Site-Specific Digital Soil Mapping Vs. SSURGO Soil Maps For Simulating Crop Yield

Accurate soil input data are essential for agroecosystem modeling, yet commonly used conventional soil maps like the soil survey geographic (SSURGO) may limit precision due to their coarse resolution, outdated information, and influence on model calibration. This study evaluates how site-specific, fine-resolution digital soil mapping (DSM) improves APSIM simulations of maize [Zea mays] grain yield compared to SSURGO for a 28-ha field in central Iowa. Yield simulations were conducted using DSM and SSURGO inputs. Results showed that SSURGO predicted significantly higher yields (mean: 1.28 ± 0.05 kg m⁻²) than DSM (means: 1.10–1.23 kg m⁻² across hillslope positions; p < 0.05) with the greatest overestimations in upland positions. DSM provided a more spatially continuous representation of the landscape and simulated yields aligned with yield reductions observed in severely eroded soils. These findings emphasize the need to integrate current fine-resolution soil data into APSIM to enhance agroecosystem modeling and precision agriculture.