A Model–Data Fusion Approach for Predicting Cover Crop Nitrogen Supply to Corn. (1st November 2016) - White, Charles M.; Finney, Denise M.; Kemanian, Armen R.; Kaye, Jason P.
Abstract : One potential benefit of cover crops (CCs) is that N mineralization from decomposing CC residues may reduce the N fertilizer requirement of a subsequent crop, but predicting this credit remains a significant challenge. This study used a model–data fusion approach to calibrate a model of CC residue N mineralization and pre‐emptive competition for soil NO3 − that occurs during CC growth to predict the yield response of an unfertilized corn ( Zea mays L.) crop. The model was calibrated with a data set of 199 observations from four CC experiments in central Pennsylvania. The most parsimonious model explained 82% of the variation in corn yield response. Parameters representing the C humification coefficients for decomposed residues from winterkilled (εwk = 0.00) and winter‐hardy (εwh = 0.40) CCs suggest that all winterkilled CCs resulted in net N mineralization, probably due to the longer period of time for decomposition of winterkilled residues. However, the yield response per unit of potentially mineralized N was greater for winter‐hardy CCs (αwh = 0.034 with tillage, αwh = 0.020 with no‐till) than for winterkilled CCs (α wk = 0.0084), probably due to the improved synchrony between corn N demand and the decomposition of winter‐hardy CC residues relative to winterkilled residues. Pre‐emptive competition for soil NO3 − led to a reduction in the corn yield response. Because the model is based on ecological processes and can be calibrated with data sets from simple field experiments, the model–data fusion approach could be widely used to guide adaptive management of CCs and N fertilizer applications in a subsequent corn crop. Core Ideas A simple model predicts how cover crops affect N availability to the next corn crop. The model highlights the ecological controls on N supply from cover crops. Site‐specific cover crop measurements can guide adaptive N management using the model. Regional model calibration could be achieved with easily collected data.