| 基于机器学习的中国农业净碳汇预测模型构建及驱动因素响应分析 |
| 摘要点击 324 全文点击 25 投稿时间：2025-04-06 修订日期：2025-07-23 |
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| 中文关键词 农业净碳汇 预测模型 灰狼优化-随机森林算法（GWO-RF） ISM算法 Lasso回归 |
| 英文关键词 agricultural net carbon sink prediction model grey wolf optimizer-random forest algorithm （GWO-RF） ISM algorithm Lasso regression |
| DOI 10.13227/j.hjkx.202504072 |
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| 中文摘要 |
| 农业净碳汇的智能化预测及驱动因素的机制研究对推进“双碳”目标下我国农业农村减排固碳政策的实施具有重要意义. 基于2000~2022年中国31个省域的农业净碳汇的测算数据和面板数据，运用多种机器学习算法构建农业净碳汇预测模型；并通过SHAP值和PDP图揭示该预测模型驱动因素对农业净碳汇的响应特征. 结果表明：①研究构建的GWO-RF模型对农业碳净汇的预测精度高且稳定性好（MSE=0.04%，MAPE=7%，R2=0.984）. ②模型驱动因素的重要性排序为：农田有效灌溉面积>耕地面积>省域特征>化肥施用强度>地区受教育水平>城镇化水平>农业机械化水平. ③农田有效灌溉面积、耕地面积和化肥施用强度这3个重要驱动因素两两组合对农业净碳汇交互影响的2D PDP结果显示：其一，农田有效灌溉面积与耕地面积组合的交互作用以农田有效灌溉面积1 600×103 hm2为界，小于该值交互作用较弱，大于该值则较强；其二，农田有效灌溉面积与化肥施用强度组合的交互作用较弱，且以农田有效灌溉面积的影响为主导；其三，耕地面积与化肥施用强度组合的交互作用以耕地面积1 600×103 hm2为界，小于该值交互作用较弱，大于该值则较强. ④上述3个驱动因素组合对农业净碳汇共同影响的3D PDP结果显示：农田有效灌溉面积对农业净碳汇的影响最为显著，几乎主导农业净碳汇变化的整个进程，其深层次原因是灌溉强度和灌溉方式均可较大程度地影响作物碳吸收和土壤碳汇的潜能. 研究成果将为农业净碳汇的预测提供一种新方法和新思路，也可为政府和相关管理部门制定农业碳减排增汇规划和政策提供决策参考. |
| 英文摘要 |
| Intelligent prediction of agricultural net carbon sink and the mechanistic analysis of its driving factors are of great significance for promoting carbon reduction and sequestration policies in China's agricultural and rural sectors under the “dual carbon” goals. Based on the calculated data of agricultural net carbon sinks and panel data of 31 provincial regions in China from 2000 to 2022， multiple machine learning algorithms were used to construct a prediction model for agricultural net carbon sinks. The SHAP values and PDP plots were employed to reveal the response characteristics of the driving factors of the prediction model to agricultural net carbon sinks. The results show that：①The GWO-RF model constructed in this study demonstrated high prediction accuracy and stability for the agricultural net carbon sink （MSE = 0.04%， MAPE = 7%， R2 = 0.984）. ② The importance ranking of the model's driving factors was as follows： effective irrigated area > cultivated land area > provincial characteristics > fertilizer application intensity > regional education level > urbanization level > agricultural mechanization level. ③The 2D PDP results of pairwise interactions among the three important driving factors， namely effective irrigated area， cultivated land area， fertilizer application intensity， on agricultural net carbon sinks showed that： First， the interaction between the effective irrigated area and cultivated land area was weak when the effective irrigated area was less than 1 600×103 hm2 and strong when it was greater than this value； second， the interaction between the effective irrigated area and fertilizer application intensity was weak， and the influence of the effective irrigated area was dominant； and third， the interaction between cultivated land area and fertilizer intensity was weak when the cultivated land area was less than 1 600×103 hm2 and strong when it was greater than this value. ④ The 3D PDP visualization of the above three driving factors on agricultural net carbon sinks showed that the effective irrigated area had the most significant impact on agricultural net carbon sinks， almost dominating the entire process. The underlying reason is that the intensity and method of irrigation can significantly affect the potential of crop carbon absorption and soil carbon sinks. The research results provide a new method and novel approach for the prediction of agricultural net carbon sinks， also providing decision-making references for the government and relevant departments in formulating agricultural carbon emission reduction and sequestration plans and policies. |
