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ARS Home » Research » Publications at this Location » Publication #154267
Title: INFLUENCE OF SOIL SPATIAL VARIABILITY ON CROP YIELD VARIABILITY

Author
item Corwin, Dennis
item LESCH, SCOTT - UC RIVERSIDE, CA
item Shouse, Peter
item SOPPE, RICHARD - UC DAVIS, CA
item Jobes, Jack
item Fargerlund, Joan
item Ayars, James

Submitted to: Agronomy Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 9/8/2003
Publication Date: 11/5/2003
Citation: Corwin, D.L., Lesch, S.M., Shouse, P.J., Soppe, R., Jobes, J.A., Fargerlund, J., Ayars, J.E. 2003. Influence of soil spatial variability on crop yield variability. Soil Science Society of America. Paper No. S05-corwin929996-P.

Interpretive Summary:

Technical Abstract: Spatial measurements of apparent soil electrical conductivity (ECa) are useful for characterizing spatial variability because ECa is influenced by several soil properties. In instances where ECa correlates with crop yield, maps of ECa are useful for devising sampling schemes to identify soil properties influencing yield. A San Joaquin Valley field (32.4 ha) was used to show how spatial distributions of ECa can guide a soil sample design to identify soil properties influencing seed cotton yield and to characterize soil spatial variability influences on yield variability. Soil samples 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 (ECe), leaching fraction (LF), 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 with ordinary least squares regression and adjusted for spatial autocorrelation using restricted maximum likelihood. The response model indicated that ECe, plant-available water, LF, and pH were the most significant properties influencing within-field variations in yield. The maps, correlations, and response model provide information for delineating site-specific management units.