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This article in AJ

  1. Vol. 95 No. 2, p. 352-364
    Received: Mar 22, 2002

    * Corresponding author(s): dcorwin@ussl.ars.usda.gov
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Identifying Soil Properties that Influence Cotton Yield Using Soil Sampling Directed by Apparent Soil Electrical Conductivity

  1. D. L. Corwin *a,
  2. S. M. Lescha,
  3. P. J. Shousea,
  4. R. Soppeb and
  5. J. E. Ayarsb
  1. a USDA-ARS, George E. Brown, Jr., Salinity Lab., 450 West Big Springs Rd., Riverside, CA 92507-4617
    b USDA-ARS, Water Manage. Res. Lab., 9611 S. Riverbend Ave., Parlier, CA 92648


Crop yield inconsistently correlates with apparent soil electrical conductivity (ECa) because of the influence of soil properties (e.g., salinity, water content, texture, etc.) that may or may not influence yield within a particular field and because of a temporal component of yield variability that is poorly captured by a state variable such as ECa Nevertheless, in instances where yield correlates with ECa, maps of ECa are useful for devising soil sampling schemes to identify soil properties influencing yield within a field. A west side San Joaquin Valley field (32.4 ha) was used to demonstrate how spatial distributions of ECa can guide a soil sample design to determine the soil properties influencing seed cotton (Gossypium hirsutum L.; ‘MAXXA’ variety) yield. Soil sample sites were selected with a statistical sample design utilizing spatial ECa measurements. Statistical results are presented from correlation and regression analyses between cotton yield and the properties of pH, B, NO3–N, Cl, salinity, leaching fraction (LF), gravimetric water content, bulk density, percentage 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. A site-specific response model of cotton yield was developed based on ordinary least squares regression analysis and adjusted for spatial autocorrelation using maximum likelihood. The response model indicated that salinity, plant-available water, LF, and pH were the most significant soil properties influencing cotton yield at the study site. The correlations and response model provide valuable information for site-specific management.

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Copyright © 2003. American Society of AgronomyPublished in Agron. J.95:352–364.