Evaluating model-based strategies for in-season nitrogen management of maize using weather data fusion

One challenge in precision nitrogen (N) management is the uncertainty future weather conditions at time of decision-making. Crop growth models require a full season data to run yield simulation, and unknown may be forecasted or substituted by historical data. The objectives this study were (1) develop model-based in-season N recommendation strategy for maize (Zea mays L.) using fusion; (2) evaluate comparison with farmers’ rate regional optimal Northeast China. CERES-Maize model was calibrated collected from field experiments conducted 2015 2016, validated 2017. At two decision dates - planting stage V8 stage, used predict grain plant uptake fusing current Using approach, simulated well (R2 = 0.85–0.89). Then, economic (EONR) determined according responses marginal return (based on predicted yield) stages. About 83% EONR fell within 20% measured values. Applying had potential increase 120–183 $ ha−1 0–83 use efficiency 8–71% 1–38% without affecting over rate, respectively. It concluded that valuable tool simulating under different densities, soil types conditions. fusion can improve compared farmer practice practice.