Article(id=1314918551421551015, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918450573701162, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1747670400000, receivedDateStr=2025-05-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791430612664, onlineDateStr=2026-10-08, pubDate=1766160000000, pubDateStr=2025-12-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791430612664, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791430612664, creator=13701087609, updateTime=1791430612664, updator=13701087609, issue=Issue{id=1314918450573701162, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='12', pageStart='6513', pageEnd='7104', issueExtLink='null', onlineDate='null', pubDate='1766160000000', pubDateStr='2025-12-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791430588620, creator='13701087609', updateTime=1791431575400, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314922589479333965, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918450573701162, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314922589479333966, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314918450573701162, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=7030, endPage=7043, ext={EN=ArticleExt(id=1314918551744512424, articleId=1314918551421551015, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Spatiotemporal evolution and influencing factors of agricultural water use efficiency in China under decoupling constraints based on machine learning, columnId=1234106417825772207, journalTitle=China Environmental Science, columnName=Environmental Impact Assessment and Management, runingTitle=null, highlight=null, articleAbstract=

Using panel data for 31 provinces from 2007 to 2022, this study applied the super-efficiency Slack-Based Measure (SBM) model to measure agricultural water use efficiency under the constraint of decoupling economic growth from water resource consumption. Spatial dynamics were assessed using Moran's I index in ArcGIS, while the Random Forest model was employed to identify key influencing factors and their nonlinear effects. The results showed that China's agricultural water use efficiency exhibited a fluctuating yet overall upward trend, with a spatial distribution pattern characterized by "higher in the east and south, lower in the west and north." The overall pattern was found to demonstrate significant spatial autocorrelation, with spatial agglomeration increasingly strengthened. At the local scale, a clear southeast-northwest differentiation was observed, where hotspots expanded toward the west and south, and cold spots shifted from the north toward the northeast. Economic development level and water resource endowment were identified as having greater impacts on agricultural water use efficiency than technological investment and cropping structure. Specifically, the effects of economic development, technological progress, and capital investment followed a stepwise upward pattern, whereas the influence of water resource endowment displayed a "decline-increase-stabilization" trajectory. Furthermore, provinces dominated by rice and maize cultivation were found to have greater potential for efficiency improvement than those dominated by wheat cultivation. Policy recommendations were proposed, including the promotion of smart irrigation in high-efficiency regions, the enhancement of water-saving infrastructure and optimization of cropping patterns in low-efficiency regions, the sharing of best practices in hotspot areas, and the strengthening of governance and structural adjustment in coldspot regions.

, authors=Yi-chen CHEN1, Hui-xin CHEN2, Jun-qing HE3, Run-xiang NI4, Wen-meng YU4, *, authorsList=Yi-chen CHEN, Hui-xin CHEN, Jun-qing HE, Run-xiang NI, Wen-meng YU, authorCompany=null, correspAuthors=Wen-meng YU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314918552147165611, articleId=1314918551421551015, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于机器学习的脱钩约束下中国省际农业用水效率时空演变及影响因素, columnId=1234106419604157142, journalTitle=中国环境科学, columnName=环境影响评价与管理, runingTitle=null, highlight=null, articleAbstract=

基于2007~2022年我国31个省份面板数据,运用超效率(SBM)模型在经济增长与水资源消耗脱钩约束下测算农业用水效率,结合ArcGIS莫兰指数分析其时空演变特征,并采用随机森林模型识别关键影响因素及其非线性响应机制.结果表明:(1)我国农业用水效率整体呈波动上升态势,区域效率呈“东高西低,南高北低”分布格局;(2)农业用水效率总体格局呈显著空间自相关,空间集聚特征持续增强;局部格局呈明显的东南和西北分异,热点区向西部和南部扩张,冷点区由华北向东北移动;(3)经济发展水平和水资源禀赋对农业用水效率的影响高于技术投入和种植结构,其中经济发展、技术进步与资本投入的影响呈阶梯式上升,而水资源禀赋的作用则呈现“下降-上升-稳定”特征,且水稻与玉米主产区的用水效率提升潜力高于小麦主产区.基于此,建议高效区推广智慧灌溉,低效区强化节水设施与结构优化;热点区注重经验推广,冷点区加强水资源管控,优化种植结构.

, authors=陈逸琛1, 陈惠鑫2, 何俊卿3, 倪润祥4, 余文梦4, *, authorsList=陈逸琛, 陈惠鑫, 何俊卿, 倪润祥, 余文梦, authorCompany=null, correspAuthors=余文梦, authorNote=

陈逸琛(1995-),男,福建福州人,副研究员,博士,主要从事资源与环境管理、减污降碳协同增效研究.发表论文10余篇..

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* 责任作者,农艺师,
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陈逸琛(1995-),男,福建福州人,副研究员,博士,主要从事资源与环境管理、减污降碳协同增效研究.发表论文10余篇..

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陈逸琛(1995-),男,福建福州人,副研究员,博士,主要从事资源与环境管理、减污降碳协同增效研究.发表论文10余篇..

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基于机器学习的脱钩约束下中国省际农业用水效率时空演变及影响因素
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陈逸琛 1 , 陈惠鑫 2 , 何俊卿 3 , 倪润祥 4 , 余文梦 4, *
中国环境科学 | 环境影响评价与管理 2025,45(12): 7030-7043
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中国环境科学 |环境影响评价与管理 2025 , 45 (12) : 7030 -7043
基于机器学习的脱钩约束下中国省际农业用水效率时空演变及影响因素
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陈逸琛(1995-),男,福建福州人,副研究员,博士,主要从事资源与环境管理、减污降碳协同增效研究.发表论文10余篇..

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陈逸琛(1995-),男,福建福州人,副研究员,博士,主要从事资源与环境管理、减污降碳协同增效研究.发表论文10余篇..

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陈逸琛1 , 陈惠鑫2, 何俊卿3, 倪润祥4, 余文梦4, *
作者信息
  • 1.福州大学,福建 福州 350108
  • 2.全国市长研修学院,北京 100029
  • 3.中国人民大学,北京 100101
  • 4.农业农村部农业生态与资源保护总站,北京 100125
通讯作者:
* 责任作者,农艺师,
作者简介:

陈逸琛(1995-),男,福建福州人,副研究员,博士,主要从事资源与环境管理、减污降碳协同增效研究.发表论文10余篇..

Spatiotemporal evolution and influencing factors of agricultural water use efficiency in China under decoupling constraints based on machine learning
Yi-chen CHEN1 , Hui-xin CHEN2, Jun-qing HE3, Run-xiang NI4, Wen-meng YU4, *
Affiliations
  • 1.Fuzhou University, Fuzhou 350108, China
  • 2.National Academy for Mayors of China, Beijing 100029, China
  • 3.Renmin University of China, Beijing 100101, China
  • 4.Rural Energy and Environment Agency, Ministry of Agriculture and Rural Affairs, Beijing 100125, China
出版时间: 2025-12-20
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基于2007~2022年我国31个省份面板数据,运用超效率(SBM)模型在经济增长与水资源消耗脱钩约束下测算农业用水效率,结合ArcGIS莫兰指数分析其时空演变特征,并采用随机森林模型识别关键影响因素及其非线性响应机制.结果表明:(1)我国农业用水效率整体呈波动上升态势,区域效率呈“东高西低,南高北低”分布格局;(2)农业用水效率总体格局呈显著空间自相关,空间集聚特征持续增强;局部格局呈明显的东南和西北分异,热点区向西部和南部扩张,冷点区由华北向东北移动;(3)经济发展水平和水资源禀赋对农业用水效率的影响高于技术投入和种植结构,其中经济发展、技术进步与资本投入的影响呈阶梯式上升,而水资源禀赋的作用则呈现“下降-上升-稳定”特征,且水稻与玉米主产区的用水效率提升潜力高于小麦主产区.基于此,建议高效区推广智慧灌溉,低效区强化节水设施与结构优化;热点区注重经验推广,冷点区加强水资源管控,优化种植结构.

农业用水  /  脱钩  /  超效率SBM  /  时空演变  /  随机森林

Using panel data for 31 provinces from 2007 to 2022, this study applied the super-efficiency Slack-Based Measure (SBM) model to measure agricultural water use efficiency under the constraint of decoupling economic growth from water resource consumption. Spatial dynamics were assessed using Moran's I index in ArcGIS, while the Random Forest model was employed to identify key influencing factors and their nonlinear effects. The results showed that China's agricultural water use efficiency exhibited a fluctuating yet overall upward trend, with a spatial distribution pattern characterized by "higher in the east and south, lower in the west and north." The overall pattern was found to demonstrate significant spatial autocorrelation, with spatial agglomeration increasingly strengthened. At the local scale, a clear southeast-northwest differentiation was observed, where hotspots expanded toward the west and south, and cold spots shifted from the north toward the northeast. Economic development level and water resource endowment were identified as having greater impacts on agricultural water use efficiency than technological investment and cropping structure. Specifically, the effects of economic development, technological progress, and capital investment followed a stepwise upward pattern, whereas the influence of water resource endowment displayed a "decline-increase-stabilization" trajectory. Furthermore, provinces dominated by rice and maize cultivation were found to have greater potential for efficiency improvement than those dominated by wheat cultivation. Policy recommendations were proposed, including the promotion of smart irrigation in high-efficiency regions, the enhancement of water-saving infrastructure and optimization of cropping patterns in low-efficiency regions, the sharing of best practices in hotspot areas, and the strengthening of governance and structural adjustment in coldspot regions.

agricultural water use  /  decoupling  /  super-efficiency SBM  /  spatiotemporal evolution  /  random forest
陈逸琛, 陈惠鑫, 何俊卿, 倪润祥, 余文梦. 基于机器学习的脱钩约束下中国省际农业用水效率时空演变及影响因素. 中国环境科学, 2025 , 45 (12) : 7030 -7043 .
Yi-chen CHEN, Hui-xin CHEN, Jun-qing HE, Run-xiang NI, Wen-meng YU. Spatiotemporal evolution and influencing factors of agricultural water use efficiency in China under decoupling constraints based on machine learning[J]. China Environmental Science, 2025 , 45 (12) : 7030 -7043 .
  • 国家重点研发计划项目(2024YFD1700505-01)
  • 福建省自然科学基金项目(2025J01454)
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  • 接收时间:2025-05-20
  • 首发时间:2026-10-08
  • 出版时间:2025-12-20
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  • 收稿日期:2025-05-20
基金
国家重点研发计划项目(2024YFD1700505-01)
福建省自然科学基金项目(2025J01454)
作者信息
    1.福州大学,福建 福州 350108
    2.全国市长研修学院,北京 100029
    3.中国人民大学,北京 100101
    4.农业农村部农业生态与资源保护总站,北京 100125

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* 责任作者,农艺师,
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2种不同金属材料的力学参数
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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