Advances in Agronomy, Volume 67

Advisory Board Martin Alexander

Ronald L. 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 Monograpbs Committee Jon Bartels Jerry M. Bigham Jerry L. Hatfield David M. Kral

Diane E. Stott, Chair Linda S. Lee David M. Miller Matthew J. Mom Donald C. Reicosky John H. Rechcigl

Wayne I? Robarge Dennis E. Rolston Richard Shbles JeffreyJ. Volenec

Edited by

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

ACADEMIC PRESS SanDiego London Boston NewYork Sydney Tokyo Toronto

This book is printed on acid-free paper. 63 Copyright 0 1999 by ACADEMIC PRESS All Rights Reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the Publisher. The appearance of the code at the bottom of the first page of a chapter in this book indicates the Publisher’s consent that copies of the chapter may be made for personal or internal use of specific clients. This consent is given on the condition, however, that the copier pay the stated per copy fee through the Copyright Clearance Center, Inc. (222 Rosewood Drive, Danvers, Massachusetts 01923), for copying beyond that permitted by Sections 107 or 108 of the U.S.Copyright Law. This consent does not extend to other kinds of copying, such as copying for general distribution, for advertising or promotional purposes, for creating new collective works, or for resale. Copy fees for pre-1999 chapters are as shown on the title pages. If no fee code appears on the title page, the copy fee is the same as for current chapters. 0065-2113/99 $30.00

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

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ASPECTS OF PRECISION AGRICULTURE Francis J. Pierce and Peter Nowak I. Introduction.. ............................................

II. Overview of the Basic Components of Precison Farming. ......... III. Conclusions .............................................. References ...............................................

2 5 65 67

SURFACE CHARGE AND SOLUTE INTERACTIONSINSOILS N. S. Bolan, R. Naidu, J. K. Syers, and R. W. Tillman I. Introduction.. ............................................ II. Types of Electrical Surface Charge. ........................... III. Development of Surface Charge. ............................. N. Components of Surface Charge .............................. V. Solution-Surface Interface .................................. VI. Concepts of Point of Zero Charge ............................ VII. Measurement of Surface Charge. ............................. W I . Factors Affecting Surface Charge ............................. M. Effect of Surface Charge on Soil Properties. .................... X. Manipulation of Surface Charge to Control Solute Interactions. . . . . XI. Conclusions and Future Research Needs ....................... References ...............................................

88 90 90 96 97 104 107 112 120 126 130 131

ALLELOPATHY: PRINCIPLES, PROCEDURES, PROCESSES, AND PROMISES FOR BIOLOGICAL CONTROL Inderjit and K. Irwin Keating I. Introduction. ............................................. 11. SomeConcerns.. ......................................... III. Allelopathy in Agroecosystems ...............................

142 145 148

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CONTENTS

W. Factors Influencing Allelopathy .............................. v. Secondary Metabolites with Allelopathic Potential . . . . . . . . . . . . . . . VI. Mechanisms of Action of Allelopathic Chemicals . . . . . . . . . . . . . . . . w. Allelopathic Growth Stimulation ............................. vm. Roles for Allelopathy in Biocontrol Programs ................... Ix. Additional Comments ...................................... X . Concluding Remarks ....................................... References ...............................................

166 181 184 189 190 195 206 207

TURFGRASS MOLECULAR GENETICIMPROVEMENT FOR ABIOTI~/EDAPHIC STRESS RESISTANCE

R . R. Duncan and R. N. Carrow I. Introduction ..............................................

TI. Molecular Genetic Improvement ............................. III. Enhancement Strategy for Multiple-Stress Resistance ............ W. Summary ................................................ References ...............................................

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

233 235 275 282 283

307

Contributors

Numbers in p n t h e s z s indicate the pages on which the authors' conbibutions begin.

N. S. BOLAN (87), Department of Soil Science, Massq University, Palmerston North $301, New Zealand R. N . CARROW (233), Department of Crop and Soil Sciences, University of Georgia at Gnfin, &fin, Georgia 30223 R. R. DUNCAN (23 3), Department of Crop and Soil Sciences, Universityof Georgia at &fin, &fin, Georgia 30223 INDERJIT (141),Department ofAgriCultzlra1Sciences (weed Science), The Royal Veterinury and Agricultral Univenity, DK-1871 Freabiksberg C., Copenhagen, Denmark K. IRWIN KEATING (141),Department ofEnvironmenta1Science, Rutgers, The State University $New Jm-ey,New Brunswick, New 'jersey 08903 R. NAIDU (87), CSIRO Land and Water, Glen Omond 5064, South Australia, Australia PETER NOWAK (l), Department of Rural Sociology, University of Wisconsin, Madison, Wisconsin 53706 FRANCISJ . PIERCE (I),Department of Crop and Soil Scimes, Michigan State University, East Lansing, Michigan 48824 J. K. SYERS (87), Department of Agrinrltzlral and Environmental Science, University of New Castle upon Tpe, New Castle upon Tpe NE1 7RU, United Kingdom R. W. TILLMAN (87), Department ofsoil Science, Massq University, Palmnston North 5301, New Zealand

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Preface

Volume 67 contains four comprehensiveand timely reviews of topics that should be of great interest to professionals and students in crop and soil sciences. Chapter 1 addresses one of the most active areas in agronomic research-precision agriculture. All aspects of the topic, including technologies, management, and economic and environmental impacts, are discussed. Chapter 2 is a thoughtful review of surface charge and solute interactions in soils. In addition to a theoretical treatment of the topic, practical applications,including surface charge effects on solute interactions and dispersiodflocculation and manipulation of surface charge by amendment additions, are included. Chapter 3 is a useful review of a topic of great interest to agronomists-allelopathy. Principles, procedures, processes, and promises for biological control are discussed. Chapter 4 thoroughly covers advances in the use of molecular genetics to enhance abioticledaphic stress resistance in turfgrass. Many thanks to the authors for their first-rate reviews.

DONALD L. SPARKS

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ASPECTS OF PRECISION AGRICULTURE Francis J. Pierce' and Peter Nowak* 'Department of Crop and Soil Sciences Michigan State University East Lansing, Michigan 48824 2Department of Rural Sociology University of Wisconsin Madison, Wisconsin 53706

I. Introduction A. Definition of Precision Agriculture B. Intuitive Appeal 11. Overview of the Basic Components of Precision Fanning A. The Enabling Technologies B. Steps in Precision Agriculture HI. Conclusions References

Precision agriculture is the application of technologies and principles to manage spatial and temporal variabilityassociated with all aspects of agricultural production for the purpose of improvingcrop performance and environmental quality. Success in precision agriculture is related to how well it can be applied to assess, manage, and evaluate the space-time continuum in crop production. This theme is used here to assess the current and potential capabilities of precision agriculture. Precision agriculture is technology enabled. It is through the integration of specific technologies that the potential is created to assess and manage variability at levels of detail never before obtainable and, when done correctly,at levels of quality never before achieved.The agronomic feasibility of precision agriculture has been intuitive, depending largely on the application of traditional management recommendations at finer scales, although new approaches are appearing. The agronomic success of precision agriculture has been limited and inconsistent although quite convincing in some cases, such as N management in sugar beet (Bera vulgaris L.).Our analysis suggests prospects for current precision management increase as the degree of spatial dependence increases, but the degree of difficulty in achieving precision management increases with temporal variance. Thus, management parameters with high spatial dependence and low temporal variance (e.g.,

I A&mctx in Agronomy, Volume 67

Copyright8 1999 by Academic Prea. All rights of reproduction in m y farm reserved. 0065-21 13/99 $30.00

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FRANCIS J. PIERCE AND PETER NOWAK liming, P, and K)will be more easily managed precisely than those with large temporal variance (e.g.. mobile insects). The potential for economic, environmental, and social benefits of precision agriculture is complex and largely unrealized because the spacetime continuum of crop production has not been adequately addressed. 0 1999 Academic Press

I. INTRODUCTION It would be a simple matter to describe the earth’s surface if it were the same everywhere. The environment,however, is not like that: there is almost endless variety. -Webster and Oliver ( I 990) The quote by Webster and Oliver (1990) is particularly applicable because precision agriculture is concerned with the management of variability in the dimensions of both space and time. Without variability, the concept of precision agriculture has little meaning (Mulla and Schepers, 1997) and would never have evolved. It appears that any component of production agriculture-from natural resources to plants, production inputs, farm machinery, and farm operators-that is variable in some way is included in the realm of precision agriculture. Aspects of precision agriculture, therefore, encompass a broad array of topics, including variability of the soil resource base, weather, plant genetics, crop diversity, machinery performance, and most physical, chemical, and biological inputs used in the production of a crop, whether natural or synthetic. By necessity, these aspects are all framed within the context of the socioeconomicaspects of production agriculture because to be successful on the farm, precision agriculture must fit the needs and capabilities of the farmer (Nowak, 1997) and must be profitable (Lowenberg-DeBoer and Swinton, 1997). Bell et al. (1995) state correctly that efforts toward precision agricultural management should recognize that the factors affecting crop yields and environmental sensitivity vary in both space and time. Managing soils and crops in space and time is the sustainable management principle for the twenty-first century, a principle exemplified by farming by soilscapes, managing zones within the field, and managing the noncrop period (Pierce and Lal, 1991). The unifying theme of this chapter is that success in precision agriculture is directly related to how well it can be applied to manage the space-time continuum in crop production. We postulate that prospects for precision management increase as the degree of spatial dependence increases, but the degree of difficulty in achieving precision management increases with temporal variance. Thus, for management parameters that vary spatially, those with high temporal correlations (e.g., liming) will be more easily man-

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aged with precision agriculture than those with large temporal variance (e.g., mobile insects). Within a given management parameter, the success to date of precision management is to a large extent determined by the degree to which the spatial variability is temporally stable. This chapter provides an overview of precision agriculture and an assessment of its current state and its potential to improve crop performance and environmental quality in production agriculture. In this chapter, we define precision agriculture, explore the technological capabilities that enable it, assess its agronomic feasibility and environmental efficacy, and evaluate its performance to date relative to economic and social impacts. The chapter concludes with an analysis to identify needed developments in precision agriculture and we provide some thoughts for a future research agenda. Given the expansive nature of precision agriculture coupled with space constraints, we attempt to synthesize the important aspects of precision agriculture while guiding the reader to the growing volume of literature on the subject. Readers seeking more detail are referred to the following major publications related to precision agriculture: Auernhammer ( 1994),American Society of Agricultural Engineers (ASAE) (1991), BIOS (1997), Lake et al. (1997), National Research Council (NRC) (1997), Pierce and Sadler (1997), Robert et al. (1993, 1995, 1996), Sawyer (1994, Schueller (1992), Stafford (1996b), and Sudduth (1998). We are aware of the rapid rate of change in precision agriculture and the inadequacies this causes in an overview of this nature.

A. DEFINITION OF PRECISION AGRICULTURE Currently, no precision agricultural systems exist; rather, various components of traditional crop management systems have been addressed separately regarding their potential for site-specific management, perhaps most notably soil fertility (Lowenberg-DeBoer and Swinton, 1997).The state of precision agriculture from a systems perspective is analogous to the early days of no-tillage crop production. Technology became available in the 1960sto plant seeds in untilled soil, but it was not until the many aspects of crop production were adequately addressed under lack of tillage and crop residue management, including the management of fertility and pests, that successful no-tillage systems were developed and implemented (Blevins et al., 1998). The adoption of no-tillage did not proceed at a significant rate until the 1980s when the integration of appropriate technologies and public policies supported its dissemination to farmers (Allmaras et al., 1998; Larson et al., 1998;Now& and Korsching, 1998).In a similar fashion, although certain technologies in the early days of precision agriculture allowed for the variable application of nutrients and pesticides, there did not exist a thorough understanding of how soil fertility and pests varied in space and time. Most important, explanations were lacking on what specifically caused the variability so that appropriate inputs

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could be matched to site-specific conditions. Today, farmers are adopting individual components of precision agriculture on the farm but a distinctive precision farming system has not yet evolved. Technological developments continue to occur and as a result of ongoing research a better understanding of underlying processes is being developed but a true system has not emerged. Therefore, any definition of precision agriculture can at best be considered only operational. Since the mid-l980s, a host of terms have been used to describe the concept of precision agriculture, including farming by the foot (Reichenberger and Russnogle, 1989), farming by soil (Carr et al., 1991; Larson and Robert, 1991). variable rate technology (VRT) (Sawyer, 1994), spatially variable, precision, prescription, or site-specific crop production (Schueller, 1991), and site-specific management (Pierce and Sadler, 1997). All these terms, however, have in common the concept of managing variability at scales that are within fields. As Stafford (1996b; p. 595) states, precision agriculture involves “the targeting of inputs to arable crop production according to crop requirements on a localized basis.” Thus, the intent of precision agriculture is to match agricultural inputs and practices to localized conditions within a field to do the right thing, in the right place, at the right time, and in the right way (Pierce ef al., 1994). A recent report of a National Research Council, Board on Agriculture Committee defined precision agriculture as “a management strategy that uses information technologies to bring data from multiple sources to bear on decisions associated with crop production” (NRC, 1997; p. 17). While the NRC definition raises important informational dimensions of precision agriculture, it fails to emphasize the basic premise of precision agriculture-the management of spatial and temporal variability. In this chapter, we use the following definition of precision agriculture as the basis of our discussions: Precision agriculture is the application of technologies and principles to manage spatial and temporal variability associated with all aspects of agricultural production for the purpose of improving crop performance and environmental quality. We provide a final note on the word precision because there is sure to be confusion regarding its meaning in precision agriculture versus its use in statistics. The term precision refers to the quality or state of being precise, where precise means minutely exact, a term synonymous with correct. Precision agriculturerefers to exactness and implies correctness or accuracy in any aspect of production. In statistics, however, precision is the closeness of repeated measurements of the same quantity to each other, whereas accuracy is the closeness of a measured or computed value to its true value (Sokal and Rohlf, 1995). In measurements, accuracy is synonymous with correctness (i.e., validity), whereas precision refers to reproducibility (i.e., reliability). Thus, something can be precise but not accurate. Another matter is measurement precision implied by number of digits reported for a given measurement. The nature of computers makes it easy to imply more precision than was possible in various aspects of data collection, analysis, and compu-

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tation in precision agriculture. Precision here refers to the limits on the measurement scale between which the true measurement is believed to lie, implied by the number of digits reported for a measurement (Sokal and Rohlf, 1995). The more digits reported for a measurement, the higher the precision implied. A pH of 5.44 implies more precision than a pH of 5.4. The appropriate precision with which to report a number is to include one additional digit beyond the last significant one measured by the observer. Statistics plays an important role in the application of precision agriculture and care should be taken in dealing with accuracy, precision, and implied precision in the reporting data.

B. INTUITIVEAPPEAL Precision agriculture is intuitively appealing because it is closely aligned with the scientific principles of management of soils, crops, and pests. Few would argue against a management philosophy that espouses matching inputs to the exact needs everywhere. Precision agriculture is intuitively appealing because it offers a means to improve crop performance and environmental quality in production agriculture (Wolf and Nowak, 1995). While the intuitive appeal creates high expectations for precision agriculture, the physical evidence supporting the agronomic (Lowenberg-DeBoer and Swinton, 1997; Sawyer, 1994) and environmental (Larson et al., 1997) benefits of precision agriculture is limited in part because it is still in its infancy. As we will demonstrate in our discussion, successful implementation of precision agriculture depends on numerous factors, including (i) the extent to which conditions within a field are known and manageable, (ii) the adequacy of input recommendations, (iii) the degree of application control, and (iv) the degree of support through private and public infrastructures. Individual success also depends on the expectationsplaced on precision agriculture which represent the difference between promotional and educational efforts versus the actual experience of farmers.

II. OVERVIEW OF THE BASIC COMPONENTS OF PRECISION FARMING The main componentsof any precision agriculture system that may emerge must first address the measurement and understanding of variability. Next, this system must use information to manage this variability by matching inputs to conditions within fields using site-specific management recommendations and mechanisms to control the accuracy of site-specific inputs. Finally, and most important, this system must provide for the measurement and recording of the efficiency and effica-

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cy of these site-specific practices in order to assess value on and off the farm. Thus, precision agriculture is technology enabled, information based, and decision focused (Pierce, 1997a).

A. THEENABLING TECHNOLOGIES While the concept of matching inputs to site-specific conditions is not new, as just discussed, there is little doubt that important advances in technology continue to enable precision agriculture. The enabling technologies of precision agriculture can be grouped into five major categories: computers, global position system (GPS), geographic information systems (GIS), sensors, and application control. Few of the enabling technologies were developed specifically for agriculture and their origins date back more than 20 years, as illustrated in the time chart in Figure 1. It is the integration of these technologies that has enabled farmers and their service providers to do things not previously possible, at levels of detail never before obtainable, and, when done correctly, at levels of quality never before achieved (Fortin and Pierce, 1998). 1. Computers

Many technologies support precision agriculture, but none is more important than computers in making precision agriculture possible. Also, it is not computers alone that are important but their ability to communicate that is so powerful for agriculture.As Taylor and Wacker (1997) suggest, it is the fusion of computers and communication that gave birth to connectivity, and it is connectivity that is driving the access of everyone to everyone, everything to everything, and everything to everyone. This electronic linkage and communication define the age of access (Taylor and Wacker, 1997). It is this notion that may have prompted the NRC (1997) to define precision agriculture in terms of a management strategy that uses information technologies for decision making. Precision agriculture requires the acquisition, management, analysis, and output of large amounts of spatial and temporal data. Mobile computing systems were needed to function on the go in farming operations because desktop systems in the farm office were not sufficient. These mobile systems needed microprocessorsthat could operate at speeds of millions of instructions per second (MIPS), had expansive memory, and could store massive amounts of data. The first microchip created by Intel in 1971 (Intel 4044 processor) contained a mere 2300 transistors and performed about 60,000instructions per second. Since 1971, the number of transistors per chip has doubled every 18 months (Fig. 2) affirming Gordon Moore’s observation in 1965 that a doubling of transistor density on a manufactured die was occurring every year, a concept referred to as “Moore’s law” (Moore, 1997).

Date

Event

1840s

Aerial photography emerges; pictures taken from balloons First image sensors incorporated in satellites; low-resolution black and white TV First commercial CIS First chlorophyll sensor (Benedict and Swidler, 1961) First multispectral photography done from space Apollo 9 manned mission Baumgardener et al. (1970) related soil organic matter to multispectral data Intel 4040 processor Launch of Earth Resources Technology Satellite-1 later renamed Landsat; permitted continuous coverage of most of the earth's surface Soil organic matter sensor (Page, 1974) Apple computer commercialized (http://www.apple.com)

1960s 1961 1968 1970 1971 1972 1974 1977 1978 1980 1981 1982

1983 1984

1985 1986

1987

1988

I990 1991 1992 1993 I994 1996 1997

Launch of first NAVSTAR GPS satellite First IBM PC Intel 80286 processor Launch of Landsat "Thematic Mapper ("h4) added The Jet Propulsion Lab produces hyperspectral sensors for use from a high-altitude aircraft platforms known as AIS (Airborne Imaging Spectrometer) GPS available for civilian use Ortlip patent issued to SoilTEQ Launch of Landsat 5 286 Intel processor Grain flow monitoring on combines @e Baerdwmeker et al., 1985) French launch an operational series of earth-observingsatellites called SPOT (SysPme Probatoire d'observation de la Terre); first offering of multispectral data to world users on a commercial basis The Jet Propulsion Lab produces a second hyperspectral sensor known as AVIRIS (Airborne VisibldnfraRed Imaging Spectrometer) Yield mapping in Texas (Bae et al.. 1987) India launches earth resources satellite (IRS-IA) that gathers data in the visible and near IR with the Linear Imaging Self-scanning sensor (LISS) Intel 40486 processor Canadian Radarsat, ERS-I, and ERS-2 managed by the European Space Agency A class of satellite remote sensors using radar systems Japan launches JERS- 1 and JERS-2 that include both optical and radar sensors Selective availability (SA) imposed on GPS signal First symposium on site-specific crop production (ASAE, 1991) Commercial yield monitors appear in the United States First international conference on soil specific crop management (Robert et al., 1993) Pentium processor Full constellation of 24 GPS satellites in NAVSTAR system complete Earth System Science Pathfinder launched by NASA Pentium I1 processor India launches the latest in the series. IRS-ID, on September 29, 1997 First European conference on precision agriculture (BIOS, 1997) Board of Agriculture, National Research Council report on precision agriculture (NRC,1997)

Figure 1 Historical developments in the technologies that enabled precision agriculture.

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Yorr

Figure 2 Illustration of Moore’s law showing the doubling of computer speed and capacity every year [Source:Intel Corporation (www.intel.com)].

As Fig. 2 indicates, Moore’s law is expected to hold until 2017 (according to Moore) and appears to hold for memory and storage capacity. Data storage capacity will need to increase rapidly as sensor technology and digital geospatial data become increasingly available to agriculture. Moore notes, By the Year 2012, Intel should have the ability to integrate 1 billion transistors onto a production die that will be operating at 10 GHz. This could result in a performance of 100,OOOMIPS, the same increase over the currently cutting edge Pentium I1 processor as the Pentium 11processor was to the 386! We see no fundamental barriers in our path to Micro 2012, and it’s not until the Year 2017 that we see the physical limitations of wafer fabrication technology being reached. We can expect, therefore, that computers will drive significant technological development to enable precision agriculture for the foreseeable future. The extent to which agriculture can utilize computer technology is important to the success of agriculture in general (Holt, 1985; Ortmann et al., 1994). However, the agricultural sector is lagging in the adoption of computer technologies on the farm relative to other business sectors. According to the 1997 annual survey of the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS, 1997), of 2,053,800 farms in the United States, only 38% had computer access, 31% owned or leased a computer, and 13% had Internet access. Part of this computer lag in agriculture is due to the lack of access or connectivity in rural areas, lack of training, and little perceived utility in available software (Nowak, 1997;

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Peterson and Beck, 1997).In any event, farmers will have to become as comfortable working with computers and their data as they are working with their farm machinery (Klein, personal communication, 1997). While it appears that computer hardware will be more than adequate for precision agriculture, the same cannot be said for the software. Advances in software logically lag behind the hardware technology. However, software for precision agriculture has been more an experience than an application. Berry (1999, in discussing the human factor in GIS, describes experience as “what you get when you don’t get what you want.” Computer software in precision agriculture has become better with time, but precision agriculture is loaded with Berry’s type of experience. Software will be adequate for precision agriculture when it becomes, as Berry (1 995) suggests, second nature to the user for assessing information and translating it into knowledge. For precision agriculture, the knowledge needed is that for managing variability on the farm, knowledge that is requisite for decision making. Computers and salient, usable software are going to play a critical role in the emergence of a precision agriculture system in the near future.

2. Geographic Information Systems Formally, GIS is an organized collection of computer hardware, software, geographic data, and personnel designed to efficiently capture, store, update, manipulate, analyze, and display all forms of geographicallyreferenced information [Environmental Systems Research Institute (ESRI), 19971. The GIS concept dates back to the 1960s when computersbecame available for use in spatial analysis and quantitative thematic mapping (Burrough, 1986).The science of GIS has evolved since the 1960s to include data management and modeling, enabling a shift from mapping to spatial reasoning (Berry, 1993, 1995). The ability to perform spatial operations on the data distinguishes a true GIS from the many software programs that do thematic mapping and database management. During the past few years, mapping software programs have been adding spatial operations, workstation GIS software programs have spawned microcomputer versions with more limited GIS capabilities to fit desktop computer technologies, and new microcomputer-based GIS systems have emerged. There are many different mapping and GIS software programs that offer different GIS features. None, however, have captured the market for application in precision agriculture. Because precision agriculture is concerned with spatial and temporal variability and because it is information based and decision focused (Pierce, 1997a), it is the spatial analysis capabilitiesof GIS that enable precision agriculture. This statement is true because the value of precision agriculture is derived only when resulting information is turned into a management decision that increases profitability, benefits the environment, or provides some other value to the farm. AGIS, in the full sense of its formal definition given previously, is key to extracting val-

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ue from information on variability. Clark and McGucken (1996) refer to GIS as the brain of a precision farming system. However, available GIS software packages are complex and difficult to learn for nonspecialists. Some GIS lack data management and spatial analysis tools needed to understand the variability observed on the farm and needed to derive site-specific management recommendations. More functional, easy to use interfaces are needed in order to fully utilize this technology in production agriculture (Berry, 1995; NRC, 1997).Computer simulation modeling can help derive the needed understanding of variability (Sadler and Russell, 1997; Verhagen and Bouma, 1997) and linking GIS to models (Goodchild et al., 1993) will be important to precision agriculture.

3. Global Positioning Systems Location control (Schueller, 1992) is essential to precision agriculture for assessing spatial variability and for site-specific application control (Auernhammer, 1994; Tyler et al., 1997). In the early days of precision agriculture, relative position within a field was determined by dead reckoning. This was a simple method in which position was measured relative to a known point determined by measuring distance using radar, ultrasound, and wheel shaft counters. Direction was determined either by using a steering-angle sensor or a gyroscope from the known point or by direction only if a field had linearized tramlines of known fixed location (Auernhammer and Muhr, 1991). Triangulation methods, in which position is determined relative to two or more known locations using, for example, radio signals transmitted from reference stations to mobile units (Palmer, 1991, 1995; Scorer, 1991), improved position accuracy to as low as 15 cm (95%probability) but such systems were time-consuming and expensive. By the early 1990s. however, the GPS known as NAVSTAR (NAVigationSystem with Time And Ranging) was becoming available for general civilian use including agriculture.This system was based on 18 satellites that were in orbit by early 1990 (Hoffmann-Wellenhofet al., 1994; Kaplan, 1996; Kennedy, 1996; Leick, 1995; NAPA, 1995). The United States NAVSTAR GPS system consists of a constellation of 24 satellites, including 3 spares. The first satellite was launched in 1978 but it was not until the Soviet downing of a Korean airliner in 1983 that the decision was made to make GPS available for civilian use [National Academy of Public Administration (NAPA), 19951. The NAVSTAR GPS system was fully deployed by 1994 and declared fully operational in 1995. The Russians also deployed a GPS system called GLONASS (Global Navigation Satellite System) consisting of 24 satellites completed in 1995. Although there are differences in time standards and coordinate systems between GLONASS and NAVSTAR, higher end GPS receivers currently available accommodate the combined use of both GPS systems resulting in increased reliability and accuracy. Although the Russian GLONASS policy called for ensured availability for 15 years, no charge on a constant global basis, and no selective availability, the system was degraded to only 14 or 15 active spacecraft

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during the fall of 1997 (Perry, 1998). Therefore, changes in GPS technology are to be expected. The GPS technology enables precision agriculture because all phases of precision agriculture require positioning information. GPS is able to provide the positioning in a practical and efficient manner for a few thousand dollars ( v l e r et al., 1997). Expensive, high-precision differential GPS (DGPS) systems are available that achieve centimeter accuracies (Lange, 1996), allow for automated machinery guidance (O’Conner et al., 1996; 51er et al., 1997) and kinematic mapping of topography (Clark, 1996), and are useful in the creation of digital elevation models needed for terrain analysis (Bell et al., 1995; Moore et al., 1993). While the GPS signal is ubiquitous, there have been problems in making available GPS at the needed precision for agriculture (Saunders et al., 1996). The U.S.Department of Defense implemented selective availability (SA) on March 25, 1990, which limited accuracy of GPS to civilians from about 8-10 m without SA to about 100 m with SA. This was done by varying the reported precise time of clocks on board the satellites and by providing incorrect orbital positioning data (NAPA, 1995). The SA has been overcome by the use of differential corrections transmitted to GPS receivers (rovers) from GPS receivers at known fixed locations (base). DGPS involves the transmission of a differential correction, that is, the difference between actual and predicted position at the base GPS receiver, to rover GPS receivers, which then apply the corrections to received GPS signals to solve for a more accurate position (Qler et al., 1997). There are four general ways of providing a differential correction: a private local GPS base receiver with a radio modem to transmit to a mobile receiver, a commercial GPS base station at which differential corrections are transmitted on FM subcarrier frequencies, a public GPS base station at which differential corrections are transmitted on AM frequencies from radio beacons with up to a 250-mile radius [U.S. Coast Guard (USCG) beacon system), and a wide area differential GPS (WADGPS) network in which differential corrections from a network of base stations are used by the roving GPS receiver to correct its position (vier et al., 1997). In all cases, DGPS requires additional receivers and antennas and is fee based for commercial correction providers. A differentialcorrection is desirable even without SA because it is needed to achieve the accuracies needed in some aspects of precision agriculture, including navigation and guidance. Currently, only WADGPS provides national coverage, whereas all others are dependent on whether the rover is close enough to a base station to receive the signal consistently. However, this is changing because FM providers are planning to offer national coverage in the near future and there are plans for completion of the USCG beacon system nationally (Divis, 1998). There is currently a debate as to whether the public sector should provide a national DGPS (NDGPS) to agriculture (NAPA, 1995;Pointon, 1997). Other sectors of the U.S. economy also need a national NDGPS, so the discussion of who benefits from a publicly supported NDGPS should not be focused on agriculture alone. The Office of Management and Budget did not support expansion of the USCG

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radio beacon system for public NDGPS in Ey98. However, some believe that a government-provided NDGPS system is so important to critical activities that it is best for the government to provide it (Divis, 1998). Certainly, precision agriculture needs DGPS and will require increased position accuracy as new technologies for navigation and guidance require higher precision, which may require DGPS accuracies not available from a government NDGPS. The prophecy of Auernhammer and Muhr (1991; p. 395) that “their use will also be without costs in the future” will probably never be realized because DGPS is big business. Regardless of who provides all aspects of DGPS, farmers and their service providers need reliable DGPS to achieve the desired positioning for precision farming operations. Farmers still experience interruptions and interferences in the GPS and/or differential correction signals, creating gaps in data collection or loss of application or guidance control. In activities at higher speeds, such as aerial applications (Kirk and Tom, 1996), time delays in differential corrections may limit positional accuracy in kinematic mode (NRC, 1997). The specified availability (four satellites in view at any location) of the NAVSTAR system is 99.85%, with a reliability (system is in service when it needs to be) of 99.97% (NAPA, 1995). However, the suitability of the satellite geometry for calculating a solution, referred to as dilution of precision (DOP), is a problem in farming in which natural or man-made structures obstruct the receivers’ view of some satellites or interfere with differential correction reception. There are also geographic locations at which DOP has been inadequate for needed location precision at certain times during the day. Additionally,some GPS receivers are susceptible to unwanted interfering signals from a variety of sources, including farm machinery, making the receiver useless in navigation or positioning. Some interferences can be overcome in the design of the GPS receiver. Regardless of problems, DGPS has greatly enabled precision agriculture. Of great importance for precision agriculture, particularly for guidance and for digital elevation modeling, position accuracies at the centimeter level are possible in DGPS receivers that use carrier phase in combination with DGPS (Lange, 1996; Tyler er al., 1997).Accurate guidance and navigation systems will allow for farming operations not currently in use, including field operations at night when wind speeds are low and more suitable for spraying and the use of night tillage to reduce the light-induced germination of certain weeds (Hartmann and Nezadal, 1990). DGPS technology changes continually and can be followed on the internet (e.g., Peter Dana’s web site hrtp://wwwhost.cc.utexus.edu/Stp/pub/grg/gcrafr/ notes/gps/gps.html or www.gpsworld.com).

4. Sensors Sensors are devices that transmit an impulse in response to a physical stimulus such as heat, light, magnetism, motion, pressure, and sound. With computers to

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record the sensor impulse, a GPS to measure position, and a GIS to map and analyze the sensor data, any sensor output can be mapped at very fine scales. Sensor technology currently lags behind other enabling technologies (Sudduth et al., 1997) and the availability of sensors has been cited as the most critical factor preventing the wider implementation of precision agriculture (Stafford, 1996b). Sensors are critical to success in the development of a precision agricultural system for three important reasons: Sensors have fixed costs, sensors can sample at very small scales of space and time, and sensors facilitate repeated measures. This means that the cost per sample is determined by the extent of sensor use, sample intensity is determined by the capability of the sensor and not the cost or difficulty in sampling associated with traditional physical sampling schemes, and sampling frequency is determined by accessibility of the target and not costs. The value of sensors and their potential for the future of precision agriculture are illustrated by yield monitoring.Yield monitoring systems, which use sensorsto measure crop flow, allow the creation of yield maps with detail not practical with other measurementtechniques (Pierce er al., 1997).Yield mapping technology may be the major factor responsible for the growing interest in precision agriculture observed since its commercial introduction in 1992 (Stafford, 1996b). Prior to 1992, the focus was on VRT, which would not in itself have sustained precision agriculture. Yield mapping bolstered precision agriculture and is currently the major precision agriculture technology in U.S. agriculture. However, the promise of sensing technologies may make yield mapping technology unnecessary in the future if high-resolution remote sensing of the growing crop leads to quantitative prediction of crop yield prior to harvest. Yield mapping will serve to validate sensor-based predictive technology, but once operational, yield monitors may not be needed. The use of remote sensing to forecastcrop yields is in use worldwide, and forecastingoffers farmers the ability to market their crops prior to harvest when prices are more favorable. Sensors are very desirable for use in precision agriculture. Every effort should be made to promote the application and adaptation of sensors developed in other industrial sectors, especially the space and defense industries, as well as to promote the development of new sensor technologies for use in assessing and managing variability in soils, plants, pests, and machinery. Sensors can be contact or remote, ground based or space based, and direct or indirect. Sensors have been developed to measure machinery, soil, plants, pests, atmosphericproperties, and water by sensing motion, sound, pressure, strain, heat, light, and magnetism and relating these to properties such as reflectance, resistance, absorbance, capacitance, and conductance. Sensors are needed in precision agriculture because such a system requires the collection,coordination, and analysis of massive quantities of data (Sudduth et al., 1997),some for strategic surveys and inventories and some for use in real-time applications. Remote sensing involves the detection and measurement of photons of differing energies emanating from distant materials. These photons may be identified

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and categorized by classhype, substance, and spatial distribution, with most designed to monitor reflected radiation (Frazier et al., 1997). Satellite remote sensing dates back to the first aerial photographs taken from balloons in the 1840s.The first satellite imagery was obtained from TV cameras mounted in satellites in the early 1960s. Since the U.S.Landsat program launched the first observation satellite in 1972, earth observation has increased and currently India, France, Russia, Japan, and the European Space Agency also operate earth observation satellites (Figure 1). Many companies now offer commercial products to agriculture from images obtained by these satellites or enhanced digital products derived from them. Remote-sensing satellites collect image data actively by sending a known signal from the satellite to the earth and measuring the portion of the signal that is returned. Passive data collection occurs by measuring the incoming energy from the sun reflected by an object or heat energy emanated from an object. The electromagnetic energy emanated from an object varies in wavelengths as determined by the object’s physical and chemical structure. Different images of an object can be constructed by combining different wavelengths, creating images far more revealing than images obtained from visible light alone. Remote-sensing systems vary in spatial resolution (meters to kilometers), spectral coverage (portion of the light spectrum covered), and temporal frequency (days to months). Different applications in agriculture will require different spatial resolutions, spectral coverages, or temporal frequencies. NASA (1998) provides an online tutorial on remote sensing and its applications. Moran et al. (1997) provide a comprehensive review of image based remote sensing for precision agriculture. Remote sensing holds great promise for precision agriculture because of its potential for monitoring spatial variability over time at high resolution (Hatfield and Pinter, 1993; Moran e? al., 1997; Stevens, 1993). For example, monitoring of a growing crop using remote sensing is critical because yield maps document yield variability but do not provide information on the cause of observed variability. However, the promise of remote sensing for agriculture has not been realized for many reasons, including costs, timeliness, and availability (Frazier et al., 1997; Stafford, 1996b).

5. Application Control Control is that portion of an automated system in which sensed information is used to influence the system’s state in order to meet an objective (Stone, 1991). For precision agriculture, control must be achieved in space and time for varying single or multiple inputs at different rates, at varying soil depths, and in a uniform and location-specific manner within fields. Because it is a required component, control technology has been a strength of precision agriculture since its inception and the state of application control was recently reviewed by Anderson and Humburg (1 997). Simply stated, if the needed accuracy cannot be achieved at the point

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of application of inputs, then precision agriculture cannot be successful (Anderson and Humburg, 1997; Schueller, 1992; Stafford, 1996b). Application control completes the precision agriculture loop. Control systems are currently available at varying degrees of precision for variable seed and metering granular fertilizers and pesticides, changing varieties on the go, anhydrous N application, sprayers, imgation, manure application, and various tillage implements (Anderson and Humburg, 1997; ASAE, 1991; Robert el al., 1993, 1995, 1996). The first patented technology for variable rate application of fertilizers was the Ortlip patent awarded in 1985 to Soil-TEQ, Inc. (now owned by AgChem), although the earliest references to precision application of fertilizers appear to be Luellen ( 1985) and Elliot ( 1987). All issues relating to the accuracy of application equipment are important to precision agriculture but not all accuracy issues are unique to precision agriculture (Anderson and Humburg, 1997). General sources of variability in application of inputs include driving precision, uniformity of distribution, topography, field surface conditions, wind conditions, and metering efficiency. Specific to precision agriculture are the transition time for changes in rate or product and positioning or location control and those aspects of application in which changing rates or products affect variability in performance. A high precision, absolute reliable DGPS will offer the positioning precision required for various tasks in precision agriculture. Some argue for a backup system, such as dead reckoning, to avoid loss of control if DGPS fails (Auernhammer and Muhr,1991). While very high position accuracy is available using DGPS, currently the major consideration is cost. Human driving precision has an expected coefficient of variation of 10% for moderate skill levels (Chaplin et al., 1995) but should be greatly improved with DGPSbased guidance systems, depending on the accuracy of the DGPS system in use (51er el al., 1997). O'Conner et al. (1996) report the use of a carrier phase differential GPS for automatic vehicle control to achieve a vehicle position accuracy within a few centimeters and heading to within 0.1". The issue of transition time is illustrated by the V-shaped spray pattern resulting from a transport delay incurred between the injection point and the nozzle discharge for a simple chemical injection system (Steward, 1994, as cited by Anderson and Humburg, 1997). Transition times of 3-9 s were reported by Bahri et al. (1996) when changing seeding rates in grain drills, with transition time depending on the magnitude of the application rate change. Their data indicated that small rate changes in seeding rate of 10 kg ha-' did not provide a real rate change, illustrating potential step size rate limitations for some inputs. A transition phase may limit the spatial resolution of variable rate application of inputs if the target area is small (Stafford, 1996b)or may cause applicationerrors if the transition time is greater than the time between detection of the need for change and the equip ment arrival at the detected position as would occur in real-time application. Current equipment may not be suitable for precision agriculture. Bashford

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(1993) and Bashford et al. (1996) report outlet CVs for grain drills ranging from 12 to 22.5%for wheat (Triticumaestivuum L.) and from 16 to 42%for soybean [Glycine 1 7 1 ~(L). Merr.]. They suggest that external fluted metering devices are not suitable for precision agriculture. Bahri et al. (1996) measured down-the-row CVs ranging from 10 to 19%.In general, variation in grain drills has been considered acceptable if the variability in grain and fertilizer delivery among row units is below a CV of 15%(Prairie Agricultural Machinery Institute, 1987). This variance, however, may exceed the desired accuracy in precision seeding systems. Centrifugal fertilizer spreaders are known to have high sensitivity of the spread pattern to flow rate variations and efforts are under way to design centrifugal spreads for precision agriculture (Olieslagers et al., 1996; Kaplan and Chaplin, 1996). Rate changes also affect nozzle performance relative to drop size and flow rate for the given nozzle design (Anderson and Humburg, 1997) and these issues are currently being addressed (Giles et al., 1996; Nuspl et al., 1996). The major issues for precision application of inputs remain transition time for changes in product or rate, uniformity of application, and rate increment control. There are other issues affecting the availability or performance of application control, including the development of standards for communication and connectivity among manufacturers, a topic being addressed by many organizations including an association of industry and the public sector called the Ag Electronics Association, ASAE, and the International Standards Organization. There are laws regulating fertilizer quality that in some states (e.g., Arkansas and Michigan) limit the blending of fertilizers on the go because of the need for a guaranteed chemical analysis, and such laws will have to be properly addressed. There are issues related to equipment wear (Ballal et al., 1996) and to weather conditions at the time of application. Kirk and Tom (1996) report that up to 13%of the variability in their tests for spray aircraft was due to wind conditions. Heterogeneity in the composition of some materials affects the flow or spreading properties (e.g., manures; Ess et al., 1996).Topography and field surface conditions also affect accuracy, in part due to their effect on flow of materials in the hoppers or tanks. Application control, including navigation and guidance, has been enabling precision agriculture since its inception and continues to improve. Farmers and their service providers have the capability to apply very precise applications of inputs site specifically. Application control technology will continue to improve and support the needs of precision agriculture. What is needed is knowledge of what inputs are required where and when.

B. STEPSINPRECISIONAGRICULTURE The basic steps in precision agriculture are assessing variation, managing variation, and evaluation. While the enabling technologies facilitate precision agri-

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culture, it is the knowledge and understanding of variability and the extent that site-specific agronomic recommendations are available to manage this variability that make precision agriculture viable. Also, because nothing is known with certainty and many factors affect crop production, evaluation must be an integral part of any precision agriculture system. These are interdependent and necessary components of a precision agriculture system yet to emerge. As will be seen, there is significant variation in the progress and research underlying each of these components. Assessing variability, precision management, and evaluation are the focus of the remainder of this chapter. 1. Assessing Variability

Assessing variability is the critical first step in precision agriculture since it is clear that one cannot manage what one does not know. The processes and properties that regulate crop performance and yield vary in space and time. Adequately quantifyingthe variability of these processes and properties and determining when and where different combinations are responsible for the spatial and temporal variation in crop yield is the challenge facing precision agriculture (Mulla and Schepers, 1997). Techniques for assessing spatial variability are readily available (Beckettand Webster, 1971; Cressie, 1991; Goovaerts, 1997,1999; Isaaks and Srivastava, 1989; Mausbach and Wilding, 1991; Mulla, 1997; Mulla et al., 1990; Rossi et al., 1992; Trangmar et al., 1985; Warrick et al., 1986; Webster and Oliver, 1990; Wollenhaupt et al., 1997) and have been applied extensively in precision agriculture.The bulk of the literature on precision management relates to some aspect of assessing spatial variability. Techniques for assessing temporal variation also exist (Shumway, 1988) but the simultaneous reporting on spatial and temporal variation is rare and the theory of these types of processes is still in its infancy (McBratney et al., 1997). Such space-time statistical applications are important to precision agriculture because many phenomena exhibit spatial patterns that develop over the course of time. They are also important because a cause-effect relationship may exist in time but not in space (Stein et al., 1997). A good example of the latter point is a yield map. A yield map defines the spatial distribution of crop yield but does not explain the observed variability. Imagery of crop growth and development over the growing season can uncover the cause-effect relationship that explains not only the yield variation within a field but also the magnitude of yield observed in a particular growing environment (Schepers et al., 1996). As we will discuss later, some variables exhibit strong spatial dependence but low temporal dependence (high temporal correlation), making them more conducive to current forms of precision management. To repeat our premise, structured spatial dependence is needed for precision management, whereas temporal variability increases the difficulty with which it can be implemented. This will become more clear in our discussion of managing variability.

’

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For precision agricultureto be useful, variation must be known, of sufficient magnitude, spatially structured (nonrandom), and manageable (Pierce, 1995). Knowing variation implies a measure of accuracy, either in measurement or in prediction. An accurate assessment of variability is essential, but the prolific use of maps without measures of accuracy indicates that this important aspect is often neglected. Knowledge also implies a sense of understanding. It is not uncommon to have detailed measures of variation within a field with little understanding of the causes of the observed variability, as suggested in our yield map example. The magnitude of both the mean and the variation determines the potential for benefits from precision agriculture.For a given parameter, a threshold variance must be exceeded for precision management to be an improvement over whole-field management for crop production.Also, the absolute magnitude of a parameter must be in a manageable range. For example, field soil fertility is already in the high range and no fertilizer would be recommended anywhere. Thus, a parameter can vary spatially but not deviate sufficiently from the mean field value or values do not fall within a manageable range to justify precision management. A high degree of spatial dependence is needed for current applications of precision agriculture. Spatial dependence drives precision agriculture because parameters with high random variation (low spatial dependence) will not be conducive to site-specific management and will be best managed on the average. Finally, accurate knowledge of large, spatially dependent variation is not sufficient for precision agriculture if the variation is not manageable. Scale is very important in this regard. For example, even with accurate maps, nutrient deficiencies or pests may be difficult to manage precisely if their areal expression is considerably smaller than the minimum area treatable by available application equipment. Biological processes that vary on very small scales are difficult if not impossible to exploit with current precision management (Groffman, 1997). Drainage ways within fields may be difficult to manage because they vary from high to low yields across years depending on seasonal precipitation patterns. While variation in drainage is known, spatially structured, and of sufficient magnitude, how to precisely manage time-dependent entities such as drainage is uncertain. Management decisions in these situations are more likely to be driven by risk assessment strategies. Ultimately, farmers must be able to delineate areas that will respond similarly to inputs that optimize crop performance (yield, quality, and environment). Maps form one basis for precision management; real-time management forms the other basis. Use of management maps is more common and these can be categorized as condition maps, prescription maps, and performance maps. In real-time precision management, maps are not necessary because inputs are triggered by real-time measurements of soil, crop, or pest condition. Condition maps are measured and/ or predicted using a broad array of technologies and techniques for estimating the spatial distribution of one or more properties or processes. Measurementsobtained in real-time precision management can be mapped as condition maps for later use.

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Prescription maps are derived from one or more condition maps and form the basis for VRT (Sawyer, 1994). Performance maps record either inputs (fertilizers, pesticides, seeds, energy, etc.) or outputs (crop yield and quality) and include derivatives of performance maps, such as profit maps (outputs-inputs). Performance maps are possible for any part of a farming enterprise that can be sensed in real time and a location recorded. Performance maps can ultimately serve as condition maps. For example, multiple-year yield maps can be used to estimate yield goal maps often needed for precision prescriptions or to estimate soil test levels using mass balance approaches. Condition maps are a critical component of precision agriculture and can be generated in four major ways: (i) surveys, (ii) interpolation of a network of point Samples, (iii) high-resolution sensing, and (iv) modeling to estimate spatial patterns. All these methods are scale dependent and have limitations to their use in precision management. We briefly discuss these here but refer the reader to more detailed references. a. Surveys Surveys are purposeful inventories of specific quantities and have been particularly useful in natural resource management. Surveys are designed with specific purposes in mind and, in general, have limitations when used for other purposes or intense applications such as precision agriculture. The National Cooperative Soil Survey of the USDA-NRCS is an extensive inventory of soil resources that includes soil data and maps needed for crop production (Soil Survey Division Staff, 1993). Surveys of varying age and scale of measurement are available for most of the United States. At first glance, the soil survey should be an important asset to the principles of precision agriculture. Farming by soils was initially thought to be a reasonable basis for precision management (Cam et al., 1991; Larson and Robert, 1991).However, existing soil surveys have proved of limited value in explaining spatial variability observed within fields. Mausbach et al. (1993) state that “Kellogg (1961; p. 58) was very clear that the soil survey and its interpretations are not site specific.” They concluded that while soil surveys are useful for planning on-farm resource management systems and for highway, urban, and other planning activities, they are not designed for specific applications such as soil-specific farming. Are soil surveys important to precision agriculture? We should not expect that variation in crop performance, either agronomic or environmental, will be explained by soil and landscape properties alone. The crop production process ia more complex than this deterministic assertion would suggest. However, within a given climate regime, we should expect crop performance to generally correspond to differences among soils and landscapes. Yield mapping has supported this general relationship.The value of the soil survey to precision agriculture could be improved by intensifying map scales to fine scale resolutions needed in detailed en-

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vironmental modeling applications or site-specific management (Moore et al., 1993). Accomplishing this is no small task. Nonetheless, new data sources (e.g., digital ortho photos, airborne and satellite imagery, and yield maps) and analysis techniques (e.g., terrain analysis) make it possible to map soils at needed resolutions (Bell et al., 1995; Moore ef al., 1993). Mausbach el al. (1993) suggest that such data collection activities should be a private-sector activity and not performed by the NRCS. However, there are two areas in which a private and public-sector cooperative relation could be developed. First, the NCSS program should complete the current soil survey in a manner that embraces available technologies for collecting data, generating finer resolution soil surveys, and publishing surveys in digital formats. Second, as will be noted later, the actual implementation of precision agriculture is spatially “lumpy” or unequal in its distribution across agricultural areas. Not all agricultural areas are of equal agronomic importance to the United States. Certain critical areas from either an agronomic or an environmental perspective could be designated for enhanced NCSS activities. The soil survey can be important to precision agricultureif the same logic (i.e., what is needed and where it is needed) is applied to defining public and private-sector roles in the allocation of scarce fiscal resources. b. Interpolation of Point Samples Another technique for assessing spatial variability involves sampling processes. A network of points in some spatial arrangement is sampled and then interpolated to produce a spatial estimation (usually a map) of the whole area using a range of statisticalprocedures. Readers are referred to references previously cited for details on the use of these procedures. These spatial statistical techniques can also be repeated over time to estimate the temporal variability (McBratney ef al., 1997; Stein et al.. 1997). Network or spatial sampling of points and interpolation into maps is useful when it is feasible to directly measure only a small number of points due to economic or temporal constraints. To a large extent, sampling depends on the nature of the entity of interest. Soil sampling for soil survey, for example, is used to determine how much of the land is of a particular type or what proportion possesses some soil attribute (Webster and Oliver, 1990). For pest management, interest may be in obtaining insect pest density maps either within a field or within a region and over time (Fleischer er al., 1997). Regardless of entity, the nature of spatial and temporal variation of that entity should affect sampling and statistical estimation procedures. The goal of network or spatial sampling for precision agriculture is to provide an accurate and affordable map of the occurrence of a specific parameter to be managed. What this parameter is will depend on the nature of the cropping system and its biophysical context. Three important issues need to be addressed regarding spatial sampling for assessing variability: sample unit, sample design (arrangement and intensity), and map accuracy. The first important question to address is what must be included in the entity un-

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der investigation and how it is defined operationally-the sample unit definition. If soil is the entity of interest, then it is important to define what constitutes a soil, for example, whether a soil is defined in terms of a map unit, a pedon, or a sample depth and if the sample is cornposited (Webster and Oliver, 1990; Wollenhaupt et al., 1997). For insects, Fleischer et al. (1997) cite Pedigo’s (1994) definition of sampling unit as the proportion of habitable space from which insect counts are taken. Here, sample unit includes unit areas, plant parts such as leaf surfaces,and time trap ping (Fleischer et al., 1997).There is little standardizationof current applicationsof precision agriculture in defining the sample unit when engaging in spatial sampling. Sample design refers to the spatial and temporal arrangement of samples and the number of samples needed to accurately estimate the spatial distribution of the parameter of interest. Sample designs will vary for different parameters and for different biophysical environmentsas parameters vary at different spatial and temporal scales and may vary in different environments.There must be a link between the capability of the enabling technologies with the underlying agronomic rules for precision management to the level of precision (scale) needed in variation assessment. Little progress in precision management will be made if something varies at the submeter level but can only be managed at scales compatible with large field application equipment (Groffman, 1997). The scale of spatial and temporal assessment depends on the spatial and temporal heterogeneity in the biophysical environment and cropping system to be managed. For example, some fields contain dissimilar soil types, whereas others are more homogeneous. They need not be sampled with the same design, nor do they have the same potential for precision management. Insects vary spatially but may require different sampling designs for each progressive generation during a growing season (Fleischer et al., 1997).Again, there has been little discussion in the precision agriculture literature of the congruency between the sample design with the enabling technologies, agronomic principles, and biophysical diversity. The objective of a specified sample unit and congruent sample design is to produce a quality map that has value for management decisions. There is no guarantee that a given sampling scheme will produce an accurate map or that an interpolation method is optimal (Gotway et al., 1996b). Techniques to evaluate map quality include cross-validationand mean square estimates from the regression of estimated versus measured values obtained from validation sets. Management decisions based on inaccurate maps appear to increase variability rather than manage it. More consideration needs to be given to map accuracy and its implication for precision management. c. High-Resolution Sensing The importance of sensing technology to precision agriculture was discussed earlier. The improvement of high-resolution sensing over interpolation of sampling points is evident when yield mapping is considered. For example, sampling

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intensity in a yield map is orders of magnitude higher than that used in grid sampling. The exercise of grid sampling a yield map would reveal the weaknesses inherent in interpolation of spatial sample points. Yield maps do not indicate the causes of the yield magnitude or its variability. However, it is the causes of variability that need to be quantified if farmers are to adjust their management practices to specific conditions within a field at appropriate times during the growing season. High-resolution remote sensing of the growing crop will reveal stresses that impact the crop during the growing season (Scheper et al., 1996).Additionally, it is not physically or economically possible to accurately map certain soil properties, crop condition, or pest status without the use of high-resolution sensing. The lower cost and ease of measure of high-resolution sensors will be critical to the future success of precision agriculture. d. Modeling Modeling is proposed as an important tool in precision agriculture to stimulate spatial and temporal variation in soil properties (Verhagen and Bouma, 1997), pests (Kropff et aL, 1997), crop yield (Bamett et al., 1997; Sadler and Russell, 1997). and environmental performance of cropping systems (Verhagen et al., 1995a).Models have been developed and calibrated for specific purposes but have not been used extensively in spatial prediction. A major problem of models is the availability of inputs needed to run them. A major advantage of models is their ability, once calibrated, to simulate the temporal component of crop production. This capability should allow models designed to account for spatial variability to evaluate different precision management scenarios that would otherwise be prohibited by time and cost considerations. The application of models to the simulation of the space-time continuum of crop production is a critical research need (Sadler and Russell, 1997).

2. ManagingVariability Once variation is adequatelyassessed, farmers must match agronomic inputs to known conditions employing management recommendationsthat are site specific and use accurate application control equipment. As already discussed, the potential for accurate application control on the farm exists but the extent to which it has reached the farm is limited. High-precision application control technology is available in many areas commercially,but it is not generally installed on farmerowned equipment. Application technology is not the only factor that can limit precision management, however. Our discussion now focuses on the agronomic feasibility of precision agriculture, i.e., whether precision agriculture improves crop performance. Precision agriculture has operated for some time on the assumption that best managementpractices developedfrom decades of agronomicresearch are applicable at any scale of management. This assumption can and will be tested as

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precision agriculture evolves on the farm. What follows is a discussion of the state of precision agriculture relative to precision management of inputs. a. Precision Soil Fertility Management Nutrient input to crop production is important because soils naturally do not supply nutrients in sufficient quantities to meet nutrient demands of commercial crops. Approximately 2 1.3 million tons of commercial N, P,O,, 60(1 1.7,4.4, and 5.1 million tons, respectively)fertilizers were applied and 1.23, 1.32, and 1.44 million tons respectively of economically recoverable nutrients in manure were available for application in the United States in 1995 [USDA-Economic Research Service (ERS), 19971. While important inputs to crop production, fertilizers and manures are also identified as major sources of nutrient contamination of surface and groundwater in agricultural areas of the United States (Mueller etal., 1995). Soils vary in their ability to supply nutrients to plants, and crops vary in their demand for nutrients. The fact that soil supply and plant demand vary in space and time and nutrient losses through leaching, erosion, and runoff also vary temporally and spatially (Sharpley, 1997)indicates that significantopportunitiesmay exist for precision management of soil fertility. In support of precision nutrient management is the fact that soil testing has been the basis for fertilizer recommendations since the late 1940s and recently has been important in determining where nutrients should not be applied as well as where they should be applied (Hergert etal., 1997). The potential for improved precision in soil fertility management combined with increased precision in application control (Anderson and Humburg, 1997) make precision soil fertility management an attractive, but largely unproven, alternative to uniform field management (Sawyer, 1994). For successful implementation, the concept of precision soil fertility management requires that within-field variability exists and is accurately identified and reliably interpreted (fertilizer recommendations are site specific), that the variability influences crop yield, crop quality, and/or the environment, and that inputs can be applied accurately (Sawyer, 1994; Pierce, 1995). We hypothesize that the ease with which precision management is accomplishedand its value vary with specific nutrients or lime. The higher the spatial dependence of a manageable soil property, the higher the potential for precision management and the greater its potential value. The degree of difficulty, however, increases as the temporal component of spatial variability increases. Applying this hypothesis to soil fertility would suggest that liming and P and K fertility are very conducive to precision management because temporal variability is low. For N, the temporal component of variability can be larger than its spatial component (Pan et al., 1997), making precision N management much more difficult in some cases. We will discuss precision management for lime, P and K, and N separately. While there is little work published on precision management of micronutrients,we expect that concepts discussed for lime and the macronutrients should apply.

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i. LIME. The value of liming acid soils is well known (Adams, 1984)and techniques to measure lime requirement are well established (McLean, 1982). Overapplication of lime is costly and may have detrimental effects on factors affecting crop yield, particularly nutrient availability (McLean and Brown, 1984). Spatial variation in soil pH is to be expected (Linsley and Bauer, 1929; Peck and Melsted, 1973). Thus, the importance of pH management in soils through liming and the expectation that pH varies spatially make the prospects of variable liming good where soil acidification is expected. Acidity can have considerable spatial dependence but the spatial dependence has a low temporal dependence (Franzen and Peck, 1995; Hergert et al., 1997). That is, exchangeable acidity varies with time but areas of acidity will remain acid until limed. This fact makes variable lime management relatively easy once the spatial variability is accurately established. Through grid soil sampling, the spatial dependence of soil pH has been verified (Peck and Melsted, 1973; Laslett et al., 1987; Tevis et al., 1991). Peck and Melsted (1973) sampled soils from two 16.2-ha fields in Illinois in 1961 on a systematic grid spacing of 25.2 m and found that pH averaged 6.55 and 6.21 for the two fields but ranged from 5.5 to 8.0, with little correlation to soil map unit. Over time, the Mansfield field was sampled and limed periodically, but the spatial pattern of soil pH remained similar between 1961 and 1991 (Franzen and Peck, 1995; Hergert et d., 1997). Pierce et al. (1995) reported that soil pH ranged from 2.0 to 3.1 pH units in three fields in Michigan, whereas mean pH values ranged from 6.0 to 6.7, with a strong spatial dependence at each site. There are, however, surprisingly few published studies that evaluate variable liming even though this practice is often identified as a major benefit of precision agriculture. Borgelt et al. (1994) showed that for an 8.8-ha field, which on average required a 3.4- to 4.5-mg ha-' lime application, 9-12% of the field would have been overlimed by a uniform application of lime and 37-41% of the field would have been underlimed, with different lime application rates depending on the method of lime determination used. Crop response was not evaluated in this study since variable lime applicationswere not made. Also, they did not evaluate map accuracy, although they used a modified composite design sampling pattern (sampling density of approximately 7.7 samples per acre) that gave a diversity of sampling points at varying distances to more precisely measure the semivariogram.Franzen and Peck (1995) applied lime to the Mansfield field originally sampled by Peck and Melsted (1973) at a uniform rate of 4.48 mg ha-' and evaluated the change in soil pH and Ca and Mg concentrations in leaf tissue of corn (&a mays L.) and soybean. Hergert el al. (1997) reported that grain yields were positively correlated to soil pH in 1991 before liming but soybean yields were unrelated to soil pH in 1992 after lime application. What is the potential for variable lime in the United States? It must first be determined whether variable liming will pay. The expectation is that soils requiring lime will benefit from increased yields and that overliming is costly and potentially yield reducing. The field in the Borgelt et al. (1994) study on average need-

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ed lime so it should be determined whether the more precise lime application improved the benefits over a uniform application. From this study, it is not possible to tell if the average application might have been sufficient to alleviate plant-limiting effects of low pH. About 12% of the Borgelt et al. (1994) field (1.1 ha) required no lime and not applying lime would have resulted in a cost saving. What effect overliming would have on crop productivity is uncertain. Another issue is missing liming opportunities by average field testing. In the fields evaluated by Pierce ef al. (1999, the average pH was adequate for crop growth and no lime would have been applied to any of the three fields. In such cases, only underliming effects would occur, assuming the lime application map was correct in the first place. Very rarely is the quality or accuracy of the map assessed and its importance can be easily demonstrated (Laslett et al., 1987). Figure 3 presents three maps of soil pH for a field in central Michigan generated from three grid sample spacings (F. Pierce, unpublished data). The average pH for this field is 6.5. Note that the area needing lime varies considerably with grid scale. In fact, grid intensity and sampling design as well as the map interpolation method all impact map accuracy (Gotway er al., 1996b). Figure 3 clearly demonstrates that the amount of overlimed or underlimed area varies with grid scale. The field average pH, however, results in considerable underliming in this field. Because lime requirement can have high spatial dependence but low temporal variability, spending the time and

Figure 3 Lime recommendation maps interpolated using inverse distance squared for 30-, 61-, and 91-m grid soil samples obtained from a central Michigan field.

26

FRANCISJ. PIERCE AND PETER NOWAK

resources to obtain quality lime application maps makes sense. This notion is further supported by the possibility that soil property, fertilizer application, and crop yield maps could be used to predict the temporal variation of soil pH, making extensive future sampling unnecessary once the spatial variability is known (Hergert et d.,1997). Real-time sensing of soil pH could also refine our ability to make accurate and cost-effective lime requirement maps and, over time, predict the need for additional variable lime applications. The caveats to variable liming are clear. For variable liming to be profitable, increased yields or lime application savings are needed to compensate for the cost of variable liming. However, acid soils in need of lime do not necessarily reduce crop yields because grain crops may not necessarily be affected by acid soil conditions (Black, 1993). McLean and Brown's (1984) summary of crop response to soil pH in the Midwest showed that corn frequently did not respond to soil pH of 5 or 6, whereas alfalfa was strongly affected by this pH range, with soybean intermediate in response. It was the beneficial effects of lime on legumes that formed the basis for lime applications prior to the 1950s, after which the need for lime was based on neutralizing the soil acidity resulting from additions of large quantities of residually acid fertilizers (McLean and Brown, 1984). Farmers may or may not experience yield changes due to liming. This may also be the case for overliming because liming soils of high pH may or may not have detrimental effects on crop yield. Negative effects of overliming are usually tied to decreased nutrient availability at high pH (Adams, 1984). Christensen et al. (1998), however, reported that applications of sugar beet lime to the high pH lakebed soils of the thumb region of Michigan increased soil pH by 0.3-0.5pH units but had no detrimental effects on crop yield and improved sucrose content in the first 2 years of the 5-year study, Where will variable liming work? The potential for variable liming is related in part to how much lime is applied and where it is applied. Lime was applied to <6% of acreage of any major crop in the United States in 1995 with an average application rate of < 2 Mg ha-' (USDA-ERS, 1997). The amount of lime applied could increase with variable liming if there are sufficient fields with an average pH near optimum but with a broad range in pH such as the fields evaluated by Franzen and Peck (1995) and Pierce et al. (1995). To some extent, the need for variable lime can be anticipated by identifying where soil acidification is a recumng problem in production agriculture. There may be equipment limitations to variable lime application because agricultural lime is difficult to apply uniformly with conventional spreaders. The availability of appropriate precision equipment may be limited due to capital equipment costs and lack of sufficient acreage for lime application services to pay for such equipment. Variable lime applications are often made by applying uniform rates to areas within fields delineated as needing lime, but the extent to which this is occurring is difficult to assess. The potential for variable lime exists where soil acidification is recurring. Accurate lime application maps are needed to ensure precision applications and avoid

ASPECTS OF PRECISION AGRICULTURE

27

over- and underliming. Expectations for crop improvement from liming should be lowered to reflect the lack of response to low pH by some crops at least in some years. In summary, the character of high spatial variability with a low temporal component for soil pH makes it ideal for precision agriculture if this management decision can be shown to be profitable. AND POTASSIUM.Precision management of P and K was an ii. PHOSPHORUS early focus of precision agriculture because there was an established basis for fertilizer recommendations in soil testing that could theoretically be applied at any scale. Moreover, the technology to variably apply fertilizers became available in the mid-1980s. Spatial variability in P and K was already known (Peck and Melsted, 1973) and was not difficult to measure within agricultural fields. The concept of VRT was very intuitive and easy to understand and implement (Luellen, 1985). The temporal component of the spatial variability of P and K is low, making it easy to soil test at a convenient time and requiring only periodic (every few years is often the recommendation) repeated sampling. Further support for precision management derives from the fact that P soil tests are increasing (Hergert er al., 1997) and P in runoff and sediment is an increasing environmental problem (Sharpley, 1997), thereby increasing the importance of soil testing and matching P inputs with crop need. The current basis for precision management of P and K, therefore, is fertilizer recommendations based on traditional soil fertility tests using various sampling schemes to assess within-field variability (Hergert et al., 1997). There are some interesting issues in this regard. While traditional soil testing and fertilizer recommendations are currently used, there are questions as to whether these are appropriate for site-specific management (Hergert er al., 1997). The improvement or verification of soil test and interpretations is considered by Fixen (1998) to be a major research need for site-specific nutrient management. Interestingly, there is a countertrend in fertilizer recommendations toward increasing the scale of soil test recommendations rather than making them more site specific. Vitosh er al. (1 996), for example, aggregated soil test recommendations from Indiana, Michigan, and Ohio into a single tristate set of recommendations. We suspect a similar change in scale of underlying agronomic recommendations is occurring elsewhere in the United States. Another complicating issue is that for a given soil test result, there are many different rate recommendations available to a farmer depending on the source of the recommendation (Cox, 1994; Olson er al., 1987). There are no standards for soil test sampling designs, sampling intensity, or methods of interpolation used in creating nutrient management maps, although some offer suggestions (Gotway et al., 1996a,b; McBratney et al., 1996; Wollenhaupt er al., 1997). In short, there appear to be no standards regarding the underlying agronomic principles that should be guiding the development and application of precision agriculture.

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FRANCIS J. PIERCE AND PETER NOWAK

Commercially, grid sampling is frequently done using a grid sampling intensity of one sample per hectare or less. Since the cost of grid soil sampling ($ ha-') is inversely proportional to the grid spacing squared, it is very easy for sampling costs to exceed the value in fertilizer savings as sampling intensity increases.There is no guarantee that a specific grid sampling intensity will result in an accurate nutrient or fertilizer recommendation map (Birrell et af., 1996). It is rare to find verification of the accuracy of nutrient or fertilizer recommendation maps either in practice (farmers)or in the literature. Long er af. (1999, however, provided an exception to this generalization because they evaluated the quality of management maps by comparing simple measures of accuracy and precision. The need for accurate identification and reliable interpretation of within-field variation was concluded by Sawyer (1994) to be the basis for VRT and is prerequisite for its success. Also, it is also not uncommon to find little correlation between grid P or K values and crop yield (Pierce et af., 1995; Smith er af., 1998). It is clear that more complex analyzes are needed to assess these relationships (Mallarino er af.,1996). Lowenberg-DeBoer and Swinton (1997) reported inconsistent results and generally low profitability of precision P and K management for the studies they reviewed, suggesting that management of one or two nutrients will not form the basis for profitability of precision agriculture. These are current or potential difficulties with precision management of nutrients such as P and K. However, the low temporal component of variability for these nutrients suggests that precision management will be of increasing value as spatial dependence increases. Studies assessing the management of P and K nationwide are being performed, although the literature is lagging behind the activity. Also, not all reports are new. Peck and Melsted (1973) reported that P soil tests for two fields in Illinois sampled on a 25.2-111 systematic grid in 1961 had means of 32.9 and 30.7 kg ha-' and ranges of 10-165 and 10-100 kg ha-', with CVs of 76 and 49%. Pierce et af. ( 1995) reported that spatial variability of P and K for three fields in Michigan sampled on a 30.5-m regular grid had means (mg kg-I) and CVs (%) of 23 (34%), 50 (50%),and 124mgkg-' (26%)forPand210(39%), 173 (50%),and 121 mgkg-' (297%) for K with strong spatial dependence for each nutrient. Han et af. (1996) reported spatial variability of P and K for two adjacent center pivots in Washington on a 6 1-mregular grid that had means (mg kg- *) and CVs (%) of 24 mg kg(24%) for P and 183 mg kg-' (21%) for K but very weak spatial structure even after data detrending.Walker ef af. (1996) examined the effects of 80 years of cultivation and slope position on soil test K levels. For the crest, midslope, and depression slope positions, the mean (mg kg-') and CVs (%) for soil test K were 283 (23%), 450 (41%), and 753 mg kg- (16%) for cultivated sites and 625 (18%), 509 (22%), and 695 mg kg-' (21%) for native sites in an east-central Alberta, Canada, location. Mallarino er al. (1996) reported variability of soil tests for three Iowa fields of 3-6 ha in size that were sampled on 15-m grid spacing. Soil test means (mg kg-I) and CVs (%) were 88 (13%), 20 (25%), and 45 mg kg-' (16%)

'

'

ASPECTS OF PRECISION AGRICULTURE

29

for P and 243 (1 3%), 107 (1 7%), and 2 13 mg kg- (28%) for K. Response to variable P and K management has been mixed (Lowenberg-DeBoer and Swinton, 1997). Carr et al. (1991) and Wibawa et al. (1993) did not find profitable returns for variable management, whereas Mulla et al. (1992) and Wollenhaupt et al. (1994) did. When and where will precision P and K management be profitable?The answer may be found in soil testing philosophies that form the basis for fertilizer recommendations. There are two fundamental fertility management philosophies-sufficiency versus buildup maintenance (Dahnke and Olson, 1990). The buildup maintenance concept promotes the application of sufficiently high rates of P and K to raise soil test levels in 1 or 2 years, followed thereafter by an annual application equivalent to the amount to be removed by the crop to be grown. This is a preventative approach to protect against yield loss because of nutrient deficiency and focuses on building soil fertility. Once the initial buildup is achieved, then the need for further soil testing is eliminated if fertilizer application adheres to the maintenance application (Dahnke and Olson, 1990).Applying this soil testing and fertilizer management philosophy to precision management, the goal would be to either increase the soil test levels of a field through fertilization or decrease the soil test levels through crop removal to the maintenance level and then fertilize based on crop removal as indicated by yield maps. Where nutrients are high within a field, no fertilizers would be applied until soil test levels were depleted to the maintenance levels, and where nutrients are low fertilizers would be applied based on a buildup program. The long-term impact of this management philosophy is to homogenize soil test levels and then rely on yield maps for fertilizer recommendations, thereby eliminating the need for grid soil sampling (Goedeken et al., 1998). The sufficiency approach, preferred by universities, is similar to the rapid buildup and maintenance except the rate of buildup is much slower and the buildup level is much lower (Dahnke and Olson, 1990). Fertilizers are applied based on probabilities of yield response to applied fertilizer at soil test classes of low, medium, and high. No fertilizers are recommended for high testing soils, a maintenance level is recommended for medium testing soils, and a larger amount than the maintenance level is applied on low testing soils. The sufficiency approach is crop specific, whereas the buildup maintenance approach builds soil fertility. Because the sufficiency approach is more conservative, less fertilizer will be recommended. Applying this approach to precision management, soil testing is still required because soil test category is required to make fertilizer recommendations.Ultimately, however, the sufficiency approach will approach the maintenance plateau as the fertility of low testing soils increases and that of high testing soils decreases over time. It appears that the only differencebetween the two management philosophies is how much and how long it will take to reach the maintenance plateau. To some extent this is related to the critical level of soil tests proposed by the different management philosophies.

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FRANCIS J. PIERCE AND PETER NOWAK

Ultimately, if precision nutrient management is based on soil testing and fertilizer management philosophies, over time variable rate applications should create soil test levels that are optimal throughout the field and would require future variable rate applicationsbased on crop removal and nutrient fixation in soils for maintenance. One might call this precision agriculture on the maintenance plateau. Goedeken et al. (1998) suggest that over time variable rate P fertilization should reduce the need for variable rate applications because soil tests will become spatially uniform. However, even if spatially uniform soil tests resulted from variable rate applications, unless crop nutrient removal was uniform there would always be a need to variably apply nutrients because the fertilizer management philosophy of maintenance requires fertilizers to match crop removal. If accurate nutrient maps were obtained for a field, then spatial mass balance calculations should preclude the need for further soil testing, except for intermittent monitoring of control points to check predictions. This also assumes that none of the nutrients are being lost to the larger environment (discussed later). The key to precision management of P and K is first and foremost to obtain accurate maps. Sawyer (1994) made this perfectly clear in his review of precision nutrient management. Accurate fertility maps require appropriate sampling designs and adequate sampling densities (Gotway er al., 1996b; Laslett er al., 1987; Wollenhaupter al., 1997) in addition to well-calibrated soil test procedures and associated interpretations (Hergert er al., 1997). Rarely is the accuracy of P and K maps evaluated. For one Michigan field, interpolation of a 91-m grid predicted only 3 1% of the variation in soil test P levels obtained from a 30.5-m independent sampling grid, whereas the 30.5-m grid interpolation predicted 73% of the variation in measured soil test P levels at the 91-m grid (Pierce, unpublished data). While the increased precision was evident from the improved regression, a significant deviation of the regression line from the 1:1 line indicated considerable inaccuracy at high P levels even with the 30.5-m grid. Smith er al. (1998) suggest that more intense grid sampling is needed to assess soil fertility in the cotton (Gossypiurn hirsuturn) fields they studied. Once an accurate map of P and K levels is created, then expectations about the relationship between yield and soil fertility need to be kept in perspective. A low or lack of correlation between soil fertility and yield in a given year means that other factors regulated yield. This outcome does not diminish the value of following a proven soil fertility management philosophy. The low temporal variability in soil tests suggests that precision P and K management will work where fields vary spatially in their need for added inputs. Since precision P and K management will ultimately lead to variable rate nutrient management based on replacing nutrients removed by crops, a precision management program based on a proven soil fertility management philosophy will be successful where crop yields vary in response to other unmanageable yield-limiting factors.

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iii. NITROGEN.The precision management of N will be applicable to situations in which the factors that control total N in soils and N availability to plants vary spatially (Pan er al., 1997). However, precision N management will be increasingly more difficult but may in fact have increasingly more environmental benefits as the temporal component of spatial variability of N availability increases. For this reason, precision N management is more complex than precision management of lime, P,and K but may have significantly more value. The difficulty presented by a large temporal component of N availability is clearly illustrated by the data of Cahn et al. (1994). From May 13 to June 15, 1992, in a 1.1ha field, soil concentrations of nitrates in the 0 to 15-cm depth decreased on the average from 8.3 to 1.6 mg kg-I and the spatial dependence observed in May was no longer present in June (Fig. 4). Conversely, early success in precision N management has occurred where the temporal variability of a significant aspect of N availability was low, a case exemplified by the success of precision N management in sugar beets in the Red River Valley (Cattanach er al., 1996; Lilleboe, 1996) and in irrigated corn in Nebraska (Hergert et al., 1996), where N leaching overwinter was low and residual nitrates were high. Total N in soils is primarily in the organic form (about 90%;Stevenson, 1982), and most of the inorganic N is mineral-fixed N ammonium (Young and Aldag, 1982). Since total N in soil varies with soil organic matter, clay mineralogy, and clay content of soils, and since N mineralization is the major natural source of inorganic N in soil, then N in soil will vary spatially with soil texture and organic matter content. Inorganic N, as nitrate and ammonium, is required by plants in large quantities and its content in soil is intricately woven into the complex soil N cycle processes of mineralization-immobilization, leaching, and denitrification (Meisinger et al., 1992).In crop production, net mineralization rates from soil organic matter are generally 1 or 2%of total N (Meisinger and Randall, 1991), which is often insufficient to supply the N requirements of crops that do not fix atmospheric N. Therefore, N in the form of inorganic fertilizers or waste materials containing N are applied to soil or a crop to meet crop N needs. More than 11.7 million metric tons of commercial N fertilizers and more than l million metric tons of N in animal manures were applied in the United States in 1995 (USDA-ERS, 1997). Both N deficiency and excess N availabilitycreate problems for production agriculture. Most fertility concerns are focused on deficiencies in N availability to plants because they reduce yield and/or quality of crops (Olson and Kurtz, 1982). Excess nitrate-N in soil can lead to N losses to the environment that reduce water quality (Mueller et al., 1995) and can reduce yield and/or quality for some crops. Excess N increases protein content but reduces sugar content in sugar beet (Hills and Ulrich, 1971) and oil content in canola (Brassica napus L.; Henry and MacDonald, 1978), whereas both yield and crop quality can be reduced in wheat (Rasmussen and Rhode, 1991). Leaching, runoff, and denitrificationare processes that

32

FRANCIS J. PIERCE AND PETER NOWAK

May 1992

a $ , h a 2 o ~ m o i m

c n

I

0,

15

P

lo

Y

v

z

5

'

0

0" 2 110

0 n

June 1992

c

I

EI,

1s

F

lo

Y

U

z 1

mw data

5 0

a

20

40

60

w1100

Lag diatanco (m)

0 "

z

Figure 4 Spatial distributions and semivariograms of raw and residual NO,-N data (0-15 cm) from the 1.1-ha area in May and June 1992 (reprinted with permission from Cahn era[., 1994).

result in loss of N from the soil-plant system creating the potential for N deficiency in crops and degradation of water and air quality. The environmental threat of N losses from soil is further compounded by the application of manures at rates in excess of crop nutrient needs (Nowak et al., 1998). This problem is exacerbated by the concentration of livestock and the high costs of transporting wastes resulting in excess animal manure being applied to limited areas (Hatfield and Stewart, 1997). Deficiencies and excesses of N can occur within the same field sometime during the year. Thus, it is the variability in space and time of the processes that

ASPECTS OF PRECISION AGRICULTURE

33

regulate the availability of N to plants and the fate of N in soil that make precision N management attractive. Nitrogen management for crop production is to varying degrees prescriptive by nature because crop yield is to a large extent determined by growing conditions that occur after the N uptake phase in plants. Nitrogen management strategies are approached in two basic ways: (i) prevention strategies, whereby prescriptive applications of N inputs are made prior to or early in the N uptake phase of plant growth to avoid nutrient deficiencies, and (ii) intervention strategies, whereby N inputs are applied to meet N requirements as determined by the nutrient status of soils or plants during the rapid N uptake phase of growing plants. Hybrid strategies supply a portion of crop needs early and intervene later. For example, the early presidedress nitrate tests used in the northeast United States were designed to adjust N fertilizer recommendations based on critical nitrate concentrations in soil following preplant applications of fertilizer N (Magdoff et al., 1990). Prevention is the most common N management strategy and is usually based on a mass balance approach in which fertilizer recommendations are based on some combination of yield goal, N requirements of the crop, residual soil N in the soil profile, and N mineralized from soil or plant residues (with some using soil organic matter as a proxy for N mineralization). Intervention strategies include foliar fertilization (Stone et al., 1996), delayed N applications based on N content in plants (Schepers et al., 1992), and chemigation (King et al., 1996) strategies that assess crop needs based on tissue sampling, sensing plant reflectance, or crop simulation. From the current literature, it appears that precision N management will be feasible using prevention strategies based on N balance approaches that rely on soil testing for residual soil profile nitrates where temporal variation in soil profile nitrates is low. Intervention strategies will be more applicable to precision N management where the temporal component of spatial variability is moderate to high. Intervention strategies that sense the occurrence of N deficiencies in plants during the uptake phase (Schepers et al., 1996) may be better adapted to precision N management than strategies that use mass balance approaches that are to a large extent soil sampling based, such as the presidressed soil nitrogen test (PSNT), even where they may be useful on a field level basis (Bock et al., 1992). The success of precision N management will depend on how well we can predict and control the dynamic soil and crop processes that regulate N availability in soils and plant N requirements (Pan et al., 1997). Research on precision N management to date has taken three basic approaches. The first is based on the application of current N recommendations to site-specific within-field scales where some form of grid soil sampling is employed. Residual soil nitrate-N values are interpolated to generate N availability maps forming the basis for N fertilizer recommendation maps (Cattanach et al., 1996; Ferguson

34

FRANCIS J. PIERCE AND PETER NOWAK

et al., 1996; Fiez et al., 1994a; Franzen et al., 1996; Hergert et al., 1996; Kitchen et al., 1995; Redulla e l al., 1996). This approach is based on the prediction of N requirement of crops using a balance sheet approach that is the basis for current N recommendations for crop production (Meisinger et al., 1992) such as that described by Eq. (1) as presented by Pan et al. (1997):

Nf = [ G ,

X

1I(NfINaVX NavlNs X Gw/Nt)]- (Nmin+ Nin)

(1)

where Nf is the fertilizer N requirement, G, is the yield goal, Nav is the available soil N in the root zone, N, is the plant N, N, is the predicted requirement for N supply to crop, Nminis the soil N supplied from net mineralization, and Ninis the preplant inorganic N. In practice, university recommendations for fertilizer N are usually much simpler than that given in Eq. (l), basically a function of the product of yield goal times an N factor adjusted for N credits and in some cases residual nitrates. The tristate N fertilizer recommendation for corn in Indiana, Michigan, and Ohio is based on yield goal and N credits from manure or legumes in rotation (Vitosh et al., 1996), whereas Nebraska’s N recommendation for corn relies on three variables-yield goal, residual soil nitrate, and percentage organic matter as a proxy for N mineralization (Hergert et al., 1995a). The second approach is to develop site-specific optimal N rate recommendations based on condition-specific N response curves (Blackmer and White, 1996; Kachanoski etal., 1996; Malzer et al., 1996) andlor related to landscape attributes (Hollands, 1996; Solohub et al., 1996; Vetsch et al., 1995).A common experimental procedure is to apply a range of N rates in replicated strips across a field, including a no N treatment, to obtain an optimal N rate by condition within a field. Ultimately, the derived site-specific optimum N rates must be related to soil or landscape properties if they are to be useful elsewhere. The third approach is to develop site-specific intervention N management based on crop monitoring of N status (Bausch et al., 1996; King et al., 1996; Schepers et al., 1992, 1996; Stone et al., 1996; Vetsch et al., 1996).The idea is to monitor plant N concentration by monitoring plant or canopy reflectance of light or some measure of plant N content such as chlorophyll content (Schepers et al., 1992), estimate N fertilizer requirement using established relationships between reflectance and N content, and fertilize the crop to the optimal N content for maximum economic yield. In some cases, a portion of a crop is fertilized to optimal levels and used as the standard for adjusting the N recommendationsfor the remainder of a field (Schepers et al., 1996) or plant N is estimated using a previously developed index calculated from measured canopy reflectance (Bausch et al., 1996). Some success has been attained with each of these approaches but none are broadly applicable. Where and when will precision N management pay off in terms of either profitability or environmental benefits? Since the key to precision N management is that the factors that regulate these processes vary in space and time we expect pre-

ASPECTS OF PRECISION AGRICULTURE

35

cision N management to be profitable and/or beneficial to the environment to varying degrees under the following conditions: Where N inputs are high: Dryland wheat would be marginal due to low inputs of N and low yields (Cam et al., 1991 ; Solohub et al., 1996), whereas high-yield wheat production in the Pacific Northwest would have potential because of high N inputs, high residual N, and high yields (Fiez et al., 1994a,b, 1995). Corn accounts for the major input of fertilizer N and use of manure, whereas potatoes (Solanurn tuberosurn L.) receive the highest average N application rate of all crops (248 kg N ha-'; USDA-ERS, 1997). These crops are the focus of much of the current research on precision N management. Where residual N is temporally stable and/or high residual N is predictable from the yield of the previous crop, for example, low yields the previous year: Residual N is reported to be spatially variable and stable from fall to spring in irrigated corn production in Nebraska (Hergert et al., 1995b) and in soil profiles prior to sugar beet production in the Red River Valley of eastern North Dakota and western Minnesota (Cattanach et al., 1996). Residual profile N is frequently leached in the humid regions over winter (Everett and Pierce, 1996; Hoeft et al., 1992), except where climatic factors, such as low annual precipitation and temperatures that keep soils frozen for extended periods overwinter, favor retention of residual profile nitrates (Bundy et al., 1992). Residual profile nitrates are not considered in nitrogen recommendations for corn or wheat in certain areas such as the tristate area of Indiana, Michigan, and Ohio (Vitosh et al., 1996).The PSNT, which is primarily an index of mineralization in the surface 30 cm of soil, may be relevant in some humid regions but not others (Bock et al., 1992). Where crop quality is affected by excess N is soil: A major benefit of grid soil testing and variable rate N application is a significant increase in sucrose content in sugar beet (Cattanach et al., 1996; Lenz, 1996; Lilleboe, 1996). Where crop yield spatial variability is high and predictable: Nitrogen recommendations are based primarily on crop yield. If yield is variable, then precision N management based on yield goal makes sense if the yield variation is predictable from year to year (low temporal variability). If yield variability is high but not predictable, then precision N management will be difficult because it is generally prescriptive. At this time, it is common to use a uniform yield goal in site-specific N fertilizer predictions while varying N recommendationsbased on residual N (Long et al., 1996) and in some cases organic carbon (Hergert et al., 1996).There have been some attempts at using previous-year yield maps to predict N requirements, although previous yield has not been very useful (Kitchen et al., 1995) and some suggest previous yield maps have no relevance (Vetsch et al., 1995).Some suggest that previous-year yield maps do not reflect N requirements because other factors besides N availability regulate yields (Kachanoski

36

FRANCIS J. PIERCE AND PETER NOWAK

et al., 1996; Malzer et al., 1996). Yields have been related to spatial variability of water availability associated with landscape position (Mulla et al., 1992).The use of digital terrain models to predict water availability in the landscape is promising (Bell et al., 1995). Where net mineralization is high and consistently related to soil and landscape properties: Soils with low residual nitrates from the previous crop can provide a significant portion of the N requirements of the succeeding crop through mineralization of soil organic N and crop residues. Pan et al. (1997) calculated that mineralization of N from the surface 30 cm ranges from 39 to 224 kg N ha-'. Vetsch et al. (1995) report that the inability of current fertilizer recommendations to predict soil N supply and fertilizer N use efficiency in the field makes them inadequate to capture the benefits of site-specific N management. Walters et al. (1996) predicted N requirements from soil organic matter maps. Stevenson and van Kessel (1996) showed that following pea in rotation, the depressional areas had higher soil N and a reduction in fertilizer N would be needed in wheat to reduce the negative effect of excessive soil N supply, but this was not the case following wheat in rotation. Corn following legumes in rotation can have little response to fertilizer N (Frye et al., 1988; Hesterman, 1988). However, spatial variation might be expected in legume contributions within a field. Where N application is not restricted in time: Sprinkler-irrigatedlands have unrestricted accessibility to the field to apply N based on crop needs using chemigation (Evans et al., 1996; King et al., 1996). Stone et al. (1996) used spectral radiance measurements for correcting in-season wheat N deficiencies at Feekes physiological stages 4 and 6. Where leaching potential is high and spatially variable prior to or during the crop N uptake period of plant growth: Within-season leaching events result in losses of nitrate either through leaching itself or due to denitrification if soils remain saturated over extended periods. Because loss of N may vary with landscape position and soil variability, intervention applications of N inputs to reduce N deficiencies could be varied spatially. On the preventative side, anticipation of areas where such losses might be expected is the basis for variable rate application of nitrification inhibitors (Malzer et al., 1995). Where variation in topographic position regulates N availability or yield: Certainly yield is very much dependent on water availability-whether drought or in excessive amounts. Effects of landscape position on wheat yields have been reported by Fiez et al. (1994a,b, 1995) and Halvorson and Doll (1991). In the Red River Valley, N availability is higher in knolls than in depressions and is key to managing variable rate N in sugar beet (Holland, 1996; Franzen et al., 1996). Techniques to predict water in the landscape have been enhanced by recent advances in digital terrain analysis (Bell et d., 1995; Moore et d., 1993). Thus,

ASPECTS OF PRECISION AGRICULTURE

37

where soils or landscapes consistently regulate water availability, precision N management would have potential. On the negative side, precision N management will not work well under the following conditions: Where the previous crop exceeds yield goal: When yield exceeds yield goal as it did in 1994 (Hergert et al., 1995b), residual nitrates were reduced. Since the benefits of grid sampling appear to increase with increasing residual N levels and increasing spatial variability (Lenz, 1996),higher than expected crop yields may diminish the prospects for variable rate N success the following year. Where large-scale leaching events occur prior to the growing season: In the areas flooded in 1993, additional fertilizers, including N, were applied to replenish flood-damaged soils (USDA-ERS, 1997). Since nitrates are soluble, large leaching events should minimize residual nitrates within the root zone. Where precision N management has successfully reduced spatial variation in yield and residual N: The short-term financial benefit of precision N management is an increase in N use efficiency by accounting for residual N in the N fertilizer recommendation, thereby reducing N fertilizer as well as reducing residual N available for leaching (Hergert et al., 1995b).Therefore, the benefits from precision N management are a result of accounting for residual N that is spatially variable. As farmers become skilled in this technique, the long-term effect of precision N management is a reduction in residual N, making precision N management less profitable although N leaching is continually reduced. Farmers do not get paid for environmental benefits so good precision management will lose its potential profit benefit to farmers. It appears that when residual N is low, grid sampling and VRT will be less profitable. On the other hand, if precision N management is to increase soil N to maximize economic yield where inadequate amounts of N would be applied under uniform management, then residual N will be higher than it would with uniform N management, unless N use efficiencies increase proportionately. Actually, residual N will be an issue when yields from the previous crop are less than yield goals and/or nitrogen use efficiencies are reduced. Therefore, yield maps combined with grain protein content and with N application maps should be a guide to precision N management but only where overwinter leaching is low. Where there is a strong temporal component of spatial variability: Where residual N is low due to high leaching potential, precision N management must rely on prediction of crop yield, net N mineralization, N losses during N uptake period, and where amendments such as nitrification inhibitors will synchronize nitrate availability with crop uptake demand. Not only does the prediction of the spatial variability of inorganic N over time make precision N management much more difficult but also we believe the potential to the farmer and the environ-

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FRANCIS J. PIERCE AND PETER NOWAK

ment are larger than when temporal variation is higher. The ability to use intervention N management strategies may allow fanners to overcome the management difficulties when the availability of N encountered has a large temporal dependence. The potential for precision N management can also be considered in terms of the steady-state and non-steady-state approximations to whole-crop N balance in the soil-crop system (Table I) discussed by Meisinger and Randall (1991). Soilcrop systems that are steady state will have fertilizer N needs that are directly proportional to crop removals and indirectly proportional to fertilizer efficiency because no soil N availability terms appear in the steady-state case. Situations conducive to steady state, such as those listed in Table I, are common in humid regions (Meisinger et al., 1992). Precision N management for the steady-state case would be based on crop yield variability and fertilizer use efficiency, both related to soil and landscape properties. Fields with dissimilar soils and/or variable landscapes would be well suited to precision N management if crop yield variability were known or predictable, whereas fields with similar soils and uniform landscape would not. Soil-crop systems that are non-steady state have high temporal variability associated with management, soil, and climatic factors. Such conditions are encountered with crop rotations involving forages, organic inputs, changes in drainage, or tillage or with large climatic fluctuations. Precision N management may be easiest when climatic factors favor N accumulation in soil profiles (low precipitation) but more difficult when management or climatic factors result in N transformation processes that are dynamic. An evaluation of the steady- versus non-steady-state approach to precision N management needs to be performed. In summary, the potential for precision N management is directly related to the extent of spatial variability in the factors that regulate N availability in soils, whereas the difficulty in precision N management is related to the degree of temporal variability. Where spatial variability is high and temporal variability is low, precision N management appears to be profitable and to reduce N available for leaching. Intervention N management strategies will be needed where the temporal component of N availability is high but access to the crop will be required for delayed N fertilizer applications. Finally, residual nitrates are human induced. As precision N management begins to control the anthropogenic sources of variation, only the natural sources will remain to be dealt with. b. Precision Pest Management Weeds, insects, and diseases are an ever-present and costly management problem to crop production because significant infestations reduce crop yield and/or quality and, if severe, can limit crop production options. The USDA-ERS (1997) reports annual expenditures of $7.5 billion on agricultural pesticides in the United States, of which two-thirds is spent on herbicides and about one-third on in-

ASPECTS OF PRECISION AGRICULTURE

39

'hble I

Characteristics of Steady-Statevs Non-Steady-StateMi-Crop Systemtf ~~

Circumstances conducive to steady-state approximation

~~

~~

Circumstances conducive to non-steady-state approximation

Goal or objective Objectives of N budget lie in making estimates Objective of N budget is for a short-term period, e.g., the next crop (1-year period) over a long-term period (10 year average) Management factors Arecent (within 1-5 years) change in the soil The same soil and water management system in and water management system, e.g., new place for an extended period (5-20 years), tillage system and new drainage or irrigation e.g., same tillage system, same soil drainage system, and same irrigation system system The same crop management practices used for A recent change in crop management, e.g., new an extended period, e.g., same rotation and rotation system and recent use of cover crops long-term use of cover crops The same N management system practiced for Arecent change in N management practices, e.g., recent use of manure to replace fertilizer N an extended period, e.g., same N source (manure or fertilizers)and same timing and placement practices Soil and climatic factors Soil total N content approximately constant over Soil total N content changing systematically a long period (10-20 years), e.g., implies that over time, e.g., decline in soil N when grassN mineralization approximates N in residues land is plowed and increase in soil N with plus immobilization continued manure applications Soil inorganic N content approximatelyconstant Soil inorganic N content changes widely from year to year, e.g.. periodic drought and over time step, e.g., soil leached out each year, irregular irrigation highly permeable soil in humid region, and excess irrigation Climate (precipitation and temperature) not Climate highly variable and slows organic N highly variable and is conducive to organic N turnover, i.e., cool and marked cold season or dry season and unpredictable hydrologic turnover, i.e., warm and humid and predictable yearly hydrologic cycle cycle Soil type has low total N content, low NO, Soil type has high total N content, readily retains content, shallow root zone, well-drained, high NO,, deep root zone, poorly drained, low infilinfiltration,deep water table, low clay content, tration, shallow water table, high clay content, e.g., a clay loam soil in a cool semiarid climate e g , sandy loam soil in warm humid climate "Reproduced with permission from Meisinger and Randall (1991).

secticides.More important, public concerns regarding the impacts of pesticide use include health risks related to food safety, water quality, and worker safety and concerns over wildlife and ecosystem health. Therefore, agricultural management practices that reduce pesticide use, improve pest management, or reduce risks of pesticides to human and ecosystem health are very desirable. The intuitive appeal of precision agriculture is that it offers the potential for such

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FRANCIS J. PIERCE AND PETER NOWAK

benefits. The potential direct economic benefit of precision pest management to farmers is a reduction in chemical/nonchemicalpest management costs, crop damage, or both due to more efficacious or efficient application of pest control measures. A reduction in pesticide use, however, does not translate into profit if the cost of obtaining information about pest populations and distribution exceeds the savings (Forcella, 1993; NRC, 1997).Environmental benefits are presumed to result from a reduction in pesticide usage, particularly in sensitive environments, although the results of the few studies on the potential of precision farming to provide environmental benefits have been inconclusiveregarding its effect on pesticide use (USDA-ERS, 1997). There are two basic questions in precision pest management: Is it possible to effectively manage pests spatially? and Will it pay? Here, we assume that spatial precision pest management is doable so that we can consider the potential benefits of it. If the main benefit of precision pest management is a reduction in input costs and applications and avoidance of unneeded applications for environmentalbenefit (NRC, 1997),then the task at hand is to determine where pesticides are used and then assess the potential for precision management. A summary of pesticide use in the United States by selected crop is given in Table 11. Four crops account for 78% of pesticide use on major crops in the United States [corn (35.6%), potatoes (15.4%), cotton (14.8%), and soybean (12.1%)]. Vegetable crops account for approximately 12%. Corn, soybean, and cotton account for 89% of the herbicide use in the United States. Cotton, corn, and potatoes account for 69% of the insecticide use, and fruits and vegetables account for 29%. Potatoes and vegetables account for 66% of the fungicide use, with fruits accounting for 30%. Potatoes and cotton account for 73% of other pesticide usage, a category which includes soil fumigants, growth regulators, desiccants, and harvest aides; vegetables account for 26%. If savings in chemical uses is sought, then we need to examine where the chemical use is taking place. Usage does not necessarily imply potential benefit from precision management. The average annual pesticide costs for corn, soybean, and cotton are 62, 59, and 124 $ ha-' compared to 15 $ ha-' for wheat (USDA-ERS, 1997). Consider a spraying operation costs about $5 ha-'. If a farmer wants input cost and applications savings to generate profits from precision management, then the fraction of input and application costs saved by precision management must exceed the cost of information gathering and variable input application costs. Crop improvement by improved pest management would add to the profit, and crop damage and/or intervention costs for pest escapes from inadequate management would add to the cost. The risks and associated codbenefit for either scenario have not been assessed. For crops that have low annual pesticide costs, precision pest management will be hard to justify economically solely on pesticide savings given the potential cost of precision management. Crop improvement or returns for environmental benefits from precision management will be needed. Currently, farmers are not compensated for environmental benefits accrued from improved pest management.

Table II Pesticide Use in 1995 in Selected U.S. Crops by Pesticide kg ai crop Corn Cotton wheat Sorghum Rice soybeans

Peanuts Potatoes Other Vegetables Citrus Apples Other Fruit Total

%

Herbicide

Insecticide

Fungicide

Other

Total

Total

Herbicide

Insecticide

Fungicide

Other

84,587 14,924 9,105 na na 30,929 na 1,314 2,778 2,118 348 898 147,001

6,790 13,638 413 na na 234 na 1,411 2,530 2,335 1,618 2,628 3 1,598

9 474 227 na na 6 na 3,620 9,902 1,825 2,125 2,154 20,341

0 8,959

91,385 37,995 9,745 0 0 31,169 0 39,454 30,325 6,359 4,133 6,235 256,800

35.6 14.8 3.8 0.0 0.0 12.1 0.0 15.4 11.8 2.5 1.6 2.4

57.5 10.2 6.2 na na 21.0 na 0.9 1.9 1.4 0.2 0.6

21.5 43.2 1.3 na na 0.7 na 4.5 8.0 7.4 5.1 8.3

0.0 2.3 1.1 na na 0.0 na 17.8 48.7 9.0 10.4 10.6

0.0 15.5 0.0 na na 0.0 na 57.2 26.1 0.1 0.1 1.o

0 na na

0 na 33,109 15,115 81 42 554 57,860

"Adapted from ESRI (1997). includes soil fumigants, growth regulators, desiccants, and harvest aides.

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FRANCISJ. PIERCE AND PETER NOWAK

Therefore, it appears that the potential for benefits from precision pest management, assuming it is doable, depends on the dependence on a particular crop for costly pest management practices (e.g., pesticide use) and actual improvementsto crop yield, crop quality, and/or to the environment from improved pest management practices. There are risks associated with being wrong, and these should enter into the discussion because farmers understand that they are managing risks in producing a crop. Next, we examine the potential for precision management for weeds and insects. There is little work on precision management of diseases reported in the literature. Diseases vary spatially within a field but are difficult to predict since they are host specific and vary temporally. i. WEEDMANAGEMENT. The application of precision agricultureto weed management is potentially beneficial to agriculture because (i) it offers an opportunity to reduce chemicalhonchemical inputs into crop production through sitespecific weed control and the use of precise application techniques and (ii) the acquisition of spatial and temporal information on weed occurrence and distribution made possible with precision agriculture technologies will lead to an improved understanding of weed biology and ecology needed to develop more effective weed management strategies (Johnson et al., 1997). Precision weed management is possible because weeds are spatially aggregated and not randomly distributed within most agronomic fields (Dessaint et al., 1991; Johnson et al., 1995; Mortensen et al., 1993, 1995, 1998; Von Groenendael, 1988) and because the efficacy, efficiency, and fate of weed control inputs vary with weed and crop conditions and with soil physical and chemical properties, all of which can vary spatially and, to a varying extent, temporally (Johnson et al., 1997). It is important to understand that precision weed management is much more than variable rate application, although this aspect receives the bulk of attention. Precision weed management also involves an understanding of the spatial and temporal interactions between landscape characteristics, pest populations, and weed management strategies; this understanding is currently lacking (Johnson et al., 1997). Consider that technologically it will be possible to perform field operations very precisely at night when environmental conditions may be more conducive to weed management operations, such as spraying in the dark to take advantage of cooler temperatures and reduced winds (Holmberg, 1998). Research has already shown that night tillage can be effective in control of the germination of weed species for which germination is light induced (Asgard, 1994; Hartmann and Nezadal, 1990; Scope1 et al., 1994). Thus, a very important dimension of precision weed management is that new weed management strategies will evolve because of it. While not the only aspect of precision weed management, its potential for reduction in herbicide use may be most important in its adoption because of public concerns regarding the environment and food safety. Management systems and inputs that foster a reduction in herbicide use in agriculture are very desirable for

ASPECTS OF PRECISION AGRICULTURE

43

economic and environmental reasons, and these aspects have been an important focus in crop protection research. Pesticide product formulations have changed to lessen environmental and human health effects, to reduce the development of pesticide-resistant pests, and to provide more cost-effective pest controls (USDAERS, 1997). Application rates have been lowered for new herbicides, which require a fraction of the previous rates. There has been a shift from preemergence to postemergence herbicides, using chemicals that have greatly reduced soil residual activities, that foster integrated pest management (IPM), that require lower rates when applied to small weeds, and that when combined with herbicide-resistant crops lower herbicide costs and amounts. These newer herbicides and crop protection technologies will benefit from precision application techniques, further supporting the case for precision weed management. However, the results of the few studies on the potential of precision farming to provide environmental benefits have been inconclusive regarding its effect on pesticide use (NRC, 1997; USDA-ERS, 1997). A common approach to precision weed management is site-specific weed control achieved by (i) applying herbicides only where weeds are present or above economic threshold levels, termed intermittent herbicide application (Mortensen et al., 1995) or patch spraying (Stafford and Miller, 1993, 1996); (ii) varying herbicide application (type, formulation, or rate) according to soil physical and chemical properties or weed characteristics (species, growth stage, and density); or (iii) some combination of the two approaches (Johnson et al., 1997). For prevention or preemergence weed control, site-specific application requires prior knowledge of historical weed distributions since no weeds are visible at the time of application. This knowledge can be obtained by mapping weed aggregation in previous years (Brown et al., 1990; Lass and Callihan, 1993).Weed control treatment or intensity can also be varied based on soil properties according to label or other recommendations if knowledge of the spatial variation of these properties is adequately known. Clearly, however, the county soil survey is generally not sufficient in describing the variability in surface soil properties affecting herbicide performance (Mausbach et al., 1993). Therefore, this precision weed management strategy requires intensive and expensive soil variability assessment. Intervention or postemergence weed control treats emerged weeds and intermittent or patch-spraying weed control systems require either a map of weeds present just prior to a weed control application or a real-time sensing of existing weeds at the time of application.The key is that weeds are aggregated into patches rather than randomly distributed. Weed mapping has been performed using remote sensing (Hanson et al., 1995). real-time ground-based detection using light reflectance (Duff, 1993; Felton et al., 1991; Felton and McCloy, 1992; Haggar et al., 1983; Nelson, 1993; Shearer and Jones, 1991; Shropshire et al., 1990) or digital image processing (Guyer et al., 1986; Sadjadi, 1996; Woebbecke et al., 1995), and GPSassisted scouting (Colliver et al., 1996; Stafford et al., 1996; Stafford and Miller,

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1996).There are limitationsto real-time systems in weed detection, in sprayer control, and in controlling herbicide type or concentration in real time. These include difficulty in separation of weeds from the crop, the time lag between weed detection and weed control application, spray pattern effects caused by the transport delay between change in herbicide type or rate and chemical discharge at a given nozzle, and nozzle performance as affected by herbicide type and rate change alterations of droplet size and flow rate for a given nozzle design or nozzle wear (Anderson and Humburg, 1997). Additionally, equipment size (boom width and nozzle spacing) will determine the scale of weed management zones, with larger equipment spraying larger areas relative to the nonweed areas (Johnson et al., 1997).Extensive research on sprayer control, weed detection, and spot spraying is under way (Ballal et al., 1996; Giles et al., 1996; Kirk and Tom, 1996; Nuspl et al., 1996). Regardless of the approach, the economic and environmental benefits of sitespecific herbicide application are derived from applying herbicides only to areas occupied by weeds at rates adequate for weed control and using environmentally safe herbicides or rates. The smaller the area occupied by weeds, the larger the potential benefits. The key, therefore, is to know the spatial distribution of weed populations. Lack of knowledge of weed distributions results in over- or underapplication of herbicides or other weed control measures-errors which have associated costs. The extent of overapplication(application to areas that did not require it or overapplication for the conditions present) is difficult to measure unless overapplication caused measurable injury to the crop. Underapplication, on the other hand, results in weed escapes that are quite visible and often lead to yield reductions and weed-induced harvest problems. The risk of underapplication of weed control practices, because it is most visible, will drive farmers’decisions more than potential environmental concerns of overapplication. The potential for weed escapes associated with inadequate knowledge of weed distributions or inability to control inputs sufficiently probably limits the adoption of precision weed management techniques. Johnson et al. (1997) provide some generalities with regard to precision weed management and we summarize them here. Weeds are aggregated within fields but are more aggregated at low populations than at high ones. However, the best economic returns to simulated site-specific weed management were obtained when weed pressure and aggregation were high. Weeds more costly to control and herbicides with high costs or with high environmental sensitivity favor the use of precision weed management. Therefore, inexpensive, low-hazard chemicals are less economical for precision weed management than expensive, hazardous chemicals. Use of economic threshold values increases the amount of area within a field that does not require herbicide. Economic thresholds were developed for uniform herbicide management and do not include the effects of aggregated weed distributions. Site-specific weed management may be more important for early emerging

ASPECTS OF PRECISION AGRICULTURE

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weeds than later emerging weeds since crops are more competitive later in the season. Stability in weed patches will affect the ability to predict weed distributions that do occur in localized patches, with weeds in no-tillage more stable than those in tilled systems. In the future, site-specific weed management may shift weed species by selecting for weeds with long seed dispersal mechanisms (nonpatchy distributions),with light reflectance properties or morphologies similar to those of crops that escape preemergence herbicides or that build resistance to herbicides in programs that use pre- and postemergence herbicides with the same mode of action. On the other hand, improved knowledge of the factors driving weed presence or absence, whether they be management, biological, or environmental, should lead to better understanding of weed biology and ecology which should lead to better weed management strategies. This aspect of precision weed management is yet to be fully explored. Ultimately, the extent of herbicide reduction in precision weed management is contingent on the weed infestation level, spatial distribution, soil heterogeneity, and performance of the application equipment. Forcella (1993) suggests that management of spatial variability is worthwhile as long as the degree of variability is large enough to justify the cost of obtaining the information and managing the differences accordingly. The growing body of evidence from research studies on intermittent or patch weed control suggests that considerable areas within fields are weed free or have weed densities below economic threshold levels (Colliver et al., 1996; Gerhards et al., 1996; Heisel et al., 1996; Johnson et al., 1995; Mortensen et al., 1995; Sadjadi, 1996; Stafford and Miller, 1996), but a thorough assessment of the economic and environmental benefits from these practices has not yet been done. The risk of weed escapes may also play a role in adoption patterns but this has not been addressed in most studies. Stafford and Miller (1996) conclude that methods for automatic detection of weeds have yet to be developed to the point where they can be used in production agriculture. Therefore, research should focus on the acquisition of knowledge and understanding of the spatial distribution of weeds and the benefits to sustainable weed management that can be derived from an increased understanding of the distribution. ii. INSECTMANAGEMENT. Precision insect management has potential because distributions of insect populations are spatially variable, in part because insects are mobile during at least part of their life cycle and in part because during the relatively nonmobile stages insects cluster in response to environmental (e.g., temperature and moisture) and behavioral responses (Heischer ef al., 1997; Taylor, 1984).Therefore, precision insect management has the potential to reduce insecticide applications and improve the efficacy of both prevention and intervention insect management strategies. A major difficulty with the management of insects is that their populations are highly dynamic and prediction of insect density is difficult or uncertain, both of which make it necessary to collect field esti-

46

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mates of pest density to monitor density over time (Fleischer et al., 1997).The difficulty and costs associated with repeated sampling of the spatial distribution of insect densities are considered by some to be an insurmountable barrier to the use of precision insect management in IPM programs (Fleischer et al., 1997). This difficulty is reflected in the limited research on the spatial management of insects. At the precision agriculture conference held in Minnesota in 1996 (Robert et al., 1996). only 1 of the 147 papers included in the proceedings dealt with insects, a spatial characterizationof corn rootworm (Diabrotica sp.) populations in corn by Ellsbury et al. (1996). The review of Fleischer et al. (1997) represents the major synthesis on this topic and is recommended reading. Beyond recognizing the sampling difficulties, they propose many important benefits of precision insect management worth repeating here. First, precision insect management is an enhancement of IPM in that it is a continuation along the path of using knowledge to replacing chemical inputs. A map of insect density is a more realistic model than a mean estimate of pest density currently used in IPM and may stimulate the use of IPM by farmers. Second, the alarming increase in resistance requires management strategies that maintain susceptiblephenotypes and ensure gene flow among susceptible and resistant subpopulations.This need, coupled with the need to maintain predator and parasitoid populations, supports the notion of leaving habitats untreated within fields, areas referred to as refugia. The creation of temporally dynamic refugia within fields is a new development in insect IPM and is a capability of precision agriculture.Third, the improved knowledge provided by precision insect management regarding insect populations across a landscapecan be used to enhance areawide and landscape pest management programs. Currently, the sampling problem inhibits progress in precision insect management. Commercially acceptable sampling schemes are not available and it is unknown which insects and which crops will respond to precision insect management (Fleischer et d., 1997). Like others dealing with other aspects of precision agriculture, Ellsbury et al. (1996) conclude their paper with the caveat about the need to know variability in that precision insect management should lead to reduced pesticide inputs “provided the spatial variation of rootworm populations can be economically and reliably monitored and predicted.” Despite sampling difficulties, positive effects of site-specific management have been reported. The development of new sampling schemes and technologies for rapid scouting of fields should create opportunities to develop new insect management interventions that are economical and environmentally friendly. c. Crop Management The potential for precision crop management derives from the genotype X environment interaction (G X E). The G X E refers to changes in the relative performance of cultivars across different environments. where environment includes

ASPECTS OF PRECISION AGRICULTURE

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all variables encountered in producing a crop, including soil type, soil fertility, moisture, temperature, and cultural practices (Fehr, 1987). Precision crop management could be achieved by varying cultivars and/or by manipulating planting geometry (populations, spacing, and seeding depth) according to variation in environments within fields. Varying cultivars or planting geomebies within fields has potential if the G X E is such that the rank among genotypes changes across environments but not if the differenceamong genotypes varies without any alteration in rank. The success or precision crop management, therefore, depends on whether cultivars are available that have been bred for adaptation to specific environments rather than adaptation over multiple environments. The technologies needed to vary cultivar or planting geometry are already available (Anderson and Humburg, 1997), but the agronomic basis for varying them is not clear. Farmers often plant more than one variety within a field and yield maps have revealed that a G X E interaction can frequently exist within fields. The value of varying cultivars within fields is illustrated by comparing the performance of two cultivars along parallel swaths within a field (Fig. 5). At some locations along the transect, the two cultivars do not vary, whereas at some locations cultivar A performs better than cultivar B or B outperforms A. The obvious management strategy is to plant each cultivar where it performs best and either cultivar where there are no performance differences. While intuitively appealing, there are three major limitations to variable cultivar management. First, of critical importance is the existence of cultivars that are responsive to specific environments. Many cultivars are bred to perform best over a range of environments, although yield mapping has revealed that this may not be as true for current cultivars as was previously believed. Criteria for variable cultivar selection may be based on risk management rather than overall performance. For example, cultivars with higher resistance to a specific stress may be preferred over less resistant cultivars even if under low stress the latter cultivar performs considerably better. Second, cultivar selection is based on expectations of past performance and there may be few data available to

Means A = B

Distance Figure 5 Illustration comparing the performance of two varieties dong parallel swaths across the length of a field.

48

FRANCIS J. PIERCE AND PETER NOWAJC

quantify the environmentunder which cultivar performance was evaluated. Therefore, there may be a limited basis on which to base within-field cultivar selection decisions. Finally, there may be limited knowledge about the spatial variation of environments within fields or the temporal variability of within-field environments is sufficient to make prediction difficult. Precision farming technologies do make it possible to evaluate cultivar performance by environment through precision planting and yield mapping. By collecting and analyzing yield maps documenting cultivar performance by environment across a region, the agronomic basis for varying cultivars within fields could be developed (Peterson, 1997). One problem may be that the life span of a commercial cultivar may be too short to develop site-specific recommendations. Conversely, the intensive on-farm evaluation of cultivars may accelerate the collection of cultivar performance data allowing for earlier release of improved cultivars or improved screening of poor performing cultivars. Varying planting geometry within fields has potential because plants respond to competition for light, water, and nutrients, and competition is reduced when spatial arrangement of plants is improved (Sojka et al., 1988). For plant distribution to be a limiting factor to crop yield, other limiting factors need to be eliminated (Porter er al., 1997). Sojka et al. conclude that a yield advantage may exist for a wide range of species if established in dense, uniformly spaced canopies than for more open canopies, provided early weed management, water availability,and fertility are adequate. The caveat suggests that within-field variability of environments might support variable planting geometries. The evidence specifically supporting variable seeding rates is limited and inconsistent. Bullock et al. (1998) reported on an extensive variable seeding rate study in corn at 170 individual locations across the corn belt from 1987 to 1996. They found that economically optimal plant densities and field quality were correlated. However, they concluded that variable rate seeding will only be profitable to the farmer who has sufficient knowledge about the relationship between yield and plant density for each section of his fields, far more knowledge than any farmer currently possesses. Others have shown positive responses to variable seeding corn. Barnhisel et al. (1996) reported higher corn yields from variable seeding rates based on topsoil depth, which greatly influenced yields at this location. They cite yield increases from variable seeding corn in Indiana of 0.125-0.878 mg ha-' as reported by Reichenberger (1996). Fiez and Miller (1995) evaluated seeding rates for winter wheat by landscapeposition in the Palouse region of eastern Washington State. They reported yield averaged over two site years increased 10.3% when seeding rates were doubled on the north backslope position but not on other landscape positions. Increased seeding costs were more than compensated by increased yields. Since the yield increases were attributed to increased spike density, they suggest that cultivars with greater tillering potential and adaptation to cooler, lower light conditions on north backslopes might result in similar yield in-

ASPECTS OF PRECISION AGRICULTURE

49

creases without increasing seeding rate. They refer to the results reported by Ciha (1984) that indicate soft white winter wheat cultivars significantly vary by landscape position and there was a cultivar-landscape position interaction for spikelets per plant and 1000-kernel weight. While not well documented, a potential for variable seeding rates is suggested by research on planting geometry. Interest in increasing plant populations in corn is fostered by the fact that modern corn hybrids tolerate higher plant density stress more than older hybrids (Tollenaar, 1991).This may be due in part to the fact that newer hybrids appear to have improved ability to resist barrenness and other types of injury associated with above optimum plant populations (Nafziger, 1994). Cox (1996) found a hybrid-plant density interaction for dry matter and grain yield in corn, suggesting that modern commercial corn hybrids interact with plant density, regardless of growing conditions. They state, however, that some modem hybrids do not tolerate high or even moderate plant density stress in dry years. A hybriddensity interaction would mean that some varieties would respond to variable seeding rates while others might not, depending on the stress conditions within a field. Thomison and Jordan (1 995) evaluated the effect of hybrid differences in ear growth habit and prolificacy and concluded they are of limited importance in determining optimum plant populations compared to environment, hybrid, and plant population main effects. Nafziger (1996) reported the net effect of doubles (two plants growing in the same space) was to increase corn yield, whereas the net effect of skips (missing plants) was to decrease corn yield, both resulting primarily through their effects on plant population. Plant spacing variability may increase with planting speed (Nielsen, 1995) or with soil conditions that affect plant stands. Therefore, where planting operations or soil conditions increase the stand loss (more skips) through poor germination or emergence, increasing the plant population in those locations in anticipation of reduced stands may increase yields over uniform seeding rates. This is consistent with Nafziger’s (1996) suggestion that the primary strategy to overcome the effects of skips in the row may be to increase the anticipated plant population. Gaps within rows can be more important in determining yield in soybeans than plant population differences when stand densities are moderate to high (Hicks et al., 1990). Devlin et al. (1995) evaluated the influence of environment on the optimum row spacing and seeding rate for soybean in Kansas from 1991 to 1993. At all 11 locations, soil fertility was adequate and rainfall levels and planting dates covered the range of conditions expected in Kansas soybean production. They concluded that narrow rows should be recommended for locations where adequate moisture and high yields (>50 bushels per acre) are expected and wide rows recommended where soil moisture is expected to limit grain yields. When soil moisture was limiting, grain yields were not affected by increased seeding rates, whereas at high yields, grain yields varied in response to changes in seeding rate, with grain yields more responsive to seeding rates in 18-cm row spacings than in 76-cm row spacings. Therefore, environment

50

FRANCIS J. PIERCE AND PETER NOWAK

and row spacing interact with seeding rate in determining the success of variable seeding rate for soybeans. Pioneer Hi-Bred (1997) reported that narrow rows for corn production increased yields but increased plant populations were not necessary for increased yields at narrow row spacings. However, increasing plant populations beyond 3 1,OOO plants per acre continued to increase yields in narrow rows but not in wide rows. Sojka ef al. (1988) identified the manipulation of row spacing as the most practical means of optimizing canopy geometry and this was supported by the data of Porter et al. (1997) for corn. Theoretically, opportunities for precision crop management appear to exist, but early field evidence does not support it. To some extent, a lack of response may result from plant breeding programs developing cultivars that tend to perform well over a range of environmental conditions rather than to optimize for specific environments. Comparison of cultivars under varied management developed under this scenario is a self-fulfilling prophecy in the sense that cultivars created to perform on the average will on average outperform those that do not. Whether breeding will focus on improvements in traits other than yields or on yielding ability when nutrients and water are suboptimal or continue to focus on yield ability in normal or highly favored environments is under discussion (Duvick, 1996). Regardless, a glance at the plethora of yield maps revealing spatial variability in crop yield is sufficient to raise expectations about what might have happened if a different cultivar had been planted or something different had been done with respect to planting geometry. Certainly, something as simple as varying planting depth based on soil moisture in the seed zone (Price and Gaultney, 1993; Weatherly and Bowers, 1997) should find some utility in production agriculture. We will not know what precision crop management can or will bring to production agriculture until it becomes a research priority in the crop science community. d. Precision Water Management

In most crops, growth can proceed unimpaired and crop yield can be maximized only when the soil moisture potential remains high (and water remains readily available) continuously throughout the growing season. -Hillel (1990) Water is critical to crop productivity since crop yields generally increase linearly with water transpired by a crop (Howell, 1990).Excess water (waterlogging) can induce nutrient and aeration stresses and encourage pests that reduce yield and quality (Wesseling, 1974; USDA-ERS, 1987). Water management is also critical to water quality because techniques to optimize water relations for plants can also impact fate and transport of pollutants to surface water and groundwater. Naturally, the adequacy of water for plant growth is primarily related to the amount, frequency, and distribution of rainfall, soil properties as they affect processes that regulate soil water availability to plants, and landscape properties that regulate the

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hydrologic cycle within a watershed. Three approaches to precision water management are therefore apparent: (i) variable rate irrigation, (ii) matching agronomic inputs to water availability defined by soil and/or landscape properties, and (iii) drainage. i. VARIABLERATEIRRIGATION.Hillel (1990) defines a well-managed irrigation system as one that optimizes the spatial and temporal distribution of water so as to promote crop growth and yield and to enhance the economic efficiency of crop production (maximum net return). He further states that since the physical circumstances and the socioeconomic conditions for irrigation are site specific (and often season specific) in each case, there can be no single solution to the problem of how best to develop and manage an irrigation project. Hillel unknowingly defined the rationale for variable rate irrigation management. Considerable progress has been made with variable rate irrigation systems primarily with sprinkler irrigation provided by center-pivot and linear-move machines (Camp and Sadler, 1994; Evans et ul., 1996; King et ul., 1995, 1996; McCann and Stark, 1993).These site-specific irrigation systems require high spatial resolution (currently 10-30 m) achieved by adding more discrete control between contiguous elements of the machine, all at higher costs than those of current systems (Sadler et al., 1998).Variable irrigation is coupled with precision nutrient and pest management via chemigation, in part because variable irrigation facilitates increased management precision in space and time (King et al., 1995) and in part because it may not be economically feasible to site-specifically manage only for water (Evans et al., 1996).The uniformity of chemical application depends on the uniformity of water application (King et al., 1995), requiring injection equipment that can vary the amount of chemical injected into the boom in proportion to the flow rate of water in order to achieve the desired chemical application rate (Sadler et al., 1996). Success of precision irrigation management has been achieved with regard to application control (Camp etal., 1996;Wall et al., 1996).The key to the agronomic success of precision irrigation management depends to a large extent on how well the water needs of the soil-plant system can be measured or predicted and the accuracy of water application (and agrichemical) prescriptions. The value of precision irrigation management depends on whether increased profits and the reduction in pollutants more than offset the cost of increased resolution needed in irrigation systems to apply irrigation and chemigate site specifically. Evans et al. (1996) concluded that the hardware, software, and communication systems to deliver a prescription work well but a major limitation lies in the ability to interpret spatially variable data and develop rational and coherent site-specific prescriptions. They further conclude that Due to the random variability in water application distributions due to wind, start-stop operations of the self-propelled machines, and sprinkler pattern vari-

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ations combined with the low cost of water and N fertilizers, it is probably not economically feasible to site-specifically manage only for water and/or nitrogen. King et al. (1995) agree that the lack of a service infrastructure to generate and deliver the maps needed to control and manage the irrigation system throughout the season remains a major limitation to variable water application. They identified the lack of variable rate sprinklers as the other major limitation, although they overcame this limitation to some extent by the use of multiple sprinklers and sizing the sprinkler nozzles to provide a stepwise variable application rate. Because of the large temporal variability in water availability, sensing plant stresses to determine water application rates may be useful (Stone et al., 1996), but its efficacy is not fully determined. Like other factors, variable imgation management requires sufficient knowledge about the spatial and temporal variability of the factors that regulate water availability to plants and water use in plants in order to achieve precision management. Thus, its potential will vary by soils, landscape, and climate. ii. SOIL-LANDSCAPE WATER MANAGEMENT. The potential for precision management of agronomic inputs increases with spatial variability in water availability within a field. Differences in water availability within a field are governed by (i) the Occurrence of dissimilar soil types; (ii) the presence of soil degradation processes (e.g., erosion, compaction, and salinization); and (iii) variation in landscapes. The evidence for spatial variation in water availability is clear. Hanna et al. (1982) reported that north-facing slopes had 20% more available water in soils than south-facing slopes throughout the year, whereas soils on east-facing slopes were the driest. Crop yields are often highest in the lower slope positions where soil water and nutrient contents are higher (Fiez et al., 1994a,b; Halvorson and Doll, 1991; Jones et al., 1989; Mulla et al., 1992; Spomer and Piest, 1982). Eroded soils often have lower infiltration rates and lower available water than their noneroded counterparts (Daniels et al., 1985; Langdale et al., 1979). Some portion of landscape variability can be attributed to the variation of soil properties with landscape position (Brubaker et al., 1993; Khakural et al., 1996; Kreznor et al., 1989; Walker et al., 1968), whereas some is attributable to redistribution of water within a landscape due to either runoff or subsurface horizontal flow of water (Miller et al., 1988; Stone et al., 1985). Compacted soils reduce infiltration or restrict plant roots, thereby limiting water availability to plants (Lindstrom and Voorhees, 1994). Areas of high salinity are known to reduce yields (McKenzie et al., 1983). Nonuniformity is easily demonstrated in dryland areas of the United States where crops generally use all the water that is available each year and stored soil water is an essential source of water for crop production (Hanna et al., 1982). Additionally, topographic trends in soil water storage or crop growth can be important even where it is not expected, such as the sand-plain landscape in central Minnesota (Tomer et al., 1995).

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Knowledge of the spatial distribution of water availability can be used as a basis for site-specific input recommendations. There are three approaches for mapping soil water variability (Bell et al., 1995): (i) county soil surveys; (ii) interpolation of a network, usually a grid, of point samples to estimate spatial distribution of soil properties or water content; and (iii) soil-landscape models to estimate spatial patterns of soil water availability. The internal variability of soil map unit delineations in Order II county soil surveys may limit their use in site-specific management (Kellogg, 1961; Mausbach et al., 1993), although where soil map units are dissimilar, map units may correspond to crop yield variability. Interpolation techniques are used extensively in precision agriculture and their use and limitations have been discussed previously. The presence of small-scale spatial variability in soil physical properties (Peck, 1983) and the high cost of network sampling may limit its use in mapping water availability. Site-specific soil water monitoring is used to some extent as a basis for variable rate irrigation (Evans er al., 1996) and in landscape studies (Khakural et al., 1996). Statistical models of soil-landscape relationships offer opportunities to map spatial patterns of soil properties where relief or some landscape attribute is a primary factor contributing to soil variability (Bell et al., 1992). Soil-landscape models are important because terrain modifiesthe distributionof hydrologic and erosional processes (i.e., soil water content, runoff, and sedimentation)and soil temperature in fields (Moore et ul., 1993), all important in regulating crop productivity and off-site movement of agrichemicals. The depiction of the spatial variability of topography with a regular grid of elevation observations is referred to as a digital terrain model (DTM) when attributes of a landscape are of interest and a digital elevation model (DEM) when merely relief is represented (Weibel and Heller, 1991). A DTM allows the estimation of derivatives of elevation including slope, curvature, aspect, catchment area, and surface drainage proximity variables that correlate to soil and land qualities (Bell et al., 1992, 1994; Moore et al., 1993; Odeh et al., 1995). A comprehensive review of relevant techniques and applicationsof DTM is presented by Weibel and Heller (1991). The value of DTM is that it increases the resolution of soil maps for use in site-specific management and in environmental modeling by using terrain attributes to spatially distribute estimated soil attribute data (Moore et al., 1993). Therefore, terrain modeling efforts have focused on its application to soil survey to model and depict the spatial variability of soil horizons in reference to the topographic surface (Bell et al., 1995) and spatial application of simulation models to evaluate current and potential management practices regarding their effects on crop production and the environment in space and time (Verhagen et al., 1995a,b). The extent of use of soil-landscape models is currently limited in field applications of precision agriculture. However, high-resolution DEMs can easily be created using DGPs and laser-based systems with high vertical accuracies (Clark, 1996; Lange, 1996). As elevation maps become available, soil-landscape modeling techniques such as DTM will be increasingly used in precision agriculture.

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iii. DRAINAGE.Poor drainage is often cited by farmers as a source of yield variability within fields. Many options for drainage currently exist and can be applied site specifically.Therefore, there is little need to design site-specificdrainage practices. The decision to install drainage is economically, not technically, limited. Regardless of scale, the decision to drain hinges on the expectation of returns on investment that exceed costs of installation. Site specifically, the cost of draining portions of fields or small isolated areas may be higher because costs to connect to drain outlets are proportional to the distance to the outlet. Yield mapping and GIS have made the cost/benefit calculation easier because the yield depression due to poor drainage can be accurately assessed if sufficient years are included in the calculation. Drainage, therefore, is a site-specific, economic decision based on the conditions at each site and cannot be generalized.

3. Evaluation of Precision Agriculture We have probed the technological capabilities and agronomic feasibility of precision agriculture, i.e., the technologies and techniques for assessing and managing spatial and temporal variation. Essentially, we have argued that initial forms of a precision agriculture system are technologically feasible and based on credible agronomic principles. However, being technologically possible while being based on sound scientific principles does not necessarily establish utility or value in the process. An evaluation of precision agriculture is also required. Three important evaluation issues surrounding precision agriculture remain unresolved: economics, environment, and technology transfer. The economic evaluation focuses on whether the documented agronomic benefits-translated into value through market mechanisms-exceed the technological and service costs. Environmental evaluation focuses on whether precision agriculture can improve soil, water, and the general ecological sustainability of our agricultural systems. Finally, and perhaps most important, is the question of whether this bundle of enabling technologies and agronomic principles will work on individual farms. Being technologically feasible and at least economically neutral are necessary conditions but, as will be shown, may not be sufficient conditions for transfer to farms. Examining precision agriculture from the perspective of the technology transfer provides a context for the ongoing debate over the scale neutrality of this technology. a. Economics The most important fact regarding the analysis of the profitability of precision agriculture is that the value comes from the application of the data and not from the use of the technology. This can be contrasted with traditional agricultural innovations in which the value is derived from the use of the new technology, e.g., new seed genetics that increase yield or a new herbicide that reduces yield loss. However, the enabling technologies discussed earlier only generate data that have

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to be analyzed into practical information (Boehjle, 1994) so that it can influence management decisions. If any value is to be derived from precision agriculture, it will come from the resulting management decisions and not through the use of the enabling technologies. The data have no value. It is only through the interpretation and application of data that value may be derived (Lowenberg-DeBoer and Boehjle, 1996).This is a critical distinction that needs to be emphasized and reemphasized because it dictates two postulates that should drive any analysis of the economics of precision agriculture. First, a theoretical or modeled analysis of the economics of precision agriculture will not necessarily have the same result as an actual application on a farm. One can model the costs of the enabling technologies (Kohls, 1996) and, based on agronomic principles, determine the scope and extent of differences in production efficiency (i.e., increases in yield per unit of input). However, an actual application on a farm may have different results because of variation in the managerial capacity of the operator relative to responding to temporal events such as climate and pest or market cycles. The profitability will differ if a piece of equipment was not properly calibrated or erroneous agronomic decision rules were used. In summary,real-world profitability of precision agriculture is highly dependent on the human capability to manage dynamic forms of spatial and temporal variability. Second, the potential profitability of precision agriculture is directly related to the nature and extent of variability in the biophysical setting in which it is applied. If this biophysical setting was homogeneous, then there would be no difference in profitability between a precision and a conventional (“one rate fits all”) agricultural system. As the heterogeneity in this biophysical system increases, especially in those salient situations or processes in which valid agronomic principles exist, then the potential for profitability will increase. Of course, at some point in this implied linear relationship the extent of heterogeneity will overwhelm the current capability of the enabling technologies and the agronomic principles. Nonetheless, the potential profitability of precision agriculture is strongly correlated with the biophysical heterogeneity of the setting to which it is applied. At issue is the extent that these two postulates have influenced research on the profitability of precision agriculture. Evidence of the economics driving the adoption of precision agriculture is fragmentary and incomplete relative to these postulatesfor several reasons. First, as has been noted (Lowenberg-DeBoer and Swinton, 1997; Nowak, 1997), there is no precision agriculture system. Rather, there are different clusters of technologies and tools that may be adopted in different sequences and combinations,each of which may be used with varying degrees of efficiency under different cropping system conditions. Moreover, the impacts of precision agriculture applications may be masked by the interaction with other new technologies or techniques simultaneouslyintroduced into the operation. New herbicides, crop genetics, or tillage tools may influence the overall profitability of a production system. In summary, it is difficult to assess the profitability of a system

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when there is little consistency in the nature of the system between comparative sites. At the other end of the spectrum is what may be called a partial analysis of the profitability of precision agriculture. To date, the profitability of precision agriculture has only been addressed by a limited number of case studies, focus groups, antidotal stories, or modeling efforts (Denton, 1996; Holmes, 1993; Hornbaker, 1996; Olson, 1995; Schnitkey er al., 1996; Swinton and Ahmad, 1996). Much of this effort is focused on examining the profitability of a specific aspect of precision agriculturefor a specific producer or a specific cropping practice such as fertility. No research studies have examined the economics of precision agriculture for an agroecological region, the type of production system, or across all dimensions of a production system. Many different factors and methods may be employed in an analysis of the profitability of precision agriculture (Fairchild, 1993). Lowenberg-DeBoer and Swinton (1997) analyzed many studies that used partial budget analysis but were characterized by little consistency in exactly what costs were considered. They suggest a broader investment analysis to capture the flow of costs and benefits across time. A related problem is the relative ease of capturing costs (Kohls, 1996), whereas benefits are more difficult to conceptualize and measure. Besides yield differences or reduction in input costs (i.e., efficiency), precision agriculture may also have benefits with regard to managing differentiated products, food safety, increasing the span of control, and environmentalprotection (Lowenberg-DeBoer and Boehjle, 1996; Nowak,1997). Future research on the economics and adoption of precision agriculture need to carefully consider the earlier postulates on how precision agriculture differs from traditional agricultural technologies. While there is consensus that profitability will dictate the adoption rate of precision agriculture technologies, care needs to be exercised in the dominanceattributed to economic rationality in these decisions. This comment is based on the apparent lack of economic rationality driving many of the adoption decisions reported in the media and case studies. That is, the adoption decision does not appear to be driven by proven short-term profits based on a partial budget analysis or long-term gains substantiated by an investment analysis. Rather, one reads in this collection of adoption stones a strong underlying belief that the information-especially that associated with spatially referenced yield data-will be valuable even if that value does not manifest itself in the short term. The spatial and temporal dimensions of precision agriculture may represent a new type of information whose value has not yet been established under traditional accounting procedures or existing market mechanisms. Profitability remains important, but current adopters of precision agriculture also report a belief that the information collected under this method will somehow, sometime pay for itself. For example, in addition to the potential to enhance production efficiency, this new type of information has been associated with on-farm experimentation, thus reducing dependence on external parties (Fortin and Pierce, 1998). It can also pro-

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vide value to the farmer in evaluating the worth of products and services offered by vendors. Furthermore, aggregated data from local firms offering mapping or analysis services may acquire additional value for private-sector vendors who develop marketing or promotional strategies based on product efficacy across a local area or region (Nowak, 1997). Finally, there is the potential to quantify environmental benefits for either self-satisfaction or regulatory avoidance (Larson et al., 1997; NRC, 1997).Consequently, rather than strict economic rationality, many current adoption decisions appear to be driven by a future and unknown potential value where the adopter is waiting for the science to “catch up” with technological applications. A more traditional economics-oriented analysis of the potential profitability of precision agriculture is found in the NRC (1997) report. This discussion, however, results in three paradoxical conclusions. First, the potential profitability of precision agriculture is directly related to the extent of natural and anthropogenic sources of manageable variance. Producers who have effectively used analytical tools (e.g., soil testing and scouting) to manage these sources in the past actually have less potential for profit versus producers who have used a “one-size-fits-all” farming approach. At the same time, the prediction of the traditional adoption model (Rogers, 1995)that the more sophisticatedor better managers will be among the first to adopt appears to be holding relative to precision agriculture. This leads us to the conclusion that those who will benefit the least economically are among the first to use precision agriculture. The second paradox is related to the sources of variation and the potential profit to be derived from the process of applying precision agricultural technologies. While indicators of the adoption of precision agriculture appear to be following the traditional logistic rate of adoption (Rogers, 1995), the greatest sources of potential profit have yet to be developed. Potential profits from managing nutrients that are relatively stable in space and time (P and K) are significantly less than profits from managing weeds, insects, disease, water, and genetics across space and time. These latter factors are much more spatially and temporally diverse, represent a greater proportion of production expenses, and generally have a greater impact on production outcomes. They will also require further development of the enabling technologies and agronomic decision rules if these larger profits are to be realized. Consequently, the adoption and diffusion of precision agriculture is continuing at expected rates, whereas the greatest benefits from this technology are yet to be realized. One might paraphrase the English poet John Donne by stating that “no man is an island onto himself’ when it comes to the process of managing precision agriculture for profit. While we have constantly alluded to the farmer or producer adopting and managing these enabling technologies, the simple fact of the matter is that the outcome of this process is highly dependent on the support of suppliers of products and services. This infrastructure, a concept discussed more fully later,

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has a major influence on the ability of any producer to achieve profits with precision agriculture. Thus, the third paradox surrounding the economics of precision agriculture is the fact that the extent of profitability for any producer will be strongly influenced by the quality of the local infrastructure. Precision agriculture is a “team” activity, and although there are a few individuals who can make it on their own, the vast majority of farmers will require quality support products and services if they are to achieve a profit. b. Environment In our opinion, one of the greatest constraints to managing for improving water quality is the inability of agricultural producers to control inputs in ways that account for the positional and temporal variability in growing conditions across a field. -Evans et al. ( 1996) Potential improvements in environmental quality are often cited as a reason for using precision agriculture (NRC, 1997). Reduced agrochemical use, higher nutrient use efficiencies, increased efficacy of managed inputs, and increased protection of soils from degradation (erosion) are frequently cited as potential benefits to the environment (Engel and Gaultney, 1990; Larson et al., 1997). Some pose precision agriculture as a “win-win for the environment” (Farm Industry News, 1994). While the impacts of precision agriculture on the environment are assumed positive, proof that it is even benign in its environmental impacts is generally undocumented. Studies evaluating the environmental benefits of precision agriculture are limited (Khakural ef al., 1995; Mulla et al., 1996; Verhagen et al., 1995a). Negative impacts of precision agriculture could derive from increased aggression by farmers on site-specific potentials or problems (Pierce, I997b). Furthermore, many factors may limit reductions in chemical applications in some situations and encourage increased use in others (NRC, 1997). Some doubt that precision agriculture can materially improve the environmental performance of agricultural production systems because current systems are fundamentally flawed compared to natural ecosystems and precision agriculture cannot fix these flaws. Groffman ( 1997) argues that precision agriculture techniques do not address many of the key factors that cause poor environmental performance in agricultural systems, that agricultural inputs are high relative to ecological processes we hope to manage, and that these processes vary on scales that are incongruent with precision management techniques. Specifically, he discusses four key factors that constrain the environmental performance of agricultural production systems: (i) temporal discontinuities in nutrient cycling processes (a non-crop period that provides no plant nutrient sink to prevent losses); (ii) high levels of soil disturbance (mainly through tillage) that exacerbates temporal discontinuities in nutrient cycling processes, creates small-scale spatial discontinuities in this

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process, and increases the susceptibility of agricultural systems to erosion; (iii) high levels of nutrient enrichment that foster high losses unless fertilizer use is reduced dramatically; and (iv) lack of resistance to the disturbance of extreme climatic events (i.e., high rainfall) that cause a major portion of nutrient and sediment losses. The uncertainty about environmental benefits of precision agriculture is underscored by the fact that environmental improvements by themselves offer little incentive for farmers to adopt precision agriculture and will be an incentive only where producers bear at least a share of the cost of agricultural pollution (NRC, 1997). The notion that agriculture is inherently leaky is reason to pursue the potential of new technologies such as precision agriculture. That soils vary considerably in their potential to pollute the environment further supports this view. For example, soil erosion (Larson er al., 1983)and nitrate leaching (Mueller er al., 1995)are major problems on only a portion of cropland in the United States and can vary within fields (Larson et al., 1997). We started this discussion with the notion that the temporal component of spatial variability is what makes precision agriculture difficult but perhaps more rewarding. While it is probably not possible for precision agriculture to make agricultural production systems as tightly coupled to ecological processes as Groffman (1997) would like, there are many opportunities to reduce inputs, increase use efficiencies, and protect the soil using precision agriculture technologies than there are without using them. We will briefly discuss the potential opportunities for precision agriculture to improve environmental quality. However, keep in mind that evaluating the environmental performance of precision agriculture is problematic since all aspects of precision agriculture to date are merely components of a system rather than a system in itself (i.e., no precision agricultural systems exist). Pollution of the environment by agriculture requires the presence of a pollutant in a form that is suitable for transport in air or water by suspension, volatilization, or dissolution. Precision agriculture can be of benefit to the environment by reducing the application of a given input, affecting its mobility or persistence, and/ or regulating the mechanisms responsible for its transport. Farmers must manipulate these aspects not discretely but over the continuum of space and time represented by the nature of inputs (type, quantity, volatility, sorptivity, solubility, and persistence) and the heterogeneity of the biophysical environment where inputs are introduced. Environmental benefits of precision agriculture could include ecologically based management practices such as the introduction of refugia within fields or landscapes to preserve or enhance biological and genetic diversity (Fleischer et al., 1997) and precision management of the non-crop period (Pierce and Lal, 1991). The environmental impacts of precision agriculture will be difficult and costly to quantify, particularly as the temporal component of variability increases. What follows is a brief listing of the areas in which potential environmental benefits of

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precision agricultureexist (We recognize that these are not well documented in the literature): Reduction in nutrient inputs where nutrient levels or supply capacity are sufficient to meet the nutritional requirements of crops: This may have short-term benefits in areas in which residual nitrates can be utilized by the crop and thereby prevented from leaching (Hergert er af., 1996) but may not have short-term benefits, as in the case of P, in which it may take decades for soil test levels to be depleted from high levels (McCollum, 1991). Addition of nutrients where needed to meet crop nutritional requirements should have minimal impact on the environment.Thus, redistribution of nutrients through precision agriculture may or may not decrease total fertilizer use but may have environmental benefits within fields. Reduction in pesticide inputs through variable rate applications:This can be particularly useful in patch spraying where significant portions of a field may not receive any pesticide treatment (Johnson et af., 1997). Reduction in irrigation water inputs in areas subject to leaching using variable rate imgation. Minimizing or avoiding nutrient and pesticide additions where the potential for significant losses exist: This can be accomplished by varying nutrient or pesticide type, formulation, and rate according to soil conditions for erosion, leaching, runoff, and volatilization. For example, Sharpley (1997) recommends the use of environmental soil tests for P and the delineation of transport zones within a field to minimize the losses of P to surface waters. Increased erosion control or a reduction in runoff achieved through site-specific tillage and residue management: Different parts of a field erode at different rates, making precision tillage and residue management very desirable (Voorhees ef al., 1993). Pierce and Gilliland (1997) suggest that precision agriculture provides the necessary level of design and control needed to achieve soil quality control on the land. It is quite remarkable that an analysis of yield maps has not been performed to demonstrate the effects of erosion on productivity, thereby quantifying the importance of erosion control on the farm. Management of field and landscape buffer zones not possible without precision management systems. Because of spatially variable control, buffer zones and refugia can be managed without interruption of farming operations creating possibilitiesfor increased biological diversity and interception of pollutant transport to surface water. Although precision agriculture is intuitively appealing, environmental benefits are not necessarily forthcoming. However, there is equally little evidence to suggest that precision agriculture will increase environmental pollution or that it cannot, as Groffman (1997) suggests, address the fundamental environmental problems facing agriculture.The best interest of agriculture is served by documenting

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the environmental performance of precision agriculture over the space-time continuum. c. Technology Transfer The third critical dimension of evaluating precision agriculture involves examining the technology transfer process. Enabling technologies can make precision agriculture feasible, agronomic principles or decision rules can make it applicable, and enhanced production efficiency or other forms of value can make it profitable. However, will precision agriculture be used on a farm? The reality check for this scientifically feasible and potentially economically viable bundle of technology comes from an assessment of what must happen on a farm to make it work. We know that producers have three basic questions relative to the adoption of precision agriculture: Will it work? Will it pay? and Can I make it work in my operation? While basic and applied research may answer the first two questions, the third question is highly dependent on farmers and the context in which they operate. Research of a different sort is required to answer this question-research that has been subnamed under the label of technology transfer. The term technology transfer could imply that precision agriculture occurs when individuals or firms simply acquire and use the enabling technologies (i.e., it is transferred to them). While precision agriculture does involve the application of enabling technologies and agronomic principles to manage spatial and temporal variability, the key term is manage. Much of the attention in what is called technology transfer has focused on how to communicate with the farmer (Crookston, 1996; Holt and Sonka, 1995; Peterson and Beck, 1997). However, communicating the answers to the three basic questions of a farmer listed previously is different than the substantive nature of those answers. If farmers are going to manage precision agriculture, then we need to understand what will influence this management process. It is here, and not in communicationtechniques, that we will begin to understand what drives the technology transfer process. Three factors will influence the transfer or diffusion of precision agriculture among a population of farmers: distributions of human capital, spatial locations and the nature of infrastructure support, and the compatibility of the enabling technologies to the needs and capabilities of producers. According to Bouma (1997), farmers make strategic, tactical, and operational decisions relative to agriculture in general and precision agriculture in particular. Strategic decisions surround the nature of the farm such as the use of crop rotations, tactical decisions focus on things such as crop varieties, and operational decisions are those focused on mounts and timing of inputs for the selected crop varieties. Bouma (1997) observes that most precision agriculture research has focused on improving the efficiency of operational decisions. However, the decision to adopt precision agriculture clearly occurs at the strategic level. Therefore, technology transfer is the process of getting farmers to make a strategic decision

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on how operational decisions will be made in the future. How do we explain why some farmers make this decision whereas others remain skeptical, uninformed, or negative regarding precision agriculture? On an individual level, perhaps the best predictor of the adoption and diffusion of precision agriculture will be based on the classic adoption model (Rogers, 1995), which places a heavy emphasis on the social psychological or human capital attributes of the potential adopter. Others have already hypothesized that the adoption of precision agriculture will follow the pattern predicted by this model (Lowenberg-DeBoer, 1997; NRC, 1997). Individuals who have greater risk propensity and who are younger, better educated, able to manage transition costs, and are well integrated into diverse information networks will be among the first to adopt precision agriculture. Individuals who are among the first to adopt are called “innovators” in the model and comprise approximately 2.5% of the population. These innovators then influence the early adopters in local areas or neighborhoods who comprise the next 13.5%who adopt the innovation. They are followed by the early majority and late majority, each comprising 34% of the population. The final group is composed of those who are either unable or unwilling to adopt for a variety of reasons. This “trickle down” process through social contagion and market processes has been well documented (Rogers, 1995). However, an individual’s social-psychological, economic, and demographic profile may be necessary, but it is not sufficient for the adoption of precision agriculture. Access to resources and support through local infrastructure is also critical for adoption. That is, one may be an innovator, but without access to dealerships, consultants, experienced farmers, manufacturing representatives, or other experts, adoption of precision agriculture is more difficult. A parallel can be found in the computer industry. There is a well-developed computer industry infrastructure in the “Silicon Valley” of California.The entrepreneur in this area is supported by the levels of innovation, adaptation, and competition for success, whereas entrepreneurs in other regions are penalized because they do not have access to similar supporting resources. It is relatively easy for the hardware or software designer to find supporting parts, services, and ideas that may not be available to the entrepreneur in the midcontinent or other regions. The same situation also applies to agriculture in general and precision agriculture in particular. Since private and public supporting resources are not equally distributed across the agricultural production landscape, some producers are going to have a greater opportunity to adopt precision agriculture than others regardless of individual managerial or economic attributes. Related to this notion of a lumpy distribution of supporting resources is the relative role of the private and public sectors. The role of infrastructure in diffusing precision agriculture is contrary to many claims of the emerging information age. Here, information technologies are supposed to create equal access to ideas, data, and management information. This “leveling of the playing field” due to information technologies, however, is not occurring with precision agriculture,

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due largely to the complexity and fragmentation of current tools and analytical processes. Making precision agriculture work on a farm is a very difficult process involving contradictory agronomic recommendations, incompatible hardware, and high learning costs with few specific objectives (other than profitability). All this creates a situation in which contracted expertise, products, and services from off the farm become the only viable strategy. This situation will change as more protocols, standards, and a uniform set of expectations are developed, at which point precision agriculture will help agriculture move into the information age. What is the importance of the private sector relative to the public sector in influencing the strategic decisions of farmers? As Wolf (1998) notes, After more than 100 years of public-sector leadership in agriculture research and extension in the United States, we appear to be entering a new era in which private and corporate interests have significantly increased authority and responsibility as information providers. In essence, the private sector largely dictates the production decisions of commercial farmers in the United States today. Since precision agriculture is being largely designed for commercial applications, we would expect the private sector to have an inordinate influence on current technology transfer efforts for precision agriculture, affecting the geographical areas where it is available, the extent to which the various products and services are accessible to the producers in these areas, and the marketing strategies used to promote precision agriculture. Dealerships, consultants, or manufacturing representatives,like the customers they serve, can be aggressive or conservative relative to new technologies. Furthermore, the geographic concentration of customers largely dictates the location of these supporting facilities due to basic market mechanisms.Agricultural technologies, precision or otherwise, are designed based on potential market demand and technological feasibility. Factors such as the number of potential customers, their ability to invest in new technology, and the geographic concentration of these customers all help to determine the spatial market demand for precision agriculture. Publicsector organizations, such as the land grant university or the USDA, will focus their precision agriculture efforts on problem solving, efficacy testing, and basic research. In essence, however, it needs to be emphasized that most of the technology transfer surrounding precision agriculture will be market driven from the private sector, commodity specific, and concentrated in certain agricultural areas. The final point about technology transfer concerns the compatibility of the enabling technologies to the needs and capabilities of the farmers. Much could be said about this issue, but attention has focused on whether precision agriculture is a scale-neutral technology.A pure economics approach to this issue would address whether there are equivalent proportionate investments across scales of operations. This approach, however, ignores several spatial dimensions associated with access to this technology. First, precision agriculture technologies have not been

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developed and adapted to work with all agricultural commodities. There was a clear sequence of developmentfrom certain cash grains to some horticulture crops to crops such as cotton. Producers of some agricultural commodities,regardless of scale of operations, cannot adopt this technology because it has not been developed for their commodity. Second, as discussed earlier, the support infrastructure has a lumpy spatial distribution. Producers in some areas will either not be able to adopt or have a very difficult time adopting because of the lack of these supporting resources. Finally, farmers are not equivalent relative to issues such as computer literacy, technical competence, or abstract reasoning. To the extent that current forms of precision agriculture call for these skills, it is not compatible with a certain proportion of these farmers. Consequently, while the scale neutrality of precision agriculture has been a contentious issue, understanding what influences the potential management of precision agriculture provides more insight than semantical debates. Precision agriculture is not scale neutral for the reasons discussed as part of the technology transfer process. Nor is there any inherent reason why it should be other than a social value related to equity. Understanding why this is the case should move the debate out of the normative and political arena and allow future research to focus on how to accelerate technology transfer for targeted regions of the country. What happens as new products or management techniques are designed specifically to work under precision agriculture?Consider the commercial release of the Windows 98 operating system. There may be little interest in moving to this new operating system as users perceive little value in that decision. The utility of a new operating system will emerge as developers create useful products that will only operate efficiently under Windows 98. The same was true of the transition from Windows 3.1 1 to Windows 95. An analogous situation exists relative to precision agriculture, a new operating system for crop production. Precision agriculture presents a new set of diagnostic, analytical, and management tools oriented toward spatial and temporal variation that must produce utility with old products and production models. Much of the current agricultural machinery, nutrients, pesticides, and genetics were designed to work with the old one-size-fits-all agricultural system that has evolved since the 1950s (Nowak, 1997). Should new products and techniques specifically designed to work in the new operating system of precision agriculture emerge, the paradigm for technology transfer changes dramatically to one in which to reap the benefits of new products and practices farmers must be equipped for precision agriculture. Consider, for example, current genotypes that optimize performance across a wide range of microenvironmentsmay not be able to compete against a suite of new genotypesthat maximize performancein selected microenvironments. It is conceivable that a series of these new genotypes developed just for precision applications will significantly outperform the old one-sizefits-all genotype currently available. Few have yet to realize how emerging biotechnology applications will also support precision agriculture and vise versa.

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Imagine nutrients whose formulation is designed for microenvironmentsbased on reducing mobility while also being in synchronization with the needs of the plant across the growth cycle. Similar “designer” approaches can be developed relative to pesticides and water management. In short, we have been evaluating the utility of a new production system under the playing rules of the old system. The technology transfer issues associated with the managerial capability of the operator, the spatial distribution of infrastructure,and the compatibility of the technology to individual farms will change radically as precision agriculture continues to develop. Anticipating these changes is important for evaluation. It is also important for understanding how the assessment and management of variation in precision agriculture must be performed. Researchers need to examine current developments. However, we also need to give attention to the “what could be” futures for precision agriculture.

III. CONCLUSIONS The value of the emerging system of precision agriculture will ultimately be measured by its success in managing the space-time continuum of all aspects of crop production. This, in turn, will have implications for environmentalprotection and farm profitability. Early successes in precision management have come where spatial dependence is moderate to high and temporal dependence is low, e.g., certain aspects of soil fertility and weed patch management. We believe that the longterm application of precision management where temporal dependence is low will reduce spatial dependenceand increase predictability. This should make precision management easier and more efficient as a system but potentially less profitable with respect to a given component. For example, through precision management, soil test P levels within a field will evolve to the maintenance plateau levels, thereby linking P fertilizer rates to crop removal rates estimated from yield maps. For spatially dependent entities, high temporal dependence makes precision management more difficult and requires more of an intervention rather than a prevention management strategy. Both IPM in general and N management in humid regions exemplify this situation. Managing entities with a high space-time dependence, however, may be potentially more profitable and environmentally beneficial than managing high spatial-low temporal-dependententities. These hypotheses remain to be tested but appear in principle to be supported by existing studies. Perhaps most problematic is the notion that the more complex the space-time situation, the more difficult it may be for farmers to manage without sophisticated decision aides. Technology transfer may be increasingly difficult as the complexity of precision agriculture increases. One conclusion is clear: To achieve success, precision agriculture must evolve

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from a collection of partially compatible technologies into an integrated management system able to accommodate the diverse biophysical settings and cropping enterprises characteristic of today’s agriculture. Precision agriculture must be structured in ways that enable farmers to complete the basic steps of assessing variability, managing variability, and evaluating the outcome of these processes. This enabling has not happened to the extent desired or possible and important issues remain in this regard. With respect to assessing variability, there is insufficient scientific guidance regarding exactly what properties or processes need to be measured. Although the tools exist to measure a given entity, this does not mean that the entity needs to be measured in assessing variability. This will be especially important as we shift the focus to the more temporally dynamic components of crop production because it will become as important to know when and where to look as it is to know what to look for. For these reasons there needs to emerge a clear description of the salient properties and processes that affect crop performance and these need to be assessed for precision agriculture by biophysical settings and cropping systems. Lack of these standard protocols has lead to situations in which interpretation of measured variability is often clouded by inaccurate assessment (due to poor measurement techniques or inadequate sampling designs) or lack of scientific understanding of underlying causal processes. Therefore, more emphasis must be placed on both the validity and the accuracy in variability assessment. Tools that increase assessment accuracy of valid properties and processes while keeping costs affordable should be a high priority for research. Sensors, both ground based and remote, should increase the detail needed for precision agriculture at reduced costs, but these will require significant improvements in software, particularly user interfaces, to fully utilize precise data. However, sensor data also need to be integrated with advances in software that will improve our understanding of the spatial and temporal variation in the properties and processes that regulate crop production as well as the fate and transport of pollutants to the environment. With regard to managing variability, the agronomic basis for most precision management techniques is not well established due to scale incongruities. Current management recommendations rely on traditional best management practices and concepts such as whole-field IPM, which may or may not have site-specific correlates at a finer resolution. A concerted effort should be made to design products and management tools based on the emerging science of precision agriculture while not forcing new enabling technologies to operate under traditional crop management strategies and guidelines. Precise management recommendations will need to be based on measurable or predictable site-specific conditions that are consistent with the capabilities of the enabling technologies. On-farm research, which is increasing with the adoption of certain enabling technologies, offers a great opportunity to evaluate current and future management practices over a broad spacetime continuum not possible under traditional small-plot research.Traditional pub-

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lic-sector research needs to integrate with the on-farm research network that is rapidly emerging in the realm of precision agriculture. With regard to evaluation, it is essential that evaluation procedures be developed that are consistent with the emerging features of precision agriculture while not relying solely on the traditional approach used for agricultural machinery, genetics, or chemicals. These evaluation procedures must involve the farmer. The farmer is the integral part of evaluating precision agriculture because the assessed variability must ultimately be managed on the farm. The enabling technologies, the agronomic rules, and even the data have no value in themselves. Value is obtained through the management of data on the farm. It is the data and the decisions they lead to on the farm that are the essence of precision agriculture. Our evaluation efforts need to begin with this farm management process. The impacts of precision agricultural systems will extend beyond crop production to the environment and to the very structure of our agriculture system. Consequently, evaluation needs to involve all sectors of agriculture. We have argued that the essence of precision agriculture is associated with spatial and temporal variability. Evaluation also needs to address this dimension. The critical impacts of precision agriculture will not be found by only asking the traditional question of what is occurring; rather, these insights will emerge when we focus on where and when it is occurring. Precision agriculture is intuitively appealing, has captured the interest of the agricultural sector, and offers many exciting and challenging research questions. Lacking in this interchange, however, has been sufficient attention to underlying scientific principles and standards. In our overview of precision agriculture, we have attempted to point out what we believe are some unifying principles and analytical deficiencies. We hope this discussion will focus future work on precision agriculture so that both scientific and practical (agronomic, economic, and environmental) objectives can be realized.

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agement. In “The State of Site-Specific Management for Agriculture” (F. J. Pierce and E. J. Sadler, Eds.), ASAMiscellaneous Publication, pp. 161-181. ASA, CSSA, and SSSA, Madison, WI. U S . Department of Agriculture, Economic Research Service (USDA-ERS) (1987). “Farm Drainage in the United States: History, Status, and Prospects” (George A. Pavelis, Ed.), Publ. No. 1455. USDA-ERS, Washington, DC. U.S. Department of Agriculture, Economic Research Service (USDA-ERS) (1997). “Agricultural Resources and Environmental Indicators, 1996-97,”Agricultural Handbook No. 712. USDA-ERS, Natural Resources and Environment Division, Washington, DC. Verhagen, J., and Bouma, J. (1997).Modeling soil variability. In “The State of Site-Specific Management for Agriculture” (F. J. Pierce and E. J. Sadler, Eds.), ASA Miscellaneous Publication, pp. 55-67. ASA, CSSA, and SSSA, Madison, WI. Verhagen, A.. Booltink, H. W. G., and Bouma, J. (1995a). Site-specific management: Balancing production and environmental requirements at farm level. Agric. Sysrems 49,369-384. Verhagen, A., Verburg, P., Sybesma, M., and Bouma, J. (1995b).Terrain modeling as a basis for optimal agroecological land management using dynamic simulation. In “Proceedings of the Second International Conference on Site Specific Management for Agricultural Systems, Bloomington/ Minneapolis, MN, 27-30 March 1994” (P. C. Robert, R. H. Rust, and W. E. Larson, Eds.), ASA Miscellaneous Publication, pp. 229-250. ASA, CSSA, and SSSA, Madison, WI. Vetsch, J. A., Malzer, G. L., Robert, P. C., and Huggins, D. R. (1995). Nitrogen specific management by soil condition: Managing fertilizer nitrogen in corn. In “Proceedings of the Second International Conference on Site Specific Management for Agricultural Systems, Bloomington/Minneapolis, MN, 27-30 March 1994” (P. C. Robert. R. H. Rust, and W. E. Larson, Eds.),ASA Miscellaneous Publication, pp. 465-473. ASA, CSSA, and SSSA, Madison, WI. Vetsch, W. C., Duke, H. R., and Iremonger, C. J. (1996).Assessment of plant nitrogen in imgated corn. In “Proceedings of the Third International Conference on Precision Agriculture, Minneapolis, MN, 23-26 June 1996” (R C. Robert, R. H. Rust, and W. E. Larson. Eds.), ASA Miscellaneous Publication, pp. 23-32. ASA, CSSA, and SSSA, Madison, WI. Vitosh, M. L., Johnson, J. W., and Mengel, D. B. (1996).Tri-state fertilizer recommendations for corn, soybeans, wheat and alfalfa, Extension Bull. No. E-2567.Michigan State University, East Lansing. Von Groenendael, J. M. (1988).Patchy distribution of weeds and some implications for modeling population dynamics: A short literature review. Weed Res. 28,437-441. Voorhees, W. B., Allmaras, R. R., and Lindstrom, M. J. (1993).Tillage considerations in managing soil variability. In “Proceedings of Soil Specific Crop Management: A Workshop on Research and Development Issues, 14-16 April 1992, Minneapolis, MN” (P. C. Robert, R. H.Rust, and w. E. Larson, Eds.),pp. 95-111. ASA, CSSA. and SSSA, Madison, WI. Walker, B. D., Haugen-Kozyra, K.,and Wang. C. (1996).Effects of long-term cultivation on a morainal landscape in Alberta, Canada. In “Proceedings of the Third International Conference on Precision Agriculture, Minneapolis, MN, 23-26 June 1996” (P. C. Robert, R. H. Rust, and W. E. Larson. Eds.), ASA Miscellaneous Publication, pp. 107-116. ASA, CSSA, and SSSA, Madison, WI. Walker, P. H., Hall, F. F., and Protz, R. (1968). Relation between landform parameters and soil properties. Soil Sci. SOC.Am. Pmc. 32, 101-104. Wall, R. W., King, B. A., and McCann, I. R. (1996). Center-pivot irrigation system control and data communications network for real-time variable water application. In “Proceedings of the Third International Conference on Precision Agriculture, Minneapolis, MN, 23-26 June 1996” (P. C. Robert, R. H.Rust, and W. E. Larson, Eds.), ASAMiscellaneous Publication, pp. 757-766. ASA, CSSA, and SSSA. Madison, WI. Walters, C. S., Frazier, B. E., Miller, B. C., and Pan, W. L. (1996). Remotely sensed organic matter mapping for site-specific N management in dryland winter wheat. In “Proceedings of the Third International Conference on Precision Agriculture, Minneapolis, MN, 23-26 June 1996 (P. C.

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Robert, R. H. Rust, and W. E. Larson, Eds.), ASAMiscellaneousPublication, p. 579. ASA, CSSA, and SSSA, Madison, WI. Wanick, A. W., Myers, D. E., and Nielsen, D. R. (1986). “GeostatisticalMethods Applied to Soil Science. Methods of Soil Analysis. Part I,” 2nd ed., Agronomy Monograph No. 9. ASA, CSSA, and SSSA, Madison, WI. Weatherly, E. T., and Bowers, C. G.,Jr. (1997). Automatic depth control of a seed planter based on soil drying front sensing. Trans. Am. SOC.Agric. Eng. 40,295-305. Webster, R., and Oliver, M. A. (1990). “Statistical Methods in Soil and Land Resource Survey.” Oxford Univ. Press, New York. Weibel, R., and Heller, M. (1991). Digital terrain modeling. In “GeographicInformation Systems, Principles and Applications” (M. F. Goodchild and D. Rhines, Eds.), pp. 269-297. Taylor & Francis, New York. Wesseling, J. (1974). Drainage and crop production. In “Drainage for Agriculture” (J. Van Schilfgaarde, Ed.), pp. 7-37. Agronomy Vol. 17. ASA, Madison, WI. Wibawa, W. D., Dludlu, D. L., Swenson, L. J., Hopkins, D. G., and Dahnke, W. C. (1993). Variable fertilizer application based on yield goal, soil fertility, and soil map unit. J. Prod. Agric. 6,255261. Woebbecke, D. M., Meyer, G. E., Von Bargen, K.,and Mortensen, D. A. (1995). Shape features for identifying young weeds using image analysis. Trans. Am SOC.Agric. Eng. 38,271-281. Wolf, S. A. (1998). Introduction. In “Privatization of Information and Agricultural Industrialization” (S. A. Wolf, Ed.), pp. xvii-xxv. CRC Press, Boca Raton, FL. Wolf, S. A., and Nowak, P. (1995). Site-specificity in agriculture: Hard or soft paths for Wisconsin’s agrichemical dealers. In “Proceedings of the 1995 Fertilizer,Aglime, and Pest Management Conference, University of Wisconsin-Madison.” pp. 121-129. Department of Soil Science, University of Wisconsin-Madison. Wollenhaupt, N. C.. Wolkowski, R. P., and Clayton, M. K. (1994). Mapping soil test phosphorus and potassium for variable-rate fertilizer application. J. Prod. Agric. 7,441-448. Wollenhaupt, N. C., Mulla. D. J., Gotway, C. A., and Crawford, L. A. (1997). Soil sampling and interpolation techniques for mapping spatial variability of soil properties. In ‘The State of Site-Specific Management for Agriculture” (p. J. Pierce and E.J. Sadler, Eds.), ASA Miscellaneous Publication, pp. 19-53. ASA, CSSA, and SSSA, Madison, WI. Young, J. L., and Aldag, R. W. (1982). Inorganic forms of nitrogen in soil. In “Nitrogen in Agricultural Soils’’ (F. J. Stevenson, Ed.), Agronomy Monograph No. 22, pp. 43-66. ASA, CSSA, and SSSA, Madison, WI.

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SURFACECHARGE AND SOLUTE INTERACTIONS INSOILS N. S. Bolan,' R. Naidu,*J. K. S y e r ~and , ~ R. W. Tillman' 'Department of Soil Science, Massey University, Palmerston North 5301, New Zealand ZCSIRO Land and Water, Glen Osmond 5064,South Australia, Australia 'Department of Agricultural and Environmental Science, University of Newcastle upon T p e , Newcastle upon Tyne NE17RU, United Kingdom

I. Introduction

II. Types of Electrical Surface Charge III. Development of Surface Charge A. Isomorphous Substitution B. Protonation/Deprotonation C. Specific Adsorption of Anions and Cations W. Components of Surface Charge V. Solution-Surface Interface A. Diffuse Double Layer B. Diffuse Double-Layer Model of Variable-ChargeMinerals VI. Concepts of Point of Zero Charge W.Measurements of Surface Charge A. Potentiomemc Titration B. Ion Retention C. ElecuokineticTechniques VIZI. Factors Affecting Surface Charge A. Soil Components B. Ionic Strength of Soil Solution C. Soil Solution Composition D. pH of Soil Solution E. Specific Adsorption of Anions and Cations M. Effect of Surface Charge on Soil Properties A. Solute Interactions B. Anion- and Cation-Induced Adsorption C. Dispersion and Flocculation X. Manipulation of Surhce Charge to Control Solute Interactions A. Liming 87 Adumm in Agrmay, VDkanr 67 Copyright 0 1999 by Academic Press. AU righa of reproduction in my form resewed. 0065-2 I 13/99 $30.00

88

N. S. BOLAN ETAL. B. Organic Matter Addition C. Phosphate and Silicate Addition XI. Conclusions and Future Research Needs References

Many soil physical and chemical properties are controlled by the nature and the amount of surface charge and the variation of surface charge with soil solution characteristics. These properties include dispersion and flocculation. electrophoretic mobility, solubility,and the adsorption and movement of solutes. The surfacereactions of charged particles are essential to the biogeochemicalcycling of nutrients and pollutants and the pathway of detoxification of the latter when present at hazardous concentrations. Surface charge can be manipulatedto take advantage of solid phase interactions relating to the movement of nutrient and pollutant ions in soils, the degradation of pesticides, and the decontaminationof soils. This chapter brings together fundamental aspects of surface charge and recent developmentson the implicationsof surface charge in relation to other soil properties, particularly solute interactions in soils. We first outline the development of charge on both permanent- and variable-charge surfaces. Then we discuss the various methods used to measure surface charge and factors affecting this charge. An attempt has been made to compare current theories on the nature of the charged solid surface-solution interface. The manipulation of surface charge can be achieved through liming and the addition of fertilizers containing specifically adsorbed ions. The practical implications of surface charge to soil properties have been discussed in relation to the dispersion and the flocculation of soils and the adsorption and leaching of inorganic cations and anions. Future research should focus on the development of methods to measure surface charge under in situ conditions and to explore further the role of surface charge in remediating 8 1999 Academic Ress contaminated soils.

I. INTRODUCTION Traditionally, soil scientists have employed the principles of physical, chemical, and biological sciences to understand the properties of soils. In recent times these basic sciences have contributed much more, especially to an understanding of the physical, chemical, and biological fertility of soils for plant growth and the long-term impact of intensive and prolonged agricultural production on soil properties. With increasing awareness of the impact of agricultural activities on the wider environment, soil is not only considered as a “source” of nutrients for plant growth but also as a “sink” for the removal of contaminants from industrial and agricultural waste materials. The “filtering” action of soil is controlled largely by the reactions of pollutants with soil components carrying surface charge. The discovery of surface charge on soil components revolutionized both fun-

SURFACE CHARGE AND SOLUTE INTERACTIONS

89

damental and applied research in soil science (Sposito, 1984; Sparks, 1986).Many soil physical and chemical properties are directly or indirectly controlled by the nature and the amount of surface charge and the variation of surface charge with soil solution characteristics. These properties include dispersion (Sumner, 1993; Quirk, 1994),flocculation/deflocculation(Suarez et al., 1984;Gregory, 1989;Bolan et al., 1996b),coagulation (Hohl et al., 1980; Heil and Sposito, 1993),electrophor-etic mobility (Schulthess and Sparks, 1988), solubility (White and Zealazny, 1986; Carroll-Webb and Walther, 1988; Polubesova et al., 1995), and the adsorption and movement of nutrient ions, toxic heavy metals, and pesticides (Barrow, 1989; Evans, 1989; Bolan et al., 1997; Naidu et al., 1996, 1997). The colloidal behavior of soil particles has been attributed to high surface charge density resulting from a combination of high charge and very small particle size (Mattson, 1931a,b). Parks and de Bruyn (1962) reported that a suspension is stable because of the repulsive effects of the like charges on particles. At the point of zero charge (PZC; see Section 111) the charge is neutralized, enabling closer approach of the particles due to van der Waals attraction resulting in coagulation or flocculation and settling. PZC is the point of minimum solubility of a solid phase in equilibrium with the solution and thus the coagulation and sedimentation rates are also maximum at the PZC (Parks, 1967; Shanmuganathan and Oades, 1982). Sposito (1992) indicated that the surface reactions of charged particles are essential to the biogeochemical cycles of trace elements and the pathway of detoxification of these elements when present in aqueous environments at hazardous concentrations. Surface charge also plays a major role in the interactions of solutes with soils (Greenland and Hayes, 1981; Banow, 1987, 1996). It can be manipulated to take advantage of solute interactions relating to the movement of pollutants and nutrient ions in soils, the degradation of pesticides, and the decontamination of soils. With the greater public awareness of contamination of soil environment there has been increasing interest among the scientific community in the interactions of pollutants with soils. In this chapter, we will first outline the development of charge on both permanent- and variable-charge surfaces. Then, we will discuss the various methods used to measure surface charge. An attempt will be made to compare current theories on charge surface and the solution interface. The practical implications of surface charge to solute interaction will be discussed in relation to adsorption and leaching of inorganic cations and anions and organic compounds in soil. The review was undertaken primarily to bring together the fundamental aspects of surface charge and recent developments on the implications of surface charge in relation to solute interactions in soils in a comprehensive manner for readers, other than surface chemists, including soil scientists, agronomists, and environmental scientists.

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N. S. BOLAN ETAL.

II. TYPES OF ELECTRICAL SURFACE CHARGE The electrical charge properties of soil colloids cover a spectrum of behavior (Arnold, 1978; Barrow, 1983,1985; Barrow etal. 1993).For one end-member, the charge on the surface is fixed or permanent and remains independent of the solution composition, but the electric potential is sensitive to indifferent electrolyte concentration.This member is usually termed the constant charge surface or permanent surface charge. For the other end-member of the constant potential surface, the charge varies with changing concentration of indifferent electrolyte. This charge is known as variable surface charge. The development of these two types of surface charge is discussed in Section III. Briefly, permanent charges are developed by isomorphous substitution of ions in the lattice structures of silicate clay minerals, whereas variable charges are developed through the dissociationof functionalgroups. Organic surfaces, such as humified organic matter, acquire their charge through the ionization of carboxylic (COOH), phenolic (OH), and possibly other functional groups. In the presence of various functional groups it is difficultto specify single acidic dissociation constants because the loss of protons (H+)tends to become increasinglydifficultas more functional groups become negatively charged. Thus, in the dissociation of COOH groups, R-COOH R-COO(1-f) f The dissociation constant (pKa) is given by:

+

H+

PKa = PH - 1% (fl(1 - f 1)

(1)

(2)

wheref is the fraction of COOH groups dissociated. The dissociation constant for organic compounds is expected to increase with increasingf (Momson and Boyd, 1973).In many soils between 10 and 90% of the total negative charges are derived from the functional groups of the organic matter.

III. DEVELOPMENT OF SURFACE CHARGE Soil constituents usually carry both positive and negative charges. Soil contains both permanent- and variable-charge surfaces. The permanent charges are fixed and are not altered by soil conditions. The permanent- or fixed-charge constituents develop surface charge as a result of substitution of metal ions in the lattice. In most silicate clay minerals, the majority of surface charges are fixed and are developed through substitution. Barrow (1985) suggested that the term “variable charge” be used to describe those constituents whose charge varies mainly with the pH of the

SURFACE CHARGE AND SOLUTE INTERACTIONS

91

soil solution. The variable charge of soils is also affected by other factors, such as ionic strength (I)of the soil solution and reactions with anions and cations. Important variable-chargeconstituents in soils include oxides and hydroxides of iron (Fe), aluminum (Al), titanium (Ti), manganese (Mn), and organic matter. In Table I the nature of charge development in various soil constituents is presented (Gillman and Uehara, 1980). Although clay minerals are considered either permanently or variably charged, these classifications are idealized end points (Lewis-Russ, 1991).Isomorphous substitution can occur in oxides and hydrous oxides, thus imparting some permanent charge to these predominantly variable-charge minerals. Single minerals may exhibit both permanent and variable charge (Schulthess and Huang, 1990). For example, the broken edges of permanently charged clay minerals, such as kaolinite and halloysite, are sites of variable charge (Bolland et al., 1976;Chorover and Sposito, 1995;Schroth and Sposito, 1997).Similarly, the variable-chargedchloritic group of minerals consists of montmorillonite and vermiculite which formed under acidic conditions and incorporated dissolved A1 in the interlayers. The interlayers compensate for any permanent charge substitution so the charge character of these clays is variable (Uehara and Gillman, 1980) Permanent and variable surface charges are developed by three processes (Stumm and Morgan, 1981; Sposito, 1992): 1. Isomorphous substitution 2. Dissociation and association of protons (H+) (protonation/deprotonation) 3. Specific adsorption of anions and cations While permanent charge is developed by the first process, variable charge is developed by the last two processes.

Table I Permanent- and Variable-Charge Constituentsin Soils Component

Permanent charge

Variable charge

Kaolinite Halloysi te Chloritic Smectite Illite Micaceous Montmorillonite Vermiculite Allophane Fe and Al oxides Organic matter

J J J J J J J J

J J J

-

-

-

J J J

92

N. S. BOLAN ETAL. Table I1 Ionic Size of Various Cations, Substitution in the Clay Layer, and the Development of Surface Charge

Ion Si4+ ~13+ Fe3+ Mg2+ Fe2+ Ca2+ Na+

Radius (pm)

Substitution by

Substitution layer

4.2 5.1 6.4 6.6

~ 1 3 +

Tetrahedral Dioctahedral Trioctahedral Trioctahedral Trioctahedral Trioctahedral No substitution No substitution Interlayer Interlayer

7.0 9.9 9.7 13.3

K+ NH:

Mg2+ Mg2+ ~ 1 3 +

Fe3+ ~ 1 3 +

NH: K+

Charge Negative Negative Negative Positive Positive Positive

No charge No charge

A. ISOMORPHOUS SUBSTTIWTON Permanent charge is developed by substitution of ions of similar sizes but with different charges in the lattice structure of clay minerals (Table 11). Permanent negative charge is developed by substitution of higher valence cations with lower valence cations. For example, if in an array of solid SiO, tetrahedra a Si4+is replaced by an A13+,a negatively charged framework is established. Similarly, isomorphous replacement of the A13+by a Mg2+in networks of A1,0, dioctahedral sheets of silicate clay minerals leads to a negatively charged lattice (Fig. l). On the other hand, permanent positive charge is developed by substitution of lower valence cations with higher valence cations. For example, substitution of a Mg2+by an A13+in the trioctahedral layer of silicate clay minerals results in net positive charge (Fig. 2) Isomorphous substitution is more common in 2: 1-type silicate minerals than in Si4+substituted by A13+

SizOI

SiAlO*-

Figure 1 Developmentof surface negative charge through the substitution of Si4+by A13+ in the tetrahedral layer of a silicate clay mineral.

SURFACE CHARGE AND SOLUTE INTERACTIONS

OR

93

OR

0

I MC I

A$+ I

OH

OK

Figure 2 Development of surface positive charge through the substitution of MgZ+by A13+in the trioctahedral layer of a silicate clay mineral.

other minerals (Table III). For example, in the common micas, muscovite and biotite, every fourth Si4+is replaced by an A13+, and the resultant charge deficit is balanced internally by K+ in the interlayer positions. While the permanent negative charge is developed in both tetrahedral and dioctahedral sheets of silicate clays, the permanent positive charges are developed only in trioctahedral sheets. Sparingly soluble salts also cany a surface charge because of lattice imperfections.

B. PROTONATION/DEPROTONA~ON When metal oxides are suspended in water, the metal ions near the surface or broken edges tend to coordinate with water molecules rather than with the hydroxyl (OH-) groups which make up the bulk of the material. These surface water molecules then tend to lose or gain protons (H+) depending on the H+ concentration in solution.When the pH is low (with a higher H+concentration)the surface gains a H+ resulting in an excess of positive charge. Similarly, when the pH is high

lbble III Examples of Isomorphow Substitutionin Clay Layers and the Amount of Charge Developed Substitutionand net charge Mineral

Vermiculite Muscovite Vermiculite

Octahedral sheet

Tetrahedral sheet

2: 1-type dioctahedral minerals AI,,,M&.3 (-0.3) Siz.6Al,, (-0.4) Si,AI (- 1.00) A4 2: I-type trioctahedral minerals Mg,,Fe,,; (+0.3) Siz,5(Al.Fe),,5(- 1.00)

Charge per unit formula

-0.70 - 1.00 -0.70

94

N. S. BOLAN ETAL. AI(OH);

-

AlOH'

A10

High pH (net negative)

Low pH (net positive)

Figure 3 Development of variable surface positive charge through the dissociation or association of H+on a mineral surface.

the water molecules tend to lose H+,resulting in an excess of negative charge (Fig. 3). Alternatively,depending on the pH, dissociation of H+ (deprotonation)or OH-, (protonation) from the exposed surfaces of minerals and organic matter results in the the creation of surface charge (Fig. 4). Most oxides and hydrous oxides exhibit such amphoteric behavior. Thus, the charge is strongly pH dependent; at low pH a positively charged surface prevails, and at high pH a negatively charged surface. The mechanism by which H+ is lost or gained by soil organic matter differs in that it involves functional groups, such as COOH and OH groups. Again, the process of charge development by organic matter through dissociation of H+ depends on the pH. The pH at which the dissociation of H+ occurs varies between the different functional groups on the organic matter (Morrison and Boyd, 1973). The development of net negative or net positive charge on organic matter depends on the relative distribution of the various functional groups (Harter and Naidu, 1995) and this can influence the metal-binding capacity of soils. Dissociation of H+ from COOH or OH functional groups in soil organic matter results in net negative charge (Fig. 4). Similarly,on a bacterium surface the charge results from protolysis of functional amino (NH,) and COOH groups (Fig. 5 ) (Stumm and Morgan, 1981;Huysman and Verstraete, 1993).

C. SPECIFICADSORPTION OF ANIONSAND CATIONS Charge can also originate by a process in which a solute becomes coordinately bound to solid surface. For example, the enrichment of iron hydrous oxides with phosphate (HPOi-) results in net negative charge: FeOOH,,,

+ HPOi- @ FeOHPO& + OH-

(3)

This phenomenon, which is frequently referred to in the literature as specific adsorption, causes charge reversal (Parfitt, 1978; Barrow, 1985). Specific adsorption

COOH

COO

+

OH

0

+

H' H+

(pH>3) @H>9)

Figure 4 Development of variable surface positive charge through the dissociation or association of H+on an organic matter surface.

SURFACE CHARGE AND SOLUTE INTERACTIONS

95

Figure 5 Developmentof variable surface positive charge through the dissociation or association of H+on a bacterium surface.

of anions and cations conveys negative (Hingston et al., 1972; Ryden and Syers, 1975; Wann and Uehara, 1978a; Naiduef al., 1990a) and positive charge (Parks, 1967; Bolan et al., 1993), respectively, to the surface. Many studies have shown that specific adsorption of anions and cations increases the net surface charge of variable-charge surfaces (Table IV). The amount and the nature of surface charge acquired through specific adsorption depends on the nature of anion and cation adsorbed and the pH and electrolyte concentration of the solute (Bowden et al., 1980; Yu, 1997). Addition of HPOi- and silicate (SO:-) to soils has been done in an attempt to increase the negative charge [or cation exchange capacity (CEC) of variable-charge soils (Wann and Uehara, 1978a,b;Naidu et al., 1990b).The mechanisms involved in the development of surface charge through specific adsorption will be discussed in detail in Section IX. In summary, permanent charge in soils originates from isomorphous substitution of metal ions in layer silicate minerals. This charge develops over a long period during pedogenic weathering and cannot easily be altered by soil management Table IV Increase in Surface Charges Due to SpecificAdsorption of Anions and Cations Soil constituent

Solute

pH

Iron hydrous oxide Allophane Soil

Phosphate Phosphate Phosphate Sulfate Phosphate

6.5 5.1 6.5 6.5

Soil

Soil Soil Aluminum oxide Soil Soil Soil

Phosphate Phosphate Sulfate Sulfate Calcium Calcium

5.0 6.5 7.5 7.0 5.8

5.0 5.6 5.8 5.8

Charge added (mol mol-' ion) 1.25

0.5 0.65 0.26 0.38 0.47 0.77 0.35-0.7

0.52 1.06 0.25 0.35-0.58 0.52

Reference Bolan et al. (1985) Rajan et al. (1974) Bolan et al. (1986b) Sawhney ( 1974)

Schalscha et al. (1974) Naidu et al. (199Ob) Rajan (1978) Curtin and Syers ( 1990) Bolan et a[. (1993) Ryden and Syers (1975)

96

N. S. BOLAN ETAL.

practices. Variable charge in soils originates from the dissociation of H+or OHfrom functional groups of soil organic matter and from the specific adsorption of anions and cations. This charge can be altered through soil management practices. It is important to note that most soils contain both permanent- and variable-charge components.

Iv. COMPONENTS OF SURFACE CHARGE Based on the structure of the solid soil components and their reactions with aqueous species, five principal charge components have been identified (Breeuwsma and Lyklema, 1971, 1973; Sposito, 1981): structural (uo),net proton (aH), inner-sphere complex (uis),outer-sphere complex (uos),and diffuse