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

Abstract

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 (EC a ) 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 tech-EC a . Nevertheless, in instances where yield correlates with EC a , maps of EC a 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 Val-manner 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 EC a 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 EC a 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, NO 3 -N, Cl Ϫ , salinity, leaching fraction (LF), tem (van Schilfgaarde, 1999). First conceived in the mid gravimetric water content, bulk density, percentage clay, and satura-1980s, the technological pieces needed to bring precision tion percentage. Correlation coefficients of Ϫ0.01, 0.50, Ϫ0.03, 0.25, agriculture into its own began to fall into place in the 0.53, Ϫ0.49, 0.42, Ϫ0.29, 0.36, and 0.38, respectively, were determined. mid 1990s with the maturation of global positioning sys-A 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 knowledg...

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