[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-272":3,"related-272":97},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":31,"primary_topic":38,"secondary_topics":44,"classification":45,"search_phrases":91,"slug":94,"view_count":95,"doi":8,"paper":8,"created_at":96},272,"数字技术能提升粮食生产韧性吗?——基于双重机器学习的因果推断——许佳彬等《智慧农业(中英文)》2026年第3期","https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176","许佳彬、秦亚茹、温浩洋、仇焕广、崔钊达在《智慧农业(中英文)》2026年第3期发表《数字技术能提升粮食生产韧性吗?——基于双重机器学习的因果推断》论文。研究聚焦数字技术对粮食生产韧性的因果效应,基于中国省级面板数据,运用双重机器学习(DML)方法有效解决高维控制变量与潜在混杂问题,准确估计数字技术对粮食生产韧性的处理效应。研究发现,数字技术显著提升粮食生产韧性,主要通过要素重组效应、数据要素与传统生产要素的协同配置等中介机制实现;数字技术的赋能效应呈现区域异质性,在粮食主产区和数字基础设施较好的地区效应更为显著。基于研究结论,建议构建数字农业社会化服务新体系,依托数字平台的规模效应,有效弥合小农户与现代农业之间的数字鸿沟,推动粮食生产韧性持续提升。",null,"欢迎您访问《智慧农业（中英文）》官方网站！[English](https:\u002F\u002Fwww.smartag.net.cn\u002FEN\u002F2096-8094\u002Fhome.shtml)\n\n[![Image 1](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002F2096-8094\u002Fimages\u002Fbanner.png)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F2096-8094\u002Fhome.shtml)\n\n[![Image 2](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002F2096-8094\u002Fimages\u002Fbanner_mobile.png)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F2096-8094\u002Fhome.shtml)\n\n[![Image 3](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002F2096-8094\u002Fimages\u002Fyyzkj.png)](https:\u002F\u002Fpano.bookan.com.cn\u002F?id=60704&cid=352)\n\nToggle navigation[智慧农业](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176)\n\n*   [网站首页](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F2096-8094\u002Fhome.shtml)\n*   [期刊介绍](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn133.shtml)\n*   [编委会](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn153.shtml)\n*   [投稿指南](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn135.shtml)\n*   [期刊服务](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn124.shtml)\n*   [下载中心](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn126.shtml)\n*   [联系我们](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn127.shtml)\n\n[Smart Agriculture](https:\u002F\u002Fwww.smartag.net.cn\u002F) ›› [2026](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FshowTenYearVolumnDetail.do?nian=2026), [Vol. 8](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FshowTenYearVolumnDetail.do?nian=2026) ›› [Issue (3)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fvolumn\u002Fvolumn_841.shtml): 176-189.doi: [10.12133\u002Fj.smartag.SA202601011](https:\u002F\u002Fdoi.org\u002F10.12133\u002Fj.smartag.SA202601011)\n\n• 专刊--数字技术赋能与农业经济范式转型 •[上一篇](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fabstract\u002Fabstract22736.shtml)[下一篇](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fabstract\u002Fabstract22779.shtml)\n\n### 数字技术能提升粮食生产韧性吗？——基于双重机器学习的因果推断\n\n许佳彬 1, 秦亚茹 1, 温浩洋 1, 仇焕广 2, 崔钊达 3([![Image 4](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Femail.png)](mailto:sdauczd@163.com))\n\n1.   1. 东北农业大学经济管理学院，黑龙江 哈尔滨 150030，中国\n\n2. 辽宁大学经济学院，辽宁 沈阳 110036，中国\n\n3. 山东农业大学经济管理学院，山东 泰安 271018，中国\n\n*   `收稿日期:`2026-01-08 `出版日期:`2026-05-30 \n*   `基金项目:`国家自然科学基金项目(72503031); 中国博士后科学基金面上项目(2025M772469); 黑龙江省自然科学基金项目(LH2024G001); 黑龙江省哲学社会科学基金项目(25JYC030); 山东省自然科学基金青年项目(ZR2025QC734) \n*   `作者简介:`许佳彬，博士，副教授，研究方向为农业绿色转型。E-mail：[xujiabin0319@neau.edu.cn](mailto:xujiabin0319@neau.edu.cn) \n*   `通信作者:`崔钊达，博士，讲师，研究方向为粮食安全。E-mail：[sdauczd@163.com](mailto:sdauczd@163.com) \n\n### Can Digital Technology Improve the Resilience of Grain Production: Causal Inference Based on Dual Machine Learning\n\nXU Jiabin 1, QIN Yaru 1, WEN Haoyang 1, QIU Huanguang 2, CUI Zhaoda 3([![Image 5](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Femail.png)](mailto:sdauczd@163.com))\n\n1.   1. College of Economics and Management, Northeast Agricultural University, Harbin 150030, China\n\n2. School of Economics, Liaoning University, Shenyang 110036, China\n\n3. College of Economics and Management, Shandong Agricultural University, Taian 271018, China\n\n*   `Received:`2026-01-08 `Online:`2026-05-30 \n*   `Foundation items:`National Natural Science Foundation of China(72503031); General Project of China Postdoctoral Science Foundation(2025M772469); Natural Science Foundation of Heilongjiang Province(LH2024G001); Heilongjiang Province Philosophy and Social Science Fund Project(25JYC030); Shandong Province Natural Science Foundation Youth Project(ZR2025QC734) \n*   `About author:`XU Jiabin, E-mail: [xujiabin0319@neau.edu.cn](mailto:xujiabin0319@neau.edu.cn) \n*   `Corresponding author:`CUI Zhaoda, E-mail: [sdauczd@163.com](mailto:sdauczd@163.com) \n\n## [在线阅读 4](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#)\n\n## [知网下载](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176)\n\n## [本地下载 20](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#1)\n\n[_![Image 6](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002Fimages\u002Fknowledge\\_map1.png)_ ## 可视化 0](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002Flexeme\u002FshowArticleByLexeme.do?articleID=22701)\n\n#### [摘要\u002FAbstract](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176)\n\n**摘要：**\n\n**【目的\u002F意义】** 运用数字技术提升粮食生产韧性对保障粮食安全、构建现代化农业保障体系具有重要意义。本研究旨在探究数字技术对粮食生产韧性的影响效应、内在机制及异质性特征，为数字技术赋能粮食生产韧性提升提供参考。 **【方法】** 采用2011—2023年中国30个省份的面板数据，采用熵值法构建粮食生产韧性综合评价指标体系并测算其水平。选取支持向量机作为基学习器的双重机器学习模型识别数字技术对粮食生产韧性的因果影响。运用中介效应模型验证数字技术通过“人-地-服”三条机制路径实现要素重组的驱动作用。通过异质性检验，分析粮食产区、地形起伏度及经济发展水平对该赋能效应的调节差异。 **【结果和讨论】** 数字技术显著促进了粮食生产韧性提升，且在不同细化维度呈现结构性差异。数字技术显著增强了调节适应力与转型创新力，但受技术依赖与资本挤占效应影响，对风险抵抗力表现出负向影响。机制分析表明，数字技术通过“人-地-服”三条路径实现要素重组，即促进劳动力非农转移带来的资本回流、降低交易成本推动的土地规模经营，以及发挥技术创新集成作用的农业生产性服务。异质性分析发现，该赋能效应在地形起伏度较高、经济发展水平较低及粮食产销平衡区与主销区更为显著。 **【结论】** 应促进数字技术集成以打通全产业链数据链，创新“数字反哺”横向补偿机制以弥补省际数字鸿沟，培育数字化新型农业社会化服务体系，并因地制宜提升自然条件劣势地区的数字化短板，从而多路径提升粮食生产韧性。\n\n**关键词:**[粮食生产韧性,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#)[数字技术,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#)[要素重组,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#)[双重机器学习,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#)[支持向量机](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#)\n\n**Abstract:**\n\n**[Objective]** Leveraging digital technology to enhance grain production resilience is pivotal for ensuring food security and constructing a modernized agricultural support system. The purposes of this study are to: (1) Define the connotation of grain production resilience and construct an evaluation index system across three dimensions: risk resistance, regulatory adaptability, and transformative innovation; (2) Identify the impact and mechanisms of digital technology as a new production factor on grain production resilience; (3) Reveal how digital technology influences resilience through the reorganization of \"People-Land-Service\" factors; (4) Analyze the heterogeneity of these effects across diverse geographical environments and economic endowments. **[Methods]** Based on panel data from 30 Chinese provinces spanning 2011 to 2023, a rigorous empirical analysis was conducted. First, the entropy method was employed to measure the levels of digital technology and grain production resilience. Second, a dual machine learning model, utilizing support vector machine as base learners with 5-fold cross-validation and double residualization, was constructed for causal inference to address the \"curse of dimensionality\" and specification biases inherent in traditional linear models. To ensure robustness, an instrumental variable approach was applied, using the product of the number of fixed-line telephones in 1984 and the national internet users of the previous year. Third, a mediation effect model was introduced to test three pathways: non-farm labor transfer, large-scale land management, and agricultural productive services. **[Results and Discussions]** Mechanism analysis indicated: (1) In the \"People\" dimension, digital technology promoted non-farm labor transfer by reducing information search costs, thereby inducing capital reflux to alleviate financial constraints; (2) In the \"Land\" dimension, digital platforms lowered transaction costs for land transfer, facilitating contiguous large-scale farming and improving the marginal efficiency of digital equipment; (3) In the \"Service\" dimension, digital technology empowered agricultural service organizations, enhancing resilience through technology diffusion and professional intervention. Heterogeneity analysis showed that the empowerment effect was more pronounced in grain production-marketing balanced zones, high-relief terrains, and economically underdeveloped regions. This highlighted the \"gap-filling\" nature of digital technology, where precision tools like drones and remote sensing compensated for the limitations of traditional machinery in complex terrains. Notably, while digital technology significantly drove overall resilience, it primarily bolstered regulatory adaptability and transformative innovation while exerting a significant negative impact on initial risk resistance. **[Conclusions]** Digital technology is not a mere superposition of factors; rather, it catalyzes a fundamental transformation in the dynamics of grain production through the systemic restructuring of elements. To this end, differentiated strategies should be implemented: Major grain-producing areas should prioritize full-chain data integration, whereas production-marketing balanced zones and ecologically disadvantaged regions should focus on overcoming digital infrastructure bottlenecks. Furthermore, a horizontal \"digital feedback\" compensation mechanism should be innovated, encouraging major consumption areas to shift from \"blood transfusion\" (passive aid) to \"blood-making\" (capacity building) through technology licensing and targeted talent support. Finally, a new system of digitalized agricultural social services should be cultivated, leveraging the scale effects of digital platforms to effectively bridge the gap between smallholder farmers and modern agriculture.\n\n**Key words:**[grain production resilience,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#)[digital technology,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#)[restructuring of elements,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#)[dual machine learning,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#)[support vector machine](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#)\n\n**中图分类号:**\n\n*   [F323](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#)\n\n#### 引用本文\n\n许佳彬, 秦亚茹, 温浩洋, 仇焕广, 崔钊达. 数字技术能提升粮食生产韧性吗？——基于双重机器学习的因果推断[J]. 智慧农业(中英文), 2026, 8(3): 176-189.\n\nXU Jiabin, QIN Yaru, WEN Haoyang, QIU Huanguang, CUI Zhaoda. Can Digital Technology Improve the Resilience of Grain Production: Causal Inference Based on Dual Machine Learning[J]. Smart Agriculture, 2026, 8(3): 176-189.\n\n#### 使用本文\n\n**[[收藏文章](javascript:;)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#) \u002F[推荐](javascript:void(null))**\n**导出引用管理器**[EndNote](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=EndNote&id=22701)|[Ris](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=Ris&id=22701)|[BibTeX](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=BibTeX&id=22701)\n\n**链接本文:**[https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202601011](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202601011)\n\n[https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176)\n\n#### [图\u002F表 10](javascript:;)\n\n[![Image 7](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-176\u002Fthumbnail\u002F7F6E0BE8-71CE-4a6e-B3E5-CDE5AD526AE2-F001.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#figureClass14687)\n\n**图1**\n\n粮食生产韧性概念示意图\n\n![Image 8](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-176\u002F7F6E0BE8-71CE-4a6e-B3E5-CDE5AD526AE2-F001.jpg)\n\n 图1 \n\n[![Image 9](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-176\u002Fthumbnail\u002F7F6E0BE8-71CE-4a6e-B3E5-CDE5AD526AE2-F002.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#figureClass14688)\n\n**图2**\n\n数字技术提升粮食生产韧性的机制框架图\n\n![Image 10](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-3\u002FImages\u002F2096-8094-2026-8-3-176\u002F7F6E0BE8-71CE-4a6e-B3E5-CDE5AD526AE2-F002.jpg)\n\n 图2 \n\n[![Image 11](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#inline_content14689)\n\n**表1**\n\n数字技术影响粮食生产韧性的变量含义与描述性统计\n\n| 变量类别 | 变量名称 | 变量赋值 | 平均值 | 标准差 | 最大值 | 最小值 |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| 被解释变量 | 粮食生产韧性 | 熵值法计算 | 0.188 | 0.068 | 0.544 | 0.079 |\n| 核心解释变量 | 数字技术 | 熵值法计算 | 0.157 | 0.010 | 0.704 | 0.029 |\n| 机制变量 | 劳动力非农转移\u002F% | （农村从业人员-第一产业从业人员）\u002F农村从业人员×100% | 0.043 | 0.019 | 0.106 | 0.012 |\n|  | 土地规模经营 | 家庭承包耕地流转总面积取对数 | 6.967 | 0.512 | 7.839 | 5.216 |\n|  | 农业生产性服务\u002F% | 农林牧渔专业及辅助性活动产值\u002F农林牧渔业总产值×100% | 0.042 | 0.019 | 0.106 | 0.012 |\n| 控制变量 | 城镇化率\u002F% | 城镇人口\u002F总人口×100% | 0.606 | 0.120 | 0.896 | 0.350 |\n|  | 地区产业结构\u002F% | （第二产业增加值+第三产业增加值）\u002F地区生产总值×100% | 0.905 | 0.052 | 0.998 | 0.748 |\n|  | 农产品价格指数 | 采用上一年度农产品生产价格指数（指数，上年=100） | 4.646 | 0.059 | 4.955 | 4.472 |\n|  | 农村人均用电量\u002F（kw·h\u002F人） | 农村用电总量\u002F乡村人口数 | 6.913 | 0.979 | 10.628 | 4.897 |\n|  | 公路里程数\u002F（万km） | 区域内公路通车总里程 | 15.674 | 8.424 | 41.830 | 1.210 |\n|  | 农田有效灌溉率\u002F% | 有效灌溉面积\u002F农作物播种面积×100% | 0.440 | 0.178 | 1.234 | 0.172 |\n|  | 自然灾害受灾率\u002F% | 农作物受灾面积\u002F农作物播种面积×100% | 0.139 | 0.181 | 2.980 | 0.001 |\n|  | 人均可支配收入 | 农村居民人均可支配收入取对数 | 10.088 | 0.443 | 11.349 | 9.044 |\n\n 表1 \n\n[![Image 12](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#inline_content14690)\n\n**表2**\n\n粮食生产韧性评价指标体系构建\n\n| 一级指标 | 二级指标 | 二级指标解释 | 属性 |\n| :---: | :---: | :---: | :---: |\n| 风险抵抗力 | 粮食产量波动率\u002F% | |当年粮食总产量－过去5年粮食总产量移动平均值|\u002F过去5年粮食总产量移动平均值 | － |\n| 粮食减损潜力\u002F（台\u002F万亩） | 粮食每万亩脱粒机械年末拥有量 | ＋ |\n| 耕地资源水平\u002F（千hm 2\u002F万人） | 耕地面积\u002F总人口 | ＋ |\n| 粮食病虫草鼠害受灾挽回情况\u002F% | 粮食病虫草鼠灾害挽回数量\u002F粮食病虫草鼠灾害受灾数量 | ＋ |\n| 调节适应力 | 农业劳动生产率\u002F（元\u002F人） | 农林牧渔业总产值\u002F第一产业从业人员数 | ＋ |\n| 粮食生产综合机械化水平\u002F% | 粮食作物机耕率×0.4+机播率×0.3+机收率×0.3 | ＋ |\n| 土地生产率\u002F（万t\u002F千hm 2） | 粮食总产量\u002F粮食播种面积 | ＋ |\n| 转型创新力 | 种质创新水平\u002F个 | 农业植物新品种权申请数量 | ＋ |\n| 科技研发潜力\u002F（人\u002F千hm 2） | 公有经济（国有）企事业单位农业技术人员\u002F农作物播种面积 | ＋ |\n| 科技研发投入\u002F% | 区域研究与试验发展经费投入强度\u002F国内生产总值 | ＋ |\n\n 表2 \n\n[![Image 13](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#inline_content14691)\n\n**表3**\n\n数字技术评价指标体系构建\n\n| 一级指标 | 二级指标 | 二级指标解释 | 属性 |\n| :---: | :---: | :---: | :---: |\n| 数字通信基础 | 农村有线广播电视普及率\u002F% | 农村有线（广播）电视用户占家庭总户数比重 | ＋ |\n|  | 互联网业务普及率\u002F% | 开通互联网业务的行政村占行政村总数比重 | ＋ |\n|  | 基础通信网络水平\u002Fkm | 长途光缆线路总长度 | ＋ |\n|  | 计算机普及率\u002F（台\u002F每百户） | 农村居民平均每百户年末家用计算机拥有量 | ＋ |\n|  | 智能手机普及率\u002F（部\u002F每百户） | 农村居民平均每百户年末移动电话拥有量 | ＋ |\n| 数字应用设施载体 | 数字基地规模\u002F个 | 区域认定的淘宝村数量 | ＋ |\n| 物联网应用支撑\u002Fkm | 农村寄递物流长度 | ＋ |\n|  | 农业气象观测能力\u002F个 | 农村气象观测业务站个数 | ＋ |\n| 邮政服务网点分布\u002F（个\u002F处） | 区域内邮政营业网点数量 | ＋ |\n| 数字创新转化服务 | 农业数字创新产出\u002F个 | 农业科技相关专利申请数量 | ＋ |\n|  | 数字金融广度（指数） | 数字普惠金融指数覆盖广度 | ＋ |\n|  | 数字金融深度（指数） | 数字普惠金融指数使用深度 | ＋ |\n\n 表3 \n\n[![Image 14](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#inline_content14692)\n\n**表4**\n\n数字技术影响粮食生产韧性的基准回归结果\n\n| 变量名称 | （1）粮食生产韧性 | （2）粮食生产韧性 | （3）风险抵抗力 | （4）调节适应力 | （5）转型创新力 |\n| :---: | :---: | :---: | :---: | :---: | :---: |\n| 数字技术 | 0.234***（0.039） | 0.309***（0.032） | -0.195***（0.027） | 0.895***（0.066） | 0.671***（0.065） |\n| 控制变量一次项 | YES | YES | YES | YES | YES |\n| 控制变量二次项 | NO | YES | YES | YES | YES |\n| 年份固定效应 | YES | YES | YES | YES | YES |\n| 省份固定效应 | YES | YES | YES | YES | YES |\n| 观测值 | 390 | 390 | 390 | 390 | 390 |\n\n 表4 \n\n[![Image 15](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#inline_content14693)\n\n**表5**\n\n数字技术影响粮食生产韧性的稳健性检验结果\n\n| 变量名称 | （1）缩尾1% | （2）缩尾5% | （3）剔除直辖市 | （4）1∶2 | （5）1∶7 | （6）弹性网络回归 | （7）套索回归 |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| 数字技术 | 0.327*** （0.028） | 0.441*** （0.027） | 0.252*** （0.025） | 0.305*** （0.033） | 0.307*** （0.031） | 0.413*** （0.096） | 0.416*** （0.097） |\n| 控制变量一次项 | YES | YES | YES | YES | YES | YES | YES |\n| 控制变量二次项 | YES | YES | YES | YES | YES | YES | YES |\n| 年份固定效应 | YES | YES | YES | YES | YES | YES | YES |\n| 省份固定效应 | YES | YES | YES | YES | YES | YES | YES |\n| 观测值 | 390 | 390 | 338 | 390 | 390 | 390 | 390 |\n\n 表5 \n\n[![Image 16](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#inline_content14694)\n\n**表6**\n\n数字技术影响粮食生产韧性的内生性检验结果\n\n| 变量名称 | Ⅳ：历史期1984年每百人固定电话数 | Ⅳ：滞后一期数字技术 |\n| :---: | :---: | :---: |\n| 数字技术 | 0.600*** （0.075） | 0.308*** （0.034） |\n| 控制变量一次项 | YES | YES |\n| 控制变量二次项 | YES | YES |\n| 年份固定效应 | YES | YES |\n| 省份固定效应 | YES | YES |\n| 观测值 | 390 | 360 |\n\n 表6 \n\n[![Image 17](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#inline_content14695)\n\n**表7**\n\n数字技术影响粮食生产韧性的机制检验结果\n\n| 变量名称 | 粮食生产韧性 | 非农劳动力转移 | 土地规模经营 | 农业生产性服务 |\n| :---: | :---: | :---: | :---: | :---: |\n| 数字技术 | 0.309*** （0.032） | 0.935*** （0.091） | 0.874*** （0.152） | 0.051*** （0.014） |\n| 控制变量一次项 | YES | YES | YES | YES |\n| 控制变量二次项 | YES | YES | YES | YES |\n| 年份固定效应 | YES | YES | YES | YES |\n| 省份固定效应 | YES | YES | YES | YES |\n| 观测值 | 390 | 390 | 390 | 390 |\n\n 表7 \n\n[![Image 18](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176#inline_content14696)\n\n**表8**\n\n数字技术影响粮食生产韧性的异质性分析结果\n\n| 变量名称 | 粮食生产区 | 地形起伏度 | 经济发展水平 |\n| :---: | :---: | :---: | :---: |\n| 主产区 | 主销区 | 平衡区 | 高 | 低 | 高 | 低 |\n| 数字技术 | 0.272***（0.038） | 0.351***（0.056） | 0.386**（0.113） | 0.461***（0.068） | 0.198***（0.030） | 0.287***（0.031） | 0.470***（0.066） |\n| 控制变量一次项 | YES | YES | YES | YES | YES | YES | YES |\n| 控制变量二次项 | YES | YES | YES | YES | YES | YES | YES |\n| 年份固定效应 | YES | YES | YES | YES | YES | YES | YES |\n| 省份固定效应 | YES | YES | YES | YES | YES | YES | YES |\n| 观测值 | 169 | 91 | 130 | 195 | 195 | 195 | 195 |\n\n 表8 \n\n[](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI3\u002F176)\n#### [参考文献 38](javascript:;)\n\n[1]李国祥. 新时代国家粮食安全的目标任务及根本要求——学习习近平关于国家粮食安全论述及十九届六中全会相关精神的体会[J]. 中国农村经济, 2022(3): 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Agricultural Economics and Management, 2024(6): 77-90.\n[13]周密, 牛浩, 魏超, 等. 农业保险保障对粮食生产韧性的影响研究[J]. 中国农业资源与区划, 2024, 45(8): 44-55.\nZHOU M, NIU H, WEI C, et al. The impact of agricultural insurance guarantee on the food production resilience[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2024, 45(8): 44-55.\n[14]SHILOMBOLENI H, EPSTEIN G, MANSINGH A. Building resilience in Africa's smallholder farming systems: Contributions from agricultural development interventions: A scoping review[J]. Ecology and Society, 2024, 29(3): art22.\n[15]FANG D, CHEN J Q, WANG S G, et al. Can agricultural mechanization enhance the climate resilience of food production? Evidence from China[J]. Applied Energy, 2024, 373: 123928.\n[16]高鸣, 杨新宇. 农业增效益: 数字技术赋能粮食产业高质量发展的实践进路[J]. 学习与探索, 2025(2): 23-32.\nGAO M, YANG X Y. Increasing agricultural benefits","智慧农业(中英文) 2026, 8(3): 176-189","2026-08-05T00:00:00Z","论文",40,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},20,22,15,2,1,"核心期刊论文，运用双重机器学习方法探究数字技术对粮食生产韧性的因果效应，方法新颖，数据详实，具有较高学术价值，但时效性较低。",[24,25,28],{"name":10,"url":6},{"name":26,"url":27},"智慧农业(中英文) 2026年第3期 176-189 · 2026-08-26","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-08-26\u002Fdoc-iniprytq2299774.shtml",{"name":29,"url":30},"智慧农业(中英文)2026年第3期","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7678304506521616942",[32,33,34,35,36,37],"智慧农业","数字技术","因果推断","粮食生产韧性","双重机器学习","智慧农业期刊",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":54},[51,59,65,71,79,86],{"code":52,"score":53,"supported":54,"title":55,"abstract":56,"tags":58},"I1",16,true,[32],[32,57],"数字农业",[33,32],{"code":60,"score":61,"supported":54,"title":62,"abstract":63,"tags":64},"I7",13,[34],[34],[36,34],{"code":39,"score":66,"supported":54,"title":67,"abstract":69,"tags":70},11,[68],"机器学习",[68],[],{"code":72,"score":73,"supported":54,"title":74,"abstract":75,"tags":78},"G2",6,[],[76,77],"社会化服务","小农户",[],{"code":80,"score":73,"supported":54,"title":81,"abstract":82,"tags":85},"I6",[],[83,84],"数据要素","数字鸿沟",[],{"code":87,"score":21,"supported":14,"title":88,"abstract":89,"tags":90},"A8",[],[],[35],[92,93],"双重机器学习 智慧农业期刊 粮食生产韧性 因果推断","双重机器学习 智慧农业期刊","双重机器学习智慧农业期刊粮食生产韧性因果推断-272",12,"2026-08-07T00:18:31.346502Z",{"total":73,"page":21,"page_size":73,"items":98},[99,143,185,247,312,375],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":8,"content":104,"source_name":105,"source_url":8,"published_at":106,"category":12,"cover_url":8,"hotness":107,"is_selected":14,"score":108,"score_detail":109,"sources":111,"tags":113,"primary_topic":117,"secondary_topics":118,"classification":119,"search_phrases":137,"slug":140,"view_count":141,"doi":8,"paper":8,"created_at":142},1117,"Evidence Based on Digital Agriculture in China (Land 2026, 15(9), 1531) — 基于700个中国县的双重机器学习证据","https:\u002F\u002Fwww.mdpi.com\u002F2073-4292\u002F15\u002F9\u002F1531","Benjian Wu, Xing Huang, Bo Zhou, Jianmin Li, Lifang Hu (中国农业科学院\u002F中国农业大学\u002F南京大学等) 共同发表论文。基于2010—2024年中国700个县的县级面板数据,将\"数字农业创新与应用基地\"试点视为准自然实验,采用双重机器学习模型考察智慧农业建设对城乡土地利用效率差距的影响。研究发现:(1)智慧农业建设显著缩小了城乡土地利用效率差距,机制渠道指向农业技术进步、资本配置优化、农业产业升级;(2)政策效应在原贫困县、土地流转率高、数字基础强的县更显著;(3)分解结果显示差距缩小主要由农村追赶驱动,而非城市衰退。本研究澄清了智慧农业建设如何通过要素再配置平衡城乡土地利用。","## Journals\n\n[Active Journals](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournals)[Find a Journal](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournalfinder)[Journal Proposal](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournals\u002Fproposal)[Proceedings Series](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fproceedings)\n\n## [Topics](https:\u002F\u002Fwww.mdpi.com\u002Ftopics)\n\n## Information\n\n[For Authors](https:\u002F\u002Fwww.mdpi.com\u002Fauthors)[For Reviewers](https:\u002F\u002Fwww.mdpi.com\u002Freviewers)[For Editors](https:\u002F\u002Fwww.mdpi.com\u002Feditors)[For Librarians](https:\u002F\u002Fwww.mdpi.com\u002Flibrarians)[For Publishers](https:\u002F\u002Fwww.mdpi.com\u002Fpublishing_services)[For 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Introduction\n\nChina’s agricultural production is still characterized by the coexistence of smallholder-based household farming and expanding scale operations. Small and fragmented plots remain widespread, while land transfer and the development of commercial family farms have gradually promoted larger-scale and more specialized production [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B1-land-15-01531)]. This production structure creates practical constraints on the transition toward smart agriculture. Fragmented farmland, relatively high technology costs, gaps in digital infrastructure and technical capacity, and uncertainty about investment returns can restrict the adoption of precision and digital technologies [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B1-land-15-01531),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B2-land-15-01531)]. Farmers do not uniformly reject agricultural modernization: evidence from China indicates broad openness to precision-agriculture technologies, but willingness and readiness to adopt them vary substantially with farm scale, perceived economic returns, access to information, financing, and service support [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B1-land-15-01531)]. Against this background, public smart agriculture programs can serve not only as technology-demonstration projects but also as institutional interventions that lower adoption barriers and facilitate the transition from traditional, decentralized production toward more digitalized, standardized, and intensive agricultural production.\n\nLand use efficiency has become an important constraint on high-quality county development in China as extensive spatial expansion becomes less sustainable [[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B3-land-15-01531),[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B4-land-15-01531)]. The issue is relational as well as aggregate: urban land benefits from denser concentrations of capital, technology, and industry, whereas rural land is more often constrained by lower technological intensity, fragmented management, and limited factor inputs [[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B5-land-15-01531),[6](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B6-land-15-01531),[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B7-land-15-01531),[8](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B8-land-15-01531)]. The resulting within-county disparity can impede urban–rural factor integration, making improvement on the rural side central to efficiency-enhancing convergence.\n\nDigital technologies, represented by the Internet of Things, big data, artificial intelligence, remote sensing, and intelligent machinery, provide a potential route for improving the precision of agricultural production and the productivity of rural land [[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B9-land-15-01531),[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B10-land-15-01531)]. Since 2017, the Digital Agriculture Innovation and Application Base program has introduced precision production, digital management, and intelligent equipment through successive county-level pilots. This staggered policy setting makes it possible to examine whether a production-oriented digital intervention narrows the urban–rural land use efficiency gap and, crucially, whether convergence occurs through rural catch-up rather than urban contraction.\n\nThe adoption constraints observed in China have close international parallels. Evidence from European farms shows that precision-agriculture uptake varies with farm scale, expected returns, investment costs, and regional conditions [[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B11-land-15-01531),[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B12-land-15-01531)]; U.S. evidence likewise documents strong scale gradients in adoption and links precision technologies to technical efficiency [[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B13-land-15-01531),[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B14-land-15-01531)]. Research on smallholders in India and Sub-Saharan Africa highlights information quality, connectivity, affordability, skills, and advisory support as recurring adoption constraints [[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B15-land-15-01531),[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B16-land-15-01531),[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B17-land-15-01531)]. These cross-country regularities provide a comparative benchmark for the Chinese case: scale, digital readiness, and complementary services are general boundary conditions, whereas the collective land system and the national pilot designation process are institutionally specific to China.\n\nThis study makes three contributions. First, it shifts attention from the level of agricultural productivity to convergence within the county urban–rural land use system and distinguishes rural catch-up from urban contraction. Second, it treats agricultural technological progress, capital allocation improvement, and agricultural industrial upgrading as complementary transmission channels rather than isolated mechanisms, clarifying their theoretical connections and the limits of comparing their relative magnitudes. Third, it uses DML to flexibly partial out high-dimensional and nonlinear observed confounding while treating the conventional TWFE event study as an auxiliary diagnostic in view of the staggered timing of treatment.\n\n## 2. Literature Review\n\nExisting research on digital agriculture identifies several routes through which technology can affect agricultural and land productivity. One strand emphasizes production efficiency: sensing, data analytics, variable-rate technologies, and other precision-agriculture tools can improve input targeting and technical efficiency [[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B10-land-15-01531),[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B13-land-15-01531),[18](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B18-land-15-01531),[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B19-land-15-01531),[20](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B20-land-15-01531)]. A second strand focuses on information and coordination. Digital advice, data platforms, and agricultural knowledge networks can reduce information frictions, support technology adoption, and strengthen coordination across production and value chains [[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B9-land-15-01531),[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B15-land-15-01531),[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B17-land-15-01531),[21](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B21-land-15-01531),[22](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B22-land-15-01531),[23](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B23-land-15-01531),[24](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B24-land-15-01531)]. A third strand highlights farm scale and fixed costs. Cross-regional evidence from Europe and the United States, together with reviews of small-scale precision farming, shows that farm size and the ability to spread technology costs materially condition adoption [[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B11-land-15-01531),[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B12-land-15-01531),[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B14-land-15-01531),[25](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B25-land-15-01531),[26](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B26-land-15-01531)].\n\nThe same literature also identifies constraints that can limit or unevenly distribute digital agriculture gains. High up-front costs, limited skills, weak connectivity, and insufficient complementary services can slow adoption, especially among smaller farms and resource-constrained producers [[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B12-land-15-01531),[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B15-land-15-01531),[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B16-land-15-01531),[25](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B25-land-15-01531)]. Data governance, trust, advisory capacity, and the organization of agricultural innovation systems can further shape whether digital tools are converted into effective production decisions [[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B9-land-15-01531),[24](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B24-land-15-01531),[27](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B27-land-15-01531),[28](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B28-land-15-01531),[29](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B29-land-15-01531)]. These constraints imply that digitalization can widen rather than narrow performance differences when access to capital, infrastructure, skills, and services is highly unequal, a concern documented particularly in smallholder settings [[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B16-land-15-01531),[30](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B30-land-15-01531)].\n\nComparative evidence therefore points to conditional rather than universal effects. European studies emphasize the roles of farm structure and adoption costs [[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B11-land-15-01531),[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B12-land-15-01531),[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B19-land-15-01531)], U.S. evidence shows that adoption is closely related to farm scale [[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B13-land-15-01531),[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B14-land-15-01531)], and field evidence from India and other developing-country settings shows that digital advisory services depend on trust, information quality, connectivity, and complementary support [[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B17-land-15-01531),[21](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B21-land-15-01531),[22](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B22-land-15-01531)]. Evidence from Sub-Saharan Africa similarly identifies affordability, infrastructure, skills, and institutional support as central barriers for smallholders [[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B15-land-15-01531),[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B16-land-15-01531)]. These results closely mirror the boundary conditions examined later in this study while also showing that the institutional route through which those constraints operate may differ across countries.\n\nAgainst this international literature, the unresolved issue is not an absence of research on digital agriculture itself. Rather, three more specific gaps remain. First, most studies evaluate farm productivity, technology adoption, technical efficiency, income, or environmental performance, while the relative efficiency of urban and rural land within the same local land system is rarely the outcome of interest [[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B9-land-15-01531),[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B11-land-15-01531),[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B13-land-15-01531),[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B19-land-15-01531),[20](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B20-land-15-01531)]. Second, technological progress, factor allocation, and rural industrial integration are often examined separately even though digital transformation can connect these processes through information, investment, and value-chain coordination [[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B9-land-15-01531),[24](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B24-land-15-01531),[31](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B31-land-15-01531),[32](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B32-land-15-01531)]. Third, conventional linear specifications can be restrictive when policy selection and outcomes depend on high-dimensional and nonlinear county characteristics. Accordingly, this study examines whether the staggered Digital Agriculture Innovation and Application Base program changes the within-county urban–rural gap in land use efficiency, how the adjustment is distributed between the rural and urban sides, and which complementary conditions shape the observed response.\n\n## 3. Institutional Background and Theoretical Analysis\n\n### 3.1. Institutional Background\n\nSmart agriculture construction is an important institutional arrangement formed against the background of the Digital China initiative and the in-depth advancement of the rural revitalization strategy. For a long time, Chinese agriculture has been dominated by traditional production methods, characterized by limited precision, low resource efficiency, and short industrial chains; rural land output has long lagged behind urban land, and the urban–rural gap in land use efficiency has been pronounced. To promote the digital transformation of agriculture and accelerate agricultural modernization, the relevant national authorities launched the construction of Digital Agriculture Innovation and Application Bases (pilots) in 2017. Relying on the Internet of Things, big data, artificial intelligence, and intelligent equipment, the bases carry out digital transformation in grain, cash crops, livestock and poultry, and aquaculture; explore models and pathways for smart agriculture; and are designated and announced in successive batches. In terms of policy content, smart agriculture construction is not only an agricultural technology-upgrading project but also carries a functional orientation toward optimizing agricultural factor allocation and promoting agricultural industrial upgrading. On the one hand, by introducing digital technologies and intelligent equipment, the program raises the precision and intensification of agricultural production and improves the allocation of land, water, fertilizer, and labor, providing technological support for higher rural land output. On the other hand, by developing digitalized, standardized, and scaled production, the program promotes the integration of agriculture with processing, logistics, and services and extends agricultural value chains, creating conditions for rural land to shift from inefficient to efficient use.\n\nChina’s land institutions are also central to interpreting the empirical design. Land in urban areas is state-owned, whereas rural and suburban land is generally collectively owned; farm households hold contractual and land management rights, and agricultural operation can be reorganized through lawful transfer or leasing of land management rights without transferring collective ownership [[33](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B33-land-15-01531),[34](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B34-land-15-01531)]. National land survey and cadastral rules distinguish agricultural land from construction land and provide the classification framework from which the study’s rural agricultural land and urban construction land measures are assembled [[35](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B35-land-15-01531)]. The county land-transfer rate is therefore a meaningful indicator of whether fragmented management rights have been consolidated into operating units that are better able to absorb machinery and precision technologies. “Formerly poverty-stricken counties” refers to counties previously included in China’s national poverty-targeting classification [[36](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B36-land-15-01531)].\n\n### 3.2. Theoretical Analysis\n\nThe urban–rural gap in land use efficiency is defined here as a within-county relative disparity in output density between two land use subsystems: urban construction land supporting secondary- and tertiary-industry output, and rural agricultural land supporting primary-industry output. Because the two subsystems produce different outputs on different land categories, the gap is a scale-free relative measure of output density rather than a like-for-like comparison of production technologies; what it captures is how unevenly economic output is generated per unit of land across the urban and rural subsystems of the same county. The formation and the narrowing of the gap can also operate through fundamentally different mechanisms on the two sides: urban land use efficiency is shaped primarily by agglomeration economies, industrial composition, and the management of construction land supply [[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B3-land-15-01531),[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B5-land-15-01531),[6](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B6-land-15-01531),[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B7-land-15-01531),[8](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B8-land-15-01531)], whereas rural land use efficiency depends on agricultural technology, factor allocation, and the scale of operation [[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B11-land-15-01531),[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B13-land-15-01531),[26](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B26-land-15-01531),[37](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B37-land-15-01531)]. The index itself is symmetric: a smaller gap could arise from higher rural efficiency, lower urban efficiency, or simultaneous changes on both sides, and these possibilities have different economic meanings. Because the Digital Agriculture Innovation and Application Base program directly targets agricultural production, its principal adjustment margin is expected to lie on the rural side: urban land already benefits from denser concentrations of capital, technology, and industry, whereas smart agriculture applications can release rural output previously constrained by information, technology, and factor allocation frictions. The theoretically relevant form of convergence is therefore rural catch-up rather than urban decline; [Section 6.2.1](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#sec6dot2dot1-land-15-01531) later separates the two sides empirically so that the side of adjustment is identified rather than assumed.\n\n**Hypothesis****H1.**\n\nSmart agriculture construction narrows the urban–rural gap in land use efficiency primarily through rural catch-up, that is, by increasing rural land use efficiency without requiring a decline in urban land use efficiency.\n\nFrom the perspective of technological progress, the primary driver of rural catch-up in land use efficiency is the upgrading of agricultural technology. Agricultural production in rural areas has long relied on experience and traditional methods, with low technological content and extensive resource use, leaving the output potential of land unrealized. By introducing the Internet of Things, remote-sensing monitoring, big data analytics, and intelligent equipment into agricultural production, smart agriculture construction promotes crop-condition sensing, precision irrigation and fertilization, intelligent pest and disease control, and intelligent machinery operations, shifting agricultural production from extensive to precision modes. Precision production reduces the waste of water, fertilizer, and pesticides and raises resource efficiency on the one hand, while it increases yields and product quality, raising agricultural output per unit of land, on the other hand [[20](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B20-land-15-01531)]. Evidence from precision-agriculture research links these tools to measurable gains in input targeting and technical efficiency [[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B10-land-15-01531),[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B13-land-15-01531),[18](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B18-land-15-01531),[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B19-land-15-01531)], and digital advisory services can substantially improve the quality and reach of production information [[22](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B22-land-15-01531)]. Meanwhile, demonstration projects and agricultural advisory networks can lower the threshold for surrounding farmers to learn and use modern technologies [[23](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B23-land-15-01531)]. Cross-country studies nevertheless show that such gains depend on complementary skills, connectivity, and service support, so a public demonstration program that bundles equipment, data","MDPI Land 2026-08-22","2026-08-22T00:00:00Z",77,{"impact":153,"substance":18,"depth":153,"authority":95,"freshness":154,"relevant":21,"comment":155},18,7,"基于700个县的双重机器学习证据，研究数字农业试点对城乡土地利用效率差距的影响，方法新颖，数据规模大，结论具有政策参考价值。",[157],{"name":149,"url":146},[32,36,159,160,114,161],"数字农业试点","城乡土地利用效率差距","MDPI Land",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":165},{"candidates":166,"agricultural_evidence":54},[167,172,176],{"code":52,"score":153,"supported":54,"title":168,"abstract":169,"tags":171},[57],[32,57,170,124],"smart agriculture",[32],{"code":39,"score":66,"supported":54,"title":173,"abstract":174,"tags":175},[68],[68],[],{"code":72,"score":132,"supported":54,"title":177,"abstract":178,"tags":179},[],[135],[],[181,182],"城乡土地利用效率差距 双重机器学习 数字农业试点 智慧农业","城乡土地利用效率差距 双重机器学习","城乡土地利用效率差距双重机器学习数字农业试点智慧农业-939","2026-08-25T00:03:29.465281Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":8,"content":190,"source_name":191,"source_url":8,"published_at":192,"category":12,"cover_url":8,"hotness":107,"is_selected":14,"score":193,"score_detail":194,"sources":197,"tags":199,"primary_topic":201,"secondary_topics":207,"classification":209,"search_phrases":240,"slug":243,"view_count":244,"doi":245,"paper":8,"created_at":246},144,"基于因果推断和机器学习的农田玉米产量预测模型","https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027","王毅、崔茜彤、王晨、熊宝伟、邵国敏、王琬莹、曹培、韩文霆(西安财经大学\u002F西北农林科技大学\u002F西安理工大学)针对传统玉米产量预测方法难以揭示变量间因果关系的问题,提出融合因果推断与机器学习的预测方法:基于遥感指数、气象数据、土壤剖面湿度和阶段性作物观测数据等多源异构信息,采用偏相关与瞬时条件独立算法系统分析玉米产量与各影响因子之间的因果关系,构建可解释的玉米产量预测模型。","欢迎您访问《智慧农业（中英文）》官方网站！[English](https:\u002F\u002Fwww.smartag.net.cn\u002FEN\u002F2096-8094\u002Fhome.shtml)\n\n[![Image 1](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002F2096-8094\u002Fimages\u002Fbanner.png)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F2096-8094\u002Fhome.shtml)\n\n[![Image 2](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002F2096-8094\u002Fimages\u002Fbanner_mobile.png)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F2096-8094\u002Fhome.shtml)\n\n[![Image 3](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002F2096-8094\u002Fimages\u002Fyyzkj.png)](https:\u002F\u002Fpano.bookan.com.cn\u002F?id=60704&cid=352)\n\nToggle navigation[智慧农业](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027)\n\n*   [网站首页](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F2096-8094\u002Fhome.shtml)\n*   [期刊介绍](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn133.shtml)\n*   [编委会](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn153.shtml)\n*   [投稿指南](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn135.shtml)\n*   [期刊服务](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn124.shtml)\n*   [下载中心](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn126.shtml)\n*   [联系我们](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fcolumn\u002Fcolumn127.shtml)\n\n[Smart Agriculture](https:\u002F\u002Fwww.smartag.net.cn\u002F) ›› [2026](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FshowTenYearVolumnDetail.do?nian=2026), [Vol. 8](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FshowTenYearVolumnDetail.do?nian=2026) ›› [Issue (2)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fvolumn\u002Fvolumn_838.shtml): 175-187.doi: [10.12133\u002Fj.smartag.SA202506027](https:\u002F\u002Fdoi.org\u002F10.12133\u002Fj.smartag.SA202506027)\n\n• 信息处理与决策 •[上一篇](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Fabstract\u002Fabstract22679.shtml)\n\n### 基于因果推断和机器学习的农田玉米产量预测模型\n\n王毅 1,6, 崔茜彤 1, 王晨 1, 熊宝伟 1, 邵国敏 2, 王琬莹 1, 曹培 3([![Image 4](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Femail.png)](mailto:caopei2020@nwafu.edu.cn)), 韩文霆 4,5([![Image 5](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Femail.png)](mailto:hanwt2000@126.com))\n\n1.   1. 西安财经大学信息学院，陕西 西安 710100，中国\n\n2. 西安理工大学西北旱区生态水利国家重点实验室，陕西 西安 710048，中国\n\n3. 西北农林科技大学机械与电子工程学院，陕西杨凌 712100，中国\n\n4. 西北农林科技大学旱区农业节水研究院，陕西杨凌 712100，中国\n\n5. 国家作物高效用水工程实验室，陕西杨凌 712100，中国\n\n6. 智财协同可信计算陕西省高等学校重点实验室，陕西 西安 710100，中国\n\n*   `收稿日期:`2025-06-17 `出版日期:`2026-03-30 \n*   `基金项目:`国家社会科学基金(23BGL252); 陕西省自然科学基础研究计划项目(2022JQ-363); 陕西省重点产业创新链项目(2024NC-ZDCYL-05-01); 陕西省重点产业创新链项目(2023-ZDLNY-58); 西安市科技计划项目(25NJSYB00014) \n*   `作者简介:`王 毅，博士，讲师，研究方向为农情信息空天地一体化智能感知与精准作业技术。E-mail：[wang_yi@xaufe.edu.cn](mailto:wang_yi@xaufe.edu.cn) \n*   `通信作者:`曹 培，博士，副教授，研究方向为农业水信息天空地一体化智能感知与精准灌溉技术及装备。E-mail：[caopei2020@nwafu.edu.cn](mailto:caopei2020@nwafu.edu.cn) 韩文霆，博士，研究员，研究方向为农业水信息天空地一体化智能感知与精准灌溉技术及装备。E-mail：[hanwt2000@126.com](mailto:hanwt2000@126.com) \n\n### Field Maize Yield Prediction Model Based on Causal Inference and Machine Learningin Agricultural Fields\n\nWANG Yi 1,6, CUI Xitong 1, WANG Chen 1, XIONG Baowei 1, SHAO Guomin 2, WANG Wanying 1, CAO Pei 3([![Image 6](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Femail.png)](mailto:caopei2020@nwafu.edu.cn)), HAN Wenting 4,5([![Image 7](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Femail.png)](mailto:hanwt2000@126.com))\n\n1.   1. School of Information Science and Engineering, Xi'an University of Finance and Economics, Xi'an 710100, China\n\n2. State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an 710048, China\n\n3. College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China\n\n4. Institute of Water-saving Agriculture in Arid Areas (IWSA), Northwest A&F University, Yangling 712100, China\n\n5. National Engineering Laboratory for Crop High-efficiency Water Use, Yangling 712100, China\n\n6. Intelligent Financial Collaborative Trusted Computing Key Laboratory of Shaanxi Province for Higher Education Institutions, Xi'an 710100, China\n\n*   `Received:`2025-06-17 `Online:`2026-03-30 \n*   `Foundation items:`National Social Science Fund Project(23BGL252); Basic Research Plan Project for Natural Sciences of Shaanxi Province(2022JQ-363); Key Innovation Chain Projects of Shaanxi Province(2024NC-ZDCYL-05-01); Key Innovation Chain Projects of Shaanxi Province(2023-ZDLNY-58); Xi'an Municipal Science and Technology Plan Project(25NJSYB00014) \n*   `About author:`WANG Yi, E-mail: [wang_yi@xaufe.edu.cn](mailto:wang_yi@xaufe.edu.cn) \n*   `Corresponding author:`CAO Pei, E-mail: [caopei2020@nwafu.edu.cn](mailto:caopei2020@nwafu.edu.cn) HAN Wenting, E-mail: [hanwt2000@126.com](mailto:hanwt2000@126.com) \n\n## [在线阅读 28](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)\n\n## [知网下载](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027)\n\n## [本地下载 47](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#1)\n\n[_![Image 8](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Fknowledge\\_map1.png)_ ## 可视化 0](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Flexeme\u002FshowArticleByLexeme.do?articleID=22685)\n\n#### [摘要\u002FAbstract](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027)\n\n**摘要：**\n\n**【目的\u002F意义】** 玉米作为中国主要粮食作物，其产量的精准预测在维护国家粮食供给体系稳定方面具有重要意义。针对传统预测方法难以揭示变量间因果关系的问题，提出了一种融合因果推断与机器学习的预测方法。 **【方法】** 首先，基于遥感指数、气象数据、土壤剖面湿度和阶段性作物观测数据等多源异构信息，采用偏相关与瞬时条件独立算法系统分析玉米产量与各影响因子之间的因果关系。其次，构建了移动平均-卷积神经网络-长短期记忆网络混合模型，先通过移动平均模块平滑数据噪声，进而利用卷积神经网络提取变量间的空间关联特征。最后，通过长短期记忆网络捕捉产量随时间变化的动态特征。 **【结果和讨论】** 10 cm和50 cm深度的土壤湿度对产量具有显著的正向影响（_P_\u003C0.01），其中深层湿度具有更强的时滞效应；反射率中的改进叶绿素吸收指数与归一化植被指数等植被指数则在玉米生长中期呈现出短期因果关系。与其他模型的对比实验结果表明，所提出的模型在多个指标上表现突出，其中决定系数（_R_ ²）达到0.955，平均绝对误差和均方根误差分别为1.201 kg\u002F亩和1.474 kg\u002F亩（1 hm 2=15亩）。 **【结论】** 因果分析可为模型提供关键变量筛选依据，显著提升产量预测性能。\n\n**关键词:**[偏相关与瞬时条件独立算法,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)[移动平均,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)[卷积神经网络,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)[长短期记忆网络,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)[产量预测,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)[机器学习,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)[因果推断](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)\n\n**Abstract:**\n\n**[Objective]** Maize is one of the most important staple crops in the world and serves as a cornerstone of food security and agricultural sustainability. Accurate and timely prediction of maize yield is essential for optimizing agricultural management practices, supporting market regulation, and guiding policy decisions related to food supply and climate adaptation. In recent years, data-driven yield prediction methods based on machine learning and deep learning have achieved notable improvements in predictive accuracy. However, most existing approaches primarily rely on statistical correlations among variables and often treat influencing factors as independent predictors, without explicitly addressing the complex causal mechanisms and time-lagged interactions that govern crop growth processes. This limitation may lead to reduced model interpretability and compromised robustness under changing environmental conditions. To address these challenges, a novel maize yield prediction framework that integrates causal inference with a hybrid deep learning model was proposed, aiming to improve both predictive performance and mechanistic understanding. **[Methods]** Multi-source heterogeneous datasets collected across the maize growing season were utilized, including remote sensing-derived vegetation indices, meteorological variables (such as temperature and precipitation), soil profile moisture measurements at multiple depths, and crop observation data corresponding to key phenological stages. First, the Peter-Clark and momentary conditional independence (PCMCI) causal discovery algorithm was applied to systematically identify causal relationships between maize yield and its potential driving factors. The PCMCI method enables the detection of both contemporaneous and time-lagged causal links while effectively controlling for confounding effects in high-dimensional time series data. Through this process, the causal structure of yield formation was explicitly characterized, and key variables with statistically significant causal impacts were selected as inputs for the prediction model. Subsequently, a hybrid moving average, convolutional neural network-long short-term memory (MA-CNN-LSTM) model was constructed to capture the complex spatiotemporal patterns in the causally screened input variables. Specifically, a moving average module was employed as a preprocessing step to suppress high-frequency noise and enhance signal stability. A CNN was then used to extract latent correlation features among multiple variables, reflecting their joint influence on yield formation. Finally, an LSTM network was adopted to model temporal dependencies and cumulative effects across the growing season, enabling effective representation of dynamic yield responses. **[Results and Discussions]** The causal analysis revealed that soil moisture at depths of 10 cm and 50 cm exerted a significant positive influence on maize yield (_P_\u003C 0.01), with deeper soil moisture showing a stronger and more persistent time-lagged effect. This finding highlighted the critical role of subsurface water availability in sustaining crop growth during later developmental stages. In addition, vegetation indiced such as the modified chlorophyll absorption ratio index and the normalized difference vegetation index exhibited significant short-term causal relationships with yield during the mid-growth stage of maize, indicating their sensitivity to canopy structure and photosynthetic activity during this period. Comparative experiments conducted against traditional statistical models and conventional machine learning approaches demonstrated that the proposed PCMCI-MA-CNN-LSTM framework consistently achieved superior predictive performance. On the test dataset, the coefficient of determination (_R_ 2) reached 0.955, while the mean absolute error (MAE) and root mean square error (RMSE) were reduced to 1.201 kg\u002Fmu and 1.474 kg\u002Fmu (1 hm 2=15 mu). These results indicated that incorporating causal variable selection effectively enhances model accuracy and stability by reducing redundant and spurious correlations. **[Conclusions]** The results confirm that incorporating causal analysis into yield modeling provides a robust basis for identifying key driving variables and effectively enhances the accuracy and interpretability of maize yield prediction. The proposed framework offers a promising approach for precision agriculture and decision support in crop yield forecasting, particularly under complex and dynamic agro-environmental conditions.\n\n**Key words:**[peter-clark and momentary conditional independence (PCMCI),](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)[moving average,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)[convolutional neural network,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)[long short-term memory,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)[yield prediction,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)[machine learning,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)[causal inference](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)\n\n**中图分类号:**\n\n*   [S513.01](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)\n\n*   [TP181](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)\n\n*   [O212.1](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#)\n\n#### 引用本文\n\n王毅, 崔茜彤, 王晨, 熊宝伟, 邵国敏, 王琬莹, 曹培, 韩文霆. 基于因果推断和机器学习的农田玉米产量预测模型[J]. 智慧农业(中英文), 2026, 8(2): 175-187.\n\nWANG Yi, CUI Xitong, WANG Chen, XIONG Baowei, SHAO Guomin, WANG Wanying, CAO Pei, HAN Wenting. Field Maize Yield Prediction Model Based on Causal Inference and Machine Learningin Agricultural Fields[J]. Smart Agriculture, 2026, 8(2): 175-187.\n\n#### 使用本文\n\n**[](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#) \u002F[推荐](javascript:void(null))**\n**导出引用管理器**[EndNote](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=EndNote&id=22685)|[Ris](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=Ris&id=22685)|[BibTeX](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002Farticle\u002FgetTxtFile.do?fileType=BibTeX&id=22685)\n\n**链接本文:**[https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027)\n\n[https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI2\u002F175](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002FY2026\u002FV8\u002FI2\u002F175)\n\n#### [图\u002F表 14](javascript:;)\n\n[![Image 9](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-175\u002Fthumbnail\u002F58730818-D102-41e5-A208-B2A22B2A852A-F001.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#figureClass13770)\n\n**图1**\n\n分期播种试验场不同播期样地分布图\n\n![Image 10](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-175\u002F58730818-D102-41e5-A208-B2A22B2A852A-F001.jpg)\n\n 图1 \n\n[![Image 11](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#inline_content13771)\n\n**表1**\n\n玉米产量影响因子\n\n| 数据类型 | 观测指标 |\n| :---: | :---: |\n| 作物数据 | 株高\u002Fcm、叶面积指数 |\n| 气象数据 | 地面气压\u002FhPa、平均气温\u002F（℃）、降水量\u002Fmm、露点温度\u002F（℃）、相对湿度\u002F%、经向风速（_U_， m\u002Fs）、纬向风速（_V_， m\u002Fs）、太阳辐射净强度（net， J\u002F（m 2\u002Fd））、太阳辐射总强度（down， J\u002F（m 2\u002Fd）） |\n| 土壤数据 | 土壤湿度（深度：10、20、30、40、50 cm） |\n| 遥感数据 | 增强植被指数（Enhanced Vegetation Index, EVI）、绿光归一化植被指数（Green Normalized Difference Vegetation Index, GNDVI）、归一化植被指数（Normalized Difference Vegetation Index, NDVI）、土壤调整植被指数（Soil-Adjusted Vegetation Index, SAVI）、转换叶绿素吸收比率指数（Transformed Chlorophyll Absorption in Reflectance Index, TCARI）、视觉大气阻力指数（Visible Atmospherically Resistant Index, VARI）、土壤饱和度（Simple Ratio, SR）、归一化差值红边（Normalized Difference Red Edge Index, NDRE）、归一化差分红边指数（Normalized Difference Red Edge Index, NDREI）、反射率中的改进叶绿素吸收指数（Modified Chlorophyll Absorption in Reflectance Index, MCARI） |\n\n 表1 \n\n[![Image 12](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#inline_content13772)\n\n**表2**\n\n植被指数及其计算公式\n\n| 植被指数 | 公式 | 引用文献 |\n| :---: | :---: | :---: |\n| EVI | E V I=2.5×(R N I R−R R)\u002F(R N I R+6×R R−7.5×R B+1)$E V I = 2.5 \\times \\left(\\right. R_{N I R} - R_{R} \\left.\\right) \u002F \\left(\\right. R_{N I R} + 6 \\times R_{R} - 7.5 \\times R_{B} + 1 \\left.\\right)$ （1） | ［14］ |\n| GNDVI | G N D V I=(R G−R R)\u002F(R G+R R)$G N D V I = \\left(\\right. R_{G} - R_{R} \\left.\\right) \u002F \\left(\\right. R_{G} + R_{R} \\left.\\right)$ （2） | ［15］ |\n| NDVI | N D V I=(R N I R−R R)\u002F(R N I R+R R)$N D V I = \\left(\\right. R_{N I R} - R_{R} \\left.\\right) \u002F \\left(\\right. R_{N I R} + R_{R} \\left.\\right)$ （3） | ［16］ |\n| SAVI | S A V I=(1+L)×(R N I R−R R)\u002F(R N I R+R R+L)$S A V I = \\left(\\right. 1 + L \\left.\\right) \\times \\left(\\right. R_{N I R} - R_{R} \\left.\\right) \u002F \\left(\\right. R_{N I R} + R_{R} + L \\left.\\right)$ （4） | ［17］ |\n| TCARI | T C A R I=3×[(R R E−R R)−0.2×(R R E−R G)×(R R E\u002FR R)]$T C A R I = 3 \\times \\left[\\left(\\right. R_{R E} - R_{R} \\left.\\right) - 0.2 \\times \\left(\\right. R_{R E} - R_{G} \\left.\\right) \\times \\left(\\right. R_{R E} \u002F R_{R} \\left.\\right)\\right]$ （5） | ［18］ |\n| VARI | V A R I=(R G−R R)\u002F(R G+R R−R B)$V A R I = \\left(\\right. R_{G} - R_{R} \\left.\\right) \u002F \\left(\\right. R_{G} + R_{R} - R_{B} \\left.\\right)$ （6） | ［19］ |\n| SR | S R=R N I R\u002FR R$S R = R_{N I R} \u002F R_{R}$ （7） | ［20］ |\n| NDRE | N D R E=(R N I R−R R E)\u002F(R N I R+R R E)$N D R E = \\left(\\right. R_{N I R} - R_{R E} \\left.\\right) \u002F \\left(\\right. R_{N I R} + R_{R E} \\left.\\right)$ （8） | ［21］ |\n| MCARI | M C A R I=[(R R E−R R)−0.2×(R R E−R G)]×(R R E\u002FR R)$M C A R I = \\left[\\left(\\right. R_{R E} - R_{R} \\left.\\right) - 0.2 \\times \\left(\\right. R_{R E} - R_{G} \\left.\\right)\\right] \\times \\left(\\right. R_{R E} \u002F R_{R} \\left.\\right)$ （9） | ［22］ |\n\n 表2 \n\n[![Image 13](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#inline_content13773)\n\n**表3**\n\n不同研究区域玉米的关键生育期\n\n| 名称 | 七叶期 | 拔节期 | 抽雄期 | 乳熟期 |\n| :---: | :---: | :---: | :---: | :---: |\n| A区 | 6月25日 | 7月3日 | 7月28日 | 8月22日 |\n| B区 | 7月4日 | 7月10日 | 8月4日 | 8月27日 |\n| C区 | 7月13日 | 7月21日 | 8月13日 | 9月5日 |\n| D区 | 7月23日 | 7月30日 | 8月22日 | 9月18日 |\n\n 表3 \n\n[![Image 14](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-175\u002Fthumbnail\u002F58730818-D102-41e5-A208-B2A22B2A852A-F002.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#figureClass13774)\n\n**图2**\n\nCNN-LSTM模型结构图\n\n![Image 15](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-175\u002F58730818-D102-41e5-A208-B2A22B2A852A-F002.jpg)\n\n 图2 \n\n[![Image 16](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-175\u002Fthumbnail\u002F58730818-D102-41e5-A208-B2A22B2A852A-F003.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#figureClass13775)\n\n**图3**\n\nMA-CNN-LSTM模型预测流程图\n\n![Image 17](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-175\u002F58730818-D102-41e5-A208-B2A22B2A852A-F003.jpg)\n\n 图3 \n\n[![Image 18](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-175\u002Fthumbnail\u002F58730818-D102-41e5-A208-B2A22B2A852A-F004.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#figureClass13776)\n\n**图4**\n\n基于因果推断和机器学习的农田玉米产量预测模型研究技术路线图\n\n![Image 19](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-175\u002F58730818-D102-41e5-A208-B2A22B2A852A-F004.jpg)\n\n 图4 \n\n[![Image 20](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-175\u002Fthumbnail\u002F58730818-D102-41e5-A208-B2A22B2A852A-F005.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#figureClass13777)\n\n**图5**\n\n不同气象、作物、土壤、遥感因子与产量之间的P值 注： P\u003C0.05表示关系显著，P\u003C0.01表示高度显著，Lag表示变量在不同时间滞后下的因果关系显著性差异。\n\n![Image 21](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-175\u002F58730818-D102-41e5-A208-B2A22B2A852A-F005.jpg)\n\n 图5 \n\n[![Image 22](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#inline_content13778)\n\n**表4**\n\n玉米产量预测模型输入特征筛选\n\n| 数据类型 | 时间滞后 |\n| :---: | :---: |\n| Lag 0 | Lag 0—Lag 1 | Lag 0—Lag 2 | Lag 0—Lag 3 |\n| 地面气压\u002FhPa | ▲ |  |  |  |\n| 平均气温\u002F°C | ▲ |  | ▲ |  |\n| 降水量\u002Fmm |  |  |  |  |\n| 露点温度\u002F°C |  |  |  |  |\n| 相对湿度\u002F% |  | ● |  |  |\n| 经向风速\u002F（m\u002Fs） | ▲ |  | ▲ | ▲ |\n| 纬向风速\u002F（m\u002Fs） | ▲ |  |  | ● |\n| 太阳辐射净强度\u002F（W\u002Fm 2） |  | ▲ | ▲ |  |\n| 太阳辐射总强度\u002F（W\u002Fm 2） |  | ▲ |  |  |\n| 土壤湿度10 cm | ● | ● | ▲ | ● |\n| 土壤湿度20 cm |  | ▲ | ● | ▲ |\n| 土壤湿度30 cm |  |  |  | ▲ |\n| 土壤湿度40 cm | ● |  |  | ▲ |\n| 土壤湿度50 cm | ▲ | ▲ | ▲ | ▲ |\n| 株高\u002Fcm |  | ▲ | ▲ |  |\n| 叶面积指数\u002Fcm 2 |  |  |  | ▲ |\n| EVI | ▲ |  |  |  |\n| GNDVI |  | ● | ● |  |\n| NDVI |  | ▲ |  |  |\n| SAVI | ● |  | ● |  |\n| TCARI |  |  | ● |  |\n| VARI |  |  | ▲ |  |\n| SR |  | ● |  |  |\n| NDRE |  | ● |  | ● |\n| MCARI | ▲ |  |  |  |\n\n 表4 \n\n[![Image 23](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#inline_content13779)\n\n**表5**\n\nMA窗口大小的梯度实验设计\n\n| 模型配置 | MAE\u002F（kg\u002F亩） | RMSE\u002F（kg\u002F亩） | _R_ 2 | 核心变化分析 |\n| :---: | :---: | :---: | :---: | :---: |\n| 无MA （窗口=1） | 1.798 | 2.105 | 0.908 | 原始数据含较多噪声，预测曲线波动明显 |\n| MA窗口=5 | 1.564 | 1.924 | 0.923 | 平衡噪声过滤与特征保留，性能最优 |\n| MA窗口=7 | 1.627 | 1.983 | 0.915 | 过度平滑导致关键时序特征（如抽雄期突增）被弱化 |\n\n 表5 \n\n[![Image 24](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-175\u002Fthumbnail\u002F58730818-D102-41e5-A208-B2A22B2A852A-F006.jpg)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#figureClass13780)\n\n**图6**\n\n不同模型预测玉米产量结果\n\n![Image 25](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-175\u002F58730818-D102-41e5-A208-B2A22B2A852A-F006.jpg)\n\n 图6 \n\n[![Image 26](https:\u002F\u002Fwww.smartag.net.cn\u002Fimages\u002Ftable-icon.gif)](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202506027#inline_content13781)\n\n**表6**\n\n不同玉米产量预测模型评估结果\n\n| Model | MAE\u002F（kg\u002F亩） | RMSE\u002F（kg\u002F亩） | _R_ 2 |\n| :---: | :---: | :---: | :---: |\n| SVR | 2.363 | 2.943 | 0.821 |\n| CNN | 2.007 | 2.564 | 0.864 |\n| LASSO | 3.473 | 3.947 | 0.679 |\n| LSTM | 1.833 | 2.217 | 0.898 |\n| MA-CNN-LSTM | 1.201 | 1.474 | 0.955 |\n\n 表6 \n\n[![Image 27](https:\u002F\u002Fwww.smartag.net.cn\u002Ffileup\u002F2096-8094\u002FFIGURE\u002F2026-8-2\u002FImages\u002F2096-8094-2026-8-2-175\u002Fthumbnail\u002F58730818-D102-41e5-A208-B2A22B2A852A-F0","智慧农业(中英文) 2026 8(2): 175-187","2026-07-28T00:00:00Z",74,{"impact":153,"substance":18,"depth":153,"authority":195,"freshness":20,"relevant":21,"comment":196},14,"核心期刊论文，结合因果推断与机器学习提升玉米产量预测精度，方法新颖，数据详实，对智慧农业有实质贡献，但出版日期较早，时效性低。",[198],{"name":191,"url":188},[32,68,200,34],"玉米产量预测",{"code":202,"slug":203,"name":204,"path":205,"parent_code":206},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[208],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":46,"locked":14,"version":47,"method":48,"evidence":210},{"candidates":211,"agricultural_evidence":54},[212,216,220,224,230,236],{"code":39,"score":95,"supported":54,"title":213,"abstract":214,"tags":215},[68],[68],[68],{"code":60,"score":95,"supported":54,"title":217,"abstract":218,"tags":219},[34],[34],[34],{"code":202,"score":66,"supported":54,"title":221,"abstract":222,"tags":223},[204],[204],[],{"code":225,"score":132,"supported":54,"title":226,"abstract":227,"tags":229},"H2",[],[228],"土壤",[],{"code":231,"score":132,"supported":54,"title":232,"abstract":233,"tags":235},"I3",[],[234],"遥感",[],{"code":52,"score":21,"supported":14,"title":237,"abstract":238,"tags":239},[],[],[32],[241,242],"玉米产量预测 因果推断 智慧农业 机器学习","玉米产量预测 因果推断","玉米产量预测因果推断智慧农业机器学习-144",9,"10.12133\u002Fj.smartag.sa202506027","2026-08-01T06:01:55.382565Z",{"id":248,"title":249,"url":250,"summary":251,"summary_zh":252,"content":8,"source_name":253,"source_url":250,"published_at":254,"category":12,"cover_url":8,"hotness":107,"is_selected":14,"score":255,"score_detail":256,"sources":260,"tags":262,"primary_topic":266,"secondary_topics":271,"classification":273,"search_phrases":290,"slug":293,"view_count":141,"doi":294,"paper":295,"created_at":311},5592,"Evaluation of Concept-Rank Collapse Audits for Computer Vision Agriculture Disease Classifiers Under Low-Label Regimes: An Interpretability Capacity Bound for Smallholder Fungicide Selectivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545193","Concept-based interpretability methods for plant-disease classifiers are typically evaluated by whether their concept activations correlate with human-annotated labels, but this criterion is silent on a prior question: whether the classifier's internal representation retains enough concept-rank structure for any audit to be informative. the present analysis formalises that prior question as an interpretability capacity bound. A classifier is modelled as a concept-rank operator whose singular values govern how much concept-level signal survives the projection from pixel space to class logits, and a collapse audit is defined as the ratio of retained concept-rank energy to total concept energy. Under a low-label regime, where concept annotations are scarce and the audit must be estimated from few labelled examples, the bound predicts that audit reliability degrades as a function of the concept-rank spectrum and the label budget. The analysis is a theoretical and methodological contribution: it derives the bound, states the conditions under which a collapse audit is identifiable, and characterises the operating characteristics of the audit as a decision procedure under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64). Power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 52 clusters were required for 5 percentage points at 80% power. The bound reframes concept audits as capacity tests rather than fidelity tests, with consequences for how smallholder fungicide-selectivity decisions should be supported by interpretable disease classifiers.","面向植物病害分类器的基于概念的可解释性方法，通常以其概念激活是否与人工标注标签相关作为评价标准，但该标准对一个更先前的问题保持沉默：分类器的内部表征是否保留了足够的概念秩结构，从而使任何审计具有信息量。本分析将该先前问题形式化为一个可解释性容量上界。分类器被建模为概念秩算子，其奇异值支配着从像素空间到类别logits的投影中概念级信号得以保留的程度，并将坍缩审计定义为保留的概念秩能量与总概念能量之比。在低标签情形下，概念标注稀缺且审计必须由少量标注样本估计，该上界预测审计可靠性会随概念秩谱和标签预算的函数而退化。本分析是一项理论和方法论贡献：推导出该上界，陈述坍缩审计可识别的条件，并在声明的方差结构（簇间SD = 6.0，残差SD = 4.5，组内相关系数 = 0.64）下刻画审计作为决策程序的操作特征。在2000个设计单元中，针对簇数为8、16、24和32以及效应量为3、5和8个百分点的情形估计了功效；在80%功效下达到5个百分点需要52个簇。该上界将概念审计重新框定为容量检验而非保真度检验，并对可解释病害分类器应如何支持小农杀菌剂选择性决策具有启示意义。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",69,{"impact":257,"substance":258,"depth":153,"authority":95,"freshness":107,"relevant":21,"comment":259},8,21,"理论方法论文，提出可解释性容量上界并给出审计功效分析，对智慧农业病害识别可信度有参考价值，但属小众方法学研究、缺乏田间实证，暂不宜进入每日精选。",[261],{"name":253,"url":250},[32,263,77,264,265],"农业人工智能","植物病害识别","可解释性",{"code":72,"slug":267,"name":268,"path":269,"parent_code":270},"farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[272],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":46,"locked":14,"version":47,"method":48,"evidence":274},{"candidates":275,"agricultural_evidence":54},[276,281,286],{"code":72,"score":95,"supported":54,"title":277,"abstract":279,"tags":280},[278],"smallholder",[278],[77],{"code":39,"score":107,"supported":54,"title":282,"abstract":284,"tags":285},[283],"computer vision",[],[263,264],{"code":52,"score":21,"supported":14,"title":287,"abstract":288,"tags":289},[],[],[32],[291,292],"农业人工智能 植物病害识别 可解释性 智慧农业","农业人工智能 植物病害识别","农业人工智能植物病害识别可解释性智慧农业-5592","10.5281\u002Fzenodo.23545193",{"doi":294,"openalex_id":296,"authors":297,"venue":253,"cited_by_count":141,"oa_url":250,"card":304,"direction":308,"ingested_from":310},"W7223137323",[298,300,302],{"name":299,"orcid":8},"Akshay Ramgoolam",{"name":301,"orcid":8},"Priya Bundhoo Appadoo",{"name":303,"orcid":8},"Jean-Noël LEspérance",{"tldr":305,"method":306,"finding":307,"direction":308,"opportunity":309},"提出可解释性容量上界，评估低标签下概念秩崩溃审计的可靠性。","将分类器建模为概念秩算子，推导容量上界并做2000格功效模拟。","审计可靠性随概念秩谱与标签预算下降；5个百分点效应需52个聚类达80%功效。","农业人工智能与决策模型","可将容量上界用于小农户杀菌剂选择性决策的可解释模型审计与标签预算设计。","openalex","2026-10-22T11:21:10.425290Z",{"id":313,"title":314,"url":315,"summary":316,"summary_zh":317,"content":8,"source_name":253,"source_url":315,"published_at":254,"category":12,"cover_url":8,"hotness":107,"is_selected":14,"score":255,"score_detail":318,"sources":320,"tags":322,"primary_topic":326,"secondary_topics":332,"classification":334,"search_phrases":357,"slug":360,"view_count":141,"doi":361,"paper":362,"created_at":374},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。",{"impact":257,"substance":18,"depth":153,"authority":95,"freshness":244,"relevant":21,"comment":319},"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[321],{"name":253,"url":315},[32,263,323,324,325],"灌溉决策","安全保证","试验设计",{"code":327,"slug":328,"name":329,"path":330,"parent_code":331},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[333],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":46,"locked":14,"version":47,"method":48,"evidence":335},{"candidates":336,"agricultural_evidence":54},[337,342,348,353],{"code":327,"score":19,"supported":54,"title":338,"abstract":339,"tags":341},[328],[340,328],"灌溉",[323],{"code":39,"score":95,"supported":54,"title":343,"abstract":345,"tags":347},[344],"artificial intelligence",[346],"decision support",[263],{"code":225,"score":73,"supported":54,"title":349,"abstract":350,"tags":352},[],[228,351],"soil",[],{"code":52,"score":21,"supported":14,"title":354,"abstract":355,"tags":356},[],[],[32],[358,359],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":361,"openalex_id":363,"authors":364,"venue":253,"cited_by_count":141,"oa_url":315,"card":369,"direction":308,"ingested_from":310},"W7223131205",[365,367],{"name":366,"orcid":8},"Nassima Benkhelifa",{"name":368,"orcid":8},"Yacine Aït Ahmed",{"tldr":370,"method":371,"finding":372,"direction":308,"opportunity":373},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z",{"id":376,"title":377,"url":378,"summary":379,"summary_zh":380,"content":8,"source_name":253,"source_url":378,"published_at":254,"category":12,"cover_url":8,"hotness":107,"is_selected":14,"score":381,"score_detail":382,"sources":384,"tags":386,"primary_topic":389,"secondary_topics":390,"classification":392,"search_phrases":407,"slug":410,"view_count":141,"doi":411,"paper":412,"created_at":426},5590,"Evaluation of Concept-Probe Temporal-Stability Audits for Computer Vision Agriculture Disease Classifiers Under Seasonal Phenology Drift: An Interpretability Decay Bound for Smallholder Fungicide Timing","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545234","Concept-probe audits are increasingly used to certify that a crop-disease classifier's internal representations track the pathological concepts a smallholder would act on, but a probe certified at one point in the season may silently decouple from those concepts as phenology shifts the visual statistics of the field. the present analysis treats that decoupling as a formal object. It defines a temporal-stability audit as a sequence of concept-probe accuracies measured across defined phenological windows, and it derives an interpretability decay bound: the rate at which probe accuracy can fall before the fungicide-timing decision it is meant to support becomes unreliable. The bound is expressed as a function of the probe's initial margin, the drift in concept prevalence between windows, and the classifier's sensitivity to the drifted features, and it yields a certified audit interval rather than a single-point certificate. The paper then characterises the operating characteristics of the audit design under a declared variance structure, using a replication-based evaluation of the before-after-control-impact estimator with between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.64. Power was estimated across 2000 design cells over cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 54 clusters were required for 5 percentage points at 80% power. The contribution is a decision-theoretic account of when a concept-probe certificate expires, and a design rule for scheduling re-audits across the season.","概念探针审计（concept-probe audit）日益被用于验证作物病害分类器的内部表征是否追踪小农户会据以采取行动的病理学概念，但在生长季某一时点获得认证的探针，可能随着物候变化改变田间的视觉统计特征而悄然与这些概念脱钩。本分析将这种脱钩视为一个形式化对象。它将时间稳定性审计（temporal-stability audit）定义为在既定物候窗口内测量的一系列概念探针准确率，并推导出一个可解释性衰减界限（interpretability decay bound）：即探针准确率在其所支持的杀菌剂施用时机决策变得不可靠之前所能下降的速率。该界限表示为探针初始裕度、窗口间概念流行率漂移以及分类器对漂移特征敏感度的函数，并给出一个经认证的审计区间而非单点认证。随后，本文在声明的方差结构下刻画该审计设计的运行特征，采用基于重复的前后对照影响估计量评估，其中组间标准差 = 6.0、残差标准差 = 4.5、组内相关系数 = 0.64。功效在 2000 个设计单元上估计，聚类数取 8、16、24 和 32，效应量取 3、5 和 8 个百分点；在 80% 功效下达到 5 个百分点需要 54 个聚类。本文的贡献在于从决策论角度阐明概念探针认证何时失效，并给出在整个生长季内安排重新审计的设计规则。",70,{"impact":257,"substance":18,"depth":153,"authority":95,"freshness":107,"relevant":21,"comment":383},"该论文提出概念探针审计的时序稳定性框架与可解释性衰减界，方法新颖、统计设计扎实，但属小众方法论研究，产业影响有限，适合作为专业精选而非头条。",[385],{"name":253,"url":378},[32,263,77,387,388],"作物病害识别","模型审计",{"code":72,"slug":267,"name":268,"path":269,"parent_code":270},[391],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":46,"locked":14,"version":47,"method":48,"evidence":393},{"candidates":394,"agricultural_evidence":54},[395,399,403],{"code":72,"score":19,"supported":54,"title":396,"abstract":397,"tags":398},[278],[77,278],[77],{"code":39,"score":107,"supported":54,"title":400,"abstract":401,"tags":402},[283],[],[387,263],{"code":52,"score":21,"supported":14,"title":404,"abstract":405,"tags":406},[],[],[32],[408,409],"概念探针 时序稳定性 作物病害分类器","杀菌剂 施药时机 可解释性衰减","概念探针时序稳定性作物病害分类器-5590","10.5281\u002Fzenodo.23545234",{"doi":411,"openalex_id":413,"authors":414,"venue":253,"cited_by_count":141,"oa_url":378,"card":421,"direction":308,"ingested_from":310},"W7223088030",[415,417,419],{"name":416,"orcid":8},"Abdi-Basid Idriss Farah",{"name":418,"orcid":8},"Amina Abdillahi Guedi",{"name":420,"orcid":8},"Moussa Omar Youssouf",{"tldr":422,"method":423,"finding":424,"direction":308,"opportunity":425},"提出概念探针时间稳定性审计与可解释性衰减界，判断何时需重新审计。","定义跨物候窗口探针精度序列，推导衰减界，用BACI复制评估统计功效。","5个百分点效应需54个聚类达80%功效，审计证书会随物候漂移过期。","可将衰减界与再审计调度规则嵌入小农户杀菌剂时机决策支持系统，并验证跨作物泛化。","2026-10-22T11:21:08.978348Z"]