[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4148":3,"related-4148":59},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":8,"secondary_topics":29,"classification":30,"search_phrases":53,"slug":56,"view_count":57,"doi":8,"paper":8,"created_at":58},4148,"安徽省农科院发布「省农科院网站迁移迭代项目」竞争性磋商公告","https:\u002F\u002Fwww.aaas.org.cn\u002F4303149\u002F71320742.html","安徽省农业科学院发布「省农科院网站迁移迭代项目」竞争性磋商公告。获取采购文件时间：2026年10月1日9:00至10月13日17:30；响应文件提交截止时间：2026年10月15日10:00；本项目为专门面向中小企业采购项目。",null,"安徽省农业科学院网·2026年10月1日","2026-10-01T00:00:00Z","报道",10,false,48,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":16,"relevant":20,"comment":21},8,14,6,12,1,"省级农科院网站迁移采购公告，属农业信息化基础设施动态，信息具体但公共价值有限，不宜进入每日精选。",[23],{"name":9,"url":6},[25,26,27,28],"数字乡村","农业信息化","政府采购","中小企业",[],{"status":31,"locked":13,"version":32,"method":33,"evidence":34},"unclassified","2026-10-22.2","rules",{"candidates":35,"agricultural_evidence":51,"reason":52},[36,41,46],{"code":37,"score":20,"supported":13,"title":38,"abstract":39,"tags":40},"I1",[],[],[26],{"code":42,"score":20,"supported":13,"title":43,"abstract":44,"tags":45},"I6",[],[],[25],{"code":47,"score":20,"supported":13,"title":48,"abstract":49,"tags":50},"J1",[],[],[27],true,"缺少足够的主题证据",[54,55],"安徽省农科院 网站迁移迭代 竞争性磋商","农业信息化 中小企业 政府采购 数字乡村","安徽省农科院网站迁移迭代竞争性磋商-4148",4,"2026-10-04T00:09:08.346534Z",{"total":18,"page":20,"page_size":18,"items":60},[61,108,182,247,328,375],{"id":62,"title":63,"url":64,"summary":65,"summary_zh":8,"content":8,"source_name":66,"source_url":64,"published_at":67,"category":68,"cover_url":8,"hotness":12,"is_selected":13,"score":69,"score_detail":70,"sources":72,"tags":74,"primary_topic":77,"secondary_topics":82,"classification":83,"search_phrases":96,"slug":99,"view_count":20,"doi":100,"paper":101,"created_at":107},3066,"山东省智慧农业发展的影响因素研究","https:\u002F\u002Fdoi.org\u002F10.64216\u002F3080-1486.26.10.012","山东省智慧农业发展的影响因素研究。社会经济导刊","社会经济导刊","2026-09-20T00:00:00Z","论文",40,{"impact":16,"substance":18,"depth":16,"authority":12,"freshness":16,"relevant":20,"comment":71},"省级期刊论文，主题相关但摘要信息量极少，缺乏方法与数据细节，仅具参考价值。",[73],{"name":66,"url":64},[25,75,26,76],"智慧农业","山东农业",{"code":37,"slug":78,"name":79,"path":80,"parent_code":81},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":84,"locked":13,"version":32,"method":33,"evidence":85},"classified",{"candidates":86,"agricultural_evidence":51},[87,92],{"code":37,"score":88,"supported":51,"title":89,"abstract":90,"tags":91},13,[75],[75],[26,75],{"code":42,"score":20,"supported":13,"title":93,"abstract":94,"tags":95},[],[],[25],[97,98],"山东 智慧农业 影响因素","农业信息化 山东农业 数字乡村 智慧农业","山东智慧农业影响因素-3066","10.64216\u002F3080-1486.26.10.012",{"doi":100,"openalex_id":102,"authors":103,"venue":66,"cited_by_count":104,"oa_url":8,"card":8,"direction":105,"ingested_from":106},"W7213767051",[],0,"智慧农业 \u002F 农业物联网","openalex","2026-09-21T23:30:16.624592Z",{"id":109,"title":110,"url":111,"summary":112,"summary_zh":113,"content":8,"source_name":114,"source_url":111,"published_at":115,"category":68,"cover_url":8,"hotness":12,"is_selected":13,"score":116,"score_detail":117,"sources":120,"tags":122,"primary_topic":126,"secondary_topics":132,"classification":133,"search_phrases":155,"slug":158,"view_count":104,"doi":159,"paper":160,"created_at":181},2539,"Sistem Informasi Manajemen Pertanian: Studi Kasus Lembang Agri Gapoktan","https:\u002F\u002Fdoi.org\u002F10.34010\u002Fjamika.v16i1.21245","Manual recordkeeping at the farmer group level leaves registration, procurement, planting, harvest, and sales data standing alone, with no link across the stages of farm management. Lembang Agri Gapoktan in Cikidang Village, West Bandung Regency, oversees 207 active members with no membership database, no digital record of seed, fertilizer, and compost procurement, and no transaction evidence for consumers. This study designs, builds, and tests a Farm Management Information System uniting the flow from registration to sales in a single database. The prototyping method runs iteratively through requirements gathering, mock-up construction and refinement, and mock-up testing. Requirements were collected through observation, interviews with the chairperson and the secretary, and review of invoices and harvest reports. The design covers five information system components, with use case and activity diagram modeling alongside data normalization. The system was built on the Laravel framework with a MySQL database, then tested through black box methods using equivalence partitioning and boundary value analysis. The system handles member registration together with land area verification, raw material procurement under tiered approval, planting records, harvest records feeding directly into salable quantity, and sales issuing invoices and notifications by SMS and email. Every test scenario in the final iteration produced output matching the specification. The contribution of this study lies in linking harvest records to sales transactions as a basis for the following planting decision.","农民小组层面的人工记录方式使登记、采购、种植、收获和销售数据彼此孤立，农场管理各阶段之间缺乏关联。位于西万隆县Cikidang村的Lembang Agri Gapoktan管理着207名活跃成员，但没有成员数据库，没有种子、化肥和堆肥采购的数字记录，也没有面向消费者的交易凭证。本研究设计、构建并测试了一个农场管理信息系统，将从登记到销售的流程统一到一个数据库中。原型化方法通过需求收集、原型构建与改进以及原型测试进行迭代。需求通过观察、对主席和秘书的访谈以及对发票和收获报告的审查来收集。设计涵盖五个信息系统组件，并使用用例图和活动图建模以及数据规范化。该系统基于Laravel框架和MySQL数据库构建，然后通过黑盒方法使用等价类划分和边界值分析进行测试。系统处理成员登记及土地面积验证、分级审批下的原材料采购、种植记录、直接生成可销售数量的收获记录，以及开具发票并通过短信和电子邮件发送通知的销售。最终迭代中的每个测试场景都产生了符合规范的输出。本研究的贡献在于将收获记录与销售交易关联起来，作为后续种植决策的依据。","Jurnal Manajemen Informatika (JAMIKA)","2026-09-14T00:00:00Z",60,{"impact":16,"substance":118,"depth":17,"authority":19,"freshness":16,"relevant":20,"comment":119},18,"印尼农民合作社的农场管理信息系统原型案例，方法规范但属地方性实践，对国内智慧农业信息化建设有参考价值。",[121],{"name":114,"url":111},[25,26,123,124,125],"农产品溯源","农场管理系统","农民合作社",{"code":127,"slug":128,"name":129,"path":130,"parent_code":131},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[],{"status":84,"locked":13,"version":32,"method":33,"evidence":134},{"candidates":135,"agricultural_evidence":51},[136,142,147,151],{"code":127,"score":137,"supported":51,"title":138,"abstract":139,"tags":141},3,[],[140],"fertilizer",[],{"code":143,"score":20,"supported":13,"title":144,"abstract":145,"tags":146},"G2",[],[],[125],{"code":37,"score":20,"supported":13,"title":148,"abstract":149,"tags":150},[],[],[26],{"code":42,"score":20,"supported":13,"title":152,"abstract":153,"tags":154},[],[],[25],[156,157],"农场管理系统 农业信息化 农产品溯源 农民合作社","农场管理系统 农业信息化","农场管理系统农业信息化农产品溯源农民合作社-2539","10.34010\u002Fjamika.v16i1.21245",{"doi":159,"openalex_id":161,"authors":162,"venue":114,"cited_by_count":104,"oa_url":111,"card":175,"direction":179,"ingested_from":106},"W7213116768",[163,165,167,169,172],{"name":164,"orcid":8},"Erlangga Nugraha",{"name":166,"orcid":8},"Dian Permata Sari",{"name":168,"orcid":8},"Muhammad Rias Ramadan",{"name":170,"orcid":171},"Valentine Kachundu","https:\u002F\u002Forcid.org\u002F0009-0009-5181-9749",{"name":173,"orcid":174},"Leonardi Paris Hasugian","https:\u002F\u002Forcid.org\u002F0000-0002-0636-6906",{"tldr":176,"method":177,"finding":178,"direction":179,"opportunity":180},"为农民合作社设计并测试一套覆盖注册到销售全流程的农业管理信息系统。","原型法迭代开发，Laravel+MySQL，黑盒测试，案例为207名成员的合作社","系统将收获记录与销售交易打通，可作为下一季种植决策依据。","数字乡村与农业信息化","可延伸研究：将收获-销售数据与遥感、气象结合，构建数据驱动的种植决策模型。","2026-09-15T23:30:29.884496Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":8,"content":187,"source_name":188,"source_url":8,"published_at":189,"category":190,"cover_url":8,"hotness":69,"is_selected":51,"score":191,"score_detail":192,"sources":198,"tags":206,"primary_topic":209,"secondary_topics":213,"classification":214,"search_phrases":241,"slug":244,"view_count":245,"doi":8,"paper":8,"created_at":246},2341,"三部门联合印发《数字乡村高质量发展行动计划(2026—2030年)》","https:\u002F\u002Fwww.cac.gov.cn\u002F2026-09\u002F11\u002Fc_1790876575314396.htm","中央网信办、农业农村部、工业和信息化部近日联合印发《行动计划》，明确到2030年数字乡村发展取得显著成效，部署5个方面24项工作举措，包括夯实数字乡村发展基础、提升乡村产业数字化水平、提升乡村建设信息化水平、提升乡村治理智慧化水平、激发数字乡村发展内生动力，重点推进智慧农业技术模式迭代创新与'人工智能+'融合应用。","2026年09月14日 星期一\n\n[设为首页](https:\u002F\u002Fwww.cac.gov.cn\u002F2026-09\u002F11\u002Fc_1790876575314396.htm###)[加入收藏](https:\u002F\u002Fwww.cac.gov.cn\u002F2026-09\u002F11\u002Fc_1790876575314396.htm###)[手机版](https:\u002F\u002Fwap.cac.gov.cn\u002F)[繁體](javascript:StranBody() \"點擊以繁體中文方式浏覽\")\n\n*   ![Image 1](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fpc\u002Fscheme\u002Fimages\u002Flogo02.png)\n*   ![Image 2](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fpc\u002Fscheme\u002Fimages\u002Flogo01.png)\n*   ![Image 3](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fpc\u002Fscheme\u002Fimages\u002Flogo02.png)\n*   ![Image 4](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fpc\u002Fscheme\u002Fimages\u002Flogo01.png)\n\n[搜索](javascript:)\n\n*   ### [首 页](https:\u002F\u002Fwww.cac.gov.cn\u002Findex.htm)\n\n*   ### [时政要闻](https:\u002F\u002Fwww.cac.gov.cn\u002Fyaowen\u002Fszyw\u002FA093601index_1.htm)\n\n*   ### [网信政务](https:\u002F\u002Fwww.cac.gov.cn\u002Fwxzw\u002FA0937index_1.htm)\n\n*   ### [互动服务](https:\u002F\u002Fwww.cac.gov.cn\u002Fhdfw\u002FA0938index_1.htm)\n\n*   ### [热点专题](https:\u002F\u002Fwww.cac.gov.cn\u002Fgzzt\u002Fztzl\u002FA092001index_1.htm)\n\n当前位置：[首页](https:\u002F\u002Fwww.cac.gov.cn\u002F)>[正文](javascript:void(0);)\n\n*   ![Image 5](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fmobile\u002Fscheme\u002Fimages\u002Flogo02.png)\n*   ![Image 6](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fmobile\u002Fscheme\u002Fimages\u002Flogo01.png)\n*   ![Image 7](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fmobile\u002Fscheme\u002Fimages\u002Flogo02.png)\n*   ![Image 8](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fmobile\u002Fscheme\u002Fimages\u002Flogo01.png)\n\n*   [首页](https:\u002F\u002Fwap.cac.gov.cn\u002Fphoneindex.htm)\n*   [时政要闻](https:\u002F\u002Fwap.cac.gov.cn\u002Fphoneindex.htm?defaultIndex=1)\n*   [网信政务](https:\u002F\u002Fwap.cac.gov.cn\u002Fphoneindex.htm?defaultIndex=2)\n*   [互动服务](https:\u002F\u002Fwap.cac.gov.cn\u002Fphoneindex.htm?defaultIndex=3)\n*   [热点专题](https:\u002F\u002Fwap.cac.gov.cn\u002Fphoneindex.htm?defaultIndex=4)\n\n![Image 9](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fmobile\u002Fscheme\u002Fimages\u002Fsearch.png)![Image 10](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fmobile\u002Fscheme\u002Fimages\u002Ffold.png)\n\n![Image 11](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fmobile\u002Fscheme\u002Fimages\u002FCAC.png)\n\n![Image 12](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fmobile\u002Fscheme\u002Fimages\u002Fashsearch.png)\n\n# 中央网信办、农业农村部、工业和信息化部联合印发《数字乡村高质量发展行动计划（2026—2030年）》\n\n 2026年09月11日 18:00 来源： [中国网信网](https:\u002F\u002Fwww.cac.gov.cn\u002F)\n\n[](https:\u002F\u002Fwww.cac.gov.cn\u002F2026-09\u002F11\u002Fc_1790876575314396.htm#)[](https:\u002F\u002Fwww.cac.gov.cn\u002F2026-09\u002F11\u002Fc_1790876575314396.htm#)\n\n[](javascript:void(0); \"分享到微信\")[](javascript:void(0); \"分享到QQ空间\")\n\n[【打印】](javascript:window.print())[【纠错】](https:\u002F\u002Fwww.cac.gov.cn\u002F2026-09\u002F11\u002Fc_1790876575314396.htm)\n\n近日，中央网信办、农业农村部、工业和信息化部联合印发《数字乡村高质量发展行动计划（2026—2030年）》（以下简称《行动计划》），明确了“十五五”时期推进数字乡村高质量发展的思路目标、重点任务和政策举措。\n\n《行动计划》坚持以习近平新时代中国特色社会主义思想为指导，深入贯彻党的二十大和二十届历次全会精神，认真落实四中全会部署，全面贯彻习近平总书记关于网络强国的重要思想、关于“三农”工作的重要论述，以学习运用“千万工程”经验为引领，以推动高质量发展为主题，以强农惠农富农为目标，统筹发展和安全，着力夯实数字乡村发展基础，着力推进乡村产业数字化、乡村建设信息化、乡村治理智慧化一体发展，着力激发数字乡村发展内生动力，加快以信息化驱动引领农业农村现代化、促进乡村全面振兴，为网络强国、农业强国建设提供有力支撑。\n\n《行动计划》明确，到2030年，数字乡村发展取得显著成效，乡村新型信息基础设施优化完善，乡村产业数字化、乡村建设信息化、乡村治理智慧化的整体水平明显提升，数字乡村可持续发展机制更加健全，农民群众在共享信息化发展成果中的获得感、幸福感、安全感更加充实。\n\n《行动计划》围绕“十五五”发展主要目标，明确到2030年的8项主要发展指标，部署了5个方面24项工作举措，包括夯实数字乡村发展基础、提升乡村产业数字化水平、提升乡村建设信息化水平、提升乡村治理智慧化水平、激发数字乡村发展内生动力等，并就加强组织实施提出工作要求，强化政策协同与资源统筹，树立和践行正确政绩观，因地制宜推进各项任务落地见效。\n\n关闭\n\n中央网络安全和信息化委员会办公室 中华人民共和国国家互联网信息办公室 © 版权所有[联系我们](https:\u002F\u002Fwww.cac.gov.cn\u002Fhdfw\u002Flxwm\u002FA093812index_1.htm)\n\n承办：国家互联网应急中心 技术支持：长安通信科技有限责任公司 [京ICP备14042428号](https:\u002F\u002Fbeian.miit.gov.cn\u002F)[京公网安备11040102700108号](https:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=11040102700108)\n\n[![Image 13: 党政机关标识](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fpc\u002Fscheme\u002Fimages\u002Fconac.png)](https:\u002F\u002Fbszs.conac.cn\u002Fsitename?method=show&id=04E495BC1FB45E52E053022819AC1EB7)\n\n*   ###### 学习强国\n\n_◆_◆ ![Image 14](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fpc\u002Fscheme\u002Fimages\u002FQR-qg.jpg) \n*   ###### 微信\n\n_◆_◆ ![Image 15](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fpc\u002Fscheme\u002Fimages\u002FQR-wx.jpg) \n*   ###### 返回顶部\n\n中央网络安全和信息化委员会办公室\n\n中华人民共和国国家互联网信息办公室 © 版权所有\n\n承办：国家互联网应急中心\n\n技术支持：长安通信科技有限责任公司\n\n京ICP备14042428号\n\n[京公网安备11040102700108号](https:\u002F\u002Fwww.beian.gov.cn\u002F\u002Fwww.cac.gov.cn\u002FregisterSystemInfo?recordcode=11040102700108)\n\n![Image 16](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fmobile\u002Fscheme\u002Fimages\u002Fconac.png)[![Image 17](https:\u002F\u002Fwww.cac.gov.cn\u002Flib\u002Fmobile\u002Fscheme\u002Fimages\u002Fcomputer.png)PC版](https:\u002F\u002Fwww.cac.gov.cn\u002F?f=pc)\n\nProduced By CMS 网站群内容管理系统 publishdate:2026\u002F09\u002F11 18:00:35\n\n分享到微信朋友圈[×](https:\u002F\u002Fwww.cac.gov.cn\u002F2026-09\u002F11\u002Fc_1790876575314396.htm#)\n\n打开微信，点击底部的“发现”，\n\n使用“扫一扫”即可将网页分享至朋友圈。","中央网信办 | 2026-09-11","2026-09-11T00:00:00Z","政策",89,{"impact":193,"substance":194,"depth":195,"authority":196,"freshness":16,"relevant":20,"comment":197},28,22,16,15,"三部门联合印发的数字乡村五年行动计划，属国家级顶层政策，明确2030年8项指标与5方面24项举措，权威性与时效性俱佳，值得进入每日精选。",[199,200,203],{"name":188,"url":185},{"name":201,"url":202},"中国经济网 2026年9月17日","https:\u002F\u002Fwww.ce.cn\u002Fxwzx\u002Fgnsz\u002Fgdxw\u002F202609\u002Ft20260917_3217698.shtml",{"name":204,"url":205},"腾讯新闻转载 2026-10-09","https:\u002F\u002Fnews.qq.com\u002Frain\u002Fa\u002F20261009A09HWH00",[207,25,208,75,26],"十五五","乡村治理",{"code":42,"slug":210,"name":211,"path":212,"parent_code":81},"digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",[],{"status":84,"locked":13,"version":32,"method":33,"evidence":215},{"candidates":216,"agricultural_evidence":51},[217,221,226,231,237],{"code":42,"score":19,"supported":51,"title":218,"abstract":219,"tags":220},[25],[25],[25],{"code":37,"score":222,"supported":51,"title":223,"abstract":224,"tags":225},5,[],[75],[26,75],{"code":227,"score":57,"supported":51,"title":228,"abstract":229,"tags":230},"G5",[],[208],[208],{"code":232,"score":137,"supported":51,"title":233,"abstract":234,"tags":236},"G4",[],[235],"乡村产业",[],{"code":47,"score":20,"supported":13,"title":238,"abstract":239,"tags":240},[],[],[207],[242,243],"农业信息化 乡村治理 数字乡村 智慧农业","农业信息化 乡村治理","农业信息化乡村治理数字乡村智慧农业-2341",2,"2026-09-14T00:06:25.305027Z",{"id":248,"title":249,"url":250,"summary":251,"summary_zh":252,"content":8,"source_name":253,"source_url":250,"published_at":254,"category":68,"cover_url":8,"hotness":12,"is_selected":13,"score":255,"score_detail":256,"sources":259,"tags":261,"primary_topic":265,"secondary_topics":271,"classification":272,"search_phrases":307,"slug":310,"view_count":245,"doi":311,"paper":312,"created_at":327},2127,"Beyond digital access: how does digital information complement agricultural production services to promote green technology adoption? Micro-evidence from Jiangxi Province, China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1916148","In the context of agricultural green transformation, whether digital information can move beyond information provision and promote smallholders’ adoption of green technologies through complementarity with agricultural production services is an important theoretical and practical question. Using survey data from 1,040 rice-farming households in Jiangxi Province, China, this study constructs indicators of digital information, agricultural production services, and the number of core green technologies adopted. A Poisson pseudo-maximum likelihood (PPML) model is used to estimate their individual and interactive effects, supplemented by marginal-effect analysis, a supermodularity test, robustness checks, endogeneity analyses, mechanism tests, and heterogeneity analyses. The results show that (1) both digital information and agricultural production services significantly promote the adoption of core green technologies. In the full model, their coefficients are 0.329 and 0.330, respectively, while the interaction coefficient is 0.702 and significant at the 5% level. (2) Further tests confirm that the marginal effect of agricultural production services increases with the level of digital information, the average cross-partial effect is significantly positive, and the predicted adoption count is highest under the high-digital-information and high-production-services scenario. The supermodularity difference is 0.132, providing further evidence of complementarity. (3) The main findings remain robust to alternative dependent variables, model specifications, sample adjustments, and the use of double machine learning, instrumental variables, and entropy balancing. (4) Mechanism analyses suggest that complementarity promotes green technology adoption by easing labor constraints and improving farmers’ awareness of green policies. (5) Within-group estimates further indicate that the effect is more pronounced among farmers who own agricultural machinery, live in villages with better transportation conditions, and operate larger rice-farming areas. Overall, digital information does not simply substitute for agricultural production services; rather, it strengthens their conversion into actual green technology adoption. These findings support closer integration between digital information platforms and agricultural service systems.","在农业绿色转型背景下，数字信息能否超越信息供给功能，通过与农业生产性服务的互补性促进小农户采纳绿色技术，是一个重要的理论与实践问题。本研究基于中国江西省1040户水稻种植农户的调查数据，构建了数字信息、农业生产性服务与核心绿色技术采纳数量的指标，采用泊松伪最大似然（PPML）模型估计其个体效应与交互效应，并辅以边际效应分析、超模性检验、稳健性检验、内生性分析、机制检验和异质性分析。结果表明：（1）数字信息与农业生产性服务均显著促进核心绿色技术采纳，在全模型中二者系数分别为0.329和0.330，交互项系数为0.702，且在5%水平上显著。（2）进一步检验证实，农业生产性服务的边际效应随数字信息水平提高而增大，平均交叉偏效应显著为正，高数字信息与高生产性服务情景下预测采纳数量最高，超模性差异为0.132，进一步佐证了互补性。（3）主要结论在替换因变量、模型设定、样本调整以及使用双重机器学习、工具变量和熵平衡等方法后依然稳健。（4）机制分析表明，互补性通过缓解劳动力约束和提高农户对绿色政策的认知来促进绿色技术采纳。（5）分组估计进一步显示，该效应在拥有农业机械、所在村庄交通条件较好以及水稻种植面积较大的农户中更为明显。总体而言，数字信息并非简单替代农业生产性服务，而是强化了其向实际绿色技术采纳的转化。上述发现支持数字信息平台与农业服务体系之间更紧密的融合。","Frontiers in Sustainable Food Systems","2026-09-10T00:00:00Z",80,{"impact":118,"substance":194,"depth":118,"authority":88,"freshness":257,"relevant":20,"comment":258},9,"基于江西1040户稻农微观调查，实证揭示数字信息与农业生产服务的互补效应，方法规范、结论可靠，对数字乡村与绿色转型政策有参考价值。",[260],{"name":253,"url":250},[25,26,262,263,264],"绿色技术采纳","农业社会化服务","水稻种植",{"code":266,"slug":267,"name":268,"path":269,"parent_code":270},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":84,"locked":13,"version":32,"method":33,"evidence":273},{"candidates":274,"agricultural_evidence":51},[275,280,287,294,299,303],{"code":266,"score":276,"supported":51,"title":277,"abstract":278,"tags":279},7,[],[268,267],[264],{"code":281,"score":18,"supported":51,"title":282,"abstract":283,"tags":286},"F5",[],[284,285],"农业机械","agricultural machinery",[],{"code":288,"score":18,"supported":51,"title":289,"abstract":290,"tags":293},"I2",[],[291,292],"机器学习","machine learning",[],{"code":143,"score":57,"supported":51,"title":295,"abstract":296,"tags":298},[],[297],"小农户",[263],{"code":37,"score":20,"supported":13,"title":300,"abstract":301,"tags":302},[],[],[26],{"code":42,"score":20,"supported":13,"title":304,"abstract":305,"tags":306},[],[],[25],[308,309],"农业社会化服务 绿色技术采纳 农业信息化 数字乡村","农业社会化服务 绿色技术采纳","农业社会化服务绿色技术采纳农业信息化数字乡村-2127","10.3389\u002Ffsufs.2026.1916148",{"doi":311,"openalex_id":313,"authors":314,"venue":253,"cited_by_count":104,"oa_url":250,"card":321,"direction":325,"ingested_from":106},"W7212107776",[315,317,319],{"name":316,"orcid":8},"Yanqiu Tong",{"name":318,"orcid":8},"Zaiwei Yang",{"name":320,"orcid":8},"Zhaojiu Chen",{"tldr":322,"method":323,"finding":324,"direction":325,"opportunity":326},"基于江西1040户稻农数据，检验数字信息与农业生产服务对绿色技术采用的互补效应。","PPML模型、边际效应、超模性检验、工具变量与双重机器学习。","数字信息与生产服务显著互补，通过缓解劳动约束和提升政策认知促进绿色技术采用。","农业绿色发展与碳","可探究数字平台与服务系统协同的机制设计，及不同作物和区域的异质性推广路径。","2026-09-11T23:30:07.699262Z",{"id":329,"title":330,"url":331,"summary":332,"summary_zh":8,"content":333,"source_name":334,"source_url":8,"published_at":335,"category":68,"cover_url":8,"hotness":12,"is_selected":13,"score":336,"score_detail":337,"sources":340,"tags":342,"primary_topic":348,"secondary_topics":353,"classification":354,"search_phrases":370,"slug":373,"view_count":222,"doi":8,"paper":8,"created_at":374},539,"ICT-Based Versus Human-Based Climate Information: Implications for Agronomic Decisions Among Smallholder Farmers in the Eastern Cape, South Africa","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114","基于南非东开普省 217 户小农户的横断面调查与多阶段抽样,采用 logistic 回归识别 ICT 气候信息采纳决定因素,并用倾向得分匹配(PSM)评估其对农事决策的影响。结果显示 65% 农户通过 ICT 平台获取气候信息,35% 依赖人力渠道;ICT 采纳驱动因素包括教育、数字素养、移动网络可靠性、实时更新感知价值与智能手机持有;PSM 估计显示基于 ICT 的气候信息在影响种植决策、投入品使用与农事时机方面比人力渠道效果低 10%—15%,提示数字接入并不自动转化为有效使用。","Logical Operator Operator\n\nSearch Text\n\nSearch Type\n\n_add\\_circle\\_outline_\n\n_remove\\_circle\\_outline_\n\n[![Image 1: sustainability-logo](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fjournals\u002Fsustainability-logo.png?3798e4e58c765aed)](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fsustainability)\n\n## Article Menu\n\nFont Type:\n\n_Arial_ _Georgia_ _Verdana_\n\nFont Size:\n\nAa Aa Aa\n\nLine Spacing:\n\n__ __ __\n\nColumn Width:\n\n__ __ __\n\nBackground:\n\nOpen Access Article\n\nby \nJabulile Zamokuhle Manyike\n\n *[](mailto:jmanyike@ufh.ac.za)[![Image 2: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0000-0001-6529-9574) and \nYanga-Inkosi Nocezo\n\n[](mailto:yanganocezo12@gmail.com)[![Image 3: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0000-0002-7607-495X)\n\nDepartment of Agricultural Economics, Extension, and Agri-Business, Faculty of Science and Agriculture, University of Fort Hare, 1 King William’s Town Road, Alice 5700, South Africa\n\n*\n\nAuthor to whom correspondence should be addressed.\n\nSubmission received: 3 February 2026 \u002F Revised: 7 March 2026 \u002F Accepted: 8 March 2026 \u002F Published: 9 August 2026\n\n## Abstract\n\nSmallholder farmers in South Africa face increasing climate variability, yet their agronomic decision-making depends on timely and reliable climate information. Although digital ICT platforms are expanding, limited evidence exists on how their effectiveness compares to human-based advisory systems. The study addresses this gap by examining the determinants of ICT-based climate information adoption using logistic regression and assessing its influence on agronomic decisions through propensity score matching (PSM). A cross-sectional survey and multistage sampling were used to collect data from 217 smallholder crop farmers in the Eastern Cape. The results indicate that 65% of farmers accessed climate information through ICT platforms, while 35% relied on human-based sources. The adoption of ICT-based information is driven by education, digital literacy, mobile network reliability, the perceived value of real-time updates, and smartphone ownership, whereas habitual dependence on traditional channels hinders digital uptake. PSM estimates show that ICT-based climate information is 10–15% less effective than human-based sources in shaping planting decisions, input use, and the timing of farm operations, likely due to digital literacy and infrastructure constraints. The study demonstrates that access to digital tools does not automatically translate into effective use and recommends a hybrid information model integrating digital platforms with trusted human intermediaries to strengthen climate resilience and agronomic decision-making.\n\n## 1. Introduction\n\nAgriculture remains central to rural livelihoods in South Africa, particularly in provinces such as the Eastern Cape, where smallholder farmers form the majority of agricultural producers [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B1-sustainability-18-08114),[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B2-sustainability-18-08114),[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B3-sustainability-18-08114)]. Despite its importance, this sector is highly vulnerable to climate-related risks, as smallholder farmers often operate with limited resources, weak extension support, and poor access to climate adaptation tools [[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B4-sustainability-18-08114),[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B5-sustainability-18-08114),[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B6-sustainability-18-08114),[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B7-sustainability-18-08114)]. Increasingly frequent droughts, delayed rainfall, rising temperatures, and shifting pest and disease pressures threaten food security and economic stability in the region [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B8-sustainability-18-08114),[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B9-sustainability-18-08114),[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B10-sustainability-18-08114)]. Timely, reliable climate information is therefore critical for guiding agronomic decisions such as planting dates, crop choices, water management, and input application [[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B11-sustainability-18-08114),[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B12-sustainability-18-08114),[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B13-sustainability-18-08114),[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B14-sustainability-18-08114),[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B15-sustainability-18-08114)]. Access to such information enhances farmers’ preparedness for climate extremes and strengthens their adaptive capacity [[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B15-sustainability-18-08114),[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B16-sustainability-18-08114),[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B17-sustainability-18-08114),[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B18-sustainability-18-08114)]. Climate information services typically include rainfall onset and cessation, seasonal duration, temperature trends, soil moisture, wind patterns, and early warnings for droughts or floods [[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B18-sustainability-18-08114),[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B19-sustainability-18-08114),[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B20-sustainability-18-08114),[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B21-sustainability-18-08114),[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B22-sustainability-18-08114)]. Traditionally, this information has been disseminated through human-based channels—extension officers, farmer groups, NGOs, and community networks [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B14-sustainability-18-08114),[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B22-sustainability-18-08114),[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B23-sustainability-18-08114),[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B24-sustainability-18-08114)]. These sources are valued for their contextual relevance and interpersonal trust [[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B21-sustainability-18-08114)]. However, in South Africa, high extension-to-farmer ratios, inconsistent service delivery, communication gaps, and sociocultural barriers, including gendered access to advisory support, limit the effectiveness of these channels [[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B25-sustainability-18-08114),[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B26-sustainability-18-08114),[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B27-sustainability-18-08114),[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B28-sustainability-18-08114),[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B29-sustainability-18-08114)].\n\nAt the same time, the expansion of digital infrastructure and mobile technologies has enabled Information and Communication Technologies (ICTs) such as mobile weather alerts, agricultural apps, WhatsApp groups, and voice-based advisory platforms to play an increasingly prominent role in climate information delivery [[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B23-sustainability-18-08114),[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B30-sustainability-18-08114),[31](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B31-sustainability-18-08114),[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B32-sustainability-18-08114),[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B33-sustainability-18-08114),[34](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B34-sustainability-18-08114),[35](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B35-sustainability-18-08114),[36](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B36-sustainability-18-08114)]. ICT-based tools have the potential to provide rapid, localized, and scalable climate information [[37](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B37-sustainability-18-08114)]. However, their adoption remains uneven due to persistent digital barriers, including poor network coverage, low digital literacy, high data costs, and language mismatches [[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B33-sustainability-18-08114),[38](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B38-sustainability-18-08114),[39](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B39-sustainability-18-08114),[40](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B40-sustainability-18-08114)]. Moreover, farmers often continue to trust interpersonal sources more than digital platforms, creating a dual system where ICT-based and human-based information coexist but are not equally utilized or valued [[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B30-sustainability-18-08114),[39](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B39-sustainability-18-08114)]. These dynamics raise important questions regarding why some farmers adopt ICT-based climate information while others rely on traditional channels, and whether ICT-based information is as effective as human-based sources in influencing agronomic decisions. Addressing these questions is essential for designing climate information systems that are both accessible and impactful.\n\nThis study, therefore, seeks to bridge this gap by (i) identifying the factors that influence smallholder farmers’ adoption of ICT-based climate information over human-based sources, and (ii) evaluating the impact of ICT-based climate information on agronomic decision-making compared to human-based sources. Focusing on the Eastern Cape Province, where both ICT infrastructure and extension systems are active but unevenly distributed, the study provides timely insights into how information flows influence climate adaptation at the farm level. By shedding light on these dynamics, the study contributes to policy discussions on how to design integrated climate information services that are inclusive, accessible, and actionable. It also offers practical recommendations for strengthening both digital and interpersonal communication channels, ensuring that no farmer is left behind in the shift toward climate-resilient agriculture.\n\n## 2. Materials and Methods\n\n### 2.1. Description of the Study Area\n\nThe study was conducted in Elundini Local Municipality, located within the Joe Gqabi District of the Eastern Cape. The Municipality is made up of three towns, Mount Fletcher in the north, Maclear in the centre, and Ugie in the South. [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#fig_body_display_sustainability-18-08114-f001) illustrates their locations along with the distribution of sampled households across the study area. The municipality is located near Mthatha [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B41-sustainability-18-08114)], the third largest city within the province [[42](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B42-sustainability-18-08114)]. Elundini’s economy is driven by agriculture, social services, and retail trade, with the agricultural sector comprising commercial, emerging, and subsistence farming [[43](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B43-sustainability-18-08114)]. Farmers commonly grow maize, potatoes, and cabbages, and keep both large and small ruminants [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B41-sustainability-18-08114)]. The area receives an annual average rainfall of approximately 1200 mm, mostly received in the summer planting season [[44](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B44-sustainability-18-08114)], but experiences significant climate variability, including erratic rainfall and periodic droughts that heighten production risks [[43](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B43-sustainability-18-08114)]. Furthermore, Elundini has many natural resources and a good climate that makes it possible for households to engage in agriculture [[45](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B45-sustainability-18-08114)]. However, its agricultural performance remains below its potential, constrained by limited market access, infrastructure, and technology uptake—conditions that contrast sharply with highly mechanized and well-serviced agricultural zones [[46](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B46-sustainability-18-08114)]. In the area, government support is available through extension services and farmer assistance programmes such as the provision of seedlings, livestock facilities, and fencing materials [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B41-sustainability-18-08114),[46](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B46-sustainability-18-08114)]. Nonetheless, the extent of their reach is limited due to high farmer-to-officer ratios and communication constraints [[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B25-sustainability-18-08114),[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B26-sustainability-18-08114),[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B27-sustainability-18-08114),[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B28-sustainability-18-08114),[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B29-sustainability-18-08114)].\n\n**Figure 1.** Map of study area. Source: Department of GIS, University of Fort Hare. Note: Numbers 1–17 show the municipal wards; red dots mark the locations of the sampled households.\n\nICT infrastructure is provided through public libraries equipped with computers and internet access [[46](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B46-sustainability-18-08114)], though network reliability and digital literacy remain barriers for many community members. Given the combination of high agricultural potential, exposure to climate-related risks, uneven technological advancement, and mixed advisory and ICT service environments, Elundini provides an ideal setting for analyzing differences in the adoption and effectiveness of ICT-based versus human-based climate information sources.\n\n### 2.2. Research Design\n\nThis study adopted a quantitative cross-sectional research design to investigate the adoption and impact of ICT-based climate information among smallholder crop farmers in the Eastern Cape Province of South Africa. A cross-sectional approach was deemed appropriate for capturing data at a single point in time, enabling an assessment of current patterns of ICT use, climate information access, and related agronomic decisions.\n\n### 2.3. Sampling Procedure and Sample Size\n\nA multistage sampling technique was employed to ensure representativeness and practicality in data collection. In the first stage, Elundini municipality was purposively selected. Within the municipality, eight wards were randomly selected based on their agricultural relevance and reported farming activity levels. Within each ward, one or two villages were randomly selected, leading to a total of twelve participating villages. Finally, within each village, a random sampling technique was used to select 217 smallholder farmers. [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#table_body_display_sustainability-18-08114-t001) presents the distribution of sampled farmers across the three towns. From the total sample, 167 farmers indicated that they had access to some form of climate information, either through ICT-based platforms or human-based sources, while 50 had no access, and were excluded from the impact assessment component of the analysis. Additionally, among the 167 farmers included, 69 were from Maclear, and 65 from Mount Fletcher, with Ugie represented by 33 farmers.\n\n**Table 1.** Sampling size per location.\n\n### 2.4. Data Collection\n\nData was collected in May 2022 using a structured questionnaire, administered through face-to-face interviews to ensure comprehension and minimize non-response. The questionnaire consisted of questions on farmer’s socio-demographic characteristics (age, education, income, household size, and gender), sources of climate information (e.g., extension agents, radio, mobile apps, WhatsApp groups, local farmer associations), ICT access and use (mobile phone ownership, digital literacy, access to internet or network), and agronomic decision-making (e.g., planting time, crop choices, irrigation practices, input application). The instrument was pre-tested with a small group of farmers outside the sample area to ensure clarity and relevance, and necessary adjustments were made before final data collection. During the data collection phase, authors ensured that verbal consent was obtained from farmers, they were treated with respect and allowed to refuse to be part of the research or withdraw from it at any given time, and that their information was kept confidential and used for academic purposes only. All of these requirements were guided by the ethical approval letter [MAN011SNOC01], which was obtained from the department of agricultural economics and extension, faculty of Science and Agriculture at the University of Fort Hare.\n\n### 2.5. Data Analysis\n\nData was first analyzed using descriptive statistics to summarize the characteristics of the sample, including age distribution, education levels, climate information sources, and ICT use. To identify the factors influencing the adoption of ICT-based climate information, a binary logistic regression model was employed. Stata\u002FSE 15.1 (Stata Corp LLC) was used for the analysis.\n\n#### 2.5.1. Logistic Regression Model\n\nThe dependent variable was a binary indicator of whether a farmer accessed climate information via ICT (1) or through human-based sources (0). The probability (\n\n$P_{i}$\n) that farmers receive climate information via ICT platforms is represented as\n\n$$\nY_{i } = \\beta_{0} + \\sum_{i = 1}^{n} \\beta_{i} X_{i ,}\n$$\n\n(1)\n\nThe equation represents a binary choice, which involves the estimation of the probability of receiving climate information via ICT platforms (Y) as a function of independent variables (X).\n\n$\\beta_{0 }$\nis constant and\n\n$Y_{i }$\nis equal to one 1 when farmers receive ICT-based climate information and 0 if through human-based sources. The logit model uses a logistic cumulative distributive function to estimate, P given by\n\n$$\nP = \\left(\\right. Y = \\frac{1}{X} \\left.\\right) = \\frac{e^{y}}{1 + e^{y}}\n$$\n\n(2)\n\n$$\nP = \\left(\\right. Y = \\frac{0}{X} \\left.\\right) = 1 - \\frac{e^{y}}{1 + e^{y}}\n$$\n\n(3)\n\n$$\nY = \\mathsf{\\beta}_{1} X_{1} + \\mathsf{\\beta}_{2} X_{2} + \\ldots + \\mathsf{\\beta}_{k} X = \\sum_{i = 1}^{k} \\mathsf{\\beta}_{i} X_{i ,}\n$$\n\n(4)\n\nwhere k represents the number of explanatory predictors that are to be included in the analysis. The model is as follows:\n\n$$\nY = \\mathit{Ln} \\left(\\right. \\frac{P}{1 - P} \\left.\\right) = \\beta_{0} + \\beta_{k} + \\epsilon ,\n$$\n\n(5)\n\nwhere Y = farmer accessed climate information via ICT; Ln\n\n$\\left(\\right. \\frac{P}{1 - P} \\left.\\right)$\nthe ratio of probability of accessing climate information via ICT (p) to receiving it through human-based sources (1 − P); β = slope of coefficient;\n\n$X_{k}$\n= vector of the independent variables presented in [Table 2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#table_body_display_sustainability-18-08114-t002); ε = error term.\n\n**Table 2.** Independent variables and expected signs in ICT adoption model.\n\n#### 2.5.2. Measuring Agronomic Decision\n\nThe Agronomic Decision Index (ADI) was employed to assess the extent of agronomic decisions made by smallholder farmers. This index serves as a tool to quantify the degree to which each household adopts key agronomic practices. While the existing literature commonly measures agronomic decision-making by simply noting whether a farmer made a decision or not [[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8114#B17-sustainability-18-08114)], this approach does not capture the depth of adoption. In contrast, ADI provides a more comprehensive measure. Each agronomic decision was coded as 1 if undertaken by the farmer and 0 otherwise. The index for each farmer was then calculated as shown in Equation (6):\n\n$$\nA D I = \\frac{D_{1} + D_{2} + D_{3} + D_{4} + D_{5} + D_{6} + D_{7}}{7}\n$$\n\n(6)\n\nwhere D1 to D7 include decisions on land preparation, planting date decis","Sustainability","2026-08-08T22:00:00Z",64,{"impact":19,"substance":338,"depth":118,"authority":19,"freshness":245,"relevant":20,"comment":339},20,"论文聚焦南非小农气候信息获取，对农业信息化有参考价值，但地域性强，时效性低。",[341],{"name":334,"url":331},[343,25,26,297,344,345,346,347],"数字农业","气候信息","ICT采纳","数字鸿沟","发展中国家",{"code":143,"slug":349,"name":350,"path":351,"parent_code":352},"farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[],{"status":84,"locked":13,"version":32,"method":33,"evidence":355},{"candidates":356,"agricultural_evidence":51},[357,362,366],{"code":143,"score":19,"supported":51,"title":358,"abstract":360,"tags":361},[359],"smallholder",[297],[297],{"code":37,"score":245,"supported":13,"title":363,"abstract":364,"tags":365},[],[],[26,343],{"code":42,"score":245,"supported":13,"title":367,"abstract":368,"tags":369},[],[],[25,346],[371,372],"农业信息化 发展中国家 数字乡村 数字农业","农业信息化 发展中国家","农业信息化发展中国家数字乡村数字农业-539","2026-08-15T00:02:55.675693Z",{"id":376,"title":377,"url":378,"summary":379,"summary_zh":8,"content":380,"source_name":381,"source_url":8,"published_at":382,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":336,"score_detail":383,"sources":385,"tags":387,"primary_topic":390,"secondary_topics":395,"classification":396,"search_phrases":418,"slug":421,"view_count":88,"doi":8,"paper":8,"created_at":422},104,"安徽农科院：聚焦智慧农业与种业创新 助力农业强省建设","https:\u002F\u002Fwww.snlbw.com\u002Fnews\u002F202607\u002F07\u002F14344.html","安徽省数字乡村进步水平达55%,比上年提升6个百分点,居全国第4位。农业生产信息化率达52.15%,比上年提升10.55个百分点,居全国第1位。安徽省农业科学院深度参与,为该省农业现代化贡献力量。","近日，中央网信办、农业农村部等部门联合印发公告，公布第二批国家数字乡村试点区域名单。其中，我省桐城市、铜陵入选，试点种类分别为范围特点型（乡村数字惠民服务方向）、地区综合型（中部区域方向）。\n\n在2026年公布的第一批国家数字乡村试点区域名单中，我省长丰、砀山、歙县、金寨4个区域入选。加上第二批入选的桐城、铜陵2个区域，现在全省共有6个试点区域，试点区域数目居各省（自治区、直辖市）并列第2位。\n\n近年来，我省以数字乡村智慧农业暨农业产业网络“5+8”试点为抓手，持续推进网络、云数据、AI等数字技术在农业生产范围应用。《中国数字乡村进步报告（2026）》显示，我省数字乡村进步水平达55%，比上年提升6个百分点，居全国第4位。其中，农业生产信息化率达52.15%，比上年提升10.55个百分点，居全国第1位。 （作者： 许昊杰）","安徽日报 2026-07-07","2026-07-07T01:00:00Z",{"impact":338,"substance":118,"depth":196,"authority":12,"freshness":20,"relevant":20,"comment":384},"报道了安徽数字乡村试点进展及全国排名数据，有一定信息增量，但时效性差，已过近一个月。",[386],{"name":381,"url":378},[25,26,388,389],"农科院","安徽",{"code":391,"slug":392,"name":393,"path":394,"parent_code":131},"F1","seed-industry","种业与品种管理","\u002Ftopic\u002Finputs-equipment\u002Fseed-industry",[],{"status":84,"locked":13,"version":32,"method":33,"evidence":397},{"candidates":398,"agricultural_evidence":51},[399,403,408,412],{"code":37,"score":257,"supported":51,"title":400,"abstract":401,"tags":402},[75],[],[26],{"code":391,"score":16,"supported":51,"title":404,"abstract":406,"tags":407},[405],"种业",[],[],{"code":42,"score":57,"supported":51,"title":409,"abstract":410,"tags":411},[],[25],[25],{"code":413,"score":137,"supported":51,"title":414,"abstract":415,"tags":417},"J2",[],[416],"农业现代化",[],[419,420],"农业信息化 数字乡村 农科院 安徽","农业信息化 数字乡村","农业信息化数字乡村农科院安徽-104","2026-07-31T00:00:54.935190Z"]