Crop yield varies within a field because conventional farming manages fields uniformly with no consideration for spatial variability. This results in an inefficient use of finite resources (e.g., fertilizer, irrigation water, etc.) and a tendency to impact the environment detrimentally. Site-Specific Management Units (SSMUs) have been proposed as a means of handling the spatial variability of various edaphic, biological, anthropogenic, topographic, and meteorological factors influencing within-field crop yield variation to increase crop productivity and reduce environmental impacts on soil and water resources. It is the objective to present a case study of an irrigated cotton field that describes the equipment, methods, and techniques for delineating SSMUs using GPS-referenced apparent soil Electrical Conductivity (ECa) measurements. A westside San Joaquin Valley field (32.4 ha) in the Broadview Water District was used to demonstrate how spatial distributions of ECa can be used to guide a soil sample design to determine the soil properties influencing seed cotton yield and from this information develop SSMUs. Soil sample sites were selected based upon a statistical sample design utilizing intensive spatial ECa measurements. Statistical results are presented from correlation and regression analyses to assess the relationship between cotton yield and the spatial variability of pH, B, NO3-N, Cl, salinity (ECe), leaching fraction (LF), gravimetric water content (Θ), bulk density (ρ), % clay, and saturation percentage. Correlation coefficients of -0.01, 0.50, -0.03, 0.25, 0.53, -0.49, 0.42,-0.29, 0.36, and 0.38, respectively, were determined. The correlation coefficient between yield and ECa was 0.51. A site-specific response model of cotton yield was developed based on ordinary least squares (OLS) regression analysis and adjusted for spatial autocorrelation using maximum likelihood. The crop-yield response model indicated that salinity, plant-available water, leaching fraction, and pH were the most significant soil properties influencing cotton yield at the study site: cotton yield (Mg/ha) = 19.28 + 0.22(ECe) - 0.02(ECe)2 - 4.42(LF)2 - 1.99(pH) + 6.93(Θ) + ε. Statistical correlations, scatter plots, and crop-yield response model provided the basic information for delineating SSMUs, which largely reflected the variability of irrigation distribution and its effect on cotton yield. The methodology for delineating SSMUs can be used whenever ECa correlates with yield. The delineated SSMUs provide the basic information needed by variable-rate irrigation technology to apply water when, where, and in the amounts needed. Variable-rate irrigation technology provides a potential means of reducing agricultural demands on water.