[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"daily-2026-09-30":3},{"date":4,"title":5,"highlights":6,"content":12,"items":13},"2026-09-30","农业农村日报：AI与精准农业赋能气候智慧型农食系统",[7,8,9,10,11],"中国退耕还林工程对作物生产的影响获实证评估，为退耕政策与粮食安全协同提供依据。","非洲土壤信息系统呼吁超越预测，构建标准化、可互操作的大陆级AI土壤数据体系。","印度精准农业大数据治理框架梳理行动者与挑战，为发展中国家数据策展提供路径。","美国提出下一代农场观测策略，以应对气候压力、保障可持续粮食生产与出口竞争力。","多篇研究聚焦干旱预警、植被恢复力与氮素创新，推动农业增产与温室气体强度脱钩。","本期汇编12篇前沿论文，覆盖退耕还林、AI土壤信息、精准农业大数据、农场观测、森林动态、种子带、河口湿地、自动化采纳、干旱预测、苜蓿修复、旱地小麦高粱及氮素创新，聚焦气候智慧型农业与数字化治理。\n\n---\n*本日报内容整理自公开来源，学术论文元数据来自 OpenAlex 等开放接口；外文资料已译为中文，翻译与摘要仅供参考；引用与决策请以官方原文与正式出版物为准。*",[14,109,216,296,372,443,519,581,644,712,813,921,992,1045,1184],{"id":15,"title":16,"url":17,"summary":18,"summary_zh":19,"content":20,"source_name":21,"source_url":17,"published_at":22,"category":23,"cover_url":20,"hotness":24,"is_selected":25,"score":26,"score_detail":27,"sources":35,"tags":37,"primary_topic":20,"secondary_topics":43,"classification":44,"search_phrases":83,"slug":86,"view_count":87,"doi":88,"paper":89,"created_at":108},3799,"How land retirement programs affect crop production: Evidence from China's Grain for Green Program","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fajae.70097","Abstract This paper evaluates the impact of one of the world's largest land conservation programs—China's Grain for Green Program—on crop production, and examines whether land retirement policies can balance ecological goals with food security objectives. By combining detailed county‐level crop production data with remote sensing data, our difference‐in‐differences estimates suggest that the program significantly reduced total cropland area but increased total crop production. This unexpected increase in production can be explained by the program's significant increase in labor input, fertilizer use, and multiple cropping on the remaining cropland. More importantly, we find that the program improved the local microclimate and substantially alleviated the damage of weather stress on crop production. Our findings suggest that, under certain institutional conditions, it is possible to adopt land‐use policy to protect the ecosystem without compromising food security.","摘要 本文评估了世界上最大的土地保护项目之一——中国退耕还林工程——对作物生产的影响，并考察休耕政策能否在生态目标与粮食安全目标之间取得平衡。通过将详细的县级作物生产数据与遥感数据相结合，我们的双重差分估计表明，该工程显著减少了耕地总面积，但提高了作物总产量。这一出乎意料的产量增长可以由该工程显著增加了剩余耕地上的劳动投入、化肥使用和复种来解释。更重要的是，我们发现该工程改善了当地微气候，并大幅缓解了天气胁迫对作物生产的损害。我们的研究结果表明，在某些制度条件下，可以采取土地利用政策来保护生态系统而不损害粮食安全。",null,"American Journal of Agricultural Economics","2026-09-27T00:00:00Z","论文",10,true,91,{"impact":28,"substance":29,"depth":30,"authority":31,"freshness":32,"relevant":33,"comment":34},26,23,19,15,8,1,"顶刊论文，用县级遥感与双重差分证据揭示退耕还林减地不减产、微气候改善缓解气象胁迫，对生态与粮食安全协同政策有重要参考价值。",[36],{"name":21,"url":17},[38,39,40,41,42],"粮食安全","耕地保护","遥感监测","生态补偿","退耕还林",[],{"status":45,"locked":46,"version":47,"method":48,"evidence":49},"needs_review",false,"2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":25,"reason":82},[51,58,65,72,77],{"code":52,"score":53,"supported":25,"title":54,"abstract":55,"tags":57},"A8",7,[],[38,56],"food security",[38],{"code":59,"score":53,"supported":25,"title":60,"abstract":61,"tags":64},"I3",[],[62,63],"遥感","remote sensing",[40],{"code":66,"score":67,"supported":25,"title":68,"abstract":69,"tags":71},"F3",3,[],[70],"fertilizer",[],{"code":73,"score":33,"supported":46,"title":74,"abstract":75,"tags":76},"H1",[],[],[39],{"code":78,"score":33,"supported":46,"title":79,"abstract":80,"tags":81},"H6",[],[],[41],"多个产业证据相当，需要确认主归属",[84,85],"中国 退耕还林 粮食安全","Grain for Green 遥感 作物产量","中国退耕还林粮食安全-3799",12,"10.1002\u002Fajae.70097",{"doi":88,"openalex_id":90,"authors":91,"venue":21,"cited_by_count":99,"oa_url":100,"card":101,"direction":105,"ingested_from":107},"W7214551215",[92,94,97],{"name":93,"orcid":20},"Xiaoxue Zong",{"name":95,"orcid":96},"Kaixing Huang","https:\u002F\u002Forcid.org\u002F0000-0002-7761-4153",{"name":98,"orcid":20},"Xi Ji",0,"https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fajae.70097",{"tldr":102,"method":103,"finding":104,"direction":105,"opportunity":106},"评估中国退耕还林工程对作物生产的影响，发现耕地减少但产量反增。","县级作物产量数据与遥感数据结合，采用双重差分法。","退耕还林减少耕地却提高产量，因增加劳动、化肥、复种并改善微气候。","农业遥感与作物表型","可探究退耕还林在不同区域和作物类型下的异质性效应及微气候机制。","openalex","2026-09-29T23:30:30.512194Z",{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":20,"source_name":115,"source_url":112,"published_at":116,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":117,"score_detail":118,"sources":122,"tags":124,"primary_topic":130,"secondary_topics":136,"classification":143,"search_phrases":186,"slug":189,"view_count":190,"doi":191,"paper":192,"created_at":215},3800,"BEYOND PREDICTION: A RESEARCH AGENDA FOR ADVANCED AI IN AFRICAN SOIL INFORMATION SYSTEMS","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx5xn6m","Digital soil mapping has produced the first continent-wide baseline estimates of soil properties in Africa, yet existing soil information systems remain fragmented, poorly standardized, and largely confined to static offline spatial prediction. Their ability to capture near-real-time soil conditions or provide timely, field-level decision support is therefore limited. This paper argues that the next step is not simply applying artificial intelligence to improve mapping accuracy, but adopting Advanced AI as the foundation for adaptive Soil Digital Twins that continuously integrate new observations and support evidence-based management. It proposes a ten-pathway research agenda structured around four interconnected pillars: (1) Unlocking legacy archives using Natural Language Processing and Computer Vision to convert unstructured historical records into machine-actionable, AI-ready data; (2) Engineering dynamic systems through Online Learning, Generative AI, and Physics-Informed Neural Networks to continuously assimilate streaming telemetry; (3) Delivering prescriptive intelligence using multi-objective recommendation engines, human-centered Explainable AI (XAI), and Human-in-the-Loop expert validation; and (4) Enforcing sovereign governance via Federated Learning, FAIR and CARE-aligned autonomous stewardship. To operationalize this vision, it calls for a Pan-African AI for Soil Health Consortium to establish sovereign compute hubs, secure sustainable financing, and cultivate a home-grown talent pipeline. This agenda provides a strategic blueprint for transforming fragmented soil observations into a dynamic, trusted public infrastructure that addresses land degradation and food insecurity, while supporting climate resilience and sustainable agriculture across the continent. Keywords: Artificial Intelligence (AI), Soil Digital Twins, soil information systems (SIS), data sovereignty, Federated Learning, Natural Language Processing (NLP), Explainable AI (XAI), Africa.","数字土壤制图已产生了非洲首个全大陆尺度的土壤属性基线估算，但现有土壤信息系统仍然碎片化、标准化程度低，且在很大程度上局限于静态的离线空间预测。因此，这些系统捕捉近实时土壤状况或提供及时、田块尺度决策支持的能力十分有限。本文认为，下一步不仅仅是应用人工智能来提高制图精度，而是采用高级人工智能作为自适应土壤数字孪生的基础，以持续整合新的观测数据并支持循证管理。本文提出了一个由十条路径组成的研究议程，围绕四个相互关联的支柱构建：(1) 利用自然语言处理（NLP）和计算机视觉解锁历史档案，将非结构化历史记录转化为机器可操作、人工智能就绪的数据；(2) 通过在线学习、生成式人工智能和物理信息神经网络（PINNs）工程化动态系统，以持续同化流式遥测数据；(3) 利用多目标推荐引擎、以人为中心的可解释人工智能（XAI）和人在回路专家验证，提供规范性智能；(4) 通过联邦学习、符合FAIR和CARE原则的自主治理，实施主权治理。为将这一愿景付诸实践，本文呼吁建立泛非土壤健康人工智能联盟，以建立主权计算中心、确保可持续融资，并培养本土人才梯队。该议程提供了一份战略蓝图，旨在将碎片化的土壤观测转化为动态、可信的公共基础设施，以应对土地退化和粮食不安全问题，同时支持整个非洲大陆的气候韧性和可持续农业。关键词：人工智能（AI）、土壤数字孪生、土壤信息系统（SIS）、数据主权、联邦学习、自然语言处理（NLP）、可解释人工智能（XAI）、非洲。","OpenAlex","2026-09-28T00:00:00Z",85,{"impact":119,"substance":29,"depth":30,"authority":120,"freshness":32,"relevant":33,"comment":121},22,13,"提出以先进AI构建非洲土壤数字孪生的十路径研究议程，方法新颖、战略性强，对农业信息化与智慧农业有参考价值。",[123],{"name":115,"url":112},[125,126,127,128,129],"农业人工智能","非洲农业","土壤数字孪生","土壤信息系统","数据主权",{"code":131,"slug":132,"name":133,"path":134,"parent_code":135},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[137],{"code":138,"slug":139,"name":140,"path":141,"parent_code":142},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":144,"locked":46,"version":47,"method":48,"evidence":145},"classified",{"candidates":146,"agricultural_evidence":25},[147,153,161,169,175,181],{"code":131,"score":148,"supported":25,"title":149,"abstract":150,"tags":152},14,[132],[151,132],"土壤",[],{"code":138,"score":24,"supported":25,"title":154,"abstract":155,"tags":160},[],[156,157,158,159],"计算机视觉","artificial intelligence","computer vision","decision support",[125],{"code":162,"score":163,"supported":25,"title":164,"abstract":165,"tags":168},"H8",6,[],[166,167],"可持续农业","sustainable agriculture",[],{"code":170,"score":67,"supported":25,"title":171,"abstract":172,"tags":174},"H7",[],[173],"climate resilience",[],{"code":176,"score":67,"supported":25,"title":177,"abstract":178,"tags":180},"I1",[],[179],"数字孪生",[],{"code":182,"score":33,"supported":46,"title":183,"abstract":184,"tags":185},"J3",[],[],[126],[187,188],"非洲 土壤信息系统 人工智能","土壤数字孪生 联邦学习","非洲土壤信息系统人工智能-3800",2,"10.31223\u002Fx5xn6m",{"doi":191,"openalex_id":193,"authors":194,"venue":20,"cited_by_count":99,"oa_url":112,"card":209,"direction":213,"ingested_from":107},"W7214598474",[195,198,200,202,204,207],{"name":196,"orcid":197},"Valentine Asong Tellen","https:\u002F\u002Forcid.org\u002F0000-0001-8513-788X",{"name":199,"orcid":20},"Chrow Khurshid",{"name":201,"orcid":20},"Luca Brocca",{"name":203,"orcid":20},"Therese Nkwatoh",{"name":205,"orcid":206},"Kwame Adjei","https:\u002F\u002Forcid.org\u002F0009-0006-8492-7111",{"name":208,"orcid":20},"Godswill Asongwe",{"tldr":210,"method":211,"finding":212,"direction":213,"opportunity":214},"提出非洲土壤信息系统应转向高级AI驱动的自适应土壤数字孪生，并给出十条研究路径。","基于NLP、在线学习、生成式AI、物理信息神经网络和联邦学习等构建数字孪生框架。","需建立泛非AI土壤健康联盟，将碎片化土壤观测转化为动态可信的公共基础设施。","数字乡村与农业信息化","可探索联邦学习与物理信息神经网络在土壤数字孪生中的落地，兼顾数据主权与实时决策。","2026-09-29T23:30:39.874981Z",{"id":217,"title":218,"url":219,"summary":220,"summary_zh":221,"content":20,"source_name":222,"source_url":219,"published_at":116,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":223,"score_detail":224,"sources":227,"tags":229,"primary_topic":235,"secondary_topics":240,"classification":241,"search_phrases":273,"slug":276,"view_count":190,"doi":277,"paper":278,"created_at":295},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":119,"substance":29,"depth":225,"authority":120,"freshness":32,"relevant":33,"comment":226},18,"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。",[228],{"name":222,"url":219},[230,231,232,233,234],"数字农业","智慧农业","农业大数据","数据治理","印度农业",{"code":236,"slug":237,"name":238,"path":239,"parent_code":142},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[],{"status":144,"locked":46,"version":47,"method":48,"evidence":242},{"candidates":243,"agricultural_evidence":25},[244,251,257,264,269],{"code":176,"score":30,"supported":25,"title":245,"abstract":247,"tags":250},[246],"precision agriculture",[248,230,246,249],"精准农业","digital agriculture",[230,231],{"code":236,"score":163,"supported":25,"title":252,"abstract":253,"tags":256},[],[254,255],"物联网","IoT",[],{"code":258,"score":259,"supported":25,"title":260,"abstract":261,"tags":263},"I6",5,[],[262],"agricultural data",[232,233],{"code":138,"score":67,"supported":25,"title":265,"abstract":266,"tags":268},[],[267],"机器学习",[],{"code":182,"score":33,"supported":46,"title":270,"abstract":271,"tags":272},[],[],[234],[274,275],"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":277,"openalex_id":279,"authors":280,"venue":222,"cited_by_count":99,"oa_url":219,"card":289,"direction":294,"ingested_from":107},"W7214617849",[281,284,287],{"name":282,"orcid":283},"Suresh Kumar Sharma","https:\u002F\u002Forcid.org\u002F0000-0002-5054-6170",{"name":285,"orcid":286},"Parul Dhull","https:\u002F\u002Forcid.org\u002F0009-0005-8368-3058",{"name":288,"orcid":20},"Pratibha Manohar",{"tldr":290,"method":291,"finding":292,"direction":213,"opportunity":293},"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。","智慧农业 \u002F 农业物联网","2026-09-29T23:30:15.187320Z",{"id":297,"title":298,"url":299,"summary":300,"summary_zh":301,"content":20,"source_name":115,"source_url":299,"published_at":116,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":302,"score_detail":303,"sources":307,"tags":309,"primary_topic":314,"secondary_topics":318,"classification":319,"search_phrases":344,"slug":347,"view_count":67,"doi":348,"paper":349,"created_at":371},3797,"A Next Generation Observing Strategy for On-Farm Crop Management","https:\u002F\u002Fdoi.org\u002F10.22541\u002Fessoar.15009564\u002Fv1","Securing sustainable food production under increasing climate stress is one of the most pressing challenges for the nation. In 2022, U.S. agricultural exports generated $412 billion in economic output, including $214.6 billion in manufacturing, trade, and transportation (Zeng et al 2025). At the same time, rising production costs and increasing vulnerability to drought and extreme weather are threatening national and global food security and economic stability. Timely information on crop health and yield is critical to anticipate short-term disruptions to food supply and inform adaptive management frameworks to enhance socioecological resilience. In practice, information on crop health is leveraged in different ways at every step throughout agricultural supply chains. However, it is arguably most powerful where the action starts: in the hands of farmers making real-time management decisions at the field scale (Parazoo and Fuchs, 2025). While the crop data analysis approaches employed by farmers have advanced significantly in recent years, they rest on a foundation of remote multiband and thermal data that has not changed materially in over two decades. Solar-Induced Chlorophyll Fluorescence (SIF) has potential to transform agricultural monitoring and management because it captures slow- to rapid- changes in how plants absorb and use light throughout the entire crop cycle, from planting to harvest, regardless of canopy density, and under variable environmental and management conditions (Yang et al. 2026). Yet despite its promise to meet agricultural needs, SIF remote sensing has not yet reached the spatial, temporal, and latency requirements necessary for operational use at field and regional scales. There is an opportunity to leverage SIF’s heritage in space and capitalize on ongoing technology developments in imaging spectroscopy and interferometry and the high demand for actionable agricultural data to dramatically advance the state of the art in crop management and yield forecasting with new measurement modalities and architectures for imaging SIF.","在日益加剧的气候压力下保障可持续的粮食生产，是美国面临的最紧迫挑战之一。2022年，美国农产品出口创造了4120亿美元的经济产出，其中包括2146亿美元的制造业、贸易和运输业产值（Zeng et al 2025）。与此同时，不断上升的生产成本以及日益加剧的干旱和极端天气脆弱性，正威胁着国家和全球的粮食安全与经济稳定。及时获取作物健康状况和产量信息，对于预判粮食供应的短期中断、为适应性管理框架提供依据以增强社会生态韧性至关重要。在实践中，作物健康信息在农业供应链的每一个环节都以不同方式得到利用。然而，可以说其作用最大的地方正是行动的起点：即农民在田块尺度上做出实时管理决策时（Parazoo and Fuchs, 2025）。尽管近年来农民所采用的作物数据分析方法已取得显著进步，但它们所依赖的遥感多波段和热红外数据基础在过去二十多年里并未发生实质性变化。日光诱导叶绿素荧光（Solar-Induced Chlorophyll Fluorescence, SIF）有潜力变革农业监测与管理，因为它能够捕捉植物在整个作物周期（从种植到收获）中吸收和利用光的方式的缓慢至快速变化，且不受冠层密度限制，并适用于多变的环境和管理条件（Yang et al. 2026）。然而，尽管SIF遥感在满足农业需求方面前景广阔，但它尚未达到在田块和区域尺度上业务化应用所需的空间、时间和延迟要求。当前存在一个机遇，可以借助SIF在太空领域的发展基础，利用成像光谱学和干涉测量学的持续技术进步，以及对可操作农业数据的高需求，通过新的测量模式和成像SIF架构，大幅推进作物管理和产量预测的技术水平。",83,{"impact":119,"substance":304,"depth":225,"authority":120,"freshness":305,"relevant":33,"comment":306},21,9,"该论文提出基于SIF的新一代农田观测策略，方法新颖且对精准农业与产量预报有实质推动，值得进入每日精选。",[308],{"name":115,"url":299},[231,310,311,312,313],"农业遥感","产量预测","作物监测","日光诱导叶绿素荧光",{"code":59,"slug":315,"name":316,"path":317,"parent_code":142},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":144,"locked":46,"version":47,"method":48,"evidence":320},{"candidates":321,"agricultural_evidence":25},[322,326,330,336,340],{"code":59,"score":305,"supported":25,"title":323,"abstract":324,"tags":325},[],[62,63],[311,312,310],{"code":52,"score":163,"supported":25,"title":327,"abstract":328,"tags":329},[],[38,56],[],{"code":170,"score":163,"supported":25,"title":331,"abstract":332,"tags":335},[],[333,334],"干旱","drought",[],{"code":258,"score":67,"supported":25,"title":337,"abstract":338,"tags":339},[],[262],[],{"code":176,"score":33,"supported":46,"title":341,"abstract":342,"tags":343},[],[],[231],[345,346],"SIF 遥感 作物监测","日光诱导叶绿素荧光 产量预测","SIF遥感作物监测-3797","10.22541\u002Fessoar.15009564\u002Fv1",{"doi":348,"openalex_id":350,"authors":351,"venue":20,"cited_by_count":99,"oa_url":20,"card":366,"direction":105,"ingested_from":107},"W7214677315",[352,354,356,359,361,363],{"name":353,"orcid":20},"Nicholas Parazoo",{"name":355,"orcid":20},"David R. Thompson",{"name":357,"orcid":358},"Jennifer E. Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-2181-8402",{"name":360,"orcid":20},"Ying Sun",{"name":362,"orcid":20},"Jochen Stutz",{"name":364,"orcid":365},"Nicholas Cody Parazoo","https:\u002F\u002Forcid.org\u002F0000-0002-4424-7780",{"tldr":367,"method":368,"finding":369,"direction":105,"opportunity":370},"提出利用日光诱导叶绿素荧光（SIF）遥感构建下一代农田作物管理观测策略。","综述SIF遥感在农业监测中的潜力，结合成像光谱与干涉测量技术发展。","SIF可捕捉全生育期作物光利用动态，但尚未满足田间业务化时空与延迟要求。","研发满足田间尺度时空与低延迟要求的SIF成像新架构，推动作物管理业务化。","2026-09-29T23:30:30.321141Z",{"id":373,"title":374,"url":375,"summary":376,"summary_zh":377,"content":20,"source_name":378,"source_url":375,"published_at":116,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":379,"score_detail":380,"sources":382,"tags":384,"primary_topic":20,"secondary_topics":388,"classification":389,"search_phrases":422,"slug":425,"view_count":33,"doi":426,"paper":427,"created_at":442},3782,"AI-driven assessment of forest dynamics and ecosystem services in response to land use change: current progress and future perspectives","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fffgc.2026.1933500","Forests cover approximately 31% of the global land surface and provide critical ecosystem services including carbon sequestration, hydrological regulation, and biodiversity support, yet ongoing deforestation and fragmentation are progressively eroding these services. The convergence of artificial intelligence (AI), multi-source remote sensing, and cloud computing offers unprecedented opportunities to monitor forest dynamics and quantify ecosystem service responses to land use change, but the literature remains fragmented, with no synthesis tracing the causal chain from land use drivers through forest responses to ecosystem service outcomes. This review addresses that gap through an original four-tier framework encompassing monitoring, assessment, prediction, and optimization, clarifying how AI and geospatial technologies perform at each stage and how outputs of one tier feed into the next. We searched Web of Science, Scopus, and Google Scholar for studies published between 2016 and 2026; screening against explicit eligibility criteria and a formal quality appraisal confirmed 27 core studies directly reporting a quantified AI\u002FML performance metric, supplemented by 70 contextual references. At the monitoring tier, deep learning architectures such as U-Net achieve F1-scores of 0.75–0.77 for forest change detection, with an advantage over conventional classifiers that is conditional rather than absolute, varying with label quality, spectral availability, and geographic transfer. At the assessment tier, AI has advanced carbon stock estimation through multi-source fusion, species distribution modeling in which convolutional neural networks substantially outperform Maximum Entropy models, an advantage that persists and even widens under stricter spatial cross-validation even as both methods' absolute accuracy declines, and hydrological service quantification coupled with process-based models. At the prediction tier, machine learning-enhanced land use simulation has reached accuracies exceeding 0.93, while reinforcement learning shows potential to jointly optimize ecosystem service and economic objectives, though only at proof-of-concept stage. Persistent challenges include limited explainability, near-absent uncertainty quantification, weak cross-domain transferability, and largely sequential AI-process model integration, with direct consequences for policy applications: AI-derived carbon stock maps and ecosystem service assessments intended to inform emissions trading, conservation planning, or land use policy currently lack the quantified uncertainty and cross-region validation such applications require. We propose a phased research agenda spanning near-term methodological consolidation, medium-term foundation model adaptation, and long-term integrated forest digital twins with equitable global coverage, offering a roadmap for translating AI and geospatial technologies into evidence-based forest ecosystem stewardship.","森林覆盖全球约31%的陆地表面，提供碳固存、水文调节和生物多样性维持等关键生态系统服务，但持续的森林砍伐和破碎化正在逐步侵蚀这些服务。人工智能（AI）、多源遥感与云计算的融合为监测森林动态和量化生态系统服务对土地利用变化的响应提供了前所未有的机遇，但相关文献仍较为分散，尚无综合研究追溯从土地利用驱动因素到森林响应再到生态系统服务结果的因果链。本综述通过一个原创的四层框架——监测、评估、预测和优化——来填补这一空白，阐明AI和地理空间技术在各阶段的表现以及某一层的输出如何输入到下一层。我们检索了Web of Science、Scopus和Google Scholar中2016年至2026年间发表的研究；依据明确的纳入标准进行筛选并经正式质量评估，最终确认27项直接报告量化AI\u002FML性能指标的核心研究，并辅以70篇背景参考文献。在监测层，U-Net等深度学习架构在森林变化检测中达到0.75–0.77的F1分数，其相对于传统分类器的优势是有条件的而非绝对的，随标签质量、光谱可用性和地理迁移性而变化。在评估层，AI通过多源融合推进了碳储量估算、物种分布建模——其中卷积神经网络显著优于最大熵模型，且这一优势在更严格的空间交叉验证下持续存在甚至扩大，尽管两种方法的绝对精度均有所下降——以及耦合过程模型的水文服务量化。在预测层，机器学习增强的土地利用模拟精度已超过0.93，而强化学习展现出联合优化生态系统服务和经济目标的潜力，但仍处于概念验证阶段。持续存在的挑战包括可解释性有限、不确定性量化几乎缺失、跨域迁移能力薄弱以及AI与过程模型的整合多为顺序式，这些问题对政策应用具有直接影响：旨在为排放目标提供信息的AI衍生碳储量图和生态系统服务评估……","Frontiers in Forests and Global Change",82,{"impact":225,"substance":119,"depth":30,"authority":148,"freshness":305,"relevant":33,"comment":381},"系统综述AI与遥感在森林动态及生态系统服务评估中的四层框架，方法学增量明确，但属国际期刊综述、非国内三农政策事件，适合主题聚合而非头条精选。",[383],{"name":378,"url":375},[125,179,385,386,387],"林业遥感","土地利用变化","生态系统服务",[],{"status":45,"locked":46,"version":47,"method":48,"evidence":390},{"candidates":391,"agricultural_evidence":46,"reason":421},[392,400,407,411,417],{"code":78,"score":393,"supported":25,"title":394,"abstract":395,"tags":399},11,[],[396,387,397,398],"生物多样性","biodiversity","ecosystem service",[386,387],{"code":138,"score":24,"supported":25,"title":401,"abstract":402,"tags":406},[],[403,267,404,405,157],"深度学习","machine learning","deep learning",[125],{"code":59,"score":163,"supported":25,"title":408,"abstract":409,"tags":410},[],[62,63],[],{"code":412,"score":67,"supported":25,"title":413,"abstract":414,"tags":416},"H4",[],[415],"carbon sequestration",[],{"code":176,"score":33,"supported":46,"title":418,"abstract":419,"tags":420},[],[],[179],"农业相关性需要复核",[423,424],"森林动态 AI 遥感","生态系统服务 土地利用变化","森林动态AI遥感-3782","10.3389\u002Fffgc.2026.1933500",{"doi":426,"openalex_id":428,"authors":429,"venue":378,"cited_by_count":99,"oa_url":436,"card":437,"direction":294,"ingested_from":107},"W7214677634",[430,432,434],{"name":431,"orcid":20},"Lili Zhao",{"name":433,"orcid":20},"Xuncheng Fan",{"name":435,"orcid":20},"Xiao Shihong","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fforests-and-global-change\u002Farticles\u002F10.3389\u002Fffgc.2026.1933500\u002Fpdf",{"tldr":438,"method":439,"finding":440,"direction":105,"opportunity":441},"综述AI结合遥感监测森林动态与生态系统服务，提出监测-评估-预测-优化四层框架。","检索2016-2026年文献，筛选27项核心研究，按四层框架归纳AI\u002FML方法。","深度学习提升森林变化检测与碳储量估算，但可解释性、不确定性量化和跨域迁移仍不足。","可探索AI与过程模型耦合的不确定性量化及跨区域迁移，支撑森林碳汇政策评估。","2026-09-29T23:30:15.293149Z",{"id":444,"title":445,"url":446,"summary":447,"summary_zh":448,"content":20,"source_name":449,"source_url":446,"published_at":116,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":450,"score_detail":451,"sources":453,"tags":455,"primary_topic":460,"secondary_topics":461,"classification":462,"search_phrases":489,"slug":492,"view_count":67,"doi":493,"paper":494,"created_at":518},3773,"Precision seed placement to advanced seed delivery: evolution, innovations, and future perspectives of seed tape systems for climate-smart agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffagro.2026.1952898","Seed placement is a critical determinant of crop establishment, resource use efficiency, and agricultural productivity. Traditional sowing methods are associated with poor seed uniformity, high sowing rates, and high labor requirements, creating a need for more sustainable sowing technologies. Seed tape technology has been demonstrated as a precision seed placement approach, particularly through paper, cellulose, natural fiber, and biodegradable polymer-based carriers. This review highlights the evolution of seed tapes from these established precision placement systems toward multifunctional and potentially intelligent seed delivery platforms for climate-smart agriculture. The review demonstrates technologies from emerging and future concepts, including established applications such as biodegradable carrier materials, precision seed placement, and mechanized seed tape systems. Emerging developments include functionalized and selected nano-enabled seed tapes incorporating hydrogels, nutrients, biological inputs, and nanomaterials, as well as smart sensing, AI, IoT, robotics, autonomous deployment, and real-time digital decision support. A technology-readiness framework is used to evaluate the maturity, agronomic relevance, sustainability potential, and commercialization prospects of these systems. Major challenges include manufacturing scale-up, formulation stability, regulatory requirements, economic viability, and field validation of advanced systems. Future research should focus on validating multifunctional materials and biological inputs, improving nano-enabled formulations, and developing digital and autonomous technologies for intelligent seed delivery. The transition from demonstrated precision seed placement to intelligent seed delivery should therefore be viewed as a technology-development trajectory rather than as a currently established integrated system. This review provides a perspective on the evolution of seed tape technology toward multifunctional and potentially intelligent seed delivery for sustainable and climate-smart crop production.","种子位置是决定作物定植、资源利用效率和农业生产力的关键因素。传统播种方式存在种子均匀度差、播种率高和劳动力需求大等问题，因此需要更可持续的播种技术。种子带技术已被证明是一种精准播种方法，尤其是通过纸、纤维素、天然纤维和可生物降解聚合物基载体实现。本文综述了种子带从这些已建立的精准放置系统向多功能乃至潜在智能化种子递送平台的发展历程，以服务于气候智慧型农业。综述展示了新兴和未来概念中的技术，包括已确立的应用，如可生物降解载体材料、精准种子放置和机械化种子带系统。新兴发展包括功能化和精选的纳米赋能种子带，其整合了水凝胶、养分、生物投入品和纳米材料，以及智能传感、人工智能、物联网、机器人技术、自主部署和实时数字决策支持。本文采用技术成熟度框架评估这些系统的成熟度、农艺相关性、可持续性潜力和商业化前景。主要挑战包括制造规模化、配方稳定性、监管要求、经济可行性以及先进系统的田间验证。未来研究应侧重于验证多功能材料和生物投入品、改进纳米赋能配方，以及开发用于智能种子递送的数字化和自主技术。因此，从已证明的精准种子放置向智能种子递送的转变应被视为一个技术发展轨迹，而非当前已建立的集成系统。本文综述了种子带技术向多功能乃至潜在智能化种子递送演进的观点，以服务于可持续和气候智慧型作物生产。","Frontiers in Agronomy",81,{"impact":225,"substance":119,"depth":225,"authority":148,"freshness":305,"relevant":33,"comment":452},"综述系统梳理种子带从精准排种到智能种子输送的技术演进，提出技术成熟度评估框架，对气候智慧型农业与智能播种装备研发有参考价值，但属概念性综述、缺乏一手数据，未达重大突破层级。",[454],{"name":449,"url":446},[231,456,457,458,459],"智能农机","气候智慧农业","精准播种","种子带",{"code":236,"slug":237,"name":238,"path":239,"parent_code":142},[],{"status":144,"locked":46,"version":47,"method":48,"evidence":463},{"candidates":464,"agricultural_evidence":25},[465,470,474,481,485],{"code":176,"score":87,"supported":25,"title":466,"abstract":468,"tags":469},[467],"smart agriculture",[467],[231],{"code":236,"score":163,"supported":25,"title":471,"abstract":472,"tags":473},[],[254,255],[],{"code":475,"score":476,"supported":25,"title":477,"abstract":478,"tags":480},"F5",4,[],[479],"机械化",[456],{"code":138,"score":67,"supported":25,"title":482,"abstract":483,"tags":484},[],[159],[],{"code":170,"score":33,"supported":46,"title":486,"abstract":487,"tags":488},[],[],[457],[490,491],"种子带 精准播种 智能种子输送","气候智慧农业 智慧农业 智能农机 精准播种","种子带精准播种智能种子输送-3773","10.3389\u002Ffagro.2026.1952898",{"doi":493,"openalex_id":495,"authors":496,"venue":449,"cited_by_count":99,"oa_url":446,"card":513,"direction":294,"ingested_from":107},"W7214664548",[497,500,503,506,508,510],{"name":498,"orcid":499},"Sourav Paramanik","https:\u002F\u002Forcid.org\u002F0009-0001-0628-4337",{"name":501,"orcid":502},"Poonguzhali Subramanian","https:\u002F\u002Forcid.org\u002F0000-0002-4282-6148",{"name":504,"orcid":505},"Mahalakshmi Mani","https:\u002F\u002Forcid.org\u002F0009-0001-6257-0928",{"name":507,"orcid":20},"Jothika Manokaran",{"name":509,"orcid":20},"Divya Nagarajan",{"name":511,"orcid":512},"Karthika Rajendran","https:\u002F\u002Forcid.org\u002F0000-0003-4175-2463",{"tldr":514,"method":515,"finding":516,"direction":294,"opportunity":517},"综述种子带技术从精准排种向多功能智能播种平台演进的路径与前景。","文献综述，结合技术成熟度框架评估材料、智能感知与自主播种技术。","种子带正从精准排种向集成纳米材料、AI与物联网的智能播种系统演进，但尚处技术开发阶段。","可研究可降解种子带与AI\u002F物联网融合的智能播种系统，并开展田间验证与商业化评估。","2026-09-29T23:30:10.437986Z",{"id":520,"title":521,"url":522,"summary":523,"summary_zh":524,"content":20,"source_name":525,"source_url":522,"published_at":116,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":526,"score_detail":527,"sources":529,"tags":531,"primary_topic":20,"secondary_topics":536,"classification":537,"search_phrases":550,"slug":553,"view_count":190,"doi":554,"paper":555,"created_at":580},3795,"Remotely sensed vegetation resilience in the four major estuarine ecosystems of eastern China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1912598","Introduction Estuarine wetlands are among the most productive ecosystems on Earth, but they have been experiencing severe degradation induced by human activities and climate change worldwide. Traditional assessments of degradation mostly rely on static indicators, which are insufficient to reflect dynamical ecosystem properties such as systemic resilience. Methods Here, we used remote sensing data and critical slowing down indicators to evaluate ecological resilience of vegetation in four major estuaries in eastern China: the Liaohe Estuary, the Yangtze Estuary, the Minjiang Estuary, and the Pearl Estuary. We identified hotspots of resilience loss during the past two decades, and then assessed relative ecological risk using the Pressure–State–Response (PSR) framework. Using random forest modeling and SHAP (SHapley Additive exPlanations) analysis, we further identified key factors associated with the classification of resilience-loss hotspots. Results Our results showed that all four estuaries have undergone resilience loss, with resilience loss areas accounting for ∼40% of the total study area. Moreover, hotspots of resilience loss were found to be mostly\u002Fstrongly\u002Flargely associated with faster sea level rise and macroclimate conditions, but showed a spatial mismatch with relatively high ecological risk areas. Defined as resilience-loss hotspots with relatively low ecological risk, potential risk areas accounted for ∼35% of the total area. Despite still exhibiting relatively favorable environmental conditions, these areas might degrade quickly under ongoing unprecedented environmental change and thus need further monitoring. Discussion Our study combined risk assessment with resilience evaluation to provide an integrated framework detecting unrecognized ecosystem states. The framework contributes to better understanding resilience dynamics of estuarine wetlands, and has useful implications for adaptive management of these important ecosystems in a changing world.","引言 河口湿地是地球上生产力最高的生态系统之一，但全球范围内正因人类活动和气候变化而经历严重退化。传统的退化评估多依赖静态指标，不足以反映系统恢复力等动态生态系统属性。方法 本研究利用遥感数据和临界慢化指标，评估了中国东部四大河口——辽河口、长江口、闽江口和珠江口——植被的生态恢复力。我们识别了过去二十年恢复力丧失的热点区域，并基于压力—状态—响应（PSR）框架评估了相对生态风险。通过随机森林建模和SHAP（SHapley Additive exPlanations）分析，我们进一步识别了与恢复力丧失热点分类相关的关键因素。结果 结果表明，四大河口均出现了恢复力丧失，恢复力丧失区域约占研究总面积的40%。此外，恢复力丧失热点主要与海平面上升加快和宏观气候条件密切相关，但与相对高生态风险区域存在空间错位。被定义为恢复力丧失热点且生态风险相对较低的区域，即潜在风险区，约占总面积的35%。尽管这些区域仍表现出相对有利的环境条件，但在当前前所未有的环境变化下可能迅速退化，因此需要进一步监测。讨论 本研究将风险评估与恢复力评估相结合，提供了一个识别未被认知生态系统状态的综合框架。该框架有助于更好地理解河口湿地的恢复力动态，并对变化世界中这些重要生态系统的适应性管理具有有益启示。","Frontiers in Environmental Science",80,{"impact":225,"substance":119,"depth":225,"authority":120,"freshness":305,"relevant":33,"comment":528},"基于遥感与临界慢化指标评估中国四大河口湿地植被韧性，方法新颖、数据扎实，对湿地生态监测与适应性管理有参考价值。",[530],{"name":525,"url":522},[40,532,533,534,535],"植被退化","生态韧性","河口湿地","生态风险评价",[],{"status":45,"locked":46,"version":47,"method":48,"evidence":538},{"candidates":539,"agricultural_evidence":46,"reason":421},[540,544],{"code":59,"score":32,"supported":25,"title":541,"abstract":542,"tags":543},[],[62,63],[532,40],{"code":170,"score":163,"supported":25,"title":545,"abstract":546,"tags":549},[],[547,548],"气候变化","climate change",[],[551,552],"辽河口 长江口 闽江口 珠江口 湿地植被","遥感 生态韧性 河口湿地","辽河口长江口闽江口珠江口湿地植被-3795","10.3389\u002Ffenvs.2026.1912598",{"doi":554,"openalex_id":556,"authors":557,"venue":525,"cited_by_count":99,"oa_url":574,"card":575,"direction":105,"ingested_from":107},"W7214657804",[558,560,562,564,566,568,571],{"name":559,"orcid":20},"Qian Dong",{"name":561,"orcid":20},"Yiwen Liu",{"name":563,"orcid":20},"Wensi Hu",{"name":565,"orcid":20},"Babak M. S. Arani",{"name":567,"orcid":20},"Ziyu Zheng",{"name":569,"orcid":570},"Chi Xu","https:\u002F\u002Forcid.org\u002F0000-0001-7389-5763",{"name":572,"orcid":573},"Shuqing N. Teng","https:\u002F\u002Forcid.org\u002F0000-0001-7431-2128","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fenvironmental-science\u002Farticles\u002F10.3389\u002Ffenvs.2026.1912598\u002Fpdf",{"tldr":576,"method":577,"finding":578,"direction":105,"opportunity":579},"用遥感与临界慢化指标评估中国四大河口湿地植被韧性，识别韧性丧失热点并评估生态风险。","遥感数据、临界慢化指标、PSR框架、随机森林与SHAP分析。","四大河口均出现韧性丧失，约40%区域丧失，热点与海平面上升和宏观气候相关，但与高风险区空间错位。","可延伸研究：将韧性评估与风险框架结合，识别低风险但韧性丧失的潜在风险区，用于湿地适应性管理。","2026-09-29T23:30:30.169110Z",{"id":582,"title":583,"url":584,"summary":585,"summary_zh":586,"content":20,"source_name":587,"source_url":584,"published_at":22,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":526,"score_detail":588,"sources":590,"tags":592,"primary_topic":595,"secondary_topics":601,"classification":602,"search_phrases":623,"slug":626,"view_count":476,"doi":627,"paper":628,"created_at":643},3780,"Epistemological Friction: Embodied Expertise and the ‘Last Mile’ of Agricultural Automation in the US Tree‐Fruit Industry","https:\u002F\u002Fdoi.org\u002F10.1111\u002Fntwe.70047","ABSTRACT Autonomous and AI‐driven agricultural technology is often framed as a panacea for labour dependency. While proponents envision autonomous technologies seamlessly optimising farming, this rationalised model misunderstands the reality of on‐farm integration. Based on an ethnographic and comparative study of 16 operations in the US West Coast Tree Fruit Industry (California and Washington), this article examines the ‘last mile’ of agricultural automation. The study reveals that integrating AI and robotics is a contested negotiation shaped by epistemological friction—the tension between the algorithmic logic of developers and the embodied, site‐specific expertise of farmers and farm workers. Rather than resulting in seamless labour replacement, we find that agricultural automation reconfigures work, creating an invisible reliance on ‘human middleware’ to navigate the biological recalcitrance of the orchard. Demonstrating how regional contexts and participatory implementation shape technological trajectories, this study extends Labour Process Theory. We argue for the need for a relational approach recognising the essential cognitive labour of farmers and farm workers that is demanded by digital agriculture. Ultimately, this study shows the ‘farm of the future’ depends on reconciling rigid systems with the situated expertise of the farm workforce.","自主与人工智能驱动的农业技术常被描绘为解决劳动力依赖问题的万能药。支持者设想自主技术能够无缝优化农业生产，然而这种理性化模型误解了农场整合的现实。本文基于对美国西海岸果树产业（加利福尼亚州和华盛顿州）16家经营主体的民族志比较研究，考察农业自动化的“最后一英里”。研究发现，人工智能与机器人技术的整合是一场充满争议的协商过程，其受到认识论摩擦的塑造——即开发者的算法逻辑与农民及农场工人具身的、场所特定的专业知识之间的张力。我们发现，农业自动化并未实现无缝的劳动力替代，而是重构了工作形态，制造了对“人类中间件”的隐性依赖，以应对果园的生物性阻力。本研究通过展示区域情境与参与式实施如何塑造技术轨迹，拓展了劳动过程理论。我们主张需要一种关系性方法，承认数字农业所要求的农民和农场工人不可或缺的认知劳动。最终，本研究表明“未来农场”取决于刚性系统与农场劳动力情境化专业知识之间的调和。","New Technology Work and Employment",{"impact":225,"substance":119,"depth":225,"authority":148,"freshness":32,"relevant":33,"comment":589},"基于美西16家果园民族志研究，揭示AI与机器人落地的“最后一公里”认识论摩擦与“人类中间件”隐性依赖，对智慧农业人机协同具参考价值。",[591],{"name":587,"url":584},[230,231,125,593,594],"农业机器人","果园自动化",{"code":596,"slug":597,"name":598,"path":599,"parent_code":600},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":144,"locked":46,"version":47,"method":48,"evidence":603},{"candidates":604,"agricultural_evidence":25},[605,609,614,619],{"code":176,"score":32,"supported":25,"title":606,"abstract":607,"tags":608},[],[230,249],[230,231],{"code":596,"score":163,"supported":25,"title":610,"abstract":611,"tags":613},[],[598,612],"orchard",[],{"code":615,"score":33,"supported":46,"title":616,"abstract":617,"tags":618},"F6",[],[],[593],{"code":138,"score":33,"supported":46,"title":620,"abstract":621,"tags":622},[],[],[125],[624,625],"美国 果树 自动化","果园 机器人 劳动力","美国果树自动化-3780","10.1111\u002Fntwe.70047",{"doi":627,"openalex_id":629,"authors":630,"venue":587,"cited_by_count":99,"oa_url":637,"card":638,"direction":294,"ingested_from":107},"W7214618881",[631,634],{"name":632,"orcid":633},"M. Anne Visser","https:\u002F\u002Forcid.org\u002F0000-0001-5047-7702",{"name":635,"orcid":636},"Alex Bladimir Hernandez","https:\u002F\u002Forcid.org\u002F0009-0001-5320-7203","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1111\u002Fntwe.70047",{"tldr":639,"method":640,"finding":641,"direction":213,"opportunity":642},"通过美西果树业民族志研究，揭示农业自动化落地中算法逻辑与农户经验知识的冲突。","对加州和华盛顿16个果园开展民族志与比较研究。","自动化未无缝替代劳动，反而依赖‘人类中间件’协调生物不确定性。","可探索参与式设计如何将农户隐性知识嵌入AI系统，弥合‘最后一英里’鸿沟。","2026-09-29T23:30:12.281947Z",{"id":645,"title":646,"url":647,"summary":648,"summary_zh":649,"content":20,"source_name":650,"source_url":647,"published_at":116,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":526,"score_detail":651,"sources":653,"tags":655,"primary_topic":658,"secondary_topics":659,"classification":664,"search_phrases":691,"slug":694,"view_count":33,"doi":695,"paper":696,"created_at":711},3772,"Integrating Vegetation Health Indices and Machine Learning for Early Prediction of Agricultural Drought","https:\u002F\u002Fdoi.org\u002F10.1080\u002F02626667.2026.2740781","Agricultural drought threatens food security across Nigeria’s Sahel, yet spatially explicit machine-learning-based prediction remains limited in the region. This study developed a spatiotemporal drought monitoring and prediction framework for Yobe State (2000–2025) integrating seven remote-sensing indicators; NDVI, EVI, VCI, TCI, SMI, LST, and rainfall within Google Earth Engine at 3 km resolution. A persistent north–south drought gradient emerged across all indicators, with northern local government areas exhibiting chronic vegetation stress, soil moisture deficit, and elevated surface temperatures throughout the study period. Three machine learning models; LSTM, Random Forest, and XGBoost were trained on Vegetation Health Index-derived drought severity classes. XGBoost achieved the highest predictive performance (accuracy = 86.9%; Kappa = 0.829; RMSE = 2.26), with rainfall and vegetation condition as dominant predictors. All three models converge on the northern LGAs as the highest-risk zone by 2030, supporting targeted early warning deployment and climate-smart agricultural investment across the Sahel.","农业干旱威胁着尼日利亚萨赫勒地区的粮食安全，但该地区基于机器学习且具有空间显式特征的预测研究仍然有限。本研究在Google Earth Engine平台上，以3 km分辨率构建了约贝州（2000—2025年）的时空干旱监测与预测框架，集成了七种遥感指标：归一化植被指数（NDVI）、增强植被指数（EVI）、植被条件指数（VCI）、温度条件指数（TCI）、土壤水分指数（SMI）、地表温度（LST）和降雨量。所有指标均呈现出持续的南北干旱梯度，北部地方政府辖区在整个研究期内表现出长期的植被胁迫、土壤水分亏缺和地表温度升高。研究以植被健康指数（VHI）衍生的干旱严重程度等级为标签，训练了三种机器学习模型：长短期记忆网络（LSTM）、随机森林（Random Forest）和极端梯度提升（XGBoost）。XGBoost取得了最高的预测性能（准确率=86.9%；Kappa=0.829；RMSE=2.26），其中降雨量和植被状况为主要预测因子。三种模型一致预测，到2030年北部地方政府辖区将成为最高风险区，这为萨赫勒地区有针对性的预警部署和气候智慧型农业投资提供了依据。","Hydrological Sciences Journal",{"impact":225,"substance":119,"depth":225,"authority":148,"freshness":32,"relevant":33,"comment":652},"基于多源遥感与机器学习构建萨赫勒地区农业干旱早期预警框架，方法扎实、结论明确，对干旱监测预警有参考价值。",[654],{"name":650,"url":647},[231,267,40,656,657],"农业干旱","预警系统",{"code":138,"slug":139,"name":140,"path":141,"parent_code":142},[660],{"code":170,"slug":661,"name":662,"path":663,"parent_code":135},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":144,"locked":46,"version":47,"method":48,"evidence":665},{"candidates":666,"agricultural_evidence":25},[667,671,675,679,683,687],{"code":138,"score":31,"supported":25,"title":668,"abstract":669,"tags":670},[404],[267,404],[267],{"code":170,"score":148,"supported":25,"title":672,"abstract":673,"tags":674},[334],[333,334],[],{"code":52,"score":163,"supported":25,"title":676,"abstract":677,"tags":678},[],[38,56],[],{"code":131,"score":163,"supported":25,"title":680,"abstract":681,"tags":682},[],[151,132],[],{"code":59,"score":476,"supported":25,"title":684,"abstract":685,"tags":686},[],[62],[40],{"code":176,"score":33,"supported":46,"title":688,"abstract":689,"tags":690},[],[],[231],[692,693],"尼日利亚 萨赫勒 农业干旱","XGBoost 植被健康指数 干旱预测","尼日利亚萨赫勒农业干旱-3772","10.1080\u002F02626667.2026.2740781",{"doi":695,"openalex_id":697,"authors":698,"venue":650,"cited_by_count":99,"oa_url":20,"card":706,"direction":294,"ingested_from":107},"W7214639469",[699,702,704],{"name":700,"orcid":701},"Gbolahan S. Badru","https:\u002F\u002Forcid.org\u002F0000-0003-4020-6356",{"name":703,"orcid":20},"Shakirudeen Odunuga",{"name":705,"orcid":20},"Michael Adebisi Adeyemi",{"tldr":707,"method":708,"finding":709,"direction":105,"opportunity":710},"基于多源遥感与机器学习构建尼日利亚萨赫勒地区农业干旱早期预测框架。","GEE整合NDVI、EVI、VCI等7个指标，用LSTM、随机森林、XGBoos","XGBoost预测精度最高（86.9%），北部LGAs为2030年前最高风险区。","可探索多模型集成与迁移学习，将干旱预警框架推广至其他萨赫勒地区并耦合作物产量预测。","2026-09-29T23:30:09.236357Z",{"id":713,"title":714,"url":715,"summary":716,"summary_zh":717,"content":20,"source_name":718,"source_url":715,"published_at":116,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":526,"score_detail":719,"sources":721,"tags":723,"primary_topic":729,"secondary_topics":735,"classification":737,"search_phrases":784,"slug":787,"view_count":33,"doi":788,"paper":789,"created_at":812},3767,"Alfalfa-mediated phytoremediation and soil restoration for sustainable cereal and fruit crop production: a review of roles, detoxification mechanisms, nitrogen enhancement, and agroecological integration","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1963157","Soil contamination by heavy metals, petroleum hydrocarbons, and excessive agrochemical inputs, coupled with declining soil fertility, poses a major threat to sustainable cereal and fruit production and global food security. This review synthesizes evidence from peer-reviewed studies published between 2000 and 2026 to evaluate the multifunctional role of alfalfa ( Medicago sativa L.) as a biological soil engineer for phytoremediation, soil restoration, and sustainable agricultural intensification. The reviewed evidence indicates that alfalfa can contribute to the remediation of contaminated soils through phytoextraction, phytostabilization, and rhizosphere-mediated degradation of organic pollutants, while simultaneously improving soil fertility through biological nitrogen fixation (BNF). Its deep and extensive root system can enhance soil structure, aggregation, water-holding capacity, and soil organic carbon, while promoting beneficial microbial communities, nutrient cycling, and suppression of soil-borne pathogens. In cereal-based systems, these processes may reduce contaminant bioavailability, decrease dependence on synthetic fertilizers, and improve the productivity and resilience of subsequent crops. In fruit-based systems, including orchards and perennial cropping systems, alfalfa can provide additional benefits through intercropping or cover-cropping, including nitrogen inputs, improved soil moisture conservation, enhanced soil biological activity, and potential economic returns. However, the accumulation of contaminants in alfalfa biomass remains an important food- and feed-safety concern, highlighting the need for rigorous contaminant monitoring and appropriate biomass management. Emerging approaches, including targeted microbial inoculation, biochar amendments, intercropping strategies, and gene-editing technologies, may further improve remediation efficiency and agroecosystem sustainability. Overall, integrating alfalfa into cereal- and fruit-based production systems represents a promising nature-based approach for restoring degraded soils, improving soil resilience, reducing external inputs, and supporting sustainable crop production. Future research should prioritize long-term, multi-site field studies to evaluate remediation performance, contaminant mass balances, plant–microbe interactions, biomass safety, and the long-term productivity of cereal and fruit production systems.","重金属、石油烃和过量农用化学品投入造成的土壤污染，加之土壤肥力下降，对可持续的谷类与水果生产及全球粮食安全构成重大威胁。本综述综合了2000年至2026年间发表的同行评审研究证据，评估紫花苜蓿（Medicago sativa L.）作为生物土壤工程师在植物修复、土壤恢复和可持续农业集约化中的多功能作用。所综述的证据表明，紫花苜蓿可通过植物提取、植物稳定和根际介导的有机污染物降解促进污染土壤的修复，同时通过生物固氮（BNF）提高土壤肥力。其深而广泛的根系能够改善土壤结构、团聚体、持水能力和土壤有机碳，同时促进有益微生物群落、养分循环并抑制土传病原菌。在谷类作物系统中，这些过程可降低污染物的生物有效性，减少对合成肥料的依赖，并提高后茬作物的生产力和韧性。在水果系统中，包括果园和多年生种植系统，紫花苜蓿可通过间作或覆盖作物提供额外效益，包括氮输入、改善土壤保水、增强土壤生物活性以及潜在的经济回报。然而，紫花苜蓿生物量中污染物的积累仍是一个重要的食品和饲料安全问题，凸显了严格污染物监测和适当生物量管理的必要性。新兴方法，包括定向微生物接种、生物炭改良、间作策略和基因编辑技术，可能进一步提高修复效率和农业生态系统可持续性。总体而言，将紫花苜蓿纳入谷类和水果生产系统，代表了一种有前景的基于自然的途径，用于恢复退化土壤、提高土壤韧性、减少外部投入并支持可持续作物生产。未来研究应优先开展长期、多地点田间研究，以评估修复性能、污染物质量平衡、植物–微生物相互作用、生物量安全性以及谷类和水果生产系统的长期生产力。","Frontiers in Sustainable Food Systems",{"impact":225,"substance":304,"depth":225,"authority":148,"freshness":305,"relevant":33,"comment":720},"系统综述紫花苜蓿在污染土壤修复与农田可持续生产中的多功能作用，对绿色农业与土壤治理有参考价值，但属综述类论文，非重大突破。",[722],{"name":718,"url":715},[724,725,726,727,728],"绿色农业","生物固氮","紫花苜蓿","土壤修复","重金属污染",{"code":730,"slug":731,"name":732,"path":733,"parent_code":734},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[736],{"code":131,"slug":132,"name":133,"path":134,"parent_code":135},{"status":144,"locked":46,"version":47,"method":48,"evidence":738},{"candidates":739,"agricultural_evidence":25},[740,746,754,760,764,769,773,779],{"code":131,"score":30,"supported":25,"title":741,"abstract":742,"tags":745},[132],[151,743,132,744],"根际","rhizosphere",[727,728],{"code":730,"score":225,"supported":25,"title":747,"abstract":749,"tags":753},[748],"alfalfa",[750,751,752,748],"间作","苜蓿","intercropping",[726],{"code":755,"score":53,"supported":25,"title":756,"abstract":757,"tags":759},"C4",[],[758,751],"饲料",[726],{"code":52,"score":163,"supported":25,"title":761,"abstract":762,"tags":763},[],[38,56],[],{"code":66,"score":476,"supported":25,"title":765,"abstract":766,"tags":768},[],[767],"肥料",[725],{"code":162,"score":476,"supported":25,"title":770,"abstract":771,"tags":772},[],[166],[724],{"code":774,"score":67,"supported":25,"title":775,"abstract":776,"tags":778},"F2",[],[777],"基因编辑",[],{"code":78,"score":67,"supported":25,"title":780,"abstract":781,"tags":783},[],[782],"农业生态",[],[785,786],"紫花苜蓿 土壤修复 重金属","苜蓿 间作 果园 固氮","紫花苜蓿土壤修复重金属-3767","10.3389\u002Ffsufs.2026.1963157",{"doi":788,"openalex_id":790,"authors":791,"venue":718,"cited_by_count":99,"oa_url":715,"card":806,"direction":810,"ingested_from":107},"W7214674081",[792,794,796,798,800,802,804],{"name":793,"orcid":20},"E. Igwe",{"name":795,"orcid":20},"S. Diakite",{"name":797,"orcid":20},"B. Y. Konyannik",{"name":799,"orcid":20},"E. Tarvids",{"name":801,"orcid":20},"N. Y. Rebouh",{"name":803,"orcid":20},"D. O. Kapralova",{"name":805,"orcid":20},"P. E. Norman",{"tldr":807,"method":808,"finding":809,"direction":810,"opportunity":811},"综述紫花苜蓿通过植物修复与土壤恢复支撑谷类与果树可持续生产的多功能作用。","综述2000–2026年同行评议文献，聚焦植物提取、根际降解、生物固氮与间作。","苜蓿可修复污染土壤、固氮增碳并改善结构，但生物量污染物累积带来食饲安全风险。","农业绿色发展与碳","可研究苜蓿-微生物-生物炭联合修复的长期田间效果及污染物质量平衡与生物量安全处置。","2026-09-29T23:30:07.577120Z",{"id":814,"title":815,"url":816,"summary":817,"summary_zh":818,"content":20,"source_name":718,"source_url":816,"published_at":116,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":526,"score_detail":819,"sources":821,"tags":823,"primary_topic":829,"secondary_topics":833,"classification":840,"search_phrases":889,"slug":892,"view_count":33,"doi":893,"paper":894,"created_at":920},3763,"Improving drought tolerance and water use efficiency in dryland wheat and sorghum in the Texas High Plains","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1952813","Texas High Plains (THP) is one of the most productive agricultural regions in the USA, and the economic value of annual total crop production is nearly $2 billion. The area also serves as a major livestock center for fed cattle and, increasingly, other feeding industries such as dairy. The THP has a semi-arid climate with annual precipitation averaging about 480 mm and the seasonal precipitation only can meet part of the evapotranspiration (ET) requirement for high crop yields. For field crop production, drought is the most common factor limiting yield and water use efficiency (WUE). In this review, we focus on research progress and future challenges of dryland cropping systems in the area. Due to the low growing season precipitation and uneven monthly distributions, dryland crops are largely produced under crop-fallow conditions and wheat-fallow and wheat-sorghum fallow are common. Over the decades, both wheat and grain sorghum yields have increased from the level of 1,000–2,000 kg ha −1 in 1950s to >4,000 kg ha −1 in current time. Although improved management practices (e.g., conservation tillage and residue management) have contributed to increased yield and WUE, genetic improvement of drought tolerance also played an important role, particularly the winter wheat. Multiple-year field studies indicated that selecting cultivars with higher biomass and greater early vigor are beneficial to wheat production under drought. We further found that newer drought tolerant (DT) cultivars used soil water more efficiently than a relative older cultivar. Also, new DT cultivars can extract more water from deeper soil profile and have cooler canopy temperature under drought. In recent years, we are also evaluating remote-sensing tools at both ground and aerial levels (airplane and drone) to characterize wheat genotypes for drought tolerance. We demonstrated that infrared thermometer, thermal imaging, and spectral reflectance data can be used to characterize genotypic variation during growing season.","德克萨斯高平原（THP）是美国最具生产力的农业区之一，年度作物总产量的经济价值接近20亿美元。该地区还是主要的肉牛育肥中心，并且日益成为乳业等其他饲养产业的聚集地。THP属于半干旱气候，年均降水量约480毫米，季节性降水仅能满足高产作物蒸散（ET）需求的一部分。对于大田作物生产而言，干旱是限制作物产量和水分利用效率（WUE）的最常见因素。本文综述了该地区旱作种植制度的研究进展与未来挑战。由于生长季降水少且月分布不均，旱地作物主要在作物-休闲制度下生产，小麦-休闲和小麦-高粱休闲是常见模式。数十年来，小麦和高粱产量均从20世纪50年代的1,000–2,000 kg ha⁻¹提高至当前的4,000 kg ha⁻¹以上。尽管改进的管理措施（如保护性耕作和残茬管理）促进了产量和WUE的提升，但耐旱性的遗传改良也发挥了重要作用，尤其是冬小麦。多年田间研究表明，选择生物量较高和早期长势较强的品种有利于干旱条件下的小麦生产。我们进一步发现，较新的耐旱（DT）品种比相对较老的品种更有效地利用土壤水分。此外，新的DT品种能够从更深土层中提取更多水分，并在干旱条件下具有更低的冠层温度。近年来，我们还在评估地面和航空（飞机和无人机）尺度的遥感工具，以表征小麦基因型的耐旱性。我们证明，红外温度计、热成像和光谱反射数据可用于表征生长季内的基因型变异。",{"impact":225,"substance":119,"depth":225,"authority":148,"freshness":32,"relevant":33,"comment":820},"综述系统梳理了德州高原旱地小麦高粱抗旱与水分利用效率的研究进展，含多年田间数据与遥感表型新方法，对旱作农业与智慧育种有参考价值。",[822],{"name":718,"url":816},[824,825,826,827,828],"小麦","旱作农业","高粱","节水灌溉","遥感育种",{"code":830,"slug":831,"name":824,"path":832,"parent_code":734},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat",[834,839],{"code":835,"slug":836,"name":837,"path":838,"parent_code":734},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains",{"code":170,"slug":661,"name":662,"path":663,"parent_code":135},{"status":144,"locked":46,"version":47,"method":48,"evidence":841},{"candidates":842,"agricultural_evidence":25},[843,848,853,857,864,868,874,880,885],{"code":830,"score":225,"supported":25,"title":844,"abstract":845,"tags":847},[831],[824,846,831],"winter wheat",[824],{"code":835,"score":31,"supported":25,"title":849,"abstract":851,"tags":852},[850],"sorghum",[826,850],[826],{"code":170,"score":148,"supported":25,"title":854,"abstract":855,"tags":856},[334],[333,334],[],{"code":858,"score":163,"supported":25,"title":859,"abstract":860,"tags":863},"C2",[],[861,862],"肉牛","cattle",[],{"code":131,"score":163,"supported":25,"title":865,"abstract":866,"tags":867},[],[151,132],[],{"code":869,"score":259,"supported":25,"title":870,"abstract":871,"tags":873},"H3",[],[872],"evapotranspiration",[825,827],{"code":875,"score":67,"supported":25,"title":876,"abstract":877,"tags":879},"C",[],[878],"livestock",[],{"code":162,"score":67,"supported":25,"title":881,"abstract":882,"tags":884},[],[883],"保护性耕作",[],{"code":59,"score":67,"supported":25,"title":886,"abstract":887,"tags":888},[],[62],[],[890,891],"Texas High Plains 小麦 高粱 抗旱","旱地小麦 水分利用效率 遥感","TexasHighPlains小麦高粱抗旱-3763","10.3389\u002Ffsufs.2026.1952813",{"doi":893,"openalex_id":895,"authors":896,"venue":718,"cited_by_count":99,"oa_url":914,"card":915,"direction":105,"ingested_from":107},"W7214633314",[897,899,902,905,908,910,912],{"name":898,"orcid":20},"薛青武",{"name":900,"orcid":901},"Sushil Thapa","https:\u002F\u002Forcid.org\u002F0000-0002-5589-8124",{"name":903,"orcid":904},"Shuyu Liu","https:\u002F\u002Forcid.org\u002F0000-0003-4748-2900",{"name":906,"orcid":907},"Jackie Rudd","https:\u002F\u002Forcid.org\u002F0000-0001-9186-3386",{"name":909,"orcid":20},"Jourdan Bell",{"name":911,"orcid":20},"Thomas Marek",{"name":913,"orcid":20},"Amir Ibrahim","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1952813\u002Fpdf",{"tldr":916,"method":917,"finding":918,"direction":105,"opportunity":919},"综述美国德州高平原旱地小麦和高粱提高耐旱性与水分利用效率的研究进展与挑战。","多年田间试验、保护性耕作、耐旱品种选育及地面与航空遥感表型工具。","耐旱新品种能更高效利用土壤水、深层取水且冠层温度更低，遥感可表征基因型变异。","可结合无人机热成像与光谱反射构建旱地作物耐旱表型高通量筛选模型，加速品种选育。","2026-09-29T23:30:07.283028Z",{"id":922,"title":923,"url":924,"summary":925,"summary_zh":926,"content":20,"source_name":718,"source_url":924,"published_at":116,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":526,"score_detail":927,"sources":929,"tags":931,"primary_topic":937,"secondary_topics":942,"classification":948,"search_phrases":974,"slug":977,"view_count":33,"doi":978,"paper":979,"created_at":991},3762,"Nitrogen-centered nutrient innovation for decoupling agricultural productivity from greenhouse-gas intensity: evidence, limits, and implementation pathways","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1934315","Increasing agricultural production while lowering greenhouse-gas (GHG) intensity is a defining challenge for climate-smart agrifood systems. This critical review evaluates nitrogen-centered nutrient innovations through a boundary-explicit, two-axis framework: evidence confidence for a stated productivity-GHG decoupling outcome and implementation readiness in a specified context. The synthesis covers enhanced-efficiency fertilizers (EEFs), biological nitrogen fixation (BNF), biological nitrification inhibition (BNI), site-specific nutrient management (SSNM), 4R stewardship, precision placement, digital decision support, circular nitrogen recovery, and integrated nutrient management (INM). Direct evidence requires paired crop-output and GHG data; nitrogen-use efficiency, fertilizer savings, nutrient-loss reduction, or soil-quality responses alone are classified as partial or supporting evidence. Evidence confidence is highest for locally calibrated SSNM, 4R rate-timing-placement practices, and selected EEFs in high-surplus or high-loss systems, although performance varies with crop, soil, climate, baseline nitrogen rate, and the measured boundary. Implementation readiness is high for established stewardship practices, moderate and context-dependent for EEFs, digital tools, recovered products, and BNI, and low for many nanofertilizers and non-legume microbial inoculants. Circular products may provide life-cycle benefits, but results depend on allocation, substitution, transport, energy, indirect N 2 O, and avoided-waste assumptions. Short-term biogenic soil CO 2 flux is not treated as fossil climate forcing; soil-carbon benefits require stock-change measurements with defined depth, baseline, duration, and permanence. Evidence is concentrated in cereal and high-input systems, and publication, language, citation-selection, and outcome-reporting biases limit generalization. Reliable decoupling therefore requires integrated portfolios, transparent accounting, independent field validation, and performance-oriented policy rather than claims based on input reduction alone.","在降低温室气体（GHG）强度的同时提高农业生产，是气候智慧型农食系统面临的一项决定性挑战。本批判性综述通过一个边界明确的双轴框架，评估以氮为中心的养分创新：针对特定生产力—温室气体脱钩结果的证据置信度，以及在特定情境下的实施就绪度。综述涵盖增效肥料（EEFs）、生物固氮（BNF）、生物硝化抑制（BNI）、位点特异性养分管理（SSNM）、4R管理、精准施肥、数字决策支持、循环氮回收和综合养分管理（INM）。直接证据要求配对的作物产量与温室气体数据；仅凭氮利用效率、肥料节约、养分损失减少或土壤质量响应，被归类为部分证据或支持性证据。对于经本地校准的SSNM、4R用量—时间—位置实践，以及在高盈余或高损失系统中选用的增效肥料，证据置信度最高，但其表现因作物、土壤、气候、基线氮用量和所测边界而异。实施就绪度对于成熟的管理实践较高，对于增效肥料、数字工具、回收产品和生物硝化抑制为中等且依赖情境，对于许多纳米肥料和非豆科微生物接种剂则较低。循环产品可能提供生命周期效益，但结果取决于分配、替代、运输、能源、间接N₂O和避免废弃物等假设。短期生物源土壤CO₂通量不被视为化石气候强迫；土壤碳效益需要具有明确深度、基线、持续时间和永久性的碳储量变化测量。证据集中于谷类作物和高投入系统，发表、语言、引用选择和结果报告偏倚限制了外推。因此，可靠的脱钩需要综合方案组合、透明核算、独立田间验证和以绩效为导向的政策，而非仅基于投入减少的宣称。",{"impact":225,"substance":119,"depth":225,"authority":120,"freshness":305,"relevant":33,"comment":928},"系统综述氮素创新对农业生产与温室气体强度脱钩的证据与落地路径，方法框架清晰、结论审慎，对农业绿色低碳与智慧养分管理有参考价值。",[930],{"name":718,"url":924},[932,933,934,935,936],"养分管理","农业减排","农业绿色低碳","氮肥增效","数字决策支持",{"code":938,"slug":939,"name":940,"path":941,"parent_code":600},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",[943],{"code":66,"slug":944,"name":945,"path":946,"parent_code":947},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":144,"locked":46,"version":47,"method":48,"evidence":949},{"candidates":950,"agricultural_evidence":25},[951,957,962,966,970],{"code":938,"score":148,"supported":25,"title":952,"abstract":954,"tags":956},[953],"greenhouse",[955,953],"温室",[],{"code":66,"score":24,"supported":25,"title":958,"abstract":959,"tags":961},[],[960,767,932,70],"施肥",[932],{"code":131,"score":163,"supported":25,"title":963,"abstract":964,"tags":965},[],[151,132],[],{"code":138,"score":67,"supported":25,"title":967,"abstract":968,"tags":969},[],[159],[],{"code":412,"score":33,"supported":46,"title":971,"abstract":972,"tags":973},[],[],[933],[975,976],"氮肥增效 温室气体 减排","4R养分管理 精准施肥","氮肥增效温室气体减排-3762","10.3389\u002Ffsufs.2026.1934315",{"doi":978,"openalex_id":980,"authors":981,"venue":718,"cited_by_count":99,"oa_url":985,"card":986,"direction":294,"ingested_from":107},"W7214627055",[982],{"name":983,"orcid":984},"Bonface Ombasa Manono","https:\u002F\u002Forcid.org\u002F0000-0001-9150-2126","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1934315\u002Fpdf",{"tldr":987,"method":988,"finding":989,"direction":810,"opportunity":990},"综述氮素创新技术能否在保障产量的同时降低农业温室气体强度，并评估证据与实施就绪度。","边界明确的双轴框架，综述EEFs、BNF、BNI、SSNM、4R、数字决策支持等","SSNM、4R和部分EEFs证据最强，但效果受作物、土壤、气候和基线氮量影响。","需在多样化作物与低投入系统中开展独立田间验证，并建立透明核算与政策评估体系。","2026-09-29T23:30:07.225900Z",{"id":993,"title":994,"url":995,"summary":996,"summary_zh":997,"content":20,"source_name":718,"source_url":995,"published_at":116,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":526,"score_detail":998,"sources":1000,"tags":1002,"primary_topic":20,"secondary_topics":1007,"classification":1008,"search_phrases":1022,"slug":1025,"view_count":163,"doi":1026,"paper":1027,"created_at":1044},3761,"Multidimensional climate physical risk and reported farmland abandonment among rural farm households in China: evidence from household panel data","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1948860","Introduction For family-operated farms, keeping contracted land in cultivation is one practical dimension of resilience to climate stress. This study asks whether multidimensional climate physical risk is associated with reported farmland abandonment among rural households in China. Methods We matched the 2014, 2016, and 2018 China Labor-force Dynamics Survey (CLDS) waves to province-level Climate Physical Risk Index (CPRI) values from the preceding agricultural years. Abandonment is coded from a household report that some contracted cropland was uncultivated during the reference period; the survey does not show whether that status was temporary or persistent or the reason for it. The resulting unbalanced panel contains 19,311 household-year observations for 11,982 households in 27 provincial-level units. Household fixed-effects linear probability models include survey-wave fixed effects, province-clustered standard errors, and Webb-weight wild cluster bootstrap inference. Results A one-standard-deviation within-province increase in CPRI is associated with a 3.02-percentage-point increase in reported abandonment, approximately one-fifth of the sample mean. The association remains positive under alternative outcomes, exposure dates, sample restrictions, and model choices. The four hazards are jointly significant; only extreme low temperature is individually significant in the exploratory decomposition. Discussion Although the estimates are observational rather than causal, they suggest that multidimensional climate exposure may weaken land-use continuity in household farming systems.","引言 对于家庭经营的农场而言，保持承包地处于耕作状态是应对气候压力的一种实际韧性维度。本研究旨在探讨多维气候物理风险是否与中国农村家庭报告的耕地撂荒存在关联。方法 我们将2014年、2016年和2018年中国劳动力动态调查（CLDS）数据与前一农业年份的省级气候物理风险指数（CPRI）值进行匹配。撂荒的编码依据是家庭报告在参考期内部分承包耕地未进行耕作；该调查并未显示该状态是暂时性的还是持续性的，也未显示其原因。由此得到的非平衡面板包含27个省级单位中11,982个家庭的19,311个家庭-年份观测值。家庭固定效应线性概率模型纳入了调查波次固定效应、省级聚类标准误以及Webb权重野生聚类自举推断。结果 省内CPRI每增加一个标准差，与报告撂荒增加3.02个百分点相关，约为样本均值的五分之一。在替代结果变量、暴露日期、样本限制和模型选择下，该关联仍为正向。四种灾害联合显著；在探索性分解中，仅极端低温单独显著。讨论 尽管这些估计是观察性的而非因果性的，但它们表明多维气候暴露可能削弱家庭农业系统中土地利用的连续性。",{"impact":225,"substance":119,"depth":225,"authority":120,"freshness":305,"relevant":33,"comment":999},"基于CLDS三期面板与省级气候风险指数，量化气候物理风险与农户耕地撂荒的关联，方法规范、数据规模大，对农业风险管理有参考价值。",[1001],{"name":718,"url":995},[547,1003,1004,1005,1006],"农业风险","农户生计","耕地撂荒","CLDS",[],{"status":1009,"locked":46,"version":47,"method":48,"evidence":1010},"unclassified",{"candidates":1011,"agricultural_evidence":25,"reason":1021},[1012,1017],{"code":1013,"score":33,"supported":46,"title":1014,"abstract":1015,"tags":1016},"G6",[],[],[1004],{"code":170,"score":33,"supported":46,"title":1018,"abstract":1019,"tags":1020},[],[],[547],"缺少足够的主题证据",[1023,1024],"中国劳动力动态调查 耕地撂荒","气候物理风险 农户 撂荒","中国劳动力动态调查耕地撂荒-3761","10.3389\u002Ffsufs.2026.1948860",{"doi":1026,"openalex_id":1028,"authors":1029,"venue":718,"cited_by_count":99,"oa_url":995,"card":1039,"direction":810,"ingested_from":107},"W7214624950",[1030,1033,1035,1037],{"name":1031,"orcid":1032},"Ruofan Liao","https:\u002F\u002Forcid.org\u002F0009-0009-5058-7368",{"name":1034,"orcid":20},"Kexin Zhang",{"name":1036,"orcid":20},"Mi Tan",{"name":1038,"orcid":20},"Jianxu Liu",{"tldr":1040,"method":1041,"finding":1042,"direction":810,"opportunity":1043},"研究中国农户多维气候物理风险与报告耕地撂荒的关联。","匹配CLDS 2014-2018面板与省级气候物理风险指数，用固定效应模型。","省内CPRI每增一个标准差，报告撂荒率上升3.02个百分点。","可结合遥感验证撂荒、区分暂时\u002F持续撂荒，并探究极端低温等具体致灾机制。","2026-09-29T23:30:07.148843Z",{"id":1046,"title":1047,"url":1048,"summary":1049,"summary_zh":1050,"content":20,"source_name":718,"source_url":1048,"published_at":116,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":526,"score_detail":1051,"sources":1053,"tags":1055,"primary_topic":1058,"secondary_topics":1059,"classification":1065,"search_phrases":1123,"slug":1126,"view_count":67,"doi":1127,"paper":1128,"created_at":1183},3759,"Synergistic interaction effects of precision phosphorus fertigation and residue management on productivity, profitability, and resource-use efficiency of wheat under conservation agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1944629","Phosphorus (P) management under conventional surface-broadcast application in calcareous soils of the Indo-Gangetic Plains (IGP) suffers from severe fixation losses, limiting phosphorus-use efficiency (PUE) to 10–25% and contributing to yield gaps in wheat, a globally important food security crops. A two-year (2023–24 and 2024–25) split-plot field experiment was conducted within a mid-term conservation agriculture (CA)-based maize-wheat-greengram system to evaluate nine phosphorus fertigation levels and split application schedules through subsurface drip fertigation (SSDF), combined with two residue management practices (retention: R+, no-retention: R-), on growth, yield, water productivity, economics, and resource-use efficiency and post-harvest available phosphorus of wheat (cv. HD 2967). Treatments included no-P (control), SSDF of P as DAP at 50, 75, and 100% of the recommended dose (RDP; 60 kg P 2 O 5 ha −1 ) applied either fully as basal (10 DAS) or split (10 and 25 DAS), along with a nano-DAP foliar spray, compared against conventional CA with flood irrigation and basal broadcast P (CA-FI-100% RDP-B). SSDF treatments consumed only 178–275 mm of total water input against 437–452 mm under flood irrigation. Among SSDF treatments, 100% RDP applied in two splits (100% RDP-2S) consistently recorded the highest growth attributes, grain yield (6.23 and 6.31 t ha −1 ), and economic returns (BCR: 1.8–2) across both years, improving grain yield by 10.6–11.5% and net returns by 13.7% over CA-FI-100% RDP-B. Notably, 75% RDP-2S was statistically comparable to 100% RDP-2S for yield and profitability, demonstrating that a 25% reduction in P dose under precision SSDF does not compromise productivity. Total and irrigation water productivity improved by 127.7 and 156.7%, respectively, under 100% RDP-2S over CA-FI-100% RDP-B. Residue retention further enhanced productivity, increasing grain yield by 14.0–15.0% and net returns by 26.4–27.5%. Post-harvest soil available P was 18.0 and 43.3% higher under SSDF than under CA-FI-100% RDP-B at 0–15 and 15–30 cm, respectively. The interaction of split P fertigation and residue retention was consistently superior for grain yield and water productivity. These findings establish 75–100% RDP delivered through split SSDF, combined with residue retention, as an agronomically superior, water-efficient, economically viable strategy for sustainable wheat intensification in the semi-arid IGPs.","在印度河-恒河平原（IGP）石灰性土壤上，常规地表撒施条件下的磷（P）管理面临严重的固定损失，导致磷利用效率（PUE）仅为10–25%，并造成小麦——一种全球重要的粮食安全作物——的产量差距。在中期保护性农业（CA）为基础的玉米-小麦-绿豆轮作体系中，开展了一项为期两年（2023–24和2024–25）的裂区田间试验，评估九种磷肥滴灌水平和分次施用方案通过地下滴灌施肥（SSDF）对小麦（品种HD 2967）生长、产量、水分生产力、经济效益、资源利用效率及收获后土壤有效磷的影响，并结合两种秸秆管理方式（还田：R+，不还田：R-）。处理包括：无磷（对照）、以DAP形式通过SSDF施用推荐施磷量（RDP；60 kg P₂O₅ ha⁻¹）的50%、75%和100%，分别以全部基施（播种后10天）或分次施用（播种后10天和25天），同时配合纳米DAP叶面喷施，并与常规保护性农业配合漫灌和基施撒施磷（CA-FI-100% RDP-B）进行比较。SSDF处理的总水分投入仅为178–275 mm，而漫灌条件下为437–452 mm。在SSDF处理中，100% RDP分两次施用（100% RDP-2S）在两年中均持续表现出最高的生长指标、籽粒产量（6.23和6.31 t ha⁻¹）和经济效益（BCR：1.8–2），较CA-FI-100% RDP-B分别提高籽粒产量10.6–11.5%和净收益13.7%。值得注意的是，75% RDP-2S在产量和盈利能力上与100% RDP-2S无统计学差异，表明在精准SSDF下减少25%的磷用量不会影响生产力。与CA-FI-100% RDP-B相比，100% RDP-2S处理下总水分生产力和灌溉水分生产力分别提高了127.7%和156.7%。秸秆还田进一步提高了生产力，使籽粒产量增加14.0–15.0%，净收益增加26.4–27.5%。收获后土壤有效磷在0–15 cm和15–30 cm土层中，SSDF较CA-FI-100% RDP-B分别高出18.0%和43.3%。分次磷肥滴灌与秸秆还田的交互作用在籽粒产量和水分生产力方面始终表现最优。这些研究结果表明，通过分次SSDF施用75–100% RDP并结合秸秆还田，是一种农艺上优越、水分高效、经济可行的策略，可用于半干旱印度河-恒河平原小麦的可持续集约化生产。",{"impact":225,"substance":119,"depth":225,"authority":120,"freshness":305,"relevant":33,"comment":1052},"两年田间试验证实分次地下滴灌施磷配合秸秆还田可减磷25%并显著提升小麦产量与水肥利用效率，数据扎实、结论可靠，对半干旱地区精准水肥管理有直接参考价值。",[1054],{"name":718,"url":1048},[231,1056,824,883,1057],"精准施肥","水肥一体化",{"code":830,"slug":831,"name":824,"path":832,"parent_code":734},[1060,1061],{"code":66,"slug":944,"name":945,"path":946,"parent_code":947},{"code":162,"slug":1062,"name":1063,"path":1064,"parent_code":135},"sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":144,"locked":46,"version":47,"method":48,"evidence":1066},{"candidates":1067,"agricultural_evidence":25},[1068,1074,1078,1083,1090,1094,1098,1104,1109,1115,1119],{"code":66,"score":1069,"supported":25,"title":1070,"abstract":1072,"tags":1073},16,[1071],"fertigation",[960,1071],[1057,1056],{"code":830,"score":31,"supported":25,"title":1075,"abstract":1076,"tags":1077},[831],[824,831],[824],{"code":162,"score":87,"supported":25,"title":1079,"abstract":1081,"tags":1082},[1080],"conservation agriculture",[1080],[883],{"code":1084,"score":163,"supported":25,"title":1085,"abstract":1086,"tags":1089},"A3",[],[1087,1088],"玉米","maize",[],{"code":52,"score":163,"supported":25,"title":1091,"abstract":1092,"tags":1093},[],[38,56],[],{"code":131,"score":163,"supported":25,"title":1095,"abstract":1096,"tags":1097},[],[151,132],[],{"code":869,"score":163,"supported":25,"title":1099,"abstract":1100,"tags":1103},[],[1101,1102],"灌溉","irrigation",[],{"code":730,"score":67,"supported":25,"title":1105,"abstract":1106,"tags":1108},[],[1107],"轮作",[],{"code":1110,"score":67,"supported":25,"title":1111,"abstract":1112,"tags":1114},"H5",[],[1113],"秸秆还田",[],{"code":170,"score":67,"supported":25,"title":1116,"abstract":1117,"tags":1118},[],[333],[],{"code":176,"score":33,"supported":46,"title":1120,"abstract":1121,"tags":1122},[],[],[231],[1124,1125],"地下滴灌 磷肥 小麦","保护性耕作 秸秆还田 小麦","地下滴灌磷肥小麦-3759","10.3389\u002Ffsufs.2026.1944629",{"doi":1127,"openalex_id":1129,"authors":1130,"venue":718,"cited_by_count":99,"oa_url":1048,"card":1178,"direction":294,"ingested_from":107},"W7214579192",[1131,1133,1136,1138,1140,1142,1144,1147,1149,1151,1153,1155,1157,1160,1162,1164,1167,1170,1173,1176],{"name":1132,"orcid":20},"Kadagonda Nithinkumar",{"name":1134,"orcid":1135},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":1137,"orcid":20},"Devika A. R.",{"name":1139,"orcid":20},"Arjun Singh",{"name":1141,"orcid":20},"Susama Sudhishri",{"name":1143,"orcid":20},"Vinod Kumar Singh",{"name":1145,"orcid":1146},"Sanjay Singh Rathore","https:\u002F\u002Forcid.org\u002F0000-0001-6725-1478",{"name":1148,"orcid":20},"Sarvendra Kumar",{"name":1150,"orcid":20},"Teekam Singh",{"name":1152,"orcid":20},"Jitendra Rajput",{"name":1154,"orcid":20},"Pravin Kumar Upadhyaya",{"name":1156,"orcid":20},"R. N. Sahoo",{"name":1158,"orcid":1159},"ARPULA SAIRAM","https:\u002F\u002Forcid.org\u002F0009-0009-9034-6032",{"name":1161,"orcid":20},"Aye Aye San",{"name":1163,"orcid":20},"K. S. Sachin",{"name":1165,"orcid":1166},"Mummasani Asritha","https:\u002F\u002Forcid.org\u002F0009-0002-4619-9943",{"name":1168,"orcid":1169},"Ramanjit Kaur","https:\u002F\u002Forcid.org\u002F0000-0001-5117-050X",{"name":1171,"orcid":1172},"Mona Nagargade","https:\u002F\u002Forcid.org\u002F0000-0003-1062-512X",{"name":1174,"orcid":1175},"Vishal Tyagi","https:\u002F\u002Forcid.org\u002F0000-0001-5593-3076",{"name":1177,"orcid":20},"Subhash Babu",{"tldr":1179,"method":1180,"finding":1181,"direction":294,"opportunity":1182},"研究保护性农业下精准磷肥滴灌与秸秆还田对小麦产量、收益和资源效率的协同效应。","两年裂区田间试验，地下滴灌分次施磷与秸秆还田处理，测产量、水生产力与经济指标。","75–100%推荐磷量分次滴灌配秸秆还田可增产10%以上、节水超一倍且不减产。","可探索基于土壤磷素实时监测的变量施磷决策模型，并与秸秆还田互作机制结合优化。","2026-09-29T23:30:06.859116Z",{"id":1185,"title":1186,"url":1187,"summary":1188,"summary_zh":1189,"content":20,"source_name":1190,"source_url":1187,"published_at":116,"category":23,"cover_url":20,"hotness":24,"is_selected":46,"score":1191,"score_detail":1192,"sources":1194,"tags":1196,"primary_topic":1201,"secondary_topics":1205,"classification":1206,"search_phrases":1234,"slug":1237,"view_count":190,"doi":1238,"paper":1239,"created_at":1261},3783,"Multi-scale lodging in rice and wheat: bridging biomechanical traits, phenotypic detection, and intelligent harvesting systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1956108","Lodging severely compromises the productivity and harvestability of rice and wheat, yet the mechanistic links among molecular regulation, tissue structure, whole-plant mechanical failure, field phenotypes, and harvesting responses remain insufficiently integrated. This review establishes a mechanically consistent, plant-centered, cross-scale framework that connects genetic and biochemical regulation to quantifiable engineering parameters, including bending moment, section modulus, flexural rigidity, and resisting moment, and links these parameters to canopy-scale structural and optical phenotypes and ultimately to harvestability. We show that tissue-level material properties and organ-scale geometry jointly determine root anchorage, flexural rigidity, breaking resistance, and whole-plant lodging behavior. We further explain how mechanical failure reorganizes canopy geometry and optical properties, generating structural and spectral phenotypes that can be quantified through field phenotyping, unmanned aerial vehicle (UAV) remote sensing, three-dimensional sensing, and machine- and deep-learning approaches. Finally, we identify root–shoot mechanical coupling, standardized biomechanical measurements, cross-environment model validation, and the integration of perception with adaptive harvesting control as major priorities for future research. By unifying plant biology, mechanics, digital phenotyping, and engineering action within a common physical framework, this review provides a basis for lodging-resistant breeding, field monitoring, and intelligent crop management.","倒伏严重制约了水稻和小麦的生产力和可收获性，然而分子调控、组织结构、整株机械失效、田间表型与收获响应之间的机制联系仍未得到充分整合。本综述建立了一个力学一致、以植株为中心、跨尺度的框架，将遗传和生化调控与可量化的工程参数（包括弯矩、截面模量、抗弯刚度和抵抗弯矩）联系起来，并将这些参数与冠层尺度的结构和光学表型相关联，最终与可收获性相衔接。我们表明，组织层面的材料特性和器官尺度的几何形态共同决定了根系锚固、抗弯刚度、抗折强度和整株倒伏行为。我们进一步阐释了机械失效如何重组冠层几何形态和光学特性，从而产生可通过田间表型分析、无人机（UAV）遥感、三维传感以及机器学习和深度学习方法进行量化的结构和光谱表型。最后，我们指出根–冠机械耦合、标准化生物力学测量、跨环境模型验证以及感知与自适应收获控制的整合是未来研究的主要优先方向。通过将植物生物学、力学、数字表型分析和工程措施统一在一个共同的物理框架内，本综述为抗倒伏育种、田间监测和智能作物管理提供了基础。","Frontiers in Plant Science",79,{"impact":225,"substance":304,"depth":225,"authority":148,"freshness":32,"relevant":33,"comment":1193},"该综述跨尺度整合力学性状、表型检测与智能收获，为抗倒育种和智慧农业提供新框架，学术价值高但属细分领域进展。",[1195],{"name":1190,"url":1187},[231,1197,1198,1199,1200],"无人机遥感","水稻小麦","抗倒伏","智能收获",{"code":734,"slug":1202,"name":1203,"path":1204,"parent_code":20},"field-crops","大田作物","\u002Ftopic\u002Ffield-crops",[],{"status":1207,"locked":46,"version":47,"method":48,"evidence":1208},"partial",{"candidates":1209,"agricultural_evidence":25,"reason":1233},[1210,1217,1221,1225,1229],{"code":1211,"score":148,"supported":25,"title":1212,"abstract":1214,"tags":1216},"A1",[1213],"rice",[1215,1213],"水稻",[],{"code":830,"score":148,"supported":25,"title":1218,"abstract":1219,"tags":1220},[831],[824,831],[],{"code":59,"score":53,"supported":25,"title":1222,"abstract":1223,"tags":1224},[],[62,63],[1197],{"code":138,"score":163,"supported":25,"title":1226,"abstract":1227,"tags":1228},[],[403,267],[],{"code":176,"score":33,"supported":46,"title":1230,"abstract":1231,"tags":1232},[],[],[231],"一级领域明确，二级方向需要细分",[1235,1236],"水稻小麦 抗倒伏 力学性状","无人机遥感 作物倒伏 表型检测","水稻小麦抗倒伏力学性状-3783","10.3389\u002Ffpls.2026.1956108",{"doi":1238,"openalex_id":1240,"authors":1241,"venue":1190,"cited_by_count":99,"oa_url":1187,"card":1256,"direction":105,"ingested_from":107},"W7214576752",[1242,1245,1247,1250,1253],{"name":1243,"orcid":1244},"Tiantian Jing","https:\u002F\u002Forcid.org\u002F0009-0003-1962-8447",{"name":1246,"orcid":20},"Xianfei Xia",{"name":1248,"orcid":1249},"Zhong Tang","https:\u002F\u002Forcid.org\u002F0000-0002-2724-115X",{"name":1251,"orcid":1252},"Zhan Su","https:\u002F\u002Forcid.org\u002F0000-0002-8021-3369",{"name":1254,"orcid":1255},"Zhao Ding","https:\u002F\u002Forcid.org\u002F0009-0002-5151-7264",{"tldr":1257,"method":1258,"finding":1259,"direction":105,"opportunity":1260},"综述构建了水稻小麦倒伏从分子到收获的跨尺度力学框架，连接生物力学性状、表型检测与智能收获。","综述整合生物力学参数、冠层光学表型、无人机遥感与机器学习方法。","组织材料与器官几何共同决定倒伏行为，倒伏改变冠层光学表型可被遥感量化。","可研究根-冠力学耦合的标准化测量与跨环境模型验证，并开发感知-自适应收获控制闭环。","2026-09-29T23:30:21.067708Z"]