Ecological farm typology and comparison in China: An unsupervised machine learning approach
文献类型：期刊论文
| 作者 | Xu, Xiangbo1,4; Liu, Shuang2; Zhou, Ziyi4; Xu, Yue3,4; Xue, Yinghao5; Xu, Zhiyu5; Hu, Xiaofang5; Yu, Xiaohua2; Zhang, Linxiu1,4 |
| 刊名 | ECOLOGICAL INDICATORS
 |
| 出版日期 | 2026 |
| 卷号 | 182页码:114560 |
| 关键词 | Ecological farms Machine learning Classification Performance evaluation Policy |
| ISSN号 | 1470-160X |
| DOI | 10.1016/j.ecolind.2025.114560 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Promoting ecological farms is a critical initiative for greening agriculture and advancing eco-agricultural theory into practice. Although China integrated ecological farm construction into national plans post-2020, its development remains nascent, necessitating objective and quantitative methods to classify farm models and evaluate their performance. This study addresses this gap by applying unsupervised machine learning-specifically Partitioning Around Medoids (PAM) clustering-to 2022 declaration data from 678 national ecological farms. Our analysis identified three distinct typologies: National Varieties Fine Management Type (NVFMT, 40.27 %), characterized by small-scale, specialty-crop operations with minimal synthetic inputs; Diversified Business Type (DBT, 33.78 %), integrating vegetable production, agritourism, and high adoption of eco-measures; and Largescale Traditional Type (LTT, 25.96 %), focused on grain cultivation with extensive land use and flood irrigation. Performance evaluation using life cycle assessment, total factor productivity (TFP), and revenue analysis revealed no significant economic differences across types. However, NVFMT and DBT exhibited 48-52 % lower greenhouse gas (GHG) emission intensity than LTT, attributable to reduced fertilizer use and diversified practices. DBT also achieved significantly higher TFP (1.18) compared to NVFMT (0.68) and LTT (0.72), linked to operational diversity and technological integration. As the first national-scale application of machine learning to ecological farm typology in China, this research provides an evidence-based framework to guide targeted policies for sustainable agricultural transformation. |
| URL标识 | 查看原文 |
| WOS关键词 | PRODUCTIVITY ; GROWTH |
| WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001660758200001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219731]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 通讯作者 | Xu, Xiangbo; Xue, Yinghao |
| 作者单位 | 1.UN Environm Programme Int Ecosyst Management Partn, Beijing 100101, Peoples R China; 2.Georg August Univ Gottingen, Dept Agr Econ & Rural Dev, D-37073 Gottingen, Germany; 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 5.Minist Agr & Rural Affairs, Rural Energy & Environm Agcy, Beijing 100125, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Xiangbo,Liu, Shuang,Zhou, Ziyi,et al. Ecological farm typology and comparison in China: An unsupervised machine learning approach[J]. ECOLOGICAL INDICATORS,2026,182:114560. |
| APA | Xu, Xiangbo.,Liu, Shuang.,Zhou, Ziyi.,Xu, Yue.,Xue, Yinghao.,...&Zhang, Linxiu.(2026).Ecological farm typology and comparison in China: An unsupervised machine learning approach.ECOLOGICAL INDICATORS,182,114560. |
| MLA | Xu, Xiangbo,et al."Ecological farm typology and comparison in China: An unsupervised machine learning approach".ECOLOGICAL INDICATORS 182(2026):114560. |
入库方式： OAI收割
来源：地理科学与资源研究所
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