Knowledge-Based Soil Inference Modeling for Estimating Soil Productivity and Grain Yield In North-Central Missouri.

We used ArcSIE (Soil Inference Engine) software to model soils resembling those mapped by NRCS soil survey, for eight 12-digit watersheds in the Central Claypan (MLRA113) in north-central Missouri. Our source data for modeling was the 10m USGS Digital Elevation Model. Environmental Covariates used in modeling included Relative Position, Slope, Planiform Curvature, Profile Curvature, and Wetness Index. The knowledge-based rules were derived by interpreting the existing SSURGO (soil survey) pattern for the area, and from the expert opinion of the authors. The resulting seven-class model simulated components of soil map units from the local SSURGO data. We assigned the appropriate Missouri Productivity Index value to each modeled soil, and used ArcSIE to create a continuous-surface Productivity Index for each watershed. We compared the SSURGO maps, the modeled soil maps and the continuous-surface PI maps with yield data from combine-mounted yield monitors. Results and discussion focus on comparisons among yield data, SSURGO data, modeling results and environmental covariates, and the implications for knowledge-based inference modeling in future soil survey projects and products.