数字农科院2.0

Multi-objective optimization of rice production and environmental sustainability under climate change in the Yangtze River Delta: A DNDC-random forest framework approach

文献类型： 外文期刊

作者： Shuoshuo Liang;Ping He;Qingnan Chu;Wentian He;Ruochen Li;Xinpeng Xu;Rong Jiang;Shuang Liu;Linkui Cao;Zhimin Sha

作者机构：

关键词： Climate change;DNDC-RF framework;Greenhouse gas emissions;Multi-objective optimization;Rice production

期刊名称： Field Crops Research

ISSN： 0378-4290

年卷期： 2026 年 339 卷

页码：

收录情况： SCIE（2025版）

摘要： The possible solutions for maintaining rice productivity while minimizing environmental impacts under climate change remain unclear. We developed a DNDC-Random Forest (DNDC-RF) framework coupling the process-based DNDC model with random forest machine learning to evaluate rice yield, NH₃ volatilization, and greenhouse gas emissions, and explored optimization potential through multi-objective fertilizer management. The DNDC model demonstrated superior performance in simulating rice yield (R² = 0.83) compared to gaseous emissions (R² = 0.80–0.86), while the DNDC-RF framework achieved enhanced predictive accuracy (R² = 0.93–0.97). Multi-objective optimization using the NSGA-III algorithm identified distinct regional variations in optimal fertilizer management strategies across Yangtze River Delta region, with partial replacement of chemical fertilizer with organic fertilizer (MF) maintaining comparable yields while reducing GHG emissions by 25–35 % compared to conventional practices (CT). Under the SSP126 scenario, both treatments-maintained productivity gains throughout 2021–2100 with yield increases of 21.8–22.4 % during the early period, while SSP585 led to progressive yield declines reaching 42.3 % below baseline levels. Spatial analysis revealed that northern counties demonstrated greater climate resilience, while southern coastal counties showed increased vulnerability. The findings of our study provide scientific support for developing climate-smart agricultural practices that simultaneously enhance productivity and environmental sustainability.

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