Improving a nitrogen mineralization model for predicting unfertilized corn yield

Crop N decision support tools are typically based on either empirical relationships that lack mechanistic underpinnings or simulation models that are too complex to use on farms with limited input data. We developed an N mineralization model for corn that lies between these endpoints; it includes a mechanistic model structure reflecting microbial and texture controls on N mineralization but requires just a few simple inputs: soil texture soil C and N concentration and cover crop N content and carbon to nitgrogen ratio (C/N). We evaluated a previous version of the model with an independent dataset to determine the accuracy in predictions of unfertilized corn (Zea mays L.) yield across a wider range of soil texture, cover crop, and growing season precipitation conditions. We tested three assumptions used in the original model: (1) soil C/N is equal to 10, (2) yield does not need to be adjusted for growing season precipitation, and (3) sand content controls humification efficiency (ε). The best new model used measured values for soil C/N, had a summertime precipitation adjustment, and included both sand and clay content as predictors of ε (root mean square error [RMSE] = 1.43 Mg ha⁻¹; r² = 0.69). In the new model, clay has a stronger influence than sand on ε, corresponding to lower predicted mineralization rates on fine-textured soils. The new model had a reasonable validation fit (RMSE = 1.71 Mg ha⁻¹; r² = 0.56) using an independent dataset. Our results indicate the new model is an improvement over the previous version because it predicts unfertilized corn yield for a wider range of conditions.