[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4745":3,"related-4745":116},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":92,"slug":95,"view_count":21,"doi":96,"paper":97,"created_at":115},4745,"Digital Agricultural Transformation under Climate Risk: A Policy and Financing Perspective within the National Economy Model","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.3038-3043.9028","Today, the global food system is undergoing a multidimensional transformation driven by climate change, population growth and rising food demand. Irregular precipitation regimes, increasing water scarcity and ecosystem degradation are making agricultural production increasingly unpredictable and fragile. In addition to these ecological pressures, shrinking arable land, the ageing agricultural labour force and rising input costs further challenge the sustainability of conventional farming systems. The growing frequency of extreme weather events demonstrates the vulnerability of existing production models and highlights the need for structural transformation in agriculture. Digital agriculture offers a potential solution through a technology-oriented paradigm shift. Technologies such as the Internet of Things (IoT), artificial intelligence (AI)-based decision support systems, remote sensing and precision agriculture enable site-specific resource management, reducing environmental impact while improving productivity. However, the adoption of these technologies is constrained by high investment costs and financial uncertainty, particularly for small and medium-scale producers. Therefore, integrating digital technologies into agricultural production depends on reducing financial and operational risks through effective public policy instruments. This conceptual analysis state-led strategic support mechanisms for digital agricultural transformation within the framework of the National Economy Model (NEM). The model’s production-oriented and cost-protection principles provide a stabilisation framework that may facilitate technological adoption. The study argues that public support mechanisms function not only as financial assistance but also as strategic policy instruments that enhance agricultural resilience and production continuity under climate-related risks.","当今，全球粮食体系正在气候变化、人口增长和粮食需求上升的驱动下经历多维转型。不规则的降水模式、日益加剧的水资源短缺和生态系统退化，使农业生产日益难以预测且脆弱。除这些生态压力外，耕地面积缩减、农业劳动力老龄化和投入成本上升，进一步挑战了传统农业系统的可持续性。极端天气事件日益频繁，表明现有生产模式的脆弱性，并凸显了农业结构性转型的必要性。数字农业通过技术导向的范式转变提供了潜在解决方案。物联网（IoT）、基于人工智能（AI）的决策支持系统、遥感和精准农业等技术，能够实现因地制宜的资源管理，在提高生产力的同时减少环境影响。然而，这些技术的采用受到高投资成本和财务不确定性的制约，对中小规模生产者尤为如此。因此，将数字技术融入农业生产，取决于通过有效的公共政策工具降低财务和运营风险。本概念性分析阐述了在国民经济模式（NEM）框架下，国家主导的战略支持机制对数字农业转型的作用。该模式以生产为导向和成本保护的原则提供了稳定化框架，可能促进技术采用。研究认为，公共支持机制不仅发挥财政援助功能，同时也是增强农业韧性和气候相关风险下生产连续性的战略性政策工具。",null,"Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z","论文",10,false,61,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,15,14,8,1,"概念性论文，从政策与融资视角讨论气候风险下数字农业转型，有一定理论参考价值但缺乏实证数据，属细分领域学术探讨。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字农业","智慧农业","农业政策","农业金融","气候风险",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,54,62,70,78,85],{"code":45,"score":46,"supported":47,"title":48,"abstract":49,"tags":53},"I1",11,true,[],[50,26,51,52],"精准农业","precision agriculture","digital agriculture",[26,27],{"code":32,"score":55,"supported":47,"title":56,"abstract":57,"tags":61},9,[],[58,59,60],"物联网","internet of things","IoT",[],{"code":63,"score":64,"supported":47,"title":65,"abstract":66,"tags":69},"H7",7,[],[67,68],"气候变化","climate change",[30],{"code":71,"score":72,"supported":47,"title":73,"abstract":74,"tags":77},"I2",6,[],[75,76],"artificial intelligence","decision support",[],{"code":79,"score":72,"supported":47,"title":80,"abstract":81,"tags":84},"I3",[],[82,83],"遥感","remote sensing",[],{"code":86,"score":87,"supported":47,"title":88,"abstract":89,"tags":91},"H3",3,[],[90],"水资源",[],[93,94],"数字农业 转型 气候风险","农业政策 农业金融 数字农业 智慧农业","数字农业转型气候风险-4745","10.24925\u002Fturjaf.v14i10.3038-3043.9028",{"doi":96,"openalex_id":98,"authors":99,"venue":10,"cited_by_count":106,"oa_url":6,"card":107,"direction":113,"ingested_from":114},"W7221460591",[100,103],{"name":101,"orcid":102},"Mehmet Metin Özgüven","https:\u002F\u002Forcid.org\u002F0000-0002-6421-4804",{"name":104,"orcid":105},"Saliha Kilavuz","https:\u002F\u002Forcid.org\u002F0009-0006-6830-0730",0,{"tldr":108,"method":109,"finding":110,"direction":111,"opportunity":112},"基于国民经济模型，从政策与融资视角分析气候风险下数字农业转型的公共支持机制。","概念分析，结合NEM框架与物联网、AI、遥感等数字农业技术。","公共支持不仅是资金援助，更是提升农业气候韧性与生产连续性的战略政策工具。","数字乡村与农业信息化","可实证检验NEM政策工具对中小农户数字技术采纳与融资约束的缓解效果。","智慧农业 \u002F 农业物联网","openalex","2026-10-12T23:30:12.745005Z",{"total":72,"page":21,"page_size":72,"items":117},[118,178,236,309,371,457],{"id":119,"title":120,"url":121,"summary":122,"summary_zh":123,"content":9,"source_name":124,"source_url":121,"published_at":125,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":126,"score_detail":127,"sources":130,"tags":132,"primary_topic":135,"secondary_topics":141,"classification":143,"search_phrases":161,"slug":164,"view_count":106,"doi":165,"paper":166,"created_at":177},4295,"Smart Soil Monitoring Systems: Challenges and Strategic Solutions for Enhanced Adoption","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v9.no12.2025.pg245.258","Smart Soil Monitoring Systems (SSMS) represent a key innovation in precision agriculture, enabling continuous monitoring of soil parameters such as moisture, pH, nutrient content, temperature, and salinity. These systems integrate Internet of Things (IoT) technologies, wireless sensor networks, and data analytics to provide real-time insights that support data-driven decision-making, optimize resource use, and enhance crop productivity. Despite their potential, the adoption of SSMS in developing institutions, including Federal Polytechnic Bali (FPB), remains limited due to financial constraints, inadequate technical skills, low awareness, and infrastructural challenges such as unreliable internet connectivity and power supply. The high cost of devices, lack of training, and insufficient institutional policies further impede practical implementation in teaching, research, and extension activities. This study employed a descriptive survey design targeting academic and technical staff in FPB’s agricultural departments. Data collected via a structured questionnaire were analyzed using descriptive statistics, with mean scores indicating key challenges and potential strategies. Findings revealed that high device costs, poor network infrastructure, and low technical capacity were the most critical barriers. To address these, respondents emphasized capacity-building workshops, improved digital infrastructure, awareness campaigns, funding support, and policy frameworks as strategic interventions. The study concludes that enhancing SSMS adoption requires integrated solutions addressing economic, technical, and institutional barriers. Implementation of these strategies is expected to strengthen hands-on learning, improve research outputs, and promote sustainable, technology-driven agriculture at FPB. The study contributes to understanding practical adoption challenges of smart agricultural technologies in resource-constrained educational settings.","智能土壤监测系统（Smart Soil Monitoring Systems, SSMS）是精准农业的一项关键创新，能够对土壤湿度、pH值、养分含量、温度和盐度等参数进行持续监测。这些系统集成了物联网（IoT）技术、无线传感器网络和数据分析，可提供实时洞察，支持数据驱动的决策、优化资源利用并提高作物生产力。尽管潜力巨大，但包括巴厘联邦理工学院（Federal Polytechnic Bali, FPB）在内的发展中机构对SSMS的采用仍然有限，原因在于资金约束、技术技能不足、认知度低以及互联网连接和电力供应不可靠等基础设施挑战。设备成本高昂、缺乏培训以及制度政策不完善，进一步阻碍了其在教学、研究和推广活动中的实际应用。本研究采用描述性调查设计，以FPB农业部门的学术和技术人员为研究对象。通过结构化问卷收集的数据采用描述性统计进行分析，以平均分表明主要挑战和潜在策略。研究发现，设备成本高、网络基础设施差和技术能力低是最关键的障碍。为应对这些问题，受访者强调能力建设研讨会、改善数字基础设施、宣传活动、资金支持和政策框架是战略性干预措施。研究得出结论，提升SSMS的采用需要综合解决方案，以应对经济、技术和制度层面的障碍。这些策略的实施有望加强实践学习、提高研究产出，并推动FPB可持续的、技术驱动的农业发展。本研究有助于理解资源受限教育环境中智能农业技术的实际采用挑战。","WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY","2026-10-05T00:00:00Z",52,{"impact":20,"substance":128,"depth":19,"authority":72,"freshness":20,"relevant":21,"comment":129},16,"聚焦资源受限院校智慧土壤监测落地障碍的实证调查，结论实用但样本局限于单一院校，影响力有限。",[131],{"name":124,"url":121},[26,27,58,133,134],"土壤监测","农业传感器",{"code":136,"slug":137,"name":138,"path":139,"parent_code":140},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[142],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":144},{"candidates":145,"agricultural_evidence":47},[146,151,157],{"code":136,"score":19,"supported":47,"title":147,"abstract":148,"tags":150},[137],[149,137],"土壤",[],{"code":32,"score":46,"supported":47,"title":152,"abstract":153,"tags":156},[],[58,154,59,60,155],"无线传感","wireless sensor",[134,58],{"code":45,"score":20,"supported":47,"title":158,"abstract":159,"tags":160},[],[50,51],[26,27],[162,163],"农业传感器 土壤监测 数字农业 智慧农业","农业传感器 土壤监测","农业传感器土壤监测数字农业智慧农业-4295","10.56201\u002Fwjimt.v9.no12.2025.pg245.258",{"doi":165,"openalex_id":167,"authors":168,"venue":124,"cited_by_count":106,"oa_url":171,"card":172,"direction":113,"ingested_from":114},"W7220426775",[169],{"name":170,"orcid":9},"NUHU ABDULLAHI","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FWJIMT\u002FVOL. 9 NO. 12 2025\u002FSmart Soil Monitoring Systems 245-258.pdf",{"tldr":173,"method":174,"finding":175,"direction":113,"opportunity":176},"调查尼日利亚某院校智能土壤监测系统采纳障碍并提出策略。","描述性问卷调研农业院系教职工，用描述统计与均值分析。","设备成本高、网络差、技术能力低是主要障碍，需培训与政策支持。","可研究资源受限院校中低成本物联网土壤监测方案与采纳机制。","2026-10-06T23:30:07.776821Z",{"id":179,"title":180,"url":181,"summary":182,"summary_zh":183,"content":9,"source_name":184,"source_url":181,"published_at":185,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":186,"score_detail":187,"sources":193,"tags":195,"primary_topic":198,"secondary_topics":199,"classification":200,"search_phrases":221,"slug":224,"view_count":21,"doi":9,"paper":225,"created_at":235},4070,"Digital Transformation of Agribusiness: Pathways Towards Smart and Sustainable Food Systems in Nordic and Indian Agricultural Contexts","https:\u002F\u002Fballisticsjournal.com\u002Findex.php\u002Fjournal\u002Farticle\u002Fview\u002F720","Digital transformation is reshaping agribusiness by integrating advanced technologies into production, processing, marketing, and supply chain management, thereby creating more efficient, resilient, and sustainable food systems. In India, increasing demand for food, climate variability, shrinking landholdings, and resource constraints have accelerated the need for smart agricultural solutions. This review examines the emerging role of digital technologies, including artificial intelligence, precision agriculture, the Internet of Things, blockchain, big data analytics, drones, and digital platforms, in modernizing agribusiness and strengthening food value chains. It synthesizes recent scientific evidence on technological innovations, adoption patterns, opportunities, barriers, and sustainability outcomes across the agricultural sector. The review highlights that digital technologies improve productivity, resource-use efficiency, traceability, market connectivity, and climate resilience while supporting informed decision-making. However, challenges related to digital infrastructure, affordability, data governance, and digital literacy remain significant. The paper concludes that supportive policies, inclusive innovation, capacity building, and multi-stakeholder collaboration are essential to accelerate India's transition toward smart, resilient, and sustainable food systems.","数字化转型正通过将先进技术融入生产、加工、营销和供应链管理，重塑农业综合企业，从而构建更高效、更具韧性且可持续的粮食系统。在印度，粮食需求不断增长、气候多变、土地持有规模缩小以及资源约束，加速了对智慧农业解决方案的需求。本文综述了人工智能、精准农业、物联网、区块链、大数据分析、无人机和数字平台等数字技术在推动农业综合企业现代化和强化食品价值链中的新兴作用。文章综合了近期关于农业领域技术创新、采用模式、机遇、障碍和可持续性成果的科学证据。综述指出，数字技术提高了生产力、资源利用效率、可追溯性、市场连通性和气候韧性，同时支持知情决策。然而，数字基础设施、可负担性、数据治理和数字素养方面的挑战依然显著。本文结论认为，支持性政策、包容性创新、能力建设和多利益相关方合作对于加速印度向智慧、有韧性且可持续的粮食系统转型至关重要。","Dandao Xuebao\u002FJournal of Ballistics","2026-10-01T00:00:00Z",76,{"impact":188,"substance":189,"depth":190,"authority":191,"freshness":20,"relevant":21,"comment":192},18,20,17,13,"综述性论文，系统梳理数字技术在印度与北欧农业的应用与障碍，具行业参考价值但非突发资讯。",[194],{"name":184,"url":181},[26,27,196,50,197],"农业人工智能","农业区块链",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":201},{"candidates":202,"agricultural_evidence":47},[203,207,211,216],{"code":45,"score":17,"supported":47,"title":204,"abstract":205,"tags":206},[],[27,50,51],[26,27,50],{"code":32,"score":72,"supported":47,"title":208,"abstract":209,"tags":210},[],[58,59],[],{"code":71,"score":212,"supported":47,"title":213,"abstract":214,"tags":215},4,[],[75],[196],{"code":63,"score":87,"supported":47,"title":217,"abstract":218,"tags":220},[],[219],"climate resilience",[],[222,223],"印度 数字农业 转型","北欧 智慧农业 食品系统","印度数字农业转型-4070",{"doi":9,"openalex_id":226,"authors":227,"venue":184,"cited_by_count":106,"oa_url":181,"card":230,"direction":113,"ingested_from":114},"W7217570283",[228],{"name":229,"orcid":9},"Anjali Adhikari",{"tldr":231,"method":232,"finding":233,"direction":111,"opportunity":234},"综述数字技术在印度农业综合企业中的应用，探讨迈向智慧可持续食品系统的路径。","综述AI、物联网、区块链、大数据、无人机等数字技术在农业价值链中的证据。","数字技术提升生产力、资源效率、可追溯性和气候韧性，但基础设施、成本和数字素养仍是障碍。","可对比北欧与印度情境，研究数字农业技术适配性与包容性创新政策。","2026-10-03T23:30:14.654788Z",{"id":237,"title":238,"url":239,"summary":240,"summary_zh":241,"content":9,"source_name":242,"source_url":239,"published_at":243,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":244,"score_detail":245,"sources":249,"tags":251,"primary_topic":255,"secondary_topics":256,"classification":257,"search_phrases":286,"slug":289,"view_count":290,"doi":291,"paper":292,"created_at":308},3781,"A Big Data Curation Framework for Precision Agriculture: Actions, Actors and Challenges in the Indian Context","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fajaar\u002F2026\u002Fv26i10763","Precision agriculture produces large volumes of heterogeneous data from IoT sensor networks, satellite imagery, unmanned aerial surveys, crop registries and weather stations. India has committed substantial public investment to agricultural data infrastructure under the Digital Agriculture Mission, which by August 2026 had generated more than 10.31 crore Farmer IDs and surveyed over 31 crore plots. No published framework specifies what curation these data require, which institutions are responsible for each activity, or what obstacles arise in the Indian operating environment. This study develops such a framework through a systematic review of 214 sources published between January 2015 and August 2026, drawn from five bibliographic databases and supplemented by Government of India policy documents. Two coders agreed on 88.5 per cent of coding decisions for a random subsample of 50 records. The framework specifies curation actions at eight stages of the agricultural data lifecycle, assigns each action to one or more of nine institutional actor categories, and documents the associated challenges. Three findings distinguish precision agriculture from the general big data case: raster imagery requires curation procedures with no counterpart in text or sensor-stream data; machine-learning models function as curation objects rather than analytical tools alone; and metadata operating only in English cannot support a national agricultural data system in a country with 22 scheduled languages, a requirement now given legal force by the Digital Personal Data Protection Rules 2025. Those Rules, notified in November 2025, impose consent, erasure and breach-notification obligations that apply across the full data lifecycle rather than at the point of collection alone. The framework gives agricultural universities, ICAR institutes and state departments a basis for assigning data management roles and planning curation training. Because it rests on published literature and policy documents, it has not yet been tested against current institutional practice.","精准农业通过物联网传感器网络、卫星影像、无人机航测、作物登记系统和气象站产生大量异构数据。印度根据“数字农业使命”对农业数据基础设施投入了大量公共资金，截至2026年8月，已生成超过1031万个农民ID，并调查了超过3100万块地块。目前尚无已发表的框架明确规定这些数据需要何种数据策管、各项活动由哪些机构负责，或在印度运行环境中会出现哪些障碍。本研究通过对2015年1月至2026年8月间发表的214份文献进行系统综述，构建了这样一个框架，文献取自五个书目数据库，并辅以印度政府政策文件。两名编码员对随机抽取的50条记录子样本的编码决定一致率为88.5%。该框架明确了农业数据生命周期八个阶段的数据策管行动，将每项行动分配给九类机构行动者类别中的一个或多个，并记录了相关挑战。三项发现将精准农业与一般大数据情形区分开来：栅格影像所需的策管程序在文本或传感器流数据中没有对应物；机器学习模型作为策管对象发挥作用，而不仅仅是分析工具；仅以英语运行的元数据无法在一个拥有22种表列语言的国家支撑国家农业数据系统，而2025年《数字个人数据保护规则》现已赋予这一要求法律效力。该规则于2025年11月通告，规定了同意、删除和违规通知义务，这些义务适用于整个数据生命周期，而不仅限于收集环节。该框架为农业院校、ICAR研究所和各邦部门分配数据管理角色和规划策管培训提供了依据。由于该框架基于已发表文献和政策文件，尚未针对当前机构实践进行检验。","Asian Journal of Advances in Agricultural Research","2026-09-28T00:00:00Z",84,{"impact":246,"substance":247,"depth":188,"authority":191,"freshness":20,"relevant":21,"comment":248},22,23,"系统综述214篇文献构建印度精准农业数据策管框架，提出栅格影像、机器学习模型与多语言元数据三大特殊性，对农业数据治理有参考价值。",[250],{"name":242,"url":239},[26,27,252,253,254],"农业大数据","数据治理","印度农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":258},{"candidates":259,"agricultural_evidence":47},[260,265,269,276,281],{"code":45,"score":261,"supported":47,"title":262,"abstract":263,"tags":264},19,[51],[50,26,51,52],[26,27],{"code":32,"score":72,"supported":47,"title":266,"abstract":267,"tags":268},[],[58,60],[],{"code":270,"score":271,"supported":47,"title":272,"abstract":273,"tags":275},"I6",5,[],[274],"agricultural data",[252,253],{"code":71,"score":87,"supported":47,"title":277,"abstract":278,"tags":280},[],[279],"机器学习",[],{"code":282,"score":21,"supported":14,"title":283,"abstract":284,"tags":285},"J3",[],[],[254],[287,288],"印度 数字农业使命 农民ID","精准农业 数据策管 框架","印度数字农业使命农民ID-3781",2,"10.9734\u002Fajaar\u002F2026\u002Fv26i10763",{"doi":291,"openalex_id":293,"authors":294,"venue":242,"cited_by_count":106,"oa_url":239,"card":303,"direction":113,"ingested_from":114},"W7214617849",[295,298,301],{"name":296,"orcid":297},"Suresh Kumar Sharma","https:\u002F\u002Forcid.org\u002F0000-0002-5054-6170",{"name":299,"orcid":300},"Parul Dhull","https:\u002F\u002Forcid.org\u002F0009-0005-8368-3058",{"name":302,"orcid":9},"Pratibha Manohar",{"tldr":304,"method":305,"finding":306,"direction":111,"opportunity":307},"通过系统综述构建印度精准农业大数据治理框架，明确8阶段数据策展行动、9类机构角色及挑战。","系统综述214篇文献与印度政策文件，双编码者一致性88.5%。","栅格影像、机器学习模型作为策展对象、多语言元数据是精准农业区别于一般大数据的三大特征。","可实证检验该框架在印度机构实践中的适用性，并探索多语言元数据与数据保护法规的落地机制。","2026-09-29T23:30:15.187320Z",{"id":310,"title":311,"url":312,"summary":313,"summary_zh":314,"content":9,"source_name":315,"source_url":312,"published_at":316,"category":12,"cover_url":9,"hotness":317,"is_selected":14,"score":318,"score_detail":319,"sources":321,"tags":325,"primary_topic":328,"secondary_topics":329,"classification":330,"search_phrases":355,"slug":358,"view_count":212,"doi":359,"paper":360,"created_at":370},3482,"Digital Transformation and the Reconfiguration of Farming Systems: Understanding How Tech-nological Adoption and Institutional Conditions Shape Sustainable and Inclusive Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22932900","Digital agriculture is progressively reshaping farming through the integration of sensors, artificial intelligence, Internet of Things technologies, digital platforms, data analytics, robotics and automated decision-support systems. This paper examines this transformation by connecting three complementary dimensions: the socio-technical development of digital agriculture, the determinants and processes of technological adoption, and the economic, organizational and inclusive consequences of digitalization for farming systems. The literature indicates that agricultural digitalization cannot be reduced to the availability of increasingly sophisticated technologies. Adoption and sustained use depend on farmers’ perceptions of usefulness and compatibility, farm resources, human capital, infrastructure, institutional support, advisory systems and the capacity to integrate technologies into established production routines. Digital technologies may improve resource efficiency, information management, productivity and sustainability, while also modifying labor organization, farmer autonomy, data governance and relationships among actors within agricultural value chains. These effects remain uneven across farms and territories, particularly where smallholders face financial, infrastructural and capability constraints. The paper therefore interprets digital agriculture as a multidimensional transformation whose outcomes depend on the interaction between technological capabilities, farmer behavior and institutional conditions. Inclusive and sustainable digitalization requires attention not only to innovation diffusion but also to governance, skills, accessibility and the distribution of technological benefits.","数字农业正通过传感器、人工智能、物联网技术、数字平台、数据分析、机器人技术和自动化决策支持系统的整合，逐步重塑农业生产方式。本文通过连接三个互补维度来考察这一转型：数字农业的社会技术发展、技术采纳的决定因素与过程，以及数字化对农业系统产生的经济、组织和包容性后果。文献表明，农业数字化不能简化为日益复杂的技术供给。采纳和持续使用取决于农民对有用性和兼容性的认知、农场资源、人力资本、基础设施、制度支持、咨询系统以及将技术整合到既定生产惯例中的能力。数字技术可能提高资源效率、信息管理、生产力和可持续性，同时也会改变劳动组织、农民自主性、数据治理以及农业价值链中参与者之间的关系。这些影响在农场和区域之间仍不均衡，尤其是小农户面临资金、基础设施和能力约束的地方。因此，本文将数字农业解释为一种多维转型，其结果取决于技术能力、农民行为和制度条件之间的相互作用。包容和可持续的数字化不仅需要关注创新扩散，还需要关注治理、技能、可及性和技术收益的分配。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-24T00:00:00Z",25,77,{"impact":188,"substance":189,"depth":190,"authority":191,"freshness":55,"relevant":21,"comment":320},"系统梳理数字农业技术采纳与制度条件的研究综述，对智慧农业政策与推广有参考价值，但属文献综述类，非重大突破。",[322,323],{"name":315,"url":312},{"name":315,"url":324},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22932899",[26,27,196,326,327],"小农户","技术采纳",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":331},{"candidates":332,"agricultural_evidence":47},[333,337,341,346,350],{"code":45,"score":20,"supported":47,"title":334,"abstract":335,"tags":336},[],[26,52],[26,27],{"code":32,"score":72,"supported":47,"title":338,"abstract":339,"tags":340},[],[58,59],[],{"code":342,"score":212,"supported":47,"title":343,"abstract":344,"tags":345},"G2",[],[326],[326],{"code":71,"score":212,"supported":47,"title":347,"abstract":348,"tags":349},[],[75],[196],{"code":351,"score":21,"supported":14,"title":352,"abstract":353,"tags":354},"G1",[],[],[327],[356,357],"农业人工智能 技术采纳 数字农业 智慧农业","农业人工智能 技术采纳","农业人工智能技术采纳数字农业智慧农业-3482","10.5281\u002Fzenodo.22932900",{"doi":359,"openalex_id":361,"authors":362,"venue":315,"cited_by_count":106,"oa_url":312,"card":365,"direction":113,"ingested_from":114},"W7214187008",[363],{"name":364,"orcid":9},"Chilufya Banda",{"tldr":366,"method":367,"finding":368,"direction":111,"opportunity":369},"综述数字农业转型，分析技术采纳与制度条件如何共同塑造可持续包容性农业。","文献综述，整合社会技术、技术采纳与数字化经济组织后果三维度。","数字化成效取决于技术能力、农户行为与制度条件的交互，小农面临多重约束。","可实证检验制度支持与农户能力如何调节数字技术对包容性和可持续性的影响。","2026-09-25T23:30:18.463733Z",{"id":372,"title":373,"url":374,"summary":375,"summary_zh":376,"content":9,"source_name":377,"source_url":374,"published_at":316,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":378,"score_detail":379,"sources":381,"tags":383,"primary_topic":386,"secondary_topics":392,"classification":394,"search_phrases":421,"slug":424,"view_count":271,"doi":425,"paper":426,"created_at":456},3347,"Integrating Material Flow Cost Accounting and IoT-Based Monitoring for Eco-Efficient Goat Farm Management","https:\u002F\u002Fdoi.org\u002F10.35145\u002F6e5wnv18","Goat farming plays an important role in supporting rural livelihoods, food production, and agricultural sustainability. However, conventional goat farm management often separates environmental monitoring, financial accounting, and livestock management, limiting the ability to identify resource inefficiencies and associated environmental impacts. This study aims to develop and implement GEMBALA (Green Eco-smart Management-Based Automation for Livestock and Accounting), an integrated digital platform that combines Internet of Things (IoT)-based environmental monitoring, Material Flow Cost Accounting (MFCA), emission analysis, artificial intelligence-based livestock management, and analytical reporting. The research employed a research and development approach in collaboration with CV Cahaya Firdaus (Fathur Farm). An IoT sensor prototype was developed, installed, and tested in a real goat farming environment to monitor temperature, humidity, Heat Index (THI), ammonia gas, and dust density. The platform also incorporates MFCA, emission, AI Estrus, AI Health, and analytical reporting modules. The results demonstrate progress toward integrating environmental, economic, and livestock management information within a unified digital platform. However, further validation is required to improve sensor data transmission, synchronization, emission calculations, MFCA data consistency, and AI performance evaluation. The study provides a foundation for eco-economic decision support, sustainable livestock management, and future commercialization of digital livestock technologies.","山羊养殖在支撑农村生计、粮食生产和农业可持续性方面发挥着重要作用。然而，传统的山羊养殖场管理往往将环境监测、财务核算和畜牧管理相互分离，限制了识别资源低效利用及相关环境影响的能力。本研究旨在开发并实施GEMBALA（基于绿色生态智能管理的畜牧与会计自动化平台），这是一个集成了基于物联网（IoT）的环境监测、物料流成本会计（MFCA）、排放分析、基于人工智能的畜牧管理以及分析报告的综合数字平台。研究采用研发方法，与CV Cahaya Firdaus（Fathur Farm）合作开展。研究开发了物联网传感器原型，并在真实山羊养殖环境中进行安装和测试，用于监测温度、湿度、热指数（THI）、氨气和粉尘密度。该平台还整合了MFCA、排放、AI发情检测、AI健康和分析报告模块。结果表明，在将环境、经济和畜牧管理信息整合到统一数字平台方面取得了进展。然而，仍需进一步验证，以改进传感器数据传输、同步、排放计算、MFCA数据一致性以及AI性能评估。本研究为生态经济决策支持、可持续畜牧管理以及数字畜牧技术的未来商业化提供了基础。","Journal of Applied Business and Technology",62,{"impact":20,"substance":188,"depth":128,"authority":13,"freshness":13,"relevant":21,"comment":380},"论文提出融合物联网监测与物料流成本核算的山羊养殖数字平台，方法有创新但尚处原型验证阶段，产业影响有限。",[382],{"name":377,"url":374},[26,27,196,384,385],"农业物联网","畜牧养殖",{"code":387,"slug":388,"name":389,"path":390,"parent_code":391},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[393],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":395},{"candidates":396,"agricultural_evidence":47},[397,401,407,411,417],{"code":32,"score":188,"supported":47,"title":398,"abstract":399,"tags":400},[60],[58,59,60],[384],{"code":387,"score":19,"supported":47,"title":402,"abstract":404,"tags":406},[403],"goat",[405,403],"山羊",[],{"code":71,"score":64,"supported":47,"title":408,"abstract":409,"tags":410},[],[75,76],[196],{"code":391,"score":72,"supported":47,"title":412,"abstract":413,"tags":416},[],[414,415],"畜牧","livestock",[],{"code":45,"score":290,"supported":14,"title":418,"abstract":419,"tags":420},[],[],[26,27],[422,423],"GEMBALA 山羊养殖 物联网","MFCA 畜牧 环境监测","GEMBALA山羊养殖物联网-3347","10.35145\u002F6e5wnv18",{"doi":425,"openalex_id":427,"authors":428,"venue":377,"cited_by_count":106,"oa_url":374,"card":451,"direction":113,"ingested_from":114},"W7214075234",[429,431,433,435,437,439,442,445,447,449],{"name":430,"orcid":9},"Nicholas Renaldo",{"name":432,"orcid":9},"Sulaiman Musa",{"name":434,"orcid":9},"Jaswar Koto",{"name":436,"orcid":9},"Kristy Veronica",{"name":438,"orcid":9},"Umar Faruq",{"name":440,"orcid":441},"Yulvia Nora Marlim","https:\u002F\u002Forcid.org\u002F0009-0007-8624-5023",{"name":443,"orcid":444},"Rangga Rahmadian Yuliendi","https:\u002F\u002Forcid.org\u002F0000-0003-2288-3580",{"name":446,"orcid":9},"Wilda Susanti",{"name":448,"orcid":9},"Achmad Tavip Junaedi",{"name":450,"orcid":9},"Nabila Wahid",{"tldr":452,"method":453,"finding":454,"direction":113,"opportunity":455},"开发集成物联网监测与物料流成本核算的山羊养殖数字平台GEMBALA。","研发方法，在真实羊场部署物联网传感器并集成MFCA、排放分析与AI模块。","平台初步实现环境、经济与养殖信息整合，但传感器传输与数据一致性仍需验证。","可延伸研究物联网数据与MFCA实时耦合的算法优化及AI模块的长期性能验证。","2026-09-24T23:30:09.863218Z",{"id":458,"title":459,"url":460,"summary":461,"summary_zh":462,"content":9,"source_name":463,"source_url":460,"published_at":464,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":465,"score_detail":466,"sources":468,"tags":470,"primary_topic":473,"secondary_topics":477,"classification":483,"search_phrases":537,"slug":540,"view_count":106,"doi":541,"paper":542,"created_at":566},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",79,{"impact":188,"substance":246,"depth":188,"authority":17,"freshness":55,"relevant":21,"comment":467},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[469],{"name":463,"url":460},[27,196,82,471,472],"作物水分胁迫","精准灌溉",{"code":86,"slug":474,"name":475,"path":476,"parent_code":140},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[478,482],{"code":71,"slug":479,"name":480,"path":481,"parent_code":36},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":484},{"candidates":485,"agricultural_evidence":47},[486,491,496,500,506,510,517,524,531],{"code":86,"score":18,"supported":47,"title":487,"abstract":488,"tags":490},[474],[489,474],"灌溉",[472],{"code":71,"score":13,"supported":47,"title":492,"abstract":493,"tags":495},[],[279,494,75],"machine learning",[196],{"code":32,"score":55,"supported":47,"title":497,"abstract":498,"tags":499},[],[58,59,60],[],{"code":45,"score":64,"supported":47,"title":501,"abstract":502,"tags":505},[],[503,504],"数字孪生","digital twin",[27],{"code":79,"score":64,"supported":47,"title":507,"abstract":508,"tags":509},[],[82,83],[82],{"code":511,"score":72,"supported":47,"title":512,"abstract":513,"tags":516},"A2",[],[514,515],"小麦","wheat",[],{"code":518,"score":72,"supported":47,"title":519,"abstract":520,"tags":523},"A3",[],[521,522],"玉米","maize",[],{"code":525,"score":72,"supported":47,"title":526,"abstract":527,"tags":530},"A5",[],[528,529],"马铃薯","potato",[],{"code":532,"score":87,"supported":47,"title":533,"abstract":534,"tags":536},"B2",[],[535],"葡萄",[],[538,539],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":541,"openalex_id":543,"authors":544,"venue":463,"cited_by_count":106,"oa_url":460,"card":560,"direction":113,"ingested_from":114},"W7223088921",[545,548,551,554,557],{"name":546,"orcid":547},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":549,"orcid":550},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":552,"orcid":553},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":555,"orcid":556},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":558,"orcid":559},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":561,"method":562,"finding":563,"direction":564,"opportunity":565},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","农业遥感与作物表型","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","2026-10-22T11:20:24.526683Z"]