Advances in Agronomy, Volume 59

V O L U M5 9E

Advisory Board Martin Alexander

Eugene J. Kamprath

Cornell University

North Carolina State University

Kenneth J. Frey

Lany P. Wilding

Iowa State University

Texas A&M University

Prepared in cooperation with the

American Society of Agronomy Monograpbs Committee William T Frankenberger, Jr., Chairman P. S. Baenziger David H. Kral Dennis E. Rolston Diane E. Storr Jon Bartels Sarah E. Lingle Jerry M. Bigham Kenneth J. Moore Joseph W. Stucki M. B. Kirkham Gary A. Peterson

DVANCES IN

Edited by

Donald L. Sparks Department of Plant and Soil Sciences University of Delaware Newark, Delaware

ACADEMIC PRESS San Diego London Boston New York Sydney Tokyo Toronto

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This book is printed on acid-free paper. @ Copyright 0 1997 by ACADEMIC PRESS All Rights Reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher.

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International Standard Serial Number: 0065-2 I I3 International Standard Book Number: 0-12-000759-2 PRINTED IN THE UNITED STATES OF AMERICA 96 97 9 8 9 9 00 01 BB 9 8 7 6 5

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Contents CONTRIBUTORS ........................................... PREFACE .................................................

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QUANTITATIVE GENETICS AND PLANT BREEDING John W. Dudley

I. Introduction .............................................. I1. History .................................................. I11. Tools of Quantitative Genetics ............................... Iv. Application of Quantitative Genetics to Plant Breeding . . . . . . . . . . . V. Future Role of Quantitative Genetics in Plant Breeding . . . . . . . . . . . References ...............................................

1 2 4 9 19 19

USEOF ORGANOCLAYS IN POLLUTION ABATEMENT Shihe Xu. Guangyao Sheng. and Stephen A . Boyd

I. I1. I11. N. V.

Introduction .............................................. Synthesis and Chemical Stability of Organoclays................. Sorptive Properties of Organoclays ........................... In Sitzl Modification ........................................ Biodegradation of Contaminants in Modified Soils ............... References ...............................................

25 28 36 44

54 57

PHENOLOGY. DEVELOPMENT. AND GROWTH OF THEWHEAT(TRITZCUMAESTWCM L.) SHOOT APEX: A &VIEW Gregory S. McMaster I. Introduction .............................................. I1. General Patterns of Grass Shoot Apex Development ............. I11. Morphological Nomenclatures ............................... Iv. Shoot Apex Developmental Sequence ......................... V Conclusion ............................................... References ...............................................

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63 64 64 67 101 102

CONTENTS

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APPLICATIONSOF MICROMORPHOLOGY OF RELEVANCE TO AGRONOMY Rienk Miedema I . Introduction .............................................. 11. Methods Used in Micromorphology .......................... I11. Soil Structure in Relation to Land Use ......................... IV. Conclusions and Future Research Needs ....................... References ...............................................

119 123 128 157 159

PHYSIOLOGICAL AND MORPHOLOGICAL RESPONSES OF PERENNIAL FORAGES TO STRESS Matt A. Sanderson. David W. Stair. and Mark A. Hussey I. htroduction .............................................. I1. Water Deficit ............................................. I11. Defoliation Stress.......................................... Low Light ............................................... v. Nutrient Stress............................................ VI. Low-Temperature Stress .................................... VII. Salt Stress ................................................ VIII. Plant Breeding for Abiotic Stress Tolerance ..................... References ...............................................

rv.

172 173 179 183 187 191 199 203 208

CROPMODELING AND APPLICATIONS: A COTTON EXAMPLE K. Raja Reddy. Harry F. Hodges. and James M . McKinion I. Introduction .............................................. I1. Phen o1ogy ............................................... 111. Growth of Individual Organs ................................ Iv. Partitioning Biomass ....................................... v High-Temperature Effects on Fruiting Structures . . . . . . . . . . . . . . . . VI. Nitrogen-Deficit Effects .................................... VII . Water-Deficit Effects....................................... VIII. Model Development ....................................... IX. Model Calibration and Validation ............................. X . Model Applications and Bridging Technologies . . . . . . . . . . . . . . . . . . XI . Summary and Conclusions .................................. References ...............................................

226 231 240 253 255 257 265 267 273 275 281 282

CONTENTS

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THEVALUEOF LONG-TERMFIELDEXPERIMENTS IN AGRICULTURAL. ECOLOGICAL. AND ENVIRONMENTAL RESEARCH A. Edward Johnston

I. I1. 111. W. V

Inaoduction .............................................. The Rothamsted Experiments ............................... The Agricultural Value of Long-Term Experiments . . . . . . . . . . . . . . Ecological Research and Long-Term Experiments . . . . . . . . . . . . . . . Long-Term Experiments and Environmental Concerns . . . . . . . . . . . VI. The Need for Long-Term Experiments ........................ VII. Approaches to New Long-Term Experiments . . . . . . . . . . . . . . . . . . . References ...............................................

291 293 294 313 319 325 327 329

INDEX...................................................

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contributors Numbers in parentheses indicate the pages on which the authors’contributions begin.

STEPHEN A. BOYD (2 S), Department of Crop and Soil Sciences, Michigan State University,East Lansing, Michigan 48824 JOHN W. DUDLEY (I), Department o f Crop Sciences, University of Illinois at Urbana-Champaign, Urbana,Illinois 61 801 HARRY F. HODGES (229, Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, Mississippi 39762 MARK A. HUSSEY (17 I), Department of Soil and Crop Sciences, Texas A&M University,College Station, Texas 77843 A. EDWARD JOHNSTON (291), MCR Rothamsted, Harpenden, Herts A L 5 ZJQ, United Kingdom JAMES M. MCKINION (226), USDA-ARS Crop Simulation Research Unit, Mississippi State, Mississippi 39762 GREGORY S. MCMASTER (63), USDA-ARS, Great Plains Systm Research, Fort Collins,Colorado 80522 RIENK MIEDEMA (1 19), Department of Soil Science and Geology, Wageningen Agricultural University,6700 AA Wageningen,The Netherlands K. RAJA REDDY (22S), Department of Plant and Soil Sciences, Mississippi State University,Mississippi State, Mississippi 39762 MATT A. SANDERSON (17 l), Texas A&M UniversityAgricllltural, Research and Extension Centq Stephenville, Texas 76401 GUANGYAO SHENG (2 S), Department of Crop and Soil Sciences, Michigan State University, East Lansing, Michigan 48824 DAVID W. STAIR (1 7 l), Department ofsoil and Crop Sciences, TexasA&M University, College Station, Texas 77843 SHIHE XU (2 5 ) , Health and Environmental Sciences, Dow Corning Corporation, Midland, Michigan 48640

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Preface Volume 59 contains seven state-of-theart reviews of various crop and soil sciences topics. The first chapter presents an overview of quantitative genetics and plant breeding, including historical aspects, the tools of quantitative genetics, the application of quantitative genetics to plant breeding, and the future role and importance of quantitative genetics in plant breeding. The second chapter reviews the use of organoclays in pollution abatement. Topics discussed include synthesis, chemical stability, sorptive properties of organoclays, in siru soil modification, and biodegradationof contaminants in modified soils. The third chapter covers the phenology, development, and growth of the wheat shoot apex, including general patterns of grass shoot apex development, morphological nomenclatures, and shoot apex developmental sequences. The fourth chapter applies micromorphology to agronomic scenarios. The discussion includes methods that are used in micromorphology and soil structure in relation to land use. The fifth chapter discusses the physiological and morphological responses of perennial forages to stresses, including water deficits, defoliation, nutrients, low temperature, and salt. The sixth chapter is a comprehensive review of crop modeling and applications, with cotton as the crop of interest. Discussions on phenology, growth of individual organs, partitioning biomass, high-temperature effects of fruiting structures,nitrogen and water deficit effects, and model development,calibration, validation, and applications are included. The seventh chapter is a historically rich overview of the importance of long-term field experiments in agricultural, ecological, and environmental research. I appreciate the first-rate reviews of the contributors. DONALD L. SPARKS

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QUANTITATIVE GENETICS AND PLANTBREEDING John W. Dudley Department of Crop Sciences University of Illinois Urbana. Illinois 61801

I. Introduction 11. History

A. Plant Breeding B. Quantitative Genetics C. Use of Quantitative Genetics in Plant Breeding 111. Tools of Quantitative Genetics A. Description of Genetic Variation B. Description of Environmental Variation C. Predicted Gain Equation D. Correlated Response Equation E. Multiple Trait Selection Index E Molecular Markers G. Generation Mean Analysis W. Application of Quantitative Genetics to Plant Breeding A. Choice of Parents B. Selection during Inbreeding C. Recurrent Selection D. Marker-Assisted Selection V. Future Role of Quantitative Genetics in Plant Breeding References

I. INTRODUCTION The objective of this chapter is to review the relationship between quantitative genetics and plant breeding from a plant breeding perspective. Plant breeding is the science and art of genetic improvement of crop plants. Quantitative genetics is the study of genetic control of traits that show a continuous distribution in segregating generations. Quantitative genetics is concerned with the inheritance of those differences between individuals that are of degree rather than kind, quanti1 A d v m r s in A p n n m y . Volume 79 Copyright 0 1YY7 by Academic Press, Inc. All rights of reproduction in any form reserved.

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tative rather than qualitative (Falconer, 1989). Where do these disciplines intersect? At one extreme, Kempthorne (1977) defined plant breeding as applied quantitative genetics. Simmonds (1984) on the other hand, considered biometrical genetics “to have helped to interpret what has already been done and to point questions, especially about the all important matter of response to selection, but to have had little impact on the actual practice of breeding.” Baker (1984) provided an intermediate view when he suggested an understanding of quantitative genetic principles is critical to the design of efficient breeding programs. In this review, Baker’s viewpoint will be followed. Because many of the most important traits with which breeders work are inherited quantitatively, quantitative genetics must be of concern to breeders.

11. HISTORY

A. PLANTBREEDING Plant breeding started with primitive people saving seed to plant in succeeding years. In the process, most of our major crops, such as maize (&a mays L.), wheat (Triticurn aestivum L.), barley (Hordeurn vulgare L.), and many others, were domesticated. Although there is a tendency to equate the beginnings of plant breeding with the rediscovery of Mendel’s laws, major plant breeding discoveries were made prior to 1900. For example, mass selection for sucrose concentration in the beet root began in 1786 and was continued until 1830. The first beet sugar factory was erected in 1802 (Smith, 1987). Thus, planned, directed plant breeding efforts resulted in a cultivar that allowed development of a new industry 100 years before the rediscovery of Mendel’s laws. The basic principles underlying maize breeding, i.e., that inbreeding reduces vigor, cross-breeding increases vigor, hybrids could be produced by detasseling one parent, and that hybridization needed to be done each generation if vigor was to be maintained, were known prior to 1900 (Zirkle, 1952) With the rediscovery of Mendel’s laws, genetic principles began to be applied to plant breeding. Smith (1966) traces the developments from 1901 to 1965, including developments in statistical theory that had important implications for plant breeders. The development of hybrid corn and the principles leading to it have been reviewed extensively (Crabb, 1947; Hayes, 1963; Wallace and Brown, 1956) and will not be reviewed in detail here. Because most of the traits of economic importance are under quantitative genetic control, quantitative genetics became an important contributor to plant breeding theory.

QUANTITATIVE GENETICS AND PLANT BREEDING

3

B. QUANTITATIVE GENETICS Selection for quantitative traits began with the first person to select for productivity of the plants from which seeds were saved for the next generation. However, the origins of quantitative genetics can be traced to Darwin’s concept of natural selection (Griffing, 1994).Early statistical concepts, such as regression (Galton, 1889) and use of correlation and multiple regression to describe relationships among relatives (Pearson, 1894),were developed prior to rediscovery of Mendel’s laws. Griffing (1 994) listed the demonstration of the environmental nature of variation among plants within lines and the genetic nature of variation among lines (Johannsen, 1903, 1909) along with the establishment of the multiple factor hypothesis for inheritance of quantitative traits by the experimental studies of Nilsson-Ehle (1909) and East (1910) as keys to demystification of inheritance of quantitative traits. On the theoretical side, the development of the Hardy-Weinberg equilibrium concept demonstrated a mechanism for maintenance of genetic variability in populations. The study that formed the basis for most of the theoretical quantitative genetics work to follow was that of Fisher (1918), which showed that biometric results (involving correlations among relatives) could be interpreted in terms of Mendelian inheritance. Griffing (1994) traces the history of quantitative genetics in detail. A few additional milestones that he identifies include the work of Cockerham (1954) and Kempthorne (1954) in partitioning epistatic variation and the contributions of Kempthorne (1957) in bringing together and interpreting in a common statistical genetic language the diverse concepts of prominent statistical geneticists. As areas of plant breeding in which they were important are considered, other important steps in the history of quantitative genetics will be reviewed.

C. USEOF QUANTITATIVE GENETICS IN PLANTBREEDING Quantitative genetic principles apply to almost any area of plant breeding. Breeders recognize the need for more extensive testing for traits of low heritability than for traits of high heritability. They cross good X good, understanding the principle that lines with similar means are likely to differ at fewer loci than dissimilar lines and thus transgressive segregants are more likely to occur. However, the formal use of such quantitativegenetic techniques as estimation of genetic variances and prediction of genetic gain is rare in most plant breeding programs. In this review, each of the steps in a plant breeding program will be examined and the utility of quantitative genetic techniques considered. However, before describing the use of these techniques in plant breeding, a brief description of the tools available from quantitative genetics is provided.

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JOHN W. DUDLEY

III. TOOLS OF QUANTITATIVE GENETICS Because quantitative traits are those for which the effects of genotype and environment cannot be readily distinguished,a major contribution of quantitative genetic theory was to provide methods for separating genetic effects from environmental effects. As a first step, genetic expectations of means and variances were obtained.

A. DESCRIPTION OF GENETIC VARIATION Based on the work of Fisher (1918) and the elaborations by Cockerham (1954) and Kempthorne (I 954), procedures for describing genetic variation in a population were developed. These procedures are based on first describing within-locus variation in terms of average effect of substitutionof an allele and deviations from that average effect. Variation associated with the average effect of substitution is called additive genetic variance and variance associated with deviations is called dominance genetic variance (see Falconer, 1989, for details). Variance associated with interaction among alleles at different loci is termed epistatic genetic variance and can be subdivided into additive X additive, additive X dominance, and dominance X dominance variance when two loci are involved. When additional loci are involved, higher-order interactions can be described. Genetic variance components can be estimated from covariances between relatives as described by Cockerham ( 1963). The general procedure for estimating genetic components of variance is to devise a mating design that will estimate covariances between relatives (such as the covariance of full-sibs or half-sibs). The mating design is then grown in an environmental design. The environmental design includes the choice of environments (usually locations and years) and environmentalstresses (such as plant population, irrigation or lack thereof, fertility levels, etc.) as well as the experimental design (such as a randomized complete block, incomplete block, or other type of design). From the appropriate analysis of variance, design components of variance are estimated and equated to covariances between relatives. Estimates of covariances between relatives are then equated to expected genetic variance components and genetic variances are estimated (Cockerham, 1963). Such estimates have limitations. Assumptions usually include linkage equilibrium in the population from which the parents of the mating design were obtained and negligible higher-order epistatic effects. The epistatic effects assumed negligible vary with the mating design, e.g., if only one covariance between relatives, such as half-sibs, is estimated, then all epistatic effects are assumed negligible if the covariance of half-sibs is assumed to be an estimate of a portion of the additive genetic variance.

QUANTITATIVE GENETICS AND PLANT BREEDING

5

As will be discussed later, estimates of genetic variance components can be used to predict gain from selection (thus allowing comparisons among breeding methods), determine degree of dominance for genes controlling quantitative traits, and compare heritability of different traits.

B. DESCRIPTION OF ENVIRONMENTAL VARZATION For any plant breeding program to be successful, the environments in which the cultivars being developed are to be grown must be defined. Selection is then concentrated on developing cultivars that can take maximum advantage of that environment. The one factor that dictates extensive, expensive testing of genotypes in a plant breeding program is the existence of genotype-environment interaction (GXE). Four aspects of GXE need to be considered. First, does GXE exist? Comstock and Moll (1963) described in detail methods of estimating GXE components of variance and detecting the existence of GXE. Second, if GXE does exist, are genotypes ranked the same in different environments? If GXE effects are significant because of differences in magnitude of differences between genotypes in different environments (non-crossover interaction) rather than differences in ranking of genotypes between environments (crossover interactions), then the GXE effects are of little consequence to the breeder. An extensive discussion of methods of measuring the importance of crossover and non-crossover interaction effects is given by Baker (1988). Third, which genotypes respond most favorably to changes in environment? Regression of performance of a genotype on the average performance of a set of genotypes in an environment (Finlay and Wilkinson, 1963; Eberhart and Russell, 1966) has been used to identify genotypes that respond favorably to environmentsor that do not respond to increased environmentalinputs. Detailed discussion is found in Lin et al. ( 1 986) and Romagosa and Fox (1993). Fourth, measures of GXE have been used to define geographic regions with similar environments in order to identify areas in which test sites should be located (Ouyang et al., 1995). Clustering procedures described by Ouyang et al., emphasize detecting crossover interaction and allow computation of distances between environments for unbalanced or missing data. Although the procedures used for dealing with GXE are primarily statistical, the traits being considered are quantitative and the genetic constitution of the entries being evaluated affects the results. For example, Eberhart and Russell (1969) determined single crosses were, on average, less stable than double crosses. However, they found individual single crosses that were as stable as most double crosses. The removal of GXE variance from estimates of genetic variance is an integral part of any attempt to estimate genetic variances for prediction of gain from selection. Choice of environments for such a study is also critical. A symposium

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volume edited by Kang (1990) provides a detailed look at the interrelationshipsof GXE and plant breeding.

C. PREDICTED GAINEQUATION One of the major contributions of quantitative genetics to plant breeding was the development of an equation for predicting gain from selection. Griffing (1994) reviews the historical developmentof the prediction equation beginning with Fisher’s ( I9 18) consideration of the ratios of U;/CT~ and as measuring the relative importance of additive genetic and dominance contributions to correlation analysis. Wright ( 1 92 1) originated the concept of broad-sense heritability and Lush (1935), using Fisher’s least squares gene model, partitioned the hereditary contribution into additive and nonadditive portions. From this work came the concept of the ratio of ui/ug as a measure of heritability in the narrow sense. A detailed discussion of the estimation of heritability is given by Nyquist (1991). In its simplest form, the predicted gain equation has been expressed as R = ia,@r,, which can be recast as R = ihiu,, where R is response to selection, i is the standardized selection differential, and h i is narrow-sense heritability. This expression assumes selection based on phenotype of individuals and recombination of selected individuals. However, there are a number of factors in plant breeding programs that complicate this simple expression. Hallauer and Miranda ( 1988), Empig et al. (1972), and Nyquist (1 99 1) explore these factors in detail. Because selection in plant breeding programs is based on progenies and these progenies vary in the types and proportions of genetic variance expressed, the appropriate types of genetic variance to be included in the numerator of the selection equation vary. In addition, the estimate of phenotypic variance to be included in the denominator varies with the experimental and environmental designs used. The basis for comparison of results from the prediction equation may also vary. For example, selection procedures may be compared on either a per year or a per cycle basis. Finally, the choice of whether recombination is such that selection is based on both the male and female parents of the next generation or only on one sex will play a role in progress from selection. Given the factors mentioned in the preceding paragraph, a generalized prediction equation for gain per year can be written as follows (Empig et al., 1972):

R = cisi/yu,,

(1)

where c is a pollen control factor (iif selection is after pollination, 1 if selection is prior to pollination, and 2 if selfed progenies are recombined), y is the number of years per cycle, i is the selection differential expressed as number of up,si is the appropriate genetic variance for the type of selection being practiced, and a,, is the appropriate phenotypic standard deviation for the progenies being evaluat-

QUANTITATIVEGENETICS AND PLANT BREEDING

ed in the selection program. If comparisons on a per cycle basis are desired, then y can be set as 1 for all types of selection being compared. This equation is critical for comparing selection procedures. Examples of its use are given by Hallauer and Miranda (1 988) and Fehr (1987).

D. CORRELATED RESPONSEEQUATION When selection is applied by plant breeders, changes are likely to occur, not only in the trait for which selection is being practiced but in other traits as well (correlated response). The extent of correlated response is a function of the heritabilities of the primary and correlated traits, as well as the genetic correlation between the traits. Falconer ( 1989) presents the correlated response equation as

CRY = ih,$iyrAupy, where CRY is the correlated response in trait Y when selection is based on trait X, i is the standardized selection differential for X , h, and h,, are the square roots of heritability of traits X and Y, respectively, rA is the additive genetic correlation between X and Y and uPyis the appropriate phenotypic standard deviation for I:Multiplying CRY by c/y generalizes the equation to a form corresponding to Eq. (1). Hallauer and Miranda (1 988) describe calculation of genetic correlations. Equation (2) becomes important not only in determining the type of correlated response that may occur under selection but also in determining effectivenessof indirect selection. If rAhx > hy then indirect selection for X will be more effective than direct selection for Y, all other factors being equal. If, in addition, selection for X allows progress in an environment where Y cannot be measured, as may be true for marker-assisted selection, then additional benefits accrue from indirect selection.

E. MULTIPLE TRAIT SELECTIONINDEX The cultivars arising from plant breeding programs must satisfy a number of criteria to be useful. For example, a high yielding cultivar susceptible to a prevalent disease would be of little use to a grower, Thus, plant breeders must select for a number of traits. Three general procedures-tandem selection, independent culling levels, and index selection-have been used to approach the question of simultaneous improvement of a population for multiple traits (Falconer, 1989). A number of forms of the equation for gain from index selection for multiple traits are available. Smith (1936) was the first to present the concept of index selection. Smith presented an index of the form: I = b,X,

+ b2X2 + . .

*

b,X,,

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JOHN W. DUDLEY

where I is an index of merit of an individual and 6, . . . 6, are weights assigned to phenotypic trait measurements represented as X,. . .X,. The b values are the product of the inverse of the phenotypic variance-covariance matrix, the genotypic variance-covariance matrix, and a vector of economic weights, A number of variations of this index, most changing the manner of computing the b values, have been developed.These include the base index of Williams (1962), the desired gain index of Pesek and Baker (1969), and retrospective indexes proposed by Johnson et al. (1988) and Bemardo (1991). The emphasis in the retrospective index developments is on quantifyingthe knowledge experienced breeders have obtained. Although breeders may not use a formal selection index in making selections, every breeder either consciously or unconsciously assigns weights to different traits when making selections.

F. MOLECULARMARKERS Although molecular markers are not a direct product of quantitative genetics, the explosion of interest in their use in plants is in large part because of the implications they have for helping solve problems that are common to quantitative genetics and plant breeding. The use of markers as a potential aid in selection dates back to Sax (1923) who found seed color related to seed size in beans. Stuber and Edwards (1986) pioneered the use of molecular markers in plant breeding with work based on isozymes. Stuber (1992) reviewed this work. The use of markers for selection in plant breeding programs is the application of a form of indirect selection. The use of markers to manipulate genes was reviewed in detail by Dudley (1993). Lee (1995) gave a comprehensive review of use of molecular markers in plant breeding. The availability of molecular markers provides an additional dimension to the use of quantitative genetics in plant breeding. Potential applications of molecular markers include marker-assisted selection, identification of the number of genes controlling quantitative traits, grouping germ plasm into related groups, selection of parents, and marker-assisted backcrossing.

G. GENERATION MEAN ANALYSIS The broad area of generation mean analysis is summarized by Mather and Jinks (1982). In essence, the procedure expresses the means of generations derived from the cross between homozygous lines in genetic terms. The generation means are then analyzed to estimate additive, dominance, and epistatic effects. The reference population is either the F, mean or the mean of homozygous lines resulting from selfing the F,. Procedures for estimating the number of effective factors affecting a particular trait in the cross being studied are provided. One of the major limita-

QUANTITATIVE GENETICS AND PLANT BREEDING

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tions of the procedure is the assumption that, for the trait being studied, one parent contains all the favorable alleles and the other all the unfavorable alleles at segregating loci. The procedure has found a great deal of use in studying genetics of disease resistance (Campbell and White, 1995; Carson and Hooker, 1981; Moll et al., 1963). An advantage cited by those using it is that the progenies used to determine segregation for single genes can also be used for generation mean analysis. In addition, means are less variable than variances.

Iv. APPLICATION OF QUANTITATIVE GENETICS TO PLAN" BREEDING Plant breeding consists of selection of parents, crossing those parents to create genetic variability, selection of elite types, and synthesis of a stable cultivar from the elite selections. Quantitative genetic principles play a role at each of these stages. In this section, the role of quantitative genetics in each of these stages of the plant breeding process is considered.

A. CHOICE OF PARENTS The choice of parental germ plasm with which to begin a breeding program is the most important decision a breeder makes. However, it is only relatively recently that quantitative genetic theory has been applied to this question.

1. Self-Pollinated Crops Discussion of choice of parents in self-pollinatedcrops will be in the context of selecting parents from which selfed lines will be derived using a pedigree system, single-seed descent, or some other method of deriving inbreds. In self-pollinated species, these lines usually are evaluated for their per se performance. In crosspollinated species, in which hybrids are the end product, similar breeding procedures are used with the exception that the end product will be a hybrid. Thus, the criterion for selection is combining ability of some form rather than line per se performance. The objective when choosing parents is to maximize the probability of generating new lines that will perform better than the best pure line currently in use. The parents chosen should generate a population for selection that will meet the criterion of usefulness described by Schnell (1983) as discussed in Lamkey et al. (1995). Usefulness of a segregating population was described by Schnell as the mean of the upper a% of the distribution expected from the population. Mathe-

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JOHN W. DUDLEY

matically, U(a)= Y 2 AG(a), where U(a)is usefulness, Y is the mean of the unselected population, and AG(a)is gain from selection. This statistic takes into account both the mean and the genetic variability, thus emphasizing a basic axiom in plant breeding: Both a high mean and adequate genetic variability are needed to produce a superior cultivar. Another basic principle of plant breeding is to cross good x good to obtain something better. The quantitative genetic basis for this axiom was demonstrated by Bailey and Comstock ( 1976).Their results demonstrated,based on probability theory and computer simulation results, the importance of each parent contributing favorable alleles from nearly equal numbers of loci that are segregating in the cross. Their results can be illustrated by considering 60 loci segregating in an F,. With no selection, the probability of a line having >39 loci fixed at homozygosity would be 0.0067, whereas the probability of a line having greater than 30 loci fixed would be 0.4487. Thus, if each parent line contributed favorable alleles at 30 loci, the probability of obtaining a line with a higher number of loci fixed with favorable alleles than the better parent would be relatively large. However, if one parent contributed favorable alleles at 40 loci and the other at only 20, the probability of obtaining a new line better than the better parent would be small. Dudley (1 982) suggested backcrossing one or more times to the superior parent if one parent was much superior to the other. The number of backcrosses needed depended on the relative number of favorable alleles coming from each parent-the greater the divergence between parents, the more backcrossing would be needed. Given the criteria of a high mean and relatively high genetic variance, what tools are available to a breeder to identify parents that will provide segregating generations with these characteristics? Baker (1984) reviewed this question in light of a paper by Busch et al. (1974) who evaluated F4 and F, bulk populations, random F,-derived F5 and F, lines, and midparent values as predictors of cross performance. Baker suggests any of these methods should be useful predictors of the mean performance of lines from an F, with the caution that midparent values might be the weakest of the methods. Toledo (1992) found use of the midparent value and the inverse of Malecot’s coefficient of parentage to be effective in selecting crosses that would produce superior lines in soybeans (Glycine m a L., Merrill). Panter and Allen (1995) suggested using best linear unbiased prediction (BLUP) methods to predict the midparent value of soybean crosses. BLUP methods take into consideration the performance of lines related to the line for which performance is being predicted. They concluded BLUP had advantages over least squares estimates of midparent values. They found a correlation of -0.47 between coefficient of parentage and genetic variance in progeny. Based on these results, they suggested that an effective method of choosing parents would be to identify pairs of lines with high midparent values estimated from BLUP and to select among such pairs those which were the most genetically diverse based on the ge-

QUANTITATrVE GENETICS AND PLANT BREEDING

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netic relationship matrix. Their suggestion is supported by the results of Toledo (1992). With the availability of genetic markers, degree of relationship between lines can be established from molecular marker data (Lee, 1995). This provides an alternative method of determining relatedness when pedigree information is unavailable or of uncertain accuracy.

2. Cross-Pollinated Crops (Hybrid Cultivars) For development of hybrid cultivars, there are two aspects to the choice of parents: (i) choice of parents to cross to form base populations for selfing, and (ii) choice of parents to form a cultivar for use by farmers. These two aspects will be addressed separately. a. Choice of Parents to Form Base Populations Conceptually, the problem of developing improved inbreds for use in hybrids is one of adding favorable alleles from a donor source to an elite inbred without materially reducing the frequency of favorable alleles already present in the elite inbred (Dudley, 1982). The basic question in choosing parents is identification of those lines or populations that contain favorable alleles not present in a hybrid being improved. Dudley ( 1984a) framed the following questions relative to choice of parents for a hybrid corn breeding program: Which hybrid should be improved? Which lines should be chosen as donors to improve the target hybrid? Which parent of the target hybrid should be improved? Should selfing begin in the F, or should backcrossing be used prior to selfing? Procedures for answering these questions were developed based on the concept of classes of loci. This concept was first explored in Dudley (1982). The basic concept assumes that for any pair of lines the loci at which the lines differ for a given trait can be divided into two classes: those loci for which P, contains favorable alleles and P, does not and those for which P, contains favorable alleles and P, does not. When a donor inbred is considered, eight classes of loci exist as illustrated in Table 1. Of critical interest is the class of loci for which the donor contains favorable alleles and both parents of the target hybrid have unfavorable alleles. Using this concept, methods of identifying donors with the greatest numbers of such loci were devised for cases in which the donor was an inbred or a population (Dudley, 1984b,c, 1987a,b). Modifications of these methods were proposed by Gerloff and Smith (1988), Bernard0 (1990a,b), and Metz (1994). Evidence for their effectiveness in selecting superior parents and identifying heterotic relationships was presented by Dudley (1988), Misevic (1989), Zanoni and Dudley (1989), Pfarr and Lamkey (1992), and Hogan and Dudley (1991). These methods are beginning to be used in commercial breeding programs in corn and sorghum [Sorghum bicolor (L.) Moench].

12

JOHN W. DUDLEY Table I Genotypes for the Classes of Loci Possible for the Parents of a Hybrid to Improve (P, and Pz) and a Donor Inbred (PJ Genotypesa for Class of loci A

B C

D E

F G

H

PI

p2

PY

++ ++ ++ ++

++ ++

++ __ ++

__ __

__ __

__ __

++ ++

__ __

__

++ __ ++ __

a + + , The line is homozygous for the dominant favorable allele; - -, homozygous for the recessive unfavorable allele.

b. Choice of Parents of a Hybrid Cultivar Choice of parents to produce a cultivar directly is usually the result of extensive testing of a number of combinations of potential parents. One of the major problems facing breeders is reducing the number of possible hybrids to be tested to a reasonable number. In general, breeders work with heterotic groups and crosses likely to be successful as cultivars are usually between inbreds from different heterotic groups (Hallauereral., 1988). However, even if breeding is restricted to two heterotic groups, thousands of potential hybrids are possible. Bernardo (1994) proposed applying BLUP to this problem. In this procedure, information on hybrid performance of a subset of lines is combined with information on genetic relationship between the lines tested and an untested set of lines to predict the performance of untested hybrids. This procedure has been widely used in dairy cattle breeding (Henderson, 1988). Bernardo (1994), using a limited number of hybrids, found correlations between observed and predicted performance ranging from 0.65 to 0.80. He compared RFLP-based estimates of relationship with pedigree-based estimates and found higher correlations for the RFLP-based estimates. In a study (Bernardo, 1996) involving 600 inbreds and 4099 tested single crosses, correlations between predicted and observed yields ranged from 0.426 to 0.762. Bernardo concluded BLUP was useful for routine identification of single crosses prior to testing.

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3. Cross-Pollinated Crops (Synthetic Cultivars) The mean of a synthetic is,a function of the mean of all possible crosses among parents and inbreeding depression (Hallauer and Miranda, 1988). Predicted mean of a synthetic is given by Wright’s equation Y2 = Y, - (Y, - Yo)/n,where Y2 is the predicted mean of the synthetic, Y, is the average performance of all possible single crosses among the parents, and Yo is the mean of the parental inbreds used to produce the synthetic. A general formula for predicting yield of synthetics that considered the frequency of selfing, the number of parents, the coefficient of parentage of the parents, and ploidy level was given by Busbice (1970).

4. Role of Molecular Marker Technology Use of molecular markers to determine relationships among potential parents has been proposed in a number of species (see Lee, 1995, for a review). Such information is useful for assigning inbreds to heterotic groups in hybrid breeding programs (Mumm and Dudley, 1994). Marker-based relationships could also be substituted for pedigree-based relationships using the methods proposed by Panter and Allen ( I 995) and Toledo (1992) for predicting genetic variability in crosses between homozygous lines. Bernard0 (1994) suggested using genetic relationships based on molecular marker information and BLUP methodology to predict performance of untested hybrids.

B. SELECTIONDURING

INBREEDING

Comstock (1978) suggested that development of a theoretical basis for comparing breeding methods was one of the most significant contributions of quantitative genetics to maize breeding. Baker (1984) suggested this statement could be extended to all economically important crops. Because breeding procedures are similar for both self- and cross-pollinatedcrops, discussion of application of quantitative genetics to selection procedures will be divided into selection during inbreeding and recurrent selection procedures. As Hallauer et a/.(1988) point out, the methods used to select during inbreeding and recurrent selection procedures are complementary parts of a breeding program. In fact, because one result of selection during inbreeding is the development of improved lines that are then crossed and another round of selection carried out, selection during inbreeding is one form of recurrent selection. Two major questions exist relative to selection during inbreeding. First, how should resources be divided between number of crosses to be evaluated and number of plants or lines to sample per cross? Second, at what stage in the inbreeding

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JOHN W. DUDLEY

process should replicated testing for yield and other traits of low heritability begin? Baker (1984) considered application of quantitative genetics to the question of the optimum allocation of resources to selection among crosses versus selection within crosses. Optimum allocation of resources was a function of among and within cross heritabilities and additive genetic variances. With a fixed number of plots, the optimum proportion of lineskross to crosses varied with heritability. Although the equations presented by Baker provided insights into the problem of allocation of resources, he concluded there was a lack of objective criteria for determining the appropriate number of crosses to evaluate. The appropriate selfing generation in which to begin testing for yield is a major question in any breeding program from which inbreds are to be produced. In species in which cultivars are inbreds, testing is for line per se performance. In species in which hybrids are to be produced, testing is for combining ability. The two cases will be considered separately. 1. Line per se Performance As inbreeding progresses, variability among lines increases and variability within lines decreases (Hallauer and Miranda, 1988). This is a basic principle of quantitative genetics. An application of this principle to breeding of self-pollinated crops that had major impact was development of the modified pedigree (singleseed descent) method. This procedure was proposed by Goulden (1941) and its advantages in quantitative genetic terms were detailed by Brim (1966). Brim noted most genetic variance in soybeans was additive. Thus, means did not change during selfing generations. Furthermore, variance among lines increased with inbreeding and an advantage in terms of gain from selection almost always occurred when selection was delayed to at least the F, and often to the F4. The advantage was particularly apparent when selfing generations could be advanced rapidly in the off-season. The extent of use of single-seed descent or a modification thereof varies with the species. In soybean [Glycine max (L.) Merrill], single-seed descent procedures are used extensively (Fehr, 1987), but less extensive use has been made in winter wheat (Allan, 1987). A breeding method related to the single-seed descent method is the use of doubled haploids. In this procedure, homozygous lines are produced by doubling haploid plants arising from gametes, thus reducing the time required to obtain homozygous lines. Choo et al. (1985) cite empirical results indicating similar efficiencies for the two methods. The most extensive use of this procedure has been in barley. Work on doubled haploids in maize was discussed by Chase (1974). Both single-seed descent and doubled haploid procedures assume that gains from early generation testing are offset by the increased gain from selection among homozygous lines and the reduced time necessary to obtain homozygous lines using these procedures.

QUANTITATIVE GENETICS AND PLANT BREEDING

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2. Combining Ability Early in the development of hybrid corn, the importance of testing for combining ability was recognized. The correlations between inbred traits and hybrid performance were generally low and not predictive of hybrid performance (Hallauer et al., 1988). Thus, some method of measuring the value of lines in hybrid combination was needed. Smith (1986) presented the theoretical basis for the correlation between testcross and per se performance. His computer simulation results suggested that for traits conditioned by a large number of genes showing complete dominance, correlations between line per se performance and testcross performance are expected to be less than 0.5. Two major decisions, which can be approached from a quantitative genetics perspective, exist. First, what tester should be used? Second, when should testing begin? The principles related to the second question are the same as those for early generation testing when the objective is a pure line. That is, as inbreeding advances testcross variation increases among lines and decreases within lines. a. Choice of Tester A major step in evaluating the type of tester to be used was the development of the concept of general and specific combining ability (Sprague and Tatum, 1942). This work supported use of a broadbase tester for preliminary screening for general combining ability, followed by testing in specific combinations. One method of evaluating for specific combining ability was use of a diallel cross. Griffing (1994) reviews the development of the analysis of the diallel cross. Griffing (1956) provided clear statementsof methods of analysis of diallel crosses in terms of general and specific combining ability and the circumstances in which each method of analysis should be used. Hallauer and Miranda (1988) review the use of diallels in corn breeding. The choice of a tester to use in a hybrid breeding program is dictated by the objectives of the program and the type of gene action controlling the traits of interest. If the objective is to improve population per se performance, then the tester should be one that has a low frequency of favorable alleles at the loci for which the population needs improvement. If additive gene action is of primary importance, then any tester will be effective. However, if dominance, partial dominance, or overdominance are important the tester should be one that has a high frequency of recessive alleles at loci for which improvement is needed. Mathematically, this can be seen from the expression for genetic variance among testcross means for a single locus presented by Homer et al. (1969): uT$= 0.5pq( 1 + F)[a + d(Q - P)I2

(3)

where p and 4 are frequencies of favorable and unfavorable alleles, respectively, in the population of lines being tested, F is the inbreeding coefficient of the lines

16

JOHN W. DUDLEY

being tested, a is half the difference between homozygotes, d is the deviation of the heterozygote value from the midparent, and P and Q are frequencies of favorable and unfavorable alleles, respectively, in the tester. If the tester is homozygous, then either P or Q = 1. Several points are apparent from this equation. If d = 0, i.e., there is no dominance, gene frequency in the tester does not affect uTz and any tester will be satisfactory. If dominance exists, then the higher the frequency of the recessive allele in the tester, the higher the testcross variance. Likewise, the greater the inbreeding of the lines being tested, the greater the testcross variance. Thus, with complete dominance maximum uTz will occur when the tester is homozygous recessive and the lines being tested are homozygous. Because interest is in increasing frequencies of favorable alleles at loci where the line to be used in combination with the line being developed has recessive alleles, the tester should be closely related to the line to be used in the ultimate hybrid. This minimizes genetic variability in testcross progeny at loci that do not need improvement and allows increased gain in gene frequency at important loci. These concepts support the generally accepted practice of identifying heterotic groups and selecting testers from an opposite heterotic group (see Hallauer, et al., 1988, for a discussion of heterotic groups). Extensive experimental data support the theory behind choice of tester (Hallauer and Lopez-Perez, 1979). b. Early vs Late Testing The question of when to begin testing for combining ability was hotly debated in the early days of corn breeding. The principle of increased variance between lines and decreased variance within lines as inbreeding progressed applies here as well as in development of inbreds for use as lines, per se. Jenkins (1935) and Sprague (1946) concluded that high-combining lines could be identified by testing early in the inbreeding process and at least half of them could be discarded, thus allowing more effort to be placed on testing the remaining lines later in the inbreeding process. Richey (1944) eloquently stated the case for selection for line per se performance prior to selecting for combining ability in a poem (to this author’s knowledge, the only poem ever published in Agronomy Journal). Bemardo (1992) developed theory for the genetic and phenotypic correlations between testcross values of lines tested in a given selfed generation and their selfed progeny. As selfing advances, the correlation increases. Bemardo showed the genetic correlationbetween lines in different generationsto be [( 1 + Fn)/( 1 + F,,)] where F,, and F,,, are inbreeding coefficients in generations n and n’.Heritability of testcross means also affect the correlation between early generation phenotypic values and expected genetic values of progeny. Based on theory and simulation results, Bemardo suggested saving approximately 25% of lines based on S, or S, testing if heritability is 0.25 or 0.5 in the S, generation. He also presented tables showing the probability of retaining lines in the upper a% of a distribution of homozygous lines given that a line selected in a preceding generation (Sn) was in the

QUANTITATIVE GENETICS AND P L m r BREEDING

17

upper a% of lines in the S,, generation. Empirical results previously published by Jensen et al. ( 1 983) agreed with these results. Hallauer and Miranda (1988) provide an extensive review of the literature dealing with early testing in corn. In general, most corn breeders use some form of early testing (Bauman, 1981).

C. RECURRENT SELECTION The objective of recurrent selection is to increase the frequency of favorable alleles affecting a trait in order to enhance the value of the population. Increased frequency of favorable genes is advantageous for either population per se performance, as in the case of synthetic cultivars, or for inbreeding to produce improved homozygous lines. Hallauer (1 985) demonstrated the theoretical advantages of increasing gene frequency prior to selection. Mechanically, recurrent selection involves repeated cycles of selection and recombination. Four major steps include selection of the starting population, development of progenies, evaluation of progenies, and recombination of selected individuals. The importance of selection of the starting population is detailed under the section on selection of parents. Comparisons among recurrent selection procedures can be made on a theoretical basis using the prediction Eq. (1). Hallauer (1985) details the types of progenies that may be used and the various forms of recurrent selection and provides examples from a number of species. Prediction equations appropriate for a number of different recurrent selection procedures are given in Empig er al. (1972) and Hallauer and Miranda (1988). The development of recurrent selection procedures was given major impetus by the controversy over the genetic causes of heterosis. Based on the data that suggested early testing should be effective, Jenkins (1940) outlined a procedure that came to be known as recurrent selection for general combining ability. In this procedure, selection was based on half-sib family selection and took advantage of additive effects. Hull (1945) considered overdominance to be of major importance in controlling grain yield in corn and suggested a recurrent selection scheme using an inbred tester that emphasized specific combining ability and would take advantage of loci showing overdominance. Comstock et d.(1949) suggested reciprocal recurrent selection based on half-sib families to take advantage of both general and specific combining ability. The procedure was designed to maximize progress regardless of whether dominance or overdominance was important in hybrid performance. Hallauer and Eberhart ( 1970) outlined reciprocal full-sib selection, which increased emphasis on nonadditive effects and provided an efficient method of simultaneously improving population cross performance and developing new inbreds. Details of these procedures and their use are provided in Hallauer and Miranda (1988). Recurrent selection principles, developed in cross-pollinated crops, have been

18

JOHN W. DUDLEY

utilized in self-pollinated crops (see Hallauer, 1985, for a review). A major limitation is the difficulty of making crosses to provide recombination between cycles. Brim and Stuber (1973) outlined a method of using genetic male sterility to facilitate recurrent selection in soybeans. They developed prediction equations for selection among and within half-sib families. Burton and Carver (1993) compared the effectiveness of S,, selfed half-sib, and selfed full-sib families for recurrent selection using male sterile genes in soybeans and wheat. The advantage of using selfed half-sib or full-sib families was an increase in the amount of seed available for testing. No consistent advantage to using S, families was found. Although the quantitative genetic basis for effective use of recurrent selection in self-pollinated species is the same as that for cross-pollinated species and procedures are available for overcoming the difficulties of recombination, use of recurrent selection in self-pollinated species has been limited (Hallauer, 1985).

D. MARKER-ASSISTED SELECTION The quantitative genetic principle behind marker-assisted selection on a single locus basis is relatively simple. Gain from selection based on marker genotype is a form of indirect selection in which the heritability of the marker is 1.O (Dudley, 1993). However, for quantitative traits several markers are usually involved. This introduces the complication of how to weight each marker’s contribution when selections are made. One method is to determine the marker genotype of each individual or line being tested, sum the additive effects of the marker loci showing a significant marker effect, and use the sum as an index value for the individual being considered for selection. This procedure has the advantage of taking into consideration the difference in magnitude of effects of the loci being included in selection. As reviewed by Dudley (1993), gain from marker-assisted selection will be greatest when the proportion of the additive variance accounted for by marker effects is greater than the heritability of the trait. This suggests selection based on markers has its greatest advantage when heritability of a trait is low. However, identification of marker-QTL associations requires precise experiments in which heritability is as high as possible (Dudley, 1993). Thus, maximum benefit from marker-assisted selection may occur when marker-QTL associations are identified under conditions of high heritability and selection is done when the trait of interest cannot be measured. In a survey reported by Lee (1993, the most common use of marker-assisted selection was to assist in transferring native monogenic factors or transgenes. Although the survey did not specifically request the information, Lee concluded that the primary breeding method involved was backcrossing. At least seven researchers indicated use of markers for transfemng QTL. Thus, marker-assisted selection is in use in some plant breeding programs.

QUANTITATIVE GENETICS AND PLANT BREEDING

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V. FUTURE ROLE OF QUANTITATIVE GENETICS IN PLANT BREEDING Predicting the future is a hazardous occupation. However, certain aspects are evident. The principles of quantitative genetics are an integral part of plant breeding and will continue to be for the foreseeablefuture. Thus, training of plant breeders will continue to require exposure to quantitative genetic principles and their use in plant breeding programs. During the past several years, the most exciting development related to quantitative genetics and plant breeding has been the development and availability of large numbers of molecular markers that allow marking relatively small segments of chromosome.At the same time, transformation procedures that allow the introduction into cultivated plants of genes from other species have become available. The availability of molecular markers has enabled investigators to attack quantitative genetic questions such as number of genes affecting a quantitative trait, the location of such genes, the type of gene action associated with them, the importance of epistasis, and the effect of environment on each gene. To date, the technology allows dealing only with chromosome segments and not individual genes, but further advances may allow this type of refinement. As transformation becomes more common, questions such as the importance of genetic background for the introduction of new genes will be important. Evaluation of questions such as this will require use of quantitative genetics. Because of the importance of molecular markers, increasing emphasis on linkage and its manipulation will be required both in training of students and in research. A question of primary interest to plant breeders is how can favorable linkage blocks be held together while introducing new favorable alleles into an existing genotype? Perhaps the combination of molecular marker technology, transformation, quantitative genetics, and the science of plant breeding can combine to answer this question. In the future, to perhaps a greater degree than in the past, integration of quantitative genetics into plant breeding programs will be a team effort. Involved in this effort will be knowledge of molecular biology principles, plant breeding principles, and quantitative genetic expertise. This combination of expertise is much more likely to be found in a team, each of whose members is an expert in one or more of these disciplines and can and is willing to communicate with other team members, than in one individual.

REFERENCES Allan, R. E. (1987). Wheat. In “Principles of Cultivar Development” (W. R. Fehr, ed.), Vol. 2, pp. 699-748. Macmillan, New York.

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Bailey, T. B., Jr., and Comstock, R. E. (1976). Linkage and the synthesis of better genotypes in selffertilizing species. Crop Sci. 16,363-370. Baker, R. J. (1984). Quantitative genetic principles in plant breeding. In “Gene Manipulation in Plant Improvement” (J. P. Gustafson, ed.), pp. 147-175. Plenum Press, New York. Baker, R. J. (1988). Tests for crossover genotype-environmental interactions. Can. J. Plant Sci. 68, 4 0 5 4 10. Bauman, L. F. (1981). Review of methods used by breeders to develop superior inbreds. Proc. 36th Annu. Corn Sorghum Ind. Res. ConJ, 199-208. Bernardo, R. (1990a).An alternative statistic for identifying lines useful for improving parents of elite single crosses. Theo,: Appl. Genet. 80(1), 105-109. Bernardo, R. (1990b).Identifying populations useful for improving parents of a single cross based on net transfer of alleles. Theol:Appl. Genet. 80(3), 349-352. Bernardo, R. (1991). Retrospective index weights used in multiple trait selection in a maize breeding program. Crop Sci. 31, 1174-1 179. Bernardo, R. ( 1992). Retention of genetically superior lines during early-generation testcrossing of maize. Crop Sci. 32,933-937. Bernardo, R. (1994). Prediction of maize single-cross performance using RFLPs and information from related hybrids. Crop Sci. 34,20-25. Bernardo, R. (1996). Best linear unbiased prediction of maize single-cross performance. Crop Sci. 36, 50-56. Brim, C. A. (1966).A modified pedigree method of selection in soybeans. Crop Sci. 6,220. Brim, C. A., and Stuber, C. W. (1973). Application of genetic male sterility to recurrent selection schemes in soybeans. Crop Sci. 13,528-530. Burton, J. W., and Carver, B. F.(1993). Selection among S , families vs. selfed half-sib or full-sib families in autogamous crops. Crop Sci. 33,21-28. Busbice, T. H. (1970). Predicting yield of synthetic varieties. Crop Sci. 10,265-269. Busch, R. H., Janke, J. C., and Frohberg, R. C. (1974). Evaluation of crosses among high and low yielding parents of spring wheat (Triticurnuestivum L.) and bulk prediction of line performance. Crop Sci. 14,47-50. Campbell, K. W., and White, D. G. (1995). Inheritance of resistance to Aspergillus ear rot and aflatoxin in corn genotypes. Phyroparhology 85,886-896. Carson, M. L., and Hooker, A. L. (1981). Inheritance of rsistance to anthracnose leafblight in five inbred lines of corn. Phyroparhology 71,488-491. Chase, S. S. (1974). Utilization of haploids in plant breeding, breeding diploid species. In “Haploids in Higher Plants” (K. J. Kasha, ed.), pp. 21 1-230. Univ. of Guelph Press, Guelph, Canada. Choo, T. M., Reinbergs, E., and Kasha, K. J. (1985). Use of haploids in breeding barley. Plant Ereeding Rev. 3,219-252. Cockerham, C. C. (1954). An extension of the concept of partitioning hereditary variance for analysis of covariances among relatives when epistasis is present. Generics 39, 859-882. Cockerham. C. C. (1963). Estimation of genetic variances. In “Statistical Genetics and Plant Breeding” (W. D. Hanson and H. F.Robinson, eds.), pp. 53-94. NAS-NRC Publ. 982, Washington, DC. Comstock, R. E. (1978). Quantitative genetics in maize breeding. In “Maize Breeding and Genetics” (D. B. Walden, ed.), pp. 191-206. Wiley, New York. Comstock, R. E., and Moll, R. H. (1963). Genotype*nvironment interaction. In “Statistical Genetics and Plant Breeding” (W. D. Hanson and H. F. Robinson, eds.), pp. 164-197. NAS-NRC Publ. 982, Washington, DC. Comstock, R. E., Robinson, H. F., and Harvey, P. H. (1949). A breeding procedure designed to make maximum use of both general and specific combining ability. Agron. J. 41,360-367. Crabb, A. R. (1947). “The Hybrid Corn Makers, Prophets of Plenty.” Rutgers Univ. Press, New Brunswick, NJ. Dudley, J. W. (1982). Theory for transfer of alleles. Crop Sci. 22,631-637.

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Dudley, J. W. (1984a). Identifying parents for use in a pedigree breeding program. In “Proceedings of the 39th Annual Corn and Sorghum Research Conference,” pp. 176188. American Seed Trade Association, Washington, DC. Dudley, J. W. (1984b). A method of identifying lines for use in improving parents of a single cross. Crop Sci. 2 4 , 3 5 3 5 7 . Dudley, J. W. (1984~).A method for identifying populations containing favorable alleles not present in elite germplasm. Crop Sci. 24, 1053-1054. Dudley, J. W. (1987a). Modification of methods for identifying populations to be used for improving parents of elite single crosses. Crop Sci. 27,940-944. Dudley, J. W. (1987b). Modification of methods for identifying inbred lines useful for improving parents of elite single crosses. Crop Sci. 27,945-947. Dudley, J . W. (1988). Evaluation of maize populations as sources of favorable alleles. Crop Sci. 28, 486-49 1. Dudley, J. W. (1993).Molecular markers in plant improvement, Manipulation of genes affecting quantitative traits. Crop Sci. 33,660-668. East, E. M. (1910).A Mendelian interpretation of variation that is apparently continuous. Am. Nafl. 44, 65-82. Eberhart, S., and Russell, W. A. (1966). Stability parameters for comparing varieties. Crop Sci. 6, 36-40. Eberhart, S. A,, and Russell, W. A. (1969). Yield and stability for a 10-line diallel of single-cross and double-cross maize hybrids. Crop Sci. 9,357-361. and Compton. W. A. (1972). “Theoretical Gains for Different Population Empig. L. T., Gardner, C. 0.. Improvement Procedures.” Nebraska Agr. Exp. Sta. M i x . Pub. 26 (revised). Falconer. D. S. ( 1989). “Introduction to Quantitative Genetics.” Wiley. New York. Fehr. W. R. (1987). “Principles of Cultivar Development: Theory and Technique.” Macmillan, New York. Finlay, K. W., and Wilkinson, G. W. (1963). The analysis of adaptation in a plant breeding programme. Ausr. J. Agric. Res. 17,742-754. Fisher, R. A. (1918). The correlation between relatives on the supposition of Mendelian inheritance. Trrrns. R. Soc. Ediilinburgh 52, 399-433. Galton, F. ( 1889). “Natural Inheritance.” MacMillan, London. Gerloff, J. E., and Smith, 0. S. (1988). Choice of method for identifying gemplasm with superior alleles. 1. Theoretical results. Theo,: Appl. Gene,. 76,209-2 16. Goulden. C. H. (1941). Problems in plant selection. In “Proceedings of the 7th International Genetics Congress,” pp. 132-1 33. Cambridge Univ. Press, Cambridge, UK. Griffing, B. (1956). Concept of general and specitic combining in relation to diallel crossing systems. Aust. J. Biol. Sci. 13,463-493. Grifting, B. ( 1994).Historical perspectives on contributions of quantitative genetics to plant breeding. I n “Historical Perspectives in Plant Science” (K. J. Frey, ed.), pp. 43-86. Iowa State Univ. Press, Ames. Hallauer, A. R. (1985). Compcndium of recurrent selection methods and their application. Crir. Rev. Pltlilt sci. 3, 1-30, Hallauer, A . R., and Eberhart, S. E. (1970). Reciprocal full-sib selection. Crop Sci. 10,315-316. Hallaurr, A. R., and Lopez-Perez, E. (1979). Comparison among testers for evaluating lines of corn. Proc. 341h Annu. H h i d Corn Ind. Res. Con/:,57-75. Hallaucr, A. R., and Mirdnda Fo. J. B. (1988). “Quantitative Genetics in Maize Breeding.” Iowa State Univ. Press, Ames. Hallauer, A. R., Russell, W. A,, and Lamkey, K. R. (1988). Corn breeding. In “Corn and Corn Improvement” (G. F, Sprague and J. W. Dudley, eds.), pp. 463-564. American Society of Agronomy. Madison, W1. Hayes, H. K. (1963). “A Professor‘s Story of Hybrid Corn.” Burgess, Minneapolis, MN.

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Henderson, C. R. (1988). Progress in statistical methods applied to quantitative genetics since 1976. In “Proceedings of the Second International Conference on Quantitative Genetics” (B. S. Weir, E. J. Eisen, M. M. Goodman, and G. Namkoong, eds.), pp. 85-90. Sinauer, Sunderland, MA. Hogan, R. M., and Dudley, J. W. (1991). Evaluation of a method for identifying sources of favorable alleles to improve an elite single cross. Crop Sci. 31,700-704. Horner, E. S., Chapman, W. H., Lutrick, M. C., and Lundy, H. W. (1969). Comparison of selection based on yield of topcross progenies and of S, progenies in maize (Zea mays L.). Crop Sci. 9, 539-543. Hull, F. H. (1945).Recurrent selection and specific combining ability in corn. J. Am. Soc. Agron. 37, 134-135. Jenkins, M. T. (1935).The effect of inbreeding and selection within inbred lines of maize upon hybrids made after successive generations of selfing. Iowa State Coil. J. Sci. 6,429-450. Jenkins, M. T. (1940). The segregation of genes affecting yield of grain in maize. J. Am. SOC.Agron. 32,55-63. Jensen. S . D., Kuhn, W. E., and McConnell, R. L. (1983). Combining ability studies in elite U.S. maize germplasm. Proc. 38th Ann. Corn and Sorghum Res. Conf, Am. Seed Trade Assoc. 38,87-96. Johannsen, W. (1903). “Uber Erblichkeit in Population und in Reinen Linien.” Gustav Fischer, Jena. Johannsen, W. (1909). “Elemente der Exakten Erblichkeitslehre.” Gustav Fischer, Jena. Johnson, B.. Gardner, C. 0..and Wrede, K. C. (1988). Application of an optimization model to multitrait selection programs. Crop Sci. 28,723-728. Kang, M. S. (ed.) (1990). “Genotype-by-Environment Interaction and Plant Breeding.” Louisiana State University Agriculture Center, Baton Rouge. Kempthorne, 0. (1954). The correlations between relatives in a random mating population. Proc. R. SOC.London B 143,103-1 13. Kempthorne, 0.(1957). “An Introduction to Genetic Statistics.” Iowa State Univ. Press, Ames. Kempthorne, 0. (1977). The international conference on quantitative genetics, introduction. In “Proceedings of the International Conference on Quantitative Genetics” (E. Pollak, 0. Kempthorne, and T. B. Bailey, Jr., eds.), pp. 3-18. Iowa State Univ. Press, Ames. Lamkey, K. R., Schnicker, B. J., and Melchinger, A. E. (1995). Epistasis in an elite maize hybrid and choice of generation for inbred line development. Crop Sci. 35, 1272-1281. Lee, M. (1995). DNA markers and plant breeding programs. Adv. Agron. 55,265-344. Lin, C. S., Binns, M. R., and Lefkovitch, L. P. (1986). Stability analysis, where do we stand? Crop Sci. 26,894-900. Lush, J. L. (1935). Progeny test and individual performance as an indicator of an animal’s breeding value. J. Dairy Sci. 18, 1-19. Mather, K., and Jinks, J. L. (1982). “Biometrical Genetics, The Study of Continuous Variation.” Chapman & Hall, London. Metz, G. (1994). Probability of net gain of favorable alleles for improving an elite single cross. Crop Sci. 34,668-672. Misevic, D. (1989). Identification of inbred lines as a source of new alleles for improvement of elite maize single crosses. Crop Sci. 29, 1120-1 125. Moll, R. H., Thompson, D. L., and Harvey, P. H. (1963). Aquantitative genetic study of the inheritance of resistance to brown spot (Physoderma maydis) of corn. Crop Sci. 3,389-391. Mumm, R. H., and Dudley, J. W. (1994). A classification of U.S. Maize inbreds: I. Cluster analysis based on RFLPs. Crop Sci. 34,842-85 1. Nilsson-Ehle, H. (1909). Kreuzungsuntersuchungen an hafer und weizen. Univ. Aarsk,: NF5,Il 1-122. Nyquist, W. E. (1991). Estimation of heritability and prediction of selection response in plant populations. Crit. Rev. Plant Sci. 10,235-322. Ouyang. Z., Mowers, R. P., Jensen, A., Wang, S., and Zheng, S. (1995). Cluster analysis for genotype X environment interaction with unbalanced data. Crop Sci. 35, 1300-1305.

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Panter, D. M., and Allen, F. L. (1995). Using best linear unbiased predictions to enhance breeding for yield in soybean, I. choosing parents. Crop Sci. 35,397-404. Pearson, K. (1894). Contributions to the mathematical theory ofevolution. 1. On the dissection of asymmetrical frequency curves. Phil. Trans. R. SOC.A 185,71-110. Pesek, J., and Baker, R. J. (1969). Desired improvement in relation to selection indices. Cun. J. Plant Sci. 49,803-804. Pfarr, D. G., and Lamkey, K. R. (1992). Comparison of methods for identifying populations for genetic improvement of maize hybrids. Crop Sci. 32,67@-676. Richey, F. D. (1944). The shattered dream of a corn breeder. Agron. J. 36,267-268. Romagosa, I., and Fox, P. N. (1993). Genotype X environment interaction and adaption. In “Plant Breeding, Principles and Prospects” (M. D. Hayward, N. 0. Bosemark, and I. Romagosa, eds.). pp. 373-389. Chapman & Hall, London. Sax, K. (1923). The association of size differences with seed coat pattern and pigmentation in Phaseolus vulgaris. Genetics 8,552-560. Schnell, F. W. (1983). Probleme der Elternwahl-Ein Uberblick. In “Arbeitstagung der Arbeitsgemeinschaft der Saatzuchtleiter in Gumpenstein, Austria,” pp. 1-1 1, November 22-24.1983. Verlag and Druck der Bundesanstalt fur alpenlandische Landwirtschaft. Gumpenstein, Austria. Simmonds, N. W. (1984).Gene manipulation and plant breeding. In “Gene Manipulation in Plant Improvement” (J. P. Gustafson, ed.), pp. 637-654. Plenum Press, New York. Smith, D. C. (1966). Plant breeding-Development and success. In “Plant Breeding” (K. J. Frey, ed.), pp. 3-54. Iowa State Univ. Press, Ames. Smith, G. A. (1987). Sugar beet. In “Genetic Contributions to Yield Gains of Five Major Crop Plants” (W. R. Fehr, ed.), pp. 577-625. CSSA Spec. Publ. No. 7. ASA, CSSA, and SSSA, Madison, WI. Smith, H. F. (1936). A discriminant function for plant selection. Ann. Eug. 7,240-250. Smith, 0.S. (1986). Covariance between line per se and testcross performance. Crop Sci. 26,540-543. Sprague, G. F. (1946). Early testing of inbred lines of corn. J. Am. Soc. Agmn. 38,108-1 17. Sprague, G. F.. and Tatum, L. A. (1942). General vs. specific combining ability in single crosses of com. J. Am. SOC.Agron. 34,923-932. Stuber, C. W. (1992). Biochemical and molecular markers in plant breeding. In “Plant Breeding Reviews” (J. Janick, ed.), pp. 37-61. Wiley, New York. Stuber, C. W., and Edwards, M. D. (1986).Genotypic selection for improvement of quantitative traits in corn using molecular marker loci. Proc. 4 f s t Ann. Corn and Sorghum Res. Con$, Am. Seed Trade Assoc. 41,4@-83. Toledo. J. F. F. (1992). Mid parent and coefficient of parentage as predictors for screening among single crosses for their inbreeding potential. Rev. Erasil. Genet. 15,429-437. Wallace, H. A., and Brown, W. L. (1956). “Corn and Its Early Fathers.” Michican State Univ. Press, East Lansing. Williams, J. S. (1962). The evaluation of a selection index. Eiometn’cs 18,375-393. Wright, S. (1921).Systems of mating I. The biometric relations between parent and offspring. Genetics 6, 11 1-123. Zanoni, U., and Dudley, J. W. (1989). Comparison of different methods of identifying inbreds useful for improving elite maize hybrids. Crop Sci. 29,577-587. Zirkle, C. (1952). Early ideas on inbreeding and crossbreeding. In “Heterosis” (J. W. Gowen, ed.), pp. 1-13. Iowa State College Press,Ames.

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USEOF ORGANOCLAYS INPOLLUTION ABATEMENT Shihe Xu,*Guangyao Sheng, and Stephen A. Boyd+ Department of Crop and Soil Sciences Michigan State University East Lansing, Michigan 48824

I. Introduction 11. Synthesis and Chemical Stability of Organoclays A. Adsorption of Organic Modifiers by Clay Minerals B. Desorption of QACs in Subsoils C. Abiotic Decomposition of the Adsorbed Organic Modifiers 111. Sorptive Properties of Organoclays A. Sorption of Hydrophobic Organic Contaminants by Organoclays B. Sorption of Ions by Organoclays C. Effect of Sorption on Contaminant Transport W. In Siru Modification A. QAC Adsorption Kinetics B. Modeling Cationic Surfactant Adsorption C. Hydraulic Conductivity of Modified Soil V. Biodegradation of Contaminants in Modified Soils A. Toxicity of QACs to Bacteria B. Bioavailability of Sorbed Contaminants References

I. INTRODUCTION The applications of surfactants in environmental remediation can be grouped into two broad categories. The first can be referred to generally as soil washing/flushing. The techniques under this category make use of the micellization and emulsification properties of surfactant molecules. In such applications, *Current address: Health and Environmental Sciences, Dow Coming Corporation,Midland, Michigan 48640-0994. +Towhom correspondence should be addressed. 2s Advancrs in Agronmq, Voiunir f9 Copyright 0 1997 by Academic Press, Inc. All rights of reproduction in any form resewed.

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surfactant solutions are injected into the subsurface or sprayed onto the surface of contaminated soils at concentrations greater than the critical micelle concentration (CMC). When the surfactant solution percolates through the soil or aquifer material, organic contaminants (e.g., petroleum hydrocarbons) are solubilized into surfactant micelles or mobilized by the emulsion. The surfactant solution is then pumped out of the soil matrix, and the volatile components are separated from the aqueous phase, for example, through air striping (Clarke et al., 1991). The nonvolatile contaminants are then separated from the surfactant solution, for example, by solvent extraction (Gannon et al., 1989). The resultant surfactant solutions are often reused. Surfactant-enhanced soil washing/flushing technologies are still in a relatively early stage of development. However, dramatic increases (10-100 times) in the efficiency of removing organic contaminants show the potential utility of this technology (Wilson and Clarke, 1994; West and Harwell, 1992; Chawla et al., 1991). In contrast to soil washing/flushing technologies that seek to mobilize organic contaminants, the second type of application attempts to use surfactants to immobilize hydrophobic organic contaminants dissolved in water. The materials utilized in these applications are cationic surfactants, which are combined with aluminosilicate clays to form organoclays. Naturally occurring clay minerals do not effectively sorb most hydrophobic organic compounds. This is due to the hydration of native inorganic exchangeable ions of clays that creates a hydrophilic environment at the clay surfaces. Replacing the strongly hydrated native inorganic exchangeable ions with organic cations, e.g., cationic surfactants such as quaternary ammonium compounds (QACs), may change the clay surfaces from hydrophilic to organophilic. The resultant organoclays have greatly enhanced sorptive capabilities for a variety of organic contaminants. In a similar application, anion surfactants such as sodium dodecyl sulfate (SDS) are used to replace the native exchangeable anions on iron and aluminum oxides (Park and Jaffi, 1993,1995). SDS adsorbed on oxide surfaces forms hemimicelles (Fuerstenau, 1970) that substantially enhance the capability of oxides to sorb hydrophobic organic compounds from water via partitioning into the hemimicelles. The primary application of such modified oxides is water treatment. An interesting point is that some iron oxides (e.g., magnetite and maghemite) are magnetic and thus SDS-treated iron oxides can be separated from water or soil suspension with magnets (Park and Jaffi, 1995). Organoclays are produced by replacing the exchangeable inorganic cations on layer silicates with organic cations such as quaternary ammonium (Boyd et al., 1988b, 1991), phosphonium (Kukkadapu and Boyd, 1995), and alkylpyridinium (Wagner et al., 1994) compounds. The resultant organoclays can have vastly improved and unique sorptive properties toward organic contaminants, depending on the nature of both organic cations and the types of layer silicates used. Importantly, organoclays have potential of being used both ex situ and in situ. For example, organoclays can be used in place of or in conjunction with activated carbon for wa-

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ter purification (Beall, 1985).The use of organoclays as landfill liner components may decrease the transport of organic compounds through the liner, reducing the potential of contamination (Smith and Jafft5, 1994a). Perhaps the most unique feature and important advantage of this chemistry is that it can be applied in situ. Subsoils and aquifer materials contain negatively charged clay minerals and hence possess a cation exchange capacity (CEC). In earlier research, we demonstrated that sorption of common organic groundwater contaminants by aquifer materials or soils can be increased by at least two orders of magnitude by using cationic surfactants to convert soil clays into sorptive organoclays (Boyd et al., 1988a, 1991; Lee et al., 1989a). These results suggested the possibility that aquifer materials or subsoils could be modified in situ via injections of cationic surfactant solutions, and the modified soil materials could function as “sorptive zones.” Such sorptive zones, if properly placed, could intercept and immobilize contaminant plumes containing dissolved organic chemicals as shown schematically in Fig. 1. The immobilized contaminants could then be detoxified by various chemical or biological means, as for example, through bioremediation using native microbial populations or introduced organisms. Containing contaminant migration in this fashion has the important advantages of preventing further downgradient aquifer contamination and of concentrating contaminants in a defined zone that can be managed to enhance remediation. For ex-

Figure 1 Schematic of proposed in siru modification of aquifer material to create contaminant sorption zone and coupled sorption and biodegradation of organic contaminants for groundwater cleanup.

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ample, in the case of bioremediation, nutrients and/or oxygen could be added to stimulate microbial activity. For any effective use of organoclays, a thorough understanding of the chemistry of organoclay synthesis as well as the properties and stability of organoclays is essential. In the following, we summarize the use of organoclays in contaminant abatement from several aspects. Under Synthesis and Chemical Stability of Organoclays, we examine the chemical reactions involved in synthesis of organoclays and estimate their chemical stability under various conditions. Under Sorptive Properties of Organoclays, we examine the mechanisms involved in sorption of various organic contaminants by organoclays and evaluate their effectiveness for removing organic contaminants from water and for retarding contaminant transport through soil profiles. Under In situ Soil Modification, we address several critical issues concerning in situ soil modification including effects on hydraulic conductivity. In the final section, we examine certain biological aspects of soil modification such as toxicity of cationic surfactants to contaminant degrading bacteria in soils and bioavailability of contaminants sorbed by organoclays.

11. SYNTHESIS AND CHEMICAL STABILITY OF ORGANOCLAYS The central concept for synthesizing organoclays is to replace the native exchangeable inorganic cations of the clays with organic cations through the cation exchange: uAX,

+ u OC”+# uA“+ + VOCX,,,

(1)

where A“+ is a native exchangeable cation, u the valence of the native cation, OC”+ an organic cation, u the valence of the organic cation, and X- denotes an exchange site on the clay surface. In general, the reaction can be accomplished by simply adding aqueous organic cation solutions to clay suspensions. We have examined in detail the adsorption chemistry of QACs on subsoils and clays. The QACs studied were of the general form [(CH,),NR]+ or [(CH,),NR,]+, where R is an alkyl or aromatic hydrocarbon. This chemistry is affected by the size and structure of the R group, the clay type, solution conditions, and the nature of the native exchangeable cation. In general, the interactions between clays and QACs are strong, and QAC-saturated clays can be readily obtained. An exception is that short-chain QACs (e.g., tetramethylammonium) have low affinities for vermiculite (Xu er al., 1995). The synthesis of organoclays using short-chain QACs is relatively straightforward. This is because short-chain QACs have high water solubilities and are adsorbed on clays exclusively by cation exchange. For QACs with large hy-

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drophobic moieties [e.g., hexadecyltrimethylammonium (HDTMA)], there are two complicating factors. First, some long-chain organic cations [e.g., dimethyldioctadecylammonium (DODMA)] have sufficiently low water solubilities so that solvents such as methanol (or methanol-water mixtures) may be needed to dissolve the organic modifiers. Second, both cation exchange and a nonexchange mechanism may contribute to the overall adsorption of such organic cations by clays. The nonexchange adsorption of QACs arises mainly from the nonelectrostatic (nonpolar) interactions between the alkyl moieties (tails) of QACs that have bound to the clay surfacesby cation exchange and the alkyl tails of QACs that have not undergone ion exchange. In addition to such "tail-tail" interactions, the repulsion of the hydrophobic chains of QACs from water also contributes to the nonexchange adsorption of these compounds (Rosen, 1987).These nonpolar interactions in aqueous systems are often referred to as "hydrophobic" bonding. The operative adsorption mechanisms strongly influence the properties of the resultant organoclays as illustrated in Fig. 2. The adsorption isotherms of HDTMA (R is a C-16 alkylhydrocarbon) by clays and soils can be divided into four regions (Xu and Boyd, 1995a). In region 1, cation exchange is the singular

0

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Figure 2 (a) Adsorption isotherms of hexadecyltrimethylammonium (HDTMA) in Na- and Casaturated Oshtemo Bt horizon soil; (b) the electrophoretic mobility of HDTh4A-soil clays; and (c) the relative turhidity of soil suspension as affected by HDTMA adsorption. The final electrolyte concentrations in all cases were 2.3 mM CI -. Reproduced from Xu and Boyd (1995a) with permission from American Chemical Society.

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mechanism for HDTMA adsorption. The alkyl tails of adsorbed HDTMA, present on opposing clay sheets, interact resulting in clay flocculation. The organic cations and remaining inorganic cations are not uniformly distributed in interlayers and on external surfaces. In fact, the external surfaces of the aggregates are occupied strictly by inorganic cations and the organic cations are in the interlayers (Xu and Boyd, 1995a,b). In region 2, both cation exchange and hydrophobic bonding are operative. HDTMA adsorbed via hydrophobic bonding is distributed on the external surfaces of the clay aggregates, reversing the surface charge. Very little increase in HDTMA adsorption via cation exchange occurs in region 3. As the equilibrium concentration of HDTMA increases in region 3, HDTMA adsorption via hydrophobic bonding increases dramatically until the equilibrium concentration of HDTMA equals its CMC. At this point (beginning of region 4), surfactant adsorption reaches a plateau. The development of positive surface charge due to HDTMA adsorption via hydrophobic bonding results in the increased degree of clay dispersion in regions 3 and 4. The adsorption mechanism also influences the chemical stability of the organoclays, which includes three aspects: the extent of adsorption of the organic modifiers, the degree of desorption of the organic modifiers, and the resistance of the adsorbed modifiers to chemical degradation. For the successful application of organoclays in environmental remediation, it is essential to maximize the chemical stability of organoclays.

A. ADSORPTIONOF ORGANIC MODIFIERS BY CLAY MINERALS Many environmental factors, such as clay type, solution composition, and type of exchangeable cations, strongly influence the adsorption of QACs. For example, montmorillonite adsorbs HDTMA more strongly than kaolinite, and vermiculite adsorbs HDTMA more strongly than illite. The HDTMA is adsorbed more strongly by Ca-montmorillonite than Na-montmorillonite. In this section, the complex factors governing the adsorption of a model QAC, viz. HDTMA, will be examined in terms of adsorption mechanisms. 1. Cation Exchange

In cation exchange reactions, the major difference between organic and inorganic cations is that the former have organic moieties of various sizes and hydrophobicities that contribute to the adsorption energy via noncoloumbic interactions. As a result, both electrostatic and van der Waals interactions are important in cation exchange reactions involving organic cations as expressed in the following equation (Theng, 1974; Maes et al., 1980):

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where AGexchis the change of Gibbs free energy of cation exchange, and AGe, and AGvanare contributions to AGexch from electrostatic interactions and lateral (Van der waals) interactions, respectively. Two factors are most important in determining AGel. First, the size of the QAC, specifically the size of the head group and the length of the alkyl chain, affects cation-water interactions. An increase in alkyl chain length for monovalent alkylammonium cations and/or a larger head group result in a less negative hydration energy and thus stronger electrostatic interactions between cations and surfaces (Aue et al., 1976a,b). The second factor is the mineral surface charge, i.e., the amount of tetrahedral charge and octahedral charge, as demonstrated for inorganic-inorganic exchange on clays (Xu and Harsh, 1990a,b, 1992). Several factors are important when considering the contribution of van der Waals interactions to AGexch.The first factor is QAC size. It is known that AGexch for the exchange of inorganic cations by alkylammonium cations on montmorillonites increases linearly with their molecular weight due primarily to increased van der Waals forces (Theng et af., 1967; Vansant and Uytterhoeven, 1972; Vansant and Peeters, 1978; Maes and Cremers, 1983). The second factor is the arrangement of organic cations in the interlayers that determines the contact area of the organic cations with the clay surfaces and the lateral interactions among the adsorbed organic cations themselves. A general conclusion is that adsorbed organic cations in montmorillonites and vermiculite tend to maximize their contact with clay surfaces. For a given surface, QACs may adopt several configurations referred to commonly as flat-lying monolayer, bilayer, pseudotrimolecular layer, and paraffin type (Fig. 3), depending on layer charge density and the size of the organic molecules (Jordan, 1949; Lagaly and Weiss, 1969,1976; Lagaly, 1982; Xu and Boyd, 1995b). The various types of arrangements shown in Fig. 3 determine the van der Waals contact among the tails of adsorbed QACs and between the QAC and clay surface. For example, both sides of organic cations in a monolayer arrangement contact the basal surface. Only one side of the organic ions in bilayers contacts the surface directly, and no direct contact between the basal surfaces and the alkyl chain occurs in a paraffin-type configuration. This may explain the general observation that HDTMA adsorbed more strongly on smectites (flat-lying configuration) than on vermiculite and illite (paraffin type). Another aspect of interlayer arrangement is the distribution of cations in clays when only a portion of inorganic cation is exchanged by QACs. The general view is that cations are not distributed uniformly in mixed cation systems, but tend to segregate themselves into various inorganic-cation-rich and organicsation-rich layers or domains (demixing) that enhance stability (Mortland and Barake, 1964;

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32

b

a

A - 4.1 A

Monolayer

Bilayer A - 8.1 A

PseudotrimolecularLayer A 12.1 A

Paraffin Complex A > 12.4A

-

Figure 3 Possible interlayer arrangements of hexadecyltrimethylammonium and corresponding interlayer spacings (A). Reproduced from Jaynes and Boyd (1991a) with permission from Soil Science Society of America.

Barrer and Brummer, 1963; McBride and Mortland, 1973; McBride, 1979; Theng et al., 1967). However, Vansant and co-workers (Vansant and Uytterhoeven, 1972; Vansant and Peeters, 1978) argued that this type of layer segregation would be less stable than a system with homogeneous mixing of the two exchange cations in every layer. For HDTMA adsorption by swelling clays, the organichnorganic cation distribution strongly depends on the degree of clay dispersion before HDTMA is added to the clay suspension. A highly dispersed clay promotes mixing, whereas flocculated clays lead to segregation (Xu and Boyd, 1995b,c). Mixing and demixing will substantially influence the degree of tail-tail association and hence the stability of the organoclay complexes and the shape of adsorption isotherms. Organic cations in a demixing distribution will have significant lateral interactions even at very low loading levels, which results in monotonic adsorption isotherms. In contrast, a mixing distribution of organichorganic cations in the interlayers can have lateral interactions only after certain (higher) loading levels (Xu and Boyd, 1995c), manifesting S-shaped nonmonotonic isotherms (e.g., Na + HDTMA exchange in Fig. 2). Another important factor that influences the adsorption strength of organic cations on layer silicates is the organophilicity of the surface that the organic cation tails contact. The hydrophilicity of the surfaces of montmorillonite and vermiculite is the subject of considerable debate. Early research on clays emphasized the irnportance of the surface silicate oxygens on water adsorption via hydrogen bonding (Hendricks and Jefferson, 1938; Bradley, 1945; Low, 1961), suggesting a hydrophilic or polar nature of the whole surface. Others argue that hydrogen bonding between water and the surface can form at tetrahedral substitution sites but not at

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33

octahedral substitution sites or on the uncharged surface (Prost, 1975). The proposed weak interactions between water and uncharged basal oxygens are supported by several pieces of evidence including theoretical calculations (Bleam, 1990), X-ray diffraction and spectroscopic analyses (Suquet et al., 1977; McBride et al., 1975; McBride and Mortland, 1973; Doner and Mortlant, 1971 ;Farmer and Russell, 1971; Farmer, 1978), and the strong adsorption of nonpolar organic compounds (aromatic hydrocarbons) from water by trimethylphenylammonium-, tetramethylammonium-, or tetramethylphosphonium-saturatedsmectites (Jaynes and Boyd, 1990; Kukkadapu and Boyd, 1995). The nature and strength of water-clay surface interactions have an important influence on organic cation adsorption. In the case of alkylammonium adsorption on montmorillonite, the alkylammonium chains lie flat on surface (as discussed previously). If we assume the radius of water is 1.38 8,(Hunt, 1963), and view water molecules on the surface in a closest packing arrangement, then each molecule will occupy 6.6 equivalent to the area occupied by 1.15
w2,

+

-

+

'

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S. XU ET AL.

cations in montmorillonite interlayers. For hydrous oxides, the H,O-surface interaction may be very strong due to hydrogen bonding, preventing the tail-surface interaction for organic ions. This is consistent with the observation that anionic surfactants adsorbed on oxide surfaces tend to form a vertical configuration (hemimicelles, admicelles, or bilayers in two-dimensional condensation) even at low loading levels. The lack of strong tail-surface interactions on oxides manifests a weaker organic4ay complex than for 2: 1 clays. Finally, inorganic cation entrapment, a nonequilibrium phenomenon, may play a role in shaping the exchange isotherms describing this chemistry, especially when vermiculite is involved (Xu and Boyd, 1994; Xu et ul., 1995; Greenland and Quirk; 1962;McBride and Mortland, 1973;McAtee, 1959).This phenomenon will produce a negative error in the cation selectivity coefficients, making AG less negative. Avoiding large decreases in cation selectivity is desirable to achieve maximal stability of the organoclay complexes. 2. Hydrophobic Bonding

Hydrophobic bonding is driven by a combination of the mutual attraction between the hydrophobic groups of organic cations and their tendency to be repelled from water (Rosen, 1987). Cation adsorption due to hydrophobic bonding is dependent on several characteristics. First, the occurrence and extent of adsorption depend on the chain length of the hydrophobic moieties. For example, the maximum uptake of small alkylammonium (e.g., n-butylammonium) was shown to be equal to the CEC of the clay even when a large excess was present in the bulk solution (Grim et ul., 1947). Under similar conditions, QACs with chain lengths greater than a critical value (e.g., eight carbons) were sorbed in excess of the CEC (Cowan and White, 1958; Kurlenko and Mikhalyuk, 1959; Sielskind and Wey, 1958).Second, adsorption by hydrophobic bonding increased as ionic strength increased (Margulies, 1992;Xu and Boyd 1995a).Third, adsorption of QAC beyond the CEC passes through a maximum as a function of temperature (Mehrian et al., 1991) similar to that of micellization of organic cations in solution (Rosen, 1987), but in contrast to the temperature-independent QAC adsorption via cation exchange. Fourth, organic cation adsorption via cation exchange results in the surface charge changing toward zero and face-to-face aggregation of clay layers. In contrast, organic cation adsorption by hydrophobic bonding develops positive charge on the surface (Xu and Boyd, 1995a; Chander et al., 1983) and causes destruction of the face-to-faceaggregates,i.e., clay dispersion (Xu and Boyd, 1995a). Fifth, the adsorption of organic cations by hydrophobic bonding results in an equivalent amount of anion adsorption but no pH change in bulk solution (Greenland and Quirk, 1962).The fact that the positive charge develops at the shear plane through this kind of adsorption indicates that at least some of the anions are swarming around the surface (e.g., in the diffuse layer), instead of directly bound to the head groups of the adsorbed organic cations (in Stern layer). Finally, organic ad-

USE OF ORGANOCLAYS INPOLLUTION ABATEMENT

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sorption via hydrophobic bonding changes the organization of adsorbed organic cations in the interlayers (Xu and Boyd, 1995b). These factors affect the stability of the organoclay complexes, the effectiveness of organoclays as sorbents, and the hydraulic properties of treated materials.

B. DESORPTION OF QACs INSUBSOILS Another important issue in clay modification is the desorption of QACs from clay surfaces. In landfill liners, water flow through the liner is limited by very low hydraulic conductivities, and the desorption of QACs may not be of much concern. In in situ modification (Fig. I), the sorptive zone must be engineered to ensure constant groundwater flow. The desorption of clay-bound QACs determines the integrity and hence the functioning of the sorptive zone. Significantdesorption would suggest that a zone of modified soil may have poor stability, thereby limiting the value of modifiers in an in situ technique. Although the importance of understanding desorption of QAC is obvious, desorption data for QACs from clays or soils are very limited. Two important conclusions concerning the desorption of HDTMA can be drawn from the existing studies. First, the binding of large cationic surfactants on interlayer exchange sites of swelling clays is highly irreversible (Zhang er al., 1993; Xu and Boyd, 1994, 1995a). By 13 repeating washings with a total 600 pore vol of 5 mM CaBr, solution, for instance, less than 10% of adsorbed HDTMA was desorbed from a vermiculitic subsoil with an initial HDTMA loading of 50% of the CEC; the desorption of HDTMA from montmorillonite is even less (Xu and Boyd, 1994).Second, the desorption of HDTMA sorbed by hydrophobic bonding is much higher than HDTMAadsorbed on exchange sites and strongly influenced by ionic strength and clay type (Xu and Boyd, 1995a). For example, desorption of HDTMA from a vermiculitic subsoil showed no hysteresis until the HDTMA loading decreased from 195% of the CEC to 110% of the CEC, indicating that HDTMA adsorbed via hydrophobic bonding by vermiculite is desorbable. However, for low-charge montmorillonite, even the HDTMA sorbed via hydrophobic bonding was only partially desorbable (Xu and Boyd, 1993). Regardless of clay type, the extent of desorption of HDTMA will be highest in pure water and will decrease with increasing ionic strength (Xu and Boyd, 1995a).

C. ABIOTIC DECOMPOSITION OF THEADSORBED ORGANIC MODIFIERS Clay minerals are heterogeneous catalysts for many degradation reactions involving organic compounds (Mortland, 1970). Under certain conditions, organic modifiers, such as QACs in montmorillonite and vermiculite interlayers, can un-

36

S. XU ET AL.

dergo abiotic decomposition (Perez-Rodriguez et al., 1988; Morillo et al., 1990; Frenkel and Solomon, 1977; Durand et al., 1972; Chou and McAtee, 1969). The ultimate degradation products are NH; and hydrocarbon derivatives (e.g., alcohols). The ammonium produced remains in the interlayers space while the hydrocarbon derivatives diffuse out, leading to collapse of interlayers. Temperature, moisture, pH, and solvent type are major factors determining the degradation rates of QACs adsorbed by 2: 1 clay minerals. At ambient temperature and neutral pH, the degradation of QACs adsorbed on clay surface is very slow (-5% decomposed in 5 months) (Frenkel and Solomon, 1977). High temperature, low moisture content, low pH, and the presence of organic solvents increase the degradation rates (Perez-Rodriguez et al., 1988; Morillo et al., 1990; Durand et al., 1972; Chou and McAtee, 1969). High water content decreases the acidity of adsorbed water and thus reduces the rates of QAC decomposition. Replacing bulk water with miscible organic solvents decreases the water content in organoclays and thus increases the acidity of the adsorbed water, resulting in increased rates of QAC degradation (Perez-Rodriguez et al., 1988). These data emphasize that low water content (e.g., air dry) is a key factor for significant abiotic degradation of QACs. As a result, abiotic decomposition may not be significant in the subsurface where the water content is normally much higher than that corresponding to airdry conditions.

III. SORPTIVE PROPERTIES OF ORGANOCLAYS Organoclays can effectively sorb a variety of organic contaminants. Although developed primarily to remove neutral organic contaminants from water, organoclays can also sorb both inorganic and organic ions (Brixie and Boyd, 1994; Haggerty and Bowman, 1994), reflecting a complex chemical environment on surfaces of clay minerals.

A. SORPTIONOF HYDROPHOBIC ORGANIC CONTAMINANTS BY ORGANOCLAYS Naturally occurring aluminosilicates ( e g , smectites and vermiculites) do not sorb significant amounts of hydrophobic organic contaminants in the presence of water due to the preferential adsorption of water around the metallic exchangeable cations (Prost, 1975). The sorptive characteristics of soils and clays for nonpolar organic compounds (NOCs) are dramatically improved when the inorganic exchangeable cations are replaced by organic cations such as quaternary ammonium (Lee etal., 1989b, 1990; Boydetal., 1988a,b, 1991; Jaynes and Boyd, 1991a). For

37

USE OF ORGANOCLAYS IN POLLUTION ABATEMENT

NWbll~l

zwo

-

-

121-

A~urourPkrrr conwmntiom, c (nvg

Figure 4 Enhanced sorption of common groundwater contaminants following treatment of soil with hexadecyltrimethylammonium. Sorption isotherms of benzene, toluene, and ethylbenzene from water by untreated and treated St. Clair (a-c) and Oshtemo (d-f) Bt horizon soils are shown. Reproduced from Lee e t a / . (1989) with permission from American Chemical Society.

example, as shown in Fig. 4, sorption coefficients for several common organic groundwater contaminants (e.g., benzene, toluene, and xylene) are increased by over two orders of magnitude by HDTMA modification of a subsoil containing vermiculite and illite as major clay minerals (Boyd et al., 1988a;Lee et al., 1989b). The large increase in sorptive capability of modified soil is partially due to an increase in organic carbon content resulting from HDTh4A adsorption, and partially due to the fact that HDTMA-derived organic matter is 10-30 times more effective than natural organic matter for sorbing NOCs (Lee et al., 1989b). The sorption properties of a given modified clay or soil depend in part on the nature of the organic modifiers. Replacing the inorganic exchangeable cations with small organic cations, such as tetramethylammonium (TMA), trimethylphenyl-

38

S. XU ET AL.

ammonium (TMPA), and tetramethylphosphonium (TMPP) increases the sorption of NOCs (Lee et al., 1990; Jaynes and Boyd, 1990,1991b; Kukkadapu and Boyd, 1995). The sorption is characterized by Langmuir-type isotherms, indicating surface adsorption as the primary retention mechanism (Jaynes and Boyd, 1991b). These clays are commonly referred to as “adsorptive organoclays” (Boyd er al., 1991). The fact that the amount of NOC adsorbed by TMPA-smectite was directly proportional to the surface area of the organoclay but inversely proportional to the layer charge of the clay or its organic carbon content (Lee et al., 1990; Jaynes and Boyd, 1991b) demonstrates that either direct interaction of sorbed NOCs with the exchangeable TMPA cations does not occur or additional mechanism@)occur simultaneously (Jaynes and Boyd, 1991b). Jaynes and Boyd ( 1991b) proposed that in the presence of bulk water the primary adsorptive sites for aromatic hydrocarbons are the uncharged siloxane oxygens, and that small organic cations such as TMPA function primarily as pillars to prop open interlayers and satisfy the cation exchange capacity. The previous interpretation is consistent with the arguments presented by others that uncharged siloxane surfaces in montmorillonites are hydrophobic, and that the hydrophilicity of untreated montmorillonites is mainly due to the strong hydration of exchangeable metal cations (Prost, 1975; Sposito and Prost, 1982). The neutral surfaces of natural clays are apparently obscured by water that hydrates the metal exchangeable cations. Replacing these highly hydrated metal cations with much less hydrated organic cations exposes the neutral surface between the organic exchange cations, leading to the adsorption of NOCs (Jaynes and Boyd, 1991b). In this model, the adsorptive organoclays are viewed as a porous adsorbent. The dimension of those micropores is fixed by the size of the QACs, the charge density of the clays, and by the distribution of the QACs in the interlayer regions. This is important because the size of the micropores may impose certain steric constraints on organic contaminant adsorption (Lee et al., 1989b, 1990). For short-chain QACs, cation exchange is the singular adsorption mechanism. The distribution of the short-chain QACs in the interlayers should correspond closely to the distribution of negatively charged sites. Due to the heterogeneity of layer charge distribution, the distribution of QACs such as TMPA in interlayers of smectite will be heterogeneous. This manifests different pore sizes within a given adsorptive organosmectite (G. Sheng, S. Xu, and S. A. Boyd, unpublished results). The heterogeneity of pore sizes influences the sorption of organic contaminants of different molecular dimensions. For example, among benzene and several alkylbenzenes, benzene can be adsorbed inside both small and large pores of TMPA-smectite, manifesting the largest sorption capacity among the solutes and the most heterogeneous distribution of adsorption energies. In contrast, larger molecules like propylbenzene can be adsorbed only in the larger pores. As a result, the sorption capacity of organosmectite for propylbenzene is only 23% of that for benzene (G. Sheng, S. Xu and S. A. Boyd, unpublished results). In addition,

USE OF ORGANOCLAYS IN POLLUTION ABATEMENT

39

the sterically restricted adsorption of propylbenzene resulted in a more narrow distribution of adsorption energies. This explains the observation that the adsorption isotherm of benzene does not exactly fit the Langmuir equation, which assumes a uniform adsorption energy, whereas that of propylbenzene does. So far, our discussion has focused on organoclays in which the clay is completely saturated with short-chain QACs. There may be instances in which the clay is only partially saturated with QACs because the last 10-20% of the exchange sites on layer silicates such as montmorillonite have comparatively low affinities for QACs (Xu and Boyd, 1995a). G . Sheng, S. Xu, and S . A. Boyd (unpublished results) have demonstrated that adsorption capacity of TMPA-smectite for benzene, alkylbenzenes, and TCE increases dramatically with the fraction of exchange sites occupied by TMPA until loading reaches 65-75% of the CEC. Further increases of TMPA loading beyond this level actually decrease the adsorption capacity of the organoclays. Replacing the inorganic exchangeable cations of clays with long-chain organic cations such as HDTMA results in an organoclay with fundamentally different sorptive properties than adsorptive organoclays (Boyd et al., 1988a,b, 1991; Jaynes and Boyd, 1991a; Lee et al., 1989a). Sorption of NOCs from water by these organoclays manifests type 111 (concave) or double-sigmoid isotherms (Fig. 5 ) , suggesting different mechanisms may be responsible for sorption of NOCs. These organoclays are commonly referred to as organophilic clays. In organophilic clays, the large organic moieties (e.g., C-16 alkyl moieties of HDTMA) fixed on the min-

0.5

-

40

0.4

EE

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0.3

30

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0.4

0.6

0.8

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Solute Relative Concentration Figure 5 Type 111 isotherms for trichloroethylene and double-sigmoid isotherms for chlorobenzene sorption by low-charge hexadecyltrimethylammonium (HDTMA)-smectite (SACH) and high-charge HDTMA-smectite (SAzH) compared to the linear isotherms for trichloroethylene and chlorobenzene sorption by a muck soil. Data from Sheng et al. (1996a).

40

S. XU ET AL.

era1 surface interact to form an organic phase. For HDTMA-modified clay, the micropolarity sensed by a fluorescent probe, pyrene, decreased as HDTMA loading increased, suggesting that modification resulted in the formation of an organic phase much less polar than the untreated clay surface (Brahimi el al., 1992; Kalyanasundaram and Thomas, 1977). The hydrophobic phase of such clays functions as a powerful partition medium for NOC sorption. Compared to linear isotherms for natural soil organic matter (Fig. 5), Type I11 isotherms result from the high sorption ability of the hydrophobic phase for NOCs. In these organoclays, the weight of the sorbed compound, e.g., chlorobenzene, may exceed that of the HDTMA phase itself (Fig. 5). The presence of such high amounts of a sorbed NOC increases the solvency of the hydrophobic phase for the NOC, which in turn manifests a Type 111 isotherm. The function of the hydrophobic phase also depends on the arrangement of organic cations in the interlayers of clay, which in turn depends on the charge density of the clay. For vermiculite, long-chain QACs such as HDTMA adopt a vertical arrangement as illustrated in Fig. 3 (paraffin type). The tails of adsorbed HDTMA are highly flexible and the hydrophobic phase functions as a solubilizing medium for NOCs analogous to a bulk organic solvent. Solute partitioning has been suggested as a major HOC sorption mechanism for this type of organophilic clays based on the observation of linear sorption isotherms. A careful study using organoclay synthesized from both nonswelling and swelling 2: 1 clay minerals with different layer charges demonstrates that double sigmoid sorption isotherms are commonly observed for organophilic clays (Fig. 5). The double sigmoid isotherms can be explained by combination of three sorption mechanisms, which are related to the arrangement of QACs in the interlayer region (Sheng et al., 1996a). In lowcharge smectites, HDTMA forms a flat-lying bilayer in the interlayers. The tails of the adsorbed HDTMA interact strongly with the mineral surfaces and with each other. The hydrophobic phases are relatively rigid and differ in this regard from a free solvent phase. As a result, NOC sorption at low relative solute concentrations (aqueous concentration/water solubility) is mainly due to the solvation of the adsorbed HDTMA and the mineral surfaces. Inserting HOCs between the hydrophobic “tails” of adsorbed HDTMA and the mineral surfaces is enhanced as solution concentrations increase. Ultimately, this results in detachment of the tails from surfaces and increases the d-spacing of the organoclay. As tails become more flexible, a more continuous solvent-like hydrophobic phase forms in the interlayer, and partitioning becomes a predominant sorption mechanism. The solvation and swelling is more easily achieved in high-charge smectite (e.g., SAz- l), in which the HDTMA tails adopt a pseudotrimolecular arrangement, manifesting weaker surface-tail interactions. Thus, the NOC sorption isotherm for HDTMA-SAz-1 is more linear at low relative concentration than that for HDTMA-SWy- 1. The sorptive characteristics of organoclays in single organic solute systems

USE OF ORGANOCLAYS IN POLLUTION ABATEMENT

41

may be different from those in multisolute systems, the latter being more representative of actual contaminated sites. These differences may arise, for example, if sorption of one solute causes interlayer expansion or a change in the solvency of organic phase in the interlayers. In a recent study of NOC sorption by HDTMA-smectites in a bisolute system, Sheng et al. (1996b) found the sorption of one contaminant may have synergistic or antagonistic effects on the sorption of another, depending on the nature of the cosolute. For example, the sorption of TCE by HDTMA-modified srnectites is much lower in a single solute system than that in the presence of other solutes such as nitrobenzene and carbon tetrachloride. Similarly, the presence of TCE increased the sorption of nitrobenzene and carbon tetrachloride. In contrast, the presence of ethyl ether decreased the sorption of TCE by HDTMA-smectites. The synergistic effects occur because the cosolute caused interlayer expansion andor increased solvency of the sorptive phase for the primary solute. Depending on the nature of the cosolute, the solvency of the sorptive phase may also decrease, as in the case of ethyl either, causing an antagonistic effect. However, sorption of most common NOCs should cause an increase in the solvency of the sorptive phase of organophilic clays (e.g., HDTMA-smectite) and hence produce a synergistic, rather than antagonistic, effect on contaminant removal from water.

B. SORPTIONOF IONSBY ORGANOCLAYS Organophilic organoclays modified with long-chain organic cations such as HDTMA can effectively remove ionizable organic compounds such as pentachlorophenol (Mortland et al., 1986; Boyd et al., 1988c; Brixie and Boyd, 1994; Stapleton et al., 1992) and some inorganic anions (Haggerty and Bowman, 1994) from water. The sorption of the ionizable organic contaminants via partitioning of the neutral species is not surprising (Mortland et ul., 1986; Stapleton et al., 1992). The sorption of organic and inorganic anions is somewhat unexpected because the high water solubilities of the charged species would preclude any significant partitioning into the hydrophobic HDTMA phase. A possible mechanism for the adsorption of anionic phenols is “hydrophobic bonding” (Rosen, 1987) similar to the adsorption of dodecyl sulfonate (DS-) on organically modified laponite (Capovilla et al., 1991). Due to the entropy effects, the DS- binds tail to tail with the hexadecylpyridium or dioctadecyldimethylammonium on exchange sites of laponite with the sulfonate head groups oriented toward the water phase (Capovilla et al., 1991). The same type of interfacial interaction is plausible for anionic chlorophenols (Toro-Suarez, 1994). A major constraint for such adsorption is the mutual repulsion of the charged head groups organized at the water-solid interface. A length of 8 carbon atoms is required to balance the repulsive effects of head groups for alkylammonium ions (Cowan and

42

S. XU ET AL.

-

White, 1958), corresponding to a maximum van der Waals contact area of 172 A*. The van der Waals contact area of a pentachlorophenol can be 143-208 A*, about the same as that of an alkyl chain with 7-10 carbon atoms. In addition, because the adsorption is limited to very low surface coverage (c0.15 mol kg-'), the large separation between head groups may reduce the repulsive force. If the previous mechanism is operative, then further increases in the pH beyond one unit above the pKa of pentachlorophenol should not result in any significant change in the degree of sorption of anionic forms. In contrast, the total cation concentration (i.e., ionic strength), which influences the repulsive effects between the anionic head groups (Rosen, 1987), may exert a significant influence on adsorption (Stapleton et al., 1992). Another possible mechanism for the adsorption of pentachlorophenolate and inorganic anions in ostensibly saturated or oversaturated (relative to the CEC) organoclays or organomodified soils is the columbic attraction between the positively charged head groups of organic cations and the anionic head groups of pentachlorophenol. Saturated organoclays are often synthesized by adding the organic cation in an amount equivalent to the CEC of either Na-saturated or Ca-saturated clay. Thus, if the ion exchange reaction is stoichiometric (as often assumed), the positive charge of the head group should be neutralized by the clay. However, for long-chain organic cations such as HDTMA, when the selectivity coefficients of a portion of exchange sites on clay surfaces are low, adsorption by hydrophobic bonding may commence before all the exchange sites are saturated with HDTMA (Xu and Boyd, 1994, 1995a,b). In addition, for vermiculite, QACs like HDTMA can entrap some Ca and even some Na, depending on how well the clay is dispersed (Xu and Boyd, 1994). Our experiments have shown that cation entrapment results in a significant portion of HDTMA being adsorbed through hydrophobic bonding before all the exchange sites are occupied by HDTMA. It is possible, therefore, that chlorophenolate binds directly to the positive head groups of HDTMA that are tail-tail adsorbed to the surface through van der Waals interactions (i.e., via hydrophobic bonding). Finally, layer silicates contain aluminol and ferrol groups that are partially positively charged at normal soil pH (5.5-7) (van Olphen, 1977). The adsorption of anions on these positively charged sites may be small for untreated clays due to repulsion of the anions from the surface region by the predominance of negative charges on the surfaces. HDTMA modification neutralizes the negative charge more effectively than common inorganic exchange ions and may lead to an increase in the anion adsorption by these positively charged sites. The ability of organoclays to immobilize organic and inorganic anions enhances the potential utility of organoclays. Because of their high water solubilities and negative charges, such compounds are typically highly mobile in soils and subsoils.An understanding of the mechanism of anion retention by organoclays is nec-

USE OF ORGANOCLAYS IN POLLUTION ABATEMENT

43

essary to take full advantage of this only recently realized capability of organoclays.

C. EFFECTOF SORPTIONON CONTAMINANT TRANSPORT Contaminant transport in soils is usually dependent on the extent of contaminant sorption and the hydraulic conductivity of the soil. The hydraulic conductivity may, however, be sufficiently low in landfill barriers, such as clay liners and bentonite slurry walls, that molecular diffusion of contaminants becomes a dominant transport mechanism. Addition of organoclays to landfill bamers will retard the mobility of contaminants through landfill barriers by greatly enhancing the extent of contaminant sorption. It is important to point out that the inclusion of Na-bentonite in such barriers is still needed because they form highly dispensed particles in water. These small particles fill voids in the liner material (e.g., soil) and effectively reduce hydraulic conductivity. One-dimensional contaminant transport under steady-state, uniform flow with equilibrium sorption in saturated, homogeneous, isotropic landfill bamers can be expressed as (Weber et al., 1991):

ac

a2c

ac

at

ax

ax

-=Dhy-Vx--ps

(I -&) aq -

ac

ac at ’

(3)

where C is the contaminant concentration, D, is the coefficient of hydrodynamic dispersion, vx is the average linear groundwater velocity, p, is the density of landfill barriers, q is the sorbed concentration, E is the porosity of landfill barriers, x is the distance in the x direction and t is time. The third term on the right side of Eq. (3) describes the effect of sorption on contaminant transport in landfill barriers. Figure 6 shows the model predictions, under given conditions, of transport of 1,2,4-tnchlorobenzene through landfill barriers with and without addition of TMPA-bentonite (Gullick er al., 1995). The addition of 0.1% TMPA-bentonite to landfill barriers reduces 1,2,4-trichlorobenzene transport by tens of times. Smith and Jaffe (1994b) evaluated benzene transport through landfill liners containing organobentonites.In this study, Ottawa sand (88%) was amended with untreated Wyoming bentonite (8%) (primarily Na-bentonite) and either 4% untreated bentonite or 4% organobentonite. The mixtures were compacted to simulate sand-bentonite landfill liners. Hydraulic conductivitiesof the organobentonite composite liners were low, all on the order of lop8c d s . Aone-dimensional solute transport model was used to simulate benzene transport in conventional sand-bentonite liners and in liners containing two sorptive organobentonites, namely HDTMA-bentonite and benzyltrirnethylarnmonium-bentonite.The addi-

S. XU ET AL.

44

o.2

t I

I I

I I I

1

10

I I

11111'

100

I

I

'

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1000

Time (years) Figure 6 Simulated transport of 1,2,4-trichlorobenzenethrough bentonite liners with and without 0.1%trimethylphenyl (TMPAtbentoniteadded. Conditions:K = lo-' c d s . dhldl = I , Dc = 2.26 X IO-' cm2/s, E = 0.40, pb = 1.60 g/cm3, x = 1 m, C, = 5000 pg/liter, Freundlich adsorption parameters Kf = 22.67, and n = 0.808. Reproduced from Gullick et al. (1995) with permission from the Clay Minerals Society.

tion of the organobentonites substantially delayed maximum benzene breakthrough from about 4 years in the conventional liner to about 275 years in the organobentonite amended liners (Fig. 7).

IV. IN SITU MODIFICATION The in siru formation of organoclays from clays contained in soils, subsoils, and aquifer materials is perhaps the most unique and appealing aspect of these materials. The ability to substantially enhance the sorptive capabilities of subsoils through additions of QACs has been clearly established (Boyd et al., 1988a; Lee er al., 1989a). Subsoils exchanged with HDTMA had significantly higher organic matter contents and displayed high sorptive uptake of common organic groundwater contaminants. The sorption coefficients for HDTMA-treated subsoils increased by over two orders of magnitude (Fig. 4).Similar increases were seen in soils of widely different textural classes (from 6 to 44% clay). The sorptive organic phase derived from adsorbed HDTMA was 10-30 times more effective on a unit weight basis than natural soil organic matter in removing NOCs from water (Boyd et al., 1988a; Lee et al., 1989a). These results suggest the possibility of injecting QAC solutions into the subsurface to form sorptive zones in situ. Such zones, if properly placed, could intercept and immobilize organic contaminant plumes. The feasibility of the approach

USE OF ORGANOCLAYS IN POLLUTION ABATEMENT

45

Time (Years) Figure 7 Benzene transport through landfill liners containing organophilic bentonite: (A) 88% sand and 12% Na-bentonite; (B) 88% sand, 8% Na-bentonite, and 4% hexadecyltrimethylammoniunbentonite; and (C) 88% sand, 8% Na-bentonite, and 4%benzyltrimethylammonium-bentonite. R, the retardation factor; v the linear velocity. Reproduced from Smith and Jaffk (1994b) with permission from American Society of Civil Engineers.

was demonstrated by Burris and Antworth (1992) using an aquifer box model to simulate the in situ formation of an organoclay (HDTMA) sorptive zone and the immobilization of a contaminant plume. The results of their study are shown in Fig. 8. The model box (30 X 30 X 120 cm) was filled with a low organic carbon content (0.02%), low clay content (8%) subsurface material obtained from the Columbus Air Force Base in Mississippi. Horizontal groundwater flow was established and then aqueous HDTMA solutions were injected into the aquifer box. The shaded area in Fig. 8 represents HDTMA-modified material, the upgradient edge coinciding with the QAC injection well locations. No changes in flow characteristics were observed after emplacement of the sorbent zone. A simulated plume of tritiated water and dissolved naphthalene was then injected upgradient from the created sorbent zone. Snapshots of the plume (Fig. 8) show that naphthalene was effectively immobilized at the beginning of the sorbent zone, where-

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Figure 8 (Top) hexadecyltrimethylamonium (HDTMA)-treated zone after injection of HDTMA into aquifer matenal box model as indicated by organic carbon contents. (Other panels) snapshots of naphthalene (heavy line) and tntiated water (thin line) plumes in the Columbus Air Force Base aquifer material box model aquifer with an in situ emplaced zone of HDTMA-modified aquifer matenal (shaded area). Naphthalene plume is effectively immobilized by the treated zone. Reproduced from Burris and Antworth (1992) with permission from Elsevier Science Publishers B.V.

USE OF ORGANOCLAYS IN POLLUTION ABATEMENT

47

as the tritiated water plume was not retarded. These results clearly demonstrate the feasibility of creating effective sorbent zones in situ by underground injections of cationic surfactants. Because soils are much more complex than the pure clays, several additional aspects of the proposed in situ modification need consideration. In the future application of in situ soil modification, the QAC will be introduced into the subsurface as an aqueous solution via injection wells. Successful placement of the sorptive zone depends on our ability to control QAC adsorption and movement in soil profiles, and thus is influenced directly by the kinetics of QAC adsorption. In addition, the ability to predict QAC adsorption in soils under different environmental conditions is desirable in order to maximize the effectiveness of soil modification, i.e., maximize the extent of QAC adsorption by cation exchange, and minimize residual QAC solution concentrations. Thus, a predictive model of QAC adsorption in soil is needed. Finally, the efficiency of the sorptive zone will depend partially on the hydraulic conductivity of the sorptive zone. If soil modification leads to a substantial loss of conductivity, the flow will be diverted around the treated zone. Knowledge of how soil modification may influence the hydraulic conductivity is therefore a crucial issue for the successful application of this technology. These issues will be addressed in the following sections.

A. QAC ADSORPTION KINETICS Two types of kinetic processes could conceivably act as rate-limiting steps that determine the time needed to reach sorptive equilibrium, and hence influence the movement of QACs injected into subsoils. First, the rate of chemical reaction between the sorbate and the binding sites in soil particles might itself limit the overall approach to sorptive equilibrium. The chemical reactions of interest are inorganic-organic cation exchange and QAC adsorption via hydrophobic bonding (Xu and Boyd, 1995a,b).These reactions are relatively rapid and probably do not represent a significant kinetic limitation in subsurface environments with typically low water flow rates (- 1 cm day-'). The second type of limitation to sorptive equilibrium may occur if QACs require extensive periods of time to move, as for example by diffusion, to all the points of contact in and on the solids where they would react chemically. These physical limitations are often referred to as mass transfer-limited processes (Schwarzenbach et al., 1993).This can be illustrated by considering the cation exchange of QACs on clay minerals. For the reaction to occur, the QACs have to diffuse through water films around aggregates (film diffusion), followed by diffusion through the immobile fluid filling the interstices of the aggregates (interparticle diffusion) (Rao et al., 1980; Wu and Gschwend, 1986; 1988; Weber et al., 1991). Finally, QACs must move through the interlayer space to the exchange sites (par-

48

S. XU ET AL.

ticle diffusion). The chemical reaction occurs when the QAC replaces the native inorganic cations that will then diffuse out in the opposite sequence, but not necessarily at the same rate. The diffusion coefficients in water films are similar to those in bulk solution cm2 s- for QAC (Jungermann, 1970)l. The upper limit of the thickness [of water film is 0.2 mm (Schwarzenbach et af., 1993), and hence it would take about 3 min for QACs to diffuse through such a film. In the laboratory, film diffusion can be reduced or eliminated with vigorous mixing. Shaking, stirring, or flowing of bulk solution all decrease the time required for film diffusion. It may, however, become more significant in the field in which such disturbance is minimal. The time scale for interparticle diffusion varies depending on the size of the aggregates (Schwarzenbach et af., 1993), water content, porosity, and the tortuosity of the diffusion path within aggregates (Bresler, 1973; Brusseau and Rao, 1989, 1991). For a water-saturated sandy aquifer with total porosity of 0.34, interparticle diffusion coefficients were &, of that in bulk solution (Papendick and Campell, 1980). Particle diffusion is the slowest of the three major mass transfer processes. The slow rate of diffusion is related to the very restricted diffusion paths in the interlayers, and this manifests a much lower diffusion coefficient than in bulk solution (Schwarzenbach et al.. 1993). For example, D' values of tetramethylammonium to 2 X ranged from 5 X cm2 s- * when clay was equilibrated with H 2 0 vapor ranging from 0 to 0.93 relative humidity (Gast and Mortland, 1971), corresponding to three to six orders of magnitude lower than in bulk solution. Studies on the kinetics of sorption and desorption of QACs in soils are very limited. We studied the sorption and desorption of HDTMA by a subsoil containing both nonswelling (e.g., kaolinite and illite) and swelling (e.g., vermiculite) clays (Xu and Boyd, 1995a). In well-mixed soil suspensions, total HDTMA adsorption and Na+HDTMA exchange were both characterized by an initial rapid rate followed by a much slower approach to apparent equilibrium (Fig. 9). The initial rapid rate was attributed to HDTMA adsorption on the external surfaces of clay minerals via both cation exchange and hydrophobic bonding (nonpolar interactions between C- 16 alkyl groups of HDTMA). The slow reaction was due to particle diffusion-controlled transfer of HDTMA, adsorbed initially via hydrophobic bonding, to exchange sites in the clay interlayers. Similar conclusions have been reached for the adsorption of aromatic QACs by smectite clay under vigorous stirring (Viaene et al., 1988). In both studies, the clay sheets formed face-to-face associations called primary aggregates, but no secondary structure, presumably due to vigorous mixing. Therefore, interparticle diffusion was not a rate-limiting factor. However, in situ modification may lead to more extensive aggregation including not only the primary aggregates but also secondary structure. Under these conditions, both interparticle diffusion and particle diffusion may be important in determining the QAC distribution in the soil profile.

USE OF ORGANOCLAYS

49

POLLUTION ABATEMENT l

~

l

-

Cs-Treated

-A *A

A A

t

"

t

-

rE

0

A

-

-

-

-

-

-

-

HDTMAAd. NaRelease

Na Rel. A A I

0.4 0

20

40

60

80

100 120 0

20

40

-

ImM 5mM I

I

I

60

EquilibriumTime (hrs) Figure 9 Kinetics of hexadecyltrimethylammonium (HDTMA) adsorption and Na release in Nasaturated untreated Oshtemo Bt horizon soil and in Na-saturated, Cs-treated Oshtemo Bt horizon soil as affected by initial NaCl concentrations. Reproduced from Xu and Boyd (1994)with permission from Soil Science Society of America.

B. MODELINGCATIONIC SURFACTANT ADSORPTION Several models have been proposed to account for organic cation adsorption in pure clay systems, each with its own limitations. For example, the tail-tail complex model (Margulies et al., 1988) assumes excess organic cations are retained beyond the CEC by adhesion of their tails to the tails of the organic cations adsorbed on exchange sites. It predicts the maximum organic cation adsorption as twice the CEC (2 CEC), whereas the experimental data indicate that maximum adsorption of QACs varies from I CEC to more than 3.2 CEC (Greenland and Quirk, 1962; Jaynes and Boyd, 1991a;Xu and Boyd, l994,1995a,b; Brahimi et al., 1992). Furthermore, it assumes all the counterions reside in the diffuse layer and hence predicts no effects of anion type, contradicting our own observations in soil (Xu and Boyd, 1994, 1995a) and clays (Xu and Boyd, 1995b,c). The neutral site model (Brownawell ef af.,1990) assumes that organic cations are adsorbed either on exchange sites or adsorbed as ion pairs on energetically heterogenous neutral sites. It does not account for the surface charge reversal and requires the use of too many flexible parameters (types of neutral sites and associated adsorption energies).

~

'

50

S. XU ET AL.

The third models, hemimicellization, has been used successfully to describe the adsorption of anionic surfactants onto strongly hydrated oxide surfaces (Fuerstenau, 1970; Somasundaran and Fuerstenau, 1966; Somasundaran and Goddard, 1979). The experimental data on which this postulate depends are typified by the SDS adsorption on alumina (Wakamatsu and Fuerstenau, 1968). According to these authors, the initial adsorption is a simple exchange of DS- with anions in the double layer of the oxide (region I). Then, at a particular concentration (before the anion exchange sites were saturated by DS-) known as the critical hemimicelle concentration, adsorption increases drastically as hemimicelles form on the absorbent; a concomitant change in zeta potential occurs (region 11). When this happens, a plot of log adsorption density (r,)vs. log surfactant concentration ( C ) should give a straight line with slope (m)equal to the average number of surfactant molecules in each hemimicelle, according to the following equation: log rs = (log 2 m? - AG,/RT)

+ m log C,

(4)

where r is the radius of ions and AGad is the adsorption free energy of hemimicelles. When applied quantitatively to the organic cation adsorption on layer silicates, the hemimicellization model is only partially successful in describing adsorption either above or below the CEC. For example, no slope rise analogous to hemimicellization in oxides has been observed at QAC loadings 5 1 CEC for swelling clays (Xu and Boyd, 1995b,c; Brownawell er al., 1990) or soil containing swelling clays (Xu and Boyd, 1995a). Instead, we observed that the slope at HDTMA loadings 5 1 CEC is very large and varies with surface loading level of organic cations and ionic strength (Xu and Boyd, 1995a). The varying slopes suggest that a simple log-log linear relationship between the amount of surfactant adsorbed and its equilibrium concentration in water (see Eq. (4),such as that observed for hemimicellization (referred to as region 11),does not apply in organic/inorganic exchange in swelling clay systems. We did observe a simple log-log relationship at HDTMA loading greater than 1 CEC, but the slope of the curve was less than 1 (Xu and Boyd, 1995a), contradicting the concept of hemimicelles (m should be 2 or greater). In addition, the adsorbed organic cations in the interlayers of low-charge swelling clays (e.g., smectites) adopt a flat-lying configuration, in contrast to the vertical orientation of the ions in hemimicelles on oxide surfaces. We have also tested two other models, a two-dimensional condensation model (Cases and Villieras, 1992) and the self-consistent field lattice model (Bohmer and Koopal, 1992), which failed to adequately describe QAC sorption by swelling clays (Xu and Boyd, 1995b) and soils containing swelling clays (Xu and Boyd, 1995a). In a recent study (Xu and Boyd, 1995c), we developed and tested a new model for cationic surfactant adsorption using HDTMA as a model compound. The model consists of two submodels describing cation exchange and hydrophobic bonding, respectively. In the cation exchange submodel, the exchange sites of clays

USE OF ORGANOCLAYS IN POLLUTION ABATEMENT

51

were treated as multitypes of sites, each having a cation selectivity coefficient (KA-HDTMA.)for any binary exchange reaction between inorganic cations (A+) and HDTMA on clay exchange sites (Xj): AXj + HDTMA+ # HDTMAXj + A+.

(5)

The cation selectivity coefficient is given by KA-H,Ti+

- (MHDTMAxj 1MAXj)(aA+/aHDTMA+)

(6)

where MHDTMAXj and MAxj are mole cation fractions of HDTMA+ and A+ on the exchange sites, respectively and uHDTMA+ and uA+ are the activities of these cations in equilibrium solution. As we mentioned previously, both electrostatic interactions and lateral interactions determine the strength of the HDTMA-surface interaction. Therefore, K,+HDTMAj

= Ke, exp(-wRT)*

(7)

where Ke, is the electrostatic contribution to the cation exchange selectivity coefficient and exp( -/RT) accounts for the contribution to the cation selectivity coefficient from lateral interactions. The parameter o is the free energy change for lateral interactions and related to the interlayer distribution of inorganic-organic cations as

= 1, and p is a paraWhere oois the minimum free energy change at MHDTMAXj meter describing the randomness of organic-inorganic cation distribution in the clay interlayers. When organic cations are segregated in the interlayers, p = 0, and as a result w = ooregardless of HDTMA loading on the exchange sites. Under this condition, KA-HDTMAjis a constant with maximal contribution from lateral interactions. When organic and inorganic cations are not completely segregated, p > 0 and o is a power function of HDTMA loading. In other words, the lateral interaction becomes significant only when HDTMA loading on the exchange sites increases beyond a certain point. The hydrophobic bonding submodel is based on the empirical observation that HDTMA adsorption at high loadings is linearly related to the square root of the aqueous concentration of monomeric HDTMA according to the equation: qHBrqHB,a

=

(cmrcm)"2,

(9)

where qHBis the amount of HDTMA adsorbed by hydrophobic bonding and qHBSm is the HDTMA adsorption plateau, and Cmand C, are equilibrium aqueous concentrations of HDTMA corresponding to qHBand qHB+ respectively. Equation (9) can be easily rearranged into

52

S. XU ET AL.

where Kd is the HDTMA distribution coefficient for hydrophobic bonding and €I is the surface coverage (qHB/qHB,m), C , is about the same as CMC of the surfactant (Xu and Boyd, 1995~).As Eq (10) shows, the distribution constant for hydrophobic bonding is reversely related to the surface coverage and the CMC of the surfactant. The new model is conceptually simple and accounts for HDTMA adsorption by clays under different experimental conditions and for the influence of interlayer swelling of clays on adsorption. For swelling clays such as montmorillonites, a random distribution of inorganic and surfactant cations (p > 0) is observed when clay is well dispersed before HDTMA is added (e.g., Na-montmorillonite in Fig. 10a). This results in a dramatic increase in cation selectivity as HDTMA cations occupy from 50 to 70% of the exchange sites, which in turn manifests an S-shaped exchange isotherm (Fig. 2). When montmorillonite is flocculated before HDTMA addition (e.g., Cs-, Ca-, and Mg-montmorillonite or Na-montmorillonite at a NaCl concentration >0.04 M), a segregated or a partially segregated distribution (p near or equal to 0) of cations in the interlayers is observed (Xu and Boyd, 1995b,c). The segregated distribution is also observed for nonswelling clays (e.g., kaolinite and illite in Fig. 10a). The segregated distribution of cations manifests a high and constant cation selectivity at low loading levels and monotonic HDTMA adsorption isotherms (Xu and Boyd, 1995~).Regardless of clay type, HDTMA adsorption via hydrophobic bonding by both nonswelling and swelling clays fits Eq. (9) well (Fig. lob), with log C- being linearly related to the logarithm of total salt concentration in solution (Xu and Boyd, 1995~).

C. HYDRAULIC CONDUCTIVITY OF MODIFIED SOIL Hydraulic conductivity of the sorptive zone is an important issue for the successful application of in situ soil modification. Our laboratory studies indicate that QAC modification may influence the hydraulic conductivity of the sorptive zone in several ways. The adsorption of QACs by swelling clay at sub-CEC saturations may cause clays to floculate; adsorption of QACs in excess of the CEC may cause clay dispersion. Xu and Boyd (1994, 1995a) determined the degree of clay dispersion as influenced by HDTMA adsorption (Fig. 2). They found that replacing inorganic exchange cations with HDTMA resulted in the formation of extensive and stable aggregates due to tail-tail interactions involving HDTMA held on exchange sites of opposing clay sheets. This should be considered as a potentially favorable effect of soil modification because the extensive aggregation resulting from the binding of many small particles together will increase the hydraulic conductivity of the treated zone. In general, the larger the QAC, the greater the degree of aggregation. In studies of sanma-bentonite landfill liners, the addition of 4% organoclay caused a slight increase in hydraulic conductivity from about 2 X

USE OF ORGANOCLAYS IN POLLUTION ABATEMENT

53

woming Montmorillonite Kaolinite

5

Predicted

2 1

0.0

0.2

0.4

0.6

0.8

1.0

Mole Fraction of Exchange Sites Saturated by HDTMA 1.2 1.o

0.8 8

m'

d 0.6 3 0-

5.3 mM NaCl 7.6mM NaCl 2.7 mM CaCI, 2.1 mM MgCI,

A

0.4

0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

(C/C,)"*

Figure 10 ( a ) Comparison of cation selectivity coefficients of hexadecyltrimethylammonium (HDTMA) (KNa-,,,,,,) predicted by the proposed model and those measured for both swelling and nonswelling clays; (b) relation between HDTMA adsorption via hydrophobic bonding (qHB) normalized to the adsorption plateau (qHB.J and the aqueous concentration of surfactant (C) normalized to thc surfactant monomer concentration at the adsorption plateau ( C - ) . Reproduced from Xu and Boyd ( 199%) with permission from American Chemical Society.

to 1-7 X cm/s (Smith and Jaff6, 1994b). However, QAC adsorption via hydrophobic bonding results in development of positive charge on clay surfaces that can dismantle clay aggregates and cause clay dispersion. The dispersed clay then may clog the pores and reduce the conductivity of the modified soil. In addition, the modified clays, especially the swelling 2: 1 clays, will have larger volume af-

S. XU ET AL.

54

ter QAC adsorption (Xu and Boyd, 1995b). This volume change under confinement (e.g., in the subsurface) may reduce the pore sizes for high clay content subsoils and decrease the hydraulic conductivity of the modified soil. Wallace et al. (1995) examined the hydraulic conductivity of HDTMA-modified soil. A sandy loam soil (19% clay) was treated in batch with 1 CEC of HDTMA. The treated soil was dry packed into soil columns and hydraulic conductivity was measured as a function of effective stress using a fixed ring consolidometer. HDTMA treatment and dry packing resulted in a 79% reduction in conductivity prior to loading (no effective stress applied). However, the organomodified soil exhibited less conductivity loss as a consequence of increased loading so that at higher stress (i.e., at greater soil depth) conductivity of the treated samples was actually higher than that of the untreated soil. Hydraulic conductivity is a critical issue in in sifu modification. Further efforts are needed to more fully evaluate the influence of in siru soil modification on hydraulic conductivity at different effective stresses. This would enable the zone to be engineered to accommodate any loss in conductivity. Furthermore, the basic physical-chemical mechanisms that manifest changes in conductivity need to be understood so that losses in conductivity can be minimized.

K BIODEGRADATION OF CONTAMINANTS IN MODIFIED SOILS A.

TOXICITY OF QACs TO BACTERIA

Protecting downgradient aquifer quality by retarding contaminant transport in sorbent zones as described previously is a potentially useful in sifu technology for environmental remediation and protection. In addition to minimizing further contamination of aquifer solids and reducing downgradient contaminant concentrations in groundwater, this approach has the advantage of confining contaminants in a zone that could be managed more effectively for remediation. One such approach would be in situ bioremediation of contarninants in the sorbent zone. Coupling enhanced contaminant immobilization with bioremediation would provide the basis for a comprehensive in situ technology to permanently remove organic contaminants from the subsurface (Fig. 1). In attempting to couple enhanced sorption and biodegradation in this fashion, two critical issues arise: toxicity of the soil modifiers (QACs) to pollutant degrading bacteria, and bioavailability of sorbed contaminants to bacteria. To assess the effects of QAC addition to soils on pollutant biodegradation we measured the toxicities of QACs, free in solution and bound to clays, to common xenobiotic degrading bacteria found in soils (Nye et al., 1994). Toxicity of free HDTMA was

USE OF ORGANOCLAYS IN POLLUTION ABATEMENT

55

quite high, with LC,, values (in parentheses) ranging from 4 to 53 FLM for Pseudomonasputida (4),Micrococcus luteus (53),Rhodococcus rhodochrous (37), Arthrobacter globifomis (7), and Alcaligenes sp. (8). The toxicities of several different QACs toward t?putida, which showed the highest sensitivity to HDTMA, were evaluated by measuring percentage survival after 1 h exposure to various cation concentrations. The QACs tested were HDTMA, cetylpyridinium (CTB), dodecyltrimethylammonium (DdTMA), nonyltrimethylammonium (NTMA), and DODMA. Generally, monoalkyl cations with shorter chain lengths (NTMA and DdTMA) were less toxic than those with longer chain lengths (HDTMA and CTB). The dialkyl cation, DODMA, exhibited the lowest toxicity, about 10 times less toxic than HDTMA. The toxicity of aqueous-phase HDTMA was largely eliminated by additions of smectite clay. Figure 11 shows that the percentage survival of F! putida to 100 FLM HDTMA (1 h exposure) increased from 0 to 100%when smectite clay was added in a stoichiometric amount based on its CEC of 90 cmmolckg. Survival increased in inverse proportion to the calculatedconcentration of unbound cation. These data clearly demonstrate that adsorption of HDTMA to clay alleviates toxicity. The effects of QAC additions on bioremediation were also assessed in biodegradability tests of 14C-labeledcompounds in QAC-treated soils (Nye et al.,

Smectite Clay Added (rng) Figure 11 Influence of smectite clay additions on the survival of I? purida (0) in 100 KM hexadecyltrimethylammonium(HDTMA) solutions following a I-h treatment. The calculated equilibrium aqueous-phase HDTMA concentrations (0)are shown on the right scale. Smectite alleviates the toxicity of HDTMA. Reproduced from Nye et al. (1994) with permission from American Chemical Society.

56

S. XU ET AL.

Time (hours) Figure 12 Time courses for the mineralization of 2.4-0 (10 p,g ml-I) in Marlette soil slurries without the addition of hexadecyltrimethylammonium(HDTMA) (0). with the addition of unbound aqueous HDTMA at 70%of the CEC (O),or with the addition of an equivalent amount of HDTMA, prebound to sterile Marlette Bt soil (0).Reproduced from Nye et al. (1994) with permission from American Chemical Society.

HDTMA additions. In the absence of HDTMA, 2,4-o mineralization occurs readily. The addition of dissolved aqueous HDTMA at a level equal to 70% of the soil CEC resulted in near-complete inhibition of mineralization. In contrast, when an equivalent amount of HDTMA was presented in a prebound form, i.e., bound to sterile soil, extensive 2,4-Dmineralization occurred. These results again demonstrate the reduced toxicity of clay-bound QACs. The effects of QAC additions to soils will probably occur in two stages. Initially, the QACs will adversely affect the activities of contaminant degrading bacteria. However, once bound to clay, toxicity will be greatly reduced. This should allow repopulation by either native or introduced bacteria and biodegradation of contaminants within the treated zone.

B. BIOAVAILABIL~~Y OF SORBEDCONTAMINANTS The influence of contaminant sorption by organoclays or organomodified soils on biodegradation is another important aspect of the coupled immobilizationbiodegradation scheme. Previous studies have shown that sorbed compounds are unavailable to bacteria and that desorption into the aqueous phase was a prerequi-

USE OF ORGANOCLAYS IN POLLUTION ABATEMENT

57

site for biodegradation (Ogram et al., 1985).In these instances, the kinetics of desorption may limit the overall biodegradation rate. The bioavailability of sorbed compounds may also be affected by the microorganisms themselves. Some organisms possess the ability to directly access sorbed contaminants as shown by Guerin and Boyd (1992) for the bacterial mineralization of naphthalene sorbed to soil. We examined the bioavailability of naphthalene sorbed to HDTMA-smectite (Crocker et ul., 1995) using a kinetic mineralization assay developed by Guerin and Boyd (1992). Sorbed naphthalene was directly available to E! putida strain 17484, in agreement with previous studies on the bioavailability of soil-sorbed naphthalene (Guerin and Boyd, 1992). For a second naphthalene degrader, Alcaligenes sp. strain NP-ALK, sorbed naphthalene was available only after its desorption. from HDTMA-smectite. However, desorption of naphthalene from HDTMA-smectite aggregates c0.25 mm in diameter was rapid and did not limit bioavailability. This agrees with earlier studies of desorption rates of aromatic hydrocarbons. Using a gas purge apparatus, Benzing (1993) showed that the desorption rate of propylbenzene from <0.25 mm diameter HDTMA-smectite was 10 times greater than that from larger aggregates and from soils. These studies suggest that contaminants sorbed to HDTMA-modified soils should be largely bioavailable to bacteria because the desorption rates from these materials are high and some degradative bacteria have the ability to directly utilize sorbed contaminants.

-

REFERENCES Allred, B., and Brown, G. 0. (1994). Surfactant-induced reductions in soil hydraulic conductivity. Groundwater Management Res. Spring, 174-1 84. Atkins, P. W. (1990). Quanta. Oxford Univ. Press, New York. Aue, D. H., Webb, H. M., and Bowers, M. T. (1976a). A thermodynamic analysis of solvation effects on the basicities of alkylamines. An electrostatic analysis of substituent effects. J. Am. Chem. Soc. 98,3 18-330. Aue, D. H., Webb, H. M.,and Bowers, M. T. (1976b). Quantitative proton affinity. ionization potentials, and hydrogen affinities of alkylamines. J. Am. Chem. SOC.98,311-317. Barrer, R.M.,and Brummer, K. (1963). Relations between partial ion exchange and interlamellar sorption in alkyammonium montmorillonite. Trans. Faraday SOC.59,959-968. Beall, G. W. (1985). Process for treating organics contaminated water. U.S. Patent P4.517.094. Benzing, T. R. (1993). The desorption kinetics of nonionic organic compounds from hexadecyltrymethylammonium-modifiedsoils and clays. Ph.D. dissertation. Michigan State University, East Lansing. MI. Bleam, W. E (1990). The nature of cation-substitution sites in phyllosilicates. Chys Clay Miner: 38, 521-536. Bohmer, M. R., and Koopal, L. K.(1992). Adsorption of ionic surfactants on constant charge surfaces. Analysis based on a self-consistent field lattice model. Langrnuir 8, 1594-1602. Boyd, S. A., Lee, J. F.,and Mortland, M. M. (1988a). Attenuating organic contaminant mobility by soil modification. Nafure 33,345-347.

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Boyd, S. A., Mortland, M. M., and Chiou, (1988b). Sorption characteristics of organic compounds on hexadecyltrimethylammoniumsmectite.Soil Sci. SOC. Am. J . 52,652-657. Boyd, S. A., Sun, S., Lee, J. F., and Mortland, M. M. (1988~).Pentachlorophenol sorption by organoclays. Clays Clay Mine,: 36, 125-130. Boyd, S. A,, Jaynes, W. F., and Ross, B. S. (1991). Immobilization of organic contaminants by organoclays: Application to soil restoration and hazardous waste contaminants. In “Organic Substances and Sediments in Water,” (R.A. Baker, Ed.), Vol. 1, pp. 181-200. Lewis, Chelsea, MI. Bradley, W. F. (1945). Molecular associations between montmorillonite and organic liquids. J. Am. Chem. SOC. 67,975-98 I . Brahimi, B., Labbe, P., and Reverdy. G. (1992). Study of the adsorption of cationic surfactants on aqueous laponite clay suspensions and laponite clay modified electrodes. Langmuir 8, 1908-1918. Bresler, E. (1973). Simultaneous transport of solutes and water under transient unsaturated flow conditions. WaferResources Res. 9,975-986. Brixie, J. M., and Boyd, S. A. (1994). Treatment of contaminated soils with organoclays to reduce leachable pentachlorophenol. J. Envirvn. Qual. 23, 1283-1290. Brownawell, B. J.. Chen, H.. Collier, J. M., and Westall, J. C. (1990). Adsorption of organic cations to natural materials. Environ. Sci. Technvl. 24, 1234-1241. Brusseau, M. L., and Rao, P. S. C. (1989). Sorption nonideality during organic contaminant transport in porous media. Crir. Rev. Envirvn. Conrroll9,33-99. Brusseau, M. L.. and Rao, P. S. C. (I99 I). Influence of sorbate structure on nonequilibrium sorption of organic compounds. Environ. Sci. Technvl. 25, 1501-1506. Burris, D. R., and Antworth, C. P. (1992). In siru modification of aquifer material by a cationic surfactant to enhance retardation of organic contaminants. J. Conram. Hydrvl. 10,325-327. Capovilla, L., Labbe, P., and Reverdy, G. (1991). Formation of cationic anionic mixed surfactant bilayers on laponite clay suspensions. Langmuir 7, 1251-1 264. Cases, J. M., and Villieras, F. (1992). Thermodynamic model of ionic and nonionic surfactant adsorption-abstaction on heterogeneous surfaces. Langmuir 8, 1251-1264. Chander, S., Fuerstenau, D. W., and Stigter, D. (1983). On hemimicelle formation at oxide water interfaces. In “Adsorption from solution” (R. H. Ottewill, C. H. Rochester, and A. L. Smith, Eds.), pp. 197-210. Academic Press, New York. Chawla. R. C., Porzucek, C., Cannon, J. N., and Hohnson, J. H., Jr. (1991). Importance of soil-contaminant-surfactant interactions for in situ soil washing. In “Emerging Technologies in Hazardous Waste Management II” (D. W. Tedder and F. G. Pohland, Eds.), pp. 316341. American Chemical Society, Washington, DC. Chou, C. C., and McAtee, J. L., Jr. (1969). Thermal decomposition of organoammonium compounds exchanged onto montmorillonite and hectorite. Clays Clay Mine,: 17,339-346. Clarke, A. N., Plumb, P. D., Subramanyan, T. K., and Wilson, D. J. (1991). Soil clean-up by surfactant washing. 1. Laboratory results and mathematical modeling. Separation Sci. Technol. 26,301-343. Cowan, C. T., and White, D. (1958). The mechanisms of exchange reactions occurring between sodium montmorillonite and various n-primary aliphatic amine salts. Trans. Faraday Svc. 54, 691-697. Criati, F., Erre, L., Micera, G.,Piu, P., and Gessa, C. (1983). Effects of layer charge on the nearinfrared spectra of water molecules in smectites and vermiculites. Clays Clay Mine,: 3 1 , 4 4 7 4 9 , Crocker. F. H., Guerin, W. F., and Boyd, S. A. (1995). Bioavailability of naphthalene sorbed to cationic surfactant-modified smectite clay. Envirvn. Sci. Technvl. 29,2953-2958. Doner, H. E., and Mortland, M. M. (1971). Charge location as a factor in the dehydration of 2: 1 clay minerals. Soil Sci. SOC.Am. Prvc. 35,360-362. Durand, B., Fripiat, J. J., and Pelet, R. (1972). Alkylammonium decomposition on montmorillonite surfaces in an inert atmosphere. Clays Clay Mine,: 20,21-35. Farmer, V. C. (1978). Water on particle surfaces. In “The Chemistry of Soil Constituents,” (D. J. Greenland and M. H. B. Hayes, Eds.), pp. 405-448. Wiley, New York.

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Farmer, V. C., and Russell, J. D. (1971). Interlayer complexes in layer silicates. The structure of water in lamellar ionic solutions. Water on particle surfaces. Trans. Faraday Soc. 67,2737-2749. Frenkel, M., and Solomon, D. H. (1977). The decomposition of organic amines on montmorillonites under ambient conditions. Clays Clay Miner: 25,463464. Fuerstenau, D. W. (1970). Interfacial processes in mineravwater systems. Pure Appl. Chem. 24, 135-164. Cannon, 0. K., Bibring, P., Raney, K., Ward, J. A., Wilson, D. J., Underwood, J. L., and Debelak, K. A. (1989).Soil clean up by in situ surfactant flushing. 111. Laboratory results. Separation Sci. Technol. 24,1073-1094. Cast, R. G., and Mortland, M. M. (1971).Self-diffusion of alkylammonium ions in montmorillonite. J . Colloid Interficc. Sci. 37, 80-92. Greenland, D. J., and Quirk. J. P. (1962). Adsorption of I-n-alkylpyridinium bromides by montmorillonite. Clays Clay Miner: 9,484499. Grim, R. E., Allaway, W. H., and Cuthbert, F. L. (1947). Reaction of different clay minerals with some organic cations. J . Am. Chem. Sue. 30, 137-142. Guerin, W. F., and Boyd, S. A. (1992). Differential bioavailability of soil-sorbed naphthalene to two bacteria species. Appl. Environ. Microbiol. 58, 1142-1 152. Gullick, R. W., Weber, W. J., Jr. and Gray, D. H. (1995). Organic contaminant transport through clay liners and slurry walls. In “Reactions.of Organic Pollutants with Clays.” The Clay Minerals Society Premeeting Workshop, Baltimore, MD. Haggerty, G. M., and Bowman, R. S. (1994). Sorption of chromate and other inorganic anions by organo-zeolite. Environ. Sci. Technol. 28,452458. Hendricks, S. B., and Jefferson, M. E. (1938). Structure of kaolin and talc-pyrophyllite hydrates and their bearing on water sorption of clays. Am. Mineral. 23,863-875. Hunt, J. P. (1963). “Metal Ions in Aqueous Solution.” Benjammin, New York. as an effective adsorbent Jaynes, W. F., and Boyd, S. A. (1990). Trimethylphenylammonium-smectite of water soluble aromatic hydrocarbons. J. Air Waste Management Assoc. 40, 1649-1653. Jaynes, W. F., and Boyd, S. A. (1991a). Clay mineral type and organic compound sorption by hexadecyltrimethylammonium-exchangedclays. Soil Sci. Sue. Am. J. 55,4348. Jaynes, W. F.. and Boyd, S. A. (1991b). Hydrophobicity of siloxane surfaces in smectites as revealed by aromatic hydrocarbon adsorption from water. Clays Clay Miner: 39,428-436. Jordan, J. W. (1949). Organophilic bentonites. 1. Swelling in organic liquids. J. Phys. Colloid Chem. 53,294-306. Jungermann, E. (Ed.) “Cationic surfactants.” Dekker, New York. Kalyanasundaram, K., and Thomas, J. K. (1977). Environmental effects on vibronic band intensities in pyrene monomer fluorescence and their application in studies of micellar systems. J . Am. Chem. SOC. 99,2039-2044. Kukkadapu, R. K.. and Boyd, S. A. (1995). Tetramethylphosphonium- and tetramethylammonium smectites as adsorbents of aromatic and chlorinated hydrocarbons: Effect of water on adsorption efficiency. C l a g Clay Miner: 43,318-323. Kurlenko. 0. D., and Mikhalyuk, R. V. (1959).Adsorption of aliphatic amines on bentonite from aqueous solution. Kulloidn. Zh. 21, 181-184. Lagaly, G. (1982). Layer charge heterogeneity in vermiculites. Clays Clay Miner: 30,215-222. Lagaly, G . , and Weiss, A. (1969). Determination of the layer charge in mica-type layer silicates. In “Proceedings of the International Clay Conference, Tokyo, 1969” (L. Heller, Ed.), Vol I., pp. 61-80.. Israel Univ. Press, Jerusalem. Lagaly, G., and Weiss, A. (1976). The layer charge of smectitic layer silicates. In “Proceedings of the International Clay Conference, MexicoCity, 1975”(S.W. Bailey, Ed.), pp. 157-1 72.Applied Publishing, Wilmette, IL. Lee, J.-F., Crum, J., and Boyd, S. A. (1989a).Enhanced retention of organic contaminants by soils exchanged with organic cations. Environ. Sci. Technol. 23, 1365-1372.

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Lee, J.-F., Mortland, M. M., Chiou, C. T., and Boyd, S. A. (1989b). Shape-selective adsorption of aroJ. Chem. Soc. Faraday Trans. I matic molecules from water by tetramethylammonium-smectite. 85,2953-2962. Lee, J.-F., Mortland, M. M., Chiou, C. T., Kile, D. E., and Boyd, S. A. (1990). Adsorption of benzene, toluene and xylene by two tetramethylammonium-smectiteshaving different charge densities. Clays Clay Miner: 38, 113-1 20. Low, P. F. (1961). Physical chemistry of clay-water interaction. Adv. Agron. 13,269-327. Maes, A., and Cremers, A. C. (1982). Water adsorption in Cu and Ca smectites of varying charge. In “International Clay Conference, 1981” (H. van Olphen and F. Vermiale, Eds.), Elsevier, New York. Maes, A., and Cremers, A. C. (1983). Mixing-demixing behavior of calcium-ethylammonium mixtures in otay montmorillonite. Cluys Clay Miner: 31,73-74. Maes, A,, Leemput, L. V., Cremers. A,, and Uytterhoeven. J. (1980). Electron density distribution as a parameter in understanding organic cation exchange in montmorillonite. J. Colloid interface Sci. 77, 14-20. Margulies, L. (1992). Using clays for stabilizing light-sensitive pesticides. In “Layer Charge Characteristics of Clays.” Premeeting Workshop, CMS-SSSA. Margulies, L., Rozen, H., and Nir, S. (1988). Model for competitive adsorption of organic cations on clays. Clays Clay Miner: 36,270-276. McAtee, J. L.,Jr. (1959). Inorganic-organic cation exchange on montmorillonite. Am. Mineral. 44, 1230-1236. McBride, M. B. (1979). Cation spin probes on hectorite surfaces: Demixing and mobility as a function of adsorption level. Clays Clay Miner: 27.97-104. McBride, M. B., and Mortland, M. M. (1973). Segregation and exchange properties of alkylammonium ions in a smectite and vermiculite. Clays Clay Miner: 21,323-329. McBride, M. B., Pinnavaia, T.J., and Mortland, M. M. (1975). Electron spin resonance studies of cation orientation in restricted water layers on phyllosilicate (smectite) surfaces. J. Phys. Chem. 79, 2430-2435. Mehrian, T.,de Keizer, A., and Lyklema, J. (1991). Effect of temperature on the adsorption of organic cation on charged surfaces. Langmuir 7,3094-3098. Morillo, E..Perez-Rodriguez, J. L., and Maqueda, C. (1990). Decomposition of alkylammonium cations adsorbed on vermiculite under ambient conditions. Appl. C l q Sci. 5, 183-187. Mortland, M. M. (1970). Clay-organic complexes and interactions. Adv. Agron. 22,75-117. Mortland, M. M., and Barake, N. (1964). Interaction of ethylamine and metal ions on montmorillonite. Trans. 8th Ini. Congr: Soil Sci. 3,433-443. Mortland. M. M., Saobai. S., and Boyd, S. A. (1986). Clay-organic complexes as adsorbents for phenol and chlorophenols. Cluys Clay Miner: 34,581-585. Nye, J. V., Guerin, W. F., and Boyd, S. A. (1994). Heterotrophic activity of microorganisms in soils treated with quaternary ammonium compounds. Environ. Sci. Techno/. 28,944-95 I. Ogram, A. V., Jessup, R. E.. Ou, L. T..and Rao, P. S. C. (1985). Effects of sorption on biological degradation rates of (2.4-dichlorophenoxy) acetic acid in soils. Appl. Environ. Microhiol. 49,582-587. Papendick, R. I., and Campell, G. S. (1980). “Theory and Measurement of Water Potential in Relation to Microbiology.” Am. SOC.Agron. Spec. Pub. No. 9. Park, J.-W., and Jaffk, P. R. (1993). Partitioning of three nonionic organic compounds between adsorbed surfactants, micelles, and water. Environ. Sci. Techno/. 27,2559-2565. Park. J.-W.. and Jaffe, P. R. (1995).Phenanthrene removal from soil slurries with surfactant-treated oxides. J . Environ. Engr: 121,430437. Perez-Rodriguez, J. L., Morillo, E., and Maqueda, C. (1988). Decomposition of alkylammonium cations intercalated in vermiculite. Clays Clay Miner: 23,379-390. Porst, R. (1975). Interactions between adsorbed water molecules and the structure of clay minerals. I n

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“Proceedings of the International Clay Conference, 1975.” (S. W. Bailey, Ed.). Applied Publishing, Wilmette, IL. Rao, P. S. C., Jessup, R. E., Rolston, D. E., and Kilerease, D. P. (1980). Experimental and mathematical description of nonadsorbed solute transfer by diffusion in spherical aggregates. Soil Sci. SOC. Am. J. 44,684-688. Rosen. M. J. (1987). “Surfactants and Interfacial Phenomena.” Wiley, New York. Schwarzenbach, R. P., Gschwend. P. M., and Imboden, D. M. (1993). “Environmental Organic Chemistry.” Wiley, New York. Sheng, G., Xu, S., and Boyd, S. A. (1996a). Mechanism(s) controlling sorption of neutral organic contaminants by surfactant derived and natural organic matter. Environ. Sci. Technol.30, 1553-1557. Sheng, G., Xu, S.. and Boyd, S. A. (1996b). Cosorption of organic contaminants from water by hexadecyltrimethylammonium-exchangedclays. Warer Res., 30, 1483-1489. Sielskind, 0..and Wey. R.(1958). Influence of pH on the adsorption of amines by H-montmorillonite. C. R. Acad. Sci. Paris 241,74-76. Smith, J. A., and Jaffk, P. R. (1994a). Adsorption selectivity of organic-cation-modified bentonite for nonionic organic contaminants. Ware6 Air; Soil Pollut. 72,205-2 I I . Smith. J. A., and Jaff6. P. R. (1994b). Benzene transport through landfill liners containing organophilic bentonite. J. Environ. Engr; 120, 1559-1577. Somasundaran, P., and Fuerstenau, D. W. (1966). Mechanisms of alkylsulfonate adsorption at the alumina-water interface. J. Phys. Chem. 70,90-96. Somasundaran, P., and Goddand, E. D. (1979). Electrochemical aspects of adsorption on mineral. Mod. Aspects Electrochem. 13,207-250. Sposito, G., and Prost, R. (1982). Structure of water adsorbed on smectites. Chem. Rev. 82,553-573. Stapleton, M. G., Sparks, D. L., and Dentel, S. K. (1992). Sorption of pentachlorophenol to a surfactant modified montmorillonite. In “Agronomy Abstracts, 1992 Annual Meetings.” ASA-CSSASSSA-CMS, Minneapolis, MN. Suquet, H.. Iiyama, J. T., Kodama, H., and Pezerat, H. (1977). Syntheses and swelling properties of saponites with increasing layer charge. Clays Chy Miner; 25,231-242. Theng, B. K. G. (1974). “The Chemistry of Clay Organic Reactions.” Wiley, New York. Theng, B. K. G. (1982). Clay polymer interactions. Summary and perspectives. Clays Clay Mineral. 30, 1-10. Theng, B. K. G., Greenland, D. J., and Quirk, J. P. (1967). Adsorption of alkylammonium cations by montmorillonite. Clays Clay Miner; 7 , 1-17. Toro-Suarez, I. (1994). Reduced transport of organic pollutants in soil modified with hexadecyltrimethylammonium. PhD dissertation. Michigan State University, East Lansing, MI. van Olphen, H., and Fripiat, J. J. (Eds.) (1979). “Data Handbook for Clay Minerals and Other Nonmetallic Minerals.” Pergamon Press, Oxford, UK. Vansant, E. F., and Peeters, J. B. (1978). The exchange of alkylammonium ions on Na-laponite. Clays Clay Miner; 26,279-284. Vansant, E. F., and Uytterhoeven, J. B. (1972). Thermodynamics of the exchange of n-alkylammoniu m ions on Na-montmorillonite. Claps Clay Miner: 20,47-54. Viaene, K., Schoonheydt, R. A., Crutzen, M.. Kunyima, B., and Be Schryver, F. C. (1988). Study of the adsorption on clay particles by means of fluorescent probes. Langtnuir 4, 749-752. Wagner, J., Chen, H., Brownawell, B. J., and Westall, J. C. (1994). Use of cationic surfactants to modify soil surfaces to promote sorption and migration of hydrophobic organic compounds. Environ. Sci. Technol. 28,231-237. Wakamatsu, T., and Fuerstenau, D. W. (1968). “Adsorption from Aqueous Solution: Advanced Chemistry Series 70,” pp. 161-172. American Chemical Society, New York. Wallace, R. B., Grant, J. M., Voice, T. C., Rakhshundehroo, G. R.,Xu, S., and Boyd, S.A. (1995). Hydraulic conductivity of organomodified soil. In “Innovative Technologies for Site Remediation

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PHENOLOGY, DEVELOPMENT, AND GROWTH OF THE WHEAT (Tritimm aestivum L.) SHOOTAPEX: A REVIEW Gregory S. McMaster USDA-ARS, Great Plains Systems Research, Fort Collins, Colorado 80522

I. Introduction 11. General Patterns of Grass Shoot Apex Development 111. Morphological Nomenclatures IV Shoot Apex Developmental Sequence A. Shoot Apex Phenology B. Shoot Apex Developmental Events V. Conclusion References

I. INTRODUCTION So nature glories in her highest growth, Showing her endless forms in orderly array. None but must marvel as the blossom stirs Above the slender framework of its leaves.

GOETHE

For centuries, people have studied the physiology and morphology of economic crops within the Poaceae family, with the result being perhaps the largest body of literature of any group of plants. Despite developmental and physiological differences, such as spike developmental patterns and C, and C, photosynthetic pathways, many similarities also exist among grasses, especially small-grain cereals 63 Advancer in Agronomy, Vohmt fY

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(Bonnett, 1966; Evans, 1940; Jeater, 1956, Rickman and Klepper, 1995). The objective of this paper is to review the vast literature on the development, phenology, and growth of the wheat shoot apex. Although focusing on wheat, other species are occasionally considered, and it is hoped that a greater understanding of grass shoot apex functioning is achieved.

II. GENERAL PAmERNS OF GRASS SHOOT APEX DEVELOPMENT As any grass shoot apex begins development, leaf primordia are initiated with their associated axillary bud (Fig, 1). The leaf, axillary bud, and node plus internode are referred to as a phytomer unit (Wilhelm and McMaster, 1995). The grass shoot is composed of the repeated addition of phytomer units, and therefore the phytomer unit is the basic building block of grass canopies (Rickman and Klepper, 1995; Wilhelm and McMaster, 1995).A wide variety of biotic (e.g., genetics, weeds, and disease) and abiotic factors (e.g., temperature, water, nutrients, light, and CO,) influence the further differentiation and growth of the phytomer unit. Shoot apexes at some later time may initiate floral structures. Each grass species differs slightly on how the floral structures develop, but normally these structures develop from the axillary bud. Evans (1940) gives a concise summary of the work of Trecul and Goebel that delineates the three patterns of developmental succession of lateral primordia on the incipient inflorescence of grasses. Wheat fits under the pattern in which inflorescence primordia are initiated acropetally and subsequent development is fastest in the central region of the inflorescence and differentiation occurs basipetally and acropetally from this point. Each shoot apex exhibits varied degrees of coordination of development and growth with other shoot apexes within a plant. Plant form can then be viewed as the net birth and death of phytomer units, modified by growth rate and form of the phytomer unit, and population ecology concepts can then be applied. The summation of all shoot apexes, and the phytomer subunits, on different plants results in the population patterns unique to the species and its environment. Because ecological plant morphology lies outside the domain of this review, no more will be said here; however, further information may be found in Dirzo and Sarukhan (1984).

III. MORPHOLOGICAL NOMENCLATURES The development of morphological nomenclatures in the past half century has facilitated the study of plant development. A variety of methods have been pro-

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- le@primordium

Figure 1. Drawing of ilwheat shoot apex at single-ridge growth stage. Leaf primordia are initiated acropetally on the shoot apex; axillary buds (not discernible in this drawing) would form on the abaxial base of the leaf primordium. Being at the single-ridge growth stage, the apical dome has elongated and the younger leafprimordia will not grow further.

posed, and subsequently modified, for naming various aboveground plant organs and defining stages of phenological development (e.g., Bruns and Croy, 1983; George, 1982; Haun, 1973; Lancashire er al., I99 1 ;Large, 1954; Noda et al., 1993; Robertson, 1968; Tottman and Makepeace, 1979; Waldren and Flowerday, 1979; Zadoks et al., 1974). Morphological naming schemes for wheat plant structures have not had universal acceptance and have just recently become more widely used. Jewiss ( 1972) and Klepper et al. (I 982, I983a) proposed a simple leaf-naming convention in which true leaves are numbered acropetally for each culm. The first foliar leaf on a culm is designated L1, followed by L2, L3, etc. Haun (1973) devised a growth staging system that allows the total number of leaves on a culm to be incorporated into the vegetative stages:

Haun srage

= (n - 1)

+2 Ln-

I

where n is the number of leaves that have appeared on the culm, L,-, is the blade length of the penultimate leaf, and L,, is the blade length of the youngest visible leaf extending from the enclosing sheath of the penultimate leaf.

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GREGORY S. McMASTER fifth /eof

Figure 2. Drawing of a young wheat plant showing identified leaves, tillers, and roots. From Klepper et al. (1982). Reproduced with permission of Elsevier Science. Nomenclature is defined in the text.

Jewiss (1972) also developed a system for naming tillers on annual grasses, which has been modified and extended (Fraser et al., 1982; Kirby et al., 1985a; Masle, 1985; Masle-Maynard and Sebillotte, 198la). One increasingly used system (Klepper et al., 1982, 1983a) identifies tillers according to the leaf axil and parent culm with which the tiller is associated (Fig. 2). The first culm that emerges from the seed is the main stem (MS). All remaining culms are primary, secondary, tertiary, etc. tillers. Primary tillers are those culms that appear in the axils of MS leaves and are given one-digit designations following a “T,” signifying “tiller.” An example would be T1, which appears in the axil of L l on the MS.Secondary tillers, or culms that appear in the axils of primary tiller leaves, are given two-digit designations. The first digit refers to the number of the parent primary tiller; the second digit refers to the parent primary tiller leaf subtending the secondary tiller. For example, T21 is a secondary tiller that appears in the axil of the first leaf (Ll) of the primary tiller T2. In a similar manner, tertiary tillers are given three-digit designations (e.g., T211). The coleoptile tiller, born in the axil of the coleoptile leaf,

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is designated TO (Klepper et al., 1982, 1983a; McMaster et al., 1994) or TC by others (Kirby and Appleyard, 1987; Kirby and Eisenberg, 1966). Most morphological naming schemes have been restricted to vegetative organs. Klepper et al. (1983b) proposed a numerical index for developmental stages of the spike, further extending other phenological growth-stage scales. Wilhelm and McMaster (1996) suggested a naming scheme for the spikelet, floret, and kernel components of a spike inflorescence. For each culm, the first spikelet at the base of the rachis is designated S I . Spikelets are sequentially incremented acropetally on the rachis (e.g., S2, S3, S4, etc.). Each floret or caryopsis position within a spikelet is sequentially numbered from base to apex as well. For example, the first floret position at the basal end of the rachilla is FI (or C1 if the caryopsis), followed by F2/C2, F3/C3, etc. To uniquely identify a specific floret or caryopsis, the spikelet position and floretkernel position are combined. For example, S3F2 would be the third spikelet from the base of the rachis and the second floret position from the base of the rachilla. The culm designation can be added at the beginning to uniquely identify each floret on any plant.

Iv. SHOOT APEX DEVELOPMENTAL SEQUENCE The preceding morphological naming schemes allow nondestructive identification of specific shoots but give no information regarding the developmental sequence and timing of events occumng within each shoot apex. Many important developmental events are not observable without magnification after plant dissection. Figure 3 (McMaster et al., 1992b) illustrates the complete developmental sequence and timing for a generic winter wheat shoot apex. Duration of various stages can vary significantlyamong cultivars and with stresses (e.g., Fisher, 1973; McMaster et al., 1992a,b; Saini and Tandon, 1983; Wang, 1960; Wilhelm et al., 1993).

A. SHOOTAPEX -NOLOGY Bauer et al. ( 1983) and Landes and Porter (1 989) discuss commonly used phenological growth-stagescales and their relationships to each other for wheat. However, it is often difficult to compare data using different scales. Harrell et al. (1993) developed a computer program that converts between three phenological growth scales: Feekes (Large, 1954), Zadoks-Chang-Konzak (1974), and Haun (1973). These scales concentrate exclusively on growth stages visible without dissection. Therefore, stages such as single ridge, double ridge, start of internode elongation, terminal spikelet initiation (TS), and beginning of floret primordium initiation are

GREGORY S. McMASTER

68

-

Kernel growth Florot A Prlmordlum Abortion

Floret Prlmordlum Inltlatlon

7 Fla#

SpikelotPrlmordium Inltiatlon

leaf appears

Rachb Blongatlon -7

T111or Abortlon .?

Internode Elongation I I

Tiller Appearance

Thermal time:

O

h a f Appearance and Growth Loaf Prlmordlum Initiation

>

I

I R

1.7

/ /

//

I Jan1

I

SR

I DR

GROWTH STAOE

-1

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1.4

1.2

--------

Poduncle Elongation 7

.7

1 I

IE

I

J

Flag leaf 2.87 1.3 0.7 8 0 0 ODD I l l

;-,

M

Figure 3. Culm developmental sequence and timing. Developmental sequence of the shoot apex correlated with phenological growth stages: germination (G), seedling emergence (E), single ridge (SR), douhle ridge (DR),jointing (J), booting (B), heading (H), anthesis (A), and physiological maturity (M). Leaf appearance is the time when the youngest expanding lamina can be seen emerging from the enclosing penultimate leaf. Question marks indicate areas of uncertainty or variability due to cultivars, whether winter or spring wheat, environment, or conflicting reports in the literature. Thermal time is either in phyllochrons or growing degree-days and assumes optimal conditions. Adapted from McMaster er al. (1992h).

not considered. No scale has all the stages that are part of shoot apex development shown in Fig. 3. The Feekes scale is commonly used to describe many of the externally visible growth stages for the main stem, with some of the stages more clearly defined. Jointing (stage 6) begins when the first node is 25 mm above the soil surface (Fig. 4). Booting (stage 10) begins when the flag leaf sheath completes growth and the spike is swollen but not yet visible within the flag leaf sheath. Because the beginning of booting is difficult to discern with confidence, I suggest a more useful definition is when the flag leaf collar is formed. Heading (stage 10.1) commences when the spike is first visible through the split in the flag leaf sheath or emerges from the collar of the flag leaf. For applicability to all varieties, I suggest that you must be able to see the glumes, paleas, or lemmas, and not just the awns, if using a cultivar with awns. Anthesis (stage 10.5.1) starts when the first anthers emerge from the spike and ends when anthers stop emerging. Physiological maturity (stage 1 1.4)is when grain dry weight reaches its maximum, which is correlated to the absence of green color in the chaff or kernels (Hanft and Wych, 1982; Singh et al., 1984).

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25 mm (=Jointing)

"m2 L&

Figure 4. Diagram of identified nodes and associated internodes and definition of jointing growth stage. In this example, the elongation of internodes 5 and 6 raises the 7th node 25 mm above the soil surface, resulting in jointing as defined by the Feekes scale (Large, 1954).From McMastereraf. (1991). Reproduced with permission of Cambridge University Press.

Other phenological stages observable only after dissection, and usually requiring magnification, are defined as follows. Single ridge begins when the shoot apex first elongates (Fig. 1). Leaf primordia formed after this point do not differentiate further and grow into a single ridge around the elongated apex, which gives rise to its name (Fisher, 1973; Williams, 1966b). Double ridge commences when both leaf and spikelet initials appear as double ridges around the shoot apex (Fig. 5 ) . The lower ridge is the leaf primordium, which does not develop further, and the upper ridge is the spikelet primordium (Barnard, 1955; Bonnett, 1966; Fisher, 1973; Oosterhuis and Cartwright, 1983; Williams, 1966b). The leaf initials subtending the spikelet primordia become progressively less developed toward the apex until the youngest leaf initials may consist of only a single cell division (Barnard, 1955;Williams, 1966b).Terminal spikelet stage is reached when the terminal spikelet initial is formed. Internode elongation begins when the first inter-

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GREGORY S. McMASTER

PROFlLE VIEW

FACE WEW

Figure 5. Drawing of a wheat shoot apex at double-ridge growth stage. Leaf and spikelet primordia are initiated acropetally on the shoot apex. The spikelet prirnordia will continue to differentiate and grow, but the leaf prirnordia will not. Profile view is viewing the apex at 90" rotation from the plane of leaves: face view is rotated 90"from profile view.

calary meristem initiates elongation in the spring and is associated with the onset of spike differentiation (Williams, 1966b). An obvious conclusion from Fig. 3, but one that merits mentioning, is that externally visible stages are not often very well correlated with shoot apex developmental events. An example of this is the external appearance of tillers and leaves and the internal initiation of spikelet and flower primordia. Phasic development is controlled by genetics and regulated by the environment. The usual approach for predicting phenological growth stages is empirically based using number of calendar days (ND), growing degree-days (GDD), or phototherma1 units (PTU) after considering the genetic tendencies of the crop or cultivar (e.g., Amir and Sinclair, 1991; Bauer et al., 1986; Davidson and Campbell, 1983; del Pozo et al., 1987; Hay and Kirby, 1991; Masoni et al., 1990; Mor and Aggarwal, 1980; Nuttonson, 1955; Robertson, 1968; Slafer and Rawson, 1995; Travis and Day, 1988). Many criticisms have been directed toward these three models (e.g., McMaster and Smika, 1988; Shaykewich, 1995; Wang, 1960),but for wheat few other approaches have been developed and those that have may not have greater accuracy. The popularity of GDD and PTU models undoubtedly is due to

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both the simplicity of the models and the overriding significance of temperature (and light for certain stages and cultivars) on wheat phenology (e.g., Allison and Day nard, 1976; Amir and Sinclair, 1991; Bauer et al., 1986; Davidson and Christian, 1984; Davidson et al., 1985; Halse and Weir, 1970; Hammes and Marshall, 1980; Kirby and Appleyard, 1984; Loss et al., 1990; Marcellos and Single, 1971, 1972; Made el al., 1989a,b;Masle-Maynard, 198la; McKinney and Sando, 1935; Omrod, 1963; Pinthus and Nerson, 1984; Slafer and Rawson, 1994, 1995; Wall and Cartwright, 1974). Bauer et al. (1988) have suggested that the evidence that photoperiod affects winter wheat development rate independent of air temperature for the Great Plains is inconclusive. Although temperature and light primarily control wheat phenology and development, water and nutrients do play a lesser role. Most studies show that water availability clearly influences phenology and development (e.g., Angus and Moncur, 1977; Baker et al., 1986; Bauer et al., 1985; Davidson and Chevalier, 1992; Frank et al., 1987; McMaster and Smika, 1988; Nuttonson, 1955; Singh et af., 1984; Sionit et al., 1980); however, not all phenological stages seem to be significantly affected by water availability (Bauer et al., 1984; Bingham, 1967; Davidson and Campbell, 1983; Doraiswamy and Thompson, 1982; Frank et af., 1987). Salinity, which can impose a water stress effect on the plant, has also been shown to hasten phenology of at least certain growth stages, especially reproductive stages (Grieve et af., 1993; Maas and Grieve, 1990; Maas and Poss, 1989). Generally, the pattern is that water stress hastens phenological timing (McMaster and Smika, 1988; McMaster et al., 1992b). The significance of nutrient availability on phenology and development is less clear. Studies can be found suggesting nutrient availability affects phenology and development (e.g., Bauer et al., 1985; Birch and Hong, 1990; Blacklow and Incoll, 1981; Erdei et al., 1986; Frank and Bauer, 1982; Frank ef al., 1987; Halse et al., 1969; Holmes, 1973; Longnecker etal., 1993; Nerson et al., 1990; Nuttonson, 1955; Whingwiri and Stem, 1982), whereas others find no significant affect on phenology and development (e.g., Bauer et al., 1984; Belford et af., 1987; Bingham, 1967; Davidson and Campbell, 1983; Frank and Bauer, 1982; Langer and Liew, 1973; Longnecker et al., 1993; McMaster and Smika, 1988; Nuttonson, 1955; Single, 1964; Whingwiri and Stem, 1982).A number of factors contribute to the uncertainty of the role of nutrients. First, there is great variation in cultivar response to nutrients. Second, different stages seem to be affected more than others, and this varies with the nutrient. Nitrogen has less affect on vegetative phases than on spikelet, floret, and grain development phases (Bauer et af.,1985; Frank and Bauer, 1982; Longnecker et al., 1993; Whingwiri and Stern, 1982). Third, most studies start with different nutrient levels, but as time progresses it becomes unclear what level of nutrient stress exists. Nutrient cycling in the soil and storage in plant tissue both contribute to uncertainty on nutrient availability later in the life cycle. Lastly, different nutrient levels are tested, and some levels probably exceed

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a threshold of nutrient stress resulting in no significant plant response. As Bauer et al. (1985) discuss for N, the interaction of soil N levels and soil water results in little consistency of plant developmental and phenological response to a given N fertilizer rate. Both water and nutrient effects on phenology and development are also confounded by their potential indirect effects on the microenvironment, particularly on meristem temperature and within canopy light quality and intensity. Previous studies have not evaluated how water and nutrients may alter the microenvironment. McMaster and Smika (1988) suggested little is to be gained in accuracy of empirical phenological models by considering N status. Other factors may also play a secondary role affecting phenological development. For instance, doubling ambient CO, has been reported to slightly shorten the time to reach various growth stages such as floral initiation and anthesis (LeCain et al., 1992; Marc and Gifford, 1984), regardless of culm considered, although Gifford (1977) found no effect of enhanced CO,, and under certain conditions of depleted CO, levels found that anthesis was reached earlier. The ND, GDD, and PTU models often do not predict growth stages near jointing well for winter wheat (McMaster and Smika, 1988; McMaster et al., 1992b) because they do not account for vernalization, The gene symbol Vrn is given to the system of genes responsible for sensitivity to vernalization (Stelmakh, 1987). The presence of dominant alleles at one or more loci results in partial or complete inhibition of the vernalization requirement. Vernalization is a complex process with many factors interacting (Purvis, 1961).Although many have tried to quantify vernalization responses, generally quantitative methods to predict vernalization are lacking (Ahrens and Loomis, 1963; Craigon ef al., 1995; Trione and Metzger, 1970). Cultivars vary greatly in their vernalization requirements (e.g., Craigon et al., 1995; Davidson and Christian, 1984; Davidson et al., 1985; Evans and Wardlaw, 1976; Flood and Halloran, 1986; Gardner and Barnett, 1990; Gott, 1961; Halse and Weir, 1970; Mosaad ef af., 1995; Slafer and Rawson, 1994; Stelmakh, 1987; Wall and Cartwright, 1974; Yasuda, 1984). A number of environmental factors, particularly photoperiod and temperature pattern, interact variably with cultivars to confound the vernalization response (Davidson et al., 1985; Purvis, 1961; Saini and Tandon, 1989). Even many spring wheats that do not require vernalization to enter the reproductive phase still respond positively to cold temperatures (Evans and Wardlaw, 1976; Halloran, 1977; Jedel et al., 1986; Levy and Peterson, 1972). Vernalization response also seems to influence the duration of phenological growth stages and rates of primordium initiation (Cutforth et al., 1992; Flood and Halloran, 1986). One rarely attempted approach for estimating the time between various growth stages is to use the phyllochron concept (Fig. 3; McMaster et al., 1992b; Rickman et al., 1996). The phyllochron is the thermal time it takes for successive leaves on a shoot to reach the same developmental stage. If thermal time is measured in ac-

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73

cumulated growing degree-days, then the phyllochron is very similar to the GDD model. However, factors that change the phyllochron are automatically incorporated into the rest of the phenological development scheme. For example, inverse relationships between planting date and time to growth stage have been reported (e.g., Fischer and Kohn, 1966; Ghadekar et al., 1992; Hay, 1986; Kirby and Appleyard, 1987; Nuttonson, 1948; Pinthus and Sar-Shalom, 1978). Given that the phyllochron usually decreases with later planting dates (Baker ef al., 1980; Stem and Kirby, 1979),the phyllochron approach should more accurately respond to this trend rather than the static GDD approach. Few estimates of the number of phyllochrons between phenological growth stages are reported in the literature. Rickman and Klepper (1991), Kirby et al. (1993), and McMaster et al. (1992b) give some estimates for winter wheat cultivars with moderate photoperiod sensitivity that are fully vernalized, and Frank and Bauer (1984) and Gardner et al. (1985) discuss a few intervals for spring wheat cultivars. The preceding discussion of phenology has generally pertained to the main stem. However, not all culms on a plant reach the same growth stage simultaneously. Normally, successively younger and smaller culms reach the same phenological growth stage later. The stagger among culms tends to be reduced as the plant approaches maturity (Baker and Gallagher, 1983a; Stem and Kirby, 1979; Whingwiri and Stem, 1982). Hay and Kirby (1991) review how the convergence of development of successively initiated organs, or of different sowing dates, results in particular stages occurring in the plant or throughout the crop in synchrony within a few days.

B. SHOOTAPEX DEVELOPMENTAL EVENTS The developmental events depicted in Fig. 3 are discussed below in approximate sequential order. The justification for the sequence and reasons for the question marks are presented in the appropriate sections. 1. Leaf Primordium Initiation Evans ( 1940) and Wilhelm and McMaster (1 995) outline the history of the plastochron dating back to 1873. Originally, the plastochron had a much more general meaning of the interval of time between two recurring successive events, such as leaf or flower initiation (Hill and Lord, 1990). Today, the plastochron is commonly used as the thermal time between the appearance of successive leaf primordia on a shoot (Fig. 1; Erickson and Michelini, 1957; Lamoreaux et al., 1978; Wilhelm and McMaster, 1995) and is distinguished from the phyllochron, which is the thermal time between the appearance of successive leaves on a shoot (Klep-

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GREGORY S. McMASTER

per et al., 1982;Wilhelm and McMaster, 1995). Generally, primordia are initiated at about twice the rate of the phyllochron (e.g.. Baker and Gallagher, 1983b; Delecolle et al., 1989; Kirby, 1985, 1995; Kirby and Appleyard, 1987; Malvoisin, 1984),and at a linear rate with growing degree-days (Gallagher, 1979; Grieve et al., 1993; Hay and Wilson, 1982; Malvoisin, 1984).About 0.03 leaf primordia per day are initiated for each degree rise in temperature above the base temperature (O'C), or about 0.4 leaf primordium per day at 15°C (Baker and Gallagher, 1983b). Temperatures above about 30°C retard leaf primordium initiation (Friend et al., 1963). Vernalization and photoperiod have little effect on the plastochron; however, because vernalization and photoperiod affect the duration of initiation of leaf primordia, these factors will affect the final number of leaf primordia (Kirby, 1985). Friend et al. (1963) reported that light intensity increased the plastochron, but they did not measure shoot apex temperature. About three or four leaf primordia are present in the seed embryo (Baker and Gallagher, 1983a;Bonnett, 1966;Bradbury etal., 1956; Hay and Kirby, 1991; Kirby and Appleyard, 1987; Lersten, 1987; Malvoisin, 1984; Williams, 1975), and nearly half the final number of leaf primordia have been initiated by seedling emergence (Baker and Gallagher, 1983b; Hay and Kirby, 1991). Leaf primordia form in the two-layer tunica region of the shoot apex (Barnard, 1955). 2. Tiller or M a r y Bud Initiation

The axillary bud, also called the tiller bud, appears somewhat later than the associated leaf primordium (Longneckeret af.,1993). Baker and Gallagher (1983a) could find no tiller bud primordium associated with leaves higher than L7 or L8, although the main stem had 12 leaves. It is likely that the axillary buds associated with leaves L9-Ll2 were insufficiently differentiated to be detectable.

3. Leaf Primordium Elongation Wheat leaf ontogeny follows a similar pattern regardless of leaf position, even though leaf development is heteroblastic between the first two true foliar leaves of the main stem (Engledow and Ramiah, 1930; Klepper er al., 1983a). In wheat leaves, the upper leaf zone (oberblatt) has been eliminated. The blade and sheath components are combined into the lower leaf zone, or unterblatt. The wheat phytomer unit follows the pattern of development typical of grasses in which the leaf blade begins growing first, followed by sheath growth, then internode elongation (Etter, 1951; Nemoto e?af., 1995; Skinner and Nelson, 1995).Early in primordial development, an intercalary meristem is created that separates into two regions: The proximal region gives rise to the sheath and the distal region creating the lamina (Dale, 1988). Cells enlarge rapidly once they are not in the meristematic region (Dale, 1988).Four distinct development stages of tall fescue leaf blades (Fes-

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tuca urundinacea Schreb.) have been described: formation of the epidermal cell division zone, formation of the epidermal cell elongation zone, linear elongation phase, and cessation of elongation due to no new cell division and existing cells being fully elongated (Skinner and Nelson, 1995). Decreasing irradiance reduces the growth zone of elongating leaves (Sanderson and Nelson, 1995). Blade growth rates change during the ontogeny of the leaf, with maximum elongation rates a few days before the blade emerges from the penultimate leaf sheath (see Skinner and Nelson, 1995, for tall fescue; Williams, 1975; Williams and Rijven, 1965). Rates for corresponding phases tend to decrease and the duration of the phases increases as later leaves commence elongation. Leaf appearance rates are slower than primordium initiation rates (e.g., Baker and Gallagher, 1983b; Delecolle et al., 1989; Kirby, 1985, 1995; Kirby and Appleyard, 1987; Malvoisin, 1984), and not all leaf primordia develop fully into leaves. Flag leaf number is determined at single ridge because all leaf primordia that are present as single ridges around the apex do not further differentiate and grow; flag leaf number is about twice the main stem Haun at single ridge (Rickman and Klepper, 19911, although this is assuming “normal” planting dates and environmental conditions. The phyllochron has been examined for various cultivars of small-grain cereals since 1960. The phyllochron seems similar for all culms within a plant, although some studies have found tillers, especially the coleoptilar tiller, to have different phyllochrons than the main stem (Anslow, 1966; Cannell, 1969; Fletcher and Dale, 1977; Kirby and Appleyard, 1987; Kirby and Riggs, 1978; Kirby ef al., 1985b; Longnecker et al., 1993; Peterson e f al., 1982; Rawson, 1971a). Normally, the phyllochron appears relatively constant during the growing season when plotted as a function of growing degree-days (e.g., Baker et al., 1980; Belford et al., 1987; Cao and Moss, 1991; Delecolle et al., 1989; Friend, 1965b; Hunt and Chapleau, 1986; Kirby, 1993, 1995; Kirby and Appleyard, 1987; Kirby and Eisenberg, 1966; Kirby etal., 1982,1989; Kirby and Perry, 1987; Klepperetal., 1982,1983a; Krenzer et al., 1991; Longnecker et al., 1993; Malvoisin, 1984; Masle et al., 1989b; Masle-Maynard, 1981b; Mosaad et al., 1995; Rawson et al., 1983; Wiegand etal., 1981). However, very detailed examination of the phyllochron, particularly in growth chamber studies, shows that the phyllochron is curvilinear with temperature (Baker et al., 1986; Cao and Moss, 1989a; Hay and Delecolle, 1989). Indeed, when examining many of the experiments that report linearity, a slight sigmoidal pattern can often be detected, particularly a slight increase in the phyllochron after about the 3-5 leaf stages. Two factors could explain this: a decrease in temperature as winter approaches, thus causing an increase in the phyllochron, and the three or four leaf primordia present in the seed embryo will have elongated and the remaining leaves must come from primordia initiated after germination. With rice, Nemoto et al. (1995) report that the phyllochron is greater for the last four or five leaves to appear. This change has been associated with inflorescence initiation and

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GREGORY S. McMASTER

internodal elongation, both of which are occurring simultaneously.Another possible factor impacting the phyllochron is the length of the whorl that the elongating leaf must pass through, which increases until internode elongation begins, resulting in a shorter distance (Miglietta, 1991a; Skinner and Nelson, 1995). However, if this is a major factor, then Nemoto et al. (1995) should not have found a greater phyllochron for the last four or five leaves to appear in rice when internode elongation was occurring. Although this issue is interesting theoretically and important in understanding factors controlling the phyllochron, under most field conditions the phyllochron can reasonably be assumed to be linear with growing degree-days. Occasionally, the phyllochron shifts in the spring for unknown reasons (Baker et al., 1986; Grieve et al., 1994; Cao and Moss, 1991; Hay and Delecolle, 1989). For field conditions, the shift (both increase and decrease) occurs shortly before or near double ridge but varies depending on planting date, and the shift is correlated with a change in the primordium initiation rate (Hay and Delecolle, 1989). The phyllochron varies among cultivars (e.g., Anslow, 1966; Baker et al., 1986; Frank and Bauer, 1984; Kirby et al., 1985a; Kirby and Perry, 1987; Mosaad et al., 1995; Syme, 1974).There appears to be little or no relationship between the phyllochron and maturity class, semidwarfing genes, or degree of vernalization requirement (McMaster et al., 1992b; Mosaad ef al., 1995). Information is currently insufficient to model or predict cultivar-related differences. Temperature and photoperiod are the major factors controlling leaf and tiller appearance, but other factors, such as nutrients, water, salinity, CO,, light intensity and quality, vernalization, seed size, planting depth, and soil strength, may effect the phyllochron (e.g., Anslow, 1966; Cutforth etal., 1992; Kirby, 1993; Made and Passioura, 1987; Rickman and Klepper, 1995; Wilhelm and McMaster, 1995). Under controlled conditions, Longnecker et al. (1993) showed a positive correlation between the phyllochron and available N, with a more pronounced effect on tillers, although there was a cultivar response. Dale and Wilson (1978) reported similar results to those of Longnecker et al. (1993) for barley in sand culture. Belford et al. (1987) found that only younger, higher-order tillers showed slight effects of N on leaf development rates. Single (1964) showed a decrease in final leaf number as N levels decreased; presumably, the duration of leaf appearance, rather than the rate of leaf appearance, was shortened by low N levels. Other studies have found no effect on N on the phyllochron (Maan et al., 1989; G. S. McMaster, unpublished data, also for P and Zn). It appears that unless N is very limiting, there is little effect on the phyllochron. The role of other nutrients has been studied rarely, if at all. Baker et al. (1986) observed a shorter phyllochron under dryland conditions than under irrigated conditions. Unpublished data from South Africa (S. Walker, 1990-1992) showed a 10% decrease in the phyllochron under dryland conditions when compared to irrigated conditions. Cutforth ef al. (1992) showed that severe

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water stress increased the phyllochron. Krenzer et al. (1991) found a slight increase in the phyllochron with water stress. Salinity has been reported to increase the phyllochron (Grieve et al., 1994; Maas and Grieve, 1990). Unfortunately, almost all water stress studies have not measured canopy or shoot temperature. LeCain et al. (1992) have shown a decrease in the phyllochron under conditions of increased canopy CO, concentrations, whereas Gifford (1 977) reported no effect. Rickman et al. (1985b) showed an increase in the phyllochron with an increase in photosynthetic photon flux densities under light-limiting conditions, as did others (Barnes and Bugbee, 1991; Bugbee and Salisbury, 1988; Friend et al., 1963; Masle et af., 1989b). Friend et al. (1962) did not find a photoperiod effect on the phyllochron, but others (Cao and Moss, 1989b; Kirby and Eisenberg, 1966) have found a photoperiod effect. Light quality (R/FRratio) has been shown to have a slight negative correlation with the phyllochron (Barnes and Bugbee, 1991; Bugbee and Salisbury, 1988; Skinner and Simmons, 1993). Seed size is positively correlated with the phyllochron (Peterson er al., 1989), and planting depth (Kirby, 1993) and soil strength (Masle and Passioura, 1987) are negatively correlated. These conflicting results on factors influencing the rate of leaf appearance can perhaps be reconciled by first discerning primary and secondary factors and then determining when the factors are influential. It seems clear that temperature is the primary factor driving the rate of leaf appearance, with light (quantity, quality, and photoperiod) also very important (Anslow, 1966; Cao and Moss, 1989a,b,c; Dale, 1988; Frank and Bauer, 1995; Friend et al., 1963; Kirby and Eisenberg, 1966; Langer, 1979; Masle eta]., 1989a; Porter and Delecolle, 1988). Other factors discussed previously are secondary factors that only become influential when very limiting or for certain cultivars. It is unknown if these factors are delaying the development of the leaf primordium or, more likely, if they are decreasing the rate of blade elongation, which would delay the appearance of the leaf from the subtending sheath. Baker et al. (1 980) were the first to report an equation to predict the phyllochron for wheat. They reported a linear relationship between the change in photoperiod following seedling emergence and the phyllochron. Others (Belford et al., 1987; Delecolle et al., 1985, 1989; Kirby, 1995; Kirby and Eisenberg, 1966; Kirby and Perry, 1987; Kirby et al., 1982, 1985a; Malvoisin, 1984; Masle et al., 1989b; McKinney and Sando, 1935; Rickman and Klepper, 1995) have reported results supporting Baker et al. ( 1980). The correlation is indirectly supported by many studies showing a relationship between leaf development rates and sowing date or photoperiod (Baker et al., 1980; Hay and Wilson, 1982; Kirby and Perry, 1987; Kirby et al., 1982, 1985a; Mosaad et al., 1995). Results such as these have led many scientists to view the phyllochron as being relatively "fixed" by the environment in which the seedling germinates. Inconsistencies with this viewpoint arise from many directions. The relationship between change in day length at seedling emergence and the phyllochron involves much variation, and as Delecolle

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et al. (1985) point out, measurement errors could account for much of the variation. This correlation also does not seem to exist in greenhouse and growth chamber experiments (Cao and Moss, 1989a,b,c, 1994; Friend et al., 1962; Hay and Delecolle, 1989;Jude1 and Mengel, 1982; Kirby etal., 1983). Unexplainable shifts of the phyllochron can occur, and indeed the phyllochron often is not constant during the growing season or is different at constant temperatures or photoperiods. Clearly, the correlation between change in day length at seedling emergence and the phyllochron is merely an environmental cue that we can determine but is not the actual cause “setting” the phyllochron shortly after emergence, if indeed it is set all. Cao and Moss (1994) hypothesized why the constant phyllochron for a planting date relationship is found. They note that the phyllochron responds nonlinearly to temperature and photoperiod but is constant within a constant temperature or photoperiod. Under field conditions in which temperatures and photoperiods vary, but generally are increasing or decreasing depending on time of year, there is differential response of the phyllochron based on the conditions. Temperature and photoperiod during the growing season can offset each other, resulting in a constant phyllochron for a planting date. Shifts in the phyllochron could be caused by unseasonably high or low temperatures disrupting the normal temperature:photoperiod relationship. Jamieson et al. (1995) also attempted to explain the observed relationship between the phyllochron and emergence date by basing the phyllochron on apical temperature (or near-surface soil temperature) rather than on air temperature. Efforts to use the relationship presented by Baker et al. (1980) between phyllochron and change in day length at the time of seedling emergence have met with some success. The relationship has been used in wheat simulation modeling efforts for English and U.S. growing conditions and had satisfactory results (McMaster et al., 1991, 1992a,b; Rickman et al., 1996; Weir et al., 1984; Wilhelm et al., 1993). Subsequent to Baker et al. (1980), other equations have been published to predict the phyllochron. Kirby and Perry (1987) used the same concept as Baker et al., but based their coefficients on Australian cultivars and conditions. Cao and Moss (1989a,b,c) used a curvilinear relationship for the effects of temperature, photoperiod, and temperature by photoperiod interaction. Volk and Bugbee ( 1991) mathematically reworked the equations of Cao and Moss. Masle et al. (1989b) examined the effects of vernalization and photoperiod. Miglietta (1991a) predicted leaf appearance on an ontogenetic decline in the rate of leaf appearance and incorporated photoperiod effects (Miglietta, 1991b). McMaster and Wilhelm (1995) tested these equations on field data for winter and spring wheat and found that no equation adequately predicted the phyllochron for all wheat cultivars across the wide range of conditions and cultural practices represented in the field data sets. Most equations predicted spring wheat phyllochrons better than winter wheat. Based on multiple criteria, the Baker et al. and Kirby and Perry approach seemed

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best. Other tests of some of these equations with different data sets found similar results (Bindi er al., 1995; Kirby, 1999, and Kirby (1995) proposed a new equation based on day length and acclimation to temperature shortly after seedling emergence. It is not known what mechanism(s) determines the phyllochron and whether the phytochrome system is involved (Cao and Moss, 1989a,b,c; Porter and Delecolle, 1988). Although Skinner and Simmons (1993) found no effect of FR on the phyllochron of barley, Barnes and Bugbee (1991) did show that as phytochrome photoequilibrium decreases (i.e., the R/FR ratio decreases), the phyllochron increases. The correlation reported by Baker et al. (1980) between change in day length and phyllochron corresponds with changes in R/FR ratio as photoperiod changes in the field. The maximum size of successive leaf blades on a culm increases (Gallagher, 1979; Hay and Wilson, 1982; Kirby, 1993; Rawson et al., 1983; Skinner and Simmons, 1993; Trought and Drew, 1980), with the exception of the flag leaf, which typically is smaller than the penultimate leaf. A linear blade elongation rate with GDD has been observed (Gallagher et al., 1979; Hay and Wilson, 1982; Kirby et al., 1985b). Growth of the first two leaves on the main stem is strongly dependent on seed reserves and aleurone area. Subsequent seedling development is controlled by size of the first two leaves (Peterson et al., 1989). Interestingly, even with adequate water and nutrients, rooting volume can affect leaf size, but not the phyllochron (Peterson etal., 1984).There appears to be a negative relationship between soil strength and leaf expansion rates (Masle and Passioura, 1987). The final number of leaves produced differs depending on the culm, with main stems producing the most leaves and a positive relationship between culm age and the number of leaves (Stern and Kirby, 1979). Stresses shorten the life span of leaves, with N being especially critical in maintaining maximum life span (Belford, 1981 ;Trought and Drew, 1980). The coleoptile leaf is important in seedling emergence, in part because it protects the shoot as it pushes through the soil. Coleoptile length limits the planting depth, below which emergence is drastically reduced (Chastain et al., 1995; Fedotov el al., 1990; Kirby, 1993; Whan, 1976). Coleoptile length will vary greatly among cultivars and increases with deeper sowing depths (Sharma, 1990). Semidwarf wheats have shorter coleoptiles than wheat varieties without the Rht genes. Stresses shorten the life span of leaves, with N being especially critical in maintaining maximum life span (Belford, 1981 ;Trought and Drew, 1980).

4. Tiller Primordium Elongation and Abortion Tiller bud differentiation and extension (and thus tiller appearance) normally ends shortly after spike development starts and well before jointing (Baker and Gallagher, 1983b; Gallagher and Biscoe, 1978; Herzog, 1986; Jewiss, 1972; Kir-

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by, 1985; Rawson, 1971a). However, other work suggests that tillering may not stop at a specific growth stage and, rather, that environmental factors interact with genetic factors causing variation in tiller appearance cessation (Darwinkel, 1978; Kirby, 1985; Longnecker ef al., 1993). Tillering varies greatly among cultivars, with semidwarf genotypes (i.e., those having Rht genes) having greater tillering rates than normal genotypes (Allan, 1989; Borrell et af., 1991; Fraser et al., 1982; Herzog, 1986). Richards (1988) reported that the recessive gene, Tin, inhibits tillering in wheat. For nonstressed conditions, tiller appearance is orderly and predictable, with specific tillers appearing only during specific windows of time (Baker and Gallagher, 1983a; Engledow and Ramiah, 1930; Hay and Kirby, 199l ; Krenzer et af., 1991; Masle, 1985; Rickman et al., 1983). For tall fescue, cessation of cell division in the leaf sheath was associated with the initiation of cell division and elongation of the associated tiller (Skinner and Nelson, 1995). Tiller buds that do not emerge may continue to slowly grow at least until the main stem reaches anthesis (Williams, 1975).Acommon approach has been to view tillering over calendar or thermal time as a function of some treatment (Maas et af., 1994; Miyasaka and Grunes, 1990; Sojka et af.,1975). A positive relationship is found between temperature and when the window of appearance occurs. An important refinement in this approach is to base tiller appearance on leaf production, or the phyllochron. For example, Table 1 gives the times specific tillers appear in the simulation models SHOOTGRO and SPIKEGRO based on the main stem Haun growth stage (McMaster et al., 1991, 1992a; Wilhelm et al., 1993). Table 1 deviates slightly from the common assumption that a tiller will appear when its subtending leaf and two subsequent leaves are fully expanded (Friend, 1965b; Harrell et al., 1993; Kirby, 1993; Kirby et af., 1985a; Masle-Maynard, 1981b), and the results in Table 1 are also supported somewhat by Longnecker et af. (1993). Rickman et af. (1 985b) reported that light intensity did not affect the relationship of tiller appearance to MS Table I Relationship between Main Stem (MS) Development and Culm Appearanceu MS Haun 0.0 1.9 2.7 3.3 4.0 5.0

Culm class

Culms that appear

1

MS

2 3

TO

4

5

6

TI T2, TOO T3, T10, TO1 T4, T20, T02. TI I , T100, T010, TOOO, T30

“Culm naming scheme is after Klepper et al. (l983a). Adapted from McMaster et al. (1991).

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Haun growth stage. Maas et al. (1994) showed that salinity could delay the window of time that certain tillers appeared. Longnecker et al. (1993) show that N can delay tiller appearance slightly, but this is related to delayed leaf appearance. It is likely that any delay in the window of tiller appearance as influenced by nutrients is due to slower growth rates, resulting in a longer duration for tiller emergence from the axil of the associated leaf rather than an actual change in the window of appearance. Often, physical and spatial constraints are ignored in wheat developmental morphology (Langer, 1979; Williams and Langer, 1975; Williams and Metcalf, 1975). For instance, the tiller bud is tightly contained in a cavity, and Williams and Langer ( 1975) view the “escape” from the cavity to be a critical event in a tiller appearing. Allometric constraints can also exist. Not all culms produce harvestable spikes (e.g., Auld et al., 1983; Darwinkel, 1978; Fraser et al., 1982; Maas el al., 1994, 1996; Roy and Gallagher, 1985; Shanahan, 1982; Watson et al., 1963). Spatial arrangement of plants has little impact on the culm number per unit area (Auld et al., 1983), although clearly plant density does have impact (Bremner, 1969; Darwinkel, 1978; Simons, 1982). Most tiller abortion normally begins when tiller appearance stops, and tiller abortion typically ends just before anthesis (Gallagher and Biscoe, 1978). Tiller abortion particularly increases shortly after internode elongation and the terminal spikelet stage (Hay, 1986). Most culms present at anthesis that have not begun aborting produce a spike. Aborting culms often can first be detected by noting the loss of chlorophyll from the youngest leaf that is emerging, whereas the penultimate and other leaves show no readily discernible loss of chlorophyll (observed by B. Klepper and R. Rickman, unpublished results, and verified by G. S. McMaster for other conditions). Quantitative relationships are not well developed for determining which tillers will survive or produce spikes. The culms on a plant are integrated so that at least some “cooperation” exists (Alaoui et al., 1992; Langer, 1979; Thorne and Wood, 1987b), and in at least some instances, tillers never become completely independent from the main stem (Rawson and Hofstra, 1969). However, the clear negative relationship between stand density and tiller number per plant (Bremner, 1969; Darwinkel, 1978; Simons, 1982) demonstrates that there is also competition between tillers for normally limiting resources. Despite the interplay between cooperation and competition, a few general qualitative patterns are apparent. First, tillers that have not produced three or four leaves by jointing, which is when the first nodal roots are produced on the tiller, do not produce a spike (Klepper et al., 1984; Masle, 1985; Masle-Maynard, 1981b; McMaster et al., 1991; Rickman et al., 1985a; Wilhelm et al., 1993). Second, younger/smaller tillers will senesce before olderAarger culms (Bremner, 1969; Darwinkel, 1978, 1980; Engledow and Ramiah, 1930; Masle, 1985; Masle-Maynard, 198la; Palfi and Dezsi, 1960; Shanahan, 1982; Thorne and Wood, 1988), although Thorne (1962) reported in-

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stances in which older tillers died before younger tillers. Third, for tillers of the same age or size, higher-order tillers will senesce before lower-order tillers. Water (Alaoui et ul., 1992; Belford, 1981;Christen et ul., 1995; Davidson and Chevalier, 1987; Krenzer et al., 1991;Langer, 1979;Masle, 1985; McMaster et al., 1994; Trought and Drew, 1980), salinity (Francois et al., 1994; Maas and Poss, 1989; Maas et al., 1994), nutrients (Blacklow and Incoll, 1981; Bremner, 1969; Masle, 1985; Power and Alessi, 1978),light (Langer, 1979; Masle, 1985; McMaster et al., 1987; Thorne and Wood, 1987a; Willey and Holliday, 1971), and high temperatures (Cannell, 1969;Rawson, 1971a; Thorne and Wood, 1987a)all can affect tiller survival. Biotic variables and management practices, such as planting density, depth, and date, surface residue cover, and tillage, affect tiller survival primarily by the affect on abiotic factors. The coleoptilar tiller seems anomalous to the other culms both physiologically and developmentally (Aggarwal and Sinha, 1984; Bingham, 1967; Brocklehurst et al., 1978; Cannell, 1969; Fletcher and Dale, 1977; Hucl and Baker, 1989; Johnson and Moss, 1976; Kirby et al., 1985b; Krenzer et al., 1991; Longnecker et al., 1993; Maas et al., 1994; Oosterhuis and Cartwright, 1983; Peterson et al., 1982; Rawson, 1971a; Richards, 1983; Smika and Greb, 1973). Semidwarfing genes significantly reduce culm height from the original standard, or tall, cultivars. For five Mexican and one Australian cultivars, the taller the cultivar the greater the internode lengths, and all cultivars had at least four or five internodes that elongated (Rawson and Evans, 1971). Assimilate for initial tiller bud growth comes from the leaf above on the parent culm (Fletcher and Dale, 1977).The role of the phytochrome system on tiller appearance and extension has been discussed in a number of studies (e.g., Casal, 1988; Kasperbauer and Karlen, 1986; Skinner and Simmons, 1993). Vernalization increases stem length without modifying the number of nodes (Blondon and Morris, 1985). Clearly, vernalization and photoperiod influence internode elongation (Chinoy and Nanda, 1951).

5. Switch from Vegetative to Reproductive Primordium Initiation The switch from the vegetative to reproductive phase occurs at single ridge if viewing a dissected apex, or at jointing if using external morphological characteristics. The transition from vegetative to reproductive development varies in duration among cultivars. Semidwarf wheat cultivars derived from Norin 10 tend to have a longer transition period and produce many more single ridges than standard wheats (Fisher, 1973). Inflorescence initiation seems to occur earlier in dwarf lines (Bush and Evans, 1988), but Brooking and Kirby (1981) concluded that the Norin 10 semidwarfing genes GdRht2 do not result in consistent differences in shoot apex morphogenesis,and much of the confusion may be due to not using isolines (Gale and Youssefian, 1985).

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The transition from vegetative to reproductive development can be influenced by at least four signals: vernalization, photoperiod, in some cases short day vernalization, and if many leaves have already formed (an internal signal). Not all cultivars will respond to all of these signals (Hay and Kirby, 1991). Several studies (Baker and Gallagher, 1983a; Tottman, 1977) have tried to relate double-ridge stage to ligule or stem height above the soil surface, but these clues are very cultivar- and site-specific dependent (Yasuda, 1984). When the outline of the first internode appears, apical primordium initiation rate increases [i.e., spikelet primordium are being produced (Malvoisin, 1984)]. It is almost ubiquitously overlooked that until the transition stage, and specifically jointing, the apical meristem is below the soil surface (Hay, 1986). From jointing through maturity the apical meristem is increasingly exposed to the canopy/aerial microenvironment. Purvis (1961) learned that the shoot apex directly perceives temperature. Most developmental concepts incorporate some type of thermal time or response using temperatures above the canopy. The assumption is that there is a consistent correlation between air temperature above the canopy and apical meristem temperature. Depending on the physical location of the meristem, and the microenvironment associated with the physical location, the degree of correlation of the relationship will vary. Given the potential variability in the relationship through time, it is amazing that the growing degree-day approach works as well as it does. Perhaps observed shifts in the phyllochron that occur in the spring near double ridge are partly related to this altered relation of the meristem to its microenvironment. Because double ridge and the start of internode elongation occur at nearly the same time, the meristem is rapidly approaching the soil surface when the shift occurs. The variable shift (increase and decrease) reported by Hay and Delecolle (1989) could be due to the highly variable relationship between air and soil surface temperature, depending on the specific conditions at the time (particularly soil water content, residue cover, and tillage practices). Another ramification of using canopy air temperature as representative of apical meristem temperature can be misinterpreting the effects of factors on various developmental processes. This was alluded to in the previous phenology discussion. Without monitoring the apical meristem temperature, it is very difficult to know if the plant is responding to the factor or the result of the factor changing the microenvironment and apical meristem temperature, or both.

6. Spikelet Primordium Initiation When the outline of the first internode appears, apical primordium initiation rate increases [i.e., spikelet primordium initiation begins (Malvoisin, 1984)], but double-ridge stage occurs before internode elongation (Harrell et al., 1993) and after under certain conditions and cultivars (Yasuda, 1984). Spikelet formation coin-

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cides with rachis internode extension in spring wheat (Holmes, 1973). Spikelet primordia are initiated acropetally (Bamard, 1955; Bonnett, 1966; Kirby, 1974). Vascular connections between the spikelet and rachis are established approximately at the time of floret initiation (Whingwiri et al., 198I), and spikelet number seems to determine the amount of vascular tissue that needs to be differentiated (Evans et al., 1970). Spikelets have their vascular tissue linked in parallel, whereas kernels within a spikelet tend to be linked in series (Bremner, 1972). Numerous transfer cells are found in the nodal regions where glumes, lemma, palea, and caryopsis are attached to the rachis and rachilla (Zee and O’Brien, 1971). Some spikelet primordia, about 50%of the final number, have been initiated prior to double ridge (Baker and Gallagher, 1983a; Kirby, 1985). Other work shows contrasting results ranging from 9 to 80% of the final spikelet number initiated by double ridge (Delecolle et al., 1989).The appropriate number is debatable and may be a function of cultivar variation in the number of single ridges established and the length of the transition phase from vegetative to reproductive primordium initiation (Fisher, 1973). The final maximum number of spikelets probably varies among cultivars, with a maximum of about 30 spikelets per spike (Allison and Daynard, 1976). Spikelet primordia are initiated about two to three times faster than leaf primordia (Baker and Gallagher, 1983a,b; Delecolle er al., 1989; Grieve et al.. 1993; Hay and Kirby, 199 I ;Kirby, 1974; Kirby and Appleyard, 1987; Malvoisin, 1984; Nerson et al., 1990; Stern and Kirby, 1979), but the rate varies greatly among cultivars (Allison and Daynard, 1976). Several studies report that spikelet primordia initiation is a linear function of temperature/GDD (Baker and Gallagher, 1983b; Hunt and Chapleau, 1986; Kirby et al., 1989; Malvoisin, 1984; Mohapatra et al., 1983), although for cereal crops, certain years and cultivars may have a slight curvilinearrelationship (Hunt and Chapleau, 1986). Kirby ( 1985)cites studies that suggest that about 0.07 spikelet primordia are formed per degree-day within a temperature range of 0 to 25°C (assuming a base temperature of 0°C). Younger tillers had faster spikelet primordium initiation rates so that the final number of spikelets per spike were similar for all culms because the higher rates compensated for the shorter duration (Hay and Kirby, 1991; Whingwiri and Stern, 1982). The vemalization response is positively related to the rate and duration of spikelet initiation (Blondon and Moms, 1985; Flood and Halloran, 1986; Halse et al., 1969). Temperature is the major variable controlling spikelet initiation rate and duration, but the role of photoperiod is much less clear. Photoperiod positively increases spikelet initiation rate (Baker and Gallagher, 1983b; Lucas, 1972; Nerson et al., 1990; Rahman and Wilson, 1978), but the effect lags several days (Davidson and Christian, 1984). An increase in photoperiod likely is correlated with an increase in apex and plant temperature and may confound the response (Kirby, 1985). Later planting dates shortened the duration of spikelet primordium initiation in calendar days (Whingwiri and Stem, 1982). Low light intensity will in-

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crease the duration and reduce spikelet initiation rate with a net result of fewer spikelets per spike (Davidson and Christian, 1984; Fischer, 1985; Friend, 1965a; Halse and Weir, 1970; Kemp and Whingwiri, 1980; McMaster et al., 1987; Stockman er al., 1983). Because low light intensity could be correlated with lower apex and plant temperature, Kirby (1985) may be correct in assuming that light, at most, has an indirect effect on spikelet initiation rates by influencing temperature. Cottrell et af. (1981) showed that gibberellin levels were higher in shoot apexes in long days, and that higher spikelet initiation rates were greater with higher levels of gibberellin. The role of the phytochrome system has not been studied. Nitrogen has variable effects on spikelet primordia initiation. The timing of double ridge was not affected by N (Frank and Bauer, 1982), and generally N does not increase the final number of spikelets (Langer and Liew, 1973; Nerson etal., 1990; Reilly et al., 1984; Single, 1964; Whingwiri and Kemp, 1980; Whingwiri and Stem, 1982;G. S. McMaster, unpublished data N, P, and Zn). N fertilizer after double ridge does not affect the final number of spikelets (Baker and Gallagher, 1983a), but this probably is because final number has already been determined. Water stress prior to the heading growth stage does not result in spikelet death unless the whole plant dies (Morgan, 1971). Salinity was found to have no effect on initiation rate but shortened the duration of two spring wheat cultivars (Grieve et al., 1993).

7. Spikelet Differentiation Spikelet differentiation commences with the appearance of flower primordia. While flower primordia are being initiated within the spikelet, each flower primordium differentiates the various organs comprising the floret (e.g., glumes at base of spikelet, lemma, palea, stamens, and pistil). As a result, a period of time occurs in which spikelet primordia, flower primordia, and floral parts are being initiated concurrently. With the onset of flower primordium initiation, a basic change in the morphological developmental pattern of the spike occurs and is maintained to physiological maturity. Until this point, leaf and spikelet primordia are initiated and develop acropetally. However, basal spikelets do not begin differentiation first. Florets in the mid-lower region of the mature spike (about spikelet positions 5-13) begin floret initiation first, with spikelet differentiation occurring both acropetally and basipetally from the mid-lower region. Some discrepancy exists between whether the basal (Kirby, 1974) or distal spikelets (Whingwiri and Stem, 1982) differentiate first. Some of the discrepancy is due to initiation starting in the lower mid-central region, resulting in more potential distal than proximal spikelets; thus, the most distal spikelets may start differentiating last if an even progression occurs both acropetally and basipetally. Within a spikelet, floret primordia are initiated acropetally. The MS begins floret formation before the tillers.

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Stem elongation has just begun (i.e., the stem is less than 5 mm and the apex is below the soil surface) when the first florets are initiated (Baker and Gallagher, 1983a; Nicholls, 1974). This would mean that spikelets begin differentiating very shortly after double ridge, if one assumes that double ridge and the start of internode elongation are simultaneous. Reports are consistent in the literature that floret formation begins prior to TS stage, but considerable variation in the number of floret primordia per spikelet at TS stage has been reported. For instance, Kirby (1974, 1985) found about two or three florets on spikelets near the mid-lower portion of the spike at TS stage. Whingwiri and Stern (1982) report that all spikelets on a culm have begun floret initiation before formation of the TS. They observed that most fourth florets were initiated between 1 and 8 days after TS formation, usually within 3 days. About 48% of the third florets were initiated before TS formation, 40% after the TS, and the remainder at the same time as TS. Time of sowing did not seem to greatly alter the interval between floret initiation and TS formation. With some exceptions, N supply appeared to advance floret initiation but not TS formation, although N did not affect the rate or duration of floret initiation (Langer and Hanif, 1973; Whingwiri and Stem, 1982). On later-formed tillers, fewer florets were initiated before TS formation. Before significant floret development occurs in Norin 10 and derivatives, the glume and lemma primordia grow within a spikelet to a greater extent than in standard wheat cultivars (Fisher, 1973). Fisher attributes this difference among genetic lines to differences in apical dominance. Differences in apical dominance may account for differences in the number of floret primordia within a spikelet before stamen primordia appear, and that Norin 10 and derivatives (i.e., those with greater tillering tendency, and presumably reduced apical dominance) will produce more floret primordia before stamen primordia are initiated than standard wheats (Fisher, 1973). Williams (1966a) gives relative growth rates, volumes, weights, and lengths of various spike organs as well as the whole spike, shows that stamen and carpel volumes in successive florets within a spikelet are lower, and shows that duration of growth is shorter within successive florets; the shorter durations of successive florets lead to synchrony within the spike (Hay and Kirby, 1991). Spikelet development rate varies considerably within the spike, with terminal spikelets, central spikelets, spikelets just above and below the central spikelets, and basal spikelets having successively decreasing developmental rates (Barnard, 1955). However, this is not an absolute pattern of spike developmental rates (Barnard, 1955). Kirby (1974) found that the difference in number of florets in spikelets differed not due to different initiation rates, but rather because the duration of initiation was shorter in non-centrally located spikelets, and Kirby and Appleyard (1987) suggest that floret initiation rate is essentially similar for all spikelets. Estimated floret initiation rates usually range from 0.02 to 0.04 florets

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"C-' day (Kirby, 1974; Kirby and Appleyard, 1987; Whingwiri and Stem, 1982), or about 25 GDD between successive florets (Williams, 1966b). Temperature, and possibly light, are the main factors influencing spikelet development rates (Friend et al., 1963; Hay and Kirby, 1991; Masle et al., 1989a). Temperatures above 30°C during floret formation have been reported to cause complete sterility (Owen, 1971; Saini and Aspinall, 1982). A maximum of 8-12 flower primordia initials are present on each mid-central spikelet and about 6-8 on basal and distal spikelets, but less than half of these are fertile florets at anthesis because at least half abort or have developed insufficiently before anthesis to be fertile (Bamard, 1955; Engledow and Ramiah, 1930; Hay and Kirby, I99 1 ;Herzog, 1986; Kirby, 1974,1985,1988; Kirby and Appleyard, 1987;Langer and Hanif, 1973; Siddique et al., 1989; Single, 1964; Whingwiri and Stem, 1982). Flower primordia initiation stops in the lower mid-central spikelets within the spike as the flag leaf begins emerging (Baker and Gallagher, 1983a; Kirby, 1988). It is not clear if flower primordium initiation ceases simultaneously for all culms and all spikelets on a culm. Floret abortion begins at booting, or when the flag leaf is fully grown, and floret initiation has ceased and lasts about two phyllochrons, after which no further floret abortion occurs; abortion ends at about heading or anthesis (Kirby, 1985, 1988; Langer and Hanif, 1973; Siddique et al., 1989). Predicting which florets will abort is difficult, but the literature is consistent in that at least half of the total floret initials within a spikelet will abort or develop insufficiently to be fertilized by anthesis. Whingwiri and Stem (1982) suggest that all florets initiated after the terminal spikelet is formed will not develop grain. Floret death occurs during the period when the stem and peduncle are growing at their most rapid rate (Siddique et al., 1989).The penultimate internode is at maximum growth rate and the peduncle growth rate is rapidly increasing. During this period, leaf area is slowly declining, although total photosynthetic rate may not initially be declining, particularly when spike photosynthesis is included. Kirby (1988) interprets this as support for the hypothesis that floret death is partly due to competition between the spike and stem for resources, presumably carbohydrates. One difficulty with this hypothesis is that often there are excess carbohydrates available during this phase and these carbohydrates are stored in the internode tissue, although the reserves are usually stored more closely to the period near or after anthesis (Asana and Williams, 1965; Blacklow et al., 1984; Jude1 and Mengel, 1982; Wardlaw, 1970). Barnard (1955) outlined the histogenesis of the spike and was led to the following conclusions. The foliage leaf, glume, lemma, palea, lodicules, and carpel are viewed as foliar appendages. Stamens, lateral spikelet primordia, and flower primordia are considered homologous with axillary vegetative shoots. Fisher (1973) agreed with this interpretation.

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8. Floret Differentiation The first glume, lemma, and palea primordia appear on spikelets near the central portion of the spike (Baker and Gallagher, 1983a; Barnard, 1955; Bonnett, 1966; Kirby, 1985; Oosterhuis, 1977). The two glumes within a spikelet differentiate first, followed by the first two lemmas on the basal florets within a spikelet. As the third lemma differentiates, the first flower primordium appears in the axil of the first lemma (Barnard, 1955). Stem elongation begins when lemma primordia first appear or slightly earlier (Kirby, 1985; Malvoisin, 1984; Tottman, 1977), but others (Masle e f al., 1989a; Wiegand er al., 1981; Yasuda, 1984) did not find this relationship. Within a floret, the order of differentiation is from the outside inward: lemma, stamens, palea, and pistil (Bonnett, 1966; Oosterhuis, 1977). However, Barnard (1 955) indicated the order of the stamens and palea was reversed, which would result in a two-ranked order of differentiation. Awns develop on lemmas; therefore, for awned cultivars, development of the awn is associated with lemma development. Awn structures begin to elongate on the lemmas after the appearance of the stamens (Oosterhuis, 1977). Conflicting reports exist on whether awns positively (Weyhrich ef al., 1994) or negatively (McKenzie, 1972) impact grain yield and test weight. A floret is particularly sensitive to stresses when the subtending lemma is being initiated (Frankel, 1976). The two lateral stamens in a floret appear first, followed by the anterior stamen and carpel (Barnard, 1955). The two lateral stamens are positioned in the keels of the palea, with the central stamen opposite the lemma on the adaxial side (Bonnett, 1966).The visible appearance of the first anther primordium on the most advanced spikelet generally coincides with the initiation of the terminal spikelet just prior to jointing (Fisher, 1973; Friend et al., 1963; Williams, 1966a). Anther initials are the first part of the stamen to differentiate, with the filament forming later beneath the anther (Bonnett, 1966). The four locules form in each anther soon after the anthers are initiated (Bonnett, 1966). For some cultivars, high temperatures (24/19 and 30125°C) can result in poor pollen development (Dawson and Wardlaw, 1989). Fewer pollen grains are produced in wheat stamens (about 1000-3800 per anther) than in other cereal grasses, and wheat stamens are smaller than those of other cereals (de Vries, 1971). Total pollen production per wheat plant is about 450,000 as contrasted to about 4 million for Secale cereale L. (rye) and 18 million for Zea mays L. (corn; de Vies, 1971). It might be speculated that fewer pollen grains are necessary for wheat, which self-pollinates, than for rye and corn, which are cross-pollinated. The pistil is the last floret structure to initiate (Bonnett, 1966). The order of pistil differentiation is ovary, styles, and stigma (Bonnett, 1966). The carpel of wheat

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is unilocular, with one ovule. Each ovule has two plumose styles with stigma branches. Throughout development, very low concentrations of polysacharides and reducing sugars and high concentrations of RNA and nuclear proteins are present in stigmatic branches; lipids increase during pollination and decrease during pollen growth (Rudramuniyappaand Panchaksharappa, 1974). Pollen grains and pollen tubes have high concentrations of polysaccharides, RNA, proteins, and lipids. Rudramuniyappa and Panchaksharappa ( 1974) infer that these macromolecular substances balance out the need for growth of the pollen tube in the stigma and style.

9. Terminal Spikelet Formation The TS is characterizedby a 90" rotation from the plane of previous spikelet initials (Fig. 6; Bonnett, 1966; Hay and krby, 1991). The TS is formed just prior to jointing, when the stem is about 20 mm long (Baker and Gallagher, 1983a), and the TS primordium appearance coincides with the appearance of the first stamen primordium (Fisher, 1973; Williams, 1966a). There does seem to be some variability, however, in the relationship of TS formation and other developmental events (Whingwiri and Stem, 1982). Nicholls (1974) suggested that cessation of spikelet primordium formation by the apical meristem is not correlated with any developmental stage, and that nutrient limitation is the cause for cessation. However, nitrogen had no effect on it when the TS appeared (Whingwiri and Stem, 1982). Photoperiod profoundly impacts when the TS is initiated, but this is likely cultivar dependent (Pinthus and Nerson, 1984); Rawson, 1971b). Probably, the TS stage is not strongly correlated with other developmentalevents. Terminal spikelet initiation occurs later on younger culms, although the range was less than about 3 days among all culms (Whingwiri and Stem, 1982). Baker and Gallagher (1983a) showed a 6-day difference among the MS and T1 culms. The presence of the TS has led Bonnett (1966) and others to view the wheat spike as determinate, although an apical meristem does remain after the TS has differentiated (Fisher, 1973). In a practical sense, the wheat spike can be viewed as determinate because further apical meristematic activity ceases under almost all conditions. 10. Rachis Growth

Rachis internode extension coincides with spikelet formation for spring wheat (Holmes, 1973).Spike growth is very slow in the early stages of development and rapid elongation starts when the flag leaf ligule is just visible (Krumm et al., 1990). It is unclear when rachis elongation ceases, but it is certainly before anthesis (McMaster et al., 1992b). Spike length increase seems fairly linear over time (Mishra

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flag leaf

Figure 6. Drawing of a wheat spike at anthesis growth stage.

and Mohapatra, 1987). The MS ear length grows at a maximum rate of 0.73 mm per degree-day and during the linear phase has a rate of 0.5 1 mm per degree-day (Kirby, 1988), although cultivar differences and varying conditions will result in different rates. Spike lengths and weights vary considerably among cultivars and conditions, with N stress reducing lengths and weights (Large, 1954; Singh and Singh, 1985; G. S. McMaster, unpublished data).

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11. Peduncle Growth Peduncle elongation occurs during booting into anthesis. Depending on conditions and cultivars, elongation and dry weight increase ends between anthesis and about 2 weeks post anthesis (e.g., Asana and Williams, 1965; Davidson and Chevalier, 1992; Jude1 and Mengel, 1982; McMaster et al., 1992b; Rawson and Evans, 1971; Spiertz, 1974; Wardlaw, 1970). Internodes other than the peduncle can sharply decrease weight at anthesis, especially at higher temperatures (Spiertz, 1974). Stem dry weight (presumably including the peduncle) increased faster than spike dry weight for the first 10 days following anthesis (Wardlaw, 1970). N has little effect on the qualitative and quantitative changes in soluble culm carbohydrates (Blacklow et al., 1984), but light intensity is positively correlated with stem weight (Spiertz, 1977). Peduncle growth rate and other internode growth rates were given by Kirby (1988). Reported peduncle lengths range from about 150 to 350 mm for seven cultivars (McMaster et al., 1992a; Rawson and Evans, 1971).

12. ChaffGrowth Chaff normally is composed of glumes, paleas, lemmas, awns, rachillas, and rachis. Asana and Williams (1965) found that chaff weight did not increase after 12 days past anthesis (about 385 GDD). Chaff dry weight at harvest can range from 6 to 54% of total spike weight, depending on cultivars and conditions (Asana and Williams, 1965; Bingham, 1967; McMaster et al., 1994; Miller, 1939). For cultured spikes, lower temperatures result in a lower proportion of the spike weight represented by nongrain factions (Donovan et al., 1983). Irrigation significantly increases glume weight, but N has no significant effect (Bingham, 1967). There is a positive correlation between kernel and chaff weight (G. S. McMaster, unpublished data). For “Gabo”, a cultivar with a short tip awn in the distal three or four spikelets, awn weight per spike ranged from about 2 to 22 mg per spike and was strongly influenced by N (Single, 1964).

13. Anthesis The two lodicules at the base of the ovary are placed against the lemma and at the edges of the palea (Bonnett, 1966). The thick bases of the lodicules swell to twice their size, probably due to sugar influx causing osmotic swelling, forcing the palea and lemma to open and allowing anther extrusion (Fig. 6; Bonnett, 1966; Craig and O’Brien, 1975; Percival, 1921). Stamen filaments can elongate to three times their original length within 3 min (de Vries, 1971), causing anther exertion. Approximately 20 min following the swelling of the lodicules, the palea and lemma close and anthesis is complete. Although the opportunity for cross-pollination

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occurs during this time, over 96% of the wheat flowers are self-pollinated (Martin et al., 1976). Pollen viability is maintained for up to 30 min under optimal conditions (de Vries, 197l), after which the pollen grains become desiccated. Pollen germinates within 1 min of reaching the stigma (Chandra and Bhatnagar, 1974), with usually only one grain germinating on a stigma branch (Rudramuniyappa and Panchaksharappa, 1974). The pollen tube enters the embryo sac from 30 to 60 min after germination (Lange and Wojciechowska, 1976). and Jensen (1 9 18) observed that fertilization occurs between 32 and 40 hr after pollination. Fertilization is kleistogamous, meaning pollination occurs prior to the phenological growth stage called anthesis (Herzog, 1986; Martin et al., 1976). Ovule fertilization begins in basal florets within spikelets in the central portion of the spike and proceeds simultaneously acropetally and basipetally along the rachis and acropetally within the spikelet (Bonnett, 1966; Oosterhuis, 1977; Rawson and Evans, 1970). All ovules within a spike are fertilized within a short span of time, usually within about 3 days (Evans et al., 1972; Rawson and Evans, 1970; Simmons and Crookston, 1979). Sterilization of basal spikelets in central florets did not affect the onset of anthesis of other florets (Rawson and Evans, 1970). Only about 80% of the fertile florets set grain (Gallagher and Biscoe, 1978). Much discussion and antidotal evidence exists for the importance of water in pollination and successful grain set; however few detailed studies examining plant water potential during pollination and wheat pollination or grain set have been published to my knowledge. Higher N seems to increase the number of fertile florets at the time of fertilization (Langer and Liew, 1973; Single, 1964), although it is unclear how this affects grain set. Single (1964) found little influence of N on grain set, but Langer and Liew (1973), Whingwiri and Kemp (1980), and others have found more kernels per spikelet under high N conditions. A parabolic pattern of number of kernels per spikelet within a spike is observed (Grieve et al., 1992; Lesch etal., 1972). Maximum number of kernels per spikelet occurred in the range of 25-50% up the rachis (Herzog, 1986). Whingwiri and Stern (1982) found that only florets that initiated prior to TS formation formed grain. Three hypotheses have been advanced to explain why florets fail to develop into kernels: (i) an inadequate supply of mineral nutrients, water, and carbohydrates; (ii) hormonal imbalances; and (iii) further development is dependent on vascular development that is a function of the interaction between sucrose and hormones such as auxin. Indirect evidence exists to support all three hypotheses (Whingwiri and Stern, 1982). 14. Kernel Growth Individual kernel growth follows a sigmoidal pattern regardless of location within the spike, among spikes, among cultivars, or any typical set of biotic or abiotic factors (e.g., Barlow et al., 1980; Darroch and Baker, 1995; Gebeyehou et al.,

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1982; Green et al., 1985; Herzog, 1986; Pinthus, 1963; Rawson and Evans, 1971; Slafer and Savin, 1994).The sigmoidal curve is frequently divided into three phases: the lag or cell division phase, the linear or grain-filling phase, and the asymptotic or maturation phase (Herzog, 1986). Precise determination of each phase is difficult, and the phases are most profitably viewed heuristically. The lag phase is dominated by cell division (Evers, 1970; Jennings and Morton, 1963). The duration of the lag phase is about 20-30% of the total grain-filling period, if one assumes that the grain-filling period ends when the curve approaches the asymptote (very difficult to precisely determine; Gebeyehou et al., 1982; Herzog, 1986). During the lag phase, both the amount of water and seed water potential increase (Barlow et al., 1980). The linear phase is the period of rapid cell growth and constitutes about 50-70% of the total grain-filling period. Herzog (1986) states that kernel growth in the linear phase is mostly caused by starch synthesis in the amyloplasts of endosperm cells. Brocklehurst (1977) suggests that assimilate supply regulates the number of endosperm cells formed, and that the rate of dry weight accumulation is primarily governed by the number of endosperm cells present. Radley ( I 978) found that endosperm and aleurone cell numbers increased when other kernels were removed, resulting in increased grain volume. The end of kernel growth is normally considered to be the point of maximum dry weight. Either because the data are often variable or because sampling is not frequent enough, it is very difficult to determine the point of maximum dry weight. End of kernel growth is often determined mathematically or visually as grain weight “stops increasing.” When considering the sigmoidal pattern of kernel growth, the two important biological variables are the duration of the grain-filling period and the instantaneous rate of grain filling. Kernel weight is commonly assumed to begin increasing at the onset of anthesis. However, Wardlaw (1970), citing others, indicated that significant dry weight gain does not begin until 6 days after anthesis. Although the error is slight, there are two inaccuracies in this assumption. The first is that fertilization occurs just prior to anthesis, where anthesis is defined as the period when stamens emerge from the floret (see Seed Ontogeny section). The second inaccuracy is that fertilization throughout the spike is not simultaneous. The duration of anthesis is normally about 3-4 days (see Phenology and Anthesis sections). It appears, and is almost universally assumed, that syngamy follows the anthesis pattern and that the time lag between fertilization and anthesis of a floret is constant regardless of floret location. a, Seed Ontogeny Knowledge of the ontogeny of the wheat seed is important in providing a developmental framework within which physiological processes can be understood. In discussing the ontogeny of the seed, two aspects are of primary importance: embryogeny and endosperm development. The triploid endosperm nucleus is formed

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2-3 hr after pollination, whereas the diploid zygote forms some hours later (Herzog, 1986, citing many references). Zygote mitotic division begins later than endosperm mitotic division (more than 12 hr after pollination compared to 6-8 hr, respectively; Herzog, 1986; Morrison, 1955), but embryo development is completed prior to endosperm maturity (Martin et af., 1976). Initial endosperm development is free-nuclear (coenocytic) mitotic divisions of varying duration (Frazier and Appalanaidu, 1965; Morrison, 1955). Endosperm cell walls begin forming about 3 days after pollination-when about several hundred nuclei have formed and 8-10 cells are present in the embryo (Deshpande and Raju, 1979; Frazier and Appalanaidu, 1965; Morrison, 1955). Endosperm development continues to proceed faster than embryo development, which is typical in flowering plants (Herzog, 1986; Lersten, 1987; Noda et af.,1993). The first phase of kernel growth was previously mentioned as a time of cell division. During this period, endosperm and embryo cell division is rapid, but little growth (either in size or in weight) is occurring (Martin et af., 1976). Endosperm cell division ceases after the first 10-20 days of the grain-filling period, after about 100,000 endosperm cells are present (Briarty et af., 1979; Evers, 1970; Jennings and Morton, 1963; Sandstedt, 1946; Wardlaw, 1970).Little endosperm cell division occurs after this period in late grain filling. Cell number of the testa-pericarp remains constant from 5 to 40 days after flowering (Jennings and Morton, 1963). It is unclear if this suggests that maximum potential size of the seed coat is determined shortly after anthesis. Once endosperm cell division has almost ceased, significant cell growth begins. Cells typically expand about 10-fold their initial cell size (Briarty et af.,1979), with cell expansion continuing until shortly before maturity. Cell growth in dry weight terms is primarily from conversion of translocated sucrose to starches and accumulation of nitrogenous organic compounds such as protein bodies (present 10 days after anthesis; Evers, 1970) that pass through the vascular bundle that extends from the base to apex of the seed through the pericarp at the base of and parallel to the crease (Frazier and Appalanaidu, 1965). Martin er af. (1976) state that starch grains and proteins fill the endosperm region in a centripetal manner (i.e., outer periphery cells first). The number of starch granules is greater in large than in small kernels, and regardless of kernel size, granules less than 10 Fm in diameter (B-type granules) contribute more than one-third of the total starch weight (Brocklehurst and Evers, 1977). The final kernel is composed of about 2.5% embryo tissue, 10% pericarp, 4% aleurone, and 85% starchy endosperm (Bradbury et af., 1956; Lersten, 1987; Martin et af., 1976).Engledow and Ramiah (1930) described major stages of grain formation, and Noda et af. (1993) propose a reclassification of the developmental stages of kernels. For spring wheat, the main vascular bundle of the caryopsis begins differentiation shortly after anthesis and is completed about the same time that caryopsis elongation ends (approximately 30% into grain filling, when cell division ends;

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Lingle and Chevalier, 1985). The sieve tubes of the main bundle appear to remain functional until physiological maturity (Lingle and Chevalier, 1985). After maturity, cultivars with red kernels normally are more resistant to sprouting than those with white kernels, which presumably is due to germination inhibitors present in the seed coat (Miyamoto et al., 1961). A complete bibliography of grass and wheat caryopsis anatomy and fine structure is available (Lersten, 1987; Rost and Lersten, 1973), and Lersten (1987) gives more detail on endosperm and embryo development. b. Kernel Growth within a Spikelet Regardless of kernel location within or among spikes, seed embryogeny and the sigmoidal pattern of growth is the same. However, the specific pattern of kernel growth, and the onset and completion dates, does vary depending on location. Within a spikelet, kernels develop and grow in an acropetal pattern (Bonnett, 1966; Kirby, 1974; Oosterhuis, 1977; Whingwiri and Stern, 1982). Therefore, the onset of kernel growth is progressively delayed for kernels closer to the apex of the rachilla. It is commonly assumed that basal kernels within a spikelet complete growth before apical kernels, but the data are not definitive on this and the lag time has not been adequately quantified. Simmons and Crookston (1979) reported for three spring wheat cultivars that all kernels within a spikelet reached maturity at about the same time. The lack of a precise determination, and definition, of kernel maturity contributes to the confusion on this point. Final kernel weight varies considerably depending on abiotic and biotic factors, but a clear trend is that kernel weight decreases acropetally within a spikelet (Bremner, 1972; Rawson and Evans, 1970), and kernel weight is positively correlated with volume of the floret cavity (Millet, 1986). Conflicting reports exist on whether the first or second kernel from the base of the rachilla has greater growth rates and final dry weight (Bremner, 1972; Rawson and Evans, 1970). Aside from possible errors in identification, this pattern may be related to cultivar differences, but more likely seems to be a function of whether stresses were present during grain filling. Bremner (1972) hypothesized that the second kernel had greater potential growth rate than the basal kernel but was affected more when resources were limiting. I have noticed that studies reporting that the second kernel tends to have slightly greater final kernel weight were usually conducted in growth chambers or greenhouses in which water and nutrient supply was plentiful. In studies in which stresses were likely to be present, particularly field studies, the first kernel tends to have the greatest final weight. c. Kernel Growth among Spikelets Kernel growth rates, duration, and final weights vary depending on location within the spike, even under favorable conditions for grain filling (Grieve et al., 1992; Lesch ef al., 1972; Mishra and Mohapatra, 1987; Slafer and Savin, 1994).

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As stresses increase, the variation among kernels within the spike increases. Sev-

eral studies (Bremner, 1972; Rawson and Evans, 1970) suggest that the variation among kernels is based on different maximum growth rate potentials and on different durations of grain filling (due primarily to different fertilization times); these two factors are accentuated by physiological conditions under which stresses result in lower final kernel dry weights. These factors imply that all kernels do not have equal sink strengths. Kernel sink strength within a spike seems to follow the fertilization pattern, although many factors determine sink strength. d. Duration of Kernel Growth within a Spike The duration of grain filling among kernels within the spike has not been definitively quantified; it is unclear whether all kernels end grain filling simultaneously or whether the stagger in kernel maturity follows the fertilization pattern, but that the stagger in maturity covers a shorter time span than the fertilization pattern. Visual observations of greenness indicate that kernels do not mature simultaneously. For example, kernels in the terminal spikelet seem to be among the first to lose chlorophyll, which is usually an indicator of maturity (Hanft and Wych, 1982; G. S. McMaster, personal observation), and physiological maturity occurred first in kernels in the apical spikelets (about 3 days earlier) and simultaneously on kernels in central and basal spikelets for eight different cultivars of spring wheat (Hanft and Wych, 1982). e. Kernel Growth among Spikes The relationships discussed previously for kernel growth within a spike apply to all spikes. The main differences between kernel growth on main stems and tillers are that kernel growth rates, grain-filling duration, and final kernel weights are less on tillers (Hucl and Baker, 1989; Shanahan el al., 1984; Zwer et al., 1995). The onset of fertilization is delayed as tiller age and size is decreased. The proportion of total yield contributed by each culm is not the same for all culms (McMaster et al., 1994; Power and Alessi, 1978). Culm age and size are both positively related to spike grain weight (Darwinkel, 1980; Hucl and Baker, 1989; Kirby et al., 1985b; Phadnawis and Saini, 1986; Saini and Nanda, 1986; Shanahan, 1982; Thome and Wood, 1988). Usually, if tillers are of the same age, higher-order tillers will have less grain weight. f. Cultivar Variations in Kernel Growth Kernel growth is significantly different among cultivars (e.g., Bruckner and Frohberg, 1987; Darroch and Baker, 1995; Housely et al., 1982; van Sanford, 1985; Vos, 1985), and the presence of semidwariing genes reduces kernel size (Pinthus and Levy, 1983).All growth parameters (duration of grain filling, growth rates, and final kernel weight) vary among cultivars. Although these growth parameters differ among cultivars, the pattern of individual kernel ontogeny and growth normally does not differ significantly among cultivars.

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g. Temperature Effects on Kernel Growth Temperature has a major effect on both the duration and the rate of grain filling. Each of these effects and their net results will be discussed in detail, but the general relationship is that as temperature increases the duration decreases and growth rates increase with a net effect of lower final kernel weight at higher temperatures (e.g., Bhullar and Jenner, 1983; Herzog, 1986; Sayed and Ghandorah, 1984; Sofield et al., 1974; Spiertz and Vos, 1985; Vos, 1985; Wardlaw et al., 1989). The duration of grain filling is highly variable, depending in part on cultivar and environmental conditions, particularly temperature (Darroch and Baker, 1995; Ford et al., 1976; Midmore et al., 1982; Wiegand and Cuellar, 1981). Wiegand and Cuellar (1981) observed a decrease of 3.1 days in duration for every "C increase in temperature. If the GDD approach is used, a nonlinear relationship with temperature is found; as temperature increases, the accumulated GDD for grain-filling duration decreases (Al-Khatib and Paulsen, 1984; Asana and Williams, 1965; Marcellos and Single, 1972; Spiertz and Vos, 1985; Vos, 1985). Some uncertainty in the qualitative pattern with temperature is due to an insufficient number of temperature treatments to adequately describe a nonlinear curve. Temperature clearly has a positive influence on kernel growth rates (Al-Khatib and Paulsen, 1984; Rawson and Evans, 1970; Wiegand and Cuellar, 1981). Most studies cite some type of linear increase in kernel dry weight with increasing temperature, although the relationship probably is not linear over the whole temperature range. Unstated assumptions in the literature include kernel growth rate responses do not vary depending on the phase of grain filling or location as in a spike. Above a threshold temperature, final kernel weights decrease (Wiegand and Cuellar, 1981). Both high and low temperatures will inhibit starch synthetase (Jenner, 1968). Some studies show an optimal maximum grain weight for temperatures between 15 and 20°C (Chowdhury and Wardlaw, 1978; Feyerherm and Paulsen, 1981; Fischer, 1985; Herzog, 1986, cites many references; Kolderup, 1979; Sofield et al., 1977b; Wardlaw et al., 1989; Wiegand and Cuellar, 1981). h. N Effects on Kernel Growth Other factors beside temperature affect the rate and duration of kernel growth, with N and water being two frequently studied factors (Blacklow and Incoll, 1981; Herzog, 1986; Simmons and Moss, 1978a; Simmons and Moss, 1978b). Nitrogen accumulation in the kernel also follows a sigmoidal pattern, and the parameters of the curve vary among cultivars (Campbell et al., 1990; Herzog, 1986; Sofield et al., 1977b; Vos, 1985). Comparing the first three basal kernels in a spikelet, limited N seems to affect the third kernel most (Whingwiri and Stem, 1982). The N concentration in a kernel ranges typically between 2 and 5% during grain filling (Anderson et al., 1991; Bhullar and Jenner, 1983; Grieve et al., 1981; Herzog, 1986; Smith ef al., 1983), with 25-50% of the grain N resulting from N uptake by plants during grain growth [Austin et al., 1977a; Grieve et al., 1981; Heitholt et a/., 1990 (less than 10%); Spiertz and de Vos, 19831. Mobilization of plant N oc-

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curs from all plant parts, with leaf, glume, stem, and root fractions contributing about 40, 23, 23, and 16%, respectively (Simpson et al., 1983). Temperature affects N and dry matter accumulation patterns in a similar manner (Bhullar and Jenner, 1983; Herzog, 1986; Spiertz and Vos, 1985). Nitrogen may (Bauer etal., 1985; Blacklow and Incoll, 1981) or may not (Bingham, 1967) affect the timing and duration of grain growth. Certainly, deficient N reduces leaf area duration during the period from anthesis to maturity, and it is likely N may indirectly effect grain-filling duration by altering senescence of the plant. Other nutrients besides N are obviously important in kernel growth, but have received much less attention. i. Water Effects on Kernel Growth Water availability has many effects on the rate and duration of grain growth such as carbon assimilation, nutrient uptake by roots, and cell division and expansion. Water availability interacts with N to decrease the mobilization of amino acids (Aggarwal and Sinha, 1984) and the interaction of water and N can result in varying effects on grain yield (Christen et al., 1995; Grieve et al., 1981; Mogensen and Talukder, 1987; Paltaetal., 1994; Palfi andDezsi, 1960; Spiertz anddeVos, 1983). Clearly, water availability is strongly correlated with kernel growth and yield (Bingham, 1967; Brocklehurst etal., 1978; Brooks etal., 1982; Fischer, 1973; Fischer and Maurer, 1978; Gallagher et al., 1976; Johnson and Moss, 1976; Richards, 1983), although some studies suggest that grain water potential is largely independent from the rest of the plant (Barlow et al., 1980; Brooks et al.. 1982). It is almost certain that the duration of grain filling is shortened by water stress [Angus and Moncur, 1977; Bauer et al., 1985; Bingham, 1967 (found no effect); Brooks et al., 1982; Frank et al., 1987; McMaster and Smika, 1988; Mogensen and Talukder, 1987; Nuttonson, 1948; Sionit et al., 19801, but the effect may be indirect because plant temperature is increased. Water stress does not seem to have as great an effect on growth rates (Mogensen and Talukder, 1987). Water deficits did not affect the number of endosperm cells (Brookset al., 1982), but presumably cell expansion was affected. j. Light Effects on Kernel Growth Temperature accounts for 75-97% of the variation in duration of the grain-filling phase, with photoperiod having no influence (Marcellos and Single, 1971). I can find no reports that photoperiod affects the duration of grain filling. Sofield et al. (1977a) reported no effect of illuminance on the duration of the linear growth phase. Shading and low light intensity reduce the number and weight of kernels per spikelet and number of spikelets per spike (e.g., Evans, 1978; Fischer, 1985; Friend, 1965a; Friend et al., 1963; Kemp and Whingwiri, 1980; McMaster et al., 1987; Sofield et al., 1974; Stockman et al., 1983). Light affects grain filling primarily by its effect on the production of carbohydrates and N accumulation (Her-

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zog, 1986), although Millet and Pinthus (1984) showed that the size of the floret cavity and the transmission of light through floret bracts are two other factors that control grain growth. k. Vascular Connections within the Spike Several studies have examined whether translocation from sources to the sink limits grain filling. Flow of sucrose is restricted by the capacity of at least one of the processes involved with transporting sugar into the grain (Jenner and Rathjen, 1972). The rate of grain filling is not related to the number of sieve tubes of the main vascular bundle of spring wheat kernels (Lingle and Chevalier, 1985). Evans et al. (1970) suggested that wild diploid progenitors of wheat may have had spikes that were largely self-supportingfor their assimilates. In hexaploid Triticum aestivum, however, this is not the case. In addressing whether the stem in I: aestivum has the capacity to transport sufficientassimilates to meet spike demand (as this is a recent result of selection), Evans et al. (1 970) examined the number of vascular bundles and phloem cross-sectional area among different evolutionary lines. They showed a positive relationship between phloem area and maximum translocation rate. Making a number of assumptions, Evans et al. (1970) concluded that the phloem present in all evolutionary lines could transport sufficient assimilates to meet spike demands. However, if this conclusion is not true, then transport limitations most likely would be found in hexaploid cultivars of recent origin. They also reported that spikelet number seemed to determine the amount of vascular tissue that needed to be differentiated. For two spring wheat cultivars, the vascular connections in the rachilla had much smaller diameter vascular bundles connecting to the fourth and fifth kernels than the first three kernels (Simmons and Moss, 1978a). In addition, all bundles that served the fourth and fifth kernels were connected to bundles that served at least one of the first three kernels. At least some of the bundles that served the first three kernels were independently connected to the rachis. Bremner (1 972) presented evidence that both spikelets and kernels within spikelets were linked both in parallel and serial, but that spikelets showed more tendency for parallel linkage and kernels within spikelets tended more toward serial linkages. This tendency might explain in part why kernels within spikelets decrease in final weight acropetally, especially when stresses increase, because resources presumably become more limiting in distal vascular regions first. This vascular system allows resources to be transported to all parts of the spikelet but also results in resources first becoming limiting in regions within the spikelet that have invested the fewest resources in development and growth-the apical florets and kernels. The greater parallel linkage among spikelets might partly account for the frequent response of basal and apical spikelets both being reduced similarly under stress conditions. The upward velocity of assimilates through the peduncle (about 80-100 cm/hr) is twice that for movement down through the leaf sheath (Wardlaw, 1965). Re-

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moving kernels slowed transfer rates upward and increased transfer down the stem. Wardlaw presented no data suggesting translocation was limiting kernel growth. 1. Sources and Sinks Present during Kernel Growth

Sources and sinks change over the course of grain filling. During early grain filling a number of sinks, in addition to kernels, are present. Peduncle growth, both in size and weight, is occurring (see Peduncle Growth section). Lower internodes often increase in dry weight and soluble sugar concentration up to about 2 weeks after anthesis, particularly under conditions of high assimilation; presumably, assimilation exceeds demand and reserves are stored in stern tissue (Asana and Williams, 1965; Rawson and Evans, 1971). Rachis growth seems to be mainly completed by anthesis. Even fewer data are available on rachilla growth, but some rachilla growth must occur after anthesis to allow space for kernels that are growing. Most chaff growth (glumes, paleas, and lemmas) is completed shortly after anthesis (Asana and Williams, 1965), although once again few data are available. One sink often ignored during grain filling is the roots. Belford et al. (1987) report that seminal roots grow until anthesis. It is unclear if new root branches develop after anthesis or if significant root extension occurs, but clearly roots continue to function until shortly before maturity (e.g., Anderson et al., 1991; Austin et al., 1977a; Grieve et al., 1981; Heitholt et al., 1990; Smith et al., 1983; Spiertz and de Vos, 1983). The primary photosynthate source during grain filling is leaves. Almost all assimilates translocated from flag leaves move upward to the grain (Carr and Wardlaw, 1965; Lupton, 1966; Patrick, 1972),whereas the penultimate and third leaves translocate both upward and downward (Lupton, 1966).As a general rule, assimilate is translocated preferentially to the closest sink (Rawson and Hofstra, 1969). The flag leaf is the main leaf source of assimilates to the spike (Patrick, 1972). Maximum LA1 is typically reached shortly before anthesis, and often each culm has two to five green leaves present at antithesis. Whether maximum LA1 is correlated with maximum photosyntheticactivity, however, is debatable, but normally a decline in flag leaf net CO, assimilation is observed after anthesis (Araus et al., 1987; Carr and Wardlaw, 1965; Hunt and van der Poorteen, 1984).Given that net carbon exchange rate (CER) decreases with leaf age, canopy net CER should decline even if LA1 remains constant as maturity is approached. Leaf sheaths are another source of photosynthate during grain filling, although they have much lower photosynthetic rates than do leaf lamina (Araus et al., 1987; Stoy, 1965). Spike components can be a potentially important photosynthate source. The assimilate contribution by spike components is variable, with some estimates that the contribution just offsets spike respiration and other estimates that up to 35% of the spike dry weight is derived from spike assimilation (Carr and Wardlaw, 1965; Evans and Rawson, 1970). Carbon exchange rates for spikes were about

PHENOLOGY, DEVELOPMENT, & GROWTH OF SHOOT APEX 101 90% of flag leaf CER, and awns could contribute from 40 to 80% of the total spike CER (Blum, 1985). Stem tissue also has some photosynthetic capacity, especially the uppermost portion of the peduncle that is not covered by the flag leaf sheath. Photosynthetic rates of peduncles are lower than those of leaf lamina (Stoy, 1965). Stem tissue can also act as a source in the sense of storing carbohydrates that can be used later in grain filling if needed. Stem nonstructural carbohydrate reserves at anthesis seem adequate to supply much of the grain-filling needs (Evans and Wardlaw, 1976; Gallagher et al., 1975, 1976; Stoy, 1965), although rarely do reserves contribute more than about 3040% to final grain yield (Aggarwal and Sinha, 1984; Bidinger et al., 1977; Gent, 1994; Palta et al., 1994; Rawson and Evans, 1971; Richards and Townley-Smith, 1987). If translocation from stem reserves does not increase under water stress conditions (Rawson et al., 1977), then the ability of the plant to access the reserves may be limited. Mobilization of stem carbohydrate reserves seems variable among cultivars (Austin ef al., 1977b; Blum ef al., 1983). Stem reserves have been postulated to serve primarily as a backup for when photoassimilation after anthesis is strongly inhibited and photorespiration rates are increased (Aggarwal and Sinha, i984; Bidinger et af., 1977; Rawson et al., 1977). which is common under dryland conditions in areas such as the Great Plains. About 63% of the net assimilation from anthesis to maturity went to the spike (Bremner, 1972). Leaf and stem photosynthetic rates remain high well into grain filling (Araus et al., 1987), and assimilation rates are in part controlled by feedback from the sinks (Blum er al., 1988).

V CONCLUSION The dynamic complexity and interaction of development, phenology, and growth challenges our ability to understand shoot apex ontogeny and growth. Simulation modeling potentially provides a puissant and heuristic tool for helping to summarize and integrate much of the research outlined here. However, of the more than 73 models that predict wheat yield (McMaster, 1993), very few (McMaster et al., 1992a,b;Rickman et al., 1996; Weir et al., 1984; Wilhelm et al., 1993) simulate near the level of shoot apex functioning. It is hoped that this review will provide the outline for building the foundation of new wheat simulation models, and that necessary references are discussed. However, much work is still necessary to understand the general developmental pattern (Fig. 3) and how abiotic and biotic factors influence the developmental pattern, both qualitatively and quantitatively. Perhaps the best legacy to be hoped from this effort is a clearer understanding of the gaps in our knowledge.

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ACKNOWLEDGMENTS This review was started by the author when beginning a project to build a simulation model integrating shoot apex development and growth. Numerous colleagues have aided throughout, particularly A. Frank, C. M. Grieve, E. J. M. Kirby, B. Klepper, E. V. Maas, J. A. Morgan, R. W. Rickman, and W. W. Wilhelm. Special appreciation is extended to the Handmade Art Company (i.e., Paul Wehr) for the drawings.

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PHENOLOGY, DEVELOPMENT, & GROWTH OF SHOOT APEX 103 Baker, C. K., Gallagher, J. N., and Monteith, J. L. (1980). Daylength change and leaf appearance in winter wheat. Plant Cell Environ. 3,285-287. Baker, J. T.,Pinter, P. J., Jr., Reginato, R. J., and Kanemasu, E. T.(1986). Effects of temperature on leaf appearance in spring and winter wheat cultivars. Agron. J. 78,605413, Barlow, E. W. R., Lee. J. W.. Munns. R., and Smart, M. G. (1980). Water relations of the developing wheat grain. Aust. J. Plant Physiol. 7,519-525. Barnard, C. (1955). Histogenesis of the inflorescence and flower of Triticum aesrivum L. Aust. J. Eof. 3, 1-24. Barnes. C., and Bugbee, B. (1991). Morphological responses of wheat to changes in phytochrome photoequilibrium. PIanr Physiol. 97,359-365. Bauer, A., Smika, D., and Black, A. (1983). Correlation of five wheat growth stage scales used in the Great Plains, USDA Am-NC-7, pp. 17. Agricultural Research Service, North Central Region, U.S. Department of Agriculture, Peoria, Illinois 61615. Bauer, A., Frank, A. B., and Black, A. L. (1984). Estimation of spring wheat leaf growth rates and anthesis from air temperature. Agron. J. 76,829-835. Bauer, A., Frank, A. B., and Black, A. L. (1985). Estimation of spring wheat grain dry matter assimilation from air temperature. Agron. J. 77,743-752. Bauer, A., Frank, A. B., and Black, A. L. (1986). Estimation of spring wheat spike water concentration and grain maturity from air temperature. Agron. J. 78,445-450. Bauer, A., Garcia, R., Kanemasu, E. T., Blad, B. L., Hatfield, J. L., Major, D. J.. Reginato, R. J., and Hubbard, K. G. (1988). Effect of latitude on phenology of 'Colt' winter wheat. Agric. Forest Meteorol. 44, 131-140. Belford, R. K. (1981). Response of winter wheat to prolonged waterlogging under outdoor conditions. J. Agric. Sci. Cambridge 97,557-568. Belford, R. K., Klepper, B., and Rickman, R. W. (1987). Studies of intact shoot-root systems of fieldgrown winter wheat. 11. Root and shoot developmental patterns as related to nitrogen fertilizer. Agron. J. 79,3 10-3 19. Bhullar, S. S., and Jenner, C. F. (1983). Responses to brief periods of elevated temperature in ears and grains of wheat. A m . J. Plant Physiol. 10,549-560. Bidinger, F,, Musgrave, R. B., and Fischer, R. A. (1977). Contribution of stored pre-anthesis assimilate to grain yield in wheat and barley. Nature 270,431433. Bindi, M., Porter, J. R., and Miglietta, F. (1995). Comparison of models to simulate leaf appearance in wheat. Eur: J. Agron. 4, 15-25. Bingham, J. (1967). Investigations on the physiology of yield in winter wheat, by comparisons of varieties and by artificial variation in grain number per ear. J. Agric. Sci. Cambridge 68,411422. Birch, C . J.. and Hong, K. E. (1990). Effect of nitrogen on the growth, yield and grain protein content of barley (Hordeum vulgare). Aust. J. Expt. Agric. 30,231-242. Blacklow, W. M., and Incoll, L. D. (1981). Nitrogen stress of winter wheat changed the determinants of yield and the distribution of nitrogen and total dry matter during grain filling. Ausr. J. Planr Physiol. 8, 191-200. Blacklow, W. M., Darbyshire. B., and Pheloung, P. (1984). Fructans polymerised and depolymerised in the internodes of winter wheat as grain-filling progressed. Plant Sci. Lett. 36,213-218. Blondon, F,, and Morris, M. (1985). Effects of vernalization and high light intensities on spike structure in two varieties of winter wheat (Triticum aestivum L.) in the phytotron. Agronomie 5, 785-794. Blum, A. (1985). Photosynthesis and transpiration in leaves and ears of wheat and barley varieties. J. Exp. Bor. 3 6 , 4 3 2 4 0 . Blum, A., Poiarkova, H.. Golan. G.,and Mayer, J. (1983). Chemical desiccation of wheat plants as a simulator of post-anthesis stress. I. Effects on translocation and kernel growth. Field Crops Res. 6,51-58.

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Singh, V. P.. Singh, M., and Kairon. M. S. (1984). Physiological maturity in aestivum wheat: Visual determination. J. Agric. Sci. Cambridge 102,285-287. Single, W. V. (1964). The influence of nitrogen supply on the fertility of the wheat ear. Aust. J . Exp. Agric. Anim. Husb. 4, 165-168. Sionit, N., Teare, I. D.. and Kramer, P. J. (1980). Effects of repeated application of water stress on water status and growth of wheat. Physiol. Plant. 50, 11-15. Skinner, R. H., and Nelson, C. J. (1995). Elongation of the grass leaf and its relationship to the phyllochron. Crop Sci. 3 5 , 4 1 0 . Skinner, R. H., and Simmons, S. R. (1993). Modulation of leaf elongation, tiller appearance, and tiller senescence in spring barley by far-red light. Plant Cell Environ. 16,555-562. Slafer, G. A., and Rawson, H. M. (1994). Sensitivity of wheat phasic development to major environmental factors: A re-examination of some assumptions made by physiologists and modelers. Aust. J. Plant Physiol. 21,393426. Slafer, G. A,, and Rawson, H. M. (1995). Base and optimum temperatures vary with genotype and stage of development in wheat. Planf Cell Environ. 18,671-679. Slafer, G. A., and Savin, R. (1994).Source-sink relationships and grain mass at different positions within the spike in wheat. Field Crops Res. 37,3949. Smika, D. E., and Greb, B. W. (1973). Protein content of winter wheat grain as related to soil and climatic factors in the semiarid central Great Plains. Agron. J. 65,433-436. Smith, T. L., Peterson, G. A., and Sander, D. H. (1983). Nitrogen distribution in roots and tops of winter wheat. Agron. J. 75, 1031-1036. Sofield, I., Evans, L. T., and Wardlaw, I. F. (1974). The effects of temperature and light on grain filling in wheat. R. SOC.New Zealand Bull. 12,909-915. Sofield, I., Evans, L. T., Cook, M. G., and Wardlaw, I. F. (1977a). Factors influencing the rate and duration of grain-filling in wheat. Aust. J. Plant Physiol. 4,785-797. Sofield, I., Wardlaw. I. F., Evans, L. T., and Zee, S. Y. (1977b). Nitrogen. phosphorus and water contents during grain development and maturation in wheat. Aust. J. Plant Physiol. 4,799-810. Sojka, R. E., Stolzy, L. H., and Kaufmann, M. R. (1975). Wheat growth related to rhizosphere temperature and oxygen levels. Agron. J. 67,591-596. Spiertz, J. H. J. (1974). Grain growth and distribution of dry matter in the wheat plant as influenced by temperature, light energy and ear size. Netherlands J. Agric. Sci. 22,207-220. Spiertz, J. H. J. (1977).The influence of temperature and light intensity on grain in relation to the carbohydrate and nitrogen economy of the wheat plant. Netherlands J. Agric. Sci. 25, 182-197. Spiertz, J. H. J., and de Vos, N. M. (1983).Agronomical and physiological aspects of the role of nitrogen in yield formation of cereals. Plant Soil 75, 379-391. Spiertz, J. H. J., and Vos, J. (1985). Grain growth of wheat and its limitation by carbohydrate and nitrogen supply. In “Wheat Growth and Modelling” (W. Day and R. K. Atkin eds.), p ~ 129-142. . Plenum Press, New York. Stelmakh, A. F. (1987). Growth habit in common wheat (Triticum aestivum L. em Thell). Euphytica 36,513-519. Stern, W. R., and Kirby, E. J. M. (1979).Primordium initiation at the shoot apex in four contrasting varieties of spring wheat in response to sowing date. J. Agric. Sci. Cambridge 93,203-215. Stockman, Y. M., Fischer, R. A., and Brittain, E. G. (1983). Assimilate supply and floret development within the spike of wheat (Triticum aestivum L.).Aust. J. Plant Physiol. 10,585-594. Stoy, V. (1965).Photosynthesis, respiration. and carbohydrate accumulation in spring wheat in relation to yield. Phvsiol. Plant. Suppl. 4, 1-125. Syme, J. R. (1974). Leaf appearance rate and associated characters in some Mexican and Australian wheats. Ausf. J. Agric. Res. 25, 1-7. Thome, G. N. (1962). Survival of tillers and distribution of dry matter between ear and shoot of barley varieties. Ann. Bot. 26,37-54.

PHENOLOGY, DEVELOPMENT, & GROWTH OF SHOOT APEX 117 Thorne, G . N., and Wood, D. W. (1987a). Effects of radiation and temperature on tiller survival, grain number and grain yield in winter wheat. Ann. Eot. 59,413426. Thorne, G . N.. and Wood, D. W. (1987b). The fate of carbon in dying tillers of winter wheat. J. Agric. Sci. Cumbridge 108,5 15-522. Thorne, G. N., and Wood, D. W. (1988). Contributions of shoot categories to growth and yield of winter wheat. J. Agric. Sci. Cornbridge 111,285-294. Tottman, D. R. 91977). The identification of growth stages in winter wheat with reference to the application of growth-regulator herbicides. Ann. Appl. Eiol. 87,2 13-224. Tottman, D. R., and Makepeace, R. J. (1979). An explanation of the decimal code for the growth stages of cereals, with illustration. Ann. Appl. Biol. 93,221-234. Travis, K. Z., and Day, W. (1988). Modelling the timing of the early development of winter wheat. Agric. Forest Mereorol. 44,67-79. Trione, E. J., and Metzger, R. J. (1970). Wheat and barley vernalization in a precise temperature gradient. Crop Sci. 10,39&392. Trought, M. C. T., and Drew, M. C. (1980). The development of waterlogging damage in wheat seedlings (Triticuni uesrivunt L.). 1. Shoot and root growth in relation to changes in the concentrations of dissolved gases and solutes in the soil solution. Plant Soil 54,77-94. van Sanford, D. A. (1985). Variation in kernel growth characters among soft red winter wheats. Crop Sci. 25,626-630. Volk, T., and Bugbee, B. (1991). Modeling light and temperature effects on leaf emergence in wheat and barley. Crop Sci. 31, 12 18-1 224. Vos, J. ( I 985). Aspects of modelling post-floral growth of wheat and calculations of the effects of temperature and radiation. In “Wheat Growth and Modelling” (W. Day and R. K.Atkin, eds.), pp. 143-148. Plenum Press, New York. Waldren, R. P., and Flowerday. A. D. (1979). Growth stages and distribution of dry matter, N, P, and K in winter wheat. Agron. J. 71,391-397. Wall, P. C., and Cartwright, P. M. (1974). Effects of photoperiod, temperature and vernalization on the phenology and spikelet numbers of spring wheats. Ann. Appl. Eiol. 76,299-309. Wang, J. Y.(1960).Acritique of the heat unit approach to plant response studies. Ecology 41,785-790. Wardlaw, 1. F, (1965). The velocity and pattern of assimilate translocation in wheat plants during grain development. Ausr. J. Eiol. Sci. 18,269-281. Wardlaw, I. F. (1970). The early stages of grain development in wheat: Response to light and temperature in a single variety. Ausf. J. Eiol. Sci. 23,765-774. Wardlaw, I. F., Dawson, 1. A,, Munibi, P., and Fewster, R. (1989). The tolerance of wheat to high temperatures during reproductive growth. 1. Survey procedures and general response patterns. Ausf. J. Agric. Res. 40, 1-1 3. Watson, D. J.. Thorne, G. N., and French, S. A. W. (1963).Analysis of growth and yield of winter and spring wheats. Ann. Bor. 27, 1-22. Weir, A. H., Brdgg, P. L., Porter, J. R., and Rayner, J. H. (1984).A winter wheat crop simulation model without water or nutrient limitations. J . Agric. Sci. Cambridge 102,371-382. Weyhrich. R. A,, Carver, B. F., and Smith, E. L. (1994). Effects of awn suppression on grain yield and agronomic traits in hard red winter wheat. Crop Sci. 34,965-969. Whan, B. R. (1976).The emergence of semidwarf and standard wheats, and its association with coleoptile length. Ausr. J. Exp. Agric. Anim. Husb. 16,411416. Whingwiri, E. E., and Kemp, D. R. (1980). Spikelet development and grain yield of the wheat ear in response to applied nitrogen. Aust. J. Agric. Res. 31,637-647. Whingwiri, E. E., and Stem, W. R. (1982). Floret survival in wheat: Significance of the time of floret initiation relative to terminal spikelet formation. J. Agric. Sci. Cambridge 98,257-268. Whingwiri, E. E., Kuo, J., and Stem, W. R. (1981). The vascular system in the rachis of a wheat ear. Ann. Eor. 84, 189-201.

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APPLICATIONS OF MICROMORPHOLOGY OF RELEVANCE TO AGRONOMY Rienk Miedema Department of Soil Science and Geology Wageningen Agricultural University The Netherlands

I. Introduction 11. Methods Used in Micromorphology A. Describing Soil Macrostructure B. Preparing Thin Sections C. Submicroscopic Techniques D. Image Analysis E. 3D Reconstructions 111. Soil Structure in Relation to Land Use A. Structure Formation and Subsequent Processes B. Functioning of Soil Structure in Relation to Land Use C. Modeling of the Functioning of Soil Structure n! Conclusions and Future Research Needs References

I. INTRODUCTION Soil structure-the spatial arrangement of individual particles, their aggregates, and of pores-plays a multifaceted key role in the factors determining crop and vegetation performance (Letey, 1985; Hamblin, 1985; Passioura, 1991;Brussaard and Kooistra, 1993). These factors not only include the physical and physicochemical processes and effects of biological activity that interact with land use and weather but also management practices (tillage, drainage, irrigation, fertilization, and mulching) intended to create and/or maintain optimum conditions for emergence, rooting, and uptake of water and nutrients. The facets of soil structure include processes of its formation by biological and physical forces, processes involved in its stability under changing weather and soil 119 Adwanrcs in A p n o n y , Volumc 59 Copynghr 0 1997 by Academic Press, Inc. All rights of reproduction in any form reserved

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moisture conditions, and its ecological and hydraulic influence on the transport and storage of heat, gas, water, and nutrients and their effects on crop and vegetation performance in different land-use systems. The interrelationships between soil structure, soil texture, soil biota, soil organic matter, and crop production were summarized by Juma (1993, 1994), who stressed the importance of quantifying pore size distribution, microbial and faunal populations, and C and N dynamics. Edwards et al. (1993) reviewed methodologies to investigate gas, water, and solute transport into and through soils in cropland ecosystems. The features in undisturbed soil samples studied in micromorphology are examined over a wide range of magnifications using techniques such as stereomicroscopy, light microscopy, scanning and transmission electron microscopy, computed X-ray or gamma ray tomography (CT scanning), and nuclear magnetic resonance imaging (NMRI). The samples often have to be pretreated in some way. In this chapter, the term micromorphology is used to include observations ranging from submicroscopy/ultramicroscopy to studies using the naked eye. The micromorphological features of the soil reflect the processes responsible for them, which also affect, e.g., land qualities important for plant growth. According to Kooistra (1990), micromorphology deals with the observation of features, the interpretation into processes, and the synthesis of the resulting effects. Ringroase-Voase (199 1) is among those to have summarized the micromorphology of soil structure (description, quantification, and application). Earlier reviews on soil structure and micromorphology and their agronomic importance include those of Jongerius (1983), Dexter (1988), Kay (1990), and Kooistra (1990). The study of soil structure ranges from the effects of particle interaction at a scale of nanometers (Quirk, 1994) to the functioning of soil structure profiles at a scale of meters (Miedema et al., 1994a; Fig. I ) . The spatial variation of soil structure on farmers’ fields (Finke, 1992, 1993) and in catenary sequences in the landscape extends the scale to kilometers. Basic knowledge of the fundamental processes involved in ecological functioning of the soil structure is still lacking (Sposito and Reginato, 1992). Knowledge of soil structure needs to be extrapolated from one scale level to the next (upscaling and downscaling). New staining techniques and fluorescence microscopy (Altemiiller, 1991), the application of submicroscopic techniques (Foster, 1994), and CT scanning in rhizosphere ecology (Anderson and Hopmans, 1994) and NMRI (MacFall and Johnson, 1994; Liu et al., 1994) are promising developments and should enhance our understanding of microbiological activities in organic matter decomposition and rhizosphere processes. Micromorphological quantification has shown rapid progress with advances in image analysis techniques (Mermut and Norton, 1992; Moran, 1994). To understand how soil structure functions, physical measurements must be performed on the soil in the field and in the laboratory (Burke et al., 1986). This reveals how the soil structure profile influences water and solute transport (Bouma,

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1992; Bouma and Hack-ten Broecke, 1993). Staining (e.g., with methylene blue) enables the pathway of water and solute transport to be studied, and quantified information can be obtained through image analysis or using fractal dimensions (e.g., Hatano and Booltink, 1993).The results can then be used in simulation models to quantitatively estimate how soil structure affects land qualities and crop performance (e.g., Van Lanen et al., 1987, 1992; Bouma et al., 1993) under different land uses. The aim of this chapter is to demonstrate that potentially micromorphology provides a unique set of tools to study soil structure because it is the study of the in situ reality of soil and undisturbed soil samples and can span many levels of scale using the appropriate observation techniques (Bisdom et al., 1990; Fig. 2).

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Figure 2 Methods available for micromorphological and submicroscopic imaging and analysis (Bisdom er al., 1990; reproduced with permission of Elsevier, Amsterdam). Morphological and imaging techniques: MRI, magnetic resonance imaging; IM,infrared microscopy; LM, light microscopy; LI, laser imaging; IA, image analysis; UM, ultraviolet microscopy; CT, computed tomography; MRG, microradiography; EDXRA, energy dispersive X-ray analysis; WDXRA. wavelength dispersive X-ray analysis; GI, gamma imaging; TEM, transmission electron microscopy; SEM, scanning electron microscopy; STEM, scanning transmission electron microscopy; HVEM, high voltage electron microscopy; AI, auger imaging; SIMI, secondary ion mass imaging. Chemical and microchemical techniques: NMR, nuclear magnetic resonance; ESR, electron spin resonance; IS, infrared spectroscopy; RS. Raman spectroscopy; US, ultraviolet spectroscopy; EDXRA, energy dispersive X-ray analysis; WDXRA, wavelength dispersive X-ray analysis; XRD, X-ray diffraction; XFS, X-ray fluorescense spectroscopy; PIXE, particle-induced X-ray emission; SR-XRF, Synchrotron radiation X-ray fluorescense; GA, gamma analysis; AES, auger electron spectroscopy; ESCA, electron spectroscopy for chemical analysis; XPS, X-ray photoelectron spectroscopy; UPS, ultraviolet photoelectron spectroscopy; SIMS, secondary ion mass spectroscopy; LAMMA, laser microprobe mass analysis.

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In combination with other soil analyses, micromorphology offers possibilities of integrating and synthesizing processes and of understanding the effects of soil diversity in agronomy from submicroscopic to field scales. A better understanding of the formation and functioning of soil structure, its stability, and its resilience under various land uses is vital in the search for sustainable land-use systems.

II. METHODS USED IN MICROMORPHOLOGY A. DESCRIBING SOILMACROSTRUCTURE The starting point in micromorphological research is normally the study of a profile pit or excavation wall. The macrostructure in pedal soils is described (FAO, 1990) in terms of the peds (type and size of peds and structure grade) and porosity (kind and amount of pores). In the case of apedal soils, Jongerius (1957) proposed using packing, stratification (undisturbed or disturbed), and porosity (amount and type of pores). Bullock el al. (1985) distinguished many different microstructures relating to macrostructure description and terminology. In such observations, the descriptive element is very strong and this limits the quantitative assessment of the influence of a given soil structure. Frequently, undisturbed samples varying in size from columns, cores, peds, to aggregates are studied in more detail by stereomicroscope, light microscope, electron microscope, CT scanning, and NMRI. The results of such research must be used to explain the macrostructure observed in relevant parts of the soil structure profile in the field, but often they are considered valuable in themselves at their microscopic or submicroscopic level of detail.

B. PREPARING THIN SECTIONS For light microscopy investigations, undisturbed soil samples taken from the field are dried and then impregnated with diluted resin with or without fluorescent dyes, which polymerizes and hardens. Murphy (1986) has summarized the main methods of thin-section preparation. Slices are cut from the resulting block and stuck on a glass slide. The bonding is much improved by applying Dinosylan (Altemiiller and Beckmann, 1991).The slice is then ground to a thin section, normally about 25-30 p,m thick, and then covered with a cover slip. Thin sections are usually studied under a polarizing microscope, generally at magnifications of 10-150. The spatial resolution is around 5 p,m.

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The impregnated blocks of soil can also be used for further studies. Bouabid et al. (1992) used epifluorescence microscopy on polished blocks to study pore size distribution in a lamellar Bt horizon. Vogel et al. (1993) studied macropore continuity from polished blocks. Zhurov (1991) used specially treated polished sections to study soil pores and pore size distribution. McBratney et al. (1992) modified the original method of Moran et al. (1989) and impregnated whole profiles in the field using an opaque epoxy resin that hardens in moist soil. They then sawed out the treated soil, impregnated it in the laboratory, and ground it back beyond the original surface to a smooth finish. They used digitized and digital gray-level image segmentation to produce a binary image from which measure depth functions for the pore structure attributes could be measured. Shimane and Nakabayashi (1988) used methyl metacrylate for impregnation. This resin can take up some water in its polymerization and thus the samples to be impregnated need not be completely dry. Improved methods of field sampling of topsoils for micromorphological research have been published by Wires and Sheldrick (1987) and Rhoton and McChesney (1991). Murphy (1986) described the various methods used to dry samples. If the purpose of the investigation allows, air drying or oven drying is easiest. However, in studies on soil structure it is often necessary to fix the sample in the moisture condition it was sampled in. Miedema (1987) suggested that the sample must be kept within the moisture range it experienced in the field. Various water-replacing techniques have been tried and new ones are still being tested. Acetone has been used to replace the water in the sample (e.g., Miedema et al., 1974; FitzPatrick and Gudmundson, 1978) and dioxane has also been used (Chartres et al. 1989). Freeze drying (e.g., Stephan, 1969; Jongerius and Heintzberger, 1975) has also been used as a drying method. The advantages and disadvantages of the various methods of removing water are discussed by Murphy (1982, 1986) and Chartres et al. (1989). Various resins (polyester resins and epoxy resins) are used (Murphy, 1986) to impregnate the sample, and a variety of diluting organic liquids are used (e.g., acetone and monostyrene). The hardening is now almost exclusively done by using gamma radiation (Bisdom et al., 1983), which saves 4-6 weeks and produces excellent results.

C. SUBMICROSCOPIC TECHNIQUES Bisdom ( 1983) reviewed state-of-the-art techniques and applications of submicroscopic examination of soils. A later paper (Bisdom et al., 1990) discussed the techniques used by soil micromorphologists. Figure 2 illustrates various imaging techniques and corresponding chemical and microchemical techniques and

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the paper by Bisdom et al. (1990) discusses the lateral resolution, penetration depth, element number, and concentrations that can be detected by each of these techniques. Sample treatment (e.g., impregnation with resin, water replacement techniques, hardening, ultramicrotomy, and micromilling) may produce artifacts that must be recognized to avoid faulty data and incorrect interpretations. Tessier (1987) has reviewed the techniques for dehydrating of samples prior to electron microscope studies [scanning electron microscopy (SEM) and transmission electron microscopy (TEM)]. For TEM studies, the sample must be ultra thin (about 5 km); this is achieved by ion milling (e.g., Bresson, 1981) or by using an ultramicrotome. For the latter, displacement of sand and silt particles must be avoided by adjusting of the hardness of the impregnating resin (Tessier, 1984). Agar impregnation in combination with cytochemical staining and use of an ultramicrotome produces an ultrathin section enabling TEM studies of organic matter distribution and microbiological processes (e.g, Foster, 1994). Ideally, submicroscopy follows field studies, stereomicroscope studies, and optical microscope studies. Whether the sampling is representative depends on the sampling procedures and sample preparations. To arrive at a sufficient number of representative samples suitable for the purpose of the investigation, the sampling technique must be statistically determined. The most frequently used submicroscopic techniques include light and electron microscopes that are widely available. Recent electron microscopes with field emission electron guns increase the brightness of the image considerably and enable uncoated samples to be used and compared with previously used gold-coated samples (Bisdom et al., 1990). Both thin sections and unhardened samples can be studied.

D. IMAGEANALYSIS Jongerius et ul. (1972) were the first to apply image analysis (Quantimet) to quantify and characterize the porosity in thin sections. The resulting quantitative data were used to calculate hydraulic conductivities (Bouma et al., 1979). Such attempts to relate morphometric measurements to physical measurements are still being carried out successfully (e.g., Gimenez et al., 1992). Micromorphometry using image analysis is frequently used to quantify the effects on soil porosity of land use and land management. The capabilities of imaging machines and the software for analyzing the resulting images or data have improved greatly. Recent reviews on digitization, processing, and quantitative interpretation of image analysis in soil science and related areas include those by Dorronsoro (1 988), Mermut and Norton (1992), and Moran (1994). Image analysis aims to quantify the pore system (in

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2D; e.g., Pagliai, 1994; Pagliai ef al., 1995). Measurements process a binary image (i.e., each element is either pore or solid). This ignores our knowledge that at any given scale the solid material has a porosity that is not resolvable at that scale. McBratney and Moran (1994) address this issue in their pore structure model using fuzzy random pseudofractal sets. Image analysis of roots in soils in minirhizotrons using neural system algorithms has been demonstrated by Nater et af. (1992). Such approaches from the soil science aspect (porosity) and from biological (root distribution) aspects should be combined (McBratney ef af., 1992). Tovey et al. (1992a,b) use automatic orientation mapping to quantify goundmass parameters that govern aspects of soil mechanics (e.g., Barton, 1974; Collins and McGown, 1983; Jim, 1990). Thompson et al. (1992) indicated that the purpose of the investigation directs the sampling procedure and, hence, the purpose should be clear from the outset of the sampling. The pretreatment of the samples (air drying, oven drying, freeze drying, acetone exchange, dioxane exchange, use of fluorescent dyes, or incomplete impregnation) may affect pores in different ways. The researcher should be aware of these artifacts. In image choice and enhancement, the scale factor (magnification and resolution) and number of samples required should be critically studied in relation to the aim of the investigation. Image processing to create a binary image by segmentation procedures can yield widely different results depending on the segmentation protocol used. The use of standard images for calibration is strongly advocated. Protz et al. (1992) presented the first application of spectral image analysis techniques (used in remote sensing studies, e.g., ERDAS) to thin sections. They produced a classified image showing all pedological features (pores, mottles, carbonates, and depletion zones) and their areal extent. Tembile and FitzPatrick (1992, 1995) employed similar techniques to identify and classify features that could not be quantified using binary black-and-white image analysis. The use of spectral information is an exciting new application to soil structure studies, opening up prospects of quantifying features other than pores.

E. 3D RECONSTRUCTIONS Microradiography, i.e., recording a shadow image obtained from X-raying a soil sample at unity magnification on a fine-grained photographic emulsion, has been used to study the pore system of soils (e.g., Rogaar and Boswinkel, 1978; Drees and Wilding, 1983). By producing images with sufficient overlap, stereoscopic 3D observations are possible. Mackie (1987) used a microcomputer to produce 3D representations of soil

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macropores, which were identified by making casts using plaster of paris (FitzPatrick et a/., 1985). The results from 2D image analysis can be converted to 3D using geometric probability or stereology (e.g., Ringroase-Voase, 1994; Vogel et al., 1993). Stereology (Weibel, 1979) is often used in studies of serial sectioning to arrive at 3D reconstructions from 1 D or 2D cross sections ( e g , McBratney and Moran, 1990; Ringroase-Voase, 1994; Ringroase-Voase, 1990).Vogel eta/.( 1993) used stereology for the 3D reconstruction of orientation and width of channels and cracks from polished blocks. Thompson et al. (1992) state that when converting from 2D to 3D, care should be taken to ensure that such conversions are permissible (i.e., sufficient number of pores, isotropic or random distribution of pores, images drawn randomly in terms of orientation, and sufficient images analyzed). X-ray CT, a technique used for medical diagnosis, involves scanning the sample (object) by revolving an X-ray tube and recording the X-rays after they have passed through the sample. CT has been used in soil science since 1982 (Petrovic et a/., 1982; bulk density). The output is a computer-integrated image and/or digital data that can be combined to form 3D images. A digital output is needed for 3D research (Bisdom et al., 1990). Many more soil science studies have since appeared on different parameters. Steude et al. (1994) presented a table summarizing most of the studies until 1993 that used CT to investigate properties of soil, rock, and related materials. The advantage of this technique is that undisturbed soil samples can be introduced in the scanner without any pretreatment. The resolution of the first-generation scanners (millimeters; e.g., Crestana et a/., 1985; Anderson et al., 1988) can now be improved (1-10 pm; e.g., Steude et al., 1994; Spanne er a/., 1994) in new generation scanners. The cost and the limited availability still hamper widespread use, but the results (i.e., water movement in macropores; Hopmans et a/., 1992; Peyton et al., 1994; Heijs er a/., 1995) and monitoring of rhizosphere processes (Anderson and Hopmans, 1994) indicate that this method has considerable potential. Aylmore (1993, 1994) summarized the applications of computer-assisted tomography and indicated that there seems to be little doubt that these techniques will become a major tool in studies of soil-water-plant systems. The inability of single-source X-ray or gamma-ray scanners to distinguish between changes in water content and bulk density in swelling and shrinking soils will remain a problem. Dual source gamma-ray scanners are promising for overcoming such problems. The computer-aided 3D reconstructions need to be quantified so that the information can be used in simulation models. Grevers and De Jong (1994) demonstrated that in Udic Borolls soil macroporosity was considerably greater, less variable, and had a greater degree of spatial continuity in subsoiled soils than in compacted soils. They applied geostatistical analysis in conjunction with image analysis to study the areal porosity. Advances in image and data analyzing software applied to images of CT scanning are promising for future research.

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III. SOIL STRUCTURE IN RELATION TO LAND USE A. STRUCTUREFORMATION AND SUBSEQUENT PROCESSES 1. Soil Physical Processes Which soil physical processes contribute to soil structure formation depends on particle size distribution (notably, clay content and clay mineralogy) and on interactions with organic matter and with the liquid phase of the soil. Cycles of changing moisture conditions and hence the ionic composition of the soil water (wetting/drying,freezingkhawing; water extraction by roots) particularly influence the interparticle forces that underlie soil physical behavior (Quirk, 1994).The pattern of moisture changes in soils is frequently very strongly anisotropic because of differential wetting and drying patterns, e.g., as a result of the crop type and its establishment. The interparticleforces include capillary condensation brought about by suction in porous material, London-van der Waals forces caused by molecular interactions, and osmotic repulsive forces that are caused by ionic-electrostatic interaction: Together, these constitute the basis of the diffuse double-layer theory, ion-ion correlation forces, and the structural component of pressure to separate two particles by water forces arising because two liquid fronts overlap. The effects of these interparticle forces, which act at scales from nanometer to micrometer level, are the swelling and shrinking of clay soils with associated changes in pore size distributions (structural porosity, macropores larger than 30 pm equivalent diameter that drain at-I0 kPa hydraulic head; textural porosity, micropores smaller than 30 p,m equivalent diameter). Swelling and shrinking interacts with the microstructure(stacking and porosity) of clay aggregates (Fig. 3) that remain water saturated in normal circumstances. Sodium clays have extensive crystalline swelling, whereas the swelling of calcium clays depends on quasicrystals (Fig. 4) and clay domains. Quirk (1994) presents details and an extensive literature review. How swelling and shrinking is translated to the microaggregate and macroaggregate level differs strongly with soil type but is the key physical force determining the shape and size of aggregation and influencing the textural and structural porosity. This may also lead to the presence of oriented domains of clay in the groundmass if the clay content and clay mineral assemblage are favorable. These have important implications for soil mechanics. Oriented domains of clay are bounded by an extensive network of voids, mostly cracks, which seem to be an intrinsic feature of the clay materials and have been described as intrinsic failure (Quirk, 1978).The intrinsic failure pores are probably the precursors of cracking leading to peds and macroaggregates and the disintegration of macroaggre-

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Figure 3 Clay aggregates in soils with different clay mineralogies resulting in distinctly different packing characteristics (Oades, 1987; reproduced with permission of ISSS).

gates into more stable microaggregates. Dexter (1988) elaborated on the mechanisms of crack formation by tensile stresses following desiccation (shrinkage). Wet soil has the least tensile strength. The distance between desiccation cracks and their distribution in cohesive loamy or clayey soils is determined by planes of weakness. Such shrinkage produces self-similar aggregation of progressively finer sizes. This is why the fractal approach (e.g., Young and Crawford, 1991, for aggregates) and the use of artificial neural system algorithms are so attractive for analyzing soil structure and roots in soil. Two examples illustrate how the formation of soil structure is dominated by physical processes. In soil ripening (Pons and Zonneveld, 1965; Fig. 5), the effects resulting from swelling and shrinking on soil macrostructure and the hydraulic implications of this macrostructure have been studied in detail (e.g., Bronswijk,

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Figure 4 Example of a TEM image of a quasicrystal of smectite from a volcanic soil from Guadeloupe (Photo courtesty Dr. Jongmans and Dr. Van Oort).

1988). Many studies (e.g., Wilding and Tessier, 1988; Blokhuis et al., 1990) have been performed on Vertisols, considering micromorphological features such as the orientation patterns in the groundmass as expressions of swellinghhrinking processes. Quirk (1994) concluded that, although research results have shown considerable progress, basic knowledge of the actual forces involved in the cementation or stabilization of aggregates is lacking. The profound effect of organic matter on soil structure and soil physical behavior is unquestioned and documented in abundant literature. The interaction of physical processes and biological processes involved in soil structure formation and functioning still needs much fundamental research; not only at the microscopic level (Brussaard and Kooistra, 1993) but also in the upscaling to soil structure functioning in agroecosystems and in simulation modeling of such functioning ( e g , Bouma, 1992).

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Figure 5 Example of soil structure profile formed by physical processes: A Dutch alluvial clay soil after physical ripening.

2. Soil Biological Processes Biological processes in the soil act in different ways in soil structure formation. Bioturbation (e.g., Humphreys, 1994) is frequently used as a term to describe the many different influences of soil macro- and mesobiota on soils. Soil biology includes the actions of fauna and flora that vary in size from micro to macro. Lee and Foster (1991) have summarized the interaction between soil macrofauna (earthworms, ants, and termites) and soil structure. Brussaard and Kooistra (1993) discussed the interrelationships between soil structure and soil biota, mainly at the microscopic level. Crossley et al. (1991) reported on modem techniques in soil ecology and Kooistra (1 99 1) discussed a micromorphological approach to study interactions between soil structure and soil biota. Lavelle (1994) summarized faunal activities (varying in size from micro to macro) and soil processes and adaptive strategies of soil biota that determine the functioning of ecosystems. Biological processes create, maintain, and modify soil structure, thus influencing soil processes such as aggregation and infiltration. In their search for organic matter as a food source, or when evading predators or adverse soil conditions or nesting, soil macro- and mesofauna travel through the soil, creating biopores of their diameter and even larger pores for nesting. Roots

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also create biopores. The occurrence of roots in thin sections is discussed by FitzPatrick (1990). The morphology of roots, the root environment, root decomposition and replacement, and soil features such as iron pipes around roots in Gleysols or the association of roots with pyrite in acid sulfate soils have been documented. Oades (1993) suggests that in future research on sustainable agriculture it is important to identify plants that are efficient at forming stable soil structure and that fit into economically feasible management systems. Geyger (1964) was one of the first to investigate the influence of grassland plant species on the formation of soil structure, including quantified information on pore size distribution. Babel and Krebs (1991) and Babel ef al. (1992) documented plant-specific microstructures in topsoils. Krebs et al. (1994) investigated the basic and relative distribution patterns of roots, channels, and cracks of different plant species resulting in different soil fabrics. Jongerius (1957) distinguished biopores in many of the apedal structures he examined. He used the term “hole structure” for an apedal soil structure with mainly vertically oriented earthworm or root channels.The term “sponge structure” was used in the case of very abundant and randomly distributed biopores. Mixing of mineral soil material through ingestion during the passage through the soil may lead to biogenic disturbance of geogenic layering (disturbed stratification).When no such disturbance occurred, he called the structure “undisturbed stratification” (e.g., that occurring in drift sand or below the water table in alluvial soils). In apedal soils, the effects of biological processes are extremely important in promoting heterogeneity in the pore size distribution and in overcoming the effects of textural stratification on water movement and rooting. The movement of the soil fauna through the soil also modifies the pedal structures, i.e., angular blocky peds become subangular blocky peds or smooth prisms are transformed into rough prisms. This has profound effects on the interpedal and transpedal porosity. The intrapedal porosity can also be significantly influenced by biological activity. Such biotic changes strongly influence the behavior of the soil structure. Biota feeding on organic matter profoundly modify this organic matter by comminution, ingestion, mixing with mineral material, and excretion (Bullock et al., 1985). Frequently, there is a whole chain of decomposition, ultimately leading to a complete loss of recognizable plant remains and transformation into humus. The humus may be present as fecal pellets whose size and shape are characteristic of the fauna that produced them (“soil morphologically characteristic fauna”; Dinq et al., 1976; Fig. 6). The biological and physicochemical processes and resulting properties of excrements or soil material strongly influenced by biological activity have been dealt with in many publications (e.g., Din$ et al., 1976; Martin and Marinissen, 1993). The processes and properties strongly depend on the species involved. Earthworms ( e g , Kretzschmar, 1992; Joschko et al., 1991; Marinissen, 1995), ants (e.g., Lobry de Bruyn and Conacher, 1990; Eschenbrenner, 1994;

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Figure 6 Example of soil microstructure formation by biological processes: The change from fibric Sphagnum peat (A) to sapric peat (C) involving Oribatid mites and fungi (B) and earthworms (C) as morphologically characteristic biota.

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Humphreys, 1994),and termites (e.g., Mermut et al., 1984;Miedema et al., 1994b) are among the macrofauna most frequently studied micromorphologically. Of the mesofauna, the role of Acari, Collembola, and Enchytraeidae has received much attention (e.g., Zachariae, 1964; Bal, 1970; Ding et al., 1976; Didden, 1990; Dawod and FitzPatrick, 1993). Fungi also play an important role in the transformation of organic matter by biochemical processes (melanosis; Bal, 1973). The exudates of roots and fungi also promote aggregation into larger macroaggregates. Fungal hyphae are also involved in aggregation and structure stability (e.g., Tisdall, 1991). Mixing of mineral and organic material by soil fauna is one of the direct processes favoring aggregation. At microscopic and submicroscopic scale, the distribution of microorganisms that help decompose organic matter has been studied by cytochemically staining ultrathin sections and examining them with TEM (e.g., Foster, 1994) or fluorescence microscopy (e.g., Altemuller and Van Vliet-Lanoe, 1990)or by staining with fluorochromes (Tippkotteret al., 1986;Tippkotter, 1990; Altemuller, 1991).Altemuller (1992) and Kretzschmar (1992) address the importance of soil fauna interacting with microflora in the formation of humus forms and soil structure. Soil biota from micro to macro size are important in soil structure profile formation (Fig. 7) and soil profile functioning.The multitude of species involved and their individual effects have spawned an overwhelming number of publications. The papers cited here are an arbitrary choice, but through their reference lists they provide access to many other important and interesting papers. Upscaling and downscaling the effects at different levels of scale, the interrelationships with physical processes involved and the input as meaningful parameters in models of soil structure behavior in the profile and at field scale, and linking these to crop and vegetation are the most important future challenges in research.

3. Soil Management Practices Soil management and soil structure degradation are reviewed by Greenland (1981); the effect of various agronomic practices on soil loss by erosion is reviewed by Norton and Schroeder (1987) and on surface crusting and soil loss by Norton et al. (1986). Aguilar et al. (1990) examined the effect of not plowing on soil structure and soil erosion in olive orchards in Spain compared to traditional tillage methods. Interpedal porosity was greater in the traditionally tilled plots, with a lower intrapedal porosity. Rodriguez et al. (1990) assessed drainpipe siltation (downward soil loss through the soil profile) in a saline-sodic soil under irrigation in Spain. Drainpipes enveloped by various materials showed layered, fine grained, and sorted deposits, from which siltation periods, flow, and sedimentation conditions could be inferred. The micromorphological results contradicted predictions of siltation risks in drainpipes based on granulometric analysis.

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Figure 7 Example of soil structure profile with strong biological influence until 90-cm depth A Dutch fluvial levee soil under an orchard.

Soil tillage is one of the agronomic management practices that has a profound effect on soil structure. Tillage operations may include deep loosening (e.g., Grevers and De Jong, 1992; Mermut et al., 1992). Different kinds of tillage implements have specific effects on soil structure. Primary and secondary plow pans (Kooistra et al., 1985) or traffic pans form a barrier between the soil structure of the tilled layer and the natural soil structure underneath.

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Pagliai ( 1994) summarized micromorphology and soil management, concluding that soils under intensive management practices have greater total porosity but a much larger seasonal variation in their porosity than soils under reduced tillage practices. The latter had a greater proportion of biopores and transmission pores useful for water and solute transport and root growth. Singh et al. (1991), however, found no statistically significant differences in porosity attributes between zero tillage and conventional tillage. Soil tillage has an influence on the porosity (total porosity, pore size distribution, pore shape, and pore continuity; e.g., Pagliai, 1994) and on the organization (orientation and compaction) of the groundmass resulting from the soil mechanical response of the soil to the exerted pressure and shear (Collins and McGown, 1983; Pagliai, 1987; Dexter, 1988; Bresson and Zambaux, 1990). By creating a certain soil structure, soil tillage influences processes such as rooting. Many studies have presented the results of comparisons between the effects on soil structure types and porosities between conventional tillage practices and various reduced tillage practices, including zero tillage. Early examples include those of Boone et al. (1976). Recent examples include the study of Moran and McBratney (1 992) on total and surface-connectedmacroporosity of a Vertisol in Queensland (Australia) under different management treatments, including zero tillage and conventional tillage. Lower macroporosity, but a better continuity of the macropores, was noted in the direct drilling treatment in the study performed by Francis et al. ( 1988)on New Zealand soils under continuous wheat compared to conventional tillage. Hall (1994) reported on the micromorphology of soil structure transformations in arable soils over the growing season. Peds become smaller and become more closely packed, but they do not lose their identity. The maintenance of good seedbed characteristics through shrinkage over the season has been reported for some Scottish soils (Mackie-Dawson et al., 1989).The latter authors also reported smaller seasonal changes in untilled soil than in neighboring tilled soils. Holden (1955), studying the temporal variation in ped shape in an old pasture soil, concluded that seasonally driven change in ped shape occurs, but that there is no simple relationship with bulk physical properties of the soil. Applications of fertilizers, manures, and slurries (e.g., Pagliai et al., 1983) received attention early in the micromorphological literature (Altemuller and Banse, 1964)in relation to their impact on soil structure.Recent micromorphological case studies include a Russian monograph on anthropogenically altered soils (Dobrovolsky, 1988), which presents many studies on effects of drainage, imgation, and fertilization on various soils in the former Soviet Union. Darmody and Norton (1994) studied the oldest agronomic plots (Aquic Argiudolls) in the United States that were established in 1876 to study the effect of crop rotations and fertilization on yield. They concluded that fertilization and liming had little effect on aggregate properties and soil fabric. However, crop rotations

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had a great impact. Aggregate size and stability decreased concomitantly with decreasing organic matter content in the order: grass sod, maize-oat-clover, maize-soybean, and continuous maize. Although greater biomass was produced with fertilization, the quality of the organic matter residues seems to be more important in maintaining a stable soil structure. Hadas et al. (1990) found a weak response to NPK fertilizer on a Chromoxerert in Israel. McBratney et al. (1992) presented the effect on porosity (based on image analysis) of 90 years of cultivation (biennial wheaupasture rotation) on a Rhodoxeralf from Australia compared to an uncultivated site under eucalypt (Figs. 8 and 9). The structural degradation brought about by plowing leads to a topsoil with fewer macropores and shattered aggregates, forming a massively structured layer, Below 20 cm, the structural attributes reflect the inherent variation in subsoils of similar origin and properties. This study demonstrates the extrapolation to the scale of the soil structure profile (Fig. 1). The authors do not mention whether the reported changes also influence the soil productivity. Therefore, it is impossible to judge if the observed structural degradation really reduces the functioning of the soil structure. In Australian Vertisols (McKenzie et al., 1994), compaction and smearing in the subsoil restrict root growth and cause cotton lint yields to drop by 30%.A very compacted furrow was compared with an undamaged cotton ridge. Vertical cracks in the dense compacted zone under the furrow (at 15 cm from the soil surface) may allow the roots to pass through this unfavorable layer to reach more favorable deeper layers. Porosity measurements using image analysis, bulk density measurements, and measurements of soil strength and water content were used to quantitatively characterize the soil structure. Puentes and Wilding (1990) studied the structural restoration in Vertisols under pasture following continuous cultivation. The combination of strong swell-shrink potential, the enhanced biotic activity in the grassland, and the absence of cultivation leads to a recovery of the self-mulching. Puentes et al. (1992) addressed the question of how to characterize the microspatial variability in a Texas Vertisol. Systematic lateral variability exists within soil horizons as a function of spatially dependent stresses generated by shrink-swell potential. The sampling scheme must take such variation into account for correct characterization. Mermut et al. (1992) used image analysis to evaluate the impact on porosity of deep ripping and paraplowing versus conventional tillage on glaciolacustnne clay soils (Haplo- and Natriborolls) in Saskatchewan, Canada. Although the total porosity (>50 pm) was increased by the deep tillage methods, no increase was found in crop yields because there were sufficient pores >50 p,m in the control (conventional tillage) in this rather dry region. In this case, conventional tillage practices did not cause deterioration of the soil. However, deep tillage (subsoiling) methods are reported to result in increased soil water recharge and increased crop

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Figure 8 Profile study of porosity in Rhodoxeralfs comparing an uncultivated site (A) with a cultivated site (B). Digital images of porosity (McBratney er al., 1992; reproduced with permission of Elsevier, Amsterdam).

growth in Solonetzic and Chernozemic clay soils in moister regions (Grevers and De Jong, 1992). Deep ripping had a more pronounced and longer lasting effect than paraplowing. Soils differ in their reaction to stresses and we need to know more about the limits to which soils can be stressed without losing adequate functioning. Research is needed to establish the resilience and "windows of opportunities" (Bouma, 1993) for different soils so that sound recommendations can be made on methods for sus-

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Figure 9 Porosity attributes of Rhodoxeralfs in Fig. 8 (McBratney era/., 1992; reproduced with permission of Elsevier, Amsterdam). A = porosity ( ~ n m ~ m m -B~ )=; surface area ( ~ n m ~ m m - C ~ )=; pore star length (mm); and D = solid star length (rnm). Dark hatched: attribute greater in uncultivated soil: light hatched: attribute greater in cultivated soil.

tainable land use. This includes fundamental research into processes governing the formation and functioning of soil structure.

B. FUNCTIONING OF SOILSTRUCTURE INRELATION TO LANDUSE 1. Structure Stability The water stability of soil aggregates is paramount in the resilience of soil structure to destructive forces. The effect of stabilizing substances (especially the effects of colloidal organic matter, but also oxides of iron, aluminum, silicon, and carbonates) depends on which part of the organic matter is involved, the mechanisms of stabilization, and the microspatial distribution of the stabilizing agents. The rate of wetting and the initial moisture content are important in the assessment of the water stability behavior of soil aggregates (Quirk, 1994).Incipient fail-

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ure (Quirk and Panabokke, 1962) or air explosion (Koenigs, 1972) may occur when dry aggregates are rapidly wetted, which may lead to slaking of the soil (e.g., Bresson, 1995).This process is especially important in the formation of crusts and seals on the soil surface. There are recent reviews that summarize the abundant literature on this subject. West et al. (1992), Bresson and Valentin (1994), and Chartres et al. (1994) summarize micromorphological contributions to crusting. Bresson (1995) reviewed physical management in crusting control in Australian cropping systems and indicated research opportunities. a. Structural Crusts Structural crusts are formed by processes directly related to raindrop impact and associated rapid wetting of the soil. Rapid wetting of the soil may also result from irrigation. These crusts have a number of microlayers including relatively thick (1 to > 10 mm) layers (Fig. 10) with fewer and smaller pores than the underlying soil. Such layers are formed by a combination of rainfall-induced aggregate breakdown or slaking, particle and aggregate rearrangement, and aggregate coalescence. A very thin (
Figure 10 Structural crust on Dutch sandy loam alluvial soil.

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Figure 11 Harrowing breaks structural crust in Fig. 10.

draulic properties and reported infiltration rates vary strongly and do not relate simply to crust thickness.Pore size distribution and the presence or absence of continuous pores are the most determining factors (Norton and Schroeder, 1987, Bresson and Boiffin, 1990, West et al., 1990).Le Bissonais and Bruand (1993) studied crust micromorphology [backscatteredelectron scanning images (BESI)] on silty soil materials during winter and spring. The spring crust is formed by coalescence of microaggregates produced by microcracking of the initial clods. The thinner and denser winter crust forms because of disaggregationby raindrop impact under continuously wet conditions. Kwaad and Mucher (1994) report that welding of aggregates is the primary cause of the development of crusts in loess soils in the Netherlands. b. Sedimentary or Depositional Crusts These crusts result from deposition of particles and microaggregates following water transport of sediment from relatively higher to relatively lower microtopographic positions. They present themselves as multiple laminated microbeds with a fining upward sequence in the individual microbeds. They may lie uncomfortably on a structural crust or on undisturbed soil. The degree of sorting within the microbeds is affected by flow regime and transport distance. Vesicles caused by air entrapment are common below sedimentary crusts. They may constitute a substantial proportion of pore space, but this pore space is ineffective for transmission of water through the crust because the vesicles are not connected to other pores.

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Miicher and De Ploey (1977) have shown experimentally that many poorly sorted laminae within depositional crusts can be formed by rainwash (splash and overland flow combined) and that well-sorted laminae may be formed by afterflow. c. Cryptogamic Crusts These crusts are formed by algae, mosses, lichens, and liversorts and may help to stabilize the soil surface. Mucher et al. (1988) investigated the micromorphology and significance of such surface crusts in semiarid rangelands in Australia. The crusts consisted of 1- or 2-mm thick, often discontinuous layers associated with both depositional and structural crusts. Sumner and Stewart (1992) presented keynote papers, including the state of the art on soil crusting research in the United States, Tropical South America, Africa, and Australia. In these contributions, the extent of the problem, contributing factors such as textural characteristics, mineralogy, and organic matter content, and soil physical effects (infiltration and crust strength) are discussed. The effects on seedling emergence, the use of amendments (gypsum,phosphogypsum,and chemical soil stabilizers), tillage practices and organic matter management, and the relation of crusting to erosion were reviewed in the previously mentioned papers. In various publications, micromorphological aspects are combined with soil physical determinations (Gimenez et al., 1992; Van der Watt and Valentin, 1992; Chartres, 1992; D’Acqui et al., 1994). Structure stability refers to the ability of soil aggregates to withstand the destructiveforces of changing water content and the influence of rainfall on bare surfaces. Structural stability increases with increasing organic matter content, which in turn is correlated with increased biotic activity. Marinissen and Dexter (1990) demonstrated that drying cycles of earthworm casts rapidly promote the stabilization of these casts, probably through the growth of superficial fungal hyphae. The concept of aggregate hierarchy in soils (Oades and Waters, 1991; Waters and Oades, 1991; Dexter, 1988; Oades, 1993) is important. It states that in soils, aggregate breakdown caused by changes in water content proceeds stepwise, until relatively stable microaggregates smaller than about 250 p,m (Fig. 12) form from less stable macroaggregates. Dexter (1988) has discussed the implications of this concept in relation to bulk density and mechanical strength of aggregates. Smaller aggregations are denser than larger aggregates because of the porosity exclusion principle. Empirical approaches used to assess aggregate stability demonstrate different results in different soils without clarifying the nature of the processes and which substances are responsible for the observed results. The frequently cited study of Kemper and Koch (1966) showed a large variation in water stability of aggregates and the general importance of factors of texture and organic matter content. Quirk (1994) advises concentrating on research into the fundamental processes governing aggregate stability and breakdown (which involve interparticle forces and the disposition of substances involved in aggregation, such as cer-

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Figure 12 SEM examples of stable microaggregates from uncropped Alfisol showing encrusted plant fragments (Oades and Waters, 1991; reproduced with permission from Australian Journal of Soil Research, CSIRO).

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tain organic matter fractions). This will enhance our fundamental knowledge of the physical behavior of the soil porosity. Recent studies concentrate on mechanisms and the exact nature of the organic matter substances involved in the stabilization of aggregates (Chenu, 1989; Tisdall, 1991; Dorioz et al., 1993). In the model of soil structure behavior (Quirk and Murray, 1991), the focus is on physical processes relating to interactions between soil and water, the disposition of organic matter and aggregate stability, and microscopic or interparticle forces within soil aggregates. Using images at the various scales would provide the means to actually see what happens and find out whether the results conform to the model assumptions. Dorioz et al. (1993) presented an example of such an approach by using CryoSEM to investigate the role of roots, fungi, and bacteria on clay particle organization.They distinguished mechanical effects leading to reorganizations of the clay around penetrating roots, root hairs, and hyphae. The zone affected ranged from 5 to 200 pm depending on the size of the living organism. Second, a polysaccharide effect that penetrated and impregnated the surrounding mineral phase due to extracellular secretion was observed. Here, the modified area varied from less than 1 pm for bacteria to 50 pm for root tips. Oades (1984, 1987) and Tisdall and Oades (1982) discussed the role of organic matter and water-stable aggregates in soil. Tisdall (1991), using SEM, attempted to find the role of mycorrhizal fungal hyphae in the mechanisms for binding microaggregates (<250 pm) into larger macroaggregates. She found that interaction between clay particles and fungal hyphae and the “sticky string bag” role of extracellular polysaccharide play an important role. This is one of the factors explaining the well-known enhanced structural stability of grassland soils. Chenu (1989) also demonstrated the influence of a fungal polysaccharide on clay microstructures. An understanding of such mechanisms enables suggestions for agricultural management to be formulated that may lead to the formation and/or maintenance of more stable soil structures. The study by Oades and Waters (1991) used empirical disaggregating techniques ranging from gentle to strong and viewed the breakdown products with SEM. The surface horizons of tilled Alfisols and Mollisols appeared to break down in steps following the aggregate hierarchy concept, whereas the topsoil of the tilled Oxisol did not show any aggregate hierarchy, but very small aggregated particles of <20 pm were released by the most vigorous disaggregation treatment. These small aggregated particles had evident clay microstructure. This suggests that organic matter fractions are the dominant stabilizing agents in larger aggregates of the Alfisols and Mollisols, but the dominant stabilizing oxides in Oxisols prevent aggregate hierarchy in those soils. Elliot (1986) and Miller and Jastrow (1990) demonstrated that such aggregate hierarchy exists in Mollisols in the United States. This type of approach seems to be a very good procedure because it informs one about mechanisms of aggregate stability and applies the fundamental effects of interparticle forces effects to the interpretation. Similar fundamental mecha-

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nisms are presented in the work of Tessier (1984) and Tessier et al. (1990) on pure clays and of Wilding and Tessier (1988) on Vertisols. d. Effects of Land Use Changes Changing land use and the effects of associated differences in land management highlight the changes in biological activity, soil structure, and humus forms following the shift from natural vegetation to arable farming (e.g., Altemuller, 1957). Studies include Kooistra er al. (1990), who studied soil degradation in cultivated Oxic Paleustalfs under different management systems in Nigeria. Secondary forest was used as control (with intense faunal activity, especially earthworms) and the control was compared with a treatment of 12 years of maize (manually managed zero tillage with or without crop residue mulch) and 6 years of maize with conventional tillage (tractor plowing and harrowing with or without crop residue mulch). The manually cropped zero tillage plots with mulch showed a stable surface layer with earthworm activity extending to the surface. Without mulch, these plots showed surface crusting, poor infiltration during rainstorms, and associated erosion. In the plowed fields, all voids available for water infiltration were unstable tillage voids. There was no intense earthworm activity and severe crusting occurred in the unmulched fields, with poor infiltration of water and severe erosion and poor rooting. In the mulched fields with conventional tillage, the activities of small fauna such as Enchytraeids and Collembola could be noted, but here too, earthworm activity was absent. Spaans et al. (1989) investigated the change from a tropical rainforest to extensive grazing lands on soils formed from young and old volcanic lahars in Costa Rica. The topsoil structure of the younger soils (Tropepts) showed only slight degradation after 20 years of bananas plus 15 years of grassland after deforestation. The older soils (Humults) showed strong structure degradation after only 3 years of grassland (Figs. 13, 14, and 15). Subsequent research (unpublished) confirmed these changes and also demonstrated the stronger recovery of soil structure in the more resilient younger soils. Livingston et al. (1 990) investigated the effects on aggregate stability, organic carbon distribution, and porosity of a virgin (under hardwood forest) and cultivated Hapludalf and a virgin (under prairie) and cultivated Argiudoll in the midwestern United States. The degradation of soil structure brought about by cultivation was greater in the Alfisol than in the Mollisol because the Alfisol had a lower organic matter content in the topsoil and the aggregates became less stable with increasing depth. However, in both soils cultivation resulted in a greater number of fine and unconnected pores compared to the virgin soils. Long-term cultivation increased the potential erosion risk by decreasing potential infiltration as a result of increased bulk density, finer pores, and diminished aggregate stability. Pagliai et al. (1995) studied the structure of two alluvial soils in Italy after 10

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Figure 13 Soil structure degradation of topsoils under extensive grassland (B and D) compared to original rainforest (A and C ) for an old Humult (A and B) and a young Tropept (B and D)(Spaans et al., 1989;reproduced with permission from Hydrological Processes, John Wiley and Sons,LTD., New York).

years of conventional and minimum tillage. In the conventionally tilled soil, the aggregate stability was less, resulting in a greater tendency of crusting and compaction. Minimum tillage seemed to bring about better soil structural conditions for plant growth [increased amount of storage pores (0.5-50 pm) and transmission pores (50-500 pm)]. Crop yields, however, were not significantly different in the silt loam variant studied, but in the clay variant the minimum tillage system gave lower yields, presumably because of problems in preparing a seedbed because the topsoil was wetter. This illustrates quite clearly that although the soil structure appears to be less favorable for plant growth, the yields need not reflect this. Critical limits of resilience in different soils are needed to be able to assess

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Figure 14 Total porosity and pore size distributions of Humult and Tropept in Fig. 13 under grassland and rainforest (Spaans et al., 1989; reproduced with permission from Hydrological Processes, John Wiley and Sons, LID., New York).

whether morphological structure differences can be translated into functional differences influencing land qualities in such a way that they affect crop yields. The results of a large research project on the soil ecology of conventional and integrated arable farming systems in The Netherlands were published recently (Brussaard, 1994). Soil macroporosity and the proportion of pores influenced by soil biota were found to be higher in the integrated farming system than in the conventional one (Boersma and Kooistra, 1994). In the conventional system, the water stability of soil macroaggregates diminished with time, whereas aggregate water stability remained high in the integrated system as a result of earthworm activity (Marinissen, 1994). 2. Ecological Functioning

Ecological functioning deals with the relationships between the activities of agents of decomposition and the processes of C and N turnover and soil properties, especially of the soil microstructure. The amount and nature of the organic matter and the location of substrates and enzyme/decomposer organisms is important. Pagliai and De Nobili ( 1993) investigated soil porosity, root development, and soil enzyme activity (urease and phosphatase) from a zero-tillage field com-

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Figure 15 Soil hydraulic behavior of Humult (A) and Tropept (B) in Fig. 13 under grassland and rainforest. Log k = (cdday); h = (crn); 8 = (volume fraction). (Spaans et ul.. 1989; reproduced with permission from Hydrologicul Processes, John Wiley and Sons, LTD., New York).

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pared with a conventionally tilled field. Total porosity was greater in the conventionally tilled plots, but the proportion of pores of 30- to 500-p,m diameter was higher in the zero-tillage plots. Root development was related to the presence of smaller pores (<500 pm). Rooting was therefore better in the zero-tillage plots, and the enzymes were also more active in the zero-tillage plots. Urease especially showed a positive correlation with soil porosity from 30 to 200 pm, a pore fraction also important for rooting. The concept that substrates are protected by being located in micropores too small to be accessed by decomposer cells or inaccessible to microfaunal predators (e.g., Heijnen et al., 1990, 1994) is linked to soil microstructure. Soil microstructure controls the availability of organic substrates for decomposition and the rates of survival of decomposer cells (Ladd et al., 1993). Physical protection of organic N in small pores in clay soils and by association with clay particles in sandy soils was noted by Hassink et al. (1993) for grassland soils in The Netherlands. Leffelaar (1986) presented experimental results on the dynamics of partial anaerobiosis, denitrification, and water in a soil aggregate. Conditions conducive to N loss through denitrification (low soil temperatures and air-filled pore volumes below critical levels for such silt loam soils) were observed frequently in the spring in Dutch alluvial soils, in a conventional and (more frequently) (Vos and Kooistra, 1994) in an integrated farm management system. This points to the importance of the soil type and the weather conditions for such denitrification losses (Van Faassen and Lebbink, 1994). With regard to the ecological functioning of the soil, it is important to realize that strongly differing conditions may coexist within the soil structure. This microspatial heterogeneity is also evident from the images presented by Foster ( 1994). Roots are responsible for the uptake of water and nutrients. In taking up water, they promote wetting/drying cycles, thus modifying soil structure. Exudates and root-associated fungal networks may enhance aggregation. As it moves through the soil, the root tip, with associated microbiota, may rearrange clay particles around the root channel but may also help loosen aggregates. Similar to mechanical disturbance (soil tillage), such loosening increases the accessibility of organic matter to bacteria and thus stimulates N mineralization. Soil structure influences root length, the root distribution pattern, and root-soil contact (Van Noordwijk et al., 1993; Kooistra et al., 1992; Schoonderbeek and Schoute, 1994). The higher organic matter content was accompanied by greater biomass and/or enhanced activity of most soil organisms in the integrated system. The effect of the soil fauna, grouped into functional groups, on the annual amount of nitrogen mineralization was considerable (Didden et al., 1994). Synlocation (i.e., concurrence) of tillage cracks or other macropores, roots, organic matter, and high biological activity suggests that the plant may benefit whenever net nitrogen mineralization occurs. Synchronization (i.e., supplying the plant with sufficient nutrients at the right time) and synlocation are aspects of a favorable soil structure. Improved knowledge of

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the effects of the soil biota on the formation or modification of soil structure and on nitrogen mineralization should make linking with experimental and modeling studies on soil organic matter dynamics possible. The dynamics of soil organic matter are linked to crop growth, with the aim of selecting cultivars best adapted to nutrient conditions (Brussaard, 1992, 1994).

3. Hydraulic Functioning a. Drainage Micromorphometric soil structure data explaining saturated hydraulic functioning using quantified information on continuous macropores stained with methylene blue and on unstained discontinuous macropores were first published by Bouma et al. (1979). The vertical and horizontal continuity of macropores is an important characteristic of soil structure. The continuity governs infiltration rates, particularly at high rainfall intensities (e.g., important in the case of crusting; see Section III,B,I), and aeration processes in fine textured soils (e.g., important for rootability and denitrification dynamics; see Section 111,B,2) with a groundmass of fine pores. Two approaches can be taken to determine macropore continuity (Bouma, 1992): (i) reconstruction of the 3D reality from the 2D micromorphological image using stereology, or in situ, nondestructive characterization of macroporosity by CT scanning or NMRI techniques (Section 11). Hopmans et al. (1992) used CT scanning to study the soil water distribution in an initially saturated draining sand core under positive pressure (one-step outflow). Nonuniform drainage was observed, with local variations of water content, presumably due to air entrapment, localized variations in packing density, and localized soil water pressure gradients. In a CT study, Heijs er al. (1995) added water containing a contrast agent and were able to distinguish water already present in the sample and to visualize the flow paths. They quantified the macroporosity and water content through image analysis of the measured CT images and were therefore able to identify preferential flow and infiltration of water from the conducting macropores into the groundmass; (ii) functional characterization of soil macropores using dye staining (e.g., methylene blue) or other tracers (e.g., gypsum; Mackie, 1987).Thin sections are used in detailed studies and image analysis can provide details on the pore sizes and shapes involved. These results are then combined with measurements of hydraulic conductivity (K-h relation) and moisture characteristics (h-0 relation) to arrive at meaningful parameters for simulation modeling (Section 1II.C) using weather data and crop parameters. When only the topsoil is involved in structure degradation (e.g., crusting or compaction), it is this layer that needs to be characterized. Spaans et al. (1989) reported a strong structure degradation (Fig. 13) and changes in pore size distributions (Fig. 14) and associated degradation of hydraulic functioning(Fig. 15)of topsoils of Costa Rican Ultisols under extensive

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grassland after clearing of tropical rainforest. Inceptisols were much more resilient and showed only slight degradation of topsoil structure and hydraulic functioning. Booltink and Bouma ( 199 1) demonstrated the reconstructed methylene blue staining patterns in a soil column (20-cm diameter) to a depth of 16 cm in a Dutch alluvial soil (Hydric Fluvaquent; Fig. 16). Using this procedure at various depths they were able to characterize the soil structure profile. Hatano and Booltink (1 993) analyzed these staining patterns using fractal dimensions of the flow patterns to predict measured bypass flow. Booltink (1995) monitored nitrate leaching and water flow in the same soil on the basis of the soil structure profile. Actively growing crops prevented nitrate leaching to the groundwater and a catch crop grown directly after slurry application also reduced the nitrate leaching during the winter period. Soil structure strongly influenced bypass flow and farmers could thus manipulate soil structure to minimize nitrate leaching. Such studies demonstrate the links between ecological functioning and hydraulic functioning and land use at the scale level of the soil structure profile. It is important to consider structure profiles in this respect because the roots of crops and vegetation exploit such large volumes of soil. This normally involves characterizingof the physical behavior of functional layers such as the plowed topsoil, the layer below the plowed layer (sometimes a plow pan or traffic pan), and the subsoil in one or more layers. Kooistra et al. (1985) illustrated this procedure in a case study on a Dutch sandy loan (Qpic Haplaquent) under different land-use management systems. Very significant changes occur in the same soil as a function of different land-use management. b. Moisture Retention This characteristic determines the feasibility of supplying water to the roots; it largely depends on the microstructure and pore size distribution. Vogel and Babel ( 1994) discussed the experimentally determined relation between quantified morphological pore size distribution from polished blocks in comparison with the equivalentpore size distributionsderived from the moisture retention curves. They noted that, as expected, compared with the morphologicalmethod there was a shift toward smaller pores for the equivalent pore sizes. However, the fraction of pores larger than 300 p n was overestimated by the water retention method, probably because of air entrapment. Optical and electron microscopy of thin sections or polished blocks yields images of porosity at various magnifications that can be quantified using image analysis (Section 11). To evaluate the capacity of the soil to adequately supply moisture to the crop, a combination with physical determinations (e.g., bulk density, aggregate density, and mercury porosimetry) and rooting density and pattern is needed. The rooting depth, density, and pattern itself interacts with the soil structure: Unfavorable layers with high soil strength (e.g., plow pans) may deflect the roots un-

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Figure 16 Methylene blue staining of pores (dark areas around pores) (A) and staining patterns with depth (B) in a Dutch Hydric Fluvaquent (Booltink and Bouma, 1991; reproduced with permission from Soil Sci. Soc. Am. J . ) .

ti1 they reach a vertical crack that allows deeper vertical penetration (Dexter, 1988). Miedema (1987) showed that old Late Weichselian Rhine soils (Alfisols) had a very dense microstructure compared with young Holocene Rhine soils (Inceptisols). There was significantly less moisture available between field capacity and the wilting point in the old soils than in the young soils. This difference was most clearly expressed in natural aggregates (3.4- to 4.8-mm diameter), but was also significantly noticeable in undisturbed core samples (100 cm3). However, when viewed on the scale of the structure profile, the old Rhine soils contained more available water for grasslandsbecause the capillary rise from the groundwater was not interrupted in these soils by horizontal cracking, whereas in the Holocene soil at a similar position in the landscape, horizontal cracking occurred, which limited the supply of water by capillary rise (Kooistra et al., 1987; Figs. 17 and 18). The microstructure is a determining factor in the moisture characteristic. Anderson et al. (1988) observed that bulk density and water content, measured by CT scanning, showed heterogeneity associated with anisotropy of the groundmass. Anisotropy of the groundmass in the distribution of fine and coarse particles and the orientation or lack or orientation of the fine particles also influences the engineering properties of soil materials and soils at different moisture content (Barton, 1974; Collins and McGown, 1983; Jim, 1990). These engineering properties are important for soil tillage. Miedema and Van Oort (1990) presented a case study

APPLICATIONS OF MICROMORPHOLOGY

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showing the microphysical elastic behavior of young Dutch Rhine soils versus the rigid behavior of old Rhine soils when drying natural aggregates from -1 kPa to -100kPa (Figs. 19 and 20). Tovey et al. (1992a,b, 1994)published methods for the automatic quantitative mapping of soil microfabrics by image analysis. The land quality, “workability,” depends on the moisture characteristic and on soil me-

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Figure 19 Micromorphology of Dutch Holocene Rhine soils (A) and of Late Weichselian Rhine soils (B) (Miedema. 1987).

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chanical behavior. Wet limits of workability for soils of different texture can be expressed in terms of the matric potential of the soil water (Dexter, 1988).

C. MODELING OF THE FUNCTIONING OF Son, STRUCTURE 1. Modeling the Physical Functioning of the Soil Structure Soil water regimes can be modeled at different scales of functioning. Bouma et al. (1993) stressed that cropsoil models require specific soil data at each scale level. Young and Crawford (1991) applied fractal analyses at the aggregate level, whereas Hatano and Booltink (1993) used fractal analyses to model bypass flow as related to the soil structure profile. The spatial and temporal dynamics of soil structure must be taken into consideration at the levels of feature, process, and effect (Puentes etal., 1992; Hall, 1994; Leffelaar, 1993; Holden, 1995). Sullivan (1994) pointed out that the current models for solute movement in soils generally consider two separate submodels: one for a microporous or dense groundmass and one for the effect of macropores (cracks and/or biopores). The properties of the walls or the pores and possible coatings on the pore wall and the

APPLICATIONS OF MICROMORF’HOLOGY

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tortuosity (occurrence of near-horizontal stretches) will significantly influence the probability of absorption into the groundmass. This “internal catchment” is being considered in modifications and refinements of currently used models. The spatial and temporal dynamics of soil structure can also be expressed as the probabilities of how certain land qualities (moisture supply capacity and moisture deficit, Fig. 17; trafficability, workability, and aeration status) relate to critical threshold values during the year or between years (Kooistra et al., 1987; Van Lanen et al. 1987, 1992; Bouma and Hack-Ten Broeke, 1993; Vos and Kooistra, 1994). Quirk and Murray (1991) discuss soil structure and physical behavior and the interrelationshipsof various areas of soil structure research. Their model is a conceptual model, not a simulation model. Microscopic and macroscopic physical behavior are analyzed for operative fundamental processes. Combining the microscopic model of clay behavior with the macroscopic model of soil behavior then yields a complete model for assessing and predicting soil physical behavior. The role of biotic influences, although mentioned in the discussion on aggregate stability, is greatly underestimated in this approach.

2. Modeling the Ecological Functioning of the Soil Structure Juma (1993,1994) suggested ways of integrating C and N cycling and soil structure formation on a whole ecosystem basis in currently used simulation models (e.g., Van Veen et al., 1984; Rappoldt, 1990, 1993; De Ruiter et al., 1994). The fundamental processes underlying such models have been discussed by authors such as Brussaard ( 1992),Ladd et al. ( 1993), and Foster (1994). Modeling of water movement, oxygen supply, and biological processes as described by Leffelaar (1993) is an example of the modeling of diffusion processes at the scale of aggregates. Micromorphological techniques enable observing the places where action actually takes place within the microstructure of the soil. The temporal and spatial anisotropy of soil structures requires modeling from the aggregate scale to the ecosystem scale. Because these models emphasize the biotic processes above the physical processes, these two aspects also need to be linked at a range of appropriate scales.

W. CONCLUSIONS AND FUTURE RESEARCH NEEDS New methods for micromorphological imaging (CT scanning and NMRI) have become available and are resulting in increasingly better resolution. Although not yet routinely available at reasonable cost, these techniques are very promising for future research on the formation and functioning of soil structure in relation to

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agronomy. The resulting images must be quantified to yield parameters pertinent to modeling. The currently available micromorphological imaging techniques and associated microchemical, micromineralogical, and microphysical analyses can bridge the scales from the particle interaction level to the macromorphological profile and field scale functioning of soil structure in relation to agronomic management practices and production ecology. Continued research is needed into new methods and improvement of existing methods. Fundamental processes of soil structure formation and functioning need to be understood in order to be able to assess the effects of current agronomic practices and to predict effects of future agronomic practices. Despite the progress made in recent years, basic knowledge on these fundamental processes is still lacking (Sposito and Reginato, 1992). In addition, the microbiological and biological approaches and the soil physical approaches in various farming systems and in comparisons with natural and seminatural ecosystems have not yet been linked sufficiently across scale levels. Linking the biological and physical processes and the fundamental role of organic matter in this respect is a major challenge for future research. The research on interactionsbetween soil structure and agronomic practices on spatial and temporal variability has made significant progress in recent years. Crop yields must be investigated too because morphologically different soil structures do not necessarily mean different crop yields. Quantitative evidence is becoming available through much more sophisticated image analysis techniques (including the application of remote sensing spectral image analysis techniques to thin-section images). This enables a quantitative analysis rather than a qualitative description of soil structure. The application of fractal analyses and artificial neural systems and the application of geostatistics in micromorphological image analyses are examples of promising research avenues for the coming years. This quantitative analysis is imperative for arriving at meaningful quantitativeparameters to be used in simulation modeling of hydraulic and ecological soil structure functioning and crop response using cropsoil models. A major challenge for future research is the linking of the different scales of observations from the nanometer scale (particle interaction) to the kilometer scale (soil sequencesAandscape). For agronomy, the upscaling and downscaling certainly needs to include the functioningof the structure profile and the temporal and spatial variations at field scale and their influences on crop yields. Recent and current attempts in this line of research have been reported in this review and they show encouraging results. Soil quality was recently defined by the Soil Science Society of America (1995) as “The capacity of a specific kind of soil to function, within natural or managed ecosystem boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and support human health and habitation.” Soil quality can be expressed in a unique set of characteristics of land units, termed “win-

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dows of opportunities”by Bouma (1994). This term expresses the natural dynamic behavior and responses of the land to different land management systems (resilience). Micromorphology provides a unique set of tools for interpreting soil processes emphasizing the in situ nature and arrangement of features. Combined with the results of other soil analyses, the results of micromorphologicalanalysis can be used to integrate and synthesize data that link processes at submicroscopic scales and extrapolation of that insight to macroscopic functioning of the soil structure. Modeling needs quantified information on relevant parameters of soil behavior. Such information is becoming easier to acquire using new methods of imaging and image analysis. Models for hydraulic functioning, ecological functioning, and cropsoil relations need to be linked across scale levels. The main future challenge remains the determination of critical boundaries for natural or agroecosystems within which the soil does not lose its capacity to function. Sufficient understanding of fundamental processes and their effects on the functioning of the soil from nanometer scales through the profile and field scales to soil sequences and landscapes is lacking.

REFERENCES Aguilar, J., Fernandez, J.. Ortega, E., De Haro, S., and Rodriguez, T. (1990). Micromorphological characteristics of soils producing olives under nonploughing compared with traditional tillage methods. Dev. Soil Sci. 19,25-32. Altemiiller, H. J. (1957). Bodentypen aus Loss im Raume Braunschweig und ihre Veriinderungen unter dem Einflusz des Ackerbaues. Doctoral thesis, Universitat Bonn. Altemiiller, H. J. ( I99 1). Praparative Grundlagen der Fluoreszens-farbung organischer Bodenkomponenten in Boden-Diinnschliffen. Mirr. Deursche Bodenk. Geseffsch.66,453-456. Altemiiller, H. J. ( 1992).Visuelle Erscheinungs-und Zustandsformen der organischer Bodensubstanz. In “Berichte iiber Landwirtschaft, Neue Folge 206. Sonderheft Bodennutzung und Bodenfruchtbarkeit: Band 4: Humushaushalt,” pp. 3 0 4 4 . Altemiiller, H. J., and Banse, H. J. (1964). Die Bedeutung der Mikromorphologie hinsichtlich der organischen Diingung. Soil Micromorp., 467-476. Altemiiller, H. J., and Beckmann, T. (1991). Verbesserung der Glashaftung von Polyesterharzen bei der Herstellung von Boden-Diinnschliffen. Zeirschr: Pflanzenern. Bodenk. 1 5 4 , 4 4 3 4 . Altemiiller, H. J., and Van Wet-Land, B. (1990). Soil Thin Section Fluorescense Microscopy. Dev. Soil Sci. 19,565-580. Anderson, S. H., and Hopmans, J. W. (Fds.) (1994). Tomography of soil-water-root processes. Soil Sci. Soc.Am. Spec. Publ. 36, pp. 148. Madison, WI. Anderson, S. H., Gantzer, C. J., Boone, J. M.,and Tully, R. J. (1988). Rapid non-destructive bulk density and soil water determination by computed tomography. Soil Sci. SOC.Am. J. 52,3540. Aylmore, L. A. G. (1993). The use of computer assisted tomography in studying water movement around roots. Adv. Agron. 49, 1-54. Aylmore, L. A. G. (1994).Application of computer assisted tomography to soil-plant-water studies: An overview. In “Tomography of Soil-Water-Root Processes” (S. H. Anderson and J. W. Hopmans, Eds.), Soil Sci. Soc.Am. Spec. Publ. 36, pp. 7-16. Madison, WI. Babel, U., and Krehs, M. (1991). Pflanzenartspezifische Mikrogefiige in Oberboden. Mirr. Deursche Bodenk. Gesellsch. 66 (11). 597-600.

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Babel, U., Ehrmann, O., and Krebs, M. (1992). Relationships between earthworms and some plant species in a meadow. Soil Biol. Biochem. 24,1477-1481. Bal, L. (1970).Morphological investigation in two moder-humus profiles and the role of soil fauna in their genesis. Geodema 4,5-36. Bal, L. (1973).“Micromorphological Analysis of Soils. Lower Levels in the Organization of Organic Soil Materials,” Soil Survey Paper 6, pp. 174. Soil Survey Institute, Wageningen, The Netherlands. Barton. C. M. (1974).The micromorphological soil investigation work of Dr. D. Lafeber. In “Soil Microscopy” (G. K. Rutherford, Ed.), pp. 1-20. Limestone Press, Kingston, Canada. Bisdom. E. B. A. (1983). Submicroscopic examination of soils. Adv. Agron. 36,55-96. Bisdom, E. B. A., Boekestein, A., Curmi, P., Lagas, P., Letch, A. C., Loch, J. P. G., Nauta, R., and Wells, C. B. (1983).Submicroscopy and chemistry of heavy metal-contamination precipitates from column experiments simulating conditions in a soil beneath a landfill. Geodema 30, 1-20. Bisdom, E. B. A., Tessier, D.. and Schoute. J. F. Th. (1990).Micromorphological techniques in research and training (submicroscopy). Dev. Soil Sci. 19,581-605. Blokhuis, W. A., Kooistra, M. J., and Wilding, L. P. (1990). Micromorphology of cracking clay soils. Dev. Soil Sci. 19, 123-148. Boersma, 0.H., and Kooistra M. J. (1994).Differences in soil structure of silt loam Typic Fluvaquents under various agricultural management practices. Agric. Ecosyst. Environ. 51,2 1-42, Booltink, H. W. G. (1993).Morphometric methods for simulation of water flow, pp. 169. Doctoral thesis, Wageningen Agricultural University. Booltink, H. W. G. (1995).Field monitoring of nitrate leaching and water flow in a structured clay soil. Agric. Ecosyst. Environ. 52, 251-261. Booltink, H. W. G., and Bouma, J. (1991).Physical and morphological characterization of bypass flow in a well structured clay soil. Soil Sci. SOC.Am. J. 55, 1249-1254. Boone, E R., Slager. S.,Miedema, R., and Eleveld, R. (1976). Some influences of zero-tillage on the structure and stability of a fine-textured river levee soil. Netherlands J. Agric. Sci. 24, 105-1 19. Bouabid, R., Nater, E. A., and Barak, P. (1992). Measurement of pore size distribution in a lamellar Bt horizon using epifluorescence microscopy and image analysis. Geodema 53,309-328. Bouma, J. (1992). Effect of soil structure, tillage and aggregation upon soil hydraulic properties. In “Interactive Processes in Soil Science” (R. J. Wagenet, P. Baveye, and B. A. Stewart, Eds.), pp. 1-36. Lewis, Boca Raton, FL. Bouma, J. (1993). Soils: A global mandate. Lecture at the annual meeting of the Soil Science Society of America, Cincinnati, OH. Bouma, J., and Hack-ten Broeke, M. J. D. (1993).Simulation modelling as a method to study land qualities and crop productivity related to soil structure differences. Geodema 57,5 1-67. Bouma, J., Jongerius, A., and Schoonderbeek, D. (1 979). Calculation of saturated hydraulic conductivity of some pedal clay soils using micromorphometric data. Soil Sci. Soc. Am. J. 43,261-264. Bouma, J., Wopereis, M. C. S., Wosten, J. H. M., and Stein, A. (1993). Soil data for crop-soil models. In “Systems Approaches for Agricultural Development” (F. W. T. Penning de Vries et al., Eds.), pp. 207-220. Kluwer Academic, Dordrecht, The Netherlands. Bresson, L. M. (1981).Ion milling applied to ultramicroscopic study of soils. Soil Sci. Soc. Am. J. 45, 568-573. Bresson, L. M. (1995).A review of physical management for crusting control in Australian cropping systems: Research opportunities. Aust. J. Soil Res. 33, 195-209. Bresson, L. M., and Boiffin, J. (1990).Morphological characterization of soil crust development stages on an experimental field. Geodema 47,301-325. Bresson, L. M., and Valentin, C. (1994).Soil surface crust formation: contribution of micromorphology. Dev. Soil Sci. 22,737-762. Bresson, L. M., and Zambaux, C. (1990). Micromorphological study of compaction induced by mechanical stress for a Dystrochreptic Fragiudalf. Dev. Soil Sci. 19,3340.

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Ringroase-Voase, A. J. ( 1991). Micromorphology of soil structure: Description, quantification, application. Aust. J. Soil Res. 29,777-813. Ringroase-Voase, A. J. (1994). Some principles to be observed in the quantitative analysis of sections of soil. Dev. Soil Sci. 22,483493. Rodriguez, R., Herrero. J., and Porta, J. (1990). Micromorphological assessment of drain siltation risk indexes in a saline-sodic soil in Monegros irrigation district (Spain). Dev. Soil Sci. 19,41-52. Rogaar, H., and Boswinkel, J. A. (1978). Some soil morphological effects of earthworm activity: field data and X-ray radiography. Netherlands J. Agric. Sci. 26, 145-160. Schoonderbeek, D., and Schoute, J. F. Th. (1994). Root and root-soil contact of winter wheat in relation to soil macroporosity. Agric. Ecosysr. Environ. 51, 89-98. Shimane, S., and Nakabayashi, K. (1988). Impregnation procedure for the preparation of soil thin sections using methyl metacrylate. Bull. Fuc. Agric. 82,29-35. Singh, P., Kanwar, R. S., and Thompson, M. L. (1991). Macropore characterization for two tillage systems using resin-impregnation technique. Soil Sci. SOC.Am. J. 55, 1674-1679. Soil Science Society of America (1995). “SSSA Statement on Soil Quality.” Agron. News, June, 7. Spanne, P., Jones, K. W., Prunty, L., and Anderson, S. H. (1994). Potential applications of synchroton computed microtomography to soil science. In “Tomography of Soil-Water-Root Processes” (S. H. Anderson and J. W. Hopmans, Eds.), Soil Sci. Soc.Am. Spec. Publ. 36, pp. 43-58. Madison, WI. Spaans, E. J. A., Baltissen, G. A. M., Bouma, J., Miedema, R., Lansu, Schoonderbeek, D., and Wielemaker, W. G. (1989). Changes in soil physical properties of young and old volcanic soils in Costa Rica after clearing of tropical rain forest. Hyd,: Proc. 3,383-392. Sposito, G., and Reginato, R. J. (1992). “Opportunities in Basic Soil Research, pp. 109. Soil Sci. Soc. Am. Inc., Madison, WI. Stephan, S. (1969). Gefriertrocknung und andere bei der Herstellung von Bodendiinnschliffen benutzbare Trocknungsverfahren. Z. Pjanzenernuh,: Diigung Bodenk. 123, 13 1-140. Steude. J. S., Hopkins, F., and Anders, J. E. (1994). Industrial X-ray computed tomography applied to soil research. In “Tomography of Soil-Water-Root Processes” (S. H. Anderson and J. W. Hopmans, Eds.), Soil Sci. Soc.Am. Spec. Publ. 36, pp. 2942. Madison, WI. Sullivan, L. A. (1994). Structural pore pedofeatures: Influence on some soil processes. Dev. Soil Sci. 22,6 13422.

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PHYSIOLOGICAL AND MORPHOLOGICAL RESPONSES OF PERENNIAL FORAGES TO STRESS Matt A. Sanderson', David W. Stair2,and Mark A. Hussey2 'Texas A&M University Agricultural Research and Extension Center Stephenville, Texas 76401 *Department of Soil and Crop Sciences Texas A&M University College Station, Texas 77843

I. Introduction 11. Water Deficit A. Whole-Plant Responses B. Effects on Photosynthesis C. Tall Fescue and Endophyte D. Water-Deficit Effects on Forage Quality E. Seedling Establishment in Grasses III. Defoliation Stress A. Whole-Plant Responses B. Remobilization of Carbon and Nitrogen IV LowLight A. Shade Responses B. Light Quality V. Nutrient Stress A. Nitrogen B. Phosphorus C. Acid Soils and Al Toxicity D. Calcareous Soils and Fe-Deficiency Chlorosis VI. Low-Temperature Stress A. Chilling Stress B. Acclimation to Low Temperature and Development of Freezing Tolerance C. Low-Temperature Stress and Organic Reserves D. Tissue Culture and Gene Expression in Low-Temperature Stress VII. Salt Stress A. Salt Accumulation B. Seedling and Adult Plant Responses C. Tissue Culture and Gene Expression in Salt Stress 171 Advanres m Agmnmny, Volume 59 Copyright 0 1997 by Academic Press, Inc. All rights of reproduction in any form reserved.

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M. A. SANDERSON ET AL. VIII. Plant Breeding for Abiotic Stress Tolerance A. Defoliation Tolerance B. Drought Tolerance C. Nutrient Stress D. Salinity Tolerance References

I. INTRODUCTION Stress reduces crop growth on nearly all arable land on earth (Solh, 1993) and severely limits agricultural productivity (Boyer, 1982). Perennial forages are grown in many different environments and must endure stresses not normally encountered by annual crops such as repeated defoliation by machines and surviving seasonal extremes in climatic conditions during several years. Forage crops account for 60-90% of feedstuff input for animal production systems (Barnes and Baylor, 1994). Rarely is the abiotic or biotic environment optimum for growth of perennial forages. Indeed, stress may be a regular feature of a particular environment. Stress has been defined as “any factor that decreases plant growth and reproduction below the genotype’s potential” (Osmond et al., 1987).Abiotic stresses include water deficit, temperatureextremes, nutrient imbalances or deficiencies, light extremes, and soil factors (e.g., salinity and pH). Perennial forages commonly are grown on soils of low water-holding capacity or infrequent irrigation, limited fertility, or high salt content. Furthermore, forages must endure subzero temperatures during winter or, in the case of tropical and subtropical forages, withstand chilling or infrequent frosts, and periodic defoliation (e.g., machine harvest). Collectively, stresses may reduce the harvested forage yield, alter its nutritive value, and change species composition of the sward. With the current societal emphasis on sustainable agricultural systems, forage crop production will become more important and, thus, our knowledge of how abiotic stresses limit forage production must increase. There are several reviews on various aspects of the general topic of abiotic stress in plants, particularly in grain crops and plants grown in extensive systems (Jones et al., 1989; Alscher and Cummings, 1990; Fowden et al., 1993; Bohnert er al., 1995). We chose to limit the scope of this review to perennial forages because of limited coverage in previous reviews. We focus on defoliation (primarily machine harvest, not herbivory), low-temperature, water-deficit, nutrient, and salinity stresses and discuss the manifestations of these stresses at the whole-plant and organ level, examine the cellular bases of stress reactions, and explore the genetics of abiotic stress in relation to plant breeding for development of more stressresistant germ plasm. We have attempted to highlight the most recent research and

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refer the reader to the pertinent, in-depth reviews in each of the appropriate sections.

II. WATER DEFICIT A. WHOLE-PLANTRESPONSES For a plant to assimilate atmospheric CO,, it must encounter evapotranspirational water loss and some level of water stress. Some forages escape water stress by completing their life cycle before water becomes limiting [e.g, crested wheatgrass (Agropryron cristutum (L.) Beauv. ssp. pectinarum (Bieb.) Tzvel.) (Bittman and Simpson, 1989; Frank, 1994)l; however, most perennial forages resist water stress with a combination of avoidance and tolerance mechanisms. Avoidance mechanisms frequently are alterations in morphology that reduce evapotranspiration and conserve water. These may include deeper roots, leaf surface modifications (wax and pubescence), leaf orientation, and leaf senescence (Chaves, 1991; Hsiao, 1973; Jones and Corlett, 1992). Tolerance mechanisms enable the plant to protect the water status of critical tissues, such as the apical meristem, and mainly include osmotic adjustment (Bray, 1993). Leaves of forage grasses typically roll or fold to reduce the transpiring leaf surface exposed to the sun (Redmann, 1985). Hardy er d.(1995) examined the leaf anatomy of C, meadow and range grasses and C, range grasses and observed that the leaves of all C, grasses examined rolled or folded adaxially such that the adaxial leaf surface was completely enclosed during extreme water stress. The C, grasses showed more variability in leaf modifications with folding, rolling, or twisting of leaves most common; however, some C, grasses did not modify their leaf display in response to stress. Water stress reduces dry matter yield of forages primarily by limiting leaf area development (Ludlow and Ng, 1977; Ludlow er al., 1980; Slatyer, 1974). Van Loo (1992) partitioned leaf area expansion in perennial ryegrass (Loliurn perenne L.) into leaf elongation rate, leaf appearance rate, specific leaf area, and tillering components, and observed that tillering rate was limited by water stress principally by a reduction in leaf appearance rate. The reduction in leaf elongation rate was speculated to be a result of loss of turgor (because of only partial osmotic adjustment) andlor alteration of cell wall extensibility in response to a hormonal signal from the roots as suggested by Davies and Zhang (1991). Neumann (1995) questioned the assumption that the loss of cellular turgor pressure in response to water stress is the principal cause of growth inhibition under water stress. Neumann’s alternative hypothesis is that cell wall adjustment (hardening or softening of the cell wall), which results in smaller mature cells, is primarily responsible.

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Onillon et al. (1995) demonstratedthat yield reduction in water-stressed tall fescue (Festuca arundinacea Schreb.) resulted principally from reduced leaf area expansion and consequent loss of light harvesting surface. Fescue did not develop deeper roots in response to water deficit, and C partitioning between root and shoot was unchanged probably because root and shoot growth were reduced similarly. Spollen and Nelson (1994) induced water stress in tall fescue in the growth chamber by withholding water for 5 days, This changed the spatial distribution of growth in the basal meristem of the elongating leaf blade by shortening the growth zone from 25 to 15 mm and stopping leaf elongation by 4 days. Water stress increased the dry matter and hexose content in the leaf growth zone but decreased the fructan content. Sucrose and hexoses contributed more as osmolytes to osmotic adjustment than did fructan. They suggested that altered C metabolism as well as a limited supply of water to expanding cells reduced leaf growth. In contrast, Parrish and Wolf (1983) reported that leaf elongation rate in tall fescue was responsive solely to water uptake and flux into the elongating leaf blade. Osmotic adjustment, the ability of the plant to accumulate solute molecules that reduce osmotic potential of the cell sap, is an important physiological mechanism for dealing with water stress (Bray, 1993). In the greenhouse, Barker et al. (1993) observed that the C, grasses indiangrass [Sorghastrum nutans (L.) Nash], big bluestem (Andropogon gerardii Vitman), and switchgrass (Panicum virgatum L.) adjusted osmotically to water deficit by 0.13421 MPa, whereas the C, grasses smooth bromegrass (Bromus inemis Leyss.) and reed canarygrass (Phaluris arundinacea L.) did not adjust. In the field, both C , and C, grasses adjusted osmotically during 24 days of drought, but the extent of adjustment was greater in the C, grasses (average of 1.05 MPa) than in the C, grasses (average of 0.58 MPa). The osmotic adjustment in the C, grasses occurred within the first 14 days, whereas the C , grasses adjusted osmotically throughout the experiment. The C, grasses were able to maintain some turgor despite less osmotic adjustment by maintaining more flexible cell walls as evidenced by a lower bulk modulus of cell wall elasticity. Drought induces retranslocation of N from shoots to roots and rhizomes of perennial C, grasses (Heckathorn and DeLucia, 1994).The resulting reduced concentration of N in leaves limits photosynthesis during recovery growth. Heckathorn and DeLucia (1994) hypothesized that the N retranslocation mechanism served to limit the loss of N in aboveground organs when soil N was less available and photosynthesis inhibited.

B. EFFECTSON PHOTOSYNTHESIS Moderate water-deficit stress reduces photosynthesis primarily by inducing stornatal closure (stomatal limitation; Chaves, 1991). Nonstomatal factors have

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been implicated in causing reduced photosynthesis under severe water deficits. Antolin and Sanchez-Diaz (1993) measured the photosynthetic response and in vifro ribulose bisphosphate carboxylase (RUBISCO) activity in alfalfa (Medicago sativa L.) exposed to increasing water deficit and found evidence for stomata1and nonstomatal effects.The intercellularCO, concentration (Ci)of alfalfa leaves, calculated from gas-exchange measurements, remained constant in control and water-stressed plants. Further evidence for nonstomatal limitations were (a) reduced C,-saturated photosynthesis during water stress, (b) only partial recovery of photosynthesis and light response after rehydration, (c) an increased CO, compensation point with decreased leaf water potential, and (d) stressed plants had a reduced quantum yield (energy required to fix a unit of CO,) as measured by in v i m RUBISCO activity (in the absence of epidermis and stomata). Activated oxygen compounds, such as H,O,, 0,-, and OH, may accumulate during water-deficit stress and damage the photosynthetic apparatus. Superoxide dismutase (SOD) and ascorbate peroxidase along with the antioxidants ascorbic acid and glutathione act to prevent oxidative damage in plants (Allen, 1995). Irigoyen et al. ( 1992) measured SOD, catalase, and peroxidase levels (enzymes associated with 0 metabolism and which may alleviate damage) and levels of ethylene and malondialdehyde (indicating lipid peroxidation) in leaves of alfalfa plants stressed at several water-deficit levels. Water deficit reduced both photosynthesis and transpiration; however, transpiration was reduced more relative to photosynthesis.At moderate stress levels (- 1.6 MPa leaf water potential), levels of H,O,, ethylene, and malondialdehyde increased but were less at lower leaf water potentials. Activity of SOD was maintained during water deficit, whereas catalase activity varied inconsistently as water deficit increased. Peroxidase activity decreased curvilinearly with increasing stress. These results indicated that oxygen free radicals had little direct effect on the photosynthetic apparatus of severely stressed alfalfa leaves. In contrast, Price and Hendry (1991) found that oxidative molecular damage in several grasses was initiated in the chloroplasts and caused a cascade of damaging effects including chlorophyll destruction, lipid peroxidation, and protein loss.

C. TALL FESCUEAND ENDOPHYTE Mutualistic relationships between plants and microbes modify the physiology of stress resistance in forages. Infection of tall fescue with an endophytic fungus (Acremonium coenophialum Morgan Jones and Gams) reduces the weight gain and disrupts the metabolism of livestock feeding on the infected forage (Joost, 1995). Ironically, infection with the fungus also confers a resistance to several biotic and abiotic stresses to the plant and enables fescue to persist for many years (Clay, 1990). Infection has been shown to affect the water-stress tolerance of tall fescue

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by inducing avoidance mechanisms such as leaf rolling, increased leaf thickness, leaf senescence, reduced leaf extension, and stomata1closure (Arachavaleta et al., 1989; Belesky et al., 1989; Richardson et af., 1993). White et af. (1992) did not find evidence for an endophyte-mediated drought tolerance in tall fescue. Elmi and West (1995), however, demonstrated an increase in the level of osmotic adjustment in basal leaf meristems and leaf blades of water-stressed infected plants compared with water-stressed noninfected plants, which could increase plant survival during long-term drought stress. The physiology of these responses has been reviewed by Bacon (1993) and West (1994).

D. WATER-DEFICIT EFFECTSON FORAGEQUALITY Water deficit frequently reduces forage yields; however, because the total value of a forage crop also depends on its quality, effects of water-deficit stress on forage quality are of interest. Drought has been shown to reduce fiber concentrations and increase the digestibility of legumes (Vough and Marten, 1971; Halim et al., 1989; Peterson etal., 1992; Petit etal., 1992) and grasses (Bittman etal., 1988; Clark and Lugg, 1986; Sheaffer et al., 1992). The changes in forage composition and quality were most often the result of reduced plant maturity and increased ratio of leaf mass (of higher quality) to stem mass (often of lower quality) (Buxton and Fales, 1994). Pitman et al. (1981), however, found evidence for a direct effect of water deficit on the in vitro digestibility of stems and leaf blades of kleingrass (Panicum coloraturn L.). As xylem pressure potential declined from -0.2 to - 1.1 MPa, leaf digestibility was reduced by 19% and stem digestibility by 32%. A companion study indicated that stressed leaves and stems had an increased proportion of cell walls and increased lignification (Pitman et al., 1982).

E. SEEDLING ESTABLISHMENT IN GRASSES Establishment of forage crops is the most critical phase of perennial forage crop production even though it accounts for a brief period in the total life of the stand. Most forages are small seeded and thus planted at depths less than 25 mm. Forage seedlings are particularly vulnerable to water deficit because the extremes of water availability occur at the soil surface (Osmond et ul., 1987). The seminal root system of perennial grass seedlings originates at the depth of planting and functions for a short time (Fig. 1). It supplies water to the developing seedling for a short time and is limited by the hydraulic conductivity of the subcoleoptile internode (Hyder et al., 1971; Wilson et al., 1976; Redmann and Qi, 1992). The permanent, adventitious root system forms at the coleoptilar node, which is at or above the soil surface in panicoid grasses (Tischler and Voigt, 1987). In festucoid

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Figure 1 Diagram of festucoid (A) and panicoid (B) seedlings indicating placement of the crown and origin of adventitious and seminal roots. In seedlings of panicoid grasses, the subcoleoptile internode elongates, placing the coleoptilar node and crown (from which adventitious roots develop) near the soil surface, whereas in festucoid grasses the coleoptilar node and crown remain at or near planting depth. Diagram and terminology based on Hyder (1974). Newman and Moser (1988). and Ries and Hofmann (1991).

grasses, only the coleoptile elongates and the crown node remains at the seeding depth; thus, the adventitious roots form deeper in the soil compared with panicoid grasses. In blue grama [Boufelouagrucilis (H.B.K.) Lag. ex Steud] grown in a semiarid environment, the maximum leaf area that could be supported by the seminal root system occurred 60-70 days after seedling emergence (Wilson et al., 1976).The cross-sectionalarea of the subcoleoptileinternode was about five times less than that of an adventitious root and could supply only 1 to 2 ml of water per day compared with 5-10 ml per day for an adventitious root. Without an adventitious root system, the seedlings died after 4 months. Newman and Moser (1988) determined that adventitious root development did not occur until the third leaf stage (about 15-24 days after emergence) for many species of the Andropogoneae tribe. Tischler et al. (1989) noted adventitiousroot developmentin kleingrass within 1 week under well-watered conditions in the growth chamber. The subcoleoptileinternode usually stops elongating when the coleoptile tip intercepts and transmits light to a putative phytochrome system in the crown (a photomorphological response; Tischler and Voigt, 1993). This occurs about 3-5 days

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Table I Subcoleoptile Internode Length in Festucoid and Panicoid Grasses Type and species

Subcoleoptile internode length (mm)

Festucoid Agropyron daysystachuma Agropyron desertorurn" Leymus angustusa Pascopyrum smirhiib Bromus inermis

0 0 0 0.8

3.7

Panicoid Andropogon scopariusa Boureloua curtipendulaU Boureloua curtipendula Panicum virgatumc Panicum colorarumC Andropogon gerardii'

12 11 24

7 8

3

"Data from Redmann and Qi (1992). Seeds were planted 15 mm deep and grown for 3 weeks in the greenhouse. bData from Ries and Hofmann (1991). Seeds were planted 25 mm deep and grown for 7 days in the growth chamber. 'Data from Tischler and Voigt (1993). Seeds were planted 5 m m deep and grown for 10 days at low light (1.5 pmol m-* SKI).

after germination (Tischler and Voigt, 1996). In some grasses, however, signal transduction to the subcoleoptile internode does not occur or is delayed, and the internode continues to elongate and elevates the crown node above the soil surface. This may occur during overcast days, in seedbeds with large amounts of residue, or in tall stubble. This response has been noted in sideoats grama [Bouteloua curtipendula (Michx.) Torr.], blue grama, kleingrass, switchgrass, and other species (Olmsted, 1941; Hyder et al., 1971; Tischler and Voigt, 1996; Redmann and Qi, 1992; Table I). The transition from seminal roots to adventitious roots is a crucial stage in seedling development of perennial grasses. Successful establishment of the adventitious root system requires moist soil for about 5-7 days (Wilson and Briske, 1979; Newman and Moser, 1988). If the soil surface is dry or the crown node is elevated above the soil surface by the subcoleoptile internode, the adventitious roots may fail to develop and the seedling will die. Tischler and Voigt (1993) hypothesized that reduced subcoleoptile internode elongation would enhance survival of some panicoid grasses by placing the crown node deeper in the soil, thus enabling adventitious root development in moist soil. Qi and Redmann (1 993) compared seedling survival of several C, and C, perennial grasses at different water stress levels (-0.5, -1.0, and - 1.5 MPa). The C, grasses had better seedling survival (average of 14% seedling mortality at

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- 1.5 MPa) than C, grasses (average of 63% mortality at - 1.5 MPa). They associated greater seedling survival in the C, grasses with a larger seed size and more vigorous seedlings and poor survival in the C, grasses with seedling morphology and reliance on seminal roots for water uptake. A shorter subcoleoptile internode may also improve water transport from seminal roots to shoots. Redmann and Qi (1992) measured the diameter of xylem vessels in seedlings of warm-season grasses that emerged from different planting depths. Vessel diameter was relatively constant, whereas the subcoleoptile internode was longer at deeper planting depths increasing the path length for water transport from the root to the shoot and reducing hydraulic conductivity. Hence, reducing subcoleoptile internode elongation would reduce path length and enhance water transport from root to shoot. Tischler and Voigt (1993) described a system for screening seedlings for crown node elevation and have selected kleingrass and switchgrass germ plasm for reduced and enhanced subcoleoptile internode elongation. Populations with greater subcoleoptile internode elongation and with little or no elongation have been developed through three cycles of divergent selection (Tischler and Voigt, 1995; Tischler et al., 1996).The value of this selection remains to be determined in the field. The literature on water stress and plant water relations is vast. Losch (1995) stated that more than 2400 reports on plant water relations appeared in the literature during 1992and 1993.The physiological mechanisms and photomorphologicalresponses involved in establishmentof warm-season perennial grasses, however, require a greater understanding. Given that establishment is the most critical phase of perennial forage crop production, research is needed to enable the development of robust, rapidly establishing cultivars and to provide insights into seedbed ecology to improve management.

III. DEFOLIATION STRESS Perennial forages are harvested (defoliated) once or more during the growing season. Removing the aboveground phytomass places a large stress on the stubble, roots, and rhizomes by depriving them partially or totally of C, whereas respiration continues. Under severe defoliation, the plant may enter into a negative C balance. Forage plants recover from this C loss through immediate (e.g., reduction in N, fixation and root growth) and long-term (e.g., rebuilding leaf area) responses.

A. WHOLE-PLANT RESPONSES The defoliation stress encountered by a forage plant depends on (a) intensity of defoliation; (b) the type of tissue removed, whether meristematic and physiologic

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age; (c) frequency of defoliation, whether in discrete well-spaced events or continuous removal; (d) timing of defoliation; and (e) whether stresses or competition have occurred before, during, or after the defoliation (Richards, 1993). Removal of young, photosynthetically active leaves affects the plant more, in terms of photosynthesis, than loss of older, shaded leaves of a lower photosynthetic capacity (Gold and Caldwell, 1989a,b, 1990). Root growth stops quickly after defoliation and fine roots may die (Luo et al., 1995; Jarvis and MacDuff, 1989). Respiration and nutrient uptake also decline quickly after defoliation. These responses, however, may be tempered by the age of the plant and availability of resources. Root growth is also affected by the frequency and severity of defoliation. Alcordo et al. (1991) clipped stargrass (Cynodon nlemfuensis Vanderyst var. nlemfuensis) at several plant and stubble height combinations and observed that root mass accumulation was reduced by up to 97% with severe defoliation compared with an unclipped control. Nitrogen fixation in legumes is greatly reduced or ceases quickly after defoliation (Hartwig et al., 1987; Denison et al., 1992; Kang and Brink, 1995). Hartwig and Nosberger (1994) hypothesized that continuation of N, fixation causes N compounds to accumulate in the nodules after defoliation and reduces the aboveground demand for N compounds (reduced N sink strength). These N compounds trigger an increase in resistance to 0, diffusion into the nodules resulting in reduced nodule respiration and reduced nitrogenase activity. What triggers the increase in 0, diffusion resistance is unknown and confirmation of the hypothesis awaits further evidence. These immediateresponses to defoliation are followed by the long-term process of recovering a positive C balance, metabolic adjustment of the remaining organs, and rebuilding the photosynthetic area (Richards, 1993).Metabolic adjustment includes an increase in photosynthetic rate of the remaining leaves. Compensatory photosynthesis (defined as increased photosynthesis in leaves of defoliated plants relative to leaves of a similar age on undefoliated plants) has been observed in the remaining leaves of defoliated plants (Nowak and Caldwell, 1984;Baysdorfer and Basham, 1985) and may be related to a delay or halt in the normal ontogenetic decline of photosynthesis in leaves (Nowak and Caldwell, 1984; Wardlaw, 1990) or exposure of shaded leaves to greater levels of light or other resources (N and water) becoming more available to the remaining leaves (Pearcy et al., 1987). Compensatory photosynthesis may be induced by increases in leaf N, carboxylase activity and amount, electron transport, or stomata1 conductance (Briske and Richards, 1995). Rebuilding the photosynthetic area depends on the plant growth form, location and activity of remaining meristems, and morphological plasticity of the plant (Chapman and Lemaire. 1993). In grasses, these characteristics include bunchgrass versus sod-forming (stoloniferous or rhizomatous species) plants, timing of tiller emergence and apical elevation (synchronous or asynchronous tillering), and

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proportion of reproductive versus vegetative culms produced. Growth form variations among species, including meristem location and activity, are important considerations for management of forages to minimize plant stress after defoliation (Briske and Richards, 1995). For example, stoloniferous and rhizomatous grasses such as bermudagrass (Cynodon dactylon L. Pers.) that maintain many active meristems at axillary buds on stem bases and at nodes of stolons orrhizomes (Dong and de Kroon, 1994) may be more tolerant of close, frequent defoliation than a bunchgrass such as switchgrass. Switchgrass elevates the growing point well above ground during early vegetative growth (Sanderson and Wolf, 1995), has a high proportion of reproductive to vegetative tillers, and is sensitive to defoliation (George and Oberman, 1989) because new growth must occur from crown buds or aerial axillary meristems (Brejda et al., 1994; Hafercamp and Copeland, 1984). If all tillers on a bunchgrass plant form and develop at once (synchronous tillering), then defoliation may remove all active meristems (Culvenor, 1993, 1994). Similarly, in legumes, there is a range of architectures that enable plants to avoid defoliation stress. Upright, crown formers such as alfalfa may be vulnerable to intensive or poorly timed defoliation, whereas stolon formers such as white clover (Trifoliurnrepens L.) avoid stress resulting from frequent and intensive defoliation because of the location and abundance of current and potentially active meristems (Forde et al., 1989). In tropical legumes, there may be even greater diversity in morphologies (Kretschmer, 1989). Defoliation management of legume seedlings may also help minimize stress and improve seedling establishment. Kang el al. (1995) recommended that white clover seedlings not be defoliated until at least four trifoliolate leaves have formed and that subsequent clipping be delayed to improve seedling survival.

B. REMOBILIZATION OF CARBON AND NITROGEN New C and N compounds are preferentially allocated to active meristems in the shoot after defoliation (Wardlaw, 1990; Baysdorfer and Basham, 1985). The active aboveground meristems are stronger sinks than the roots and enable the plant to recover quickly from defoliation stress. This imbalance in sink strength is maintained until the amount of leaf area is large enough to meet the demands of the active sinks. Habben and Volenec (1990) demonstrated that starch stored in alfalfa taproots was used for shoot growth and root respiration for 14 days following defoliation. After 14 days, the taproot became a sink for C and by 28 days after defoliation starch again accumulated to high levels. Breakdown and synthesis of starch grains were spatially separated in the taproots of alfalfa during regrowth. Starch grains near the vascular cambium were used before those near the center of the taproot. Habben and Volenec ( 1990)concluded that during the early stage of carbohydrate

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remobilization starch was degraded first in the taproot bark and in medullary ray cells near the bark phloem, then in ray cells near the center of the taproot. Boyce et al. (1992) showed that endoamylase activity in alfalfa taproots was positively related with starch degradation during shoot regrowth. Kallenbach et al. (1995) hypothesized that day length regulated carbohydrate metabolism during summer regrowth of sainfoin (Onobrychis viciifolia Scop.), which has poor regrowth and persistence during summer. Although day length altered plant form (plants were taller and had more blooms under long days), there was no effect on plant yield, total nonstructural carbohydrate in root, enzyme activity (a-and P-amylase, aglucosidase, and starch-debranchingenzymes) or metabolic heat rates of the whole plant. In many temperate grasses, fructan is the storage carbohydrate remobilized for regrowth (Pollock and Cairns, 1991). Prud’homme er al. (1992) demonstrated that perennial ryegrass remobilized carbohydrates and soluble proteins from the stubble and roots to newly formed leaves during the first 6 days of regrowth. During Days 6-29, carbohydrates and proteins were replenished to levels present before cutting. These changes in carbohydrate levels were associated with changes in enzyme activities. During early regrowth, activities of anabolic enzymes declined, whereas activities of hydrolyzing enzymes increased. During carbohydrate replenishment, this pattern was reversed. Golovko and Tabalenkova (1994) demonstrated active remobilization of stored carbohydrate from stubble of annual ryegrass (Lolium multijlorum Lam.) and reported that in defoliated plants reserve carbohydrate was used only during regrowth and was not used at all in nondefoliated plants. The importance of reserve carbohydrates in regrowth has been questioned (Volenec and Nelson, 1994). Volenec (1985) observed that the rate and extent of leaf area development and stem extension during regrowth of alfalfa did not vary directly with root carbohydrate reserve levels. Hall et al. (1988) reported that even though drought-stressed alfalfa had higher concentrations of storage carbohydrates in the roots compared with nonstressed plants, there was no increase in regrowth yield of stressed plants versus nonstressed plants. Boyce and Volenec (1992) used high- and low-starch lines of alfalfa to show that shoot regrowth was not related to starch levels. Richards and Caldwell (1985) noted that the number and activity of meristems remaining after defoliation was more important than the level of carbohydrate reserve in the crown during regrowth of crested wheatgrass and bluebunch wheatgrass [Pseudoroegeneriaspicara (Pursh) A. Love]. Busso et al. (1990) demonstrated that regrowth of drought-stressed wheatgrasses was positively correlated with carbohydrate pools; however, regrowth was enhanced only when there were active meristems available. New evidence indicates that N reserves may be as important as C reserves during regrowth. Ta et al. (1990) found that plant respiration consumed most of the C stores in roots of defoliated 8-week-old alfalfa and that one-fourth of the N re-

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serves in the roots were exported to support shoot regrowth. Lemaire et al. (1992) reported that up to 40 kg ha- * of N was remobilized from alfalfa taproots and exported to support aerial regrowth. Hendershot and Volenec (1993b) determined that specific pools of N in the alfalfa taproot were used for regrowth after cutting. Aspartate and asparagine were the most prevalent amino acids in taproots and along with buffer-soluble proteins decreased greatly in concentration after defoliation. These N compounds were then replenished during shoot regrowth. The amino-N compounds were postulated to serve as readily available forms of N, whereas the proteins may be a long-term storage form. Ourry et al. (1994) generated alfalfa plants with similar crown and root dry weights but that had either high starch-low N concentrations or low starch-high N concentrations and found that the highest plant yields during regrowth occurred with high tissue N concentrations despite low starch levels. Nitrogen uptake and remobilization and shoot dry weight during regrowth were more closely related to the amount of N remaining in the source organs before clipping than to C reserves. Nitrogen compounds have also been shown to be very important in regrowth of grasses (Thornton et al., 1994; Jarvis and MacDuff, 1989; MacDuff et al., 1989). These recent advances in understanding the role of carbohydrates and plant morphology have shifted the emphasis of defoliation management based solely on optimizing or maximizing organic reserves to a recognition of the concept of morphological plasticity and the importance of maintaining active meristems that may capitalize on stored C and N compounds. Indeed, Kemp and Culvenor (1994) stated that defoliation tolerance is best enhanced by maintaining a high density of plants and tillers that have low growing points so that regrowth is rapid after defoliation or in unfavorable environmental conditions.

Tv. LOWLIGHT Leaf area development, growth rate, and yield of forages vary directly with the amount of sunlight intercepted by the canopy (Gifford et al., 1984; Lawlor, 1995). Light regulates plant growth and development via informational signals detected by phytochromes (Quail et al., 1995; Smith, 1995; Hock, 1995). The amount of sunlight intercepted by a forage plant may be reduced by the cropping system [e.g., woodland pastures (Mordelet, 1993), agroforestry systems (Pearson and Ison, 1987), or grass-legume mixtures (Beuselinck et al., 1994)],canopy structure [leaf erectness and tiller density (Rhodes, 1973)], litter accumulation (Tilman and Wedin, 1991),and climate. The responses to low-light stress generally include an increase in plant leaf area to maximize light interception and changes in physiological processes to enhance the efficiency of C utilization.

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A. SHADERESPONSES In forage grasses, responses to reduced light (shade) include larger leaves with fewer mesophyll cells and stomata per unit leaf area, more intercellular air space, higher leaf area ratios (LAR), and reduced specific leaf weight (SLW) (Allard et al., 1991a; Kephart et al., 1992). The increased leaf area is the result of longer leaves because of an increased duration of leaf elongation (Allard et al., 1991a). Leaves that develop in shade also have a reduced photosynthetic rate capacity (Evans, 1993a; Labhart et al., 1983). Kephart er al. (1992) observed that C, and C, grasses responded similarly to shade in terms of morphological adaptations (e.g., reduced SLW and increased LAR); however, C exchange rate (CER, per unit leaf area) and crop growth rate of C, grasses were reduced more by shade than C, grasses. Allard et al. (1991b) also showed a reduced CER (per unit leaf area) for shaded tall fescue along with a reduced stomata1 conductance and suggested that leaves adapted to shade by changes in both anatomical and physiological characteristics. They also determined the response of CER to light and CO, and found that the initial slope of the response curve did not differ between shade treatments, but maximal responses were less for shade-grown leaves than for leaves grown in full sun. This suggested that the efficiencies of photosynthetic reactions at low levels of input were not affected by light level during leaf development, but that the capacity of the processes was affected. Leaves in low-light environments undergo photosynthetic acclimation with a reduced photosynthetic capacity per unit of chlorophyll (Evans, 1993a,b). Leaves acclimated to low-light environments redistribute N to thylakoid membranes to maintain a constant ratio of photosynthetic capacity to total leaf N concentration, which facilitates maximum light absorption and optimizes photosynthesis in a range of light environments (Evans, 1989). Reduced light interception also influences the kinetics of leaf growth and assimilatepartitioning.Schnyder and Nelson (1989) noted that leaf blades of tall fescue grown in low versus high (60 vs 300 pmol m-’ s-l photosynthetic photon flux density) continuous light had 33% greater leaf elongation rate, a longer elongation zone, and a decreased rate of deposition of water-soluble carbohydrate. Fructan accounted for 64%of water-soluble carbohydrate deposition (25% of dry matter import into the elongation zone of the leaf blade) indicating that the elongation zone was a strong sink even at low irradiance. Sanderson and Nelson (1995) showed that reducing light in a stepwise manner resulted in longer leaves with a larger area and lower SLW, a greater leaf elongation rate, and reduced dry matter deposition in high yield per tiller and low yield per tiller genotypes of tall fescue (Table 11; Fig. 2). Increasing light at graded levels reversed these responses. Leaf elongation rate was severely reduced in darkness and dry matter deposition was stopped. The greater leaf elongation rate at low light was due to a longer zone of cell elongation in the leaf blade meristem. The data indicated that the longitudinal growth rates and spatial distribution of growth in leaf blades of tall fescue were

185

RESPONSES OF PERENNIAL FORAGES TO STRESS Table I1 Morphological and Kinetic Responses of Tall Fescue Leaf Blades to Decreasing or Increasing Light in the Growth Chamber ~

Irradiance (pmol PPFD m-* Decreasing light 550 190 50 0 Increasing light 50 130 400

Leaf area s-I)

~

(mm)

Specific leaf weight (mg m-’)

Leaf elongation rate (mm h-I)

I74 180 195 218

6. I 6.4 6.3 6.4

57.3 42.8 26.3 26.3

0.62 0.60 0.88 0.39

294 321 227

5.3 6.0 5.8

20.1 35.9 48.0

0.86 0.88 0.68

Leaf width

(nun2)

Leaf length (mm)

1089 1167 1252 I365 I573 1998 1330

“Leaf elongation rate data from Sanderson and Nelson (1995).Leaf dimension data from M. Sanderson and C. Nelson (unpublished data).

nearly quantitatively reversible with increases or decreases in light, similar to the photosynthetic and specific leaf area responses of annual ryegrass to alternate low and high light (Prioul et al., 1980a,b). Legumes growing in a mixture with grasses may respond to the plant canopy by placing leaves in a favorable light environment. Woledge et al. (1992) found that short and tall (small- and large-leaved, respectively) cultivars of white clover displayed leaves in the upper canopy of tall- and short-stature grasses to maximize light interception. Tolerance of some warm-season grass seedlings to shaded or high-light habitats may be related to the type of C, photosynthetic pathway used by the plant. Veenendaal etal. (1993) noted that seedlings of grasses with the phosphoenolpyruvate carboxykinase (PCK) pathway emerged and were more prevalent in shaded areas (i.e., under tree canopies), whereas in full sun, seedlings with the NAD-dependent malic enzyme pathway were more prevalent. They noted that PCK-type grasses occurred most often in moist habitats and speculated that the shade of the tree canopy reduced heat and drought stress resulting from direct sunlight. Forage quality of both C, and C, grasses was increased by shade treatments with a small decrease in fiber concentration,a small increase in digestibility,and a large increase in N concentration (Kephart and Buxton, 1993). The authors speculated that the reduced fiber concentration in shaded grasses resulted from dilution by accumulated N and perhaps because of a limited supply of photosynthate. Samarakoon er al. (1990) also reported small increases in digestibility of tropical grasses with increasing shade. Ellen and Van Oene (1989), however, showed a small

M. A. SANDERSON ET AL.

186 0 7

I

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.

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,

.

I

.

I

.

I

.

Decreasing light

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4-

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600

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PHOTOSYNTHETIC PHOTON FLUX DENSITY (umol Figure 2 Rates of dry matter deposition within the growth zone (basal 30 mm) of tall fescue leaf blades in response to graded levels of light. Data are average rates for the entire growth zone. HYT, high yield per tiller genotype: LYT, low yield per tiller genotype. Source: Sanderson and Nelson (1995).

increase in cell wall constituents and a large decrease in water-soluble carbohydrates of barley (Hordeum distichurn L.) in response to low light. Buxton and Fales (1994) noted contradicting reports of shade on forage quality and concluded that most effects of shade on forage quality were small.

B. LIGHTQUALITY Changes in light quality result in photomorphogenic changes in forages. The plant canopy not only intercepts photosynthetically active radiation (PAR) (400-700 nm wavelengths) and reduces exponentially the quantity of light that

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reaches the base of the canopy (Evans, 1993a) but also selectively absorbs red wavelengths (approximately 660 nm) more than far-red wavelengths (approximately 730 nm) such that the red:far-red ratio of light decreases toward the base of the canopy (Holmes and Smith, 1977). Common responses to altered light quality include elongation of petioles, leaves, and stems and reduced tillering in grasses and branching in legumes (BallerC et al., 1995). Robin et al. (1994) isolated the apical bud on the main stolon of white clover and exposed it to light with red:farred ratios of 2.1, 1.6, or 0.25 without affecting the amount of PAR received by the apical bud or the remainder of the plant. Light with a reduced red:far-red ratio delayed branch appearance by 0.5 phyllochrons, which subsequently resulted in a reduced number of primary branches on the plant. Frank and Hofmann (1994) demonstrated that defoliation management of perennial cool-season grasses affects the red:far-red light ratio in the canopy and stem density. Management that resulted in increased standing forage (e.g., exclusion of grazing) reduced the red:far-red ratio at the base of the plant canopy and reduced the number of stems per unit area. Removal of forage by grazing or haying increased the red:far-red ratio at the canopy base and increased stem density in the stand. As discussed under Section II,E (Seedling Establishment in Grasses), light quantity and quality affect the physiology and morphology of grass seedlings during establishment. Seedlings of kleingrass and switchgrass selected for increased subcoleoptile internode elongation required a much greater light level to inhibit crown node elevation than did seedlings selected for reduced subcoleoptile internode elongation or parent germ plasm (Elbersen et al., 1995). Preliminary data suggest that selection for reduced subcoleoptile internode elongation has increased the sensitivity of the phytochrome A/B system in the seedlings.

V NUTRIENTSTRESS Nutrient stresses in forage plants can result from either deficiency or excess. A common response to nutrient deficiency stress is a decline in growth rate and a decline in the rate at which all resources are acquired (Chapin, 1991). Other responses may include an increased ability to absorb nutrients, perhaps through altered activity or number of ion-specific carriers in root cells, increased partitioning of dry matter to roots relative to shoots, remobilization of tissue nutrients, reduced photosynthesis, and hormonal responses (Chapin, 1991).

A. NITROGEN Nitrogen is the largest fertilizer input in forage systems (Muchovej and Rechcigl, 1994). Nitrogen deficiency results in reduced photosynthesis (Woledge and

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Pearse, 1985; Bowman, 1991), reduced plant growth, and chlorosis (Fernandes and Rossiello, 1995). Nitrogen deficiency reduces cell size, volume, and protein content and reduces the number and size of chloroplasts (Nhtr, 1992). In perennial grasses, reduced leaf elongation associated with N limitation (Pilbeam, 1992; Volenec and Nelson, 1983)results from reduced cell division and not from reduced cell elongation (MacAdam et al., 1989). Gastal and Nelson (1994) examined N deposition in the growth zone of elongating tall fescue leaves and found that the rate of N deposition was greater in plants grown with high N than in those transferred to no N. Most of the N was found in the cell division zone and the least amount of N was found in the cell elongation zone. Gastal and Nelson (1994) speculated that synthesis of RUBISCO and other chloroplast proteins that occurs in the cell maturation zone uses recycled N from proteins manufactured during cell division. BClanger et al. (1992) showed that C partitioning to roots relative to shoots increased in N-limited tall fescue. Gastal and BClanger (1993) reported that N fertilization improved dry matter yield principally by speeding up leaf area development and increasing light interception rather than by increasing canopy photosynthesis. Nitrogen may be remobilized from other plant organs during periods of N stress (Engels and Marschner, 1995), and forage plants often rely on internal stores of N during regrowth (Ourry et al., 1994; Hendershot and Volenec, 1993b; Thornton et al., 1994). In response to a reduced N supply, perennial grasses increase fine roots at microsites of high N availability and increase root hair length and density (Boot and Mensink, 1990; Crick and Grime, 1987). The primary effect on forage quality is reduced crude protein resulting from N deficiency. Other effects of N on forage quality result from changes in plant morphology (leaf to stem ratio) or maturity. There are no direct effects of N on structural carbohydrate composition. Excess N in forage may accumulate as NO, and affect animal health. Also, protein quality may be affected by changes in soluble protein, nonprotein N, and protein degradability by the ruminant animal. Hanson et al. (1983) reported that N fertilization increased in v i m dry matter digestibility of smooth bromegrass due solely to an increased crude protein concentration and not to an increased digestibility of nonprotein components (i.e., cell walls). Sanderson and Wedin (1989) reported no effect of N fertilizer on in vitro dry matter digestibility or detergent fiber concentrations of smooth bromegrass. Nitrogen fertilization of grasses increased the concentration of N in cell walls (Sanderson and Wedin, 1989; Wilman and Wright, 1978). The increased crude protein in Nfertilized herbage is usually accompanied by a decrease in water-soluble carbohydrates (Wilman and Wright, 1983).

B. PHOSPHORUS Phosphorus functions centrally in plant metabolism as (a) a structural component of nucleic acids and phospholipids, (b) as an enzyme regulator through phos-

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phorylation and dephosphorylation, (c) as a regulator and substrate in photosynthesis, and (d) as a modulator of gene transcription (Mimura, 1995). Several plant responses may improve uptake and utilization of P including altered root morphology (larger, finer, and more highly branched roots, and increased root hair density and length) to enable the plant to more fully explore and exploit soil P, altered transport of P across cell membranes, improved cellular metabolism and internal utilization of P, and alterations in the rhizosphere to increase P uptake (Gourley et al., 1993).In white clover, Gourley et al. (1993) found that larger root systems enabled greater shoot growth and accumulation of P, but they did not find evidence that P was absorbed or utilized more efficiently. In contrast, Dunlop and Gardiner (1993) demonstrated an enhanced rate of PO, uptake by PO,-deficient plants compared with phosphate-adequatewhite clover plants. They attributed the increased uptake to a difference in the stoichiometry of a H+/H,PO,- symport in the plants. Phosphorus is remobilized from older to younger leaves during deficiency, and the cell cytoplasm maintains a constant pool of inorganic P by using the vacuole as a reservoir of inorganic P (Mimura, 1995). Phosphorus deficiency reduces root development and alters root morphology (Christie, 1975),and roots may concentrate in the upper soil layer where P may be more available (Sanderson and Jones, 1992). Some forages, particularly C, grasses, develop symbiotic relationships with vesicular arbuscular mycorrhizae (VAM) that confer several advantages to plant survival (Hetrick et al., 1991). Mycorrhizal hyphae enhance P acquisition by increasing the volume of soil that can be exploited (Hetrick et al., 1990).Brejda et al. (1993) showed that mycorrhizal warmseason grasses had more and heavier tillers, increased root mass, greater tissue P concentrations and P recovery, and a lower root:shoot ratio than nonmycorrhizal controls. Root architecture influences the degree of colonization by VAM with greater colonization occurring on large-diameter roots than on small-diameter roots (Reinhardt and Miller, 1990; Hetrick et al., 1991). Association with VAM may enhance P uptake in calcareous soils in which P availability may be low. Azc6n and Barea (1 992) grew alfalfa in three calcareous soils and reported that VAM-inoculated alfalfa yielded as much as P-supplemented alfalfa and had similar P concentrations but lower Ca concentrations. They speculated that the P uptake mechanism of VAM suppressed excessive Ca uptake. Mycorrhizae infection may also enable nutrient acquisition strategies such as differential timing of P uptake during cool weather for cool-season grasses versus warm-season grasses (Bentivinga and Hetrick, 1992) or access to different pools of organic and inorganic P by mycorrhizal and nonmycorrhizal plants (Jayachandran et al., 1992). Mycorrhizal infection also influences competition among plants (Hetrick et al., 1994). In alfalfa, deficiencies of P result in reduced forage yield and may affect stand longevity. Walworth et al. (1986) speculated that plant death due to low P at establishment limited yield response to P applied 3 years later. Nelson et al. (1992) reported that P deficiency reduced alfalfa plant densities 3-5 years after establish-

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ment. Sanderson and Jones (1993) also demonstrated severely reduced yields of alfalfa under P stress; however, plant and shoot density was not affected after 3 years. They concluded that low yields at low P levels were a function of reduced yield per shoot. Petit et al. (1992) noted an increase in acid detergent fiber, acid detergent lignin, and stem length and a reduced leaf-to-stem ratio with increasing P fertilization of alfalfa grown in a cold (15°C day/9"C night) greenhouse environment but not in a warm (25"C/19"C) environment. Sanderson (1993) reported that alfalfa grown in a low P soil had less neutral detergent fiber and was more digestible than alfalfa grown with sufficient P. The differences in digestibility and fiber in both studies were likely due to plant maturity and/or plant morphology differences and not to direct effects on cell walls.

C. ACIDSOILSAND AL TOXICITY Large areas of the world have acid subsoils in which the low pH renders A1 more readily available (von Uexkiill and Mutert, 1994). Delhaize and Ryan (1995), Kochian (1995),and Foy (1992) have reviewed the physiology, genetics, and management of several crop species, including forages, on acid subsoils with aluminum toxicity potential. Aluminum injury in plants results in altered root growth, mainly affecting the root apex and restricts root proliferation and, hence, soil exploitation for nutrients and water. Mechanisms of A1 tolerance include Al exclusion through modification of the rhizosphere and internal detoxification of A1 via binding to proteins or complexing with other organic molecules.

D. CALCAREOUS SOILSAND FE-DEFICIENCY CHLOROSIS Calcareous soils can induce Fe-deficiency chlorosis in several forages including many forage legumes (Ocumpaugh er al., 1991;Gildersleeveand Ocumpaugh, 1988). Chlorotic plants have reduced rates of photosynthesis caused by reduced chlorophyll content in the leaves. Wei er al. (1994, 1995) described several characteristics of resistance to Fe-deficiency chlorosis in subterranean clover (Trifoliurn subterraneum L.) including a greater root:shoot ratio, lower concentrations of tissue P that may interact with tissue Fe, mechanisms for immobilizing Fe in the soil, reduced Fe requirements for plant metabolism, and a greater efficiency of Fe use. Plants respond to Fe deficiency by two mechanisms: altering the rhizosphere by releasing H+ to increase reduction of Fe3+(strategy I plants), and synthesis and release of a phytosiderophore to chelate Fe3+,along with an associated transport mechanism in the membrane (strategy I1 plants) (Loeppert et al., 1994).Nongrass plants primarily use strategy I, whereas grasses use strategy I1 to cope with Fe-de-

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ficiency stress. Management of Fe-deficiency stress is limited to the use of resistant species or cultivars.

VI. LOW-TEMPERATURE STRESS Changes in temperature cause a pronounced effect on plant growth and productivity (Ong and Baker, 1985). Most temperate species are exposed to temperatures below the optimum for growth during winter months, and freezing or low temperatures limit persistence of tropical plants in northern latitudes or high altitudes. Chen (1994) classified low-temperature stress into chilling (10-15°C and below) and freezing (below O”C, when ice forms in tissues) stresses.

A. CHILLINGSTRESS Initially, low temperatures stop cell division and elongation (Pollock et al., 1984).The timing of the response to low temperaturesin extending cells compared with the cell cycle (Frances and Barlow, 1988) suggests that the extending cells themselves respond directly to temperature changes (Pollock and Eagles, 1988). Carbon fixation and translocation do not limit cell growth at chilling temperatures. Leaf growth in dallisgrass (Paspalum dilarutum Poir.) was more sensitive to low temperature than was photosynthesis or starch accumulation (Forde er al., 1975). Chilling-resistant plants did not reduce translocation of C at low temperatures (Berry and Raison, 1982). Simultaneous measurements of extending cell turgor, leaf temperature, and leaf extension rate suggested that unacclimated leaves of Lolium temulentum did not change in turgor pressure between 20 and 2”C, which would limit cell expansion (Pollock and Eagles, 1988). Pollock and Eagles (1988) interpreted these results to imply that the sites at which low-temperature signals were perceived and transduced were linked to and acted directly on the cell wall in affecting cell growth.

B. ACCLIMATION TO Low TEMPERATURE AND DEVELOPMENT OF FREEZING TOLERANCE The secondary effects of low temperature occur during extended exposure and increase tolerance to suboptimum temperatures. Plants that grow in suboptimum temperatures and acquire increased tolerance to freezing have “hardened.” The hardening process takes several weeks to reach a maximum in many perennial forages. Different levels of freeze tolerance may develop depending on the genotype

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and hardening conditions. Dehardening may take less than 1 week and occurs without subsequent lowering of temperature or on reintroduction to warm temperatures. Acclimation results from active metabolic processes associated with changes in gene expression (Hughes and Pearce, 1988; Howarth, 1990). Changes in cellular lipids occur within hours after exposure to low temperatures (Dickens and Thompson, 1981; Lynch and Thompson, 1984) and may be very important to subsequent freeze tolerance (Webb et al., 1994; Uemura and Steponkus, 1994). Both qualitative and quantitative changes in protein populations (Uemura and Yoshida, 1984) and isozyrnic shifts (Roberts, 1974) occur during growth at low temperatures. Many of these changes function to adjust metabolism at low temperatures. Physiological adjustments to low temperature may alter plant growth. For example, growth traits of “fall-dormant” alfalfa cultivars conditioned by cool short days include a prostrate growth habit, shorter internodes, and slower regrowth in fall. These traits also correlate with increased winter hardiness (Barnes et al., 1979). Similarly, fall yield has been negatively correlated with winter survival; however, limited fall growth of fall-dormant alfalfa is correlated with a reduced yield in the spring and summer (Stout and Hall, 1989). Reciprocal grafts of falldormant and nondormant alfalfa cultivars grown in warm long days or cool short days indicated that expression of prostrate growth and shorter internodes was strongly conditioned by the plant shoot mass with some mediation by the root, whereas slower regrowth was conditioned by both shoots and roots (Heichel and Henjum, 1990). Changes in growth in response to low temperature similar to fall-dormant alfalfa have been reported in timothy (Phleum pratense L.) (Klebedsadel and Helm, 1986), white clover (Woledge and Suarez, 1983; Ollerenshaw and Baker, 1981), and berseem clover (Trifolium alexundrinium L.) (Barnes and Wilson, 1986). Similar changes were noted when abscisic acid (ABA) was applied to berseem clover at higher temperatures (Barnes and Wilson, 1986) and may mediate the changes in other species. Nitrogen fertilization during growth at low temperatures increased leaf, stolon, and petiole dry weight, leaf area, and photosynthetic rate of white clover. The number of leaves and stolons and the respiration rate at low temperature were unaffected by added N (Woledge and Suarez, 1983). In white clover, cold hardiness increases with time spent at temperatures less than 0°C under short days (Collins and Rhodes, 1995) and frost hardiness at 0.5 or 6°C increased with increased N supply (Sandli et al., 1993). Cool-season forages in colder parts of the upper south in the United States must develop some level of winter dormancy so that root and crown reserves are not depleted by top growth during brief warm spells followed by killing freezes (Ball et al., 1991) Although fall growth can indicate relative winter hardiness among divergent groups of alfalfa, no correlation with freezing tolerance was found among 15 alfalfa populations with similar fall growth habits (Bowley and McKersie, 1990).

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The type and placement of perennating organs of perennial forages affect lowtemperature survival. Perennating structures in forages include crowns and rhizomes in rhizomatous species. Both structures often have a similar freeze tolerance (Schwarz and Reaney, 1989). Subcrown internode length of 7-day-old perennial ryegrass seedlings was negatively correlated with winter survival of the same cultivars in the field (Wood and Cohen, 1983). Reduced internode length enhanced winter hardiness because the meristem was placed deeper in the soil. Rhizomatous grasses with subterranean perennating structures survived severe winters in Alaska better than timothy, which required an insulating snow cover for maximum winter survival because the crown was exposed (Klebesadel and Helm, 1986). Differences in rhizome depth among ecotypes also acount for differences in vegetative overwintering in Johnsongrass [Sorghum halepense (L.) Pers.] (Warwick et al., 1986). Water content of plants may change during growth at low temperature and affect the dessication aspects of freeze tolerance. Freeze-tolerant cultivars of timothy in Alaska had less moisture in the crown compared with nontolerant cultivars (Klebesadel and Helm, 1986). In switchgrass, however, crown moisture was not correlated with freeze tolerance (Hope and McElroy, 1990), and Bowley and McKersie (1990) found no correlation between crown moisture and freeze tolerance in alfalfa plants of the same chronological age. Genotypes vary in the time and temperature required for low-temperature hardening. Gay and Eagles (1991) modeled this process and calculated hardening kinetics. The model indicated that the perennial ryegrass cultivar Grasslands Ruanui was only 34% hardened at 14 days at 2"C, whereas the cultivar S23 was 78% hardened at 14 days, though both cultivars had similar maximal levels of freezing tolerance. Rates of deacclimation were also cultivar specific. Low-temperature hardening also depends on genotype and age. Limin and Fowler (1987) classified seedlings of fall-seeded forage grasses in Canada into three categories of risk for winter-kill. One group had less freeze tolerance than winter wheat (Triticum aestivum L.) and needed the insulation of deep snow cover for winter survival. A second group attained the same freeze tolerance as winter wheat and a minimal snow cover was needed for survival. The last group attained the greatest degree of freeze tolerance, similar to that of winter rye (Secafe cereafe L.). When seeded in the spring and well established by fall, all cultivars attained at least the freeze-tolerance level of winter wheat. Freeze tolerance of 1to 3-week-old alfalfa seedlings increased during 4 weeks of growth at 1°C and older seedlings attained a greater degree of tolerance (Cloutier et af., 1990). The amount of messenger RNA (mRNA) induced by stress was correlated with the age of alfalfa plants (Luo et af., 1992) and may account for the age dependence observed in other studies. Warm-season C, forage grasses increased in freeze tolerance with exposure to low nonfreezing temperatures and dehardened on return to warm temperatures

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(Anderson et al., 1988; D. W. Stair, unpublished data) similar to temperate grasses though usually not to the same extent. Some C, grasses in northern latitudes, however, increased their level of freezing tolerance equal to that of temperate grasses. Northern ecotypes of switchgrass have been shown to increase their freeze tolerance and survive temperatures of - 19 to -22"C, similar to timothy and alfalfa in the same location (Hope and McElroy, 1990). Four C, grasses [Distichlis stricta (Torr.) Rydb.; Sparfina gracilis Trin.; Schizachyrium scoparium (Michx.) Nash; and blue grama] reached the same degree of freeze tolerance (-27°C). The more northern species achieved these levels faster when grown at similar temperatures (Schwarz and Reaney, 1989). Freeze tolerance in alfalfa increased when exposed to 2°C for 2 weeks; however, after 2 weeks the freeze tolerance declined somewhat. Exposing plants to -2°C after the first 2 weeks at 2°C allowed acclimation to continue (Monroy et al., 1993a; Castonguay et al., 1993, 1995). ABA is associated with many stress responses (Luo et al., 1993).Application of ABA to alfalfa (Mohapatra et al., 1988) and berseem clover (Barnes and Wilson, 1986) plants increased freezing tolerance at nonhardening temperatures; however, greater freezing tolerance was acquired with low temperatures in alfalfa. Giberellic acid (GA) counteracts many effects of ABA and inhibited ABA-induced freezing tolerance in berseem clover (Barnes and Wilson, 1986). The changes in freezing tolerance may be regulated by cellular Ca movement and Ca-dependent phosphorylation of proteins. By blocking Ca2+ channels, calmodulin activity, and Ca2+-dependent protein kinase activity, Monroy et al. ( 1993b) inhibited cellular freezing tolerance acclimation. Freezing damage to cellular components results from a change in chemical potential of water during freezing. Supercooling suspension cultures of smooth bromegrass cells to -5°C produced a profile of electrolyte leakage similar to that of nonstressed samples and their viability was similar. Samples frozen at -5"C, however, showed acute electrolyte leakage characteristic of samples frozen at all temperatures, suggesting that temperature alone has little effect on cellular damage (Zhang and Willison, 1989). Winter-hardy cultivars of alfalfa progressively increased in LT,, (the lethal temperature for 50% of the population) as measured solutes increased in size from ions to macromolecules, suggesting progressive damage to the plasma membrane as the freezing temperature decreased. Nonhardy cultivars, however, showed an acute damage to the plasma membrane as evidenced by the concurrent leakage of solutes of all sizes at the same freezing temperature (Sulc et al., 1991). Cellular adjustments to growth at low temperature improve physiological functions at the prevailing temperature. Alfalfa roots maintain higher respiration, nodulation ability, and acetylene reduction levels, and activity levels of some enzymes increase more in winter-hardy cultivars of alfalfa during low-temperature growth than in less hardy cultivars (Duke and Doehlert, 1981). After a cold-induced period of growth cessation in alfalfa, nitrogenase relative efficiency was restored and

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nodules grew, and the temperature optimum for photosynthesis broadened in winter-hardy types (MacDowall et d., 1988). Zhang et al. (1992) observed that electrical resistance of the cytoplasm and the vacuole increased in birdsfoot trefoil (Lotus corniculatus L.) during cold acclimation.The increase in resistance was attributed to increased concentrations of sugars, which increased viscosity. At the same time, the capacitance of the plasmalemma and the vacuolar membrane decreased during cold acclimation. This was attributed to a decrease in cell size or relative increase in xylem. Enzyme stability at low temperature contributes to low-temperature survival. Krall and Edwards (1993) reported that phosphoenol pyruvate carboxylases from guinea grass lost up to 50% of activity after 60 min at O"C, whereas the enzyme from Panicum miliaceum L. maintained its activity at 0°C. This difference in stability at 0°C may result from different hydrophobic bonding in the active tetramer (Krall and Edwards, 1993). In the C, grass, Echinochloa crus-galli, several enzymes in the C fixation pathway of plants from warm environments were more affected by low temperature than the same enzymes in plants from cold environments. The most cold-labile enzyme was NADP+-malate dehydrogenase (Potvin et al., 1986). Zhang and Willison (1989) found that a freezehhaw cycle caused acute leakage immediately after thawing with subsequent chronic leakage. The only difference between treatments was the severity of the acute leakage after thawing. The length of time spent at a specific freezing temperature had no effect on electrolyte leakage. The authors suggested that the acute leakage is associated with membrane rupture and the chronic leakage is associated with altered permeability of the membrane after resealing. Activated oxygen radicals may cause damage in freezing stress and are alleviated by superoxide dismutase activity. Four alfalfa plants transformed with SOD attached to a constitutive promoter had increased regrowth after freezing stress (McKersie et al., 1993). Regrowth after freezing stress was faster for F, progeny from one of the plants that had a single functional SOD copy in the chloroplast. This suggests a protective role for SOD in freezing stress.

C. LOW-TEMPERATURE STRESSAND ORGANIC RESERVES The most commonly studied metabolites of perennial forages under cold stress are nonstructural carbohydrates.Storage carbohydrates may provide food reserves and C skeletons for conversion to sugars by the overwintering plant. An alfalfa genotype that accumulated large amounts of starch had better winter survival than a low-starch genotype. Starch decreased in both genotypes during the late acclimation period, and concentrationsof total sugars increased concomitantly (Boyce and Volenec, 1992).The decrease in starch in alfalfa crowns was correlated with

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an increase in freeze tolerance (Castonguay et al., 1995). A greater content of carbohydrate in stolons of white clover improves overwintering ability (Collins and Rhodes, 1995). Low nonfreezing temperatures increased the amount of fructans in leaves and increased the partitioning of fructans to roots of tall fescue (Prud’homme et al., 1993). Similar increases in fructan concentrations occurred in roots of Poa pratensis (Solhaug, 1991) and Agropyron and Agrosris alba (Chatterton et al., 1987).The most freeze-tolerant cultivars of timothy also had the highest levels of nonstructural carbohydrates as measured by etiolated growth of the overwintering tissue in warm temperatures (Klebesadel and Helm, 1986). Temperatures lower than 10°C may inhibit starch mobilization in leaves of tropical grasses (Shatters and West, 1995). The accumulation and translocation of soluble sugars changes in response to low temperature.A non-winter-hardy cultivar of alfalfa accumulated fructose, glucose, and maltose in stems and leaves during cold hardening, whereas a winterhardy cultivar had reduced concentrations of soluble sugars in stems and leaves (Green, 1983) and soluble sugars increased in the crown during cold hardening (Castonguay et al., 1995; Duke and Doehlert, 1981). A winter-hardy high-starch genotype of alfalfa also had higher levels of total sugars during the winter than low-starch genotypes (Boyce and Volenec, 1992).The accumulation of simple and complex nonstructural carbohydrates may be related more to decreased utilization than to photosynthetic production (Farrar, 1988). Specific sugars may protect against damage due to dessication at freezing temperatures. Differences in freeze tolerance among alfalfa cultivars were related to levels of raffinose and stachyose (Castonguay et al., 1995). Bruni and Leopold (1991) suggested that disaccharides protect membranes and proteins from desiccation by forming a glassy state that slows molecular motion and prevents damaging interactions. Sucrose may enable plants to survive desiccation by stabilizing membranes and proteins (Hoekstra e? al., 1989). Sucrose concentrations in alfalfa, however, were not correlated with maximum freezing tolerance, and glucose concentrations decreased during low-temperature hardening (Castonguay et al., 1995). Glucose may be negatively correlated with desiccation tolerance because it may participate in the Maillard reaction, which can deactivate protein and change DNA (Koster and Leopold, 1988). Total N, soluble amino-N, and buffer-soluble protein increased in alfalfa taproots during the autumn and subsequently decreased during spring regrowth. Non-winter-hardy types of alfalfa accumulated less soluble protein than winter-hardy types (Hendershot and Volenec, 1993a). Other changes may act directly in gene expression. In cold-hardy alfalfa cultivars, putrescine concentrations increased during cold hardening at 2°C and levels of spermidine remained constant, whereas concentrations of both compounds decreased in plants of similar age grown at 22°C. Spermine concentrations decreased in both temperature treatments. This trend was similar to that found in wheat under the same treatments (Nadeau et al., 1987).

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D. TISSUE CULTLTRE AND GENEEXPRESSION IN LOW-TEMPERATURE STRESS Tissue culture has been used in many studies to identify cellular mechanisms and molecular aspects of tolerance to low-temperature stress and to identify stresstolerant germ plasm. Freeze tolerance was induced by ABA in suspension cultures of smooth bromegrass (Reaney and Gusta, 1987), birdsfoot trefoil callus (Keith and McKersie, 1986), and alfalfa cell cultures (Orr et al., 1985), though kinetin had to be excluded for birds-foot trefoil and alfalfa. The degree of tolerance induced in smooth bromegrass cultures was similar to that attained by whole plants at low temperature and decreased the time needed for hardening to 7 days (Tanino et al.. 1990). Reaney et al. (1989) found that the application of GA 4, GA 7, and GA 9 blocked the ABA-induced freeze tolerance, whereas GA 3 was not effective, and that kinetin hastened the dehardening process in smooth bromegrass cell cultures. Alfalfa cell cultures treated with ABA also had slower dehardening rates than untreated cell cultures (Reaney and Gusta, 1987). Both cell expansion and division decrease at low temperatures; however, metabolic activity continues. Robertson et al. (1987) found that protein synthesis continued in bromegrass suspension cultures despite the cessation of net growth in cells at 3°C. Most major proteins synthesized in cells grown at 23°C were also detected in cells grown at 3°C. New proteins were also synthesized in cells grown at 3°C or treated with ABA, whereas synthesis of other proteins was inhibited (Robertson et al., 1988). The pattern of protein synthesis in alfalfa cell cultures was also altered by both low temperature and ABA in smooth bromegrass cell suspensions (Robertson er al., 1988) and alfalfa cell cultures (Mohapatra et al., 1988). Some overlap in the change in protein expression occurred between ABAand low temperatures, whereas some changes were specific to either treatment. One protein was regulated by ABA or low temperature and showed greater response in alfalfa cultivars that are more freezing tolerant (Mohapatra et al., 1988). In smooth bromegrass, one of the low-temperature-induced proteins was located in a membrane, whereas another was soluble in water (Robertson et al., 1988). The changes in protein and RNA populations due to low temperature were dependent on alfalfa cultivar. More rapid rates of 35Sincorporation and increased levels of protein and RNA were found in Saranac than in Anik as well as faster acclimation and deacclimation; however, Anik was more freezing tolerant at maximum acclimation (Mohapatra et al., 1987). Gatschet et al. (1994) and Anderson and Taliaferro (1995) have demonstrated freezing tolerance acclimation in warm-season C, grasses. The LT,, of bermudagrass decreased 5°C in response to low temperature and was associated with the induction of cold-regulated (COT) proteins, which are correlated with freezing tol-

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erance. These studies suggest the importance of protein expression; however, the significance of many proteins is unknown. Lee et al. (199 1) compared mRNA populations induced by ABA or low temperature in cell suspension cultures of smooth bromegrass and found that ABA induced more changes in the mRNA and protein population than did low temperature. Three of the changes were common to ABA and low temperature. Abscisic acid-induced mRNA were still present after 7 days without ABA and the LT,, of these cell cultures remained lower. The second stage of hardening in alfalfa also has associated changes in the pool of mRNA (Castonguay et al., 1993). Many of these were found to be glycine-rich proteins on translation. Some were specific to the freeze-tolerantcultivar, whereas others were altered in response to water stress and ABA. Dehardening reduced the expression of many cor mRNA. Analysis of isolated cDNAs associated with freezing tolerance induced by ABA in smooth bromegrass suspension culture showed new expression, upregulated expression, and transient expression (Lee and Chen, 1993). Genes cloned from each of these groups had homology with genes associated with sugar metabolism, osmotic stress, and protease activity. Monroy et al. (l993a) found that the increase in freeze tolerance at -2°C in alfalfa was paralleled by an increase in the expression of casl5, a cold-induced gene. Expression of this nuclear-targeted product occurred even when protein synthesis was inhibited, suggesting that expression depended on preexisting gene products. The structure of this gene was different between freezing-tolerant and freezingsensitive cultivars. Three cold acclimation specific (cas) genes have been identified in alfalfa by Mohapatra et al. (1989) and are coordinately regulated at the level of transcription. Expression of these genes was positively correlated with the degree of freeze tolerance. Other genes induced by ABA in alfalfa were induced by many environmental stresses including low temperature (Luo et al., 1992). The cDNA sequence of a cas gene from alfalfa indicated that the product was a small hydrophilic glycine-rich protein with homology to proteins associated with dessication tolerance. These transcripts accumulated slowly during cold acclimation, whereas they disappeared rapidly during cold hardening. Transcription experiments indicated that the stability of the transcript was greatly increased and may have accounted for the increased expression (Wolfraim et al., 1993). Dessication tolerance is important in freezing stress due to the loss of cellular water across the plasmalemma to the frozen extracellular matrix. Expression of a dehydrin gene was increased in smooth bromegrass acclimated in the field during the fall, smooth bromegrass cell suspension cultures treated with ABA, and bromegrass grown hydroponically at a 2°C day temperature and a -2°C night temperature for 28 days (Robertson et al., 1994). Dehardening of the plant material decreased the levels of dehydrin expression. Increases in dehydrin expression were accompanied by an increase in freeze tolerance and treatments that did not result

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in the increased expression of dehydrin genes did not show an increase in freeze tolerance.

VII. SALTSTRESS Scarcity of fresh water, competition for fresh water, and soil salinization have resulted in a need for grasses and legumes with increased salt tolerance, especially in irrigated regions of the world. Salts lower the solute potential of the external solution, presenting the plant with increased water potential gradients to overcome. Salinity reduces seed germination, stand establishment, and yield in forage grasses and legumes, but susceptibility of plants to salinity stress at different developmental stages varies among species. Plants cope with salinity stress by accumulating (primarily in tissues removed from meristems), extruding, or diluting ions, and by selective ion absorption (e.g., absorbing K in the presence of high Na).

A. SALTACCUMULATION Most plants accumulate ions in saline environments.Ando et al. (1985) reported intraspecific variation in the degree of salt accumulation, ranging from 0.32% for colored guineagrass (Panicurn coloraturn var. coloraturn) to 2.33% for Kabulabula grass [ P . colorarum var. rnakarikariense (Gossens) Van Rensb.]. Interspecific variation in accumulation exists as well. The pattern of Na and C1 uptake with an increasing level of salt differed between tropical legumes but was not related to the degree of tolerance observed (Keating et al., 1986). Shoot and root concentrations of Na and C1 were elevated under high salt treatments for St. Augustine grass (Stenoraphrurn secondturn Walt.), a moderately salt-tolerant grass, and seashore paspalum (Paspalurn vaginaturn Swartz.), a very salt-tolerant grass. Seashore paspalum also maintained higher K concentrations under salt stress. In contrast, bermudagrass maintained lower concentrations of Na and C1 under high salinity, but was less tolerant to salt (Marcum and Murdoch, 1990). High productivity under salinity stress in subterranean clover was positively correlated with restricted Na uptake in the shoot and the maintenance of high K/Na ratios (Shannon and Noble, 1995). In white clover, an extremely salt-sensitive species, salt-tolerant cultivars had lower concentrationsof Na and C1 in shoots than nontolerant cultivars, suggesting that tolerant cultivars had an improved ability to regulate uptake of both ions (Rogerset al., 1994).Plant tissues also vary in salt content. Tall wheatgrass [Thinopyrurn ponticurn (Popd.) Barkworth and Dewey] and salt-tolerant lines of crested wheatgrass had greater concentrations of K, lower Na, and lower NdK ratios in leaves than salt-intolerant lines of crested wheatgrass. Tall wheat-

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grass, however, had greater concentrationsof K, Na, C1, and lower NdK ratios in roots than crested wheatgrass (Johnson, 1991).

B. SEEDLING AND ADULTPLANTRESPONSES Increasing levels of salinity generally reduce seed germination and seedling emergence. Significant differences in germination over several saline solutions have been reported in perennial ryegrass (Horst and Dunning, 1989), subterranean clover (Shannon and Noble, 1995),and alfalfa (Al-Niemi et al., 1992).Simple correlations between alfalfa cultivars and germination in various salts indicated that germination was most affected by Na concentration; however, when all independent variables were considered,concentrationsof C1 and Mg were most important. Thus, selection in NaCl solutions should account for most of the potential gain in germination salt tolerance (Rumbaugh et af., 1993). A selected alfalfa line maintained constant germination to twice the salt osmotic pressure that the parents did. The selected line also germinated faster (Robinson et al., 1986). The level of germination of alfalfa in saline conditions was not related to postgermination performances (Al-Niemi ef al., 1992). Rhodesgrass (Chloris guyana Kunth.) populations from five cycles of selection under high salt conditions showed a significant improvement in survival and regrowth compared with unselected material. The selected material also had increased germination at moderate salt levels and decreased water use per unit leaf area. There were no apparent differences between the selected and unselected material in growth, ion content, or protein synthesis of the plants (Malkin and Waisel, 1986). An alfalfa population selected for high NaCl tolerance during germination had greater improvement in germination in NaCl than in other salt solutions and a higher germination percentage in high osmotic solutions of mannitol. The selected and parent populations did not differ in accumulation of Na or C1 during 48 h or in absorption of tritiated water during 12 h. The major physiological difference determined was that the population tolerant to low salt had a higher seed respiration rate than the population tolerant to high salt. Tolerance appeared to be due to specific ion inhibitions and osmotic tolerance and not to differences in ion uptake or imbibition rates (Allen et al., 1986). Salt tolerance at germination for seed lots of different ages from the same germ plasm source differed significantly as a percentage of nonsaline germination. Solute leakage during imbibition increased as seed age increased and was correlated with declines in germination of aged seed but not with fresh seed (Smith and Dobrenz, 1987). Degradation of plasmalemma function may allow the entry of more ions than the germinating embryo can tolerate. Examination of parental and selected alfalfa populations for different saccharides revealed no differences. Although raffinose and sucrose were higher in the seeds from the selected line, Dobrenz et af. (1993) suggested that the increase was not enough to account for the increased salt tolerance.

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Irrigation with saline water results in salt accumulating in the surface soil. Imgation with water with a conductivity of greater than 4.3 dS m-' strongly inhibited final emergence of alfalfa seedlings because of salt accumulation even though the seed had the potential to germinate in 28 dS m- water (Assadian and Miyamoto, 1987). The authors suggested that reduced emergence at seeding depths less than 5 mm resulted from reduced seed germination, whereas reduced emergence from greater depths resulted from damage to the hypocotyl caused by accumlated salts in the surface soil. Grasses and legumes vary within and among species for growth responses to salinity, often showing increased dry weight with moderate increases in Na levels (Ando et al., 1985; Ashraf et al., 1986a; Shannon and Noble, 1995). Panicum coloratum var. Bambatsi was more tolerant to salt than several tropical legumes; it sustained a 50% yield reduction at 16.4 dS m- I , whereas the best tropical legume had a 50% yield reduction at 10.6 dS m-I (Keating et al., 1986). Rhodesgrass, a halophytic forage grass, shows stimulated growth of single roots under NaCl concentrations that inhibit growth of the whole plant (Waisel, 1985). A salt-tolerant St. Augustine grass cultivar (Seville) had a 50% reduction in top growth at 28 dS m-', whereas the less tolerant cultivars exhibited a 50% reduction at 22 dS m-I. The differences between cultivars were largest at moderate salinity levels (Dudeck et al., 1993). Seville reacted to salt stress by increasing the root length, whereas a less salt-tolerant cultivar had stunted root growth under saline treatments (Meyer et al., 1989). Kallargrass [Leptochloafusca (L.) Kunth.] tolerates high salinity by secreting ions (Jeschke etal., 1995). Salt glands on its leaf blades resemble two-celled structures described in some halophytic genera of Poaceae. Secreted Na and C1 crystallizes in larger amounts than K when plants are grown under high-salt conditions (Wieneke et al., 1987). Vesicles (swollen epidermal hairs resembling the salt glands of Bouteloua sp.) occur on the leaf and sheath surfaces of kallargrass and are more common on the upper surface of leaves, whereas on the sheath they occur mainly on the lower (outer) surface. These structures may help regulate plant salt content in kallargrass (Bhatti et al., 1992a). The greatest variation in salt tolerance and Na, C1, and K uptake in kallargrass accessions came from different collection sites (Warwick and Halloran, 1991). For an accession, the internal concentrations of K, Ca, Na, and C1 in the shoots and roots were constant over a wide range of external NdCa ratios. In contrast, the shoot K and Ca and the root Ca concentrations in Panicum turgidurn remained unchanged at all external NdCa ratios, whereas the root K concentration decreased significantly at high external NdCa ratios (Ashraf and Naqvi, 1991). The Na and CI concentrations of kallargrass were higher in leaf sheath than blades and increased greatly with leaf age, whereas K concentrations were highest in young leaves and decreased with age. The authors state that K retranslocation occurs and suggested that K recycling and the use of Na to maintain turgor in old leaves is important in t? turgidum. Both Mg and Ca concentrations increased with leaf age

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(Bhatti et al., 1992b).Time-dependent secretion from leaves of kallargrass showed a large linear secretion of Na and C1 during 24 h, whereas secretion of K and Ca could not be detected before 2 and 6 h, respectively. Light stimulated the secretion of Na and C1 from the leaves and chilling the roots decreased the secretion of Na and C1 (Bhatti and Sarwar, 1993). The influence of NaCl treatment on the activity of three photorespiratory enzymes from three legumes [Cicer arietinum L. (salt sensitive), Arachis hypogaea L. (intermediate salt sensitivity), and Sesbania aculeata Poir. (salt tolerant)] was investigated. Moderate levels of NaCl inhibited the activity of 3-phosphoglycolate phosphatase from C. arietinum and A. hypogaea, whereas enzyme activity was not inhibited by NaCl in S. aculeata. Inhibition of this activity can elevate the concentration of phosphoglycolate, which is a strong inhibitor of part of the Calvin cycle. The activity of glycolate oxidase was increased slightly in C. arietinum and strongly in A. hypogaea. Salt treatment also increased the catalase activity in S. aculeata, whereas catalase activity was reduced in the other two species (Murumkar et al., 1985). The reduction of catalase activity can lead to elevated H 2 0 2 levels in conjunction with the increase in oxidase activity leading to oxidative damage in the cell. Compatible osmolytes are often used by plants to counter the chemical potential of sequestered ions in the cell. Total organic acid concentration in alfalfa nodules and roots was depressed by more than 40% in a moderately saline environment; however, lactate and amino acid concentrations increased. Proline increased the most in roots and nodules, indicating an osmoregulatory use in nodules as well as roots. Asparagine increased as well and was a major osmoregulatory component in bacteriods. The salt treatment had very little effect on other amino acids. The carbohydrate pool was increased also with pinitol increasing significantly in the cytosol and bacteroids, whereas trehalose concentration remained low (Fougere et al., 1991). Histone acetylation of alfalfa was similar in salt-tolerant and salt-sensitivecells under normal growth conditions. Exposure to short-term salt stress in salt-sensitive cells or continued growth at 1% NaCl for salt-tolerant cells resulted in large increases in multiacetylated forms of histone H4 and two forms of H3. Waterborg et al. (1989) state that the increase is an in vivo reporter suggesting an altered intranuclear ionic environment and may be an adaptive response to allow chromatin function in a more saline environment.

C. TISSUE CULTURE AND GENEEXPRESSION INSALTSTRESS Tissue culture has also been used to identify tolerance mechanisms and germ plasm tolerant to salinity. Cellular mechanisms for dealing with salinity stress may be different from

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whole-plant mechanisms and selection in vitro may not result in plants with increased salt tolerance. Young inflorescence explants of napiergrass (Penniseturn purpureurn Schumach.) exhibited delayed initiation of callus and a gradual decrease in growth with increasing salt level. Cell lines that tolerated up to 2% salt in the media were isolated and complete plants were regenerated from cell cultures tolerant to 0.5% salt (Bajaj and Gupta, 1986).Afalfa cell lines tolerant to NaCl retained their tolerance after 16 weeks of subculturingon media with no NaCI. Plants regenerated from these salt-tolerant lines exhibited somaclonal variation for salt tolerance compared with plants regenerated from control cell lines. All plants were morphologically abnormal with some extreme dwarfs. Regenerated plants from this study had unbalanced chromosome sets with one variant having 106 chromosomes. Isozyme phenotypes were also altered compared with control lines. Although the in v i m salt tolerance was maintained after regeneration for 9 of 12 regenerates, whole-plant tolerance was found only in two plants, one of which was sterile and the other did not flower (McCoy, 1987). Saline environments can alter gene regulation.A salt-inducedcDNA specific to a salt-tolerantalfalfa line had a charateristic zinc finger motif common to proteins that are nuclear transcription factors (Winicov, 1993). This will alter subsequent patterns of expression and stability. Eleven cDNA clones are induced within 2 h after exposure to high levels of salt. The decline in expression divided these genes into two groups, one returned to basal levels by 24 h and the other group between 3 and 7 days. The authors termed the rapid coordinate expression of this large number of genes an “early salt stress response” (Gulick and Dvorak, 1992). A salt-inducible cDNA from alfalfa encoded a 40-kDa cell wall protein containing a repetitive proline-rich sequence and a cysteine-rich carboxyl-terminalsequence with homology to nonspecific lipid transferases. The accumulation of this transcript in salt-tolerant alfalfa cells due to the presence of salt is primarily due to increased stability of the mRNA (Deutch and Winicov, 1995).

VIII. PLANT BREEDING FOR ABIOTIC STRESS TOLERANCE Cultivars of perennial forages have been developed with tolerance to the abiotic stresses discussed in this chapter; however, improved tolerance to abiotic stress has generally been an indirect response of selecting for superior agronomic traits such as establishment and yield among others. Recent reviews of breeding for stress tolerance (Ashraf, 1994; Gay, 1994; Hall, 1992; Noble and Rogers, 1993; Thomas, 1994) have been published in which physiological processes are discussed in detail relative to whole-plant stress tolerance. The most successful approaches to breeding perennial forages have historically used field-based evalua-

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tions to identify tolerant species, followed by selection within that species for genotypes that combine performance in stressful environments with forage quality traits (Barker and Kalton, 1989; Meyer and Funk, 1989; Busey, 1989; Beuselinck er al., 1994). Studies of plant response to abiotic stress indicate that genetic variation is available for traits such as water-use efficiency (Asay and Johnson, 1990; Barker et al., 1989; Frank, 1994; Johnson et al., 1990; Johnson and Rumbaugh, 1995; Read et al., 1993), growth under nutrient stress (Baligar et al., 1989; Edmeades er al., 1991; Lafever, 1981; Mackay et al., 1991; Wheeler et al., 1993ab), salinity tolerance (Al-Niemi et al., 1992; Ashraf er al., 1986b, 1987; Asins et al., 1993; Gregorio and Senadhira, 1993; Rumbaugh and Pendery, 1990; Smith er al., 1994), and defoliation tolerance (Brummer and Bouton, 1992;Jaindl et al., 1994; Jones et al., 1991; Smith and Bouton, 1993). Unfortunately, plant breeders have not incorporated this variation into cultivars that exhibit whole-plant stress tolerance. There are several reasons for this: (a) a poor understanding of genetic control of stress tolerance, (b) stress tolerance is often controlled by multiple genes, and (c) variation for stress tolerance usually exhibits a large environmental component or large environment by genotype interaction making direct selection for a physiological trait in a single environment difficult. Furthermore, tolerance at one developmental stage does not always confer tolerance at another stage. In addition, many methods proposed to monitor stress tolerance are based on the performance of individual cells, tissues, organs, or individual plants and do not provide a good indication of whole-plant response to stress either when grown in a spaced-plant nursery or in a competitive environment. Although competition has rarely been addressed as a selection criterion by plant breeders, both inter- and intraspecific competition have been shown to affect traits such as stable carbon isotope ratios and nutrient acquisition of plants (Williams et al., 1991;Caldwell et al., 1987). Similarly, Smith et al. ( 1992) demonstrated that grazing-tolerant alfalfa cultivars exhibited reduced persistence when grown in mixtures with tall fescue and grazed rather than when grazed in monocultures.

A. DEFOLIATION TOLERANCE Breeding perennial forage crops for improved defoliation tolerance is often a direct product of the selection that the breeder places on a cultivar before its release. Multiple defoliations within and between years by both man and grazers are commonly used in perennial forage breeding programs and are designed to allow only the most defoliation-tolerant genotypes to be released. Although studies have been conducted that compare interspecific variation for defoliation tolerance (Muir and Pitman, 1991), there have been few reports of breeders selecting for improved defoliation or grazing tolerance. Exceptions to this include breeding efforts in perennial turf grass species and efforts to develop grazing-tolerant alfalfa.

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20s

Efforts to improve the grazing tolerance of alfalfa have resulted in the release of Alfagraze (Bouton ef al., 1991). Unlike earlier efforts to improve grazing tolerance, Alfagraze was developed under grazing. Two cycles of recurrent phenotypic selection were used in which a broad-based alfalfa population consisting of plants from 22 germ plasms or cultivars and from 1070 plant introductions were grazed for about I20 days in 3 years in cycle 1 and for 2 years in cycle 2. Subsequent evaluations of Alfagraze have indicated that it generally has many thick stems, intermediate decumbency, high herbage yield, many crown buds (Brummer and Bouton, 1991), maintains high levels of stubble carbohydrates, and high residual leaf areas (Brummer and Bouton, 1992) under frequent defoliation. Selection for rhizomes in perennial grasses and legumes is another trait that has been associated with improved persistence. Bouton ef al. (1989) reported that highly rhizomatous accessions of tall fescue survived better in competition with bermudagrass than did nonrhizomatous genotypes. Although Bermuda grass competition depressed rhizome production, tillering, and plant size across all genotypes, highly rhizomatous genotypes survived better than weakly or nonrhizomatous genotypes indicating that selection for rhizomatous cultivars could improve persistence in fescue. The presence of the endophyte did not affect rhizome production, making selection for rhizomes independent of endophyte infection status (De-Battista er af., 1990). In many tropical grass species, winter survival is associated with rhizome production. Bashaw ( 1980) selected Llano and Nueces buffelgrass (Cenchrus ciliaris L.) for rhizome production, which significantly improved the northern range of this species. The production of rhizomes, however, is influenced by soil fertility (M. A. Hussey and E. C. Bashaw, unpublished data) so that expression of rhizomes in buffelgrass and, thus, winter survival, depends on management. Similar results have been reported in nonrhizomatous warm-season grasses, where strategic use of defoliation and N fertilizer has been suggested as a method for the identification of winter-hardy genotypes of weeping lovegrass [Eragrosriscurvufa(Schrad.) Nees var. curvula Nees] (Voigt and DeWald, 1985). Recently, molecular markers associated with the rhizome trait have been reported in interspecific crosses between Sorghum bicolor x S. propinquurn (Paterson er af., 1995). The use of closely linked DNA markers and megabase DNA libraries provides the tools essential for map-based cloning of the genes regulating rhizome production.

B. DROUGHT TOLERANCE The use of controlled irrigation systems, such as line-source irrigation, rain-out shelters, and others, has enabled plant breeders and physiologists to identify genetic variation for crop water use and response to drought. Numerous reports have been published on the usefulness of carbon isotope discrimination (Cid) for pre-

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dicting water use in annual and perennial plants (Donovan and Ehleringer, 1994; Nageswara-Rao and Wright, 1994;Wright el al., 1994).Although heritabilities for water use efficiency (WUE) have generally been high, large environmental effects have been reported. Johnson et al. (1990) observed genetic variation in both crested wheatgrass and Altai wildrye [Leymus angustus (Trin.) Pilger] under both greenhouse and field conditions. Barker et al. (1989) also reported genetic variation for W E in crested wheatgrass, western wheatgrass [Pascopyrum smithii (Rydb.) A. Love], and intermediate wheatgrass [Thinopyrum intermedium (Host) Barkworth & D. R. Dewey], but noted that environmentalvariance was large. Asay and Johnson (1990) reported genetic variation for forage yield under water stress but observed that genetic variation for yield decreased as stress increased. In alfalfa, Johnson and Rumbaugh (1995) reported variation in Cid in some alfalfa populations but not in others. They concluded that, although variation in Cid was present, more diverse germ plasm would be needed to make substantial improvements in water use. Johnson and Tieszen ( I 994) measured Cid in a diverse set of alfalfa germ plasm originating from 13 countries. Total plant WUE was correlated with Cid only under drought stress; however, shoot WUE and Cid were correlated within well-watered and drought-stressed treatments. Reproducible variation for Cid was obtained in alfalfa germ plasm, suggesting that Cid is useful in evaluating alfalfa germ plasm for improved WUE. The use of seedling selection systems has indicated that genetic variation is available for seedling drought tolerance in lovegrass (Tischler et al., 1991). Hycrest crested wheatgrass was developed based on seedling drought tolerance (Asay et al., 1986). Because of its hybrid vigor and selection pressure in drought environments, Hycrest has had about 30% higher dry matter yields than Nordan in certain stress environments. Although selection for rooting depth in perennial forages has not been widely used, genetic variation for rooting depth has been reported in several species (Lehman and Engelke, 1993; Salaiz et al., 1991). In tall fescue, genotypes with thicker roots were reported to penetrate hardpans, have deeper rooting depths, and avoid water stress (Tolbert et al., 1990). Differences in rooting depth were noted between and within species with those having deeper rooting being more drought tolerant.

Most attempts to breed perennial forages under nutrient stress have focused on acid soils and genetic variation for growth in response to A1 (Edmeades et al., 1991; Lafever, 1981; Mackay et al., 1991;Mugwira andHague, 1993a,b,c,d). Genetic variation has been observed in many species including sorghum (Sorghum bicolor L. Moench) (Foy et al., 1993),perennial ryegrass (Wheeler et al., 1993a),

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alfalfa (Baligar et al., 1989; Bouton et al., 1986; Parrott and Bouton, 1990),lablab bean (Lablab purpureous L. Sweet) (Mugwira and Hague, 1993c,d), sesbania (Sesbania sesban L. Merr.) (Mugwira and Hague, 1993d), orchardgrass (Dactylis glomerata L.) (Wheeler et al., 1993b),tall fescue (Wheeler et al., 1993b),reed canarygrass (Phalaris arundinacea L.) (Oram et al., 1993), and others. Other research has focused on genetic variation for growth on acid soils (Alison and Hoveland, 1989; Bouton e f al., 1986)without consideration of specific nutrient stresses. Genetic variation for tolerance to high levels of Mg (Keisling et al., 1990), Cu (Plenderleith and Bell, 1990), or growth under low P (Mugwira and Hague, 1993a,b,c,d) has been identified. Although differences in genetic variation for ability to grow under nutrient stress have been identified, few cultivars have been developed with improved tolerance to acid soils or low nutrient status. This apparent lack of success by plant breeders is due to the complexity of nutrient uptake and the large interactions that exist between nutrients. Where tolerant cultivars have been developed, they have generally resulted from growing plants on nutrient-deficient soils and selecting those species with the best agronomic performance, rather than cultivars selected specifically for response to low nutrient stress.

D. SALINITYTOLERANCE Despite the existence of genetic variation for salinity tolerance, few salt-tolerant cultivars have been released (Noble and Rogers, 1993). Selection has been based primarily on agronomic characters such as yield and those traits that integrate physiological mechanisms responsible for tolerance. Genetic variation for salinity tolerance in perennial forage crops has been assessed during germination, seedling growth, and regrowth. Accessions of crested wheatgrass were identified with good germination and forage production under moderately saline (-0.6 MPa) conditions; however, consistent differences in salinity tolerance were not observed (Johnson, 1991). One of the most comprehensive studies of genetic variation of seedling salt tolerance compared populations of forage rape (Brassica napus L.), berseem clover, alfalfa, and red clover (Trifoliumpratense L.). Approximately 10,000 seedlings of each species were screened for growth at NaCl concentrations ranging from 200 to 250 mmol liter-'. A selection intensity of 1% was used with the plants exhibiting salinity tolerance being polycrossed and realized and narrow sense heritabilities were calculated. Realized heritabilities of 0.62, 0.34,0.31, and 0.57 were observed for forage rape, berseem, alfalfa, and red clover, respectively, suggesting that it should be possible to improve seedling response to saline conditions in these species (Ashraf et al., 1987). Fourteen half-sib families were randomly selected from an experimental alfal-

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fa population after two cycles of mass selection for improved forage growth at 80 mMNaCI. In separate experiments, the effect of salt stress was measured at germination (radicle growth at 7 days), during seedling growth (40 days after planting), and postharvest growth (forage yield at 67 and 95 days) under 0 and 80 mM NaCl. Although genetic correlations between seedling and regrowth yields were positive, it was concluded that selection for increased alfalfa forage yield in saline environments may not be optimum if conducted under saline conditions. Selection methods for improved salt tolerance should include several growth stages to develop alfalfa cultivars with improved yield under saline environments (Johnson et al., 1992). Recent advances in plant physiology and plant molecular biology have greatly expanded our understanding of physiological processes. Nevertheless, as with other agronomic crops, perennial forage cultivars must combine both quality and yield traits with tolerance to abiotic and biotic stresses. Except in rare circumstances, the advances made in understanding the physiology of abiotic stress tolerance will be used by plant breeders to develop selection techniques in which the genetic variation for specific traits can be quantified. Because of the wealth of interspecific and intergeneric variation that exists for tolerance to abiotic stresses, improvement efforts with perennial forages will continue to be targeted at the identification of tolerant species. Although genetic variation exists, most collections of perennial forages are small compared with cultivated species. Therefore, advancements in breeding for stress tolerance in perennial forages will likely follow the lead of cultivated crops, except for stress-tolerance traits that are uniquely important to perennial forages (e.g., winter hardiness, grazing tolerance, among others).

ACKNOWLEDGMENTS The authors thank Dr. David Briske, Texas A&M University, and Dr. Stan Wullschleger, Oak Ridge National Laboratory, for helpful critical reviews of an earlier version of the manuscript. M. Sanderson thanks the Texas Agricultural Experiment Station for granting a study leave at the Danish Institute of Plant and Soil Science, The Research Center Foulum, Denmark, to prepare this review. M. Sanderson also thanks Lise Molkier, Librarian at The Research Center Foulum for her help, and Villy J~rgensen and Dr. Christer Ohlsson, Department of Forage Crops and Potatoes, for the use of office space and facilities.

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Williams, K., Richards, J. H.,and Caldwell, M. M. (1991). Effect of competition on stable carbon isotope ratios of two tussock grass species. Oecologia 88, 148-151. Wilman, D., and Wright, P. T.(1978). The proportions of cell content, nitrogen, nitrate-nitrogen and water soluble carbohydrate in three grasses in the early stages of regrowth after defoliation with and without applied nitrogen. J. Agric. Sci. (Cambridge) 91, 381-394. Wilman, D., and Wright, P. T. (1983). Some effects of applied nitrogen on the growth and chemical composition of temperate grasses. Herbage Abstr: 53,387-393. Wilson, A. M., and Briske, D. D. (1979). Seminal and adventitious root growth of blue grama seedlings on the central plains. J. Range. Management 32,209-213. Wilson, A. M., Hyder, D. N., and Briske, D. D. (1976). Drought resistance characteristics of blue grama seedlings. Agron. J. 68,479-484. Winicov, I. (1993). cDNA encoding putative zinc finger motifs from salt tolerant alfalfa (Medicago sativa L.) cells. Plant Physiol. 102,68 1-682. Woledge, J., and Pearse, P. J. (1985).The effect of nitrogenous fertilizer on the photosynthesis of leaves of a ryegrass sward. Grass Forage Sci. 40,305-309. Woledge, J., and Suarez, A. C. (1983).The growth and photosynthesis of seedling plants of white clover at low temperature. Ann. Bor. 52,239-245. Woledge, J., Davidson. K.,and Dennis, W. D. (1992). Growth and photosynthesis of tall and short cultivars of white clover with tall and short grasses. Grass Forage Sci. 47,230-238. Wolfraim, L. A., Langis, R., Tyson, H., and Dhindsa, R. S. (1993). cDNA sequence, expression, and transcript stability of a cold acclimation-specific gene, casl8, of alfalfa (Medicagofalcara) cells. Planr Physiol. 101, 1275-1282. Wood, G. M., and Cohen, R. P. (1983). Predicting cold tolerance in perennial ryegrass from subcrown internode length. Agron. J. 76,516-517. Wright, G. C., Nageswara-Rao, R. C., and Farquhar, G. D. (1994).Water-use efficiency and carbon isotope discrimination in peanut under water deficit conditions. Crop Sci. 34,92-97. Zhang, M. I. N., and Willison, J. H. M. (1989). Electrolyte release from frozen-thawed Bromus inermis suspension-cultured cells. Acra Bor. Neerl. 38,279-285. Zhang, M. I. N., Stout, D. G., and Willison, J. H. M. (1992). Plant tissue impedance and cold acclimation: A re-analysis. J. Exp. Bor. 43,263-266.

CROPMODELING AND APPLICATIONS: A COTTONEXAMPLE K. Raja Reddy', Harry F. Hodges', and James M. McKinion2 'Department of Plant and Soil Sciences Mississippi State University Mississippi State, Mississippi 39762 WSDA-ARS Crop Simulation Research Unit Mississippi State, Mississippi 39762

I. Introduction 11. Phenology A. Reproductive Initiation B. Square Maturation Period C. Boll Maturation Period D. Leaf Unfolding Interval Rates E. Leaf Expansion and Internode Elongation Duration 111. Growth of Individual Organs A. Leaf Area Expansion and Internode Elongation Rates B. Leaf Area and Internode Length at Leaf Unfolding C. Specific Leaf Weight and Starch Accumulation D. Internode Mass Accumulation Rate E. RootGrowth F. Square and Boll Growth G. Whole Plant Leaf Area Development H. Whole Plant Growth Rate N. Partitioning Biomass V. High-Temperature Effects on Fruiting Structures VI. Nitrogen-Deficit Effects A. Leaf Nitrogen and Phenology B. Leaf Nitrogen and Leaf and Stem Expansion C. Leaf Nitrogen and Specific Leaf Weight D. Leaf Nitrogen and Photosynthesis E. Leaf Nitrogen and Transpiration F. Modeling the Effects of N Deficits VII. Water-Deficit Effects A. Tissue Expansion B. Photosynthesis C. Modeling the Effects of Water Deficits VIII. Model Development A. Cotton Simulation Model GOSSYM B. Expert System COMAX 22s Advances in A p n m . v , Volume 19

Copyright 0 1997 by Academic Press, Inc. All righs of reproduction in any form reserved

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M. Model Calibration and Validation A. Model Calibration B. Model Validation C. Model Testing Methods X. Model Applications and Bridging Technologies A. Computer Technology B. Object-OrientedSimulation C. Decision Support Systems D. Geographic Positioning System E. Geographic Information Systems F. Intelligent Implements G. Site-SpecificAgriculture for Farm Management XI. Summary and Conclusions References

I. INTRODUCTION One of the unusual features of agriculturalproduction is the uniqueness of every season. Each year is unique in the timing of the rain, temperature regimes, etc., and when the uniqueness of the weather is combined with the individuality of cultural practices, soils, and variety characteristics, the crop production manager has more variables to consider than the human mind can reasonably organize. Resource managers need information organized within some sort of theoretical framework that can assist decision-making processes. They do not want to be confronted by a huge body of research data. With the availability of computers and a comprehensive knowledge of how crops respond to weather variables, mechanistic crop models can be developed to assist in making production-management decisions. The purpose of this chapter is to describe the type of information useful for developing a physiologically based crop simulation model using cotton (Gossypium hirsuturn L.) as an example. This report brings together data from experiments designed for a crop model, and it provides a general description of how a crop model can be developed, calibrated, validated, and used. It also shows how crop models may be used for technology transfer, including use in combination with precision agriculture and other forms of new technologies. Application of geographic information systems (GIS), global positioning satellites (GPS), and related improvements in field equipment monitoring and delivering devices to agriculture production systems will generate information not previously available. Simulation methodologies combined with expert system technologies will close the gap between the GPS, GIS, and intelligent implements (11) and precision agri-

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culture. That information will spawn a need for new and more powerful diagnostic tools to identify crop production problems and their solutions. Crop models that have physiological processes realistically incorporated into them, along with appropriate expert systems, may be integrated with these new technologies to provide such diagnostic tools. Others have done considerable work on photosynthesis (Baker et al., 1983; Acock, 1991; Boote and Loomis, 1991; Evans and Farquhar, 1991; Gutschick, 1991; Harley and Tenhunen, 1991;Norman and Arkebauer, 1991; Sinclair, 1991) so that will be discussed in only a limited way here. Similarly, root-zone processes and the topic of abscission are avoided. Those topics need to be developed and reviewed, but we cannot do so here. Growth and development of plants in the natural environment are the result of the interactions of two major regimes: the genetic potential of individual plants and the external environment. Potential growth and developmental rates for a particular genotype are defined as the maximum rates achievable at a given temperature. These rates will be expressed as functions of temperature under optimum water and nutrient conditions. These potential rates may be decreased by stress factors. A stress factor is defined as any factor that reduces organ growth andlor developmental rate below its genetic potential at a given temperature. Controlledenvironment facilities are necessary sources of process-rate data because they allow varying one environmental factor while maintaining other factors in nonlimiting conditions. Crop data obtained in this manner are less ambiguous and allow understanding of the responses to environmental variables and nutrient status. After potential rates of development or growth for a particular genotype have been established, the actual rates may be delayed or reduced by environmental and nutritional (including carbon) stresses. If major changes in genetic potential occur, then the model needs to be adjusted to reflect those changes. Typically small differences due to cultivar changes that are unique to the cultivar can be simulated with minor calibration adjustments to potential rates of the given species. The minimum, optimum, and maximum temperatures at which plants grow and develop vary among species (Kiniry and Bonhomme, 1991). Although cotton is produced worldwide, it is grown in a relatively narrow temperature range compared to other species. For example, the minimum temperature threshold for cotton is 12-15"C, whereas for maize, pearl millet, rice, sorghum, soybean, and sunflower it is 7-9°C. The optimum and maximum temperatures also vary among species and are poorly defined and quantified. Planting dates of the crops are varied from year to year and among locations depending on temperature conditions. The suitability of a crop to a new location depends not only on the threshold temperatures but also on the length of growing season. Long-term average daily temperature is shown for several U.S. cotton-producing regions and Maros, Indonesia, a maritime equatorial region of the world (Fig. 1). These long-term temperatures illustrate that several days per year can be ex-

REDDY ET AL.

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pected to be above optimum (28°C) for cotton production. Average maximum daily temperaturesat Phoenix,Arizona exceeded 40°C over 90 days per year, and longterm average daily mean temperaturesexceeded 30°C 88 days per year (K. R. Reddy et al., 1995~).However, there is a linear relationship between air temperature minus leaf temperature and leaf water potential (Fig. 2). This suggests that as the plants become dehydrated and stomata close, because of low leaf water potential the leaf temperature becomes warmer than its surrounding environment, whereas in well-watered plants transpirational cooling lowers leaf temperature below air temperature by several degrees (Jackson, 1988; Kimball et al., 1994). Such an association is not new, but relatively little quantitative information is available. Crop modelers need to be aware of the relationship and realize that canopy rather than air temperature controls plant growth and developmental processes. There is concern that present-day societies are clearly causing major changes in

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the atmospheric concentration [CO,] and other "greenhouse gases." Atmospheric [CO,] has increased globally during the past 200 years. Recent [CO,] measurements at Mauna Loa, Hawaii show a 12% increase in the mean annual concentration in 32 years, from 316 pl liter-' in 1959 to 354 p1 liter-' in 1990 (Keeling and Whorf, 1991).That increase, and the continuing rise at the rate of 1.8 pl liter-' per annum, are likely to cause a change in climate (Rotty and Marland, 1986; IPCC, 1990). Global circulation models predict a I S-5.9"C rise in global surface temperature, change in precipitation patterns, and cloud cover in the next 50-70 years (Washington and Meehl, 1984; Manabe and Wetherald, 1987; Hansen et al., 1988; Wilson and Mitchell, 1987; Schneider, 1989; Adams er al., 1990). Agricultural productivity is very sensitive to these climate change variables, and understanding their effects on major agricultural crops would allow lead time in adjusting to these changes. If there is a rise in average global temperature, as has been hypothesized based on global circulation models, a 5°C increase would cause most areas where cotton is currently grown to have above-optimum temperatures during much of the summer. If such temperature changes occur, temperature will surely hamper production of cotton and other seed-bearing crops (Baker and Allen, 1994; Conroy et al., 1994; Rawson, 1995; K. R. Reddy er al., 199%). It will also increase our need for understanding and predicting crop responses to temperature and other environmental factors. A 5°C increased daily temperature at Stoneville, Mississippi, would bring tedperatures to what we now consider acceptable cotton planting temperature about

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45 days earlier in the year. If cotton planting occurs 45 days earlier than is nor-

mally practiced today, and if other production practices remain the same, then harvest would begin about August 15. Such production practices would allow much of the cotton growing season to occur in a higher solar radiation environment than is experienced today; however, a major portion of the flowering and boll growth period would occur during the period when temperatures are predicted to be above optimum. Environmental factors are often covariants resulting in several interacting changes occurring simultaneously, e.g., more rain is usually associated with less solar radiation. Crop responses to changing weather conditions also involve covariants; e.g., higher temperature increases transpiration. These crop- and weather-related changes quickly become too complex to accurately analyze by conventional ways and allow one to find the most appropriatecombination of management practices to utilize the new conditions.A crop model that simulates plant responses to its physical world provides quantitative information needed to predict the effect of new management practices on crop performance. It is difficult to build process-level simulation models from data collected in the field because many factors often simultaneously affect these processes; and because many environmental and biological factors are covariants.This makes it literally impossible to reasonably assess the causes and effects with accuracy. We have conducted numerous experiments in sealed, naturally lit plant growth chambers in which temperature, atmospheric [CO,], water, and nutrients were varied independently while other factors were maintained at nearly optimum conditions (K. R. Reddy et al., 1992a,b, 1993a,b, 1995d, 1996a,b).This allowed us to determine the relationships of the varied factor with crop response(s). These experiments were conducted in environments in which major efforts were made to allow only the variable being tested to be limiting. Plants were typically grown in wellwatered sand and adequately fertilized with all known mineral nutrients. Plants were also grown in higher [CO,] to enhance photosynthesis to avoid carbon deficits, and the plants were not subjected to disease or insect infestations. Thus, the plants were grown at their potential rates and limited only by the variable being tested. Potential rates may be estimated from relationships developed in this manner and then corrected by stress factors known to occur in the natural environment. Thus, potential rates may be estimated from the temperatures that occurred and actual rates simulated by reducing the potential rates with “stress factors.” Cotton belongs to the genus Gossypium of the Malvaceae family. Of the 39 species of the genus that are diverse in habitat, only 4 produce commercial lint. The upland and acala varieties belong to G. hirsutum L., and extra-long staple pima and sea island or Egyptian varieties (G. barbadense L.) are grown commercially throughout the world. Upland cotton is grown on more than 5 million hectares in the United States and more than 34 million hectares worldwide (USDA, 1989). Much of the world’s production is in arid and semiarid climates and must be irrigated for commercial production.

CROP MODELING AND APPLICATIONS

23 1

To comprehend a plant’s responses to its environment, one needs to understand the morphological characteristicsof the crop and its responses to temperature, water, and nutrient supply. It is necessary to describe the plant as a whole and each facet of growth at the organ level. The growth and development of each plant organ are also influenced by competition from other organs as well as by environmental conditions.Mauney (1968) described the anatomy and morphology of cultivated cotton. The primary axis of the cotton plant results from elongation and development of the embryo. In the dormant seed, the primary axis consists of a radicle, a hypocotyl, and a poorly developed epicotyl. The epicotyl contains one true leaf initial and a dome of meristematic cells. The growing cotton plant actively proliferates new cells on many fronts. Thus, all the differentiation of the vegetative framework above the cotyledons takes place after germination. Cotton is indeterminate in growth habit in that the mainstem apex continuously initiates leaves and axillary buds. The axillary buds on the lower nodes develop into vegetative or monopodial branches if conditions are favorable. The axillary buds in the upper nodes, normally above node 5 , develop into fruiting or sympodial branches. Vegetative branches behave much like the mainstem in that they produce both vegetative and fruiting branches. Fruiting branches, on the other hand, initiate one true leaf and then terminate as a flower. Branch elongation is accomplished by growth of axillary buds producing a sympodial zigzag structure (Mauney, 1984; Mutsaers, 1983a). The phyllotaxy (arrangementof leaves on the stem) is spiral in cotton, with each leaf beingQturn above the last. A stem may have either a counterclockwise or a clockwise phyllotaxy. Mauney (1968) stated that half of the stems tend to spiral to the right and half to the left, and occasionally a stem may be seen in which the phyllotaxy has reversed direction. Thus, potential patterns of growth are determined by the apical and axillary meristems.

11. PHENOLOGY Crop growth and development are driven by canopy temperature and will be modulated by water and nutritional supply (Gepts, 1987; Hodges, 1991). Plant phenology is the study of the time between like events, dissimilar events, or the duration of a process. Like events include the time intervals between mainstem leaves or branch leaves on a plant. Unlike events include the intervals between plant emergence and formation of a flower bud, flower, or mature fruit. Duration of a process might include the period between unfolding of a leaf, or the appearance of an internode, and the time the leaf or internode reaches maximum size. Potential developmental events were defined as the genetic potential of a species at a given temperature where the plants are grown in a stress-free environment. These

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rates were obtained from experiments conducted in controlled environmental facilities (K. R. Reddy et al., 1992a,b, 1993a,b, 1995a,b,c, 1996a,b;V. R. Reddy et al., 1994a,b).It is nearly impossible to determine potential process rates with any other known techniques. The important periods in the life of most plants can be distinguished and are predictable. Phenological information is essential for the analysis and understanding of source:sink interactions in plants. Hesketh and co-workers performed a series of experiments in the late 1960sand early 1970s on many phenological events of cotton cultivars (Hesketh and Low, 1968; Moraghan et al., 1968; Low et al., 1969, Hesketh et al., 1972). K. R. Reddy et al. (1993b, 1996a,b) have studied the phenological rate functions for modem upland and pima varieties. They also added duration of expansion or elongation functions for leaves and internodes that were not previously available. The data of K. R. Reddy et al. (1993b, 1996a) are in the form of daily developmental rates. This is the form needed by modelers for building process-level dynamic simulation models. The data of K. R. Reddy et al. (1993b, 1996a) were collected in a wide range of temperatures and in both ambient and twice-ambient carbon dioxide levels. Compared to the earlier data on phenology (Hesketh and Low, 1968; Hesketh et al., 1972), this has broader applicability for model development. Genetic improvements that occurred in recent years resulted in cultivars that are earlier in maturity than cultivars grown 25-30 years ago (Wells and Meredith, 1984; K. R. Reddy et al., 1993b, 1996a). Models developed from such a database may be useful both in present-day crop production environments and in the future hypothesized warmer environments with higher CO,. Crop simulation models that were developed to study global climate (Wall et al., 1994) or used to study the possible impacts of climate change on crop production (Curry et al., 1990;Fisher et al., 1995;Adams et al., 1990) did not use data that were collected in potential growing conditions. These studies, however, should be considered somewhat preliminary because the crop models used were based on a much nmower temperature range and the phenological information was collected at present-day atmospheric [CO,] or at slightly lower levels.

A. REPRODUCTIVEINITIATION Temperature and photoperiod are the two main environmental factors that determine flowering in young and established plants. Commercially grown cotton cultivars are very sensitive to temperature but not sensitive to photoperiod, so floral formation and floral development in these cultivars are relatively simple to understand.The formation of squaring marks the beginning of the reproductive phase in cotton. The rate of the first flower bud, or square formation, is very temperature dependent. The time from emergenceto observance of first square, 3 mm in length which coincidences with the unfolding of the subtending leaf, is expressed as the

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reciprocal of days (Fig. 3A). That allows one to use the information by simply adding daily developmental rate at any temperature and predicting square formation when the sum equals one or greater (K. R. Reddy et al., 1993b).The base temperature, below which no progress occurred toward flower bud formation in cotton, was 15"C, the maximum rate of progress occurred at about 3OoC,and progress at temperatures above 30°C was slower than that at 30°C. One of the primary ways cultivars differ in maturity is determined by the time from emergence to first square (K. R.Reddy et al., 1993b). Cotton breeders have successfully shortened this developmental phase, and many of the modem upland

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and pima cultivars produce squares a few days earlier than cultivars used three to four decades ago (K. R. Reddy et al., 1993b; Moraghan et al., 1968). For example, at 27"C, the plants used by Moraghan et al. (1968) required 38 days to square for cv. M-8, the plants in Hesketh et al. (1972) took about 34 days, whereas the modern upland cultivars took only 25 days at the same temperature (K. R. Reddy et al., 1993b).The cultivars were different in all three studies. Baker et al. (1983) developed a cotton model, GOSSYM, that simulated this phenological response based on the temperature-responsedata of Moraghan et al. (1968).When this model was first tested, they found that the model predicted 20% more time to reach first square than was observed in field conditions. They calibrated their model by introducing multipliers into the temperature response function for this phenological event. This adjustment improved the predictions at nearoptimum temperature, but predicted rates were still too fast at both below- and above-optimum temperatures. We found a modern pima cotton cultivar decreased its rate of progress toward first square more than a modem upland cotton cultivar at temperatures above 27°C suggesting that it was more sensitive to high temperature than upland cotton (Fig. 3A). The modern cultivar took 18% less time to reach first square at 20 and 25°C than the cultivar reported by Moraghan et al. (1968), but 36% less time to form first square at 30°C. This suggests that modern pima cultivars are not only earlier but are also more heat tolerant than those pima cotton cultivars grown four decades earlier (Lu et al., 1994). There are several reasons for the differences between our results and the earlier published work. One is differences among cultivars (Wells and Meredith, 1984), and another is uncertainty of the environments from which the developmental rates in the earlier work were determined and the definition of square formation. Our data show that modem cultivars of both cotton species formed squares much earlier and produced squares or other fruiting structures at higher temperatures than cultivars grown several years ago. Such data are needed to improve the predictive capabilities for crop management and yield forecasting models. The effect of temperature on development rate has often been described by using a thermal time concept.The most widely used thermal time method is the growing degree days procedure, which relates developmental rate linearly to temperature above a species- or cultivar-specific base temperature at or below which developmental rate remains zero (Hodges, 1991; Ritchie and NeSmith, 1991). The growing degree days concept has been used for simulating days to first square in cotton and several other developmental events (Jackson, 1991; Hearn, 1994).This procedure has been only moderately successful because of the variability in plant responses, but may be satisfactory if above-optimum and low-temperaturethresholds are adequately considered in developing the relationships. The reason for variability in numbers of heat units required to reach first square in different weather conditions is apparent from Fig. 3A. The response curves are nearly linear to

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about 27”C,but at higher temperature the rate of progress toward squaring is slower. This results in accumulating degree days very rapidly on hot days, but phenological progress is in fact slower at high temperatures than at 27°C. K. R. Reddy et al. ( 1993b) and Cogn’ee (1988) found that a quadratic function was superior to any other form to describe the relationship of developmental rate to temperature in cotton. Because we need to predict crop performance in a range of temperatures (Fig. l), it was important to know the appropriate response functions at all meaningful temperature conditions. The rate functions describing the reciprocal of days from emergence to first square (Vduration, day-’) for upland and pima cotton cultivars are as follows: Upland, Y = -0.1265 Pima, Y = -0.1593

+ 0.01 142-X- 0.0001949.P; 3 = 0.98 + 0.01473.X - 0.0002749.P; 1.2 = 0.99,

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where X is average temperature for that period. Above-optimum temperatures delayed progress toward fruiting and extended the vegetative period. In the natural environment, high temperatures are often associated with water deficits, which result in partially closed stomates and even higher leaf temperatures (Fig. 2).

B. SQUAREMATURATION PERIOD The interval from square formation to flowering is presented in the same way as square formation (Fig. 3B). Rate of progress toward flowering was equally sensitive to temperature throughout the temperature range except at the above-optimum temperature for growth (27°C).Daily progress from squaring to flowering at above-optimum temperature did not decrease as rapidly as formation of squares. Similar temperature response functions were published by Hesketh and Low (1968) for the cultivars used two or three decades ago. At 27”C, the plants of Hesketh and Low (1968) required 20 days from square to bloom, whereas plants in Hesketh er al. (1972) took about 26 days. The modem cultivars reported by K. R. Reddy et af. (1993b) required about 24 days. The pima cotton cultivars required more time to form flowers from squares at all temperatures. After flowering begins, the plants are more subject to source/sink imbalance because bolls are major photosynthetic and mineral nutrient sinks in cotton plants. As fruit load increases, the rate of vegetative growth and development decreases because carbon becomes limiting, fruit loss increases, and vegetative growth eventually stops completely. This process is often described as “cutout” in cotton (Guinn, 1986; Sadras, 1995). If insects or environmental conditions cause boll abscission, vegetative growth will resume. Vegetative growth will also begin, if weather is suitable, when bolls

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mature and are no longer energy sinks. The daily developmental rates for square maturation period (Uduration, day-') as functions of temperature for upland and pima cotton cultivars are as follows: Upland, Y = -0.1148 Pima, Y = -0.06096

+ 0.009670-X- 0.0001432.P;

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C. BOLLMATURATION PERIOD Daily progress from flowerto mature fruit (open boll) was nearly linear throughout the temperature range tested (Fig. 3C). Progress to mature fruit showed no evidence of slowing at temperatures above 30°C as had the earlier-formed reproductive structures. The boll maturation period of these modern upland cultivars was slightly faster at low temperatures,but relatively slower at high temperatures, than those reported by Hesketh and Low (1968).The difference between pima and upland cotton cultivars for boll maturation period was about 2-6 days at temperatures above 25"C, and the regression lines are not parallel. As the bolls matured and opened, the carbohydrate stress was slowly alleviated and regrowth occurred if conditions were favorable. The daily developmental rates from bloom to open boll or boll maturation period (Uduration, day-') as functions of temperature for upland and pima cotton cultivars are as follows: Upland, Y = -0.02610 Pima, Y = -0.01863

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D. LEAFUNFOLDING INTERVAL RATES Developing mainstem and fruiting branch nodes are important aspects of cotton development because these processes determine the number of leaves produced and thus canopy development and interception of photosynthetically active radiation before the canopy closure in cotton. The rate of leaf formation was defined as the time required from the day the leaf unfolded until the next leaf unfolded on the mainstem. In cotton, we assumed a leaf unfolded the day three main

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veins were clearly visible. Defined in this way, leaf appearance can be used as a discrete event. Others have used the Haun scale of leaf emergence over short periods, but that method estimates leaf emergence by comparing the size of the emerging leaf relative to the preceding leaf. The rates of leaf unfolding on the mainstem and on fruiting branches were functions of temperature (Fig. 4A). Daily developmental rates were accumulated until the summed value was equal to one or greater, which predicts a new leaf should be initiated either on the mainstem or on the fruiting branches. Developmental rates were not linear over the biologically meaningful temperature range. Leaf unfolding rates of both mainstem and fruiting branch leaves increased with increasing temperature. At 30°C, only 2.2 days were required to produce a new leaf on the mainstem, whereas at 20°C, 5 days were needed to produce a mainstem leaf.

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REDDY ET AL.

Fruiting branches, on the other hand, required 6 days at 30°C and 9.5 days at 20°C to produce a leaf. The rate of leaf formation on fruiting branches is considerably slower than that on the mainstem because the branch primordium develops a flower. An axillary meristem then forms from which the next leaf and internode and a flower are produced. Thus, the ratio of mainstem and fruiting branch leaf unfolding interval was not constant at different temperatures as assumed by others (Hearn, 1969; Mutsaers, 1983a). The most important differences between their data and the data we obtained were decreased rates of leaf formation at higher temperatures found in our data and that our conditionsalso included a wider temperature range. Consequently, the growing temperature alters the architectural form of the plant. Leaf unfolding intervals and flower appearance intervals were not different from each other. Similar results were reported by Hesketh er al. (1972). Their plants required 2.4 days at 27°C to produce a leaf on the mainstem, whereas modern upland cultivars required 2.7 days (K. R. Reddy et al., 1996a). Fruiting or sympodial branch leaf unfolding interval at 27°C was 7 days for cultivars used by Hesketh et al. (1972), whereas the modem cultivars reported by K. R. Reddy et al. (1996a) required only 5.8 days. Unlike many other phenological events, there were no differences between upland and pima cotton cultivars for leaf developmental events. The daily developmental rates for mainstem and fruiting or sympodial branch leaf unfolding intervals(Uduration, day- ') are as follows: Mainstem plastochron, Y = -0.6698 + 0.0570.X - 0.0006765*P;3 = 0.94

(7)

Sympodial plastochron, Y = -0.3645 + 0.03389.X - 0.0005199.X; 3 = 0.84,

(8)

where X is average temperature for that period. Position on the plant also had some effect, probably indirectly, on rate of mainstem leaf development. For some unexplained reason, the rate of prefruiting leaf development was considerably slower than that of postfruiting branch leaf development (Fig. 4B). The prefruiting leaves were produced at progressively more rapid rates as plants added mainstem leaves. Because young plants partition a larger proportion of their photosynthates to roots, we hypothesized that prefruiting leaves were delayed due to carbohydrate deficits (Hodges et al., 1993). However, seedlings grown in twice-ambient [CO,] did not produce prefruiting nodes any more rapidly than plants grown in ambient [CO,]. Because photosynthesis is more rapid in twice-ambient [CO,] (K. R. Reddy ef al., 199%; V. R. Reddy et al., 1995), one would have expected the additional carbohydrate to overcome the hypothesized carbon deficit if that was the rate-limiting factor. Production of mainstem leaves or nodes after node 17 was probably slower due to carbohydrate deficits. The leaf at node 17 was produced when the first flower at

CROP MODELING AND APPLICATIONS

239

node 6 was produced. After that time, flowers were added rapidly in the lower positions on the plant. The developing bolls soon became important sinks for all available metabolites and probably delayed vegetative growth (Sadras, 1995, and references cited therein). Leaf unfolding intervals, generally referred to in the literature as phyllochron intervals, were not different from the square appearance intervals in cotton (Hesketh et al., 1972; K. R. Reddy et al., 1993b, 1996a). Squares will normally appear when the leaf at a given node is unfolded with main veins visible from the top. Defined in this way, the same response rate functions can be applied for square intervals to mark the appearance of reproductive organs. Other researchers have distinguished between plastochron intervals, the time between two successive leaf primordial initiations that can be observed with a dissecting microscope, and phyllochron intervals. Phyllochron intervals may be more easily verifiable in the field than plastochrons.

E. LEAFEXPANSIONAND INTERNODE ELONGATION DURATION The reciprocal of duration of leaf expansion and internode elongation is a measure of the rate at which these processes are completed (Fig. 5 ) . Internodes typically took less time at all temperatures to reach final length compared to leaves. 0.12 r

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0.04

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25

30

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Temperature, "C Figure 5 Role of temperature on duration of mainstem leaf expansion and internode elongation rates. Daily progress is the reciprocal of days to expand mainstem upland and pima cotton leaves. Expansion duration was similar regardless of position on the mainstem (K. R. Reddy ef al.. 1996a).

2 40

E D D Y ET AL.

Leaf petiole elongation occurred simultaneouslywith lamina expansion. Leaf expansion duration at a particular temperature was similar despite leaf position on the mainstem (K. R. Reddy et aZ., 1993a). Leaf expansion duration and internode elongation duration data were not available for cotton prior to the reports of K. R. Reddy et al. (1993b, 1996a). The equations describing the rate of leaf expansion duration (Uduration, day- ') and internode elongation duration (Uduration, day-') are as follows: Leaves, Y = -0.09365

+ 0.01070.X - 0.0001697-X?

9= 0.95

Internodes, Y = -0.04312

+ 0.007383.X - 0.0001046.X?

?- = 0.96,

(9) (10)

where X is average temperature for that period.

III. GROWTH OF INDMDUAL ORGANS A. LEAFAREAEXPANSION AND INTERNODE ELONGATION UTES To mechanistically simulate plant height and leaf area development throughout the season, it was essential to simulate potential leaf and internode growth rates. The mechanism of internode elongation is similar in both dicots and monocots, although development is acropetal in dicots and basipetal in monocots because of the position of intercalary meristem (Evans, 1965; Kaufman er al., 1965; Sachs, 1965;Morrison er aZ., 1994).Growth is defined as increase in mass, area, or length. Because much of the plant is not growing, one needs to simulate the responses of the only growing organs. Such detail is necessary because the individual organs have a sigmoidal growth pattern. An internodejust beginning to elongate has a different growth potential in a particular set of conditions than it has in its linear phase of expansion in the same conditions. Thus, to simulate plant height one should model the potential responses of the elongating internodes, including the duration of the elongation process, to the conditions that prevail during elongation. It is assumed that we are simulatingplants grown in the natural environment, and the primary role of solar radiation is to drive photosynthesis and transpiration. Rates of elongation were calculated by plotting relative leaf expansion rate (RLER) and relative internode elongation rate (RIER) as functions of days after leaf unfolding. These functions were calculated from daily measurements of leaf area and the subtending internode lengths for each leaf and internode on plants

241

CROP MODELING AND APPLICATIONS

grown in a range of temperatures and CO, environments. The RLER and RIER decreased with age. The linearly extrapolated intercepts provided estimates of the maximum RLER (cm2 an-,) or RIER (cm cm-') on Day 1. The slopes of the RLER or RIER with age for each leaf (cm2 day-') or internode (cm cm-' day- l ) were also calculated. The maximum RLER or RIER and slopes are functions of temperature (Figs. 6 and 7). The intercepts and slopes for leaves and internodes changed progressively with temperature, and the two were inversely related. The maximum RLER (Day 1) was 23%higher than the maximum RlER at all temperatures, whereas the slope, or the rate of growth reduction with age, was 5% lower for the leaves than for the internodes. The effect of temperature on final leaf area or final internode length is the net result of both temperature effects on duration (Fig. 5) and rates of growth

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Temperature, "C Figure 6 Influence of temperature on maximum relative leaf expansion rate (RLER) and rate of reduction with age (reduction rate or the slope, cmz day-'). The maximum RLER and slope were calculated by linear regressions fitted between leaf age and relative leaf expansion rate for each leaf at each temperature and [CO,] (K. R. Reddy et al., 1996a).

E D D Y ET AL.

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Temperature, "C Figure 7 Influence of temperature on maximum relative internode elongationrate (RIER) and rate of reduction with age (reduction rate or the slope, cm cm-' day-'). The maximum RIER and slope were calculated by linear regressions fitted between internode age and relative internode elongation rate for each internode at each temperature and [CO,] (K. R. Reddy et al., 1996a).

(Figs. 6 and 7). The equations describing these rate parameters for leaves as functions of temperature are as follows: Intercept (cm2 cmW2),Y = -0.03390

9 = 0.95

+ 0.02041.X

Slope (cm2 cm-2 day-'), Y = 0.01341 - 0.001879-X

3 = 0.98,

(11) (12)

where X is average temperature for that period. The equations describing the rate parameters for internodes as functions of temperature are as follows: Intercept (cm cm-I), Y = -0.001427

9 = 0.97

+ 0.0166.X

CROP MODELING AND APPLICATIONS

243

Slope (cm cm-l day-'), Y = 0.02479 - 0.001994.X 1-2 = 0.97

(14)

where X is average temperature for that period.

B. LEAFAREA AND INTERNODELENGTHAT LEAFUNFOLDING Leaf area and internode length at leaf unfolding increased progressively at higher positions on the mainstem until first square (Fig. 8A) or first flower (Fig. 8B) were formed. After first square was initiated, the initial leaf area decreased at higher positions on the mainstem. The length of the internodes at leaf unfolding also decreased after the first flowers were formed. Mature leaf areas followed a similar pattern on the mainstem nodes as found for leaf areas at leaf unfolding (Fig. 9A). Mature leaf areas increased as mainstem node number increased until node 6 was produced, then succeeding mature mainstem leaves were progressively smaller with higher node position on the plant. The first square was produced when the fruiting branch formed on node 6. A possible explanation is that initial leaf sizes

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Figure 8 Profile of leaf areas (A) and internode lengths (B) at leaf unfolding on the mainstem at both CO, concentrations for plants grown at 27°C. Arrows and nodes 5 and 15 indicate appearance of a first square and first flower on the mainstem (K. R. Reddy er al., 1996a).

REDDY ET AL.

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10 15 20 25 30 Mainstem NodeReaf Number Profile of leaf areas (A) and internode lengths (B)93 days after emergence for plants 5

Figure 9 grown in two CO, concentrations and at 27°C. Arrow at node 5 indicates the appearance of first square when the leaf was unfolding at node 5, and arrow at node 15 shows the appearance of first flower at node 5 when the leaf at node 15 was unfolding.

and leaf area expansion were competing with branches, roots, and reproductive structures for available photosynthates. Squares were formed when the leaf at nodes 5-7 unfolds; then, fruiting branches and other reproductive structures are initiated more rapidly with time and compete for the same resources. Initial internode length, on the other hand, increased linearly as node number increased until the plants started producing bolls, and then each succeeding internode was shorter (Fig. 9B). Bolls were first produced when the leaves at nodes 15-1 7 were unfolded. Mature internode lengths also followed a similar pattern on the mainstem as found for internode lengths at leaf unfolding. Similar patterns in mature leaf areas were observed in the field (Constable and Rawson, 1980; Constable, 1986) and growth chamber studies (Mutsaers, 1983a). Mutsaers (1983a) found a positive relationship between leaf sizes and cell number. Mature inter-

CROP MODELING AND APPLICATIONS

245

node lengths increased as mainstem node number increased until nodes 15-17, then subsequently produced internodes were progressively shorter. Again, the first flower was produced at the time the leaf on node 17 was unfolding. Mature in.ternode lengths were correlated with internode lengths at leaf unfolding (? = 0.78). It seems likely that potential internode lengths and leaf areas were determined by the time of leaf unfolding. Similar results were observed for mature internode lengths in growth chamber studies by Mutsaers (1984). The equations describing initial leaf areas (cm2) and internode lengths (cm) as functions of mainstem nodes are as follows (Fig. 8): Leaves 1-6, Y = 6.061 + 1.8069.X ? = 0.91

(15)

Leaves 7 and above, Y = 18.3812 - 0.523.X; ? = 0.95

(16)

+ 0.05605.X ? = 0.93

(17)

Internodes 1-14, Y = 0.05738

Internodes 15 and above, Y = 1.3589 - 0.0407-X;? = 0.91,

(18)

where X is mainstem node number. Leaf area and internode lengths at leaf unfolding for leaves 10-12 increased as temperature increased to about 27-30°C and declined at higher temperatures (K. R. Reddy et al., 1996a). The equations describing these processes for leaves (cm2)and internodes (cm) as functions of temperature are as follows:

+ 2.186-X - 0.0381.P; ? = 0.62 Internodes, Y = -0.06853 + 0.1077-X - 0.00203 1 -F;

Leaves, Y = - 18.599

9 = 0.11,

(19) (20)

where X is average temperature for that period. The initial branch leaf area decreased linearly with nodes on the branches. This was consistent with the change in mature leaf area by position on the branch (Mutsaers, 1983a,b). This suggests that leaf area was largely determined by the number of cells formed before the leaf began to expand. The equation describing initial leaf area (cm,) as a function of branch node number is as follows (K. R. Reddy et al., 1996a): Y = 13.457 - 1.179.X ? = 0.98,

(21)

where X is branch node number.

C. SPECIFIC LEAFWEIGHTAND STARCHACCUMULATION The specific leaf weight of the leaves on plants grown at 20/12"C dayhight temperatures was more than that of leaves produced at other temperatures, and the plants grown in the high CO, environments at that low temperature had signifi-

REDDY ET AL.

2 46

'

cantly higher mass per unit area than plants grown in the 350 pl liter- [CO,] (Fig. 1OA). Leaf expansion characteristics and specific leaf weight determined the leaf contribution to the total sink strength. Plants grown in the 700 pl liter-' [CO,] at 25/17"C dayhight and higher temperatures had leaves that were slightly more dense than leaves of plants grown in 350 pl liter-' [CO,]. These changes in leaf densities may have been due in part to the nonstructural carbohydrate status of those leaves (Fig. 10B).The nonstructural carbohydrate concentration was higher in the leaves of all the high [CO,] grown plants except those grown in 40/32"C dayhight temperature. The plants grown in 20/12"C dayhight temperatures had the highest concentrations of nonstructural carbohydrates compared to those grown at any warmer temperatures; the plants growing in the 700 c1.1 liter-' [CO,] and in 20/12"Cdayhight temperatures had nearly twice the concentration of those growing in the ambient [CO,] at the same temperature. These results reflect the growth and developmental rates of plants in the various temperatures and [CO,]. 1.4

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Temperature, "C Figure 10 Effect of temperature and atmospheric CO, on specific leaf weight (A) and nonstructural carbohydrate content (B)of leaves (K. R. Reddy et a/., 1996b).

CROP MODELING AND APPLICATIONS

2 47

Cotton growth rates were severely limited by 20/12"C dayhight temperatures. Photosynthesis was affected less by temperature than by growth, resulting in the accumulation of nonstructural carbohydrates. The high CO, environment favored the production of additional carbohydrates, but temperature limited growth and thus carbohydrate utilization. Transmission electron micrographs of leaf chloroplasts grown in high and ambient [CO,] at various temperatures also illustrate the previous result (K. R. Reddy et al., 1996b). When leaf cross sections were examined at lower magnification, chloroplast starch grains were conspicuous and copious in all the leaves except those in which growth was rapid. Starch grains were more abundant in the chloroplasts of leaves grown in high CO, environments and the most abundant in plants grown at low temperatures, where growth was slowest. The equations describing these processes for specific leaf weight (g dm-,) and total nonstructural carbohydrate contents (%) as functions of temperature are as follows: Specific leaf weight:

350 pl liter-' CO,, Y = 2.050 - 0.101O.X + 0.001593.P; 9 = 0.95

(22)

700 pI liter-' CO,, Y = 3.884 - 0.2240-X+ 0.003692.F; 9 = 0.94

(23)

Total nonstructural carbohydrate content:

350 pl liter-' CO,, Y = 78.79 - 5.2919.X + 0.0887-P; 1-2 = 0.91 700 pl liter-' CO,, Y = 129.272 - 8.3081.X 3 = 0.84

+ 0.1348.P;

(24) (25)

where X is average temperature for that period.

D. INTERNODEMASS ACCUMULATION RATE Internode mass accumulation of cotton plants grown in optimum water and nutrient conditions increased as a function of internode age (Fig. ll). Unlike elongation duration (Fig. 5 ) and rate of elongation (Fig. 7), internode growth rate (g day- I ) continuously increased throughout the growing season. The growth rate followed two distinct patterns, one during the expansion phase (0-20 days) and the other during the rest of the growth period (20+ days). The growth rate from 20 days onwards was 338% more than the growth rate during the expansion phase

E D D Y ET AL.

248

o

10

20

30

40

50

60

70

ao

90 100 110 120

Age of the Internode, d Figure 11 Internode weight vs age. Plants were harvested several times during the growing season and internode lengths and weights determined.

(0-20 days). Thus, internode growth rate, along with internode elongation patterns, was an important factor in determining sink strength. The equations describing potential internode growth rate (g cm- day-') as functions of age (days) are as follows:

'

+ 0.002693.X r;? = 0.46 21 + days, Y = -0.01828 + 0.01178.X r;? = 0.91,

0-20 days, Y = 0.005032

(26)

(27)

where X is the age of the internode after initiation.

E. ROOTGROWTH In our growth chambers, the aboveground cabinet temperatures are controlled within -COS"C, but root environment temperatures are not controlled. Therefore, the soil surface temperature reflects the effects of the aerial chamber temperature, but only small or no differences caused by chamber air temperature occur in soil temperature at 25 cm or deeper (V. R. Reddy et al., 1994a).The soil bins have one face (1 X 2 m) that is glass. This allows observation of root growth on regular intervals and then estimation of root growth activity by treatment. It appears that root numbers were limited by temperature when the aboveground parts were below 22"C, but differences in root numbers were not found at higher temperatures (Fig. 12). The temperature response curves of the data on root length were similar to those of root numbers. Root length per axis increased as temperature increased throughout the temperature range tested. If conditions prevailed that caused flower

CROP MODELING AND APPLICATIONS

249

30

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v

5

40

c" 30

F 20

f

c)

20

10

16

18

20

22

24

26

28

30

32

Temperature, "C

Figure 12 The role of temperature and CO, on rate of root development. Number of root sightings per m2 per day on vertical glass plane (top), root growth per day (middle), and root growth per root (bottom) (V.R. Reddy er af., 1994a).

or fruit abscission, then greater root growth occurred, apparently because of less competition for available nutrients. The most root growth occurred at the temperature optimum for vegetative shoot growth. Apparently, if temperature caused fruit abscision, additional root growth occurred because of sparing nutrients that might have been used for fruit growth. Without the fruit, those nutrients, presumably carbon, were available for additional vegetative growth, including roots (Pearson et al., 1970). The experiments were not designed to determine the effect of temper-

E D D Y ET AL.

250

ature on root growth. There is only limited information on root growth and environmental conditions. Recent advances in minirhizotron techniques and image analysis software will help unravel this area of research in future studies, but root growth response to temperatures are needed.

F. SQUAREAND BOLLGROWTH The data for square and boll development were collected from plants grown in both ambient and twice-ambient [CO,] to derive potential growth rates. Potential growth of developing squares was temperature dependent and had a curvilinear response; growth rate increased to 22°C but did not grow more rapidly at higher temperatures (Fig. 13A). Square growth rate was low and less sensitive to high temperature than boll growth (Fig. 13B). Boll growth was much more rapid, had a distinct optimum temperature at about 28°C and declined rapidly at higher tem0.01 6

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Temperature, "C Figure 13 The role of temperature on (A) flower bud (square) and on (B) boll growth rates (Baker e?al., 1983).

CROP MODELING AND APPLICATIONS I

I

10

20

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co,, pl I-'

0

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Boll Age, d Figure 14 Effect of carbon dioxide concentration on pima cotton boll growth. Plants were grown from emergence in controlled atmospheric CO, at 30/22"C dayhight temperatures. The means are not significantly different between CO, concentrations as determined by student's f test at the 0.05 probability level (K. R. Reddy etal., 1995d).

perature. Boll mass increased with age of the bolls, but no difference in weight per boll occurred due to different CO, environments ( P = 0.05) (Fig. 14). Plants grown in elevated [CO,], however, produced more fruiting mass per plant and the differences in total fruiting structure mass were the result of more bolls and squares produced rather than boll size (K. R. Reddy et al., 1995d). With increasing temperature, there was a shortening of duration of boll filling and slowing of the rate of filling at temperatures above 28°C (Fig. 3C and 13). Faster crop developmental rates resulted in less time available for the process or event to occur. Above-optimum temperature caused less time to be available for a fruit to develop, and it also reduced the rate of growth per day. Thus, any fruit grown at high temperatures was smaller than a similar fruit grown at optimum temperatures. This result was also true in rice (Baker et al., 1992), wheat (Wardlaw and Wrigley, 1994), as well as in cotton. The square and boll growth rate (g day-') as functions of temperature are as follows: Square, Y = -0.010168

+ 0.001253.X - 0.0000195.x2; 1.2 = 0.98

(28)

Boll: 0-28.5"C, Y = -0.0508125

+ 0.003125.X

28.5"C and above, Y = 2.73285 - 0.08285.X,

(29) (30)

REDDY E T AL.

252

where X is average temperature for that period.

G. WHOLE PLANT LEAFAREA DEVELOPMENT Whole plant leaf area development depends on the summation of all the leaves growing on the plant at any one time and their respective growth rates. This was obviously controlled by temperature when other factors were not limiting. Whole plant leaf area expansion rate was plotted for plants of different ages that were grown at different temperatures (Fig. 15A). Obviously, the small young plants had much slower rates of leaf tissue addition because fewer leaves were growing. Op-

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Temperature, "C Figure 15 The role of temperature and age (A) on whole plant leaf area expansion and (B) biomass accumulation rates of young cotton (K. R. Reddy et al., 1992b).

253

CROP MODELING AND APPLICATIONS

timum temperature for whole plant leaf area development was 27"C, and leaf development decreased at other temperatures.

H. WHOLEPLANTGROWTHRATE Whole plant growth rate responses to temperature were similar to those of many of the previously mentioned plant parts (Fig. 15B).The maximum growth rate was at 27°C with lower growth rates at other temperatures higher than 27°C. This response to temperature was quite similar to whole plant leaf area response to temperature (Fig. 15A).

N.PARTITIONING BIOMASS The apparent priority for organs receiving available nutrients varies with growth stage. The specific factors controlling an overall priority system are not well understood; therefore, a general mechanism (if it exits) controlling the allocation of nutrients among competing plant organs is not known. The controlled environment studies, in which potential growth rate conditions were maintained, may be used to draw some inferences concerning resource partitioning. Early in the growing season, in the natural environment, survival is often de-

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Days after Emergence Figure 16 The effect of plant age on biomass partitioning of biomass to roots and aboveground parts (Hodges e t a / . . 1993).

E D D Y ET AL.

2 54

pendent on the ability of plants to extract water and nutrients from the soil. In our experiments, a large proportion of the available carbon was used for root growth early in the life of the plant and proportionally less was allocated for roots as the season progressed (Fig. 16). In environments in which the temperature caused fruit to grow slowly, to not be produced, or to be abscised soon after flowering, plants allocated much of their resources to stems, leaves, and roots (Fig. 17). In the growing environments in which the temperature is favorable for fruit growth, nearly 50%of the total biomass produced was in the fruit. Expansion of leaves and stems was limited during the fruit-growing period, and new leaves and internode additions were delayed because of the intraplant competition (Figs. 4B, 9A, and 15). However, even during the fruit growth period, when intraplant competition for nutrients was important, dry matter accumulation in the stem continued (Fig. l l ) and root growth probably continued, but slowly. Recent field observations using a minirhizotron technique found root growth recovered quickly as bolls matured (data not shown). Sadras (1995) reviewed and proposed a framework for compensatory growth in cotton due to fruit loss. In other experiments (Ben-Porath and Baker, 1990), in which root volume was very restricted but adequate nutrients and water were maintained, the total root mass was reduced. Acompensatory amount of growth occurred in developing fruit, however. In that situation, less taproot was produced, but larger numbers of fine roots were added to the plants growing in small containers. There is some evidence that extensive root growth is a vital part of overall plant development. The implication is that growth regulators produced in the roots enhance aboveground growth and play a role in regulating source-sink interactions. Such interactions of growth

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Temperature, "C

Figure 17 The role of temperature on cotton plant partitioning among different organs (V. R. Reddy er al., 1991).

CROP MODELING AND APPLICATIONS

255

rates among different plant parts, and therefore different partitioning coefficients, have caused some to hypothesize that plant growth regulators, such as PIX, an antigibberlin type plant growth regulator, or herbicides may be used to manage stem growth (K. R. Reddy et al., 1995b; Hodges et al., 1991, and references therein).

Y HIGH-TEMPERATURE EFFECTS ON FRUITING STRUCTURES Baker and Landivar (199 1) described a method for modeling abscission of fruiting structures in cotton. Basically, their method estimated the amount of carbohydrate produced plus any available from previous reserves, which they called C supply, and they estimated sink-demand potential based on the number of growing sites. They also estimated respiratory requirement, which was added to the carbon demand. When their supp1y:demand ratio became less than 1, they assumed that there was a carbon stress and the youngest reproductive organs (squares and bolls less than 7 days old) were subject to abscission. The supply4emand method has provided a relatively effective way to predict fruit abscission when other factors are not complicating the crop performance. With the recognition and partial quantification of high-temperature injury, which also causes square and young boll abscission, a more complicated mechanism for predicting fruiting structure abscission is needed. The observation of Constable (1991) that there is shedding of very small squares at the beginning of the season also complicates the issue, although Constable acknowledges that squares do compete with vegetative growth for available carbohydrates. The fact that mature leaf areas and areas at unfolding (Figs. 8A and 9A), and mature square lengths (Holt et al., 1994), follow the same trend on the mainstem nodes suggests that they both compete for available carbohydrate. Thus, the concept of supply-demand may adequately explain early square loss in cotton. The physiological mechanism responsible for high-temperature injury is not known and the process is not modeled. However, such a model is needed because crops frequently experience above-optimum temperatures in today’s crop production environments; such conditions will be more frequent and severe if global warming occurs as predicted (K. R. Reddy et al., 199%; Adams etal., 1990; Fisher et al., 1995). Neither of the crop models described by Wall etal. (1994) and Fisher et al. ( 1995)has any mechanism to predict this injurious aspect of high temperature on food and fiber production. K. R. Reddy et al. (1992~)found flower retention was negatively associated with the number of hours per day the plants were exposed to 40°C. Several other studies indicated that square and fruit abscission increased when average daily canopy temperature during the square- or fruit-formation period was above 30°C (V. R. Reddy et al., 1991; K. R. Reddy et al., 1992c; Hodges and Reddy, 1995).

REDDY ET AL.

256 100 80 7

-a

60

d

40

cn"

t

m

Produced

0

Retained

Upland

A

T

Em E

0

20

a

P o U g

100

m

80

-5cn

60

40

20 0 15

20

25 30 Temperature, "C

40

35

Figure 18 The role of temperature on number of (A) upland and (B)pima cotton fruiting structures produced and retained (K. R. Reddy e? al., 1992a.b).

Bolls Produced

25

26

27

28

29

30

31

32

33

34

35

Temperature, "C Figure 19 The role of temperature and CO, on upland cotton fruit produced and retained. Plants were grown in similar conditions until flowering, then placed in mid-South typical July diurnal temperatures. A range of temperature was obtained by varying some chambers maximum and minimum temperatures -2,2,5, or 7°C from typical July, maximum and minimum. Average daily temperature is the mean of 15-min averages throughout the period.

CROP MODELING AND APPLICATIONS

257

As temperature increased, upland cotton produced more fruiting sites, but fewer squares and bolls were retained (Fig. 18A). Pima was even more high-temperature sensitive than upland cotton. It failed to produce fruiting branches and therefore fruiting sites when the average daily temperature was 36°C (Fig. 18B). These results occurred in well-watered and fertilized plants in which insects or diseases were not a problem. In other experiments in which atmospheric [CO,] was varied, and high atmospheric [CO,] increased total photosynthesis, similar responses to temperature were observed (Fig. 19). Thus, high-temperature effects must be the result of temperature injury or physiological damage that is not affected by carbohydrate supply. Preliminary studies of flowers produced on plants grown in 42-year average daily July temperatures at Stoneville, Mississippi, plus 5 or 7°C found that pollen produced was viable if the plants had been exposed to high temperatures no more than 8 days. Almost no seeds were produced on flowers grown at ambient temperature, but that were pollinated with pollen from plants exposed to high temperature for 12 or more days. All parts of the flowers produced in the high-temperature treatments were smaller than flowers produced in 42-year average daily July mean temperature (data not shown). Thus, high-temperatureeffects on reproductive development are not clearly understood. Future studies should address reproductive developmental responses to environmental variables, particularly high-temperature and water-deficit conditions.

VI. NITROGEN-DEFICIT EFFECTS Growth and nutrient uptake by plants have been the subjects of numerous studies. Crop yields are much higher now than they were several years ago probably because of improved fertility practices. Nitrogen (N) fertility practices, in particular, have resulted in dramatically improved crop yields. Crop responses to additions of N have resulted in such gains that excesses in its application have sometimes occurred, and even been encouraged, because of the economic advantage. As a result, streams and aquifers have sometimes been contaminated by nitrate leached from the soil. In addition, it is now recognized that excessive nitrogen applied to cotton can sometimes cause lower fruitfulness. The relative amounts of vegetative to reproductive growth are modified by the plants’ environment, including nitrogen fertility. The function of N in crop growth and development has been studied extensively. The underlying assumption is that N deficiency causes several often related, yet discrete and identifiable, effects. These effects are integrated at the whole plant level to produce systematic alterations in growth, altered ratios of plant parts to one another, yield, and earliness. These effects are also dependent on environmental and genetic factors. Although crop plants interact with the environment in

258

REDDY ET AL.

a complex way, a careful understanding must be developed if reliable predictions of growth are to be made. Such predictions will help to enable the most productive fanning and environmental quality to be maintained. In an excellent review, Radin and Mauney (1986) described many of the responses of plants to N deficiency. They described the effects of low N nutrition of plants as causing (i) lower photosynthetic rates, (ii) slower leaf expansion resulting from lower hydraulic conductivity, and (iii) altered responses (largely stomatal) to water stress. They argued convincingly, and are supported by the results of Wong (1979), that although photosynthesis has received the most attention, it is probably the least important effect of N deficits in agricultural production situations. Leaf size is a primary visual symptom of N deficiency. Smaller leaves are produced by plants grown in low N (Radin and Mauney, 1986, and references cited therein). Radin and Parker (1979a) showed that N effects on leaf area were caused largely by differences in leaf cell expansion. Those differences were apparently the result of decreased root hydraulic conductivity in low-N-grown plants (Radin and Boyer, 1982). Their work was extended to show that the increased resistance occurred in the cortical root cells (Radin and Matthews, 1989).The lower root conductivity resulted in sufficiently lower leaf cell turgor to reduce expansion during the day but not during the night when water potential was higher. Cell wall softening was not affected by low N. Thus, these data, and those of Radin and Parker (1979b) and Radin (1983), show that N deficiency mainly inhibits leaf expansion by altering plant water relations. Probably for this reason, N deficiency causes many symptoms that are similar to plant responses to water deficits. Water deficits cause an earlier leaf-expansion response than photosynthesis response, which results in more carbon being available for other purposes (root growth, osmotic adjustment, cell wall thickening, etc.). The stomata of N-deficient cotton plants are more sensitive to water deficits and close long before wilting occurs (Radin and Parker, 1979b). This characteristic has ecological significance in that it allows N-deficient plants to essentially slow water use during periods of moderate stress. Slower water use and enhanced root growth allow greater exploitation of soil water and nutrients, effectively delaying the impact of drought (Shimshi and Kafkafi, 1978). Despite enormous amounts of research on plant nutrition during the past several decades, many of the specific relationships required for modeling appear to be totally missing or not quantified in many crops including cotton. For example, the relationship between leaf N and many physiological responses is not known, and the interaction of leaf nitrogen and growth in high-C0,-grown plants is not clear. How plants respond to nitrogen as carbon becomes more readily available is not well known. Results from fertility experiments and nutrient uptake studies often do not provide relationships between tissue N and plant growth or development processes. The rate of appearance of both vegetative and reproductive organs is

CROP MODELING AND APPLICATIONS

2 59

primarily governed by canopy temperature modulated by water and nutrient deficiencies. Some researchers have found no effect of nitrogen supply on the rate of leaf emergence (Davidson and Campbell, 1983; Bauer et al., 1984). Others have observed a decreased leaf emergence rate in response to low N supplies (Single, 1964; Terry, 1970; Dale and Wilson, 1978; Radin and Mauney, 1986; Boquet, 1989; Longnecker et al., 1993; Gerik et al., 1993; Parker et al., 1993). The discrepancies in these observations have several reasons: the different intensities of N-deficiency stress, growth stages, the methods used in the calculation of developmental events, and poorly controlled temperature conditions. It was unclear from the literature how severely N-starved plants must be before there is an effect on leaf emergence. Therefore, we conducted experiments in which plants were grown in naturally lit chambers at optimum temperature, water, and nutrient conditions until the plants had first squares. Various N treatments were imposed at 17 days after emergence. Growth, development, and photosynthesis were measured frequently along with leaf nitrogen. The data were used to generate the rate functions in response to leaf N content.

A. LEAFNITROGEN AND PHENOLOGY The rate of cotton leaf development increased as leaf N increased (Fig. 20). There was no difference in N response between plants grown at twice-ambient atmospheric [CO,] (700 pI liter-') and ambient atmospheric [CO,]; therefore, the data from both [CO,] were combined to generate the relationship. A quadratic equation fit the data better than any other form. The results show that the formation of new leaves or other organs was considerably slowed as leaf nitrogen diminished. To our knowledge, there are no reports directly dealing with leaf N and morphogenetic delays. The relationship describing this developmental rate (Vduration, day-') as a function of leaf N ( g N mP2) is as follows: Plastochron interval, Y = -0.805 + 0.856.N - 0.160-N2; 9 = 0.86.

(30)

The maximum rate of node development was achieved at about 2.5 g N m-,, and development was projected to stop at about 1.25 g N m-,, although leaf N never reached that low of a concentration. In the natural environment, plants nearly always function under somewhat nutrient-deficit conditions and canopy development is restricted by the nutrient concentrations in the tissue. The rate of development from square to flower was not affected by nitrogen nutrition. Several others have reported no differences of nitrogen treatments for time to first flower or time between flower and open boll (Waldeigh, 1944; Tewolde et al., 1993; Gerik et al., 1989). This suggests that once the organs are formed the

E D D Y ET AL.

2 60

a

0.5

-

1.25

. o

7Wp1I"CO, 35Opl1"CO2

1.50

1.75

om

2.00

2.25

2.50

2.75

Leaf Nitrogen, g rn-'

Figure 20 The relationship between leaf nitrogen (g N m-2) and rate of node development (Uduration, day-').

rate of development is governed by canopy temperature, and canopy nutrient status rarely gets so low that rates are not directly altered. The number of bolls and squares produced may be limited by nitrogen effects on node development and the initiation of both vegetative and fruiting branches. Media nitrogen concentration up to 2 mM caused more fruiting sites to be produced, but at higher concentrations additional increases were very small. Similar results were observed by several others concerning the effect of N deficits on the fruiting sites produced (Jackson and Gerik, 1990; Gerik et al., 1989). They, too, concluded that the number of fruiting sites was probably controlled by plastochron intervals and other developmental events such as branch development.

B. LEAFNITROGEN AND LEAFAND STEMEXPANSION Nitrogen-deficit effects on the processes underlying leaf area expansion rates are only occasionally documented in mathematical terms in the literature. Leaf growth parameters, such as initial leaf sizes, relative leaf expansion rate, and leaf appearance rates, are essential information to modeling canopy development in field situations in which leaf N content varies. Leaf area at leaf unfolding was greater in plants with more leaf N, and plants grown in high [CO,] had significantly bigger leaves at unfolding than plants grown at ambient [CO,] (Fig. 2 1). The fact is that any parameter that affects leaf area will cause variation in initial leaf sizes (Terry, 1970; Dale, 1972; Robson and Deacon, 1978; Tolley-Henry and Raper, 1986; Gerik et al., 1993). The equations describing these initial leaf sizes (cm2) as a function of leaf N (g N mP2)are as follows:

CROP MODELING AND APPLICATIONS 12

I

261

zo I

I

I

I

I

I

70OplI"CO,

l1

-

350 pl1" CO,

0

10 9 -

-

0

8 -

0 00

O0

7 -

Y-

m

al

4

6

,

I

Figure

350 p1 liter-' CO,, Y = -6.5774 + 13.4476.N - 2.8023*N2;3 = 0.81

(31)

700 pl liter-' CO,, Y = -4.8437 + 12.1494.N - 2.4555.N2; 3 = 0.69.

(32)

The RLER increased as the leaf N concentration increased (Fig. 22) with the maximum RLER obtained at the highest leaf N concentration. Similar increases in leaf area have been observed with increased N (Radin and Sell, 1975; Oosterhuis et al., 1983; Jackson and Gerik, 1990; Gerik ef al., 1989; Fernandez et al., 1993). 0.15 0

B

4

0

0.05

a

0.00 1.25

1.50

1.75

2.00

2.25

2.50

Leaf Nitrogen, g ni' Figure 22 The relationship between relative leaf expansion rate (cm2 cm-* day-') and leaf nitrogen (g N m-*).

REDDY ET AL.

262 5 iJ

5 4

0

700plI"CO,

0

350 pI r1CO,

a

d a mr

2 3

c

.-0

9 2

k w E l

d

0 1.25

1.50

1.75

2.00

2.25

2.50

2.75

Leaf Nitrogen, g m-* Figure 23 The relationship between stem extension rates (cm day-') and leaf nitrogen (g N m-2).

The RLER was not different between CO, treatments; therefore, the data were combined to generate the rate parameters. The RLER (cm2 cm-2 day-') and leaf N (g N m-,) relationship is expressed as follows:

Y = -0.5374

+ 0.5063.N - 0.0981*N2;r;! = 0.84.

(33)

Stem elongation rate increased as leaf N increased (Fig. 23). Stem elongation rate followed a quadratic trend similar to that of leaf expansion rate. Plants grown in high-CO, environments had consistently higher stem elongation rates at all N levels compared to plants grown in ambient CO, environments. The shapes of the curves in both CO, levels, however, were similar. These data suggest that stem elongation was also carbohydrate limited and was consistent with the sensitivity of stem growth to boll load. Rates of stem elongation (cm day-') as a function of leaf N (g N mP2) are as follows: 350 pl liter-' CO,, Y = - 10.967 r;! = 0.91

+ 11.633.X - 2.389.P;

700 p,l liter-' CO,, Y = - 10.238 + 11.20.X - 2.282.P; r;! = 0.90.

(34) (35)

C. LEAFNITROGEN AND SPECIFIC LEAFWEIGHT Specific leaf weight was maximum when leaf N concentration was low and decreased as leaf N increased. Specific leaf area (cm2 g-I), the inverse of specific leaf weight as a function of leaf N (g N m-,), is as follows: Y = -0.946

+ 1.438.N - 0.264*N2;r;! = 0.80.

(36)

CROP MODELING AND APPLICATIONS 2.5 I

263

1

I

4

I

I

a

I

I

1.75

2.00

2.25

2.50

I

r

a

Y

E 2.0 -

Er

r1CO,

0

700pl

0

350plI" CO,

1.5

.-u?

ga

1.0

CI

* C

a

9 0.5 0

E

'

0.0 1.25

I

1S O

Leaf Nitrogen, g nf2

Figure 24 The relationship between leaf nitrogen (g N m-*) and rate of photosynthesis (mg CO, m-2sec-')measuredat 1600pmolm-Zsec-'.

D. LEAFNITROGEN AND PHOTOSYNTHESIS The rate of photosynthesis was uniquely related to leaf N both in ambient and twice ambient [CO,] (Fig. 24). Plants grown in twice-ambient [CO,] had consistently higher photosynthetic rates at all N levels compared to plants grown in ambient [CO,]. Such a relationship between leaf N and photosynthetic rates has been observed in several other species, including soybean (Boote et al., 1978; Sinclair and Hone, 1989),rice (Cook and Evans, 1983; Yoshida and Coronel, 1976), corn (Ryle and Hesketh, 1969; Wong et al., 1985), wheat (Osman etal., 1977; Evans, 1983), sunflower (Goudriaan and Van Keulen, 1979; Connor et al., 1993) and Eucalyptus spp. (Reich and Walters, 1994). The photosynthetic rates (mg CO, rn-, sec-l) and leaf N (g N m-,) relationships are as follows:

350 pl liter-' CO,, Y = - 1.6642 + 2.01 16.N - 0.3172.N2; 1.2 = 0.88

(37)

700 pl liter-' CO,, Y = -1.7204 + 2.1967.N - 0.3092.N2; $= 0.90.

(38)

E. LEAFNITROGEN AND TRANSPIRATION The potential transpiration rate of a canopy is dictated by meteorological conditions, but it also depends on the availability of water in the root zone. Nitrogen

REDDY ET AL.

2 64 500

i 0

I

I

I

I

1.6

2.0

2.4

2.8

300

.U

g 200

I

8 C E I-

100 0 1.2

Leaf Nitrogen, g m-' Figure 25 The relationship between transpiration (mg H,O rn-, sec-I) and leaf nitrogen (g N m-*).

deficiency causes stomates to partially close because of greater resistance in root cortical cells (Radin and Boyer, 1982) and thus affected transpiration (Fig. 25). Nitrogen deficit causes reduced leaf growth, in both size and number, as well as stomatal opening and closing. Therefore, studies in which N is a variable are always confounded with water relations. Transpiration increased linearly with increased leaf N in both CO, levels in well-watered conditions, but the rates of increase were not parallel. The equations describing transpiration rates (mg H,O mP2sec- ') and leaf N (g m-,) are as follows: 350 pl liter-' CO,, Y = -278.218 700 pl liter-' CO,, Y = -120.352

+ 250.22.N; 3 = 0.92 + 171.43.N; 1.2 = 0.91.

(39)

(40)

F. MODELING THEEFFECTSOF N DEFICITS Potential growth, development, and photosynthetic rates were calculated as functions of canopy temperatures under optimum water and nutrient conditions. Reduction or delay factors were calculated by assigning maximum growth, development, or photosynthetic rates at maximum leaf N, and the values at leaf N below the maximum were expressed as a fraction of the maximum rate. The new scaled values range from 0, where no growth or development occurred, to 1, where potential growth or development occurred at maximum leaf N. The relationships calculated in this way can be used as multiplication factors to decrease the potential growth and developmental rates in nitrogen-deficit environments.

CROP MODELING AND APPLICATIONS

265

The factor limiting growth response to multivariables is not always readily indisputable, nor can one always be confident of the most appropriate way to model the stress results. Acock (1990) discussed several processes that may control partitioning of photosynthates and other plant growth resources into different plant parts. For an extended discussion of how plants respond to two or more simultaneously varying limiting factors see Sinclair (1992). Compensation occurs in plants when an apparent single-most limiting nutrient, e.g., nitrogen, is supplied with an additional nutrient, e.g., carbon. The results from this experiment are consistent with the hypothesis proposed by Bloom et al. (1985). They proposed that when simultaneously limiting factors that control plant growth are increased, then growth is also increased in proportion to the extent the deficit of either nutrient was overcome.

VII. WATER-DEFICIT EFFECTS Plants growing in the natural environment are often prevented from expressing their full genetic potential for yields. Environmental stresses have been estimated to reduce crop yields in the United States by about 71% compared with maximum achievable yields (Boyer, 1982).Similar problems are encountered worldwide because much of the world’s cotton production is in arid or semiarid climates. With irrigation, attempts are made to optimize moisture conditions by controlling amounts and timing of water application. Within the United States, for example, yields in the irrigated Southwest are approximately double those of the nation as a whole (USDA, 1989). Such high yields are the result of growing crops in a high-radiation environment and correcting the most limiting environmental constraint-water deficits. When a plant experiences a shortage of water, its water content decreases and tissue water potentials become more negative. The negative tissue water potentials cause reduced tissue expansion, lower photosynthetic rates, and closed stomata, and proportionally more dry matter is partitioned to roots. In extreme conditions, the leaves wilt, senesce prematurely, and abscise; ultimately, the whole plant dies. Plants that are exposed to relatively long cycles between irrigation are less able to extract water from the soil than frequently irrigated plants (Radin et al., 1989). This is particularly true during the fruiting period when intraplant competition is strong between fruiting structures and roots for limited supplies of carbohydrates. Increased hydraulic resistance of root systems of water-deficit-exposed plants and intraplant competition for resources limits root growth and delays recovery of plants after watering. Such delayed recovery extends the negative effects of drought on crop productivity. Leaf water potential is commonly accepted as an index of plant water status and

E D D Y ET AL.

266

is relatively easy to measure. It is a function of both availability of water in the root profile of the soil and prevailing atmospheric demand. It is also influenced by the hydraulic conductivity of the system, which may be influenced by the age of the plants and their previous environmental conditions.

A. TISSUE EXPANSION Water deficits have very large effects on tissue expansion in both stems and leaves. Rates of increase in plant height are plotted against midday leaf water potential (Fig. 26). Stem elongation rate declined in response to leaf water potential. Stem growth was maximum when midday leaf water potential was - I .2 MPa; it decreased to zero at about -2.2 MPa. Stem elongation rate (cm day-') as a function of midday leaf water potential (MPa) is as follows: Y = 4.2904 - 0.6491.X - 0.9737.P;1.2 = 0.77,

(41)

where X is midday leaf water potential (MPa). Water deficits also had a very large effect on the expansion of leaves that was similar to that found for stem elongation (Fig. 26). Leaf growth rate was maximum (m2day-') at about - 1.2 MPa and decreased to zero at -2.4 MPa. The relationship describing leaf expansion rate (m2 dayF1)as a function of midday leaf water potential (MPa) is as follows (Marani ef al., 1985):

Y = 44.89 + 33.98.X + 6.38.P; 9 = 0.73,

(42)

where X is midday leaf water potential (MPa).

I

cn l Y 5

Photosynthesis

E

f 4 a"

-8

$ 2

P,

C

a

C

P

0 -3.5

-3.0

-2.5

-2.0

-1.5

-1.o

Midday Leaf Water Potentential, MPa Figure 26 The relationship between midday leaf water potential (MPa) and the rates of canopy photosynthesis(rng CO, rn-2 sec-I), and stem elongation (cm day-').

CROP MODELING AND APPLICATIONS

267

B. PHOTOSYNTHESIS Photosynthetic rate reduction in cotton as a result of moisture deficits is well documented (Hsiao, 1973; Ackerson and Kreig, 1977; Sung and Krieg, 1979). There is a close relationship between photosynthetic rate and leaf water potential (Fig. 26). Several others observed similar results both in the growth chambers and in tield-grown cotton (Ackerson and Kreig, 1977; McMichael and Hesketh, 1982; Marani et al., 1985; Radin, 1992; Turner el al., 1986). The photosynthetic reduction rate (mg CO, rn-, sec-') as a function of midday leaf water potential (MPa) is as follows: Y = 4.7929 - 0.1318.X - 0.3319-X2; 9 = 0.63,

(43)

where X is midday leaf water potential. The inhibition of photosynthetic rate was assumed to be caused by both stomatal closure and nonstomatal factors when the plant experienced water deficits (Hutmacher and Krieg, 1983; Farquhar and Sharkey, 1982; Jordan and Ritchie, 1971).

C. MODELING THE EFFECTS OF WATER DEFICITS In order to apply the functional relationships between leaf water potential and various growth and photosynthetic processes in any environmental situation and in different plant growth stages, the data on the effect of leaf water potential on stem and leaf growth and on photosynthesis had to be reanalyzed assuming that maximum growth potential occurs at the minimum midday leaf water potential. At the minimum midday leaf water potential when the midday water potential was no less than 1.O MPa, the growth potential or the process is given a value of 1, and the rest of the values are expressed as a fraction of that minimum. The new scaled value now ranges from 0 at no growth to 1 at the potential or the maximum growth. For simulation purposes, these values are used as multiplication factors to decrease the potential growth, development, and process rates that were calculated as functions of temperature.

VIII. MODEL DEVELOPMENT A physiologically based crop model can be assembled using the equations previously described. The equations were based on the data collected from controlledenvironment studies with only the limiting variable being tested. Therefore, the equations provide a basis to model potential crop growth and development rates. The quantitative information introduces only briefly how one might model waterand nutrient-deficit conditions.

268

REDDY ET AL.

The step-by-step model development and applications are as follows: 1. The time required to produce the first square from emergence can be calculated by summing the daily developmental rates from Eq. (1) for upland cultivars and Eq. (2) for pima cultivars until the summed value equals 1 or greater. 2. Once the squares are formed, their potential developmental rates to become flowers can be calculated from Eq. (3) for upland and Eq. (4) for pima cotton cultivars. 3. The boll maturation period, or the time from flower to open boll, can be calculated from Eq. ( 5 ) for upland and Eq. (6) for pima cotton cultivars similar to the procedure used in the calculation of time to first square by summing the.daily developmental rates. 4. Cotton potential leaf unfolding interval rates on the mainstem and on fruiting branches can be described with Eqs. (7) and (8), respectively. Vegetative branch leaf unfolding intervals follow the mainstem axis. Nutrient-deficit effects may be added to account for morphogenetic delays. Equation (30) is used to simulate N effects on leaf development. 5. Once leaves and internodes are initiated, their potential growth can be simulated with three rate functions: growth duration, maximum RLER or maximum RIER, and rates of growth reduction with age [Eqs. (9)-( 14)]. 6. Variable internode lengths or leaf areas on the mainstem and on branches can be simulated by using initial values, size at the time of leaf unfolding, from Eqs. (15)-(21) assuming ontogenetic patterns seen in the mature internodes and leaves are set at or before leaf unfolding. 7. Potential growth of plant height or whole plant leaf area simulation can be predicted by integrating the growth rates of successive internodes on the mainstem or all the leaves both on the mainstem and on the branches capable of growth. 8. Nitrogen-deficit effects on leaf and stem growth can be added with Eqs. (3 1)-(35). These growth processes can be decremented by water-deficit effects on stem elongation and leaf area development based on Eqs. (41) and (42). 9. Potential leaf weight per unit area increase can be calculated from Eqs. (22) and (23).The effect of nutrient status on specific leaf weight can be calculated with Eq. (36). 10. Potential internode growth rate can be calculated from Eqs. (26) and (27), and total mainstem weight can be calculated by summing growth rates of all internodes. A similar approach can be employed for fruiting branch internodes. 11. Potential canopy photosynthetic rates in the model from Baker ef al. (1983) and water- and nutrient-deficit effects on photosynthesis could be calculated from Eqs. (37), (38), and (43). 12. The effect of high temperature on fruit and square abortion needs much more attention in the future to develop models in that area. The data available in the literature are not adequate for developing models for fruit abscission in response to short- or long-term high-temperature stress.

CROP MODELING AND APPLICATIONS

2 69

From these equations, a crop model can be developed that describes the responses of cotton to many aspects of its physical environment. The data from which these equations were derived represent conditions in which a concerted effort was made to obtain potential rates. It is also shown how potential morphogenetic rates may be delayed or how growth rates may be reduced by water and nutrient deficits. These potential growth and developmental rates, and the effects of water and nitrogen deficits on these potential growth and developmental rates, have been incorporated into a comprehensive simulation model for cotton, GOSSYM/COMAX/WHIMS, which will be discussed briefly later. A cotton crop simulation model was developed in the mid-1980s by several collaborators (Baker et al., 1983; Whisler et al., 1986). This model, called GOSSYMKOMAX, was the first of its kind available for assisting agronomic production-type decisions. It is a materials-balance model that simulated the location of water, nitrogen, and carbon in the plant and soil system. It simulates the soil processes, including roots, using two-dimensional geometry. The concept of simulating potential growth and developmental rates allowed model developers to incorporate growth reduction and organ abscission factors into the model. For the first time, this provided a vehicle to simulate deficits of water and nutrition factors and relate those deficits to the plant’s potential growth requirements. They used a supp1y:demand ratio concept to reduce the growth rates of particular organs in water- and nutritional-deficit environments. This concept proved to be a useful mechanism to simulate plant responses to environmental-stress conditions (Baker and Acock, 1986). The GOSSYMKOMAX model expert system has been used commercially by cotton producers and consultants to assist management decisions since 1984 (McKinion et al., 1989). It has undergone many changes as new information has became available (K. R. Reddy et al., 1993b, 1995a,b,d; K. R. Reddy, 1995) for producers and consultants (Ladewig and Thomas, 1992; McKinion et al., 1989) and for scientists to study and identify the principles of plant-environment interactions (V. R. Reddy et al., 1989a.b; Landivar et al., 1983; Whisler et af., 1982, 1993).

A. THECOTTONSIMULATION MODELGOSSYM The development, characteristics, and applications of GOSSYMEOMAX have been previously described (Baker et al.,1983; McKinion et al., 1989; Baker and Landivar, 1991). Briefly, GOSSYM, an acronym from the word Gossypium, the genus of cotton, is a cotton crop simulation model that is linked to a rule-based expert system called COMAX ( C a p MAnagement expert). GOSSYM simulates crop responses to environmental variables such as solar radiation, temperature, rainhigation, and wind, as well as variation in soil and cultural practices. Growth and development are estimated and a record is kept of leaf,

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internode, square, and fruit age. GOSSYM estimates growth and development rates by calculating potential rates from daily temperatures assuming other conditions are not limiting; then it adjusts the potential rates by intensity of environmental stresses as discussed previously. GOSSYM provides its users with the size and stage of the crop as well as its present growth rate and the intensity of the stress factors. Therefore, a grower can assume certain weather conditions (e.g., last year’s temperature, rainfall, solar radiation, and wind speed) to determine yield estimates depending on the current maturity of the crop. A flowchart of GOSSYM shows the general organization of the model and program flow (Fig. 27). GOSSYM is the main program from which all the subroutines vertically below it in the diagram are called. CLYMATreads the daily weather information and calls DATES, which keeps track of both Julian day number and the calendar date being simulated; CLYMAT also calls TMPSOL, which calculates the soil temperatures by soil layer. SOIL is a mini-main program, which calls the soil subprograms (Boone eral., 1995).The soil routines provide the plant model with estimates of soil water potential in the rooted portion of the soil profile, an estimate of the nitrogen entrained in the transpiration stream available for growth, and an estimate of metabolic sink strength in the root system. The belowground processes are treated in a two-dimensional grid. The material balances of water and nitrate, ammonia, and organic matter are maintained and

Figure 27 Flowchart of GOSSYM cotton crop simulation model showing the organization of the model and program flow.

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updated several times per day. FERTLIZ distributes ammonium, nitrate, and urea fertilizers in the soil profiles. GRAFLO simulates the movement of both rain and irrigation water into the soil profile by gravitational flow. ET estimates the rate of evaporation from the soil surface and transpiration from the plant. UPTAKE calculates the amount of soil water taken from the soil region in which roots are present. CAPFLO estimates the rewetting of dry soil from wetter soil by capillary flow. NITRIF calculates the conversion of ammonium to nitrates by bacterial action in the soil medium. CHEM is also a mini-main program that calls subprograms, PIX (K. R. Reddy et al., 1995b)and PREP (V.R. Reddy, 1995), which calculate the effect of chemicals on plant physiological processes. PIX deals with the effects of the plant growth regulator, mepiquat chloride, and PREP deals with the effect of a boll opener, ethephon, and defoliant chemicals. In PNET leaf water potential, canopy light interception, photosynthesis, and respiration are calculated. Then, in GROWTH, potential dry matter accretion of each organ is calculated from temperature. These potential organ growth rates are adjusted for turgor and nitrogen availability. Photosynthates and any reserve carbohydrates are partitioned to the various organs in proportion to the total growth requirements. The partition control factor is the carbohydrate supp1y:demandratio. RUTGRO calculates the potential and actual growth rates of roots. RIMPED calculates the effect of increasing soil bulk density on the capability of roots to elongate. NITRO calculates the partitioning of nitrogen in the plant. MATBAL keeps track of the nitrogen and carbon material balance in all parts of the plant and soil complex. In PLTMAP, fruit loss and developmental delays are calculated using both carbohydrate and nitrogen supp1y:demand ratios. These developmental delays are used to allow the simulator to slow the plastochron intervals that are calculated as functions of temperature depending on the intensity of the stress. ABSCISE estimates the abscission rate of fruit, squares, and leaves caused by nutrient or water deficits. PMAPS, COTPLT, and OUTPUT print various userselected reports from the model. The program cycles through these subroutines one day at a time from emergence to the end of the season. A more complete description of the subroutines and how GOSSYM works can be found in Baker er al. (1983).

B. EXPERTSYSTEM COMAX Briefly, COMAX is an expert system that was explicitly developed for working with the crop simulation model GOSSYM ( L e m o n , 1986). Its organization and utility have changed considerably since its inception (McKinion et al., 1989). COMAX is a forward-chaining,rule-based system that contains an inference en-

gine, a file maintenance system for the simulation model requirements, a database

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system for the knowledge base, and a “user friendly” menu-driven system for user interaction. The inference engine applies rules to (i) set up weather and cultural practice input data files, including plant growth regulator application used by GOSSYM program; (ii) execute the GOSSYM program; and (iii) interpret model results, making recommendations on timing and amounts of irrigation, fertilizers, plant growth regulators, and harvest-aid chemicals. For more detailed information on COMAX, see L e m o n (1986). The GOSSYM/COMAX system is more properly called a model-based reasoning system rather than an expert system. Such a model/expert system captures the expertise of the modeling team’s ability to develop the model and to interpret its results. The availability of such a crop growth model would be highly desirable for any intensively managed crop. A more complete description of COMAX, and how COMAX works in conjunction with the simulation model GOSSYM, may be found in Hodges et al. (1996).

C. EXPERTSYSTEMWHIMS Insect control for economical cotton production is consistently more critical than for any other major field crop. After testing and application of GOSSYM/COMAX as an agronomic management decision aid, it became apparent that many entomological decisions in cotton production were equally complex. A decision to spray an insecticide might control the problem, delay a problem, or kill beneficial insects and cause the problem to become worse. As a result of an untimely or inappropriate insecticide application, the producer might add both economic and environmental cost. Therefore, a rule-based expert system was developed to help control cotton-damaging insects in the mid-South that could be used in conjunction with GOSSYM/COMAX. This system is called rbWHIMS [rule-based (W)Holistic Insect Management System]. It makes recommendations on I3 arthropod pests. The insect-control expert system uses a Microsoft Windows graphical interface. A specifically designed scouting protocol was also developed to improve scouting efficiency and to provide the necessary data input for rbWHIMS (Olson and Wagner, 1992; Wagner et af., 1995a,b; Willers et af., 1992, 1995; Williams et af., 1991, 1995). The rbWHIMS is most useful when field conditions do not lead to definitive decisions or clear-cut actions on specific pests. The greatest possibility to waste or conserve resources directed to pest management occurs when such a condition exists, and it is at these times that the crop production manager needs a second opinion. The rbWHIMS is based on the combined judgment of several cotton insect-control specialists and may be used to provide the manager with such an opinion. However, insect-control situations are so unique that the rules may not be universal in application. Because we do not know

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much about the biology associated with insect control in a complex agricultural system, such rules and control strategy must be developed regionally.

M.MODEL CALIBRATION AND VALIDATION A. MODELCALIBRATION The simulation model developed from the response rate functions collected from the controlled environmentchambers needs to be calibrated against field data for each cultivar. Cultivars may have different rate functions to environmental variables. However, it is expensive and time-consuming to generate the processrate data for all the cultivars used commercially (Curry and Feldman, 1987). Several methods have been proposed to calibrate the simulation models including iterative and heuristic approaches to applying genetic algorithm techniques (V. R. Reddy et al., 1985; Boone et al., 1993; Sequeira et al., 1994).Assuming that the cultivars of a particular species have similar response rate functions to the environmental variables, one can shift the function by moving either direction with multiplying factors. For example, daily developmental rates from emergence to first square are presented in Fig. 3 and the response functions for these rate functions can be calculated by Eq. (1) for upland cotton, by Eq. (2) for pima cotton as described by Boone et al. (1993), or by automating the techniques to parameterize the models as described by Sequeira et al. ( 1994). The data for upland cotton were collected for two cultivars and there were no differences between cultivars for this trait. Therefore, the data were pooled to generate the function. If different cultivars take more or less time for this particular response function, the function can be modified by multiplication factors: 1.1 for early season cultivars and 0.90 for late-season cultivars compared to that generated in the present study. However, it is not possible to accurately calibrate the model for pima cotton cultivars based on the response functions developed for upland cotton because the response functions are not parallel. Thus, a new response function had to be developed for pima cultivar in order to simulate its behavior.

B. MODEL VALIDATION Validation is a critical stage in model development if the models are intended for use as an on-farm decision-aidingtool. Validation data should be obtained from real-world field experiments. The data sets must include a wide range of soil and weather conditions with an array of cultural practices and genetic resources. Val-

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idation should not include the data sets that were used to calibrate the model. Here, the researcher needs data to represent the diverse conditions to which the crops to be simulated will be subjected. Environmental extremes and stress conditions should be included in the database so that simulation problems caused by those conditions can be identified. Also, validation data should include more information than conditions and yield. Yield may be limited by several factors that occur at one time, or at different times, during the growing season. Uniqueness of weather patterns, such as early cool or hot periods, rainy conditions, late season droughts, or nutritional deficits caused by inadequate fertilization or root zone leaching, are only a few of many environmental factors to which the model should be responsive. Validation data should be screened for freedom from production factors that are not being simulated before those data are used to test the model for yield. If weeds or weed control, for example, are not being simulated in the model, then weeds or weed control should not be a limiting production factor in the data set used to test the model. Also, a database including crop responses to an herbicide that damages the crop’s root system should not be included in the validation data set. This damage causes unpredictable responses to drought and nutrition that the model was not designed to simulate.

C. MODELTESTING METHODS Evaluating the accuracy of various simulation model results through comparison with observed values is not always instructive and straightforward.This is partially due to the fact that it is easy to replicate observed data, but it is not easy to replicate the simulations.As attempts to build and validate simulation models have increased, several methods to judge the performance of simulations have been proposed (Feldman et al., 1984; Gardner er af., 1990; Garratt, 1975; Talpaz et al., 1987; Kleijnen, 1982; Law, 1983; Loague and Green, 1991; Pritstker, 1984; K. R. Reddy et af.,1995b; Reynolds, 1984; Shaffer, 1988; Shannon, 1975; Welch et af., 1981; Willers et al., 1995). A common statistical technique is to plot observed and simulated results and fit a straight line through the data with a zero intercept. The slope and coefficient of determination (?) values are used as indices of agreement with the observed data. However, the distributional assumptions of the standard regression may be violated if these performance statistics are used alone (Welch ef af., 198 1). Furthermore, environmental data frequently exhibit skewed distributions rather than normal distributions (Parkin and Robinson, 1992).Thus, the relationship between 9 and performance of models is not always instructive. The additive main effects and multiplicativeinteraction model (Willers et al., 1995), for comparing unreplicated model output to the mean response of replicated data, and the mean of the mean sum square errors (K. R. Reddy et al.,

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1995b),for evaluating the performanceof two similar models dealing with the same process, provide new techniques in evaluating the simulation model performances.

X. MODEL APPLICATIONS AND BRIDGING TECHNOLOGIES Before we can discuss model applications, we need to discuss the types of bridging technologies that make additional use of a biological model possible. Members of the Crop Simulation Research Unit have had many years of experience in building crop models, insect models, and associated decision support systems. Simply building a crop decision support system is not enough. It must be easy to use. The user interface must be intuitive. The system must limit the requests made by the user to supply labor-intensive information. The system within its selfdescribed limits must work successfully. The system must be thoroughly tested and debugged. Software used by consultants and growers for crop management decision support must be essentially bug free or the developers will face the unpleasant realization that users will say the system does not work, whether or not the bug affects system accuracy.

A. COMPUTER TECHNOLOGY The advent of the personal computer, with increasingly more available memory, disk space, and computational power, starting in the mid-l980s, is largely responsible for use of models and decision-support systems for cropping systems. This phenomenon was predicted by McKinion and Baker (1983). They observed that computer technology, driven by the integrated circuit industry, had an orderly, predictable increase in computer speed and memory availability. This increasing computing power was making minicomputers competitive with mainframe computers for running software. Industry had published data showing that, over a 5-yearperiod, users could expect memory costs to decrease by a factor of 90% and the cost of the central processing unit to likewise decrease by 75%. At the same time, the speed of the computer was doubling every 2 years while the price essentially remained constant. Integrated circuits were just then being developed to build the first desktop computers, i.e., the Altair microcomputer. Even then, it became apparent that the same process that made minicomputers competitive with mainframes would be at work to improve the microcomputer. For the foreseeable future, computer capabilities will continue to increase while costs continue to decline. Computer capabilities doubled about every 2 years dur-

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ing the period from 1960 to 1985. Since then, this rate of technology introduction has accelerated. From 1985 through 1992, the doubling period diminished from 24 months to 18 months. From 1992 to 1995, the doubling period was approaching 12 months. When technological change in the past has allowed an incremental (2 X to 4X) increase in computer capability over a 5-year period, software technology has followed a smooth evolutionarypath. Now, with the order of magnitude change in computational power (not only just speed but also memory size, disk storage size, and high-speed access to the Internet) over a 5-year period, we face revolutionary changes in the way we do business with computers. These dramatic increases in computer technology will cause even more radical changes in the way information is handled and delivered to the user. This discussion will not be concerned with many applications of computers on the farm. The list is endless and will continue to grow for the foreseeable future. We do, however, identify some developing technologies that we see as possibilities of facilitating major changes in crop production management and related activities.

B. OBJECT-ORIENTED SIMULATION Advances in software technology will allow new computer programs to take advantage of increased computational capability. Foremost among these is the object-oriented programming paradigm. For the first time, it appears that a programming methodology will finally deliver on promises of code reusability. As part of its assignment in the U.S. Global Change Research Program, the Agricultural Research Service (ARS) recently identified the need for comprehensive process-level crop models. Such models should respond mechanistically to future high-CO, climatic conditions for seven major crops: cotton, corn, wheat, soybean, rice, potato, and an unspecified forage legume. These crops constitute the great majority of tilled agricultural land, provide most of our food energy and protein either directly or indirectly, and should receive priority in model development. The construction of comprehensive, process-level models of these crops will require a significant modeling effort. In the past, models for different crops have been developed with varying levels of detail and capability. These models include relatively comprehensive, process-level models, such as GOSSYM, COTC02, OZCOT, and GLYCIM, less comprehensive models, such as SOYGRO, CERESWHEAT, KUTUN, and CERES-MAIZE, and finally the purely statistical regression models (Baker efal., 1983; Mutsaers, 1984; Acock and Trent, 1991; Hodges, 1991; Boote and Loomis, 1991; Hearn, 1994; Wall ef al., 1994). Of these models, only two, GOSSYM and GLYCIM for cotton and soybean, respectively, have been field tested on commercial farms for use as decision-support systems in crop production. Research is currently under way to give SOYGRO and GOSSYM the

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mechanisms necessary to respond to and predict the effects of a high-CO, future climate. However, these are only two of the targeted high-priority crop models. ARS, in a national modeling effort encompassing multiple locations and research units, is attempting to develop a next-generation cotton crop model. At the same time, and complementary to that effort, standardizedcrop model components will be developed. An informal project of cooperating ARS and state research groups has been organized under the banner cotton production modeling. These groups have agreed to use object-orientedprogramming technology to develop interchangeable modules using a standardizedmodel structure. Computer crop models have been written in the past using procedural languages, such as FORTRAN or PASCAL, or in computer-simulation languages such as CSMP. The most comprehensive crop models consist of several thousand lines of computer code and have been built by teams of interdisciplinary research scientists. These models have been a burden for nonmodelers, or even modelers of other crops, to understand. It may take as much as a year of training and examination of such a model before a scientist feels comfortable to modify it. Most researchers cannot afford to devote this much time to learning a crop model, particularly when they are interested in only certain subsystems. A potential answer to this problem has arrived in the form of object-orientedprogramming or, as applied to simulation, object-oriented simulation (00s).The specifications and capabilities of 00s offer exactly what is needed to provide easy access to comprehensive crop simulators. Using OOS, models can be written so that modules correspond directly to objects found in nature, thus providing easy recognition of system components by nonmodelers. Ideally, modules communicate through carefully and completely specified interfaces, relieving the scientist concerned with only the operation of one module from being forced to know the details of the rest of the model. Modules or objects should contain all the code and data necessary to completely describe that object. Thus, if a scientist knowledgeable about photosynthesis wanted to use a particular photosynthesis module, he or she would simply replace the model’s photosynthesis object. The user should not be concerned with the operation of other modules as long as the module responds correctly to the message-passing interface. Extensive and careful design early in the project is essential. To ensure that model hierarchy will easily accommodate such proposed changes as outlined previously requires extensive and careful design up front. For a good presentationof object-orientedprogramming, see Booch (199 l), and for a discussion of OOS, see Sequeira et al. (1991). The crop production modeling group has already spent several man months developing a generalized crop object-oriented model hierarchy. Perhaps even more design time must be allocated to object-oriented design than in top-down programming design efforts using structured systems design with the PASCAL or C programming languages. It has already been the experience of the Crop Simulation Research Unit at Mississippi State, Mississippi, with cotton and soybean models that there are a number of algorithms common among these models when writ-

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ten in FORTRAN. We expect that 00s will accelerate the development of generalized crop modules. Object-oriented simulation models may provide the necessary incentive to separate process and organ objects, which are plant species dependent, from the process and organ objects, which are species independent. For example, all crop models should respond to soil water potential, which is the output of a process common to all crops. Conversely, an object that describes the salient features of a soybean plant root nodule is an example of an object that is species dependent. Such developments will likely help to spread mechanistic, process-level crop modeling more rapidly and easily to other crop species.

C. DECISION SUPPORTSYSTEMS The GOSSYWCOMAX cotton crop management system is a simulationbased, decision support system that is widely used today in cotton production (McKinion et al., 1989).This system is currently used as a decision aid on a commercial basis for determining the (i) timing and amount of irrigation applications, (ii) timing and amount of fertilizer applications, (iii) timing and amount of plant growth regulators to be applied, and (iv) timing and amount of crop termination chemicals. The system for use as a decision aid for the application of pesticides for insect control, G O S S W C O M M H I M S , is scheduled to be released in the mid-South area in 1996 as an integrated system. All these decision-aid questions involve agronomic and entomological issues that are weather dependent and have traditionally been made by experienced managers for each management unit (often a field). Often, however, managers have limited information availableon which to base decisions. This has resulted at times in misuse of chemicals, resulting in poor choices for both environmental and economic reasons. Crop models also allow users to study the cropping system. Users can specify soils and test croppingpractices with multiple weather scenarios and ultimately make fundamental decisionson the basis of reasonable probabilitiesof weather. With a greater number of weather files, one can develop reasonable probabilities of simulated results and stronger risk management analysis. The use of expert system technology, coupled with detailed crop models, has already shown that a complex simulation system can be used by laymen as a crop management decision aid. These can and must be tested under current environmental conditions to establish validity.

D. GEOGRAPHIC POSITIONING SYSTEM The U.S. Department of Defense satellite navigation system was completed in the late 1980s and permits precise navigation and location of the receiving sensor. The space segment of the system consists of the GPS. These satellites send radio

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signals from space. The GPS Operational Constellation consists of 24 satellites and provides users with between five and eight position signals visible from any point on the earth. By using GPS sensors, which can receive the satellite signals, users can precisely compute their location and altitude on or above the surface of the planet. Using this technique, accuracies of +5 cm have been achieved. This technology makes possible the concept known as precision agriculture.

E. GEOGRAPHIC INFORMATION SYSTEMS GIS provide the integrating software technology in which the pieces of this concept are brought together. GIS allow the collection and display of megabytes of spatially registered data through a graphical user interface, but require largecapacity computers and data memory capability. GIS were only feasible for agricultural applications after such hardware became realistically inexpensive. GIS technology allows the coding of information, which is tied to spatial coordinates, and displays the information through maps. In agriculture, the basic map that a grower would use is the map of each management unit, which displays where each soil type is located. Not only could the soil type be known for each location, but also the soil physical properties such as bulk density, a soil water retention curve by horizon, sand, silt, and clay content, and percentage slope. Other data layers might include percentage organic matter, residual nitrate, ammonia content by horizon, preplant fertilizer content, and micronutrient content. After the crop is planted, additional data layers might include rainfall or irrigation amount, variety, plant population, tillage operations, and applied fertilizers, insecticides, herbicides, and plant growth regulators. Also, locations of historical hot spots for insect pests could be entered into the GIS system. When plant samples are taken or insect scouting information is gathered, all this information can be geo-referenced via the use of portable differential sensors. Thus, plant and insect information can be entered into the GIS database with precise location information. GIS can be defined with many layers of data, which can be specific to management units. When a crop model and expert system decision aid is interfaced to the GIS structure, users can activate the system by simply pointing and clicking with a mouse pointing device at the map location from which information is desired. Users will be able to simulate the growth of their crop, query the decision-support system for advice on crop management (both agronomic and entomological), and develop “control tapes” for use with farm implements coupled with harsh environmenttolerant microcomputers for applying seed and agrochemicalsin a precise manner.

F. INTELLIGENT IMPLEMENTS Tractors, harvesters, airplanes, and other powered equipment used to apply seed and agrochemicals for crop production, when equipped with powerful, harsh en-

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vironment-tolerant microcomputers, differential global positing satellite (DGPS) sensors, GIS software, and a control tape, can be called intelligent implements. The DGPS sensor tells the I1 where it is within a management unit. The GIS software with stored geographicaldatabases provides the context within which the intelligent implements are working. The control tape gives the microcomputer instructions on rate of application of materials, depending on which operation is being performed. The control tape can be a %inch diskette generated from the GIS-based GOSSYM/COMAX/WHIMSsystem. This diskette will contain the information on the rates by location within the managementunit. For example, a user has a field with six different soil types. The GOSSYMKOMAXNHIMS system is then run to develop first optimum planting densities by soil type. It then writes information to a diskette, which is then transferred to the intelligent implements, which are a tractor and variable-rate seed planter. As the tractor and planter travel across the management unit, the computer adjusts the seed planting rate for each of the six soil types. Similarly, as other production management operations are performed, the grower runs GOSSYM/COMAX/WHIMS to determine optimum application of fertilizer, plant growth regulators, water, herbicide, pesticides, and harvest-aid termination chemicals. The DGPS sensor tells the implement where its precise geographic location is. The GIS system provides the context of the operation by telling the intelligent implements which soil type they are on, etc.

G. SITE-SPECIFIC AGRICULTURE FOR FARM MANAGEMENT With intelligent implements and GOSSYM/COMAX/WHIMS, cotton growers can literally “farm by the square foot.” Precise applications of agrochemicals can be justified to potential regulators, and the growers can apply “what is right,” “when it is needed,” thereby becoming better stewards of the land. The model and expert system was developed from the beginning as a precision agriculture system, even before the term “precision agriculture” became popular. The model and expert system uses information on soil series type: soil water retention, sand, silt, and clay content, bulk density, initial nitrogen carryover from the preceding season, and percentage organic matter, all by soil horizon. These factors are by definition site specific. The American Farm Bureau Federation recently completed a 2-year study (March 3, 1995) of precision agriculture and its implications in today’s-tomorrow’s production agricultural world. They concluded that the missing element was a way to incorporate the biologically important and dynamic information into the proposed precision agriculture system. The crop model and expert system can provide the knowledge-based system’s requirement identified as the missing part needed for effective precision agriculture.The American Farm Bureau Federation report said that all the key components for precision agriculture are now available.

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These include variable-rate planters and chemical applicators, DGPS sensors with 55-cm accuracy, and GPS software. In other words, by linking dynamic, physiologically sound crop models and expert system decision aids with GPS, GIS, and intelligent implements, the package becomes complete for applying appropriate agronomic and entomological site-specific production practices. All extension recommendations from the experimental point of view were designed for general application on a wide area basis. Precision agriculture is the opposite extreme. Expert systems alone will not be able to help because they do not have predictive capability, and we do not have experts who can answer those questions on plant population, chemical application rate, etc. by soil type. Knowledgebased systems can provide those answers because they are a combination of a predictive model component of the system, which responds correctly to soil, weather, and cultural practice variation, and a rule-based expert system. The GIS software and DGPS provide location-based input information that, when combined with the GOSSYM/COMAX/WHIMSsystem, can generate the large quantity of information that will be needed by intelligent implements to carry out their operations. Today, the model and expert system has DGPS input capability, is coupled to a GIS system and database, and generates site-specific recommendations. Cotton production equipment is available that has implement-mounted computers, DGPS sensors, and variable application rates of materials under computer control. The final loop is now being closed by making the site-specific output of GOSSYM/COMAX/WHIMS available to the intelligent implements for use as the control algorithm for precision application of seed and agrochemicals. The aerial pesticide industry is already using DGPS sensors in airplanes to precisely determine where the aircraft is and when to apply the pesticide. This technology has already reduced grower’s and applicator’s liability by eliminating ground personnel formerly used as flaggers to help the aircraft keep track of where it has been and by logging the time and place of application of pesticides to reduce liability exposure to charges of misapplication of chemicals by site or air drift errors on other crop producers adjacent land.

XI. SUMMARY AND CONCLUSIONS In this chapter, we have attempted to bring together a unique data set appropriate for modeling cotton and to illustrate how it can be used to develop such a model. It is also suggested that models of other crop species can provide fundamental tools that can be useful for both in-season and preseason crop management decisions. Several additional crop management tools are becoming available that appear to provide additional impetus to allow precision agriculture to become feasible and real. As progress toward precision agriculture continues, the need for

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yield-limiting diagnostic tools and other crop management decision aids will become more urgent. Mechanistic crop models and automated, user-friendly expert systems that can facilitate selection of the optimum and environmentallysound solutions to problems with many variables are a way to select best-management alternatives. The engineering and computing technologies that are needed to allow precision agriculture to evolve with many of its ramifications are essentially all now available. Unfortunately, our understanding of the biology of crop production is less well advanced, and currently the models are not perfect. There are many aspects of crop growth, crop interactions with other plants, insects, and diseases, and the responses of these organisms to their physical environment that are not properly understood. Modeling forces organization of known information and concepts. Although we may not know enough to develop a comprehensive model that includes all aspects of the farm or crop production system, modeling a meaningful portion of the system provides clarity. For a model to correctly predict plant responses to physical conditions, the concepts and the response functions must be appropriately modeled. When a crop model is built on appropriate concepts and processes, it has predictive capability in new environments and could be used either alone or with other emerging newer technologies to disseminate information to the end users.

ACKNOWLEDGMENTS Appreciation is expressed for the fine technical assistance provided by Gary Burrel, Kim Gourley, Wendell Ladner, and Sam Turner. Part of the research was funded by the USDE National Institute for Global Environment Change through the South Central Regional Center at Tulane University (DE cooperative agreement No. DE-FCO3-BOER 61010). We thank Drs. Dave Albers, Susan Bridges, Greg Constable, Tom Hodges, Howard Rawson, Keith Remy, Victor Sadras, Stephen Welch, and Gail Wilkerson for critical review and suggestions.

REFERENCES Ackerson, R. C., and Krieg, D. R. (1977). Stomata1 and non-stomatal regulation of water use in cotton, corn, and sorghum. Plant Physiol. 60,850-853. Acock, A,, and Trent, A. (1991). The soybean crop simulator, GLYCIM: Documentation for the modular version 91. In “Responses of Vegetation to Carbon Dioxide No. 017.” U. S. Department of Energy and USDA, U.S. Department of Energy, Carbon Dioxide Research Division, Office of Energy Research, Washington, DC. Acock, B. (1990). Effects of carbon dioxide on photosynthesis, plant growth, and other processes. In “Impact of Carbon Dioxide, Trace Gases, and Climate Change on Global Agriculture” (B. A. Kimball er al., eds.), pp. 45-60. ASA Spec. Publ. No. 53. ASA, Madison, WI. Acock, B. (1991). Modeling canopy photosynthetic responses to carbon dioxide, light interception, temperature, and leaf traits. In “Modeling Crop Photosynthesis-From Biochemistry to Canopy” (K. J. Boote and R. L. Loomis, eds.), pp. 109-140. CSSA Spec. Publ. No. 19. CSSA, Madison, WI.

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Sinclair, R. 0..and Horie, T. (1989). Leaf nitrogen, photosynthesis, and crop radiation use efficiency: A review. Crop Sci. 29,90-98. Sinclair, T. R. (1992). Mineral nutrition and plant growth response to climate change. J. Exp. Bot. 43, 1141-1146. Single, W. V. (1964). The influence of nitrogen supply on the fertility of the wheat ear. Ausc. J. Exp. Agric. Animal Hus.4, 165-168. Sung, F. J. M., and Krieg, D. R. (1979). Relative sensitivity of photosynthetic assimilation and translocation of I4C to water stress. Plant Physiol. 64, 852-856. Talpaz, H., Da Roza, G. D., and Hearn, A. B. (1987). Parameter estimation and calibration of simulation models as a non-linear optimization problem. Agric. Sys. 23, 107-1 16. Terry, N. (1970). Developmental physiology of sugar-beet. 11. Effects of temperature and nitrogen supply on the growth, soluble carbohydrate content and nitrogen content of leaves and roots. J. Exp. Bor. 21,477-498. Tewolde, H., Fernandez, C. J., and Foss, D. (1993). Growth, yield and maturity of nitrogen and phosphorus deficient pima cotton. In “Proceeding Beltwide Cotton Conferences” (D. J. Herber and D. A. Ritcher, eds.), pp. 1184-1 190. National Cotton Council, Memphis, TN. Tolley-Henry, L., and Raper, C. D. (1986). Expansion and photosynthetic rate of leaves of soybean plants during onset of and recovery from nitrogen stress. Bot. Gaz. 147.400406. Turner, N. C., Hearn, A. B., Begg, J. E., and Constable, G. A. (1986). Cotton (Gossypium hirsurutn L.): Physiological and morphological responses to water deficits and their relationship to yield. Field Crops Res. 14, 153-170. U.S. Department of Agriculture (USDA) ( 1989). “Agricultural Statistics 1989.” US. Government Printing Office, Washington, DC. Wagner, T. L., Akins, D. C., Willers, J. L., and Williams, M. R. (1995a).User’s guide for rbWHIMS: An expert system for managing cotton arthropod pests in the Midsouth. Mississippi Agric. Exp. Sta. Tech. Bull. 207, Mississippi State, MS. Wagner, T. L., Williams, M.R., Willers, J. L.,Akins, D. C., Olson, R. L., and McKinion, J. M. (1995b.) Knowledge-base of rbWHIMS: An expert system for managing cotton arthropod pests in the Midsouth. Mississippi Agric. Exp. Sta. Tech. Bull. 205, Mississippi State, MS. Waldeigh, C. H. (1944). Growth status of the cotton plants as influenced by the supply of nitrogen. Arkansas Agric. Exp. Sta. Bull. No. 446, Fayetteville, AR. Wall, G . W., Amthor, J. S., and Kimball, B. A. (1994). COTC02: A cotton growth simulation model for global change. Agric. Fo,: Mereorol. 70,289-342. Wardlaw, I. F., and Wrigley, C. W.(1994). Heat tolerance in temperate cereals: An overview. Ausr. J. Plant Phsyiol. 21,695-703. Washington, W. M., and Meehl, G. A. (1984). Seasonal scale experiment on the climatic sensitivity due to a doubling of CO, with an atmospheric general circulation model coupled to a simple mixedlayer Ocean model. J. Geophys. Res. 89,9475-9503. Welch, S. M., Croft, B. A,, and Michels, M. F. (1981). Validation of pest management models. Environ. SOC.Am. 10,425-432. Wells, R., and Meredith, W. R. (1984). Comparative growth of obsolete and modern cotton cultivars. 111. Relationship of yield to observed growth characteristics. Crop Sci. 24,868-872. Whisler, F. D., Lambert, J. R., and Landivar, J. A. (1982). “Predicting Tillage Effects on Cotton Growth and Yield. Predicting Tillage Effects on Soil Physical Properties and Processes,” pp. 179-198. ASA, CSSA, SSSA, Madison, WI. Whisler, F. D.. Acock, B.. Baker, D. N., Fye, R. E.. Hodges, H. F., Lambert, J. R. Lemon., H. E., McKinion, J. M., and Reddy, V. R. (1986). Crop simulation models in agronomic systems. Adv. Agron. 40,142-208. Whisler, F. D., Reddy, V. R., Baker, D. N., and McKinion, J. M. (1993). Analysis of the effects of soil compaction on cotton yield trends. Agric. Sys. 42, 199-207. Willers, J. L., Yatham, S. R., Williams. M. R., and Akins, D. C. (1992).Utilization of the line-intercept

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method to estimate the coverage, density, and average length of row skips in cotton and other row crops. In “Proceedings of Applied Statistics in Agriculture: Kansas State University.” Kansas State Univ., Manhattan. Willers, J. L., Wagner, T.L., Sequeira, R. A.. Theseira, G. W., and Boykin, D. L. (1995). Analysis of deterministic simulation models using methods applicable to two-way treatment structures without replication. Agron. J. 87,478-492. Williams, M. R., Wagner, T.L., Willers, J. L., and Olson, R. L. (1991). Scouting protocol for arthropod pests of cotton in the Midsouth. Mississippi Agric. For. Exp. Sta. Bull. 977, Mississippi State, MS. Williams, M. R., Wagner, T.L., and Willers, J. L. (1995). Revised protocol for scouting arthropod pests of cotton in the Midsouth. MississippiAgric. Fo,: Exp. Sta. Bull., Mississippi State, MS (in press). Wilson, C. A., and Mitchell, J. F. (1987). A doubled CO, climate sensitivity experiment with global climate model including a simple ocean. J. Ceophys. Res. 92, 13315-13343. Wong, S.C. (1979). Elevated atmospheric partial pressure of CO, and plant growth. I. Interactions of nitrogen nutrition and photosynthetic capacity in C, and C, plants. Oecologia (Berlin) 44,68-74. Wong, S.C., Cowan, I. R., and Farquhar, G. D. (1985). Leaf conductance in relation to fate of CO, assimilation. I. Influence of nitrogen nutrition, phosphorus nutrition, photon flux density, and ambient CO, during ontogeny. Plant Physiol. 78,821-825. Yoshida, S., and Coronel, V. (1976). Nitrogen nutrition, leaf resistance and leaf photosynthetic rate of the rice plant. Soil Sci. Plant Nut,: 22,207-2 11.

THEVALUEOF LONG-TERM FIELD EXPERIMENTS IN AGRICULTURAL, ECOLOGICAL, AND ENVIRONMENTAL RESEARCH A. Edward Johnston IACR Rothamsted Harpenden, Herts AL5 2JQ United Kingdom

I. Introduction 11. The Rothamsted Experiments 111. The Agricultural Value of Long-Term Experiments A. Assessing Sustainability B. Testing New Ideas C. Effect of Soil Type and Farming Systems on Soil Organic Matter n! Ecological Research and Long-Term Experiments A. Park Grass, the Most Long-Term Ecological Study in the World B. The Rothamsted Insect Survey V. Long-Term Experiments and Environmental Concerns A. Soil Pollution VI. The Need for Long-Term Experiments VII. Approaches to New Long-Term Experimene References

I. INTRODUCTION The title of the conference held to celebrate the 150th anniversary of Rothamsted Experimental Station in July 1993 was “Insight from foresight: The role of long-term experimentsand databases in agricultural and ecological sciences.” This title reflected major aspects of Rothamsted’s work. The first was the scientific insight that has been gained because of the foresight shown by the station’s founder, Sir John Bennet Lawes and his scientific collaborator for 57 years, Sir Joseph Henry Gilbert, in retaining their long-term field experiments after their initial aims had been fulfilled. In this they recognized that future generations of scientists might 291 Advancer in Aflononry, Volrrme F9 Copyright 0 1997 hy Academic Press, Inc. All rights of reprodtiction in any form reserved.

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see uses for the experiments that they could not and this foresight has been amply rewarded. Other aspects reflected the station’s interest and reputation in long-term research in agriculture and ecology and, recently, with the interaction of agriculture and the wider environment of which agriculture is but a part. The published proceedings of the conference (Leigh and Johnston, 1994) have a number of common themes. One is that long-term data sets acquire a value not often predicted at the outset. The development of new biological, chemical, physical, and statistical analytical techniques allows additional information to be extracted provided the data are easily retrieved and archived samples have been kept in appropriate conditions for future analysis. Today, much attention is given to modeling, often thought to be a reliable way of predicting future events, but models are only as good as the data on which they are based. Modeling biological systems requires reliable data from long-term experiments because, often, short-term changes are but a part of much longer cycles that can be related to slow changes in climatic or other environmental parameters. Another common theme was the uncertainty that surrounds long-term research. Every paper demonstrated that long-term experiments continually provided new hypotheses to be tested. Some of these hypotheses could be tested within the experiment and some outside of it, but all were the basis for valuable ongoing research. There is an increasing awareness that human activities, including food production, should be environmentally benign. Thus, it becomes ever more important to continue existing long-term experiments and start others to measure and evaluate the effects of both farming practices and nonagricultural anthropogenic activities on soil fertility, water quality, and the sustainability of crop production. It also becomes increasingly obvious that, although the principles of crop growth and soil processes have wide applicability, crops and soils interact and respond to other external factors, like weather, in very different ways in different parts of the world. Thus, there is a need for long-term experiments in a range of agroecological situations with varying climatic conditions. The value of well-designed and executed experiments increases with time, but longevity makes experiments ever more costly. However, their cost effectiveness can be increased if they serve a number of different objectives and if they are conducted on well-characterized sites so that the results can be extrapolated as widely as possible. Among the more easily identified objectives are 1. to test the sustainabilityof a particular agro-ecosystem over a long time span and determine what changes, if any, are needed to maintain sustainability 2. to provide data of immediate value to farmers, ecologists, and environmentalists to improve best husbandry practices 3. to provide a resource of soil and plant material to further scientific research into soil and plant processes that control soil fertility, plant productivity, and water and habitat quality

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293

4. to allow a realistic assessment of nonagricultural anthropogenic activities on soil fertility and plant quality 5 . to provide long-term data sets that can be used to develop mathematical models to predict the likely effects of management practices and of climate change on soil properties, on the productive capacity of soils, and on the wider environment It is therefore unfortunate that currently much of the political and financial climate in which research in general and agricultural and ecological and environmental research in particular functions is for “short-term fixes,” rather than having a longer-term approach so that political decisions can be based on sound scientific facts.

II. THE ROTHAMSTED EXPERIMENTS This paper discusses some of the points raised in the Introduction using examples from experimental sites now controlled by Rothamsted. The world’s oldest long-term agricultural field experiments were begun between 1843 and 1856 by J. B. Lawes and J. H. Gilbert at Rothamsted, an agricultural estate on the outskirts of Harpenden, a town 40 km north of London in southeast England. Eight of these experiments still continue.All were started as agronomic experimentsto determine the nutrient requirements of agricultural crops (Johnston, 1994) and although the original questions have long since been answered they continue to provide data of considerable agronomic, ecological (Tilman et al., 1994),environmental (Jones et al., 1994; Johnston and Jones, 1999, and scientific value (Jenkinson et al., 1994). Other examples are taken from the Woburn Experimental Station, begun in 1876 (Johnston, 1977), and the Saxmundham Experimental Station, begun in 1899 (Johnston, 1987). The soil and rainfall at each of the three sites are as follows: Rothamsted, a silty clay loam, 700 mm rainfall; Woburn, a sandy loam, 600 mm; Saxmundham, a sandy clay loam, 600 mm. In no small measure the current value of these experiments arises because during their early years, but more so in recent times, various carefully considered modifications have been made to introduce tests of new hypotheses and to respond to events that might otherwise have brought an experiment to an end. The benefits derived from such changes have shown that the usefulness of long-term experiments is enhanced when their design has flexibility to accommodate change and plot size is large enough to allow subplot tests. However, whereas the design of long-term experiments must have flexibility to introduce changes, it is crucial to avoid frequent, mindless changes to accommodate ephemeral fads and fashions. In addition, the Rothamsted experience has also shown that the value of such experiments is increased immeasurably when crop and soil samples are archived in appropriate conditions. Such samples have been analyzed retrospectively using

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new or improved techniques for elements or compounds about which there is a developing scientific interest or environmental concern some of which, such as the organic pollutants, were unknown in the middle of the last century.

III. THE AGRICULTUUL V U 'E OF LONG-TERM EXPERIMENTS If long-term experiments are to assess the sustainability of agricultural systems, provide research material to better understand soil and plant processes, and produce data of benefit to farmers then changes must be made occasionally. This is especially so if both successes and failures of husbandry practices are to be explained by measurable parameters. Changes to achieve these goals have been made in Rothamsted experiments and they have enhanced their value considerably.

A. ASSESSING SUSTAINABILITY 1. Combined Effects of Chemical and Biological Changes It is probably unusual for a single factor to cause a farming system to collapse but identifying the probable factors and the way they have interacted is difficult. A complicated, but excellent example of slow changes in chemical and biological properties and their effect on sustainability comes from the Woburn Ley Arable experiment. Because the experiment had contrasted treatments it was possible, in some cases, to decide why a particular husbandry system was not sustainable. Begun in 1938, the design of the experiment was very ambitious (Boyd et d., 1968). Initially, four contrasted cropping sequences, each lasting 3 years, were compared. They were (A) a grass-clover ley grazed by sheep; (B) lucerne (alfalfa) cut three or four times each year for conservation; (C) potatoes, wheat, and kale; and (D) potatoes, wheat, and I-year ley. The effects of these sequences on two arable crops, initially potatoes and then barley, which followed were measured. The 5-year rotations were, therefore, 3 years of ley and two arable crops or 5 years of arable crops; each phase of the rotation was present each year on one of the five blocks that constituted the experiment. Half the plots always had the same rotation, designated continuousrotation plots, and in 20 years there were four replicates of each 5-year rotation. The other half of the plots tested a practice that might well have occurred in commercial farming. Each arable rotation was followed by a ley rotation and vice versa, e.g., sequences were C A D B or D A C B. Designated alternating rotation plots, there was the least chance of a buildup of soil-borne pests and diseases, but on these plots it took 20 years to complete one cycle.

THE VALUE OF LONG-TERM FIELD EXPERIMENTS

295

At the beginning, the same total amounts of phosphorus and potassium wereapplied to each of the 5-year rotations, which followed the accepted principles of experimental design to aid statistical analysis. However, in 1951-1952, yields of potatoes following lucerne (25.2 t ha-') were less than those following grazed ley, (31.6 t ha-') and, unexpectedly, only a little better than those in the all-arable rotation (2 1.8 t ha-'). That there was little benefit from the lucerne ley was contrary to the perceived wisdom. Subsequently, lucerne was shown to be removing more potassium than was being applied and more than the crops in any of the other three rotations. This depletion of soil K reserves led to K deficiency in the potatoes. A test of additional P and K, first to potatoes, and then to sugar beet, showed this to be so. The P and K manuring was increased and in 1962 altered again so that each rotation was manured according to its estimated needs (Boyd et al., 1968). The experiment had been ongoing for 18 years before the first biological problem became apparent. In 1955, the potatoes in the treatment phase of the arable rotations yielded much less in the continuous rotation than in the alternating rotation cycle (Table I). Potatoes grown on these plots in 1955 were the eighth and sixth potato crops, respectively, and there had been a greater buildup of potato cyst nematodes (Heteroderu rosrochiensis) where potatoes were grown more frequently. Yields of winter rye were not affected (Table I). Sugar beet replaced potatoes as Table I Yields (tha-I) of Potatoes and Winter Rye in Arable Rotations following Different Previous Rotations-Woburn Ley Arable 1955 Rotation cycle and previous rotation Alternating

Potatoes Tubers Rye Grain

Continuous

Ley

Lucerne

Arable (hay)

Arable (roots)

19.4

14.7

4.8

3.8

4.54

4.07

4.29

4.15

Rotations were

Treatment phase

Test phase

b Y Lucerne Arable (hay) Arable (roots)

3-Year grass ley grazed by sheep 3-Year lucerne ley cut for conservation Potatoes, rye, hay Potatoes, rye, sugarbeet

Potatoes, barley Potatoes, barley Potatoes, barley Potatoes, barley

Note. For detinitions of alternating and continuous rotations see text. These rotations, with some minor modifications in the arable crops, had been maintained for 18 years before the results quoted here. Adapted from Rothamsted Experimental Station (1955).

2 96

A. EDWARD JOHNSTON Table I1 Yields of Lucerne (tha-' Dry Matter)Woburn Ley Arable Years after sowing Period

1

2

3

Total

1944-1956 1957-1964

1.94 3.32

7.09 5.77

7.91 5.27

16.94 14.36

the first test crop and the cropping in the all-arable rotations was changed so that no crop was grown more than once in 5 years to minimize the effects of soil-borne pathogens. After 1957, lucerne in the second and third year began to yield much less than in earlier years (Table 11) because of stem eelworm (Dirylenchus dipsaci). This problem was not solved by soil fumigation or by the use of fumigated seed, and lucerne was eventually replaced by clover. Lucerne yielded more (3.32 t ha") in the first year, during 1957-1964, than in 1944-1956 (1.94 t ha-') because more K was applied in the second period. Sugar beet was grown for 12 years. During 1965-1967, the best yields with optimal amounts of fertilizer N were those after lucerne; there was, on average, about 0.95 t ha-' more sugar than after the grazed ley (Table 111). This large difference was unexpected and the effects of lucerne and grazed ley were now reversed comTable 111 Yields of Sugar from Sugar Beet with Optimum Nitrogen in Different Rotations--Woburn Ley Arable 1965-1967 Rotation

Total sugar (tha-I)

Ley

Lucerne

Arable (hay)

Arable (roots)

8.36

9.31

8.70

9.14

Rotations were

Treatment phase

Test phase

h Y Lucerne Arable (hay) Arable (roots)

3-Year grass ley grazed by sheep 3-Year lucerne ley cut for conservation Potatoes, rye, hay Potatoes, rye, carrots

Sugar beet, barley Sugar beet, barley Sugar beet, barley Sugar beet, barley

Note. These rotations, with some minor modifications in the arable crops. had been maintained for 28 years before the results quoted here. Adapted from Boyd et al. (1968).

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297

Table IV Yields (tha-') of Potato Tubers, Average of Susceptible and Resistant Cultivars, Grown with (F)and without (0)Soil Fumigation-Woburn Ley Arable 1970-1973 Rotation

0

F F-0

Grass ley

Sainfoin

Arable (hay)

Arable (roots)

56.0 62.5 6.5

55.2 62.3 7.1

52.8 59.9 7.1

51.4 58.9 7.5

pared to those recorded earlier for potatoes. This was because there were many more free-living nematodes (Longidorus and Trichodorus) in the grazed ley plots. Yields in the arable rotations were similar to those following lucerne, provided that the correct amount of nitrogen was applied. In the early 1970s, potatoes were reintroduced as the first test crop and nematode susceptible and resistant cultivars were compared on soils with and without fumigation. Fumigation increased yields by, on average, 7.0 t ha-' in all four rotations but, with and without fumigation, yields were about 3.0 t ha-' larger after the two leys, grass and sainfoin, than after the all-arable rotations (Table IV). This suggested that potatoes might benefit from the small amount of extra humus in soils in a 5-year rotation that included 3 years of ley. The experiment was modified yet again and the benefits of 3- and 8-year grass-clover and all-grass leys with N are now being measured on cereals. After 40 years, much had been learned about how to do the experiment and maintaining productivity, the paramount aim of sustainable agriculture. It was necessary to manure with P and K to meet the needs of the crop. It was essential to be aware of the role played by pests and diseases and to try to either eliminate them or estimate their effect on yield. Such problems were not foreseen at the outset. Only in the past few years has it been possible to measure the effects of extra organic matter accumulated in soil by growing leys, an original goal of the experiment. 2. Effects of Acidity

Soil acidity, particularly when it results from some of the treatments being tested, can mask treatment effects. Soil acidity is now well understood; it was not in the late 1880s when it began to seriously decrease yields of spring barley in the Continuous Barley experimentbegun in 1877 on the sandy loam at Woburn. Table V shows that yields declined most where N, as ammonium sulfate, was given to-

298

A. EDWARD JOHNSTON Table V

Yields of Wheat and Barley (Grain tha-’) Grown Continuously and Effect of Calcium Carbonate on Plots Given Ammonium Sulfate-Continuous Wheat and Barley Experiments,Woburn 1877-1926 Period Crop and treatmentu Winter wheat Unmanured NPK No chalk Chalkb FYM Spring barley Unmanured NPK No chalk Chalk FYM

1877-1886

1887-1 896

1897-1 906

1907-19 16

I9 17-1 926

1.08

0.83

0.61

0.66

0.46

2.04 1.76

1.94 1.83

1.68 1.69

1.11 1.25 1.38

0.64 0.66 1.20

1.56

0.98

0.60

0.60

0.49

2.57 2.39

2.10 2.30

0.19 1.39 1.87

0.19 1.39 1.87

0.30 0.90 1.54

-

Note. pH, in water, of soils sampled in 1927 were: wheat, no chalk 4.6, chalk 5.0 barley, no chalk 4.8, chalk 5.8. Applying chalk would have raised soil pH sooner after application. Adapted from Johnston (1975). u46 kg N ha-’ as ammonium sulfate; FYM, 17.6 tha-’ per year on average. b2.5 t CaO ha-’ to winter wheat; half in 1905, half in 1918. 10.0t CaO ha-’ to spring barley; half in 1898, half in 1912.

gether with P and K and that yields were not increased to those at the beginning of the experiment when chalk was applied and pH increased. The factor, other than acidity, that caused the failure of the system was probably cereal cyst nematodes that increased when cereals were grown continuously on this light-textured soil. On the heavier-textured soils at Rothamsted, it took 80 years for the effects of soil acidity to decrease yields of turnips in the Agdell experiment (Powlson and Johnston, 1994). In this case, it was not a direct effect of acidity, but rather because the fungus, Plasmodiophora infesfans,which causes “finger and toe,” built up in the acid soil. Other ways in which soil acidity can affect yields through factors that are not being tested include the mobilization of Al, Fe, and Mn in very acid soils; varying the populations of fungal pathogens, besides the previous example-the seventy of Guaemannomyces graminis appears to depend on soil pH (Glynne, 1935); and varying the efficiency of some soil-applied herbidices. It is essential, therefore, to decide on a policy at the outset to either test the effects of treatments on soil pH or eliminate any effects by maintaining soil pH at some acceptable value.

THE VALUE OF LONG-TERM FIELD EXPERIMENTS

299

Table VI Effect of Using Weedkillers on Yields of Winter Wheat Grown Continuously during 1985-1990-Broadbalk, Rothamsted Mean annual yield of grain (tha-I) at 85% dry matter

Treatment

No weedkillers Weedkillers

FYM plus N (kg ha-')

PK plus N (kg ha- ') 0 2.9 1.4

48 3.9 4.0

96 4.4 5.3

144

4.9 6.6

192 5.4 6.2

240

4.5 6.3

288 5.1 6.9

0 5.0 6.6

96 4.9 8.2

Nore. The comparison of with and without weedkillers started in 1963 after 120 years of continuous wheat cropping with occasional bare fallows to control weeds. Since 1963, the section without weedkillers is bare fallowed occasionally when weed populations become excessive.

3. Effects of Weeds Quantifying the benefits of weed control to justify the cost can only be done in long-term experiments because the seed bank in both treated and untreated soils takes many years to stabilize. Johnston and Powlson (1994) described the early history of weed control in winter wheat on the Broadbalk experiment begun at Rothamsted in 1843. Since 1965, weedkillers have been used on all but one half section and the effects of weeds can now be estimated. Weed competition on the higher-yielding plots decreased yield during 1985-1990 by as much as 1.8 t ha-' grain (Table VI), whereas on the plot with FYM plus fertilizer N, the lost yield exceeded 3.0 t ha-'. On the plot that receives PK fertilizers but no N, two legumes, Vicia safiva and Medicago ZupuZina, fix nitrogen that either directly or indirectly through ploughed in residues increased yield by more than 1.O t ha-' grain compared to where these legumes were killed by spraying (Table VI). In a recent development, this treatment difference is being exploited further; weeds from the sprayed and unsprayed sections are being compared for the buildup of resistance to weedkillers.

4. Availability of Cultivars Whether to change cultivars of annual crops and if so, how frequently, is perhaps one of the more difficult decisions for sponsors of long-term experiments. This is especially so now that new cultivars, particularly of cereals, are introduced more frequently than in the past. The benefits to yield of changing cultivars are well illustrated by results from Broadbalk (Fig. 1). SquareheadsMaster was grown

t 'OL

-6 -8

-L -9

-S

d I

I

-t -€

-z pamueuun o

-L -0

Figure 1 Yields of winter wheat grown on Broadbalk, Rothamsted, from 1852 to 1990 with fertilizers and with farmyard manure showing the effects of changing cultivars and the introduction of weed control, fungicides. and crop rotation to minimize effects of soil-borne pathogens. Reprinted from Johnston (1994).

THE VALUE OF LONG-TERM FIELD EXPERIMENTS

301

for a long period to investigate the effects of weather on wheat yields, but Yates (1969) considered it impossible to do this because many of the other sources of variation could not be quantified. Current practice for long-term experiments at Rothamsted is to review cultivars regularly. On Broadbalk, for example, this is done every 5 years when a cultivar has been grown on all sections used for the five-course rotation. Carefully considered changes in the cultivar grown can have major benefits. On Broadbalk, there has been a great improvement in the recovery of fertilizer nitrogen (Table VII), from about 30% up until the late 1960s to over 70% in the late 1980s, because new cultivars have a much improved harvest index and grain contains more nitrogen than straw. Within each period, recovery of nitrogen varied little with the amount applied, No matter how little fertilizer nitrogen was used, there was always some that could not be accounted for. Such information obtained on one site over many years is invaluable in discussions with those concerned with the environmental aspects of fertilizer N use because it provides reliable long-term data on the amounts of N not accounted for by crop removal and changes in soil organic N. The need to change cultivars may also focus attention on other soil factors that affect yield. The Hoosfield Continuous Barley experiment, begun in 1852 at Rothamsted, has two treatments that compare annual applications of PK fertilizers (33 kg P ha-' and 90 kg K ha-') and animal manure (FYM) (35t ha-'). By 1968, the FYM-treated soils contained two and a half times as much soil organic matter as fertilizer-treated soils, and in that year both plots were divided to test four amounts of N as inorganic fertilizer: 0,48,96, and 144 kg ha-'. Between 1970 and Table VII PercentageRecovery of Fertilizer Nitrogen Applied to Winter Wheat Grown Continuously on Broadbalk, Rothamsted % Recovery a

N applied (kg ha-') Period

48

96

144

1852-1871 19661967 1970-1 978 1979- 1984 1985-1987

32 32 56 69 67

33 39 63 83 77

32 36 59 76 67

192 29

52 69 57

"In grain plus straw and determined using N in crop grown on plot where no N was applied as control. Adapted from Johnston and Jenkinson (1989).

A. EDWARD JOHNSTON

3 02

7L

6

d

't.

0

0

Georgie 1980-83

Julia 1970-79

, 48

,

1

96 144

N

I

Triumph 1984-89 1

48 96 144 0 48 (kg ha-1) applied annually 0

I

96 144

Figure 2 Mean annual yields of three cultivars of spring barley grown continuously on soils that each year since 1852. Hoosfield Barhave received either PK fertilizers (0)or farmyard manure (0) ley experiment, Rothamsted. Adapted from Johnston (1991).

1979, yields of cv Julia on fertilizer-treatedsoils given 96 kg N ha-' were the same as on FYM-treated soils (Fig. 2). This equivalence of the yields with these two treatments had been an unchanging feature of the results since the experiment began and had led to the view that the extra soil organic matter on the FYM-treated plot was of little importance. In 1980-1984, yields of cv Georgie on fertilizertreated soils were again equal to those given by FYM, but 144 kg N ha-' was needed and there was some indication that on FYM-treated soils yields were further increased by giving extra fertilizer N. During 1985-1990, cv Triumph yielded more on FYM-treated soil than with most fertilizer N and there was an even greater response to extra fertilizer N on FYM-treated soil. Apparently, benefits from having extra soil organic matter had developed over a short period of time although the amount of humus in both soils had changed little. The benefit observed may be explained because recently introduced cultivars have a high yield potential and have to grow quickly to achieve good yields. This requires good soil physical conditions for rapid root growth to explore the soil for nutrients and water. A similar benefit of having extra organic matter in soil has also been seen for winter wheat on Broadbalk (Table VIII). In recent years, yields have always been largest when extra fertilizer N was given to crops grown on FVM-treated soil. On these plots, the readily available N from the annual application of FYM and the nitrate mineralized from the soil organic matter were not sufficient for current cultivars to reach their yield potential when grown on this soil. It is thought that improved soil

THE VALUE OF LONG-TERM FIELD EXPERIMENTS

303

Table VIII Yields of Winter Wheat (Grain tha-I) Given by Fertilizers, Farmyard Manure, and Farmyard Manure Plus Fertilizer N-Broadbalk, Rothamsted Cultivar grown Flanders 1979-1984

Brimstone 1985-1 990

Treatment

Continuously

In rotation

Continuously

In rotation

NPKU FYM FYM+N'

6.93 6.40 8.13

8.09 7.20

6.69 6.17 7.92

8.61 7.89 9.36

~

8.52

'

agest yields of cv Flanders were given by 192 kg N ha- and of cv Brimstone by 288 kg N ha-'. 'FYM plus 96 kg ha-' fertilizer N.

structure and the slightly enhanced water-holding capacity in the FYM-treated soil is the explanation for this difference, but it is also possible that part of the benefit comes from mineralization of organic N late in the growing season although this has a greater effect on grain percentage of N than on yield (Johnston and Jenkinson, 1989).

5. Plot Size and Soil Movement Although related to the sustainability of an experiment rather than to an agricultural system, a poorly designed and managed experiment could lead to erroneous conclusions about the sustainability of an agricultural system. Decisions about plot size in long-term experiments must take into account the need for soil cultivation, especially ploughing and harrowing, which can move soil across plot boundaries. That soil had been moved between plots during 100 years of cultivation was recognized on the Hoosfield Continuous Barley experiment in the 1960s (Warren and Johnston, 1967) and the harvested area of the plot was truncated to allow for this. Sibbesen et al. (1985) introduced the concept of a diffusion process to describe soil movement and modeled it in one dimension using data from an experiment at Askov in Denmark. McGrath and Lane (1989) developed a more complex two-dimensional model using data for concentrationsof heavy metals in soils along transects across plots to which very different amounts of metals had been added over a period of 20 years. Figure 3 shows the phosphorus concentration along a transect across four plots in the Market Garden experiment on the sandy loam at Woburn after 20 years. Initially, two crops were grown each year and then three in 2 years, so there had been much ploughing and cultivation. Each plot was

A. EDWARD JOHNSTON

3 04 2400

2000

7

1800

m

I-"

1200 1000 -

600

P+NM2 1

0

I

5

I

10

I

15 Distance (m)

P

P+S2 I

20

I

25

30

Figure 3 The total Pcontent of soils sampled in 1984 along a transect crossing plots that had received varying amounts of P. All plots had received superphosphate (P) supplying 1.1 t P ha-' between 1942 and 1984; in addition, farmyard manure (FYMZ) and sewage sludge (S2) had supplied 7.7 and 13.1 t P ha-', respectively,between 1942 and 1967. Adapted from Johnston (1989).

only 6.04 m wide X 8.38 m long and ploughing was always at right angles to the length of the plot. Such soil movement is difficult to observe unless crops respond to a soil nutrient gradient with a gradient in crop growth. However, the effect of cultivation, together with that of soil movement due to soil erosion processes, can seriouslyjeopardize the useful life of an experiment. If plots have to be small, soil movement between plots must be prevented, e.g., by concrete barriers or grass guard strips, and on larger plots every effort should be made to minimize soil movement.

B. TESTING NEWIDEAS 1. Effects of Soil-Borne Pathogens

Johnston (1994) discussed the reasons why, with one exception, Lawes and Gilbert grew arable crops in monoculture. Growing winter wheat on Broadbalk and spring barley on Hoosfield has been spectacularly successful,especially when considered against our present knowledge of fungal pathogens and root diseases of cereals. Since the 1930s, knowledge of plant pathology has developed rapidly and two major diseases of cereals, eyespot caused by Cercosporella herpotrichoides and take-all caused by Guaemannomyces graminis, have been studied extensively on Broadbalk. The low incidence of take-all in wheat on Broadbalk when

THE VALUE OF LONG-TERM FIELD EXPERIMENTS

305

compared to that in other fields led to the idea of take-all decline-that is, when wheat is grown continuously, factors inimical to take-all prevented it from developing in its most severe form (Glynne et al., 1956). However, other evidence suggested that even with maximum take-all decline, yields could be less than those in the absence of take-all. Because the plots on Broadbalk are large, it has been possible to test this hypothesis. The 5 sections, made in 1925 to introduce fallowing on a 5-year cycle for the control of weeds, were halved in 1968 to create 10 sections. On some sections, wheat was grown continuously, and on others a 2-year break from cereals was introduced because this was known to minimize the risk of take-all affecting the next crop. From 1970 to 1978, cv Cappelle Desprez was grown. On plots given fertilizers, yields of wheat grown continuously increased up to 96 kg N ha-' with little further increase to more N; when grown after a 2year break, yields peaked at 96 kg N ha-' and then declined (Fig. 4). With 96 kg N ha-', the benefit of the 2-year break was 1.18 t ha-' grain. On plots with more organic matter from repeated applications of FYM, the 2-year break increased yields by only 0.63 t ha-' and yields declined when extra fertilizer N was given. During 1979-1984, cv Flanders was grown and yields in all situations increased up to the maximum amount of N tested. The benefit of the 2-year break was 1.45 t ha-' on fertilizer-treated soils with 96 kg N ha-', whereas on FYM-treated soils

'r

A 1970-78 Cappelle Desprez

1979-84

Flanders

"t 7

0

48

96 144 192

N

0

48

96

144 192

(kg ha-1) applied in spring

Figure 4 Yields of two cultivars of winter wheat grown either continuously (circles) or after a 2year break (squares) and on soils with either 1.8% soil organic matter (open symbols) or 4.6%organic matter (solid symbols). Broadbalk, Rothamsted. From Johnston (1994).

A. EDWARD JOHNSTON

3 06

'LBble IX Yields (Grain tha-') of First, Second, and Third Winter Wheat after a 2-Year Break Compared with Those of Wheat Grown Continuously-Broadbalk, Rothamsted 1985-1990. Wheat after a 2-year break Treatment

1st

2nd

3rd

NPKU EYM FYM + Nb

8.61 7.89 9.36

7.85 5.86 8.64

6.47 5.37 7.59

~~

Continuous wheat

6.69 6.17 7.93

~~

aBest yield of cv Brimstone was given by 288 kg N ha-'. bFYh4 plus 96 kg ha-' fertilizer N.

the benefit was 0.80 t ha-'. For both cultivars, the largest benefit was where the least amount of fertilizer N was applied; 1.56 and 1.78 t ha-' grain for Cappelle and Flanders, respectively. The reason was probably because there was a better root system in the absence of take-all that took up more of the small amount of nitrogen available to the crop. The difference in yields with the larger amounts of fertilizer N in the two periods was a result of fungicides being applied to control foliar pathogens in the second period. The effect of breaking take-all decline by having a 2-year break and then growing three consecutive wheats has been tested since 1985. With fertilizers, FYM and FYM + N, best yields of the third wheat after a 2-year break were always less than those of wheat grown continuously (Table IX) because after a 2-year break from cereals the causative agent for take-all decline built up less quickly than did G. graminis. Thus, in this long-term experiment, continuous wheat production has not only been sustained but increased, the value of extra organic matter in soil has been demonstrated, and the role of take-all and take-all decline has been shown. There is also an important message for farmers; namely, that although it has been possible to grow wheat continuously on this soil with careful attention to management, there are benefits to be obtained if profitable crop rotations can be devized in which most wheat crops are grown after a 2-year break. 2. Effects of P and K Accumulated in Soil from Fertilizers and Manures

Lawes and Gilbert applied large amounts of P and K in their experiments; annual applications were 33 kg P ha-' to all crops, 90 kg K ha'' to cereals, and 200 kg K ha'' to root crops and grass. When smaller quantities were used, yields were less because at that time soils were deficient in both P and K, but especially P. The

THE VALUE OF LONG-TERM FIELD EXPERIMENTS

3 07

amounts used far exceeded offtakes in the harvested produce and Lawes and Gilbert made some simple tests of the effects of the residues (Johnston, 1970). Liebig (1872) and Dyer (1901, 1902) both showed that dilute acid extractants removed more P and K from fertilized than from unmanured soils after fertilization had continued for some years, but only a fraction of the estimated residue remained readily soluble. Studies have shown that only about 13% of the increase in total soil P from applications of fertilizers and FYM remains soluble in 0.5 M NaHCO, (Johnston and Poulton, 1992) and about 40% of K residues remain exchangeable to 1M NH,OAc (Johnston and Goulding, 1990). However, the cumulative effects of residues from many applications may be important for soil fertility and sustainable land use and estimating the value of such residues became a major research topic at Rothamsted for some 40 years after 1950. Yields of spring barley in the Exhaustion Land experiment at Rothamsted (Table X) showed that, on this soil, with pH above 6.0, P and K residues from inorganic fertilizers and FYM had remained in forms available to plants for more than 50 years. Similarly, in the Hoosfield Barley experiment, P and K residues from 20 applications of FYM were still plant available after 100 years (Table XI). In both experiments, the benefits were from the combined effects of P and K. The plant availability of these residues after many years was indisputable. The reason they had such a large effect from 1949 on Exhaustion Land and 1970 on Hoosfield was because no nitrogen was applied between 1901 and 1948 on Exhaustion Land and 1872 and 1967 on the FYM residue plot on Hoosfield and, in consequence, little P and K had been removed in the small yields. Once sufficient N was applied, the soils were stressed to supply P and K and the effectiveness of the residues began to decline (cfi yields for 1970-1973 and 1988-1991 in Table XI). Table X Effect of Residual P and K in Soil on Yields of Spring Barley-Exhaustion Land, Rothamsted, 1949-1974. Grain (tha-’ at 85% dry matter) Treatment 1856-1901a Period

Unrnanured

FYM

NPK

Pb

PK

1949-1 953 1954-1959 1960-1962 1963 1964-1969 1970-1 975

1.59 1.80 2.02 I .90 1.71 1.83

3.03 3.32 3.09 3.27 4.28 4.75

2.87 3.10 2.62 2.86 3.61 4.22

2.12 2.8 1 2.59 3.2 1 3.59 3.76

3.05 3.08 2.73 2.99 3.61 4.53

“For details see Johnston and Poulton (1977). bK applied 1856-1 875.

3 08

A. EDWARD JOHNSTON Table XI

Effect of P and K Residues Accumulated in Soil between 1852 and 1871 on Yields (tha-') of Spring Barley in 1970-1973 and 1988-1991-Hoasfield, Rothamsteda Treatment Nitrogen since 1968 (kg ha-') None 48 96 144

1852-199 1, Unmanured

1852-1871 only,

1852-199 1,

FYM

FYM

1970-1973

1988-1991

1970-1973

1988-1991

1970-1973

1988-1991

1.46 2.01 2.12 2.26

0.98 1.43 1.73 1.66

1.07 3.20 5.27 4.94

2.20 3.24 3.48 3.63

5.38 5.86 5.62 5.38

5.50 5.94 6.06 6.00

uAdapted from Johnston and Pulton (1992).

Laws and Gilbert's legacy of large plots made it possible to subdivide them in a number of experiments to test P and K residues separately (Johnston and Warren, 1970).A selection of results for P (Table XII) and K (Table XIII) showed, not surprisingly, that soils with residues gave larger yields than those without. More important, yields on impoverished soils were not increased to those on enriched soils, except on the Exhaustion Land, even when generous quantities of P or K fertilizers were given. The results on the Exhaustion Land are atypical and the large Table XI1 Effect of Residual P in Soil and Freshly Applied P Fertilizer on Yields (tha-I) of Three Arable Crops Experiment and bicarbonate soluble P (mg kg-')

Crop Spring barley Grain Potatoes

P applied (kg ha-')

Tubers

0 56 0 56

Sugar From beet

56

0

Exhaustion Land

Agdell

Woburn

4

13

4

12

18

42

1.54 2.89 12.1 25.4 3.38 4.79

3.41 3.88 29.9 38.2 5.77 6.00

2.03 3.49 12.8 32.6 3.84 5.72

3.11 3.48 21.1 32.6 5.67 6.00

2.62 2.86 35.1 38.2 5.17 5.15

3.34 3.61 40.7 43.4 5.90 6.15

Note. For each experiment, the soil with the lower level of bicarbonate soluble P had received no P fertilizer since the start: Agdell, 1848;Exhaustion Land, 1852;Woburn, 1876. For details see Johnston et al. (1970a).

THE VALUE OF LONG-TERM FIELD EXPERIMENTS

3 09

Table XU1 Effect of Residual K in Soil and Freshly Applied K Fertilizer on Yields (tha-') of Five Arable Crops Experiment and exchangeable K (mg kg-I)

K applied (kg ha-') Spring barley Grain Winter wheat Grain Potatoes Tubers Sugar From beet Beansd Grain

0 63 0 52 0 125' 0 1 25c 0 52

Exhaustion Land

Woburn

Saxmundham

83

Ill

61

84

113

166

3.34 3.56 nt" nt 17.1 31.1 4.87 5.67 nt

3.54 3.56 nt nt 27.6 36.7 6.15 5.43 nt nt

3.14 3.38 nt

3.32 3.31 nt nt 41.2 47.2 4.60 5.80 nt nt

nt nt 8.49 8.54 28.8 39.6

nt nt 8.50 8.60 43.1 44.0 nt nt 4.42 4.38

nt

nt

32.9 44.2 3.59 5.8 I nt nt

nt

nt 2.52 3.60

Note. From each experiment, the soil with the lower level of exchangeable K had received no K fertilizer since the start: Exhaustion Land, 1852; Woburn, 1876; Saxmundham, 1899. For details of the Exhaustion Land and Woburn experiments, see Johnston et al. (1970b). "nt, not tested. bK to potatoes, 208 kg ha-' at Saxmundham. 'K to sugar beet, 376 kg ha-' at Woburn. dVicia faba.

response to newly applied P and K on impoverished soil may be because the soil is well structured, allowing excellent root growth to explore soil for nutrients. These results, derived from long-term experiments, highlight the importance of maintaining the P and K fertility of soil both to the immediate benefit of farmers and to the longer-term sustainability of crop production.

3. Testing Efficient Use of Nitrogen Fertilizers There have been a number of major concerns in the agriculture/environment debate, some of which can be answered only by long-term experiments. For example, the increasing levels of nitrate in potable waters in the 1970s and 1980s was perceived by many people as a direct consequence of the increasing use of inorganic nitrogen fertilizers. However, one of the main difficulties with this conclusion was that, in England and Wales, it was some years after reports of increasing levels of nitrate in water that the amounts of fertilizer N applied to, for example, cereals first exceeded the amount of N in the harvested crop (Sylvester-Bradley et

310

A. EDWARD JOHNSTON

al., 1987). Research into the efficient use of fertilizer N necessitated the use of labeled 15N fertilizer and, more important, sites at which there was no net immobilization or release of organic N from humus, especially at which it was not possible to measure leaching and denitrification losses. Only in long-term experiments is it possible to find soil organic matter levels that are in reasonably stable equilibrium. Experiments with varying rates of I5N-labeledinorganic N fertilizers applied to winter wheat were performed for 4 years on Broadbalk (Powlson et al., 1986). On average, about 20% of the spring-applied N fertilizer was found in the soil after harvest, mostly as organic N; less than 2% of the applied N was mineral N (TableXIV). This N balance used measured values for N in soil, grain, and straw, and showed, by difference, that about 20% of the applied N was unaccounted for. In these and similar experiments, there was a strong relationship between the loss of spring-applied labeled N and rainfall in the 3 weeks after fertilizer application (Powlson et al., 1992). The losses could have been from volatilization of ammonia on application, from leaching, or from denitrification.In 13 of 16 of these experiments, denitrification was the most likely cause of nitrate loss because rainfall did not exceed evapotranspiration for soils already below field capacity (Addiscott and Powlson, 1992). These results suggested that much of the nitrate that appears in soil in autumn comes from the mineralization of organic matter, an observation important for those framing legislation on N fertilizer use. The exceptions are where fertilizer N has been applied in excessive amounts relative to the yield potential of the site for a particular crop or where the crop has failed for some reason (see Johnston and Jenkinson, 1989 for examples). It is recognized, however, that the long-continued use of nitrogen fertilizers can lead to the accumulation of more humus in fertilized soils compared to those that are unmanured (see example in Fig. 6). On Broadbalk, straw has always been reTable XIV Percentage Distribution at Harvest of Fertilizer-Derived NitrogenApplied to Winter Wheat at 144 kg N ha-' Labeled with ISN-Broadbalk, Rothamst& % Fertilizer nitrogen in

Year

Grain

Straw

Soil

Unaccounted for

1980 1981 1982 1983 Mean

55

13 16 23 13 16

17 20 24 16 19

15 27 8 27 19

37 45

44 45

OAdapted from Johnston and Jenkinson (1989).

THE VALUE OF LONG-TERM FIELD EXPERIMENTS

311

I___ 00

5

10 N added, t ha-1

15

20

Figure 5 The relationship between nitrogen added in four organic manures during 1942-1 960 and FYM; 0, compost percentage N in soil in 1951 and 196QMarket Garden experiment, Woburn. 0, of FYM and green material; A,sewage sludge; V, compost of sewage sludge and straw. Adapted from Johnston (1975).

moved at harvest and the only return of organic matter has been in stubble, roots, and fallen leaves. Shen ef al. (1989) showed that the soil that had received 144 kg N ha-’ as fertilizer each year for 137 years contained 3600 kg organic nitrogen in the top 23 cm compared to 2900 kg in soil receiving no nitrogen. When these two soils were incubated in the laboratory, the amount of nitrogen mineralized was the equivalent of 22 and 14 kg N ha-’, respectively, but the wheat crop at harvest contained 72 and 3 1 kg N ha-’, respectively; these values were determined using the 15N-labeledfertilizer experiment data. However, per unit area of land, these amounts of “available” nitrogen are small relative to the much larger quantities of nitrate mineralized in the first autumn or spring following the ploughing of grassclover leys (Johnston et al., 1994) or the incorporation of residues after growing grain legumes. 4. The Fate of Nitrogen in Organic Manures A large proportion of the N applied in organic manures can be unaccounted for. For 20 years, FYM, sewage sludge, and two composts were applied at 37.5 and 75 t ha-’ fresh manure in the Market Garden experiment at Woburn. The straight line relationship (Fig. 5 ) between the accumulation of N in soil and that applied indicated that only about 37% of the applied N could be found in extra soil organic

A. EDWARD JOHNSTON

3 12

3-02.0-

/

I I\

T &

3.0-

**O-

I I

1.0

3-I---- - -o-c-G-*

1.0A \

A 0-

1

I

,

B 0-

I

1

matter at the end of the period in which these large applications were made (Johnston, 1975). Similarly, during the first 135 years of the Broadbalk experiment, about 95 kg N ha-' of the 225 kg applied in the annual application of FYM could not be accounted for (Table XV). Much of this loss was probably as nitrate because Powlson et al. (1989) found much larger concentrations of nitrate in soil throughout the winter on similar FYM-treated soils compared to those given fertilizers only. In experiments in which differences in soil humus content have accumulatTable XV Increase in the Amounts of Nitrogen in the Soil and the NitrogenApplied to and Removed in Winter Wheat from the Farmyard Manure-Treated Plot on Broadbalk, Rothamsted, at Various Periodsu

Period

Annual increase in soil nitrogen (kg ha-I)

Nitrogen in harvested crop (kg ha-')

Nitrogen applied (kg ha-')

Nitrogen not accounted for (kg ha-')

1852-1861 1892-1901 1970-1978

70 30 5

65 90 125

225 225 225

90 105 95

uThe amount of FYM applied has remained constant at 35 t ha-' each year and its nitrogen content has varied little; an average value of 225 kg ha-' has been used. Adapted from Johnston et al. (1989).

THE VALUE OF LONG-TERM FIELD EXPERIMENTS

313

ed over long periods, it is possible to show enhanced denitrification with extra organic matter (Goulding and Webster, 1989).

C. EFFECTOF SOILTYPE AND FARMINGSYSTEMS ON SOIL ORGANIC MATTER Figure 6 shows changes in the soil organic matter or humus on variously treated plots on Hoosfield at Rothamsted and at Woburn. On Hoosfield soil, humus content has been constant for about 100 years on both the unmanured plot and that given NPK fertilizers. The quantity is a little larger in the fertilized soil because larger crops have been grown and, although straw is removed each year, there have been larger residues from stubble, leaves, and roots. Annual additions of 35 t ha-' fresh FYM have increased soil humus, rapidly at first and then more slowly as the appropriate equilibrium value for this system was approached. The time scale is important: 130 years for the buildup from a large annual application of FYM on this medium-textured soil in a temperate climate. The importance of soil texture on soil humus levels is well illustrated in Fig. 6. The sandy loam at Woburn has about 10%clay, the silty clay loam at Rothamsted about 20%.At the beginning of the experiments at Woburn, the soil contained more humus than the soil at Rothamsted had contained 30 years previously (for an explanation, see Johnston, 1991),but under continuous arable cropping with a range of treatments there is now less humus in Woburn soil than in Rothamsted soil. 0ther examples for the two sites are given by Johnston (1991), whereas Christensen and Johnston (1 997) have discussed interesting similarities for similar soil types using data from Askov in Denmark and Woburn in England. Such long-term data sets make it possible to develop models to describe these changes (Jenkinson et al., 1994).Such models can then be used to predict likely effects of changing farming systems on soil humus content and also possible effects of climate change (Jenkinson et al., 1991).

W. ECOLOGICAL, RESEARCH AND LONG-TERM EXPERIMENTS A. PARKGRASS,THEMOSTLONG-TERM ECOLOGICAL STUDY IN THE WORLD

The Park Grass experiment, begun in 1856, was the last of Lawes and Gilberts' major field experiments. The results from the experiments on arable crops, begun periodically between 1843 and 1852, had highlighted differences between crops representative of the great botanical families, especially in the amounts and ratios

3 14

A. EDWARD JOHNSTON

of nitrogen, phosphorus, and potassium they required. Lawes and Gilbert were intrigued as to whether different species from these same families but those found in the mixed sward of permanent grassland would respond similarly.

1. Treatments and Management The chosen site had been in permanent grassland for some hundreds of years and had never received the large applications (up to 250 t ha-') of chalk (CaCO,) that had been applied to the arable fields. Initially, plots ranging in size from 0.1 to 0.2 ha were established to test N, P, and K in various combinations and compare the effects with those of FYM.All fertilizers were applied each year, P, K, Na, and Mg in late winter and N once only in spring. Initially, FYM (35 t ha-') was also applied each year on the surface of the sward but it decomposed so slowly that the accumulated residues adversely affected yields. FYM was not applied between 1864 and 1904. For details of treatments testing FYM since 1905 see Warren and Johnston (1964). Small amounts of lime (CaCO,), first tested in 1881 and again in 1883 and 1896, had little effect on either yield or botanical composition. In 1903, a test of 4 t ha-' CaCO, applied cumulatively every fourth year to half plots was begun (Warren and Johnston, 1964). In 1923, Crowther (1925)determined the pH of the 0-23 and 23-46 cm soil horizons on all plots. Where 96 or 144 kg N ha-' had been applied as ammonium sulfate, the pH of the surface soil was below 4.0, having declined from about 5.8 in 1856. The pH on the limed halves of the plots ranged from 4.1 to 4.8. By 1959, the pH of these unlimed soils had decreased only a little, but that of the limed soils had increased further (Warren and Johnston, 1964). In 1965, the half plots were halved yet again to give subplots a, b, c, and d and during the years that followed chalk was applied to try to achieve pH 7,6, and 5 on subplots a, b, and c, whereas subplot d remained without lime to assess the extent to which treatment and other acidifying inputs would decrease soil pH. On plots with a mat or thatch of only partially decomposed organic material, it proved very difficult to raise the pH of the underlying mineral soil until the mat had been decomposed by increased microbial activity following liming (Johnston, 1972). In June each year, the herbage on each plot was cut and made into hay on its own plot before being weighed and a sample taken for dry matter determination, chemical analysis, and storing in the archive. This continued until 1959. From 1960, a part of each plot was cut by flail harvester at about the same time as previously, the herbage was weighed green and yields, unaffected by haymaking losses, were recorded as dry matter. The traditional haymaking was maintained on the rest of the plot because it was considered that mature shed seed might have helped maintain the botanical composition of the sward. The growth between haymaking and late autumn, the aftermath, was grazed by sheep until 1874, but after that it was cut, weighed green, and yields were recorded as dry matter.

THE VALUE OF LONG-TERM FIELD EXPERIMENTS

315

2. Results The fertilizer treatments quickly changed the proportions of grasses, legumes, and herbs (dicotyledons excluding legumes) in the sward. After only 2 years, PK fertilizers had increased legumes from 5 to 20% (dry weight basis) and herbs were rare. Ammonium sulfate alone or with PK fertilizers eliminated the legumes and most of the herbs leaving a herbage with 90% or more grasses. In 1859, Lawes and Gilbert wrote “the very varying degree in which they (the manures) respectively developed the different kinds of plants that the experimentalground looked almost as much as if it were devoted to trials with different seeds as with different manures.” In 1896, Lawes commented that the experiment was “the battlefield of the plants.” The early results led Lawes and Gilbert to realize that, unlike the wheat, barley, and turnips in their other experiments, weight alone was not a satisfactory measure of the worth of the crop. In fact, they concluded (Lawes and Gilbert, 1880) that the experiment was of greater interest to the “botanist, vegetable physiologist and the chemist than to the farmer.” This has proved to be so, especially now that there is such a long data set to provide insights into issues in evolutionary, population, community, and ecosystem ecology. The species composition of the sward on individual plots at different times has been discussed frequently (Lawes and Gilbert, 1863, 1880; Lawes er al., 1882; Brenchley and Wamngton, 1958; Thurston, 1969; Williams, 1978) and recently by Tilman et al. (1994). For example, Warren and Johnston (1964) showed how grasses comprising more than 10% of the hay weight varied with treatment (Table XVI). The effects of external factors, such as weather, have also been studied (Cashen, 1947; Smith, 1960; Silvertown, 1980, 1987; Tilman, 1982, 1986). It has been concluded that, in the absence of any experimental perturbation, plant species composition, diversity, and productivity are quasi-stable but are influenced by climatic variation and probably also by atmospheric inputs to the soil. On these untreated plots, some usually rare species have brief periods of dominance, whereas a few, usually dominant, species have brief periods of rarity. Where nutrients are applied, plant community composition is highly dependent on both the rate and the ratio of nitrogen, phosphorus, and potassium, the major limiting nutrient resources in this experiment, and these data have been used to develop a theory of resource competition (Tilman, 1982). In addition, soil pH, modified in this experiment by liming, and fertilization greatly affect species composition and diversity. For example, nitrogen is supplied as either ammonium sulfate or sodium nitrate and should have the same effect on yield and species composition but repeated use of ammonium sulfate has appreciably acidified the soil and acidity has greatly affected species composition. An example of these interactions is given in Table XVII.The extra yield on plot 7 compared to plot 8 is because potassium supplied to plot 8 encourages legumes. One of the most intriguing features of Park Grass is the fact that differences in

A. EDWARD JOHNSTON

316

a b l e XVI

Effect of Manuring and Soil Reaction on Grass Species during 1947-194ePark Grass, Rothamsted ISpecies present in more than 10% by weight of grass fraction

Treatment

pH 3.74.1

N1

A. tenuis, 79 E rubra, 16

N2P

N2PK

A. tenuis, 44 E rubra, 23 H. lanatus, 20 A. odoraturn, 10 H. lanatus, 9 1

N3PK

H. lanatus, 96

pH 4.2-6.0

pH 6.0-7.5

D. glomerata, 36 A. pratensis, 20 E rubra, 13 A. odoraturn, 12 H. lanatus, 12 R rubra, 59 A. pratensis, 28

D. glomerata, 21 E rubra. 26 H. pubescens, 22

A. prarensis, 38 A. elatius, 28 C. cristatus, 15 D. glomerata, 13 f! pratensis, 10 A. pratensis, 72 A. elatius, 18 I! pratensis, 11 D. glomerata, 10

A. elatius, 48 D. glomerata, 14 C. cristatus. 14 A. pratensis, 13

Note. Fertilizers (kg ha-’): N, 1, 2, 3; 48, 96, 144; P, 33; K, 200. Agrostis tenuis, fine bent; Alopecurus pratensis, meadow foxtail; Anthoxanthum odoratum, sweet vernal; Arrhenatherum elatius, tall oat; Cynosurus cristatus, crested dogstail; Dactylis glomerata, cocksfoot; Festuca rubra, fed fescue; Heliototrichon pubescens, downy oat; Holcus lanatus, Yorkshire fog; Poa pratensis, smooth-stalked meadow grass.

soil conditions and nutrient supply have acted as a selective force on plant populations causing measurable evolutionary change. Anthoxunthum odoraturn has always been found on many plots and the populations found on different plots have evolved to be both morphologically and physiologically different (Snaydon, 1970; Snaydon and Davis, 1972). The adaptive nature of the differences between populations was confirmed by reproducing vegetatively a number of plants from each population under uniform conditions and then transplanting them back into either their own or other plots. Each population survived and grew fastest on its own plot (Table XVIII). The number of plants surviving, as a percentage of the total planted, decreased rapidly with time. On average, after 18 months 67% survived on their native plot but only 43% survived on the alien plot. The two largest differences were first for plants grown on soil at pH 5.3 and given full nutrients (plot 9L); when planted into an unmanured soil (plot 3U) at the same pH only 14% survived. When grown on soil given P only at pH 7.0 (plot 8L) and then planted into

THE VALUE OF LONG-TERM FIELD EXPEFUMENTS

317

lsble XVII

Effect of Nutrient Supply and Soil pH on Species Richness and Yield of the Hay Crop-Park Grassa Plot and treatmentb

pHC

Species richnessd

Yielde

pH

Species richness

Yield

3, None 8, p 7, PK II/I,NPK

6.4 6.4 6.2 5.4

33 32 23 13

3.28 3.43 5.47 5.76

4.8 4.9 4.8 3.4

35 29 25 2

2.16 3.56 4.35 5.59

"Adapted from Tilman ef al. (1994). bP, 33 kg P ha-'; K, 200 kg K ha-'; N, 144 kg N/ha as ammonium sulfate. 'pH in water, different pHs on two of the quarters on each whole plot in 1991, 0-23 cm depth. dTotal species per 10 X 15-m area. etha-' dry matter from the early June hay crop mean 1991-1993.

soil with both P and K but at pH 4.9, only 38% survived. Surviving plants of populations transplanted into their native plots generally produced more tillers (average 8.8) compared to transplants on alien plots (5.8 tillers). Mean plant dry weight was also affected (470 and 345 mg on native and alien plots) but there appeared to be some compensatory mechanisms at work. For example, for plot 9L there was the least difference in survival but the most difference in plant weight, whereas on Table XVIII Plant Surival and Mean Plant Weight of Si Populationsof Anthoxanthurn odomtum 18 Months after Being 'kansplanted into Their Native and into Constrasted PloLs-Park Grass, Rothamst& Plant weight Population pairs by plot number and treatment

9L, N,PK 3U, none IL, N , IU, N, 8L, P 7U, PK Mean

Vegetation height, 1969

(mm) 580 110 160 120 230 210

(mid

% Survival

Soil pH

5.3 5.2 1.2 4.0 7.0

4.9

Native

Alien

Native

Alien

60 55 82 61 16 69 67

50 14 54 49 54 38 43

614 45 1 416 49 1 39 I 395 470

364 49 1 395 310 264 248 345

~

Note. U plots without CaCO, since 1856; L plots 2250 kg CaO ha-' every 4 years since 1903. N, and N, : 48 and 96 kg N ha- I as ammonium sulfate. P and K: 33 kg P ha-' and 200 kg K ha-' since 1856; plots with PK also get I I kg Mg, 16 kg Na; pH in water 1:2.5 s0il:water. "Adapted from Davis and Snaydon (1976).

318

A. EDWARD JOHNSTON

plot 3U there was the most difference in survival but the least difference in plant weight. Although to the practicing farmer the yields on this experiment are of little interest, there are some interesting aspects related to yield. There was a stepwise increase in yield on all plots after 1959 when the method of estimating yields of hay was changed. However, examination of the yields of herbage dry matter both before and after 1959 shows that yields have been sustained with all treatments but at very different levels of productivity. Some treatments gave yields that would have been too low for economic viability of a farming system based on such inputs. Recently, it has been possible to test suggestions that increasing carbon dioxide concentrations in the air would enhance plant growth. The relative stability of the botanical composition of the unmanured plots and those getting nitrogen as sodium nitrate with P and K has allowed a test of this hypothesis (Jenkinson et al., 1994). Using the statistical approach adopted, no increase in yield attributable to increased carbon dioxide could be detected. The authors concluded that the same was likely to be true for all ecosystems limited by water andor nitrogen. Other interesting soil studies ecologically important have compared changes in pH on the unmanured soils on Park Grass and Geescroft Wilderness (Johnston et al., 1986). Left untended since 1886, the Geescroft Wilderness is now deciduous (mainly oak) woodland. The 0- to 23-cm depth of soil on Geescroft has become more acid (by 3 pH units) more quickly (in 100 years) than the Park Grass soil (by 1 pH unit) in 150 years (Table XIX). This difference probably relates to the efficiency with which a tree canopy can slow airflow and trap acidifying compounds in the atmosphere so that they are transferred to the soil. Where superphosphate (supplying 33 kg P ha-’) has been applied annually to Table X M Effect of Both “Natural” and Additional Fertilizer Acidifying Inputs on Soil pH at Different Depths under Woodland and Grassland at Rothamsted‘ Year and experiment ~

Geescroft Wilderness woodland Horizon(cm) Natural inputs 0-23 23-46 46-69 Additional fertilizer inputs 0-23 2346

~~

Park Grass grassland

1883

1904

1965

1983

1991

1876

1923

1959

1984

1991

7.1 7.1 7.1

6.1 6.9 7.1

4.5 5.5 6.2

4.2 4.6 5.7

4.3 5.1 6.0

5.4 6.3 6.5

5.7 6.2

5.2 5.3 -

5.0 5.7 -

4.8 5.4 5.7

-

-

-

-

-

4.2 6.3

3.8 4.4

3.7 4.1

3.4 4.0

3.2 3.8

-

uAdapted from Johnston et al. (1986)with additional unpublished data.

THE VALUE OF LONG-TERM FIELD EXPEFUMENTS

3 19

the surface of the Park Grass plots for more than 130 years, P now enriches the subsoils below 23 cm. Very much smaller enrichment of subsoils below 23 cm has been detected where the same annual applications of superphosphate have been applied to the arable experiments for 140 years. However, a downward movement of P has occurred on FYM-treated soils in the arable experiments. This suggests some relationship between organic matter and the downward movement of Pin the aqueous phase (Johnston and Poulton, 1992).This has implications for the causes of enrichment of surface water with P, which is related to the increased risk of algal blooms occurring.

B. THEROTHAMSTEDINSECT SURVEY This survey is a good example of the benefit of long-term data sets in ecological research (Woiwood and Harrington, 1994).Although not specifically related to the long-term experiments,the monitoring sites used were located adjacent to them so that cropping and treatment would be constant from year to year. Founded in the early 1960s, the survey formally amalgamated studies on insect migration beginning in the 1920s and on butterflies and moths beginning in the 1950s. Longterm fluctuations in insect populations and the prediction of changes in pest status help to understand the balance between pest and nonpest species and can be used to develop and test fundamental ideas of population biology and dynamics. Changes in insect populations may be very useful indicators of environmental changes. For example, insect populations responding to changes in plant quality may reflect deleterious changes in an ecosystem long before they become apparent by conventional botanical observations. In the same way, changes in soil microbial biomass often indicate the direction of change in total soil organic matter, as a result of changing husbandry, much sooner than changes in soil organic matter itself. Other interesting monitoring examples include bird populations (Greenwood et al., 1994),plants (Grime et al., 1994),marine organisms (Gamble, 1994), and tropical forests (Swaine, 1994).

V. LONG-TERM EXPERIMENTS AND ENVIRONMENTAL CONCERNS A. SOILPOLLUTION One of the most exciting unforeseen benefits of the long-term Rothamsted experiments has been the opportunity to study contaminant trends over time in response to increasing public and scientific interest of the impact of humankind on the environment. Sustainable land use is being increasingly threatened by pollu-

320

A. EDWARD JOHNSTON

tion causing not only readily recognized soil acidification but also accumulation of heavy metals and organic pollutants, the effects of which are still only imperfectly understood. Measuring rates of accumulation, assessing bioavailability,and setting critical limits for such pollutants requires reliable data, only available from longterm experiments, if policymakers are to make good legislation for their control. Research on pollutants in archived and contemporary samples from the Rothamsted long-term experiments has been an excellent example of collaboration between different groups with complementary skills; in this case at Rothamsted and at Lancaster University. As a result, a number of papers have been published on historical trends in both inorganic and organic contaminants and their fate and behavior in soil-plant systems.

1. Organic Pollutants Polynuclear aromatic hydrocarbons (PAHs),polychlorinateddibenzo-p-dioxins and -furans (PCDDFs), and the polychlorinated biphenyls (PCBs) have been studied. All are ubiquitous in the environment and research interest in their environmental fate and behavior begin in the 1960s because of their persistent (recalcitrant) nature, the propensity for some of them to accumulate through the food chain, and concerns about their toxicity. Contemporary concentrations of these compounds in normal agricultural soils range from pg kg-' for PCDD/Fs to mg kg-' for PAHs, and it is only recently that sufficiently sensitive and reliable analytical procedures have become available to determine these concentrations.Thus, little reliable environmental analytical data existed for these compounds prior to the 1970s.As a result, retrospective analysis of the Rothamsted archived samples has provided a unique and valuable insight into the influence of human activity on the inputs, environmental cycling, and time trends of these contaminants. Concentrations of PAHs, PCDD/Fs, and PCBs have been measured in control plots on Broadbalk and the Market Garden experiment at Woburn because the concentrations (Table XX) in plough-layer (0-23 cm) depth soil and in the vegetation grown on these soils have been influenced only by air-soil exchanges (Jones et al., 1989a,b; Wild et af., 1990; Kjeller et af., 1991; Jones et al., 1992; Alcock et af., 1994).The data for six selected compounds highlight the contrasting behavior of the different compounds, both within and between homolog groups. Figure 7 shows the data from Table XX diagrammatically, by normalizing the average concentration for each compound from the two experiments to its peak concentration over time. Four compounds, phenanthrene, benzo(a)pyrene (B(a)P), 2,3,7,8TCDD, and OCDD, were detectable in the earliest samples indicating that these compounds occur naturally at low concentrations in soils, the source being combustion of organic materials. All four compounds increased in concentration, supporting the assertion that anthropogenicactivities have increased their atmospheric burden (and hence deposition) during this century. However, phenanthrene con-

Table XX Concentrations of the Six Selected Compounds in Soils (0- to 23-cm Depth) Taken at Various Times from the Control Plots of Broadbalk and Woburn Experiments Year

1846 Broadbalk Phenanthrene (pg kg-I) Benzo(a)pyrene (pg kg2,3,7,8-TCDD (pg kg-I) OCDD (ng kg-') PCB-28 (pg kg-I) PCB-138 (pg kg-I)

I)

Woburn Phenanthrene (pg kg-I) Benzo(a)pyrene (pg kg-') PCB-28 (pg kg-') F'CB-138 (pg kg-I)

1856

1881

1893

1914

1944

1956

68 6.7

45

110 23 40 10 1.7 0.26

120 13 49 13 3.4 (0.15

1966

1980

1986

160

140

28 60 32 106 3.4

120 79 20 1.9 0.2 1

48 72

-

-

-

89 12 40 11 -

1942

1951

1960

19661967

1972

1980

1984

1992

17 17 9.1 0.6

110 18 50 <1.2

340 30 154

250 38 146 3.9

130 45 183 5.5

160 39

150 35 4.1 2.3

0.38 1.O

46 18 33 7.6 -b

-

-

=Adapted from Jones et al. (1994). b-, Indicates not detected; space indicates not analyzed.

29

4.5

11

110

2.1

110

25

322

A. EDWARD JOHNSTON

u

0.0

1840

1.2

1860

1880

1900

1920

1940

1960

2000

1980

* 2.3.7,8 TCDD

1.0-

+OCDD

(I)

c

0 .-

5

0.8

-

0.6

-

0.4

-

L

c

a, 0

0

wm E z

-0

.-

0.2

0.0

A Y

1

~

*

1

~

1

~

1

1

centrations peaked in the 1960s and have declined subsequently, whereas B(a)P, 2,3,7,8-TCDD,and OCDD concentrations have either continued to increase up to the present or have stabilized. Although this observation could be explained by the potential volatility of phenenthrene (Wild and Jones, 1992) and/or its susceptibil-

~

~

THE VALUE OF LONG-TERM FIELD EXPERIMENTS

1840

1860

1880

1900

1920

1940

1960

1980

323

2000

Figure 7 (continued)

ity to biological degradation (Wild and Jones, 1993), it also implies that the inputs of PAHs have declined in recent years. Evidence for declining inputs comes from a decline in ZPAH concentrations in archived vegetation samples since the 1960s (Jones er al., 1989b, 1992) and data from dated lake sediment cores indicating maximum ZPAH deposition fluxes in the 1950s (Sanders et al., 1993). Coal consumption is thought to be the largest contemporary source of PAHs to the UK environment (Wild and Jones, 1994),and total UK consumptionpeaked in the 1950s. In addition, there has been a shift in coal usage away from the relatively inefficient domestic combustion of coal at lower temperatures to higher temperature combustion in very large quantities at power stations (Jones er al., 1989a). It is important to note, however, that despite the reductions in PAH input, the concentrations of B(a)P have not declined in Broadbalk and Woburn soils (Table XX, Fig. 7) probably because the potential loss pathways for B(a)P from soil are very limited. In other words, B(a)P has a long residence time in soils (Wild et al.. 1990). The continued increase in concentrationsof 2,3,7,8-TCDDand OCDD throughout the 20th century are consistent with our knowledge of the likely inputs (Kjeller et al., 1991) and the physicochemical properties of these compounds. PCDD/Fs can be formed and released from a variety of sources (Hmad and Jones, 1992) and it seems likely that contemporary inputs, notably from municipal waste incineration and coal combustion, have not declined. Concentrations of both PCB-28 and PCB-138 peaked in the 1960s and 1970s, resembling the temporal trend for phenanthrene (Table XX). Contemporary concentrations are similar to those in the 1940s. These trends are consistent with our

324

A. EDWARD JOHNSTON

knowledge of the production and history of use of these compounds in the United Kingdom (Alcock et al., 1993); the peak in worldwide usage occurred in the late 1960s (Harrad et al., 1994). One interesting aspect of the PCB time trend data is the rapid loss of these congeners from the soil beginning about 1970 probably due to volatilization back to the atmosphere from where they came. In summary, the PCDD/Fs and high-molecular-weight PAHs can be expected to reside in soils over many yearddecades, firmly retained by soil constituents. In contrast, UK soils, such as those at Rothamsted, now appear to be acting as a source of PCBs and lowmolecular-weight PAHs back to the atmosphere. This is of significance for the global cycling of these compounds. 2. Inorganic Pollutants

Studies on the trends in metals in soils and herbage have focused especially on cadmium, but lead and selenium have been included. Such studies are complicated because the speciation of metals in waste materials added to soil may be different from that of the metals taken up by plant roots, speciation may change in soil, and long periods may be required for added metals to equilibrate with other soil constituents.Also, concentrationsof metals that do not affect crop growth may affect other parameters contributing to soil fertility. Brookes and McGrath ( 1 984) showed that concentrationsof heavy metals below those at which crop growth was affected had adverse effects on some components of the soil microbial population. The amount of microbial biomass in metal-contaminatedsoil was only half that in soil low in metals. In particular, the effects of metals on nitrogen-fixing bacteria were of agronomic concern because they were observed 20 years after the last application of metal-contaminated sewage sludge. Only one strain of Rhizobiurn survived in the contaminated soils and this was not able to form effective nodules on the roots of clover (McGrath et al., 1988). Point sources of pollution such as those from sewage sludge can be readily identified and remedial action taken. However, equally damaging to sustainable land use can be the very slow buildup in soil of pollutants from atmosphericdeposition. Jones et al. (1987) analyzed soils from the Rothamsted archive to estimate changes in concentration of 12 metals over periods of up to 140 years. For 8 of these metals, there were no consistent changes that could be associated with either atmospheric or treatment inputs over this time scale. There was a small (- 15%) increase in Pb from atmospheric deposition and there were increases in Cu, Pb, and Zn associated with the large (35 t ha-') annual applications of FYM. Johnston and Jones (1992,1995) have summarized changes in the cadmium burden in soils and crops at Rothamsted and discussed their results in relation to others throughout the world. Exposure to cadmium is a major health hazard and dietary cadmium is a major source. Figure 8 shows that at Rothamsted there has been a rapid increase in soil cadmium since the 1940s on soils without any agricultural

325

THE VALUE OF LONG-TERM FIELD EXPERIMENTS

1840

1860

1880

1900

1920

1940

1960

1980

Year

Figure 8 Changes in soil Cd levels in the untreated Broadbalk plot between 1846 and 1980. Measured soil Cd data (x). Predicted soil Cd concentration (0)based on assumed global emission of Cd to the atmosphere. Error bars relate to analyses of eight separately digested samples for both 1846 and 1980. All other data points are the mean of duplicate analyses. Adapted from Jones et al. (1987).

amendment, an increase that must be due to aerial deposition. It was also observed that there had been no additional accumulation of cadmium in soils low in organic matter and with pH above 6.5, to which 0.4t ha-' superphosphate had been added each year for I 0 0 years; there was some retention of cadmium added in superphosphate, however, on acid soils with more organic matter. There was no change in the cadmium concentration in cereal grain over time, but that in herbage had increased. However, cadmium in herbage could be lessened by maintaining soil pH above 6.5.

VI. THE NEED FOR LONG-TERM EXPERIMENTS Long-term or continuing field experiments are essential to our understanding of, and opportunity to test, the sustainabilityof agroecological systems. It is impossible, however, to say how long an experiment should continue, but undoubtedly the

326

A. EDWARD JOHNSTON

opportunity for slow changes, which have adverse or beneficial effects, to manifest themselves increases with time. It is more realistic and perhaps important to suggest that experiments continue until they demonstrate that the system being tested or studied is not sustainable. If doubts about sustainability are raised, every effort should be made to seek modifications to the system to ensure its continuity. In 1843, Lawes and Gilbert could not have conceived that, of the field experiments they were to start over the next 14 years, eight would still be continued in 1996 and continue to provide agronomic and scientific data of immeasurable value. However, some of the experiments they, and others at Rothamsted, begun have not survived. These experiments were often stopped because yields became too small and at the time the decrease in yield could not be explained. Where yields declined because of increasing soil acidity, this was corrected by liming but adverse effects of nematodes have only been realized in recent decades, whereas the failure to grow legumes continuously has never been explained satisfactorily. Such results highlight the need for multidisciplinary teams to be responsible for and take an active interest in long-term experiments. This is well illustrated in the account of the Woburn Ley Arable experiment and in the way those responsible for the experiment reacted by changing treatments to explain failures and ensure continuity. Experiments can be too short and the results misleading; a good example are the data in Fig. 9. Over a 30-year period (1957-1989), herbage lead concentrationsdeclined in an experiment at Rothamsted. Although there was much seasonal varia-

1955

‘60

‘65

‘70

‘75

‘80

‘85

‘90

Figure 9 Lead concentrations in herbage at Rothamsted 1956-1988. Soils treated with fertilizers

(0)or farmyard manure (0). Adapted from Jones et al. (1991).

THE VALUE OF LONG-TERM FIELD EXPERIMENTS

327

tion, the decline mirrored a decline in atmospheric lead concentrations (Jones et al., 1991). Viewed in isolation, however, data from the mid-1970s to the early 1980s could be used to show a slight increase in lead concentrations over a period of about 7 years that, although real, was nevertheless against the overall downward trend.

VII. APPROACHES T O NEW LONG-TERM EXPERIMENTS A number of criteria must be satisfied before setting up long-term experiments. Two factors need to be settled before much time is spent on detailed planning. One is tenure of the site, the other is funding. In no small measure the Rothamsted long-term experiments have continued through many decades because there was security of tenure of the sites; the Lawes Agricultural Trust either owns or has very long leases on them. Thus, scientists could be assured that the sites would continue to be there for the experiments to continue. Funding for long-term experiments should ideally differentiatebetween the running costs of doing and managing an experiment and the cost of the research done on it. The latter should be charged to the appropriate research budget. Also, it would be realistic to consider that funding should be multiresourced if the different objectives set out in the Introduction are to be achieved. Partial fanner funding should be available where data are provided that help farmers reach decisions about their day-to-day management practices. However, studies at the interface of agricultural practice, the maintenance, or increase in productivity in the long-term and environmental objectives, on which administrative decisions will be based, should be funded by an appropriate agency of government. Funding for field experimentsmust be justifiable on the basis of getting reliable and valuable data that could not be otherwise obtained. The data must be well interpreted and then made freely available. Policymakers especially need good interpretation if they are to reach rational decisions so that future generations enjoy both adequate food and a safe and pleasant environment.The need for good quantitative data has never been more apparent. It is frightening that today so many people are trying to predict the possible effects of, for example, global warming on the one hand and alternative husbandry systems on the other, on future food supplies on the basis of totally inadequate data. Even with well-founded data, especially those for complex biological and physicochemical processes such as occur in soils and plants, there is almost always scope for a variety of interpretations. Trying to infer processes from a poor data base and then model it and make global predictions leaves much to be desired. The importance of long-term, continuing

328

A. EDWARD JOHNSTON

experiments.to global concerns is something to which all funding agencies should give their attention and funds. As long as site tenure and funding can be secured, other aspects of setting up long-term experimentscan then be considered. Building on current experience and some of the examples discussed previously, it is essential that any new long-term field experiments should be multidisciplinary in approach. Hence, the first task is to assemble a group of likeminded researchers willing to invest their time and expertise under the leadership of a chairperson with whom they can work. This group must then decide not only the questions they want to ask of the experiment but also, and much more difficult, the likelihood of their experiment providing the answers and over what time scale. In seeking compromises in design to include treatments to meet individual research needs, it is essential to ensure that the achievement of the original aims will not be jeopardized. The scale of an experiment and its design will depend on the system being tested and the number of treatments to be imposed. For agricultural experiments, it is essential to consider possible cropping sequences at the beginning of any longterm experiment. Crop rotations should exclude, wherever possible, the risk of buildup of pests and diseases unless their control chemically, biologically, or by rotation can be included in the experimental design and the damaging effects of the pest or disease estimated. Long-term experiments will almost certainly outlive cultivars of arable crops and the agrochemicals that may be used as treatments or basal applications. The protocol adopted must be flexible enough to allow for change when change is essential. The size of plots must also be sufficient to exclude edge and soil creep effects and permit the opportunity to test additional treatments. In addition to treatment effects, it is also essential to monitor those factors that can affect soil fertility and plant composition or health but are not under test. Weather at the site should be monitored rigorously, especially those parameters that are needed to derive other data, such as evapotranspiration,which may be important in helping understand differences in yield. In addition to yield, long-term data sets about factors controlling growth, which are of interest to the individual members of the research group, will accumulate. However, it is also essential to decide initially on a policy for collecting and archiving crop and soil samples. Here there must be a rigid protocol if results are to be compared over time. It is especially important to take a soil sample from each plot before any treatments are applied and to sample subsoils as well as surface soils. Three other major problems of long-term experiments must never be underestimated. One is the publication of results, especially when they take some years to accumulate. Too often today, publication and promotion, perhaps even job security, appear inseparable to the young research worker. Senior management within research organizations must give credit for good work on long-term experiments. However, within the framework of established treatments, there is often consider-

THE VALUE OF LONG-TERM FIELD EXPERIMENTS

329

able opportunity for carefully supervised projects leading to higher degrees and postdoctoral research and these possibilities should never be overlooked or dismissed lightly. Also, there is the continuity of experiments beyond the active participation of the initiators. Within the framework of multidisciplinarysponsorship, it is likely that there will be a range of age, experience, and management skill. Thus, as any one sponsor leaves the group a suitable replacement can be found and their enthusiasm and skill used to maintain or enhance the research program. Finally, it is extremely important to recognize that all long-term experiments are likely to have periods of great interest alternating with a routineness that threatens the morale of even those closely involved. Such interludes can also lead to questions about the need to continue by those responsible for funding or wanting to make alternative use of the site. Such periods of doubt must be minimized. One of the advantages of the long-term experiments at Rothamsted is that there is more than one. Invariably, there is something new and interesting in one experiment or another.

REFERENCES Addiscott. T. M., and Powlson, D. S. (1992). Partitioning losses of nitrogen fertilizer between leaching and denitrification. J. Agric. Sci. (Cambridge) 118, 101-107. Alcock, R. E., Johnston, A. E., McGrath, S. P., Berrow, M. L., and Jones, K. C. (1993). Long-term changes in the polychlorinated biphenyl content of United Kingdom soils. Environ. Sci. Technol. 27,1918-1923. Alcock, R. E., Halsall, C. J., Harris, C. A,, Johnston, A. E., Lead, W. A,, Sanders, G., and Jones, K. C. (1994).Contamination of environmental samples prepared for PCB analysis. Environ. Sci. Technol. 28(11), 1838-1842. Boyd, D. A., and The Ley Arable Sponsors (1968). Experiments with Ley and Arable Fanning Systems. Rorhumsted Exp. Srarion Rep. 1967, 3 16-33 I . Brenchley, W. E., and Warington, K. (1958). “The Park Grass Plots at Rothamsted 1856-1949.” Rothamsted Experimental Station. Harpenden, Hens, UK. [Reprinted 19691 Brookes, P. C., and McGrath, S. P. (1984). Effects of metal toxicity on the size of the soil microbial biomass. J. Soil Sci. 35,341-346. Cashen, R. 0. (1947). The influence of rainfall on the yield and botanical composition of permanent grass at Rothamsted. J. Agric. Sci. 37, 1-10. Christensen, B. T., and Johnston, A. E. (1997). Soil organic matter and soil quality: Lessons learned from long-term field experiments at Askov and Rothamsted. In “Soil Quuliryfor Crop Production” (E. G . Gregorich and M. R. Carter, eds.). Elsevier, Amsterdam. Crowther, E. M. (1925). Studies on soil reaction, 3, 4, 5. J. Agric. Sci. (Cambridge) 15, 201-221, 222-23 I , 232-236. Davis, M. S., and Snaydon, R. W. (1976). Rapid population differentiation in a mosaic environment. 111. Measurements of selection pressures. Heredity 36,5946. Dyer, B. (1901). A chemical study of the phosphoric acid and potash contents of the wheat soils of Broadbalk field, Rothamsted. Philos. Trans. R. SOC.B 194,235-290. Dyer, B. (1902). Results of investigations on the Rothamsted soils. In “Bullerin ofOficiu1 Experiment Srafions. No. 106, pp. 180. U S . Department of Agriculture. Washington, DC. ”

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Gamble, J. C. (1994). Long-term planktonic time series as monitors of marine environmental change. In “Long-Term Experiments in Agricultural and Ecological Sciences” (R. A. Leigh, and A. E. Johnston, eds.), pp. 365-386. CAB International, Wallingford, UK. Greenwood, J. J. D., Baillie, S. R., and Crick, H. Q. P. (1994). Long-term studies and monitoring of bird populations. In “Long-Term Experiments in Agricultural and Ecological Sciences” (R. A. Leigh and A. E. Johnston, eds.), pp. 343-364. CAB International, Wallingford, UK. Grime, J. P., Willis, A. J., Hunt, R., and Dunnett, N. P. (1994). Climate-vegetation relationships in the Bibury Road verge experiments. In “Long-Term Experiments in Agricultural and Ecological Sciences. ” (R. A. Leigh, and A. E. Johnston, eds.), pp. 271-286. CAB International, Wallingford, UK. Glynne, M. D. (1935). Incidence of take-all on wheat and barley on experimental plots at Woburn. Ann. Appl. Biol. 22,225-235. Glynne, M. D., Salt, G. A., and Slope, D. B. (1956). Rothamsted Exp. Station Rep. 1955, 103. Goulding, K. W. T., and Webster, C. P. (1989). Denitrification losses of nitrogen from arable soils as affected by old and new organic matter from leys and farmyard manure. In “Nitrogen in Organic Wastes Applied to Soils” (I. A. Hansen, and K. Henriksen, eds.), pp. 225-234. Academic Press, London. Harrad, S. J., and Jones, K. C. (1992). A source inventory and budget for chlorinated dioxins and furans in the United Kingdom environment. Sci. Total Environ. 126,89-107. Harrad, S. J., Sewart, A. S., Alcock, R. E., Boumphrey, R., Burnett, V., Duarte-Davidson, R., Halsall, C., Sanders, G., Waterhouse, K. S.,Wild, S. R., and Jones, K. C. (1994). Polychlorinated biphenyls (PCBs) in the British environment: Sinks, sources and temporal trends. Environ. Pollur. 85, 131-147. Jenkinson, D. S., Adams, D. E., and Wild, A. (1991). Model estimates of CO, emissions from soil in response to global warming. Nature 351,304-306. Jenkinson, D. S., Bradbury, N. J., and Coleman, K. (1994). How the Rothamsted Classical Experiments have been used to develop and test models for the turnover of carbon and nitrogen in soil. In “Long-Term Experiments in Agricultural and Ecological Sciences (R. A. Leigh, and A. E. Johnston, eds.), pp. 117-138. CAB International, Wallingford, UK. Johnston, A. E. (1970). The value of residues from long-period manuring at Rothamsted and Woburn. II. A summary of the results of experiments started by Lawes and Gilbert. Rothamsted Exp. Station Rep. 1969, Part 2,7-2 1. Johnston, A. E. (1972). Changes in soil properties caused by the new liming scheme on Park Grass. Rothamsted Exp. Station Rep. 1971, Part 2. 177-180. Johnston, A. E. (1975). The Woburn Market Garden experiment, 194269.11.Effects of the treatments on soil pH, soil carbon, nitrogen, phosphorus and potassium. Rothamsted Exp. Starion Rep. 1974, Part 2, 102-131. Johnston, A. E. (1977).“Woburn Experimental Farm: A hundred years of agricultural research devoted to improving the productivity of light land,” pp. 43. Lawes Agricultural Trust, Harpenden, UK. Johnston. A. E. (1987). Saxmundham Experimental Station 1899-1986. Areview of the achievements during 1965-1986. Rothamsted Exp. Station Rep. 1986, 265-278. Johnston, A. E. (1989). Phosphorus cycling in intensive arable agriculture. In “Phosphorus Cycles in Terrestrial and Aquatic Ecosystems” (H.Tiessen, ed.), pp. 123-136. SCOPE Regional Workshop I: Europe, Saskatchewan Institute of Pedology, Saskatoon, Canada. Johnston, A. E. (1991). Soil fertility and soil organic matter. In “Advances in Soil Organic Matter Research: The Impact on Agriculture and the Environment” (W. S. Wilson, ed.), pp. 299-314. Royal Society of Chemistry, Cambridge, UK. Johnston, A. E. (1994). The Rothamsted Classical Experiments. In “Long-Term Experiments in Agricultural and Ecological Sciences” (R. A. Leigh, and A. E. Johnston, eds.), pp. 9-38. CAB International, Wallingford, UK. Johnston, A. E., and Goulding, K. W. T.(1990). The use of plant and soil analyses to predict the potas‘I

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sium supplying capacity of soil. In “Development of K-Fertilizer Recommendations,” pp. 177-204. International Potash Institute, Berne. Johnston, A. E., and Jenkinson, D. S. (1989). The nitrogen cycle in UK arable agriculture. In “Pmreedings No. 286. The Fertilizer Society” pp. 1-24. Peterborough, UK. Johnston, A. E., and Jones, K. C. (1992). The cadmium issue-Long-term changes in the cadmium content of soils and the crops grown on them. In “Phosphorus Fertilizers and the Environment” (J. J. Schultz, ed.), pp. 255-269. International Fertilizer Development Centre, Muscle Shoals, USA. Johnston, A. E., and Jones, K.C. (1995). The origin and fate of cadmium in soil. In “Proceedings No. 366. The Fertilizer Society, ” pp. 3-39. Peterborough, UK. Johnston, A. E., and Poulton, P. R. (1977). Yields on the Exhaustion Land and changes in the NPK contents of the soils due to cropping and manuring, 1852-1975. Rothamsted Exp. Station Rep. 1976. Part 2.53-85. Johnston, A. E., and Poulton, P. R. (1992). The role of phosphorus in crop production and soil fertility: 150 years of field experiments at Rothamsted, United Kingdom. In “Phosphate Fertilizers and the Environment” (J. J. Schultz, ed.), pp. 45-64. International Fertilizer Development Centre, Muscle Shoals, USA. Johnston, A. E., and Powlson, D. S. (1994). The setting-up, conduct and applicability of long-term, continuing field experiments in agricultural research. In “Soil Resilience and Sustainable Land Use, (D. J. Greenland, and I. Szabolcs, eds.), pp. 395421. CAB International, Wallingford, UK. Johnston, A. E., and Warren, R. G. (1970). The value of residues from long-period manuring at Rothamsted and Woburn. HI. The experiments made from 1957 to 1962, the soils and histories of the sites on which they were made. Rothamsted Exp. Station Rep. 1969, Part 2.22-38. Johnston, A. E., Warren, R. G., and Penny, A. (1970a). The value of residues from long-period manuring at Rothamsted and Woburn. IV. The value to arable crops of residues accumulated from superphosphate. Rothamsted Exp. Station Rep. 1969. Part 2.39-68. Johnston, A. E.. Warren, R. G., and Penny, A. (1970b). The value of residues from long-period manuring at Rothamsted and Woburn. V. The value to arable crops of residues accumulated from K fertilisers. Rothamsted Exp. Station Rep. 1969, Part 2, 69-90. Johnston, A. E., Goulding, K. W. T., and Poulton. P. R. (1986). Soil acidification during more than 100 years under permanent grassland and woodland at Rothamsted. Soil Use Manage. 2,3-l0. Johnston, A. E., McGrath, S. P., Poulton, P. R., and Lane, P. W. (1989). Accumulation and loss of nitrogen from manure, sludge and compost: Long-term experiments at Rothamsted and Woburn. In “Nitrogen in Organic Wastes Applied to Soils” (J. A. A. Hansen, and K. Henriksen, eds.), pp. 126-1 39. Academic Press, London. Johnston, A. E., McEwen, J., Lane, P. W., Hewitt, M. V., Poulton, P.R., and Yeoman, D. P. (1994). Effects of one to six year old ryegrass-clover leys on soil nitrogen and on subsequent yields and fertilizer nitrogen requirements of the arable sequence winter wheat, potatoes, winter wheat, winter beans. J. Agric. Sci. (Cambridge) 122,73-89. Jones, K.C., Symon, C.J., and Johnston, A.E. (1987). Retrospective analysis of an archived soil collection. I. Metals. Sci. Total Environ. 61, 131-144. Jones, K. C., Stratford, J. A,, Waterhouse, K. S., Furlong, E. T., Giger, W., Hites, R. A., Schaffner, C., and Johnst0n.A. E. (1989a). Increases in the polynucleararomatic hydrocarbon content of an agricultural soil over the last century. Environ. Sci. Technol. 23,95-101. Jones, K. C., Grimmer, G., Jacob, J., and Johnston, A. E. (1989b). Changes in the polynuclear aromatic hydrocarbon content of wheat grain and pasture grassland over the last century from one site in the U.K. Sci. Total Envimn. 78, 117-130. Jones, K. C., Symon, C., Taylor, P. J. L., Walsh, J., and Johnston, A. E. (1991). Evidence for a decline in rural herbage lead levels in the UK. Amos. Envimn. 25A, 361-369. Jones, K. C., Sanders, G., Wild, S. R., Burnett, V., and Johnston, A. E. (1992). Evidence for a decline of PCBs and PAHs in rural vegetation and air in the United Kingdom. Nature 356, 137-140. ”

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Jones, K. C., Johnston, A. E., and McGrath, S. P. (1994). Historical monitoring of organic contaminants in soils. In “Long-TermExperiments in Agricultural and Ecological Sciences” (R. A. Leigh, and A. E. Johnston, eds.), pp. 147-163. CAB International, Wallingford, UK. Kjeller, L.-0.. Jones, K.C., Johnston, A. E., and Rappe, C. (1991). Increases in the polychlorinated dibenzo-p-dioxin and -furan content of soils and vegetation since the 1840s. Environ. Sci. Technol. 25,1619-1627. Lawes, J. B., and Gilbert, J. H. (1859). Report of experiments with different manures on permanent meadow land. J.R. Agric. SOC.England, Part I, 19, 552-573. Part II, 20, 228-246, Part 111, 20, 246-272, Part N,2 0 , 3 9 8 4 1 . Lawes, J. B., and Gilbert, J. H. (1863). The effect of different manures on the mixed herbage of grassland. J. R. Agric. SOC.England 24, Part I, 1-36. Lawes, J. B., and Gilbert, J. H. ( I 880). Agricultural, botanical and chemical results of experiments on the mixed herbage of permanent meadow, conducted for more than twenty years in succession on the same land. Part I. The agricultural results. fhilos. Trans R. SOC.171, 139-210. Lawes, J. B., Gilbert, J. H., and Masters, M. T. (1882). Agricultural, botanical and chemical results of experiments on the mixed herbage of permanent meadow, conducted for more than twenty years in succession on the same land. Part II. The botanical results. fhilos. Trans. R. SOC. 173, 1181-1413.

Leigh, R. A., and Johnston, A. E. (Eds.) (1994). “Long-Term Experiments in Agricultural and Ecological Sciences” pp. 448. CAB International, Wallingford, UK. Liebig, H. (1872). Soil statics and soil analyses. J. Chem. SOC.25,318,837. [English abstract] McGrath, S . P., and Lane, P. W. (1989). An explanation for the apparent losses of metals in a long-term experiment with sewage sludge. Environ. follur. 60,235-256. McGrath, S. P., Brwkes, P. C., and Giller, K. E. (1988). Effects of potentially toxic metals in soil derived from past applications of sewage sludge on nitrogen fixation by Trifolium repens L. Soil Biol. Biochem. 20,415-424. Powlson, D. S., and Johnston, A. E. (1994). Long-term field experiments: Their importance in understanding sustainable land use. In “Soil Resilience and Sustainable Land Use” (D. 3. Greenland, and I. Szabolcs, eds.), pp. 367-393. CAB International, Wallingford, UK. Powlson, D. S., Pruden, G.,Johnston, A. E., and Jenkinson, D. S. (1986). The nitrogen cycle in the Broadbalk Wheat Experiment-recovery and losses of 15N-labelled fertilizer applied in spring and inputs of nitrogen from the atmosphere. J. Agric. Sci. (Cambridge) 107,591-609. Powlson, D. S., Poulton, P. R., Addiscott, T. M., and McCann, D. S. (1989). Leaching of nitrate from soils receiving organic or inorganic fertilizers continuously for 135 years. In “Nitrogen in Organic Wastes Applied to Soils. ” (I. A. Hansen, and K. Henriksen, eds.),pp. 334-345. Academic Press, London. Powlson, D. S., Hart, P. B. S., Poulton, P. R., Johnston, A. E., and Jenkinson, D. S. (1992). Influence of soil type, crop management and weather on the recovery of ‘SN-labelled fertilizer applied to winter wheat in spring. J. Agric. Sci. (Cambridge) 118,83-100. Rothamsted Experimental Station (1955). “Results of the Field Experiments 1955,” p. 55. Lawes Agricultural Trust, Harpenden, UK. Sanders, G.,Jones, K. C., Hamilton-Taylor, J., and Dorr, H..( 1993). Concentrations and deposition fluxes of polynuclear aromatic hydrocarbons and heavy metals in the dated sediments of a rural English lake. Environ. Toxicol. Chem. 12, 1567-1581. Shen, S. M., Hart, P. B. S., Powlson, D. S., and Jenkinson, D.S. (1989). The nitrogen cycle in the Broadbalk Wheat Experiment: ’SN-labelled fertilizer residues in the soil and in the soil microbial biomass. Soil Biol. Biochem. 21,529-533. Sibbesen, E.,Andersen, C. E., Andersen, S., and Flensted-Jensen, M. (1985). Soil movement in longterm field experiments as a result of soil cultivation. I. A model for approximating soil movement in one horizontal dimension by repeated tillage. Exp. Agric. 21, 101-107.

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Silvertown, J. (1987). Ecological stability: A test case. Am. Nuturulisr 130,807-810. Silvertown, J. W. (1980). The dynamics of a grassland ecosystem: Botanical equilibrium in the Park Grass Experiment. J. Appl. Ecol. 17,491-504. Smith, L. P. (1960). The relation between weather and meadow hay yields in England. J. BI: Grassland SOC.15,203-208. Snaydon, R. W. (1970). Rapid population differentiation in a mosaic environment. I. Response of Anthoxanthum odorarum to soils. Evolution 24,257-269. Snaydon, R. W., and Davies, M. S. (1972). Rapid population differentiation in a mosaic environment. 11. Morphological variation in Anthoxanrhum odoratum L. Evolution 26,390405. Swaine, M. D. (1994). Long-term studies in tropical forest dynamics. In “Long-Term Experiments in Agricultural and Ecological Sciences. (R. A. Leigh, and A. E. Johnston, eds.), pp. 305-320. CAB International, Wallingford, UK. Sylvester-Bradley, R., Addiscott, T. M., Vaidyanathan, L. V., Murray, A. W. A,, and Whitmore, A. P. ( 1987). Nitrogen advice for cereals: present realities and future possibilities. In “Proceedings No. 263. The Ferfiliser Society,” pp. 3-36. Peterborough, UK. Thurston, J. M. (1969). The effect of liming and fertilizers on the botanical composition of permanent grassland, and on the yield of hay. In “Ecological Aspects of the Mineral Nutrition of Plants. ’’ (I. Rorison, ed.), pp. 3-10. Blackwell, Oxford. Tilman, D. ( 1982). “Resource Comperirion and Community Structure.” Princeton Univ. Press, Princeton, NJ. Tilman, D. (1986). Resources, competition and the dynamics of plant communities. In “Plant Ecology,” (M. J. Crawley, ed.), pp. 51-75. Blackwell, Oxford. Tilman, D., Dodd, M. E., Silvertown, J., Poulton, P. R., Johnston, A. E., and Crawley, M. J. (1994). The Park Grass experiment: Insights from the most long-term ecological study. In “Long-Term Experiments in Agriculrural and Ecological Science. ” (R. A. Leigh and A. E. Johnston, eds.), pp. 287-303. CAB International, Wallingford, UK. Warren, R. G., and Johnston, A. E. (1964). The Park Grass experiment. Rothamsred Exp. Sturion Rep. 1963. 240-262. Warren, R. G.. and Johnst0n.A. E. (1967). Hoosfield Continuous Barley. Rothamsred Exp. Sration Rep. 1966, 32CL338. Wild, S. R., and Jones, K. C. (1992). Organic chemicals entering agricultural soils in sewage sludges: Screening for their potential to transfer to crop plants and livestock. Sci. Toral Environ. 119, 85-1 19. Wild, S. R., and Jones, K. C. (1993). Biological and abiotic losses of polynuclear aromatic hydrocarbons (PAHs) from soils freshly amended with sewage sludge. Environ. Toxicol. Chem. 12,5-12. Wild, S . R., and Jones, K. C. (1995). Polynuclear aromatic hydrocarbons in the United Kingdom environment: A preliminary source inventory and budget. Environ. Polluf. 88,91-108. Wild, S. R., Waterhouse, K. S., McGrath, S. P., and Jones, K. C. (1990). Organic contaminants in an agricultural soil with a known history of sewage sludge amendments: Polynuclear aromatic hydrocarbons. Environ. Sci. Technol. 24, 1706-171 1. Williams, E. D. (1978). “Botanical Composition of the Park Grass Plots at Rothamsted 1856-1976.” Lawes Agricultural Trust, Harpenden, UK. Woiwood, I. P., and Harrington, R. (1994). Flying in the face of change: The Rothamsted Insect Survey. In “Long-Term Experiments in Agricultural and Ecological Sciences” (R. A. Leigh and A. E. Johnston, eds.), pp. 287-303. CAB International, Wallingford, UK. Yates, F. ( 1969). Investigations into the effects of weather on yield. Rothamsred Exp. Sfarion Rep. 1968, Part 2 , 4 6 4 9 . ”

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Index A

Anthropogenic activity evaluation, 292-293 and organic pollutant trends, 320-324 Apical dominance, differences in genetic lines, 86 Aquifer materials, modification in situ, 27 Avoidance mechanisms, perennial forages, 173-1 74 Awns, wheat, development, 88

Abortion floret, 87 tiller primordium, 79-82 Abscisic acid, induced freezing tolerance, 194. 197-1 98 Abscission flower or fruit, and root growth, 249 modeling, 255 Acidity, soil and aluminum toxicity, 190 effect on crop yields, 297-299 Adsorption cationic surfactant, modeling, 49-52 organic modifiers by clay minerals, 30-35 QACS, 28-35,52-54 kinetics, 47-49 Adsorption isotherms, organophilic clays, 39-40 Aggregates, see also Macroaggregates hierarchy, 142-145 rainfall-induced breakdown, 140- I41 Aggregation face-to-face, clay layers, 34-35,48 processes favoring, 134 Agricultural Research Service, 276-277 Agriculture, site-specific, for farm management, 280-281 Alfalfa freezing tolerance, 192-195 grazing tolerance, 204-205 phosphorus deficiency effect, 189-190 taproots, carbon and nitrogen remobilization, 181-183 Alleles favorable, 10-1 I , 15-17 substitution, 4 Aluminum toxicity, and acid soils, 190 Ammonium sulfate, treated Park Grass plots, 315 Anther, initiation, 88 Anthesis, wheat, 91-92

B Backcrossing, in choosing parents, 10 Bacteria in metal-contaminated soil, 324 QAC toxicity, 54-56 Barley, yield, fertilizer effects, 307-309 Beet root, sucrose concentration, selection for, 2 Belowground processes, in GOSSYM, 270-271 Bentonite, complex with TMPA, 43-44 Benzo[a]pyrene, temporal trend, 320-324 Best linear unbiased prediction, 10-13 Bioavailability, sorbed contaminants, 56-57 Biodegradation, contaminants in modified soils, 54-57 Biological changes, effects on sustainability, 294-297 Biomass microbial, in metal-contaminated soil, 324 partitioning, 253-255 Biopores, creation in soil, 131-132 Bioturbation, micromorphological approach, 131 Boll, cotton growth, 250-252 maturation period, 235-236 Booting, initiation in wheat, 68 Branches, fruiting, leaf unfolding rates, 236-239 Broadbalk experiment cereal disease study, 304-306 at Rothamsted, 299-302

335 Advanref m Agmnmny. Volume I9 Copyright 0 1997 by Academic Press,Inc. All rights of reproduction in any form reserved.

3 36

INDEX C

Cadmium, soil burden, trends at Rothamsted, 324-325 Calibration, cotton simulation model, 273 Canopy air temperature, and apical meristem temperature, 83 base, red:far-red ratio, 187 Carbohydrates deficits, and leaf formation, 238-239 nonstructural, accumulation, 246-247 relationship to floret death, 87 remobilization in grasses, 181-183 reserves in stem, 101 Carbon available for root growth, 254 fixation, and chilling stress, 191 isotope discrimination, 205-206 remobilization, defoliation effect, 181-183 Carbon dioxide atmospheric, Hawaii, 229 high conditions in future, 276-277 increase, effect on plant growth, 3 18 Carbon exchange rate, in response to shade, 184 Cation exchange in adsorption of organic modifiers, 30-34 modeling, 50-52 Cellular adjustments, to growth at low temperatures, 194-195 Cereals, diseases, study at Broadbalk, 304-306 Chaff, growth in wheat, 91 Chemical changes, effects on sustainability, 294-297 Chemical stability, organoclays, 28-36 Chilling, and carbon fixation, 191 Chlorosis, iron-deficiency, and calcareous soils, 190-191 Classes of loci, in choice of parents, 1 1 Clay surface interaction with water, 33-34 QAC adsorption effect, 53-54 Coleoptile leaf, in seedling emergence, 79 Combining ability, testing for, 15-17 Competition intraplant, for resources, 265 as selection criterion, 204 Computer technology, in crop management, 275-276 Contaminants biodegradation in modified soils, 54-57

hydrophobic organic immobilization, 26-28 sorption by organoclays, 36-41 transport, sorption effect, 43-44 Correlated response equation, 7 Cotton commercial varieties, 230 fruiting structures, high-temperature effects, 255-257 individual organs, growth, 240-253 model development, 267-273 nitrogen deficit and water deficit effects, 257-267 partitioning biomass, 253-255 phenology, 231-240 Cracks, formation in clay soils, 128-129 Crop management, 275-280,3 14 Crops modeling and applications, 225-282 self- and cross-pollinated and choice of parents, 9-13 recurrent selection, 17-18 yield soil acidity effect, 297-299 and soil morphology, 158 Crowns, freezing tolerance, 193 Crusts cryptogamic, 142- 1 45 structural and sedimentary, 140-142 CT scanning in analysis of hydraulic functioning, 150-156 in soil science, 127 Culms, plant density effect, 81-82 Cultivars annual crops, availability, 299-303 cotton, phenology, 23 1-240 hybrid, choice of parents, 11-12 synthetic, 13 wheat development patterns, 82-83 kernel growth variations, 96 tillering variation, 80 vernalization requirements, 72-73 Cultivation, effect on porosity, 137

D Decision support systems, cotton crop management, 278

INDEX Defoliation and remobilization of carbon and nitrogen, 181-183 tolerance, breeding for, 204-205 whole-plant responses, 179-181 Degradation, soil structure, 150-15 I Dehardening forage grasses, 192-194 and gene expression, 198-199 Denitrification, losses through, 149 Density rooting, 150-151 stand, relationship to tiller number, 81-82 Desiccation damage, protection by sugars, 196 tolerance, in freezing stress, 198-199 Desorption, QAC in subsoils, 35 Development cotton and leaf unfolding, 236-239 temperature effects, 234-235 leaf area, whole plant, 252-253 model, for crops, 267-273 shoot apex, sequence, 67-101 vegetative to reproductive switch, 82-83 Differentiation floret, 88-89 spikelet, 85-87 Diffusion, particle. slow rate, 48 Digestibility forage stems and leaf blades, 176 shade effects, 185-1 86 Diseases, cereals, study at Broadbalk, 304-306 Dodecyl sulfonate adsorption on modified laponite, 41 exchange with anions, 50 Drainage, micromorphological imaging, 150-151 Drought tolerance, grasses, breeding for, 205-206

E Earthworm, activity in Oxic Paleustalfs, 145 Ecological functioning soil microstructure, 147-150 soil structure, modeling, 157 Ecological research, and long-term experiments, 3 13-3 19 Electrolyte leakage, by supercooling, 194-195 Elongation

337

cotton leaf internode, duration and rate, 239-243 grass leaf. in response to shade, 184-185 leaf primordium, 74-79 stem, and leaf nitrogen, 262 subcoleoptile internode, 177-179 tiller primordium, 79-82 Endophyte, tall fescue infected with, 175-1 76 Endosperm, wheat, development, 93-94 Environmental variation, description, 5-6 Enzymes photorespiratory, salt effects, 202 stability at low temperatures, 195 Epoxy resin, impregnated soil, 124 Equations correlated response, 7 describing processes for specific leaf weight, 241 predicted gain, 6-7 prediction, for recurrent selection, 17-18 rate parameters for leaf and internode, 242-243 Exhaustion Land, experiment at Rothamsted, 307-309 Expert systems, COMAX and WHIMS, 271-273

F Fanning conventional and integrated systems, 147 system, effect on organic matter, 313 Farm management, site-specific agriculture for, 280-281 Fertilization effect on yield, 136-137 kleistogamous, 92 residues, effect on soil, 307-308 Fertilizers nitrogen, 301-303,305-306.309-311 treatment of Park Grass plots, 314-319 Field experiments, long-term, 291-329 Floret differentiation, 88-89 initiation and abortion, 85-87 Flowchart, GOSSYh4.270-271 Forages, perennial, responses to stress, 17 1-208 Fractal analysis, soil structure functioning, 156 Freezing tolerance development in forages, 191-195 and gene expression, 197-199

338

INDEX

Fruiting structures, cotton, high-temperature effects, 251,255-257 Funding, long-term experiments, 327-328 Fungus, buildup in acidic soil, 298 FYM manure application to Park Grass plots, 314 increased soil organic matter, 301-303

G Gene expression in low-temperature stress, 197-199 in salt stress, 202-203 Generation mean analysis, 8-9 Genetics, quantitative, and plant breeding, 1-19 Genotype-environment interaction, 5-6 Genotypes for classes of loci, 12 low-temperature hardening, 193 potential growth and developmental rates, 227 Geographic Information Systems, 279 Geographic Positioning System, 278-279 Germination, salt tolerance during, 200 Gibbs free energy, and van der Waals interactions, 3 1 Global circulation model, 229 Grain-filling, during kernel growth, 93-101 Grasses carbon and nitrogen remobilization, 181-183 dehardening, 192-194 seedling and adult, responses to salt stress, 200-202 establishment, 176-179 shoot apex, developmental patterns, 64 treated and untreated, at Park Grass, 315-316 Growing degree-days model, 70-73 phyllochron linear with, 75-76 Growth, see also Regrowth cotton, individual organs, 240-253 grasses at low temperatures, 192-195 responses to salinity, 201 Growth stages anthesis, wheat spike, 90 phenological, 67-73 Growth staging system, 65

H Haploids, doubled, in inbreeding, 14 Hardening, forage grasses, 191-194

HDTMA, see Hexadecyltrimethylammonium Hemimicellization, model of anionic surfactant adsorption, 50 Heritability, trait and marker, 18 Hexadecyltrimethylammonium adsorption, 29-32,42 in aquifer box model, 45-47 complex with smectites, 40-41, 55-57 loading on exchange sites, 5 1 sorption and desorption, 48 History plant breeding, 2 quantitative genetics, 3 Humus accumulation in fertilized soils, 310-31 1 formation, 132-134 Hybrids, choice of parents, 11-12 Hydraulic conductivity, modified soil, 52-54 Hydraulic functioning, micromorphological imaging, 150-156 Hydrophilicity, surface of montmorillonite, 32-34 Hydrophobic bonding in adsorption of organic modifiers, 34-35 submodel to cationic surfactant adsorption model, 51-52

I Image analysis, porosity in thin sections, 125-126 Immobilization, coupled with biodegradation, 54-57 Inbreeding, selection during, 13- 17 Infection potato with nematode, 295-297 tall fescue with endophyte, 175-176 Inference engine, in expert system COMAX, 27 1-272 Insect control, addressed by WHIMS, 272-273 Insect survey, at Rothamsted, 319 Intelligent implements, in crop management, 279-280 Internode elongation duration and rate, 239-243 length at leaf unfolding, 243-245

339

INDEX mass, accumulation rate. 247-248 potential growth rate, 268 Ions, sorption by organoclays, 41-43 Iron-deficiency chlorosis, and calcareous soils, 190-191

K Kernel, growth in wheat, 92-101 Kinetics, QAC adsorption, 47-49

L Land use, soil structure in relation to, 128-157 Layers cationic-rich, segregation, 3 1-32 clay, face-to-face aggregation, 34-35.48 Lead, herbage concentrations at Rothamsted, 326-327 Leaf area development, whole plant, 252-253 expansion, and internode elongation, 240-243 at leaf unfolding, 243-245 ratios, in response to shade, 184 Leaf nitrogen and leaf and stem expansion, 260-262 and phenology, 259-260 and photosynthesis and transpiration, 263-264 Leaf primordium elongation, 74-79 initiation, 73-74 Leaf water potential, midday, 266-267 Leaves, see also Specific leaf weight coleoptile, in seedling emergence, 79 cotton expansion and internode elongation, 239-240 unfolding interval rates, 236-239 and leaf area, 243-245 forage grasses, water deficit effects, 173-174 as photosynthate source during grain filling, 100

temperature, and air temperature, 228 Light effect on kernel growth, 98-99 low intensity effect on spikelet initiation, 84-85 responses of perennial forages, 183-187

Light quality (red:far-red ratio) changes, effects on forages, 186-187 correlation with phyllochron, 77,79 Line per se performance, in inbreeding, 14, 16 Long-term experiments agricultural value, 294-3 13 ecological research related to. 3 13-3 19 environmental concerns, 3 19-325 need for, 325-321 Rothamsted, 293-294 Lucerne, depletion of potassium reserves, 295-297

M Macroaggregates formation, 128-129 fungal hyphae role. 144 Macrostructure, soil, 123 Mainstem growth stage, and tiller appearance, 80-8 1 leaf unfolding rates, 236-239 Manures

FYM application to Park Grass plots, 314 increased soil organic matter, 301-303 organic, nitrogen fate in, 311-313 P and K accumulation in soils, 306-309 Mating design. in estimating genetic components of variance, 4-5 Maturation period, cotton square and boll, 235-236 Maturity, wheat kernel, 95-96 Mendel’s laws, rediscovery, 2-3 Meristem, grasses, defoliation effect, 18 1 Micromorphology in analysis of hydraulic functioning, 150-156 approach to bioturbation, 13 1 methods, 123-127 Microradiography, soil pore system, 126 Microstructure, soil, 147-150 Mineralization nitrogen, 149-150 organic matter, 310 Modeling cationic surfactant adsorption, 49-52 deficits effects nitrogen, 264-265 water, 267 functioning of soil structure, 156-159

340

INDEX

Models aquifer box, 45-47 cotton crop applications, 275-28 1 calibration and validation, 273-275 development, 267-273 expert systems COMAX and WHIMS, 271-273 GOSSYM, 234,269-271 for predicting phenological growth stages, 70-73 Moisture retention, micromorphological analysis, 151-156 Molecular markers in choosing parents, I3 for selection, 8, 18-19 Montmorillonite, surface hydrophilicity, 32-34 Morphology, nomenclatures, 64-67

Ontogeny, wheat seed, 93-95 Organic matter in aggregate stabilization, 144 biochemical transformation, 132-134 effects of soil type and farming system, 31 3 increase with FYM application, 301-303 Organic modifiers abiotic decomposition, 35-36 adsorption by clay minerals, 30-35 Organic reserves, and low-temperature stress, 195-196 Organoclays production and use, 26-28 sorptive properties, 36-44 synthesis and chemical stability, 28-36 Organophilic clays, adsorption isotherms, 39-40 Osmotic adjustment, perennial forages, 174

N

P

Naphthalene, sorbed to HDTMA-smectite, bioavailability, 57 Nematode infection, potatoes, 295-297 Nitrate leaching, reduction, 151 Nitrogen, see also Leaf nitrogen deficiency effects on cotton, 257-265 in forage plants, 187-188 effects kernel growth, 97-98 leaf developmental rates, 76 spikelet initiation, 85 fate in organic manures, 3 11-3 13 fertilizer, 301-303.305-306.309-31 I fixation and remobilization, defoliation effect, 180-183 mineralization, 149-150 retranslocation, 174 Nomenclatures, morphological, 64-67 Nonpolar organic compounds, sorption, 37-41 Number of calendar days model, 70-73 Nutrients availability, role in wheat phenology, 71-72 organs receiving, priority, 253-255 stress, in forage plants, 187-191, 206-207

Parents, choosing for breeding programs, 9-13 Park Grass, long-term ecological study, 3 13-3 19 Particle diffusion, slow rate, 48 Partitioning, biomass, 253-255 Pathogens, soil-borne, effect on crops, 304-306 Peduncle, growth in wheat, 91 Pentachlorophenol, van der Waals contact area, 42 PH Park Grass plots, 314-319 soil, effect on crop yields, 297-299 Phenanthrene, temporal trend, 320-324 Phenology cotton cultivars, 231-240 and leaf nitrogen, 259-260 shoot apex, 67-73 Phosphorus accumulation in soil, 306-309 deficiency in forage plants, 188-190 Photoperiod, correlation with phyllochron, 78-79 Photosynthesis compensatory, 180 and leaf nitrogen, 263 water deficit effect, 174175.267 Photothermal units model, 70-73 Phyllochron in estimating time between growth stages, 72-73 intervals, 239

0 Object-oriented simulation, crop modeling, 276-278

341

INDEX linear with growing degree-days, 75-76 prediction, 78-79 water stress effect, 76-77 Physical functioning, soil structure, modeling, 156-157 Plant breeding for abiotic stress tolerance, 203-208 history, 2 quantitative genetics, 3,9-18 Plastochron, and leaf primordium initiation, 73-74 Plot size, and soil movement, 303-304 Pollen grains, production per wheat plant, 88-89 Pollination, self and cross, and choice of parents, 9-13 Pollutants, organic and inorganic, long-term experiments, 3 19-325 Polynuclear aromatic hydrocarbons, temporal trend, 320-324 Populations base, formation, 11 segregating, in choosing parents, 9-10 Pore system, image analysis, 125-126 Porosity interpedal and intradpedal, 132-1 34 tillage effect, 136-138 Potassium accumulation in soil, 306-309 reserves, depletion in lucerne, 295-297 retranslocation, 201-202 Potato, nematode infection, 295-297 Precision agriculture, 280-282 Predicted gain equation, 6-7 Process-level simulation model, 230 Propylbenzene, sorption by organosmectite,38-39

Q QACs, see Quaternary ammonium compounds Quantitative genetics in plant breeding, 3,9-18 tools, 4-9 Quaternary ammonium compounds addition to subsoils, 44-54 adsorption chemistry, 28-35 toxicity to bacteria, 54-56

R Rachis, internode extension, 89-90 Raindrop impact, disaggregation by, 140-141

Reconstructions, 3-D, in micromorphology, 126-127 Regrowth, grasses, and carbohydrate remobilization, 182-183 Relative internode elongation rate, 240-243 Relative leaf expansion rate, 240-243.262 Remediation, environmental, use of surfactants, 25-28 Remobilization. carbon and nitrogen, 181-183 Reproduction, cotton, initiation, 232-235 Research, value of long-term field experiments, 291-329 Residues, from fertilizations, effect on soil, 307-308 Resilience, soil structure, 139-140 Rhine soils, moisture deficits, 152-156 Rhizomes freezing tolerance, 193 tall fescue, selection for, 205 Ribulose bisphosphate carboxylase, water deficit effects, 175 Ridge, single and double, commencement in wheat, 69 Ripening, soil, 129-130 Roots adventitious and seminal, grass seedlings, 177-178 cotton, growth, 248-250.254255 development phosphorus deficiency effect, 189 and porosity, 149 role in soil biology, 131-132 as sink during grain filling, 100 Rotations, 5-year. in long-term field experiments, 295-297 Rothamsted, England insect survey, 3 19 long-term field experiments, 293-329

S Salt stress, in grasses, 199-203 tolerance, breeding for, 207-208 Seedlings emergence, and phyllochron, 77-79 grasses establishment, 176-1 79 responses to salt stress, 200-202 Seeds, wheat, ontogeny, 93-95

3 42 Selection criterion, competition as, 204 gain from, predicting, 6-7 during inbreeding, 13-17 for quantitative traits, history, 3 recurrent, 17-18 for rhizomes in tall fescue, 205 use of molecular markers, 8, 18-19 Shade responses, forage grasses, 184-1 86 Shoot apex developmental events, 73-101 phenology, 67-73 Shrinking, clay soils, 128-130 Sigmoidal pattern, kernel growth, 92-93 Single-seed descent, in inbreeding, 14 Sinks, during kernel growth, 100-101 Site tenure, long-term experiments, 327-328 Smectite-HDTMA, 40-41.55-57 Smectite-TMPA, adsorption capacity, 38-39 Soils acidity and aluminum toxicity, 190 effect on crop yields, 297-299 biological processes, 13 1-134 calcareous, and iron-deficiency chlorosis, 190-191 macrostructure, 123 management practices, 134-1 39 microstructure, 147-150 modified contaminant biodegradation, 54-57 hydraulic conductivity, 52-54 movement, and plot size, 303-304 physical processes, 128-130 pohtion, long-term experiments, 3 19-325 quality, 158-159 Rhine, moisture deficits, 152-156 type,effect on organic matter, 313 Sorption effect on contaminant transport, 43-44 hydrophobic organic contaminants by organoclays, 36-41 ions by organoclays, 41-43 Sorptive zone, 27-28.35.47.52-54 Sources, translocation to sinks, effect on grain filling, 99-101 Species composition, in treated and untreated plots, 315-319 Specific leaf weight in response to shade, 184 and starch accumulation, 245-247

Spikelet differentiation, 85-87 kernel growth within or among, 95-96 naming scheme, 67 primordium initiation, 83-85 terminal, formation, 89 Square, cotton formation, 233-235,243-244 growth, 250-252 maturation period, 235-236 Stability aggregate, 145-147 enzyme, at low temperatures, 195 soil structure, 139-147 Starch accumulation, and specific leaf weight, 245-247 granules, in wheat kernels, 94 Stems carbohydrate reserves, I01 expansion, and leaf nitrogen, 260-262 Stereology, in 3-D reconstructions, 127 Stomata, nitrogen-deficient cotton plants, 258 Stress defoliation, whole-plant responses, 179-1 8 1 low-temperature, 191-199 nutrient, in forage plants, 187-191 salt, 199-203 tolerance, plant breeding for, 203-208 water, effect on phyllochron, 76-77 Subcoleoptile internode, elongation, 177-1 79 Submicroscopy, soils, 124-125 Subsoils HDTMA-treated, 44 modification in siru, 27 QAC desorption, 35 Sugars, protection of desiccation damage, 196 Superoxide dismutase role in freezing stress, 195 water deficit effects, 175 Superphosphate, applications at Park Grass, 3 18-3 19 Surfactants cationic, adsorption modeling, 49-52 in environmental remediation, 25-28 Survival grass seedlings, 178-179 plants mycorrhizal hyphae effects, 189 in Park Grass plots, 316-318 winter, and rhizome production, 205

INDEX Sustainability, assessment in long-term experiments, 294-304 Swelling, clay soils, 128-130 Synthesis, organoclays, 28-36

T Take-all, pathogen-induced, study at Broadbalk, 304-306 Tall fescue development stages, 74-75 infection with endophyte, 175-176 selection for rhizomes, 205 Taproots, alfalfa, remobilization of carbon and nitrogen, 181-183 2.3.7.8-TCCD. temporal trend, 320-324 Temperature canopy and apical meristem, correlation, 83 controlling leaf appearance, 76-77 dayhight, and specific leaf weight, 245-247 effects cotton fruit, 25 1 cultivar vernalization response, 72 development rate, 234-235 kernel growth, 97 spikelet initiation, 84-85 high, effects on fruiting structures, 255-257 long-term, in crop modeling, 227-23 1 low, acclimation to. 191-195 Testing for combining ability, 15-17 cotton simulation models, 274-275 for yield, 14 Thin sections, preparation, 123-124 Tillage effects on soil structure, 134-138 minimum, and yield, 146-147 Tillers bud initiation, 74 naming system, 66-67 primordium elongation and abortion, 79-82 Tissue culture in low-temperature stress, 197-199 in salt stress, 202-203 Tissue expansion, water deficit effect, 266 TMPA, see Trimethylphenylammonium Tolerance freezing, 191-195, 197-1 99 salt, 199-203 stress, plant breeding for, 203-208

343

Toxicity aluminum, and acid soils, 190 QACs to bacteria, 54-56 Traits, multiple, selection index, 7-8 Transformation organic matter, 132-134 in plant breeding, 19 Transmission electron microscopy, sectioning for, 125 Transpiration, and leaf nitrogen, 263-264 Transplantation, back to native plot, 316-318 Transport, contaminants, sorption effect, 43-44 Trimethylpheny lammonium complex with bentonite, 43-44 complex with smectite, adsorption capacity, 38-39

U Unfolding, cotton leaf, interval rates, 236-239

V Validation, cotton simulation model, 273-274 Van der Waals interactions, and Gibbs free energy. 31 Variation environmental, description, 5-6 genetic estimations, 4-5 for stress tolerance, 204-208 Vascular connections, within spike, 99-100 Vernalization increase of stem length, 82 requirements of wheat cultivars, 72-73 Vertisols, structural restoration, 137 Vesicles below sedimentary crusts, 141 on grass leaf, regulation of salt content, 201 Vesicular arbuscular mycorrhizae, and phosphorus uptake, 189

W Washing/flushing, with surfactants, 25-26 Water effects on kernel growth, 98 interaction with clay surface. 33-34 potable, nitrate levels, 309-310 role in wheat phenology, 7 1 stress, effect on phyllochron, 76-77

344 Water deficit effects cotton, 265-267 forage quality, 176 photosynthesis, 174-1 75 and grass seedling establishment, 176-179 Rhine soils, 152-156 tall fescue and endophyte, 175-176 whole-plant responses, 173-174 Water use efficiency, and carbon isotope discrimination, 206 Weeds, in long-term field experiments, 299 Wheat shoot apex, growth and phenology, 63-101 winter, FYM effect, 302-303 Whole-plant responses defoliation stress, 179-18 1 water deficit, 173-174

INDEX Woburn Ley Arable, long-term field experiment, 294-297

Y Yield agronomic plots, fertilization effect, 136-137 crop soil acidity effect, 297-299 and soil morphology, 158 herbage dry matter, 3 18 and minimum tillage, 146-147 spring barley, fertilizer effects, 307-309 testing for, 14

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