A Modeling Approach to Quantify the Effects of Spatial Soybean Yield Limiting Factors

Spatial yield variability is a complex interaction of many factors, including soil properties, weather, pests, fertility,and management. Crop models are excellent tools to evaluate these complex interactions and provide insight into causes ofspatial yield variability. The goal of this study was to use a soybean crop growth model to determine the contribution of threefactors that cause spatial yield variability and to test several calibration and validation strategies for yield prediction. Aprocedure was developed to calibrate the CROPGROSoybean model and to compare predicted and measured soybeanyields, assuming that water stress, soybean cyst nematodes (SCN), and weeds were the dominant yieldlimiting factors. Theprocedure involved calibrating drainage properties and rooting depth over three seasons for each grid. These procedures weretested on 77 grids (0.2 ha in size) in the McGarvey field in Perry, Iowa, for 1995, 1997, and 1999. Predicted soybean yieldswere in good agreement (r2 = 0.80) with measured yield after calibrating three model parameters. The calibrated model wasused to quantify the effects of three yieldlimiting factors on soybean. The maximum soybean yield potential in 1997 wasestimated by running the calibrated model with no water, SCN, or weed stress. The model was then run for 1997, turning eachyieldlimiting factor off to assess its relative impact on yield reduction. Average estimated yield loss due to the combinedeffects of water stress, SCN, and weeds in each grid was 842 kg ha1. Soybean yields were significantly reduced by an averageof 626 kg ha1 as a result of water stress. The presence of SCN in several grids accounted for an average yield reduction of105 kg ha1. The effects of weeds on soybean yield were not significant.