Apparent soil electrical conductivity measurements in agriculture
Introduction
Over the past three decades, global agriculture has made tremendous progress in expanding the world's supply of food. Even though the world population has doubled over this time period, food production has risen even faster with per capita food supplies increasing from less than 2000 calories per day in 1962 to more than 2500 calories in 1995 (World Resources Institute, 1998). The rise in global food production has been credited to better seeds, expanded irrigation, and higher fertilizer and pesticide use, commonly referred to as the Green Revolution.
The prospect of feeding a projected additional 3 billion people over the next 30 years poses more challenges than encountered in the past 30 years. In the short term, global resource experts predict that there will be adequate global food supplies, but the distribution of those supplies to malnourished people will be the primary problem. Longer term, however, the obstacles become more formidable, though not insurmountable. Although total yields continue to rise on a global basis, there is a disturbing decline in yield growth with some major crops such as wheat and maze reaching a “yield plateau” (World Resources Institute, 1998). Feeding the ever-increasing world population will require a sustainable agricultural system that can keep pace with population growth.
In an effort to feed the world population, agriculture has had detrimental impacts due to the loss of natural habitat, the use and misuse of pesticides and fertilizers, and soil and water resource degradation. By 1990, poor agricultural practices had contributed to the degradation of 38% of the roughly 1.5 billion ha of crop land worldwide and since 1990 the losses have continued at a rate of 5–6 million ha annually (World Resources Institute, 1998). From a global perspective, irrigated agriculture makes an essential contribution to the food needs of the world. While only 15% of the world's farmland is irrigated, roughly 35–40% of the total supply of food and fiber comes from irrigated agriculture (Rhoades and Loveday, 1990). Yet, poor water management on irrigated crop land has resulted in 10–15% of all irrigated land suffering some degree of waterlogging and salinization. In fact, waterlogging and salinization alone represent a significant threat to the world's productivity capacity (Alexandratos, 1995).
Barring unexpected technological breakthroughs, sustainable agriculture is viewed as the most viable means of meeting the food demands of the projected world's population. The concept of sustainable agriculture is predicated on a delicate balance of maximizing crop productivity and maintaining economic stability while minimizing the utilization of finite natural resources and the detrimental environmental impacts of associated agrichemical pollutants. Arguably, the most promising approach for attaining sustainable agriculture, and thereby keeping agricultural productivity apace with population growth, is precision agriculture. Site-specific crop management refers to the application of precision agriculture to crop production.
Conventional farming currently treats a field uniformly, ignoring the naturally inherent variability of soil and crop conditions between and within fields. Ever since the classic paper by Nielson et al. (1973) concerning the variability of field-measured soil water properties, the significance of within-field spatial variability of soil properties has been scientifically acknowledged and documented. However, until recently, with the introduction of global positioning systems (GPS; see Appendix A for a list of abbreviations) and yield-monitoring equipment, documentation of crop yield and soil variability at field-scale was difficult to establish. Now there is well-documented evidence that spatial variability within a field is highly significant and amounts to a factor of 3–4 or more for crops (Birrel et al., 1995, Verhagen et al., 1995) and up to an order of magnitude or more for soils (Corwin et al., 2003a).
Spatial variation in crops is the result of a complex interaction of biological (e.g., pests, earthworms, microbes), edaphic (e.g., salinity, organic matter, nutrients, texture), anthropogenic (e.g., leaching efficiency, soil compaction due to farm equipment), topographic (e.g., slope, elevation), and climatic (e.g., relative humidity, temperature, rainfall) factors. Site-specific crop management aims to manage soils, pests, and crops based upon spatial variations within a field (Larson and Robert, 1991). Specifically, site-specific crop management is the management of agricultural crops at a spatial scale smaller than the whole field by considering local variability with the aim of cost effectively maximizing crop production and making efficient use of agrichemicals to minimize detrimental environmental impacts.
Precision agriculture utilizes rapidly evolving electronic information technologies to modify land management in a site-specific manner as conditions change spatially and temporally (van Schilfgaarde, 1999). First conceived in the mid-1980s, the technological pieces needed to bring precision agriculture into its own fell into place in the mid-1990s with the maturation of global positioning systems (GPS) and geographical information systems (GIS). As such, precision agriculture is a technologically driven system (van Schilfgaarde, 1999). The fundamental components of precision agriculture include newly commercialized technologies of GPS, yield-monitoring, and variable rate agrichemical application combined with adaptation of existing technologies of GIS and remote sensing (e.g., electromagnetic induction, aerial photography, satellite- and airborne multispectral imagery, microwave, and hyperspectral imagery) or rapid invasive soil property measurement technologies (e.g., electrical resistivity, time domain reflectometry) (Plant, 2001).
To manage within-field variability, geo-referenced areas displaying similar behavior with respect to a specified characteristic (e.g., yield potential, leaching potential) must be identified (van Uffelen et al., 1997). It must also be established to what extent and under what conditions these spatial patterns are stable. Yield maps provide information on the integrated effects of the physical, chemical, and biological processes under certain weather conditions (van Uffelen et al., 1997) and provide the basis for implementing site-specific crop management by indicating where varying cropping inputs are needed based upon spatial patterns of crop productivity (Long, 1998). However, the cropping inputs necessary to optimize productivity and minimize environmental impacts can be derived only if it is known what factors gave rise to the observed spatial crop patterns (Long, 1998). Yield maps alone cannot provide information to distinguish between the various sources of variability and cannot provide clear guidelines without information concerning the influence of the variability of weather, pests and diseases, and soil physico-chemical properties on the variability of a crop for a particular year (van Uffelen et al., 1997).
To a varying extent from one field to the next, crop patterns are influenced by edaphic or soil-related properties. Bullock and Bullock (2000) point out that efficient methods for accurately measuring within-field variations in soil physical and chemical properties are important for precision agriculture. The measurement of apparent soil electrical conductivity (ECa) is a technology that has become an invaluable tool for identifying the soil physico-chemical properties influencing crop yield patterns and for establishing the spatial variation of these soil properties (Corwin et al., 2003b).
Precision agriculture not only requires spatial information to determine where and how much of an input (e.g., fertilizers, pesticides, irrigation water) to apply, but also requires temporal information to know when to apply. To know when to apply an input, particularly when to irrigate, requires real-time measurements of plant and/or soil conditions. Real-time measurements of plant condition, and to a limited extent soil condition, are best obtained with multi- and hyper-spectral imagery. Even though multi- and hyper-spectral imagery are still in their infancy for answering questions related to when inputs should be applied, their potential for answering time-related management questions is greater than for geospatial ECa measurements. Imagery has the advantage of monitoring plant condition over large areas in a short time frame, whereas ECa monitors the soil, which must be related back to plant response. However, the problem with imagery has been that in some instances (e.g., water stress) by the time imagery detects a change in plant condition, such as exceeding the wilting point, it may be too late to rectify the condition and damage may have already occurred. Nonetheless, the extremely rapid, landscape-scale measurement of plant response with multi- and hyper-spectral imagery makes it more practical for real-time measurements of plant condition, which are necessary to determine the timing of inputs within a precision agriculture management framework. Spatio-temporal measurements of ECa are best suited for historical or year-to-year assessments of trend, such as salinization of a soil or reclamation of a salt-affected soil.
It is the objective of this review to provide the reader with (i) an understanding of the basic theories and principles of the ECa measurement and what it actually measures, (ii) an overview of various ECa measurement techniques (i.e., electromagnetic induction, electrical resistivity, time domain reflectometry), (iii) applications of ECa measurements in agriculture, particularly site-specific crop management, (iv) guidelines for conducting an ECa survey, and (v) current trends and future developments in the application of ECa to precision agriculture.
Section snippets
Basic principles of the ECa measurement
A comprehensive and instructive discussion of the theory and principles of the ECa measurement is presented by Hendrickx et al. (2002a). An overview of the basic theories and principles is presented herein.
Original application of the ECa measurement in agriculture
The first application of ECa in agriculture was for the measurement of soil salinity. Research in this area was primarily conducted by Rhoades and colleagues in the 1970's at the USDA-ARS Salinity Laboratory in Riverside, CA. Soil salinity refers to the presence of major dissolved inorganic solutes in the soil aqueous phase, which consist of soluble and readily dissolvable salts including charged species (e.g., Na+, K+, Mg+2, Ca+2, Cl−, HCO3−, NO3−, SO4−2 and CO3−2), non-ionic solutes, and ions
Applications of ECa measurements in precision agriculture
Efficient methods for accurately measuring within-field variations in soil physical and chemical properties are a crucial element of precision agriculture (Bullock and Bullock, 2000). The ability to delineate geo-referenced areas within a field that display similar behavior with respect to crop yield potential, referred to as site-specific management units (SSMUs), is difficult due to the complex combination of edaphic, anthropogenic, biological, and meteorological factors that affect crop
Guidelines for conducting a field-scale ECa survey
Geo-referenced measurements of ECa are potentially useful for determining the spatial distribution of those soil properties influencing ECa at that particular location. In instances where ECa correlates with a particular soil property, an ECa-directed soil sampling approach will establish the spatial distribution of that property with an optimum number of site locations to characterize the variability and keep labor costs minimal (Corwin et al., 2003a). Also, if ECa is correlated with crop
Future needed developments and current trends in the application of ECa to precision agriculture
Future developments that are needed to better focus current research in the application of ECa to precision agriculture include protocols and guidelines for (i) conducting an ECa survey and (ii) delineating SSMUs. There are many previous examples of ECa surveys applied to precision agriculture or to soil spatial variability characterizations that have been misused, misunderstood, and/or misinterpreted. For this reason, a recent USDA-ARS Precision Agriculture Workshop (Kansas City, MO; 25–27
Acknowledgments
The senior author wishes to thank Dr. Dan Schmoldt, Editor-in-Chief of Computers and Electronics in Agriculture, for the invitation to serve as the guest editor for the Special Issue entitled “Applications of ECa Measurements in Precision Agriculture.” The authors also wish to extend their appreciation to Dr. Dan Schmoldt and the staff of Computers and Electronics in Agriculture for their assistance and hard work in bringing this Special Issue to publication. Their professionalism and
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