|Lesch, Scott - UC RIVERSIDE, CA|
|Soppe, Richard - PARLIER, CA|
Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 22, 2002
Publication Date: March 15, 2003
Repository URL: http://www.ars.usda.gov/SP2UserFiles/Place/53102000/pdf_pubs/P1831.pdf
Citation: Corwin, D.L., Lesch, S.M., Shouse, P.J., Soppe, R.W., Ayars, J.E. 2003. Identifing soil properties that influence cotton yield using soil sampling directed by apparent bulk soil electrical conductivity. Agronomy Journal. Vol 95:352-364 Interpretive Summary: Precision agriculture depends upon the ability to determine within a spatial context those soil-related, anthropogenic, and meteorologic factors that influence yield and thereby cause spatial variation in yield across a field. A technique is described that identifies the soil properties influencing the spatial variation of cotton yield within a particular field. The technique is demonstrated on a cotton field in the Broadview Water District of central California's San Joaquin Valley. Soil samples are taken based on maps of bulk soil electrical conductivity. The soil samples are then analyzed for a variety of soil properties known to influence crop yield: salinity, pH, boron, NO_3-N, leaching fraction, bulk density, water content, clay content and saturation percentage. Spatial statistics are then applied to the spatial data of the various soil properties and to a map of the field's cotton yield to identify those soil properties that are most significant in influencing the cotton yield. In this particular field, leaching fraction, salinity, pH, and available water were identified as the important soil-related factors influencing cotton yield. This technique makes it possible to make site-specific recommendations to improve cotton yield.
Technical Abstract: Bivariate regression and boundary line analysis have shown that crop yield inconsistently correlates with apparent bulk soil electrical conductivity (EC_a). This inconsistency is because EC_a is influenced by several soil properties (i.e., salinity, water content, texture, bulk density, organic matter, and temperature) that may or may not influence yield within a particular field. Nevertheless, in instances where yield correlates with EC_a, maps of EC_a are useful for devising soil sampling schemes that can be used to identify soil properties influencing yield within a field. A westside San Joaquin Valley field (32.4 ha) in Broadview Water District was used to demonstrate how spatial distributions of EC_a can be utilized to determine those soil properties influencing cotton yield. Soil sample sites were selected based upon a statistical sample design utilizing intensive spatial EC_a measurements. Statistical results are presented from ordinary least squares (OLS) correlation and regression analyses to assess the relationship between cotton yield and the spatial variability of pH, B, NO_3-N, Cl^-, EC_e, leaching fraction, bulk density, volumetric water content, % clay, and saturation percentage. The Pearson correlation coefficient between yield and EC_a was 0.51. A site-specific response model of cotton yield was developed based on OLS regression analysis and adjusted for spatial autocorrelation with the maximum likelihood approach. The crop-yield response model indicated that salinity, available water, leaching fraction, and pH were the most significant soil properties influencing cotton yield at the study site. The correlations and crop yield response model provide information for site-specific management of fertilizers, plant density, salinity, and water distribution to optimize crop yield while minimizing drainage return flows and detrimental environmental impacts with an acceptable economic return.