LAUSR

Abstract Production of irrigated sweet corn on sandy soils requires ample N inputs and results in substantial leaching of nitrate to groundwater. The irrigation infrastructure of this system provides the opportunity for fertilizer additions throughout the season, opening the prospect for adaptive, real-time and site-specific management that might synchronize N availability with crop demand, minimizing leaching. This requires validated process-based and mechanistic simulations of the crop N budget and soil N cycling. We adapted the AmaizeN model to sweet corn, validated the model for groundwater NO3-N leaching estimation on sandy soils, and assessed the potential of model application for adaptive, in-season N management in this cropping system. The model was calibrated and tested with a two-season dataset by comparing predicted and measured leaf area index (LAI), above ground biomass (AGB), yield, and cumulative crop N uptake (CNUP). The model prediction and measurement comparisons yielded high coefficients of determination (R2, 0.82–0.95) and low root-mean-square errors (RMSE, 6.0–9.5%) for the whole range of the target crop attributes across years and wide-ranging N treatments. The difference between simulated and measured groundwater NO3-N loadings from lysimeter experiments ranged from 2.1–19.8% as relative absolute errors in 2014, and 1.7–7.2% in 2015. The adaptive N management strategy was proposed and demonstrated in a moderate-N treatment using the model prediction for real-time soil N availability and crop N demand dynamics. This approach significantly enhanced crop productivity by approximately 40% compared to conventional practice, while reducing N fertilizer inputs and NO3-N loading by 30–48% and 27–52%, respectively, relative to the highest N input treatments. The adaptive strategy shows potential to achieve target crop yields while minimizing NO3-N leaching.