[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2308":3,"related-2308":127},{"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":32,"secondary_topics":38,"classification":45,"search_phrases":102,"slug":105,"view_count":106,"doi":107,"paper":108,"created_at":126},2308,"Coffee phytobiome dynamics: integrating multitrophic interactions and plant physiology for climate-resilient production","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffmicb.2026.1951767","Coffee ( Coffea arabica L. and Coffea canephora Pierre ex A. Froehner) is a globally important perennial plantation crop that sustains the livelihoods of millions of smallholder farmers while making substantial contributions to agricultural economies worldwide global agricultural economies. Nevertheless, sustainable coffee production is increasingly constrained by climate change, declining soil fertility, emerging pests and diseases and the environmental costs associated with intensive use of synthetic agrochemicals. Recent advances in plant microbiome research have transformed the understanding of coffee from an individual organism to a holobiont, where the host plant and its associated microorganisms operate as an integrated biological system. The coffee phytobiome encompasses diverse microbial communities inhabiting the rhizosphere, rhizoplane, endosphere, phyllosphere, anthosphere, carposphere and spermosphere, together with complex multitrophic interactions involving shade trees, soil fauna, insects and the surrounding environment. These interactions collectively regulate nutrient acquisition, carbon assimilation, water-use efficiency, hormonal balance, stress tolerance, immune responses and overall plant productivity. This review critically examines current advances in multitrophic plant–microbe interactions that influence physiological adaptation in coffee, with particular emphasis on sustainable production under Indian agroecological conditions. It highlights the functional functions of plant growth-promoting rhizobacteria (PGPR) and arbuscular mycorrhizal fungi (AMF), endophytic microorganisms and other beneficial microbes in enhancing root development, nutrient cycling, and resilience to biotic and abiotic stresses. Furthermore, the review evaluates recent progress in metagenomics, metatranscriptomics, metabolomics and microbiome engineering for harnessing indigenous microbial resources. Finally, it outlines future research priorities integrating plant physiology, microbial ecology, systems biology, and precision agriculture to develop climate-resilient, resource-efficient, and environmentally sustainable coffee production systems.","咖啡（Coffea arabica L. 和 Coffea canephora Pierre ex A. Froehner）是全球重要的多年生种植作物，维系着数百万小农户的生计，同时为全球农业经济作出重大贡献。然而，可持续咖啡生产日益受到气候变化、土壤肥力下降、新发病虫害以及集约化使用合成农用化学品所带来的环境成本的制约。近年来植物微生物组研究的进展已将人们对咖啡的认知从单一生物体转变为全息生物（holobiont），即宿主植物与其相关微生物作为一个整合的生物系统协同运作。咖啡植物微生物组（phytobiome）涵盖栖息于根际、根表、内生圈、叶际、花际、果际和种际的多样化微生物群落，以及涉及遮荫树、土壤动物、昆虫和周围环境的复杂多营养级相互作用。这些相互作用共同调控养分获取、碳同化、水分利用效率、激素平衡、胁迫耐受性、免疫应答和整体植物生产力。本文综述批判性地审视了影响咖啡生理适应的多营养级植物–微生物相互作用的最新进展，特别关注印度农业生态条件下的可持续生产。文章重点阐述了植物促生根际细菌（PGPR）和丛枝菌根真菌（AMF）、内生微生物及其他有益微生物在促进根系发育、养分循环以及增强生物和非生物胁迫韧性方面的功能。此外，本文评估了宏基因组学、宏转录组学、代谢组学和微生物组工程在利用本土微生物资源方面的最新进展。最后，文章展望了未来研究重点，即整合植物生理学、微生物生态学、系统生物学和精准农业，以开发气候韧性、资源高效和环境可持续的咖啡生产系统。",null,"Frontiers in Microbiology","2026-09-10T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,14,8,1,"系统综述咖啡植物微生物组与多营养级互作，为气候韧性生产提供微生物组工程与组学新思路，专业性强但属细分作物领域，产业影响中等。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","气候韧性","土壤健康","咖啡产业","植物微生物组",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,59,66,74,81,89,96],{"code":33,"score":52,"supported":53,"title":54,"abstract":56,"tags":58},15,true,[55],"coffee",[57,55],"咖啡",[30],{"code":40,"score":13,"supported":53,"title":60,"abstract":61,"tags":65},[],[62,63,41,64],"土壤","根际","rhizosphere",[29],{"code":67,"score":68,"supported":53,"title":69,"abstract":70,"tags":73},"H7",7,[],[71,72],"气候变化","climate change",[28],{"code":75,"score":68,"supported":53,"title":76,"abstract":77,"tags":80},"I1",[],[78,79],"精准农业","precision agriculture",[27],{"code":82,"score":83,"supported":53,"title":84,"abstract":85,"tags":88},"G2",6,[],[86,87],"小农户","smallholder",[],{"code":90,"score":91,"supported":53,"title":92,"abstract":93,"tags":95},"H6",3,[],[94],"农业生态",[],{"code":97,"score":91,"supported":53,"title":98,"abstract":99,"tags":101},"J3",[],[100],"全球农业",[],[103,104],"植物微生物组 咖啡产业 土壤健康 智慧农业","植物微生物组 咖啡产业","植物微生物组咖啡产业土壤健康智慧农业-2308",2,"10.3389\u002Ffmicb.2026.1951767",{"doi":107,"openalex_id":109,"authors":110,"venue":10,"cited_by_count":116,"oa_url":117,"card":118,"direction":124,"ingested_from":125},"W7212145113",[111,114],{"name":112,"orcid":113},"Somashekhargouda Patil","https:\u002F\u002Forcid.org\u002F0000-0002-0362-0745",{"name":115,"orcid":9},"C. S. Rudragouda",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fmicrobiology\u002Farticles\u002F10.3389\u002Ffmicb.2026.1951767\u002Fpdf",{"tldr":119,"method":120,"finding":121,"direction":122,"opportunity":123},"综述咖啡植物微生物组与多营养级互作，提出整合植物生理与微生物组提升气候韧性生产。","综述微生物组学、多组学与微生物组工程，聚焦印度咖啡农生态。","咖啡作为全生物体，其根际、内生等微生物组通过多营养级互作调控养分、抗逆与生产力。","农业绿色发展与碳","可探索咖啡全生物体多组学数据整合与微生物组工程，构建气候韧性精准调控模型。","智慧农业 \u002F 农业物联网","openalex","2026-09-13T23:30:11.231069Z",{"total":83,"page":22,"page_size":83,"items":128},[129,173,259,387,456,558],{"id":130,"title":131,"url":132,"summary":133,"summary_zh":9,"content":9,"source_name":134,"source_url":9,"published_at":135,"category":136,"cover_url":9,"hotness":13,"is_selected":14,"score":137,"score_detail":138,"sources":144,"tags":146,"primary_topic":152,"secondary_topics":153,"classification":155,"search_phrases":168,"slug":171,"view_count":106,"doi":9,"paper":9,"created_at":172},2386,"安徽省农科院茶叶所专家团队赴休宁县开展茶园土壤质量安全专项调查","https:\u002F\u002Fcys.aaas.org.cn\u002F4304505\u002F71046161.html","9月7日国家茶叶产业技术体系岗位专家、安徽省农科院茶园环境与地力提升团队负责人苏有健率团队深入休宁县重点产茶乡镇，开展茶园土壤质量安全调查取样工作。调查发现部分茶园存在土壤酸化趋势加剧、有机质下降、氮磷钾比例失衡等潜在风险，专家组当场提出'对症开方'。","安徽省农业科学院茶叶所 | 2026-09-09","2026-09-09T00:00:00Z","报道",60,{"impact":139,"substance":140,"depth":20,"authority":141,"freshness":142,"relevant":22,"comment":143},12,16,13,5,"省级科研机构专家实地调查，揭示茶园土壤酸化、有机质下降等实质风险并提出对策，专业性与信源权威度较好，但属区域性技术调查，影响层级有限，时效性一般。",[145],{"name":134,"url":132},[147,148,149,150,151],"土壤酸化","农业科技服务","茶园土壤","地力提升","茶叶产业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[154],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":156},{"candidates":157,"agricultural_evidence":53},[158,164],{"code":33,"score":159,"supported":53,"title":160,"abstract":162,"tags":163},11,[161],"茶叶",[161],[],{"code":40,"score":159,"supported":53,"title":165,"abstract":166,"tags":167},[62],[62],[],[169,170],"农业科技服务 土壤酸化 地力提升 茶叶产业","农业科技服务 土壤酸化","农业科技服务土壤酸化地力提升茶叶产业-2386","2026-09-14T00:06:30.257175Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":9,"source_name":179,"source_url":176,"published_at":180,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":181,"score_detail":182,"sources":186,"tags":188,"primary_topic":191,"secondary_topics":192,"classification":199,"search_phrases":240,"slug":243,"view_count":22,"doi":244,"paper":245,"created_at":258},4896,"Site-Specific Precision Cover Cropping for Soil Health and Environmental Sustainability Using Remote Sensing and Multi-Criteria Analysis","https:\u002F\u002Fdoi.org\u002F10.21663\u002Feeg-d-26-00041","ABSTRACT Cover crops, which are plants or mixtures of plants grown primarily to enhance and protect soil health, are a key strategy in sustainable agriculture. Traditionally, they have been uniformly planted in a field typically during off-season when the main crops are not in competition. However, advancements in precision agriculture, particularly the integration of remote-sensing technology, demand more efficient seeding strategies based on field variability. The aims of this study were to assess the specific needs of a field in Aurora County, SD, and determine the type and optimal combinations of the cover crop mix. The cover crop species considered in this study included legumes, grasses, and brassicas. The remote-sensing data were extracted from Sentinel-2 satellite imagery. Soil samples were collected from 0 to 30 cm depth, and soil properties were measured. Additionally, topographic maps such as slope and aspect were extracted from light detection and ranging (LiDAR) data. The results of precision cover crop analyses showed that 6.5 percent (7.8 ha), 20.6 percent (24.7 ha), 38.1 percent (45.7 ha), 29.6 percent (35.5 ha), and 5.3 percent (6.3 ha) of the studied field should planted in the following mixtures of crops: rye, radish, and vetch; triticale, turnip, and vetch; triticale, radish, and clover; triticale, rapeseed, and pea; and barley, flax, and clover, respectively. The mixtures significantly improved soil properties by reducing compaction, increasing residue C\u002FN ratio, and enhancing weed suppression compared with the control. The resulting map should be interpreted as a decision-support recommendation rather than a fully validated field prescription.","摘要 覆盖作物（cover crops）是指主要为了提升和保护土壤健康而种植的植物或植物混合体，是可持续农业的一项关键策略。传统上，覆盖作物通常在非主作物竞争的季节以均匀方式播种于田间。然而，精准农业的进步，尤其是遥感技术的整合，要求基于田块变异性制定更高效的播种策略。本研究旨在评估南达科他州奥罗拉县某田块的具体需求，并确定覆盖作物混合物的类型及最优组合。本研究考虑的覆盖作物种类包括豆科、禾本科和十字花科植物。遥感数据提取自Sentinel-2卫星影像。土壤样品采集深度为0至30 cm，并测定了土壤性质。此外，从激光雷达（LiDAR）数据中提取了坡度和坡向等地形图。精准覆盖作物分析结果表明，研究田块中分别有6.5%（7.8 ha）、20.6%（24.7 ha）、38.1%（45.7 ha）、29.6%（35.5 ha）和5.3%（6.3 ha）的面积应种植以下作物混合物：黑麦、萝卜和野豌豆；小黑麦、芜菁和野豌豆；小黑麦、萝卜和三叶草；小黑麦、油菜和豌豆；以及大麦、亚麻和三叶草。与对照相比，这些混合物通过降低紧实度、提高残体碳氮比和增强杂草抑制效果，显著改善了土壤性质。所得图谱应被解读为决策支持建议，而非经过充分验证的田间处方。","Environmental and Engineering Geoscience","2026-10-13T00:00:00Z",72,{"impact":139,"substance":183,"depth":19,"authority":141,"freshness":184,"relevant":22,"comment":185},21,9,"该研究将遥感与多准则分析结合用于精准覆盖作物制图，方法新颖、数据具体，对农业信息化与可持续农业有参考价值，但属单点田间案例，影响范围有限。",[187],{"name":179,"url":176},[27,78,189,29,190],"遥感","覆盖作物",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[193],{"code":194,"slug":195,"name":196,"path":197,"parent_code":198},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":200},{"candidates":201,"agricultural_evidence":53},[202,206,211,215,222,229,235],{"code":40,"score":52,"supported":53,"title":203,"abstract":204,"tags":205},[41],[62,41],[29],{"code":194,"score":139,"supported":53,"title":207,"abstract":209,"tags":210},[208],"remote sensing",[189],[189],{"code":75,"score":21,"supported":53,"title":212,"abstract":213,"tags":214},[],[78,79],[27,78],{"code":216,"score":83,"supported":53,"title":217,"abstract":218,"tags":221},"A4",[],[219,220],"油菜","rapeseed",[],{"code":223,"score":83,"supported":53,"title":224,"abstract":225,"tags":228},"H8",[],[226,227],"可持续农业","sustainable agriculture",[],{"code":230,"score":91,"supported":53,"title":231,"abstract":232,"tags":234},"B1",[],[233],"萝卜",[],{"code":236,"score":22,"supported":14,"title":237,"abstract":238,"tags":239},"A7",[],[],[190],[241,242],"Sentinel-2 遥感 覆盖作物","精准覆盖作物 土壤健康","Sentinel-2遥感覆盖作物-4896","10.21663\u002Feeg-d-26-00041",{"doi":244,"openalex_id":246,"authors":247,"venue":179,"cited_by_count":116,"oa_url":9,"card":252,"direction":256,"ingested_from":125},"W7221669942",[248,250],{"name":249,"orcid":9},"Ali Mirzakhani Nafchi",{"name":251,"orcid":9},"Salman Mirzaee",{"tldr":253,"method":254,"finding":255,"direction":256,"opportunity":257},"利用遥感与多准则分析为南达科他州田块制定精准覆盖作物混播方案。","Sentinel-2影像、LiDAR地形与土壤采样，结合多准则分析。","田块被划分为5个管理区，各推荐不同覆盖作物混播，显著改善土壤紧实度、残体C\u002FN比和抑草效果。","农业遥感与作物表型","可验证该决策支持地图的田间效果，并融合实时物联网数据优化动态推荐。","2026-10-14T23:30:27.077312Z",{"id":260,"title":261,"url":262,"summary":263,"summary_zh":264,"content":9,"source_name":265,"source_url":262,"published_at":266,"category":12,"cover_url":9,"hotness":13,"is_selected":53,"score":267,"score_detail":268,"sources":271,"tags":273,"primary_topic":277,"secondary_topics":278,"classification":279,"search_phrases":351,"slug":354,"view_count":116,"doi":355,"paper":356,"created_at":386},4832,"Applications and Future Prospects of Plant Multi-Omics Technologies in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms27209034","Against the backdrop of global population growth, intensifying climate change, and limited arable land, ensuring food security and promoting sustainable agriculture have become critical issues. The limitations of conventional agricultural systems are becoming increasingly apparent, creating an urgent need for innovation driven by science and technology. In this context, plant multi-omics technologies serve as the foundational data and technical cornerstone of systems biology and precision agriculture. Integrating multi-level data from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics offers substantial advantages for systematically understanding and improving plants. Unlike previous reviews that mainly catalog omics technologies or focus on individual crops, this review adopts a unified discovery-to-translation framework and critically evaluates the strength of evidence across applications. This review focuses on cutting-edge applications of multi-omics in three key areas: improving the traits of cereal crops (rice, wheat and maize), remediating soil health, and innovating products such as biofertilizers and biostimulants for agriculture. We systematically explore the roles of multi-omics in accelerating breeding processes, deciphering complex agronomic traits, restoring soil ecosystems, and constructing intelligent decision-support systems. The review also examines current challenges, including the complexity of data integration and the difficulties of predicting genotype-by-environment interactions. Furthermore, it outlines future directions involving deeper convergence with artificial intelligence, high-throughput phenomics, and digital twins. In summary, plant multi-omics technology is a vital tool for unravelling the mysteries of plant life and is also a key enabler of intelligent, precise and sustainable agricultural transformation.","在全球人口增长、气候变化加剧和耕地有限的背景下，保障粮食安全和促进可持续农业已成为关键议题。传统农业系统的局限性日益显现，迫切需要由科学和技术驱动的创新。在此背景下，植物多组学技术作为系统生物学和精准农业的基础数据与技术基石，整合基因组学、转录组学、蛋白质组学、代谢组学、表观基因组学和微生物组学等多层次数据，为系统理解和改良植物提供了显著优势。与以往主要罗列组学技术或聚焦单一作物的综述不同，本综述采用统一的“从发现到转化”框架，并批判性评估各应用中的证据强度。本综述聚焦多组学在三个关键领域的前沿应用：改良谷类作物（水稻、小麦和玉米）的性状、修复土壤健康，以及创新生物肥料和生物刺激素等农业产品。我们系统探讨了多组学在加速育种进程、解析复杂农艺性状、恢复土壤生态系统和构建智能决策支持系统中的作用。本综述还审视了当前挑战，包括数据整合的复杂性和预测基因型与环境互作的困难。此外，文章展望了未来方向，涉及与人工智能、高通量表型组学和数字孪生的更深层次融合。总之，植物多组学技术是揭示植物生命奥秘的重要工具，也是智能、精准和可持续农业转型的关键使能技术。","International Journal of Molecular Sciences","2026-10-12T00:00:00Z",83,{"impact":269,"substance":183,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":270},22,"核心期刊综述，系统梳理植物多组学在精准农业中的应用与AI、数字孪生等未来方向，专业深度与信源权威度俱佳，值得进入每日精选。",[272],{"name":265,"url":262},[27,274,275,78,29,276],"农业人工智能","种业振兴","多组学",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":280},{"candidates":281,"agricultural_evidence":53},[282,288,292,299,306,313,320,324,328,335,341,346],{"code":75,"score":283,"supported":53,"title":284,"abstract":285,"tags":287},19,[79],[78,286,79],"数字孪生",[27,78],{"code":40,"score":68,"supported":53,"title":289,"abstract":290,"tags":291},[],[62,41],[29],{"code":293,"score":83,"supported":53,"title":294,"abstract":295,"tags":298},"A1",[],[296,297],"水稻","rice",[],{"code":300,"score":83,"supported":53,"title":301,"abstract":302,"tags":305},"A2",[],[303,304],"小麦","wheat",[],{"code":307,"score":83,"supported":53,"title":308,"abstract":309,"tags":312},"A3",[],[310,311],"玉米","maize",[],{"code":314,"score":83,"supported":53,"title":315,"abstract":316,"tags":319},"A8",[],[317,318],"粮食安全","food security",[],{"code":67,"score":83,"supported":53,"title":321,"abstract":322,"tags":323},[],[71,72],[],{"code":223,"score":83,"supported":53,"title":325,"abstract":326,"tags":327},[],[226,227],[],{"code":329,"score":330,"supported":53,"title":331,"abstract":332,"tags":334},"I2",4,[],[333],"artificial intelligence",[274],{"code":336,"score":91,"supported":53,"title":337,"abstract":338,"tags":340},"F3",[],[339],"肥料",[],{"code":342,"score":22,"supported":14,"title":343,"abstract":344,"tags":345},"F1",[],[],[275],{"code":347,"score":22,"supported":14,"title":348,"abstract":349,"tags":350},"F2",[],[],[276],[352,353],"植物多组学 精准农业","多组学 智能育种","植物多组学精准农业-4832","10.3390\u002Fijms27209034",{"doi":355,"openalex_id":357,"authors":358,"venue":265,"cited_by_count":116,"oa_url":262,"card":379,"direction":385,"ingested_from":125},"W7221567543",[359,362,364,367,369,371,374,377],{"name":360,"orcid":361},"Tao Tong","https:\u002F\u002Forcid.org\u002F0000-0002-9003-9667",{"name":363,"orcid":9},"Wang Qingxia",{"name":365,"orcid":366},"Fanrong Zeng","https:\u002F\u002Forcid.org\u002F0000-0001-7742-5195",{"name":368,"orcid":9},"Shuzhen Ye",{"name":370,"orcid":9},"Zhijuan Ji",{"name":372,"orcid":373},"Zhong‐Hua Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7531-320X",{"name":375,"orcid":376},"Jiaoyu Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0249-808X",{"name":378,"orcid":9},"Younan Ouyang",{"tldr":380,"method":381,"finding":382,"direction":383,"opportunity":384},"综述植物多组学在精准农业中的应用，涵盖作物改良、土壤修复和生物产品创新。","整合基因组、转录组、蛋白组、代谢组等多组学数据，采用发现到转化框架。","多组学可加速育种、解析复杂农艺性状、修复土壤生态并构建智能决策系统。","农业人工智能与决策模型","多组学与AI、高通量表型、数字孪生融合，解决基因型-环境互作预测难题。","数字乡村与农业信息化","2026-10-13T23:30:29.504594Z",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":392,"content":9,"source_name":393,"source_url":390,"published_at":394,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":395,"score_detail":396,"sources":398,"tags":400,"primary_topic":403,"secondary_topics":404,"classification":410,"search_phrases":438,"slug":441,"view_count":106,"doi":442,"paper":443,"created_at":455},3680,"AI-Driven Nutrient Optimization Systems for Precision Agriculture: A Data-Integrated Approach to Reducing Nutrient Runoff and Enhancing Soil Health in Agricultural Systems","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.85286","Nutrients play a fundamental role in plant growth, soil fertility, and overall agricultural productivity. However, inefficient fertilizer application in conventional farming systems often leads to nutrient imbalance, reduced soil quality, and increased environmental degradation. The emergence of precision agriculture and artificial intelligence (AI) has introduced new opportunities for improving nutrient management through data-driven decision-making. This study investigates an AI-driven nutrient optimization system designed to enhance fertilizer efficiency while reducing nutrient runoff and improving soil health. Findings indicate that AI-based systems significantly improve nutrient use efficiency by enabling site-specific fertilizer recommendations and adaptive management strategies. The integration of multi-source agricultural data enhances prediction accuracy for soil nutrient status and crop demand, while also supporting early identification of nutrient loss risks such as runoff and leaching. The study concludes that AI-driven nutrient optimization provides a sustainable and scalable approach for modern agriculture, supporting both productivity enhancement and environmental protection through intelligent, data-integrated farming systems.","养分在植物生长、土壤肥力和农业整体生产力中发挥着基础性作用。然而，传统农业系统中低效的施肥方式往往导致养分失衡、土壤质量下降以及环境退化加剧。精准农业与人工智能（AI）的出现，为通过数据驱动决策改善养分管理带来了新的机遇。本研究探讨了一种AI驱动的养分优化系统，旨在提高肥料利用效率，同时减少养分径流并改善土壤健康。研究结果表明，基于AI的系统通过实现特定地块的施肥建议和适应性管理策略，显著提高了养分利用效率。多源农业数据的整合提升了土壤养分状况和作物需求的预测精度，同时也有助于早期识别径流和淋溶等养分流失风险。研究得出结论，AI驱动的养分优化为现代农业提供了一种可持续且可扩展的方法，通过智能化、数据整合的农业系统，同时支持生产力提升和环境保护。","International Journal For Multidisciplinary Research","2026-09-26T00:00:00Z",61,{"impact":139,"substance":17,"depth":52,"authority":21,"freshness":21,"relevant":22,"comment":397},"论文提出AI驱动的养分优化系统，方法有新意但属学术探讨，产业落地影响有限，可作为智慧农业技术动态收录。",[399],{"name":393,"url":390},[27,274,401,29,402],"精准施肥","养分流失",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[405],{"code":336,"slug":406,"name":407,"path":408,"parent_code":409},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":46,"locked":14,"version":47,"method":48,"evidence":411},{"candidates":412,"agricultural_evidence":53},[413,417,421,428,432],{"code":40,"score":52,"supported":53,"title":414,"abstract":415,"tags":416},[41],[62,41],[29],{"code":75,"score":52,"supported":53,"title":418,"abstract":419,"tags":420},[79],[78,79],[27],{"code":336,"score":13,"supported":53,"title":422,"abstract":423,"tags":427},[],[424,339,425,426],"施肥","养分管理","fertilizer",[401],{"code":329,"score":330,"supported":53,"title":429,"abstract":430,"tags":431},[],[333],[274],{"code":433,"score":91,"supported":53,"title":434,"abstract":435,"tags":437},"I6",[],[436],"agricultural data",[],[439,440],"AI 养分优化 精准农业","养分流失 土壤健康","AI养分优化精准农业-3680","10.36948\u002Fijfmr.2026.v08i05.85286",{"doi":442,"openalex_id":444,"authors":445,"venue":393,"cited_by_count":116,"oa_url":390,"card":450,"direction":383,"ingested_from":125},"W7214553198",[446,448],{"name":447,"orcid":9},"Olufisayo Obebe",{"name":449,"orcid":9},"Ebenezer Akinola",{"tldr":451,"method":452,"finding":453,"direction":383,"opportunity":454},"研究AI驱动的养分优化系统，通过数据集成实现精准施肥，减少养分流失并改善土壤健康。","集成多源农业数据与AI算法，进行站点特异性施肥推荐和自适应管理。","AI系统显著提高养分利用效率，增强土壤养分与作物需求预测精度，并早期识别流失风险。","可探索多源数据融合的实时决策模型，以及在不同土壤气候条件下AI系统的可扩展性与长期环境效应评估。","2026-09-28T23:30:49.535963Z",{"id":457,"title":458,"url":459,"summary":460,"summary_zh":461,"content":9,"source_name":462,"source_url":459,"published_at":463,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":464,"score_detail":465,"sources":467,"tags":469,"primary_topic":474,"secondary_topics":475,"classification":476,"search_phrases":502,"slug":505,"view_count":91,"doi":506,"paper":507,"created_at":557},3351,"Integrated Land, Soil and Crop Information Systems in Ethiopia, Kenya, and Rwanda: Institutional Readiness and Implications for Climate-Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101776","Integrated land–soil–crop information systems are increasingly important for supporting climate-smart agricultural planning and implementation, yet their development in Eastern Africa is constrained by fragmented mandates, weak technical capacity, and limited data interoperability. This study assesses how institutional readiness, user demand, and technical and human-capacity conditions shape the integration of such systems into national Agricultural Knowledge and Innovation Systems (AKIS). A mixed-methods, multi-country assessment was conducted in Ethiopia, Kenya, and Rwanda (2022–2024), drawing on 145 semi-structured key-informant interviews, stakeholder mapping, national workshops, and a regional synthesis consultation. The analysis addressed three objectives: (i) diagnose institutional readiness and user demand; (ii) assess technical, infrastructural, and human-capacity requirements; and (iii) identify governance and design principles for embedding integrated information systems within AKIS and Climate-Smart Agriculture (CSA) strategies. The results show consistently high demand for spatially explicit soil and crop data but persistent fragmentation of mandates, uneven coordination, and substantial subnational capacity gaps. Despite these constraints, emerging digital-agriculture strategies, open-data policies, and regional soil-health initiatives provide potential entry points for integration. The study offers a comparative evidence base and design principles—covering governance, interoperability standards, co-production processes, and capacity strengthening—needed to transition from project-driven fragmentation toward interoperable and sustainable information systems. These findings provide diagnostic and design-oriented insights for national and regional efforts to strengthen agricultural information systems that support CSA implementation in Eastern Africa.","综合的土地—土壤—作物信息系统在支持气候智慧型农业规划与实施方面日益重要，但其在东非的发展受到职责分散、技术能力薄弱和数据互操作性有限的制约。本研究评估了制度准备度、用户需求以及技术和人力能力条件如何影响此类系统融入国家农业知识与创新系统（AKIS）。研究于2022—2024年在埃塞俄比亚、肯尼亚和卢旺达开展了混合方法、多国评估，基于145次半结构化关键知情人访谈、利益相关方映射、国家研讨会以及一次区域综合磋商。分析围绕三个目标展开：（i）诊断制度准备度和用户需求；（ii）评估技术、基础设施和人力能力需求；（iii）确定将综合信息系统嵌入AKIS和气候智慧型农业（CSA）战略的治理与设计原则。结果表明，对空间显式土壤和作物数据的需求持续较高，但职责分散、协调不均衡以及地方层面能力差距显著等问题长期存在。尽管存在这些制约，新兴的数字农业战略、开放数据政策和区域土壤健康倡议为整合提供了潜在切入点。本研究提供了比较性证据基础和设计原则——涵盖治理、互操作性标准、共同生产过程和能力建设——这些是从项目驱动的碎片化转向可互操作且可持续的信息系统所必需的。这些发现为国家和区域层面加强支持东非CSA实施的农业信息系统提供了诊断性和设计导向的见解。","Land","2026-09-23T00:00:00Z",81,{"impact":17,"substance":269,"depth":17,"authority":20,"freshness":184,"relevant":22,"comment":466},"基于三国145位关键知情人访谈的混合方法研究，为东非农业信息系统整合与气候智慧农业提供治理与设计原则，方法扎实、结论可靠，具区域政策参考价值。",[468],{"name":462,"url":459},[470,27,29,471,472,473],"数字农业","气候智慧农业","农业数据","数据互操作",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":477},{"candidates":478,"agricultural_evidence":53},[479,484,488,494,498],{"code":75,"score":140,"supported":53,"title":480,"abstract":482,"tags":483},[481],"smart agriculture",[470,481],[470,27],{"code":40,"score":52,"supported":53,"title":485,"abstract":486,"tags":487},[41],[62,41],[29],{"code":489,"score":91,"supported":53,"title":490,"abstract":491,"tags":493},"J1",[],[492],"农业规划",[],{"code":67,"score":22,"supported":14,"title":495,"abstract":496,"tags":497},[],[],[471],{"code":433,"score":22,"supported":14,"title":499,"abstract":500,"tags":501},[],[],[472],[503,504],"埃塞俄比亚 肯尼亚 卢旺达 农业信息系统","气候智慧农业 土壤作物数据","埃塞俄比亚肯尼亚卢旺达农业信息系统-3351","10.3390\u002Fland15101776",{"doi":506,"openalex_id":508,"authors":509,"venue":462,"cited_by_count":116,"oa_url":459,"card":552,"direction":124,"ingested_from":125},"W7214079859",[510,513,516,518,520,523,526,529,531,534,537,540,543,546,549],{"name":511,"orcid":512},"John Walker Recha","https:\u002F\u002Forcid.org\u002F0000-0002-1146-7197",{"name":514,"orcid":515},"A. Kooiman","https:\u002F\u002Forcid.org\u002F0000-0001-8208-6781",{"name":517,"orcid":9},"Thaïsa van der Woude",{"name":519,"orcid":9},"Hanneke Heesmans",{"name":521,"orcid":522},"Ermias Aynekulu","https:\u002F\u002Forcid.org\u002F0000-0002-1955-6995",{"name":524,"orcid":525},"Angela Nduta Gitau","https:\u002F\u002Forcid.org\u002F0000-0002-8963-2375",{"name":527,"orcid":528},"Pascal Debons","https:\u002F\u002Forcid.org\u002F0000-0001-6314-9935",{"name":530,"orcid":9},"Frank van Weert",{"name":532,"orcid":533},"Michael Okoti","https:\u002F\u002Forcid.org\u002F0000-0002-9550-8258",{"name":535,"orcid":536},"Elizabeth A. Okwuosa","https:\u002F\u002Forcid.org\u002F0000-0001-5941-7423",{"name":538,"orcid":539},"Kennedy Were","https:\u002F\u002Forcid.org\u002F0000-0002-8012-6812",{"name":541,"orcid":542},"Girma Mamo Diga","https:\u002F\u002Forcid.org\u002F0000-0002-2593-3187",{"name":544,"orcid":545},"Dejene Abera","https:\u002F\u002Forcid.org\u002F0000-0003-3692-8620",{"name":547,"orcid":548},"Pierre Celestin Ndayisaba","https:\u002F\u002Forcid.org\u002F0000-0002-8400-9146",{"name":550,"orcid":551},"Jules Rutebuka","https:\u002F\u002Forcid.org\u002F0000-0002-5236-3503",{"tldr":553,"method":554,"finding":555,"direction":385,"opportunity":556},"评估埃塞俄比亚、肯尼亚和卢旺达三国土地-土壤-作物综合信息系统的机构准备度与整合路径。","2022-2024年三国混合方法评估，含145个关键知情人访谈、利益相关方映射与","三国对空间化土壤作物数据需求高，但机构职责碎片化、协调不均、地方能力缺口大。","可研究开放数据政策与区域土壤健康倡议如何作为切入点，推动跨部门互操作标准与联合生产能力建设。","2026-09-24T23:30:10.120978Z",{"id":559,"title":560,"url":561,"summary":562,"summary_zh":9,"content":9,"source_name":563,"source_url":9,"published_at":564,"category":136,"cover_url":9,"hotness":13,"is_selected":14,"score":565,"score_detail":566,"sources":568,"tags":570,"primary_topic":573,"secondary_topics":574,"classification":575,"search_phrases":590,"slug":593,"view_count":91,"doi":9,"paper":9,"created_at":594},2879,"中国咖啡的品质根基与创新驱动论坛在昆明举办:云南省农科院聚焦全产业链标准体系","https:\u002F\u002Fnews.qq.com\u002Frain\u002Fa\u002F20260918A07YT800","9月17日,2026中国热带作物产业大会分论坛在昆明举行,来自中国标准化研究院、云南省农业科学院等单位的80余名代表参会。论坛以标准筑基科技赋能为主线,中国农业大学副教授李想作AI助力种好咖啡报告,介绍AI农技大脑一田一策精准决策、AI撮合平台、田间移动服务站、智慧农场ERP等农事服务新模式,提出AI-农业服务人员-农民服务路径。","中国新闻网\u002F腾讯新闻","2026-09-18T00:00:00Z",68,{"impact":17,"substance":140,"depth":20,"authority":159,"freshness":184,"relevant":22,"comment":567},"省级科研机构主导的咖啡全产业链标准与AI农技服务论坛，行业细分进展有实质信息，但影响层级限于产业与省级范围。",[569],{"name":563,"url":561},[27,274,30,571,572],"热带作物","农业标准体系",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":576},{"candidates":577,"agricultural_evidence":53},[578,582,586],{"code":33,"score":139,"supported":53,"title":579,"abstract":580,"tags":581},[57],[57],[30],{"code":75,"score":22,"supported":14,"title":583,"abstract":584,"tags":585},[],[],[27],{"code":329,"score":22,"supported":14,"title":587,"abstract":588,"tags":589},[],[],[274],[591,592],"云南省农科院 咖啡 标准体系","AI农技大脑 咖啡","云南省农科院咖啡标准体系-2879","2026-09-19T00:06:04.718237Z"]