Adaption of the Amaizen Model for N Management in Sweet Corn (Zea mays L.).

Conventional N management strategies based on spatial and temporal generalizations lack time-dependent soil and crop N dynamics, which largely restrict the level of precision and efficiency of N management decisions. AmaizeN is a daily-time-step decision-support system for optimizing nitrogen management developed for maize crops. The dynamic modeling method moves us from generalized and static recommendations to adaptive, real-time and site-specific management depending on process-based and mechanistic simulations of the crop N budget and soil N cycling. The objective of this study is to evaluate the ability of the AmaizeN model to simulate sweet corn production on the sandy soils in Central Sands region of Wisconsin with respect to yield and N leaching over different N fertilizer levels. The AmaizeN model was calibrated for sweet corn growth, yield and N deficiency effects using field experiments data in 2014 and 2015. Predictions of LAI, above ground biomass, ear dry matter and cumulative crop N uptake showed good agreement with measurements (R²: 0.82-0.95, RMSE: 6.00-13.34% of the whole range of the target crop attributes). The model ability to simulate NO₃-N loading was validated with 2-year lysimeter experiments. The relative absolute errors (RAE) ranged from 1.7% to 18.5% between the simulated and measured NO₃-N loading, which appeared better than comparable studies. The calibrated model was applied to provide implications for adaptive, in-season N management. Different N management strategies including a combination of various N sources (ESN and urea), N levels and split application timings were tested with 30-year weather data to determine the best management practices yielding highest yield and lowest groundwater NO₃-N loading in sweet corn on sandy soils.