• 关键词：碳源  碳汇  时空分布  LMDI模型  影响因素  农田生态系统  河南

• 基金项目：国家水体污染控制与治理科技重大专项（No.2015ZX07204- 002-05）；河南省生态碳汇能力提升方法路径及措施研究项目（No.20210717A）

• 王莉
• 郑州大学生态与环境学院，河南省减污降碳协同工程技术研究中心，郑州 450001

• 刘莹莹
• 郑州大学生态与环境学院，河南省减污降碳协同工程技术研究中心，郑州 450001

• 张亚慧
• 郑州大学生态与环境学院，河南省减污降碳协同工程技术研究中心，郑州 450001

• 董素涵
• 郑州大学生态与环境学院，河南省减污降碳协同工程技术研究中心，郑州 450001

• 摘要：为明确河南省农田生态系统碳源/汇分布特征及影响因素，利用2010—2020年河南省农作物播种面积、产量等数据估算农田生态系统 碳源/碳汇量，分析其时空特征，并采用对数平均迪式指数法（LMDI加法分解模型）分别对碳源/汇的影响因素进行分解.结果表明，2010—2020年，河南省农田生态系统碳排放呈先增后降的趋势，田间氮肥施用所造成的碳排放贡献率最大，达74.39%~76.35%.各地市农田生态系统的 碳排放量、增幅及单位播种面积碳排放强度均存在显著差异.河南省农田生态系统碳吸收呈上升趋势，净增6.0×10⁷ t，增幅为22.93%.各地市碳吸收量及单位播种面积碳吸收强度均呈增长态势，河南省农田生态系统碳汇能力不断增强，且碳吸收量大于碳排放量.在碳源影响因素分解中，农业能源利用水平和农业低碳技术水平的提高、劳动力规模的控制可有效减少碳排放；在碳汇影响因素分解中，碳汇系数、碳汇技术水平与碳吸收量呈正效应，且小麦碳汇能力最强.建议通过提高能源利用水平和低碳技术水平、调整农作物种植结构、发展循环农业模式等措施，实现农田生态系统降碳增汇.

• Abstract：In order to understand the carbon source （C-source） and sink （C-sink） of farmland ecosystem in Henan Province， this study analyzed the amount of C-source， C-sink and their spatial-temporal characteristics from 2010 to 2020 based on sown area， yield etc. In the meantime， the influence factors of C-source and C-sink were decomposed by employing a Logarithmic Mean Divisia Index （LMDI） additive decomposition model. The results demonstrates that， from 2010 to 2020， for C-source， it presented a tendency of increase first and then decrease， and 74.39%~76.35% of C-source was contributed via nitrogen fertilization. Different districts presented significant differences in their C-source amount， increase rate and emission intensity per unit sown area. For C-sink， it showed a rising trend by 22.93%， with a net increase of 6.0×10⁷ t. All districts presented increase tendencies in both amount of C-sink and absorption intensity per unit sown area. The capacity of C-sink kept increasing， and the amount of C-sink was larger than the amount of C-source. The LMDI analysis shows that， for the amount of C-source， it could be effectively decreased by improving the utilization of agricultural energy， promoting the application of low-carbon technologies in agriculture， and reducing the size of agricultural labor force. For the amount of C-sink， it had a positive correlation with the coefficient of C-sink and the level of C-sink technology. Also， among all crops， the wheat poses the strongest capacity of C-sink. The findings in this study suggest that， to decrease the C-source and increase the C-sink， measures could be taken by promoting the energy utilization level， enhancing low-carbon technologies， optimizing the crop planting structure， developing circular agriculture modes.

• Key words：carbon source  carbon sink  spatial and temporal distribution  Logarithmic mean divisia index （LMDI）  influence factors  farmland ecosystem  Henan Province

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