Modeling Inorganic Soil Nitrogen Status in Maize Agroecosystems
Published Web Locationhttps://dl.sciencesocieties.org/publications/sssaj/articles/83/5/1564
Core Ideas DSSAT was calibrated for simulating soil N concentration during the maize growing season. Two-step validation included research experiments and 49 commercial maize fields. Model performance was fair in predicting SOM mineralization and N management impacts. The model captured effects of early season rainfall on soil N variability across sites. Farmers have limited knowledge of inorganic soil nitrogen (N) concentration during maize (Zea mays L.) growth in the US Midwest, particularly after periods of wet spring weather. The objectives of this study were to calibrate the Decision Support System for Agrotechnology Transfer (DSSAT) model for predicting inorganic soil N concentration using data from three field experiments in Illinois, to evaluate model performance against three independent sites and additional data from 49 commercial maize fields, and to assess the impacts of rainfall variability on the predicted decrease in soil N concentration early in the growing season. Model calibration included adjustments to soil organic matter (SOM) decomposition parameters based on predicted soil organic carbon concentration (obtained from gSSURGO) and soil drainage rates. Model performance was considered “fair” in predicting SOM mineralization dynamics and the effects of fall vs. spring N fertilizer application across the validation datasets (normalized RMSE, 21.2–25.7%). The model also captured the variability in soil N concentration across 49 commercial fields (R2 = 0.68–0.88; slope, 0.99–1.24), with higher cumulative rainfall from January to July (>800 mm) reducing predicted soil N availability compared with fields receiving less rainfall (500–600 mm). Results suggest that DSSAT has the potential to estimate soil N availability across variable weather patterns, soil properties, and fertilizer management scenarios in Illinois. However, future work is needed to further improve model accuracy, especially if it is to be used as a decision support tool for farmers.
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