Validating a Digital Soil Map with Corn Yield Data for Precision Agriculture Decision Support

Abstract

Capturing the variability in soil‐landscape properties is a challenge for grain producers attempting to integrate spatial information into the decision process of precision agriculture (PA). Digital soil maps (DSMs) use traditional soil survey information and can be the basis for PA subfield delineation (e.g., management zones). However, public soil survey maps provide only general descriptions of soil‐landscape features. Therefore, improved DSMs are needed that use high‐resolution data that more precisely model soil‐landscape characteristics. Additionally, reliable methods are needed to validate DSM products for PA. The objective of this study was to validate with corn ( Zea mays L.) yield data the performance of a new DSM product, termed Environmental Response Unit (ERU), compared with the USDA Soil Survey Geographic (SSURGO) soil map. The ERU was developed by integrating SSURGO information with high‐resolution elevation data. For validation, corn yield maps were collected and corrected for common data collection errors from 409 fields across Indiana, Iowa, Minnesota, and Nebraska in 2010 to 2012. Reductions in the area‐weighted variance ( R v ) of corn yield for ERU and SSURGO were calculated relative to the whole‐field variance. The average R v across all site‐years for SSURGO and ERU was 16 and 25%, respectively, which equated to a 57% higher median yield variance reduction with ERU over SSURGO. This variance reduction technique showed the potential of ERU as an improved model better representing soil‐landscape properties that impact corn yield. This research also has application potential for determining the success of a DSM for identifying management zones in PA. The variance reduction metric ( R v ) provided a direct comparison between DSM models. The greatest reduction in corn yield variance was exhibited by the ERU DSM model. Physiographic derivatives from high‐precision data sets improved DSM functionality. Yield data helped outline interrelationships among various soil properties.