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.

Meeting crop nitrogen (N) demand while minimizing N losses to the environment has proven difficult despite significant field research and modeling efforts. To improve N management, several real-time N management tools have been developed with a primary focus on enhancing crop production. However, no coordinated effort exists to simultaneously address sustainability concerns related to N losses at field- and regional-scales. In this perspective, we highlight the opportunity for incorporating environmental effects into N management decision support tools for United States maize production systems by integrating publicly available crop models with grower-entered management information and gridded soil and climate data in a geospatial framework specifically designed to quantify environmental and crop production tradeoffs. To facilitate advances in this area, we assess the capability of existing crop models to provide in-season N recommendations while estimating N leaching and nitrous oxide emissions, discuss several considerations for initial framework development, and highlight important challenges related to improving the accuracy of crop model predictions. Such a framework would benefit the development of regional sustainable intensification strategies by enabling the identification of N loss hotspots which could be used to implement spatially explicit mitigation efforts in relation to current environmental quality goals and real-time weather conditions. Nevertheless, we argue that this long-term vision can only be realized by leveraging a variety of existing research efforts to overcome challenges related to improving model structure, accessing field data to enhance model performance, and addressing the numerous social difficulties in delivery and adoption of such tool by stakeholders.

Nutrient loss reduction strategies have recently been developed in the U.S. Midwest to decrease the environmental footprint associated with nitrogen (N) fertilizer use. Although these strategies generally suggest decreasing N rates and shifting the timing of N application from fall to spring, the spatiotemporal impacts of these practices on maize yield and fertilizer N use efficiency (NUE, kg grain yield increase per kg N applied) have not been assessed at the watershed scale using crop simulation models. We simulated the effects of N fertilizer rate (0, 168, 190, 224 kg N ha-1) and application timing [fall-applied N (FN): 100% N applied on 1 December; spring-applied N (SN): 100% N applied 10 days before planting; split N: 66% N applied on 1 December + 34% N applied 10 days before planting] on maize grain yield (GY) across 3042 points in Illinois during 2011-2015 using the DSSAT-CERES-Maize model. When simulations were scaled up to the watershed level, results suggest that increases in average maize GY for SN compared to FN occurred in years with higher than average winter rainfall (2011, 2013), whereas yields were similar (+/- 4%) in 2012, 2014, and 2015. Accordingly, differences in NUE for SN compared to FN were small (0.0-1.4 kg GY/kg N) when cumulative winter rainfall was < 300 mm, but increased to 0.1-9.2 kg GY/kg N when winter rainfall was > 500 mm at both 168 kg N ha-1 and 224 kg N ha-1. The combined practice of reducing N fertilizer amounts from 224 kg N ha-1 to 190 kg N ha-1 and shifting from FN to SN resulted in a wide range of yield responses during 2011-2015, with the probability of increasing yields varying from <10% to >70% of simulation points within a watershed. Positive impacts on both GY and NUE occurred in only 60% of simulations for this scenario, highlighting the challenge of simultaneously improving yield and NUE with a 15% N rate reduction in this region.