[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4812":3,"related-4812":124},{"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":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":93,"slug":96,"view_count":22,"doi":97,"paper":98,"created_at":123},4812,"Improving energy efficiency and carbon balance through conservation and precision agriculture with respect to conventional agriculture: a methodological approach","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffagro.2026.1916869","Introduction Conservation Agriculture (CA) is based on three core principles that, at farm scale, imply minimizing soil disturbance through tillage, maintaining a permanent soil cover with residues and diversifying the species in crop rotations. Precision Agriculture (PA) envisages a more efficient use of inputs by reducing the overlaps between machine passes. The combination of both contributes to a more sustainable agricultural system. Methods A four-season study was carried out to assess the energy consumption and carbon balance of a combined CA and PA system. No-tillage (NT) with residue retention and tractor guidance assistance (GA) was compared with conventional tillage (CT) in three annual crops: wheat, sunflower and legumes. To analyse the data obtained, a simple methodological approach was developed to facilitate energy and carbon balance calculations. Results No significant differences were found in yield between systems. NT with GA reduced fuel consumption by around 50%, and the energy parameters studied improved compared with CT. Across crops, energy efficiency relative to CT ranked as follows: sunflower > wheat > legumes. A similar pattern was observed for carbon use efficiency and carbon use productivity. Conclusions The results show the suitability of jointly using CA and PA techniques, such as tractor guidance under NT soil management.","引言 保护性农业（Conservation Agriculture，CA）基于三项核心原则，在农场尺度上即通过耕作最小化土壤扰动、利用残留物保持永久性土壤覆盖，以及通过轮作实现物种多样化。精准农业（Precision Agriculture，PA）则通过减少机具作业重叠来实现投入品的更高效利用。二者的结合有助于构建更可持续的农业系统。方法 本研究开展了为期四季的试验，以评估保护性农业与精准农业联合系统的能耗与碳平衡。研究在三种一年生作物——小麦、向日葵和豆科作物——中，比较了采用残茬保留与拖拉机导航辅助（guidance assistance，GA）的免耕（no-tillage，NT）与常规耕作（conventional tillage，CT）。为分析所获数据，开发了一种简便的方法学框架，以便于能耗与碳平衡的计算。结果 各系统间产量无显著差异。采用导航辅助的免耕使燃料消耗降低约50%，且所研究的各项能量参数较常规耕作均有改善。就各作物而言，相对于常规耕作的能量效率排序为：向日葵 > 小麦 > 豆科作物。碳利用效率和碳利用生产力也呈现类似规律。结论 结果表明，将保护性农业与精准农业技术（如免耕土壤管理下的拖拉机导航）联合应用具有适宜性。",null,"Frontiers in Agronomy","2026-10-12T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,21,17,13,9,1,"四季节田间试验量化免耕加导航对能耗与碳平衡的改善，方法可复用，对智慧农业绿色低碳方向有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"精准农业","农业碳排放","保护性农业","碳平衡","免耕",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":41,"reason":92},[39,46,54,61,68,75,81,87],{"code":40,"score":17,"supported":41,"title":42,"abstract":44,"tags":45},"I1",true,[43],"precision agriculture",[27,43],[27],{"code":47,"score":48,"supported":41,"title":49,"abstract":50,"tags":53},"A2",6,[],[51,52],"小麦","wheat",[],{"code":55,"score":48,"supported":41,"title":56,"abstract":57,"tags":60},"H2",[],[58,59],"土壤","soil",[],{"code":62,"score":63,"supported":41,"title":64,"abstract":65,"tags":67},"H8",4,[],[66],"conservation agriculture",[29],{"code":69,"score":70,"supported":41,"title":71,"abstract":72,"tags":74},"A4",3,[],[73],"向日葵",[],{"code":76,"score":70,"supported":41,"title":77,"abstract":78,"tags":80},"A7",[],[79],"轮作",[],{"code":82,"score":70,"supported":41,"title":83,"abstract":84,"tags":86},"F5",[],[85],"tractor",[],{"code":88,"score":22,"supported":14,"title":89,"abstract":90,"tags":91},"H4",[],[],[28],"多个产业证据相当，需要确认主归属",[94,95],"保护性农业 精准农业 碳平衡","免耕 拖拉机导航 能耗","保护性农业精准农业碳平衡-4812","10.3389\u002Ffagro.2026.1916869",{"doi":97,"openalex_id":99,"authors":100,"venue":10,"cited_by_count":113,"oa_url":114,"card":115,"direction":121,"ingested_from":122},"W7221566817",[101,104,107,110],{"name":102,"orcid":103},"Francisco Márquez‐García","https:\u002F\u002Forcid.org\u002F0000-0002-4333-0099",{"name":105,"orcid":106},"Miguel Angel Repullo-Ruibérriz de Torres","https:\u002F\u002Forcid.org\u002F0000-0001-7537-6983",{"name":108,"orcid":109},"Gottlieb Basch","https:\u002F\u002Forcid.org\u002F0000-0002-4696-6287",{"name":111,"orcid":112},"Emilio Jesús González-Sánchez","https:\u002F\u002Forcid.org\u002F0000-0002-8031-6595",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fagronomy\u002Farticles\u002F10.3389\u002Ffagro.2026.1916869\u002Fpdf",{"tldr":116,"method":117,"finding":118,"direction":119,"opportunity":120},"研究免耕结合拖拉机导航的保育与精准农业对能耗和碳平衡的影响。","四季田间试验，比较免耕+导航与常规耕作，计算能耗与碳平衡。","免耕+导航减油耗约50%，能效和碳效率提升，作物间向日葵最优。","农业绿色发展与碳","可扩展至更多作物和区域，量化长期碳汇与经济效益，优化智能导航策略。","智慧农业 \u002F 农业物联网","openalex","2026-10-13T23:30:10.612877Z",{"total":48,"page":22,"page_size":48,"items":125},[126,245,300,414,485,537],{"id":127,"title":128,"url":129,"summary":130,"summary_zh":131,"content":9,"source_name":132,"source_url":129,"published_at":133,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":134,"score_detail":135,"sources":139,"tags":141,"primary_topic":146,"secondary_topics":152,"classification":164,"search_phrases":220,"slug":223,"view_count":113,"doi":224,"paper":225,"created_at":244},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)","2026-10-21T00:00:00Z",80,{"impact":136,"substance":137,"depth":136,"authority":20,"freshness":21,"relevant":22,"comment":138},18,22,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[140],{"name":132,"url":129},[142,143,144,27,145],"智慧农业","农业人工智能","物联网","气候韧性",{"code":147,"slug":148,"name":149,"path":150,"parent_code":151},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[153,158],{"code":154,"slug":155,"name":156,"path":157,"parent_code":151},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":159,"slug":160,"name":161,"path":162,"parent_code":163},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":165,"locked":14,"version":35,"method":36,"evidence":166},"classified",{"candidates":167,"agricultural_evidence":41},[168,179,184,191,197,205,209,214],{"code":147,"score":136,"supported":41,"title":169,"abstract":171,"tags":178},[170],"artificial intelligence",[172,173,174,175,176,177],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[143],{"code":40,"score":180,"supported":41,"title":181,"abstract":182,"tags":183},16,[43],[27,43],[142,27],{"code":154,"score":13,"supported":41,"title":185,"abstract":186,"tags":190},[],[144,187,188,189],"边缘计算","IoT","edge computing",[144],{"code":159,"score":21,"supported":41,"title":192,"abstract":193,"tags":196},[],[194,195,160],"灌溉","水资源",[],{"code":198,"score":199,"supported":41,"title":200,"abstract":201,"tags":204},"H7",7,[],[202,203],"气候变化","climate change",[145],{"code":55,"score":48,"supported":41,"title":206,"abstract":207,"tags":208},[],[58,59],[],{"code":76,"score":70,"supported":41,"title":210,"abstract":211,"tags":213},[],[212],"间作",[],{"code":215,"score":70,"supported":41,"title":216,"abstract":217,"tags":219},"F3",[],[218],"养分管理",[],[221,222],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":224,"openalex_id":226,"authors":227,"venue":132,"cited_by_count":113,"oa_url":129,"card":238,"direction":121,"ingested_from":122},"W7223121456",[228,231,233,235],{"name":229,"orcid":230},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":232,"orcid":9},"Oluwadare OYEBODE",{"name":234,"orcid":9},"Iretiayo Adelaiye",{"name":236,"orcid":237},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":239,"method":240,"finding":241,"direction":242,"opportunity":243},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","农业人工智能与决策模型","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":246,"title":247,"url":248,"summary":249,"summary_zh":9,"content":9,"source_name":250,"source_url":248,"published_at":251,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":252,"score_detail":253,"sources":256,"tags":258,"primary_topic":261,"secondary_topics":265,"classification":266,"search_phrases":279,"slug":282,"view_count":113,"doi":283,"paper":284,"created_at":299},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":180,"substance":18,"depth":136,"authority":254,"freshness":13,"relevant":22,"comment":255},14,"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[257],{"name":250,"url":248},[142,143,27,259,260],"机器视觉","作物行检测",{"code":40,"slug":262,"name":263,"path":264,"parent_code":151},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":165,"locked":14,"version":35,"method":36,"evidence":267},{"candidates":268,"agricultural_evidence":41},[269,274],{"code":40,"score":270,"supported":41,"title":271,"abstract":272,"tags":273},5,[],[43],[142,27],{"code":147,"score":275,"supported":14,"title":276,"abstract":277,"tags":278},2,[],[],[143,259],[280,281],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539","10.1007\u002Fs11119-026-10473-z",{"doi":283,"openalex_id":285,"authors":286,"venue":250,"cited_by_count":113,"oa_url":9,"card":9,"direction":9,"ingested_from":122},"W7223117828",[287,290,293,296],{"name":288,"orcid":289},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":291,"orcid":292},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":294,"orcid":295},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":297,"orcid":298},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":9,"source_name":306,"source_url":303,"published_at":307,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":308,"score_detail":309,"sources":313,"tags":315,"primary_topic":320,"secondary_topics":326,"classification":327,"search_phrases":376,"slug":379,"view_count":113,"doi":380,"paper":381,"created_at":413},5480,"Sustainable Plant Disease Management: Current Advances, Challenges, and Future Directions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010661","Sustainable plant disease management is essential for maintaining agricultural productivity, environmental health, and global food security. Climate change has increased crop vulnerability to emerging and re-emerging plant diseases, posing significant challenges for agricultural sustainability. Although synthetic chemical pesticides have long been effective for disease control, their extensive use is increasingly questioned because of environmental contamination, pathogen resistance, and potential risks to human and ecosystem health. Consequently, plant disease management (PDM) is shifting toward more sustainable and integrated approaches. Future strategies are expected to incorporate multiple disciplines, including: (i) biological control and microbiome engineering to enhance natural plant defense mechanisms; (ii) advanced molecular and genomic technologies, such as CRISPR\u002FCas systems, RNA interference (RNAi), and epigenome editing, to develop durable disease resistance; (iii) smart delivery systems for the precise application of antimicrobial compounds and defense elicitors; (iv) nanotechnology-based platforms for the targeted delivery of nutrients, pesticides, and biocontrol agents, improving efficacy while reducing chemical inputs; (v) digital and precision agriculture technologies, including remote sensing, unmanned aerial vehicles (UAVs), and artificial intelligence (AI)-based diagnostics, to facilitate real-time disease surveillance and targeted interventions; and (vi) agroecological practices such as crop diversification, soil health restoration, and climate-smart farming to enhance agroecosystem resilience. These innovations aim not only to suppress plant pathogens but also to build sustainable, adaptive, and resilient agricultural systems that address future environmental and biological challenges.","可持续的植物病害管理对于维持农业生产力、环境健康和全球粮食安全至关重要。气候变化加剧了作物对新发和再发植物病害的脆弱性，给农业可持续性带来了重大挑战。尽管合成化学农药长期以来在病害防控中行之有效，但其广泛使用正日益受到质疑，原因包括环境污染、病原菌抗药性以及对人类和生态系统健康的潜在风险。因此，植物病害管理（plant disease management, PDM）正转向更加可持续和综合的途径。未来的策略预计将融合多个学科，包括：(i) 生物防治和微生物组工程，以增强植物天然防御机制；(ii) 先进的分子和基因组技术，如CRISPR\u002FCas系统、RNA干扰（RNAi）和表观基因组编辑，以培育持久抗病性；(iii) 用于精准施用抗菌化合物和防御诱导子的智能递送系统；(iv) 基于纳米技术的平台，用于靶向递送养分、农药和生物防治剂，在提高功效的同时减少化学投入；(v) 数字和精准农业技术，包括遥感、无人机（UAVs）和基于人工智能（AI）的诊断，以促进实时病害监测和靶向干预；以及(vi) 农业生态实践，如作物多样化、土壤健康恢复和气候智慧型农业，以增强农业生态系统的韧性。这些创新不仅旨在抑制植物病原菌，还旨在构建可持续、适应性强且具有韧性的农业系统，以应对未来的环境和生物挑战。","Sustainability","2026-10-20T00:00:00Z",81,{"impact":137,"substance":310,"depth":136,"authority":20,"freshness":311,"relevant":22,"comment":312},20,8,"系统综述可持续植物病害管理六大技术方向，涵盖AI诊断、纳米递送与基因编辑，对智慧农业与绿色防控具参考价值。",[314],{"name":306,"url":303},[142,27,316,317,318,319],"基因编辑","植物病害","生物防治","纳米技术",{"code":321,"slug":322,"name":323,"path":324,"parent_code":325},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",[],{"status":165,"locked":14,"version":35,"method":36,"evidence":328},{"candidates":329,"agricultural_evidence":41},[330,334,339,346,350,354,361,367,371],{"code":40,"score":311,"supported":41,"title":331,"abstract":332,"tags":333},[],[27,43],[142,27],{"code":321,"score":199,"supported":41,"title":335,"abstract":336,"tags":338},[],[337,318],"农药",[318],{"code":340,"score":48,"supported":41,"title":341,"abstract":342,"tags":345},"A8",[],[343,344],"粮食安全","food security",[],{"code":55,"score":48,"supported":41,"title":347,"abstract":348,"tags":349},[],[58,59],[],{"code":198,"score":48,"supported":41,"title":351,"abstract":352,"tags":353},[],[202,203],[],{"code":355,"score":48,"supported":41,"title":356,"abstract":357,"tags":360},"I3",[],[358,359],"遥感","remote sensing",[],{"code":362,"score":70,"supported":41,"title":363,"abstract":364,"tags":366},"H6",[],[365],"农业生态",[],{"code":147,"score":70,"supported":41,"title":368,"abstract":369,"tags":370},[],[170],[],{"code":372,"score":22,"supported":14,"title":373,"abstract":374,"tags":375},"F2",[],[],[316],[377,378],"CRISPR RNAi 作物抗病","基因编辑 智慧农业 植物病害 生物防治","CRISPRRNAi作物抗病-5480","10.3390\u002Fsu182010661",{"doi":380,"openalex_id":382,"authors":383,"venue":306,"cited_by_count":113,"oa_url":303,"card":408,"direction":121,"ingested_from":122},"W7223044776",[384,387,390,393,396,399,402,405],{"name":385,"orcid":386},"Karim M. Hassan","https:\u002F\u002Forcid.org\u002F0000-0002-1241-2307",{"name":388,"orcid":389},"Basma T. Abd-Elhalim","https:\u002F\u002Forcid.org\u002F0000-0002-3009-332X",{"name":391,"orcid":392},"Mohamed K. Abou El-Nasr","https:\u002F\u002Forcid.org\u002F0000-0003-1451-833X",{"name":394,"orcid":395},"Essam Yosef Abdul-Hafeez","https:\u002F\u002Forcid.org\u002F0000-0003-4744-4672",{"name":397,"orcid":398},"Ahmed N. Abdelhamid","https:\u002F\u002Forcid.org\u002F0000-0002-1326-7885",{"name":400,"orcid":401},"Mostafa Abdelkader","https:\u002F\u002Forcid.org\u002F0000-0003-2339-5087",{"name":403,"orcid":404},"Mohamed Abd El-Hamed Nasser","https:\u002F\u002Forcid.org\u002F0000-0002-0328-2969",{"name":406,"orcid":407},"Mahmoud A. A. Ali","https:\u002F\u002Forcid.org\u002F0000-0002-9062-0048",{"tldr":409,"method":410,"finding":411,"direction":121,"opportunity":412},"综述植物病害可持续管理的多学科前沿进展、挑战与未来方向。","文献综述，整合生物防治、基因编辑、纳米递送、数字农业与生态农艺。","未来病害管理需融合生物、分子、纳米与数字技术构建韧性农业系统。","遥感、无人机与AI诊断的实时病害监测闭环仍缺田间验证与低成本落地研究。","2026-10-21T23:30:09.242484Z",{"id":415,"title":416,"url":417,"summary":418,"summary_zh":419,"content":9,"source_name":420,"source_url":417,"published_at":307,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":252,"score_detail":421,"sources":424,"tags":426,"primary_topic":431,"secondary_topics":435,"classification":436,"search_phrases":465,"slug":468,"view_count":113,"doi":469,"paper":470,"created_at":484},5473,"Can regional value-chain integration decouple China's agri-food export growth from production-side direct CO2 attributed to export domestic value added? Evidence from belt and road partner economies","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1977325","This study examines whether regional value-chain (RVC) integration can support agri-food export growth while improving the carbon performance of China's export sectors. We build a 2007–2023 bilateral country–industry–year panel for C01 agriculture, forestry, and fishing and C03 food processing across 39 economies currently classified as Belt and Road partners. Asian Development Bank multi-regional input–output tables identify bilateral value-added paths, while Chinese sectoral fossil-energy data and Intergovernmental Panel on Climate Change emission factors are used to measure production-side direct fossil-energy CO 2 attributed to export domestic value added. The analysis combines logarithmic mean Divisia index decomposition, fixed-effects models, an external shift-share instrument, value-chain configuration tests, and a within–between decomposition. Four findings stand out. First, long-run carbon-intensity improvement more than offsets cumulative scale and structural pressures. Second, the baseline fixed-effects and IV estimates show a negative overall association between RVC cooperation and absolute production-side direct CO 2 , and this relationship remains negative in post-2013 samples. Third, within established partner–industry relationships, deeper RVC is significantly associated with lower carbon per unit of exports, while the export-scale and absolute-carbon coefficients are statistically insignificant. Fourth, denser two-way embedment is associated with higher carbon pressure, a more forward-oriented structure with lower carbon, and deep cooperation with a higher carbon association than shallow cooperation. The results suggest that climate-smart agri-food integration should combine production-efficiency gains with forward-oriented domestic value creation and careful monitoring of the scale effects of deeper production networks.","本研究考察区域价值链（RVC）整合能否在促进农食产品出口增长的同时改善中国出口部门的碳绩效。我们构建了2007—2023年双边国家—行业—年份面板数据，涵盖目前被归类为“一带一路”伙伴的39个经济体中的C01农、林、渔业和C03食品加工业。亚洲开发银行多区域投入产出表用于识别双边增加值路径，中国部门化石能源数据和政府间气候变化专门委员会排放因子用于测算出口国内增加值所对应的生产侧直接化石能源CO₂。分析结合了对数平均迪氏指数分解、固定效应模型、外部移动份额工具变量、价值链构型检验以及组内—组间分解。有四項发现尤为突出。第一，长期碳强度的改善足以抵消累积的规模压力和结构压力。第二，基准固定效应和IV估计显示，RVC合作与绝对生产侧直接CO₂总体上呈负相关，且这一关系在2013年之后的样本中仍为负。第三，在既有的伙伴—行业关系中，更深的RVC与单位出口碳含量较低显著相关，而出口规模和绝对碳的系数在统计上不显著。第四，更密集的双向嵌入与更高的碳压力相关，更偏向前的结构与更低的碳相关，深度合作比浅层合作具有更高的碳关联。结果表明，气候智慧型农食一体化应将生产效率提升与偏向前的国内价值创造相结合，并审慎监测更深生产网络的规模效应。","Frontiers in Sustainable Food Systems",{"impact":180,"substance":137,"depth":422,"authority":20,"freshness":21,"relevant":22,"comment":423},19,"基于39个一带一路伙伴国2007–2023年面板数据，方法扎实、结论有新意，对农业绿色贸易政策有参考价值，但属学术论文，公共影响有限。",[425],{"name":420,"url":417},[28,427,428,429,430],"一带一路","气候智慧农业","农产品贸易","全球价值链",{"code":198,"slug":432,"name":433,"path":434,"parent_code":163},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[],{"status":165,"locked":14,"version":35,"method":36,"evidence":437},{"candidates":438,"agricultural_evidence":41},[439,443,450,456,461],{"code":198,"score":199,"supported":41,"title":440,"abstract":441,"tags":442},[],[202,203],[428],{"code":444,"score":48,"supported":41,"title":445,"abstract":446,"tags":449},"E1",[],[447,448],"食品加工","food processing",[],{"code":451,"score":70,"supported":41,"title":452,"abstract":453,"tags":455},"D",[],[454],"渔业",[],{"code":457,"score":22,"supported":14,"title":458,"abstract":459,"tags":460},"E5",[],[],[429],{"code":88,"score":22,"supported":14,"title":462,"abstract":463,"tags":464},[],[],[28],[466,467],"中国 农食产品 出口 碳排放","一带一路 区域价值链","中国农食产品出口碳排放-5473","10.3389\u002Ffsufs.2026.1977325",{"doi":469,"openalex_id":471,"authors":472,"venue":420,"cited_by_count":113,"oa_url":417,"card":479,"direction":119,"ingested_from":122},"W7223067445",[473,475,477],{"name":474,"orcid":9},"Kunlin Tang",{"name":476,"orcid":9},"Wei Yang",{"name":478,"orcid":9},"Yuan Huang",{"tldr":480,"method":481,"finding":482,"direction":119,"opportunity":483},"研究区域价值链整合能否使中国农食出口增长与生产侧直接CO2脱钩。","2007–2023年39个一带一路经济体的双边面板，用ADB多区域投入产出表与L","长期碳强度改善抵消规模压力，RVC合作总体降低绝对CO2，但深度双向嵌入会推高碳压力。","可探究前向型国内增加值创造与深度生产网络的规模效应监测机制，识别气候智慧型农食整合路径。","2026-10-21T23:30:07.454815Z",{"id":486,"title":487,"url":488,"summary":489,"summary_zh":9,"content":9,"source_name":490,"source_url":9,"published_at":491,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":492,"score_detail":493,"sources":496,"tags":498,"primary_topic":501,"secondary_topics":504,"classification":505,"search_phrases":524,"slug":527,"view_count":113,"doi":9,"paper":528,"created_at":536},5462,"Architecture diverse TabNet ensemble for crop type classification using harmonized multisource soil data（基于多源土壤数据融合的架构多样性TabNet集成作物类型分类）","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75366-7","对18,237条多源土壤记录、7类作物构建TabNet集成框架。TPE优化使对数损失由0.3325降至0.2566，集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。作者称尚未做外部验证。","Scientific Reports 2026-10-18","2026-10-18T00:00:00Z",70,{"impact":494,"substance":310,"depth":19,"authority":20,"freshness":311,"relevant":22,"comment":495},12,"方法新颖、数据规模可观且指标有实质提升，但缺乏外部验证、属细分方法研究，公共影响有限，可作候选而非必选。",[497],{"name":490,"url":488},[143,173,27,499,500],"土壤数据","作物分类",{"code":55,"slug":59,"name":502,"path":503,"parent_code":163},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[],{"status":165,"locked":14,"version":35,"method":36,"evidence":506},{"candidates":507,"agricultural_evidence":41},[508,512,516,520],{"code":55,"score":310,"supported":41,"title":509,"abstract":510,"tags":511},[58,59],[58],[499],{"code":147,"score":275,"supported":14,"title":513,"abstract":514,"tags":515},[],[],[143,173],{"code":40,"score":22,"supported":14,"title":517,"abstract":518,"tags":519},[],[],[27],{"code":355,"score":22,"supported":14,"title":521,"abstract":522,"tags":523},[],[],[500],[525,526],"TabNet 作物类型分类","多源土壤数据 作物分类","TabNet作物类型分类-5462",{"doi":9,"openalex_id":9,"authors":529,"venue":9,"cited_by_count":113,"oa_url":9,"card":530,"direction":242,"ingested_from":535},[],{"tldr":531,"method":532,"finding":533,"direction":242,"opportunity":534},"构建架构多样性TabNet集成，用多源土壤数据分类7类作物。","18,237条多源土壤记录，TPE调参的TabNet集成，对比XGBoost。","集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。","尚未外部验证，可跨区域迁移验证并融合遥感时序特征提升泛化。","agent","2026-10-21T00:10:08.428584Z",{"id":538,"title":539,"url":540,"summary":541,"summary_zh":542,"content":9,"source_name":543,"source_url":540,"published_at":544,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":545,"score_detail":546,"sources":550,"tags":552,"primary_topic":554,"secondary_topics":555,"classification":556,"search_phrases":581,"slug":584,"view_count":63,"doi":585,"paper":586,"created_at":612},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences","2026-10-19T00:00:00Z",93,{"impact":547,"substance":548,"depth":422,"authority":17,"freshness":21,"relevant":22,"comment":549},26,24,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[551],{"name":543,"url":540},[142,143,27,358,553],"全球评估",{"code":147,"slug":148,"name":149,"path":150,"parent_code":151},[],{"status":165,"locked":14,"version":35,"method":36,"evidence":557},{"candidates":558,"agricultural_evidence":41},[559,564,568,572,576],{"code":40,"score":560,"supported":41,"title":561,"abstract":562,"tags":563},11,[],[142,27,43],[142,27],{"code":147,"score":199,"supported":41,"title":565,"abstract":566,"tags":567},[],[173,175],[143],{"code":159,"score":48,"supported":41,"title":569,"abstract":570,"tags":571},[],[194,160],[],{"code":355,"score":63,"supported":41,"title":573,"abstract":574,"tags":575},[],[358],[358],{"code":321,"score":70,"supported":41,"title":577,"abstract":578,"tags":580},[],[579],"除草",[],[582,583],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":585,"openalex_id":587,"authors":588,"venue":543,"cited_by_count":113,"oa_url":540,"card":607,"direction":121,"ingested_from":122},"W7222974188",[589,592,595,598,601,604],{"name":590,"orcid":591},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":593,"orcid":594},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":596,"orcid":597},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":599,"orcid":600},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":602,"orcid":603},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":605,"orcid":606},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":608,"method":609,"finding":610,"direction":121,"opportunity":611},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z"]