Analysis quantifies greenhouse gas emissions and identifies regional factors, suggesting mitigation strategies.
Global warming and climate deterioration are primarily driven by massive greenhouse gas emissions, making the comprehensive assessment of agricultural emissions imperative. This study integrates multiple datasets to achieve three objectives: (1) quantifying agricultural greenhouse gas emissions, (2) identifying regional influencing factors, and (3) exploring mitigation strategies. In this study, a random forest regression model was used to fit the data, providing a new perspective for the analysis of emission factors. Key findings reveal fertilization and irrigation as the dominant emission drivers, with significant regional variations. Specifically, (1) fertilization practices, particularly nitrogen application, exert a greater influence than phosphorus on carbon emissions; (2) irrigation impacts correlate strongly with regional water usage patterns among staple crops; (3) distinct emission patterns emerge across China’s northeast–southwest divide, reflecting variations in grain crop impacts and climatic responses. The study proposes three mitigation approaches: precision fertilization, adaptive irrigation management, and crop structure optimization. These strategies provide actionable pathways for China to meet agricultural emission reduction targets while advancing sustainable development goals.
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Zhou et al. (2025) studied this question.
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