SOIL MANAGEMENT Identifying Soil Properties that Influence Cotton Yield Using Soil Sampling Directed by Apparent Soil Electrical Conductivity

tially variable soil properties that affect crop yield to better optimize crop productivity and to maintain the Crop yield inconsistently correlates with apparent soil electrical sustainability of agriculture. conductivity (ECa) because of the influence of soil properties (e.g., Site-specific crop management is the management of salinity, water content, texture, etc.) that may or may not influence soils, pests, and crops based on spatial variations within yield within a particular field and because of a temporal component a field (Larson and Robert, 1991). Site-specific manageof yield variability that is poorly captured by a state variable such as ment utilizes rapidly evolving electronic information techECa. Nevertheless, in instances where yield correlates with ECa, maps of ECa are useful for devising soil sampling schemes to identify soil nologies to modify land management in a site-specific properties influencing yield within a field. A west side San Joaquin Valmanner as conditions change spatially and temporally ley field (32.4 ha) was used to demonstrate how spatial distributions (van Schilfgaarde, 1999). The aim of precision agriculof ECa can guide a soil sample design to determine the soil properties ture is to improve management to increase profitability, influencing seed cotton (Gossypium hirsutum L.; ‘MAXXA’ variety) increase crop productivity, sustain the soil–plant–water yield. Soil sample sites were selected with a statistical sample design environment, and/or reduce detrimental environmental utilizing spatial ECa measurements. Statistical results are presented impacts (Atherton et al., 1999). from correlation and regression analyses between cotton yield and Precision agriculture is a technologically driven systhe properties of pH, B, NO3–N, Cl , salinity, leaching fraction (LF), tem (van Schilfgaarde, 1999). First conceived in the mid gravimetric water content, bulk density, percentage clay, and satura1980s, the technological pieces needed to bring precision tion percentage. Correlation coefficients of 0.01, 0.50, 0.03, 0.25, agriculture into its own began to fall into place in the 0.53, 0.49, 0.42, 0.29, 0.36, and 0.38, respectively, were determined. mid 1990s with the maturation of global positioning sysA site-specific response model of cotton yield was developed based tems (GPS) and geographical information systems (GIS). on ordinary least squares regression analysis and adjusted for spatial These and other new technologies potentially provide autocorrelation using maximum likelihood. The response model indicated that salinity, plant-available water, LF, and pH were the most the ability to (i) quantify yield variability in small areas significant soil properties influencing cotton yield at the study site. of the field; (ii) quantify the spatial variability of soil The correlations and response model provide valuable information properties influencing yield; and (iii) adjust inputs such for site-specific management. as fertilizer, pesticide, and seeding rates based on knowledge of soil and yield variability (Atherton et al., 1999). The measurement of ECa is among the technologies that P and anthropogenic factors result in soil are helping to bring precision agriculture from a concept variation within agricultural fields that affects crop to a tool for addressing the issue of agricultural sustainproductivity. A variety of physicochemical properties of ability. soil influence crop production, including plant-available Bullock and Bullock (2000) point out that efficient water; infiltration; permeability; soil texture and strucmethods for accurately measuring within-field variations ture; soil depth; restrictive soil layers; organic matter; in soil physical and chemical properties are important chemical constituents such as salinity, fertilizers, pestifor precision agriculture. Soil ECa has become one of the cides, trace elements, and toxic ions; meteorology; and most reliable and frequently used measurements to landscape features such as microelevation and topogracharacterize field variability for application to precision phy (Black, 1968; Thornley and Johnson, 1990; Hanks agriculture due to its ease of measurement and reliaand Ritchie, 1991; Tanji, 1996). In laser-leveled, irribility (Rhoades et al., 1999a, 1999b; Corwin and Lesch, gated agricultural lands of the arid southwestern USA, 2003). For instance, it has been previously shown by soil physicochemical properties such as salinity, soil texKitchen et al. (1999) using boundary-line analysis that ture and structure, plant-available water, trace elements soil ECa provides a measure of the within-field soil dif(particularly B), and ion toxicity (Na and Cl ) are the ferences associated with topsoil thickness, which for primary soil factors influencing crop yield (Tanji, 1996). claypan soils, is a measure of root zone suitability for These properties tend to be highly spatially variable. crop growth and yield. The potential of the spatial meaSite-specific crop management (or precision agriculsurement of profile ECa for predicting crop yield due ture) has been proposed as a means of coping with spato soil differences has been reported by Jaynes et al. (1995) and Sudduth et al. (1995). The rapid spatial meaD.L. Corwin, S.M. Lesch, P.J. Shouse, USDA-ARS, George E. Brown, Jr., Salinity Lab., 450 West Big Springs Rd., Riverside, CA 92507Abbreviations: ECa, apparent soil electrical conductivity; ECe, electri4617; and R. Soppe and J.E. Ayars, USDA-ARS, Water Manage. cal conductivity of the saturation extract; GIS, geographical informaRes. Lab., 9611 S. Riverbend Ave., Parlier, CA 92648. Received 22 tion systems; GPS, global positioning systems; LF, leaching fraction; Mar. 2002. *Corresponding author ([email protected]). OLS, ordinary least squares; SP, saturation percentage; g, gravimetric water content; b, bulk density. Published in Agron. J. 95:352–364 (2003).