Scott M. Lesch

Application of soil electrical conductivity to precision agriculture: Theory, principles, and guidelines

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 acknowlJoaquin 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 worldEC 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 faciliSalinity 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 for the declining resource base for agriculture, we do not know the exact T predominant mechanism causing the salt accuextent to which soils in our country are salinized, the mulation in irrigated agricultural soils is evapotransdegree to which productivity is being reduced by salinpiration. The salt contained in the irrigation water is ity, the increasing or decreasing trend in soil salinity left behind in the soil as the pure water passes back to development, and the location of contributory sources the atmosphere through the processes of evaporation of salt loading to ground and drainage waters. Suitable and plant transpiration. The effects of salinity are manisoil inventories do not exist and until recently, neither fested in loss of stand, reduced rates of plant growth, did practical techniques to monitor salinity or assess the reduced yields, and in severe cases, total crop failure (Rhoades and Loveday, 1990). Salinity limits water upAbbreviations: EC, electrical conductivity; ECa, apparent soil electritake by plants by reducing the osmotic potential and cal conductivity; ECe, electrical conductivity of the saturated soil paste thus the total soil water potential. Salinity may also extract; ECw, electrical conductivity of soil water; EM, electromagnetic cause specific ion toxicity or upset the nutritional balinduction; EMavg, the geometric mean of the vertical and horizontal electromagnetic induction readings; EMh, electromagnetic induction measurement in the horizontal coil-mode configuration; EMv, electroUSDA-ARS, George E. Brown, Jr., Salinity Lab., 450 West Big magnetic induction measurement in the vertical coil-mode configuraSprings Rd., Riverside, CA 92507-4617. Received 23 Apr. 2001. *Cortion; GIS, geographical information system; GPS, global positioning responding author (dcorwin@ussl.ars.usda.gov). systems; NPS, nonpoint source; SP, saturation percentage; TDR, time domain reflectometry; w, total volumetric water content. Published in Agron. J. 95:455–471 (2003).

Remote- and Ground-Based Sensor Techniques to Map Soil Properties

Farm managers are becoming increasingly aware of the spatial variability in crop production with the growing availability of yield monitors. Often this variability can be related to differences in soil properties (e.g., texture, organic matter, salinity levels, and nutrient status) within the field. To develop management approaches to address this variability, high spatial resolution soil property maps are often needed. Some soil properties have been related directly to a soil spectral response, or inferred based on remotely sensed measurements of crop canopies, including soil texture, nitrogen level, organic matter content, and salinity status. While many studies have obtained promising results, several interfering factors can limit approaches solely based on spectral response, including tillage conditions and crop residue. A number of different ground-based sensors have been used to rapidly assess soil properties “on the go” (e.g., sensor mounted on a tractor and data mapped with coincident position information) and the data from these sensors compliment image-based data. On-the-go sensors have been developed to rapidly map soil organic matter content, electrical conductivity, nitrate content, and compaction. Model and statistical methods show promise to integrate these groundand image-based data sources to maximize the information from each source for soil property mapping.

Assessment and field-scale mapping of soil quality properties of a saline-sodic soil

Salt-affected soils could produce useful forages when irrigated with saline drainage water. To assess the productive potential and sustainability of using drainage water for forage production, a saline-sodic site (32.4 ha) in Californias San Joaquin Valley was characterized for soil quality. The objectives were (1) to spatially characterize initial soil physicochemical properties relevant to maintaining soil quality on an arid zone soil and (2) to characterize soil quality relationships and spatial variability. An initial mobile electromagnetic (EM) induction survey was conducted in 1999, with bulk soil electrical conductivity (ECa) readings taken at 384 geo-referenced locations, followed by an intensive mobile fixed-array survey with a total of 7288 geo-referenced ECa readings. Using the EM data and a spatial statistics program (ESAP v2.0), 40 sites were selected that reflected the spatial heterogeneity of the ECa measurements for the study area. At these sites, soil-core samples were taken at 0.3-m intervals to a depth of 1.2 m. Duplicate samples were taken at eight sites to study the local-scale variability of soil properties. Soil-core samples were analyzed for a variety of physical and chemical properties related to the soil quality of arid zone soils. Soils were found to be highly spatially heterogeneous. For composite soil-core samples taken to a depth of 1.2 m, ECe (electrical conductivity of the saturation extract) varied from 12.8 to 36.6 dS m−1, SAR from 28.8 to 88.8, and clay content from 2.5% to 48.3%. B and Mo concentrations varied from 11.5 to 32.2 mg l−1 and 476.8 to 1959.6 μg l−1, respectively. CaCO3, NO3− in the saturation extract, exchangeable Ca2+, Se, and As consistently had the highest coefficients of variation (CV) while pHe, ρb, and Ca2+ in the saturation extract consistently had the lowest CVs at all depths. A one-way analysis of variance (ANOVA) was used to spatially partition the local- and global-scale variability. Local-scale variability was greatest for pHe. Laboratory measurements of saturated hydraulic conductivity (Ks) were very low (0.0000846–0.0456 cm h−1), whereas field measurements were considerably higher (0.49–1.79 cm h−1). Based on the Cl− data, the leaching fraction (LF) for the entire study area was estimated to be 17%. Soil quality was reflected in yield and chemical analysis of forage. Forage Mo contents determined from newly established Bermuda grass varied from 1 to 5 mg kg−1 on a dry matter basis, and Cu/Mo ratios averaged 3.3, while forage yield in the establishment year declined with ECe, and failed to grow above ECe levels of approximately 22 dS m−1. The initial soil quality assessment of the research site indicated that the sustainability of drainage water reuse at this location would depend upon maintaining a sufficient LF with careful consideration and management of salinity, boron, molybdenum, and sodium levels.

Regional-scale Assessment of Soil Salinity in the Red River Valley Using Multi-year MODIS EVI and NDVI

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.

Influence Of Anion Competition On Boron Adsorption By Clays And Soils

Boron adsorption on the clay minerals, kaolinite and montmorillonite, and two arid zone soils was investigated as a function of solution pH (3-12) and presence of competing anions (nitrate, sulfate, molybdate, and phosphate) after 2 h of reaction time. Boron adsorption on all materials increased from pH 3 to 8, exhibited a peak at pH 8 to 10, and decreased from pH 10 to 12. Boron adsorption was greatest using a NaNOJ background electrolyte. The competitive anion effects on B adsorption increased in the order sulfate < molybdate < phosphate. The competitive effect on B adsorption was small even for the strongly adsorbing anion, phosphate. Our results suggest that B-adsorbing sites are, generally, specific to B and act independently of competing anions. This result will simplify the description of B transport in that changes in solution concentration of competing anions may not have to be considered.

Effect of high boron application on boron content and growth of melons

Management options for reducing drainage water volumes on the west side of the San Joaquin Valley of California, such as reuse of saline drainage water and water table control, have the potential to adversely impact crop yields due to a build up in soil solution boron concentration. An earlier experiment had shown that extrapolation of B soil tests to field conditions provided poor predictability of B content of melons despite statistically significant relationships. Consequently, three tests for extractable soil B were evaluated for their ability to predict conditions of potential B toxicity in melons grown under controlled conditions. Melons were grown for 95 days in two consecutive years in containers of Lillis soil (very-fine, smectitic, thermic Halic Haploxerert) that had been pretreated with solutions containing B concentrations as great as 5.3 mmol L−1. Extractable soil B was determined using ammonium acetate, DTPA-sorbitol, and a 1:1 aqueous soil extract at the beginning and end of the experiment. The B treatments caused various deleterious effects on melon growth and development. Fresh and dry plant matter decreased significantly with increasing B concentrations, while B concentration of plant leaves, stems, and fruits increased significantly with increasing B. The number of days to first flowering was significantly delayed from 35 days at B treatments < 2 mmol L−1 to 51 days at B treatments > 3 mmol L−1. Fruit set was completely inhibited at the highest B treatment of 5.3 mmol L−1. Plant analysis revealed a highly significant relationship between soil extract B obtained with all three extractants and leaf, stem, and fruit B content. Correlation coefficients for plant stems and fruits were much higher than for plant leaves. Correlation coefficients for all soil tests were almost equivalent, although the highest values were obtained for the DTPA-sorbitol extract indicating the greatest predictive capability. The soil tests were well able to predict B damage to melons in a container experiment.

Using the dual-pathway parallel conductance model to determine how different soil properties influence conductivity survey data

1995), sand deposition (Kitchen et al., 1996), and moisture content (Kachanoski et al., 1988; Sheets and HenThe correlation structure between apparent soil electrical conducdrickx, 1995). Additionally, there are articles documenttivity (ECa) and various soil properties can often appear radically dissimilar in different field surveys. Ideally, some type of methodology ing the use of conductivity survey information for yield for survey data validation should be developed that can predict the mapping (Jaynes et al., 1995), determining salt loading expected correlation structure between ECa survey data and various and field irrigation efficiency (Rhoades et al., 1997), soil properties, given information about the soil properties themselves. estimating leaching and salt loading (Corwin et al., 1996, In this paper, we review an existing model for ECa and hypothesize that 1999; Rhoades et al., 1999a), estimating deep drainage this model can be used to accurately predict the expected correlation (Triantafilis et al., 1998), and designing optimal salinity structure between ECa data and multiple soil properties of interest sampling and monitoring strategies (Lesch et al., 1995b, (such as soil salinity, saturation-paste percentage, and soil water con1998). A comprehensive review of the various methods tent). Our objective is twofold: (i) to demonstrate how this model of soil salinity assessment via electrical conductivity can be employed to produce the expected correlation structure and measurements is given in Rhoades et al. (1999b) and (ii) to extend this ECa model to handle survey data collected under low water content situations by dynamically adjusting the model’s Hendrickx and Kachanoski (2002), and the use of conassumed water content function. This adjustment can be estimated ductivity survey information for precision-farming apusing acquired ECa signal and soil sample data, and its statistical plications is discussed in Rhoades et al. (1999b) and significance can be determined for each specific survey situation. We Corwin and Lesch (2003). demonstrate both of these techniques using acquired electromagnetic Particular interest in any given ECa survey is often induction signal data and measured soil properties of interest from focused toward ensuring that the acquired ECa data 12 different field salinity surveys performed in California and Colocorrelate well with the prespecified target soil variable. rado and in Alberta, Canada. Results from these 12 surveys suggest For example, in a soil salinity survey, one generally that the ordinary model is able to accurately predict the expected attempts to maximize the correlation between salinity correlation structure between conductivity and soil property when and ECa by minimizing the corresponding variation in the water content is near field capacity and that the dynamically adjusted model is able to substantially improve the accuracy of the soil texture and water content using different area subspredicted correlation structure when the water content is significantly tratification schemes (for minimizing texture variation) below field capacity. or timing strategies (for minimizing water content variation). In spite of these efforts, considerable variation is sometimes observed in the observed correlation beW the last decade, the collection of spatial tween salinity and ECa. Indeed, as indicated by the soil electrical conductivity data has played an inabove-mentioned references, ECa often correlates to creasingly important role in precision-farming research. some degree with several different soil properties. FurThe collection of such data typically focuses on the thermore, the strength of these observed correlation assessment of spatial variation in one or more soil propestimates can vary widely from one survey to the next. erties, as inferred by the observed spatial variation in To a certain extent, these apparent inconsistencies have the ECa survey data. Depending on the specific survey resulted in some unfortunate confusion in the general application, the target variable of interest is usually soil soils literature with respect to how well different soil salinity, soil texture, and/or soil water content although properties are expected to correlate with ECa data. sometimes information about additional soil properties Rhoades et al. (1989) developed a model for ECa may also be ascertained (i.e., organic matter, clay conbased on data collected across the arid southwestern tent, sodium adsorption ratio, B, etc.). United States. This model was developed to predict the There are numerous technical articles that document effects that soil salinity, soil texture, bulk density ( b), the relationships between ECa and various soil physicochemical properties, including soil salinity (Williams and Abbreviations: CalcECa, calculated soil electrical conductivity; DPPC, Baker, 1982; Rhoades et al., 1989; Slavich and Petterson, dual-pathway parallel conductance (model); Dy-DPPC, dynamic wa1990; Hendrickx et al., 1992; Rhoades, 1992, 1996a, ter content partitioning (model); ECa, apparent soil electrical conductivity; ECe, electrical conductivity of the saturated soil extract; ECwc, 1996b; Lesch et al., 1995a;), clay content (Williams and specific electrical conductivity of the continuous soil water phase; ECws, Hoey, 1987; Cook et al., 1992), depth to clay layers specific electrical conductivity of the series-coupled soil water phase; (Doolittle et al., 1994), nutrient status (Suddeth et al., EM, electromagnetic induction; EMavg, average electromagnetic induction; SP, saturation percentage; g, gravimetric soil water content; w, volumetric soil water content; wc, volumetric soil water content USDA-ARS George E. Brown, Jr., Salinity Lab., 450 West Big Springs in the continuous liquid pathway; ws, volumetric soil water content in Rd., Riverside, CA 92507-4617. Received 29 Mar. 2002. *Correspondthe series-coupled pathway; b, bulk density; wc, adjusted volumetric ing author (slesch@ussl.ars.usda.gov). soil water content of continuous liquid pathway; ws, adjusted volumetric soil water content of series-coupled pathway. Published in Agron. J. 95:365–379 (2003). 365 Published March, 2003

Assessing irrigation/drainage/salinity management using spatially referenced salinity measurements

Abstract A unique technology-package for measuring the spatial distributions of salinity in irrigated soils and fields and for evaluating the appropriateness of some related irrigation-, drainage- and salinity control-management practices is described. This assessment technology is based on the use of: (1) geophysical-instrumental systems for intensively measuring bulk soil electrical conductivity and associated spatial coordinates; (2) statistical algorithms for site selection and salinity calibration; and (3) algorithms for data analysis and graphical display to facilitate interpretation. Results are presented to demonstrate some of the utility of the technology. Additionally, examples are given which show that much of the apparent chaos observed in the spatial pattern of soil salinity in irrigated fields is man-induced and related to such management practices as irrigation, drainage, and tillage.

Field level digital soil mapping of cation exchange capacity using electromagnetic induction and a hierarchical spatial regression model

At the field level the demand for spatial information of soil properties is rapidly increasing owing to its requirements in precision agriculture and soil management. One of the most important properties is the cation exchange capacity (CEC, cmol(+)/kg) because it is an index of the shrink–swell potential and hence is a measure of soil structural resilience to tillage. However, CEC is time-consuming and expensive to measure. Various ancillary datasets and statistical methods can be used to predict CEC, but there is little scientific literature which implements this approach to map CEC or addresses the issue of the amount of ancillary data required to maximise precision and minimise bias of spatial prediction at the field level. We compare a standard least-squares multiple linear regression (MLR) model which includes 2 proximally sensed (EM38 and EM31), 3 remotely sensed (Red, Green and Blue spectral brightness), and 2 trend surface (Easting and Northing) variables as ancillary data or independent variables, and a stepwise MLR model which only includes the statistically valid EM38 signal data and the Easting trend surface vector. The latter is used as the basis for developing a hierarchical spatial regression model to predict CEC. The reliability of the model is analysed by comparing prediction precision (root mean square error) and bias (mean error) using degraded EM38 transect spacing (i.e. 96, 144, 192, 240, and 288 m) and comparing these with predictions achieved with the 48-m spacing. We conclude that the EM38 data available on the 96- and 144-m spacing are suitable at a reconnaissance level (i.e. broad-scale farming) and 24- or 48-m spacing are suitable at smaller levels where detailed information is necessary for siting the location of water reservoirs. In terms of soil management, CEC predictions determine where suitable subsoil exists for the purpose of soil profile inversion to improve the structural resilience of a topsoil that is susceptible to dispersion and surface crusting.

Geostatistical Analysis of a Soil Salinity Data Set

Publisher Summary This chapter presents a typical geostatistical analysis of a data set representative of the diversity and complexity of data sets handled through GIs. There is much more to geographical (spatial) data analysis than performing elementary operations of overlay, merge, and split and then merely mapping data with somewhat arbitrary, eye-pleasing, spline algorithms. The data talk when their geographic interdependence is revealed; there is an essential third component to any two data values taken at two different locations in space or time-their relation is seen as a function of the separation vector linking these two locations. Pictorial and numerical models of patterns of space or time dependence allow us to go far beyond data locations into alternative (stochastic equiprobable) maps that depict the true complexity of the data while always preserving an assessment of uncertainty. This chapter also illustrates the toolbox aspect of geostatistics, presenting several alternative ways to reach the same goal and proposing cross-validation exercises to help the operator in his or her decision.