Advances in Agronomy, Volume 63

V O L U M E 63

$:

T Martin Alexander

Ronald Phillips

Cornell University

University of Minnesota

Kenneth J. Frey

Larry P. Wilding

Iowa State University

Texas A&M University

Prepared in cooperation with the

American Society of Agronomy Monographs Committee P. S. Baenziger Jon Bartels Jerry M. Bigham M. B. Kirkham

William T Frankenberger, Jr., Chaimnan David H. Kral Dennis E. Rolston Diane E. Storr Sarah E. Lingle Kenneth J. Moore Joseph W. Stucki Gary A. Peterson

L D V A N C E SI N T T

VOLUME 63

. I

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

vii ix

APPLICATIONSOF FRACTALS TO SOILSTUDIES Alison N. Anderson, Alex. B. McBratney, and John W. Crawford I. TI. 111. N. V VI. VII. VIII.

lX.

Fractals.. . . . . . . . . . . . . . . ...................... Fractal Dimensions and Their Measurement. . . Approximating Soil Smicture Using Fractals. . . . . . . . . . . . . . . . . . . . Soil Fragmentation . . . . . . . . . . . . . . . . . . . . . . . ............ Applications of Fractal Models in the Prediction of Soil Physical Processes . . . . . . . . . . . . - ........................ Other Applications of Fractal Geometry in Soil Science. . . . . . . . . . . Fractal Geometry and Spatial and Temporal Variation . . . . . . . . . . . . Fractal Eclectica. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Directions and Conclusions References . . . . . . . . . . .............................

2 6 17 29 42 48 52 64 68 70

RESPONSES OF COOLSEASONGRAINLEGUMES TO SOIL ABIOTICSTRESSES H. P. S. Jayasundara, B. D. Thomson, and C. Tang Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soil Acidi ty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soil Salinity and Sodicity . . .......................... Soil Alkalinity. . . . . . . . . . . ......................... Soil Compaction . . . . . . . . ............................ VI. Waterlogging.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I. 11. 111. W. V.

78 80 91 104 113 122 133 135

KURACLOVER(Trifoolium ambipurn M.B.) BREEDING, CULTURE, AND UTILIZATION N. L. Taylor and R. R. Smith I. Introducuon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11. Taxonomy ................................................ V

154 155

vi

CONTENTS

I11. Morphology and Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV Culture and Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Utilization ............................................... VI. Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII . Further Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

156 158 166 168 173 174

ULTISOLS: CHARACTERISTICS AND IMPACTS ON SOCIETY L . T. West. F. H . Beinroth. M . E . Sumner. and B . T. Kang ............................. ................... I11. Classification: Historical and Current . . . . . . . . . . . . . . . . . . .

W. Geographic Distribution .............................. V. Formation of Ultisols ................................ VI. Morphological Properties . . . . . .................... VII. Mineralogical Properties . . . . . . .................... .................................. VIII . Physical Properties . . . IX . Chemical Properties ...................... ............. X . Biological Properties ...................... ............. XI. Management of Ultisols .................................... XI1. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . .............................

195 202 205

211 216 218 227 229

FORMATION MECHANISMS OF COMPLEX ORGANIC STRUCTURES INSOILHABITATS J.-M. Bollag. J . Dec. and P. M . Huang I . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I1. Organisms Involved in Organic Matter Formation . . . . . . . . . . . . . . . I11. Degradative Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tv. SyntheticProcesses ........................................ V. Significance of Synthetic Reactions in Soil ...................... References .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .................

237 238 230 242 259 260

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

267

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

ALISON N. ANDERSON (l), Depaitment of Agricultural Chemistry and Soil Science, University of Sydney, Sydney, New South Wales 2006, Australia F. H. BEINROTH (1 79), Agvononzy and Soils Department, University of Puerto Rico, Mayaguez, Puerto Rico 00681 J.-M. BOLLAG (2 3 7), Laboratory of Soil Biochemistry, Centerfor Bioremediation and Detoxi$cation, Pennsylvania State University, University Park, Pennsylvania 16802 JOHN W. CRAWFORD (I), Soil-Plant Dynamics Unit, Scottish Crop Research Institute, Dundee DD2 FDA, Scotlaland J. DEC (237), Laboratory of Soil Biochmiim, Centerfor Bioremediation and Detoxification, Pennsylvania State Univenxity, University Park, Pennsylvania 1 6802 P. M. H U N G (237), Department of Soil Science, University of Saskatchewan, Saskatoon, Saskatchewan S7N OWO, Canada H. P. S. JAYASUNDARA (77), Cooperative Research Centre for Legumes in Mediterranean Agriculture, University of WesternAustralia, Nedlands, Western Australia 6907, Australia B. T. KANG (1 79), Department of Crop and Soil Sciences, University of Georgia, Athens, Georgia 30602 ALEX. B. MCBRATNEY (l), Department of Agricultural Chemistry and Soil Science, University of Sydney, Sydney, New South Wales 2006, Australia R. R. SMITH (1S 3 ) , U S . Daipy Forage Research Center, Univeipsity of Wisconsin, Madison, Wisconsin 53 706 M. E. SUMNER (179), Department of Crop and Soil Sciences, University of Georgia, Athens, Georgia 30602 C. TANG (7 7), Cooperative Research Centrefor Legumes in Mediterranean Ayiculture, Universityof WesternAristt-alia,Nedands, WesternAustralia 6907,Australia N. L. TAYLOR (15 3 ) , Department OfAgroriomy, University ofKentucky, Lexington, Kentucky 40546 B. D. THOMSON (77), Cooperative Research Centre for Legumes in Mediterranean Agriculture, University of Western Australia, Nedlands, Western Azistralia 6907, Australia L. T. WEST (179), Department of Crop and Soil Sciences, Univevsity of Georgia, Athens, Georgia 30602 vi i

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Volume 63 contains five first-rate reviews on various plant and soil sciences topics. Chapter 1 is a cutting-edge review of applications of fractals to soil studies, a topic that is of great interest to soil scientists. Fractals are defined, and their dimensions and measurements are discussed. Other topics, including the use of fractals to approximate soil structure, soil fragmentation, applications of fractal models in the prediction of soil physical processes, and fractal geometry and spatial and temporal variation, are covered. Chapter 2 discusses responses of cool season grain legumes to soil abiotic stresses, including soil acidity, soil salinity and sodicity, soil alkalinity, soil compaction, and waterlogging. Chapter 3 is an up-to-date review of kura clover. Topics that are covered include taxonomy, morphology and description, culture and management, utilization, and breeding. Chapter 4 comprehensively reviews Ultisols, an important soil order. Details on classification; distribution; formation; morphological, mineralogical, physical, chemical, and biological properties; and management of Ultisols are included. Chapter 5 is a contemporary overview of formation mechanisms of complex organic structures in soil habitats. Topics that are discussed include organisms involved in organic matter formation, degradative and synthetic processes, and the significance of synthetic processes. Many thanks to the authors for their thoughtful and timely reviews.

DONALD L. SPARKS

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APPLICATIONS OF FRACTALS TO SOILSTUDIES Alison N. Anderson,' Alex B. McBramey,' and John W. Crawford' 'Department of Agricultural Chemistry and Soil Science University of Sydney, Sydney New South Wales 2006 Australia ?Soil-Plant Dynamics Unit Scottish Crop Research Institute Dundee DD2 SDA Scotland

I. Fractals A. History 11. Fractal Dimensions and Their Measurement A. Surface Fractal Dimension B. Mass Fractal Dimension C. Spectral Dimension D. Fragmentation Fractal Dimension E. Techniques for Estimating the Surface Fractal Dimension F. Techniques for Estimating the Mass Fractal Dimension G. Techniques for Estimating the Spectral Dimension H. Techniques for Estimating the Fragmentation Fractal Dimension 111. Approximating Soil Structure Using Fractals A. Derivation o f a Mass Fractal Model from Bulk Density Data B. Estimating Mass Fractal and Spectral Dimensions from Two-Dimensional Soil Images C . Characterizing the Surface Fractal Dimension of Soil Iv. Soil Fragmentation A. Studies o f t h e Fragmentation Fractal Dimension of Soil B. Probability of Failure of Aggregates C . Shortcomings of the Fragmentation Fractal Dimension D. Applications of the Fragmentation Fractal Dimension V. Applications of Fractal Models in the Prediction of Soil Physical Processes A. Gas Diffusion B. Water Retention Curve and I Iydraulic Conductivity C. Other Applications to Physical Processes VI. Other Applications of Fractal Geometry in Soil Science A. Soil Fauna Dynamics and Habitat Space B. Soil Mechanics C. Adsorption Studies

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ALISON N. ANDERSON ETAL. VII. Fractal Geometry and Spatial and Temporal Variation A. Studies of Spatial Variability at the Landscape Scale B. Studies of Spatial Variability a t Finer Scales C . Implications and Limitations of Using Fractal Geometry in Soil Spatial Variability Studies D. Temporal Variability VIII. Fractal Eclectica A. Scaling Processes in Soil Using Fractal Geometry B. Distinguishing a Fractal from its Complement C. Deterministic Uncertainty and Chaotic Behavior D. Describing Root Morphology Using Fractal Geometry M. Future Directions and Conclusions References

I. FRACTALS Although the concepts underlying fractals were conceived around the turn of the century by Cantor, Peano, Fatou, and others, their application to real-world problems is a relatively new and promising research field. In recent years the subject of fractal geometry has received attention in the soil science community. Evidence reported in the soil science literature suggests that fractals may provide a useful approximation to the structure of soil and may have a role to play in the prediction and understanding of physical, chemical, and biological processes occurring within. The popularity of fractal geometry has increased, particularly in the 1990s, and this reflects the growing awareness of the theory and recognition of areas of application by soil scientists. Figure 1 shows the distribution of papers that have been published in the soil science literature since 1983. For an introduction to fractal geometry and its applications, Kaye (1989a) is recommended, since it is intended for readers without prior knowledge of fractal theory. For more detailed theory, Feder (1988) and Schroeder (1991) are two of the many books that have been written about fractals. In particular, readers should be aware of Baveye et al. (1997), a book that deals specifically with fractals in soil science and introduces fractal theory relevant to soil science that is beyond the scope of this chapter.

A. HISTORY For more than two millennia classical Euclidean geometry has been employed to describe the irregular shapes found in nature, despite nature being largely de-

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APPLICATIONS OF FRACTALS TO SOIL STUDIES

n ”

81 8 2 8 3 8 4 85 86 8 7 88 8 9 90 91 92 93 9 4 95

96

Year Figure 1 The distribution of papers published since 1983 that examine some aspect of fractal geometry in soil science.

void of Euclidean shapes. Euclidean lines, circles, spheres, and tetrahedra have served, traditionally, as the basis of the intuitive understanding of the geometry of nature (Feder, 1988). Nature, however, is not regular and easily simplified, and it is difficult for one to satisfactorily describe it using Euclidean geometry. The architecture of the soil is clearly a case in point. The geometry that the Greeks held in great esteem has been so strong an influence that shapes that do not reduce locally to straight lines have been labeled “monsters” and “pathological” (Mandelbrot, 1989). Mandelbrot (1975, 1977,1982,1983) popularised this new geometry by demonstrating its appropriateness in describing natural phenomena. The irregular shapes of nature were identified with fractals by Mandelbrot, and fractals now provide a workable new middle ground between the excessive geometric order of Euclid and the geometric chaos of roughness and fragmentation (Mandelbrot, 1989). The term fructul was coined by Benoit B. Mandelbrot in 1975 with the publication of his essay in French, “Les objets fractals: Forme, hasard et dimension.” How Mandelbrot arrived at this term is described in his 1983 essay “The Fractal Geometry of Nature”: 1 coined fructul from the Latin adjective fructus. The corresponding Latin verb frungere means “to break:” to create irregular fragments. It is therefore sensible-and how appropriate for our needs!-that, in addition to “fragmented” (as infraction or refraction), fructus should also mean “irregular,” both meanings being preserved in frugment.

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ALISON N. ANDERSON ETAL.

The concept of fractional dimensions was first introduced by Mandelbrot in his 1967 paper “How Long Is the Coast of Britain? Statistical Self-similarity and Fractional Dimension” (Mandelbrot, 1967). In this paper he discussed the earlier work of Steinhaus (1954) and Richardson (1961), who noted that the lengths of banks of rivers, coastlines, and frontiers increased as they were measured with increasing precision. If we were to step along a coastline with stride length E, the polygon created would have a perimeter that is an estimate of the coastline length. The perimeter of the polygon L(E)can be calculated by multiplying the number of strides taken by E. If we were to step along the coastline again we would notice that as our stride lengths became smaller and smaller the length of the coastline would appear to increase. If this were to be done for a Euclidean shape, such as a circle, we would find L(E)to settle rapidly to a scale-independent value, the true length. Richardson (1961) proposed an empirical formula to describe this phenomenom:

where F and D are constants. This came to the attention of Mandelbrot (1967) who proposed that D be interpreted as a dimension, despite not being an integer. The value of D depends on the coastline chosen and may even change for different parts of a coastline. Its value reflects the irregularity of a coastline. A highly rugged and irregular coastline such as that found in the southern part of Norway, has a value of D equal to 1.52 (Feder, 1988), whereas a smooth coastline, such as that found in South Africa, has a value of D equal to 1.02 (Mandelbrot, 1967). In the time since Mandelbrot’s essays, many scientists have used fractal geometry as a means of quantifying natural structures and also as an aid in the understanding of physical processes occurring within these structures. A passage from Avnir (1989) describes the achievement of fractal geometry to date: As its ability to help many scientists to overcome the psychological barriers of treating problems which involve very complex geometries. . . . It has initiated a massive scientific effort at looking at problems, of the interplay between natural and man-made shapes and structures, and the processes which form these structures or the processes which take place in the environments created by these structures. A fractal is an object that appears the same, regardless of the scale of observation. At all levels of magnification the morphological detail of a fractal will be the same. Mandelbrot ( 1989) defines them as “shapes whose roughness and fragmentation neither tend to vanish, nor fluctuate up and down, but remain essentially unchanged as one zooms in continually and examination is refined.” This property is

APPLICATIONS OF FRACTALS T O SOIL STUDIES

Figure 2 First four stages in the construction of Koch’s triadic ctirve (Mandefbrot, 1982; reprinted with kind permission of B. B. Mandelbrot).

known as scale invariance; that is, there is no characteristic scale defining the ‘‘size’’ of a fractal, and the properties of fractals are invariant under certain transformations of scale (Mandelbrot, 1983). If these transformations are independent of direction, the fractal is self-similar. However, if the transformations are different in different directions, the fractal is self-affine (Falconer, 1990). Because fractals are scale-invariant, they are not smooth at any resolution and as such are nondifferentiable. An example of a self-similar fractal is Koch’s triadic island (Fig. 2 ) . It is nondifferentiable and nonrectifiable (i.e., has an infinite perimeter). The construction of this simplest type off fractal begins with an “initiator” (Mandelbrot, 1983) that in this case is an equilateral triangle. A “generator” (Mandelbrot, 1983) determines the scaling properties of the fractal. Construction of Koch’s triadic island involves placing an equilateral triangle with sides of length 5 upon the mid-third of each side of the original triangle, producing the Star of David (Mandelbrot, 1983). This process of adding equilateral triangles is then repeated, ad infinitum. Two examples of fractals occurring in nature that are easy to comprehend are trees (Crawford and Young, 1990) and the human respiratory system (West er a!., 1986). The branch of a tree shares the scaling properties of the whole tree (Crawford and Young, 1990). If we were then to break off a smaller branch again, it too would look similar to the original tree and also similar to the first branch. The respiratory system begins with the trachea branching into two tubes, the bronchi. Each bronchus then branches and rebranches, forming bronchioles that, in their turn, repeatedly form smaller and smaller ducts (Keeton and Gould, 1986). Natural structures are not fractals in the strict mathematical sense because they do not display power-law scaling over an infinite range in scale. However, fractals may provide a useful characterisation of heterogeneity between lower and upper length scale limits, rminand rmax,respectively. An important test of the adequacy of the approximation is in the level of agreement between predictions and measurements of associated processes. Mere descriptions of heterogeneity do not add to our knowledge of the significance of soil structure.

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11. FRACTAL DIMENSIONS AND THEIR MEASUREMENT Fractal dimension can be defined in an infinite number of different ways depending on the measurement being made. In the array of papers recently written, some of these different definitions are being used. The appropriate definition depends upon the nature of the work being carried out. The fact that there are different fractal dimensions has led to confusion as to which fractal dimension is actually being referred to and what can be inferred from it. This confusion has been observed in soil science papers dealing with fractals, and particular misinterpretations of fractal dimensions will be discussed in the appropriate sections of this chapter. The following sections will discuss the different types of fractal dimensions used in soil science and the most popular methods for estimating them. There are other types of fractal dimensions that have not been used in the soil science literature.

A. SURFACEFRACTAL DIIVIENSION The boundary of any figure is a surface or perimeter, depending on whether the topological dimension, D,, is one or two. Koch’s triadic island (Fig. 2) is built from a triangular initiator of three lines and so has D, = 1. A fractal built from an initiator made up of planes has D, = 2 , and so is bounded by an irregular surface. The minimum number of coordinates required to locate all the points in a figure is the embedding dimension, d,. For all fractals, and for any definition of the fractal dimension, the value of the fractal dimension lies between D, and d,. Thus any fractal dimension of a figure built up from lines or curves embedded in a two-dimensional space must lie between 1 and 2. The Koch triadic island in Fig. 2 is an example. Similarly, for a surface embedded in a three-dimensional space, any fractal dimension must lie between 2 and 3. Many rugged landscapes can be approximated by such a surface fractal. The surface fractal dimension ( D J will be introduced first because the properties of a fractal surface are probably the easiest to conceptualize. In this chapter we refer to any boundary, as defined earlier, as a surface. Surface fractals have a fractal boundary or perimeter but a compact, nonfractal bulk (Van Damme et al., 1988). An example of this is Koch’s triadic island (Fig. 2), which has a boundary line of Ds= 1.23. Quartz may be a natural example of particulate material with a fractal surface and a compact mass distribution (Van Damme et al., 1988). Research has shown that a wide range of materials (e.g., quartz, coal dust, soil, and crushed rock) have surface fractal characteristics (Avnir et al., 1985). In fact, Avnir et al. (1985) concluded that nonfractal surfaces are the exception rather than the rule.

APPLICATIONS OF FRACTALS TO SOIL STUDIES

7

Some fractal surfaces or perimeters can be characterised by two distinct values of Dsassociated with different scales of irregularity, and this has been observed by Orford and Whalley (1983) and Kaye (1989a). Kaye (1989a) termed these two types of surface fractal dimension, textural (denoted D,,) and structural (denoted D J . The structural fractal dimension is larger because the overall structure of the surface appears more rugged or irregular at low resolution. The smaller values of the textural fractal dimension, found at finer resolutions, indicate that the boundary is becoming more Euclidean (less rugged).

B. MASSFRACTAL DIMENSION Objects that have a nonuniform, self-similar, internal mass distribution are termed mass fractals. The bulk of soil, for example, is not compact, but is porous, and even the mass distribution within the solid fraction may not be uniform. For mass fractals the mass (M) inside a characteristic r scales according to

M

cx

r*m

(2)

where Dm is the mass fractal dimension, and Dm is less than d,. An example of a self-similar mass fractal is the Sierpinski carpet (Fig. 3). The initiator is a filled black square. The filled black square is divided into nine squares, and the middle square is removed. This is the generator and is shown in Fig. 3 along with further steps in the construction of the Sierpinski carpet. The area of the Sierpinski carpet vanishes, while the total perimeter of its holes is infinite (Mandelbrot, 1983). The mass fractal dimension of an object lies within the range 0 to d,, where d, is the embedding dimension. The embedding dimension is equal to 2 for two-dimensional objects and 3 for three-dimensional objects. An object having a value of Dm equal to the embedding dimension (e.g., 3 for a soil aggregate) would not be a mass fractal and would need to be described as a surface fractal, if the surface were indeed rugged.

Figure 3 First three stages in the construction of the Sierpinski carpet (Mandelbrot, 1982; reprinted with kind permission of B. B. Mandelbrot).

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ALISON N. ANDERSON ETAL.

Figure 4 The Menger sponge (Mandelbrot, 1982; reprinted with kind permission of B. B. Mandelbrot).

The Sierpinski carpet and the Menger sponge (Fig. 4) have been used to represent soil structure, and at least some of the features of the Menger sponge may be useful to describe the properties of soil aggregates (Baveye and Boast, 1976). Perfect (1996) used the Menger sponge as a conceptual model for soil structure and stated that if the algorithm used to construct the sponge is executed stochastically, it will contain both connected and disconnected pores or voids. Baveye and Boast (1997) explain that the Menger sponge cannot be used as a model of soil aggregates because soil aggregates have a porosity less than unity and a mass density different from zero, whereas the Menger sponge has both porosity equal to unity and zero density. However, the properties of the prefractals (sets of points obtained at intermediate steps in the construction of the Menger sponge) associated with the Menger sponge are directly related to those of soil aggregates (Baveye and Boast, 1997). If the soil is fractal for a range of scales, the soil mass will be selfsimilar and will behave similarly to the mass of the Sierpinski carpet. When the soil is fractal, pores that cannot be seen at coarse scales will be observed at finer scales. These pores that are observed at fine scales will obviously be micro-pores. Crawford (1994) used a random recursive lattice (constructed according to Clerc et al., 1990) to represent a heterogeneous soil with a tortuous pore space. The construction is simply a random version of the Sierpinski carpet (Fig. 3), which itself belongs to the class of random Cantor sets (Falconer, 1990).The struc-

APPLICATIONS OF FRACTALS TO SOIL STUDIES

9

ture is based on a 4 X 4 Latin square design. As for the method used by Crawford (1994) we begin with a square lattice comprising Mde cells, where d, is the Euclidean dimension of the space containing the lattice, 2 in this case. A probability P is defined such that PMde cells are chosen at random to occupy the fractal, while (1 - P)Mde are removed and represent pore space; e.g., for M = 4, de = 2, P = 0.75, four cells are removed. At the next stage all occupied cells (solid matrix) are treated as the original square lattice was. They are divided into Mde cells of which (1 - P)Mde are removed. If we continue this process ad infinitum, the resulting structure is a statistically self-similar fractal of mass dimension = ln(PMde)/lnM. An example of the Latin square design fractal is shown in Fig. 5. To show selfsimilarity the top-left square (from the original 16) has been magnified. The structures shown in Fig. 5 have a value of Dm = 1.79, calculated using an equation given in Crawford and Matsui (1996), and stated earlier. The density of solid objects or particles is not necessarily independent of their size, r (Van Damme er al., 1988). Scale-variant bulk density (p) is characteristic of mass fractals, since their mass scales with size or length scale. This is compared to dense objects whose mass distribution does not change with scale and therefore have a density independent of size. An equation by Orbach (1 986) may be used to calculate the density of fractal objects: p(r) = M(r)N(r) = BrDm/Crde ci r D m P d e

(3)

where p is the density, M is the mass, V is the volume, B varies according to the lacunarity, and Cis a constant. Because Dm 5 d,, the density of a mass fractal falls off with increasing length scale, implying that fractal objects of large size will be

Figure 5 A random recursive lattice based on a 4 X 4 Latin square design. To show the fractal nature of the structure the top-left square (from the original 16 squares) of structure (a) has been magnified and is shown as structure (b).Structure ( b ) I S self-similar to structure (a).The structures shown have a value of Dm = 1.79.

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ALISON N. ANDERSON ETAL.

Figure 6 The second stage in the construction of two Sierpinski carpets (Mandelbrot, 1983) with identical values of Dn,. The image on the right appears much more lacunar, while both have a value of Dn,equal to 1.904 (Crawford er al., 1993a; reprinted with kind permission of Elsevier Science-NL).

extraordinarily light (Orbach, 1986). Decreases in bulk density with increasing size have been reported for soil aggregates by Chepil ( 1950), Lin ( 197 l), Young and Crawford (1 99 l), and Eghball et a f .(1993a). An important characteristic of a mass fractal is its lacunarity. Mandelbrot (1983) first observed lacunarity by studying gaIaxies and described it as follows: “Lacuna (related to lake) is Latin for gap, hence a fractal is to be called lacunar if its gaps tend to be large, in the sense that they include large intervals (discs, or balls).” Mandelbrot (1983) illustrated lacunarity using Sierpinski carpets that were constructed using different generators. The Sierpinski carpets shown in Fig. 6 have a mass fractal dimension of 1.904. This is not obvious, mainly because the carpet shown on the right has larger gaps or pores if we liken this example to soil. It is said to be more lacunar. Intuitively we would expect the lacunarity of an object to influence physical processes occurring within the object.

C. SPECTRAL DIMENSION Whereas fractal dimensions such as the mass fractal dimension (D,) and the surface fractal dimension ( D J are measures of static structural characteristics, the spectral (or fracton) dimension ( d )characterizes dynamic properties of fractal networks. The spectral dimension may be used to describe vibrations of the atoms that make up a fractal structure or to describe the diffusion or flow of particles along paths constrained to fractal geometry (Orbach, 1986). In Sec. 1I.B it was demonstrated that two mass fractals can have similar mass fractal dimensions while being completely different in appearance because they have differing lacunarities. The spectral dimension may be used to distinguish between structures with similar values of Dm. Spectral dimension is a measure of the connectedness of a frac-

APPLICATIONS OF FRACTALS TO SOIL STUDIES

11

tal, and a large value of d reflects a more continuous, less tortuous pathway. Values of d for soil pore networks, estimated from two-dimensional images of soil structure, have been reported to lie in the range 1 < d < 2 (Crawford et al., 1993a; Anderson et al., 1996). Most fractals have d < 2, although larger values are possible (Havlin and Ben-Avraham, 1987). The spectral dimension replaces tortuosity and connectivity in earlier soil physics.

D.

FRAGMENTATION FRACTAL DIMENSION

Many cases of fragmentation occur in nature, whether as the result of slow weathering, impact, or the use of explosives, and the distribution of fragment sizes will be related to the distribution of joints and preexisting planes of weakness (Turcotte, 1989). Number-size distributions, resulting from fragmentation, can often be described by a power-law relationship. A number-size distribution is fractal when the number of fragments or objects, N, with a characteristic size greater than r scales with the relation N rpDf, where D, is the fragmentation fractal dimension (Turcotte, 1986; Turcotte, 1989). Phenomena occumng in nature that have frequency-size distributions that can be described with a fractal dimension include islands, earthquakes, fragments, ore deposits, and oil fields (Turcotte, 1989). The power-law distribution observed in the fragmentation of natural materials is a result of the scale invariance of the fragmentation mechanism (i.e., planes of weakness) (Korvin, 1992). Scale invariance of fragmentation mechanisms mean that zones of weakness that are predisposed to failure exist at all scales. The existence of a fractal dimension for frequency-or number-size distributions is direct evidence for scale invariance (Turcotte, 1986). The fragmentation fractal dimension arose from the work of Korcak in the late 1930s. He studied the size distribution of the islands of the world and claimed that they could be described by the hyperbolic (or “Pareto”) distribution:

-

Nr(A > a ) = F l u p B

(4)

where Nr(A > a) is the number of islands of size greater than the stated area a, and F’ and B are two positive constants (Mandelbrot, 1983). Korcak (1938) claimed that B was equal to 0.5, but Mandelbrot (1983) criticizes this claim and states that the value of B varies between regions of the world and that it is always greater than 0.5. Mandelbrot (1983) developed a diameter-number relationship that can be written as N r ( R > A)

=

FApDf

(5)

where N r ( h > A) is the number of fragments greater than the given diameter A, F is a constant, and D, is the fragmentation fractal dimension. Furthermore D , can be linked to the B in Eq. (4) by the relationship B = Dd2.

12

ALISON N. ANDERSON ETAL.

Turcotte (1986, 1989, 1992) gave several examples of power-law fragmentation. Turcotte (1989, 1992) observed that the values of D, vary considerably for a wide variety of processes, but most lie in the range 2 < D, < 3. A simple model described in Turcotte (1986, 1989, 1992) illustrates how a fractal distribution can result from fragmentation.

E. TECHNIQUES FOR ESTIMAT~VG THESURFACE FRACTAL DIMENSION Measurement of the surface fractal dimension ( D s )can be carried out using a number of methods, and several will be described here. The original method of measuring the surface fractal dimension is the stride method. Other simple methods are box counting (e.g., Feder, 1988; Pfeifer and Obert, 1989) and Minkowski’s sausage logic (Kaye, 1989a).These methods require an image from which the boundary can be directly studied. Indirect laboratory methods involve probing the surface of a material by mercury porosimetry (e.g.. Bartoli et al., 1991) and studying the dependence of monolayer values on particle radii (e.g., Avnir et al., 1985). The ideas behind the stride method, box counting, and Minkowski’s sausage logic are similar, so they will be discussed together. They all involve measuring the length of a boundary or surface using progressively smaller strides, boxes, or discs. The concept of stepping along a coastline and estimating the length of a coastline as stride length decreased was introduced in Sec. LA. This method may be used to estimate the surface fractal dimension of any irregular line. In order to estimate Dba log-log plot of measured length L(E)versus E is required. The plot gives a straight line that corresponds with Eq. ( 1 ) and so D s= 1 - m,where m is the slope of the line. The slope will be negative; therefore, Ds for a perimeter will lie in the range 1 5 Ds I 2. In order to stride along a line it is necessary to know exactly where the line is. Many boundaries in nature, for example, that of a cloud, are fuzzy, and when scrutinizing a fuzzy boundary, one does not know exactly where the boundary is located (Kaye, 1989a). In cases where the boundary of an object is fuzzy we can use methods to measure Ds that do not depend on us knowing exactly where the boundary is. Two methods that may be used are box counting (Pfeifer and Obert, 1989), similar to the mosaic amalgamation method described by Kaye (1989a), and Minkowski’s sausage logic (Kaye, 1989a).Feder ( 1988)favored the box-counting method because it allowed him to overcome the problem of dealing with islands, fjords, and rivers, a problem when stxiding around a coastline with dividers. The box-counting system is also applicable regardless of whether the system to be rneasured is a dust, curve, surface, or bulk solid, and N ( E ) (the number off boxes required to cover the object of interest) may be interpreted as the number of pixels required to represent the object to within resolution E (Pfeifer and Obert, 1989).

APPLICATIONS OF FRACTALS TO SOIL STUDIES

13

Rather than use boxes, disks of radii E are drawn around each point of a curve for the method known as Minkowski’s sausage logic; the method is fully described in Kaye ( 1989a). Dilation and erosion logic have been used to obtain estimates of the surface fractal dimension. The usefulness of dilation logic in estimating Dsof a boundary was first acknowledged by Flook (1978). When an irregular boundary of an object is dilated the boundary loses more and more of its finer irregularities, and the perimeter decreases as more and more pixels are added to the boundary. When the object is eroded back to its original size the boundary is simplified. Anderson el al. ( 1996) estimated values of Ds of pore-solid interfaces by counting the difference (in pixels) between the dilated and eroded pore space. Kaye (1989a,b) discusses how dilation and erosion logic helps overcome the problem of inspecting fine particles that may be touching rather than actually fused together. Erosion of a carbon-black profile allowed Kaye (1989a) to determine how many subunits must have collided to form the cluster, Intrusion porosimetry can be used to study the structure of pores in a material, the process of determining the pore-size distribution of porous solids by mercury intrusion under pressure being first suggested by Washburn ( 192 1). The technique has been used by powder technologists (e.g., Kaye, 1989a) and soil scientists (Bartoli er al., 1991, 1992a,b, 1993). Nagpal er d. (1972) used mercury intrusion porosimetry to determine the pore-size distribution of soil. This had previously been determined from soil water characteristic curves (e.g., Learner and Lutz, 1940; Childs, 1940). In fact, Ah1 and Niemeyer (1989) used the water-retention curve to calculate the surface fractal dimension using the same technique as for mercury intrusion porosimetry. Mercury intrusion porosimetry involves forcing mercury, under pressure, into a sample that has previously been evacuated of water and gas. The applied pressure is increased in discrete steps, and the volume of pores intruded between steps is obtained (Nagpal, 1972). The pore radius that corresponds to a particular applied pressure can be calculated using the Washburn equation:

P

=

(2ycosO)/r

(6)

where P is the applied pressure, r is the radius of the pore, y is the surface tension of mercury, and 0 is the contact angle between the mercury and the soil. Bartoli et al. (1991) used an equation from Friesen and Mikula (1987) to measure the surface fractal dimension of soil: dVpldr

rzP1’s

(7)

where Vp is the pore volume, r is the average pore radius, and Dhis the surface fractal dimension. The surface fractal dimension can be estimated from the slope of the double logarithmic plot of dVJdr versus z Therefore Dscan be determined by measurement of the pore volume as a function of pore radius (Friesen and Mikula, 1987).

14

ALISON N. ANDERSON ET AL.

Friesen and Mikula (1987) found that two types of porous structure are probed by mercury intrusion. At very low pressures the mercury intrudes only the pore space between the particles in the sample, whereas at high pressures the mercury is forced into the pores of the individual particles. This corresponds with inter-aggregate and intra-aggregate pore space. Errors encountered with mercury intrusion porosimetry may come about because it is assumed that the accessibility of any pore does not depend on a smaller pore (Friesen and Mikula, 1987). In the case where a larger pore is only accessible by smaller pores it will be counted as a number of smaller pores rather than a single large pore. Kaye (1989a) suggests that it would be better to say that this method measures access throat diameter rather than pore size. Compared to image analysis techniques for estimating Ds, the advantage of mercury porosimetry experiments is that they cost little time (Bartoli etal., 1993), and a wider range of sizes may be probed (Bartoli et al., 1992b). Molecular probing can also be used to measure the surface fractal dimension; Avnir et al. (1985) state that the major advantage of using this method is that it is based on probing surfaces with molecules, and, consequently, the range of selfsimilarity, if it exists, covers the molecular size range beyond the limits of most microscopic instrumentation. Another advantage of this method is that it directly probes the structure of surfaces (Avnir et al., 1984), whereas striding, box counting, and the Minkowski sausage methods probe perimeters and can only indirectly provide estimates for the dimensions of surfaces by making stereological assumptions (Weibel, 1979). A number of equations that can be used to calculate Ds from molecular probing experiments are listed in Avnir et al. ( 1 984). Avnir et al. ( 1 985), for example, used the equation n

m r13s-3

(8)

where n represents the apparent monolayer values (e.g., in moVg adsorbent) for different average radii ( r ) of spheroidal particles. A log-log graph of Eq. (8) will give a straight line if a surface is fractal and Dsis estimated from the slope. A second equation, used by Okuda et al. (1995) is

where Nm(u)is the number of probe molecules of size u required to cover the surface of the given sorbent. The value of Dscan be estimated from the slope of the straight line produced from a plot of N,(u) versus u on a double logarithmic scale. The monolayer value N,(u) is generally estimated from adsorption data using the classical BET (Brauner-Emmett-Teller)equation. Avnir ef al. (1985) and Farin and Avnir (1989) report values of Ds(estimated from molecular probing data) for many different adsorbents and the adsorbates used. In most cases the value of Ds fell within the theoretically possible range of 2 IDs 5 3. Mandelbrot ef al. (1984) used the slit-island technique for evaluating the frac-

APPLICATIONS OF FRACTALS TO SOIL STUDIES

15

tal dimension of a fracture surface. This method involves embedding a fracture surface in a resin matrix. The specimen is then sectioned parallel to the fracture surface. As one proceeds to polish the specimen, “islands” of the fracture surface appear. These islands appear to grow as polishing continues, and it is possible to relate the surface fractal dimension of the perimeter of the islands to the surface fractal dimension of the surface. Pachepsky et al. (1996), for example, used this technique to measure the surface fractal dimension of pore outlines. Measurements of area and perimeters for the outlines were required.

F. TECHNIQUES FOR ESTIMATING THE MASS FRACTAL DIMENSION Techniques for estimating the mass fractal dimension are based largely on the box-counting method described by Pfeifer and Obert (1989). The following description of a box-counting method is from Anderson et al. (1996), and similar box-counting methods have been used by Hatano et al. (1992), Hatano and Booltink (1992), Booltink et a/. (1993), and Peyton et al. (1994). It is possible to estimate the mass fractal dimension of either the pore space (Dmp)or the solid fraction (DmJ by filling the phase of interest with progressively larger boxes. Each box measures m pixels by m pixels. For each value of m the number of boxes, N(m), that contain the phase of interest is counted. As m is increased N ( m )decreases. The value of Dmpor Dmsfor any image may be estimated from a plot of In N ( m ) versus In m where Dmis equal to the negative of the slope. A slightly different method was used by Crawford et al. (1993a). They used the standard method of Voss (1988), which involves counting the number, N ( r ) , of pixels residing in pore space (for example) within radius r of some origin in the network, a plot of log N(r) versus log r yielding Dm. Bartoli et al. (1991) picked the center of each image as the center for a series of eight squares of side length I: Occupied and unoccupied 1-pixel squares were analysed in each of the squares and additions of either occupied or unoccupied pixels were calculated.

G. TECHNIQUES FOR ESTIMATING THESPECTRAL DIMENSION The spectral dimension may be estimated for a particular fractal network by conducting a random walk through the network. From Orbach (1986)

where S(t) is the number of distinct sites visited in time r and is determined solely by d. Crawford et al. (1993a) and Anderson et al. (1996) calculated d for soil pore

16

ALISON N. ANDERSON ETAL.

space, but the method can be applied to the solid fraction if it is the phase of interest. For each random walk a pore pixel from the image of soil structure is chosen randomly as a starting pixel. The particle moves by stepping randomly into any of the eight pore pixels that surround it. This is continually repeated, and if the pixel being visited has not been visited previously, 1 is added to the number of distinct sites visited, S(n),and if the pixel has been visited previously, 0 is added to S(n) and 1 is added to the number of steps taken, n. Crawford et a/. (1993a) terminated a walk if the particle reached the boundary of an image, as did Anderson et al. (1996). Anderson et al. (1996) also terminated a walk after 100 null steps (steps into previously visited pixels) were taken in an attempt to reduce the time required to calculate d. A plot of log S(n) versus log n yields a line of slope d12 (Crawford et al., 1993a). Values of d were based on the average between lo3 and lo4 individual random walks on 440 X 320 grids by Crawford et al. (1993a), whereas they were based on 5000 individual random walks on images of 1000 X 1000 pixels by Anderson er al. (1996).

H. TECHNIQUES FOR ESTIMATING THE FRAGMENTATION FRACTAL DIMENSION The value of the fragmentation fractal dimension is measured from a double logarithmic plot of the number of fragments (aggregates or particles of soil) of greater size than a characteristic size (e.g., the diameter A in Eq. [ 5 ] )versus the characteristic size (i.e., A). D, is estimated as the negative of the slope of the line produced. Measurement of the fragmentation fractal dimension (Of) of soil begins with either an aggregate-size distribution or a particle-size distribution. Generally we can conclude that as the size of the aggregates or particles decreases, a greater number of aggregates or particles of a particular size will be present. A higher value of D, indicates greater fragmentation and a greater proportion of smaller aggregates or particles. When calculating the number-size distribution it is not usually possible to count the number of all aggregates or particles of a given size, especially at the smallest sizes. It is common practice to infer the number-size distribution from the masssize distribution, which is much more easily determined. This method has been used by Bartoli et al. (1991), Eghball et al. (1993a), Perfect and Kay (1991), Perfect et a/. (1992), Rasiah et u/. (1992), Rieu and Sposito (1991b), and Tyler and Wheatcraft (1989), for example. The equation (as written in Rieu and Sposito [ 199 1b] used to calculate the number of aggregates in the ith size class from the total mass of aggregates in the ith size class is N(dJ

=

M(d,)/(d,”p,) ( i

=

0, 1, . . .)

(11)

APPLICATIONS OF FRACTALS TO SOIL STUDIES

17

where N(d,) is the number of aggregates of size class i, M ( d J is the mass of class i, d, is the mean diameter of size class i and pi is the bulk density of size class i. As can be seen in Eq. ( 1 l), assumptions may have to be made when nurnbersize distribution data is calculated from mass-size distribution data. The first assumption involves aggregate bulk density or particle density. This is at times assumed to be scale-invariant (e.g., Bartoli etal., 1991; Perfect and Kay, 1991; Tyler and Wheatcraft, 1992a) and at other times it has been measured for each size class (Eghball rt al., 1993; Rieu and Sposito, 1991b). The problems that may be associated with this assumption will be discussed in Section 1V.A.Another assumption regards scale-invariant aggregate shape or particle shape. Aggregates are generally assumed to be cubic (e.g., Perfect and Kay, 199 1;Rasiah et al., 1992) and particles assumed to be spherical (e.g., Bartoli er d., 1991; Wu et al., 1993). The size of aggregates is usually assumed to be the mean diameter of a range of size of aggregates that have been placed in the one size class. The lower and upper cutoffs to a particular size class are generally two sieve sizes. All fragments that pass through one sieve (upper cutoff) but do not pass through the next sieve (lower cutoff) are grouped together. This assumes that aggregate- and particle-size distributions are made up of a discontinuous distribution of aggregates or particles rather than a continuum of aggregate or particle sizes. This assumption may cause some errors in the estimation of D,. The value of N(d,)in Eq. (1 1) is strongly dependent on the choice of d j , especially when dj is small, because it appears raised to the power three (Tyler and Wheatcraft, 1992a).

111. APPROXIMATING SOIL STRUCTURE USING FRACTALS In recent years soil scientists have pursued methods that allow a quantitative description of soil structure, an area that has been predominantly treated qualitatively in the past. The mathematical basis of fractal geometry makes it a potentially useful tool to describe the heterogeneity of soil structure quantitatively. A quantitative description of soil structure would ideally be able to be directly related to the processes occurring within the soil. Depending on the processes under study, a full characterization of soil structure requires that more than one type of fractal dimension is estimated. However, seldom is more than one geometrical attribute (and therefore fractal dimension) evaluated simultaneously (GimCnez, 1995). Relationships between fractal dimensions of the soil structure and soil physical processes will be discussed further in Section V. Soil structure has been characterized by fractal dimensions, estimated either directly from images of soil structure or indirectly from bulk density or mercury porosirnetry data, for example.

18

ALISON N. ANDERSON ET AL.

A. DERIVATION OF A MASSFRACTAL MODEL FROM BULKDENSITY DATA Fractal models have been verified, and the mass fractal dimension, Dm, has been estimated from soil aggregate bulk density- or mass-size data by Rieu and Sposito (1991b), Young and Crawford (1991), and Anderson and McBratney (1995). Rieu and Sposito (1991a,b) referred to Dmas the bulk fractal dimension. Although bulk density-size data is a seemingly simple method of estimating Dm,it is rarely collected for soil. There is much room for error in calculating bulk density-size relationships, and different methods are tested and discussed by Chepil ( 1950). Estimating Dmfrom bulk density data is one of only a few methods for estimating Dm directly for soil in three dimensions. The mass fractal dimension for three dimensions can be predicted from values of Dm estimated from images of soil thin sections by adding 1, but this can only be done realistically if the soil is isotropic (Crawford et al., 1995), which is difficult to verify and may be unlikely in general. If soil aggregates obey fractal scaling and are mass fractals, their bulk density will decrease as aggregate or ped size increases. A plot of the bulk density-size data from Chepil(1950) shows that aggregate bulk density does just that (Fig. 7). Young and Crawford (1991) collected mass-size data for two sandy loam soil types and estimated Dm from a plot of In mass (8) versus In radius (mm), where the slope of the line was equal to Dm. Values of Dm ranged from 2.75 to 2.95; larger values of Dm were a result of cultivation. Values of Dm reflect the heterogeneity of the aggregates, and therefore it can be concluded from the results of Young and Crawford (1991) that rotary cultivation (and probably most cultivation techniques) increases the homogeneity of the aggregates.They attribute this increase in homogeneity to the combined forces of compaction, slicing, and piercing that occur during cultivation and also suggest that moisture content at the time of cultivation will have a significant effect on Dm values. Wet soil will undergo plastic deformation, resulting in a more homogeneous structure. Bulk density-size data from Chepil(l950) and Wittmus and Mazurak (1956) as well as for one other soil and a hypothetical sandy and a clayey soil type was used by Rieu and Sposito (1991b) to estimate Dm.Plots of log (uiluo)versus log (di/do) produced lines from which Dmwas estimated (Dm = 3 + m, where m is the gradient), where ui and di are the bulk density and mean diameter of the ith size class respectively, and uoand do are the bulk density and diameter of the largest aggregate respectively. Values of Dmfor the Chepil (1950) data ranged from 2.88 for a fine sandy loam to 2.95 for a clay. Increases in Dmwere correlated with increases in clay content. As the aggregate size increases for a fine textured soil, bulk density does not drop off as rapidly as for a coarse-textured soil. Similarly, values of Dm were 2.88 and 2.95 for their hypothetical sandy and clayey soil types, respectively. Results from the Wittmus and Mazurak (1958) soil showed two distinct val-

APPLICATIONS OF FRACTALS T O SOIL STUDIES

0.0

0.5

1.o

1.5

2.0

19

2.5

Radius (mm)

Figure 7 Bulk density-aggregate \ize relationship for the data from Chepil (1950). The data are from the nonlinear fit to the Chepil (1950) data reported by Anderson and McBratney (1995).

ues of Dm ( D , = 2.75 for the smaller, coarser textured aggregates and 2.95 for the larger aggregates). This highlights the fact that soil is a natural fractal, and changes in fractal scaling are possible. Rieu and Sposito (1991b) attribute the change in D , value to differences in the texture of larger and smaller aggregates. The Chepil (1950) data were also used by Anderson and McBratney (1995) to estimate Dm,along with bulk density data reported by Eghball et al. (1993a). These data sets were used to compare linear and nonlinear methods of estimating Dm. Both methods were found to give similar estimates of Dm, and neither method could be said to give a superior estimate of D,,,. The linear equation used is written as

log pi = c

+ s x log Ti +

Ei

(12)

where pi is the bulk density of the ith size class, T , is the mean radius of the ith size class, s is the slope (s = Dm, - d,), c is the intercept, and E; is the residual of the ith size class. Dm,is the mass fractal dimension estimated using the linear method,

20

ALISON N. ANDERSON E T A L .

and d, is the embedding dimension (3 in this case). Eq. ( 3 ) ,from Orbach (1986), was used for the nonlinear fit and is written as p. = k

rifDmn-(felef

+ E,.

(13)

where Dmnis the mass fractal dimension estimated using the nonlinear method, and k is equal to B/C in Eq. (3). Values of Dm, (2.880-2.953) and Dmn (2.883-2.955) were very similar for identical samples, as were values of k and 10' (placing c back on the original scale). Orbach (1986) states that the B in Eq. (3) varies according to the lacunarity and that C is a constant. Because k = B/C then k, and most likely lo', must also vary with the lacunarity of the aggregates. Additionally, GimCnez et al. (1994) estimated Dm from bulk density data reported by Larsen and Padilla (1990). Values of Dm agreed with those that have been reported elsewhere, with values falling in the range 2.85-2.89. The results from bulk density-size studies form a convincing argument that fractals are a better approximation to soil structure than non scale-invariant models. They all show that as aggregate size increases, bulk density decreases (Fig. 7). This gives evidence for fractal aggregates and is reinforced by the work of Gumbs and Warkentin (1976), which showed that pore volume increased with increasing aggregate size.

B. ESTIMATING MASSFRACTAL AND SPECTRALDIMENSIONS FROM TWO-DIMENSIONAL SOILIMAGES Mass fractal (D,) and spectral (d)dimensions are able to be estimated directly from two-dimensional images of soil structure. The images may be of thin sections (Bartoli et al., 1991; Crawford et al., 1993a; Peyton et al., 1994; Anderson et al., 1996; Crawford and Matsui, 1996) or stained flow patterns (Hatano et al., 1992; Hatano and Booltink, 1992; Booltink ef al., 1993). The first method includes all pores that are visible at the resolution of the thin section or the photograph taken of the thin section, whereas the second method includes only those pores that contribute to the flow of water through the soil. Measuring fractal dimensions directly from images of soil structure (rather than from bulk density data) avoids the problem of non-uniqueness, which arises when estimates of structural heterogeneity are based on properties of that structure that can arise as a consequence of different types of heterogeneity (e.g., see Sec. 1V.C). Furthermore, it will become obvious in Sec. V, which deals with the prediction of physical processes from the fractal properties of the soil structure, that it is necessary to have estimations of more than one fractal dimension for the complete characterization of soil structure and for the prediction of soil physical processes. The fractal dimensions that may be estimated from two-dimensional images of soil structure are the mass fractal dimension, the spectral dimension, and the surface fractal dimension (discussed in Sec. 1II.C).

21

APPLICATIONS OF FRACTALS TO SOIL STUDIES

A list of studies that estimated either D , or d is given in Table I. The mass fractal dimension of the solid fraction will be referred to as Dms,and the mass fractal dimension of the pore space will be referred to as Dmp.Bartoli et al. (1991) were the first to measure the mass fractal dimension of both the solid and pore space (named the mass and porosity fractal dimensions, respectively, by Bartoli et al. (1991)) from images of thin sections. Determined on silty and sandy soil types, thin, very-thin, and ultra-thin sections were used to measure Dmsand Dmpby image analysis (pixel resolution was 0.25 mm) on a continuous scale from lop9 to 10-'m by Bartoli et al. (1991). They concluded that the porosities of the soil types studied were nonfractal because the standard errors were much larger for Dmpthan for D,,, and values of Dmpalways approached 2, whereas values of Dmswere significantly smaller than 2. They were able to differentiate between a compact soil

Table 1 Summary of Papers Reporting Estimates of Dm and d Paper Bartoli ef al. (1991) Hatano and Booltink ( 1992)

Method Box counting Box counting

Hatano er al. (1992)

Box counting

Booltink et al. ( 1993)

Box counting

Crawford et al. ( 1993)

Radius method for Dn, and random walks ford Box counting

Peyton et al. ( 1994) Anderson ef a!. ( 1996)

Box counting for Dm and random walks ford Crawford and Calculating the qth moments of the Matsui ( 1 996) "mass" distribution

D,,, or d" value

Scale

D,,,\ = I .77-2.02 D,,,, = 1.95-2.07 Dmp(whole,3h = 2.27-2.42 Dmp(upper)3 = 2.29-2.43 D,np(lowcr,3 = 2.04-2.58 Dmp(wuho,rl = 1.21-1.85 Drnp(lower) = 0.90-1.75 Dmp,whc,lc13 = 2.067-2.380 Dmp,upper13 = 2.069-2.425 D,,,pl,,,wer)3 = 1.784-2.274 Dmp= 1.71-1.94 dp = I .04-I .33

Continuous scale. 10-"-10- m Images made from cross-sections of soil cores, diameter 20 cm

Dmp= 1.25-1.88

Images S 1.2 X 5 1.2 mm

D,np= 1,682-1.852 dp = 1.236-1.668

Images SO

D,,,p(,,,,r; = 1.06-1.73 Dmn(ve,fl = 1.20-1.72 r . Dm,(hor) = 1.93-1.98 Dn,5(vrrt) = I .93-1.99

Images made from circular thin sections, 5 cni diameter

'

Images 113

X

106mm

Images made from cross-sections of soil cores, diameter 20 cm Images 45 X 34 mm

X

50 mm

"Subscripts p and s represent pore space and solid matrix, respectively. hD,p3 represents values of Dlnpestimated for three dimensions. Subscripls of whole, upper, and lower refer to averaged values for a whole soil column, values for the upper half of a column, and values for the lower half of a column, respectively. Values of Dm were estimated from images in both the horizontal and vertical planes.

22

ALISON N. ANDERSON ET AL.

and a more porous soil using values of Dms,values being higher for the more compact soil, and subsequently it was shown that Dmswas inversely related to the average soil porosity. Values of Dmsreported by Bartoli er al. (1991) vary slightly for different scale ranges, but overall only one scaling regime is found. Dmsand Dmpare estimated over a much larger range of scales here than in other studies. Flow paths were characterized by Hatano and Booltink (1992), Hatano et al. (1992), and Booltink et al. (1993) by estimating the mass fractal dimension of stained flow patterns (Dmp).Two-dimensional images were obtained from horizontal layers (2 cm thick) taken at several depths from soil cores (1 5 cm in diameter and height in Hatano et al. [ 19921 and 20 cm in diameter and height in Hatano and Booltink [ 19921 and Booltink er af. [ 19931) that had been stained by water passing through them with methylene blue added. Images produced by Hatano er al. (1992) were 512 X 480 pixels, each pixel having a resolution of 0.221 mm. In Hatano and Booltink (1992) pixel resolution was 1 mm. In all cases Dmpproved to be useful for the morphological characterization of soil structure, and in general values of Dmpdecreased with depth. It was observed that Dmpvalues were correlated with the form of the staining patterns. For example, Hatano et al. (1992) found that images characterized by many fine planar stains had larger values of DmP,whereas images with stains confined to root channels or cracks had smaller values of Dmp.Observations by Hatano and Booltink ( 1 992) include the fact that staining patterns in the upper half of cores tend to be similar, whereas they vary considerably in the lower half. In Booltink er af. (1993) the upper half of the core was from above the plough layer, and more horizontal pedfaces were present than in the lower half where vertical continuous cracks were dominant and values of Dmpwere smaller. Hatano and Booltink (1992) developed a method for estimating Dmp3(the mass fractal dimension of the stained flow patterns in three-dimensional space). Values of Dmp3were calculated using values of Dmpand stained area in cross-sections. For isotropic structures Dmp3may be estimated by 1 + DmP.For the samples studied by Hatano and Booltink (1992) values of Dmpand stained area varied with depth, and they found that a better approximation of the depth average of Dmpis given by Dmp3- 1, which they state takes the effect of an anisotropic structure into account. Crawford et al. (1993a) and Anderson et af. (1996) estimated Dmpand dp for images of thin sections with contrasting structures. In both, values of Dmpand dp discriminated well between the different structures and agreed well with what would have been expected from a visual examination of the structures. Generally, a soil structure with large continuous pores had larger values of Dmpand d, than a structure with small discrete pores and low lacunarity. In Anderson et al. (1996) each image was 1000 X 1000 pixels with a pixel resolution of 0.05 mm. Anderson et al. (1996) showed that Dmpand d, do not depend wholly on porosity even though structures with greater porosity generally have larger values of D,,. Im-

APPLICATIONS OF FRACTALS TO SOIL STUDIES

23

ages with the same porosity but with different pore formations were compared. Different values of Dmp and dp were obtained for structures of equal porosity because the pore spaces had different spatial distributions. It should be pointed out that while three-dimensional values of D, can be estimated from twodimensional values of Dm and vice versa there is no such relationship for the spectral dimension. Images produced from X-ray computed topography were used by Peyton et at. (1994) to estimate values of the fractal dimension of areas formed by unoccupied pixels (D,,). Unoccupied pixels were those with bulk densities less than a particular cutoff (known as the X-ray computed tomography cutoff number or CTC). At low cutoff values only the macropores are included in the unoccupied space, whereas for high cutoff values the occupied space is only made up of those pixels with high bulk densities. Peyton et al. ( 1994) chose an appropriate cutoff to enable comparison between the soil samples. Each image was 5 12 X 5 12 pixels, with a pixel resolution of 0.1 mm. As Peyton et a/. ( 1994) increased their CTC the number of unoccupied pixels increased, and Dmpapproached 1.91 for the soil cores examined. Log-log plots used to estimate Dmpwere not constant over the entire range of box sizes. To avoid artifacts caused by using very small or very large boxes Peyton et al. (1994) used only boxes in the size range 1 .O to 10.0 mm to estimate D,,. Larger values of Dmpwere found for uniformly packed soil columns than for undisturbed columns (a forest and a field soil). The difference between the forest and field samples was only significant at a probability level of 0.059, and Peyton et al. (1994) attribute this to the large variation within the forest and field groups. Crawford and Matsui ( 1996) estimated Dn,pand Dms in both the horizontal and vertical planes from images produced from a range of thin sections. Pixel resolution of the computer images was 0.1 mm. Values of D,,l, were consistently higher (and close to 2) than those for D,,,,, and values of Dmpwere much more variable. We would generally assume that Dn,, values would be larger than Drnpvalues because we would expect the solid fraction of soil to be more homogeneous. Crawford and Matsui (1996) reported that values of Dn15were independent of the orientation of the plane of the section and the size of the subsample, although this was not the case for values of Dmp.The implications of these results will be discussed in Sec. V1II.B.

C. CHARACTERIZING THE SURFACE FRACTAL DIMENSION OF SOIL The surface fractal dimension (D,) of the solid-pore interface (pore-wall roughness) or surface roughness has been estimated directly from images of soil structure (e.g., Hatano et al., 1992; Anderson era!., 1996) and using indirect methods, of which mercury porosimetry has been the most popular (e.g., Bartoli el al., 1991 :

24

ALISON N. ANDERSON ETAL.

Pachepsky et al., 1995). The surface fractal dimension may have application in understanding physical processes occurring within the soil, soil microbe movement, and adsorption phenomena as well as allowing for a quantitative comparison of surface and pore-wall roughness between soil types and experimental treatments. Most of these applications will be dealt with in later sections of the chapter. Interest in using fractals to characterize surface roughness was first expressed by Bertuzzi et al. (1990). Surface roughness, as measured by a noncontact laser reliefmeter, was described by six soil-surface roughness indices, including a fractal index, after three successive periods of simulated rainfall on a field plot. Their fractal index was not the surface fractal dimension as such, but it did indicate the fractal nature of the soil surface and it did describe the changes in soil surface roughness with rain. The methods of Bertuzzi er al. (1990) would tend to underestimate Ds for tortuous surfaces but have the advantage of being noninvasive (Young and Crawford, 1992). Studies of the surface fractal dimension of soil are summarised in Table 11. It is important to note that when Ds is estimated directly from soil images, Ds lies within the range 1 5 Ds 5 2 because Dsis being estimated from profiles of surface roughness or pore-wall roughness, whereas when Ds is estimated from mercury porosimetry, Ds lies within the range 2 5 Ds 5 3. The value of D,2 (the surface fractal dimension in two-dimensional space) is approximately equal to D\3 (the surface fractal dimension in three-dimensional space) minus one. Stereological principles (Weibel, 1979) would suggest, if isotropy can be assumed, that Ds2 + 1 = Ds3. Serra (1982) also states that Ds3 can be obtained by adding 1 to Ds2. However, it is likely that many soil structures do not display isotropy. The surface fractal dimension of fracture profiles was estimated by Young and Crawford (1992). They found that a fractal description of soil roughness, particularly when fracture paths were tortuous, was a much more appropriate method of describing roughness as compared to the methods of Dexter and Horn (1988) and Bertuzzi ef al. ( 1990). In the case of a tortuous path doubling back on itself some of the path will be omitted from the analysis for these other measures of roughness, but this will not be the case for a fractal analysis (Young and Crawford, 1992). Surface fractal dimensions of terrain profiles were reported by Ohmiya (1993), terrain profiles being random and nonperiodic. The characteristics of terrain profiles and fractional Brownian motion were found to be very similar. By covering an irregular terrain profile with different-sized circles Ohmiya found profiles of meadows to have values of Ds in the range 1.16-1.29. The perimeter-area method of estimating the surface fractal dimension was used by Hatano et al. (1992) and Kamplicher and Hauser (1993). The images from which Hatano et al. (1992) estimated Ds were generated by taking horizontal sections at various depths from soil cores (1 5 cm in diameter, 15 cm in height) after a methylene blue solution had passed through the cores, consequently staining the flow paths. It was found that for any particular soil column, values of Ds remained

AF'PLICATIONS OF FRACTALS T O SOIL STUDIES

25

Table I1 Summary of Papers Reporting Estimates of D5 Paper

D, value

Method

Scale

Ah1 and Niemeyer. 1989 Bartoli etal., 1991

Water-rerention curve

D, = 2.10-2.84

Mercury porosimetry

D' = 2.69-2.90

Young and Crawford, 1992

Box counting, images of fracture profiles

D, = 0.97-1.36

Hatano et a/., 1992

Perimeter-area method. images of \wined flow paths

D, = 1.30-1.64

Bartoli eta/., 1992a

Mercury porosimetry

D. = 2.44-2.65

Pore radius range of 3.7-IO5 nm

Bartoli et al.. 1992b

Mercury porosimetry

D,

2.45-3.06

Pore radius range of 4-lohnm

Bartoli e t a / . , 1993

Mercury porotimetry

D, = 2.60-2.90

Pore radius range of 4-loh nm

Kamplicher and Hauser. 1993

Perimeter-area method, images of thin sections

D,

Image size varied. e.g.,4 X 6.4 mm

Ohmiya, 1993

Circle counting Box counting, images from CT scans Mercury porosimetry

D5 = 1.16-1.29 D\ = 1.22-1.87

Less than 5-50 m Images51.2X 51.2mm

D,, = 2.66-3.39 D,,, = 1.97-2.73 D5,,a = 1.65-2.1 1 D,ii, = 2.3-4.15

Pore radius range of 4 nm to lOmm

Anderson et al., 1996

Dilation and erosion logic, images of thin sections

Dsi = 1.15-1.25

Images 50 X 50 mm

Pachepsky etal., 1996

Perimeter-area method. images of thin sections

D,, = 1.06-1.12

Peyton et a/., I994 Pachepsky et a/., 199s

=

=

2.26-2.39

Pore radius range 0f3.7-10~nm Fracture profiles approximately 10 cm long Images I13 X 106 rnm

D,2 = 1.91-1.99

D,z = 1.42-1.51

Four subsample areas generated from samples approximately 3 X 5 cm

relatively constant with depth. This implies that while values of D,, decrease with depth for any particular column, the degree of irregularity in the boundaries of staining patterns does not vary. There were, however, some differences in average values of Dc for the different columns. This enabled some conclusions to be made about the values of Db to be expected for different soil types by Hatano el al. (1992). Porous pumice stones have rough and irregular surfaces as do well-developed subangular blocky peds compared to large prismatic or blocky peds and root

26

ALISON N. ANDERSON ETAL.

channels. This was not the case for the images of soil structure prepared by Kamplicher and Hauser (1993), which showed little variability in values of Dz for the different soil types. Kamplicher and Hauser (1993) felt that this narrow range of values of D swas possibly due to their strict criteria on the selection of soil sections. A limitation to the area-perimeter method is that it is restricted to images of soil that have patches of discrete pores as images that have interwoven networks of soil matter, and crevices cannot be analysed by using the computer program used by Kamplicher and Hauser (1993). The perimeter-area method of "slit-island" method (Mandelbrot et al., 1984) was also used by Pachepsky et al. (1996) to study the irregularity of soil pore boundaries revealed on soil thin sections. They also aimed to use the values of Ds of the pore outlines to determine any differences between different management practices (conventional, manure, legume, and grass strip). Two distinct values of Dswere found. Small pores had values of D g , in the range 1.06-1.12 and large pores had values of Ds, in the range 1.42- 1.51. The cutoff between the two fractal intervals was between pore radii 0.02 to 0.03 mm. A possible reason for the two intervals of fractal behavior is that the resolution of the computer used is not sufficient to register fine irregularity of soil particle surfaces (Pachepsky et al., 1996). However, Pachepsky et al. (1996) also state that it is possible that the small pore scaling is real. Some differences between management practices were also found by Pachepsky et al. (1996). Values of D I , were significantly larger for samples from the legume and grass strip plots than for the other samples. Samples from the legume plots had significantly larger values of Ds, than for samples from the other plots. In a study on the two-dimensional quantification of ped shape, Holden (1993) measured the surface fractal dimensions of the boundary of peds. Holden (1993) used the stride method to measure Ds at one scale of observation, stating that additional detail is revealed by reducing stride lengths to estimate perimeter in the same manner that investigation at finer detail does. Holden ( 1993) identified two distinct values of the surface fractal dimension, D s , (1.007-1.163) and Ds, ( 1.0311.106),but also estimated the overall value of Ds (DsT= 1.105- 1.129). Ds I corresponds with the textural fractal and Ds2with the structural fractal of Kaye (1989a) as discussed in Sec. 1I.A. Although finding all types of Ds to be useful in distinguishing roughness classes of peds, Holden (1993) found D s , to be the best because Ds2could not reliably distinguish the smooth and undulating classes of peds. Peyton et al. (1994) estimated Ds for images generated by X-ray computed tomography, the process for obtaining the images having been discussed in Sec. 1II.B. The uniformly packed soil columns had the largest values of Ds and therefore the most irregular boundaries. Of the undisturbed samples the forest soil had a larger value of Ds compared to the field soil. An important observation by Peyton et al. (1994) is that the slope (generated from a log-log plot of number of boxes of a given size versus box size) used to estimate Ds was not constant for all

APPLICATIONS OF FRACTALS TO SOIL STUDIES

27

box sizes, and so only the portion with a constant gradient was used. This corresponded to the region between box sizes of 1 .O to 10.0 mm. The dilation and erosion method of estimating the surface fractal dimension of the pore-solid interface, reported by Anderson era/. (1996), gave two distinct values of Ds, D s , , and Ds2. The plot used to estimate Dshad two distinct gradients, and from these two gradients Ds,and Ds,were calculated. Although values of D s , and Ds2 were very different, variability within the values of D s , and Ds,for the different soil structures was small. Values of D s , ranged from 1.15 to 1.25 and values of Ds, ranged from 1.91 to 1.99. Therefore the overall structure of the poresolid interface appears more rugged at low resolution (DS2),and at higher resolutions ( D s , )the boundary becomes more Euclidean. Anderson et al. ( 1996)question whether all values of Dsare likely to be similar at this scale, even for different soil types. The images analysed by Anderson e f a/. ( 1996) were all 1000 X 1000 pixels or 50 X 50 mm, representing a pixel resolution of 0.05 mm. Pixel resolution in Peyton et al. ( 1994) was 0.1 mm, each image being 5 1.2 X 5 1.2 mm, and in Hatano et al. (1992) it was 0.221 mm, each image being 113.2 X 106.1 mm. Pixel resolution in Kamplicher and Hauser (1993) was either 0.006 or 0.012 mm, depending on the size of the image, an example size being 4 X 6.4 mm. In Young and Crawford (1991) pixel resolution was either 0.2 or 0.16 mm. It appears that an increase in pixel resolution does not increase the likelihood of statistical differences being found between values of Ds for different soil samples. In the cases reported here the studies with the best pixel resolution (Kamplicher and Hauser, 1993; Anderson et al., 1996) had small variability between values of Ds. Sand grains (> 50 pm) largely composed of quartz were found to have nonfractal surfaces by Barak e t a / . (1996) when Ds was estimated from image analysis. No increase in the perimeter of sand grains was apparent for the range of magnifications studied ( X 1 to X20). Using the perimeter-area method, values of Ds were found to be close to that expected for circles, ellipses, and regular polygons. Barak et al. (1996) suggest that quartz may not have a fractal surface because it lacks cleavage planes and because it is often subjected to prolonged reworking by soil-forming and geological processes that may smooth surface irregularities. Rather than use the classical method of image analysis to estimate Ds, Bartoli ef al. (1991), Bartoli et al. (1992a,b), Bartoli et al. (1993), and Pachepsky et a/. (1995) used mercury as a surface probe. Bartoli e f at. (1993) state that the Dhestimated using this method is only a similitude Ds because the probe only describes the surface accessible by the probe and not the real surface. In this case Ds is an intrinsic measurement of the degree of pore-volume filling of different soil pore systems (Bartoli et al., 1992a). For all the listed studies air-dried samples (1 g) were used, and all used the < 2 mm fraction with the exception of Pachepsky et al. (1999, who used the < 1 mm fraction. Mercury porosimetry was used to determine the distribution of pore radii in the range or part thereof 3.7-106 nm.

28

ALISON N. ANDERSON ETAL.

All but Bartoli et al. (199 1) reported limits to fractal behavior. A region of selfsimilarity (as observed in log-log plots of AV/Ar versus v ) was between pores of radius 0.05 ym and 5 ym (Bartoli et al., 1992a). Bartoli et al. (1992b) reported that the lower limit to fractal behavior fell between pores of radius 7.8 nm to pores of radius 250 nm, whereas the upper limit fell between 1.4 y m and 45 ym. The domain that was fractal corresponded to the inter-assemblage and inter-microaggregate pores. The upper limits, as observed by Bartoli et al. (1993), varied from 2.5 to 45 ym. The predominant domain of fractal behavior in this case corresponded to the intra-aggregate pore systems. Pachepsky et al. (1995), however, found up to four distinct intervals (DsI,Dsll,Dslla,and Dslllin Table 11) with different values of Ds in the range of pore radii 4-5 ym, and they attributed these different values to differences in composition of soil particles of different sizes. Similarly, Serra (1982) found when studying the microstructures of clay rocks that values of Ds sometimes vary with the size distribution of the clay particles. Bartoli et al. (199 1) estimated Dsas part of a study of soil structure using fractals. Different values of Dswere observed for two horizons of a silty soil and one horizon of a sandy soil. They also estimated Dsfor some sand-clay and silt-clay mixtures studied by Fies (1984).For one silt-clay mixture Dswas observed to be greater than 3, clearly a nonphysical situation. Bartoli er al. (199 1) attribute values of D5 greater than 3 to be the result of compression of a sample during mercury intrusion. Pachepsky et al. ( 1 995) also obtained some nonphysical values, also attributing them to the compressibility of bulk material as well as to air entrapment. The modification of the structure of a poorly structured silty soil, by adding poorly ordered femhydrite (with or without model humic macromolecules), was studied using mercury porosimetry, among other methods, by Bartoli et al. (1992a). They found that Dsincreased as a function of adsorbed iron and so were able to characterize the structure formed by adding poorly ordered Fe hydrous oxides. Bartoli et al. (1992a) also estimated Dsfrom water retention data, and when values of Ds for mercury and water were plotted as a function of adsorbed iron, values of Dsfor water were larger than values of Dsfor mercury for a particular amount of iron adsorbed (%). This is because the progressive pore volume filling appeared greater with water than with mercury (Bartoli et al., 1992a). Values of Ds were used by Bartoli e f al. ( 1992b) to characterize the influence of organic matter on aggregation in Oxisols. Two Oxisols were used-ne rich in gibbsite, the other in goethite. Generally it was found that Dswas inversely proportional to depth and increased with organic carbon content. Bartoli et al. (1992b) noted that values of Dsfor the gibbsite-rich Oxisol were less than that for the Oxis01 rich in geothite and suggested that their values of Ds reflected the values of Ds that would be found for their main constituents. Differences may also be due to differences in salt concentration, and Bartoli er al. ( 1992b) report the work of Amal et al. (1989) who found that Dsdecreased as KCI concentrations in hematite suspensions increased. Increasing salt concentrations were said to increase aggrega-

APPLICATIONS OF FRACTALS T O SOIL STUDIES

24,

tion rates therefore yielding increasingly tenuous structures. Third, Bartoli et al. ( 1992b) stated that differences may be the result of different microcrack patterns that result from different wet-dry cycles. Aggregate structures were studied and Dsestimated for a root zone horizon and a nonroot zone horizon for a sandy acid brown soil by Bartoli et al. (1993). Along a 23 m transect they found Ds to be a better discriminator than the porosity between the two zones, and values of Dswere significantly smaller for the root zone soil than for the nonroot zone soil. Bartoli et al. (1993) point out that the samples show repetition of disorder at all length scales as did the temperate silty and sandy soil types studied by Bartoli et al. ( 199 1) and that this large degree of disorder can be somewhat explained by the relative flexibility of clays (predominantly illites, illite-vermiculites associated with kaolinites) that occur in these temperate soil types. Pachepsky et al. (1995) successfully used Dsto characterise pore surface area for three different soil types before and after simulated soil degradation. As mentioned earlier they found three or four distinct fractal scaling regimes. Simulated degradation had the effect of increasing values of Dsin one or more fractal intervals, indicating an increase in the roughness of the pore surfaces. The largest values of Dswere found for the interval of the smallest radii (0.004 p m to approximately 0.06 Fm). Degradation processes that were simulated by Pachepsky et al. (1995) were loss of organic matter, intensive cyclic wetting-drying, and movement of silica acid solution. Increases in values of D bfollowing simulated degradation reflect the loss of bonds between particles (Pachepsky et ul., 1995). This work, along with that of Bartoli et al. (1991), Bartoli e f al. (1992a,b), and Bartoli et al. (1993), strongly suggests that D 5has a role to play in any complete description of aggregate form and structure. Ah1 and Niemeyer (1989) used data from water retention curves to obtain the fractal dimension of the pore volume in the same way that it is estimated using mercury porosimetry. In this case, however, the fractal dimension was referred to as the bulk fractal dimension (or mass fractal dimension), and values in the range 2.1-2.8 were found. In Friesen and Mikula (1987) equations that only differ in the fact that the surface fractal dimension replaces the bulk fractal dimension are discussed. It is stressed by Friesen and Mikula (1987) that this does not imply that the two are equal.

Iv. SOIL FRAGMENTATION Power-law distributions of soil fragmentation data (either as an aggregate- or particle-size distribution) have been assigned a fractal dimension (e.g., Eghball et at., 1993; Perfect and Kay, 199 I ; Perfect et ul., 1992; Rasiah e t a / . , 1992; Rieu and

30

ALISON N. ANDERSON ETAL.

Sposito, 1991b). Although the fractal dimension of soil fragmentation data has been referred to as a number of different dimensions, it will be named the fragmentation fractal dimension (of) here, and it is suggested that this name be adopted in the future to avoid confusion with the other fractal dimensions. The fragmentation fractal dimension has been subject to a number of criticisms in the past (Anderson and McBratney, 1995; Crawford et al., 1993b; McBratney, 1993). The calculation, applications, and limitations of the fragmentation fractal dimension will be discussed here.

A. STUDIESOF THEFRAGMENTATION FRACTAL DIMENSION OF SOIL Tables 111 and IV contain summaries of papers in soil science journals that have calculated the fragmentation fractal dimension from either particle- or aggregatesize distributions. These tables (adapted from Anderson and McBratney, 1995) Table 111 Summary of Papers Reporting Estimates of D , Calculated from Particle-Size Distribution Data Paper

Data type

Tyler and Wheatcraft, 1989

Particle-size distribution (PSD) data, scale-invariant particle density (p,,) assumed PSD data, scale-invariant pprn assumed PSD data, scale-invariant p,, assumed for number-based approach

Bartoli e r n / . . 1991 Tyler and Wheatcraft, l992a Wu et of., 1993 Barak rt u/., I996

Kozak et a/., I996

PSD data, scale-invariant ppa, assumed Sand PSD data, grains counted and measured using image analysis PSD and aggregatesize distribution data, scaleinvariant p“ f B assumed

Constraints on D,

D,value

D,> 3 shows clear

D,= 2.7-3.485

D,. >3

D, = 2.69-3.26

fractal behavior

0 < D,. <3

Dtlnunlher, = 3.01 1-3.499 D1l,na,,l= 2.06-2.839

No constraints

D,. = 2.8 between lower and upper cutoffs

Not stated

Not fractal; has lognormal distribution

D, < 3

DIILJIC, = 2.68-3.42

D,,,,,,, = 2.76-3.19 ,)

Table IV Summary of Papers Reporting Estimates of D, Calculated from Aggregate-Size Distribution Data Paper

Constraints on D,

Data type

D, value

Perfect and Kay, Aggregate-size 1991 distribution (ASD) data, scaleinvariant aggregate density (pa,g) assumed

No constraints

Rieu and Sposito, ASD data, pagP measured for 1991b each size class Rasiah et d.. ASD data, scaleI992 invariant pa, assumed Perfect et 01.. ASD data, scaleI992 invariant pa,, assumed for D ,,,,,~,,, Perfect el of., ASD data, scale199% invariant p€', assumed Rasiah rr al.., ASD data, scale1993 invariant p'W for

D, 5 3

D, = 2.58-2.91

No constraints

DfIrapld = 3.175-3.557 Dfl,lowwet) = 1.8163.807

No conqtraints

D,(", 0.67-3.92 Dflm,= 0.794.06

0 < D, < 3, otherwise D, = 1.46-5.15 multifractal D, < 3 for D,,,,

and DflT)' and scale-variant pa, and probability of failure ( P ) for

Eghball er d., I993

DflRJ

ASD data, no assumptions for p,,, necessary ASD data,, ,p measured for each size class

2.51-2.86 2.98-3.52

=

Dflnfter wet ,,cr,ng, =

DI(mJ

Perfect et ol., I993b

Dfthet,,re wet \,r"!ngl

Dtlm,= 2.32-3.28 DIlT, = 2.46-2.84 Dtl,, = 2.02-3.19

D, 5 3 if P scale D,= 3.03 invariant, D, > 3 Dl,,ncremen,a,, = 2.53-3.46 if P scale variant (multifractal) D, = 2.281-3.306 D, < 3 (from Rieu and Sposito, 1991a)

0

5

5 D, 5

Perfect et al., I994

ASD data, scaleinvariant pa,, assumed

0

Rasiah era!., 1995

ASD data, scaleinvariant,,p, assumed

D r > 3if

Logsdon et a1 I996

ASD scaleinvariant p assumed

3 (from Turcotte, 1986)

fragmentation process exhibits multifractal behavior Not stated

D,,, = 1.179-2.803 D,(,,,) = 1.818-2.663 D,.ld,= 1.878-2.775 D,,, = 1.383-3.088 D,,n,, = 1.307-3.06 I Also recalculated D, for data in Perfect et al, ( 1992) and Rasiah et al., (1992,1993) DfIcoun,, = 2.44 and 2.41 D,.(calc, = 2.5 I and 2.03

32

ALISON N. ANDERSON E T A .

have information regarding how the aggregate- or particle-size distribution was generated, constraints on D,, and values of 0, found. For the first cases of the fragmentation fractal dimension being reported, D, was calculated using the simple method described previously in Sec. 1I.H. As the problems associated with the assumptions made to calculate D, became apparent, soil scientists began experimenting with other methods in order to obtain a more reliable estimate of D,. The first to take into account the variation in bulk density with aggregate size was Rieu and Sposito ( 199 1b). The only others to do this were Eghball et al. ( 1993a). The importance of accounting for scale-variant bulk density has been the subject of much discussion. With scale-variant bulk density taken into account Rieu and Sposito (1991b) obtained values of D, < 3, and Eghball et al. (1993a) obtained values of D, < 3 for a11 but one aggregate number-size distribution. Rieu and Sposito (199 1a) were the first to show mathematically that D, 5 3 for fractal soil structures. This contradicted the notion by Tyler and Wheatcraft (1989) and Bartoli et al. (1991) that D, > 3 if the fragmentation process shows clear fractal behavior. Tyler and Wheatcraft ( 1 992a) reported that their earlier findings of D, > 3 were an artifact of the plotting algorithms and assumptions on grain density and size. They developed a method to estimate D, from the cumulative mass distribution because it is less sensitive to the assumed grain density and characteristic size (D,(,,,ass) in Table 111). This is compared to the traditional cumulative number approach (Df(number) in Table 111). D , was also constrained to 0 < D, < 3. Values of D , estimated using the number-based approach were predominately greater than 3, whereas all values of D,estimated using the mass-based approach were less than 3; this led Tyler and Wheatcraft (1992a) to conclude that the latter method was more appropriate for the estimation of D, for field soils. Contrary to the findings of Tyler and Wheatcraft (1992a) were the findings reported by Perfect ef al. (1992). The value of D, estimated from a number-size distribution (calculated from a mass-size distribution assuming scale-invariant shape and aggregate density) was compared with that estimated from a number-size distribution determined by actual counting of the aggregates to determine whether scale-invariant shape and density is a valid assumption. These two estimates of D, are called D,(,,) and D,(m), respectively, in Table IV. A 1: 1 linear relationship was obtained between the two and so they were able to conclude that the assumptions mentioned were valid. Perfect et al. ( 1992) did have a number of values of D, > 3, and this implies that some of their fragmentation processes are not fractal (McBratney, 1993). A number of different methods to estimate D, were compared by Rasiah ef al. (1993). They developed a new mass-size-based model that does not assume that bulk density and probabihty of failure ( P ) are scale invariant for the estimation of D , (D,,, in Table IV). They also estimated D,(D,,,, and D,,,,) using the methods of Perfect er al. (1992) and Df0,, which is the mass-based model of Tyler and

APPLICATIONS OF FRACTALS T O SOIL STUDIES

33

Wheatcraft ( 1992a). Significant 1 : 1 linear relationships existed between DfCn) and Df(m)and between D,,, and D,,,.Although D,(,, did not have a 1:l linear correlation with the other values of D, it is the only method of estimating D, that produced all estimates of D , to be less than 3. These values are consistent with those reported by Tyler and Wheatcraft ( 1992a), and despite the fact that they agree with the constraints placed on D, earlier, Rasiah et al. (1993) has concluded that D,,, is inferior to the method that estimated D,,,. This can be justified by the fact that D4,) values are statistically similar to Df(,,,)values, which are statistically similar to values of Df(n,,estimated from actual counting of aggregates. It is questionable which is the best estimate of D, since each estimate has its advantages. Amodified number-based method for estimating D, was proposed by Kozak ef al. ( 1 996) because of the inconsistencies involved in the general method of obtaining a number-size relationship from mass-size data. Three inconsistencies were identified by Kozak et al. ( 1996).The inconsistencies involved the characteristic grain size, arithmetic averaging (of a size class), and the fact that the number of grains of sizes greater than the upper size limit of the first fraction is equal to zero. Kozak et al. (1996) assumed that scale-invariant fragmentation was a valid model of particle-size distributions within size ranges of fractions and so derived a formula expressing the characteristic grain size as a function of the fractal dimension and limits of the grain size range. The modified equation gave smaller values of D, (Df(mod) in Table 111) than the original method of calculating D , (Df(ca,c, in Table 111). Kozak et al. (1996) also applied the modified equation to 2600 particle-size distributions, and in 80% of the samples fractal scaling was not applicable across the whole range of particle sizes, but only 1.5% of the samples had values of D, exceeding 3. They concluded that models more sophisticated than scale invariant fragmentation may be required to satisfactorily describe soil particle-size distributions. Of note is the feelings of Turcotte ( 1992), who stated that we should accept the physical view rather than the mathematical view. For distributions that had a value of D, > 3, Turcotte (1992) considered them fractal because they are not precluded physically even though their values of D , lie outside the geometrically allowed range. Geometric fractals, such as D,,,, are constrained by their embedding dimensions, but no such restriction applies to the exponent D, in Eq. (5) (Baveye and Boast, 1996). The Pareto distribution has applications in areas such as the size of insurance claims, and in cases such as this we cannot expect the exponent to be constrained to a particular range for geometrical or physical reasons (Baveye and Boast, 1997). However, the value of D , does not have a geometrical interpretation in these circumstances. In other cases values of D, > 3 may be an artifact of measurement error, estimation model, and their underlying assumptions. Values of D, > 3 are theoretically possible and are explained by Perfect et al. ( 1 993a) using the concept of a scale-dependent value of D,. For mass-conserving cubic fragmentation D , can be related to { P ( x , ) ) u (the probability of failure of a

34

ALISON N. ANDERSON ETAL.

dry aggregate of normalized equivalent cubic length x, for a specified stress, u)by assuming scale dependency in D,. Perfect et al. (1993a) found that a number-size distribution determined by visual counting gave a spectrum of values of D, as predicted by the theory. The log-log plot of the cumulative number-size distribution produced a line with a slope that gave a value of D, equal to 3.03 + / - 0.09. The line showed deviations from linearity, and estimates of the incremental values of D, ranged from 2.53 to 3.46, suggesting that the number-size distribution cannot be fully characterized by a single value of D,. Perfect et al. (1993a) called this behavior “multifractal”; however, Perfect and Kay ( 1995a) propose that the behavior be called “scale-dependent fragmentation” so as to avoid confusion with the correct definition of multifractal. Estimations of D,may be biased using the linear method of estimating D,. Perfect et al. ( 1994) and Rasiah et al. ( 1995) compared values of D, estimated using linear and nonlinear techniques and Df(nl)in Table IV). Additionally, Perfect et al. (1994) estimated D, with the linear method using data after the fourth stage of fragmentation only (D,,,, in Table IV). This was done because fractal models indicate deviations from ideal behavior during the initial stages of fragmentation. An unbiased estimate of D, was considered to be given by D,,, by Perfect et al. (1994). Perfect et at. (1994) obtained a 1: 1 linear relationship between D,,, and Dt(”,),whereas values of Df(l,were biased toward data from the early stages of fragmentation. In contrast, estimates of Df(,) and Df(”,)are weighted toward the smallest size fractions, where linearity on log-log plots is most pronounced (Perfect et al., 1994). Rasiah et al. (1995) found the nonlinear method of estimating D, to be more appropriate. Criteria used to come to this conclusion included RZ values of the fits (marginally higher for the nonlinear fit) and the standard errors of the estimates (less for Dfcnl,). Errors associated with the nonlinear fitting procedure showed random distribution, but those associated with the linear fitting procedure did not, being biased toward the early stages of fragmentation, as observed by Perfect et al. (1994). Rasiah et al. (1995) postulate that values of D, > 3 reported in earlier papers can be attributed at least partially to the linear fitting procedure and prove this by recalculating some values of D, using the nonlinear fitting procedure. The assumptions of scale invariance of several factors when calculating number-size distributions from mass-size data tempted Logsdon et ul. (1996) to question these assumptions. Aggregate density, shape, and relative diameter are often treated as scale invariant. Logsdon et al. (1996) combined these into a single unknown factor, GI, to test the assumption of scale invariant GI and also compared values of D, from calculated cumulative number of aggregates (Df(calc, in Table IV) and counted cumulative number of aggregates (Df(count, in Table IV). The value of Df(calc) was significantly larger than Df(count), values for which were 2.5 1 and 2.44, respectively, for one data set but significantly smaller for the other, with values being 2.03 and 2.4 I , respectively. The two different estimates of D , were sig-

APPLICATIONS OF FRACTALS T O SOIL STUDIES

35

nificantly correlated however. Logsdon et al. (1996) found the Gi factor to be scale variant, but which subcomponent(s) was scale variant could not be determined. Barak et al. (1996), in contrast to what has been reported in this chapter, found that the number-size distribution of sand grains (individually counted) was better described as having a lognormal distribution than a fractal one. The plots used to estimate D, showed marked nonlinearity, and a single estimate of D, was not possible. Perhaps estimates of incremental values of D, (as calculated in Perfect er al., 1993a) would have been relevant here, but it must be taken into account that the whole particle-size distribution has not been used in the studies of Barak et al. (1996). A lognormal distribution as favored by Barak et al. ( I 996) should perhaps be used in place of a fractal distribution when the plot used to estimate D, is nonlinear. It is rather subjective as to whether a fractal description of the data is appropriate or not, particularly when the plot used to estimate D, is linear for small sections of data. Earlier work by Russell (1976) showed the superiority of the incomplete gamma distributions over the Weibull and lognormal distributions for the coarse fraction of granitic soil. Barndorff-Nielsen (1977) and Barndorff-Nielsen et al. (1982) showed that the log-hyperbolic function best fitted the size distribution of aeolian sand. The range of scales over which D, was determined varied between studies, and of course the scales at which D, of aggregate-size distributions was estimated were larger than that for D, of particle-size distributions. Table V lists the range of sizes over which D, was estimated for the papers reported in Tables I11 and IV. Because soil is a natural fractal, the values of D, found will only apply for a limited range of sizes, and it is not possible to accurately predict D, for scales for which D, has not been measured. This is highlighted by the scale-dependent nature of D, observed by Perfect et al. (1993a). Wu et a / . (1993) used an array of methods to estimate D, depending on the size of the soil particles. These methods included sieving, sedimentation, static, and dynamic light scattering and electron microscopy. This enabled Wu et al. ( I 993) to estimate D, over more than six orders of magnitude in length scale, and they found that the power law usually held over two to five decades of length scale. Barak et al. (1996) reported that the number of size fractions used for fractal analysis has been in most cases small (< 10) and that Jelinek (1970) felt that five size fractions is the absolute minimum of size classes that should be used for drawing a cumulative particle distribution function.

B. PROBABILITY OF FAILURE OF AGGREGATES Although it has been discussed in Sec.1I.D that the fragmentation mechanism is scale invariant for true fractal behavior, there is evidence to suggest that this is not the case for soil aggregates (e.g., Perfect and Kay, 1991; Rasiah er al., 1992; Perfect et al., 1993a). The renormalization group approach to fragmentation (Tur-

36

ALISON N. ANDERSON ETAL. Table V Range of Scales over Which D, Has Been Estimated Paper ~

~

Range (diameter)" ~~

Tyler and Wheatcraft, 1989 Perfect and Kay, 199 I Bartoli et a/., 1991 Rieu and Sposito, 1991b Rasiah et a[., 1992 Perfect et al., 1992 Tyler and Wheatcraft, 1992 Wu et al., I993 Perfect er al.. I993a Rasiah et ul., I993 Perfect er al., 1993b Eghball et ul., 1993 Perfect et a/., 1994 Rasiah et nl., 1995 Barak e f nl., 1996 Kozak et a/., I996 Logsdon et af., 1996

2.00-2000 p.m 0.25-8.00 mm 0.02-2000 p.m 0.03-7.00 mm 0.25-10 mm 0.50-32 mm 0.001-10 mm 40 nm-I0 cm 0.50-32 mm 0.25-8 mm 0.50-3 1.5 mm 0.074-16 mm 0.50-3 I .5 mm 0.25-8 mm Sand > SO p m 0.002-1 mm 0.5-32 mm

"In some cases the ranges are approximate because values have been estimated from log-log plots. Some number-size distributions were not calculated over the full range reported but within the range reported because different size ranges were used for different data sets. When sieve sizes were stated, the range is considered to lie between the smallest-sized and the largest-sized opening cieve.

cotte, 1986) assumes that the probability that a cube will fragment into eight smaller cubes is the same at all orders. The fragmentation mechanism or probability of failure ( P ) will be scale invariant for a mathematical fractal, but for objects in nature it is more likely that P will be scale variant or will only be constant over increments of range. The probability of failure of an aggregate ( P ) tends to increase with aggregate size, and the rate of change of P with aggregate size is not constant (Perfect and Kay, 1991; Rasiah et al., 1992). As well as P varying with aggregate size, it may also vary with cropping treatment and soil type; this was observed by Rasiah et al. (1993). Rasiah et al. (1993) point out that much greater variation in P is encountered for different sized aggregates than for bulk density, highlighting the need to

APPLICATIONS OF FRACTALS TO SOIL STUDIES

37

measure probabilities of failure in fragmentation experiments. Perfect et a/. ( 1993a) describe both fractal cubic fragmentation (scale invariant P is assumed and 0 5 D, 5 3) and multifractal cubic fragmentation that allows for scale-dependent P and therefore a scale-dependent value of D, that may be greater than 3. If scale-invariant P is assumed, the number-size distribution is characterized with a single value of D,, but for the situation of scale-dependent F: it is characterized by a spectrum of D,values (Perfect er al., 1993a). The occurrence of scale-dependent P brings us to question whether all the problems associated with estimating practical values of D, are a result of the assumption of scale-invariant aggregate bulk density and shape. Some of the errors associated with estimating D,can be attributed to the assumption of scale-independent l? If the log-log plot used to estimate D, does not produce a line with a single gradient, we have the case of scale-dependent values of D , as observed by Perfect et al. (1993a) and scale-dependent values of F! Scale-dependent D , highlights that soil aggregate fragmentation does not exhibit ideal fractal behavior, and we must take into account the scales over which D , is constant and any changeover or cutoff scales. Wu et al. (1993) observed the lower and upper cutoffs to true fractal behavior, and in between these cutoffs the number-size distribution was characterized by a single value of D,. The utility of these measures remains questionable.

C. SHORTCOM~VGS OF THE FRAGMENTATION FRACTAL DIMENSION The main factor that limits the number of applications that the fragmentation fractal dimension (D,) has is that D, is estimated from a distribution of aggregates or particles that bear no resemblance to the original soil matrix. A limited amount of information regarding the original soil structure can be obtained from D,. Even for individual fragments, D, only characterizes their geometry in a very crude way (Baveye and Boast, 1997). Kaye (1989a) emphasized that D , is purely a measure of the size distribution of a set of fragments. The fragmentation fractal dimension is not related to the surface fractal dimension (D<)of individual fragments or aggregates or to the mass fractal dimension (D,) of aggregates or of the parent structure (Crawford et a/., 1993b). In fact, when the bulk density of aggregates is treated as scale invariant, aggregates are not being treated as mass fractals at all; instead, they are regarded as homogeneous. Crawford et al. ( 1993b) state that the major limitation of D, is that it is difficult to relate a size distribution to physical processes in the soil. Physical processes need to be related to fractal dimensions that have some relationship with the soil structure as it is observed in the field, Dm for example. Tyler and Wheatcraft (1989) implied a relationship between D, and pore-wall roughness (which would be char-

38

AJJSON N. ANDERSON ETAL.

acterized by D,). Perfect and Kay (199 1 ) also implied a relationship between D , and surface roughness. They interpreted D , as the fractal dimension for a threedimensional system or Dm. Crawford et af. (1993b) discusses cases where D, has been misinterpreted as Dm and show how D, cannot be equal to Dm. Later, Tyler and Wheatcraft ( 1992) retracted the ideas related in their earlier work, realizing that it would be unlikely that information regarding the structure of pores can be obtained solely from particle-size distribution data. Only if the soil matrix has a fractal boundary can the index of the power-law number-size distribution provide information regarding the soil structure and be related to Ds(Crawford et af., 1993b). Crawford et al. (1993b) point out that this will only be true if the fragmentation process results in complete fragmentation or if the probability of fragmentation is independent of scale. It has been discussed earlier how it is unlikely that the probability of failure will be independent of scale. Complete fragmentation is a question that needs to be addressed in regards to the fragmentation studies mentioned in Sec. 1V.A. Errors in the calculation of D, may come about from incomplete fragmentation of soil aggregates. The utility of such measures of D, is questionable because it cannot be uniquely related to a property of the soil since its value is an unknowable convolution of the many factors involved in the fragmentation process.

D. APPLICATIONS OF THE FRAGMENTATION FRACTAL DIMENSION Predominantly, the fragmentation fractal dimension (D,) has been used as an indicator of fragmentation caused by different tillage operations or as a result of different cropping strategies (Eghball et af., 1993a; Perfect and Kay, 1991; Perfect et af., 1993b, 1994; Rasiah et ul., 1992, 1993, 1995). For example, Eghball e f al. (1993a) used D, to quantify soil fragmentation under various crop sequence and tillage treatments. Large values of D, represented a highly fragmented soil with a dominance of small aggregates. They collected values of D, for the different treatments during one growing season and into the following growing season. This allowed for differences to be seen between treatments and also within treatments with time, and significant differences between values of D, were found. Eghball et al. (1993a) were able to use D, as an indicator of temporal deterioration of aggregation (an increase in D,), differences in tillage operation as to how they fragment and mix the soil, differences in crop effects (soybean appeared to have a negative effect on the formation of large aggregates in no-till), and of the formation of larger aggregates during the growing season. The formation of larger aggregates is attributed to increased biological activity and the subsequent production of metabolic by-products and to living plant roots by Eghball et af. (1993a). They also no-

APPLICATIONS OF FRACTALS TO SOIL STUDIES

39

ticed a difference between a wet and a dry season, with more aggregation in the wet season. This is perhaps a result of increased microorganism activity. The effect of cropping and tillage treatments on D, has been equally noted by others. Tillage implements affected the aggregate-size distribution (Perfect et al., 1993b; Perfect et al., 1994; Rasiah et al., 1995), and D, can evaluate tillage implement performance and can be used in studies comparing seed-bed conditions across a range of soil types (Perfect et al., 1993b). D, is strongly influenced by cropping treatments (Perfect and Kay, 1991; Rasiah efal., 1992, 1993, 1995), and in particular forage treatments are characterized by lower values of D , (Rasiah et al., 1992). Perfect and Kay (199 1) noticed, however, as indicated by values of D,, that the structural benefits of long-term forages may be lost within a matter of months after ploughing and seeding with corn. Tillage and cropping systems, along with soil properties, accounted for over 80% of the variability in D, (Rasiah et al., 1995). Different values of D,can be explained to some extent by soil properties. Intuitively, we would expect soil properties that have a role in soil aggregation to also affect values of D , since it is a measure of fragmentation. We would presume that soil properties that enhance aggregate stability would give rise to aggregate-size distributions with smaller values of D, because of the dominance of larger aggregates. In clayey soil types a much larger proportion of smaller particles are found that would give rise to larger values of D,. A relationship between soil texture and D, is apparent in Bartoli et al. ( 1 991), Rieu and Sposito (199 lb), Tyler and Wheatcraft (1992), and Wu et al. (1993). With the exception of Rieu and Sposito (1991b) these papers all dealt with particle-size distributions. Finer textured soil types give rise to particle-size distributions with larger values of D,. Tyler and Wheatcraft (1992) showed, using their mass-based equation and choosing various values of D,, the dependence of texture on D,. They also showed on soil textural triangles the loci of soil types whose mass distribution follows their mass-based equation exactly. From the textural triangles it is apparent that the particle-size distributions of some soil types are not fractal because their particle-size distributions are not self-similar as described by their massbased equation. The loci of soil types showing fractal scaling in particle-size distribution changes when the largest grain radius is changed. Using these textural triangles Tyler and Wheatcraft (1 992) also showed that even if several soil types have similar values of D, (estimated from log-transformed particle-size distribution data), they will not all fit the scale-invariance requirement and suggested poor sensitivity relating D, to soil texture. These results indicate that even though we can describe aggregate- and particle-size distributions using a simple power-law relationship, it does not necessarily imply a fractal distribution. In contrast to what was found in the particle-size distribution studies and in the aggregate-size distribution study of Rieu and Sposito (1991b), D, was found to de-

40

ALISON N. ANDERSON ET AL.

crease with increasing clay content in the aggregate-size distribution studies of Perfect et al. (1993b), Rasiah et al. (1993), and Rasiah et al. (1995). They found that clay content was the most important inherent soil property influencing D,. Smaller values of D, were found with increases in clay content because of cloddiness (Perfect et al., 1993b) and because clay cements soil particles together, which increases aggregate strength and reduces the number of failure zones (Rasiah er al., 1993). Increases in organic matter content (which increases aggregate binding) also resulted in smaller values of D, (Rasiah et al., 1993, 1995). Perfect et al. ( 1993b) found organic matter content and wet aggregate stability to both influence cloddiness, which in turn would effect values of D,. Both clay and organic matter content tend to increase the proportion of large aggregates found in aggregate-size distributions. The apparent contradiction between effects on values of D, calculated from either a particle- or aggregate-size distribution is a consequence of the different type of measurement being made. In particle-size distributions the particles are completely fragmented and individual particles are being examined. Obviously more small (clay) particles will be present than large (sand particles). With aggregatesize distributions we are not examining completely fragmented soil but only the degree of aggregate fragmentation that results from the input of energy (e.g., tillage and sieving). Inherent soil properties such as clay and organic matter content have a negative effect on fragmentation as they hold the soil together. The differing results of Rieu and Sposito (199 1b) compared with those from the other aggregatesize distributions may be a consequence of the fact that the smallest aggregates of Rieu and Sposito (1991 b) were smaller than those reported in the other aggregatesize distribution studies. The upper class size in Rieu and Sposito (1991b) was similar to the upper class size in Rasiah et al. (1 993, 1995) but much smaller than that for Perfect et al. (1993b). The value of D, has been used in the prediction of soil physical properties such as the soil water retention function. Tyler and Wheatcraft (1989) used fractal mathematics to show that a (the fitting parameter ofthe Arya and Paris [1981] soil water retention model) is equal to the fractal dimension of the pore trace. They calculated the fractal dimension of the pore trace using the fractal increment ( D J , calculated from their values of D,. The fractal increment is the difference between a fractal dimension and the topological dimension. So, for an irregular line with a value of DF= 1.23 and a topological dimension of 1, D jwill be equal to 0.23. The problem of relating D, to soil physical properties was discussed in Sec. 1V.C. Because the values of D, derived by Tyler and Wheatcraft (1989) are all above 3 and therefore unrealistic, the deduction of dimensions for the pore networks from the results is perplexing (Young and Crawford, 1991). Despite the fact that Tyler and Wheatcraft (1989) were able to estimate soil water retention data quite well from particle-size distributions, Tyler and Wheatcraft (1992) state that particle-size dis-

APPLICATIONS OF FRACTALS TO SOIL STUDIES

41

tribution data alone are not sufficient to fully characterize the fractal scaling that plays an important role in soil water retention and porosity. Rieu and Sposito (199 la,b) made a clear distinction between D, (the fractal dimension of a completely fragmented porous medium) and the bulk fractal dimension (the mass fractal dimension, D,ll), which is the fractal dimension for an incompletely fragmented porous medium. The latter represents the soil in the field rather than individual aggregates as would be collected for aggregate-size analysis. Rieu and Sposito (1991a) reported that at a given water content the corresponding hydraulic conductivity for an incompletely fragmented fractal porous medium could be expressed in terms of a similarity ratio and the two fractal dimensions, D, and Dm. The moisture characteristic for the medium was also found to be able to be expressed in terms of these parameters. Limited testing of the model of soil water properties reported by Rieu and Sposito (199 1b) suggested that soil aggregates are fractal and that the pore space in natural soils may exhibit structure characteristics of an incompletely fragmented fractal medium. However, Crawford (1994) discussed the fact that there is a fundamental error in the calculation of conductivity in Rieu and Sposito (1991a). Crawford (1994) states that Rieu and Sposito (1991a) have not recognized the critical role of porosity because they have made simplifying assumptions relating to connectivity. In order to verify whether the estimation of hydraulic conductivity ( K ) from aggregate-size distributions can be successfully done, Logsdon (1995) backcalculated fractal dimensions and aggregate densities (scale-invariant, p , and scalevariant, p,) from hydraulic conductivity. Fractal dimensions calculated were D, and Dm (or bulk fractal dimension) as described by Rieu and Sposito (1991a,b). Backcalculated values were compared with measured values calculated from aggregatesize distributions taken from close to where K was measured. Both back-calculated p and pi were significantly less than measured p. The values of back-calculated D, were greater than 3, whereas values of Dm were less than 3 despite the fact that Dm should be larger than D, (Rieu and Sposito, 1991a). The mean value of backcalculated Dmwas larger than the actual measured mean value of D, however. Both back-calculated D, and Dill were significantly different from measured D,. Despite having possible explanations for these inconsistencies, Logsdon (1995) concluded that further study is required to determine the relationship between aggregate-size distributions and K, if there is any, and also to determine whether fractal geometry can be reliably used to provide an accurate description of aggregate-size distributions. In summary it appears that the fragmentation fractal dimension has applications in quantifying aggregate-size distributions caused by tillage operations and crop effects on aggregate-size distributions but has extremely limited application in the prediction of soil physical properties. This is because D, is a descriptor of aggregate- or particle-size distributions produced by fragmentation processes only and not of the structure of soil as it occurs in the field.

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ALISON N. ANDERSON ETAL.

V. APPLICATIONS OF FRACTAL MODELS IN THE PREDICTION OF SOIL PHYSICAL PROCESSES The prospect of using fractal models in the prediction of soil physical processes is promising due to the very nature of fractals. First, physical processes occurring within fractal entities will be restricted to the fractal networks occurring within those entities, and they cannot be likened to physical processes occurring in free space. Evidence presented in this chapter suggests that in many cases soil can be adequately described as a fractal, and for soil types that are fractal this should be taken into account when predicting and describing physical processes occurring within them. Second, as fractals have no characteristic scale and because soil physical processes occur across many orders of magnitude in length scale, a fractal description of soil may be particularly appropriate.

A. GASDIFFUSION Applications of fractal theory to gas diffusion through the soil have been reported by Crawford et al. (1993a) and Anderson et al. (1996). The case of diffusion through a fractal network was addressed by Orbach (1986). A particle moving through a fractal network is restrained to that network, and for diffusion through a fractal network the conventional diffusion coefficient (D)is replaced by a length-dependent diffusion coefficient: D(r)

rPB

(14)

where D(r) is the (Euclidean) length-dependent diffusion constant, r is the Pythagorean length, and 8 is a constant that equals zero in the Euclidean limit. Crawford et al. (1993a) show that 0 = 2(D, - d)/d,and values of 8 for pore networks have been reported to fall in the range 0.219-1.29 (Crawford et al., 1993a, Anderson er al., 1996). Orbach (1986) showed that r 2 ( t )= t d / D m

(15)

where ?(r) is the mean square distance traveled in time, t. The exponent &D, is equal to unity for diffusion in Euclidean space but is less than 1 for diffusion through a fractal network. While d solely determines the number of distinct sites visited in time t (Eq. [lo]), the mean square distance traveled in the same time t depends on the ratio d D , (Orbach, 1986).A graph showing how a fractal network can impede ? ( f ) is shown in Fig. 8. Associating Dm with the heterogeneity of the pore network and d with the tortuosity or the degree to which the network impedes the progress of a diffusing par-

APPLICATIONS OF FRACTALS TO SOIL STUDIES

43

1

T W

0.0

2.5

5.0

7.5

10.0

Time ( t ) Figure 8 Mean-square displacement as a function of time for three values of dD,; 0.85, and ( c )0.70.

( a ) 1 .00, ( b )

ticle in a given direction, Crawford et al. (1993a) was able to relate measurements of D, and d to diffusion through the soil. For soil, diffusing particles move out from their origin more slowly than under the assumption of classical (Brownian) flow because d 5 D, (Crawford et al., 1993a). For two contrasting structures Crawford et al. (1993a) reported dD, to be equal to 0.6 1 (d = 1.04 and Dm = 1.71) and 0.69 (d = 1.33 and D, = 1.94). Despite values of d and D m being considerably larger for one of the soil images, the values of dD, are comparable. For a range of structures Anderson er al. (1996) also found this to be the case, and in some instances a structure with larger values of both d and Dm than another structure had a smaller value of &D,,,. Crawford et af. (1993a) explain this apparent contradiction by considering the separate and competing effects of heterogeneity and tortuosity on the rate of progression of diffusing particles. Tortuosity is easily understood to retard the forward movement of particles, whereas heterogeneity has the effect of reducing the effect of dilution of flow as particles move outward from their origin. Despite this it would be possible for particles to travel greater

44

ALISON N. ANDERSON ETAL.

overall distances, regardless oft, in fractal networks with large values of d and Dm (in these cases representing large continuous pores).

B. WATERRETENTION CURVE

AND

HYDRAULIC CONDUCTMTY

Applying fractal theory to soil water properties has received considerable attention and began with studies by Toledo et al. (1990) and Tyler and Wheatcraft (1990) on hydraulic conductivity at low water content (0) and the soil water retention curve. Earlier, Tyler and Wheatcraft (1989) used fractal geometry in conjunction with the Arya and Paris (1981) soil retention model, and this application was discussed in See. IV.D, as was the fractal application to soil water properties by Rieu and Sposito (1991a,b). Matric potential (T) and hydraulic conductivity ( K ) often obey power laws in 0, the exponents of which are largely empirical. Toledo et al. (1990) used theories of fractal geometry and of thin-film physics to provide a basis for the observed power-law behavior of T and K. They obtained the relationships tI x 0- I l ( 3 - D ) (16)

where m is the exponent in the relation of disjoining pressure and film thickness. The fractal dimension D was interpreted to be the surface fractal dimension of the pore-solid interface. Toledo el al. ( 1990)used Eq. (16) to determine D for the poresolid interface at low 0 and obtained values in the range 2.1 < D < 2.7 across length scales between 5 and 20 pm. Both Toledo et at. (1990) and Anderson and McBratney (1995) were able to achieve a theoretical basis forb in the relationship ZI’ V bproposed by Brooks and Corey (1964). Anderson and McBratney ( 1995) showed that the saturated moisture content (0,) is proportional to r@mPde),where Dn, is the mass fractal dimension, d, is the embedding dimension, and r is the radius of an aggregate. The saturated moisture content corresponds to zero water potential (T).It is expected that the moisture content (8) will vary with T ( ” m P d c ) , and Crawford (1994) has shown this theoretically. By equating Crawford’s relationship 0 o( q ( & - d e )

(18)

with Campbell’s (1985) version of the Brooks-Corey water retention function 0

q(-l/b)

(19)

it is suggested that Dm = d, - l/b. This is mathematically identical to the relationship obtained by Toledo et al. (1990) where b = 1/(3 - D ) except that the frac-

APPLICATIONS OF FRACTALS TO SOIL STUDIES

45

tal dimension is the mass fractal dimension here, and Toledo et af. (1990) interpreted D to be the surface fractal dimension. The value of d, is equal to 3 for a soil aggregate. Campbell (1985) suggests that b ranges from 2 for coarse sand to 24 for clay. This gives values of D,,, in the range 2.50-2.96, which is in accord with values of Dm reported in Anderson and McBratney (1995). Toledo et al. (1990) was also able to interpret the value of the empirical exponent, b, in the relationship K 3~ OhK from Campbell (1974) as 3/m(3 - D ) , where m is the exponent in the relation of disjoining pressure and film thickness. The analogy between real soil and Sierpinski carpets made by Tyler and Wheatcraft ( 1990) indicates that the soil-pore size-number distribution may follow fractal scaling concepts. Tyler and Wheatcraft (1990) were able to evaluate the water retention properties of regular and random Sierpinski carpets and relate these properties to the Brooks and Corey water retention model, giving the empirical coefficient a physical meaning. They were also able to extend their results to hydraulic conductivity. Commenting on the work of Tyler and Wheatcraft (1990), Brakensiek and Rawls (1992) further verified the role and applications of the Sierpinski carpet for modeling soil water retention properties. This led to Tyler and Wheatcraft (1992b) further investigating the physical significance of the Sierpinski carpet parameters. A fractal description of macroporosity was given by Brakensiek et al, (1992). Based on the Sierpinski carpet analog of a porous medium (Tyler and Wheatcraft, 1990) they calculated D (which they termed the carpet dimension) from the pore number-size distribution, obtained from image analysis. Their D, obtained from macroporosity data, was able to be used in the calculation of areal porosity. Brakensiek e?al. (1992) suggest this as an alternative method to Poiseuille’s equation using hydraulic conductivity measured with an infiltrometer (Watson and Luxmoore, 1986) for calculating areal porosity. Shepard (1993) proposed a method for calculating the hydraulic conductivity of unsaturated soil from moisture retention data and a fractal description of soil water flow paths. Shepard (1993) based his fractal model on a triadic Koch curve and compared calculated hydraulic conductivities with those measured by Clapp and Hornberger (1978). Calculated hydraulic conductivities approximated measured hydraulic conductivities, the best approximation being for a sand. For the clay and loam the fractal model overpredicted the hydraulic conductivity at the wet end and underpredicted it at the dry end. Shepard (1993) concluded that retention curves do not contain all of the information required to calculate the conductivity functions. Modifying the Marshall ( 1958) saturated hydraulic conductivity equation using fractal properties of the Sierpinski carpet allowed Rawls et al. (1993) to develop equations for predicting the matrix and macropore saturated conductivity. Their prediction equations were developed by relating the number of pore size classes and maximum pore radius to soil properties. The D reported by Rawls et al. ( 1993)

46

ALISON N. ANDERSON ET AL.

was the fractal dimension of soil porosity as derived by Tyler and Wheatcraft (1990) and ranged from 1.42 for a sand to 1.87 for a clay. The modified Marshall equation provided reasonable estimates of both matrix and macropore saturated hydraulic conductivity for a wide range of differently textured soil types. However, D is not being measured independently, and it cannot be assumed that the fitted value is a fractal dimension, so it has to be questioned whether the relationship developed here is any more useful than a Campbell-type relation. In order to capture the characteristics of a heterogeneous soil structure with a tortuous pore space Crawford (1994) used a random fractal matrix comprising a hierarchical aggregation of primary structural elements. It was found that hydraulic conductivity depends on the heterogeneity as well as the degree of connectivity of the solid matrix. These are related to the mass fractal dimension and the spectral dimension of the solid matrix, respectively. Crawford (1994) showed that under simplifying assumptions, structure influences the water retention function through the mass fractal dimension of the solid matrix only. It was also shown that both the saturated and unsaturated conductivity show a power-law dependency on the length scale, L, of measurement at low fluid velocities, where Darcy’s equation is relevant and where the solid matrix may be approximated by a fractal. Crawford (1994) points out that other studies that have returned K(Y)expressions (Tyler and Wheatcraft, 1990; Rieu and Sposito, 1991a) have failed to fully account for the role of connectivity of the soil matrix. The Kozeny-Carman equation, rederived using fractal properties, was used to successfully predict field and laboratory measurements of saturated hydraulic conductivity by GimCnez et al. (1994). Incorporated into the new equation were the fractal dimensions of the pore volume ( D ) and pore-solid interface ( D J . mp Crawford er al. (1995) studied the precision with which moisture-release data may be related to soil structure. In an earlier work (Young and Crawford, 1991) they predicted that the fractal dimension, estimated from log-log plots of matric potential versus moisture content will be underestimated where pore necks are present. In Crawford et af. (1995) they were able to compare values of the fractal dimension estimated from moisture release data to values obtained from direct examination of the soil structure as observed in thin sections taken from cores that had previously had moisture-release data taken. For the soil samples in Crawford et al. (1995) it is the solid matrix that is shown to be best approximated by a fractal, and so values of the mass fractal dimension of the solid matrix, estimated from thin sections, are compared to fractal dimensions estimated from moisture-release data. Although the latter was always significantly less than or not significantly different to the former, the differences were less than 2%, suggesting that the effect of pore necks is negligible in this case. Crawford et at. (1995) state that the interpretation of moisture release data can be ambiguous because a power-law exponent is a consequence of either a fractal solid volume, a fractal pore wall, or a nonfractal, self-similar pore wall, and one cannot infer from the moisture-release data which is the case. They also stated that a power-law exponent can arise from a frac-

APPLICATIONS OF FRACTALS TO SOIL STUDIES

47

tal pore volume, but this is not true and arose from an error in a length scale argument in the paper. The results confirm that the mass fractal dimension of the solid matrix is a good predictor of the Brooks-Corey exponent, but that the solution to the inverse problem is non-unique in the sense that the soil structural characteristics cannot be unambiguously deduced from the Brooks-Corey exponent. Anew three-parameter fractal model for the water retention curve O(w),derived by Perfect er al. (1996), was found to be an improvement on existing models. The parameters of the Perfect et al. ( 1996) equation were D (defines the fractal nature of the void size distribution), 9(, (the air-entry value), and u’,(the tension draining the smallest pores), which correspond to the upper and lower scaling limits, respectively. The existing equations that were compared with the new equation contained only D and YC, and were from Ah1 and Niemeyer (1989) and Rieu and Sposito (1991a). It was shown, using goodness-of-fit statistics, that the new equation fitted experimental measurements better than the two existing fractal equations. Perfect et al. (1996) were able to use the new equation to estimate the three parameters for a range of materials from glass beads to undisturbed soil with textures ranging from a very fine sandy loam to a heavy clay.

C. OTHERAPPLICATIONS TO PHYSICAL PROCESSES Values of fractal dimensions, measured from methylene blue staining patterns (described in Sec. 1II.B and 1II.C) were related to Brenner numbers of chloride breakthrough, measured under saturated steady-state conditions in soil cores by Hatano et al. ( 1992). They obtained an empirical equation, explaining the Brenner number for chloride breakthrough as a function of depth averages of both the mass fractal dimension (D,J and surface fractal dimension ( D s )of the staining patterns and macroporosity. Hatano et al. (1992) concluded that miscible displacement is contributed to by macropore flow and that this contribution depends on both the internal structure of macropores (as highlighted by methylene blue) and on the smoothness of their walls. Values of D,, and Ds can be used to characterize miscible displacement in different soil samples, which will vary from soil to soil because of the variability in values of D,, and Dqfound for different soil samples. Using fractal dimensions of methylene blue staining patterns, Hatano and Booltink (1992) were able to predict bypass flow. An empirical equation relating the total amount of outflow to both upper and lower values of DmP3(obtained for upper and lower sections of soil cores) and to the volume fraction of stained parts was obtained. Higher values of DmP3in the upper half of the core (reflecting interpedal macropores developed around relatively small peds) and a lower value of DmP3(reflecting fragments of cracks) in the lower half resulted in larger amounts of outflow, allowing Hatano and Booltink (1992) to conclude that vertically continuous macropores play a significant role in bypass flow. A physico-morphological approach to the measurement and simulation of bypass flow was taken by

48

ALISON N. ANDERSON ET AL.

Booltink el al. (1993). Macropore geometry, as characterized by fractal dimensions (D,,3) of staining patterns, was the base of a pedotransfer function used to calculate the time of initial breakthrough at the bottom of soil cylinders. Booltink et al. (1993) used this pedotransfer function in a computer model that successfully simulated bypass flow in 15 larger soil columns. The application of fractal geometry to unstable interfaces. a phenomenon occurring between two fluids, was used to describe fingering structure and to estimate the effective surface tension at the wetting front during infiltration in soil by Chang et al. (1994). Fingering occurs in soil when water moves downwards through a two-layer soil, with the upper layer having a finer texture than the underlying layer (Hill and Parlange, 1972). Fingering of a wetting front is driven by gravity, unlike viscous fingering. However, surface tension stabilizes the fingering of the front in both (Chang et af., 1994). For the infiltration experiments camed out by Chang et al. (1994) the wetting front was almost linear for homogeneous media (coarse sand, fine sand, or clay). The geometry of wetting fronts at various stages of infiltration was described using the surface fractal dimension (estimated using the stride method). For the coarse sand underlying either fine sand or clay, unstable flow produced fingering. Wetting fronts had values of D, up to 1.419. The fractal characteristics of horizontal movement of water in soil were studied by Guerrini and Swartzendruber (1994). They were able to consider observed deviations from traditional concepts of soil-water movement in terms of fractals. The position of a given water content is directly proporrtional to tn, where t is time and n is an exponent found to be less than the traditional value of 0.50 for unsaturated soil. Guerrini and Swartzendruber (1 994) were able to make a connection between soil-water movement and a diffusion process defined in terms of a modified Brownian motion (a random, self-affine fractal) to account for the soil-water diffusivity function having auxiliary time dependence for unsaturated soil. They identified unsaturated horizontal soil-water absorption ( n < 0.50) as a macroscopic fractional Brownian motion (fBm), whereas saturated soil-water absorption ( n approaching 0.50) was identified as a macroscopic regular Brownian motion (rBm).

VI. OTHER APPLICATIONS OF FRACTAL GEOMETRY IN SOIL SCIENCE A. SOILFAUNADYNAMICS AND HABITAT SPACE The roughness of the soil-pore interface will undoubtedly have an effect on the space able to be utilized by soil fauna. This is particularly important for soil microbes and microarthropods. Smaller microbes will be able to utilize more space along a fractal interface than larger ones and we would therefore expect greater numbers of small individuals than large ones. As we inspect a fractal surface at

APPLICATIONS OF FRACTALS TO SOIL STUDIES

49

higher and higher magnifications the surface area increases because of nested irregularity. Morse et al. (1985) observed that the vegetation on which arthropods live is fractal, therefore providing smaller arthropods with much more living space than for larger arthropods. Arthropods are advanced animals with jointed legs and a hard outer skeleton and include insects, centipeds, millipeds, and spiders (Keeton and Gould, 1986). Sugihara and May (1990) discuss how fractals may be used by ecologists to answer questions regarding scale, measurement, and hierarchy in ecological systems. Assessment of habitat space available to microarthropods was carried out by Kampichler and Hauser (1993) by studying soil pore surface roughness. Their measurements of Dswere reported in Sec. IILC, and values close to 2.3 were obtained. Larger values of D5 will give rise to a steeper size-abundance distribution of microarthropods. For the values of D\ observed, a decrease in body length by an order of magnitude should result in a four-fold increase in density of individuals on a given pore area. Further investigations of this kind should concentrate on the variability of the fractal dimension of habitable pore surface in time and space, with particular attention being paid to organic layers where the most diverse soil microarthrood communities occur (Kamplicher and Hauser, 1993). However, Kamplicher (1995) showed that a combination of hypotheses concerning the fractal dimension of a pore surface, metabolic rate, and accessibility of soil pores overestimates the steepness of the size-abundance relationship. Crawford et al. (1993a) provide insights into the interaction between structure and biotic processes in the soil using fractal geometry. In the case of a species of soil fungus (Trichoderma viride) they showed that the distribution of biomass responds to a heterogeneous nutrient base. Some discussion was also provided on the significance of soil structure for the population dynamics of microbial communities. They discuss that for a soil from Young and Crawford (199 1) with a value of Dq = 2.36 almost half of the potential habitable space for a bacterium of size 5 p m is inaccessible to protozoa of size 30 p m that may prey on the bacteria. Also discussed by Crawford et af. (1993a) is the influence structure has on the spatial dynamics of small microbes compared to large microbes. A small species would be able to initially move faster due to a greater motility but would finally diffuse more slowly than a larger species due to the constrains of tortuosity. It is concluded by Crawford et al. (1993a) that reference will need to be made to soil structure in studies of nutrient cycling because rates of nutrient cycling depend on the spatio-temporal dynamics of microbes.

B. SOILMECHANICS Examples of fractals in soil mechanics are discussed by Herrmann ef al. (1 993). They focus on hydraulic fracturing at constant pressure and also present a modelization for a fluid penetrating under a pressure gradient into a fractal crack that

50

ALISON N. ANDERSON ETAL.

it is creating itself. The crack patterns observed by Herrmann et al. (1993) were fractal with a fractal dimension of 1.56 0.05. Cracking in soil is similar to the fingering seen when a colloidal fluid is displaced with a miscible fluid. Van Damme (1989) discusses this phenomenon, and a picture of the radial pattern obtained by displacing a clay suspension (a non-Newtonian colloidal fluid) with water (a miscible liquid) shows many armed, finely branched interfaces. Perfect and Kay (1995b) modified the conventional Weibull model for brittle fracture because it assumes that soil aggregates are Euclidean cubes, an unlikely situation. In their modified model soil aggregates were represented as fractal cubes, and the modified model also had an extra parameter, the mass fractal dimension of the solid matrix (Dms).Comparing the two models using previously published brittle fracture data for air-dry aggregates (with equivalent cubic length in the range 1.19-25.275 mm) from a range of soil samples, Perfect and Kay (1995b) found that the modified model fitted the data much better than the conventional model. In some cases Perfect and Kay (1995b) obtained values of D,, less than zero, clearly physically impossible, and this was attributed to the lack of a relationship between strength and aggregate size for the size classes studied. From the discussions in Sec. IV the discrepancy is likely to have arisen from the fact that the observed power-law exponent is not related to the fractal dimension of the matrix. It was also found that Dms increased with increasing clay content, this being consistent with results discussed previously.

*

C. ADSORPTION STUDIES Adsorption data has been used to obtain an estimate of the surface fractal dimension of soil. For seven soil types Avnir ef al. (1985) obtained values of Ds (using Eq. [ti]), ranging from 2.19 to 2.99. The soil samples, fractionated into 12 grain sizes ranging from 2 to 325.5 km, had surface area measured by malachite green adsorption. Over the whole range of particle sizes, two distinct regions of fractality (self-similarity) were observed. The fine particles, composed primarily of kaolinite, corresponded to one region, whereas the other region corresponded with coarse particles, composed of feldspar, quartz, and limonite. Despite the obvious assumption of a gradual change in composition, Avnir et al. (1 985) found an abrupt change in composition around 25-45 km with the fractal analysis. High values of Ds were associated with soil grains composed of a clay mineral. A new theory was proposed by Sokolowska (1989) to describe the adsorption of gases on geometrically and energetically nonuniform surfaces prevalent in soil. The geometrically nonuniform surfaces were described by Ds, as measured from adsorption isotherms using water and nitrogen as the adsorbates. Even though the fine particles (0.005-0.002 mm) were composed mainly of illite, mica, quartz, and some smectite and the coarse particles (0.5-1 .O mm) of quartz and feldspars, only

APPLICATIONS OF FRACTALS TO SOIL STUDIES

51

one region of self-similarity is reported. Sokolowska reports that despite the fact that the standard BET method of evaluating the monolayer capacity assumes geometric uniform surfaces, previous investigations (Fripiat et al., 1986; Sokolowska and Sokolowska, 1988) have shown that the fractality of mildly geometrically nonuniform surfaces has little effect on the measured values of the monolayer capacity. For the four horizons studied, Dsranged from 2.28 to 2.40 for N, and from 2.25 to 2.35 for H,O. The fractal dimensions evaluated using the two different absorbates were quite different. Nitrogen only probes external surfaces, whereas water may also explore the internal surfaces of soil particles. This shows similarity of values of Dsfor external and total surfaces. Sokolowska (1989) used values of D5to compute energy distribution functions for water vapour adsorbed on soil samples. Okuda er al. (1996) explored the surfaces of three simple sorbents, a kaolinite, a silica-gel, and a sand, using a range of adsorbates (e.g., n-pentane, benzene, water, and nitrogen), covering a range of sizes (a).Using both the classical BET equation and a fractal BET equation from Fripiat et al. (1986), modified because of the unrealistic assumptions associated with the classical BET equation, to estimate Nm(a) (Eq. [9]) Okuda et al. (1995) were able to interpret the surfaces of the kaolinite and silica-gel as fractal-like. Values of DI in the 10-50 X lop2' m2 size range were 2.17 (classical BET) and 2.13 (fractal BET) for the kaolinite and 2.56 (classical BET) and 2.41 (ffractal BET) for the silica-gel. The relationship between Nm(a) and a for the sand could not be explained with fractal theory, and Okuda et af.( 1995) attribute this to the high sensitivity of Nm(a)to the chemical reactivity of the probes. They state that the N,,,(a) values should be a function of the physical size of the probe (a)only and not of the chemical characteristics of the probe. Problems may arise when determining the surface irregularity of soil, a complex natural sorbent. One of the most important factors determining the environmental distribution of chemicals is the sorption of toxic organic chemicals by natural sorbents (Okuda et al., 1995), and so the study of heterogeneous surfaces, as adsorbates perceive them, may have particular application in this area. Okuda et al. (1995) also believe that the characterization of heterogeneous natural sorbents at the small scale (molecular to pore level) is necessary to further understand sorption behavior. From the studies reported here it may also be possible to estimate the surface fractal dimension of a particular soil if we have knowledge of its mineralogical makeup. Many adsorption studies have been carried out on soil (e.g., Quirk, 1955)for which data is available to calculate surface fractal dimensions. Avnir e? al. (1985) were intrigued as to why so many scientists have presented their experimental data as log n versus log r (from Eq. [8]),obtained a straight line, and did not seek or give an explanation for the linearity. The notion of self-similarity, or fractal surfaces, is suggested by such results. As an example we will calculate values of D, for some of the data reported in

52

ALISON N. ANDERSON ETAL. Table VI Adsorption Data for Two Soil Types" SSA (m2/g)

Nm (mM/g)

Soil (< 0.3 m)

Water

Nitrogen

Water

Nitrogen

Miami silt loam Carrington loam

127 94

72 74

I .95 2.98

0.738 0.759

"Reported in Quirk, 1955.

Quirk (1955). A Miami silt loam (< 0.3 km) and a Carrington loam (< 0.3 pm) were studied using water and nitrogen as adsorbates. The amount of each of these adsorbed by the silt loam and loam are shown in Table VI. Given the probe size (a)as indicated by cross-sectional area for water (10.8 X m2) and nitrogen (16.2 X m2) in Okuda et al. (1995) it is possible to calculate Nm(a)using N , (mollg) = (SSA/a)/6.02e+23

(20)

where SSA is the specific surface area reported in Table VI and 6.02ef23 is Avogadro's number. Values of N , are reported in Table VI. According to Eq. (9) it is possible to estimate Dsfrom a plot of log N , versus log a where Dsis equal to the slope X - 2. The values of Ds for the Miami silt loam and the Carrington loam were found to be equal to 2.08 and 2.93, respectively. Of course, these are only estimates since only two data points were available for each soil. A large value of Ds was found for the Miami silt loam, suggesting a highly irregular surface, reflected by the fact that a greater number of small molecules (water) were adsorbed compared to the Carrington loam.

VII. FRACTAL GEOMETRY AND SPATIAL AND TEMPORAL VARIATION The soil as we see it at any point in time is a function of many initial variables and ongoing processes and conditions. Soil is a function of climate, organisms, relief, parent material, and time (Jenny, 1941). which can shape the soil independently and interdependently over time and space. Additionally we must not discount the influence of human intervention on the shaping of our soil. These soil forming factors may act over a range of spatial scales, and within each factor there may be many spatial scales of interaction (Burrough, 1983a). In addition to spatial variability, there is also temporal variability. Numerical classifi-

APPLICATIONS OF FRACTALS TO SOIL STUDIES

53

cation, multivariate statistical methods, continuous (fuzzy) classification, geostatistics, fractal methods, mathematical morphology, and chaos theory (which will be discussed in Sec. V1II.C) have all been employed to approach the inexactness of soil as a phenomenon (Burrough, 1993). Some of these methods, including fractal methods, will probably remain research-oriented for the near future (Burrough, 1993). The following sections focus on the research orientated toward using fractal geometry as a tool for quantifying soil and crop variability over space and time. Fractal geometry may be used to describe soil variation from the microscopic scale to the macroscopic or landscape scale. Soil variation is usually considered to be composed of two different types of variation. These are “functional” variation, or variation that can be explained, and a random variation, or “noise” that is unresolved (Burrough, 1983a). However, this variability that appears as noise at low resolution will most likely reveal structure as the scale of observation is increased (Burrough, 1983a). Spatial and temporal variability data that may be classed as fractal will therefore be continuous but nondifferentiable (Burrough, 198 1). The fractal dimension as it pertains to soil and crop variability will be written as Dv so as to avoid confusion with the other types of fractal dimensions. Armstrong ( 1986) describes the virtue of using Dv as a standardized description of the irregularity of a set of measurements. This is not the case for the variance or the standard deviation. The use of fractals in soil variability studies was introduced by Burrough (1981, 1983a,b). With Dv it is possible to compare the irregularity of different soil properties that may have been measured over a multitude of length scales (Armstrong, 1986). Intuitively, Dv is actually a surface fractal dimension, but because it is estimated from a variogram rather than using methods discussed in Sec. 1I.E it has been given a different name. A method of estimating the fractal dimension of soil variability uses the “semivariogram” and is described in Burrough (1983a). The semivariogram is drawn by plotting the semivariance, y ( h ) against h, the separation distance of data points or lag. The form of the semivariogram indicates whether the semivariance increases with h to a constant value, which is termed the sill, or whether it increases infinitely. The value of h at the point where the semivariance becomes constant is referred to as the range. When h is equal to zero the semivariance is also expected to be equal to zero. This is not usually the case for soil and other environmental data due to local variability and measurement errors. The variance when h = 0 is known as the nugget variance. Burrough (l983a) discusses how fractional Brownian processes can be used to approximate the variations of soil properties and also shows that if the semivariogram of a fractional Brownian function is plotted on a log-log scale, it will be a straight line of slope rn = (4 - 2 0 , ) . The fractal dimension ( D v )is able to distinguish between long- and short-range variation (e.g., Bartoli et al., 1995; Burrough, 1983a; Eghball and Power, 1995). Small values of Dv indicate a dominance of

54

ALISON N. ANDERSON ETAL.

long-range variation, whereas large values of Dv indicate the domination of shortrange variation. A value of Dv = 1.5 corresponds to a completely random Brownian motion. A value of Dvless than 1.5 implies that the increments along the sample series tend to be positively correlated (Burrough, 1983a). This expresses persistence of positive values, therefore reflecting the importance of long-range variations in the Brownian fractal function. Values of Dvfor soil properties are most often greater than 1.5, which implies that the increments along the sampled series are negatively correlated with each other, meaning that large positive increments tend to be followed by large negative increments, therefore giving rise to large short-range variation (Burrough, 1983a). The study of Dvis important because the existence of large variation over small scales poses problems to soil surveyors and map makers (Culling, 1986).

A. STUDIESOF SPATIALVARIABILITY AT THE LANDSCAPE SCALE Fractals have been applied to a range of soil data at the landscape scale in order to obtain a fractal dimension for the spatial variability of the data ( D J Semivariograms are estimated in many studies of soil spatial variability, and so data required to calculate Dv should be readily available. Studies at the landscape scale have been reported by Bartoli er al. (199% Burrough (1981), Burrough (1983a), and Culling (1986). Burrough (1981) calculated the fractal dimension (Dv) for a selection of previously reported environmental data and found values of Dvto be quite high (> 1.5) and to vary greatly. Culling (1986) calculated the value of Dvfor the spatial variability of soil pH along two transects. Values of Dvin the range 1.7-1.9 were reported, indicating highly erratic variation. Bartoli ef al. ( 1 995) studied the spatial variability of topsoil characteristics (total clay, water-dispersed clay, water-stable aggregates) for three topsoil units, each occurring at a different topographic position in the landscape. The topsoil variability could be modeled as a fractal Brownian process. Low values of Dvwere found for the total clay (1.28, 1.42, 1.42 for the plateau area, the midslope, and downslope topsoil units, respectively), whereas large values of Dv were found for water-dispersed clays (1.6, 1.82, and 1 S ) and water-stable aggregates (1.96, 1.83, and I .97). For this chapter we will focus on the work of Burrough (1983a), who carried out a fractal analysis for four sets of data: ( 1 ) thickness of two deposits, the Zuiderzee sand and the Almere clay deposits along a 266 m transect (sampled every meter) in the IJsselmeer, The Netherlands; ( 2 ) a 3200 m transect from the English Midlands sampled every 10 m for pH, percentage clay, and percentage silt at different depths (Webster and Cuanalo, 1975); (3) soil moisture capacity estimations for 530 stratified randomly distributed sites from a 2 X 2 km area in the Netherlands (van Kuilenburg et af.,1982);and (4) the surveyed topographic height

5s

APPLICATIONS OF FRACTALS TO SOIL STUDIES

of each boring, thickness of the A 1 horizon and percentage clay content at depths of 0-20 cm and 180-200 cm from a 1060 m transect in The Netherlands, sampled every 20 m (Veens, 1982). Figure 9 (from Burrough, 1983a) shows plots for some of this soil data. All data have been standardized to zero mean and unit variance to enable comparisons between series.

0

I 0

1000

3000

2000

I

I

100

200 m

Figure 9 Plots from data sets reported in Burrough (1983a); ( n ) data from the English Midlands transect, and ( b )thicknesses of the ZuiderLee sand and Alniere clay deposits (Burrough, 1983a: reprinted with kind permission of Blackwell Science Ltd.).

56

ALISON N. ANDERSON ETAL.

The semivariograms were plotted on a log-log scale-the dataused by Burrough (1983a) provided estimations of Dv. The slopes of the plots, used to calculate Dv, were obtained by fitting a straight line (by least squares) to the data points lying within any clearly defined range of the semivariogram. Values of Dv reported by Burrough (1983a) are shown in Table VII. Additionally, Burrough (1983a) reported the apparent values of Dv for some geographical and geophysical data (e.g., landform, river discharge, geological sediments, and climatic data) and their values of Dv were predominantly less than 1.5. Values of Dv for a range of natural phenomena can also be found in Burrough (1981, 1989). A major observation of Burrough (1983a) is that values of Dvfor soil properties (Table VII) are much higher than values of Dv for other environmental data, meaning that the soil data is much more irregular or "noisy" and has large shortrange variation. The landscape properties or soil-forming processes that have the greatest influence on the property being studied largely determine whether the value of Dv for a particular soil property will be small or large. Burrough (1983a) suggests that parent material has a long-range effect (small D J , whereas local influences such as biological action and microrelief have short-range effects (large 0,). If local influences override the effect of say, parent material, large values of D y

Table VII Values of DVfor Soil Properties in Four Data Sets" Data set I

Property

Lag (m)

Transect length (m)

Dv

Zuiderzee sand thickness (cm) Alrnere clay thickness (cm)

1

266

I .9

I 10

266 3210 3210 3210 3210 3210 3210

2.0

pH, 8 cm pH, 65 cm % clay, 8 ern % clay, 65 ern % silt, 8 cm 9% silt. 65 cm Estimated soil moisture capacity (mm) Topographic height (m) 96 clay, 0-20 cm % clay, 80-100 cm % clay, 18&200 cm Thickness A I horizon (cm)

1060 I060 I060 I060 I060

1.3

"Reported in Burrough, 1983a

10

10 10

I0 10 50

20 20 20

20 20

1.6

I .8 1.7 I .7 I .7 1.8 I .8

1 .s

I .6

I .s 1.7

APPLICATIONS OF FRACTALS TO SOIL STUDIES

57

will be found. Burrough (1983a) also states that large values of D,, may be the result of using a sample spacing that is too large to resolve the patterns adequately. Burrough ( 1983b) proposed a non-Brownian, nested model because the model (a fractional Brownian model) in Burrough (1983a) fails to account for abrupt changes in the mean of a soil property, as observed at soil boundaries, as well as for second-order stationarity and for the non-self-similarity of variations at different scales that are observed in real soil data. Burrough (1983b) states that this nested model is most likely to be applicable to situations in which lateral change in soil results form a number of clearly independent and superimposed sources of variation. An example of this is a change of texture as a result of a change in parent material on level sites. The nested model is based on independent spatial random functions, denoted RFq, which operate over range rq. The random functions represent each class of spatial variation affecting soil, for example, worm activity and geological formation. Burrough ( 1983b) found the nested model to be a good fit to many of the data examined. In the cases where superposition of soil-forming processes occurs, more detail may be revealed as the scale of observation is refined as it is with fractals, but the variation may not necessarily be Brownian or self-similar (Burrough, 1983b). The practical implications of this study for survey, as reported by Burrough (1983b), is that when the soil variation fits the nested model, abrupt changes in the mean level of a particular soil property should be identified.

B. STUDIESOF SPATIALVARIABILITY AT FINERSCALES Studies of soil variability at scales finer than the landscape scale have focused on measurements of soil surface strength (as measured by a penetrometer or shear vane) and microtopography. Armstrong (1986) referred to these soil properties as transient soil properties (measured over small distances, from 0.01 to 10 m) as compared to permanent soil properties (at least within human time scales), as discussed by Burrough (1983a,b) and Culling ( 1 986), that were mainly measured over distances in the range 1-1000 m. The first application of fractals to describe the spatial variability of transient soil properties was by Armstrong (1986). Soil structure was chosen by Armstrong (1986) because it is important for purposes such as the ability of the soil to bear stock or vehicular traffic. and it is affected by intrinsic and transient properties. Surface roughness was also studied because it gives an indication of the damage done to the soil surface by grazing stock. For soil strength, as determined using a cone penetrometer and a field shear vane, Armstrong (1986) chose two sites, one under pasture and the other in arable cultivation. For the pasture site two transects 50 m long (lag = 0.5 m) were used, the first being on drained land and the second on undrained land. For the cultivated

58

ALISON N. ANDERSON ETAL.

site one transect was used (initially set out at right angles to the last cultivation operation); samples were taken every 10 cm, and 200 samples were collected whenever possible. Samples were collected for three samples in time. The first was at the end of winter weathering, the second a week later but the field had been cultivated and direct drilled with spring cereals, and the third another week later following a rolling operation. The estimated values of D, for the soil strength measurements are shown in Table VIII. Armstrong (1986) noted that the variograms for the soil strength measurements from the pasture sites had very similar shapes. This indicated that the spatial structures were similar despite the mean levels of the transects differing. The values for the arable site indicate that cultivation introduces order into the data. Microtopography data from 1 m long profiles with readings every 1 cm (for different sites over a period of time) gave values of Dv close to 1.7 (Armstrong, 1986). Values of Dv did vary from 1.53 to 1.9 1, but there was very little obvious pattern in the results, between sites or temporally. Armstrong (1986) found the calculation of D, to be problematic where semivariograms showed an initial straight-line segment and a subsequent fall. and he attributed this fall to be a function of the small number of data points (100) used to calculate the semivariograms. Armstrong (1986) stated that despite the fact that the technique of least squares regression (used to calculate D,)is objective, the choice of the number of lags over which to calculate Dvis arbitrary. Armstrong (1986) then suggested that an ensemble viewpoint (an ensemble average of Dv or amalgamation of several semivariograms) should be taken for a satisfactory interpretation of D,.

Table VIII

Values of D yfor Soil Strength Data" Site

Instrument

Lag (m)

Pasture (drained) Pasture (drained) Pasture (undrained) Pasture (undrained) Cultivatedh Cultivated' Cultivatedd

Penetrometer Shear vane Penetrometer Shear vane Shear vane Shear vane Shear vane

0.5 0.5 0.5 0.5 0. I 0. I 0.1

Sample size 100 100 100

I00 I60 200 20 I

D, 1.944 ,939 ,910 ,960 ,972 ,826 .I64

"Reported in Armstrong, 1986. hThe cultivated site at the end of winter weathering. 'The cultivated site a week later following cultivation and direct drilling with spring cereals. dThe cultivated site another week later following a rolling operation.

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Armstrong (1986) concluded that Dv is a useful tool for describing soil variability but challenged Culling (1 986) who stated that the derivation of D ypresents few problems. There are problems in deriving Dv,and in particular these are associated with the lack of a firm theoretical basis for the derivation of the semivariogram (Armstrong, 1986). Perfect et al. (1990) performed laboratory penetrometer measurements for 10 undisturbed soil cores from two soil types. This allowed them to carry out an analysis of spatial variation at the mesoscopic scale ( 10-3-100 m). A constant-load needle penetrometer and a constant-rate micropenetrometer were used. Perfect et at. (1990) expressed their data as a percentage of the maximum depth attainable with the needle penetrometer (PMP), and as the slope (m)and intercept ( n )of linearized percent penetrability versus tip-pressure relationships for the micropenetrometer. The fractal dimension ( D J was estimated for PMP, m,and n and was reported as a fractal dimension for a surface; a value of Dv close to 2 represented a well-organized surface, and a value of Dvclose to 3 signified complete spatial independence. From the semivariograms plotted on a double-logarithmic scale, D bfor a surface is given by D,, = 3 - Hl2, where H is the slope off the log-log plot as h (the lag) approaches zero. For PMP the mean value of D yand the standard deviation was found to be D,, = 2.83 ? 0.5; for n, Dv = 2.93 t 0.06; and form, D y= 2.89 t 0.06. This indicates that short-range variation dominates for soil penetrability. This is similar to the findings of Armstrong (1 986). Soil surface roughness is the result of many elements acting over a range of scales. Elements contributing to roughness at their respective scales include individual grains, aggregates, tillage marks, and landform features (Huang and Bradford, 1992). Huang and Bradford (1992) proposed that a combination model of fractional Brownian motion and Markov-Gaussian processes at different scales be used to quantify soil roughness. The fractal dimension (Dy) was used to describe roughness for the sloping straight-line portions of the variogram, whereas the Markov-Gaussian process was used to describe the portion of the variogram where the slope approached zero. Huang and Bradford (1992) believed that roughness could not be fully characterized using Dv and suggested that the parameter 1 (termed the crossover length) be estimated as well. The D yand 1 parameters are analogous to the slope and intercept parameters required to quantify a straight line. They stated that Dv is an index for the proportional distribution of different-sized roughness elements in a relative scale, and 1 is the scaling parameter transforming the relative size to actual scale. Data on soil roughness was obtained from natural fallow plots, field rain-simulator plots, and laboratory soil pans. Results showed that rainfall can have the effect of decreasing the surface roughness, but continued rain can cause the development of microrills, and the surface will then appear to have increased in roughness. An increase in D,, following rainfall indicates erosion. Huang and Bradford (1992) suggest using the rate of change in surface topography under the erosive forces of rain as a measure of surface stability or strength.

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Huang and Bradford (1992) found the fractal model to describe soil roughness for a limited range in scale, and this break in fractal scaling implied that elevational variations at small scales do not scale up proportionally when the areal scale is increased. They also found 1to reflect changes in surface roughness more efficiently than Dv in some instances. Larger 1 values reflected greater roughness. Kemblowski and Chang ( 1993) report the fractal analysis of saturated hydraulic conductivities along a horizontal and a vertical transect. Values of Dv were 1.475 and 1.825, respectively. The large value for the vertical transect indicates that the distribution is highly variable, and its increments are negatively correlated. Kemblowski and Chang (1993) explain this by the fact that the horizontal measurements are usually located within one depositional stratum. Neuman (1990) studied the universal scaling of hydraulic conductivities and dispersivities in geologic media. Longitudinal data of log hydraulic conductivities constituted a self-similar random field with homogeneous increments characterized by a semivariogram with a value of Dv D, + 0.75, where D, is the topological dimension of interest. Neuman (1990) concluded that this could be viewed as a universal scaling rule about which large deviations occur as a result of local influences. This includes discrete natural scales at which log hydraulic conductivity is statistically homogeneous. Soil surface strength was studied for fractal analysis by Folorunso et al. (1994) in order to improve understanding of crust spatial characteristics. Maximum penetration forces were measured at intervals of 0.005, 0.01, 0.05, and 0.5 m along four parallel transects 0.25 m apart for nine different sites. In the first transect, 300 measurements were made (0.005 m apart), in the second, 200 measurements were made (0.01 m apart), and in the third and fourth, 100 measurements were made (spacings of 0.05 and 0.5 m), giving transects with lengths of 1.5, 2,5, and 50 m. Although Dv has been obtained from the semivariogram in the other papers discussed here, Folorunso et al. (1994) used the variation method for one-dimensional profiles (Dubuc ef al., 19891, which approximates box counting. They used this method because the variogram may give errors due to non-stationarity in the mean of data along the transects. Folorunso et al. (1994) found that mean forces for the different transect lengths and variances did not remain constant but tended to increase with sample spacing. However, most statistical techniques for looking at spatial characteristics of soil properties assume stationarity of the mean and variance. The fractal dimension was found to exceed 1.5,and in most cases nearly constant values were observed for different sites. The fractal dimensions allowed Folorunso et al. (1994) some quantitative discrimination between soil types; for example, a cohesive soil has less variability in Dv than a loose soil. Folorunso et al. (1994) also examined multifractal spectra (for data sets normalized and interpreted as probability measures), and these spectra gave similar entropy dimensions for all sampling schemes, despite variability on the whole multifractal spectra. Entropy dimensions did, in general, decrease with larger spac-

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ings. The multifractal spectra can be used along with Dv in the optimization of sampling schemes. Folorunso et al. (1994) noted that soil types whose entropy dimensions remain high at alternative scales could be sampled at the largest spacings. However, soil types that show decay in the entropy dimension as the sample spacing increases should be sampled at the smallest spacing possible. Studies of soil strength were also carried out by Pan and Lu (1994), who measured soil strength in harvested paddy fields and on ridges. Transects were either 25.6 or 5 1.2 m long, with a sample separation of 0.1 m. Values of Dv were always close to 2, irrespective of site, date, direction, or location of measurements, indicating random data sets.

C. IMPLICATIONS AND LIMITATIONS OF USING FRACTAL GEOMETRY IN SOILSPATIAL V A R ~ A ~ ~ STUDIES ITY Soil properties are not ideal fractals and as such cannot be modeled perfectly or always successfully by a Brownian fractal function, and Burrough (1983a) states two reasons why this is so. First, the semivariance of soil properties does not always increase uniformly with increasing intersample distance and area studied but instead tends to increase in a series of steps. Variation in soil properties is controlled by many independent processes that can cause abrupt transitions. Second, the distribution of (Z,l.+lr- ZJZ, (where Z,, Zx+,,are the values of the Brownian line function at points x, x + h) for lag h = 1 for soil properties is often different from that found for a fractal Brownian function. Even though soil is not an ideal example of a fractional Brownian process, soil does have nested levels of variation, and the relationships between these nested levels are controlled by the importance and scales of all the soil-forming properties and processes that have been active (Burrough, 1983a). A limitation for calculating Dv from a semivariogram is that the semivariogram can be highly dependent on sampling direction and interval (Burrough, 198 1). The variance of increments can fall rapidly when the sample spacing for the soil property of interest tends to match the scale of a spatial pattern, and therefore an apparently small Dv can be found (Burrough, 1981). The technique used to calculate Dv from the semivariogram can fail under certain conditions. Bartoli et al. ( 1 995) found that when the log-log representation of the semivariogram is a decreasing curve, local fractal dimensions greater than 2 were found, and this is, of course, not physically possible. Bartoli et al. (1995) stated that this situation often occurs in the case of periodic long-range structures, and it is called hole effects. This type of situation is mostly avoided because Dv is generally calculated from the slope of the log-log variogram close to the variogram origin. The value of Db,can give a guide as to the preferred distance between samples

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required for future sampling. A small value of Dvfor a particular soil property means that values for that property change gradually, whereas a large value of Dv means that values for that property are irregular. It is best to choose map scales that can most efficiently resolve the most important sources of variation present for a particular area (Burrough, 1983a). Large values of Dv, despite improvements in the resolution of surveys, indicate the presence of important short-range sources of variation, and if D yis found to vary over a range of closely related scales, the one that returns the smallest value of Dv should be regarded as the most efficient spacing (Burrough, 1983a). When values of Dv are large (the result of several closely spaced nested scales of variation), the success with which interpolation of values of soil properties at unvisited sites can be carried out may be questionable (Burrough, 1981; Burrough, 1983a; Culling, 1986). Values of D ycan assist in the tailoring of sampling strategies, and this will improve the efficiency of field surveys and of the interpolations resulting from it (Burrough, 1981). Sampling error may also contribute to high values of Dv. The vagaries of soil pH testing were discussed in Culling (1 986) and include different pH meters and electrodes, testing procedures, and methods of collection and preparation. Laslett and McBratney (1 990) have suggested that the highly erratic spatial behavior of soil pH as reported by Culling (1986) could well be due to instrumental drift as well as measurement error, which is inevitable in the case of most soil sampling. Additionally, they feel that a temporal variation effect could have been present because the sampling was carried out over several months. Laslett and McBratney (1990) propose a general model for soil pH measurement that includes instrumental drift, random measurement error, and random and correlated spatial variation. The studies reported here indicate that fractal geometry can be a useful tool in the quantitative description of soil spatial variability as long as the problems that may be associated with estimating Dv from semivariograms are taken into account. In particular it has implications for the design of sampling strategies. A review of the application of fractal concepts to the characterization of spatial variability of soil properties is reported in GimCnez and Rawls (1996). They also suggest areas of potential research, including multifractal techniques, how the distribution of hydraulic properties relate to one another, and measurements of soil properties of different soil volumes as an alternative to regularly spaced measurements.

D. TEMPORAL VARIABILITY A fractal analysis of temporal variability in plant parameters has been carried out by Eghball and Power ( 1995) and Eghball et al. (1995). The results of these two studies gave an indication as to whether crop yields are dominated by long-

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term or short-term variation. Again, the semivariogram was used for the fractal analyses, and the semivariance was calculated for a range of crops for different year intervals (h). The fractal dimension ( D v ) was calculated using the same method as for spatial transects. Eghball and Power (1995) used the average grain yields of barley, maize, oat, peanut, rice, rye, sorghum, soybean, and wheat and also the fiber yield of cotton in the United States in the 6 1-year period from 1930 to 1990 for their fractal analyses. As a result of improvements through plant breeding, and the increasing use of fertilizers, pesticides, and herbicides, yields increased during this period. An analysis of covariance showed that there were significant differences between the slopes of the regression lines (from the plot of log semivariance versus log h) for the 10 crops. Resulting values of Dv ranged from 1.20 for rice to 1.47 for oats. Rice, therefore, had the least short-term variation, whereas oats and soybeans had more pronounced short-term variation, suggesting that these two crops may be particularly sensitive to annual variation in some growth factors. The advantage of using a fractal analysis to compare temporal variability between crops is that the results of regression analyses cannot be compared because the slope depends on yield levels, and they would need to be standardized to allow comparison (Eghball and Power, 1985). As with spatial variability studies the fractal analysis is scale independent, and the values of Dv depend on variability rather than yield and so values of Dv may be compared. Grain yield data (1953 to 1993) for maize under different management regimes was used in the temporal yield variability study by Eghball etal. (1995). Plots were divided into manure and nonmanure sections and to each of these sections different fertilizer treatments (0,45, 90, 135, 180 kg N ha-' and 135 kg N + 80 kg P ha- I ) were applied. Values of Dv for the manure and nonmanure plots were 1.37 1 and 1.981, respectively, and there was no significant difference between the two. This was also the case for the different fertilizer treatments with values of Dv ranging from 1.958 to 1.996. These results indicate that short-term variation dominates. Eghball et al. (1995) conclude that for the location of this study (western Nebraska) management practices cannot override the strong influence of variable environmental conditions. This compares to the study by Eghball and Power ( 1995). Long-term variability dominated in that study, probably because the yield data reported were averages for the United States. Intuitively, we would expect greater fluctuation in yield data for smaller case studies. Using fractal analysis in the evaluation of long-term cropping studies has many advantages. It allows the variability of yields for different crops to be compared. In some cases the value of Dv indicates that environmental factors rather than management practices effect the year-to-year variability of crop yields. Where longterm variability dominates, however, it may be possible to predict crop yields over time and model the temporal plant growth (Eghball ef al., 1995). Values of Dv also

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indicate the risk involved in growing a particular crop in a particular location. Increased yield variability in the short-term (higher D,) indicates greater risk in crop production. This certainly has enormous implications for site-specific crop management.

VIII. FRACTAL ECLECTICA A. SCALING PROCESSES IN SOILUSING FRACTAL GEOMETRY The value of being able to upscale or downscale soil physical processes has long been observed by soil physicists. Scale factors used in soil science are discussed in Tillotson and Nielsen ( 1 984), for example, and McBratney ( 1997) also discusses scaling in soil science. Knowledge of scaling factors allows us to predict the characteristics of some process or system at a particular scale based on the characteristics of the same process or system at another scale. Fractal geometry has a potential role to play in the scaling of soil attributes and physical processes because fractals have no characteristic scale and physical processes occur across a multitude of length scales. If soil can be described as a fractal as a result of self-similarity over a range of length scales it will be possible to predict the behavior of physical processes at scales not directly examined. To be able to do this with any degree of success will require greater attention to the fractal nature of soil structure at a range of scales. As mentioned in Sec. 1.A fractals occurring in nature will only display fractal scaling between rminand rmax.These are natural cutoffs to fractal behavior, and within this range more than one fractal regime may be observed. The scale at which the value of D changes will be termed the crossover region, and this may or may not be abrupt. Changes in values of D may be due to differences in composition of soil particles of different sizes (Pachepsky et al., 1995) or due to a shift in the processes generating structure (Sugihara and May, 1990), for example. Pfeifer and Obert (1989) state that rminand rm,x should span one decade of length or more in order to accept a value of D as a well-defined fractal dimension. Additionally, the minimal condition is that rmax/rmin must exceed 2'lD (Pfeifer and Obert, 1989). Knowledge of rminand r,,, and of any crossover regions is necessary if processes or properties are to be scaled appropriately. When scaling processes that occur in the soil the question of relevant resolution is important. It is also important because at high resolutions, pores that appeared unconnected at lower resolutions may appear connected (Crawford et al., 1993a). This again stresses the importance of observing the soil structure for a range of length scales. Crawford et al. (1993a) suggest doing this and obtaining an asymptotic value of d (for example) as the resolution increases.

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B. DISTINGUISHING A FRACTAL FROM ITS COMPLEMENT Much evidence supports the use of fractals to approximate the heterogeneity of the solid matrix of soil, and the definition of a mass fractal means that both the solid fraction and pore space cannot be simultaneously fractal. This is because the density of a mass fractal decreases as the size of the fractal object increases. Therefore, either (or neither) the solid phase or pore space can be a mass fractal and the other the fractal complement. This subject is dealt with in detail in Crawford and Matsui (1 996), and they conclude, for the soil in their studies, that it is the solid phase that can be approximated by a fractal while the pore space is the fractal complement. Crawford and Matsui ( 1996) discuss how distinguishing between a fractal and its complement is made difficult by the fact that both can be characterized by power-law scaling exponents over a limited range in scale. Only the fractal will have a constant exponent, independent of the size of the subsample examined. This will not be the case for the fractal complement, with the exponent increasing toward 2 as the size of the subsample increases. Crawford and Matsui (1996) showed that it was the solid phase that was fractal by estimating Dn,for a number of subsamples of different size. However, when the value of Dm for the solid phase is close to 3 (or 2 in the case of thin sections), artifactual scaling of the pore space occurs. This has important implications for the scaling of soil physical processes. Scaling functions, based on incorrectly associating a structure with a fractal, will lead to incorrect extrapolation of the behavior of processes taking place at smaller or larger spatial scales (Crawford and Matsui, 1996). These results suggest that fractal models cannot be used to scale both gas and water transport in soil, since gas diffusion depends on the heterogeneity and connectivity of the pore space and hydraulic properties depend on the heterogeneity and connectivity of the solid phase.

C. DETERMINISTIC UNCERTAINTY AND CHAOTIC BEHAVIOR A discussion on the applications of fractals in soil science would not be complete without a discussion on deterministic uncertainty and chaotic behavior in soil information. Chaos theory is characterized by a sensitive dependence on initial conditions (Gleick, 1987). Edward Lorenz (1963) was the first to observe this. Studying computer weather patterns, similar starting positions produced weather patterns that diverged until all resemblance disappeared. Similarly, chaotic behavior may occur in soil when similar initial conditions result in soil profiles that have very different characteristics. For example, Fig. 10 shows the boundary between two horizons in a podzol, located at Cooloola, Queensland, Australia. The irregularity observed in the boundary could be due to chaotic behavior. As the profile has developed the boundary region between the two horizons has developed different characteristics within a small spatial scale.

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Figure 10 Photograph showing the irregular boundary between the albic and spodic horizons in a podzol. A line has been drawn to highlight the boundary. Other regions where the characteristics of one horizon appear within the other are also highlighted (photograph courtesy of A. J. Koppi, University of Sydney).

McBratney (1992) identifies three types of uncertainty associated with soil information: stochastic, deterministic, and semantic (or vagueness). The latter arises when the soil data in question is qualitative. Stochastic uncertainty is the most well known, and it is believed that if the property or process in question is observed with greater precision, the uncertainty decreases (Laslett and McBratney, 1990). However, this will not be the case for deterministic uncertainty (McBratney, 1992). If soil systems do exhibit deterministic chaos, a greater proportion of soil variability can be ascribed to systematic variation and less to random noise (Phillips, 1993). The link with fractals is two-fold. In the phase space describing the behavior of the system the chaotic region is itself a fractal set (Gleick, 1987), so small changes in the phase space give quite different outcomes. Second, phenomenom displaying critical-point behavior show self-similar scaling around the critical point (Bruce and Wallace, 1989). This might be expected for moisture flow at critical moisture contents and porosities (Crawford, 1994). Phillips (1993) reinterpreted Jenny’s (1941) state factor model (soil is a function of climate, organisms, relief, parent material, and time) as a nonlinear dynamical system (rather than linear), while emphasizing deterministic chaos. One

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advantage of using nonlinear dynamical system theory is that it recognizes the interdependence of the components of the model (Phillips, 1993). Phillips ef aZ. (1996) suggested that deterministic uncertainty reconciles the traditional view that soil variability can be explained by more and better measurements and nonlinear dynamical systems-based views. Deterministic uncertainty arises from the effects of known or hypothesized, but unobserved or unmeasureable, underlying constraints and differs from nonlinear dynamical systems theory in that it does recognize that uncertainty could be possibly reduced if more information could be obtained about the underlying constraints or effects (Phillips et al., 1996). It was found for the podzolized soils (where age, parent material, and climate are constant) studied by Phillips et al., (1996) that only about 20% of the spatial variation in soil morphology could be explained by topography and drainage variables, leading them to say that deterministic complexity (chaos), associated with the unstable growth of minor perturbations, could explain the observed soil variability. Concepts such as chaos and deterministic uncertainty are little known in soil science, and this is an area for future research.

D. DESCRIBING ROOTMORPHOLOGY USING FRACTAL GEOMETRY Root systems can be considered as mass fractals, and therefore morphological descriptions of roots have been quantified using fractal geometry (Tatsumi et al., 1989;Eghball et al., 1993b). Intuitively, we would expect the fractal dimension of the root system to be influenced by soil structure, plant nutrition, and root diseases. A higher mass fractal dimension would indicate that the root system fills the soil space effectively, and this could indicate the plant’s ability to take up water and nutrients. Tatsumi et af. (1989) reported values of D,,, in the range 1.48-1.58 for the root systems of several crops, including maize, wheat, and peanut. Eghball et al. (1993b) concluded that fractal analysis of root systems may be useful in distinguishing treatment differences after studying the effect of nitrogen stress on the morphology of corn roots. The value of D , for a root system that had no applied N was significantly less than for root systems that had received applications of N. Additionally, greater amounts of branching were found directly beneath the crown ( D , = 1.73) compared to deeper in the soil ( D , = 1.19). Of course, these estimates of D , were made from two-dimensional data (by laying the root systems flat) and can only be used as an indication of the intracacy and space-filling ability of roots in three-dimensional space. In the future, studies that characterize root morphology (this could equally be applied to fungal hyphae) and soil structure in siru could be done. It would probably involve staining the roots in a two-dimensional plane of soil structure. This

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type of study would allow the quantification of root morphology-soil structure relationships.

IX. FUTURE DIRECTIONS AND CONCLUSIONS Fractal geometry, although a relatively new research area in soil science, has been explored by many soil scientists in a great range of applications. In just over 10 years we have come a long way in our understanding of fractal geometry, particularly in the area of its applications and limitations. Fractal geometry is emerging as a powerful tool for describing soil heterogeneity and assisting in the understanding of soil processes, physical, chemical, and biological, as they relate to soil structure. Further research is required in many areas if the true potential or limitations of fractal geometry are to be realized and if the wider soil science community is to take more than a passing glance at the subject. Methods that can reliably estimate fractal dimensions seem to have been well studied and so research into areas where these fractal dimensions may have a useful application should be the focus of our attentions. Clearly, there is more that we can do than simply measure another fractal dimension. For example, we can use bulk density-aggregate size relationships to obtain estimates of Dm.Practically, it would be useful if we could predict features of aggregates, other than bulk density, using this fractal dimension (Baveye and Boast, 1997). Rigorous testing of a theory that makes use of fractals is important. Until tested, we do not know if the theory is valid. This sentiment was expressed by Rieu and Sposito (1991b), stating that precise concurrent data on soil aggregate physical properties and soil-water parameters are required to evaluate the applicability of fractal concepts to soil science. Crawford (1994) developed a relationship between soil structure and the hydraulic conductivity of soil and stated that the theory lent itself to more rigorous testing using experiments designed to facilitate the measurement of Dm and d for soil cores for which hydraulic properties have previously been derived. Following this, the theoretical relation between moisture content and matric potential derived in Crawford ef al. (1995) was tested and verified using direct measurement of Dm. A final example is the theory for diffusion through fractal networks (Crawford et al., 1993a; Anderson er al., 1996), which needs to be tested by making actual diffusion measurements on soil cores and then estimating fractal dimensions from images of soil structure made from the cores. Estimated rates of diffusion (e.g., see Glasbey, 1995) can then be tested for reliability against actual measured rates. Empirical relationships, relating fractal properties to the Brenner number for chloride breakthrough (Hatano ef al., 1992), and total amount of outflow (Hatano and Booltink, 1992), for example, have been successful.

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In some of the studies reported here soil chemical properties and soil mineralogy have been discussed in relation to the fractal dimension estimated for a particular soil, or they have been used to explain apparent changes in fractal scaling. Further research into relating chemical properties and mineralogy of different soil types to their fractal dimensions may help in the understanding of the origin of this type of fractal scaling. This type of research could not discount the influences of physical properties and biological influences on soil structure, however. Reliable methods of extrapolating values of fractal dimensions measured in two dimensions to three dimensions are required. Simply adding one, assuming isotropy, is not necessarily realistic, and isotropic soil is probably an exception rather than the rule. Hatano and Booltink (1992) have suggested one method. Concurrent measurements of D,,, estimated from bulk density data and from two-dimensional images of soil structure need to be made to determine a relationship, if any, between the two. Reported estimations of Dmusing the two methods are similar. Crawford et al. (1993b) suggest that there may be problems relating Dm estimated from the two different methods because in the case of Dm for two-dimensional soil images it represents the structure of the saturated aggregate prior to impregnation, which may cause swelling in some soil types. A small point, but nevertheless important, is that a concerted effort should be made by soil scientists to keep the terminology simple and consistent in the literature dealing with fractal geometry. If symbols for the different types of fractal dimensions are reported consistently, it will help with the understanding of papers and for comparisons between papers, particularly for soil scientists that are new to the world of fractal geometry. Symbols such as Ds,Dm, and D, for the surface fractal dimension, mass fractal dimension, and fragmentation fractal dimension, respectively, seem the most appropriate and are self-explanatory. The type of fractal dimension being estimated should also be clarified by authors when only one type of dimension is being estimated, rather than simply referring to it as the fractal dimension. This will also help in the transfer of information from authors to readers and make the interpretation of results all the much easier. Overall it appears that fractal geometry will have a role to play in the future of soil science. Natural structure is, by and large, variable across a broad range in scale, and the use of fractal geometry to describe nature can only become more widespread as the theory of fractals becomes better understood by the scientific community. The fractal characteristics of nature, and indeed soil, also have to be taken into account when describing and predicting the processes occurring within them.

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REFERENCES Ahl, C., and Niemeyer, J. ( I 989). The fractal dimension of the pore-volume inside soils. Zeifschrififur P~ffn~enrrnuiirum und Bodenkunde 152,457458. Amal, R., Raper, J. A,, and Waite, T. D. (1989). Fractal structure of hematite aggregates. J. Colloid and Interfnce Sci. 140, 158-168. Anderson, A. N.. and McBratney, A. B. (1995). Soil aggregates as mass fractals. Ausr. J. Soil Rex 33, 757-772. Anderson, A. N., McBratney, A. B., and FitzPatrick, E. A. (1996). Soil mass, surface, and spectral fractal dimensions estimated from thin section photographs. Soil Sci. Soc. Am. J. 60,962-969. Armstrong, A. C. (1986). On the fractal dimensions of some transient soil properties. J. Soil Sci. 37, 641-652. Arya, L. M., and Paris, J. F. (198 I). A physicoempirical model to predict the soil moisture characteristic from particle-size distribution and bulk density data. Soil Sci. Soc. Am. J. 45, 1023-1030. Avnir. D. (1989). “The Fractal Approach to Heterogeneous Chemistry.” John Wiley & Sons, ChiChester, UK. Avnir, D., Farin, D., and Pfeifer, P. (1994). Molecular fractal surfaces. Ncrfurr 308,261-263. Avnir, D., Farin, D., and Pfeifer, P. (1985). Surface geometric irregularity of particulate materials: The fractal approach. J. Colloid und Intef‘ice Sci. 103, 112-123. Barak, P., Seybold. C. A,, and McSweeney, K. (1996). Self-similitude and fractal dimension of sand grains. Soi( Sci. Suc. Am. J. 60,72-76. Barndorff-Nielsen, 0.( 1977). Exponentially decreasing distributions for the logarithm of particle size. Proc. Royal SOC.London A 353,401 -4 19. Rarndorff-Nielsen, O., Dalsgaard, K.. Halgreen, C., Kuhlman, H., Mpller, J . T., and Schou, G. (1982). Variation in particle size distribution over a small dune. Sedimenrology 29,53-65. Bartoli, F., Philippy, R., Doirisse, M., Niquet, S., and Dubuit, M. (1991). Structure and self-similarity in silty and sandy soils: The fractal approach. J. Soil Sci. 42, 167-1 85. Bartoli, F., Philippy, R., and Burtin, G. (1992a). Poorly ordered hydrous Fe oxides, colloidal dispersion and soil aggregation. 11. Modification of silty soil aggregation with Fe(II1) polycations and model humic macromolecules. J. Soil Sci. 43,49-75. Bartoli, F., Philippy, R., and Burtin, G. (1992b). Influence of organic matter on aggregation in Oxisols rich in gibbsite or in goethite. I. Structures: The fractal approach. Geoderma 54,23 1-257. Bartoli, F., Burtin, G., Philippy, R., and Gras, F. (1993). Influence of fir root zone on soil structure in a 23 m forest transect: The fractal approach. Geoderma 56,67-85. Bartoli, F., Burtin, G., Royer, J. J., Gury, M., Gomendy. V., Philippy, R., Leviandier, T., and Gafrej, R. (1995). Spatial variability of topsoil characteristics within one silty soil type. Effects on clay migration. Geoderma 68,279-300. Baveye, P., and Boast, C. W. (1997). Fractal geometry, fragmentation processes and the physics of scale-invariance: An introduction. I n “Fractals in Soil Science” (P. Baveye, J.-Y. Parlange, and B. A. Stewart, eds.), pp. 1-54. Lewis Publishers, Boca Raton, FL. Baveye, P., Parlange, J.-Y., and Stewart, B. A. (eds.) (1997). “Fractals in Soil Science.“ Lewis Publishers, Boca Raton, FL. Bertuzzi, P., Rauws, G., and Courault, D. (1990). Testing roughness indices to estimate soil surface roughness changes due to simulated rainfall. Soil and Tillage Res. 1 7 , 8 7 4 9 . Booltink, H. W. G., Hatano, R., and Bouma, J . (1993). Measurement and simulation of bypass flow in a structured clay soil: A physico-morphological approach. J. Hydro/. 148, 149-1 68. Brakensiek, D. L., and Rawls, W. J. (1992). Comment on “Fractal processes in soil water retention” by Scott W. Tyler and Stephen W. Wheatcraft. WaferResour. Rex 28,601-602. Brakensiek, D. L.. Rawls, W. J., Logsdon, S. D., and Edwards, W. M. (1992). Fractal description of macroporosity. Soil Sci. Soc. Am. J. 56, 1721-1723.

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COOLSEASON GRAINLEGUMESTO SOIL

RESPONSES OF

ABIOTIC STRESSES H. P. S. Jayasundara, B. D. Thomson, and C. Tang Cooperative Research Centre for Legumes in Mediterranean Agriculture University of Western Australia Nedlands, Western Australia 6907 Australia

I. Introduction 11. Soil ,4cidity A. Responses of Cool Season Grain Legumes to Soil Acidity B. Factors Relating to Poor Growth in Acidic Soils C. Effects of Soil Acidity on Nodulation and N2 Fixation D. Genetic Variation in Response to Soil Acidity 111. Soil Salinity and Sodicity A. Responses of Cool Season Grain Legumes to Soil Salinity B. Responses of Cool Season Grain Legumes to Sodicity C. Factors Relating to Poor Growth in Saline and Sodic Soils D. Factors Influencing Salinity Response E. Effects of Salinity and Sodicity on Nodulation and N z Fixation F. Genetic Variation in Response to Salinity and Sodicity W. Soil Alkalinity A. Responses of Cool Season Grain Legumes to Soil Alkalinity B. Factors Relating to Poor Growth in Alkaline Soils C. Effects of Soil Alkalinity on Nodulation and N 2 Fixation D. Genetic Variation in Response to Soil Alkalinity V. Soil Compaction A. Responses of Cool Season Grain Legumes to Soil Compaction B. Factors Relating to Poor Growth in Compacted Soils C. Effects of Soil Compaction on Nodulation and N2 Fixation D. Genetic Variation in Response to Soil Compaction VI. Waterlogging A. Responses of Cool Season Grain Legumes to Waterlogging B. Physiology of Waterlogging Stress C. Effects of Waterlogging on Nodulation and N, Fixation D. Genetic Variation in Tolerance to Waterlogging VII. Conclusions References

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I. INTRODUCTION Grain legumes are an important source of dietary protein for many people throughout the world, particularly for those in developing countries. They also constitute a major component of animal feed in developed countries. Grain legumes play a major role in low-input agricultural systems because of their ability to fix atmospheric nitrogen. Their contribution of biologically fixed nitrogen is a key factor in sustaining long-term soil fertility in cereal production both in the developed and developing worlds. Grain legumes can be categorized into two major groups, based on their geographical and climatic distribution in the world: (1) warm season grain legumes and (2) cool season grain legumes. Warm season grain legumes, which include soybean (Glycine m a ) , common bean (Phaseolus vulgaris), cowpea (Vigna unguiculata),mungbean (Vigna radiata),blackgram (Vigna mungo), pigeon pea (Cujanus cajan),and several other species with regional importance, e.g., adzuki bean (Vigna angularis)and lima bean (Phaseoluslunatus), are cultivated in tropical and subtropical climatic regions of the world. Major cool season grain legumes include chickpea (Cicer arietinum L.), faba bean (Vicia faba L.), lentil (Lens culinaris Medik.), and field pea (Pisum sativum L.), which are the main food legumes, and lupins (predominantly Lupinus angustifolius, L. abtus, and L. luteus) and vetches (Viciasp.), which are mainly used as animal feed. There is a considerable production of Luthyrus safivus (chickling pea or chickling vetch), particularly in India, Bangladesh, Nepal, and Ethiopia, but the future potential of this species as a food or feed legume largely depends c n introduction of cultivars with low concentrations of antinutritional factors (Oram and Agcaoili, 1994). Cool season grain legumes are primarily cultivated in temperate, mediterranean, and subtropical regions in the world but are also grown at high altitudes in the tropics (e.g., Ethiopia). Cool season grain legumes occupied nearly 40% of the total world grain legume producing area (67.2 X lo6 ha) but contributed more than 50% of the total world grain legume production (58.0 X lo6 tonnes) during the period from 1992 to 1994 (FAO, 1994). Of the total cool season grain legume production (29.5 X lo6 tonnes), field pea comprised the biggest share with 49% of the total, followed by chickpea (24%), faba bean (13%), lentil (9%), and lupins (4%) (FAO, 1994; Cox, 1997). Over 80% of the total world production of faba bean and lentil and almost all of the world production of chickpea occurred in developing countries, whereas about 85% of total field pea production occurred in developed countries (FAO, 1994). Australia produces the greatest share (80%) of world lupin (Cox, 1997). Current production and future expansion of cool season grain legumes are constrained by biotic and abiotic factors. Biotic constraints for cool season grain legumes include diseases, insect pests, and, for some species, competition from weeds, e.g., Orobanche spp. (Saxena, 1993), and these aspects have been dis-

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cussed in several recent reviews (Bond et al., 1991; Singh el at., 1991; Ali et al., 1991; Erskine et ai.,1991). The most common abiotic factors that constrain the yield of cool season grain legumes are moisture stress and extremes of temperatures (both heat and cold), and these constraints have received the most attention (Buddenhagen and Richards, 1988; Saxena, 1993). Other abiotic constraints for cool season grain legumes are mainly soil related and include acidity, alkalinity, salinity, sodicity, waterlogging, and deficiency or toxicity of mineral nutrients (Saxena, 1993). Soil abiotic stresses may act directly on plant growth but may also interact with root growth to affect the water and nutrient relations of the plant. The extent of limitation imposed by soil-related abiotic stresses on cool season grain legume production has not been estimated on a worldwide scale, but it is of considerable importance at the regional level. Currently, there are about 950 X lo6 ha (about 6% of the world’s land area) of “salt-affected land” worldwide and include both saline and sodic soils (Flowers and Ye0 1995). A large proportion of this land is distributed in the major cool season grain legume producing regions, e.g., in the Indian subcontinent, where more than 70% of the world’s chickpea production and nearly 30% of the world’s lentil production originates (FAO, 1994). Between 14.1 X lo6 and 34.3 X 106 ha is salt affected (Singh, 1992; Abrol er al., 1988). Moreover, the problem of secondary salinization is ever increasing due to either rising water tables resulting from clearing of native vegetation for cultivation (e.g., Australia, McWilliam, 1986) or inadequate drainage in irrigated land (e.g., India and Pakistan, Singh. 1992). According to Szabolcs (1 987) about half of the existing irrigation systems in the world are more or less under the influence of secondary salinization, alkalization, and waterlogging due to mismanagement. In India alone an average of 11% of the potential irrigated area is subjected to salinity, and an average of 18% is waterlogged (Singh, 1992). Transient waterlogging can also affect the growth of cool season grain legumes in rain-fed mediterranean environments due to the high concentration of rainfall during winter, particularly where soil drainage is poor. We were unable to find reliable estimates of world production losses of cool season grain legumes due to waterlogging or to the total area of land affected by waterlogging. As with other soil abiotic stresses, the extent of constraint imposed by soil acidity on cool season grain legume production has not been accurately estimated. According to Uexkull and Mutert (1994), of about 3190 X lo6 ha total potentially arable land in the world, nearly 80% (about 2500 X lo6 ha) is acidic. Although a large proportion (about 68%) of these acidic soils occur in the humid tropics, a considerable proportion is also distributed in Australia, North America, and Europe (Uexkull and Mutert, 1994) where the potential for cool season grain legume production is high. As a regional example, acidic soils (pH H,O < 6.0) occupy around 15 million ha of arable land in southern Australia (Porter and McLaughlin, 1992), and this is already a considerable barrier for expansion of cool season grain legumes (particularly chickpea and faba bean) production in this region.

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The primary propose of this review is to pool current knowledge and understanding of the responses of different cool season grain legumes to soil abiotic stresses and to identify the major limitations to plant growth, as well as to explore the extent of inter- and intraspecific genetic variation in response to soil abiotic stresses for most commonly cultivated cool season grain legumes. We have mainly focused on experimental evidence directly relevant to cool season grain legumes, but in discussing principals examples from tropical grain legumes are considered, particularly when information is not available for cool season grain legumes. We have divided the major soil abiotic stresses into five sections (acidity, salinity and sodicity, alkalinity, soil structural problems, and waterlogging), but it should be remembered that in most situations these stresses are closely related, e.g., sodicity and soil structural problems, salinity and waterlogging. In each section, the responses of cool season grain legumes to a particular stress are discussed first and followed by the major factors contributing to reduced growth and yield. The effects of these factors on symbiotic nitrogen fixation are considered separately. We end each section by considering the extent of intraspecific variation in response of particular stress factor for the major cool season grain legumes.

11. SOIL ACIDITY Acidification is a natural soil-forming process favoured by high rainfall, low evaporation, leaching of basic cations, and high oxidative biological activity that produces acids (Rowell, 1978).Human activities, such as intensive agriculture and industrialization, can accelerate the rate of acidification (Helyar and Porter, 1989). The major constraints to plant growth in acidic soils are toxicities of H+, aluminium, and manganese and deficiencies of calcium, magnesium, phosphorus, and molybdenum (Foy, 1984). The relative importance of these constraints may vary with soil type, parent material, soil pH, soil structure and texture, and plant species. Acidity in the surface soil can be ameliorated with liming, but subsoil acidity is a serious constraint to plant growth because it can not be easily ameliorated with surface application of lime (Helyar and Porter, 1989).The chemistry of acidic soils has been comprehensively reviewed by Thomas and Hargrove ( 1984) and Ritchie (1989).

A. RESPONSESOF COOLSEASONGRAINLEGUMES TO SOILACIDITY Soil acidity considerably limits the productivity of cool season grain legumes. This is clearly demonstrated by large improvements in growth and yield observed

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for many species in response to liming (Jessop and Mahoney, 1982; Mahler and McDole, 1985; Brooke eta[., 1989).Under field conditions in northeastern Victoria, liming acidic soils with pH (CaCI,) 4.4 improved grain yield of field pea by about 50% and chickpea by more than 140% (Brook er al., 1989). Vegetative growth of faba bean, field pea, and chickpea on acid soils with pH 5.4 (H,O) was increased by more than 200% in response to liming (Jessop and Mahoney, 1982). Mahler and McDole (1987) found that a decrease in soil pH by one unit from a critical level of about 5.5 (H,O) caused around 80% decrease in the grain yield of field pea and lentil. The responses of cool season grain legumes to soil acidity may vary between species (Fig. 1). Among commonly cultivated species, lentil appears to be the most sensitive species, growth being reduced at pH < 7 in nutrient solution even when mineral nitrogen was supplied (Tang and Thomson, 1996). In contrast, the growth of lupins (L. nlbus and L. u ~ g u ~were ~ not ~ affected ~ ~ ~until i pH ~ ~< 5) (Fig. 1). Chickpea was also affected by pH below 7.0 when dependent on biological N, fixation but was relatively less affected when mineral nitrogen was supplied (Tang and Thomson, 1996). Field pea and faba bean were moderately sensitive to low pH, growth being reduced at pH < 6 (Fig. 1 ) . These comparisons between species, however, were based on growth responses to one factor in acidity stress, i.e., high H + activity or low pH. Our present knowledge on the differential responses of cool

. -0- L angustifohus -0-L albus +V faba +C

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-.

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~

d 3

4

5

6

7

8

KHCO, (8.7)

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Figure 1 Effects of pH and bicarbonate ions on relative shoot fresh weight (N2-fixing plants) of six cool season grain legume species grown i n nutrient solutions for 23 days (redrawn from Tang and Thornson, 1996).

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season grain legumes to other factors associated with soil acidity, i.e., A1 and Mn toxicity, are limited to only a few species. Results from several studies, for example, have shown that lupins are relatively more tolerant of high concentrations of A1 than are faba bean (Guerrier and Morard, 1978; Horst and Goppel, 1986a; Grauer and Horst, 1990). Among lupin species L. luteus is relatively more tolerant of high A1 than is L. angusfifolius (Can and Sweetingham, 1994). In an acidic soil containing 30 ppm exchangeable A13+ and 50 ppm exchangeable Mn2+ at pH (CaCl,) 4.4, chickpea yield responded more to liming than that of field pea, which in turn responded more than that of lupin (Brooke et al., 1989). Cool season grain legumes generally appear to be more sensitive to acidity than do cereals (Mahler and McDole, 1987; Brook et al., 1989; Yan et a/., 1992); for example, the minimum acceptable pH (H,O) for maximum grain yield of lentil and field pea were 5.65 and 5.52, respectively, compared with 5.23 for barley and 5.19 for wheat (Mahler and McDole, 1987).

B. FACTORS RELATINGTO POORGROWTHIN ACIDICSOILS 1. Hydrogen Ion Toxicity High H+ activity at low pH can directly affect grain legume growth; however, this is compounded and often overshadowed by Al toxicity, Mn toxicity, and deficiencies of Ca, Mg, and P (Foy, 1984). Thus, most evidence for inhibitory effects of H+ on growth of cool season grain legumes has come from solution culture experiments (Evans et al., 1980; van Beusichem, 1982; Schubert et al., 1990a; Yan et al., 1992;Tang and Thomson, 1996). Schubert et al. (1990a), for example, found that shoot dry matter of faba bean dependent on mineral nitrogen decreased by more than 25% when solution pH was reduced from 7.0 to 5.5. Similarly, Tang and Thomson (1996) found that shoot growth of a number of cool season grain legumes (lentil, chickpea, faba bean, and field pea) supplied with mineral N declined at pH below 5.0 in the nutrient solution. These results suggest a direct effect of high H+ on grain legume growth. The reductions in shoot growth of grain legume species at low pH is normally associated with more severe depressions in root growth; for example, solution pH at 5.5 reduced root dry matter of field pea by about 40% even without a decrease in shoot dry matter (van Beusichem, 1982). In faba bean, the rate of root elongation was about 80% lower at pH 4.0 than at pH 6.5 in the solution culture (Yan et a/., 1992). The effects of low pH in the bulk soil in reducing root growth can be accentuated by decreased rhizosphere pH in plants dependent on N, fixation compared with plants fed with nitrate (van Beusichem and Langelaan, 1984). The depressions in root elongation and shoot growth associated with high concentrations of Ht are related to decreased proton extrusion from roots (van Beu-

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sichem, 1982; Schubert etal., 1990a; Yan elal., 1992). In faba bean growth in nutrient solutions, increasing H+ activity decreased net H+ extrusion from roots with a concurrent reduction in the rate of root elongation (Yan et a[., 1992). At pH Values below 4.0 both root elongation and net extrusion of H + in faba bean had ceased (Yan er al., 1992). The impairment of net H + extrusion at low pH may be attributed to an inhibition of H+-ATPase activity (Marschner, 1995) or reentry of H+ into root cells (Yan et ai., 1992). Low pH can impair membrane integrity, particularly at low external Ca2+ concentrations (Foy, 1984). Increasing Ca2+ concentration from 0.1 mM to 5 mM in the rooting medium significantly increased both net H+ release from the roots and the rate of root elongation in faba bean at pH 4.1 (Yan et ul., 1992). Hydrogen ion extrusion from the root is an important process required for the uptake of nutrients (Marschner, 1995) and the regulation of cytoplasmic pH (Felle, 1988). Consequently, the inhibition of H + extrusion at low pH may lead to both limited nutrient uptake and disturbed cytoplasmic pH regulation.

2. Aluminium Toxicity Aluminium toxicity is probably the most dominant growth-limiting factor in acidic soils. With decreasing pH, the solubility of A1 in soils increases sharply, particularly at pH below 5.0 (Foy, 1984). However, Al-mediated inhibition of growth is not directly correlated with total soluble A1 in the soil because of the high variation in relative toxicity of different A1 ion species (Taylor, 1988). The chemical reactions that form different A1 species are highly pH dependent; thus the phytotoxicity of A1 also varies with soil pH (Ritchie, 1989). In addition, many other soil factors-ie., type of clay minerals, organic matter levels, and ionic strength-as well as plant factors may influence the extent of phytotoxicity of different A1 species (Foy, 1984). In general, toxicity is decreased when A1 is complexed with organic ligands, F- and SO:-, whereas the activity of A13+ and Al-hydroxy species are considered to be most phytotoxic (Ritchie, 1989). Aluminium primarily affects root growth, particularly root elongation and lateral branching. In faba bean, root elongation is decreased by about 50% in the presence of 9.3 mM m-3 AICI, in the rooting medium (Grauer and Horst, 1990), whereas in field pea, root elongation was completely inhibited by 100 mM m p 3 AlCl, in the solution (Matsumoto, 1991). Numerous reports of tropical grain legumes also demonstrate the inhibitory effects of A1 on root growth (e.g., cowpea, Horst et al., 1983; common bean, Buerkert et al., 1990; soybean, Foy er al., 1993). Aluminium-affected roots are stubby as a result of the inhibition of elongation and lateral branching (Foy, 1984; Matsumoto, 1991 ). In the field, poor root penetration into acidic subsoils results in plants that are shallow rooted ( e g , faba bean, Hartmann and Aldag, 1989; chickpea and field pea, Seifu, 1993) and therefore inefficient in exploring nutrients and water from deeper soil layers (Foy, 1992).

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Most shoot symptoms resulting from A1 toxicity are secondary and often similar to deficiencies of P, Ca, and Mg (Foy, 1984). Aluminium taken up by plants is normally sequestered in roots and not easily translocated to shoots until at least the retaining capacity of roots is exceeded (e.g., faba bean and field pea, Wagatsuma, 1984). Thus, growth depressions resulting from A1 toxicity may not be well correlated with leaf A1 concentrations (Taylor, 1988). Some evidence indicates that A1 might inhibit the biosynthesis of cytokinins in the root apex, which regulates shoot growth under normal conditions (Pan et al., 1989). It is generally believed, however, that reduced shoot growth due to A1 toxicity is primarily due to limited supply of nutrients, particularly Ca, Mg, and P, as well as water stress (Marschner, 1995). The mechanisms of A1 toxicity are complex and still not fully understood. Aluminium has been found to interfere with a large number of physiological processes in root cells (Matsumoto, 1991). These may be grouped into four categories (Taylor, 1988): (1) inhibition of DNA synthesis and mitosis, (2) disruption of membrane structure and function, (3) inhibition of cell elongation, and (4) disruption of uptake and translocation of mineral nutrients and metabolism. Inhibition of cell division by high concentrations of AI3+ is the primary cause for restricted root elongation in some grain legumes (e.g., cowpea, Host et al., 1983; field pea, Matsumoto, 1991). Aluminium interferes with nucleic acid metabolism in root meristematic cells of field pea (Matsumoto, 1991). Restricted cell elongation, however, also contributes greatly to reduced root elongation, especially at moderate concentrations of external A13+ (Marschner, 1991). AIuminium has a high affinity for phospholipids in plasma membranes (Haug and Shi, 199 1); thus A1 can bind to the plasma membrane of the root rhizodermal and cortical cells and thereby impair its normal functioning (e.g., field pea, Wagatsuma ef al., 1995). All cool season grain legume species are not equally affected by high concentrations of A1 in the rooting medium. Roots of some species can withstand relatively high concentrations of A1 without significant inhibition of elongation. In lupin (L. luteus), for example, between 3 and 4 mg A1 g-I dry weight might accumulate in root apical zone before root elongation is inhibited, whereas less than 1 mg A1 g-’ dry weight is sufficient to inhibit root elongation in faba bean (Horst and Goppel, 1986b). Low concentrations of A]“+ (0.5-2 mg 1-’) at low pH may even stimulate plant growth, particularly in Al-tolerant species or cultivars (e.g., field pea cv. Uspekh, Klimashevskii et al., 1970; lupin, Horst and Goppel, 1986b), which may be related to a possible amelioration of H + ion toxicity by small concentrations of Aln+ (Marschner, 1995). Although some plant species can tolerate high concentrations of aluminium by internal activation in older leaves (e.g., tea, Matsumoto ef al., 1976), the majority of crop species or cultivars tolerant to A1 achieve this ability mainly by “exclusion” of A1 from sensitive sites, i.e., cytoplasm, plasma membrane-apoplasm interface, and the apoplasm in root cells (Marschner, 1995). The mechanisms that may be involved in A1 exclusion are

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(Taylor, 1995) (1) immobilization of A1 at the cell wall or low cell wall CEC, (2) selective permeability of the plasma membrane, (3) formation of a plant-induced pH barrier in the rhizosphere or root appoplasm, (4) exudation of chelator ligands, (5) exudation of phosphate, and (6) A1 efflux. Of these, the exudation of chelator legands is a frequently observed adaptive mechanism in a number of grain legumes species (e.g., L. luteus, Guerrier et a/., 1977; cowpea, Host et al., 1983; soybean, Horst et a/., 1990; chickpea, Rai, 1991). In some instances A1 tolerance has been associated with greater efficiency of uptake and translocation of P (e.g., field pea cv. Uspekh, Klimashevskii et al., 1970) and Ca (e.g., cowpea, Horst, 1987). 3. Manganese Toxicity The availability of Mn2+ and, hence, the potential for manganese toxicity increases with decreasing pH in the soil provided sufficient quantities of total manganese are present (Foy, 1984). In general, manganese toxicity can occur at relatively higher pH levels than those required for A1 toxicity (Fox et al., 1991). Unlike aluminium, manganese absorbed by plant roots is easily transported to shoots. Thus, in most plants, including grain legumes, manganese toxicity first affects shoot growth (Foy, 1984). Roots, however, may also be affected if the toxicity becomes severe (e.g., soybean, Suresh et al., 1989). In general, the reduction in shoot growth due to Mn toxicity is correlated well with leaf Mn concentration (e.g., soybean, Bethlenfalvay and Franson, 1989; cowpea, Vega et a/., 1992). The common symptoms of Mn toxicity in grain legumes are interveinal chlorosis and “crinkle leaf’ in young leaves (e.g., soybean, Heenan and Carter, 1977) and formation of brown speckles in mature leaves (e.g., common bean, Horst and Marschner, 1978; cowpea, Wissemeier and Horst, 1992). Some of these symptoms (such as crinkle leaf and chlorosis) are probably related to induced deficiencies of Ca and Mg (Maschner, 1995). Apart from the interferences with Ca and Mg nutrition, excess Mn disrupts phytohormone balance (e.g., activities of IAA), certain enzyme activities (e.g., RuBPcarboxilase) and, membrane functions in leaf tissues (Horst, 1988). For cool season grain legumes, only limited information is available on critical toxic levels of Mn at which growth is adversely affected. Of different species, lupins (L. angustifolius and L. albus) appear to be relatively tolerant to high concentrations of Mn since they can accumulate relatively higher concentrations of Mn than most other species without apparent toxicity symptoms (Gladstones, 1962). In L. angustifolius toxicity was reported at shoot Mn 2 2000 ppm, whereas in chickpea toxicity occurs at 2 520 ppm manganese in shoots (Reuter and Robinson, 1986). Among other cool season grain legumes, toxicity symptoms were apparent when plants had > 1000 ppm Mn in faba bean and > 1700 ppm Mn in field pea (Snowball and Robson, 1991; Weir and Cresswell, 1993). For comparison, in some common tropical grain legumes (e.g., soybean, common bean,

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cowpea, pigeon pea) growth was reduced by Mn at concentrations between 140 and 1600 ppm in the shoots (Edvards and Asher, 1982). The mechanisms of Mn tolerance are located predominantly in shoots (Horst, 1988). In Mn-tolerant cowpea cultivars manganese was uniformly distributed in mature leaves, whereas in sensitive cultivars with the same manganese content, manganese was accumulated in localized spots that correlated with chlorotic and necrotic patches (Horst, 1983). Other proposed mechanisms for tolerance to high concentrations of manganese are ( 1) restricted translocation of manganese to young leaves, ( 2 ) undisturbed translocation of Ca from mature leaves to young leaves, and (3) formation of stable manganese complexes, for example, with oxalic acid and polyphenols (Horst, 1988).

4. Nutrient Deficiencies Apart from low pH and toxicities of Al"+ and Mn3+, decrease of Ca, Mg and P uptake, and Mo deficiency can also limit growth of cool season grain legumes in acidic soils. Decreasing medium pH, for example, from 6.0 to 4.0 depressed the uptake of Ca and Mg by field pea and L. albus (Findenegg, 1987) and decreasing pH from 7.0 to 4.0 depressed the uptake of Ca, Mg, and P by faba bean (Schubert et al., 1990a). Deficiencies of these nutrients at low pH occur either due to reduced availability in soil or to interactions with high concentrations of H', A]"+, and Mn2+.Increasing external Al"+ concentrations decreased the uptake of Ca and Mg in faba bean and L. luteus (Horst and Goppel, 1986b). Marschner (1995) implicated high concentrations of Hi, Al"+, and Mn2+ as the primary reason for inhibited uptake or deficiencies of Ca and Mg in acidic soils. High external concentrations of H + at low pH can inhibit net Ht extrusion from roots (e.g., field pea, van Beusichem, 1982; faba bean, Yan ef al., 1992), which is essential for the uptake of nutrients (Marschner, 1991). In addition, high concentrations of Al"+ and Mn2+ can compete with Ca2+ and Mg2+ for binding at cation exchange sites in the apoplasm, further reducing the uptake of Ca2+ and Mg2+ (Marschner, 1991). In many instances, the risk of Al"+ and/or Mn2+-induced deficiencies of Ca and Mg and, thus, the severity of acidity-induced growth inhibition can be ameliorated by increasing external concentrations of Ca2+ and/or Mg2+ (Foy, 1992). In many instances plants effected by Al toxicity may exhibit symptoms similar to those of P deficiency (Foy, 1992). In a majority of acidic soils, P deficiency may occur due to high P-fixing capacity and/or precipitation of P to form less soluble aluminium phosphates in the soil (Rowell, 1987). Moreover, A1 can immobilize P on root surfaces, cell walls, and in the free spaces of plant roots presumably as aluminium phosphate, preventing P from being available for translocation to shoots and metabolism (e.g., cowpea, de Manzi and Cartwright, 1984). In addition, restricted root growth by A1 may further limit the uptake of relatively immobile P in the soil (Foy, 1992).

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In contrast with other micronutrients the availability of Mo is reduced with decreasing pH; thus deficiency of Mo is highly likely in acidic soils (Gupta and Lipsett, 1981). Molybdenum deficiency may particularly affect growth of legumes because of high requirements for Mo of plants dependent on biological N, fixation (see discussion later) (Coventry and Evans, 1989). Liming generally corrects Mo deficiency unless the soil is absolutely deficient of Mo (Foy, 1992).Large yield improvements in response to application of Mo have been observed in acidic soils deficient in Mo (e.g., soybean, Burmester et al., 1988; groundnut (Amchis hypogaea), Hafner et a/., 1992).

C. EFFECTSOF SOILACIDITYON NODULATION AND N, FIXATION Nodulation and N,fixation are particularly sensitive to soil acidity. This is clearly indicated by higher sensitivity of these species to acidity when dependent on biological N, fixation than on mineral nitrogen (Evans er d., 1980; van Beusichem and Langelaan, 1984; Schubert et at.. 1990b; Tang and Thomson, 1996). In general, nodulation declines at pH below 5.0 in most species including lupin, which is regarded as relatively acidity tolerant (Fig. 2). The impairment of the symbiosis arises from poor survival of the microsymbiont (field pea, Evans e f al., 1993; faba

3

4

5

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Figure 2 Effects of pH and bicarbonate ions on nodulation of six cool season grain legume species grown in nutrient solutions for 13 days (redrawn from Tang and Thomson, 1996).

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bean, Carter etal., 1995), inhibition of the nodulation process (field pea, Lie, 1969; Evans et af., 1980), and inhibition of nodule functioning (lentil, Rai and Prasad, 1983; field pea, Paulino et al., 1987; faba bean, Schubert et af.,1990b; chickpea, Rai, 1991). In most cool season grain legumes (except lupins), the nodule bacteria are fastgrowing rhizobia, and these are relatively more sensitive to acidity than slowgrowing bradyrhizobia, which nodulate most tropical grain legumes and lupins (Graham and Paker, 1964). Thus, rhizobial survival is an important limiting factor for nodulation in most cool season grain legumes growing in acidic soils. In some acidic soils, populations of Rhizobium spp. are extremely low. As an example, Amarger (1988) found that in acidic soils in France with pH < 5.5, Rhizobium leguminosarum bv. viciae was undetectable, whereas sufficient numbers of effective rhizobia of this species were often present in soils with alkaline reaction. Similar observations have been made in some acidic soils in Western Australia and southwest Victoria (Evans et al., 1993; Carter et al., 1995). That inadequate rhizobial populations limit nodulation in acidic soils was demonstrated by increased nodulation achieved through inoculation with appropriate rhizobia (e.g., faba bean, Carter et al., 1995; field pea, Evens et al., 1993; Seifu, 1993) and by increased rhizobial numbers and nodulation achieved through correcting soil acidity by liming (e.g., lentil, field pea, Mahler and McDole, 1985; Mohebbi and Mahler, 1989). Of various acidity factors, high concentrations of Hi andAl"+ and P deficiency appeared to be the major inhibitory factors for rhizobial growth (Coventry and Evans, 1989). In addition, Ca deficiency is an important limiting factor for some Rhizobium spp. (Howieson et al., 1995). Considerable genetic variation in response to acidity exists in rhizobia nodulating different cool season grain legumes (e.g., lentil, Rai and Prasad, 1983; chickpea, Rai, 1991; faba bean, Carter et al., 1995). Thus, it may be possible to extend the growth of cool season grain legumes to acidic soils by selection of acidic soil tolerant rhizobia (Howieson, 1995). Nodule initiation is also highly sensitive to acidity (Lie, 1969; Evans et al., 1980). In field pea, nodule initiation was 10 times more sensitive to acidity than was rhizobial growth, nodulation being completely inhibited at a medium pH 4.8 even when adequate populations of rhizobia were present (Evens et al., 1980). The inhibition of nodulation in field pea by acidity occurred in early stages of the infection process (Lie, 1969), suggesting that the most sensitive steps are probably rhizobial attachment, invasion, and development of the infection thread. Attachment of rhizobia to the host root is quite rapid, occurring within 2 hours of inoculation in field pea (Broughton et al., 1980). Evidence with other legumes also indicates that rhizobial attachment to root hairs (e.g., common bean, Vargas and Graham, 1988) as well as nod gene induction (e.g., subterranean clover, Richardson et al., 1988) are inhibited by acidity. The most adverse acidity factors inhibit-

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ing these processes are excess H+ and Al"+ and deficiency of Ca and P (Coventry and Evens, 1989). Howieson (1995) suggested that symbiotic tolerance achieved through the host plant in some species (e.g., Medicugo spp.) is largely due to the plant's ability to produce rhizobial nod gene inducers and stable exudates required for rhizobial attachment under acid soil stress. The development of nodules that have already been initiated appear to be little affected by soil acidity, and their specific nitrogenase activity may also not be significantly reduced (Lie, 1969). Nitrogen fixation per plant, however, may be significantly reduced in line with decreased nodule weight per plant (due to fewer nodule numbers). In field pea grown in nutrient solutions, for example, decreasing pH from 7.0 to 5 . 2 resulted in more than 60% reduction in total nitrogenase activity per plant (Paulino et af., 1987). Adding 3 p,M A1 (as Al,(SO,),) to the nutrient solution at pH 5.2 reduced nitrogenase activity by a further 20%, whereas number of nodules per plant and plant dry matter were not affected, suggesting a possible direct effect of Al"+ on nitrogenase activity (Paulino et al., 1987). Both nodule development and N, fixation may be restricted by Mo deficiency in acidic soils (Coventry and Evans, 1989). Molybdenum is a component of several enzymes, including nitrogenase, which is essential for N, fixation (Marschner, 1995). The requirement for Mo is several times higher in nodules than in any other plant part (Brodrick and Giller, 1991), and thus its deficiency can considerably reduce N, fixation. In groundnut growing in Mo-deficient acid soils, addition of Mo and P increased nodule dry weight (125%), specific nitrogenase activity (20%), and N uptake (55%)compared to P application alone (Hafner et al., 1992). Application of Mo to soybean grown in a Mo-deficient soil increased both N yield (33%) and seed yield (22%) in plants dependent on N, fixation but not in plants dependent on mineral N, indicating the greater importance of Mo for N, fixation compared to host plant growth (Parker and Harris, 1977).

D. GENETICVARIATION INRESPONSETO Son. ACIDITY The use of acid-tolerant species is an important agronomic practice complementary to liming. It is perhaps more important in situations where subsoil acidity inhibits root growth. Since soil acidity is a complex stress factor, plant adaptation to acidic soils often involves a combination of adaptive mechanisms, such as the link between Al-tolerance and efficient uptake and translocation of P (e.g., field pea cv. Uspekh, Klimashevskii etal., 1970; pigeon pea, Fujita et al., 1995) and the link between Al-tolerance and low internal requirements of Ca (e.g., cowpea, Horst, 1987). Similarly, Mn tolerance may be linked with efficient uptake and translocation of Ca (Horst, 1988). Since Al toxicity and Mn toxicity are the most

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dominant constraints in many acidic soils, tolerance to these two stresses has been used as a criteria for acid soil tolerance. These two characters, however, are not interconnected (Marschner, 1995). Although interspecific variation in tolerance to soil acidity has been demonstrated in cool season grain legumes (Horst and Goppel, 1986a,b; Grauer and Horst, 1990; Carr and Sweetingham, 1994; Tang and Thomson, 1996), the extent of intraspecific variation is almost unknown. In two separate studies where few genotypes from field pea and chickpea have been tested for Al-tolerance, results indicate that a considerable intraspecific variation is present. Klimanshevskii et al. (1970), for example, found that in two field pea cultivars grown in nutrient solutions containing aluminium (supplied as Al2(SO&), a relatively Al-tolerant cultivar showed only 32% reduction in growth (total dry matter) at 11 mg A13+ I - ' , whereas this level of A13' was completely detrimental to an Al-sensitive cultivar. Similarly, Rai (1991) found that at a solution concentration of 5 ppmA13+, shoot dry weight of a sensitive chickpea cultivar reduced by 70%, whereas that of a relatively tolerant cultivar reduced only by 27%. A wide intraspecific variation has been found in tropical grain legumes for both A1 tolerance (cowpea, Horst et al., 1983; Horst, 1987; common bean, Vargas and Graham, 1988; soybean, Campbell and Carter, 1990; Foy et al., 1993) and Mn tolerance (cowpea, Horst, 1982; soybean, Mascarenhas et al., 1995). Results from these studies indicate that, in general, intraspecific variation for Al-tolerance is more prominent in root growth than in shoot growth. Thus, the rate of root elongation in the presence of A1 appeared to be a useful selection criteria for the identification of intraspecific variation for A1 tolerance (Campbell and Carter, 1990; Foy et al., 1993). In 12 diverse soybean genotypes, the relative rate of root elongation in solutions containing A1 was well correlated with the relative rate of shoot growth in acidic soils (Campbell and Carter, 1990). In contrast to A1 tolerance, Mn tolerance is primarily related to the plants' ability to tolerate high concentrations of Mn in leaves (soybean, Heenan and Carter, 1977; Horst, 1983). Cultivars susceptible to Mn toxicity often develop distinct toxicity symptoms, making it relatively easy to recognize. Application of Mn to petiole and rating the induced Mn toxicity symptoms, for example, appeared to be a suitable screening technique for selection of tolerant cowpea genotypes at the vegetative stage (Horst, 1982). In cowpea, however, Mn tolerance at the vegetative stage is not necessarily correlated with Mn tolerance at the reproductive stage (Kang and Fox, 1980; Horst, 1982) because grain yield can be reduced severely by high internal concentrations of Mn without a significant decrease in vegetative growth (Kang and Fox, 1980). A separate screening is therefore suggested for identifying Mn tolerance at the reproductive stage (Horst, 1982). The reduction in grain formation following application of Mn to the peduncle was found to be a promising method for identifying genotypes tolerant to excess Mn at the reproductive stage (Horst, 1982).

GRAIN LEGUME RESPONSES TO SOIL ABIOTIC STRESSES

91

111. SOIL SALJNTY AND SODICITY Salt-affected soils are abundant in arid and semiarid regions of the world. Common ions contributing to this problem are Ca2+, Mg2+, Nat, CI-, SO:-, HCO;, and in some instances K C and No; (Bernstein, 1975). In a saline soil, electrical conductivity of the saturated extract (EC,) is > 4 dS m- (equivalent to 40 mM NaCl) and exchangeable sodium percentage (ESP) is < 15 (Gupta and Abrol, 1990). In these soils chlorides and sulphate salts of Ca and Mg are predominant, and soil pH is usually < 8.2. In some saline soils, however, ESPexceeds 15 and these are defined as saline-sodic soils (Gupta and Abrol, 1990). The effects of salinity and sodicity on plant growth in saline-sodic soils are nonadditive and noninteractive, with growth being primarily limited by salinity (Bemstein, 1975; Gupta and Abrol, 1990). In nonsaline sodic soils (ESP > 15, ECe < 4 dS m- I ) the total salt concentration is low with decreases in exchangeable Ca2+ and Mg'+ being balanced by increases in exchangeable Naf (Bemstein, 1975). Bicarbonate and carbonates are prevalent anions in these soils, and pH is often greater than 8.2 (Gupta and Abrol, 1990). The physical conditions of sodic soils are poor (Naidu and Rengasamy, 1993),and in addition to Na toxicity, conditions such as hard setting, surface crusting, compaction, and transient waterlogging may limit plant growth in these soils (discussed in detail in Sec. V and VI).

'

-

A. RESPONSESOF COOLSEASONGRAINLEGUMES TO SOILS A L ~ N I - ~ Y The majority of published reports on the responses of cool season grain legumes to salinity are based on experiments conducted under greenhouse conditions and to a limited extent experiments using confined field microplots with artificially created saline soils. Moreover, these experiments are highly variable both in the duration of the treatments and the stage of growth at which treatments were imposed. Notwithstanding these problems, the results from these experiments demonstrate that all aspects of growth are severely inhibited by soil salinity. The experimental results also show that the sensitivity to salinity can vary between stages of crop growth.

1. Germination and Seedling Growth Germination and seedling emergence are two indispensable parameters for the establishment of a better crop because failure at this phase will reflect badly in the final yield. Therefore, a substantial amount of research has been conducted to document the effect of soil salinity on germination and seedling growth. In most cool season grain legumes, seed germination is progressively delayed by salinity, but final germination percentage is not reduced until salinity is increased to relatively

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high levels, i.e., 6.2-9.0 dS m-' (e.g., faba bean, Hamid and Thalibudeen, 1976; lentil, Ayoub 1977; faba bean and field pea, Dua er al., 1989; chickpea, Yadav et al., 1989; faba bean and L. albus, Shaddad et al., 1990). The results from these experiments also show that most species are more tolerant to salinity at germination than at subsequent growth. There are considerable differences between species in terms of the level of salinity when germination and seedling emergence are significantly reduced. Germination and seedling emergence of faba bean, for example, were not reduced even at 9 dS m-' EC,, whereas both parameters of field pea were significantly reduced at salinity levels greater than 6 dS m-I (Dua et al., 1989). Faba bean also appears to be more tolerant to NaCl salinity than does L. albus at germination (Shaddad et al., 1990). In lentil, germination and seedling emergence (up 7 days after the radicle emergence) were not reduced by NaCl up to 7.9 dS m- (Ayoub, 1977), whereas those of chickpea were reduced by 4.2 dS m-' (Yadav et al., 1989). It is difficult to make generalizations about the response to salinity at germination and seedling emergence of different cool season grain legumes because comparisons have not been made under uniform conditions. The delay in germination under saline conditions may be mainly attributed to reduced rate of water uptake, particularly at initial stages of the germination process. In common bean, for example, initial stages of germination (up to radicle emergence) were more affected by osmotic stress created by polyethylene glycol (carbowax 1540)than by iso-osmotic concentrations of NaCl (Prisco and O'Leary, 1970). In contrast, seedling growth was inhibited more by NaCl stress than by polyethylene glycol. After radicle emergence, early seedling growth is largely dependent on the mobilization of reserves from the cotyledons to embryonic axis. Inhibition of seedling growth by external NaCl may largely be due to restricted translocation of reserves from the cotyledons and various assimilatory processes resulting from specific ion effects (Corchete and Guerra, 1986).

'

2. Plant Growth and Seed Yield Salinity reduces vegetative growth depending on the sensitivity of the species and the intensity of salinity. Generally, root growth is less affected than shoot growth (e.g., common bean, Wignarajah, 1990; field pea, soybean and common bean, Cordovilla etal., 1995a; faba bean, Cordovilla er al., 1996), thus the shoot-root ratio is decreased. Lupins (L. albus and L. luteus), however, are exceptions, root growth being more reduced by salinity than shoot growth (van Stevenink et d., 1982; Jeschke et al., 1986). Although root growth is less affected than shoot growth, some morphological and structural changes may occur in roots under salinity stress. In field pea such changes include thickening and curving of the roots and a reduction in the diameter of the roots and vascular cylinder because of the suppression of meristematic activity (Setia and Narang, 1985; Soloman et al., 1986).

93

GRAIN LEGUME RESPONSES TO SOIL AEUOTIC STRESSES

The sensitivity or tolerance of different crop species to salinity is commonly expressed as relative reduction in dry matter or seed yield as a function of increasing salinity (Mass and Hoffman, 1977). After a threshold salinity level the relative growth or yield is reduced linearly with increasing salinity (EC,). This is expressed as Y, = 100 - b (EC, - U ) where Y, = relative growth or yield; n = threshold salinity level in EC, units (dS m- '), which is maximum allowable salinity without significant reduction in yield; b = slope or the percentage yield reduction per unit of EC, after the threshold level. Different species can be classified into four groups: ( 1 ) sensitive, (2) moderately sensitive, ( 3 )moderately tolerant, and (4) tolerant, using the threshold salinity level and slope of the response curve (Mass and Hoffman, 1977). According to this classification, vegetative growth of the majority of cool season

~~~

3

80-

U

m

c

U

8

b

8

70

--

0 Faba bean (1)

rn Faba bean (2) A Faba bean (3)

60--

x Pea (4)

50-~

0 Chickpea(6)

4i--

0 Chickpea (7)

0 Lentil (5)

~

Figure 3 Effects of salinity on relative dry matter yield of' different cool season grain legumes compared to nonsalinized control plants; data are from iI ) Hamid and Talibudeen, 1976; (2)Abd-Alla, 1992; (3)Ayers and Eberhard, 1950; ( 4 )Cerda ei a/..1982; ( 5 )Ayoub, 1977;(6) Manchanda and Sharma, 1989; (7) Johansen et d.,(1990).Planrs in all experiments have been grown in artificially salinized (mainly with NaCl and Na,SO,) soil or sand in the greenhouse for 2-3 months. Classification of salinity tolerance (groups separated by solid lines) is baaed on Maas and Hoffman, 1977.

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H. P. S. JAYASUNDAEU ETAL.

Often the response of seed yield to salinity is not similar to the response of vegetative growth to salinity. In field micro-plots containing artificially salinized soils (with NaCl), for example, increasing salinity from 1.3 to 5.3 dS m- almost completely inhibited the seed yield of lentil (by 92%), whereas total plant dry matter was reduced only by 48% (a decrease in the harvest index from 0.3 to 0.03) (Ayoub, 1977). Similar decreases in harvest index have been observed for field pea growing in soil with mixed salinity (Cl- and SO:) (Lal, 1985). For chickpea, increasing salinity (Cl- dominate) from 1.9 to 5.1 dS m- completely inhibited pod formation, whereas plant dry matter was reduced only by 50% under greenhouse conditions (Manchanda and Sharma, 1989). Thus species comparisons based only on vegetative growth may not be adequate for selection of crops to saline soils. Some examples for threshold and critical salinity levels and slope of salinity response curve based on seed yield of commonly cultivated cool season grain legumes (except lupins for which published data are not available) are summarized in Table I. From these data as well as those in Fig. 3, it is apparent that faba bean

'

'

Table I Threshold Salinity Level (CJ, Critical Salinity Level (C5& and Slope of the Response Curve (Percentage Decline in Yield per Unit Increase in Salinity) for Seed Yield of Some Widely Cultivated Cool Season Grain Legumes" ~~~

Species Chickpea (C. urietinum)

Type of salinity

CI dominant (C I-SO, ratio SO, dominant (CI-SO, ratio NaCI-CaS0,MgSO, ratio

Lentil (L. culirzaris) Faba bean (V faho)

Pea ( P . sativum)

=

7:3)

=

3:7)

=

c,

C.4,)

(dSm-')

(dSm-')

Slope (9)

2.1

4

55

Manchanda and Sharma, 1989

5.1

8

46

Manchanda and Sharma, 1989

1.7

4

24

Dua. 1992

1.8

5

26

Ayoub, 1917

ND" 5.5

9 8.5

ND

El Karouri, 1979

2.6

9

9

2.8

8

11.5

Reference

7:2:1

NaCl CI dominant SO, dominant

NaCI-CaCI, ratio

=

I :1

NaCl and CaClz NaCI-CaCI, ratio

=

I :I

"Values were recalculated from the references indicated. "C,,, salinity at 50% yield reduction. 'ND = no data.

12

1986 Rabie er d.. Cerda ei a/., 1982 Lal, 1985

GRAIN LEGUME RESPONSES TO SOIL ABIOTIC STRESSES

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is relatively more tolerant to salinity than other grain legumes, whereas chickpea and lentil are highly sensitive. These observations need to be confirmed in experiments, both in the greenhouse and in the field, that compare the response of a range of grain legumes to salinity under uniform conditions.

B. RESPONSES OF COOLSEASON GRAINLEGUMES TO SODICITY Compared with the substantial amount of research on the response of cool season grain legumes to soil salinity, only few research publications have considered their response to soil sodicity. As with salinity, soil sodicity adversely affects cool season grain legumes from the beginning of crop establishment. In the few experiments where effects of increasing ESP (at EC, < 4 dS m-I) on growth of cool season grain legumes have been studied, results indicate that both germination and seedling emergence are significantly reduced with increasing ESP. For example, in nine chickpea cultivars tested in isolated field plots with ESP from 15 to 28 at EC, < 2.4 dS m- I, seedling emergence was markedly reduced at ESP > 20 (Kumar, 1985).For lentil, although germination was not reduced by sodicity up to ESP 30, seedlings survived only when ESP was less than 25 (Tewari and Singh, I99 I). The adverse effects of sodicity on chickpea progressively increased through the establishment phase, and seedling mortality increased from 10% in the control (15 ESP) to 83% at 28 ESP (Kumar, 1985). Toxicity due to excess Na+ was concluded to be the main reason for the seedling mortality in chickpea. Vegetative and reproductive growth of cool season grain legumes is also reduced by soil sodicity (Abrol and Bhumbla, 1979; Kumar, 1985; Singh and Abrol, 1987; Singh et al., 1993). In chickpea grown on field microplots, an increase of sodicity from 15 to 20 ESP reduced leaf area by 50% and leaf dry weight by 48% (Kumar, 1985). Similarly, in lentil an increase of sodicity from 10 to 15 ESP reduced total plant biomass by 60% (Singh et al., 1993). As with salinity, seed yield appears more sensitive to sodicity than does vegetative growth; for example, the level at which a 50% reduction of seed yield occur was around 10 ESP for chickpea (Abrol and Bhumbla, 1979) and less than 15 ESP for lentil (Abrol and Bhumbla, 1979; Singh et al., 1993). In both species, number of pods per plant and 1000 seed weight were severely decreased, whereas number of seeds per pod was little affected. In contrast, both vegetative and reproductive growth of pea appear relatively less affected by sodicity, with 50% reduction in seed yield occurring at 35 ESP(Dua and Sharma, 1993). We are not aware of any studies related to yield response of faba bean and lupins to sodicity. In some common tropical grain legumes, however, a 50% reduction of seed yield occurred at ESPof around 15-20 (groundnut, Singh and Abrol, 1985; soybean, Singh and Abrol, 1986). In comparison, rapeseed (Brassicu napus), a moderately sodicity-tolerant species, can withstand 35 ESP without significant reduction of growth or yield (Porcelli e f al., 1995).

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H. P. S. JAYASUNDARA ETAL.

C. FACTORSRELATINGTO POORGROWTHIN SALINE A N D SODK SOILS

Salt stress has three major components: ( 1 ) water stress caused by salt acting as an osmoticum, ( 2 ) ion toxicity, and ( 3 ) nutrient imbalances. In saline soils and saline-sodic soils, all three factors may contribute to reduced growth, whereas in sodic soils ion toxicity (particularly Na+) and nutrient imbalances may contribute to reduced growth. In addition, other soil physical problems in sodic soils, i.e., surface crusting, hard setting (compaction), and transient waterlogging, may also contribute to reduced growth (Naidu and Rengasamy, 1993). In sodic soils it is difficult to isolate any single growth-limiting factor as the most important one, but intolerant crops may be affected by Na toxicity at relatively low levels of ESP when physical effects of sodicity are absent (Bernstein, 1975). The effects of sodic soils on cool season grain legume growth resulting from Na toxicity are considered in this section. The problems associated with structural degradation are discussed under topics on effects of poor structural conditions and waterlogging on cool season grain legumes (Sec. V and VI, respectively), and the constraints associated with high pH are considered under effects of soil alkalinity (Sec. IV).

1. Water Stress Leaf expansion is the earliest affected visible plant parameter by salinity (Munns and Termaat, 1986). The rate of transpiration is also reduced (Abbas et al., 1991). These rapid responses to root-zone salinity are largely related to changes in leafwater status in response to low external water potential (Munns andTermaat, 1986). In L. albus the hydraulic resistance of the plant is increased by approximately four times soon after exposure to 100 mM NaCl (Munns and Passiora, 1984), and this may greatly exacerbate the effect of low external water potential in decreasing the leaf-water potential. The reduced leaf-water potential is not directly responsible for the retardation of growth, as indicated by results that if the leaf-water potential is artificially increased (applying an external pressure similar to osmotic potential), leaf expansion rate remains reduced (e.g., L. albus, Munns and Termaat, 1986). Plant hormone-mediated signals originating from roots in response to low external water potential have been implicated in immediate inhibition of shoot growth in salt stressed plants (Munns and Termaat, 1986). It is believed that, in most plants, stress induces an accumulation of abscisic acid. Abscisic acid has been found to be involved in the regulation of growth and development of plants and in transmitting signals from roots to shoots (e.g., L. albus, Wolf et al., 1990).

2. Ion Toxicity In plants exposed to salt stress for a long duration, growth inhibition may be largely related to a gradual buildup of toxic Na+ and C1- ions in the plant (Munns

GRAIN LEGUME RESPONSES TO SOIL ABIOTIC STRESSES

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and Termaat, 1986). For a plant to grow well under saline conditions the supply of ions to the leaves must be regulated. Ideally, most of the salts should be excluded from the roots, and the small fraction arriving in the shoot should be partitioned so that it does not accumulate to toxic concentrations. Most grain legume species, however, are generally poor salt regulators and poor in cornpartmentalising Na+ and CI- in the leaves (Lauchli, 1984). In L. albus exposed to NaCl ranging from 0 to 100 mM, the concentrations of Na+ and C1- in the xylem sap increased linearly with increasing salinity and averaged 10% of the external NaCl concentration. By contrast in barley, a moderately salt-tolerant species, Na' and C1- concentrations in the xylem reached only 4% of the external concentration in plants exposed to 100 mM NaCl (Munns, 1988). For some species, a minor buildup of Naf and C1V ions in the shoot may not be adverse, especially if the ion concentration does not increase with time due to an equivalent shoot growth (L. luteus, Van Stevenink eral., 1982; common bean, Wignarajah, 1990). Under these conditions growth may be maintained or even stimulated (e.g., L. luteus, Van Stevenink et al., 1982; faba bean and L. albus at < 80 mM NaC1, shaddad et a f . , 1990). An increase in the leaf fresh-weight-dry-weight ratio (succulence) has also been observed along with growth stimulation (van Steveninck et al., 1982; Wignarajah, 1990). These salinity responses are halophytic in nature and are considered mechanisms for tolerance (Greenway and Munns, 1980). These responses, however, are not common for most grain legumes and can only be seen under low levels of salinity. In most cool season grain legumes internal Na+ and CI- concentrations generally increase gradually with time and with increasing external salt concentration (Ayoub, 1977; Yousef and Sprent, 1983; Lauter and Munns, 1987). In chickpea exposed to combined NaCl and Na,SO, (30 mM Na+, 15 mM CI-, and 7.5 mM SO:-), Na+ and C1- concentrations increased rapidly during the first 4 days and then gradually to reach a steady state after 10 days. By this time relative growth rate was reduced, and stress symptoms had appeared (Lauter and Munns, 1987). The relative importance of Na+ and C1- as the major ion causing toxicity may vary among species. In field pea, for example, internal Na concentration remained unchanged, whereas C1 concentration increased significantly with increasing NaCl salinity from 1.8 to 10 dS/m EC,. Within this range plant dry matter decreased by more than 60% (Cerda et al., 1982). In contrast, dry matter yield reduction in chickpea was more strongly correlated with shoot Na concentration than with shoot CI concentration (Lauter and Munns, 1987). The rate of photosynthesis is also reduced by high leaf Na+ and C1- concentrations, especially when ionic relations in the chloroplast are affected (Seeman and Critchley, 1985). In common bean, photosynthetic rate per unit leaf area declined by 75% over a range of 0-300 mM CI- in leaves (Seeman and Critchley, 1985). This decrease may result either from a feedback inhibition, as carbohydrates often accumulate in leaves after exposure to salinity due to reduced sink activity (Munns and Termaat, 1986), or from reduced chlorophyll concentration

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H. P. S. JAYASUNDARA ETAL.

(Seeman and Critchley, 1985). In plants subjected to prolonged salt stress, reduced growth is more associated with a reduction in photosynthetic leaf area than a reduction in photosynthetic rate per unit leaf area (Munns and Termaat, 1986). Accumulated Naf and C1- in a salt-stressed plant are not uniformly distributed in different plant parts (Greenway and Munns, 1980). Generally, ion concentrations are higher in mature leaves than in younger leaves, and this corresponds with earlier death in older leaves (lupin, Munns, 1988; chickpea, Dua, 1992). In L. albus Nat concentration in the leaflet midrib sap of the oldest leaves increased to about 40 mM 7 days after exposure to 160 mM external NaCl, and the salt injury was first developed in these leaves (Munns, 1988). The concentration at which toxic symptoms appear in old leaves depends on the ability of the plant to compartmentalize salt in the vacuole (Munns and Termaat, 1986). An accumulation of salt in the cytoplasm interferes with metabolism and membrane permeability, and excessive concentrations in the cell wall cause a loss of turgor and loss of water (Munns and Termaat, 1986).

3. Nutrient Imbalances The uptake of excessive amounts of Na+ and C1- by salt-affected plants generally interferes with the uptake of essential macronutrients, and this may also disturb normal growth. There is a general trend for decreased uptake of K', Ca2+, Mg2+, and NO-; with increasing internal Na+ and C1- in grain legumes (Munns, 1988; Jeschke et af.,1992; Cachorro et al., 1994; Gouia et al., 1994). The presence of 50 mM NaCl in the culture solution decreased K + uptake by 41% in common bean, compared to only 12% in cotton, a salt-tolerant plant (Gouia et al., 1994). The uptake and xylem transport of Ca2+ and Mg2+ were also decreased (Gouia et al., 1994). A significant negative correlation between the uptake of Na+ and the uptake of K + is generally found in many cool season grain legume species (e.g., field pea, Cerda et al., 1982; faba beans, Yousef and Sprent, 1983; lentils, Ashraf and Waheed, 1993a). Thus internal K-Na ratio is decreased under saline conditions. When the K-Na ratio is decreased below a critical level, normal functioning of metabolic processes are disturbed in nonhalophytes (Wyn Jones et al., 1979). Under saline conditions high external Na+ greatly reduces the activity of Ca2+ in the solution (Grattan and Grieve, 1994) and may displace Ca2+ from the plasmalemma of the root cells, leading to disruption of ion uptake and transport regulation and membrane integrity (Cramer et al., 1985). Often the increasing external Ca2' concentration has been found to improve the relative salinity tolerance in many plants, including grain legumes (Guerrier and Pinel, 1989; Cachorro et al., 1994). This beneficial effect of Ca2+ appears to be associated with counteracting effects on toxic Na+ accumulation. In common bean grown in solutions containing 80 mM NaC1, for example, increasing external Ca2+ supply from 0.2 to 5 mM

G

W LEGUME RESPONSES T O SOIL ABIOTIC STRESSES

99

reduced shoot Na concentration by about 40% with a concurrent increase in shoot growth by about 64% (Cachorro et al., 1994). High phosphorus supply under saline conditions may increase P uptake considerably, leading to growth depressions resulting from P toxicity (Grattan and Grieve, 1994). L. luteus, for example, a species tolerant to 50 mM external NaCl (van Steveninck etal., 1982). is highly sensitive to salinity (50 d e x t e r n a l NaCl) in the presence of 2 mM inorganic P (Treeby and van Steveninck, 1988). Similarly, several soybean cultivars were severely injured when solution P concentration exceeds 0.12 mM (Gratton and Mass, 1988). These cultivars accumulated large quantities of P in the leaf when they were grown above this critical P concentration (0.12 mM) in the solution regardless of the Ca*+-Na+ ratio or type of salt used. In soybean salinity injury was related both to high P and to CI- concentrations in the leaf (Gratton and Mass, 1988).

D. FACTORS INFLUENCINGSALINITYRESPONSE Salinity response of plants is dependent on several other factors, including stage of growth, type of anions (C1- or SO:-), nutritional factors, and environment. Generally, most cool season grain legumes can withstand a comparatively higher level of salinity at germination than at later growth (compare the values given in Sec. 1II.A.I with threshold salinity levels for growth and yield in Fig. 3 and Table I). This may be beneficial for establishment in the field where salt concentration is relatively high at the soil surface compared to the sub soil. Sensitivity to salinity, however, may increase over time as growth progresses (Ayoub, 1977; El Karouri, 1979; Siddiqui and Kumar, 1985; Mor and Manchanda, 1992), mainly due to salt accumulation over a longer period. In some species late vegetative growth and reproductive growth appears relatively more tolerant to moderate salinity compared with earlier growth (e.g., faba bean, Hamid and Thalibudeen, 1976; Abd-Alla, 1992). In general, CI- appears more harmful than SO:- for many cool season grain legumes (Manchanda and Sharma, 1989; Dua, 1992). Leopold and Willing ( 1 984) suggested that CI- may be more deleterious to membrane functions than SO:-. When 133 m M Na,SO, was replaced by iso-osmotic 200 mM NaCI, membrane leakage increased by about 28% in soybean leaf tissues (teopold and Willing, 1984). Lauter and Munns (1986) reported that harmful effects of salinity on chickpea at low levels of salinity are related to the accumulation of Na in shoots regardless of the dominant anion, whether it is CI- or SO:-. Application of fertilizer, i.e., NO,, may sometimes modify the severity of salinity damage (Lauter et al., 1981; Rabie et al., 1986; Cordovilla et al., 1996). The nature of the response to applied nutrients, however, may depend on the intensity of salinity (Rabie et al., 1986; Grattan and Grieve, 1994). Under mild salinity, nu-

100

H. P. S. JAYASUNDARA E T A .

trient deficiency may limit growth more than salinity; thus, a positive interaction or an “improved salt tolerance” can occur in response to nutrients supply. At moderate to high salinity, addition of nutrients may either have no interaction or a negative interaction on plant growth (Grattan and Grieve, 1994). Comparisons of salinity response of different cool season grain legumes can be misleading because of the influence of environmental factors. Any climatic factor that reduces the amount of transpiration per unit of carbon fixed will also reduce the rate of accumulation of salt in leaves and therefore prolong their effective life (Munns and Termaat, 1986). This would occur if the plant had an inherently high water-use efficiency (Munns and Termaat, 1986) or was grown under conditions that increase the ratio of photosynthesis to transpiration, such as high humidity (e.g., chickpea; faba bean, El Karouri, 1979; Lauter and Munns, 1987), high CO, (e.g., common bean; Schwarz and Gale, 1984), and low light intensity (e.g., faba bean; Helal and Mengel, 1981). High air temperature, on the other hand, increases rate of transpiration and thus the flow of ion to the shoot and the severity of salinity injury (e.g., lentil, Ayoub, 1977).

E. EFFECTSOF SALNTY AND SODICI-I-Y ON NODULATION AND N, FIXATION Results from a few experiments have shown that plants relying on biological N, fixation are more sensitive to salinity than plants relying on fertilizer N (Lauter et al., 1981; Yousef and Sprent, 1983), suggesting relatively higher sensitivity of the symbiosis to salinity than host plant growth. The adverse effects of salinity on the legume-rhizobium symbiosis include decreased nodulation, nodule dry weight, and decreased N, fixation (Lauter et al., 1981; Yousef and Sprent, 1983; Sigleton and Bohlool, 1983; Subbarao ef al., 1990; Abd-Alla, 1992; Delgado et al., 1993). Rhizobia nodulating most cool season grain legumes appear to be relatively tolerant of soil salinity (Graham and Parker, 1964). The growth of 14 strains of chickpea rhizobia for example, was not affected in yeast extract mannitol medium salinized with 340 mM NaC1, a level at which most grain legumes could not survive (Elsheikh and Wood, 1990). Thus, the survival and growth of rhizobia in the rhizosphere may not be a major limiting factor for poor nodulation in cool season grain legumes under saline conditions. The early steps of nodulation appear to be the most sensitive to salinity. An external NaCl of 80 mM, for example, inhibited the nodule formation in soybean without reducing the number of rhizobia in the medium and root colonization (Singleton and Bohlool, 1984). Moreover, delaying the application of salt stress by 96 hours increased nodule number, nodule dry weight, and shoot N concentration (Singleton and Bohlool, 1984). The impairment of nodulation by salinity has been suggested to be due to a reduction in number of root hairs and the formation of in-

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fection threads in lucerne (Lakshmi-Kumari et al., 1974) and in lateral expansion of root hairs, reduced root curling, and a reduction in both the number of root hairs that contained the infection threads and the proportion of these that led to nodule initiation in faba bean (Zahran and Sprent, 1986). It appears that once the symbiosis is well established the salinity response is similar in both N,-fixing and N-fed plants. In pigeonpea, for example, when salt stress was applied to well-nodulated plants, shoot dry matter was not significantly different from that of N-fed plants, but when the salt stress was imposed at seed sowing, shoot dry matter of symbiotically dependent plants was depressed more (70%) than that in N-fed plants (40%) (Subbarao et al., 1990). There was no difference in Na+ and C1- uptake behavior or distribution in pigeon pea as influenced by the type of N nutrition under salt stress (Subbarao et al., 1990). The nodules already formed are unaffected by salinity since average weight per nodule was either increased or remained unchanged (e.g., chickpea, Lauter et al., 1981; faba bean, Yousef and Sprent, 1983; soybean, Singleton and Bohlool, 1984; pigeon pea, Subbarao er al., 1990). This response may be a compensation for reduced specific nitrogenase activity in salt-affected plants (Sprent et al., 1988). Total nitrogenase activity, however, is decreased under salt stress in proportion to reduced nodulation and nodule dry weight (Singleton and Bohlool, 1984).Nitrogen fixation may also be affected either by direct inhibitory effects of salt on the nitrogenase enzyme (e.g., field pea, Delgado et al., 1993; Faba bean, Cordovilla er af.. 1996)and 0, diffusion (Serraj et al., 1994) or indirectly by restricted host plant growth (e.g., soybean, Singleton and Bohlool, 1983). Host plant appears to have a major role in the tolerance of the symbiosis under salt stress (Rai et at., 1985; Velagaleti et al., 1990; Cordovilla et al., 1995b). In 15 genotypes of faba bean inoculated with a salt-tolerant strain of R. leguminosarum biovar. viciae, three genotypes with relatively more tolerance to 75 mM NaCl (based on relative shoot and root growth) also had a greater number of nodules and nodule dry weight per plant (Cordovilla et al., 1995b). Similarly, in 16 soybean cultivars screened for salinity tolerance (80 mM NaCl), shoot and root growth, nodulation, nodule dry weight, and nitrogenase activity were severely depressed in 11 salt-sensitive cultivars, whereas 5 salt-tolerant cultivars continued to grow and fix N,, although their shoot growth, nodulation, and nodule dry weights were marginally reduced (Velagaleti et al., 1990). For soybean, the important features of successful symbiosis under salt stress were the resistance of roots to salt stress, only moderate reduction in nodulation and nitrogenase activity, and continued growth and nitrogenase activity (though slightly depressed) until seed setting (Velagaleti et al., 1990). Only a few studies have been conducted on the effect of sodicity on nodulation and N, fixation in cool season grain legumes. Available evidence suggests that sodicity affects the legume-rhizobium symbiosis in a similar manner to salinity. In lentil, for example, both nodule number and nodule dry weight decreased by

H. P. S. JAYASUNDARA ETAL.

102

67-76% at 20 ESP relative to those at 10 ESP (Singh et al., 1993). Nitrogenase activity was completely inhibited in lentil nodules at 20 ESP (Singh ef al., 1993). Impaired N, fixation was further evident by progressive decline in N concentration in different plant parts of lentil with increasing sodicity.

F. GENETIC VARIATION INRESPONSE TO SALINITY AND SODICITY For cool season grain legumes several studies have been conducted on intraspecific variation in salinity tolerance within a particular species (Table 11). Few

Table I1 Some Examples of Intraspecific Variation of Salinity Response in Four Widely Cultivated Cool Season Grain Legumes

Species Chickpea (C.arietinum)

Number of

Parameter

genotypes

evaluated

4

Reference

% germination

2.8

Survival C,,,for dry matter"

NA"

C,, C,,, and the slope' % germination

2.6 13

C,, for dry matter

10.5 NA

Ashraf and Waheed, 1990

11

YOgermination and relative seedling dry matter Relative seed yield

NA

Ashraf and Waheed, 1993

15

Relative dry matter

NA

C,, pod yield and the slope Rate of root elongation

4

Cordovilla eta!., I995 Cerda et al., 1982

160 81

20 10 Lentil

d Cso(1

S

< 1.5

Kheradnam and Ghorashy, 1973 Lauter and Munns, 1986 Johansen eta/.. 1990 Dua, 1992 Saxena and Rewari, 1992 Rai, 1983

(L. culinaris) 133

Faba bean (V faba) Field pea ( P sativum)

2 3

8.3

Poljakoff-Mayber etal., 1981

"The difference in C,, for most-sensitive genotype and most-tolerance genotypes (dS m-I). "C,, salinity at SO% yield reduction (dS m- I). 'C, = threshold salinity level (dS m-'). %A = not available.

GRAIN LEGUME RESPONSES TO SOIL ABIOTIC STRESSES

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studies have been conducted on intraspecific variation in response to sodicity. In most instances where intraspecific variation in salinity tolerance has been studied only a few genotypes or cultivars have been included; thus potential intraspecific variation has not been thoroughly investigated. Of most widely cultivated cool season grain legumes, chickpea has received the greatest evaluation. Lauter and Munns (1986), for example, tested 160 genotypes of chickpea for their tolerance to salinity (imposed at I week after emergence) in terms of survival after 9 weeks in solution cultures salinized with 50 mM NaCl or 25 mM Na,SO,. There was some variation in the development of salinity damage and survival of different genotypes, but this variation was very narrow. Quantifying salinity tolerance accurately by observations based only on survival is difficult however. In another series of experiments, 81 genotypes of chickpea were screened under glasshouse conditions in sand culture salinized with a mixture of NaCI, Na,SO,, and CaCl, in the ratio 7: 1:2 (Johansen et al., 1990). Plants were grown in sand with different salinity levels (1, 2, 3, and 5 dS m-I) for 3 months. Results from these experiments indicated that the extent of variation based on the critical salinity level was also very narrow (e.g., < 1.5 dS/m between most-tolerant genotype and most-sensitive genotype) and thus unlikely to be of any practical importance. Under field conditions in microplots filled with artificially salinized soils (C1- dominant, ECe 2 - 8 dS m-'), Dua (1992) tested 20 genotypes of chickpea for their threshold and critical salinity levels and slope of the response curve in terms of seed yield. The threshold salinity level varied from 4.0 dS m- in the most-tolerant genotype to 1.O dS m-I in the most-intolerant genotype. Values for the slope of the response curve (the percentage decrease in relative yield per unit increase in salinity beyond the threshold level) were higher for genotypes with high threshold values than those with low threshold values. Since a combination of a high threshold salinity value and a low slope value is considered for optimum salt tolerance (Mass and Hoffman, 1977), these genotypes were considered undesirable (Dua, 1992). Therefore, it appears that a substantial improvement of salinity tolerance in chickpea would be difficult by conventional breeding methods. The variation in the response of the chickpea-rhizobia symbiosis to salinity has not yet been explored. For lentil, a considerable genotypic variation in response to salinity has been observed at germination and early seedling growth (Ashraf and Waheed, 1990). Of 133 genotypes (from Pakistan) evaluated, 7 genotypes produced relative dry matter (relative to dry matter of plant grown without NaCl) greater than 120% in sand cultures salinized with SO mM NaC1, whereas another set of 20 genotypes produced relative dry matter in the range of 80- 120%. In all other genotypes relative dry matter yield was less than 65%. In a followup genotypic evaluation in lentil, the three most tolerant and two moderately tolerant genotypes showed a positive correlation between the degrees of salt tolerance at different growth stages up to physiological maturity, indicating that for these genotypes salinity tolerance exhibited at early growth stage was conferred at the adult stage (Ashraf and Waheed,

'

104

H. P. S. JAYASUNDARA ETAL.

1993a).Rai (1983) has also demonstrated a broad variation in salinity response for lentil with only five genotypes. The critical salinity level ranged from 0.5% NaCl (-7 dS m- EC,) for most sensitive genotype to 1.1% NaCl (- 15 dS m-' EC,) for most tolerant genotype for plants grown in sand culture salinized with NaCl. These results suggest that there may be a greater possibility for genetic improvement of salinity tolerance for lentil than for chickpea. Two additional studies also indicate a large intraspecific variation in response to salinity in field pea (Cerda et al., 1982) and faba bean (Cordovilla et al., 1995a). A substantial genetic variation in these two legumes may be found if a large number of genotypes were evaluated. Wild relatives of cultivated grain legumes may be a substantial source for genetic variation for salinity tolerance (e.g., pigeon pea, Johansen et al., 1990); however, only limited efforts have been made for evaluation of genetic variation in the wild types of cultivated cool season grain legumes. Where attempts have been made to evaluate wild relatives for salinity tolerance, the results so far have been discouraging; for example, the wild species of Cicer tested were more intolerant to salinity than were the cultivated species (Johansen etal., 1990). In contrast, two wild pea species (P elatius and P. falvum) had a greater salinity tolerance compared to three cultivated pea cultivars based on relative rates of root elongation under saline conditions (Poljakoff-Mayber efal., 1981). A more comprehensive evaluation of wild species related to cultivated cool season grain legumes may therefore be warranted if adequate genetic variation among existing genotypes cannot be found.

w.SOILALKALINITY Soil alkalinity (pH > 7 in H,O) is associated with carbonates in the soil. The most common carbonate present in soils is calcite or pure CaCO,. Soils with free CaCO, (defined as calcareous soils) may develop pH greater than 7 (usually 7-8.5) depending on the equilibria between CaCO,, H,O, and CO,. Calcerous subsoils may develop even higher alkalinity (pH > 8.5) due to poor aeration (Rowel, 1987). Some calcareous soils also have excess Na (exchangeable Nat > 15%), and these (sodic) soils may be extremely alkaline (pH > 8.5) due to the powerful alkaline hydrolysis of sodium carbonates and bicarbonate salts (Gupta and Abrol, 1990). Soil alkalinity adversely affects plant growth mainly by nutritional disorders, Fe deficiency in particular, and P, Zn, and Mn deficiencies (Naidu and Rengasamy, 1993). Growth of some species may also be affected directly by high pH, excessive concentrations of HCO;, and excessive Ca2+ in alkaline soils (Tang et al., 1992a; de Silva et al., 1994; Pissaloux et al., 1995).Apart from alkalinity, alkaline sodic soils may pose additional problems to plant growth, such as sodium toxicity and mechanical impedance (Sec. III.C.2 and V, respectively).

GFUIN LEGUME RESPONSES TO SOIL ABIOTIC STRESSES

105

A. RESPONSESOF COOLSEASON GRAINLEGUMES TO SOILALKALINITY The response of different cool season grain legumes to soil alkalinity varies depending on the species and growing conditions (Saxena and Sheldarke, 1980; Atwell, 1991; Tang et al., 1995a). A slight increase in soil alkalinity can result in a drastic reduction in growth of some species, whereas others are not affected; for example, a subsoil alkalinity of pH 7.2 with 2% CaCO, resulted in about 50% reduction in shoot dry matter of narrow-leafed lupin (L. angustifolius),whereas field pea was not affected (Tang et al., 1993a). The growth of other widely cultivated cool season grain legumes are generally less affected by alkalinity up to pH 8 but can be severely depressed at pH above 8 (Fig. 1). Stunted growth and the development of leaf chlorosis are common adverse effects induced by soil alkalinity in these species (Saxena and Sheldarke, 1980; Sakal et al., 1984; Singh et al., 1986; Hamze et a/., 1987; Chaney et al., 1992; Erskine et al., 1993). The components of growth most affected by soil alkalinity may vary among species. Generally, in lupins root growth is more affected than shoot growth (White and Robson, 1989a; Tang et al., 1993b). In contrast, in chickpea, lentil, field pea, and faba bean shoot growth was more affected than root growth in alkaline nutrient solutions (Tang and Thomson, 1996). These differences among species may reflect differences in the mode of action of soil alkalinity on plant growth. In narrow-leafed lupin, growth can be reduced by alkalinity without symptoms of iron deficiency (Tang et al., 1993a). It has been proposed that for this species poor root growth directly limits plant growth in alkaline soils (Tang et al., 1993b). For other cool season grain legumes iron deficiency is thought to be the important factor limiting growth and yield in alkaline soils (Saxena and Sheldrake, 1980; Kannan, 1983; Sakal et al., 1984; Erskine el al., 1993).

B. FACTORSRELATING TO POORGROWTHnv ALwm Sons 1. Iron Deficiency Iron deficiency is probably the predominant growth-limiting factor for many cool season grain legumes in alkaline soils (Saxena and Sheldarke, 1980; Sakal et al., 1984; 1984; Kaur et al., 1984; Singh et al., 1986; White, 1990; Erskine et al., 1993). In lentil, iron deficiency resulted in up to 47% reductions of seed yield, in sensitive genotypes (Erskine et a/., 1993). Similarly, iron deficiency depressed both vegetative growth (up to 52%) and seed yield (up to 77%) in chickpea growing in calcareous vertisols in India (Saxena and Sheldrake, 1980; Kaur et al., 1984). Narrow-leafed lupins growing in fine-textured alkaline soils in Western Australia often produce low dry matter and may sometimes display iron deficien-

106

H. P. S. JAYASUNDARA ET AL.

cy symptoms (White and Robson, 1989b). Incidence of iron deficiency appears relatively less in faba bean (Tang and Thomson, 1996) and pea (Atwell, 1991;Tang et al., 1992a) compared with other species. The symptoms of iron deficiency are usually manifested as interveinal chlorosis of the younger leaves (Korcak, 1987). This symptom, often termed “lime-induced chlorosis”, appears very early in susceptible cultivars (Saxena and Sheldrake, 1980; Plessner etal., 1992; Tang etal., 1995a).The expression of symptoms in young leaves is attributed to the inability to redistribute iron within the plant (Korcak, 1987). Under severe deficiency, leaflets may develop necrosis and die (e.g., chickpea, Saxena and Sheldrake, 1980; White and Robson, 1989; lupins, Plessner et al., 1992). In most instances the development and severity of the symptoms are exacerbated by environmental and soil conditions prevailing in the growing season (discussed later). Early symptoms of iron deficiency may disappear as plants mature, but adverse effects can persist even after the disappearance of symptoms and may be reflected in low seed yields (Saxena and Sheldrake, 1980). The causes of iron deficiency in plants growing in alkaline soils are complex. The deficiency may be due to the unavailability of iron to plants or failure in susceptible plants to translocate iron that is already absorbed (Korcak, 1987). Most plants are able to respond to “Fe-stress” in alkaline soils by modification of their rhizosphere in order to alter Fe solubility and thus avoid Fe deficiency (Romheld and Marschner, 1986). Rhizosphere acidification, release of Fe’+ reductants, release of Fe chelates, and reduction of Fe3+ to Fez+ at the plasma membrane of the root cells are some adaptive mechanisms by which plants increase Fe availability in the rhizosphere (Romheld and Marschner, 1986; Korcak, 1987). In addition, distinct root morphological and anatomical changes, i.e., thickening of the root apex, increased root hair formation, increased development of lateral roots, and formation of rhizodermal transfer cells, that may lead to greater localized changes in the rhizosphere may occur in response to Fe stress (Romheld and Marschner, 1986). These root responses are under genetic control of the plant, and species able to strongly acidify and reduce the rhizosphere grow better in calcareous soils than species poor in acidifying and reducing the rhizosphere (Romheld and Marschner, 1986). Chickpea cultivars resistant to Fe deficiency, for example, decreased the solution pH from 6.4 to 3.5 over a 17-day period, whereas susceptible cultivars did not cause significant changes to the solution pH (Kannan, 1981). Various soil chemical factors (CaCO,, Ca2+, HCO;, CO:-) have been implicated in the incidence and severity of iron deficiency. In most situations high concentration of HCO; in the soil soiution is the most important factor inducing iron deficiency (Fleming et al., 1984; Inskeep and Bloom, 1986; Loeppert et al., 1988). Increasing HCO, concentration is highly correlated with the development of iron chlorosis in sensitive plants in both solution culture (Hamze et al., 1987; Chaney er al., 1992) and in the field (Inskeep and Bloom, 1986). The mechanisms by which HCO; induces iron deficiency may be related to its buffering ability and

GRAIN LEGUME RESPONSES TO SOIL ABIOTIC STRESSES

107

resulting deleterious effects on the plant’s expression of Fe stress response and probably reduced translocation of iron from roots to shoots (Flemming et al., 1984). Often the visual symptoms of iron deficiency are not correlated with the total soil CaCO, content but are well correlated with active CaCO, (the fraction of CaCO, in clay and fine silt) in the soil (Inskeep and Bloom, 1986; Loeppert et al., 1988). Since the dissolution of CaCO, occurs as a surface reaction, the HCO; concentration in the soil solution is dependent on the reactive surface area of CaCO,. Environmental factors (particularly wet conditions and low temperatures) and soil physical properties that adversely affect soil water relations and soil aeration exacerbate iron chlorosis (White and Robson, 1989b; Hamze e? ul., 1987). Inskeep and Bloom (1986) found that iron chlorosis was often more severe in soybean in relatively wet areas in the field. Also, iron chlorosis is often severe in fine-textured soils in which aeration is slower compared to coarse-textured soils (White, 1990; Chaney et al., 1992). Furthermore, iron deficiency did not occur in narrow-leafed lupins grown in well-aerated soils with 2% CaCO, but occurred in the same soil when aeration was impaired (White and Robson, 1989b).The aeration effects in inducing iron deficiency symptoms are not due to an oxygen deficiency to the root system, because the changes in soil aeration required to induce iron deficiency are marginal (e.g., a decrease of air-filled porosity from 27 to 21% for lupin) and considerably higher than the critical soil aeration level (10% air-filled porosity) below which root functioning is impaired by oxygen deficiency (White and Robson, 1989b).The effects of high soil moisture and low aeration on exacerbating iron deficiency arise from increased soil HCO; concentrations resulting from a build-up of CO, in the soil environment (Inskeep and Bloom, 1986).Restricted aeration may be due either to soil compaction or poor water relations, which are common in calcareous and sodic soils (Gupta and Abrol, 1990, Naidu and Rengasamy, 1993). It is not clear whether HCO; has a direct physiological effect on plant growth. In most cool season grain legumes, shoot growth is severely depressed by HCO; without symptoms of iron deficiency or reduction in root growth (Tang and Thomson, 1996). High concentrations of HCO; is also associated with high pH. Thus, it is difficult to separate the effect of HCO, from that of high pH on plant growth. Millar and Thorn (1956) reported that the respiration of excised bean roots immersed in Hoagland’s solution was significantly inhibited by HCO,. The inhibitory effects increased with increasing HCO; concentration, independent of solution pH, and were more severe in chlorosis-susceptible plants, i.e., common bean, lupins and soybean, than in chlorosis resistant plants, i.e., wheat, barley and tomato (Millar and Thom, 1956). Soil Na+ concentration is positively correlated with the severity of iron chlorosis (Inskeep and Bloom, 1986; Loeppert et al., 1988). In alkaline sodic soils pH is dependent on the amount of NaHCO, in the soil solution (Rowel, 1987) and may generally be higher than that of a normal alkaline soil (Gupta and Abrol, 1990). Nutritional problems, as a consequence of higher pH and excess Na, intensify in

108

H. P. S. JAYASUNDARA ET AL.

these soils compared with those in calcareous soils (Rowel, 1987). Atwell (1991) found that growth of L. pilosus and field pea, two alkaline tolerant species, was severely depressed (by 42 and 55%, respectively) in a alkaline sodic soil (solonized brown soils with 83 mol m P 3 Na concentration and 8.13 pH). Growth reductions for these species were not due to iron deficiency, because no symptoms were apparent. In the same soil L. angustifolius developed severe chlorosis, and its growth was depressed by more than 70%. Thus, factors other than iron deficiency may be important in limiting the growth of L. pilosus and field pea under these conditions.

2. High pH The high pH in alkaline soils can directly inhibit root growth and thereby shoot growth (Atwell, 1991; Tang et ul., 1992a). In buffered nutrient solutions bubbled with C0,-free air to prevent the buildup of high concentrations of HCO;, root elongation in L. angustifolius was reduced by 40% when the pH increased from 5.5 to 6.0, whereas that of field pea was not affected (Fig. 4). The root elongation in other commonly cultivated cool season grain legumes (chickpea, lentil, faba

100

-

80 -

60

-

40 -

.

20

I

3

4

L. angustfoli.us L. albus P. sativum I

I

I

1

5

6

7

8

9

Solution pH Figure 4 Relative root elongation rates of two lupin species and field pea grown in buffered nutrient solutions between pH 4 and 8 (redrawn from Tang et 01.. 1992a; and Tang and Thornson, 1996).

GRAIN LEGUME RESPONSES TO SOIL ABIOTIC STRESSES

109

bean) and in several potentially important species (i.e., Vicia satiua, V narbonensis, l? benghalensis, and Lathyrus sativus) appears less sensitive to alkalinity up to pH 8 (Tang and Thomson, 1996). The decreased root elongation in L. angustifofius above pH 6.0 is due to decreased cell elongation (50-65% lower cell length relative to that at pH 5.0),rather than to a reduced cell division (Tang et al., 1992a). The inhibition of cell elongation by high pH was rapid (occurring within 1 hour after the exposure of roots to high pH) and readily reversible, suggesting that high pH directly affects the cells in the elongating zone (Tang er al., 1992a). Furthermore, the roots exposed to pH 6 or above exhibited physical damage of the root surface and reduced root-hair formation (Tang et al., 1993~). The mechanisms by which the high pH impairs cell elongation in L. angustifolus is not properly understood but may be associated with the regulation of cellwall rheological properties (Tang et al., 1992a). It has been suggested that cell growth is dependent on cell-wall acidification and related cell-wall loosening characteristics (Taiz, 1984). Thus, pH above 6.0 possibly decreases the degree of cellwall acidification, thereby preventing the loosening of cellulose microfibrils in the wall. Alternatively, pH above 6.0 may impair plasma membrane integrity, leading to poor cell-wall formation (Tang et al., 1992a). Differences in root elongation between these species may be due to different efficiencies in proton efflux by their roots at pH above 6.0 or to different buffering capacity in their apoplast (Tang et al., 1992a). Roots grown at high pH may be thicker than roots grown at optimum pH; thus, root dry matter may not necessarily change under alkaline conditions (Tang and Thomson, 1996). Shoot growth, however, is inhibited by high pH (Tang and Thomson, 1996), possibly as a consequence of the impairment of uptake of water and nutrients resulting from reduced root length, physical damage to root surface, and reduced root-hair formation (Tang et al., I992a, 1 9 9 3 ~ Tang ) . and Thomson (1996) found that shoot fresh weights were well correlated with the total root length in a range of cool season grain legumes (chickpea, lentil, faba bean, pea, and lupins) growing in high pH nutrient solutions.

3. Excess Ca2+ High calcium concentration in the rhizosphere may have direct adverse effects on some species growing in alkaline soils (Jessop et al., 1990; de Silva et af., 1994; Pissaloux et al., 1995). In L. luteus, high concentrations of Ca2+ in the rhizosphere resulted in reduced leaf conductance (Ruiz et al., 1993). Moreover, de Silva et al. (1994) found considerable reductions in the rate of leaf expansion, rate of transpiration, and rate of net assimilation in L. luteus with increasing external Ca2+ concentration (supplied as Ca(NO,),) from 1 to 15 mol mP3 under glasshouse conditions. High rhizosphere Ca2+ also had direct effects on carbon assimilation of the plant, which was independent from the effects of reduced stomata1 conductance

110

H. P. S. JAYASUNDARA ETAL.

(De Silva e fal., 1994). Pissaloux et al. (1995) also found that increasing Ca2+ concentration in the nutrient solution (supplied as CaCl,) from 2.5 to 10 mol mP3 decreased growth of L. albus significantly. There was no visible sign of leaf chlorosis in either study (De Silva et al., 1994;Pissaloux etal., 1995).Tang e f al. (1995b), however, found that adding 0.33 g CaSO, per kg soil containing 0-10 g of CaCO, per kg soil (soil pH ranging from 4.8 to 7.3) increased Ca concentration in the soil solution by 1.7 to 6.6-fold and in leaves by 10-30% but did not depress shoot growth of L. angusfifolius. Reasons for these inconsistencies are not clear.

4. Other Nutritional Disorders Apart from iron, the availability of P, Zn, and Mn, and in some instances Cu and B, is low in alkaline soils. The availability of P decreases with increasing pH in the soil, particularly when the total P and soil organic matter levels are low (Marschner, 1995). Decreased availability coupled with restricted root growth often cause P deficiency in cool season grain legumes growing in alkaline soils (Mahler et al., 1988). The solubility of Zn decreases 30-45 times for each unit increase in soil pH in the range of pH 5.5-7.0 (Marschner, 1995). Zinc deficiency is a widespread problem in calcareous soils in India where chickpea and lentil are important components in the farming systems. (Takkar, 1993). Limitations to growth and yield due to Zn deficiency are demonstrated by considerable yield responses to Zn application for chickpea (Yadav and Shukla, 1983; Singh and Grupta, 1986; Ahlawat, 1990).The activity of Mn2+ decreases logarithmically for each unit increase in pH, with minimum activity at around pH 9.0 (Lindsay, 1979, cited in Naidu and Rengasamy, 1993). Manganese deficiency has been reported in chickpea (Rashid et at., 1990), and application of Mn to chickpea improved nodulation, dry matter production, and yield (Ahlawat, 1990). Atwell (1991) observed that shoot Cu concentration in narrow-leafed lupin growing in alkaline sodic soils was lower than the normal sufficiency range. The availability of B is largely influenced by soil pH, being most available in the acidic pH range and less available with increasing pH, primarily due to the adsorption by soil colloids (Mahler et d., 1988); for example, yield responses of chickpea to B application (2.5 Kg B ha- ') has been observed in calcareous soils in India (Ahlawat, 1990). B concentration in some other alkaline soils, however, may be inherently high, particularly where the parent material contained high concentrations of B (Nable and Paull, 1991). Continuous irrigation using water containing moderately high concentrations of B can also lead to accumulation of B in these soils (Nable and Paull, 1991). Boron toxicity is, therefore, much more likely to occur than B deficiency under these conditions. Additionally, toxic concentrations of B may develop with over-application of B fertilizer when correcting B deficiency (Nable and Paull, 1991). Boron toxicity in crops growing in alkaline soils has been reported in the United States, southern Australia, India, and Pak-

GRAIN LEGUME RESPONSES TO SOIL ABIOTIC STRESSES

111

istan (Nable and Paull, 1991). In southern Australia reductions of cereal yield up to 17% resulting from B toxicity have been reported (Nable and Paull, 1991). Cool season grain legumes are also likely to be affected by B toxicity in these soils. Under controlled environmental conditions the majority of commercial cultivars of chickpea, lentil, field pea, faba bean, and lupin are highly sensitive to B toxicity (J. Paull, unpublished data). Faba bean, however, appeared to be relatively more tolerant to B toxicity compared with field pea, lentil, and chickpea (J. Paull, unpublished data). Experimental evidence, particularly with field pea, has shown a large genetic variation in tolerance to high B concentrations in the soil and nutrient solutions (Nable and Paull, 1991; Baheri et al., 1992).

C. EFFECTS OF ALKALNI-~Y ON NODULATION AND N, FIXATION Soil alkalinity can impair symbiotic N, fixation in cool season grain legumes through adverse effects on the microsymbiont as well as on infection and nodule development. Nodule functioning may also be affected directly by nutritional disorders associated with alkalinity or indirectly by poor host plant growth. Survival of the rhizobia that nodulate cool season grain legumes in alkaline soils may vary depending on their genetic characteristics. Generally, fast-growing biovars of R. leguminosarum, which nodulate lentil, faba bean, and pea, and R. ciceri, which nodulate chickpea, are relatively tolerant to moderate soil alkalinity (pH 7.0-8.3) (Graham and Parker, 1964; Elsheikh and Wood, 1989). In contrast, Bradyrhizobia (slow growing), which nodulate lupin, appear to be highly sensitive (Parker and Oakley, 1964). At pH > 8.5, the growth of most rhizobia is reduced. Bhardwaj (1975) found that the growth of R. leguminosarum and R. trifolii isolated from lentil and berseem (7: ulexundrinum), respectively, growing in various soils with acid, neutral, or alkaline reactions, was equally limited by pH 8.7; however, this does not preclude the possibility of genetic variation. Some strains of rhizobia are able to survive and even grow, though very slowly, at pH levels around 10 (Lakshmi-Kumari et al., 1974; Rao et al., 1994). A fast-growing strain of Bradyrhizobium sp. isolated from a native Lupinus species from the Sonoran Desert, Mexico, survived and grew in a soil with pH 8.2 (Miller and Pepper, 1988). The effects of soil alkalinity on nodulation may also vary in different cool season grain legumes. In chickpea, lentil, faba bean, and pea, nodulation is relatively unaffected by alkalinity up to about pH 8.0 (Fig. 2). In comparison, nodulation in lupins decreases dramatically with increasing alkalinity (Fig. 2, and Tang and Robson, 1993), and this response is independent of host plant growth (Tang and Robson, 1993). For lupins (L. angustifolius and L. albus) the optimum pH for nodulation is in the range of pH 5.0-6.0 (Tang and Thomson, 1996). The exact processes involved in reduced nodulation in alkaline soils are poorly understood

112

H. P. S. JAYASUNDARA ETAL.

but may relate to inhibitory effects on rhizobial-root recognition and attachment processes (Lakshmi-Kumari et al., 1974; Kijne et al., 1985). The optimum pH for attachment of R. leguminosarum to pea root hairs in YEM culture medium is 7.5 (Kijne et al., 1985); thus a higher pH may inhibit the attachment. Alkalinity appears more inhibitory than salinity to nodule formation. Nodulation in lucerne (Medicago sativa), for example, was completely inhibited by 0.2% NaHCO, in the medium but occurred, though delayed, at 0.6% NaCl (Lakshmi-Kumari et al., 1974). Micronutritional disorders, common in alkaline soils, may also affect the legume-rhizobium symbiosis (Robson, 1988). In particular, iron deficiency is an important factor affecting both nodulation and N, fixation (Rai et a/., 1982 and 1984; Tang et al., 1990). Plants reliant on N, fixation have a higher requirement for iron than those supplied with mineral nitrogen (Tang et al., 1992b). Generally, nodule initiation is more sensitive to iron deficiency than other phases of the symbiosis, whereas the division of cortical cells and bradyrhizobial proliferation in the developing nodule may also be affected (Tang et al., 1992b). In iron deficient lupin plants, nodule leghaemoglobin concentration is significantly decreased (Tang et al., 1990). Boron is another micronutrient that is important in the legume-rhizobium symbiosis (Bolanos et al., 1996) and is likely to be deficient in some alkaline soils. Nodule development in faba bean is reduced by B deficiency (Robson, 1988). Zinc application has also been reported to increase nodule dry weight of chickpea (Shukla and Yadav, 1982), but this may be due to increased host plant growth rather than a direct effect on nodule development. The growth of legumes dependent on N, fixation does not appear to be more sensitive to Zn deficiency than that of mineral N-fed legumes (Shukla and Yadav, 1982).

D. GENETIC VARIATION INRESPONSE TO SOILALKALINITY It is clear that some species are better adapted to alkaline soils than others and possess certain adaptive mechanisms to avoid or tolerate the adverse chemical factors. Also, adapted species are capable of obtaining nutrients with low availability, i.e., iron, phosphorus, zinc, and manganese from the soil for optimum growth, and their root extension growth is less affected by high concentrations of Ca2+, HCO;, or high pH (Tang ef al., 1996). Although it is recognized that the interspecific variation in response to soil alkalinity is important, what is more useful may be the extent of the intraspecific variation. Due to the complex nature of the causes for poor growth in alkaline soils and the differences in the response exhibited by species, it may be difficult to select common selection criteria in determining the extent of genetic variation in tolerance of different genotypes to soil alkalinity. This is particularly so for species such as lupin, for which iron deficiency is not the single cause for poor growth in alka-

GRAIN LEGUME RESPONSES T O SOIL ABIOTIC STRESSES

1 13

line soils. Also, in some instances, iron deficiency symptoms occur, but the loss of seed yield is not economically significant (Zaiter and Ghalayini, 1994). For most widely cultivated cool season grain legumes, however, where attempts have been made to determine the extent of intraspecific genetic variation, research has been focused mainly on “iron efficiency” of different cultivars or germplasms (Saxena and Sheldrake, 1980; Kannan, 1983; Kaur et al., 1984; Rai et al., 1984; Singh et al., 1986; Hamze et al., 1987; Saxena et al., 1990; Erskine et al., 1993; Zaiter and Ghalayini, 1994); for example, evaluation of 3267 lines of chickpea and 35 12 lines of lentil for iron deficiency chlorosis symptoms on a calcareous soil (CaCO, content 20%, pH 8.5) at the International Centre forAgricultura1 Research in Dry Areas (ICARDA) revealed a large intraspecific genetic variation in the development of lime-induced chlorosis symptoms in both species (Saxena etal., 1990; Erskine et al., 1993). The characteristic appearance of chlorosis in younger leaves in susceptible germplasm and lack of incipient deficiency in tolerant germplasm permits selection of tolerant germplasm on the basis of visual scoring (Hamze et al., 1987; Saxena et al., 1990). In chickpea, susceptibility to iron deficiency is governed by a single recessive gene (Gowda and Rao, 1986; Saxena et af., 1990); thus susceptible types can be identified easily in segregating populations in breeding problems. Since lime-induced chlorosis resistance is related to adaptive mechanisms in the root system of efficient genotypes, such genotypes may generally be efficient in obtaining other marginally available nutrients from calcareous soils (Marschner, 1995). For species in which iron deficiency often correlates poorly with shoot growth and seed yield in alkaline soils, iron chlorosis scores alone may not be useful as a selection criteria for intraspecific variation in tolerance to alkalinity. Tang et al. (1996) suggested that rate of tap-root elongation and shoot weight in plants growing in alkaline nutrient solutions might be useful selection criteria for lupin genotypes tolerant to alkaline soils. The early root elongation rate of 16 lupin genotypes (from six species) at pH 7.0 in a buffered nutrient solution was well correlated with shoot growth and seed yield of these genotypes in alkaline soils under field conditions (Tang et al., 1996). Using the same screening technique a large intraspecific variation in response to alkalinity has been found among 30 wild genotypes of L. angustifolius (C. Tang, unpublished data).

-

-

V. SOIL COMPACTION Soil structure can deteriorate as a result of a number of processes. Compaction from agricultural traffic is a common process of deterioration of the soil structure (Voorhees, 1992). Surface crusting and hard setting are other forms of physical degradation, particularly in soils with low-activity clays, low organic matter con-

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H. P. S. JAYASUNDARA ETAL.

tents, and high exchangeable sodium percentage (Mullins et al., 1990). Some soils naturally have a relatively light-textured shallow top soil, overlying a heavier finetextured subsoil with a high bulk density and lower permeability (Dracup et d., 1992).All these conditions can severely limit the productivity of cool season grain legumes by poor seedling emergence and/or restricted root growth.

A. RESPONSESOF COOLSEASONGRA~N LEGUMES TO S o n COMPACTION 1. Seedling Emergence

Mechanical resistance to seedling emergence can be an important limiting factor for establishment of cool season grain legumes in soils prone to surface crusting (Sivaprasad and Sarma, 1987; White and Robson, 1989a). Surface crusts can be formed by beating action by rain followed by drying. When crusts are formed after sowing, seedlings attempting to penetrate the crust may be weakened or fail to emerge. A greater proportion of seed reserves may be utilized by seedlings emerging through a hard surface crust compared to seedlings emerging from a normal seed bed with optimum conditions (Goyal et al., 1980). Seedlings stressed at emergence may develop slowly and may be more susceptible to pests, diseases, and other environmental stresses (White, 1990). The adverse effects of surface crusts may vary depending on the pattern of seedling emergence. In species with an epigeal pattern of emergence (e.g., lupin), the relatively large cotyledons have to be pushed through the soil surface, and this process may face considerable resistance. Seedlings of these species initially rupture the soil surface with the hypocotyl hook while the cotyledons still remain in the soil, and further growth of the hypocotyl pulls the cotyledon out of the soil. If the strength of the soil crust is high, the cotyledons may become trapped in the soil, causing severe seedling damage (Rathore et al., 1981; White and Robson, 1989a). In contrast, species with a hypogeal pattern of emergence (e.g., faba bean and field pea) face less mechanical resistance because their cotyledons remain below the soil surface while the relatively small plumule emerges through the soil surface (Inouye el al., 1979; White and Robson, 1989a). Seed size has an effect on seedling emergence through surface crusts. The greater reserves in the larger cotyledons may produce a stronger seedling with a large emergence force (Inouye et al., 1979). In 12 leguminous species, including faba bean, pea, and lupin, the mean maximum emergence force of a species is strongly and positively correlated with the seed size and cross-sectional area of the stem (Inouye et al., 1979), with faba bean being highest, followed by lupin and pea. In lupin, total emergence and seedling vigour were more affected in the smaller seeded L. angust$olius than in larger seeded L. albus in a hard-setting soil

GRAIN LEGUME RESPONSES T O SOIL ABIOTIC STRESSES

1 15

(White and Robson, 1989a). Similar results have been reported for different cultivars of soybean with varying seed size (Longer e t a / . , 1986). The development of the maximum emergence force, however, is quicker in species with smaller seeds (Inouye et al., 1979), so these species may avoid the resistance from surface by emerging before surface crusts develop maximum hardness (White, 1990).

2. Plant Growth and Seed Yield Soil compaction may considerably reduce the growth of cool season grain legumes; for example, compaction reduced 20-30% of shoot dry matter in lupin (Henderson, 1991) and faba bean (Kahnt et al., 1986; Brereton et al., 1986) and more than 50% of shoot dry matter in field pea (Hebbelethwaite and McGowan, 1980; Whiteley and Dexter, 1982) and chickpea (Agrawal, 1985). Such reductions are usually associated with restricted growth and functioning of the root system. In faba bean, increasing bulk density from 1.25 to 1.65 Mg mp3 at 20 cm soil depth decreased the total root length by more than 55% and shoot dry matter by 30% (Kahnt et al., 1986). Plants growing in compacted soils often produce shallow roots. This is particularly so when the root-impeding layer is near the surface (Olsson et al., 1995). This can cause severe adverse effects on growth and yield when surface soils are dry. Under field conditions in Western Australia, deep ripping increased the yield of pea by 60% and that of lupin by 20%, indicating the limitations of soil compaction on the productivity of these species (Henderson, 1991). In pigeon pea, compaction at 5 cm depth reduced root penetration into the subsoil considerably, resulting in a 50% reduction in shoot dry weight and 65% reduction in seed yield in a dry year (Kirkegaard el a/., 1992), whereas compaction did not reduce yield in a wet year.

B. FACTORSRELATING TO POORGROWTH IN COMPACTED SOILS 1. Physical Resistance to Root Growth

Some degree of resistance is common for all roots growing in soil, but this is considerably higher in compacted soils (Voorhees, 1992). It has been shown that radicle elongation decreases with increasing soil resistance (Fig. 5). The values of soil resistance that limit root growth may range from 0.8 to 5 MPa penetrometer resistance or 1.4 to 1.8 Mg mp3 bulk density (Vepraskas, 1988). These values are largely influenced by soil water content (Eavis, 1972), soil texture (Jones, 1983), soil structure (Bennie, 1991),and plant species (Materechera et a/., 1991). In many crops, including grain legumes, commonly encountered mechanical impedance under field conditions, i.e., around 2 MPa penetrometer resistance (Atwell, 1993),

116

H. P. S. JAYASUNDARA ETAL. 807

70 - -

-

60 ~-

.-z0a

50

E E

--

-Fa m - c)

a

u

3

0 0

0

0 0 0.

0 0 0

30.20

0

0 --

10 - -

.

0

-.. 0

0

O.

07

0

m

0

usually decreases the total root length by at least 50% (e.g., common bean, Asady and Smucker, 1989; pigeon pea, Kirkegaard et al., 1992; lupins, Patterson et al., 1995). For some soils, the interpretation of root elongation relative to penetrometer resistance may be complicated due to high gravel content (Hamblin, 1985), high spatial variability of texture, structure and water content of the soil (Bengough and Mullins, 1990), and the existence of biopores (Wang et al., 1986). Root elongation in some grain legumes can proceed slowly at soil strengths greater than 2 MPa (e.g., groundnuts, Taylor and Ratliff, 1969; L. angustifolius, Materechera, et al., 1991) (discussed in more detail in Sec. V.8.3). 2. Interactions with Soil Aeration Soil pore volumes are considerably decreased with compaction; thus the rate of gaseous diffusion is reduced in a compacted soil. Asady and Smucker (1989) found that the oxygen diffusion rate decreased by more than 50% when the soil bulk density increased from 1.1 to 1.4 Mg mP3 (0.43and 2.14 MPa penetrometer resis-

GRAIN LEGUME RESPONSES TO SOIL ABIOTIC STRESSES

117

tance at a constant moisture level). Therefore, inadequate aeration may exert adverse effects on root growth in addition to physical resistance in a compacted soil. These two limiting conditions are often synergistic and difficult to separate (Eavis, 1972). Using compressed artificial granules (ballotini) circulated with nutrient solutions containing different concentrations of 0,, Gill and Miller (1956) and Barley (1962) demonstrated the interactive effects of poor aeration with mechanical resistance on the rate of root elongation in maize. Increasing external pressure from 0 to 0.05 MPa at a high concentration of 0, (20%) decreased the root elongation by 31%, whereas the same increase of external pressure at a low concentration of 0, (5%) decreased root elongation by 50% (Barley, 1962). Although root penetration into subsoil can be restricted by high mechanical resistance or bulk density, proliferation of lateral roots in the topsoil may proceed (Asady and Smucker, 1989; Kirkegaard et al., 1992). In such situations, the normal activities of the root system and its associated rhizosphere microflora in the topsoil could exacerbate the problem of inadequate aeration down the soil profile. Consumption of oxygen may be faster than the rate of replenishment due to reduced diffusion rates. Asady and Smucker (1989), for example, measured the oxygen diffusion rates at different depths of soil columns compacted to three levels of bulk densities: 1.1, 1.4, and 1.7 Mg m p 3 (0.43, 2.14, and 5.50 MPa penetrometer resistance at a constant metric suction) and planted with common bean. The oxygen diffusion rate was below the critical level (02diffusion rate required for optimum root growth) at the bottom of soil columns with highest bulk density (1.7 Mg m-3) at all stages of plant growth, whereas it was not limiting at the other two levels of bulk densities at early stages of growth (0-20 days after planting). As root accumulations increased at the 0.10-0.25 m depths, oxygen diffusion rates dropped below critical levels for all bulk densities (Asady and Smucker, 1989).

3. Changes in Root Characteristics Several anatomical and morphological changes may occur in mechanically impeded roots (Atwell, 1988; Bennie, 199 I). Both cell division and cell elongation in the root meristem may be reduced. In field pea, root penetration resistance of 0.34 MPa reduced the rate of cell division by 40% and the rate of root elongation by 70% (Eavis, 1969). The reduced cell elongation is, however, accompanied by a radial expansion of the cortical cells (Atwell, 1988; Bennie, 1991). This may lead to increased root diameter (Table 111). Mechanically impeded roots are, therefore, shorter and thicker compared with roots developed under unimpeded conditions. It has been suggested that the root penetration into compacted soils may be facilitated by the thickening of roots in response to mechanical impedance (Abdalla et al., 1969). An increase in radial root pressure resulting from root swelling may relieve the resistance at the root apex thereby permitting further axial growth until a

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H. P. S. JAYASUNDARA ETAL.

limiting situation occurs again (Abdalla et al., 1969). Thicker roots may also have a greater resistance to bending (Materechera et al., 1992) and higher axial rootgrowth pressure (Misra et al., 1986), which may further facilitate elongation in a compacted soil. Roots of different species may vary in their ability to elongate in compacted soils (Taylor and Ratliff, 1969; Materechera et al., 1991, 1993). This variation is largely related to the thickness of the roots and the tendency of roots to swell in response to mechanical impedance (Taylor and Ratliff, 1969; Atwell, 1988; Materechera et al., 1991, 1992, 1993). Materachera et al. (1993) found that in a range of crop species, the proportion of roots that penetrated into a compacted soil layer was higher in dicotyledonous species (with thicker roots) than in mono-

Table 111 Rates of Root Elongation and Root Diameters of Some Cool Season Grain Legumes as Affected by Increasing Soil Bulk Density

Species Faba bean (% faba)

Pea

(I? sativum)

Soil bulk density (Mg rn-.’)

Elongation rate (mm day-')

Root diameter

Low" 1.35

9.87 0.68

0.95 2.07

Materechera et a1 , 1991

Reference

I .25 1.57

ND" ND

0.81 1.52

Materechera efa/., 1992

Low" I .35

10.46 0.70

0.78 I .62

Materechera et a/.. 1991

I .25 1.57

ND ND

0.7 I I .28

Materechera et ul.. 1992

0.98 1.81

Materechera ef a/., 19Y1

Lupin Low" (L. ~rig~,~t~ffl;ffs) 1.35

Vetch (V. sativa)

(mm)

6.94 0.7 1

1.23 1.42 1.64

45.5 33.2 28. I

1.56 I .68 1.81

Atwell, 1988

1.3 1.7

22.9 5.7

1.7 2. I

Patterson e t a / . , 1995

Low" 1.35

11.27 0.65

0.74 1.33

Materechera et ul., 1991

"Roots grown in vermiculite. "ND = no data.

GRAIN LEGUME RESPONSES T O SOIL ABIOTIC STRESSES

119

cotyledonous species (with thinner roots). Results from a few experiments where several cool season grain legumes have been tested under uniform conditions show that root growth in lupin (L. angustifilius) was least affected by soil compaction (compared to that in deeply tilled soil), followed by the root growth in faba bean, pea (Materechera era/., 1992, 1993), and vetch (Vicia saliva) (Materechera e f al., 1991). Other morphological changes in roots growing in compacted soils are increased production of sclerified cells in the cortical and vescular tissues, thicker casparian strips, and ruptured epidermal cells (Beligar et a/., 1975). Tissues with such sclerified cells probably prevent the deformation of interior cells of the root (Prihar et a/., 1971). The growth of lateral roots may also be considerably inhibited due to the compression of surrounding soils resulting from root thickening (Dexter, 1987). In lupin growing through a layer of fine-textured sandy loam, there was little or no proliferation of lateral roots due to compaction (Atwell, 1988). The zone of lateral root growth is sometimes advanced nearer to the apex of the main axis due to compaction (Atwell, 1988). The mechanisms for root responses to mechanical impedance are complex. Greacen and Oh (1972) suggested that root cells may change their osmotic potential to exert more pressure on the surrounding soil. Atwell ( 1 988) found an increase of about 27% in the osmotic pressure of compacted root apices of lupin (L. angusrifolius). Many root morphological changes occumng in compacted soils, however, do not relate to osmotic adjustments alone (Bengough and Mullins, 1990). An involvement of ethylene has been implicated in the root response to mechanical impedance (Kays e t a / . , 1974; Veen, 1982). Applications of exogenous ethylene causes changes in root morphology similar to those of mechanically impeded roots (Kays et al., 1974). Moss ef a/. (1988), however, recently suggested that the increased ethylene evolution by mechanically impeded roots is not a direct effect of mechanical impedance but an effect of physical wounding of radially expanding roots. Thus, many physiological aspects of the root response to mechanical impedance still remain unclear.

4. Functioning of Roots Root functioning is also affected by soil compaction. Roots developed under high mechanical impedance are usually shorter and thicker with reduced proliferation in the subsoil; thus the amount of soil volume they explore is reduced (Bennie, 1991). As an example, field pea roots, which penetrate to around 1.5 m under well-drained deep sandy soils (Armstrong et ul., 1994), exhibit variable rooting depths (sometimes as low as 0.4 m) in soils with a compacted subsoil (Reid et al., 1987; Henderson, 1991). The demand for water and nutrients by the shoots may also lead to a rapid depletion of resources within the limited soil volume early in the plant growth (Bennie, 1991). In a number of grain legumes water uptake, depth of soil water ex-

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H. P. S. JAYASUNDARA ETAL.

traction, and crop transpiration rates were decreased significantly by soil compaction (e.g. faba bean, Brereton et al., 1986; field pea, Reid et at., 1987; field pea and L. angustifolius, Henderson, 1991; pigeon pea, Kirkegaard et al., 1992). In pigeon pea compaction at 5 cm below the surface decreased the maximum depth of water extraction at flowering by approximately 30% relative to that in deep-tilled soils with a lower bulk density (Kirkegaard et al., 1992). Low oxygen diffusion in compacted soils may also reduce water and nutrient uptake by roots. Roots may sometimes penetrate into the compacted subsoil by extending through biopores or cracks (Wang et al., 1986), but their ability to extract marginally available water may be considerably limited because they are often clumped in pores (Passioura, 1991). Moreover, the usually low water storage capacity and restricted movement of water in compacted soils may further exacerbate limitations in water uptake by roots (Olsson et al., 1995). Many studies with various crop species, including cool season grain legumes, have shown that if the limitations to root growth are alleviated by soil management practices such as deep tilling, root growth can proceed into the subsoil, and plant growth and yield are increased, presumably due to improved uptake of water (Hamblin, 1985). The effects of soil compaction on the uptake of nutrients may vary depending on differences in their availability and mobility in the bulk soil (Cornish et al., 1984) and possibly on plant species (Castillo et al., 1982). Generally, the reduced root-length density and decreased root-length-root-weight ratio of plants grown in compacted soils decrease both the availability of nutrients to the root system as a whole and the rate of nutrient absorption per unit length of root (Bennie, 1991). Conversely, a higher bulk density can sometimes improve the ability of roots to extract relatively immobile or poorly soluble nutrients, possibly due to the increased contract between roots and soil and increased mobility of the nutrient (Passioura and Leeper, 1963; Cornish et al., 1984). For cool season grain legumes, information is limited on these aspects. Castillo et al. (1982) investigated the effects of mechanical stress on root growth and nutrient uptake by field pea grown in soil cores under controlled environmental conditions. Application of an external pressure of 0.18 MPa to a soil core (initial bulk density = 1.16 Mg m-3) decreased root length and root-length-root-weight ratio by 86% and 76%, respectively, resulting in a 25-40% decrease in the uptake of K, Ca, and Mg. Similar decreases in the uptake of nutrients resulting from increased bulk density have been reported for field pea (Grath and Hakansson, 1992) and faba bean (Rowse and Stone, 1980) under field conditions and for soybean under glasshouse conditions (Hallmark and Barber, 1981). 5. Shoot Responses

Depressions in plant height, number of branches per plant, leaf area index, leaf area duration, and shoot dry matter resulting from compaction are common with

GRAIN LEGUME RESPONSES T O SOIL ABIOTIC STRESSES

12 1

many cool season grain legumes (Hebblethwaite and McGowan, 1980; Agrawal, 1985; Brereton et al., 1986; Kahnt ef a/.. 1986). Limitations of nutrient and water uptake resulting from restricted root growth are thought to be the main reason for these responses. Brereton et al. (1986) and Passioura (1991), however, have suggested that the restricted root growth or distorted roots do not necessarily impair shoot growth because in some situations shoot growth is slow even when roots are able to supply adequate amounts of water and nutrients. Hormonal mechanisms have been proposed through which roots “sense” the physical impedance of the soil or possibly the restricted water supply and communicate these to the shoot to modify water relations and lower the leaf expansion rates and growth of shoots (Masle and Passioura, 1987). Contradictory results have been reported, however, for some grain legumes, especially in nonuniformly compacted soils. In pigeon pea, for example, growing in soil columns with different subsoil bulk densities (0.95-1.46 Mg m p 3 or 0.5 1-3.47 MPa penetrometer resistance) under optimum supply of water and nutrients, root growth (root length and weight) and root distribution in the compacted subsoil were inhibited with increasing mechanical strength, but the proliferation of lateral roots in the top soil was unaffected (Kirkegaard et al., 1992). As a result the total root length per plant was not affected, and plant height, leaf area, and shoot dry weight were not significantly different. Under field conditions a similar proliferation of roots in the topsoil and a reduced penetration in the subsoil may still be disadvantages, particularly under suboptimal conditions (i.e., in a dry year).

C. EFFECTSOF SOILCOMPACTION ON NODUTION AND N, FIXATION Among soil factors affecting nodulation and nitrogen fixation in cool season grain legumes, soil compaction is probably the least studied. Since soil compaction can modify several aspects of the soil physical environment, such as soil aeration and soil water relations, which influence both nodule formation and functioning (Sprint, 1971),biological nitrogen fixation is likely to be adversely affected by soil compaction. In addition, soil strength may directly affect nodule development, as it does root elongation (Kirkegaard et al., 1992). Grath and Hakansson (1992) studying the relationship between poor growth of field pea and several soil parameters under field conditions in Sweden, found that decreases of about 40% in total plant dry matter, 52% in shoot N concentration, and 60% in number of nodules in the main roots of pea were associated with soil compaction resulting from agricultural traffic and its accompanied indirect effects, i.e., poor aeration and low hydraulic conductivity. In soybean and common bean, increasing soil bulk density from 1.2 to 1.6 Mg m-3 resulted in a 30-50% reduction in nodule number and a 25-30% reduction in nodule fresh weight per plant (Tu and Buttery, 1988). The

122

H. P. S. JAYASUNDARA ETAL.

corresponding reductions in N, fixation (measured as acetylene-reduction activity) for both species were about 60% in total nitrogenase activity and about 50% in specific nitrogenase activity (Tu and Buttery, 1988). That nodule functioning (nitrogenase activity) was more affected than number of nodules and nodule growth in soybean and common bean suggests that the N, fixation was particularly sensitive to changes in the soil environment.

D. GENETIC VARIATION IN RESPONSETO SOILCOMPACTION To some extent interspecific variation in root responses to mechanical impedance has been demonstrated for a number of cool season grain legumes (e.g., lupins, field pea, faba bean, and vetch, Materecheraet al., 1991, 1992, 1993); however, the extent of intraspecific variation in these species is still not known. Although soil management practices play an important role in optimizing soil conditions for crop growth in poorly structured soils, selection of genotypes tolerant to soil compaction may still be valuable. In experiments where interspecific variation has been demonstrated, a significant positive correlation exists between root thickening and rate of root elongation in stressed plants both under controlled environment conditions and in the field (Materechera et al., 1991, 1992, 1993). Knowledge based on these studies may now be used in identifying the extent of such genetic variability within a species. A similar approach for common bean has demonstrated the possibility of selecting cultivars better adapted to soils with high bulk densities (Asady et at., 1985).

VI. WATERLOGGING Waterlogging is defined as saturation of the soil root zone with water. Waterlogging is harmful to plant growth because it prevents the diffusion of gases between the soil system and the atmosphere. As the soil becomes saturated with water, airfilled pore space gradually decreases. The concentration of 0, in the soil also decreases because it is used by microorganisms and plant roots. The depletion of 0, can range from partial depletion (hypoxia) to complete depletion (anoxia) depending upon several factors, including soil temperature, plant and microbial biomass, and the length of the waterlogging period. Once depleted, 0, concentrations remain low in a soil saturated with water because of low solubility and low diffusivity of 0, in water. As the 0, concentration depletes, the soil aerobic microorganisms are replaced sequentially by facultative anaerobic and then obligatory, anaerobic microorganisms. Anaerobic microorganisms use other substances from the environment for terminal electron acceptors instead of 0,, and the soil becomes increas-

GRAIN LEGUME RESPONSES T O SOIL ABIOTIC STRESSES

123

ingly reduced. Under these conditions a number of chemical changes, such as denitrification and reduction of manganese, iron, and sulphates, can occur in the soil (Ponnamperuma, 1984). At the same time various toxic end-products of anaerobic respiration, such as lactic acid, ethanol, acetic acid, butyric acid, and amines, may accumulate. Ethylene, a physiologically active organic substance (Jackson and Drew, 1984), increases in concentration in waterlogged soils through production by both soil microorganisms and plant roots. The chemical processes occurring in waterlogged soils have been comprehensively reviewed by Ponnamperuma ( 1984).

A. RESPONSESOF COOLSEASON GRAINLEGUMES TO WATERLOGGING 1. Germination and Seedling Emergence Seed germination is very susceptible to waterlogging at least partly because at this stage of growth the whole organism is subjected to the stress. Germination is also an intensely energy-dependent process in which energy is provided by respiration. Thus, both anoxic and hypoxic conditions may inhibit germination of nonwetland species, including cool season grain legumes (Crawford, 1977; Rowland and Gusta, 1977; Sarlistyaningsih, et al., 1995, 1996). In addition, the leakage of nutrients from germinating seeds can create favorable conditions for microbial growth, and this may also affect the germination and survival of seeds (Rowland and Gusta, 1977). Poor crop establishment is a common problem when waterlogging occurs at seedling emergence. Waterlogging 6 days after the germination of pea, for example, delayed seedling emergence for 2 4 days and reduced the final plant density by 80% relative to plant density in freely drained soil (Belford and Thornson, 1979). In lupin (L. angustifolius) seed germination was also completely inhibited after 4 days of waterlogging, and seed survival (assessed 5 days after the recovery of waterlogging) was decreased to 0% (Sarlistyaningsih et al., 1995, 1996). In faba bean and pea, soaking seeds for 4 hours in nonaerated water decreased germination by about 50% (Rowland and Gusta, 1977). Crawford (1977) found that in faba bean and field pea alcoholic fermentation resulting from excess water reduced both germination and survival of seeds. In comparison with cereals, grain legumes are intolerant to waterlogging at germination (Crawford, 1977). Information is scarce on comparative responses of different cool season grain legumes to waterlogging at germination. Limited data suggest that faba bean is relatively tolerant to waterlogging at germination compared with field pea (Crawford, 1977) and L. ungustifalius (Sarlistyaningsih, 1993). Viability of pea seeds is completely lost within 72 hours after exposure to excess water, whereas faba bean was able to maintain over 50% germination after soaking 72 hours in water (Crawford,

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1977). Sarlistyaningsih (1993) recorded 60% seed germination in faba bean even after 6 days waterlogging. We are not aware of any studies on the comparative response to waterlogging at seed germination in other cool season grain legumes.

2. Plant Growth and Seed Yield Waterlogging severely depresses vegetative growth of plants. Generally, root growth is more affected than shoot growth (Fig. 6.; B. Thomson, unpublished data; Gallacher and Sprent, 1978; Ashraf and Chisti, 1993). The initial response to waterlogging is reduced rate of root elongation (Jackson and Drew, 1984). Thus, a significant reduction in root growth may occur before any significant change in the shoot growth. Hanbury (1988), for example, found that root dry weight of L. albus decreased within 5 days of imposing waterlogging, but shoot dry weight did not significantly decrease for 10 days. Similar observations have been made by Broue et al. (1976) with a range of lupin species subjected to waterlogging. The capacity of roots to tolerate waterlogging may vary among different cool season grain legumes. Roots of L. luteus and L. angustifolius exhibited a greater tolerance (based on the relative growth rate) to waterlogging between 35 and 42 days after sowing than did the roots of faba bean, which in turn exhibited a greater tolerance than did the roots of chickpea, lentil, and field pea (Fig. 6). In many plants tolerant of waterlogging, including grain legumes, adventitious roots are formed from the base of the submerged stem while original roots are dying (e.g., lupin, Hanbury, 1988; Dracup er al., 1992; Davies et al., 1996; lentil, Alcalde and Summerfield, 1994; soybean, Singh, 1988a; K sinensis, Nawata et al., 1991). The development of an adventitious root system relates to the capacity of these species to adapt to waterlogged conditions because these are normally distributed closer to soil surface where 0, concentration is relatively high. In L. luteus and L. angustifolius waterlogged at 8-10 weeks after sowing, original root dry weights measured at 12 weeks were reduced by 70-94%, but the adventitious root dry weights were increased by 200% in waterlogging-tolerant L. luteus compared with only 20% in waterlogging-sensitive L. angustifolius (C. Davies, unpublished data). Waterlogging-sensitive species, such as pea, fail to form adventitious roots (Jackson, 1979). In plants adapted to wetlands, roots usually have aerenchyma (tissues with large intercellular spaces), facilitating continuous gas diffusion from shoots to roots (Jackson and Drew, 1984). Such tissues may also form in newly developed adventitious roots and old roots in some legume species in response to waterlogging (e.g., L. ulbus, Hanbury, 1988; Vigna sinerzsis, Nawata et ul., 1991; Trifolium spp., Rogers and West, 1993; cowpea, Takele and McDavid, 1994), and this may greatly contribute to the waterlogging tolerance of these species. Waterlogging decreases stem elongation, leaf orientation, leaf expansion, and dry matter accumulation (Jackson and Drew, 1984). Many of these responses are common to cool season grain legume species (e.g., chickpea, Cowie et al., 1996;

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Figure 6 Effects of waterlogging at between 3.5 and 42 days after sowing on relative growth rates of two lupin species and four widely cultivated cool season grain legumes grown in soils under glasshouse conditions (from B. D. Thornson, unpublished data). Solid bars represent nonwaterlogged controls, and open bars represent waterlogged treatments.

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lentil, Alcalde and Summerfield, 1994; field pea, Belford et al., 1980; Jackson, 1979; lupin, Davies et al., 1996). In species relatively tolerant to waterlogging, however, such as L. luteus (Broue et al., 1976) and faba bean (Fig. 6), a slight to moderate reduction in root growth in response to waterlogging is sometimes accompanied by an increase in shoot growth, particularly when waterlogging stress is of relatively short duration. Such “compensatory” interactions between root and shoot growth during short-term waterlogging have been attributed to the inhibition of root respiration and the consequent “removal” of the root sink for assimilates (Luxmoore et al., 1973). With prolonged waterlogging shoot growth is drastically decreased in many grain legumes (Broue et al., 1976; Ashraf and Christi, 1993; Davies e f al., 1996). Reduced shoot growth under waterlogging is generally associated with reduced leaf growth. Alcalede and Summerfield (1994) found about 60% reduction in total leaf area of lentil at flowering when the plants were waterlogged for 6 days between 16 and 22 days after sowing. The reduction in leaf area per plant was due to inhibition of leaf expansion as well as a reduction in number of leaves per plant. Similar results have been reported for field pea (Jackson, 1979; Belford ef al, 1980) and chickpea (Cowie et al., 1996). In L. luteus and L. angustifolius, waterlogging between 8 and 10 weeks reduced leaf expansion more than leaf dry matter, causing a 38-60% reduction in specific leaf area (C. Davies, 1996, unpublished data). Development of leaf chlorosis, necrosis, and premature senescence are common symptoms of waterlogging stress in grain legumes (e.g., field pea, Jackson, 1979; Belford et al., 1980; lentil, Alcalde and Summerfield, 1993; chickpea, Cowie et al., 1996; lupin, Broue et al., 1976; faba bean, Younis et al., 1993). The adverse effects of waterlogging increase with the length of the period of waterlogging (Belford et al., 1980; Alcalde and Summerfield, 1994) and are decreased if waterlogging is only intermittent (Broue et al., 1976). For field pea at the 6-7 leaf stage, complete saturation of the root zone for 2 days or partial saturation for 5 days had little adverse effects relative to complete saturation of the root zone for 5 days (Belford et al., 1980). Thus, the greater the proportion of the pea root system in saturated soil and the longer this occurred, the greater the adverse effect. Furthermore, once seedlings are established, the sensitivity of plants to waterlogging stress is generally increased with increasing age (Broue et ul., 1976; Jackson, 1979; Belford et al., 1980; Cowie er al., 1996). Thus, the ability to survive and recover following waterlogging is dependent on timing of waterlogging relative to the stage of growth and generally declines sharply as reproductive growth approaches (Jackson, 1979; Cowie et al., 1996). High temperatures during waterlogging usually increase the severity of waterlogging damage compared to moderate or low temperatures (Cowie et al., 1995). Therefore, the potential confounding factors of plant age, duration of waterlogging, the extent of the root zone saturated, and soil temperature should not be ignored when comparing the waterlogging tolerance of different plant genotypes.

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Seed yield loss resulting from waterlogging can be substantial. Limited data available for lupin, field pea, and chickpea show that reductions in yield can vary from negligible to almost loo%, depending on the species, stage of growth when waterlogged, duration, and the extent of root zone affected by waterlogging. Some species are very sensitive to waterlogging; thus, a significant loss of seed yield may occur in response to only a brief inundation, such as excess irrigation, particularly in poorly drained soils (e.g., field pea, Greenwood and McNamara, 1987; chickpea, Cowie et al., 1995). On the other hand, some species can withstand waterlogging for up to 2 weeks without significant loss of seed yield (e.g., L. luteus, Fig. 7). At its most severe level of damage, waterlogging can kill plants (e.g., field pea, Cannell, 1979; chickpea, Cowie et d.,1996). Generally, the loss of seed yield is more severe when waterlogging occurs at the reproductive stage (flowering or pod filling) than at vegetative growth. Waterlogging for 10 days starting at 21 days after sowing, for example, reduced the seed yield of chickpea by 35% compared with 53 and 67% loss of seed yield if waterlogging occurred at flowering (48 days after sowing) and pod filling (75 days after sowing), respectively (Cowie et a/., 1996). The loss of seed yield in chickpea due to waterlogging at vegetative growth was mainly due to a decreased number of pods per plant, whereas the number of seeds per pod, hundred seed weight, and the harvest index were not significantly

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Figure 7 Effects of waterlogging for 14 days between 56 and 70 days after sowing on seed yield of L. lureus (yellow lupin) and L. clngrr.rtifiJius (narrow-leafed lupin) grown in hydraulically isolated

plots (2.25 mZ)in a duplex soil near Beverly, Western Australia (from C. L. Davies, unpublished data).

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affected. In contrast, the majority of plants died when waterlogging occurred at flowering or pod filling, and in surviving plants all the yield components were severely affected (Cowie et al., 1996a). In the field, seed yield of pea was reduced by 60-70% by waterlogging for 4 days at reproductive growth (Cannell, 1979). The severity of yield loss also varies with the duration of waterlogging. Thus, in field pea, 2 days waterlogging at the 6-7 leaf stage had little effect on seed yield, whereas 4 days of waterlogging reduced the yield by 42% (Belford et af., 1980).

B. PHYSIOLOGY OF WATERLOGGING STRESS 1. Root Growth

The adverse effects of waterlogging on roots may arise for a number of reasons; for example, inhibition of aerobic respiration may deprive the root system of energy required for physiological functions, such as cell division, cell elongation, and nutrient uptake (Jackson and Drew, 1984). The accumulation of various end products of chemical and biochemical reducing reactions resulting from waterlogging may also be responsible for root injury (e.g., nitrous oxide and CO,, Jackson, 1979). Moreover, anaerobic metabolism in the root system may also generate toxic end products. As an example, in many plants, including grain legumes, endogenous ethylene accumulates under waterlogging (e.g., faba bean, El-Beltagy and Hall, 1974; lupin, Young and Newhook, 1977; field pea, Huber ef aL, 1979), and this has been implicated in the reduction of root extension in field pea (Goodlass and Smith, 1979). The relative importance of each of these factors in causing waterlogging injury has not been quantified, however. Under prolonged waterlogging, roots start to degenerate, first at the tips, which have high energy requirements relative to fully expanded tissues (Jackson, 1979). The duration of root survival approximately corresponds to the period over which mitochondria1 structure undergoes no irreversible structural degeneration (Jackson and Drew, 1984), and this may vary between plant species. Roots of pumpkin (Cucurbitu pepo), for example, can survive for 12 hours in an anaerobic environment at 25"C, whereas roots of pea can only survive for 6 hours (Webb and Amstrong, 1983). Roots of faba bean appear relatively tolerant and are able to survive 12-48 hours under anaerobic conditions at 23°C (Williamson, 1968). Physiological reasons for such differences in the survival of roots in the absence of 0, are complex and may include both avoidance of accumulation of toxic compounds and maintenance of a continuous supply of energy (Jackson and Drew, 1984; Pezeshki, 1994). Root primordia have a greater resistance to waterlogging relative to root apices possibly because of the low energy requirement for cell maintenance (Jackson and Drew, 1984).Thus, the recovery of the root system after death of root apices main-

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ly relies on the recovery of root primordia. The new roots, mostly adventitious, formed during and after waterlogging tend to concentrate near the soil surface, leading to a shallower root system compared with nonwaterlogged plants (e.g., lupin, Dracup et al., 1992; Davies et al., 1996; V sinensis var. sesquipedalis, Nawata et al., 1991; soybean, Singh, 1988). Development of adventitious roots may be related to better 0, supply near the soil surface or through the stem tissues (Jackson and Drew, 1984). These roots may take over the functions of the original root system, although they would presumably be vulnerable to surface drying when the waterlogging stress ceases. The functioning of these roots during waterlogging is facilitated by the development of aerenchyma (Pezeshki, 1994). The formation of aerenchyma involves partial breakdown of the cortex with cell lysis, which in many plants is mediated through endogenous ethylene (Kawase and Whitmoyer, 1980; He et al., 1996).

2. IonUptake Waterlogging markedly reduces the nutrient uptake, particularly the uptake of nitrogen (e.g., field pea, Belford et al.. 1980; lentil, chickpea, and faba bean, B. Thomson, unpublished data). Jackson ( 1979) found that 4 days waterlogging at the 8-1 0 leaf stage decreased the concentrations of nitrogen, phosphorous, and potassium in the shoots of field pea, with greater inhibition in N uptake (82%) than in P (41%) and K (46%) uptake. This inhibition of N uptake appears at least partly responsible for the premature chlorosis and leaf senescence of waterlogged plants. As an example, following 2 weeks of waterlogging of 4-week old L. luteus and L. angustifolius supplied with inorganic N, Davies (unpublished data) found that N content was reduced by 30% in relatively waterlogging-tolerant L. luteus and by 60% in waterlogging-sensitive L. angustifolius compared to control plants. Furthermore, premature chlorosis and leaf senescence occurred earlier in L. angustifolius than in L. luteus. In several cool season grain legumes, waterloggingintolerant species or cultivars exhibited a greater decrease in leaf chlorophyll concentration compared with relatively waterlogging-tolerant species or cultivars (e.g., lupin and field pea, Phuphak, 1989; lentil, Ashraf and Chisti, 1993). The physiological reasons for the differences in N uptake by different grain legume species during waterlogging are not well understood, however. In contrast to the essential nutrients, shoot concentrations of Na and C1 are increased drastically under waterlogging (Rogers and West, 1993). The mechanisms for the exclusion of these ions are generally energy dependent (Greenway and Munns, 1980); thus, under waterlogging, these mechanisms may be disrupted. In I? vulgaris exposed to 40 mM NaCl under waterlogged conditions, leaf Na and C1 concentrations increased substantially relative to those in plants grown under welldrained conditions (West and Taylor, 1980). Similar observations have been made with Trifolium species grown under saline waterlogging (Rogers and West, 1993).

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Therefore, under a combination of salt and waterlogging stress, plant growth may be more depressed than under either stress separately. There is no published information on the response of cool season grain legumes to combined salt and waterlogging stress. Under waterlogging, transfer of ions from the rooting medium to the shoots may take place passively, ions moving with little selectivity across damaged cell membranes by mass flow with the transpiration steam (Trought and Drew, 1980). Increased concentrations of Fe and Mn in shoots under waterlogging (e.g., lentil, Ashraf and Chishti, 1993) may be related to such passive uptake, since the solubility of Fe and Mn increases considerably under waterlogged conditions (Ponnamperuma, 1984). Conversely, the provision of greater concentrations of essential nutrients in the external medium may partially offset waterlogging-induced inhibition of nutrient transport to the shoot; for example, application of nitrogen fertilizer has been reported to reduce waterlogging damage in pea (Jackson, 1979), soybean (Buttery, 1987), and cowpea (Minchin and Summerfield, 1976).

3. Shoot Responses Shoot responses to waterlogging may primarily arise from the modifications to the internal flow of substances between root and shoot (Jackson and Kowalewska, 1983). Stomata1 closure is an early response to waterlogging (e.g., field pea, Jackson and Hall, 1987), perhaps due to increased resistance to water flow in flooded roots soon after waterlogging (Pezeshki, 1994; Zhang and Zhang, 1994). Stomata may remain closed until the waterlogging stress is removed or new adventitious roots or aerenchyma are formed (Pezeshki, 1994). The causes for the flood-induced stomatal closure are not fully understood, but both nutritional (particularly potassium nutrition) and hormonal (abscisic acid and ethylene) imbalances have been implicated (Pezeshki, 1994). Generally, stomatal closure in response to environmental stress is associated with increase in abscisic acid (ABA) concentration in response to a leaf water deficit. In many plants, however, including grain legumes, the concentration of endogenous ABA increases following waterlogging without significant change in the leaf water potential (Jackson and Kowelewska, 1983; Zhang and Davies, 1987; Jackson and Hall, 1987). In the absence of a leaf water deficit, an accumulation (due to waterlogging-induced inhibition of translocation from shoots to roots) rather than increased synthesis of ABA in leaves has been implicated as the cause for stomatal closure (Jackson and Hall, 1987). More recently, Zhang and Zhang (1994) have shown that under waterlogging ABA is produced due to loss of turgor in the older leaves and translocated to younger leaves, thus leading to stomatal closure. Wilting in response to waterlogging is common in many plants (Pezeshki, 1994), and in most cases this is alleviated by the closure of stomata (Pezeshki, 1994).Therefore, stomatal closure may be an important adaptive response to conserve water during the initial few days of water-

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logging stress. Under prolonged waterlogging the resumption of normal stomatal functioning may occur depending on the plants’ adaptive mechanisms (Pezeshki, 1994). In waterlogging intolerant plants, however, a drastic loss of leaf water content may occur a few days after waterlogging followed by leaf desiccation (e.g., field pea, Jackson and Kowalewska, 1983). This severe loss of water from leaves also coincides with increased leakiness of nutrients and large molecular organic compounds from leaves, suggesting membrane damage (e.g., field pea, Jackson and Kowaleska, 1983). Ion leakage, loss of water, and leaf desiccation under extended waterlogging have been attributed to injurious substances, but the exact nature of such substances is not known (Jackson and Kowalewska, 1983). The rate of photosynthesis may be decreased in response to waterlogging; for example, waterlogging (four alternative cycles of 6 days of waterlogging and 6 days of drainage) decreased average net photosynthesis in cowpea by about 45% relative to that of control plants (Takele and McDavid, 1994). In common bean, rate of photosynthesis was reduced by 17% within 24 hours after waterlogging and reached near zero 7 days after waterlogging (Singh et al., 1991). The reduction in photosynthesis may be due to inhibition of metabolic processes (Pezeshki, 1994) as well as stomatal closure (Younis et a/., 1993). The rate of photosynthesis may recover in waterlogging-tolerant species following the initial reduction, whereas it progressively declines in intolerant species (Pezeshki, 1994).

C. EFFECTSOF WATERLOGGING ON NODULATION AND N, FIXATION Apart from a few studies with field pea and faba bean, relatively little information is available on nodulation and N, fixation in cool season grain legumes in response to Waterlogging. In many instances where the effects of waterlogging on legume growth have been studied, the adverse effects on the performance of symbiosis have not been investigated. The reduced root growth, particularly loss of root hairs under waterlogging, may inevitably reduce nodule initiation (Minchin et al., 1978). In cowpea, waterlogging during early vegetative growth (16 days of waterlogging starting from 8 days after sowing), reduced the nodule dry weight by about 70%, relative to that of plants grown at field capacity (Minchin and Summerfield, 1976). In the absence of 0, supply in field pea, nodulation was completely inhibited and only very small nodules were formed with limited 0, supply (Virtanen and von Hausen, 1936).Nitrogen accumulation in these plants was much lower, and their growth was improved by the supply of chemical N (Virtanen and von Hausen, 1936). A later study with field pea has also shown that accumulation of nitrogen was severely inhibited by waterlogging in plants dependent on N, fixation, but nitrate-fed plants showed relatively smaller reduction in nitrogen accumulation (Minchin and Pate, 1975). These results suggest that nodule formation

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and functioning may be more sensitive than the growth of the host plant to waterlogging. Oxygen is required for bacteriodes in root nodules; thus limited 0, supply may depress N, fixation by inhibiting the energy supply to the bacteroides (Schwinghamer et al., 1970; Trinick et al., 1976).Furthermore, the respiratory quotient (RQ) increases with declining PO,, so that N, fixation is much less efficient in terms of carbohydrate consumed at low 0, tensions (Smith, 1987). In most grain legumes the nitrogenase activity reduces drastically under waterlogging (e.g., faba bean, Sprent, 1972; field pea, Minchin and Pate, 1975; cowpea, Minchin and Summerfield, 1976; soybean, Sung, 1993). In soybean, the imposition of waterlogging either at anthesis or the commencement of seed filling suppressed the nitrogenase activity by more than 60% relative to the controls (Sung, 1993). When a 10-day waterlogging stress at anthesis was removed, nitrogenase activity recovered to some extent, but there was irreversible damage to the nitrogenase system by a 4-day waterlogging stress at seed filling (Sung, 1993).The inhibitory effects of low 0, on N, fixation may arise from (1) restricted supply of ATP and other intermediates from aerobic pathways of carbohydrate metabolism (Sprent, 197I), ( 2 ) direct effects of ethylene (Grobbelaar et al., 1971; Goodlass and Smith, 1979; Smith, 1987), and (3) decreased synthesis of the nitrogenase enzyme (Bisseling et al., 1980). The nodules already formed when waterlogging stress was imposed may exhibit certain adaptive mechanisms to the stress depending on the species. Enhanced production of lenticels and/or aerenchyma, increases in the ratio of uninfected cells to infected cells, and increases in the size of intercellular spaces in both cortex and the infected region are some examples of adaptation shown in root nodules in response to waterlogging (e.g., soybean, Pankhurst and Sprent, 1975; cowpea, Minchin and Summerfield, 1976; faba bean, Gallacher and Sprent, 1978). A substantial enlargement of infected cell vacuoles has been observed in nodules of waterlogged white clover (Pugh et ul., 1995). These enlarged vacuoles push the cell contents outwards toward the cell walls, thus increasing the surface-volume ratio of the protoplast, and this may result in 0, within the intercellular spaces between infected cells becoming more accessible to the bacteroids (Pugh et al., 1995). The presence of bacteriods in the center of anatomically adapted nodules of cowpea, even after 32 days of waterlogging, provides strong evidence of a continued 0, supply to these tissues (Minchin et al., 1978). Nodules with such anatomical adaptations have relatively high nitrogenase activity under waterlogging compared with more compact nodules developed under better aerated conditions (Smith, 1987). In contrast, legumes intolerant to waterlogging exhibit pronounced degeneration of nodules soon after waterlogging (e.g., field pea, Minchin and Pate, 1975; lupin, Farrington eral. 1977).The recovery of symbiosis in these species may well depend on the formation of new nodules after waterlogging is relieved, and may occur at the expense of the recovery of shoot growth (Hong et al., 1977; Minchin et al., 1978).

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D. GENETICVARIATION r~ TOLERANCE TO WATERLOGGING Published results have shown a range of interspecific variation in waterlogging tolerance in different cool season grain legumes, from greatest tolerance of faba bean and L. lureus to intolerance of field pea and lentil. For the selection of cultivars for soils prone to transient waterlogging, it is also useful if intraspecific variation in response to waterlogging can be exploited. Such intraspecific variation in response to waterlogging has been observed in soybean (Hartley et a/., 1993) and cowpea (Takele and David, 1994). Very few studies, however, have explored the intraspecific variation for tolerance to waterlogging within cool season grain legumes. Results from limited studies with chickpea have indicated a substantial variation in the waterlogging tolerance among different cultivars or assessions. Begiga and Anbessa (1995), for example, recorded a large genotypic variation in the ability of 100 chickpea lines to survive a 50-day waterlogging period imposed at 30 days after sowing. Of 100 lines tested about 30 lines were able to survive more than 40 days of waterlogging. Considerable variation in the mortality rate (10-65%) of ten chickpea lines in response to 10 days of waterlogging during the vegetative growth has also been recorded by Cowie et al. (1995). In these studies comparative responses of different genotypes have been made by comparing their growth and development under waterlogged conditions without any attempt to relate the observed differences to physiological parameters. Although these observations are valuable in terms of recognizing the existing variation among different assessions within a species, in view of compounding effects of plant age and other environmental factors, such as soil temperature and organic matter content in the development of waterlogging damage, it would be useful if morphological, anatomical, or physiological parameters associated with the observed responses were identified (Smith, 19887).

VII. CONCLUSIONS Cool season grain legumes are a major source of protein for both humans and animals in many parts of the world. These crops play a key role in sustaining longterm soil fertility in cereal production systems, particularly in temperate, mediterranean, and subtropical environments. With a contribution of more than 50% to the world’s current total pulse production (FAO, 1994), cool season grain legumes already play a major role in world food supply, and this is likely to increase with time. The production and expansion of cool season grain legumes on a worldwide scale are limited by major abiotic stresses such as drought, heat, and cold (Buddenhagen and Richards, 1988; Saxena, 1993), but they are also limited by soil abiotic stresses such as acidity, salinity and sodicity, alkalinity, poor soil structure,

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and waterlogging. Considerable interaction is likely to occur between soil abiotic stresses and drought (and nutrient) stress through effects on root growth. Management practices that reduce the effects of abiotic stresses on cool season grain legumes will play a significant role in overcoming the barriers for increased production and expansion of these crops; however, the selection of species or cultivars better adapted to soil stresses is also likely to have an important role in improving crop growth on marginal soils. The success of both approaches for increasing production of cool season grain legumes, will depend on an improved understanding of physiological and biochemical processes involved in tolerance to these stress conditions. Historically, for cool season grain legumes, a large proportion of research has concentrated on responses to soil salinity, with relatively little research concerning the responses to other soil abiotic stresses. The knowledge base for research on responses of tropical grain legumes to soil stresses such as acidity, alkalinity, soil compaction, and waterlogging has some relevance to improving the understanding of physiological and biochemical aspects of adaptation; however, these responses need to be confirmed for cool season grain legumes. Responses to soil abiotic stresses also need to be tested for a wider range of species and genotypes than have been tested in the past and under uniform experimental conditions. Furthermore, in defining species adaptation to a particular soil condition, results obtained under controlled conditions need to be verified under field conditions. Cool season grain legumes are generally more sensitive to soil abiotic stresses than are cereals, although considerable variation exists among species in their sensitivity. Faba bean, for example, appears relatively better adapted to salinity, alkalinity, and transient waterlogging compared to chickpea and lentil. Field pea also appears relatively more tolerant to salinity and alkalinity than chickpea and lentil but is very sensitive to waterlogging. Lupins are the most well-adapted cool season grain legume to acidic soils, whereas faba bean and field pea are moderately sensitive, and chickpea and lentil are very sensitive to acidity. Considerable variation also exists within species in their response to a given soil stress. In this context also, a large amount of research has considered intraspecific variation in response to soil salinity (particularly with chickpea and lentil), with relatively little research concerning other species or stress factors. In some instances the extent of intraspecific variation appears too narrow (e.g., chickpea for soil salinity) to be of any practical significance in terms of conventional breeding purposes, whereas for others intraspecific variation appears considerable (e.g., lentil for salinity and chickpea and lentil for iron chlorosis). A major limitation of studies looking at intraspecific variation is the limited number of genotypes that have been tested. In only a few instances has a broadscale evaluation of genotypes to a particular stress been undertaken, e.g., chickpea and lentil for iron deficiency (Saxena et al., 1990; Erskine et al., 1993).Thus, the existing intraspecific variation in response to aparticular stress has not been fully explored. Future studies of the intraspecific varia-

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tion in cool season grain legumes should concentrate on defining practical selection criteria that can be used in screening programs. Responses of the legume-(brady)rhizobium symbiosis to soil abiotic stresses have received insufficient attention in the past. The symbiosis appears more sensitive than host plant growth to some stresses, e.g., soil acidity, and this implies that under these conditions the performance of the symbiosis is limiting grain legume growth. In situations where the poor performance of the symbiosis is due to the intolerance of the microsymbiont it may be necessary to select nodule bacterial strains adapted to the abiotic stress. For most grain legume species, however, the symbiosis appears particularly sensitive to soil abiotic stresses at the infection stage. In such situations the selection of host cultivars capable of nodulating under stress conditions may also be an important requirement in the selection of adapted cultivars. There appears to be substantial intraspecific variation in the response of the legume-(brady)rhizobium symbiosis to major soil abiotic stresses in different cool season grain legumes, but this variation has not been fully explored.

ACKNOWLEDGMENTS The authors thank the Grains Research and Development Corporation (Australia) for financial assistance; Mike Perry for valuable discussions throughout the review, and Nancy Longnecker, Miles Dracup, and Tim Setter for constructive comments on the manuscript.

REFERENCES Abbas, M., Younis, M., and Shukry, W. (1991 ). Plant growth, metabolism, and adaptation in relation to stress conditions. XIV.Effect of salinity on the internal solute concentrations in Phaseolus i d garis. J. Plant Physiol. 138,722-727. Abdalla, A. M., Hettiaratchi, D. R. P., and Reece, A. R. (1969). The mechanics of root growth in granular media. J. Agric. Eng. Res. 14,236-248. Abd-Alla, M. H. (1992). Nodulation and nitrogen fixation in faba bean (Viciafaba L.) plants under salt stress. Symbiosis 12, 31 1-319. Abrol. I. P., and Bhumbla, D. R. (1979). Crop responses to differential gypsum application in a highly sodic soil and the tolerance of several crops to exchangeable sodium under field conditions. Soil Sci. 127,79-85. Agrawal, R. (1985). Soil physical conditions and growth of chickpea (Cicrr arirfinuni L.). J. Agron. Crop Sci. 155, 89-92. Ahlawat, I. P. S. (1990).Diagnosis and alleviation of mineral nutrient constraints in chickpea. In “Chickpea in the Nineties: Proc. 2nd Int. Workshop on Chickpea Improvement,” pp. 93-99. ICRISAT, India. Alcalde, J., and Summerfield, R. ( 1994). Effect of waterlogging during the vegetative stage of growth and development of lentil. Lens Newsletrer 21, 21-29. Ah, S. M., Sharma, B., and Ambrose, M. J. ( 1994). Current status and future strategy in breeding pea to improve resistance to biotic and abiotic stresses. In “Expanding the Production and Use of Cool

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Singh, B., Tucker, K., Sutton, J., and Bharadwaj, H. (1991). Flooding reduces gas exchange and growth in snap bean. Hort. Science, 26,273-373. Singh, 8 . B., Tewari, T. N., and Singh, A. K. (1993). Stress studies in lentil (Lens esculenta Moench). 111. Leaf growth, nitrate reductdse activity, and nodulation of two lentil genotypes exposed to sodicity. J . Agron. Crop Sci. 171, 196205. Singh, K., and Gupta, V. K. (1986). Response of chickpea (Cicer arietinum) to zinc fertilization and its critical levels in soils of semi-arid tropics. Fert. Res. 8, 2 13-218. Singh, K. B.. Malhotra, R. S., Halila, M. H., Knights, E. J., and Verma, M. M. (1994). Current status and future strategy in breeding chickpea for resistance to biotic and abiotic stresses. In “Expanding the Production and Use of Cool Season Food Legumes’’ (F. J . Muehlbauer, and W. J. Kaiser, eds.), pp. 572-591. Kluwer Academic Publ., Netherlands. Singh, N. T. (1992). Dry land salinity in the Indo-Pakistan subcontinent. In “Degradation and Restoration of Arid Lands” (H. E. Dregne, ed.), pp. 179-248. Texas Technical Univ., Lubbock, TX. Singh, R. A., Sinha, N. P., Singh, B. B., and Sharma, S.G. (1986). Reaction of chickpea genotypes to iron deficiency in a calcareous soil. J . Plan? Nurr. 9,417-422. Singh, S. B. (1988a). Tolerance of soybean varieties to waterlogging. Ind. J . Plant Physiol. 31, 410412. Singh, S. B. (1988b). Effects of sodicity on the growth and chemical composition of lentil. Ind. J . Pulse Res. 1, 152-155. Singh, S. B., and Abrol, I. P. ( 1985). Effects of soil sodicity on the growth, yield, and chemical composition of groundnut (Arachis hypognen L,). Plant Soil 84, 123-127. Singh, S. B., and Abrol, I. P. (1986). Effects of soil sodicity on the growth, yield, and chemical composition of soybean. J. Ind. Soc. Soil Sci. 34,568-57 I . Singh, S. B., and Abrol, I. P. (1987). Effect of exchangeable sodium percentage on the growth, yield, and chemical composition of chickpea. Ind. J . Agric. Sci. 57,457478. Singleton, P. W., and Bohlool B. B. (1983). Effects of salinity on the functional components of the soybean--Rhizobiurnjapnnicrtni symbiosis. Crop Sci. 23,8 15-8 18. Singleton, P. W., and Bohlool B. B. (1984). Effect of salinity on nodule formation by soybean. Plant Phy.yiol. 74,72-76. Sivaprasad, B., and Sarma, K. (1987). Seedling emergence of chickpea (Cicer arietinum L.), pigeonpea (Cajanus cujan L.), and pearl millet (Penniserurn typhoides L.), Effect of differential soil crusting, as induced by raindrop size, and depth of sowing. Plant Soil 104,263-268. Smith, K. (1987). The effects of waterlogging on root growth and on symbiotic nitrogen fixation by legumes. In “Adaptation of Chickpea and Pigeonpea to Abiotic Stress.” Proc. Consultants Workshop, pp. 77-89. ICRISAT, India. Snowball, K., and Robson, A. D. (1986). Symptoms of nutrient deficiencies: Lupins. Univ. of Western Australia Press, Nedlands, Western Australia. Solomon, M., Gedalovich, E., Mayer, A., and Poljakoff, M. A. ( I 986). Changes induced by salinity to the anatomy and morphology of excised pea roots in culture. Ann. Bot. (London) 57, 8 11-8 18. Sprent, J. (1971). Effects of water stress on nitrogen fixation in root nodules. Plant Soil spec. vol. 225-228. Sprent, J. ( 1972).The effects of water stress on nitrogen fixing root nodules. IV. Effects on whole plants of Viciafabn and Glycine mux. New Phytol. 71,603-6 I I , Sprent, J., Zahran, H., Beck, D., and Materon, L. (1988). Infection, development, and functioning of nodules under drought and salinity. In “Nitrogen Fixation by Legumes in Mediterranean Agriculture,” Developments in Plant and Soil Science, No. 32, pp. 145-150. Martinus Nijhoff Publishers, Dordrecht, The Netherlands. Subbarao, G., Johansen, C., Rao, J., and Jana, M. (1990). Response of the pigeonpea Rhizobium symbiosis to salinity stress: Variation among Rhizobium strains in symbiotic ability. Biol. Fertil. S d s . 9,49-53.

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Sung. F. (1993). Waterlogging effect o n nodule nitrogenase and leaf nitrate reductase activities in soybean. Field Crops Res. 35, 183-189. Suresh, R., Foy, C., and Weidner, J. (1987). Effects of excess soil manganese on stomata1 function in two soybean cultivars. J. Plant Nutr. 10,749-760. Szabolcs, I. (1987). The global problems o f salt-affected soils. Acru Agron. Hung. 36, 159-172. Taiz, L. ( 1 984). Plant cell expansion: Regulation of cell wall mechanical properties. Ann. Rev. PIunr Physiol. 35,585-667. Takele, A,, and McDavid, C. ( 1994). Effects of short term waterlogging on cultivars of cowpea (Vignu unguiculafa [L.] Watp.). Tr(~p.Agric. (Trinidad) 71,275-280. Takkar, P. N. (1993).Requirement and response of crop cultivars to micronutrients in India-A review. In “Genetic Aspects of Plant Mineral Nutririon” (P. J. Randall, ed.), pp. 341-348. Kluwer Academic Publ., Netherlands. Tang, C., Adams, H., Longnecker, N. E., and Robson. A. D. (1996). A method to identify lupin species tolerant of alkaline soils. Ausf. J. Agric. Rex 36,595-601. Tang, C., Buirchell, B., Longnecker, N., and Robson, A. (1993a). Variation in the growth of lupin species and genotypes on alkaline soil. Plunf Soil 155-156,s 13-5 16. Tang. C., Kuo, J., Longnecker, N., Thomson, C., and Robson, A. (1993~).High pH causes disintegration of the root surface in Lupinus angustifolius L. A m . Bot. (London) 71,201-207. Tang, C., Longnecker, N.. Thomson, C., Greenway, H., and Robson, A. (l992a). Lupin (Lupinus ongusfifolius L.) and pea (Pisum sotivim L.) roots differ in their sensitivity to pH above 6.0. J . Plant PhysioI. 140,7 15-7 19. Tang, C., and Robson, A. ( 1993). pH above 6 reduces nodulation i n Lupinus species. Plan! Soil 152, 269-276. Tang, C., Robson, A,, and Dilworth, M. J. (1990).The role of iron in nodulation and nitrogen fixation in Lupinus angusf(fo1iusL. New Phytol. 114, 173-182. Tang, C., Robson, A., Longnecker, N., and Buirchell, B. (1995a).The growth of Lupinus species on alkaline soils. Ausr. J. A g r k Rex 46, 255-268. Tang, C., Robson, A., Longnecker, N., and Greenway. H. (l993b). Physiological responses of lupin roots to high pH. Plonr Soil 155-156, 509-5 12. Tang, C., Robs0n.A. D., and Adams, H. ( 199%). High calcium is not the primary factor in poor growth of Lupinus angusrifulius L. in high pH soil. ANSI. J . Agrir. Res. 46, 1051-1062. Tang, C., Robs0n.A. D., and Dilworth, M. J. ( I992b). The role of iron in the (Brady)Rhizobiurn legume symbiosis. J . Plum Nurr 15,2235-2252. Tang, C., and Thomson, B. D. (1996). Effects of solution pH and bicarbonate on the growth and nodulation of a range of grain legume species. Plrrit Soil. 186, 321-330. Taylor, G. J. (1988).The physiology of aluminium phytotoxicity. In “Aluminium and Its Role in Biology” (H. Sigel, and A. Sigcl, eds.), pp. 123-1 52. Marcel Dekker, New York. Taylor. G. J. ( I 995). Overcoming barriers to understanding the cellular basis of aluminium resistance. In “Plant Soil Interactions at Low pH” (R. A. Date, N. J . Grundon, G. E. Rayment, and M. E. Probert, eds.) pp. 255-269. Kluwer Academic Publ., Netherlands. Taylor, H. M., and Ratliff, L. F. ( 1969). Root growth pressures of cotton, peas, and peanuts. Agron. J . 61,398402. Tewari, T. N., and Singh, B. B. (1991). Stress studies in lentil (Lens erculenta Moench). 11. sodicityinduced changes in chlorophyll, nitrate and nitrite reductase, nucleic acids, proline, yield and yield components in lentil. Plant Soil 136, 225-230. Thomas, G. W., and Hargrove, W. L. (1984). The chemistry of soil acidity. In “Soil Acidity and Liming” (F. Adams, ed.) 2nd ed. Soil Sci. Soc. Am., Amer. SOC.Agron. and Crop Sci. SOC.Am.. Madison, WI. Treeby, M., Steveninck, R. V., and Van, S. R. ( 1988). Effects of salinity and phosphate on ion distribution in lupin leaflets. Physiol. Plunt. 73,3 17-322.

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KURACLOVER(Trijioliamambipum M.B.) BREEDING,CULTURE, AND UTILIZATION N. L. Taylor' and R. R. Smith' [Department of Agronomy University of Kentucky Lexington, Kentucky 40546 * U S Dairy Forage Research Center University of Wisconsin Madison, Wisconsin 53706

I. Introduction A. Economic Importance B. Origin and Distribution 11. Taxonomy A. Related Species B. Cytology 111. Morphology and Description A. Root B. Stein and Leaf C. Flowers D. Seeds If? Culture and Management A. Adaptation and Soil Requirements B. Establishment C. Persistence and Productivity D. Pests E. Seed Production V. Utilization A. Forage Quality B. Antiquality Components VI. Breeding A. Genetic Variation B. Objectives C. Breeding Methods D. Cultivars E. Germplasms F. Interspecific Hybridization VII. Future Outlook References

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I. INTRODUCTION

A. ECONOMIC IMPORTANCE Trifolium ambiguum M. B. is a relatively new forage legume that has potential to be a major grazing crop. One of the long-term goals of specialists in the forageproducing areas of the world has been the development of a legume that would persist along with the pasture grasses. The most commonly used legumes, alfalfa (Medicago sativa L.), white clover (Trifolium repens L.), and red clover (Trifoliurn pratense L.) are nonpersistent under continuous grazing, particularly in periods of heat and drought. 7: ambiguum offers the potential to be the long-sought legume due to its extensive rhizome and root development. Because of limitations of establishment and slow recovery after harvesting its potential has not been fully realized. Like many new crops, 7: ambiguum had a long incubation period, beginning about 1944, although it was first introduced into the United States in 1911. Asymbiotic strain of Rhizobium that would inoculate T ambiguum was not available until 1954 (Erdman and Means, 1956).The potential value of 7: ambiguum was recognized very early in Australia (Hely, 1957). Further development of 7: ambiguum was delayed by downsizing in many forage breeding programs and the consequent reduction in the number of introduction trials. In addition, seedings of ir: ambiguum invariably failed in establishment, which led to discounting the value of the crop. At present, some 40 years after the first significant introduction, seed supplies are only now being increased sufficiently to allow significant areas to be sown. It has been more than two decades since the review of 7: ambiguum by Bryant (1974) and more than a decade since the review by Speer and Allinson (1985). Heightened interest in the crop is evidenced by the increased amount of research, particularly since 1990. The present review will examine the progress, the present status of research, development, usage, and potential of this relatively new forage crop.

B. ORIGINAND DISTRIBUTION Trifolium ambiguum is indigenous to Georgia, Armenia, Azerbaydzhan, the Crimea, the Black Sea coast of the Ukraine, eastern Turkey, and northern Iran (Hussain, 1961; Bryant, 1974). The common name in the United States comes from the Kura River of Georgia, and the name “Caucasian clover” used in New Zealand and Australia is also a reference to the region of origin. In these areas, 7: ambiguum occurs in ecological niches varying from poorly drained low lands of the Chernozen steppes up to 3200 m in the meadows of the Caucasus mountain range (Speer and Allinson, 1985).

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At present, 7: ambiguum is not domesticated in its native lands but is regarded as a promising species for introduction into cultivation. Wild forms have been collected and examined in the former Soviet Union for forage and seed yields (Khoroshailov and Fedorenko, 1973; Fedorenko, 1981) and for amino acid composition (Mamsurov et al., 1985). The collection of 90 accessions in the southern region of the former Soviet Union in 1976-1978 (Kovaleva, 1986) and 116 seed samples in the Caucasus and Armenia is evidence of interest in the species as a forage crop in Europe and Asia Minor (Khoroshailov ef al., 1987). Trifolium ambiguum also has been evaluated in the former Czechoslovakia, Australia, New Zealand, Canada, and the United States. It has been distributed in the United States since 1911, but lack of an effective Rhizobium inoculant limited its use. Beginning about 1944, small seed samples and root stock cuttings were distributed by Pellet Gardens in Atlantic, Iowa, and perhaps by other gardens. Its abundance of nectar led to its initial use as a honey plant; hence its two common names in the United States: Pellet’s clover and honey clover (E. A. Hollowell, unpublished manuscript). Since the discovery of effective Rhizobium species, interest has increased, particularly in Australia where Hely and co-workers (Anonymous, 1977) produced several cultivars in the 1970s from introductions. Nevertheless, expansion of 7: ambiguum hectarage in most countries has been very slow, perhaps due to its extremely low seedling vigor and difficulty of establishment. In the United States, for example, the only hectarage is that involved in the seed increase of the recently released cultivar Rhizo (Henry and Taylor, 1989).

11. TAXONOMY

A. RELATEDSPECIES Trifolium ambiguum is botanically classified in the Trifolium section Lotoidea Crantz, according to Zohary and Heller (1984). They further divide this section into the subsection and series Platyphylium. In addition to 7: ambiguum, this series includes three Eurasian, nine African, and two North American species; however, other taxonomists classify 7: ambiguum closer to 7: repens and 7: hybridum L., species with which it has been hybridized (see Sec. V1.F.).

B. CYTOLOGY Trifolium ambiguum is rather unique in the genus, having diploid (2n = 16), tetraploid (2n = 32), and hexaploid forms (2n = 48). Generally the hexaploid form is the most vigorous, but intergrading occurs (Bryant, 1974). In a 4-year test in

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New South Wales, Australia, at an altitude of 1150 m, hexaploids and diploids had higher dry matter production than did tetraploids (Dear and Zorin, 1985). Ploidy affected flowering date, persistence, leaf shape, and leaflet area but was not closely related to productivity. The different forms are usually separated geographically, but when brought together they may intercross. Interploidal fertility increases with ploidy, i.e., 2x X 4x crosses produced the lowest and 5x X 6x crosses produced the highest seed set (Townsend, 1970). The hexaploid form was the most widespread, with tetraploids being one-half and the diploids one-third as frequent in the former Soviet Union (Khoroshailov et al., 1987). The Crimea, Stavropol, and Krasnodar region contained mostly hexaploids and the Caucasus mostly tetraploids and diploids above 2000 m. In Dagestan, all three ploidies were encountered (Kovaleva, 1986). An octoploid (2n = 8x = 64) has been reported from Armenia (Khoroshailov ef al., 1987). Similar to other perennial clovers, 7: ambiguum has a base number of n = 8. Most polyploid clovers are perennial, but conversely, not all perennials are polyploid. Only a few clover species contain 2n = 4x = 32 forms, i.e., white clover (7: repens L.), and all (like T. ambiguum) are perennial and cross-pollinated (Taylor et al., 1979). The 2n = 2x = 16 forms are generally regular in meiosis, having eight bivalents (Williams et a/., 1982). The 2n = 4x = 32 form of the cultivar Treeline has somewhat less regular pairing of chromosomes, producing 12.18 and 13.56 bivalents (two plants) (Williams et al., 1982) and 14.35 bivalents (one plant) (Anderson et al., 1991). Some homology of the four sets of chromosome occurs as shown by quadrivalent pairing (1.86, 1.19, and 0.53, respectively, in the previously listed plants). No cytological studies of hexaploid plants have been conducted nor has mode of inheritance been examined, but polygenic control would be expected in the polyploids depending on the degree of diploidization that has occurred.

III. MORPHOLOGY AND DESCRIPTION A. ROOT Trifolium ambiguum has, similar to most perennial clovers, a crown system but is somewhat unique in producing lateral roots. The rhizomatous root stock may be exhibited in the first season as early as 3 months after sowing and after 2 years may spread as much as 1 m in diameter (Kim, 1996). The roots may ultimately reach a depth of 60 cm (Speer and Allinson, 1985). One of the unusual characteristics of 7: ambiguum is its preponderance of roots. In a mountainous Australian site, a 17-month-old stand had considerably more root mass than aerial parts (2.74 root-shoot ratio vs 0.16 for 7: repens) (Spencer et al., 1975). A 13-year-old stand in New Zealand had more than 20 t/ha root biomass (rhizomes and tap roots) (Stra-

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chan et al., 1994).The content of nutrients in the root mass was approximately five times that of the herbage. This massive root system conveys resistance to frost heaving and ability to survive long periods with minimal photosynthesis. The extensive root system suggests usefulness for soil and water conservation but implies that the species may produce insignificant aerial biomass for livestock production. The question arises as to whether some of the root mass could be transferred to aerial herbage without greatly influencing longevity.

B. STEMANDLEAF Most cultivars of T ambiguum bloom very little in the first season of growth and produce stems and heads arising from the crown in the second season (after induction by cold). Stems may range up to 50 cm and may be erect or prostrate. Stems branch from the base (crown) or slightly above. They are generally nonpubescent (glabrous). Leaves are usually trifoliolate (rarely more). Leaflets are lanceolate to ovate, slightly longer, and more pointed than red clover. Size and shape vary somewhat with ploidy and cultivar (Kannenberg and Elliot, 1962). Leaflets are 1-8 cm long, 0.5-5 cm broad, and setose dentate. They may be marked with a white “ V ’ as in red and white clover, but leaflets without marks are not rare. Leaflets are glaucous and waxy on the dorsal side. Petioles may be glabrous or very slightly pubescent. The frequency of V leaflet markings varies among sites of origin (Szabo and Chiorean, 1987). Of 15 collections from the former Soviet Union, two collections had plants that were all leafmarked, and the other 13 collections were about 50% marked. In one group of 22 accessions, a significant correlation ( r = 0.72) existed between the frequency of no-mark phenotype and altitude of the collection. In another group, the correlation was r = 0.42. Overall, only 10.7% of the leafmark variation could be associated with altitude.

C . FLOWERS Inflorescences of 7: arnbiguwn are capitate and indeterminate; stems may carry several heads at various heights varying in date of anthesis. Heads 1-2 cm elongating later up to 3.5 cm are borne on peduncles up to 100 mm long. Heads are composed of up to 175 white florets (flowers) that become pink with age. The corolla is twice the length of the calyx, and ovoid to oblong pods are enclosed in the calyx. Lower florets deflex after seed is set (Zohary and Heller, 1984).

D. SEEDS Pods usually contain two dull yellow to reddish brown seeds. Seed size varies with ploidy. Diploid seeds are about 1.2 mm across and lenticular (oblong to reni-

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form). Seed weights were 0.1510, 0.1716, and 0.298 g per 100 seeds for 2x, 4x, and 6x respectively (Kannenberg and Elliott, 1962) but may vary depending upon environmental conditions.

N. CULTURE AND MANAGEMENT A. ADAFTATION AND SOILREQUIREMENTS 1. Adaptation The general adaptation of 7: umbiguum was reviewed by Speer and Allinson (1985). Being introduced from the Caucasus region of the former Soviet Union, the species would be expected to be adapted to similar temperate regions in other parts of the world. In the United States, it is adapted to and makes excellent growth in the north central and northeastern regions (Sheaffer et al., 1992; Peterson et al., 1994a; Kim, 1996). Although persisting adequately, it makes little growth in Georgia and Alabama (C. S. Hoveland, personal communication). In extensive trials in Kentucky, 7: ambiguum persists well and makes excellent growth under a fourharvest regime if adequate rainfall is available. Without adequate rainfall after the first cut, it may be unproductive. In Australia and New Zealand, it is well adapted to montane, subalpine, and alpine regions as attested by numerous experiments. The legume succeeds well in dry regions, such as those in Australia, and also has been reported to grow well under wet conditions (Speer and Allinson, 1985). On the other hand, evidence of poor growth on wet soils has been reported from Tasmania (Yates, 1993) and by greenhouse research (Taylor and Meche, undated). More research is needed to ascertain adaptation to hot, humid, wet regions and hot, dry regions typical of the southeastern and southwestern United States, respectively.

2. Soil Requirements a. Phosphorus (P) Early research has shown that in some climates, 7: ambiguum is productive on infertile soils (low pH and P availability) (Speer and Allinson, 1984). One example is that of Daly and Mason (1987) on a soil testing P = 16 (Olsen) and pH 5.7. Without additional fertilizer 7: umbiguum outyielded white clover by an average of 2.6 t dm/ha/yr. The authors surmised that this capability was due to the ability of 7: ambiguum to mine the soil by virtue of its extensive rhizomes and deep tap roots. Other research (Davis, 1991), however, has shown that i7 ambiguum responds

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to P applications. In one test, increasing P from 50 to 800 k g h a resulted in a seven-fold increase in dry matter yield. Davis indicated that slow establishment may give the impression that 7: ambiguum has a low P requirement, but given similar maximum yields, it probably has a P requirement and response similar to 7: repens. Likewise, on an infertile soil (P = 1 1.4 ppm Bray) in the Snowy Mountains of New South Wales, Australia, 7: ambiguurn responded to Papplications. Treatments were high fertility (280 k g h a superphosphate) and low fertility (no P). The hexaploid cultivar Monaro yielded 1960 k g h a and had a legume component of 75% in the high fertility treatment compared with 1040 kg/ha and 22%, respectively, in the low fertility treatment. The diploid cultivar Alpine was less productive under both treatments but responded similarly (Virgona and Dear, 1996). An infertile soil in New Zealand (South Island) high country was fertilized in a phosphorous gradient experiment sown to 7: umbiguum (Strachan et al., 1994). The trial was not grazed or fertilized after establishment, and by year 12, ir: ambiguum established and maintained dominance in all plots that had more than 100 kg P h a of applied fertilizer; however, 200 kg P/ha was required for maximum production, indicating that it responded to P. Phosphorus stored in the underground biomass amounted to 58 kgha. The authors concluded that the ability to store and remobilize nutrients from the roots over the growing season is a valuable attribute of the species. b. Lime Although 7: ambiguum will grow and persist in acid soils, it does respond to lime (Bamard and Folsher, 1988). On a soil with a pH of 4.65, dry matter yields of 7: ambiguum and other legumes increased as lime was added to raise the pH to 6.0. Foliar analysis indicated that poor growth on acid soils could largely be attributed to inadequate Ca and Mo uptake. These data all indicate that 7: ambiguum benefits from additions of lime and phosphorus to infertile soils; however, 7: umbiguum, in some climates at least, is more productive and persistent than other legumes on infertile soils due to its extensive rhizome and root system. Additional research on other major fertilizer elements is needed, but results are likely to be similar to those for phosphorus.

B. ESTABLISHMENT Because one of the major deficiencies of 7: ambiguum is low seedling vigor (Taylor and Henry, 1989), it is not surprising that a considerable amount of research has been conducted to ascertain the most efficient establishment procedures. Even extreme measures such as vegetative propagation have been attempted. Rhizome fragments and terminal sections with buds were transplanted at 2-m intervals in a Festuca grassland in New Zealand. Rhizome fragment size, termi-

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nal bud presence, and fertilizer applications all affected initial survival (Scott and Mason, 1992). In seed-sown plots, large seed size improved stand establishment, which was reflected in increased second-year yield (Smith, 1995).

1. Germination a. Temperature Effects In growth chambers, seeds of a number of legumes, including 7: ambiguum, exhibited reductions in germination and increases in time to germination at day and night temperatures at 12 and 6 and 8 and 12°C but not at 10 and 15 and 15 and 10°C. The overall conclusion was that 7: ambiguum appeared to germinate satisfactorily under most Australian conditions (Hill and Luck, 1991). b. Drought Most studies indicate that T. ambiguum will establish under drought conditions provided sufficient moisture is available for initial germination. Seeds of T. ambiguum germinated well and plants were adapted to the semiarid tussock grasslands of the south island of New Zealand (Woodman et al., 1992). In the north island of New Zealand, seeds of T. ambiguum germinated as well as other legume seeds under warm-dry conditions but not as adequately under cool-dry conditions (Awan et af.,1993). Kentucky data, however, indicate that drought after germination when seedlings are establishing root biomass is a primary factor in many stand failures (N. Taylor, personal observation).

2. Frost Tolerance Another factor that might possibly result in poor stand establishment is inability to withstand severe frosts (as low as - 16°C). T. ambiguum was found to be sensitive to frost damage in New Zealand, probably because of poor vigor of seedlings (Caradus, 1994). 3. Seeding Rates Seed size and recommended seeding rates for hexaploid T. ambiguum are about the same as for Medicago sativa and T. pratense, i.e., about 11-13 kgha (Taylor and Henry, 1989; Strachan et al., 1994). (Seeds of diploid and tetraploid forms are smaller, and, theoretically at least, seeding rates could be reduced). Seeding rates as low as 1.9 kgha in rows 46 cm apart and 4.5 kgha in broadcast plantings have been successful (Bryant, 1974). At these low seeding rates, if seeds are hard (impermeable seed coats), scarification may be recommended (Moorhead et al., 1994). Benefits of scarification at high seeding rates as a commercial practice are unknown but may be unnecessary for most seed lots. Seeding rates for difficult

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sites, such as mine spoils, possibly should be increased beyond the recommended rate (13.5 kgha) (Collins et al., in review). In Wisconsin, total second-year forage yield of 7: ambiguum when seeded at 6.8, 11.0, and 20.4 k g h a was 5.0, 5.8, and 6.5 M g h a , respectively. At the end of the second year of production, however, only 34% of the ground cover was 7: ambiguum in the plots seeded at the rate of 6.8 k g h a compared with 65 and 87% for the 1 1 .O and 20.4 k g h a seeding rates, respectively (R. R. Smith, unpublished data).

4. Nodulation and Pelleting Effective nodulation has been implicated as a major factor for the establishment of 7: ambiguum in overseeding tussock grassland sites (Lowther and Patrick, 1992). The tetraploid cultivar Treeline required a different strain of Rhizobium from the hexaploid cultivar Monaro. More effective nodulation of the hexaploid cultivar resulted from increasing the peat inoculant level from the recommended rate (9.6gk.g seed) to 6.3 times the rate (Patrick et al., 1994). Number of established plants of 7: ambiguum was strongly correlated with percentage nodulation 7 months after sowing on seven of nine sites (Patrick and Lowther, 1995). Although pelleting is generally recommended for overseeding 7: ambiguum in New Zealand, Collins et a/. (manuscript in preparation) found no differences in seedling density as affected by coating either with or without a fungicide as compared to uncoated seeds; data were collected on two sites-a mine spoil and a silt loam soil. Obviously, more research is needed to elucidate the relationship of seed inoculation and pelleting to stand establishment.

5. Companion Grasses In greenhouse pot trials, Festuca arundinacea Schreb. more severely competed with 7: ambiguum than with 7: repens or Lotus corniculatus L. Roots of 7: ambiguum did not branch and spread among the root mass of E arundinacea in the same manner as 7: repens. Hill and Hoveland (1993) concluded that T. ambiguuni could be very sensitive to the sowing density of companion grass species, particularly in shallow soils with restricted rooting depth. The previously described research was repeated by Hill and Mulcahy ( 1995) using larger pots (390 mm wide and 330 mm deep). They concluded that early growth of 7: ambiguum-in particular, root and rhizome development-may be better where the density of the companion grass is low or grass vigor is low due to inadequate nitrogen. They suggested that as a means of overcoming this difficulty, 7: ambiguum should be established as a pure stand with a companion grass being introduced later. In Wisconsin, a mixture of 7: ambiguum and Kentucky bluegrass (Poa prutensis L.) produced more total forage, had the most consistent clovergrass ratio, and the greatest proportion of clover over a 3-year period compared

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with mixtures with smooth bromegrass (Bromus inermis Ley.) and orchardgrass (Dactylis glomerata L.) (Kim, 1996).Rhizomes of 7: ambiguum generally spread more in smooth bromegrass swards than in Kentucky bluegrass swards.

6. Sowing Method Efforts have been made to determine the most effective method of establishing 7: ambiguum for pasture renovation. Strip-seeding, sod-seeding, and broadcastsowing methods were compared for introducing I: ambiguum seeds into a short tussock grassland in New Zealand. The least successful was the broadcast method (Moorhead et al., 1994).The most effective method, in which 7: ambiguum established as quickly and as successfully as white clover, was the strip method, followed by sod seeding. The strip method cut and inverted a ribbon of turf, placing it adjacent to the drilling strip, after which the soil was cultivated; a seed coulter sowed the seed, and a presswheel firmed the seed bed. An inverted T coulter was used for the sod-seeding method, and in the broadcast treatment seeds were sown by a plot drill to fall directly from the coulters over a 200-mm wide strip. Removal of competing grass sod was critical in determining the most effective method.

7. Herbicides Somewhat contrary results to those of Moorhead et al. (1994)were reported by Decker et al. (undated) who compared broadcast and banded (about 13-mm wide) paraquat over row seedings of 17: ambiguum, 7:pratense, and other legumes. By the end of the season, stands of 7:ambiguum in both methods were poor, whereas stands of most of the other legumes were good. Both s-ethyl dipropylthiocarbamate (eptam) and ~-butyl-~-ethyl-cl,cl,cl-t~flouro-2,6-dinitro-p-toluidine (balan) have been used successfully in Wisconsin and Kentucky for weed control when establishing 7: ambiguum without a companion crop (R. R. Smith and N. L. Taylor, unpublished data). In summary, the preponderance of research data indicates that 17: ambiguum seeds germinate at about the same rate as 7: prarense seeds and are not differentially affected by extremes of temperature, drought, or fertilization applications. The need for adequate nodulation is evident, but whether this is the main factor in poor seedling establishment remains to be elucidated. This species will not tolerate competition with companion species, either small grain nurse crops or established grasses. Methods that control competitive grasses and/or weeds have resulted in successful establishment. The slow stand development of 17: ambiguum is apparently not related to poor germination but to its tendency to partition more photosynthate preferentially to root and rhizome tissues than other legumes. Typically, after germination the shoot does not enlarge, but its underground mass increases. In the study by Collins eta!. (manuscript in preparation) 1-year old plants on a mine site had 52-59% of their

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total mass below the soil surface compared with only 29% for red clover. Apparently, when the seedlings are increasing root mass, they are very susceptible to drought and to competition from other plants. Further investigations are needed to quantify the length of the period when the seedlings are most susceptible and to elucidate methods of rendering the plants less subject to stand losses.

C. PERSISTENCE AND PRODUCTMTY The strongest attribute of 7: ambiguum is its longevity. Investigations are underway to augment, quantify, and manipulate this character for maximum efficiency in grazing systems.

1. Length of Stand In the high country of New Zealand, stands have persisted as long as 13 years (Strachan et al., 1994). A large proportion of the plant consisted of coarse roots (rhizomes, crowns, and larger roots) up to 20 t k a dm. This is five times that of the herbage and probably accounts for its ability to persist under extremes of heat and cold. In the high rainfall zones of southeast Australia, 7: ambiguum has persisted up to 17 years, much longer than 7: repens (Hill et al., 1993). In Tasmania, Australia, stands of the cultivar Summit have survived over 22 years and have spread beyond the original plot boundaries. It did not survive well on heavy, black soils that were wet over winter (Yates, 1993). 2. Grazing Management Many of the long-lived stands have persisted under very lax grazing, if grazed at all. It is critical that 7: amhiguum persist under utilization if it is to become a major contributor to animal agriculture. In a study of 7: ambiguum transplanted to overcome the difficulty and uncertainty of stand establishment, three grazing managements were initiated at the Tara Hills Research Station in New Zealand: low, medium, and high stocking rates with continuous, alternating, or rotational grazing. Spread of original transplants over the 1984-1993 period was greater under medium than under low or high stocking rates. Plant spread, which averaged 58.2 cm under continuous, alternating, and rotational grazing, was not different with variable stocking rates (Allan and Keoghan, 1994). Because of the difference between transplants and sown plants, additional data are needed to determine the response of 7: ambiguum to different harvesting regimes. Such a study was conducted by Sheaffer and Marten (1991) in Minnesota. They subjected plots sown in 1984 to a 2-cut, 3-cut, or 4-cut harvest schedule in 1985 and 1986. Initial stands of 7: ambiguum were poorer than those of the oth-

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er legumes (Medicago sativa, 7:pratense, Coronilla varia L., Lotus corniculatus, and Astrugalus cicer L.), but by 1987 they approximated 95% ground cover, greater than any other legume. Yields over the 2-year period under all cutting managements were comparable to the aforementioned legumes. They concluded that 7: arnbiguurn would continue to produce and maintain stands under the different clipping managements and merited further investigation, particularly under grazing management. Their next investigation compared the above-ground productivity of 5-year-old stands of 7: arnbiguurn under 3,4, 5 , or 6 cuts annually and under continuous or rotational grazing with sheep. The rotational grazed pastures were further subdivided into those with a 14- or 28-day rest period (Peterson et al., 1994a). Treatments were conducted over a 3-year period (1990-1992). In the third year of the stand, the 5- and 6-cut treatments produced only 80 and 70%, respectively, as much forage as the 3- and 4-cut treatments. In the second and third years, the 14-day rest period treatments produced 28 and 16% less forage, respectively, than the 28-day rest period. Below-ground productivity was examined in the same experiment (Peterson et al., 1994b). Cutting treatments had little effect on below-ground morphology or total nonstructural carbohydrates (TNC), apparently because of residual leaf area after cutting. TNC concentration, similar to other legumes, was lowest in the spring and greatest in autumn. Persistence was excellent under all defoliation treatments. The authors concluded that the extensive crown and root system of established 7: arnbiguurn conferred the ability to maintain adequate TNC for persistence under a range of defoliation regimes. Although no data have been reported from sown experiments, the ability to withstand trampling by cattle has been observed in natural populations in the Transcaucasus region of the former Soviet Union (Khoroshailov and Fedorenko, 1973).

1. Diseases As with many new crops, disease problems are slow to arise, but eventually, as the crop becomes more widespread, diseases become more important. Trifolium arnbiguurn has been reported to be resistant or immune to Sclerotinia trifoliorurn, the causal agent of root rot, and Aureobasidum caulivora and A. pullulans, causal agents of anthracnose (Slesaravichyus et al., 1986). Ninety percent of 3-week-old 7: arnbiguum seedlings, however, were highly susceptible (killed) to S. trifoliorum when inoculated in a growth chamber with S. trifoliorurn ascospores (R. R. Smith, unpublished data). The reaction of 7: arnbiguum to Sclerotinia under natural conditions was not observed. Trifolium arnbiguurn is also very resistant to most virus diseases, including bean yellow mosaic virus, clover yellow bean mosaic, peatop mosaic, and peanut stunt

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virus (Smrz el al., 1985; Pederson and McLaughlin, 1989; Anderson et al., 1991). The only known instance of susceptibility is a single report of faint symptoms caused by tomato ringspot virus (Scott and Bamett, 1991). Resistance to root-knot nematode has also been reported (Pederson and Windham, 1989). The citation by Speer and Allinson (1985) that 7: ambiguum is resistant to Eqsiphe may be incorrect. Trifoliunz ambiguum (cultivar Rhizo) is severely infected with powdery mildew (causal agent not determined) in Kentucky greenhouses (personal observation). Khoroshailov and Fedorenko ( 1973) also observed mildew on plants of some accessions in the former Soviet Union.

2. Insects Attacks on 7: ambiguum have been infrequent and perhaps opportunistic. The dark-winged fungus gnat (Bradysia spp.) caused damage to greenhouse-grown seedlings of several legumes, including 7: ambiguum (Springer and Carlton, 1993). Importance under field conditions was not determined. Larval feeding by the grass grub (Costehytra zealandica, [White]) apparently is a problem in New Zealand (Dymock et al., 1989). Feeding of the western spotted cucumber beetle (Diabrotica undecimpueta Barber) on seedlings was controlled by application of 0.89 k g k a malathion (0,O-dimethyl phosphorodithiate of diethyl mercaptosuccinate) (Steiner and Snelling, 1994). In Wisconsin, 7: ambiguum has been noted to be susceptible to the potato leafhopper, Empoasca fabae (Harris), but variability for the reaction to this insect exists among all ploidy levels (Germplasm Resources Information Network, USDA, ARS, National Plant Germplasm System).

3. Nematodes Little data are available on nematode attacks. Trifolium ambiguum, however, is reported to be susceptible to the root-lesion nematode, Pratylenchus penetrans (Cobb) Filipjev and Schur-Stekhoven, in Minnesota (Thies, et al., 1995).

E. SEEDPRODUCTION Seed of 7: ambiguum is produced mainly on the first growth, at least in the United States, since the second growth flowers only sporadically. Vernalization is apparently a requirement for flowering, but the temperature requirements are unknown and may vary with ploidy level and cultivars. At the Lithuanian Agricultural Institute the highest proportion of inflorescences was found in the first cut (9.3-15% vs 2.8-7.1% in the second cut and 0% in the third) (Sprainaitis, 1989). The results suggested that breeding should be aimed at improving the capacity to form reproductive organs. Because main crowns produced more shoots per crown (7.4) (and thus more seed) than those from secondary crowns (1.7), Coolbear et

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al. (1994) suggested that stands of 7: ambiguum may take several years to reach their maximum reproductive potential, and that interrow cultivation may be an inappropriate management practice. Phenotypic recurrent selection was undertaken at Kentucky by Taylor and Cornelius (1994) to overcome the need for vernalization and to increase seed yields. Seed yields were low, however, 55 kgha, and were unaffected by the selection regime (see Sec. VI on breeding). In Wisconsin, seed yields of up to 192 kgka have been obtained from increase plots (R. R. Smith, unpublished data). The most extensive research on seed production has been conducted in Oregon (Steiner, 1992). On two sites, when the first crop was not cut for hay, seed yields were 113-169 kgka and 289 kgha. When the first crop was cut for hay, comparable seed yields were 18-43 and 11-15 kgha. Obviously, cutting the first crop for hay reduced blooming. When 7: ambiguum was intercropped with wheat, yields ranged up to 145 kgha and were much reduced by sowing wheat as a nurse crop. Over a 2-year period, the highest yield was from T. ambiguum sown alone (Steiner and Snelling, 1994). The results of this study agreed with other research that companion or nurse crops severely compete with 7: ambiguum, particularly during establishment.

V. UTILIZATION A. FORAGE QUALITY Nutritive value and acceptability of 7: ambiguum to livestock would be expected, a priori, to be similar to that of T. repens, except in the first harvest when 7: ambiguum might have more fibrous stems and flowering heads. In fact, the acceptability of 7: ambiguum was similar to T. repens in a “cafeteria style” grazing experiment in New South Wales, Australia (Hill et al., 1995). Although the growth pattern of 7: ambiguum and 7: repens differed, (7: ambiguum had less growth in November), sheep grazed more readily the leaves of both species that were easy to harvest, i.e., the large-leafed tall types. One reason for high nutritive value of forage species is a high leaf-stem ratio. In Czechoslovakia, 7: ambiguum grown in pots and harvested at bud or flower stage had a higher leaf-stem ratio than did 7: hybridum, T. repens, T. resupinaturn L., and 7: pratense (Macuha, 1989). Likewise in Minnesota, 7: ambiguum had greater leafiness than Lotus corniculatus (Sheaffer et al., 1992).

1. Dry Matter Digestibility In Minnesota, digestibility of T. nmbiguum was 672-748 g k g , higher than the other legumes in the experiment: Astragalus cicer; Coronilla varia, Lotus comiculatus, 7: pratense, and 1: hybridum (Sheaffer and Marten, 1991). In another ex-

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periment first, second, and third cuts of 7: arnbiguum averaged 744, 702, and 764 g/kg, respectively, compared with 616-681, 592-632, and 640-715 kg/ha, respectively, for Medicago sativa, Coronilla varia, Astralagus cicer; and L. corniculatus (Allinson et al., 1985). In other experiments, in vitro digestibility (IVDDM) values of 7: ambiguum consistently exceeded those of L. corniculutus, which was apparently related to the greater leafiness of 7: ambiguum (Sheaffer et al.. 1992).

2. Neutral Detergent Fiber (NDF) and Acid Detergent Fiber (ADF) In Wisconsin 7: ambiguum forage averaged 264 and 192 g/ha NDF and ADF, respectively, over a 3-year period when harvested five times at 4-cm height each year (Kim, 1996). When harvested only three times at 10-cm height NDF and ADF concentrations averaged 3 11 and 229 g/ha, respectively.

3. Crude Protein The other important factor of nutritive value, crude protein, was found for 7: umbiguum by Sheaffer et ul. (1992) to be in excess of the needs for growing lambs (National Academy of Science Standards 1985).Crude protein figures obtained by Allinson et at. (1985) were 183-203 g/ha. Kim (1996) reported similar values for crude protein (209-254 g/ha depending on height and frequency of harvest).

4. Average Daily Gain (ADG) For lambs, ADG values over a 4-year period were 198, 190, and 205 g for L. corniculatus, L. corniculatus-7: ambiguum mixture, and 7: ambiguum, respectively (Sheaffer et al., 1992). A gain of 150 g/lamb/day was considered good in this environment (Minnesota).

B. ANTIQUALITY COMPONENTS Trifolium ambiguum possesses nonlethal amounts of hydrocyanic acid (HCN) (Sprainaitis and Kyarshulis, 1984) but usually much less than 7: repens. High levels of HCN occur only rarely (according to personal communication by F. S. Pickering, cited by Hill et al., 1995). A more important problem with 7: ambiguum is bloat, which is similar in occurrence to other succulent legumes. In one study, up to 7% of lambs bloated, and 4% died despite remedial drenching with poloxalene. Because a 5 0 5 0 mixture of 7: ambiguum and L. corniculatus did not reduce the incidence of bloat, evaluation of the compatibility of grasses in mixtures with 7: ambiguum was recommended (Sheaffer et al., 1992). In summary, all studies indicate that 7: ambiguum has excellent nutritive value, possibly because of its high leaf-stem ratio. This same factor, on the other hand,

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increases the possibility of bloat. It is expected that the same preventative measures that are used with other legumes will have to be utilized in grazing management systems.

VI. BREEDING A. GENETICVARIATION For breeding to be successful, genetic variation must exist. Being a cross-pollinated species, 7: ambiguum is quite variable, as was shown by Townsend (1970). Variation among different sources and ploidy were identified for vigor, height, spread, flowering date, and foliage color. Rhizobium inoculant was not used, and it is not known what the effect of adequate nodulation and nitrogen fixation would have had on these characters. A few self-compatible plants were found, but by far the majority of plants were self-incompatible, as is common in cross-pollinated species. Stewart (1979) obtained broad sense heritability estimates of a broad range of plant characteristics. Morphological characteristics had the highest heritabilities (35-70%), and total plant yield and its components had medium heritabilities (16-32%). The number and dry weight of flowers exhibited the lowest heritabilities (7-1 2%). Polyploid cultivars were more genetically variable than diploids. Plant characteristics vary among ploidy levels (Kannenberg and Elliott, 1962). The hexaploid collections were highest for most desirable agronomic traits, but intergradation existed. Low interploidal fertility indicated that breeding would have to be conducted within each ploidy level.

B. OBJECTIVES Although 7: ambiguum has many desirable attributes, it also has some serious shortcomings, and most breeding efforts have been designed to take these into consideration. These include seed and forage yield, drought resistance, seedling vigor, early flowering, second crop flowering, and symbiotic performance in association with Rhizobium strains. The greatest shortcomings, low seedling vigor and lack of competitiveness, may be ameliorated somewhat by use of herbicides to prevent weed competition and by sowing the crop in pure stands.

C. BREEDING METHODS Most of the research conducted on 7: ambiguum has involved phenotypic selection. Hely (1957, 1972), working in Australia, was probably the first to breed

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the species. He made paired crosses among diploid plants introduced from the Caucasus region and evaluated specific and general combining ability relative to earliness of nodulation (Hely, 1972). A combination of phenotypic and genotypic selection techniques resulted in the development of the cultivar, Summit, suitable for use at high altitudes. Hely also selected plants for up to seven generations to produce other cultivars at the tetraploid and hexaploid levels. Usually up to 30-45 plants were selected within each ploidy level. In addition to early nodulation, characters selected included plant habit, seedling vigor, establishment capacity, high seed yield, and winter hardiness (Hely, 1972) (see Sec. V1.D for further discussion of characters selected). Mass selection was used by Henry and Taylor (1989) in Kentucky to develop the cultivar Rhizo, which was essentially an increase of plant introductions. In other phenotypic selection, Taylor and Cornelius ( 1 994) selected plants for first- and second-season blooming (two programs). The rationale was that firstseason blooming might be associated with greater seedling and aftermath vigor. Six to nine cycles of selections were conducted with number of half-sib families per cycle ranging from 22 to 53. The first-season program led to increased flowering in the first, and in the second season as well, but led to progressive reductions in vigor and forage and seed yields. The second-season selection program had little or no effect. The yield reductions may have been associated with inbreeding depression or detrimental effects as a consequence of first-season flowering. Hely and Zorin (1975), for example, showed that the earlier nodulating plants acted as nurse plants for less well-nodulated plants, which were more rhizomatous and spread early to form most of the stand. As only the well-nodulated less rhizomatous plants flowered in the first year, seed collected in the first year of the stand would be biased toward lower rhizome production. Selection of more half-sib families in each generation might help to prevent inbreeding, but the other possibility cannot be eliminated. Smith ( 1 995) used two cycles of phenotypic recurrent selection to increase seed size in diploid, tetraploid, and hexaploid germplasms. Seed size in the hexaploid was increased from 1.76 g to 2.24 g/IOOO seeds (27% increase). When seeded at the rate of 10.5 kgha, twice as many seedlings of the large-seeded population were established after 6 weeks compared with the original population. Total forage yield in the second year was increased by 1.05 Mg/ha for the large-seeded population.

D. CULTWARS 1. Diploid Three cultivars were released in Australia by CSIRO. The first of these, Summit, derived from material received in 1931 from Georgia, in the former Soviet

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Union (Barnard, 1972). Selection was conducted continuously for six generations by Hely (1963, 197 1, 1972). The cultivar is adapted to survival at high elevations and is resistant to seasonally waterlogged conditions. Forest is another diploid derived from Georgia accessions but is better adapted to lower elevations than Summit (Anonymous, 1977). Plants of the Forest cultivar are lower growing and spread more than Summit, Treeline, or Prairie. The third diploid cultivar is Alpine, which was selected out of Summit over 4 years for uniformity, productivity, seed setting ability, and high symbiotic performance in association with Rhizobium strains (Anonymous, 1983).

2. Tetraploid The Treeline cultivar was from material received in 1931 from Georgia, in the former Soviet Union (Barnard, 1972). It was selected continuously for six generations in Australia by Hely (1957, 1971). As its name implies, it is best suited for use at treeline elevations of the high country of southeast Australia. It requires warmer conditions for growth and flowering, and it flowers more unevenly and sparsely than Summit.

3. Hexaploid The cultivar Prairie was selected by F. W. Hely over seven generations from material received from Leningrad (Anonymous, 1977). Plants are more vigorous, leaves and flowers are larger, and seeds are much larger than diploid or tetraploid cultivars. It is a continental type, very winter-hardy, and continues to grow during long snow-free seasons at Canberra (Australian Capital Territory) and in the coastal plain of New Zealand. It is best adapted to moist, medium elevations. Monoro is a hexaploid cultivar developed in Australia from seeds received from the Markop region of Krasnodar Province, in the former Soviet Union, in 1969 (Anonymous, 1983). It was selected over two generations for 6 years by F. W. Hely and M. Zorin for effective nodulation, resistance to clover stunt virus and drought, spreading capacity, and flowering uniformity. It was the most productive of all lines tested at medium elevations (1 150 m) (Dear and Zorin, 1985). It is 2 weeks later than the diploid cultivars Summit and Forest. The only cultivar released in the United States is Rhizo, developed by the USDA Soil Conservation Service in cooperation with the University of Kentucky (Taylor and Henry, 1989). This was a seed increase of PI 325489 after several generations of natural selection at Quicksand, Kentucky. In tests at the University of Kentucky during 1983-1985, Rhizo yielded more than 7: medium, about equal to I: repens, and less than 7:prarense when managed as hay; however, it was more persistent than any of these species. Breeding programs are underway in several countries involving both public and private agencies. One such private development is

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Endura kura clover, a hexaploid cultivar from Wrightson Seeds Company of New Zealand. It is expected that several new cultivars will become available in the next decade.

E. GERMPLASMS One of the first germplasms to be released was C-2, a hexaploid developed in Colorado (Townsend, 1975). C-2 is the first generation progeny of 20 plants selected for vigor, susceptibility to nodulation, and for dark green color in a 1200plant nursery. KY 1, also a hexaploid germplasm, was released from Kentucky (Taylor, 1991). It was produced by six cycles of selection for first-year flowering from the base population of Rhizo (PI 325489). For seed production, 26 plants of cycle six were outcrossed with the other five generations, and six generations of plants selected for strong second-season flowering. Rumbaugh, et al. (1991) in Utah released ARS2678, a hexaploid that was selected on the basis of winter hardiness, tolerance of drought and high temperatures, extensive spreading of rhizomes, superior forage and seed yield, and increased nodulation. Three germplasms were released from Mississippi: MS-2X, MS-4X, and MS6X (diploid, tetraploid, and hexaploid respectively) (Pederson e f al., 199 1). These germplasms were produced from plant introductions originating from Australia, Iran, Turkey, and the former Soviet Union. All parent plants of the three germplasms are resistant to mechanical infection by clover yellow vein potyvirus and peanut stunt cucumovirus and resistant to Meloidogyne incognita race 4.

F. INTERSPECIFIC HYBRIDIZATION The success of hybridizing 7: umbiguum with its relatives has been summarized by Cleveland (1985). The present review is based on post-1985 literature. Species affinities of 7: ambiguum are the same as those diagrammed by Cleveland for 7: repens. The primary gene pool (a term defined by Harlan and deWit [ 197 I ] and Harlan [ 19751) for 7: ambiguum includes 7: repens, 7: occidentule Combe, and 7: hybridurn. The secondary gene pool includes 7: nigrescens Viv., 7: isthmocarpun? Brot., and 7: uniflorum L. Added for the tertiary pool is 7: montanum L. and 7: argutum Sol. (those species that may be crossed with either 7: umbiguum or 7: repens only with special techniques). It is obvious that a wealth of genetic material is available for manipulation. The research of Williams (summarized by Cleveland, 1985) reported between 1976 and 1982 in which 7: ambiguum was hybridized with 7: repens and T. hybridum has stimulated worldwide efforts to repeat and study these hybrids. In chronological order, the species hybridized with 7: ambiguum include 7: hvbridum

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(Firsova et al., 1980); 7: hybridum (Rubtsov and Komkova, 1983); 7: repens and 7: hybridum (Mezentsev, 1983); 7: repens (Yamada and Fukuoka, 1986; Yamada et al., 1989); 7: hybridurn (Slesaravichyus et al., 1988); a trihybrid (designated RUO) of 7: repens, 7: uniJorum, and T occidentale (Ferguson et al., 1990); and 7: repens (Meredith et al., 1995). Enhancement of hybrids has primarily involved 7: repens and conceivably could take several approaches: (1) backcrosses to improve 7: repens by adding disease resistance and rhizomatous growth habit from 7: ambiguum; (2) backcrosses to improve 7: ambiguum by adding stoloniferous growth habit from 7: repens; and (3) development of a new species by using the hybrid in various genetic constitutions. The original hybrid (No. 435) produced by Williams and Verry (1981) had 10-15% pollen stainability and was only slightly fertile in backcrosses to 7: repens and infertile in backcrosses to 7: ambiguum. Hybrid 435 has been released as a germplasm (Williams eta!., 1990). To increase the fertility of the hybrid, an in vitro technique was devised to double the chromosomes of hybrid 435 (Anderson er al., 1990). Pollen stainability of the 8x hybrid increased to 33.6%. The octoploid was somewhat more self fertile than the 4x hybrid. Pollen was tetrahedral in shape. (Anderson et al., 1991). Crosses among 8x plants under field cages, however, were low seed setting (Taylor, unpublished). The octoploid hybrid has been released as a germplasm (Taylor et al., 1991). Backcrosses of the 8x hybrid to 4x 7: arnbiguurn would be expected to produce a 48-chromosome plant that in turn might be crossed with 6x 7: ambiguum forms that are agronomically superior to the 4x forms. Further backcrosses of the hybrid 435 to 7: ambiguum again produced no progeny, but backcrosses to 7: repens resulted in two kinds of progeny (Anderson et al., 1991). First, 10 plants had 48 chromosomes and were explained by assuming there was an unreduced hybrid gamete (with 32 chromosomes) pollinated by a reduced gamete from 7: repens (16 Chromosomes). These 6 x hybrid plants, if stable, are candidates for direct use as a new species and may be useful for further backcrosses to white clover or 4x or 6x 7: arnbiguum. One 6x hybrid plant has been released as a germplasm (Taylor, et al., in press). Second, five plants had 32 chromosomes with an average of 77.9% pollen stainability. Meiotic analyses showed most of the chromosomes associating as bivalents (24 pairs) indicative of allosynditic pairing, which should allow recombination of genes between genomes of 7: repens and 7: ambiguurn (Anderson et al., 199 1 ). Backcrosses of 7: ambiguum X 7: repens hybrid to 7: repens also were made by Meredith et at. (1995). They found evidence of chiasmata among chromosomes of the two species and concluded that, by further backcrossing, it should be possible to isolate genotypes in which single alien chromosomes are added to the white clover complement. Further enhancement of these hybrid materials has been conducted by Hussain and Williams (1996). The octoploid (2n = 8x = 64)produced by Anderson et a/.

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(1991) was backcrossed to 7: repens (2n = 4x = 32) and 48 chromosome B C , F , plants resulted that were very similar to those produced by Anderson. Pollen stainability was 32%, whereas those produced by Anderson et a/. ( 199 1 ) averaged 43%. Hussain and Williams intercrossed the BC , F , to give a BC,F, with a pollen stainability of 40.8%. A backcross of the 6x BC,F, with 4x 7: umbiguum resulted in a (2n = Sx = SO) plant with pollen stainability of 59.3%. Hybrid materials at the Sx, 6x, and 8x levels are now available. No hybrid materials have been released as a cultivar from any breeding program. 1. Disease Resistance of the Hybrid

Several studies have shown that 7: umbiguum has greater virus resistance than 7: repens (Alconero et a!., 1983, 1986). How this resistance is transmitted to hybrid material is of considerable interest. Pederson and McLaughlin (1989) reported that 15 of 24 F, plants from hybrid 435 were resistant to peanut stunt virus (PSV), clover yellow vein virus (CYVV) and alfalfa mosaic virus (AMV). Trifolium ambiguum plants had 99 and 100% resistance to PSV and CYVV, respectively. Anderson et af. (1991) evaluated hybrids and parents for PSV infection after 1 1 months of field exposure and found none in 7: ambiguum, but all 7: repens plants were infected. Two F, hybrids had severe symptoms and a high incidence of infection. Second backcross progenies and backcross intercross progenies were apparently tolerant to PSV infection and had few or no symptoms despite very high virus incidence. Introgression of genes for PSV resistance from 7: ambiguum to 7: repens was not evident. Anderson et ul. (1991) concluded that prospects for obtaining genes for resistance to PSV from the two hybrid tests are doubtful. Trifolium ambiguum populations also have been shown to contain plants (38%) resistant to root-knot nematode (Meloidogyne incognita) (Pederson and Windham, 1989). Resistance of hybrids of 7: urnbiguum X T. repens was not examined.

VII. FUTURE OUTLOOK This review shows that T. ambiguum has the potential to become a major forage crop in the temperate forage-producing areas of the world. It produces well, even under intensive utilization, and the forage is of excellent quality. Where it is adapted it has the ability to respond to fertilizer applications after periods of disuse or misuse. Establishment difficulties, once thought to be insurmountable, can be overcome by controlling weeds with herbicides. This legume however, is difficult to establish when sown directly into companion crops, and its lack of suffi-

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cient seedling vigor to be sod-seeded in pasture renovation is a formidable obstacle that requires much research attention. The lag period that seedlings undergo while enlarging root systems at the expense of shoots also deserves research to determine if this period can be altered or if altering it would diminish the otherwise desirable characteristics; e.g., the ability to withstand extremes of drought, moisture, overgrazing, and other hazards. The area of adaptation deserves to be extended from cool, humid regions to wamier latitudes in order to make a greater contribution to grassland agriculture. Perhaps this should be a major goal of breeding programs. Seed yields are surprisingly good for a recently introduced crop, but breeding for even higher seed yields is a worthwhile objective. Breaking the vernalization requirement so that all flushes of growth will produce flower heads may lead to higher seed yields and more vigorous regrowth after cutting if there are no associated disadvantages. Inasmuch as 7: ambiguum causes bloat similar to other forage legumes, compatibility with grasses is a necessity. Methods of introducing grasses into 7: ambiguum stands deserve investigation. If these and other problems that likely will arise as 7: ambiguum becomes more widespread can be dealt with, this species bids to be the long-sought legume that will persist with companion grasses in pasture systems.

ACKNOWLEDGMENTS The authors thank A. V. Stewart, Pyne Could Guinness Ltd., New Zealand, for the loan of literature pertinent to New Zealand; C. T. Dougherty and M. Collins for helpful discussions; and R. E. Mundell and Susan Leopold, University of Kentucky, for help in preparing the manuscript.

REFERENCES Alconero, R. (1983). Viruses infecting six species of perennial clovers (Trifolium spp.) in field evaluations of plant introductions and cultivars. Plant Dis. 67, 1270-1271. Alconero, R., Fiori, B., and Sherring, W. (1986). Relationships of virus infections to field performance of six clover species. Plant Dis. 70, 1 19-1 2 I . Allan. B. E., and Keoghan, J. M. (1994). More persistent legumes and grasses for oversown tussock country. Proc. New Zeal. Grassl. Assoc. 56, 143- 147. Allinson, D. W., Speer, G. S., Taylor, R. W., and Guillard, K. (1985). Nutritional characteristics of kura clover (Trifo/ium ambiguurn Beib.) compared with other forage legumes. J . Agric. Sci. 104, 227-229. Anderson, J. A,, Ghabrial, S. A., and Taylor, N. L. (1991). Natural incidence of peanut stunt virus infection in hybrid populations of Trifdium ambiguum X 7: repens. Plant Dis. 75, 156-159. Anderson, J. A,, Mousset-Declas, C., Taylor, N. L., and Williams, E. G. (1990).An in vitro chrornosome doubling method for clovers (Trifolium spp.). Genome 35, 1-5. Anderson, J. A,, Taylor, N. L., and Williams, E. G. (1991).Cytology and fertility of the interspecific hybrid Trifo/iurn nmbiguum X 7: repens and backcross populations. Crop Sci. 31,683487. Anonymous. (1977). Register of Australian herbage cultivars. J . Aust. Inst. Agric. Sci. 43,92-96.

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Anonymous. (1983). Register of Australian herbage cultivars. .I Ausr. . Inst. Agric. Sci. 49, 242-243. Awan. M. H., Kemp, P. D., Choudary, M . A,, and Barker, D. J . (1993). Pasture legume establishment from oversowing in drought-prone hill country. Prnc. New Zeal. Grass/. Assoc. 55, 101-104. Barnard, C. (1972). “Register of Australian Herbage Cultivars,” pp. 148-1 5 I . CISRO, Aust. Div. Plant Ind., Canberra, Australia. Barnard, R. O., and Folscher, W. J. (1988). Growth of legumes at different levels of liming. Trop. Agr 65, 113-116. Bryant, W. G. (1974). Caucasian clover (Trifolium anthiguum Bieb.): Areview. 1.Aust. Inst. Agric. Sci. 40, 11-19. Caradus, J. R. (1994). Frost tolerance of Trfolium species. New Zeal. J. Agric. Res. 38, 157-162. Cleveland, R. W. ( 1985). Reproductive cycle and cytogenetics. In “Clover Science and Technology” (N. L. Taylor, ed.), pp. 71-1 10. Mono. 25. Am. SOC.Agron., Crop Sci. SOC.of Am., and Soil Sci. Soc. Am. Madison, WI. Collins, M.,Ditsch, D. C., and Taylor, N. L. (11.d.).Establishment of kura and red clover on soil and mine spoil. Manuscript in preparation. Coolbear, P.,Hill, M. J., and Efendi, F. ( 1994). Relationship between vegetative and reproductive growth in a four-year-old stand of Caucasian clover (Trfo[iuinambiguum M. Bieb.) cv. Monaro. Ann. Conj: Agron. SOC.New Zeal. Proc. 24,77-82. Daly, G. T., and Mason, C. R. (1987). Performance off Caucasian and zigzag clovers. Proc. New Zeal. Grassl. A ~ s o c48, . 15 1 - 156. Davis, M. R. (1991). The comparative phosphorus requirement of some temperate perennial legumes. P h r and Soil 133, 17-30. Dear, B. S . , and Zorin, M . (1985). Persistence and productivity of Trfolium nrn6iguuni M. Bieb. (Caucasian clover) in a high altitude region of southeastern Australia. Auht. 1.E.rp. Agric. 25,124-1 32. Decker, A. M., Dudley, R. F., Vough, L. R . , Specknoll, M. I., and Miller, T. H. (n.d.). Band vs. broadcast paraquat and alternative new species seeding in a no-till pasture renovation. In “Proc. Am. For. Grassl. Coun.,” pp. 254-261. Lexington, KY. Dymock, J. J . , Van den Bosch, J., Caradus, J . R.. and Lane, G. A. (1989). Growth and survival of the grass grub, Cosre/yfrazealmidicci (white) (Coleoprercr:Scarahaeidae) on Trifoliunt species and 7: repens X 7: urtijlorum hybrids. N m Zeiil. J. Agr Rex 32, 389-394. Erdman, L. W., and Means, U. M. (1956). Strains of Rhizobiuni effective on Trifoliurn amhiguurn. Agron. J. 48, 341-343. Fedorenko, I. N. (1981). Resources of Trjfoliurn arnhiguurn in the Krasnodar region. Trudy PO Priklodnoi Botanike, Cenetike Selekrsii 71, 59-62. Ferguson, N. H., Rupert, E. A,. and Evans, P. T. (1990). Interspecific Trifidiurn hybrids produced by embryo and ovule culture. Crop Sci. 30, 1145-1 149. Firsova, E. K., Mezentsev, A. V., and Rubtsov, M. 1. ( 1980). Interspecific hybridization of clover with the aid of embryo culture. Selekr.riytr I Seritenovodstvo 1980, 16-1 8. Harlan, J . C. (1975). “Crops and Man.” Ain. Soc. Agron., Madison, W1. Harlan, J. R., and deWit, J . M. J . (1971). Toward a rational classification of cultivated plants. tax or^. 20,509-5 17. Hely, F. W. (1957). Symbiotic variation in Trifdiuni arnhiguum M. Bieb. with special reference to the nature of resistance. Ausl. J. Biol. k i . 10, 1-16. Hely, F, W. (1963). Relationship between effective nodulation and time to initial nodulation in adiploid line of Trfolium ambiguum M. Bieb. Aiist. J. Biol. Sci. 16,43-54. Hely. F. W. (197 I ). Adaptation of wild crws-fertilized clovers for better nodulation and other characters required in cultivars. CSIRO, Ausr. Dia PIarir Inrrod. Rev. 8, 29-30. Hely, F. W. (1972). Genetic studies with wild diploid Tr(fidiumarnbiguurn M. Bieb. with respect to timc of nodulation. Aust. J. Agric. Res. 23. 4 3 7 4 4 6 . Hely, F. W., and Zorin, M. (1975). Ecological significance of fully effective, early nodulating members of the population i n the establishment and persistence of long-lived perennial (climax) legumes.

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Patrick. H. N., Lowther, W. L.. and Trainor. K. D. (1994). Inoculation for successful establishment of Caucasian clover. Proc. New Zrtrl. Grcr.c.sl.Assoc.. 56, 10 1-105. Pederson, G. A., and McLaughlin, M. R. ( 1989). Resistance to viruses i n Trifdiurii interspecific hybrids related to white clover. Plrrrir Dis. 73,997-999. Pederson. G. A.. McLaughlin, M. R., and Windham, G. L. (199 I ) . Registration of MS-2X, MS-4X, and MS-6X kura clover gerniplasms. Crop Sci. 31, I7 14. Pederson, G. A., and Windham, G. L. ( 1989). Resistance to Mefoidogwe ificogr>ircr in Tr[fi>/iut77interspecific hybrids and species related to white clover. Plnnr Dis. 73,567-569. Peterson, P. R., Sheaffer, C. C., Jordan, R. M., and Christians. C. J . (1994a). Responses of kura clover to sheep grazing and clipping. 1. Yield and forage quality. Agmn. J. 86,655-660. Peterson, P. R.. Sheaffer, C. C., Jordan, R. M., and Christians, C. J. (1994b). Responses of kura clover to sheep grazing and clipping. 11. Below-ground morphology, persiztence, and total nonstructura1 carbohydrates. Agrori. J . 86,66&667. Rubtsov, M. I., and Komkova, T. N. ( 1983). Interspecific hybridization in clover. Sr”skokho:yaistvennoyn Biologiytr 1983, 55-58. Rumbaugh, M. D., Johnson, D. A,, and Carlson, J. R. (1991). Registration of ARS-2678 kura clover germplasm. Crop Sci. 31,497. Scott, D., and Mason, C. R. (1992). Potential for high country pasture improvement from planting of rhizome fragments of spreading legumes. Proc. New Z e d . Grnssl. Assoc. 54, 127- 129. Scott, S. W., and Barnett, 0. W. ( 1991). An isolate of tomato ringspot virus from Trifoliuni arnhiguutn. Platit Dis. 7573-77. Sheaffer, C. C., and Marten, G. C. ( 199 I 1. Kura clover forage yield, forage quality, and stand dynamics. Ctrri. J. PlnntSci. 71, 1169-1 172. Sheaffer, C. C., Marten, G. C., Jordan, R. M., and Ristau, E. A. ( 1992).Forage potential of kura clover and birdsfoot trefoil when grazed by sheep. Agrori. J. 84, 176-180. Slesaruvichyus, A. K., Brazauskene, 1. M.. Gauril’chikene. I. V.. Tarashkyavichyus. V. A,, Petrauskas, P. Y . ,and Sliesaravicius, A. (1986).Sources of r e h a n c e to certain diseases in the genera TrIfoliU J H and Festuca. Seleklsiycr I Serneiroi~odsti~o 1986, 2 1-23, Slcsrtravichyus, A . K., Dabkyovirchene. G. A,. and Sliesaravicius, A. ( 1988).Obtaining interspecific clover hybrids. Seiektsiyir 1 Srmrrioi~otlsti~c, 1988, 24. Smith. R. R. (1995). Recurrent phenotype selection for seed size in kura clover. Agron. Absi. 1995,78. Smrz. J., Musil, M., and Vacek, V. ( 1985). Susceptibility to virus diseases of some species of the family Viciaceae. Sbornik Vedeckych Praci Vyzkunineho a Slechtitelskeho Uctavu Picninarskeno v Troubsku u Brna. 9, 163-1 70. Speer, G. S., and Allinson. D. W. (1984). Response of kura clover (Tr[fi>/iurncmbiguutn Bieb.) to defoliation, phosphorus, and boron treatments. hi “Am. For. G r a d Coun. Proc.,” pp. 73-78. Lexington, KY. Speer, G. S., and Allinson, D. W. (1985). Kura clover (Trtfoliurti nrtihiguwti): Legume for forage and soil conservation. Econ. Bot. 32, 165-1 76. Spencer, K., Hely, F. W., Govans, A. G., Zorin, M.. and Hamilton, L. J . (1975). Adaptability of Trifoliuni arnhiguuni Bieb. to a Victorian montane environment. J. Aust. Inst. Agr: Sci. 41,268-270. Sprainaitis, A. (I989). Structural analysis of yield in source material of Trijiiliurn anzbiguurn. Sostoyanie i perspekling razvitiya genetiki i selektsii v Litve: Tezisy Respublikankoi Konferents, pp. 112-1 14. Sprainaitis. A. P., and Kyarshu1is.A. S. ( 1984). The content ofcyanogenic glycocides in certain species of the genus Trifoliurn L. Rastir. rrsursy 20,462469. Springer, T. L., and Carlton, C. E. (1993). Oviposition preference of darkwinged fungus gnats (DiptemSciaridae) among Trifiliurn species. J. Ecori. 071. 86, 1420- 1423. Steiner, J. J. (1992). Effect of haying 011 kura clover (Trtfolium cunbiguuni) grown for seed. J. Appl. Seedfrod. 10, 15-18.

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Steiner, J. J., and Snelling, J . P. (1994). Kura clover seed production when intercropped with wheat. Crop Sci. 34, 1330-1335. Stewart, A. V. ( 1979).Genotypic evaluation of Trifi,liuni cmibiguum. Master of Agric. Sci. Thesis, Univ. of Canterbury, New Zealand. Strachan, D. E., Nordmeyer, A. H., and White, J. G. H. ( 1994). Nutrient storage in roots and rhizomes of hexaploid Caucasian clover. Proc. New Zeal. Grass/.A.ssac. 56,97-99. Szabo, A. T., and Chiorean, D. (1987). V-leaf marking in a Trifolium cimbiguum Bieb. germplasm collection. Notulae Botnnicae, Horti. Agrobolonici. Cluj Napocrr. 17, 77-84. Taylor, N. L. (1991). Registration of KY I kura clover germplasm. Crop Sci. 31,237. Taylor. N. L., Anderson, J. A., and Williams, E. G. (n.d.1. Registration of hexaploid germplasm from the cross of Trifoliurn cimbiguum X 7: repens. Crop Sci. Manuscript in preparation. Taylor, N. L., Anderson, J. A., Williams, E. G., and Williams, W. M. (1991). Registration of the octoploid hybrid germplasm from the cross of Trifolium nnibiguum X 7: repens. Crop Sci. 31, 1395. Taylor, N. L.. and Cornelius, P. L. (1994). Influence of recurrent selection for flowering on flowering and yields in kura clover. Euphytica 72,9-14. Taylor, N. L., and Henry, D. ( 1989). “Kura Clover for Kentucky.” Univ. of Kentucky Agric. Exp. Stn. Pub. AGR-141. Lexington, KY. Taylor, N. L., Quesenberry, K. H., and Anderson, M. K. ( 1979). Genetic system relationships in TriJoliurn. Econ. Bof. 33,43 1-44 I. Taylor, R. W., and Meche. G.A. (n.d.1. “Kura Clover Development and Response to Cutting Frequency and Height.” 74th Ann. Rept., pp. 448-45 1. Rice Expt. Station, Crowley, LA. Thies, J. A,, Petersen, A. D., and Barnes, D. K. ( 1995). Host suitability of forage grasses and legumes for root-lesion nematode Pmtylerichus penerrcins. Crop. Sri. 35, 1647-165 I . Townsend, C. E. (1970). Phenotypic diversity for agronomic characters and frequency of self-compatible plants in Trifolium ambiguum. Can. J . Plcint Sci. 50,331-338. Townsend, C . E. ( 1 975). Registration of C-2 kura clover germplasm. Crop Sci. 15,738. Virgona, J. M., and Dear, B. S. ( I 996). Comparative performance of Caucasian clover (Trifolium a m higuuni cv. Monaro) after I I years under low-input conditions in southeastem Australia. New Zeol. J . Agr: Res. 39,245-253. Williams, E. F.. Plummer, J., and Phung, M. (1982). Cytology and fertility of Trfoliuni repens. 7: ambiguum. 7: hybridurn, and interspecific hybrids. New Zeal. J . Bot. 20, 11 5-120. Williams, E. G., Taylor, N. L., Van Den Bosch, J., and Williams, W. M. (1990). Registration of tetraploid hybrid germplasm from the cross of Trifolium arnbiguirrrt X 7: repens. Crop Sci. 30, 427. Williams, E. G., and Verry, I. M. ( 198 1 ). A partially fertile hybrid between Trifolium reperis and 7: amhiguum. New Zeal. J . Bor. 19, 1-7. Woodman, R. F., Keoghan, J. M., and Allan, B. E. (1992). Pasture species for drought-prone lower slopes of the South Island high country. Proc. New Zeal. Gmssl. Assoc. 54, 1 15-120. Yamada, T., and Fukuoka, H. (1986). Production of interspecific hybrids between Trifolium ambiguum M. Bieb. and 7: repens L. by ovule culture. Jap. J. Breed. 36,233-239. Yamada, T., Fukuoka, H., and Higuchi, S. (1989). Interspecific hybridization of tetraploid kura clover (Tr(fo1iumarnbiguum M. Bieb.) and white clover using ovule culture. J . Jap. Soc. Grassl. Sci. 35, 180-185. Yates, J. J. ( 1993).Growth and persistence of Trifoliuin crrnbiguum on “high country” in Tasmania, Australia. In “Proc. 17th Intl. Grass]. Congr.,” pp. I79 1-1792. New Zealand and Australis. Zohary, M., and Heller, D. (1984). “The genus Trifolium.” Israel Academy of Sciences and Humanities, Jerusalem.

ULTISOLS: CHARACTERISTICS AND IMPACTS ON SOCIETY L. T. West,' F. H. Beinroth,2 M. E. Sumner,' and B. T. Kang' 'Department of Crop and Soil Sciences University of Georgia Athens, Georgia 30602 lAgronorny and Soils Department University o f Puerto Rico

Mayaguez Puerto Rico 00681

I. Introduction 11. Concept and Definition of Ultisols 111. Classification: Historical and Current Iv. Geographic Distribution A. World Distribution B. Distribution in the United States V Formation of Ultisols A. Environmental Factors B. Pedogenic Processes W. Morphological Properties A. Argillic and Kandic Horizons B. Soil Color C. Plinthite VII. Mineralogical Properties A. Clay Minerals B. Sand and Silt Minerals VIII. Physical Properties A. Bulk Density and COLE B. Water Retention C. Hydraulic Conductivity D. Infiltration and Surface Crusting IX. Chemical Properties A. Charge Characteristics and Source of Charge B. Cation Exchange Capacity C. Anion Exchange Capacity D. Acidity and Exchangeable Aluniinuin E. Phosphorous X. Biological Properties A. Organic Matter B. Biologic Populations and Processes

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L. T. WEST ETAL. XI. Management of Ultisols A. High-Input Cultural Systems B. Low-Input Cultural Systems: Case in West AFrica with Special Reference to Southern Nigeria

XII. Conclusion References

I. INTRODUCTION Ukisols are soils with a clay increase in subsoil horizons and low base saturation. Ultisols are extensive in warm humid climates, and most have developed under forest vegetation. Most areas of Ultisols have sufficient rainfall for crop production, and physical properties of Ultisol subsoil horizons commonly are favorable for most agricultural and nonagricultural uses. When Ultisols are converted from forest to cultivation, however, rapid losses of organic matter are common, and bare soil surfaces become subject to crusting, which limits rainfall infiltration and may cause problems for seedling emergence. In addition, Ultisols are commonly acid and dominated by kaolinitic clays. As a result, cation exchange capacities are often low, subsoils often have high amounts of exchangeable Al, and most have low reserves of nutrients needed for plant growth. Thus, Ultisols are often considered unproductive and undesirable for agricultural production. In many areas of the world, however, Ultisols are the most productive soils available and with proper management productivity can be sustained. The purpose of this chapter is to present a brief overview of Ultisols in terms of their genesis, classification, distribution, and properties and to illustrate crop management systems that have been used for agricultural production on these soils. Research on Ultisols would fill volumes. As such, any attempt to summarize all knowledge of Ultisols will not be complete. It is hoped that this summary will present enough pertinent information about Ultisols, their properties, and their response to management for the reader to develop a general understanding of this important group of soils.

11. CONCEPT AND DEFINITION OF ULTISOLS The central concept of Ultisols is a group of soils with an argillic or kandic horizon and few basic cations that have developed under forest vegetation in humid climates. Ultisols may be found on a variety of parent materials and under a range of climatic conditions. Two unifying characteristics for the development of Ultisols are (1) the parent material either contained sufficient weatherable minerals to form silicate clays, or silicate clays were present when the material was deposit-

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ed; and (2) that the climate under which the soil developed resulted in seasonal desiccation of the soils and seasons when precipitation exceeded evapotranspiration and water moved through the soil into subjacent strata. For a comprehensive definition of Ultisols, the reader is referred to the Keys to Soil Taxonomy (Soil Survey Staff, 1996). In brief, Ultisols are soils that have an argillic or kandic horizon and a base saturation (by sum of cations) of less than 35% at the shallowest of the following depths: (1) 125 cm below the upper boundary of the argillic or kandic horizon; or (2) 180 cm below the mineral soil surface; or (3) at a lithic, paralithic, or petroferric contact.

111. CLASSIFICATION: HISTORICAL AND CURRENT Soils currently considered to be Ultisols were recognized as Pedalfers at the highest level (Category VI) of the first soil classification system recognized in the United States (Marbut, 1928). Depending on a particular pedon’s color and other properties, most current Ultisols would have placed in one of three groups in Category IV (mature soils) of this system; Red soils, Yellow soils, or Lateritic soils. Poorly drained Ultisols would have been part of Swamp soils or Glei soils in Category 111 (immature but related soils). Category I1 was the soil series, and the names of many series established and mapped at that time are still used today, i.e., Cecil, Orangeburg, Norfolk, and Tifton. The 1938 Classification System in the United States (Baldwin et al., 1938) kept many of Marbut’s concepts and classes but introduced the concept of zonality into soil classification in the United States, i.e., zonal, intrazonal, and azonal soils. Present Ultisols would have been considered to be Pedalfers within the Zonal soil order. Their suborder would have been Lateritic soils of forested warm-temperate and tropical regions, and they would have been placed in either Yellow Podzolic or Red Podzolic great soil groups. The Yellow Podzolic soils were defined as “thin dark-colored organic covering over pale yellowish-gray leached layer 6 inches to 3 feet thick over heavy yellow B horizon over yellow, red, and gray mottled parent materials; acid.” The definition of Red Podzolic soils was “Thin organic layer over yellowish-brown or grayish-brown leached surface soil over deep-red B horizon. Parent material frequently reticulately mottled red, yellow, and gray; acid.” Poorly drained soils were placed in the Azonal order, and wet Ultisols would be part of Swamp soils in this order. The 1938 classification system was revised in 1949 (Thorp and Smith, 1949), but most changes were minor. The terms “Pedalfer” and “Pedocal” were dropped from the system. The major change that affected classification of present-day U1tisols was the combination of Red Podzolic and Yellow Podzolic soils into a single great soil group, the Red-Yellow Podzolic group. These soils were defined as

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“A group of well-developed, well-drained acid soils having thin organic horizons (A,) over a light-colored bleached (A,) horizon over a red, yellowish-red, or yellow and more clayey (B) horizon. Parent materials are all more or less siliceous. Coarse reticulate streaks of mottles of red, yellow, brown, and light gray are characteristic of deep horizons of the Red-Yellow Podzolic soils where parent materials are thick.” This is the first mention of a clay increase as a characteristic of these soils. No chemical properties were included in the definition. The term Ultisols as a soil order to include low base status forest soils was introduced in the seventh approximation (Soil Survey Staff, 1960),and the definition of the order included a base saturation criterion in addition to textural and morphological criteria. In the first edition of Soil Taxonomy (Soil Survey Staff, 1975), Ultisols were briefly defined as soils with an argillic horizon and base saturation less than 35% in lower subsoil horizons. In a departure from the earlier classification systems, poorly drained soils with other properties characteristic for Ultisols were included in the order as the Aquults suborder. The choice of 35% base saturation as the criterion to separate Alfisols and Ultisols was based on review of data available at the time that suggested this limit would separate soils that sustain permanent cultivation without amendments (Alfisols) from those that required amendments for continuous cultivation (Ultisols) (Smith, 1986). Placement of the diagnostic depth for this limit in the lower subsoil was to avoid changes in base saturation in the upper part of the soil that might be induced by long-term additions of soil amendments. By keeping soils that had been modified by management and similar unmanaged soils together taxonomically, the experience gained in management could be extended to the unmanaged counterpart (Smith, 1986). Since the original publication of Soil Taxonomy (Soil Survey Staff, 1975), two major changes have been made in the lower-level classification of Ultisols. Following the recommendations of the International Committee on the Classification of Low Activity Clays (ICOMLAC) (Moorman, 1985), the kandic horizon as a diagnostic subsurface horizon with low-activity clays was introduced, and Kandi and Kanhapl great groups were added to Ultisols and Alfisols. With the addition of these great groups, Trop great groups (is0 temperature regimes) of Ultisols were dropped from the system (Soil Survey Staff, 1987). With this change, presence of either an argillic or a kandic horizon was considered diagnostic for placement of a soil as an Alfisol or Ultisol. Recognition of the kandic horizon at a high level in the taxonomy emphasized the importance of a clay increase between topsoil and subsoil horizons to soil water relationships. The kandic horizon also recognized differences in nutrient amounts and relationships between Alfisols and Ultisols with low-activity clays (Kandi and Kanhapl great groups) and soils in these orders with higher cation exchange capacity. Because the kandic horizon does not require evidence of translocated clay, however, genetic implications associated with clay illuviation in an argillic horizon no longer apply to Alfisols and Ultisols in Kandi and Kanhapl great groups.

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The second major change in Ultisol classification was dropping the original requirement that Ultisols have a mesic, isomesic, or warmer temperature regime (mean annual soil temperature of 8°C or more). In 1992, this restriction was dropped, and currently Ultisols can be in any temperature regime (Soil Survey Staff, 1992). Prior to this change, soils occurring in cold climates with other properties diagnostic for Ultisols were classed as Alfisols. Other changes in the lower-level classification of Ultisols have been made as these concepts and definitions were introduced into Soil Taxonomy, i.e., introduction of Eqiaquults and Endoaquults to separate perched from apparent water tables. Currently, there are five suborders, 30 great groups, and more than 200 subgroups of Ultisols. As new knowledge is gained, especially for soils in tropical climates where most Ultisols occur, these numbers will probably increase.

IV. GEOGRAPHIC DISTRIBUTION A. WORLDDISTRIBUTION Ultisols are currently estimated to cover about 11,330,000 km’ of the earth's surface, which equates to about 8.7% of the global land mass (Eswaran, 1993). Most Ultisols occur in a global band that lies between 40"N and 40"s latitude (Fig. I ) . Ultisols occupy extensive areas of the southeastern and eastern United States, South America, central Africa, coastal areas of India, southern China, and the islands and mainland of southeast Asia. Because substantial time and extensive leaching is required to develop most Ultisols, this distribution is related to regions that have old stable land surfaces and both a seasonal moisture surplus and a seasonal deficit. Ultisols are allowed to have any temperature regime, but most occur in mesic, thermic, and hyperthermic temperature regimes (mean annual soil temperature of 8°C or more) and their is0 counterparts. About 80% of the area covered by Ultisols is in tropical regions, and about 18% of the land area of the tropics is covered by Ultisols (9,018,000 km2) (Edwaran, 1993). About half of the Ultisols currently recognized have a udic moisture regime, and half have a ustic or xeric moisture regime with long seasonal moisture deficits.

B. DISTRIBUTION IN THE UNITED STATES Ultisols comprise about 860,000 km' in the United States (Table I) and occur in 3 1 states (Fig. 2). The largest area of Ultisols in the United States is in the eastern and southern parts of the country, with the states of Alabama, Georgia, Kentucky, Mississippi, North Carolina, Pennsylvania, South Carolina, Tennessee, Virginia, and West Virginia accounting for about 64% of the Ultisols (550,000 km2; Fig. 2 ) .

0

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2 c m

C

P

i

sk ii c

c

i

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c

i:

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i

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ULTISOLS Table 1 Distribution of Ultisols in the Conterminous United States and Puerto Rico" State

Area (krn')

% of total

Alabama

97,965

11.4

Arkansas California Delaware Florida

68.644 8418 342 I 28,106

8.0

Georgia Idaho Indiana

114,802 17 3158 44

Illinois Kansas

79 27,402

Kentucky Louisiana Massachusetts Maryland Missouri Mississippi

23,395 22 16,004 28,534 49,826 85,057 7617

North Carolina New Jersey New York

579 11.195

Oregon

7137 16,784

Ohio Oklahoma Pennsylvania Puerto Rico South Carolina Tennessee

39,375 I784 54.720 44,3 I7 24,967 69,Y 10 3574 25.435 862,288

Texas

Virginia Washington West Virginia Total

1 .0 0.4 3.3 13.3

0.0 0.4 0.0 0.0 3.2 2.7 0.0 I .9 3.3 5.8 9.9 0.9 0.1 1.3 0.8 1.9 4.6 0.2 6.3 5.1 2.9 8.1 0.4 2.9 100.0

~

"Courtesy of USDA-NRCS, National Soil Survey Center

Other concentrations of Ultisols are in the southcentral United States ( 160,000 h2) and in a band along the West Coast from central California to central Washington (Fig. 2 ) . Ultisols are also extensive on the older islands of Hawaii and oc-

c

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cur in the northeastern and northcentral parts of the country in unglaciated areas with acid parent materials.

V. FORMATION OF ULTISOLS Soil-formation theory originated in Russia during the late nineteenth century, and Vasili V. Dokuchaev is credited with developing the concept of soil as an independent natural body that is a constantly changing function of five soil-forming factors: local climate, parent materials, organisms, relief, and the age of the country (Dokuchaev, 1883).The ideas of the Russian school diffused into Western Europe and North America and became fundamental principles of the emerging science of pedology. Jenny (1941), in his seminal treatise on the Factors of Soil Formation-A System of Quantitative Pedology, expressed the framework postulated by Dokuchaev in a factorial model that states that soil (s) is a function of climate (cl),organisms ( o ) , relief ( r ) ,parent material ( p ) , and time (t).This is expressed in the equation s =,f(cl,

0,c

p , t, . . .),

where the elipses indicate unspecified additional factors. Simonson (1959) outlined a generalized theory of soil genesis in which he proposed to consider soil formation as consisting of two overlapping steps: the accumulation of parent materials and the differentiation of horizons in the solum. The former is largely a geochemical-geologic process, whereas the latter is ascribed to additions, removals, transfers, and transformations within the soil system. Van Wambeke (1991) makes a similar distinction and refers to regolith formation as geogenesis and to horizon development as pedogenesis. Although the factorial approach has great appeal, especially for teaching students of the environmental sciences, it has serious limitations, including the presumed independence of the state factors and the failure to accommodate polygenetic pathways of soil formation. Smeck rt al. (1983) and Wilding (1994) comprehensively reviewed the strengths and weaknesses of Jenny’s model, not the least of which is that the equation has never been solved. A final difficulty is that, though one may gain great insight into the factors, one may learn very little about the soil itself. These drawbacks notwithstanding, the factorial approach to soil formation continues to be a unifying philosophy in pedology, and the formation of Ultisols is therefore discussed here in the framework of this paradigm.

A. ENVIRONMENTAL FACTORS 1. Climate Climate is one of the most critical factors in soil formation, and this probably is the reason why it occupies the first position in Jenny’s equation. Climate affects

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all of the other factors, except time, because it controls the kind and rate of weathering, the type of vegetation and other biota, and the nature of landscapes. With respect to rainfall, the definition of Ultisols implies two conditions. First, in the case of those Ultisols with argillic horizons, there must be some time during the year when evapotranspiration exceeds precipitation since this appears to be a prerequisite for the formation of the argillic horizon (Van Wambeke, 1991). Second, precipitation must exceed the capacity of the soil to retain water during some time of the year so that water percolates through the solum (Miller, 1983). This is essential to producing and maintaining a low base saturation in the subsoil. The wide variety of climates that meet these conditions correspond to the udic, ustic, and xeric soil moisture regimes of Soil Tclxonorny (Soil Survey Staff, 1975). Locally, the correlation of atmospheric climate and soil moisture regimes may be obfuscated by the presence of seasonally high groundwater levels or aquic conditions (Soil Survey Staff, 1996) which taxonomically override the effect of rainfall patterns. Most Ultisol areas have mean annual air temperatures above 6°C and, correspondingly, mesic, thermic, or hyperthermic soil temperature regimes. In the tropics, where annual temperature fluctuations are minimal, these become isomesic, isothermic, and isohyperthermic temperature regimes. Ultisols are therefore soils of warm areas. This characteristic was diagnostic in the original definition of U1tisols, which required them to have “a mesic, isomesic, or warmer temperature regime” (Soil Survey Staff, 1975), a criterion that was subsequently dropped. The climate conducive to the formation of Ultisols is thus typically humid tropical or humid warm-temperate. The formation of Ultisols with argillic horizons is favored either by seasonally concentrated precipitation or by high rainfall patterns with dry spells that cause sporadic soil moisture deficits. As many tropical Ultisols have evolved over geologic time periods, knowledge of the paleoclimate is essential to understanding and analyzing their development.

2. Parent Material The definition of Ultisols, as stipulated in Soil Taxonomy (Soil Survey Staff, 1996), requires the presence of either an argillic or a kandic horizon, both of which contain considerable amounts of silicate clays. It is obvious, therefore, that the parent material of Ultisols must be one that contains either (1) appreciable quantities of phyllosilicates, or (2) primary minerals that can weather to produce them. The most abundant elements in the rocks of the earth’s crust are oxygen and silicon, accounting for 46.6 and 27.7% by weight, respectively. Consequently, igneous rocks, from which all other rocks are ultimately derived, consist almost entirely of silicate materials. The structural units of silicates are silica tetrahedra, which may be linked in various ways to form, through cations or by elimination of some oxygen ions, chain, ribbon, sheet, or framework structures. An important

189

ULTISOLS

example of the framework silicates are feldspars. In the presence of water, hydrolysis occurs and produces phyllosilicates according to this equation: 2KAlSiO,O, + 11H,O + Al,Si,O,(OH), 4H,SiO, + 2K+ + 2 0 H -

+ (1)

the right-hand side of which represents kaolinite. Other kinds of clay minerals, such as those belonging to the smectite group, may form depending on the microenvironment prevailing at the weathering site, particularly drainage. As the vast majority of rocks contain silicates, a wide variety of geologic formations, dating from pre-Cambrian to Pleistocene, are capable of producing carent materials suitable for the formation of Ultisols. There are exceptions, however. Sedimentary rocks such as limestone or dolomite, if they are exceptionally pure and thus consist only of CaCO, and CaMg(CO,),, will obviously not be able to weather to produce phyllosilicates, and neither will their metamorphic derivates marble. Similarly, a sandstone consisting only of quartz, and its metamorphic equivalent, quartzite, cannot generate layer silicates because they are extremely resistant to chemical weathering, unless they are reduced to silt size or finer particles, and lack the bases for synthesizing phyllosilicates. Except in such cases, practically any rock can provide the parent material for Ultisols. A distinction must be made between transported or allochthonous and residual or autochthonous parent materials. Autochthonous parent materials have weathered in situ, whereas allochthonous materials have been transported by fluvial, colluvial, aerial, or glacial processes. The sediments that form the parent material of Ultisols have frequently been preweathered and, particularly in tropical environments, may have gone through more than one weathering cycle. Where Ultisol development occurs in sku, the parent material is usually saprolite, i.e., a chemically highly weathered parent rock that, unless it is collapsed, still preserves most of the original spatial fabric of the country rock. It may be mentioned as a curiosity that some areas in Subsahelian Africa and possibly other areas of the lower latitudes experience recurring episodes during which dust is deposited by easterly winds blowing toward the equator from desert areas. These dust storms deposit silicate clay on the landscape, and this aeolian addition of clay may be a factor in Ultisol formation at the northern fringe of equatorial Africa where leaching is sufficiently strong to decalcify the dust deposits. There is thus an abundance of plutonic, volcanic, sedimentary, and metamorphic rocks of all geologic periods that can produce Ultisols, provided that the other exogenic environmental and the endogenic factors of pedogenesis are suitable.

3. Biota Although Ultisols occur under many tropical and subtropical ecosystems, forests are the dominant vegetation of most natural Ultisol landscapes. The dy-

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L. T. WEST ETAL.

namics in forest soils appear to accelerate the formation of argillic horizons through the desiccating effect of tree roots that absorb water but not the colloids in suspension. In Africa and South America, vast areas of Ultisols are under savanna vegetation. It cannot be ascertained, however, whether the soils formed under the present vegetation or under the forests of one or more humid periods in the past. Termites and ants may also play a role in the formation of Ultisols, especially in the tropics. Lee and Wood (197 1) studied the termite activity in soils and its effect on modifications in the solum. Van Wambeke (1991) cites a French study by Aloni et al. that reports that the amount of soil that termites displace per hectare and year varies between 300 and 1000 kg and that their mounds typically comprise 250 m3. Leaf-cutter ants not only transport soil particles upward but carry large amounts of plant tissue below the surface. A comprehensive account of the role of termites and mesofauna in tropical pedogenesis has recently been compiled by Van Wambeke (1991). The impact of termite activity on Ultisol formation may be destructive since the transport of clay to the surface counters the downward movement of clay in the soil solution. The termite-caused clay distribution with depth may thus no longer meet the criteria for the argillic or kandic horizons and hence prevent their classification as Ultisols. Although Jenny’s original deliberations on the “ 0 ’ factor fail to mention the action of humans, their impact on soil formation cannot be ignored. Clearing of the native forest vegetation certainly has an effect on biomechanical processes occurring in the solum. In a recent paper, Buol (1 996) reported that long-term, high-input management of Ultisols in North Carolina significantly increased the levels of exchangeable bases with corresponding decreases in extractable aluminum levels in subsoil horizons. These human-induced profile alterations can conceivably convert Ultisols to Alfisols.

4. Relief The classic exposition of soil-forming factors referred to “relief’ and regarded it as static (Jenny, 1941). Jenny regretted that more was known of the effect of topography in the erosion of soils than in their formation; in fact, his account of the relief factor is distinguished by its brevity. As a discussion of relief should not be separated from the landscape of which it is a segment, a geomorphic approach may provide more insight. Ultisols occur on, among other landforms, uplands, backslopes, interfluves, and river and marine terraces. The one characteristic that all of these loci have in common is geomorphic stability over long periods of time. This condition relates to the time required to form an argillic or kandic horizon. Commonly this implies slope angles of 60% or less. Lower slope angles, however, are not necessarily an

ULTISOLS

191

indication of stability or geomorphic age as, e.g., a level surface may be a recent floodplain or a Tertiary peneplain. As pointed out by Daniels et a/. (197 l ) , the occurrence of Ultisols, as with other soils, is controlled by the interaction of the geomorphic and other formative factors and the resulting rates and degrees of expression of pedogenic processes. Geomorphic studies of tropical landscapes, e.g., in Brazil (Lepsch and Buol, 1974), Hawaii (Beinroth et al., 1974), and Puerto Rico (Beinroth, 1981), invariably show that Ultisols occupy geomorphic positions that are younger and less stable than the surfaces where Oxisols occur but older and more stable than those of the other soils with which they are geographically associated. In the absence of Oxisols in temperate regions, Ultisols have developed on the oldest, most stable, and highly weathered landscapes, such as the central Missouri Ozarks (Scrivner et al., 1966). The Ultisols of the coastal plain of the southeastern United States occur on nearly level geomorphic surfaces that have been stable for millions of years (Daniels et al., 1971). The landscape patterns of Ultisols in the temperate region and their geomorphic control are fairly uncomplicated. In the tropics, however, the recent introduction of the kandic horizon, which may be diagnostic for both Ultisols and Oxisols, has made the geomorphic boundary between the two orders somewhat fuzzy.

5. Time The time required for the formation of Ultisols obviously depends on the other pedogenetic factors, notably the weatherability of the parent rock, the composition of transported parent materials, and the climate and its fluctuations over time. There is much room for variability in these conditions and consequently for the age of Ultisols. There is also considerable pedologic diversity among Ultisols, including variable degrees of clay activity, which, if low, is an indication of advanced stages of weathering. The kandic horizon is identical to the oxic horizon in this regard, and it will clearly require more time to reach this stage than to develop an argillic horizon. A further complication is that, in residual Ultisols, weathering of parent material and soil development overlap, and it is difficult, therefore, to state when geogenesis ends and pedogenesis begins. Statements about the time factor in Ultisol formation are therefore necessarily speculative. The Soil Survey Staff (1975) concluded “that formation of the argillic horizon ordinarily requires a few thousands of years.” This relatively short time may suffice to produce the incipient stage of an argillic horizon, since the definition of this horizon only requires a minimum thickness of 7.5 cm. The development of the thick argillic horizons that typify the Pale great groups of Ultisols would certainly take longer. For Ultisols developing in transported sediments, the geologic age of these stra-

192

L. T. WEST ETAL.

ta determines the absolute maximum of time available for soil formation. According to Miller (1983), the Hapludults developed in the highly weathered fluvial Deweyville terrace (18,000-30,000 years B.P.) of the U S . Gulf Coast represent the shortest known time period of Ultisol formation. Gamble er al. (1970), however, reported the presence of Ultisols on late Pleistocene and Holocene surfaces in parts of the Coastal Plain of the southeastern United States, which would place their age in the neighborhood of 20,000 years. Yet, in the same region Daniels er ul. (1971) described Ultisols on geomorphic surfaces that have been weathering from 2 or 3 to about 8 million years. In Puerto Rico, Ultisols developed in the preweathered materials of marine terraces of Quaternary age, but the in siru Ultisols commonly occur on Pliocene surfaces that have been exposed to subaerial weathering for perhaps 15 million years. Ultisols in landscapes of comparable age have been reported in Africa, South America, and Australia (Miller, 1983).

B. PEDOGENIC PROCESSES Miller (1983) speculated that early concepts of Ultisol formation reflect a fascination with laterite, which was a uniquely new topic for the temperate region pedologists of the time, and efforts were focused on identifying laterite on the basis of low Si0,-AI,O, and Si0,-R,O, ratios commonly observed. Podzol-like features were also noted, however. According to Miller (1983), this prompted Joffe to describe soil formation in the Southeastern United States as “a struggle between podzolization and laterization, with podzolization having the lead.” As a consequence, the resulting soils were called Red and Yellow Podzolic soils. Subsequently, the role of podzolization was downplayed or discounted (Simonson, 1949), and more emphasis was placed on eluviation and illuviation. Various processes combine to produce, either concurrently or sequentially, the features that typify Ultisols. Prominent among these processes are those that (1) produce and maintain a low base saturation in the subsoil and ( 2 ) cause an increase of silicate clay in subsurface horizons. The first characteristic is the result of either intensive leaching or the absence of bases in the parent material. Clay increase with depth is more difficult to explain since it may result from several different processes. The argillic horizon is an “illuvial horizon which contains significant accumulations of illuviated layer-silicate clays” (Soil Survey Staff, 1975). All argillic horizons, except those that are clayey and have 2: 1 lattice clays, must have clay films in at least some part. The process known as lessivage that causes the development of cutans and the associated increase in clay is therefore critical to the formation of the argillic horizon. Eluviution of clay occurs in the part of the solum above the argillic horizon, and under favorable conditions leads to the formation of an albic horizon, as in the Albaquults. In kandic horizons, which do not require the presence of argillans, the increase

ULTISOLS

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in clay with depth may result from several processes. First, there may be vertical downward translocation of clay without accumulation in an illuvial horizon. French pedologists refer to this process that leads to lighter textured surface honzons as appauvrissment or impoverishment (Van Wambeke, 199 1). Second, clay depletion in the soil surface may be caused by the selective removal of fine particles from the surface soil by erosion or the mesofauna. Third, in Ultisols that are seasonally saturated, ferrolysis can cause the destruction of clay in the topsoil (Brinkman, 1970). Fourth, as postulated by Simonson (1949), in situ formation of clay in the B horizon may occur. Fifth, lithological discontinuities may also account for textural changes in the solum. Laterization is of major importance in the formation of most Ultisols. This process involves desilicntion, .ferraltization, ferritization, and allitization and causes the chemical migration of silica out of the solum and the relative concentration of sesquioxides in the soil. The effect of laterization is most noticeable in the Kandi and Kanhapl great groups of the Aquults, Humults, Udults, and Ustults. These taxa have kandic horizons with the chemical characteristics of the oxic horizon, indicating advanced stages of weathering and pedogenesis. The formation of plinthite is often considered an extreme manifestation of laterization. The Soil Survey Staff (1996) characterizes plinthite as an iron-rich, humus-poor mixture of sesquioxides, clay, quartz, and other dilutents that commonly occurs as dark red mottles in platy, polygonal, or reticulate patterns and generally forms in a horizon that is saturated with water for some time during the year. In a moist soil, plinthite is soft enough to be cut with a spade, but it changes irreversibly to ironstone or petroplinthite when exposed to repeated wetting and drying. The definition, genesis, and kinds of plinthite and petroplinthite have been discussed in detail by Van Wambeke (1991). Plinthite of various forms and degrees of expression occurs in Plinthaquults, Plinthohumults, Plinthudults, and Plinthustults as well as in the Plinthic and Plinthudic subgroups of Aquults, in the Plinthaquic and Plinthic subgroups of Udults, and in the Plinthic subgroups of Humults and Ustults. The evidence for podzolization is less tangible. Simonson ( 1949) discounted “pure” podzolization on the basis of the similarity of the Fe,O,-SiO, ratios in the A and B horizons of most Ultisols. Buol et al. (1973) nevertheless maintain “that iron has moved from the albic to the argillic horizon.” Ultisols with well-developed albic horizons are not common, however, and Soil Taxonomy (Soil Survey Staff, 1996) recognizes only one taxon-Albaquults-where the presence of such a horizon is an alternative requirement. Podzolization is more pronounced in the Spodic Paleudults, which have a horizon with some, but not all, of the properties of the spodic horizon. This is the only Spodic subgroup of Ultisols provided in Soil Taxonomy. Gfeizationis a process of considerably more importance in Ultisol formation. It refers to the reduction of Fe and Mn under seasonally anaerobic soil conditions,

194

L. T. WEST ET AL.

which produces bluish to greenish gray matrix colors with or without yellowish brown, brown, or black mottles and ferric and manganiferous concretions. These redoximorphic features are striking characteristics of the Aquults and the Anthraquic, Aquic, Glossaquic, Ombraquic, Oxyaquic, Plinthaquic, and Psammaquentic subgroups of Humults, Udults, Ustults, and Xerults. The transformation of raw organic material into soil organic matter, known as humification, occurs in all Ultisols but is of particular importance in all Humults, in Umbric subgroups of Aquults, and in Humic subgroups of Udults. The concurrent process of melanization produces a thin surface horizon darkened by organic matter in most well-drained Ultisols but is more prominent in poorly drained Humults and Urnbraquults (Buol et al., 1973). A related process is the illuviation of humus that results in the formation of the sombric horizon. Soil Taxonomy (Soil Survey Staff, 1996) provides only one taxon for Ultisols with this horizon, Sombrihumults. They are restricted to the cool high plateaus of the tropics at altitudes of between 1400 and 3000 m above sea level with isothermic or colder temperature regimes and a udic soil moisture regime (Van Wambeke, 1991). Other processes operating in Ultisol formation include rub$ication, which results in the dark red colors with a hue of 2.5YR or redder and a value of 3 or less that are diagnostic for Rhodudults and Rhodustults and the Rhodic subgroups of Udults and Ustults. Vertic Albaquults, Vertic Hapludults, and Vertic Paleudults have cracks and slickensides, which are indicative of some pedoturbation. The decrease in the concentration of bases with depth that is observed in many Ultisols of the southeastern United States is ascribed to biocycling (Buol et al., 1973). The differentiating feature of the Ultisols classified as Fragiaquults, Fragiudults, and Fragaquic and Fragic Paleudults is the presence of a fragipan: a subsurface horizon with a low organic matter content and a high bulk density that is slowly permeable to water. Fragipans are found mainly in temperate-region soils with some indication of poor drainage. Their origin has been explained by glacial or periglacial phenomena, but these do not explain their occasional occurrence in tropical lowlands (Van Wambeke, 1991). Although fragipans are common in Ultisols of the Southeastern United States and important to soil management, their mode of formation remains obscure. In summary, a broad range of environmental conditions and pedogenetic processes and mechanisms may be involved in the formation of Ultisols. Yet, there is no single set of formative factors, processes, and mechanisms that accounts for the formation of all Ultisols. The fact that the causative conditions may not have been the same, may have operated at different intensities over time, or may have occurred simultaneously or sequentially adds complexity to Ultisol genesis. Although many attempts to mathematically capture soil formation in computer-based models are currently in progress and in vogue, the development of process-based and mechanistic algorithms that realistically simulate the formation of all Ultisols still lacks an adequate knowledge- and database.

ULTISOLS

19s

VI. MORPHOLOGICAL PROPERTIES A. ARGILLIC AND KANDIc HORIZONS Other than low base saturation, the one property that distinguishes Ultisols from other soil orders is the requirement of a clay increase between surface and subsoil horizons. This textural differentiation with its commonly associated distribution of Fe oxides essentially determines the morphology of Ultisols and influences many chemical and physical properties of soils in this order. Until the introduction of the kandic horizon, the clay increase was required to be in the form of an argillic horizon with evidence of illuvial clay in Bt Horizons (Soil Survey Staff, 1975). After the addition of the kandic horizon, however, the clay increase in subsoil horizons of Ultisols may be in the form of a kandic horizon, which does not require the clay increase to be due to translocation. The kandic horizon was added to Soil Taxonomy to differentiate Ultisols and Alfisols that have a clay fraction dominated by clay minerals with low CEC (low activity clays, LAC) from Ultisols and Alfisols dominated by clay minerals that have high CEC. Although not required to be so, the clay increase between the epipedon and subsurface horizons in many, if not most, Ultisols with kandic horizons is, at least partly, due to eluviation and illuviation. In many Ultisols, however, clay coatings as evidence for clay translocation may only be found in lower Bt and BC horizons. The lack of identifiable translocated-oriented clay in upper horizons may be related to the abundance of kaolinite and sesquioxides commonly found in these soils (Isbell, 1980; Robertus and Buol, 1985). In addition, bioturbation and other mixing processes may have destroyed clay coatings in upper horizons (Robertus and Buol, 1985). Inclusion of the kandic horizon as a criterion for placement in Ultisols allows recognition of the subsoil clay increase at a high categorical level without firm identification of translocated clay. Though specific processes contributing to clay eluviation and illuviation vary among soils, most models include clay suspension through dispersion and slaking as dry soils are wetted, downward movement through large pores with percolating water, and redeposition as the soils dry (Buol and Hole, 1961; Soil Survey Staff, 1975; Eswaran and Sys, 1979; McKeague, 1983). Rapid wetting as would be expected during initial stages of a rainfall event and low electrolyte concentration in the soil solution of surface horizons enhance slaking and dispersion (Sumner, 1992). In most cases, downward movement of suspended clay is stopped as the soil dries due to evapotranspiration. As water moves into fine pores in the soil matrix in response to matric potential gradients, suspended clay particles are deposited on the walls of large pores, including interpedal pores. An abrupt increase in pH or ionic strength may also cause suspended clays to flocculate and stop moving downward. Achange in pore-size distribution with depth may cause water and

196

L. T. WEST ETAL.

suspended clay to perch for short periods, and while this change may not affect deposition of suspended clay, it may influence the depth at which the argillic horizon occurs in the soil. Because of the platy morphology of most silicate clays, illuvial clays are commonly oriented with the short axis (c-axis) normal to the depositional surface (Thorp and Smith, 1949). The orientation of the clay particles causes the clay coatings to have specific optical interference characteristics in thin section under crossed-polarized light, which can be used to identify the clay as illuvial. At a macroscale under reflected light, thin clay coatings have a smooth, glazed surface, and thick ones have an uneven, ropy appearance with a waxy luster (Brewer, 1976). In most cases, the mineralogy of translocated clay is similar to that in the soil matrix. However, argillic horizons often have a greater proportion of fine clay (<0.0002 mm) than overlying or underlying horizons because of its preferential transport (McKeague and St. Arnaud, 1969; Oertel, 1968). Because the mineralogy of translocated clays is similar to the matrix, the R,O,-SiO, ratio of the clay will be similar to that in overlying and underlying horizons. The R,O,-SiO, ratio of the whole soil, however, will narrow in the argillic horizon as clays are added. Similarly, illuviation of clay will dilute the coarse fraction, and quartz, zircon, and other resistant minerals will be less abundant in the argillic horizon than in overlying or underlying horizons (Soil Survey Staff, 1975). Clay translocation may be an intermittent process since illuviation can only occur if new clay-sized material is available. Once clay has been deposited and stabilized, it is less probable that forces of dispersion will overcome the stabilizing forces holding the clay in place (Nettleton et al., 1987). Rebertus and Buol(l985) reported two cycles of clay illuviation in Hapludults of North Carolina. The first occurred as clay was formed from easily weathered plagioclase feldspars. At about the same stage of pedogenesis, biotite in the parent material was weathering to silt and sand-sized kaolinite, but only after the coarse-grained kaolinite had been communited to clay-sized particles did the second cycle of illuviation occur. These workers attributed the lack of clay coatings in upper horizons of the most developed soils in the sequence to pedoturbation over the period since cessation of illuviation. Clay illuviation does not necessarily have to be from epipedons to subsurface horizons. Though there is no method to differentiate clay that has moved tens of centimeters from that that has only moved a few millimeters, clay appears to move considerable distances under certain conditions. For Ultisols, Entisols, and Spodosols in Florida with sandy horizons from 90 to more than 200 cm thick, lack of evidence for a lithologic discontinuity and presence of clay coatings in loamy subjacent horizons suggested that clay eluviation and illuviation have been major processes in formation of these soils (Cabrera-Martinez et al., 1989). Similar results have been reported for Ultisols with thick sandy epipedons in North Caroli-

ULTISOLS

197

na (Gamble et al., 1970), and Daniels et al. ( 1967) reported thicker E horizons in Ultisols on better-drained surfaces than in those on more poorly drained surfaces, implying deeper illuviation of clay when water tables were deeper. While evidence of clay translocation is necessary for the soil to have an argillic horizon, it should be recognized that in many Ultisols only a minor part of the clay in the argillic horizon is illuvial. Formation of clay in B horizons from weatherable minerals or by precipitation from solution is an important process in many Ultisols as well as soils in other orders (Rebertus and Buol, 1985, Norfleet and Smith, 1989).There is no reason to expect clay formation from primary minerals would occur in only surface horizons. While all Ultisols have an increase in clay between surface and subsoil horizons, the depth and thickness of the argillic or kandic horizon are criteria for differentiation of Ultisols at lower categorical levels and have considerable impact on interpretation of these soils for management. The Typic Kanhapludult in Fig. 3 is a soil developed from mica schist in the Southern Piedmont in Georgia. Weathering has been most intense in horizons near the surface, and much of the clay in the upper part of the kandic horizon is probably the result of neoformation rather than illuviation. With depth and decreased weathering intensity, clay content decreases rapidly in C horizons to levels similar to that observed in surface horizons. Clay increases rather abruptly at the surface of the argillic horizon, and much of the clay lost from A and E horizons has probably illuviated into Bt horizons, although dissolution of clay in these acid surface horizons cannot be discounted nor can loss of clay from the surface from selective erosion of fine particles. The Typic Kandiudult shows a much deeper profile with no decrease in clay observed within 2 m (Fig. 3 ) .This soil is developed from loamy and clayey Mioceneaged coastal plain sediments. It is assumed that much of the clay in the kandic horizon of this Ultisol was deposited with the parent material. Presence of clay coatings indicates that eluviation and illuviation have been active, however. The Grossarenic Kandiudult illustrates the great depth at which an argillic or kandic horizon can occur (Fig. 3). In the areas of the Coastal Plain of the southeastern United States, investigations have suggested that the combined thickness of A and E horizons increases with increasing soil age and increasing contents of quartz and other resistant minerals in the parent material (Daniels et al., 1966, 1970; Cady and Daniels, 1968). For fluvial-marine sediments from which these soils have developed, however, textural contrasts are common and may contribute to the textural differentiation observed in these soils. Clay coatings are commonly present in Bt horizons, however, which suggests illuviation has been an active process in these Ultisols (Cabrera-Martinez et al., 1989). From the clay distribution for the Grossarenic Kandiudult in Fig. 3, it would appear that soils with more than 2 m of sandy horizons would be found in the same landscapes. This is indeed the case. Many of the sandy Entisols (Quartzipsamments) found in the Coastal Plain of the southeastern United States have loamy

198

L. T. WEST ETAL.

Clay, % 0

20

n

40

60

t

I

50-

T

I

A

I

75

- '

I

+ I

loo-

I

I

\

125

\

'

- -

. .

\

I

150

\

\

*

\

175

Figure 3

Psammeniic Paleuduit Grossarenic Kandiuctult

I

A

i \

\ 200

Typic Kandiudult

\

'*

\

w

Clay distribution with depth o f four subgroups of Ultisols.

horizons morphologically similar to argillic or kandic horizons at depths greater than 2 m that do not enter into the soil's classification.The clay differentiation may be partially related to depositional facies, but the presence of clay coatings indicates that clay illuviation has also been an active process.

B. SOILCOLOR Color of surface horizons in Ultisols is commonly brown to yellowish brown to dark brown as would be expected for soils with relatively low amounts of organic C. However, in cool climates or poorly drained conditions with slow organic matter decomposition, thick, dark surface horizons (umbric epipedons) are common.

ULTISC)LS

199

Color of subsoil horizons in Ultisols varies from dark red to yellowish brown to light gray depending on internal drainage of the soil and the content and mineralogy of Fe and Mn oxides. In general, well-drained UItisols have reddish B horizons because of the presence of hematite as a pigmenting agent. Small amounts of hematite in a soil horizon will result in the horizon having a yellowish red or redder color because of the effectiveness of hematite in masking the yellowish brown color of geothite (Schwertmann, 1993). Because of the strong pigmenting ability of hematite, soil color has a stronger relationship with hematite content than with total Fe oxide content (Torrent ef al., 1983). The common occurrence of hematite and associated related red colors in subsoil horizons of Ultisols are related to the environment under which the soils formed. Hematite formation in soils is favored by high temperatures, seasonal moisture deficits, and rapid organic matter turnover (Schwertmann, 1993). These environmental factors are also often associated with Ultisol development. Not all Ultisols have red subsoil horizons. Yellowish-brown subsoil horizons are also common. The lack of hematite in these horizons may be due to parent material and environmental conditions that precluded formation of hematite (Schwertmann and Taylor, 1989). Yellowish brown B horizons in tropical environments, however, have been related to short periods of saturation that preferentially reduce hematite leaving behind the more stable goethite (Torrent and Schwertmann, 1987; Macedo and Bryant, 1989; Dobos e f al., 1990). Under extended periods of saturation and reduction, Ultisols will develop redox depletions and gray matrices. Similar to soils in other orders, these colors are related to loss of pigmenting Fe oxides from the depletion or horizon by mass flow and diffusion or, in a few cases, the color of reduced Fe.

C . PLINTHITE Plinthite is defined in Soil Taxonomy as an iron-rich, humus-poor mixture of clay with quartz and other dilutents (Soil Survey Staff, 1996). Much of what is now called plinthite has been referred to in the past as laterite. In general, laterites are intensively weathered materials high in Fe and A1 oxides that are hard or capable of hardening on exposure to wetting and drying (Alexander and Cady, 1962). This material is normally low in basic cations and primary silicate minerals other than quartz, and kaolinite is usually the only clay mineral present in any abundance. These characteristics describe a wide range of materials, ranging from bauxite to almost pure ironstone. Because of the imprecise definition of laterite, the term plinthite was introduced in 1949 to describe “doughy” and concretionary ferruginous soil materials that have not yet hardened (Kellogg, 1949). Plinthite is most commonly found in Bt horizons between 1 and 2 m but can be found at shallower and deeper depths and in E, Bw, and C horizons. Abundance of plinthite in a horizon can range from less than 1% to greater than 50%. For hori-

2 00

L. T. WEST ETAL.

zons with low amounts of plinthite, redistribution of Fe within the horizon or pedon may account for the Fe in the plinthite and ironstone. For soils with high amounts of plinthite or petroplinthite, however, Fe must be added to the pedon by movement with ground water (Van Wambeke, 1991). Plinthite hardens irreversibly to ironstone or petroplinthite with repeated wetting and drying, especially if it is also exposed to heat from the sun (Soil Survey Staff, 1996). Formation of plinthite is related to oxidation and reduction of Fe associated with fluctuating water table (Wood and Perkins, 1976a; Dougherty and Arnold, 1982). As Fe becomes reduced and mobile, it may move by mass flow with the soil solution and ground water or by diffusion to zones with lower concentration. When redox potential (Eh) and pH conditions that permit precipitation are encountered, the Fe precipitates around whatever insoluble material is present in the matrix. The Eh and pH often differ within a single horizon, which may result in formation of redox depletions and concentrations. Over long periods of seasonal water table fluctuations, this process can lead to sufficient accumulation of Fe to cement the matrix and form plinthite. Continued wetting and drying of plinthite with additional Fe accumulation and increased Fe crystallization may result in irreversibly hardened ironstone. Wood and Perkins (1976a) described the variation in Fe oxide content among redox depletions, the soil matrix, and plinthite in soils from the Coastal Plain of the southeastern United States. They reported that Fe,O, contents of less than 1% for redox depletions, 1.2-5.1% for the soil matrix, and 5.4-17.5% for plinthite. Perkins and Lawrence (1982) reported similar results for depletions, matrix, and plinthite, and also reported Fe oxide contents for ironstone nodules ranging from 7 to 24%. In both studies, Fe oxide content for a particular pedon increased in the order redox depletions < soil matrix < plinthite < ironstone. Similar results were reported for plinthic soils in Brazil (Dosanjos er al., 1995). Kaolinite commonly is the dominant clay mineral in soils containing plinthite, although smectite has been reported in soils with plinthite (Wood and Perkins, 1976a; Dougherty and Arnold, 1982; Dosanjos el af., 1995). Wood and Perkins (1976a) reported geothite to be the only Fe mineral in the matrix of horizons with plinthite, but plinthite from these horizons contained both geothite and hematite. In soils from Brazil, the greatest abundance of plinthite occurred in soils at footslope positions with redoximorphic features. Fe mineralogy of plinthite from these soils was dominantly geothite and lepidocrocite (Dosanjos et al., 1995). In these soils, the ratio of oxalate to dithionite-extractable Fe in the matrix was greater than that in plinthite, which was greater than this ratio in ironstone, suggesting that the Fe oxides become more crystalline as the concentrations become more indurated. Eswaran and Mohan (1973) reported that the microfabric of petroplinthite was a network of closely crystallizing geothite crystals that cemented silicates, giving the material its hardness. The variation in amount, mineralogy, and crystallinity of Fe oxides in redox con-

ULTISOLS

201

centrations results in a continuum of redox concentrations with soft, red Fe segregations on one end and ironstone on the other. Thus, field identification of plinthite in a soil horizon is, at times, difficult. Daniels et al. (1978) stated that plinthite could be differentiated from soft Fe segregations by rolling the material in question between the thumb and forefinger. If field-moist material remains intact with moderate rolling, it is plinthite or ironstone. Soft segregations can also be separated from plinthite by their slaking characteristics. Soft segregations will slake in water in a few minutes, whereas plinthite will not slake within 2 hours even with gentle agitation (Wood and Perkins, 1976b; Daniels et al., 1978). The separation between plinthite and ironstone is based on its consistence or rupture resistance (Soil Survey Division Staff, 1993). Plinthite is firm or very firm when moist (barely crushable between the thumb and forefinger), and ironstone is extremely firm when moist (fragments < 30 mm long cannot be broken or crusted by hand). Plinthite is common in Ultisols in the southeastern United States. More than 3.5 million hectares of soils in Plinthic subgroups have been mapped to date. Maximum plinthite content in subsoil horizons is 15-20%. Thus, no soils in Plinthic great groups have been recognized in the United States. Both platy and nodular forms of plinthite occur. Platy plinthite in this region is commonly less than 1 cm thick, 2-4 cm on the long axis, and is horizontally oriented. Nodular plinthite is found as irregular or spherical bodies that are normally less than 2 cm in diameter but may be larger (Daniels et ul., 1978).The boundary between the plinthite body and the soil matrix is sharp in both forms. Factors that determine the morphological form of plinthite in a soil are not completely understood. Both forms often occur in the same soil, with nodular forms overlying platy plinthite. Daniels et ul. (1978) suggested that soils with platy plinthite were mostly found on f a t or gently undulating landscapes with limited horizontal water movement above restrictive layers. In contrast, these authors suggested that soils with nodular plinthite were found on gently sloping landscapes that would induce lateral movement of water above water-restrictive horizons immediately subjacent to horizons with plinthite. Further complicating landscape distribution of plinthite is the common occurrence of morphologically similar soils with and without plinthite on similar landscapes (Perkins and Lawrence, 1982; Washer and Collins, 1988). Perkins and Lawrence (1982) found that sediments underlying soils with plinthite in southeast U.S. Coastal Plain landscapes had greater amounts of Fe than did sediments underlying soils without plinthite. In north Florida, soils with plinthite were reported to have a parent material discontinuity immediately subjacent to the horizons with plinthite that perched water. From a pedological perspective, plinthite is an interesting soil feature, but it also has significance in terms of movement of water through soils. Daniels et al. (1978) suggested that 10% platy plinthite would perch water. A similar amount of nodular plinthite was not considered to be water restrictive, but horizons subjacent to

202

L. T. WEST ETAL.

the plinthic horizon did restrict water movement. Carlan er al. (1985) reported perching of water in and above horizons containing plinthite and that movement of water and solutes through the horizon containing plinthite was restricted to redox depletions. Similar results were reported by Blume et al. (1987). In addition, these authors were able to find a Br- tracer in and above the horizon with plinthite 60 m downslope from the point of application. Both of these studies suggested that horizons subjacent to the horizons with plinthite were more water restrictive than the plinthic horizons. Data from Shaw et al. (1997) also suggest that subjacent horizons are more water restrictive than horizons containing low amounts of plinthite. Presence of a water restrictive layer below the plinthite may, in fact, be responsible for the fluctuating water tables that contributed to plinthite formation. Regardless of which horizons-those with plinthite or those subjacent to the plinthite-are most water restrictive, extensive plinthite in soils on a landscape suggests water is perching in the soil and that a large amount of the water and solutes are moving to low landscape positions as shallow subsurface flow (interflow). Shirmohammadi el al. (1 984) reported that 60-80% of total streamflow in Southeastern U.S. Coastal Plain landscapes dominated by soils with plinthite was from interflow. Hubbard and Sheridan (1983) reported that 79% of the water and 99% of the nitrate loss from upland landscapes in this region was through interflow.

VII. MINERALOGICAL PROPERTIES A. CLAYM~VERALS 1. Phyllosilicates The clay fraction of most Ultisols is dominated by kaolinite. Hajek and Zelazny (1985) estimated that more than 60%of the soils in the Southern Piedmont of the United States (dominantly Ultisols) had more than 50% kaolinite in their clay fraction. Other references to kaolinite as the dominant clay mineral in Ultisols are too numerous to list. The major reason for the dominance of kaolinite in Ultisols is the stability of kaolinite as compared to other phyllosilicates (Jackson et al., 1948; Allen and Fanning, 1983). In most cases Ultisols have undergone long periods of weathering and, thus, would be expected to be dominated by mineral components that are most resistant to weathering, such as kaolinite. In other cases, Ultisols have developed in previously weathered parent materials and have inherited kaolinite as the dominant clay mineral from these parent materials. Fiskell and Perkins (1970) reported kaolinite to be the major component of the clay fraction in 45 pedons from

ULTISOLS

203

across the southeastern Coastal Plain of the United States. Because sediments in this region largely originated from previously weathered soils and saprolites, much of the kaolinite in these soils is assumed to be inherited from the parent sediments (Allen and Fanning, 1983). In addition to its resistance to weathering, the thermodynamic stability field of kaolinite is bounded by those of gibbsite, K-mica, K-feldspar, and amorphous silica (Garrels and Christ, 1965). Thus, these minerals may transform directly to kaolinite without passing through other phyllosilicate intermediates. For Ultisols and underlying saprolite in the Piedmont and Blue Ridge Mountains of the southeastern United States, kaolinite has been shown to be a relatively early weathering product of biotite and K-feldspar in acid gneiss and schist parent materials (Robertus and Buol, 1985; Robertus et al., 1986; Norfleet and Smith, 1989). Though kaolinite is commonly the dominant clay mineral in subsoil horizons of Ultisols, hydroxy-A1 interlayered vermiculite (HIV) is often the dominant clay mineral found in A and E horizons of Ultisols and may be a major component of upper Bt horizons of these soils. (HIV will be used in this chapter to refer to both hydroxy-A1 interlayered vermiculite and smectite, since the two minerals are not commonly differentiated in the literature. This mineral has been referred to in the past by a variety of names, including 1.4 nm intergrade mineral, 2:l to 2:2 intergradational mineral, chlorite, pedogenic chlorite, and chlorite-vermiculite intergrade.) Formation and stability of HIV in Ultisols is related to high A1 activity in the soil solution and the formation of hydroxy-A1 polymers in the interlayer of vermiculite and smectite (Barnhisel and Bertsch, 1989). The degree of hydroxyA1 interlayering is variable and determines cation exchange and other properties of the mineral. In Ultisols containing appreciable amounts of both HIV and kaolinite, HIV is often dominant in surface horizons, and kaolinite dominates subsurface horizons (Bryant and Dixon, 1964; Fiskell and Perkins, 1970; Carlisle and Zelazny, 1973; Harris et al., 1980; Karathanasis et a/., 1983). Because weathering intensity is greatest in surface horizons, high amounts of HIV near the soil surface suggest HIV is as or more stable than kaolinite in these environments (Carlisle and Zelazny, 1973; Karathanasis et d.,1983). Preferential eluviation of kaolinite from A and E horizons (Hams et nl., 1989) and more rapid formation of HIV as compared to kaolinite in surface horizons (Harris et al., 1980) also have been proposed to account for the depth distribution of HIV and kaolinite in Ultisols. Even though kaolinite is the dominant clay mineral in most Ultisols, appreciable amounts of mica, vermiculite, and smectite occur in many of these soils. These more weatherable minerals may only be found in lower subsoil horizons with overlying horizons dominated by kaolinite or HIV, suggesting formation of the 2:l minerals as an initial weathering product with subsequent decomposition-recrystallization or alteration to form kaolinite and HIV (Kantor and Schwertmann, 1974; Nash et al., 1988).

2 04

L. T. WEST ETAL.

Appreciable amounts of 2: 1 minerals in Ultisols have commonly been hypothesized to have been inherited from the soil’s parent material. Harris et al. (1984) reported appreciable talc in Ultisols developed from ultramafic rocks in the Virginia Piedmont. The persistence of this unstable mineral was attributed to protection by Fe oxide coatings. Areas of Ultisols in the southeastern U.S. Coastal Plain have subsoil horizons with the clay fraction dominated by smectite (Nash, 1979; Karathanasis er al., 1986; Nash et al., 1988). The smectite in these soils was inherited from clayey marine and alluvial parent materials, and in most cases, upper horizons of these soils have abundant kaolinite and HIV formed by weathering and alteration of the smectite. The low permeability of these clayey soils has restricted leaching sufficiently to preserve smectite, but enough water has passed through the soils over the period of soil development to remove most basic cations.

2. Nonsilicate Minerals A common but minor mineral component of many Ultisols is gibbsite (Carlisle and Zelazny, 1973; Hajek and Zelazny, 1985). This mineral is often considered as the end product of silicate mineral weathering (Jackson et al., 1948), and as such, minor amounts should be expected to be found in highly weathered Ultisols. Clay fractions of Ultisols developed from acid gneiss and schist in the southeastern United States, however, have been reported to contain as much as 70% gibbsite formed as a product of K-feldspar weathering at the rock-saprolite interface (Losche etal., 1970; McCracken et al., 1971; Norfleet and Smith, 1989). Apart of the gibbsite may be subsequently resilicated to form kaolinite as weathering proceeds (Calvert et al., 1980). Iron oxides and oxy-hydroxides are also common but minor components in the clay fraction of Ultisols. Goethite is the most common Fe-oxide mineral in most Ultisols, but many well-drained Ultisols have appreciable amounts of hematite. Hematite imparts a red color to subsoil horizons (Schwertmann, 1993), and its formation is favored by warm temperatures and distinct dry seasons (Schwertmann and Taylor, 1989), which are common environmental conditions associated with Ultisols. Lepidocrocite is often present in Ultisols subject to saturation and reduction. High A1 activity in the soil solution, however, retards lepidocrocite crystallization, and this Fe mineral seldom occurs in strongly acid soils (Schwertmann and Taylor, 1989). Under certain environmental and soil conditions, minor quantities of other Fe-oxide minerals may also be present in Ultisols.

B. SANDAND SILTMINERALS As would be expected for soils that have been extensively weathered, the sand and silt fraction of Ultisols commonly is dominated by quartz, and many Ultisols

ULTI SOL S

205

have >90% quartz and other resistant minerals in their coarse fractions. Weakly to moderately developed Ultisols, however, may have appreciable quantities of mica, feldspars, and other weatherable minerals in their sand and silt separates. Bockheim et al. ( 1 996) reported that the coarse fraction of Ultisols on the coast of Oregon contained 40-50% feldspars, mica, chlorite, hornblende, and pyroxene. Sand fractions from subsoil horizons of Ultisols developed from mica gneiss and schist in the Blue Ridge Mountains in the United States were reported to have more than 50% biotite, muscovite, and feldspar (Norfleet and Smith, 1989). In more weathered Ultisols developed from similar parent materials, however, most of the sand-sized biotite had pseudomorphically altered to interstratified biotite-vermiculite, HIV, and kaolinite (Robertus et a/., 1986).

VIII. PHYSICAL PROPERTIES Even though Ultisols are considered to be nutrient poor and often present a harsh chemical environment for plant growth, physical properties of these soils as they relate to water infiltration and storage often present major constraints to their optimum use. The kaolinitic mineralogy of most Ultisols results in low shrink-swell potential and relatively favorable water-retention properties. Textural differentiation between surface and subsoil horizons as required for argillic and kandic horizons, however, may cause water perching, which, in turn, may limit infiltration and increase runoff. Sandy and loamy surface horizons with low organic matter and weak structure are subject to compaction and surface crusting, which can have a large impact on infiltration, rooting, and seedling emergence. Many Ultisols have thick sandy epipedons and/or horizons with high bulk density, which may limit water storage and root proliferation.

A. BULKDENSITY AND COLE The bulk density of Ultisols varies considerably depending on the horizon, parent material, presence of plinthite, and, for surface horizons, management. In general, A horizons of uncultivated Ultisols have lower bulk density than subsoil horizons because of the effects of organic C and bioturbation. With tillage and the accompanying loss of organic matter, compaction, and aggregate breakdown, however, the A horizon may become as dense or more dense than underlying Bt horizons (Hubbard et al., 1985). Ranges of reported bulk density for A and Bt horizons are given in Table 11. In many Ultisols, surface and/or subsurface bulk density is greater than the bulk density generally considered limiting to root proliferation. Presence of zones in sub-

2 06

L. T. WEST ET AL. Table I1

Ranges in Selected Physical Properties of Ultisols Property

References

Bulk Density (Mg m- ) A horizon

0.54-1.85

Bruce eral., 1983

Bt horizon

I .05-1.70

Hubbard et al., 1985 Quisenberry eral., 1987 Southard and Buol, 1988

COLE (Bt horizons)

0.006-0. I57

Water retention (cm’ A horizons 10 kPa

30 Wa I500 kPa Bt horizons 10 kPa 30 kPa I500 kPa Saturated Hydraulic Conductivity (pm S K I ) Bt horizons Btv horizons

0.13-0.2 1 0.09-0.18

Norfleet and Smith, 1989 Karathanasis and Hajek, 1985 Griffin and Buol, 1988 Southard and Buol. 1988 Bruce el a/., 1983 Hubbard ef al., 1985 Quisenbeny et al., 1987

0.05-0.09 0.2M.42 0.17-0.40 0.09-0.33

0.0 1-1 3. I 0.3-1.3

Hubbard er al., 1985 Southard and Buol, 1988

soil horizons with high macroporosity (Franklin, 1994; Shaw et al., 1997) and assumed lower bulk density may allow root penetration into horizons with overall high bulk density. If roots are restricted to these zones, however, the volume of soil exploited for water and nutrients will be limited. As would be expected for soils with kaolinitic mineralogy, shrink-swell potential for Ultisols is low (Table 11). Southard and Buol (1988a) reported coefficient of linear extensibility (COLE) values for Bt horizons of Hapludults and Paleudults in the Coastal Plain of North Carolina with kaolinitic mineralogy that ranged from 0.003 to 0.014. Similar values were reported by Hubbard et al. (1985). As the amount of smectite in Ultisols increases, however, so does shrink-swell. Coefficient of linear extensibility values from 0.090 to 0.157 have been reported for Bt horizons from Ultisols with smectitic mineralogy (Karathanasis and Hajek, 1985; Griffin and Buol, 1988). Even with high COLE values smectitic Ultisols often lack wide cracks common to Vertisols and Vertic subgroups of other orders (Karathana-

ULTISOLS

207

sis and Hajek, 1985). These authors suggested that this discrepancy between laboratory-measured COLE and cracking patterns was better explained by thickness and texture of surface horizons, structural characteristics, solum thickness, duration of seasonal moisture deficit, drainage, and type of vegetation than by variation in physical, chemical, and mineralogical properties.

B. WATERRETENTION Because texture, organic matter content, mineralogy, bulk density, and porosity vary widely, water-retention characteristics of Ultisols vary widely both among pedons and among horizons within a pedon (Table 11). In general, Ultisols that place in Typic subgroups and fine-loamy or fine particle-size families would be considered to be capable of retaining and supplying sufficient moisture for plant growth in humid climates (Table 11). However, water often becomes a limiting factor for crop growth and yield for Ultisols that place in sandy or coarse-loamy particle-size families or that have thick sandy epipedons (Arenic and Grossarenic subgroups).

C . HYDRAULIC CONDUCTMTY As is the case with water-retention characteristics, hydraulic-conductivity characteristics of Ultisols vary widely within a pedon and among pedons depending on texture, bulk density, total porosity, pore size distribution, mineralogy, shrinkswell potential, and presence of plinthite and other cementing agents in the horizon or pedon (Table 11). Most studies of saturated hydraulic conductivity (Ksat) in Ultisols have found macroporosity to be the best predictor of Ksat. For a 5-m thick sequence of soil and saprolite in North Carolina, O’Brien and Buol(l984) reported Ksat for the uppermost saprolite horizon was only half that of the overlying Bt horizon even though the Bt horizon had a higher clay content. The Bt horizon had 40% more macroporosity than the upper saprolite horizon, which these authors attributed to better structure in the Bt horizon. Southard and Buol (1988b) found Ksat to be most strongly related to macroporosity in Paleudults and Paleaquults in the southeastern U.S. coastal plain. Because these soils were weakly structured for the most part, they proposed a biological origin for the macropores. Studies of Ksat and preferential flow paths by dye staining undisturbed columns from soils in the Piedmont and Coastal Plain of Georgia indicate that water movement through subsoil horizons is limited to zones within the soil with greater abundance of macroporosity (Franklin, 1994; Shaw el al., 1997).Dyed and undyed zones in these soils had similar texture, but the dyed areas had a significantly greater number and area of pores >0.05 mm diameter

2 08

L. T. WEST ETAL. Table 111 Mean Pore Area for Dyed and Undyed Areas After Breakthrough of Methylene-blue for Ultisols from the Piedmont and Coastal Plain of Georgia" Mean pore areah Horizon Typic Kanhapludit (Piedmont) A BA Bt Bt Plinthic Kandiudult (Coastal Plain) Bf Btv BC

Dyed (%)

Undyed (%)

19.0 17.7 15.8

3 .O 5.0 2.4

14.4

2.8

5.0

1.1

5.9

0.8

NA"

0.9

"Data from Franklin. 1994; Shaw er d.,1997. "Area of pores > 0.05 mm equivalent circular diameter determined from impregnated polished blocks with image analysis. 'The BC horizon had insufficient dyed area for evaluation of pore area.

(Table 111). Biological channels and chambers were common in dyed areas, and when channels were not abundant, microfabric differences between dyed areas suggested that the zones of soil contributing to flow had been biologically reworked over the period of soil development (Fig. 4).

D. INFILTRATION AND SURFACE CRUSTING In many Ultisols, one of the most important factors affecting infiltration of water is formation of a surface crust or seal that reduces infiltration, increases runoff, and may potentially interfere with seedling emergence. Most studies suggest that surface crusts form through processes directly related to raindrop impact and rapid wetting of the soil surface, including slaking, aggregate disruption, aggregate and particle rearrangement, and dispersion of clays (Farres, 1978; Gal et af., 1984; Onofiok and Singer, 1984; Norton, 1987; Arshad and Mermut, 1988; Bresson and Boiffin, 1990; Miller and Radcliffe, 1992; Chiang et a/., 1994). These processes produce a thin layer at the soil surface with fewer and finer pores than underlying soil, pores clogged by dispersed clay, and for selected soils, a thin layer of almost

UL'I'ISOLS

209

Figure 4 Thin-section micrograph of dye staining in large columns from a Plinthic Paludult. ( A ) BC horizon; note the dyed area (dark area) around a biologic channel ( c ) ; (B) Bt horizon; field completely dyed; note the difference in porosity and microfabric between this dyed area and the undyed regions of A; (4) quartz; ( v ) void; ( A ) plane polarized light; ( B ) partially crossed polars; bar length = I mm.

pure clay at the immediate soil surface (McIntyer, 1958; Arshad and Mermut, 1988; West et al., 1992a; Chiang et ul., 1994). For 25 Ultisols from the southeastern United States tested under 50-90 mm h-' simulated rainfall, infiltration rate decreased rapidly during the first 10-20 mm of the rainfall event, and the infiltration rate at the end of rainfall was less than 10 mm h-l for all of the soils (Miller and Radcliffe, 1992). In this region, soils with sandy loam texture are most prone to crusting. Soils higher in clay tend to take longer to form a surface crust, and sands and loamy sands form only weakly expressed crusts (Radcliffe et al., 1991; Miller and Radcliffe, 1992). The propensity of a soil to form a surface crust has been related to water dispersibility of clay (Miller and Bahruddin, 1986; Miller and Radcliffe, 1992), and because electrolyte concentration in the soil solution in many Ultisols is low, these soils may have a large portion of their clay in a water-dispersible form (Miller and Bahruddin, 1986). Water dispersion of clay may not be spontaneous, but energy imparted by raindrop impact is, in many cases, sufficient to cause dispersion. For soils in the southeastern United States, normal electrolyte concentrations are generally less than 1 mM(+) liter- I , whereas 5-10 mM(+) liter- total salt concentration is required to flocculate Ca-saturated clays (Miller et al., 1990).

2 10

L. T. WEST ETAL. Table IV Comparison of Organic C, Aggregate Stability, Infiltration Rate, and Percentage of Moist Days under Conventional and No-Till Cropping Systems" Infiltration ratec

Cropping system"

Organic C (g kg-')

Aggregate stability (g k g - ' )

CTG

10.4 23.3

580 890

NTG

Residue (mm h-I)

Residue removed (mm h-I)

(76 of growing season)

Moist days"

36

so

22 46

49

29

"Data from West et al., 1991; Bruce et al., 1992. "CTG = conventional-tilled grain sorghum, Sorghum bicolor L. Moench; NTG = no-till grain sorghum with clover winter cover. 'Infiltration rate at the end of a 1-hour-of-rainfall simulation at approximately 64 mm h-I. dMoist days are defined as the days with soil moisture tension >O. 1 MPa.

Because crust formation is associated with dispersion, slaking, and aggregate breakdown, soils with properties that add resistance to these processes are less prone to form surface crusts than soils laclung these properties. For seven Ultisols from Georgia, Chiang et al. (1993) found that only exchangeable Na percentage and water-dispersible clay were significantly correlated with measurements of the degree of crust formation. In general, however, A horizons with sandy loam texture and low amounts of Fe oxides were more prone to crust formation than soils with more clayey textures or soils with relatively high amounts of Fe oxides. Because energy inputs from rainfall are needed to form surface crusts, crusting is minimal if the soils have residue or vegetative cover. Crusting is also greatly reduced if organic matter is maintained in the upper part of the soil and the soil is not disturbed by tillage. For Ultisols in the Piedmont of Georgia with varying surface texture, infiltration rates after 1 hour of simulated rainfall were about 40% higher for soils that had been under no-tillage for 5 years than for soils that had been conventionally tilled (Table IV, Bruce ef al., 1992). High infiltration was maintained in the no-till soils when surface residue was removed, but infiltration rates for the conventionally tilled soils decreased because of enhanced crust formation. Maintenance of high infiltration rates in Ultisols under no-tillage even with residue removed was attributed to greater aggregate stability induced by increases in organic C in the upper few millimeters of the soil (Table IV, Fig. 5). Greater infiltration into the soil results in reduced runoff, which relates to more water available for plant use. Over the 5-year study period, Ultisols under notillage had 20% more days during the growing season with soil moisture tension >0.1 Mpa than the soils being conventionally tilled (Bruce et al., 1992).

ULTISOLS

211

Figure 5 Thin-section micrograph\ of surface of a Typic Kanhapludult managed under different cropping systems. ( A ) Conventional tillage; note presence of thin surface crust at the immediate surface. (B) No-tillage with winter cover; note greater aggregation and porosity. (q) quartz; (v) void: partially crossed polars; bar length = 1 mni.

M.CHEMICAL PROPERTIES Ultisols are partially defined by their chemical properties in that they must have base saturation less than 35% in the lower part of the subsoil. This defining characteristic carries with it certain accessory properties, including low pH, potentially high A 1 saturation, appreciable weathering and associated kaolinitic mineralogy, and in many cases, relatively high contents of Fe and A1 oxides and oxy-hydroxides. This suite of properties has significant impact on the chemical properties of Ultisols and how these properties affect use and management of this soil order.

A. CHARGE CHARACTERISTICS AND SOURCE OF CHARGE The amount and source of surface charge has a large impact on nutrient retention, nutrient availability, and retention of contaminants in by-products applied to Ultisols. Clay minerals in Ultisols can be divided into two groups: those with main-

+

+OHg

A'

+H+

OH2

1 Fe \

OH

0

+OH-

+H+

OH

1 Fe \

C+

OH

Figure 6 Charging of an iron-oxide surface as a function of potential determining ions (H+ and OH-) (after Sumner, 1992).

ly permanent charge and those with variable or pH-dependent charge. Clay minerals with permanent negative charge originating from isomorphous substitution of cations within the clay structure include mica, vermiculite, smectite, and chlorite. These minerals have the capacity to retain cations, and other than a small amount of variable charge from broken edges, the magnitude of their cation exchange capacity is independent of pH and electrolyte concentration. Sources of variable charge in soils include organic matter, broken edges of clay minerals, and Fe, Al, and Mn oxides and oxyhydroxides. Hydroxy-A1 interlayered vermiculite has both permanent charge from isomorphous substitution in the clay structure and variable charge from the hydroxy-A1 in the interlayer of the mineral (Barnhisel and Bertsch, 1989). The amount of positive and negative charges from variable charge components varies with both pH and electrolyte concentration changes in the ambient solution (Uehara and Gillman, 1981). The pH-dependent negative charges develop by deprotonation of hydroxide groups on clays, organic matter, and Fe, Al, and Mn oxides as illustrated in Fig. 6. As OH- ions are added to the soil solution, positive charges are created. The diagram in Fig. 6 is for an Fe hydroxide, but similar reactions occur for Al and Mn oxides and hydroxides, A1 exposed and broken edges of 1: 1 and 2: 1 clay minerals, and carboxyl, phenol, and other reactive groups in organic matter. Soils, being multicomponent systems, expose a wide variety of surfaces to the soil solution, and many, if not most, Ultisols exhibit both permanent and variable charge. The way in which positive and negative charges change with pH and electrolyte concentration of the soil solution is illustrated in Fig. 7. For permanently charged surfaces, the charge is independent of pH and electrolyte concentration, which is illustrated by the horizontal line. In contrast, charge of variable charge systems is entirely a function of pH and electrolyte concentration. As can be seen in Fig. 7, there is a pH at which the variable-charge component

213

U LTI S0L S

+I

Permanent

Va riabI e

Mixed

Figure 7 Variation in charge with pH and electrolyte concentration for permanent, variable, and mixed charge systems (after Sumner, 1992).

has zero net charge (pH,). Above this pH, the net charge of the component becomes increasingly negative, and below this pH the net charge becomes increasingly positive. It is important to note, however, that pH, is the pH at which the component has zero net charge. In other words, the component has an equal number of positive and negative charges and not zero charge. For the mixed permanent and variable charged systems, as is the case for most Ultisols, there is a point of zero net charge (PZNC) where positive and negative charges on the whole system (permanent plus variable charge) are equal. For these mixed systems, this will always be at a pH lower than pH, for the variable charge components (Fig. 7). Thus, most Ultisols with mixtures of permanent and variable components will have a net negative charge.

B. CATIONEXCHANGE CAPACITY The cation exchange capacity (CEC) of a soil is a general indicator of the ability of a soil to retain and supply nutrients for plant growth and to adsorb and immobilize cations added to the soil. As such, it is a commonly measured soil property for both soil fertility and resource evaluation. The CEC of Ultisol horizons depends on the amount and type of clay, Fe, Al, and Mn oxide content, organic matter content, soil solution electrolyte concentration, and pH of the horizon. Reported CEC values for Ultisol horizons range from less than 3 to more than 20 cmolc kg-' (Sanchez, 1976; Carlisle et a/., 1985; Karathanasis etal., 1986). Generally, Ultisols with mixed or smectitic clay mineralogy have a greater proportion of fixed charge than those with kaolinitic mineralogy and have CEC values in the upper half of the range shown earlier.

2 14

L. T. WEST ETAL.

Cation exchange capacity has traditionally been measured by adsorption and replacement of cations from solutions buffered at pH 7 or at pH 8.2 (Sanchez, 1976). For soils with field pH in this general range or with little or no variable charge components, these methods generally represent the CEC of the soil in the field. For acid Ultisols with appreciable variable charge components, however, these methods overestimate the field exchange capacity. For these soils, replacement and measurement of adsorbed cations by an unbuffered salt, such as KCI or CaCI,, more accurately reflects the exchange properties of the soil at the field pH (Sanchez, 1976). This unbuffered CEC is commonly referred to as the effective cation exchange capacity or ECEC (Coleman and Thomas, 1967). The 35% base saturation used to differentiate Alfisols and Ultisols is calculated with CEC measured at pH 8.2. Correlations between base saturation from CEC at pH 8.2 and base saturation from ECEC for 88 soils from the midwestern and southeastern United States and Puerto Rico indicated that 35% base saturation from CEC at pH 8.2 was equivalent to 55% base saturation based on the ECEC (Buol, 1973b). Because of the large database of CEC and base saturation measured at pH 7 and pH 8.2 that has been collected over the years, these measurements are useful to estimate clay mineralogy and to compare and group soils. Interpretation of CEC, base saturation, and Al saturation values in terms of plant response, however, is probably best made with ECEC.

C. ANIONEXCHANGE CAPACITY Even when the net charge is negative, many Ultisols have an appreciable positive charge and an appreciable anion exchange capacity (AEC). Gillman and Sumner (1987) in a study of four Ultisols from the Piedmont of Georgia reported AEC values at the pH of the soil that ranged from 0.1 to 1.2 cmolc kg-', which was attributed to Fe and Al oxides in these soils. For Ultisols from Georgia and South Africa, Grove et al. (1982) reported AEC values ranging from 0.03 to 1.91 cmolc kg- I . Measurable AEC has also been reported for Ultisols from tropical Australia and Peru (Gillman and Sumpter, 1986; Gillman and Sinclair, 1987). The importance of AEC in retarding movement and leaching losses of NO,has been demonstrated for Ultisols, Oxisols, and acid Alfisols and lnceptisols from Africa, South America, and Southeast Asia (Wong ei a]., 1987; Wong er al., 1990). Bellini ef al. (1996) reported anion adsorption more than doubled the amount of drainage required to leach a monovalent anion (NO,- or CI-) through columns packed with the Bt horizon of an Typic Kanhapludult from Georgia (AEC = 1.43 cmolc kg-I). Gupte er al. ( 1996), working with undisturbed columns from the upper 50 cm of a Typic Kanhapludult in Georgia, reported an average of I .3 1 pore volumes for 50% breakthrough of Br-, which they attributed to anion adsorption.

ULTIS01, S

215

These types of results have wide implications in terms of NO,- retention for plant nutrition as well as protection of groundwater from NO,- contamination.

D. ACIDITYAND EXCHANGEABLE ALUMINUM Below a pH of about 5.5, A1 released from weathering of primary and secondary minerals is present as part of the exchange complex. As this exchangeable Al is released to the soil solution, A1 toxicity may develop. The primary effect of high levels of A1 on plant growth is impairment of root development, which, in turn, reduces water and nutrient uptake. Aluminum also has been shown to accumulate in the roots and interfere with uptake and translocation of Ca and P to above-ground plant parts (Foy, 1974). Research on Ultisols in North Carolina indicates that an A1 saturation of greater than 60% (measured as percentage of the unbuffered CEC or ECEC) is needed to raise soil-solution A1 concentrations to more than 1 mg liter-' (Nye et ul., 1961; Kamprath, 1970). This value has been suggested to be the lower limit of A1 concentration that results in direct yield reductions (Sanchez, 1976). This critical level, however, varies with the tolerance of the crop and the total electrolyte concentration in the soil solution (Freinsen et al., 1982; Van Wambeke, 1991).

E. PHOSPHORUS After nitrogen, phosphorus is generally the most limiting nutrient element for crop growth, especially in tropical climates where most Ultisols occur. Phosphorus occurs in a number of forms in the soil, including both organic and inorganic, and the reaction of inorganic P in its various forms has received considerable attention over the years. Total P i n the soil has little implication in terms of soil fertility or plant nutrition, but has limited utility as an index of weathering (Sanchez, 1976). An important soil process for P nutrition of plants is the fixation or adsorption of phosphate. Phosphate is specifically adsorbed by Fe, Al, and Mn oxides and amorphous or poorly crystalline aluminosilicates such as allophane, and this adsorption is only slightly affected by changes in pH and associated changes in surface charge. Most Ultisols have low contents of clay, oxides, and poorly crystalline components in surface horizons because of eluviation and weathering. Thus, P sorption is generally lower in Ultisols than in Oxisols and Andisols (Sanchez, 1976; Van Wambeke, 1991). Pratt et a/. (1969), however, found that a Ultisol sorbed more phosphate than an Oxisol in Brazil. They attributed this difference to less crystalline Fe oxides in the Ultisol as compared with those in the Oxisol. Similar results were reported for Oxisols and Ultisols in Hawaii (Fox ej ul., 1971).

2 16

L. T. WEST ETAL.

X. BIOLOGICAL PROPERTIES Research reports that describe morphological, chemical, physical, and mineralogical properties of Ultisols and how these properties influence crop productivity and other uses of Ultisols are voluminous. However, studies describing biological properties of Ultisols as a group of soils and comparing the biological properties of Ultisols to other orders are rare. This is probably as it should be. Biological properties of soils are highly sensitive to nutrient additions, tillage practices, and other soil perturbations. Thus, biological properties can change rapidly, and in most cases, these changes in response to use and management are more important that biological properties that have resulted from long-term soil development processes.

A. ORGANICMATTER A common misconception is that Ultisols have low contents of organic matter compared with other soil orders. Although Ultisols generally have lower amounts of organic C than do Mollisols, Oxisols, and Andisols, levels of organic C in U1tisols are similar to those in Alfisols both within and across geographic areas (Table V). There is also little difference in organic C content between Ultisols in temperate and tropical climates (Table V, Buol, 1973a). In poorly drained Ultisols in the southeastern United States, organic C levels as high as 50-120 g kg-' are common in umbric epipedons (Carlisle et al., 1985) as are thick 0 horizons and histic epipedons. Accumulation of organic matter in these wet soils is because of reduced decomposition rates under anaerobic conditions. Similar organic matter accumulations in poorly drained Ultisols in tropical climates may not be as common. At high temperatures, organic matter decomposition rates under anaerobic conditions are sufficiently high that poor drainage does not necessarily lead to accumulation of organic matter (Neue and Scharpenseel; 1987; Greenland et al., 1992). Organic matter contents in Ultisols decrease rapidly when these soils are converted from forest or pasture to intensive cultivation. For Ultisols in Georgia, Giddens (1957) found that organic matter contents decreased from 20.5 to 16.6 g kgin 24 months after conversion of forest to intensive cultivation. Much of the decrease in organic matter with conversion of forest to cultivation can be attributed to decreases in organic matter inputs. Crop residues supply only a fraction of the dry matter previously supplied by forest litter and root decomposition (Sanchez, 1976).Also contributing to the rapid decrease in organic C in cultivated soils is an increase in decomposition rate from increased aeration of the soil with cultivation (Sanchez, 1976; Beare et al., 1992) and exposure of pools of organic matter to de-

217

ULTISOLS Table V

Comparison of Average Organic C Contents of Selected Soil Orders in the United States, Brazil, and Zaire (Amounts Are the Average of 16 Randomly Selected Pedons in g kgg'y United States

Brazil

24.4

-

-

-

20.1 16.1 10.6

21.3

10.7 8.8 5.3

10.3 4.5 5.5

Soil order

0-15 cm Depth Mollisols Oxisols Ultisols Alfisols

15.8 15.5 LSD,,,,,

=

Zaire

9.8 13.0

Means

24.4 20.7 13.9 13.0

3.8

0-100 cm Depth

Mollisols Oxisols Ultisols Alfisols

11.1

11.1

-

4.9 5.2 LSD,, ,,~ = 1.9

1.05 6. I 5.3

"Data from Sanchez. 1976.

composition that were protected within aggregates prior to cultivation (Beare et al., 1994). Use of no-tillage, cover crops, and residue management can help to restore and maintain organic C levels in Ultisols at precultivation levels. Bruce et al. (1992) reported that organic C levels in the upper 15 mm of degraded Ultisols were increased from 10.4 to 23.3 g kg- by use of no-tillage and winter cover crops. Associated with the increase in organic C in these soils were significant increases in water-stable aggregates and infiltration rates and decreases in soil erodibility (West et al., 1991; Bruce et al., 1992; West et al., 1992b).

'

B. BIOLOGICPOPULATIONS AND PROCESSES Populations of microorganisms in Ultisols are not appreciably different from soils in any other orders. Surface horizons of most Ultisols have acid pHs. Thus, fungi may comprise a greater proportion of the microbial biomass in these soils because of the fungi's ability to survive under acid conditions better than other types of microorganisms (Alexander, 1977). Similarly, low pHs have been reported to reduce rates of nitrification and denitrification in pure cultures (Alexander, 1977). Most studies of environmental influences on microbial populations are made in laboratory cultures, however. Natural adaptation by microorganisms to lo-

218

L. T. WEST ETAL.

cal field conditions probably alters the effects of pH and other environmental conditions observed in the laboratory.

XI. MANAGEMENT OF ULTISOLS A. HIGH-INPUT CULTURAL SYSTEMS Extensive areas of Ultisols occur in South America, southeastern United States, East and West Africa, Asia, and Australia. Generally speaking, many of these soils are so infertile that little sustained productivity is possible without inputs of fertilizer and lime. In addition, under intensive cultivation, organic matter content often declines rapidly, resulting in reduced capacity to hold nutrients and in many cases structural decline. Thus, both physical and chemical constraints to crop production must be considered in the management of Ultisols.

1. Chemical Constraints Ultisols often exhibit multiple nutrient deficiencies that must all be corrected in order to achieve good yields. An example will be used to illustrate how Ultisols have been brought into production using modem techniques. The area selected is in the eastern highlands of South Africa where, in the higher rainfall areas (>750 mm year- I ) , the landscape is dominated by Ultisols with some Oxisols. This area is known as the Highland Sourveld, which refers to the fact that the natural grassland is unpalatable to cattle for a large part of the year (8 months). The grassland is composed of species such as Themeda triandra, Aristida juncifomis, Eragrostic curvula, and others, all of which rapidly lignify towards the end of spring. As a result, starving cattle can often be found in these areas with grass growing up to their navels. Because of the need to supplement the diets of the cattle with improved pastures and grain, many farmers attempted to cultivate row crops. The early attempts to produce crops such as maize (Zea mays), sunflower (Helianthus annus), and sorghum (Sorghum bicolor) failed in the second or third year of cultivation due to acidity and lack of nutrients. In the first year after plowing out the grassland, crop yields would be satisfactory but, in subsequent years, yields declined to virtually zero. The cycling grassland vegetation resulted in a concentration of a little fertility in the topsoil, which was rapidly depleted by row crops. The only technology available to produce satisfactory crops on these soils prior to World War I1 involved the use of organic sources of nutrients such as manures and composts. The first experiments conducted in this area compared organic and inorganic sources of nutrients (Table VI). Clearly, the compost treatment pro-

2 19

ULTISOLS Table VI Average Maize Yields (1940-1946) on a

Natal Ultisol with ’hatments Comparing Organic and Inorganic Sources of NPK’ Treatment

Grain yield (kg ha-’)

Control (no nutrient inputs) Local dry grass (45 t ha- I )

50 818

Compost (45 t ha-’)

324 I 0

NPK equivalent to that in the compost “Data from Scott. I950

moted yields, whereas the NPK treatment, which contained the same amounts of these nutrients as the compost, was a complete failure. Soil pH in the NPK treatment fell below 4.0 from an initial value of 4.5 due to the acidifying effects of the ammonium sulfate used to supply N. The local grass treatment was not very effective because of the lack of nutrients in this material. In 1956, a series of factorial experiments was started using only inorganic sources of nutrients and lime to supply all limiting elements and neutralize soil acidity simultaneously. The soil test values for the soil before experimentation were pH 4.78; resin P 1.64 mg kg- I ; and exchangeable K, Ca, and Mg, 0.17,0.34, and 0.75 cmolc kg-’, respectively. The fertilizer rates (kg ha-’) utilized were N, 0, 21 and 42; P, 0, 19 and 38; K, 0, 53 and 106; dolomitic lime, 0, 2100, 4200 in combination with a basal application of Zn, Cu, B, Mo, and S. The N, P, and K applications were made annually, and the lime treatments were applied once every 5 years. The best treatment in this experiment, which corresponded to the highest rates of N, P, K, and lime, yielded an average of 4232 kg grain ha over the period 1956-1963. These results demonstrated that with the correct combinations of nutrient and lime additions, it was possible to grow acceptable crops without the use of organic sources of nutrients (Orchard and Sumner, 1966). Soil analyses prior to the 1961 crop showed that in the highest yielding treatment, pH had reached 5.2, resin P was 2.6 mg kg- I , and exchangeable K, Ca and Mg were 0.5, 1.6, and 3.0 cmolc kg- I , respectively (Marques, 1961). Treatment combinations involving only one or two nutrients or lime yielded less than 100 kg grain hap1. At this stage of the experiment, the cultivar used was open pollinated at a population of 35,000 plants ha-’. In 1963, a new experiment was commenced with much higher fertilizer and lime rates (kg h a p 1 )(N, 79, 132, 185,238; P, 46,92; K, 44, 88, 132, 176; dolomitic lime, 3200, 6400) and using hybrid corn at a popula-

220

L. T. WEST ETAL.

tion of 50,000 plants ha- I . The highest average yield (1963-1972) of 6909 kg grain ha- I was obtained at the 185 N, 92 P, 132 K, and 6400 lime levels. A study of root growth in this experiment demonstrated that the roots were not penetrating into the highly acidic subsoil. As a result, a new experiment was initiated in which the variables were depth of lime incorporation (sufficient to neutralize all exchangeable Al; 0 and 75 cm) in factorial combination with a single initial application of P at 300,600, 1200, and 2400 kg ha-'. The highest average yield (1972-1978) of 8700 kg ha-' was obtained with deep lime incorporation at the highest P level. The results of this experiment demonstrated the importance of utilizing as much of the water present in the profile as possible. In most cases in such soils, however, this water is unavailable due to lack of roots in the acid subsoil. Subsequent work by Reeve and Sumner (1972) showed that gypsum applied on the soil surface could be effective in offsetting the deleterious effects of subsoil acidity. This early work has been followed up in many areas of the world, resulting in substantial yield increases in many crops (Sumner, 1993). Under field conditions, Farina and Channon ( 1988b) have clearly demonstrated that subsoil amelioration can be readily achieved through the use of surface-applied gypsum, which is capable of moving down the profile and neutralizing part of the subsoil acidity. Farina and Channon ( 1988a) have demonstrated the superiority of gypsum over deep incorporation of lime in the subsoil (Table VII). The beneficial effect of the gypsum is attributable to the improved root proliferation in the subsoil resulting from a reduction in the A1 saturation and an increase in Ca and Mg saturation, allowing more prolific root development in the subsoil. This has been shown by Sumner (1993, 1994) to result in increased water availability in the subsoil, which translates into higher yields. Thus, fertility management of Ultisols first involves the identification of the nutrients most limiting to crop yield followed by remedial fertilizer applications to ensure balanced crop nutrition. In addition, amelioration of subsoil acidity, which

Table VII

Comparison of Effects of Surface-AppliedGypsum and Lime Incorporated by Deep Tillage on Yield of Maize on a Plinthic Paleudult in South Africa" ~

Treatment

Grain yield (kg ha-')

Limed topsoil plus conventional tillage

6,158

Limed topsoil plus 10 t lime hac' incorporated with Wye double digger Limed topsoil PIUS 10 t surface-applied gypsum ha-'

7,096

"Data from Farina and Channon, 1995.

10,272

22 1

ULTISOLS

is frequently a limiting factor in these soils, results in improved water relations for the crop, allowing partial avoidance of yield reduction during droughty periods during the season.

2 . Physical Constraints During the nineteenth century in the southeastern United States when cotton was king, soil erosion was a problem of vast magnitude. Recent research has shown that one of the primary factors promoting this erosion was the tendency of Ultisols that had under prolonged cultivation formed crusts or seals. Surface crusting in these soils is a direct result of the loss of organic matter under row cropping, which results in structural decline. Because organic matter is the primary bonding agent for aggregates, its loss results in the clay in the soil becoming dispersible under the influence of impacting raindrops (Shainberg et al., 1989). This dispersible clay then blocks pores at the soil surface, which reduces infiltration and promotes increased runoff. Miller and Radcliffe (1992) have shown that many Ultisol topsoils are highly dispersible with inputs of energy supplied in the form of raindrops. Consequently, the management strategy to reduce runoff and erosion necessarily involves the prevention of clay dispersion at the soil surface. This can be achieved by using one of two strategies, namely, interception of the energy of the falling raindrops and promotion of clay flocculation and water-stable aggregation. The former involves using mulches or conservation tillage systems, which provide permanent soil cover and promote infiltration (Table VIII), or promoting the buildup of organic matter under no-till cropping systems (Bruce e t a / . , 1990); the latter can be achieved by providing adequate electrolyte at the soil surface to prevent dispersion (Table IX). The importance of avoiding soil disturbance and providing soil cover can be clearly seen in Table VIII, and the effects of surface-applied gypsum in increasing Table V l l l

Soil Loss and Runoff from Three Ultisols with Varying Tillage and Surface Cover under 50 mrnh Rainfall Intensity for 1 Hour on 5 9 % Slopes" Total runoff (mm)

Soil loss (g m-?) Tillage

Surface condition

Soil I

Soil 2

Soil 3

Soil I

Soil 2

Soil 3

0.33 0.48

16

18

23 23

17 16

0.07 0.02

3 I

3 I

6 I

Conventional

Crusted

0.46

0.56

No-till

Tilled Bare Residue

0.72 0.14 0.07

0.79 0.19 0.03

"Data from West rt ( I/.. 199 I

222

L. T. WEST E T A L . Table IX Effects of Surface-Applied Phosphogypsum on Final Infiltration Rate and Soil Loss from Georgia Ultisols" Final infiltration rate (mm h - ' )

Soil loss (kg ha- ' )

Soil

Control

Gypsum

Control

Gypsum

Typic Kanhapludult Typic Ochraquult Typic Hapludult Rhodic Kandiudult

7.3 0.8 2.3 I1

22.2 2.2 11.0 23

266 1315 I I35 939

96 732 442 50

"Data from Miller, 1987; and Miller and Scifres, 1988

infiltration rate and decreasing soil loss are evident in Table IX. The strategy of choice would be to enhance surface cover because the gypsum effect is transitory in nature being maintained only while gypsum is available to dissolve at the soil surface. In addition to having physical limitations at the soil surface, the dense Bt horizons in Ultisols can limit the vertical passage of water and in many cases also limit the proliferation of roots due to high mechanical impedance. The incorporation of substantial amounts of gypsum (10 Mg ha-') into the topsoil has been shown to both benefit root penetration and improve subsoil hydraulic conductivity (Chiang et al., 1987) provided that enough time has passed for transport into the subsoil. Thus, as far as physical condition of Ultisols is concerned, any strategies that lead to reduced rates of organic matter loss, protect the soil surface from impacting raindrops, and provide electrolyte for flocculation are likely to be highly beneficial in the management of these soils.

B. Low-INPUTCULTURAL SYSTEMS: CASEINWESTAFRICA WITH SPECIAL REFERENCETO SOUTHERN NIGEIUA 1. Land Use Estimates indicate that Ultisols cover over 69 million hectares or over 16.6% of the land area in tropical Africa (NAS, 1982). In western Africa, Ultisols are found in the forested zones of Sierra Leone, in Ivory Coast, in parts of Liberia, and in the forested coastal strip from Ivory Coast to Cameroon. These soils are mainly found in forested areas with annual rainfall of > 1300 mm and a monomodal or pseudo-

ULTISOLS

223

bimodal rainfall distribution. Soil moisture is usually not a constraint for crop production in the area, but low solar radiation during the cropping season can limit the potential of crop production, particularly when grown in intercropping systems. Under rain-fed conditions, root and tuber crops (cassava, yams [Dioscorea sp.] and cocoyams [ Colocasia sp. and Xunthosoma sp.]), upland rice, banana, and plantains are the dominant annual food crops. Groundnuts and cowpeas, which are tolerant to soil acidity, are also grown in small fields on Ultisols. Maize is usually grown only following land clearing and burning of the vegetation. Annual food crops are mainly grown in mixed cropping systems (Okigbo and Greenland, 1976). In nearby fields or home gardens, the food crops are grown in multistory or agroforestry systems in association with various multipurpose fruit and food woody species. These include indigenous fruit species such as Chtysophyllum albidurn (star apple), Dactyodis edulis (African pear), Elueis guinensis (oil palm), Iniingia gabonensis (wild African mango), Pentacletra macrophylla (oil bean tree), Raphia hookeri (raphia palm), and Treculia africana (African breadfruit) and woody vegetable species such as Pterocarpus milbraedii, Pterocarpus soyauxiz, and Vernona urnyldalina (bitter leaf). Farmers also commonly retain in the fields various woody browse species such as Dactyladenia barteri (Icheiko), Microdesmis puberula, and Milletia aboensis and Miletia thorningii for feeding small ruminants. Large areas of Ultisols in this region are under primary and secondary forest vegetation and are used for oil palm and rubber plantations or for timber production with native and exotic species.

2. Soil Characteristics Ultisols in the area are mainly kaolinitic Ultisols, which are acidic with high levels of extractable Al, particularly in the subsoils. These are low-activity clay soils, with low ECEC, low base saturation, inherently very low nutrient levels (Juo, 1981; Juo and Adams, 1986), and deficiencies in most of the macro nutrients (N, P, and K). Soils in coastal areas can have high levels of extractable P (Table X) because of the marine origin of the parent material. These Ultisols have moderate phosphate absorption capacity (Juo an Fox, 1977). In addition, Ultisols in this area have low levels of secondary (Ca and Mg) and micronutrient (Zn) levels. Subsurface horizons of these Ultisols have very low levels of extractable K, Ca, and Mg. Magnesium deficiency is common with continuous and intensive cropping (Kang, 1984). Soils require only low dosages of lime application for continuous intensive and sustained crop production. Lime can be considered more as a fertilizer than as a soil amendment measure (IITA, 1981), and because of low buffering capacity of the soil, lime can be easily overapplied, which can create nutrient imbalances, particularly for micronutrients (Juo and Uzu, 1977).

224

L. T. WEST ETAL. Table X Chemical Properties of Surface Soil (0-7.5 cm) of a Typic Paleudult from Southeastern Nigeria Before and After Burning' Exchangeable cations (cmolc kg-')

pH (water)

Organic C (g kg-’)

BB

4.3

AB

5.0

Treatment"

Ca

Mg

K

A1

17.3

1.32

0.34

0.16

1.44

18.2

2.96

0.85

0.33

0.08

Extractable P ( m g kgBray I

I)

I08 123

"Data from Kang and Juo, 1986. "BE = before burning; AB = after burning of fallow vegetation.

Ultisols in this region are characterized by coarse-textured surface horizons and low structural stability (La1 and Greenland, 1986).Fallowed and uncultivated soils have high infiltration rate due to the presence of macropores created by biotic activity. Infiltration rates, however, decline rapidly with cultivation. These soils are easily compacted and are susceptible to drought in short dry periods and under supraoptimal soil temperatures without vegetation cover. These Ultisols are prone to erosion without adequate soil surface cover and with conventional mechanized tillage operations. As such, soil surface management practices are crucial for maintaining the long-term productivity of these soils. Maintaining a layer of crop residue mulch on the soil surface to intercept high intensity rains, combined with minimum tillage and intercropping, as already practiced by traditional farmers, are valuable means for maintaining soil productivity.

3. Traditional Soil Management Systems Shifting cultivation and the related bush-fallow slash-and-bum cultivation are still the dominant systems for food crop production. These systems are based on the use of a fallow period for regenerating soil fertility exhausted during the short cropping cycle and for suppression of noxious weeds. During the fallow period, most of the plant nutrients are held in the fallow vegetation. In addition, the presence of deep-rooted woody species in the fallow vegetation contributes to nutrient cycling from lower soil horizons to soil surface through litter fall, and N,-fixing species contribute nitrogen to the system. Canopy cover of the woody species also provides a better microclimate for higher biotic and soil faunal activities that contribute to improvement of soil chemical and physical conditions. This is eco-

ULTISOLS

22s

logically a balanced system when practiced with short cropping cycles and long fallow periods. In this system, traditional farmers practice various methods of land clearing and seed bed preparation depending on the natural vegetation and population density of the area. In areas with low population densities and dense forest vegetation, farmers practice step-wise land clearing. In the first year, underbrush and small trees are manually removed with a machette, which is followed by burning of the vegetation and planting of food crops. In the second year, larger trees are felled and burned, which is followed by planting of food crops. In the third and subsequent years, farmers bum the outer shells of the large trees and grow crops until the soil fertility is exhausted. In the nonforested areas (where natural vegetation has been degraded) with secondary thicket regrowth, farmers clear the undergrowth, bum the vegetation, and grow crops. The main purpose for burning plant biomass is to speed up the release of cations and phosphorus held in the fallow biomass. Burning also reduces soil acidity and extractable A1 levels (Table X) and allows cultivation of acid-sensitive crops such as maize. However, the effect of burning is normally short-lived and lasts for only one season in the humid zone where cation leaching rate is high (Okoro, 1981). The burning effect depends on the amount and quality of biomass and intensity of the fire. Farmers usually practice “flash burning” where fire moves quickly. This method of burning only affects the upper 5 cm of the soil and results in partial burning of the biomass (Okoro, 1981). With this burning method, only the leaves and small branches are burned, and most of the woody material is left. Farmers may occasionally follow this bum with concentrated intensive spot burning where unburned material is piled and reburned. This intensive burning can reach temperatures of over 500°C and cause irreversible damage to the soil (Kang and Sajjapongse, 1980; Okoro, 1981). Although burning is widely practiced by farmers, application of plant residue as mulch is more beneficial to the crop with no fertilizer input (Table XI). Farmers normally do not practice clean clearing of their fields. They usually retain a portion of the woody regrowth that will be used as staking material for climbers, such as yams, and allow rapid revegetation of the field after the cropping cycle. In some areas of southeastern Nigeria where population density is too high to allow long fallow periods, farmers have retained or planted more productive and useful fallow species. These species were selected because of their purported soilenriching properties and also as sources of browse and staking materials. This includes species such as Alchornea cordifola, Anthonata macrophyllu, Baphia nitida, Baphia pubescence, Dialium guineense, Hurungana madugascarensis, Miletiu aboensis, Nupoleonia imperalis, and Nuclea latifolia. In the Mbaise area close to Umuahia in southeastern Nigeria, farmers have for

Table XI Effect of Plant Residue Management and FertilizerApplication on Grain Yield of Maize (cv. TZPB) Grown on Typic Paleudult at Oune in Southeastern Nigeria Year

2

1

3

4

5

6

Treatment

M"

B

M

B

M

B

M

B

M

B

M

N

Control (no fertilizer) (Mg ha-')

1.74 2.88

1.19 2.80

0.56 1.68

0.60 2.23

1.49 3.14

1.39 3.59

2.49 3.64

2.16 4.03

0.79 4.10

0.74 4.78

0.85 3.08

0.62 3.02

NPK Mg Zn (Mg h a - ' ) LSD 0.05 (Mg ha-')

" M = mulch; B= burned

0.87

0.69

0.72

0.9 1

0.97

0.6 I

ULTISOLS

227

generations practiced an innovative no-input traditional alley cropping system consisting of 1 to 2 years of cropping followed by 3 to 4 years of fallow (Kang et al., 1991).In this system, they grow Dactyladenia barteri shrub in hedgerows with 2-3 m spacing at a high-population density of more than 5000 plants per hectare. At the beginning of the cropping cycle, the shrubs are cut to a height of 10-20 cm above the ground and burned, and plots are then planted with yam, cassava, and maize. During the second year, only the cassava remains, growing between the Dactyladenia hedgerows. In the third year, the hedgerows cover the entire field for another fallow cycle. Home gardens in this region have higher soil fertility than nearby fields, due to nutrient cycling by woody species and regular additions of home refuse, kitchen ash, and goat manure (Table XII). This practice contributes to the development of “anthrophic” soils around the dwellings. On degraded grassland areas such as those found in Cameroon, farmers utilized the “mafuku” system. In this system, farmers gather weeds and pile them up at spots in the field. These weed piles are later covered by soil, burned, and planted with crops. The fertility gain in this method depends on the amount of biomass gathered in each of the piles and the intensity of burning. Because of the absence of animal power due to the prevalence of sleeping sickness in the forest zone, land preparation in traditional farming is done manually. For tilling the land, farmers use narrow or wide blade hoes that are fixed to a short handle at a narrow angle. These unique hoes are suitable for weeding or for building mounds that are traditionally used to grow crops, particularly root and tuber crops. Because only the upper part of the surface soil is used to build the mounds, a large volume of the surface soil is used for crop production. These mounds can be easily flattened and other mounds built for the next cropping cycle. In this way, surface soil is continually recycled without cultivating the more acid and poorer subsoil. This is contrary to the use of mechanized cultivation that mixes the shallow surface soil with the subsoil, which can create new soil management problems. These traditional soil management systems are geared for subsistence farming with low or modest yield levels. With increasing population pressure and shorter fallow periods, however, these systems become unsustainable. To meet population demands on these Ultisols, a combination of conservation practices already used in the traditional system combined with judicious levels of external inputs will be needed.

XII. CONCLUSION In general, Ultisols are not highly productive soils. In fact, the original concept for soils in this order was a group of soils that could not sustain continuous crop

Table XI1 Soil Profiles in Different Qpes of Compound Farms and a Nearby Field at Bera in Southeastern Nigeria"

Field type

Major crops

Compound farm

Plantain and

under tree crops Compound farm under plantain

Nearby field

fruil trees

Plantain

Yam

under food and vegetable crops "Data from Snelder. I987

Exchangeable cations (cmolc kg-I)

Depth (cm)

PH (water)

0-10

5.5

17.8

I .6

0.34

10-20 2e30 30-45 0-10 10-20 20-30 30-45

4.9 4.7 4.7

11.4 8.9 7.1

I .o 0.8 0.7

0.2 1 0.23 0.2 1

6.7 6.8

28.4 18.3

2. I I .4

11.1

0.9 0.6 I .2 0.7 0.6 0.5

0-10

6.9 6.7 5.0

I @20 2&30 30-45

4.8 5.0 4.7

K

10.7 13.8 9.1 6.4 8.1

1.15

0.57 0.49 0.61 0.2 1 0.09 0.08 0.05

Ca

M g

Acidity (cmol kg- ')

ECEC (cmolc kg- I )

3.25 1.47 0.93 0.45 9.15 8.03

0.88

0

0.65 I .23 0.78 2.18 I .70

7.2 1

1.09 1.31 0.45 0.17

2.49 2.18 1.80 0 0 0 2.02 2.86

4.60 4.93 4.69 3.36 12.64 10.43 8.94 11.13 4.34

2.9 I 3.32 1.22

3.56 3.86 I .65

7.06 0.72 0.30 0.21 0.18

0.15

0.12

ULTISOLS

229

production with addition of soil amendments. By definition, Ultisols have low base saturation and many are acid and with high A1 saturation, which may result in A1 toxicity in A1 sensitive crops. In addition, most Ultisols have dominantly low activity clays and associated low CEC and capacity to retain nutrients. Phosphorous fixation is a problem in many Ultisols. Cultivation of these soils often results in rapid loss of the relatively low amounts of organic matter in surface horizons. Ultisols are, in many cases, extremely fragile and subject to physical degradation. Surface horizons are often weakly structured, and tillage and other mechanical disturbances can result in structural degradation, surface crusting, and compaction. All of these forms of degradation tend to reduce rainfall infiltration and increase runoff. Reduced infiltration and increased runoff reduce the amount of water available for crop use and increase erosion, which further degrades the soil and contaminates surface water through sediment additions. Compared with Alfisols and Mollisols, Ultisols, as a whole, have a low potential for agricultural production. Ultisols are not “bad’ soils, however. They are as they should be given the combination of climate, parent material, vegetation, and topography under which they have been exposed over long periods of time. In many regions of the world, especially tropical and subtropical regions, Ultisols are the best soils available. In general, the combination of chemical, physical, and mineralogical properties of Ultisols are more favorable for crop production and other uses than are Oxisols, Vertisols, and many Andisols. With residue and tillage management, most Ultisols have physical properties that enable the soils to accept, retain, and supply adequate water for plant growth. With proper management, animal wastes and other by-products can be disposed of on Ultisols without contamination of ground and surface waters. With additions of plant nutrients and lime, Ultisols can be highly productive. Even if nutrient addition is not a viable option because of availability or economics, fallow periods with improved species, intercropping, and careful management of residue and nutrient resources can help sustain productivity of these soils. Ultisols are an extensive and valuable resource base, and we have extensive knowledge of the formation, properties, and management of this resource. There is much to be learned, however. Numerous examples can be found of how this resource has been abused and degraded, mostly because of improper management. We must continue our efforts to understand the behavior of these soils and their response to human intervention. Only through these efforts can the productivity of this valuable resource be maintained and enhanced.

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FORMATION MECHANISMS OF COMPLEX ORGANIC STRUCTURES IN SOILHABITATS J.-M. Bollag,' J. Dec,' and P. M. Huang* 'Laboratory of Soil Biochemistry Center for Bioremediation and Detoxification Pennsylvania State University University Park, Pennsylvania 16802 'Department of soil Science University of Saskatchewan Saskatoon, Saskatchewan S7N OW0 Canada

I. Introduction 11. Organisms Involved in Organic Matter Formation 111. Degradative Processes Tv. Synthetic Processes A. T h e Role of Oxidative Coupling in Humus Formation B. T h e Role of Abiotic Catalysts in Humus Formation C. Covalent Binding of Xenohiotics to Humus D. Other Synthetic Mechanisms V. Significance of Synthetic Reactions in Soil References

I. INTRODUCTION Soil is a natural reservoir of biotic debris consisting of plant remains, dead animals, and microorganisms that are subject to continuous turnover. With time, dead biomass is either mineralized or transformed to yield diverse complex organic substances. Transformation proceeds in two stages (Hayes, 1991): The first stage involves degradative processes that lead to the formation of substrates. The more recalcitrant lignin components are selectively preserved (Kogel-Knabner, 1993). In the second stage, the substrates and the preservation products are further transformed by synthetic processes to result in the formation of humus. 237

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Humus formation is essentially a biochemical process (Haider et al., 1975). During the degradation of organic residues, reactions are catalyzed mainly by microbial enzymes. The subsequent synthesis of the humic material can be mediated by both enzymes and abiotic catalysts that are present in the soil. Soil enzymes originate mostly from microorganisms and to a lesser extent from plants (Haider et al., 1975), and clay minerals or metal oxides constitute abiotic catalysts (Wang and Huang, 1986; Huang, 1990). Polycondensation reactions between reducing sugars and amino acids play a role in humus formation (Maillard, 1916; Kononova, 1966). One of the most important catalytic reactions in soil is oxidative coupling that links phenolic products of the degradative processes into humic polymers through enzymatic reaction (Flaig et al., 1975; Haider er al., 1975; Bollag et al., 1980) and mineral catalysis (Shindo and Huang, 1982; Wang and Huang, 1986). Oxidative coupling can also result in the binding of xenobiotics to humus. This reaction is usually accompanied by a detoxification effect (Bollag, 1983; Calderbank, 1989) and may be catalyzed by either enzymes or minerals. Organic chemicals can be bound to soil through various mechanisms involving either physical sorption or covalent binding. By definition, the adsorption process is reversible; in practice, however, xenobiotics are increasingly difficult to desorb as they become gradually sequestered in inaccessible microsites within the soil matrix (Alexander, 1995; Pignatello and Xing, 1996; Weber and Huang, 1996). The compounds that are bound through stable covalent linkages are believed to be an integral part of humus, thus representing little or no threat to the environment (Bollag, 1983; Fiihr, 1987; Calderbank, 1989). The purpose of this chapter is to review the existing knowledge about the synthetic processes previously outlined and to discuss their origins, importance, and practical implications.

11. ORGANISMS INVOLVED IN ORGANIC MATTER FORMATION Living organisms make up less than 1% of the total volume of the soil, whereas minerals account for about 50%, organic matter for 3-6%, and air and water for up to SO% (Alexander, 1977). Without this small biological component, however, the organic matter would not exhibit such richness of form and structure. Essentially, all four trophic groups of living organisms can be found in soil: (1) photoautotrophic higher plants, algae and photosynthetic bacteria, (2) photoheterotrophic bacteria, (3) chemoautotrophic bacteria, and (4) chemoheterotrophic organisms, which include all soil animals, both vertebrates and invertebrates, most bacteria, and most fungi (Ross, 1989). This last group of organisms is pri-

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marily responsible for the biological and biochemical processes of humus formation. Chemoheterotrophs are the only organisms that can perform the two critical functions necessary to the humification process: ( 1 ) an active role of transforming raw materials into humus, and ( 2 ) a passive one of serving as a source of biomass after death. Among the chemoheterotrophs, microorganisms play the most important active role in humification. Plants, however, are most important in passive processes because they serve as the major source of biomass. The three other groups of organisms play only a passive role in the humification process. Chemoheterotrophic animals, such as beetle larvae, centipedes, millipedes, and termites in tropical soils, are primary decomposers that simply break up plant litter into smaller pieces. Some saprophytic fungi and protozoa are also important in the primary decomposition process because these organisms produce extracellular enzymes that catalyze the dissolution of outer protective tissues. Secondary decomposition is performed exclusively by microorganisms, which include most of the soil saprophytic bacteria and fungi (Ross, 1989). These microorganisms specialize in selectively degrading various large organic molecules such as lignin and cellulose. The products of microbial degradation can also be linked into humic polymers by the action of the same microorganisms. The microorganisms involved in humification are those that synthesize the oxidoreductive enzymes required to mediate the processes of synthesis. Oxidoreductive enzymes are designated as either peroxidases or mono-phenol monooxygenases. The latter include two distinct classes of enzymes commonly referred to as laccases and tyrosinases (Sjoblad and Bollag, 1981). Peroxidases are found in most higher plants and numerous bacteria and fungi. Laccase activity was first detected in the Japanese lacquer tree Rhus vernicifera (Yoshida, 1883). Microbial laccases were subsequently found in many fungi (e.g., Agaricales, Aspergillus fumigatus, Rhizoctonia praticola, Trumetes versicolor; and others) and in actinomycetes like Actinnmyces albocrustosus, A. galbus, and Streptomyces spp. (Krasil'nikov et al., 1971; Matsubara and Iwasaki, 1972; Bollag etal., 1979). Tyrosinase activity has been detected in fungi such as Agaricales, Aspergilfus nidulans, brown-rot fungi, Psalliotu arvense, and Russula nigricans (Bourquelot and Bertrand, 1895; Safe et al., 1976) and in various actinomycetes (Krasil'nikov et al., 1971). Both peroxidase and laccase activity have also been detected in soil extracts, apparently as a result of the production of the extracellular oxidoreductases (Galstyan, 1958; Kozlov, 1964; Bartha and Bordeleau, 1969; Bollag et al., 1987). Synthetic reactions are quite common in soil. The extent of these reactions is limited only by proliferation of the oxidoreductase producers, and the ability of these microorganisms to proliferate appears to be practically limitless. Various studies have shown that essentially all microbial species can be found in most soils, differing only in numbers and proportions. Even in deep layers of soils (up to 500

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J.-M. BOLLAG ETAL.

m) where only anaerobic conditions are expected to prevail, the composition of the microflora is essentially the same as that observed on the surface. Also, the transformation of organic compounds in both types of environments differs little except in the extent of the reactions (Kaiser and Bollag, 1990; Shanker et al., 199 1). Thus, the microorganisms responsible for the humification process are ubiquitous.

111. DEGRADATWE PROCESSES Humus possesses limited stability and undergoes a constant slow decomposition at a rate of about 2-5% a year (Jenkinson and Rayner, 1977). In spite of this degradation, the content of humus in soil remains fairly constant over considerable periods of time due to its systematic replenishment. As previously stated, plant materials are the major source of raw material for new humus synthesis. These materials decompose fairly rapidly, with half-lives varying from days to months (Paul and Van Veen, 1978). Only a small portion of the total plant input, however, is converted to humic substances. During the first year, about 70% of the plant material is degraded to CO,, and about 5-10% forms new biomass (Haider, 1992). Thus, 7 5 4 0 % of plant substances undergo an initial rapid phase of plant litter decomposition. The material remaining is decomposed more slowly, and only the products of this second phase are incorporated into soil humus. The plant residues that are subjected to decomposition consist of a wide range of organic constituents differing in their resistance to microbial activity. Alexander (1977) divided these substances into six broad categories: (1) cellulose, which constitutes on average 15-60% of the dry weight; (2) hemicelluloses (10-30%); (3) lignins (5-30%); (4) water-soluble sugars, amino acids, and aliphatic acids (5-30%); ( 5 )ether- and methanol-soluble fats, oils, waxes, and pigments; and (6) nitrogen- and sulfur-containing proteins. These constituents do not decompose in the soil at the same rate. According to Hatcher and Spiker ( 1 SSS), transformation is a process involving the “preferential decomposition” of easily mineralizable materials and the “selective preservation” of refractory plant components. Of the plant components, cellulose, hemicelluloses, and proteins are easily mineralized, whereas lignins and lipids are refractory to decomposition (Allison, 1973). During the period of rapid decomposition, the former are quickly mineralized to CO, by microorganisms as a source of carbon and energy (Stott ef al., 1983). Nitrogen contained in proteins is mineralized to ammonium and nitrates (Alexander, 1977). The pathway of lignin decomposition in soil is not yet fully understood. Lignin has a complicated structure of phenyl propanoid units connected irregularly by C 0 - C and C-C side chain and direct ring-to-ring linkages. The CO, released during lignin degradation is the product of cometabolism rather than the result of direct

241

COMPLEX ORGANIC STRUCTURES IN SOIL

H-C-OR +oc;iyN.*-

OR

9-

I

H-C-OR

OCH3

OR

Radical Cation

02

1

H2F0H4;;

H-C

I

-0-0

-

Peroxyradical

Figure 1 Action of fungi peroxidases on lignin models: R represents connection to lignin subunits or ethyl methyl substituents (from Haider, 1992).

microbial mineralization (Stott et ul., 1983; Kirk and Farrell, 1987). Compared to cellulose, very little (less than 1%) lignin carbon is incorporated into new biomass (Haider and Martin, 198 1). Lignin degradation, however, is critical for humification to occur because the process generates phenols and phenolic acids, which are fundamental constituents of the core humus polymers. White-rot fungi are the most well known of the lignin biodegraders. These fungi contain peroxidases that remove electrons from lignin aromatic rings with completely etherified hydroxyl groups (Kogel-Knabner et ul., 1991; Haider, 1992). This reaction yields cationic radicals that are then stabilized by the cleavage of CC bonds in the lignin side chains. The radicals then react with 0, or water and become stabilized by the formation of hydroxy or keto derivatives (Fig. 1). Because lignin is highly resistant to biodegradation, it may stabilize reactive components like cellulose, hemicellulose, or proteins by forming chemical or physical linkages to these molecules (Alexander, 1977). A similar protective effect is produced by soil minerals and humus polymers that anchor unstable plant constituents by various adsorption forces or chemical binding (Allison, 1973; Huang, 1990). Highly degradable proteins, for example, may be protected against rapid biodegradation by their nucleophilic addition to aromatic polymers through free NH, or SH groups andlor by adsorption on humus or clay (Huang, 1990). As a result of this acquired stability, the plant components may not be completely mineralized during the initial rapid decomposition phase. Therefore, many relatively unstable plant constituents, such as polysaccharides, proteins, soluble sugars, and amino acids, can survive in the soil for a sufficient length of time to participate directly in humus formation.

2 42

J.-M. BOLLAG ETAL.

IV SYNTHETIC PROCESSES A. THEROLEOF OXIDATIVE COUPLING INHUMUSFORMATION Humus is a substance of incomparably complicated structure. According to Stevenson (1994), all three main fractions of humus (humic acid, fulvic acid, and humin) have fundamentally similar structures-that of a large aromatic polymer that in nature occurs as micelles. Incorporated into such polymers are various phenols, quinones, nitrogen heterocycles, and benzoic acids having reactive carboxyl, hydroxyl, carbonyl, and thiol groups that can act as binding sites for carbohydrates and amino acids. Humus also contains aliphatic moieties, often hydrophobic in nature, and abounds in stable free radicals that give oxidative properties to humus and enable the attraction of ionized or protonated compounds (Steelink and Tollin, 1967; Stevenson, 1994). Several theories have been developed to explain humus formation (Felbeck, 1971; Stevenson, 1994). One of the oldest theories proposes that humic polymers are lignins or other resistant plant materials that have undergone minor modifications under soil conditions (Waksman and Iyer, 1932). According to another theory, plant materials serve exclusively as a source of energy for certain bacteria and fungi, which then synthesize high-molecular weight polymers and release them into the soil environment when they die. A third theory suggests that the role of microorganisms is limited to the production of various amino compounds and phenols that, once discharged into soil, polymerize to form humus. It seems possible that each of these processes can, to a certain extent, contribute to humus formation (Bollag and Loll, 1983). In each of the proposed modem theories of humification, a central role is played by the process of polymerization. Traditionally, phenolic compounds derived from lignins, such as ferulic acid, pyrogallol, orcinol, syringic acid, and catechol, are considered to be the most important substrates for these polymerization reactions. In order to confirm this hypothesis and to learn more about the nature of humus, many researchers have successfully synthesized humic-like substances by polymerizing one or more phenol-derived compounds (Ladd and Butler, 1975; Mathur and Schnitzer, 1978; Martin and Haider, 1980; Dec and Bollag, 1988). The synthetic polymers produced in this way are quite similar to natural humus, particularly with respect to elemental analysis, exchange capacity, total acidity, resistance to microbial degradation in soil, and identity of phenols recovered upon Na-amalgam degradation (Martin and Haider, 197I). Phenolic compounds may be polymerized during the process of humus formation through oxidative coupling reactions that, as with most biological processes, require catalysts. This function is fulfilled by a series of biotic catalysts (the oxidoreductive enzymes) produced by soil microorganisms (Sjoblad and Bollag,

COMPLEX ORGANIC STRUCTURES IN SOIL

243

R-iH

I

(Pcplldr)

c.0

Figure 2 Type structure for hurnic acid (from Stevenson, 1976).

198 1 ) and abiotic catalysts (clay minerals and metal oxides) present in soils (Wang and Huang, 1986; Huang, 1990). Humus itself also exhibits catalytic properties due to the presence of free radical components (Stevenson, 1994). Figure 2 presents the type structure for humic acid proposed by Stevenson (1976); the structure is based on a survey of many spectra of natural humus. Stevenson's type structure consists of aromatic rings possessing hydroxyl and carboxyl groups. These aromatic rings are connected to each other either directly by carbon-carbon bonds or through oxygen and nitrogen or sulfur bridges (not included here). The structure is only hypothetical; in reality, the sequences of bonds and constituents may occur in an unlimited number of combinations. According to Stevenson (1976) and Dubach and Mehta ( 1963), no two molecules of humic acid are alike, no matter how similar they might appear. The mechanism of coupling requires the oxidation of phenolic compounds involving the loss of a proton and an electron from the hydroxyl group. This loss results in the production of free radicals or reactive quinones that covalently couple to form chains of humic polymers (Bollag, 1983). Such coupling has been shown by Liu et ul. (1981) who used a simple model involving one of the components of humus, namely syringic acid, to demonstrate the formation of various oligomers, ranging from dimers to hexamers, by a laccase isolated from the fungus Rhizoctonia praticolu. A similar approach was taken by Simmons er al. (1988). Mass spectroscopy and nuclear magnetic resonance (NMR) techniques were used to identify the products of an enzymatic reaction in which horseradish peroxidase was used to catalyze the oligomerization of guaiacol (Fig. 3). Such studies have contributed to an understanding of mechanisms involved in humus formation. The polymers of syringic acid (Liu et ul., 1981) and guaiacol (Simmons et ul., 1988) are similar to the structure presented by Stevenson (1976), particularly with regard to the oxygen bridges and the direct carbon-carbon linkages between aro-

a

c

n

e

e - zn

H 3C 0

0 H 0 H

OCH,

zn

on

t

E

A GUAIACOL zn _ I

c

-zn

c -214

H,CO

2H

C

H

Figure 3 Reaction pathway scheme for the oligomerization of guaiacol (from Simmons et al.. 1988; used with permission of Soil Sci. SOC.Am. J . ) .

COMPLEX ORGANIC STRUCTURES IN SOIL

245

matic rings. Liu et al. (1985) demonstrated that it is possible to incorporate a peptide unit similar to that depicted in Stevenson’s structure into a simplified synthetic model of humic acid. This incorporation could also be mediated by oxidoreductive enzymes, such as the laccase isolated from the fungus R. pruticofa. As previously mentioned, the process of humification is in constant competition with biodegradative reactions for potential humus constituents. By amending soil with various I4C-labe1ed phenolic substrates, it has been demonstrated that only a minor portion of the phenolic substrate is actually incorporated into humus. The majority is used by microbial populations as a carbon and energy source (Haider, 1976). Yet, when the I4C-labeled substrates were enzymatically incorporated into humic acid before being added to soil, the components were decomposed only to a limited extent. This result supports the theory that compounds incorporated into humus become stabilized and are resistant to microbial degradation (Batistic and Mayaudon, 1970; Wang et al., 1971; Verma et al., 1975). This protective effect explains the ability of natural humic acids to remain intact over hundreds or thousands of years.

B. THEROLEOF ABIOTICCATALYSTS IN HUMUSFORMATION Abiotic catalysts play a vital role in the transformation of phenolic compounds to humic substances. These catalysts include primary minerals, layer silicates, metal oxides, hydroxides, and oxyhydroxides, and poorly crystalline aluminosilicates as discussed later. Shindo and Huang (1985a) reported that primary minerals differ in their ability to catalyze the abiotic polymerization of hydroquinone. The sequence of the catalytic power of the primary minerals is tephroite > actinolite > hornblende > fayalite > augite > biotite > muscovite = albite = orthoclase = microcline = quartz. Except for the tephroite, which is a Mn-bearing olivine, the hydroquinone-derived products are largely low-molecular weight phenolic polymers. The infrared spectrum of the hydroquinone-derived polymers (Shindo and Huang, 1985a) is similar to that of humic substances (Schnitzer and Khan, 1972; Schnitzer, 1978). The scanning electron micrographs of the hydroquinone-derived polymers formed by catalysis of tephroite (Shindo and Huang, 1985a) is similar to those of humic substances of soil (Stevenson and Schnitzer, 1982). Kumada and Kato (1970) are among the pioneers in the study of browning of pyrogallol as affected by clay-size layer silicates. Polymerization of 14C-labeled p-coumaric and ferulic acids to humic acids was reported by Wang ef al. (1971). In a series of studies, Wang and his co-workers reported that layer silicates catalyze the abiotic formation of model humic substances through oxidative polymerization of many phenolic compounds common in soils, plants, and microbial metabolites (Wang and Li, 1977; Wang ef al., 1978a,b, 1980). The formation of

246

J.-M. BOLLAG ETAL.

aromatic radical cation can be catalyzed by intracrystal surfaces of transition metal saturated layer silicates (Pinnavaia et al., 1974). Aromatic molecules may donate electrons to metal ions, such as Cu (11) or Fe (111), on the cation exchange complex of smectite (Mortland and Halloran, 1976). The ability of muscovite to catalyze the oxidation of some phenolic compounds to form humic polymers has also been reported (Filip ef al., 1977). Since the early 1980s, Huang and co-workers have studied the sequence of catalytic power of layer silicates and their reaction sites in the polymerization of phenolic compounds and the subsequent formation of humic substances. The promoting effect of 2: 1 layer silicates is higher than 1: 1 layer silicates (Shindo and Huang, 198Sb). This is attributed to the higher specific surface and higher lattice imperfection of the former than of the latter. The edges of kaolinite are virtually the only catalytic sites for the formation of hydroquinone-derived macromolecules. The edges of nontronite have a very important role as catalytic sites in the formation of hydroquinone-derived macromolecules (Wang and Huang, 1988). The Fe (111) in the octahedral sheet of nontronite also serves as a Lewis acid site to catalyze the oxidative polymerization of hydroquinone (Wang and Huang, 1986). Furthermore, nontronite has the ability to cleave the ring of pyrogallol, catechol, and hydroquinone (Wang and Huang, 1994). The extent of ring cleavage of the polyphenols to release CO, varies with their structures; nontronite greatly catalyzes the reaction (Table I). Catechol, which has two hydroxyls in ortho positions, is evidently more easily cleaved than hydroquinone, which has two hydroxyls in para positions. PyrogaIlol, which has three hydroxyls in consecutive positions, is even more easily cleaved than catechol. In the reaction systems, intermediate products including aliphatic fragments may undergo polycondensation to form humic macromolecules. The structure and functionality of polyphenols are important in influencing the extent of ring cleavage of polyphenols, the release of CO,, the formation of aliphatic fragments, the content of carboxyl groups, and the yield of humic macromolecules formed by catalysis of nontronite. Scheffer et at. (1959) reported that hydroquinone can be oxidized and polymerized in the presence of iron oxides and oxyhydroxides at pH 3-7. Shindo and Huang (1984b) reported that the importance of hydrous oxides of Fe in catalyzing the oxidative polymerization of phenolic compounds is also influenced by the structure and functionality of phenolic compounds. Iron oxides and oxyhydroxides may form surface coatings on layer silicates and humic substances. The surface coatings may catalyze the oxidative transformation of phenolic compounds to humic substances in soils. Hydrous oxides of Al catalyze the oxidative polymerization of phenolic compounds (Wang et al., 1983; Shindo and Huang, 1984b; Wang, 1987). The yields of humic substances from catechol and pyrogallol are significantly greater in the presence of A1 oxides than in its absence. The ability of various Al hydroxides and oxy-

COMPLEX ORGANIC STRUCTURES IN SOIL

247

Table I Release of Carbon Dioxide in the Nontronite-Polypheno1 Systems at the End of a 90-Hour Reaction Period" Reaction condition Nontronite

Polyphenol

f'

Pyrogallol

-d

Pyrogallol Catechol Catechol Hydroquinone H ydroquinone

+ ~

+ -

COz release (pmol") 263

54 88 34 49 21

"Data from Wang and Huang, 1994. "Amount of CO, released in the systems containing I g of Canontronite (0.2-2pm), 5 inmol of pyrogallol. catechol, or hydroquinone i n 30 mL of aqueous solution adjusted to pH 6.00. <'Inthe presence. "In the absence.

hydroxides and their surface coatings on soil particles to promote oxidative polymerization of phenolic compounds remains to be uncovered. Among metal oxides, hydroxides, and oxyhydroxides, Mn oxides are the most powerful catalysts in the transformation of phenolic compounds (Shindo and Huang, 1982,1984b). Manganese oxides (birnessite, cryptomelane, and pyrolusite), which are common in soils, act as Lewis acids that accept electrons from phenolic compounds, leading to their formation of semiquinone, their oxidative polymerization, and the subsequent formation of humic macromolecules. The coupling of semiquinone free radicals requires little heat of activation; coupling of semiquinones rather than the formation of quinones should, thus, be kinetically the preferred reaction path (Shindo and Huang, 1984b).Recently Wang and Huang ( 1992) reported that the solid-state cross-polarization magic angle spinning (CPMAS) "C NMR spectrum of humic acid formed in the presence of the Mn (IV) oxide-pyrogallol system (Fig. 4) is very similar to spectra of humic acids extracted from natural humus. The importance of oxidative polymerization of dissolved phenols by soluble and insoluble inorganic species such as Mn was also recognized in limnology and oceanography (Larson and Hufnal, 1980). Poorly crystalline ahminosilicates are present in soil environments (Wada, 1989). The ability of allophane to catalyze the polymerization of polyphenols has been reported (Kyuma and Kawaguchi, 1964). The infrared spectra of humified polyphenols catalyzed by silicoalumina resemble those of natural humic sub-

J.-M. BOLLAG ETAL.

248

149 8 1

131 9 I

144 6

1

170.7

\I3

I

I

196.3

---A/ , l I I , l ,

300

1

200

1

1

1

1

100

I

I

1

0

,

,

I

I

l

,

-1 0 0

l

l

PPM Figure 4 Solid state l3C-NMR spectra of synthesized HA: (a) pyrogallol and (b) birnessitepyrogallol systems (from Wang and tiuang, 1992).

COMPLEX ORGANIC STRUCTURES IN SOIL

2 49

stances (Wang et al., 1983).There is a series of poorly crystalline aluminosilicates in soils (Huang, 1991). Their catalytic effects on transformation of phenolic compounds to humic substances remain to be studied. Besides the already mentioned reactions, similar catalytic processes can be mediated by salts of Cu, Zn, Pb, and Ag as well as oxides of Zn and Cu (Musso, 1967; Larson and Hufnal, 1980). Sorokin et al. (1995) reported the identification and quantitative analysis of compounds resulting from the oxidative dechlorination and aromatic ring cleavage of trichlorophenol catalyzed by Fe- and Mn-tetrasulphonate phtalocyanine complexes.

C. COVALENT BINDING OF XENOBIOTICS TO HUMUS 1. Covalent Binding Through Oxidative Coupling Reaction From the available information, it appears that humic acid does not have a definitive structure, but rather is an open structure in a state of constant formation and modification. For this reason, any substance that chemically resembles a natural humus constituent can participate in humification and become incorporated into soil organic matter. In particular, xenobiotics such as substituted phenols are analogues of numerous natural phenolic compounds occurring in soil and as such may become an integral part of soil humus. Oxidative coupling constitutes one of several possible mechanisms of incorporation. The contribution of oxidative coupling to the overall binding effect is unknown, however. Nevertheless, there are two groups of xenobiotics that are mostly involved in their binding to humus through oxidative coupling reactionssubstituted phenols and anilines. These compounds are often degradation products of commonly used pesticides. Wolf and Martin (1976) observed that the I4C-labeled herbicides 2,4-D and chloropham could, by the action of fungi, be covalently incorporated as phenolic derivatives into humic-like polymers. Similar observations were made by Smith and Muir (1980) and McCall et al. (1981) who studied transformation of 14C-2,4-D and I4C-2,4,5-T in soil. A study by Kazano et al. ( 1972) indicated that 1-naphthol, a carbaryl derivative, may also covalently couple with humic acids. Sarkar et al. (1988) provided direct evidence that binding of chlorophenols to humic polymers may be mediated by oxidoreductive enzymes. In this study, incubations were carried out in vitro involving I4C-labeled 2,4-dichlorophenol (a degradation product of 2,4-D), natural fulvic acid isolated from stream water, and four oxidoreductive enzymes (tyrosinase, horseradish peroxidase, laccase from R. praticola, and laccase from is versicolor).It was found that upon incubation of the radio-labeled substrate and the enzymes substantial amounts of radioactivity were incorporated into fulvic acid, whereas no binding was observed in control samples

J.-M. BOLLAG ETAL.

0

12

24

36

HOURS Figure 5 Removal of ''C-2,4-dichlorophenol through binding to fulvic acid and polymerization in the presence of oxidoreductases (Sarkar et al., 1988; used with permission of Soil Sci. SOC.Am. J . ) .

that contained boiled enzymes (Fig. 5). The rate of binding with active enzymes depended on pH, temperature, and the amount of enzyme added. Several studies have elucidated the binding mechanism using model systems that contained at least two substrates-one, a toxic phenol, and the other, a representative of the natural humus components (Bollag et al., 1980; Bollag and Liu, 1985). Each of four natural phenolic substances commonly found in soil (orcinol, syringic acid, vanillic acid and vaniilin), for example, was incubated with 2,4-

COMPLEX ORGANIC STRUCTURES I N SOIL

251

Table I1 Formation of Oligomeric and Hybrid Products After Incubation of Phenolic Humus Constitutents and 2,4-Dichlorophenol in the Presence of a Fungal Laccase" Phenolic oligomers ( d z value of molecular ion) ~

Substrate

Monomer

Dirner

+

124

246

2,4-dichlorophenol

I62

284''

Syringic acid + 2,4-dichlorophenol

I98

3 14"

Vanillic acid + 2,4-dichlorophenol

168

284"

I62

Vanillin + 2.4-dichlorophenol

I52

290 304 322 334 302 3 12"

Orcinol

~~

Trimer

Tetramer

Pentamer

466" 5 10" 444"

6 18" 626h

770"

322 I62

I62

450" 482

"Data from Bollag rr ul.. 1980 "Hybrid products

dichlorophenol in the presence of a laccase from R. pruricolu. As revealed by mass spectroscopy, incubations resulted in the formation of hybrid products ranging from dimers to pentamers (Table 11). The proposed structures of the hybrid oligomers formed by cross-coupling of 2,4-dichlorophenol with syringic acid are presented in Fig. 6. In further studies, Bollag and Liu (1985) determined that in addition to oligomers of phenolic character, quinonoid hybrids can also be formed. Other chlorophenols incubated with syringic acid in the presence of a laccase from R. pruticola also formed quinonoid oligomers. In each case, hybrid dimers and trimers were formed that contained not more than one chlorophenol unit. Reactions involving other substituted phenols appeared to be similar to those observed with chlorophenols. When, for instance, 2,6-dimethylphenol was incubated at pH 4.5 with syringic acid in the presence of a laccase from 7: versicolor; hybrid dimers of quinonoid and phenolic character were formed (Liu and Bollag, 1985). The binding to organic matter of substituted anilines from pesticides such as phenylureas, phenylcarbamates, acylanilides, dinitroaniline herbicides, and certain fungicides has also been reported. Formation of bound residues from the herbicide propanil, for example, was shown by Bartha (197 I ) and from the fungicide

J.-M. BOLLAG ETAL.

252

OH CI

+

__t

CI

-

2.4 DICHLOROPHENOL

on

SYRlNGlC ACID

CI

X"3

on

Figure 6 Suggested scheme of oxidative coupling products resulting from the combined incubation of syringic acid and 2,4-dichlorophenol with a laccase (Bollag era/., 1980: used with permission of Soil Sci. Soc. Am. J . ).

2,6-dichloro-4-nitroaniline (DCNA) by Van Alfen and Kosuge ( 1976). Binding has also been reported for pirirnicarb (Hill, 1976) and for various dinitroaniline herbicides (Helling and Krivonak, 1978). In a study by Katan et al. (1976). it was

COMPLEX ORGANIC STRUCTURES IN SOIL

253

demonstrated that binding of I4C-parathion to soil was the result of the activity of soil microorganisms that degraded this pesticide to amino compounds, which were then rapidly and tightly bound to soil. Hsu and Bartha (1974, 1976) reported binding of ''C-3,4-dichloroaniline to humus and ascribed this binding to the covalent linking of the amino groups to the heterocyclic ring structures of humus. Similar observations were made by Parris (1980) who proposed that the quinoidal groups of humic acid molecules are the most likely attachment sites for anilines. Bollag et al. (1983) suggested that the binding of anilines is similar to that of phenols and may be explained by oxidative coupling: the anilines are oxidized to arylamino radicals, which are then linked with phenolic humus constituents. To examine this theory, an investigation was performed using three aniline compounds: 4-chloroaniline, 3,4-dichloroaniline, and 2,6-diethylaniline. The compounds were incubated with various phenolic humus constituents (vanillic, ferulic, syringic, and protocatechuic acid) in the presence of a laccase from the fungus R. pruticolu. The incubations resulted in the formation of various hybrids ranging from dimers to tetramers. The anilines coupled via imine linkages to quinones that originated from the humic acid constituents. Investigations by You et al. (1982) and Hsu and Bartha (1976) have demonstrated that the binding of anilines to humic materials may occur without the presence of any catalysts. For instance, stirring 3,4-dichloroaniline and 4-methylcatechol for 3 days in phosphate buffer (pH 6) resulted in the formation of an adduct identified as N-(3,4-dichlorophenyl)-3-hydroxyd-methyl-p-benzoquinoimide, which proved to be resistant to alkaline hydrolysis and to biodegradation by soil microorganisms. This characteristic distinguishes the binding of anilines from the binding of phenols, in that the binding of phenols requires the presence of a catalyst. The noncatalyzed binding of anilines, however, was slow (up to 3 days) and, as demonstrated by Bollag ef al. (1983), could be greatly enhanced by oxidoreductases. In a study with 4-chloroaniline, Simmons er al. (1989) showed a similar enhancement by an inorganic catalyst, manganese oxide. They also examined the products formed using manganese oxide and four enzymes (tyrosinase, horseradish peroxidase, and laccases from 7: versicolor and R. praticola) and found that each of these catalysts promoted the formation of the same oligomeric products. The formation of hybrids during model reactions with humus components strongly supports the assumption that substituted phenols and anilines may bind covalently to humus. To provide proof for this hypothesis, studies have recently been conducted using I3C-NMR. This technique is a nondestructive method that allows direct observation of the I3C label and its associations. Using NMR, Hatcher e f al. ( 1993) analyzed the enzymatic incorporation of '3C-labeled 2,4dichlorophenol into humic acid and found that the 13Clabel generated sharp distinct signals in specific regions of the spectrum. From the position of the signals in the spectra, it was concluded that 2.4-dichlorophenol was covalently bound to humic acid through ester, phenolic ether, and carbon-carbon linkages. Some l3C-NMR signals could be explained by interactions at chlorinated sites

2 54

J.-M. BOLLAG ETAL.

of the 2,4-dichlorophenol molecule, indicating removal of the chlorine atom. Such a dehalogenation reaction was determined recently through direct measurement of chloride ions released into the reaction mixture during horseradish peroxidase-mediated incorporation of several chlorophenols to humic acid (Dec and Bollag, 1994). The percentage of chloride released during the incorporation reaction ranged from 6.7% (of chlorine initially attached to the benzene ring) for 3chlorophenol to 41.4% for 4-dichlorophenol. None of the weak adsorption or binding interactions responsible for incorporation of xenobiotics into soil organic matter (such as Van der Waal’s forces, H-bonding, and hydrophobic bonding) can result in a dehalogenation reaction. Because only covalent bonding can cause a dehalogenation reaction, the release of chloride ions constitutes more direct evidence for the formation of covalent bonds during enzymatic coupling of chlorophenols to humic substances.

2. Covalent Binding Through Other Reactions Although oxidative coupling is an important binding mechanism for phenols and anilines, other interactions should not be excluded for linking these compounds with organic matter. In the case of anilines, Hsu and Bartha (1 974) proposed an alternative chemical binding mechanism, through condensation of the amino group and the carbonyl of the carboxyl group positioned on the humic acid molecule. Thorne et al. (1996)used I5N-NMR spectroscopy to demonstrate covalent binding of I5N-labeled aniline to humic acid when the two components were dissolved in water and stirred for 5 days at pH 6. The changes in the chemical shifts of the I5N atom indicated that binding was due to nucleophilic addition reactions of aniline with the quinone or carbonyl groups typical for humic substances. The labeled chemical was incorporated in the form of anilinohydroquinone, anilinoquinone, anilide, imine, and heterocyclic nitrogen. The latter comprised over 50% of the bound amines. The NMR technique helped obtain evidence for covalent binding of secondary amines through mechanisms that do not involve the nitrogen atom. In the study of Haider et al. ( 1993), for instance, the fungicide anilazine [4,6-dichloro-N-(2chlorophenyl)-2,3,5-triazine-2-amine] labeled with 14Cand I3Cin the triazine ring was found to be immobilized in soil through ligand exchange. The immobilization occurred within a few days (about 80% of the initial radioactivity) under both sterile and nonsterile conditions, indicating the abiotic character of the binding reaction. The NMR spectra of humic acid extracted from soil together with the bound fungicide revealed that the chlorine substituents located at the C-4 and C-6 positions were removed from the triazine ring and replaced by the oxygen-containing functional groups of soil organic matter. This exchange resulted in the formation of strong ether and ester linkages between the dehalogenated molecule of anilazine and the humic matrix.

COMPLEX ORGANIC STRUCTURES IN SOIL

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Recently, I 'C-NMR spectroscopy was applied to evaluate soil-bound residues of the fungicide cyprodinil [4-cyclopropyld-rnethyl-2-phenylaminopyrimidine], which was labeled with I3C and I4C in either the phenyl or the pyrimidyl ring (Dec et al., 1997a,b). The transformation of cyprodinil by aerobic microorganisms was a prerequisite to binding; negligible immobilization of the fungicide was observed under anaerobic or sterile conditions. The rates of binding determined for the phenyl- and pyrimidyl-labeled compound were essentially the same. Significant differences, however, occurred in the rates of I4CO, evolution upon the incubation of 14C-cyprodinilin soil (a three-fold difference in favor of the phenyl label) and in the rates of plant uptake (a four-fold difference in favor of the pyrimidyl label). The differences suggested significant changes in the chemical structure of the studied compound, including cleavage between the phenyl and pyrimidyl rings. The possibility of cleavage and separate binding of both aromatic moieties to soil was confirmed by the results of I 3C-NMR analysis of the humic acid fraction dissolved in 1% NaOD (Dec et al., 1997a,b). The covalent nature of the formed linkages was verified by derivatization of the humic acid fraction through silylation and analysis of the silylated extracts by I 'C-NMR, size-exclusion chromatography (SEC), and thin-layer chromatography (Dec et al., 1 9 9 7 ~ )As . determined by the SEC analysis, the molecular size of the silylated humic acid fraction with the bound aromatic moieties was in the range of 2 X lo3 daltons.

D. OTHERSYNTHETIC MECHANISMS 1. Mechanisms Involved in Humus Formation Although oxidative coupling mechanisms have been extensively studied, few other mechanisms of humification have been investigated. Little is known, for example, about the incorporation of protein, saccharide, and aliphatic moieties into humus. In an earlier report, Kononova (1966) briefly discussed the polycondensation reaction and proposed that this reaction prevailed over polymerization during humus formation. Polycondensation, like polymerization, results in the formation of high-molecular weight compounds. Contrary to polymerization, however, polycondensation is a reaction brought about by chemical interactions between functional groups of substrates in which at least two such functional groups are present. Among the substances that contain two functional groups are the saccharides (e.g., glucose) and the amino acids (e.g., glycine). As shown by Maillard (1916), glucose can condense with glycine to form brown, humic-like macromolecules. Enders ( 1943) considered humic acids to be condensation products of methylglyoxal and amino acids. When he reproduced this condensation reaction experi-

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mentally, the products he obtained had some features characteristic of humic acids. These types of products are often referred to as melanins. Since polyphenols and phenolic acids possess at least two functional groups, theoretically these compounds can also undergo polycondensation. Unless the reaction is catalyzed, however, polycondensation might not compete with oxidative coupling since some of the former reactions demand severe conditions that are unlikely to be found in soil. In the experiments conducted by Enders (1943), for example, the reaction mixture was heated for 9 hours at temperatures of up to 150°C before polycondensation of methylglyoxal and glycine was obtained. It is possible that many polycondensation processes are the result of metabolic reactions of fungi and other microorganisms. According to Haider (1976) and Musso (1967), it is also possible that melanin formation may be in part an autooxidative process during which phenols, at neutral or alkaline pH, react with atmospheric oxygen to form reactive quinones and radicals that can polymerize with other phenols. Protein and saccharide moieties that bind to humic material are protected from biodegradation. The linkage can occur through the polymerization process (Liu et al., 1985), but, as shown by Mayaudon (1968), proteins and saccharides can also be adsorbed to humic material through hydrogen bonding and electrostatic forces. Humic and fulvic acids isolated from various soils appear to contain appreciable amounts of long-chain aliphatics of lipid origin (Schnitzer and Khan, 1972; Dine1 ef al., 1990). Most of these compounds can be extracted from soil with hexane or chloroform, which indicates that there is no covalent linkage of these moieties to humic substances (Fusteck-Mathon et al., 1977; Capriel et al., 1990). The aliphatic moieties that are covalently linked with humus are probably inherent side chains or cleaved rings of lignin monomers that were earlier incorporated into the humus structure (Haider, 1992). Layer silicates such as Ca-saturated illite have the ability to catalyze the polycondensation of phenolic compounds with amino acids to form N-containing humic acids (Wang et al., 1985). The yields and nitrogen contents vary with the lund of amino acids. The formation of free radicals from phenolic compounds by catalysis and the subsequent reaction of free radicals with amino acids are apparently the primary mechanism for the formation of N-containing humic macromolecules. Birnessite, a poorly crystalline Mn oxide, also catalyzes the abiotic formation of NH,-N and nitrogenous polymers in hydroquinone-glycine systems in the pH range common in soil environments (Shindo and Huang, 1984a).The deamination of glycine from the bimessite-glycine-pyrogallol systems at an initial pH 7.0 and 5.0 are 96 and 69 times higher, respectively, than that from the glycine-pyrogallol systems at the corresponding pH (Wang and Huang, 1987). The formation of Ncontaining humic macromolecules is also greatly increased by catalysis of birnessite. Birnessite has an extensive exposed edge surface, since it is poorly crystalline. These edges contain Mn of high oxidation numbers, e.g., Mn (111) and Mn (IV) (McKenzie, 1989), which can adsorb and polarize 0, molecules, leading to the

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catalysis of the ring cleavage of pyrogallol, the formation of aliphatic fragments, and the decarboxylation and the dealkylation of glycine. The presence of the Mn (11) in the supernatants of the reaction systems, coupled with the electron spin resonance data of humic macromolecules derived from pyrogallol, indicates that Mn (IV) is reduced to Mn (11) in the oxidation of pyrogallol to form semiquinone free radicals (Wang and Huang, 1987). The semiquinones apparently react with amino acids to form N-containing humic polycondensates.

2. Mechanisms Involved in Binding of Xenobiotics Adsorption mechanisms constitute another important type of synthetic interaction responsible for binding of xenobiotics to soil organic matter. Some possible binding sites are indicated in the structure of humic acid by Stevenson (Fig. 2). Positively charged cationic compounds, for example, such as diquat or paraquat, can be attached to ionized carboxyl or hydroxyl groups of humic acid by electrostatic bonding. The carboxyl, hydroxyl, and carbonyl groups can also facilitate binding of various xenobiotics through H-bonding and exchange of ligands, i.e., by the formation of covalent bonds with metal ions attached to these functional groups. As mentioned previously, humus also abounds in stable free radicals that may become binding sites for certain pesticides, particularly those capable of being ionized or protonated to the cationic form (Steelink and Tollin, 1967; Stevenson, 1976). Phenylurea herbicides do not appear to undergo oxidative coupling unless they are degraded to anilines. Some of these herbicides, however, can be bound to organic matter through a cation exchange mechanism (Weber, 1980). They may also be attached to humus through H-bonding, Van der Waals forces, and ligand exchange (Hance, 1969; Senesi and Testini, 1980).In some cases, hydrophobic bonding may also be involved (Carringer et al., 1975). Since triazines can be easily protonated, cation exchange is the primary adsorption mechanism by which they bind to soil organic matter (Carringer et d., 1975). At higher pH values (pH 8-10) triazines are not protonated and binding is instead mediated by hydrogen-bonding and Van der Waals forces (Hayes, 1970). In addition, Khan (1982) suggested that hydrophobic binding and trapping in the pores of the humic structure may also account for the binding of triazines. As previously discussed, diquat and paraquat, the major representatives of bipyridilium herbicides, bind to humus primarily by ion exchange mechanisms (Khan, 1974). To a lesser extent, these compounds can also be adsorbed through H-bonding and Van der Waals forces (Burns et al., 1973; Khan, 1974). Hydrophobic bonding is the major mechanism responsible for adsorption of water-insoluble compounds, such as the chlorinated pesticides DDT, dieldrin, and polychlorinated biphenyls (PCBs). Bound DDT was reported to be associated with the lipophilic fraction of organic matter (Pierce et al., 1974). According to Math-

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ur and Morley (1978), electrostatic forces may also play a role in the adsorption of DDT and PCBs.

3. Mechanisms Involved in Sequestration of Xenobiotics Xenobiotics retained through adsorption are considered reversibly bound, because they can be desorbed through extraction with organic solvents. According to recent observations, however, the desorption rates are subject to reduction with the time that chemicals remain in soil (Pignatello and Xing, 1996). Apparently, during prolonged residence or aging in soil, xenobiotic molecules undergo an entrapment (sequestration) in structural voids and hydrophobic interiors of micellelike humic aggregates or in the micropores of clay minerals (Wershaw, 1993; Engebretson and Wandruszka, 1994, 1996; Alexander, 1995). As a result of these phenomena, chemicals exhibit declining availability to extraction or biodegradation (Alexander, 1995). Wershaw (1993) suggested that humic compounds are arranged in cage-like, pseudomicellar structures that form an intramolecular hydrophobic microenvironment. In the presence of the hydrogen or metal ions, these molecular assemblies may undergo reorganization with the formation of more compact, aggregatelike structures that are kept together by hydrogen bonds and other physical forces (Buffle and Leppard, 1995; Piccolo er al., 1996). The interior voids of the contracting aggregates can sequester xenobiotic compounds, such as pyrene, 2,4-D, or prometryn, thus preventing them from microbial degradation (Engebretson and von Wandruszka, 1994, 1996; Khan, 1982). According to Alexander (1995), sequestration can be attributed to slow diffusion of xenobiotics into inaccessible microsites in the soil aggregates. This concept is consistent with views of Pignatello and Xing (1996) who indicated that nonionic organic chemicals are subject to a transition from a labile to a nonlabile phase of sorption in soil. Sequestration as a process of slow sorption may require weeks or months to reach equilibrium (Alexander, 1995; Pignatello and Xing, 1996; Weber and Huang, 1996). Weber and Huang (1996) attributed this sorption to intraparticle diffusion of chemicals to the internal surfaces of meso (20-5OOA) and micro- (<20 A) porous minerals andor to diffusion of chemicals within soil inorganic matter. The latter seems to occur mostly in the so-called condensed fraction of soil organic matter exhibiting a certain degree of microcrystallinity (Weber and Huang, 1996). The sequestration phenomena were evaluated using a number of xenobiotic chemicals, including phenanthrene, nitrotoluene, several s-triazines (atrazine, prometon, cyanazine, and 2-chloro-4,6-dimethoxy-s-triazine), 5-chloro- 1,3dimethoxybenzene, and trichloroethylene (Hatzinger and Alexander, 1995; Weber and Huang, 1996; Xing et al., 1996). In a recent study with cyprodinil (Dec et al., 1997a), the sequestered material was found in the methylene chloride extract from

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fulvic acid that previously was isolated from soil by NaOH extraction and precipitation of the coextracted humic acid by HCl. Through application of the silylation procedure, an additional portion of the sequestered compound was detected in the humin fraction (Dec et al., 1 9 9 7 ~ )These . findings indicated that compounds retained through sequestration do not constitute a uniform fraction; to the contrary, they can be viewed as a gradient of the increasingly stable fractions depending on how deeply the diffusion proceeded.

V. SIGNIFICANCE OF SYNTHETIC REACTIONS IN SOIL Humus is one of the most important components of the soil system. It constitutes a habitat for a variety of living organisms that digest the continuous inflow of plant and animal debris and thus prevent excessive accumulation of the dead biomass. Humus also contains nutrient substances that support growth of new generations of plants and microorganisms. In addition, organic matter affects the water-holding capacity and determines to a large degree the cation-exchange capacity and buffering properties of the soil. As is well known, these factors greatly influence the retention of nutrients in the soil and their uptake by plants. The same factors also control the detrimental effects of soil acidity. Thus, the presence of organic matter is critical for maintaining the quality of soil structure and consequently the ability of soil to support plant growth. The synthetic reactions discussed in this chapter are essential to the constant replenishment of humus. Another significant benefit of these reactions is the neutralization of certain natural soil organic substances, such as benzoic acid, vanillin, and other lignin decomposition products, that have proved to be toxic to plants (Kononova, 1966). As illustrated in this chapter, these compounds are incorporated into humus, and thus made unavailable to plants, through the humification process. The synthetic reactions typical for soil habitats may also fulfill a detoxifying function in relation to anthropogenic chemicals, such as pesticides, that are known to bind to humus with a loss of toxicity. When the binding of xenobiotics to soil organic matter was first observed, it raised serious concerns about possible delayed adverse effects due to release of the bound pollutants. At present, however, the binding of xenobiotics is considered beneficial, since all available data indicate only minimal release of the bound chemicals. For that reason, numerous studies have been conducted in an attempt to determine whether the detoxification effect can be enhanced through stimulation of the binding process. Oxidative coupling appears to be a very convenient tool for this purpose since it can be enhanced by oxidoreductive enzymes and abiotic catalysts. Of course, some xenobiotics are not good substrates for enzymes, but they can still be detoxified indirectly by increas-

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ing the content of humus in soil. In this case, enhanced detoxification will be the result of intensified adsorption or sequestration. It should be noted that amending soil with additional organic matter to enhance detoxification is not only a theory, but is already used in practice. In fact, amendment is becoming more common as the market is joined by companies that offer humus preparations possessing such detoxifying capabilities. This method is applicable not only for organic pollutants, but also for heavy metals in order to limit their uptake by crops. In spite of considerable progress made in humus science in recent years, our present knowledge of various important aspects of humification and their practical implications still is far from complete. Thus, continued research is necessary to obtain the information needed for controlling the important process of humification.

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Index A Abiotic catalysts, in humification, 245-249 Abscisic acid salt stress and, 96 in waterlogged conditions, I30 Acid detergent fiber, in kura clover, I67 Acid soil aluminum toxicity, 83-85 cool season grain legumes and, 79.80-90 effects on nodulation and nitrogen fixation, 87-89 hydrogen ion toxicity, 82-83 manganese toxicity, 85-86 nutrient deficiencies, 86-87 tolerance, genetic variability, 89-90 Adaptation, in kura clover, 158 Adsorption in humification, 238, 257 in soil, fractal characterization, 50-52 Adventitious roots, in waterlogged soil, 124, 129 Aeration in compacted soil, 116-1 17 in iron deficiency, 107 Aerenchyma, in waterlogged conditions, 124, 129 Africa, Ultisol use and management, 222-227 Alfisols, 182-1 83 Aliphatics, in humification, 256 Alkaline soil calcium and, 109-1 10 cool season grain legumes and, 104-1 I3 effects on nodulation and nitrogen fixation, 1 1 1-1 12 iron deficiency, 105-108 nutrient deficiencies, 104, 1 1 0 - 1 1 1 plant growth in, 105-108 tolerance, genetic variability, 112-1 13 Allitization, 193 Allochthonous rock, in Ultisol formation, I89 Alpine kura clover, I70 Aluminosilicates, in humic polymerization, 247, 249

A 1u mi nu m tolerance, 84-85, 89-90 toxicity, 82, 83-85, 86 in Ultisols, 2 15 Aluminum oxide, in humic polymerization, 246 Anaerobes, in waterlogged soil, 122-123 Anilazine, incorporation in humus, 254-255 Anilines, xenobiotic, incorporation in humus, 251-253,254-255 Anion exchange capacity, in Ultisols, 214-215 Anoxia, in waterlogged soil, 122 Anthracnose, in kura clover, 164 Anthropogenic compounds, see Xenobiotics Ants, in Ultisol formation, 190 Appauvrissment, 193 Argillic horizon formation, 190, 191 moisture regime and, 188 pedogenesis in, 192 in Ultisols, 180-181, 182, 195-198 Arthropods, soil habitat space, 48-49 Aureobasidum, 164 Autochthonous rock, in Ultisol formation, 189 Average daily gain, kura clover and, 167

B Bacteria, in humification, 238-240 BET equation, 51 Bicarbonate, in iron deficiency, 106-107 Biocycling, 194 Biota, in Ultisol formation, 189-1 90 Birnessite, in hurnification, 256-257 Bloat, kura clover and, 167, 168 Boron, toxicity, alkaline soil and, 110-1 1 1 , 1 12 Boundary, estimations, 4, 12-15 Box-counting method, estimation of fractal dimensions, 12-13, 15 Brcid.ysin, 165 Breeding, in kura clover, 168-173 Broadcast-sowing, of kura clover, 162 Brownian function, application to soils, 53-54, 61

267

268

INDEX

Bulk density estimation of soil fractal dimensions, 9-10, 18-20,32 of Ultisols, 205-207 Burning, in traditional African farming, 224-227 Bush-fallow cultivation, 224-227 Bypass flow, fractal characterization, 4 7 4 8

c Calcite in alkaline soil, 104 in iron deficiency, 107 Calcium in alkaline soil, 109-1 10 in aluminum toxicity, 84, 85 in manganese toxicity, 85, 86 salt stress and, 98-99 Cameroon, traditional farming practices, 227 Carbonate, in alkaline soil, 104 Carrington loam, adsorption properties, 52 Catalysts, abiotic, in humification, 245-249 Cation exchange capacity, in Ultisols, 213-2 14 Caucasian clover, see Kura clover Cell physiology, aluminum toxicity, 84 Chaos theory, applied to soil science, 65-67 Charge, in Ultisols, 21 1-213 Chemoheterotrophs, in humification, 238-240 Chickpea, see also Grain legumes salt tolerance, 103 seed yield, in waterlogged conditions, 127-128 tolerance to iron deficiency, I13 tolerance to waterlogging, I33 Chloride in salt stress, 96-98, 99 shoot concentrations, in waterlogged soil, I29 Chlorophenols, incorporation in humus, 249-25 I , 253-254 Chlorosis iron deficiency, 106 manganese toxicity, 85 Clay effects on soil fractal dimensions, 18 effects on soil fragmentation, 40 soil charge and, 2 1 1-2 12 in soil classification, 182 in surface crusting, 209 in Ultisols, 180-181, 192-193, 195-198, 202-204

Climate, in Ultisol formation, 187-188 Clover, see Kura clover Coastlines, fractals and, 4 Coefficient of linear extensibility, in Ultisols, 206207 COLE, in Ultisols, 206-207 Companion grasses, for kura clover, 161-162 Covalent bonding, in humification, 249-255 Cover crops, effects on soil organic matter, 2 I7 Cowpea, see olso Grain legumes manganese tolerance, 86,90 Cracks, in soil, fractal characterization, 49-50 Crinkle leaf, 85 Cropping strategies, effects on soil fragmentation, 38-39 Crop progenitors, salt tolerance, 104 Crops, African, on Ultisols, 223 Crop yield, temporal variability, fractal characterization, 6 2 4 4 Crude protein, in kura clover, 167 Culrivars, in kura clover, 169-171 Cultivation effects on soil organic matter, 2 16-217 effects on soil strength, 57-58 effects on soil structure, I8 effects on surface crusting, 210 traditional practices, Africa, 224-227 Cyprodinil, incorporation in humus, 255,258-259 Cytology, in kura clover, 155-156 Cytoplasm, salt and, 98

D Ductyludenia harteri, 227 Dark-winged fungus gnat, 165 DDT, adsorption in humus, 257-258 Decomposition, see Degradation Degradation in humification, 240-24 I i n soil, fractal characterization, 29 Desiccation, in waterlogging, 131 Desilication. 193 Deterministic uncertainty, applied to soil sci ence, 65-67 Detoxification, soil, humus in, 259-260 2,4-dichlorophenol, incorporation in humus, 249-25 I , 253-254 Diffusion, in soil, 4 2 4 4 , 116-1 17 Digestibility, in kura clover, 166167 Dilation logic, estimation of surface fractal dimension, 13, 27

INDEX Disease resistance, in kura clover, 173 Diseases, of kura clover, 164- 165 Dokuchaev, Vasili V.. I87 Drought, effects on germination in kura clover, I60 Dust storms, Ultisol formation and, 189

E Effective cation exchange capacity, in Ultisols, 214 Electrolytes, soil charge and, 2 12 Eluviation, in Ultisol formation, 192, 195-198 Embedding dimension, in fractals, 6, 7 Empoasca. 165 Entisols, 197-198 Entropy dimension, in soil spatial variability, 6 0 41 Erosion, in Ultisols, 221-222 Erosion logic, estimation of surface fractal dimension, 13, 27 Ethylene, in impeded roots, 1 19 Exchangeable sodium percentage, 91, 95, 102

F Farming, traditional practices, Africa, 224-227 Ferralization, 193 Ferrihydrite, modification of soil structure, 28 Ferritization, 193 Ferrolysis, 193 Fertilizer inorganic, in Ultisol management, 2 19-220 salt damage and, 99-100 Fesruca arundinacea, I6 1 Field pea, see also Grain legumes aluminum tolerance, 90 nodulation, soil acidity and, 88, 89 Fingering structure, fractal characterization, 48 Flower morphology, in kura clover, 157 Flow paths, in soil, 22-23,4748, 207-208 Forage, effects on soil fragmentation, 39 Forest kura clover, I70 Forest soil, see Ultisols Fractals characterization of root morphology, 67-68 characterization of soil, 2 , 6 8 4 9 adsorption, 50-52 chaos theory and, 66 faunal habitat space, 48-49 fragmentation, 2 9 4 1

2 69

mechanics, 49-50 physical processes, 4 2 4 8 pore-solid interface, 23-29 spatial variability, 5 4 4 2 structure, 17-29 temporal variability, 62-64 complements, 65 defined, 4 fragmentation dimension, 11-12, 16-17, 2 9 4I history, 2-6 mass dimension, 7-10, 15, 18-23, 37-38, 4247 scale invariance, 5 , 34-35 scaling, 64 self-similar, 5 spectral dimension, 1 0 - 1 1, 15-16, 2&23 surface dimension, 6-7, 12-15, 23-29 variability dimension, 53-54 Fracton dimension, in fractals, 10-1 I Fractures, in soil, fractal characterization, 49-50 Fragipan, 194 Fragmentation fractal dimension, 11-12 estimation, 16-17 scale-dependent, 34,35 in soil, 2 9 4 1 Frost tolerance, in kura clover, 160 Fungi in humification. 238, 239 in lignin degradation, 241 soil habitat space, 49 Ultisols and, 217 Fungicide, incorporation in humus, 254-255

G Gas diffusion, in soil, 4 2 4 4 , 116-1 17 Generator, in fractals, 5 Genetic variation in cool season grain legumes, 89-90, 102-104, 112-113, 122, 133, 134-135 in kura clover, 168 Geomorphic traits, Ultisol formation and, 190-191 Germination of kura clover, I60 in saline soil, 91-92 in sodic soil, 95 in waterlogged soil, 123-124 Germplasms, in kura clover, 171 Gibbsite, 204

2 70

INDEX

Gleization, in Ultisol formation, 193-194 Glucose, pol ycondensation, 255 Glycine, polycondensation, 255 Goethite, 204 Grain legumes abiotic constraints on, 79, 133-135 acid soil and, 79, 80-90 alkaline soil and, 104-1 13 biotic constraints on, 78-79 genetic variability, 89-90, 102-104, 112-1 13, 122, 133, 134-135 saline soil and, 91-95 sodic soil and, 91,95-99, 101-102 soil compaction and, 113-122 types, 78 waterlogging and, 122-133 in world food supply, I33 Grain yield, temporal variability, fractal characterization, 63 Grasses, companion, for kura clover, 161-162 Grazing management, for productivity in kura clover, 163-164 Grossarenic Kandiudult soil, 197 Growth in alkaline soil, 105-1 1 1 in saline soil, 91-95, 96,97, 98 in sodic soil, 95 soil compaction and, 115 in waterlogged soil, 124-126 Guaiacol, polymerization, 243-245 Gypsum soil erosion prevention, 22 1-222 subsoil acidity and, 220

H Habitat space, in soil, fractal characterization, 4849 Hematite, 199, 204 Herbicides incorporation in humus, 249-255,257-258 kura clover and, 162-163 Highland Sourveld, Ultisol management in, 2 18-220 HIV, in Ultisols, 203-204 Human impact, on Ultisol formation, 190 Humic acid, structure, 243 Humic polymers, 242-245 Humification, 194 abiotic catalysts in, 245-249 covalent bonding in, 249-255

degradation and, 240-241 detoxification and, 259-260 hydrophobic binding in, 257 overview, 237-238 oxidative coupling in, 238, 242-245, 249-254 polycondensation in, 255-256 polymerization in, 242-245 role of organisms, 238-240 xenobiotics in, 249-255, 257-260 Humus, see ufsoHumification functions, 259 general composition, 242 ilkuviation, I94 nitrogen in, 256-257 radiocarbon labeling, 245 sequestration of xenobiotics, 258-259 in soil detoxification, 259-260 Hybrids, in kura clover, 171-173 Hydraulic conductivity fractal characterization, 41,44-47, 60 in Ultisols, 207-208 Hydrocyanic acid, in kura clover, 167 Hydrogen ion, toxicity, 82-83 Hydrophobic binding, in humification, 257 Hydroquinone, abiotic polymerization, 245-249 Hydroxy-Al interlayered vermiculite, in Ultisols, 203-204 Hypoxia, in waterlogged soil, 122

I Illuviation of clay, 195-198 ofhumus, 194 Impoverishment, in Ultisol formation, 193 Infiltration, in Ultisols, 208-210 Inflorescence, in kura clover, 157 Initiator, in fractals, 5 Inoculation, of kura clover seeds, 161 Inorganic fertilizer, in Ultisol management, 2 19-220 Insects, kura clover and, 165 Interspecific hybridization, in kura clover, 171-173 Intrusion porosimetry, 13-14,27, 28 Ion toxicity, in saline conditions, 96-98 Ion uptake, in waterlogged soil, 129-130 Iron deficiency, in alkaline soil, 105-108, 112, 113 shoot concentrations, in waterlogged soil, 130

271

INDEX Iron oxide in humic polymerization, 246 in plinthite, 199-201 in Ultisols, 204 Irrigation, mismanagement, 79 Islands, size distribution, I 1

K Kandic horizon pedogenesis in, 192-193 in Ultisols, 18&181, 182, 195-198 Kaolinite adsorption properties, 5 1 in Ultisols, 202-203 Kentucky bluegrass, as companion gras\, 161-162 K:Na ratio, in saline conditions, 98 Koch's triadic island, 5, 6 Kura clover adaptation in, 158 breeding, 168-173 cytology, 155-156 establishment, factors affecting, 159-163 forage qualities, 166-1 68 future outlooks, 173-174 germplasms, 17 I history, 154 interspecific hybridization, 171-173 longevity, I63 morphology, 156-1 58 origin and distribution, 154-155 pests, 164-165 photosynthate partitioning, 162-1 63 productivity, grazing management and, 163- 164 related species, I55 seedling vigor, 159 seed production, 165- I66 soil requirements, 158-1 59 vernalization, 165, 166

Lateral roots, in compacted soil, 119 Laterites, characteristics, I99 Laterization, in Ultisol formation, 193 Latin square design, 9 Layer silicates, in humification, 245-246, 256 Leaching, anion exchange capacity and, 2 14-2 15 Leaf morphology, in kura clover, 157 Leaf-stem ratio, in kura clover, 166, 167-168 Leaves excess calcium, 109-1 10 iron deficiency, 106 manganese toxicity, 85 salt stress and, 96, 98 in waterlogged conditions, 126, 13 I Legumes, see also Grain legumes: Kura clover as forage, 154 Lentil, see also Grain legumes salt tolerance, 103-104 Lepidocrocite, in Ultisols, 204 Lessivage, 192 Lignin, in humification, 240-241 Lime effects on cool season grain legumes, 81, 82 kura clover and, I59 in Ultisol management, 219-220,223 Linear method, estimation of surface fractal dimension, 34 Loam, adsorption properties, 52 Lupin, see also Grain legumes in alkaline soil, 105, 109 aluminum and, 84 excess calcium and, 109-1 10 ion toxicity, 98 manganese and, 85 salt-induced water stress and, 96 tolerance to iron deficiency, 113 in waterlogged soil, 124, 129

M

L Laccase, in humification, 239 Lacunarity, in mass fractals, 10 Lambs, kura clover and, 167 Landscape spatial variability, fractal characterization, 54-57 in Ultisol formation, 190-191

Macropores in bypass flow, 47-48 fractal characterization, 45 hydraulic conductivity and, 207-208 Magnesium in aluminum toxicity, 84, 86 in manganese toxicity, 85, 86 Maize, yield variability, 63

2 72

INDEX

Mandelbrot, Benoit B., fractals and, 3 4 , 10 Manganese deficiency, alkaline soil and, I10 in humification, 256-257 shoot concentrations, in waterlogged soil, 130 tolerance, 86.89-90 toxicity, 82, 85-86 Manganese oxide, in humic polymerization, 247 Mass fractal dimension, 7-10 derivation from soil bulk density data, 18-20 estimation, 15 in soil fragmentation, 37-38 in soil gas diffusion, 4 2 4 4 in soil water properties, 4 4 4 7 from two-dimensional soil images, 20-23 Mass selection, in kura clover breeding, 169 Mass-size distribution, estimation of soil fractal dimensions, 16-17, 18.32-33 Matric potential, fractal characterization, 44 Melanin, formation, 256 Melanization, 194 Menger sponge, 8 Mercury intrusion porosimetry, 13-14, 27, 28 Mesofauna, in Ultisol formation, 190 Miami silt loam, adsorption properties, 52 Microarthropods, soil habitat space, 4 8 4 9 Micronutrients, see ulso specific nutrients soil acidity and, 86-87 Microorganisms in humification, 238-240 soil habitat space, 4 8 4 9 Ultisols and, 217-218 in waterlogged soil, 122-123 Microrelief, in soil spatial variability, 56 Mildew, in kura clover, I65 Minerals in humic polymerization, 245 in Ultisols, 204-205 Minkowski’s sausage logic, 12, 13 Moisture regime, in Ultisol formation, 183, I88 Molecular probing, determination of surface fractal dimension, 14 Molybdenum, deficiency, 87,89 Monophenol monooxygenase, in humification, 239 Morphology, see ulso Root morphology; Soil morphology in kura clover, 156-1 58

N Nematodes, kura clover and, I65 Nested model, of soil spatial variability, 57 Neutral detergent fiber, in kura clover, 167 Nigeria, traditional farming practices, 225-227 Nitrate leaching, anion exchange capacity and, 214-215 salt damage and, 99-100 Nitrogen in humus, 256-257 uptake, in waterlogged soil, 129 Nitrogenase salt stress and, 101 in waterlogged conditions, 132 Nitrogen fixation in acid soil, 87-89 in alkaline soil, 11 1-1 12 in saline soil, 100-101 in sodic soil, 101-102 soil compaction and, 12 1-122 in waterlogged soil, 131-132 Nodulation in acid soil, 87-89 in alkaline soil, 1 1 1-1 12 in kura clover, 161 in saline soil, 100-101 in sodic soil, 101-102 soil compaction and, 121-122 in waterlogged soil, 131-132 Nonsilicate minerals, in Ultisols, 204 Nontronite, in humic polymerization, 246 No-ti1 lage effects on soil organic matter, 2 I 7 effects on surface crusting, 2 10 NPK fertilizer. in Ultisol management, 219-220 Nuclear magnetic resonance, analysis of xenobiotics in humus, 253-255 Number-size distribution estimation of soil fractal dimensions, 16-17, 32.34, 35 in sand grains, 35 Nutrient deficiency in acid soil, 86-87 in alkaline soil, 104, 110-1 I I in saline soil, 98-99 Nutrient uptake in compacted soil, 120 in waterlogged soil, I29

INDEX 0 Oligomerization, of phenolics, 243-245 Organic matter effects on soil fragmentation. 40 effects on surface crusting, 210 Organisms, in humification, 238-240 Oxidative coupling, in humification. 238, 242-245,249-254 Oxidoreductive enzymes. in humification. 239, 249-250 Oxisols, fractal characterization, 28-29 Oxygen depletion, in waterlogging, I22 diffusion, in compacted soil, 116-1 17 effects on nitrogen fixation, 132 Oxyhydroxide. in humic polymerization, 246

P Parent material in soil spatial variability, 56 in Ultisol formation, 188-189 Particle-size distribution, estimation of soil fractal dimensions, 40 Pasture, soil strength, 57-58 PCBs, adsorption in humus, 257-258 Pedalfer soils, 18 I Pedogenesis, in Ultisol formation, 192-194 Pedoturbation, 194 Peds, fractal characterization, 26 Pelleting, in kura clover, 161 Penetrometers, in soil penetrability studies, 59 Perimeter-area method, estimation of surface fractal dimension, 24-26 Peroxidase, in humification, 239 Pesticides, incorporation in humus, 249-255, 257-258 Pests, of kura clover, I 6 4 1 65 PH aluminum toxicity and, 2 I5 soil charge and, 2 12-2 13 Phenolics, polymerization in humus. 242-245 Phenols polycondensation, 256 xenobiotic, incorporation in humus, 249-25 I , 253-254 Phenotypic selection, in kura clover breeding, f68-169

273

Phenylurea herbicides. incorporation in humus, 257 Phosphorus aluminum toxicity and, 84. 85, 86 deficiency, alkaline soil and, I10 kura clover and, 158-1 59 in salt stress. 99 in Ultisols, 215 Photosynthate, partitioning, in kura clover, 162- 163 Photosynthesis salt stres.s and, 97-98 in waterlogged conditions, 131 Phyllosilic;ltes, in Ultisols, 188-189,202-204 Physiology, effects of waterlogging, 128- 13I Plant residues, degradation, 240-241 Plinthite characteristics. 199-20 1 morphological forms, 20 I soil water movement and, 201-202 in Ultisols, 193. 201 Ploidy, in kura clover, 155-156 Po0 pratensis. 16I - I62 Podzolic soils, 181-182 Podzolization, in Ultisol formation, 193 Polycondensation, in humification, 255-256 Polymerization abiotic catalysts, 245-249 in humification, 242-245 Polyphenols abiotic polymerization, 245-249 polycondensation, 256 Pore-solid interface, fractal characterization, 23-29.37-38 Porosimetry, mercury intrusion, 13-14, 27, 28 Porosity, see also Soil pores fractal characterization, 45 hydraulic conductivity and. 207-208 Potassium, in saline conditions, 98 Potato leafhopper, I65 Powdery mildew, in kura clover, 165 Prairie kura clover, I70 Precipitation in surface crusting, 208-209 in Ultisol formation, 183, 188 Primary minerals, in humic polymerization, 245 Probability, in soil aggregate failure, 35-37 Productivity, in kura clover, 163-164 Protein crude, in kura clover, 167 in humification, 240.24 I , 256

274

INDEX

Q Quartz fractal characterization, 27 in Ultisols, 204-205

R Radiocarbon labeling, in humus, 245 Rainfall effects on soil roughness, 59 in surface crusting, 208-209 in Ultisol formation, 183, 188 Random walk, estimation of mass fractal dimension, 15-16 Redox potential, in formation of plinthite, 200 Red-Yellow Podzolic soils, 181-1 82 Relative growth, in saline soil, 93 Relief, in Ultisol formation, 190-191 Residues, degradation, 240-24 I Resistance, in compacted soil, 115-1 16 Resolution, in fractal characterization of soil, 64 Rhizobacteria in acid soil, 88 in alkaline soil, 1 1 1-1 12 in saline soil, 100, 101 Rhizo kura clover, 170 Rhizomes, in kura clover, 156-157 Roots adventitious, in waterlogged soil, 124, 129 uptake, in compacted soil, 119-120, 129 Root growth in alkaline soil, 106, 107, 108-109 aluminum toxicity, 83-85 in compacted soil, 115-121 hydrogen ion toxicity, 82-83 in saline soil, 92 in waterlogged soil, 124-126, 128-129 Root meristem, in compacted soil, 117 Root morphology in compacted soil, 117-1 19 fractal characterization, 67-68 iron deficiency, 106 in kura clover, 156-157 Root primordia, in waterlogged conditions, 128-129 Root rot, in kura clover, 164 Root zone horizon, fractal characterization, 29 Rotary cultivation, effect on soil structure, 18 Rubification, 194

S Saccharide in humification, 256 polycondensation, 255 Saline soil cool season grain legumes and, 79.91-104 effects on nodulation and nitrogen fixation, 100-102 ion toxicity, 96-98 nutrient deficiencies, 98-99 plant growth in, 91-95 plant responses to, 99-100, 102-104 secondary salinization, 79 tolerance, genetic variability, 102-104 water stress, 96 world acreage, 79 Salt-affected soil, see Saline soil; Sodic soil Salt stress, major components, 96-99 Sampling error, in soil variability studies, 62 Sand adsorption properties, 5 1 fractal characterization, 27 number-size distribution, 35 in Ultisols, 204-205 Saprolite, I89 Scale invariance, in fractals, 5,34-35 Scaling, in soil processes, 64 Sclerotinia, 164 Secondary salinization, 79 Seeding rates, for kura clover, 160-16 I Seed inoculation, in kura clover, I6 I Seedling emergence in saline soil, 91-92 in sodic soil, 95 soil compaction and, 114-1 15 in waterlogged soil, 123-124 Seedling vigor, in kura clover, 159 Seed morphology, in kura clover, 157-158 Seed production, in kura clover, 165-166 Seed size, effect on seedling emergence, 114-1 15 Seed yield in saline soil, 93-95 in sodic soil, 95 soil compaction and, I15 in waterlogged soil, 127-128 Semantic uncertainty, in soil information, 66 Semivariogram crop variability, 63 soil variability, 53, 61

INDEX Sequestration, of xenobiotics in humus, 258-259 Sheep, kura clover and, 167 Shoots ion concentrations, in waterlogged soil. 129-130 responses to compacted soil, 120- I2 I responses to waterlogging, 130- I3 1 Shoot growth aluminum toxicity, 84 hydrogen ion toxicity, 82-83 manganese toxicity, 85 saline soil and, 92 in waterlogged conditions, 124-126 Shoot morphology, in kura clover, I57 Sierpinski carpet, 7-8, 10,45 Silica-gel, adsorption properties, 5 I Silicate clay, in Ultisols, 180-181, 192-193 Silicates in humification, 245-246, 256 in Ultisol formation, 188-1 89 Silicoalumina, in huniic polymerization. 247, 249 Silt minerals, in Ultisols, 204-205 Slash-and-burn cultivation, 224-227 Slit-island technique, estimation of surface fractal dimension, 14-15 Slope, in Ultisol formation, 19CL-191 Smectite, 204 Sodic soil cool season grain legumes and. 9 I effects on nodulation and nitrogen fixation, 101-102 physical problems in, 96 plant growth in, 95-99 Sodium, see also Exchangeable sodium percentage in iron deficiency, 107-108 shoot concentrations, in waterlogged soil, 129 Sodium toxicity in salt stress, 9 6 9 8 in sodic soil, 96 Sod-seeding, of kura clover, I62 Soil, see aiso Ultisols charge in, 2 I 1-2 13 classification systems, 181-1 83 fractal characterization, 2, 6 8 4 9 adsorption, 50-52 chaos theory and, 66

275

chloride breakthrough, 47 degradation, 29 faunal habitat space, 4 8 4 9 fingering structure, 48 flow paths, 22-23.47-48 fragmentation, 16-17,2941 gas diffusion, 4 2 4 4 mechanics, 49-50 penetrability, 59 pore-wall roughness, 37-38 root zone horizon, 29 scaling in, 64 spatial variability, 52-62 strength, 57-58, 6 M 1 surface roughness, 23-29, 59-60 temporal variability, 52-62 terminology, 69 water properties, 4 W I . 4 4 4 8 interaction with root morphology, 6 7 4 8 particle-size distribution modeling, 33 salt-affected, see Saline soil; Sodic soil theory of formation, I87 waterlogging, 122- 123 Soil acidity, see Acid soil Soil aeration incompactedsoil, 116-117 in iron deficiency, 107 Soil aggregates formation, 38-39 fractal characterization, 16-17, 18-20. 29 probability of failure, 35-37 size distribution. 40, 41 Soil biota, Ultisols and, 217-218 Soil bulk density in estimation of soil fractal dimensions, 9-1 0. I8-20,32 in Ultisols, 205-207 Soil color, in Ultisols, 198-199 Soil compaction cool season grain legumes and, 113-122 effects on nodulation and nitrogen fixation, I2 1-1 22 root growth and, 115-121 seedling emergence and, 114-1 15 seed yield and, 1 15 tolerance, genetic variability, I22 Soil fauna habitat space, fractal characterization, 4849 in humification, 238-240

276

INDEX

Soil management traditional practices, Africa, 224-227 of Ultisols, 2 18-227 Soil morphology, in Ultisols, 195-202 Soil organic matter, see also Humus in Ultisols, 216-217 Soil pH, see also Acid soil; Alkaline soil measurement errors, 62 Soil pores fractal characterization, 8-10, 11, 15, 21-29, 45 fractal complement, 65 mercury intrusion porosimetry, 13-14 microfaunal habitat space, 4 8 4 9 soil water properties, 4 5 4 8 Soil salinity, see Saline soil Soil science chaos theory and, 65-67 fractals and, 2 Soil sodicity, see Sodic soil Soil structure fractal characterization, 8-10, 17-29 mercury intrusion porosimetry, 13-14 modification with ferrihydrite, 28 rotary cultivation and, 18 Soil water, see also Waterlogging flow paths, 22-23,4748,207-208 fractal characterization, 40-41,44-48 plinthite and, 201-202 retention in Ultisols, 207 Solid matrix fractal complement, 65 in soil water properties, 46-47 Solid-pore interface fractal characterization, 23-29 as microfaunal habitat space, 48-49 South Africa, Ultisol management in, 218-220 Sowing, of kura clover, 162 Soybean, see also Grain legumes salt stress and, 101 Spatial variability, in soil, fractal characterization, 52-62 Spectral fractal dimension, 10-1 I , 15-16, 20-23 Stochastic uncertainty, in soil information, 66 Stomata, in waterlogged conditions, 130-13 I Stride method, estimation of surface fractal dimension, 12,26 Strip-seeding, of kura clover, I62 Structural fractal dimension, 7 Subsoil acidity, gypsum and, 220 Sulfate, in salt stress, 99

Summit kura clover, 169-170 Surface crust, see ulso Soil compaction erosion and, 221 in Ultisols, 208-210 Surface fractal dimension, 6-7, 12-15, 23-29 Syringic acid, polymerization, 243,245

T Temperature effects on germination in kura clover, 160 in Ultisol formation, 188 Temporal variability, in soil, fractal characterization, 62-64 Termites, in Ultisol formation, 190 Terrain profiles, fractal characterization, 24 Textural dimension, in fractals, 7 Tillage effects on soil fragmentation, 38-39 effects on soil organic matter, 216217 effects on surface crusting, 210 Time, in Ultisol formation, 191-192 Tomography, X-ray computed, 2 6 2 7 Topological dimension, in fractals, 6 Total nonstructural carbohydrate, in kura clover, 164 Transient soil properties, fractal characterization, 5 7 4 1 Treeline kura clover, 170 Triazine, incorporation in humus, 257 Trfolium ambiguum, see Kura clover Trfolium repens. interspecific hybridization, 171-173 Typic Kanhapludult soil, 197 Tyrosine, in humification, 239

U Ultisols in Africa, 222-227 chemical properties, 2 11-215 classification, 181-183 clay minerals in, 202-204 defining concepts, 180-181 distribution, 183-187, 222 erosion in, 221-22 formation, 187-194 general description, 180,227, 229 management, 218-227 morphology, 195-202 organic matter and, 2 16-2 17

INDEX physical properties, 205-210 sand and silt minerals in, 204-205 soil microorganisms and, 21 7-2 I8 United States, Ultisol soil distribution, 183, 185-1 87

V Variability fractal dimension, 53-54 Vernalization, in kura clover, 165, 166 Virus, in kura clover, 164-165

w Wall-pore roughness, fractal characterization, 37-38 Water, see also Soil water flow paths in soil, 22-23,4748,207-208 movement in soil, fractal characterization, 48 root uptake, in compacted soil, 119-120 Waterlogging confounding factors, I26 cool season grain legumes and, 79, 122-1 33 effects on nodulation and nitrogen fixation, 131-132 ion uptake in, 129-130 physiological effects, 128-1 3 1 plant growth in, 123-128 tolerance, genetic variability, 133

277

Water retention curves estimation of soil surface fractal dimension, 29 fractal Characterization, 40-4 1, 44-47 Water stress, in saline conditions, 96 Western spotted cucumber beetle, 165 Wetting fronts, fractal characterization, 48 White-rot fungi, in lignin degradation, 241 Wild plants, salt tolerance, 104

X Xenobiotics, in humification, 249-255, 257-260 X-ray computed tomography, 26-27 Xylem, salt stress and, 97

Y Yield in saline soil, 93-95 in sodic soil, 95 soil compaction and, I I5 temporal variability, fractal characterization, 62-64 in waterlogged soil, 127-128

2 Zinc, deficiency, in alkaline soil, 110, 112

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