Global agricultural adaptation case database and trend analysis based on large language models
文献类型：期刊论文
| 作者 | Zhong, Jing-Wen1,3; Zhang, Xue-Yan3; Ma, Xin2 |
| 刊名 | ADVANCES IN CLIMATE CHANGE RESEARCH
 |
| 出版日期 | 2025-08-01 |
| 卷号 | 16期号:4页码:747-761 |
| 关键词 | Agriculture Climate change adaptation Large language models ChatGPT Natural language processing Trend analysis |
| ISSN号 | 1674-9278 |
| DOI | 10.1016/j.accre.2025.03.013 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | The Paris Agreement mandates that countries report on their adaptation efforts to evaluate the adequacy and effectiveness of these measures. Agriculture, a critical sector in climate change adaptation, benefits significantly from global case studies that provide evidence, share experiences, and disseminate knowledge. However, the rapid expansion of these case studies presents challenges in extracting and analyzing relevant information effectively. To address this, this study developed a question-answering information extraction framework that combines geographic analysis with ChatGPT. Guided by the Systematic Evidence Synthesis (ROSES) review protocol, we established a comprehensive global database of agricultural adaptation cases from 2000 to 2024. This database includes key information such as case distribution, climate stressors, adaptation measures, cost-effectiveness, and constraints, aimed at identifying major trends in agricultural adaptation. Our findings reveal the following: 1) Natural language processing technologies, particularly Large Language Models (LLMs), greatly enhance the efficiency and depth of extracting key information from adaptation cases. This advancement supports the frequent updating of the agricultural adaptation database. 2) There is a notable geographic imbalance in agricultural adaptation efforts globally. Adaptation cases are concentrated in central and southern Africa, southern Asia, Europe, and other regions. While there is diversity in responses to slow onset events, measures for extreme climate events are less common, indicating a gap in the sector's ability to address sudden and uncertain challenges. 3) Agricultural adaptation measures are evolving from individual technologies to more comprehensive approaches. The shift is from methods like crop improvement and irrigation adjustments to integrated measures such as climate-smart agriculture, conservation agriculture, and sustainable practices. These approaches collectively enhance adaptation capacity through technological, managerial, infrastructural, and biodiversity improvements, reflecting a deeper understanding and ongoing refinement of adaptation practices. This study highlights the significant potential of LLMs in improving the efficiency of information extraction and analysis for global adaptation research. It offers new methods for quickly summarizing adaptation cases in agriculture and potentially other fields, providing valuable insights and recommendations for global agricultural policymakers. |
| URL标识 | 查看原文 |
| WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001565832400001 |
| 出版者 | KEAI PUBLISHING LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216109]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Zhang, Xue-Yan |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 2.Chinese Acad Agr Sci, Inst Environm & Sustainable Dev Agr, Beijing 100081, Peoples R China 3.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Zhong, Jing-Wen,Zhang, Xue-Yan,Ma, Xin. Global agricultural adaptation case database and trend analysis based on large language models[J]. ADVANCES IN CLIMATE CHANGE RESEARCH,2025,16(4):747-761. |
| APA | Zhong, Jing-Wen,Zhang, Xue-Yan,&Ma, Xin.(2025).Global agricultural adaptation case database and trend analysis based on large language models.ADVANCES IN CLIMATE CHANGE RESEARCH,16(4),747-761. |
| MLA | Zhong, Jing-Wen,et al."Global agricultural adaptation case database and trend analysis based on large language models".ADVANCES IN CLIMATE CHANGE RESEARCH 16.4(2025):747-761. |
入库方式： OAI收割
来源：地理科学与资源研究所
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