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ance. In addition, the salt composition of the soil water influences the composition of cations on the exchange Due in large measure to the prodigious research efforts of Rhoades complex of soil particles, which influences soil permeand his colleagues at the George E. Brown, Jr., Salinity Laboratory ability and tilth, depending on salinity level and exover the past two decades, soil electrical conductivity (EC), measured changeable cation composition. Aside from decreasing using electrical resistivity and electromagnetic induction (EM), is among the most useful and easily obtained spatial properties of soil crop yield and impacting soil hydraulics, salinity can that influences crop productivity. As a result, soil EC has become detrimentally impact ground water, and in areas where one of the most frequently used measurements to characterize field tile drainage occurs, drainage water can become a disvariability for application to precision agriculture. The value of spatial posal problem as demonstrated in the southern San measurements of soil EC to precision agriculture is widely acknowl-Joaquin Valley of central California. edged, but soil EC is still often misunderstood and misinterpreted. From a global perspective, irrigated agriculture makes To help clarify misconceptions, a general overview of the application an essential contribution to the food needs of the world. of soil EC to precision agriculture is presented. The following areas While only 15% of the world's farmland is irrigated, are discussed with particular emphasis on spatial EC measurements: roughly 35 to 40% of the total supply of food and fiber a brief history of the measurement of soil salinity with EC, the basic comes from irrigated agriculture (Rhoades and Lovetheories and principles of the soil EC measurement and what it actually day, 1990). However, vast areas of irrigated land are measures, an overview of the measurement of soil salinity with various threatened by salinization. Although accurate world-EC measurement techniques and equipment (specifically, electrical wide data are not available, it is estimated that roughly resistivity with the Wenner array and EM), examples of spatial EC half of all existing irrigation systems (totaling about 250 surveys and their interpretation, applications and value of spatial measurements of soil EC to precision agriculture, and current and million ha) are affected by salinity and waterlogging future developments. Precision agriculture is an outgrowth of techno-(Rhoades and Loveday, 1990). logical developments, such as the soil EC measurement, which facili-Salinity within irrigated soils clearly limits productivtate a spatial understanding of soil-water-plant relationships. The ity in vast areas of the USA and other parts of the world. future of precision agriculture rests on the reliability, reproducibility, It is generally accepted that the extent of salt-affected and understanding of these technologies. soil is increasing. In spite of the fact that salinity buildup on irrigated lands is responsible fo...

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The ability to inventory and map soil salinity at regional scales remains a significant challenge to scientists concerned with the salinization of agricultural soils throughout the world. Previous attempts to use satellite or aerial imagery to assess soil salinity have found limited success in part because of the inability of methods to isolate the effects of soil salinity on vegetative growth from other factors. This study evaluated the use of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery in conjunction with directed soil sampling to assess and map soil salinity at a regional scale (i.e., 10-10(5) km(2)) in a parsimonious manner. Correlations with three soil salinity ground truth datasets differing in scale were made in Kittson County within the Red River Valley (RRV) of North Dakota and Minnesota, an area where soil salinity assessment is a top priority for the Natural Resource Conservation Service (NRCS). Multi-year MODIS imagery was used to mitigate the influence of temporally dynamic factors such as weather, pests, disease, and management influences. The average of the MODIS enhanced vegetation index (EVI) for a 7-yr period exhibited a strong relationship with soil salinity in all three datasets, and outperformed the normalized difference vegetation index (NDVI). One-third to one-half of the spatial variability in soil salinity could be captured by measuring average MODIS EVI and whether the land qualified for the Conservation Reserve Program (a USDA program that sets aside marginally productive land based on conservation principles). The approach has the practical simplicity to allow broad application in areas where limited resources are available for salinity assessment.

We describe a regression-based statistical methodology suitable for predicting field scale spatial salinity (ECe) conditions from rapidly acquired electromagnetic induction (ECa) data. This technique uses multiple linear regression (MLR) models to estimate soil salinity from ECa survey data. The MLR models incorporate multiple ECa measurements and trend surface parameters to increase the prediction accuracy and can be fitted from limited amounts of ECe calibration data. This estimation technique is compared to some commonly recommended cokriging techniques, with respect to statistical modeling assumptions, calibration sample size requirements, and prediction capabilities. We show that MLR models are theoretically equivalent to and cost-effective relative to cokriging for estimating a spatially distributed random variable when the residuals from the regression model are spatially uncorrelated. MLR modeling and prediction techniques are demonstrated with data from three salinity surveys.