One challenge in precision nitrogen (N) management is the uncertainty in future weather conditions at the time of decision-making. Crop growth models require a full season of weather data to run yield simulation, and the unknown weather data may be forecasted or substituted by historical data. The objectives of this study were to (1) develop a model-based in-season N recommendation strategy for maize (Zea mays L.) using weather data fusion; and (2) evaluate this strategy in comparison with farmers’ N rate and regional optimal N rate in Northeast China. The CERES-Maize model was calibrated using data collected from field experiments conducted in 2015 and 2016, and validated using data from 2017. At two N decision dates - planting stage and V8 stage, the calibrated CERES-Maize model was used to predict grain yield and plant N uptake by fusing current and historical weather data. Using this approach, the model simulated grain yield and plant N uptake well (R2 = 0.85–0.89). Then, in-season economic optimal N rate (EONR) was determined according to responses of simulated marginal return (based on predicted grain yield) to N rate at planting and V8 stages. About 83% of predicted EONR fell within 20% of measured values. Applying the model-based in-season EONR had the potential to increase marginal return by 120–183 $ ha−1 and 0–83 $ ha−1 and N use efficiency by 8–71% and 1–38% without affecting grain yield over farmers’ N rate and regional optimal N rate, respectively. It is concluded that the CERES-Maize model is a valuable tool for simulating yield responses to N under different planting densities, soil types and weather conditions. The model-based in-season N recommendation strategy with weather data fusion can improve maize N use efficiency compared with current farmer practice and regional optimal management practice.
Bibliographical noteFunding Information:
The research was supported by National Key Research and Development Program of China ( 2016 YFD0200600 , 2016YFD0200602 ), the Norwegian Ministry of Foreign Affairs ( SINOGRAIN II , CHN-17/0019 ), Minnesota Department of Agriculture/Agricultural Fertilizer Research and Education Council ( MDA/AFREC R2019-20 and R2020-32 ), the USDA National Institute of Food and Agriculture ( State project 1016571 ) and the National Institute of Food and Agriculture, U.S. Department of Agriculture , Hatch project ALA014-1-16016 . We also would like to thank Yanjie Guan, Xuezhi, Yue, Zheng Fang, Weidong Lou, and Guiyin Jiang for their help with the field experiments.
© 2021 Elsevier B.V.
- Economic optimal nitrogen rate
- Historical weather data
- In-season nitrogen recommendation
- Maize grain yield
- Plant nitrogen uptake
- Precision nitrogen management