Optimizing nitrogen fertilizer application in Chinese rice production under current and warming climatic scenarios

Optimizing the nitrogen (N) fertilizer use is the key to facilitating the sustainable development of agricultural systems. In this study, a DeNitrification–DeComposition model was used to analyze the effects of N fertilization on yield, profit, and reactive N losses in single-season rice production of China. The comprehensive optimum N application rate (CONR) considering the trade-off between economy and environment was calculated for each rice production county. The results showed that the high CONR values were mainly concentrated in middle and south China, like Yunnan-guizhou plateau, and Guangxi and Guangdong Provinces, where the rice yield potential is high. In addition to the Qinghai-tibet rice cultivation zone (RZ), the mean values of CONR in each RZ were mainly in the range of 150 kg N ha^-1 to 200 kg N ha^-1. Moreover, the 1.5 °C warming scenario with CO₂ concentration up to 423 ppm increased the CONR in most RZs of China. A generalized additive model (GAM) was used to develop quick prediction models of CONR using soil, terrain, climate, and crop data as predictors. The outcomes revealed that the GAM model could well predict the CONR across China's single-season rice production counties under both current (R² = 0.58, RRMSE = 18.3 %) and warming (R² = 0.61, RRMSE = 18.3 %) climatic scenarios. The rice yield potential, soil clay fraction, and soil organic carbon were the most important factors affecting CONR among different RZs. The cost-effectiveness analysis showed that CONR may save 9.7 % – 9.8 % more N fertilizer use and reduce 13 % – 14.5 % more N pollutant emissions in Chinese single-season rice production than the economic optimum N rate under current and warming climatic scenarios, and the net profit is only reduced by 1.1 % – 1.3 %. This research would offer a novel strategy for rice N fertilization management across China by optimizing the economic and environmental consequences at the same time.