[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4045":3,"related-4045":99},{"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":9,"secondary_topics":31,"classification":32,"search_phrases":50,"slug":53,"view_count":54,"doi":55,"paper":56,"created_at":98},4045,"Controlled flexibility for agronomic and natural-science research embedded in Living Labs safeguarding scientific rigor under adaptive conditions","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105001","CONTEXT Living Labs (LLs) bring agricultural research into real-world settings where researchers and practitioners co-create, test and adapt solutions as conditions change. This flexibility is valuable, but creates a less visible problem: if reference conditions (comparators), sampling, measurements or research questions change during the process, the scientific claim may no longer be supported in the same way. OBJECTIVE We propose controlled flexibility as a framework for keeping evidence scientifically interpretable while preserving the adaptiveness and co-creation that make LLs useful. METHODS This Perspective focuses on quantitative, experimental and structured observational research embedded within LLs. It distinguishes two questions that are often conflated: did the LL generate useful learning or change, and can the resulting data support a specific scientific claim?, thus differentiating between “evaluation for impact” and “evidence for publication”. Systems thinking helps match the measurements to the claimed level of change. RESULTS AND CONCLUSIONS The framework separates a protected evidential core (research question and claim, analytical unit, comparator or case-selection logic, core indicators, sampling rules, metadata, analysis plan and model of change) from elements that may be adapted locally. Decision logs, deviation records, shared scientific rationale and data audits make adaptations visible. If a change affects the evidence supporting the claim, a new claim version is defined before further analysis. SIGNIFICANCE Controlled flexibility offers a practical way to remain adaptive without weakening scientific credibility. It helps LLs produce evidence that is transparent, proportionate to the claim and publishable, without sacrificing co-creation and situated learning.","背景 生活实验室（Living Labs, LLs）将农业研究带入现实场景，使研究者与实践者能够在条件变化时共同创造、测试和调整解决方案。这种灵活性很有价值，但也带来一个不易察觉的问题：如果参考条件（对照）、采样、测量或研究问题在过程中发生变化，科学主张可能不再以同样的方式得到支持。目标 我们提出“受控灵活性”框架，以在保持证据科学可解释性的同时，保留使生活实验室有用的适应性和共同创造。方法 本视角文章聚焦于嵌入生活实验室中的定量、实验和结构化观察研究。它区分了两个常被混淆的问题：生活实验室是否产生了有用的学习或变化，以及由此产生的数据能否支持某一特定科学主张？从而区分“影响评估”与“发表证据”。系统思维有助于使测量与所声称的变化层级相匹配。结果与结论 该框架将受保护的证据核心（研究问题与主张、分析单元、对照或案例选择逻辑、核心指标、采样规则、元数据、分析计划和变化模型）与可在本地调整的要素区分开来。决策日志、偏差记录、共享科学依据和数据审计使调整变得可见。如果某项变化影响了支持该主张的证据，则在进一步分析之前定义新的主张版本。意义 受控灵活性提供了一种实用方式，既能保持适应性，又不削弱科学可信度。它帮助生活实验室产生透明、与主张相称且可发表的证据，同时不牺牲共同创造和情境化学习。",null,"Agricultural Systems","2026-10-02T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,20,18,14,1,"提出受控灵活性框架以在Living Labs自适应条件下保障科研严谨性，方法学创新明确但属小众方法论议题，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","数据治理","农业科研","Living Labs","科研方法",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"unclassified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":48,"reason":49},[38,43],{"code":39,"score":21,"supported":14,"title":40,"abstract":41,"tags":42},"I1",[],[],[26],{"code":44,"score":21,"supported":14,"title":45,"abstract":46,"tags":47},"I6",[],[],[27],true,"缺少足够的主题证据",[51,52],"Living Labs 农业研究","controlled flexibility 科研","LivingLabs农业研究-4045",0,"10.1016\u002Fj.agsy.2026.105001",{"doi":55,"openalex_id":57,"authors":58,"venue":10,"cited_by_count":54,"oa_url":6,"card":91,"direction":95,"ingested_from":97},"W7216180303",[59,61,64,66,68,70,72,74,76,79,81,83,85,87,89],{"name":60,"orcid":9},"Beatrix Alsanius",{"name":62,"orcid":63},"Liselot Bourgeois","https:\u002F\u002Forcid.org\u002F0009-0008-8426-5706",{"name":65,"orcid":9},"Saskia Buysens",{"name":67,"orcid":9},"Thibault Cloet",{"name":69,"orcid":9},"Christina Elshoff",{"name":71,"orcid":9},"Lisa Heyman",{"name":73,"orcid":9},"Jérôme Lambion",{"name":75,"orcid":9},"Anne-Violette Lavoir",{"name":77,"orcid":78},"Mira Lehberger","https:\u002F\u002Forcid.org\u002F0000-0001-7756-9519",{"name":80,"orcid":9},"Anna Karin Rosberg",{"name":82,"orcid":9},"Denise Sewkaransing",{"name":84,"orcid":9},"An Van De Walle",{"name":86,"orcid":9},"Elien Van den Bossche",{"name":88,"orcid":9},"Hélène Védie",{"name":90,"orcid":9},"Gerben Messelink",{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"提出受控灵活性框架，使生活实验室在适应变化时仍能保持科研证据的可解释性与可信度。","基于系统思维，区分影响评估与发表证据，划分受保护证据核心与可本地调整要素。","通过决策日志、偏差记录和数据审计使调整可见，若影响证据则需先定义新主张版本。","数字乡村与农业信息化","可将受控灵活性框架落地为生活实验室数据治理工具，并检验其对跨站点证据合成的影响。","openalex","2026-10-03T23:30:05.152391Z",{"total":100,"page":21,"page_size":100,"items":101},6,[102,185,267,339,413,483],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":116,"tags":118,"primary_topic":122,"secondary_topics":128,"classification":129,"search_phrases":157,"slug":160,"view_count":54,"doi":161,"paper":162,"created_at":184},4663,"Sensor Data Transmission in Smart Agriculture: Challenges, Potentials, and Limitations in the Fourth Industrial Revolution","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsignals7050098","Smart agriculture is a key application domain of the Fourth Industrial Revolution (4IR), where cyber–physical systems, the Internet of Things (IoT), and data-driven analytics converge to improve productivity, sustainability, and resource efficiency. In this context, large volumes of sensor data collected in the field form the basis for automated monitoring, decision support, and adaptive control across agricultural processes. However, the value of such data depends not only on sensing technologies and connectivity, but also on how data are generated, transferred, processed, and interpreted throughout their lifecycle. This paper is organized in two parts. The first part reports a narrative review of the literature, based on a documented search protocol, and provides an overview of sensor data and sensor data transmission networks in smart agriculture. It examines the main challenges, potentials, and limitations associated with IoT-based field deployments in 4IR environments, and consolidates them into ten open problems. These problems are ordered from the device layer to the socio-economic layer and span physical robustness, device security, rural connectivity, aerial sensing, interoperability, labelled-data scarcity, explainability, federated access control, data ownership, and cost. The second part responds to these open problems with the conceptualization of a layered architecture and a set of design proposals, covering hierarchical edge security, offline-first synchronization, semantic interoperability, explainable decision support, and farmer-centric data governance, that are grounded in established techniques and standards but have not been implemented or empirically validated in this work; they are intended as a design blueprint and a starting point for future prototyping and evaluation rather than as measured results. The paper is accordingly positioned as a conceptual, perspective-oriented contribution: it states the procedure by which the open problems were derived from the reviewed works, it states what the proposed integration is claimed to add over existing smart-agriculture architectures, and it sets out an evaluation plan that fixes the metrics and the baselines against which each proposed component would have to be tested. Throughout both parts, particular attention is given to the role of data integrity and provenance as foundational elements for trustworthy agricultural data, enabling reliable analysis, auditability, and long-term reuse. By synthesizing current approaches, open issues, and conceptual solution patterns, this paper aims to support the design of resilient, transparent, and future-ready smart agriculture systems aligned with the principles of the 4IR.","智慧农业是第四次工业革命（4IR）的关键应用领域，信息物理系统、物联网（IoT）和数据驱动分析在此汇聚，以提升生产力、可持续性和资源效率。在此背景下，田间采集的大量传感器数据构成了农业过程自动化监测、决策支持和自适应控制的基础。然而，此类数据的价值不仅取决于传感技术和连接能力，还取决于数据在其生命周期中如何生成、传输、处理和解释。本文分为两部分。第一部分基于有据可查的检索方案，对文献进行叙述性综述，概述智慧农业中的传感器数据及传感器数据传输网络。它考察了4IR环境下基于物联网的田间部署所面临的主要挑战、潜力和局限，并将其归纳为十个开放性问题。这些问题从设备层到社会经济层依次排列，涵盖物理鲁棒性、设备安全、农村连接、空中传感、互操作性、标注数据稀缺、可解释性、联邦访问控制、数据所有权和成本。第二部分针对这些开放性问题，提出了分层架构的概念化和一组设计建议，涵盖分层边缘安全、离线优先同步、语义互操作性、可解释决策支持以及以农户为中心的数据治理。这些建议以已有技术和标准为基础，但尚未在本文中实现或经过实证验证；它们旨在作为设计蓝图和未来原型开发与评估的起点，而非实测结果。因此，本文定位为概念性、视角导向的贡献：它说明了开放性问题如何从所综述的工作中推导出来的过程，阐明了所提出的集成方案相对于现有智慧农业架构声称增加的贡献，并提出了一个评估计划，确定了每个所提组件必须接受测试的指标和基线。在两部分中，特别关注数据完整性和来源作为可信农业数据基础要素的作用，从而实现可靠分析、可审计性和长期复用。","Signals","2026-10-10T00:00:00Z",71,{"impact":112,"substance":19,"depth":113,"authority":114,"freshness":17,"relevant":21,"comment":115},15,17,13,"系统梳理智慧农业传感器数据传输的十大开放问题并提出分层架构蓝图，属概念性综述，有参考价值但未经实证验证。",[117],{"name":108,"url":105},[26,119,120,27,121],"农业人工智能","农业物联网","传感器网络",{"code":123,"slug":124,"name":125,"path":126,"parent_code":127},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":130,"locked":14,"version":34,"method":35,"evidence":131},"classified",{"candidates":132,"agricultural_evidence":48},[133,138,145,152],{"code":39,"score":112,"supported":48,"title":134,"abstract":136,"tags":137},[135],"smart agriculture",[26,135],[26],{"code":123,"score":13,"supported":48,"title":139,"abstract":140,"tags":144},[],[141,142,143],"物联网","internet of things","IoT",[120],{"code":146,"score":147,"supported":48,"title":148,"abstract":149,"tags":151},"I2",4,[],[150],"decision support",[119],{"code":44,"score":147,"supported":48,"title":153,"abstract":154,"tags":156},[],[155],"agricultural data",[27],[158,159],"智慧农业 传感器数据传输","农业物联网 数据完整性","智慧农业传感器数据传输-4663","10.3390\u002Fsignals7050098",{"doi":161,"openalex_id":163,"authors":164,"venue":108,"cited_by_count":54,"oa_url":105,"card":178,"direction":182,"ingested_from":97},"W7221365687",[165,167,170,173,175],{"name":166,"orcid":9},"Alvertos Ioannis Mourikis",{"name":168,"orcid":169},"Iosif Polenakis","https:\u002F\u002Forcid.org\u002F0000-0002-6427-5519",{"name":171,"orcid":172},"Christos Sarantidis","https:\u002F\u002Forcid.org\u002F0009-0003-9486-8483",{"name":174,"orcid":9},"Romanos Kalamatianos",{"name":176,"orcid":177},"Ioannis Karydis","https:\u002F\u002Forcid.org\u002F0000-0002-9470-2729",{"tldr":179,"method":180,"finding":181,"direction":182,"opportunity":183},"综述智慧农业传感器数据传输挑战，归纳十大开放问题并提出分层架构设计蓝图。","文献综述与概念架构设计，基于搜索协议梳理IoT部署问题。","从设备到社会经济层归纳十大开放问题，提出边缘安全、语义互操作等设计提案。","智慧农业 \u002F 农业物联网","所提分层架构尚未实现与实证，可开展原型验证并量化数据完整性、溯源与离线同步效果。","2026-10-11T23:30:08.094185Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":9,"source_name":191,"source_url":188,"published_at":192,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":193,"score_detail":194,"sources":198,"tags":200,"primary_topic":204,"secondary_topics":208,"classification":209,"search_phrases":226,"slug":229,"view_count":218,"doi":230,"paper":231,"created_at":266},3806,"Data Management in Agricultural Enterprises: A Framework for Data-Driven Decision Support","https:\u002F\u002Fdoi.org\u002F10.7160\u002Faol.2026.180305","The growing digitalisation of agriculture is leading to a rapid increase in the volume of data produced by agricultural enterprises; however, its practical use is often limited by fragmented data sources, varying data quality, insufficient metadata, and inconsistent storage and management practices. The aim of this article is to propose a practical framework for the systematic management of data in agricultural enterprises to support its long-term availability, reuse, and integration into decision-making processes. The proposed approach is based on four interlinked steps: identifying data needs and issues, creating and recording a data inventory, drawing up a Data Management Plan (DMP), and implementing it in practice. The framework links technical aspects of data management – such as data formats, metadata, quality, integrity, security, backup and archiving – with the definition of responsibilities for individual staff members and farm (agricultural enterprise) management. For practical implementation, two basic approaches to data storage are distinguished: file systems and data platforms or repositories. The more advanced platform-based approach enables the centralisation of heterogeneous data sources, the automation of their transfer and processing, and subsequently the integration of the acquired data with analytical tools and decision-support systems. The proposed framework thus represents a path from the isolated collection of operational data to its systematic management and utilisation as a long-term information source for both operational and strategic decision-making within an agricultural enterprise.","农业日益数字化，导致农业企业产生的数据量迅速增加；然而，其实际应用往往受限于数据来源分散、数据质量参差不齐、元数据不足以及存储和管理实践不一致。本文旨在提出一个实用框架，用于农业企业数据的系统化管理，以支持其长期可用性、再利用以及融入决策过程。所提出的方法基于四个相互关联的步骤：识别数据需求与问题、创建并记录数据清单、制定数据管理计划（Data Management Plan, DMP），以及在实践中实施该计划。该框架将数据管理的技术层面——如数据格式、元数据、质量、完整性、安全性、备份与归档——与个别工作人员及农场（农业企业）管理层的责任界定联系起来。在实践实施方面，区分了两种基本的数据存储方式：文件系统和数据平台或存储库。更为先进的基于平台的方式能够实现异构数据源的集中化、其传输与处理的自动化，并随后将所获取的数据与分析工具及决策支持系统相集成。因此，所提出的框架代表了一条从孤立收集运营数据到系统化管理并将其作为农业企业内部运营和战略决策长期信息源加以利用的路径。","Agris on-line Papers in Economics and Informatics","2026-09-28T00:00:00Z",67,{"impact":195,"substance":19,"depth":196,"authority":114,"freshness":17,"relevant":21,"comment":197},12,16,"核心期刊论文，提出农业企业数据管理四步框架，方法清晰但偏理论，对农业数据治理实践有参考价值。",[199],{"name":191,"url":188},[201,26,27,202,203],"数字农业","决策支持","农业数据管理",{"code":146,"slug":205,"name":206,"path":207,"parent_code":127},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":130,"locked":14,"version":34,"method":35,"evidence":210},{"candidates":211,"agricultural_evidence":48},[212,217,222],{"code":146,"score":213,"supported":48,"title":214,"abstract":215,"tags":216},9,[150],[],[202],{"code":39,"score":218,"supported":14,"title":219,"abstract":220,"tags":221},2,[],[],[201,26],{"code":44,"score":21,"supported":14,"title":223,"abstract":224,"tags":225},[],[],[27],[227,228],"农业企业 数据管理 框架","农业数据管理计划 DMP","农业企业数据管理框架-3806","10.7160\u002Faol.2026.180305",{"doi":230,"openalex_id":232,"authors":233,"venue":191,"cited_by_count":54,"oa_url":188,"card":260,"direction":265,"ingested_from":97},"W7214583099",[234,237,240,243,246,248,251,253,256,258],{"name":235,"orcid":236},"Michal Stočes","https:\u002F\u002Forcid.org\u002F0000-0001-7128-1071",{"name":238,"orcid":239},"Jan Jarolímek","https:\u002F\u002Forcid.org\u002F0000-0001-7194-3055",{"name":241,"orcid":242},"Irena Benešová","https:\u002F\u002Forcid.org\u002F0000-0002-9381-063X",{"name":244,"orcid":245},"Pavel Šimek","https:\u002F\u002Forcid.org\u002F0000-0001-9244-1476",{"name":247,"orcid":9},"Zdeňka Žáková Kroupová",{"name":249,"orcid":250},"Ludmila Pánková","https:\u002F\u002Forcid.org\u002F0000-0001-8861-4733",{"name":252,"orcid":9},"Jiří Vaněk",{"name":254,"orcid":255},"Eva Kánská","https:\u002F\u002Forcid.org\u002F0000-0001-5249-6405",{"name":257,"orcid":9},"Martin Havránek",{"name":259,"orcid":9},"Anderle Michael",{"tldr":261,"method":262,"finding":263,"direction":95,"opportunity":264},"提出农业企业数据管理框架，实现数据从孤立采集到系统化决策支持。","四步框架：识别需求、数据清单、管理计划、实施；区分文件系统与数据平台。","平台化存储可集中异构数据、自动化处理并集成决策支持系统。","可研究农业数据管理成熟度评估模型及平台落地对决策绩效的实证影响。","农业人工智能与决策模型","2026-09-29T23:31:01.874610Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":272,"content":9,"source_name":273,"source_url":270,"published_at":192,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":274,"score_detail":275,"sources":279,"tags":281,"primary_topic":284,"secondary_topics":285,"classification":286,"search_phrases":317,"slug":320,"view_count":218,"doi":321,"paper":322,"created_at":338},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",84,{"impact":276,"substance":277,"depth":19,"authority":114,"freshness":17,"relevant":21,"comment":278},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。",[280],{"name":273,"url":270},[201,26,282,27,283],"农业大数据","印度农业",{"code":123,"slug":124,"name":125,"path":126,"parent_code":127},[],{"status":130,"locked":14,"version":34,"method":35,"evidence":287},{"candidates":288,"agricultural_evidence":48},[289,297,301,306,312],{"code":39,"score":290,"supported":48,"title":291,"abstract":293,"tags":296},19,[292],"precision agriculture",[294,201,292,295],"精准农业","digital agriculture",[201,26],{"code":123,"score":100,"supported":48,"title":298,"abstract":299,"tags":300},[],[141,143],[],{"code":44,"score":302,"supported":48,"title":303,"abstract":304,"tags":305},5,[],[155],[282,27],{"code":146,"score":307,"supported":48,"title":308,"abstract":309,"tags":311},3,[],[310],"机器学习",[],{"code":313,"score":21,"supported":14,"title":314,"abstract":315,"tags":316},"J3",[],[],[283],[318,319],"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","10.9734\u002Fajaar\u002F2026\u002Fv26i10763",{"doi":321,"openalex_id":323,"authors":324,"venue":273,"cited_by_count":54,"oa_url":270,"card":333,"direction":182,"ingested_from":97},"W7214617849",[325,328,331],{"name":326,"orcid":327},"Suresh Kumar Sharma","https:\u002F\u002Forcid.org\u002F0000-0002-5054-6170",{"name":329,"orcid":330},"Parul Dhull","https:\u002F\u002Forcid.org\u002F0009-0005-8368-3058",{"name":332,"orcid":9},"Pratibha Manohar",{"tldr":334,"method":335,"finding":336,"direction":95,"opportunity":337},"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":340,"title":341,"url":342,"summary":343,"summary_zh":344,"content":9,"source_name":345,"source_url":342,"published_at":346,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":347,"score_detail":348,"sources":350,"tags":352,"primary_topic":354,"secondary_topics":355,"classification":356,"search_phrases":386,"slug":389,"view_count":147,"doi":390,"paper":391,"created_at":412},3621,"Beyond Precision Agriculture: AI and Data-driven Information Systems as Catalysts for Sustainable Agribusiness Transformation","https:\u002F\u002Fdoi.org\u002F10.56557\u002Fjgembr\u002F2026\u002Fv18i311156","The dominant narrative in digital agriculture centres on discrete technologies, including sensors, unmanned aerial vehicles, global positioning systems, remote sensing, and machine learning, evaluated primarily for their capacity to increase yields and improve input-use efficiency. This narrows the analytical lens to the farm gate and understates a more consequential shift: individual technologies generate value for agribusiness only when information systems capture, integrate, and route the data they produce into real operational and strategic decisions. This review critically examines the AI- and data-driven agribusiness transformation literature through a single organising pathway: data, analytics, artificial intelligence, information systems, decisions, sustainable agribusiness outcomes; it asks whether the field's technical achievements have matched comparable progress in systems integration and governance. A transparent narrative search and appraisal protocol synthesised evidence across ten thematic domains: agricultural big-data integration, predictive and prescriptive analytics, farm management information systems (FMIS), AI-enabled supply-chain analytics, climate-risk adaptation, sustainability and ESG analytics, data governance and cybersecurity, digital infrastructure and adoption, circular agriculture, and decision-support systems. The evidence shows that sensing and algorithmic capabilities have matured faster than the organisational and governance capacity required to convert those capabilities into adopted agribusiness decisions. Data-governance barriers, rather than technological ones, now dominate constraints on information-system integration in the literature published since 2023. Research, investment, and policy attention should therefore shift further toward the interoperability, governance, and organisational design layers of digital agriculture, rather than continuing to concentrate primarily on sensor accuracy or model performance.","数字农业的主流叙事聚焦于各类离散技术，包括传感器、无人机、全球定位系统、遥感和机器学习，并主要依据其提高产量和改善投入品利用效率的能力加以评价。这种视角将分析视野收窄至农场大门，低估了一个更具深远意义的转变：只有当信息系统能够捕获、整合单个技术所产生的数据，并将其导入实际运营和战略决策时，这些技术才能为农业企业创造价值。本综述通过一条单一的组织路径——数据、分析、人工智能、信息系统、决策、可持续农业企业成果——批判性地审视人工智能和数据驱动的农业企业转型文献，并追问该领域的技术成就是否与系统集成和治理方面的相应进展相匹配。一项透明的叙述性检索与评价方案综合了十个主题领域的证据：农业大数据集成、预测性与规范性分析、农场管理信息系统(FMIS)、人工智能赋能的供应链分析、气候风险适应、可持续性与ESG分析、数据治理与网络安全、数字基础设施与采用、循环农业以及决策支持系统。证据表明，感知和算法能力的成熟速度快于将这些能力转化为已被采用的农业企业决策所需的组织和治理能力。在2023年以来发表的文献中，数据治理障碍而非技术障碍，已成为信息系统集成的主要制约因素。因此，研究、投资和政策关注应进一步转向数字农业的互操作性、治理和组织设计层面，而非继续主要集中于传感器精度或模型性能。","Journal of Global Economics Management and Business Research","2026-09-26T00:00:00Z",80,{"impact":19,"substance":276,"depth":19,"authority":114,"freshness":213,"relevant":21,"comment":349},"该综述指出数据治理与系统集成而非传感技术本身才是数字农业落地的关键瓶颈，观点有实质增量，但属学术综述、产业影响偏间接。",[351],{"name":345,"url":342},[26,119,282,27,353],"农业信息体系",{"code":146,"slug":205,"name":206,"path":207,"parent_code":127},[],{"status":130,"locked":14,"version":34,"method":35,"evidence":357},{"candidates":358,"agricultural_evidence":48},[359,363,369,376,380],{"code":39,"score":112,"supported":48,"title":360,"abstract":361,"tags":362},[292],[201,295],[26],{"code":146,"score":13,"supported":48,"title":364,"abstract":365,"tags":368},[],[310,366,367],"machine learning","artificial intelligence",[119],{"code":370,"score":100,"supported":48,"title":371,"abstract":372,"tags":375},"I3",[],[373,374],"遥感","remote sensing",[],{"code":44,"score":302,"supported":48,"title":377,"abstract":378,"tags":379},[],[282],[282,27],{"code":381,"score":307,"supported":48,"title":382,"abstract":383,"tags":385},"H8",[],[384],"可持续农业",[],[387,388],"农业信息体系 数据治理","农场管理信息系统 FMIS","农业信息体系数据治理-3621","10.56557\u002Fjgembr\u002F2026\u002Fv18i311156",{"doi":390,"openalex_id":392,"authors":393,"venue":345,"cited_by_count":54,"oa_url":342,"card":406,"direction":411,"ingested_from":97},"W7214471201",[394,396,398,400,402,404],{"name":395,"orcid":9},"Timilehin Fawibe",{"name":397,"orcid":9},"Gbenga Samuel Ernest",{"name":399,"orcid":9},"Joshua Emmanuel",{"name":401,"orcid":9},"Simon Jacob",{"name":403,"orcid":9},"Olawale Akinleye",{"name":405,"orcid":9},"Tosin Olorunwo",{"tldr":407,"method":408,"finding":409,"direction":95,"opportunity":410},"综述指出农业数字化瓶颈已从技术转向数据治理与系统集成，呼吁研究重心转向互操作与组织设计。","采用透明叙事检索与评价协议，综合十大主题领域的AI与数据驱动农业文献。","2023年后数据治理障碍而非技术障碍，成为农业信息系统集成的主要制约。","可研究农业数据互操作标准、治理机制与组织采纳路径如何影响可持续农业商业决策。","农业遥感与作物表型","2026-09-27T23:30:46.092194Z",{"id":414,"title":415,"url":416,"summary":417,"summary_zh":418,"content":9,"source_name":419,"source_url":416,"published_at":420,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":421,"score_detail":422,"sources":425,"tags":427,"primary_topic":431,"secondary_topics":432,"classification":433,"search_phrases":458,"slug":461,"view_count":307,"doi":462,"paper":463,"created_at":482},2292,"A governance-first framework integrates artificial intelligence and blockchain to advance sustainable and inclusive horticultural value chains","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1894057","Horticultural value chains face persistent agronomic challenges including production inefficiencies, postharvest losses, poor resource management, and limited supply-chain transparency, particularly in smallholder-dominated systems across the Global South. These constraints undermine agricultural productivity, market access, and the long-term sustainability of horticultural farming. Despite growing research interest in digital agriculture, artificial intelligence (AI) and blockchain technology are increasingly explored together, including in emerging integrated AI-blockchain-IoT architectures for agricultural traceability. However, these existing integrations remain predominantly technology-centric, prioritizing traceability, automation, and data verification, without an explicit governance, equity, or data-sovereignty architecture designed specifically for smallholder-dominated horticultural production and distribution systems. This study proposes a comprehensive conceptual framework that integrates AI’s predictive and decision-support capabilities with blockchain’s data integrity, traceability, and automation features to enhance sustainability, inclusivity, and governance in horticultural value chains. Drawing on an integrative review of peer-reviewed sources identified through Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar, and grounded in the Technology-Organisation-Environment (TOE) framework, Sociotechnical Systems Theory, and Sustainable Digital Transformation Theory, the study addresses the agronomic, governance, and equity dimensions of digital transformation in horticulture. The framework is structured around five interlinked operational layers, data collection, AI-driven analytics and decision-making, blockchain validation and traceability, smart contracts and decision support, and user interface and stakeholder feedback, all operating within a governance architecture and ethical design layer that functions as a structural precondition for the framework rather than a downstream feature. This governing layer embeds algorithmic transparency, data sovereignty through community-governed data cooperatives, and inclusive digital access for smallholder farmers as non-negotiable design principles. The framework explicitly aligns with Sustainable Development Goals 2, 9, 10, 12, 13, and 16, and includes a three-phase empirical validation pathway comprising expert Delphi elicitation, simulation modeling, and pilot implementation in horticultural cooperatives. The integration of AI and blockchain within an institutionally governed, equity-oriented architecture represents a paradigm shift from technology-first to governance-first digital transformation in horticulture, providing a theoretically robust and practically actionable foundation for future research, systems design, and policy development, with direct application to resource-constrained smallholder systems in sub-Saharan Africa and comparable Global South contexts.","园艺价值链面临持续存在的农艺挑战，包括生产效率低下、采后损失、资源管理不善以及供应链透明度有限，在全球南方以小农为主的体系中尤为突出。这些制约因素削弱了农业生产力、市场准入以及园艺种植的长期可持续性。尽管数字农业的研究兴趣日益增长，人工智能（AI）与区块链技术正越来越多地被共同探索，包括用于农业溯源的新兴AI-区块链-物联网集成架构。然而，现有这些集成仍以技术为中心，优先考虑溯源、自动化和数据验证，缺乏专门针对以小农为主的园艺生产和分销体系而设计的明确的治理、公平或数据主权架构。本研究提出一个综合性概念框架，将AI的预测与决策支持能力与区块链的数据完整性、溯源性和自动化特征相结合，以增强园艺价值链的可持续性、包容性和治理水平。基于对通过Scopus、Web of Science、IEEE Xplore、ScienceDirect和Google Scholar识别的同行评审文献的整合性综述，并以技术-组织-环境（TOE）框架、社会技术系统理论和可持续数字化转型理论为基础，本研究探讨了园艺数字转型中的农艺、治理和公平维度。该框架围绕五个相互关联的操作层构建：数据采集、AI驱动的分析与决策、区块链验证与溯源、智能合约与决策支持，以及用户界面与利益相关者反馈，所有这些都在治理架构与伦理设计层内运作，该层作为框架的结构性前提而非下游功能发挥作用。这一治理层将算法透明度、通过社区治理的数据合作社实现的数据主权，以及小农的包容性数字接入作为不可协商的设计原则。该框架明确与可持续发展目标2、9、10、12、13和16相一致，并包含一个三阶段实证验证路径，包括专家德尔菲征询、仿真建模以及在","Frontiers in Sustainable Food Systems","2026-09-11T00:00:00Z",78,{"impact":19,"substance":276,"depth":19,"authority":114,"freshness":423,"relevant":21,"comment":424},7,"该研究提出治理优先的AI-区块链融合框架，聚焦小农园艺价值链的可持续与包容性，方法新颖、理论扎实，对数字农业政策设计有参考价值，但属概念框架尚未实证，时效性一般。",[426],{"name":419,"url":416},[26,119,27,428,429,430],"小农户","农业区块链","园艺价值链",{"code":146,"slug":205,"name":206,"path":207,"parent_code":127},[],{"status":130,"locked":14,"version":34,"method":35,"evidence":434},{"candidates":435,"agricultural_evidence":48},[436,440,444,448,454],{"code":146,"score":112,"supported":48,"title":437,"abstract":438,"tags":439},[367],[367,150],[119],{"code":39,"score":423,"supported":48,"title":441,"abstract":442,"tags":443},[],[201,295],[26],{"code":123,"score":100,"supported":48,"title":445,"abstract":446,"tags":447},[],[141,143],[],{"code":449,"score":147,"supported":48,"title":450,"abstract":451,"tags":453},"G2",[],[452],"smallholder",[428],{"code":44,"score":21,"supported":14,"title":455,"abstract":456,"tags":457},[],[],[27],[459,460],"农业人工智能 农业区块链 园艺价值链 数据治理","农业人工智能 农业区块链","农业人工智能农业区块链园艺价值链数据治理-2292","10.3389\u002Ffsufs.2026.1894057",{"doi":462,"openalex_id":464,"authors":465,"venue":419,"cited_by_count":54,"oa_url":416,"card":477,"direction":182,"ingested_from":97},"W7212272522",[466,468,471,474],{"name":467,"orcid":9},"Olaoluwa Olarewaju",{"name":469,"orcid":470},"Tobi Fadiji","https:\u002F\u002Forcid.org\u002F0000-0001-5525-4495",{"name":472,"orcid":473},"Olaniyi Amos Fawole","https:\u002F\u002Forcid.org\u002F0000-0001-5591-4633",{"name":475,"orcid":476},"Lembe Samukelo Magwaza","https:\u002F\u002Forcid.org\u002F0000-0001-9809-2254",{"tldr":478,"method":479,"finding":480,"direction":95,"opportunity":481},"提出治理优先框架，整合AI与区块链提升园艺价值链可持续性与包容性。","整合性文献综述，基于TOE、社会技术系统与可持续数字化转型理论。","治理架构应作为结构性前提，嵌入算法透明、数据主权与包容性数字接入。","可实证检验社区数据合作社与治理层对小型农户采纳和公平收益的影响。","2026-09-13T23:30:07.102172Z",{"id":484,"title":485,"url":486,"summary":487,"summary_zh":9,"content":9,"source_name":488,"source_url":9,"published_at":489,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":490,"score_detail":491,"sources":494,"tags":496,"primary_topic":9,"secondary_topics":500,"classification":501,"search_phrases":521,"slug":524,"view_count":147,"doi":9,"paper":525,"created_at":533},2108,"《AI in Agriculture: Transforming the Food System from Data to Decisions to Action》 美国密西西比州立CAST论文","https:\u002F\u002Fpublications.cast-science.org\u002FCAST\u002Fen\u002Farticle\u002Fview\u002F245","密西西比州立大学农业自主研究所所长Thomasson发表CAST评论,系统论述AI正成为美国食物系统的基础技术,深度嵌入田间装备、生产系统、食物加工、供应链与自然资源管理。引入\"物理AI\"概念——能在生物变异性与环境不确定性下感知、推理、行动的AI系统,农业是其最严苛的试验场。生成式AI被视为让先进分析民主化的力量。约束AI负责任采用的核心不是技术,而是制度——数据质量、田间验证、推荐透明度、网络安全与互操作性。","Council for Agricultural Science and Technology (CAST)","2026-09-08T00:00:00Z",86,{"impact":492,"substance":276,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":493},24,"CAST权威评论系统提出物理AI概念并指出制度而非技术是负责任采用的核心约束，观点新颖、信息增量大，值得进入每日精选。",[495],{"name":488,"url":486},[26,119,27,497,498,499],"食物系统","物理AI","生成式AI",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":502},{"candidates":503,"agricultural_evidence":48,"reason":49},[504,509,513,517],{"code":505,"score":21,"supported":14,"title":506,"abstract":507,"tags":508},"E2",[],[],[497],{"code":39,"score":21,"supported":14,"title":510,"abstract":511,"tags":512},[],[],[26],{"code":146,"score":21,"supported":14,"title":514,"abstract":515,"tags":516},[],[],[119],{"code":44,"score":21,"supported":14,"title":518,"abstract":519,"tags":520},[],[],[27],[522,523],"农业人工智能 数据治理 智慧农业 食物系统","农业人工智能 数据治理","农业人工智能数据治理智慧农业食物系统-2108",{"doi":9,"openalex_id":9,"authors":526,"venue":9,"cited_by_count":54,"oa_url":9,"card":527,"direction":265,"ingested_from":532},[],{"tldr":528,"method":529,"finding":530,"direction":265,"opportunity":531},"系统论述AI正成为美国食物系统基础技术，提出物理AI概念并强调制度约束。","CAST评论文章，基于田间装备、供应链等应用场景的系统分析。","约束AI负责任采用的核心是制度而非技术，如数据质量与互操作性。","物理AI在生物变异与环境不确定性下的感知推理行动机制，及制度适配研究。","agent","2026-09-11T00:04:22.740798Z"]