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United States Department of Agriculture

Agricultural Research Service

Title: Characterizing Soil Spatial Variability for Precision Agriculture Using Geophysical Measurements

Authors
item Corwin, Dennis
item Lesch, Scott - UC RIVERSIDE, CA
item Shouse, Peter
item Soppe, Richard - PARLIER, CA
item Ayars, James

Submitted to: Meeting Abstract
Publication Type: Proceedings
Publication Acceptance Date: February 9, 2004
Publication Date: February 22, 2004
Citation: Corwin, D.L., Lesch, S.M., Shouse, P.J., Soppe, R., Ayars, J.E. 2004. Characterizing soil spatial variability for precision agriculture using geophysical measurements. Proceedings of 17th Annual Symposium on the Application of Geophysics to Engineering and Environmental Problesm (SAGEEP). Colorado Springs, CO on February 22-26, 2004. CDROM.

Interpretive Summary: Key components of precision agriculture are the ability (i) to identify the soil factors that influence within-field variations in crop yield and (ii) to map these factors so that precision agriculture recommendations can be made to the farmer to increase their crop yield. An overview is presented of recent precision agriculture work conducted by Corwin and colleagues (Corwin and Lesch 2003, 2004; Corwin et al. 2003), which provide the methodology for developing site-specific crop management recommendations for the purpose of increasing crop yield on irrigated arid zone soils. The overview is an invited publication for the Proceeding of the 2004 Symposium of the Application of Geophysics to Engineering and Environmental Problems (SAGEEP; Colorado Springs; 22-26 Feb. 2004). The methodology uses soil electrical conductivity measurements guidelines and survey protocols by Corwin and Lesch (2003, 2004) for taking mobilized electromagnetic measurements to direct soil sampling using a statistical software package to determine the sampling site locations. The soil samples are analyzed for physical and chemical properties known to potentially influence a crop's yield and are statistically analyzed in association with crop yield information to establish the significant properties influencing yield and to create a crop yield model based on the work of Corwin et al. (2003). Using a case study from the San Joaquin Valley, the steps are presented for the development, analysis, and interpretation of maps of the soil properties and of the crop yield model to establish site-specific crop management recommendations. These recommendations indicate to the farmer where and what variable-rate inputs need to be applied to increase crop yield.

Technical Abstract: Key components of precision agriculture are (i) identifying the site-specific factors that influence within-field crop yield variation and (ii) spatially characterizing those factors. Geo-referenced measurements of apparent soil electrical conductivity (ECa) provide a potential means of characterizing the spatial variability of edaphic properties that influence crop yield. It is the objective to present a methodology for characterizing the spatial variability of soil properties influencing crop yield for site-specific crop management applications. The presented methodology is based on guidelines and protocols developed by Corwin and colleagues, which utilize mobile geophysical measurements of ECa to direct soil sampling to characterize the spatial variability of edaphic properties influencing yield. The methodology consists of 7 stages: (i) site description and ECa survey design, (ii) ECa data collection with mobile GPS-based equipment, (iii) soil sample design based on geo-referenced ECa data, (iv) soil core sampling at specified sites designated by the sample design, (v) laboratory analysis of soil properties, (vi) spatial statistical analysis, and (vii) GIS database development and graphic display of spatial data. A case study is provided for a cotton field in the San Joaquin Valley. A site-specific response model of cotton yield based on ordinary least squares (OLS) and adjusted for spatial autocorrelation using restricted maximum likelihood was developed. The spatial information and response model provide maps of site-specific crop management recommendations to increase crop yield.

Last Modified: 10/1/2014
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