[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3351":3,"related-3351":136},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":76,"slug":79,"view_count":61,"doi":80,"paper":81,"created_at":135},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。",null,"Land","2026-09-23T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30,31],"数字农业","智慧农业","土壤健康","气候智慧农业","农业数据","数据互操作",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":48},[45,53,59,66,71],{"code":46,"score":47,"supported":48,"title":49,"abstract":51,"tags":52},"I1",16,true,[50],"smart agriculture",[26,50],[26,27],{"code":33,"score":54,"supported":48,"title":55,"abstract":56,"tags":58},15,[34],[57,34],"土壤",[28],{"code":60,"score":61,"supported":48,"title":62,"abstract":63,"tags":65},"J1",3,[],[64],"农业规划",[],{"code":67,"score":21,"supported":14,"title":68,"abstract":69,"tags":70},"H7",[],[],[29],{"code":72,"score":21,"supported":14,"title":73,"abstract":74,"tags":75},"I6",[],[],[30],[77,78],"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":80,"openalex_id":82,"authors":83,"venue":10,"cited_by_count":126,"oa_url":6,"card":127,"direction":133,"ingested_from":134},"W7214079859",[84,87,90,92,94,97,100,103,105,108,111,114,117,120,123],{"name":85,"orcid":86},"John Walker Recha","https:\u002F\u002Forcid.org\u002F0000-0002-1146-7197",{"name":88,"orcid":89},"A. Kooiman","https:\u002F\u002Forcid.org\u002F0000-0001-8208-6781",{"name":91,"orcid":9},"Thaïsa van der Woude",{"name":93,"orcid":9},"Hanneke Heesmans",{"name":95,"orcid":96},"Ermias Aynekulu","https:\u002F\u002Forcid.org\u002F0000-0002-1955-6995",{"name":98,"orcid":99},"Angela Nduta Gitau","https:\u002F\u002Forcid.org\u002F0000-0002-8963-2375",{"name":101,"orcid":102},"Pascal Debons","https:\u002F\u002Forcid.org\u002F0000-0001-6314-9935",{"name":104,"orcid":9},"Frank van Weert",{"name":106,"orcid":107},"Michael Okoti","https:\u002F\u002Forcid.org\u002F0000-0002-9550-8258",{"name":109,"orcid":110},"Elizabeth A. Okwuosa","https:\u002F\u002Forcid.org\u002F0000-0001-5941-7423",{"name":112,"orcid":113},"Kennedy Were","https:\u002F\u002Forcid.org\u002F0000-0002-8012-6812",{"name":115,"orcid":116},"Girma Mamo Diga","https:\u002F\u002Forcid.org\u002F0000-0002-2593-3187",{"name":118,"orcid":119},"Dejene Abera","https:\u002F\u002Forcid.org\u002F0000-0003-3692-8620",{"name":121,"orcid":122},"Pierre Celestin Ndayisaba","https:\u002F\u002Forcid.org\u002F0000-0002-8400-9146",{"name":124,"orcid":125},"Jules Rutebuka","https:\u002F\u002Forcid.org\u002F0000-0002-5236-3503",0,{"tldr":128,"method":129,"finding":130,"direction":131,"opportunity":132},"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。","数字乡村与农业信息化","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。","智慧农业 \u002F 农业物联网","openalex","2026-09-24T23:30:10.120978Z",{"total":137,"page":21,"page_size":137,"items":138},6,[139,230,359,420,467,536],{"id":140,"title":141,"url":142,"summary":143,"summary_zh":144,"content":9,"source_name":145,"source_url":142,"published_at":146,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":147,"score_detail":148,"sources":154,"tags":156,"primary_topic":160,"secondary_topics":161,"classification":168,"search_phrases":211,"slug":214,"view_count":21,"doi":215,"paper":216,"created_at":229},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":149,"substance":150,"depth":151,"authority":152,"freshness":20,"relevant":21,"comment":153},12,21,17,13,"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。",[155],{"name":145,"url":142},[27,157,158,28,159],"精准农业","遥感","覆盖作物",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[162],{"code":163,"slug":164,"name":165,"path":166,"parent_code":167},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":169},{"candidates":170,"agricultural_evidence":48},[171,175,180,186,193,200,206],{"code":33,"score":54,"supported":48,"title":172,"abstract":173,"tags":174},[34],[57,34],[28],{"code":163,"score":149,"supported":48,"title":176,"abstract":178,"tags":179},[177],"remote sensing",[158],[158],{"code":46,"score":181,"supported":48,"title":182,"abstract":183,"tags":185},8,[],[157,184],"precision agriculture",[27,157],{"code":187,"score":137,"supported":48,"title":188,"abstract":189,"tags":192},"A4",[],[190,191],"油菜","rapeseed",[],{"code":194,"score":137,"supported":48,"title":195,"abstract":196,"tags":199},"H8",[],[197,198],"可持续农业","sustainable agriculture",[],{"code":201,"score":61,"supported":48,"title":202,"abstract":203,"tags":205},"B1",[],[204],"萝卜",[],{"code":207,"score":21,"supported":14,"title":208,"abstract":209,"tags":210},"A7",[],[],[159],[212,213],"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":215,"openalex_id":217,"authors":218,"venue":145,"cited_by_count":126,"oa_url":9,"card":223,"direction":227,"ingested_from":134},"W7221669942",[219,221],{"name":220,"orcid":9},"Ali Mirzakhani Nafchi",{"name":222,"orcid":9},"Salman Mirzaee",{"tldr":224,"method":225,"finding":226,"direction":227,"opportunity":228},"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":231,"title":232,"url":233,"summary":234,"summary_zh":235,"content":9,"source_name":236,"source_url":233,"published_at":237,"category":12,"cover_url":9,"hotness":13,"is_selected":48,"score":238,"score_detail":239,"sources":241,"tags":243,"primary_topic":247,"secondary_topics":248,"classification":249,"search_phrases":324,"slug":327,"view_count":126,"doi":328,"paper":329,"created_at":358},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":18,"substance":150,"depth":17,"authority":19,"freshness":181,"relevant":21,"comment":240},"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。",[242],{"name":236,"url":233},[27,244,245,157,28,246],"农业人工智能","种业振兴","多组学",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":250},{"candidates":251,"agricultural_evidence":48},[252,258,263,270,277,284,291,297,301,308,314,319],{"code":46,"score":253,"supported":48,"title":254,"abstract":255,"tags":257},19,[184],[157,256,184],"数字孪生",[27,157],{"code":33,"score":259,"supported":48,"title":260,"abstract":261,"tags":262},7,[],[57,34],[28],{"code":264,"score":137,"supported":48,"title":265,"abstract":266,"tags":269},"A1",[],[267,268],"水稻","rice",[],{"code":271,"score":137,"supported":48,"title":272,"abstract":273,"tags":276},"A2",[],[274,275],"小麦","wheat",[],{"code":278,"score":137,"supported":48,"title":279,"abstract":280,"tags":283},"A3",[],[281,282],"玉米","maize",[],{"code":285,"score":137,"supported":48,"title":286,"abstract":287,"tags":290},"A8",[],[288,289],"粮食安全","food security",[],{"code":67,"score":137,"supported":48,"title":292,"abstract":293,"tags":296},[],[294,295],"气候变化","climate change",[],{"code":194,"score":137,"supported":48,"title":298,"abstract":299,"tags":300},[],[197,198],[],{"code":302,"score":303,"supported":48,"title":304,"abstract":305,"tags":307},"I2",4,[],[306],"artificial intelligence",[244],{"code":309,"score":61,"supported":48,"title":310,"abstract":311,"tags":313},"F3",[],[312],"肥料",[],{"code":315,"score":21,"supported":14,"title":316,"abstract":317,"tags":318},"F1",[],[],[245],{"code":320,"score":21,"supported":14,"title":321,"abstract":322,"tags":323},"F2",[],[],[246],[325,326],"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":328,"openalex_id":330,"authors":331,"venue":236,"cited_by_count":126,"oa_url":233,"card":352,"direction":131,"ingested_from":134},"W7221567543",[332,335,337,340,342,344,347,350],{"name":333,"orcid":334},"Tao Tong","https:\u002F\u002Forcid.org\u002F0000-0002-9003-9667",{"name":336,"orcid":9},"Wang Qingxia",{"name":338,"orcid":339},"Fanrong Zeng","https:\u002F\u002Forcid.org\u002F0000-0001-7742-5195",{"name":341,"orcid":9},"Shuzhen Ye",{"name":343,"orcid":9},"Zhijuan Ji",{"name":345,"orcid":346},"Zhong‐Hua Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7531-320X",{"name":348,"orcid":349},"Jiaoyu Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0249-808X",{"name":351,"orcid":9},"Younan Ouyang",{"tldr":353,"method":354,"finding":355,"direction":356,"opportunity":357},"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。","2026-10-13T23:30:29.504594Z",{"id":360,"title":361,"url":362,"summary":363,"summary_zh":364,"content":9,"source_name":365,"source_url":362,"published_at":366,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":367,"score_detail":368,"sources":370,"tags":372,"primary_topic":376,"secondary_topics":377,"classification":383,"search_phrases":403,"slug":406,"view_count":126,"doi":407,"paper":408,"created_at":419},4295,"Smart Soil Monitoring Systems: Challenges and Strategic Solutions for Enhanced Adoption","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v9.no12.2025.pg245.258","Smart Soil Monitoring Systems (SSMS) represent a key innovation in precision agriculture, enabling continuous monitoring of soil parameters such as moisture, pH, nutrient content, temperature, and salinity. These systems integrate Internet of Things (IoT) technologies, wireless sensor networks, and data analytics to provide real-time insights that support data-driven decision-making, optimize resource use, and enhance crop productivity. Despite their potential, the adoption of SSMS in developing institutions, including Federal Polytechnic Bali (FPB), remains limited due to financial constraints, inadequate technical skills, low awareness, and infrastructural challenges such as unreliable internet connectivity and power supply. The high cost of devices, lack of training, and insufficient institutional policies further impede practical implementation in teaching, research, and extension activities. This study employed a descriptive survey design targeting academic and technical staff in FPB’s agricultural departments. Data collected via a structured questionnaire were analyzed using descriptive statistics, with mean scores indicating key challenges and potential strategies. Findings revealed that high device costs, poor network infrastructure, and low technical capacity were the most critical barriers. To address these, respondents emphasized capacity-building workshops, improved digital infrastructure, awareness campaigns, funding support, and policy frameworks as strategic interventions. The study concludes that enhancing SSMS adoption requires integrated solutions addressing economic, technical, and institutional barriers. Implementation of these strategies is expected to strengthen hands-on learning, improve research outputs, and promote sustainable, technology-driven agriculture at FPB. The study contributes to understanding practical adoption challenges of smart agricultural technologies in resource-constrained educational settings.","智能土壤监测系统（Smart Soil Monitoring Systems, SSMS）是精准农业的一项关键创新，能够对土壤湿度、pH值、养分含量、温度和盐度等参数进行持续监测。这些系统集成了物联网（IoT）技术、无线传感器网络和数据分析，可提供实时洞察，支持数据驱动的决策、优化资源利用并提高作物生产力。尽管潜力巨大，但包括巴厘联邦理工学院（Federal Polytechnic Bali, FPB）在内的发展中机构对SSMS的采用仍然有限，原因在于资金约束、技术技能不足、认知度低以及互联网连接和电力供应不可靠等基础设施挑战。设备成本高昂、缺乏培训以及制度政策不完善，进一步阻碍了其在教学、研究和推广活动中的实际应用。本研究采用描述性调查设计，以FPB农业部门的学术和技术人员为研究对象。通过结构化问卷收集的数据采用描述性统计进行分析，以平均分表明主要挑战和潜在策略。研究发现，设备成本高、网络基础设施差和技术能力低是最关键的障碍。为应对这些问题，受访者强调能力建设研讨会、改善数字基础设施、宣传活动、资金支持和政策框架是战略性干预措施。研究得出结论，提升SSMS的采用需要综合解决方案，以应对经济、技术和制度层面的障碍。这些策略的实施有望加强实践学习、提高研究产出，并推动FPB可持续的、技术驱动的农业发展。本研究有助于理解资源受限教育环境中智能农业技术的实际采用挑战。","WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY","2026-10-05T00:00:00Z",52,{"impact":181,"substance":47,"depth":19,"authority":137,"freshness":181,"relevant":21,"comment":369},"聚焦资源受限院校智慧土壤监测落地障碍的实证调查，结论实用但样本局限于单一院校，影响力有限。",[371],{"name":365,"url":362},[26,27,373,374,375],"物联网","土壤监测","农业传感器",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[378],{"code":379,"slug":380,"name":381,"path":382,"parent_code":167},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":40,"locked":14,"version":41,"method":42,"evidence":384},{"candidates":385,"agricultural_evidence":48},[386,390,399],{"code":33,"score":19,"supported":48,"title":387,"abstract":388,"tags":389},[34],[57,34],[],{"code":379,"score":391,"supported":48,"title":392,"abstract":393,"tags":398},11,[],[373,394,395,396,397],"无线传感","internet of things","IoT","wireless sensor",[375,373],{"code":46,"score":181,"supported":48,"title":400,"abstract":401,"tags":402},[],[157,184],[26,27],[404,405],"农业传感器 土壤监测 数字农业 智慧农业","农业传感器 土壤监测","农业传感器土壤监测数字农业智慧农业-4295","10.56201\u002Fwjimt.v9.no12.2025.pg245.258",{"doi":407,"openalex_id":409,"authors":410,"venue":365,"cited_by_count":126,"oa_url":413,"card":414,"direction":133,"ingested_from":134},"W7220426775",[411],{"name":412,"orcid":9},"NUHU ABDULLAHI","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FWJIMT\u002FVOL. 9 NO. 12 2025\u002FSmart Soil Monitoring Systems 245-258.pdf",{"tldr":415,"method":416,"finding":417,"direction":133,"opportunity":418},"调查尼日利亚某院校智能土壤监测系统采纳障碍并提出策略。","描述性问卷调研农业院系教职工，用描述统计与均值分析。","设备成本高、网络差、技术能力低是主要障碍，需培训与政策支持。","可研究资源受限院校中低成本物联网土壤监测方案与采纳机制。","2026-10-06T23:30:07.776821Z",{"id":421,"title":422,"url":423,"summary":424,"summary_zh":425,"content":9,"source_name":365,"source_url":423,"published_at":426,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":427,"score_detail":428,"sources":430,"tags":432,"primary_topic":434,"secondary_topics":435,"classification":436,"search_phrases":451,"slug":454,"view_count":21,"doi":455,"paper":456,"created_at":466},4069,"Adoption of Smart Soil Monitoring Systems among Biological Science Staff in Federal Polytechnic Bali: Trends, Benefits, and Challenges","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v9.no10.2025.pg255.268","Smart Soil Monitoring Systems (SSMS) have emerged as innovative digital solutions for improving soil data collection, analyzing environmental variables, and enhancing precision in agricultural and scientific research. Despite the global shift toward smart agriculture and environmental sustainability, the level of awareness and adoption of SSMS in many Nigerian institutions remains significantly low. This study investigated the trends, benefits, and challenges in the adoption of Smart Soil Monitoring Systems among Biological Science staff of Federal Polytechnic Bali, Taraba State. A descriptive survey design was adopted, and a census sampling technique was used to involve all 20 Biological Science staff as respondents. A validated structured questionnaire titled “Adoption of Smart Soil Monitoring Systems Questionnaire” was used for data collection, while frequency, percentage, mean, and standard deviation served as analytical tools. Findings revealed a moderate level of awareness of SSMS but very low practical adoption due to limited access to modern monitoring technologies, lack of training, insufficient funding, and inadequate institutional policy support. However, respondents acknowledged clear benefits such as improved soil analysis accuracy, enhanced environmental sustainability, and better support for agricultural research and teaching. It was recommended that the institution should invest in digital soil monitoring tools, organize continuous capacitybuilding workshops, and encourage integration of SSMS into academic laboratory activities. This research contributes to the understanding of digital innovation adoption in higher education, particularly in the context of agricultural and environmental studies.","智能土壤监测系统（Smart Soil Monitoring Systems, SSMS）已成为创新性数字解决方案，用于改善土壤数据采集、分析环境变量，并提升农业与科学研究中的精准度。尽管全球正朝着智慧农业和环境可持续方向转型，但尼日利亚许多机构对SSMS的认知和采用水平仍然显著偏低。本研究调查了塔拉巴州巴利联邦理工学院生物科学系教职工在采用智能土壤监测系统方面的趋势、益处与挑战。研究采用描述性调查设计，并运用普查抽样技术，将全部20名生物科学系教职工纳入为调查对象。数据收集使用了一份经过验证的结构化问卷，题为“智能土壤监测系统采用情况问卷”，分析工具包括频数、百分比、均值和标准差。研究发现，受访者对SSMS的认知处于中等水平，但实际采用率极低，原因包括获取现代监测技术的机会有限、缺乏培训、资金不足以及机构政策支持不到位。然而，受访者认可其明显益处，如提高土壤分析准确性、增强环境可持续性，以及更好地支持农业研究与教学。研究建议该机构应投资数字土壤监测工具，组织持续的能力建设研讨会，并鼓励将SSMS纳入学术实验室活动。本研究有助于理解高等教育中数字创新的采用情况，尤其是在农业与环境研究背景下。","2026-10-01T00:00:00Z",42,{"impact":303,"substance":19,"depth":149,"authority":303,"freshness":181,"relevant":21,"comment":429},"尼日利亚地方院校小样本调查，主题相关但影响范围与信源权威度有限，不建议进入每日精选。",[431],{"name":365,"url":423},[26,27,374,375,433],"尼日利亚",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":437},{"candidates":438,"agricultural_evidence":48},[439,443,447],{"code":33,"score":19,"supported":48,"title":440,"abstract":441,"tags":442},[34],[57,34],[],{"code":46,"score":181,"supported":48,"title":444,"abstract":445,"tags":446},[],[27,50],[26,27],{"code":379,"score":21,"supported":14,"title":448,"abstract":449,"tags":450},[],[],[375],[452,453],"Federal Polytechnic Bali 土壤监测","尼日利亚 智慧土壤监测系统","FederalPolytechnicBali土壤监测-4069","10.56201\u002Fwjimt.v9.no10.2025.pg255.268",{"doi":455,"openalex_id":457,"authors":458,"venue":365,"cited_by_count":126,"oa_url":9,"card":461,"direction":133,"ingested_from":134},"W7216559522",[459],{"name":460,"orcid":9},"Usman Mohammed",{"tldr":462,"method":463,"finding":464,"direction":131,"opportunity":465},"调查尼日利亚一所理工学院生物科学教职工对智能土壤监测系统的认知、采用趋势、益处与挑战。","描述性问卷普查，20名教职工，频数、百分比、均值与标准差分析。","认知中等但实际采用率极低，受技术获取、培训、资金与政策支持不足制约，但益处获认可。","可研究发展中国家高校农业数字化采用障碍机制，并设计低成本培训与政策干预方案。","2026-10-03T23:30:14.576760Z",{"id":468,"title":469,"url":470,"summary":471,"summary_zh":472,"content":9,"source_name":473,"source_url":470,"published_at":474,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":475,"score_detail":476,"sources":478,"tags":480,"primary_topic":483,"secondary_topics":484,"classification":490,"search_phrases":517,"slug":520,"view_count":521,"doi":522,"paper":523,"created_at":535},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":149,"substance":17,"depth":54,"authority":181,"freshness":181,"relevant":21,"comment":477},"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。",[479],{"name":473,"url":470},[27,244,481,28,482],"精准施肥","养分流失",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[485],{"code":309,"slug":486,"name":487,"path":488,"parent_code":489},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":40,"locked":14,"version":41,"method":42,"evidence":491},{"candidates":492,"agricultural_evidence":48},[493,497,501,508,512],{"code":33,"score":54,"supported":48,"title":494,"abstract":495,"tags":496},[34],[57,34],[28],{"code":46,"score":54,"supported":48,"title":498,"abstract":499,"tags":500},[184],[157,184],[27],{"code":309,"score":13,"supported":48,"title":502,"abstract":503,"tags":507},[],[504,312,505,506],"施肥","养分管理","fertilizer",[481],{"code":302,"score":303,"supported":48,"title":509,"abstract":510,"tags":511},[],[306],[244],{"code":72,"score":61,"supported":48,"title":513,"abstract":514,"tags":516},[],[515],"agricultural data",[],[518,519],"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",2,"10.36948\u002Fijfmr.2026.v08i05.85286",{"doi":522,"openalex_id":524,"authors":525,"venue":473,"cited_by_count":126,"oa_url":470,"card":530,"direction":356,"ingested_from":134},"W7214553198",[526,528],{"name":527,"orcid":9},"Olufisayo Obebe",{"name":529,"orcid":9},"Ebenezer Akinola",{"tldr":531,"method":532,"finding":533,"direction":356,"opportunity":534},"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":537,"title":538,"url":539,"summary":540,"summary_zh":541,"content":9,"source_name":542,"source_url":539,"published_at":543,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":544,"score_detail":545,"sources":547,"tags":549,"primary_topic":554,"secondary_topics":560,"classification":562,"search_phrases":587,"slug":590,"view_count":21,"doi":591,"paper":592,"created_at":610},2928,"Biochar 2.0 in Climate-Smart Agriculture: A Critical Review of Designer Biochars, Soil Microbiome Responses, Nutrient-Use Efficiency and Greenhouse-Gas Mitigation","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fajsspn\u002F2026\u002Fv12i4764","Biochar has moved from a broadly promoted soil amendment to a material that is deliberately engineered for defined agronomic and climatic functions. This transition, described here as a shift towards designer biochars, raises questions that the earlier literature was not designed to answer: whether tailoring feedstock, pyrolysis conditions and post-synthesis modification produces reproducible gains in nutrient-use efficiency, whether the accompanying soil microbiome changes are functionally meaningful or largely descriptive, and whether greenhouse-gas benefits observed in short experiments persist under field management and withstand carbon-accounting scrutiny. This critical narrative review examines the evidence linking biochar properties to soil microbial responses, nutrient retention and transformation, and the mitigation of nitrous oxide, methane and carbon dioxide in agricultural soils. Literature was identified through structured searching of open scholarly indexes, agricultural and intergovernmental repositories, and citation tracking, with every retained source verified against an authoritative bibliographic record. The synthesis indicates that the strongest and most reproducible effects are pedoclimatic rather than material-specific: responses concentrate in acidic, coarse-textured, nutrient-depleted and tropical soils, whereas temperate fertile systems frequently show negligible agronomic gain. Microbiome studies consistently report compositional turnover with little change in alpha diversity, and the functional interpretation of these shifts rests heavily on marker-gene abundance rather than demonstrated process rates. Nitrous oxide suppression is well supported in aggregate but varies by a factor of five across syntheses, and recent field work shows that freshly applied high-rate biochar can increase emissions. Methane outcomes in flooded systems depend on water management and application history. Carbon permanence remains contested because the principal proxies used for crediting are informative about carbonisation rather than about in-soil residence under field weathering. Confidence is highest for liming, cation retention and short-term nitrogen conservation, and lowest for long-term net climate benefit at landscape scale. Priorities include multi-year factorial field trials that pair engineered materials with process-level measurement, standardised reporting of biochar properties, and accounting frameworks calibrated on weathered rather than freshly produced material.","生物炭已从一种被广泛推广的土壤改良剂，转变为一种为特定农艺和气候功能而刻意设计的材料。这一转变——本文将其描述为向“设计型生物炭”的转向——提出了早期文献未曾设计回答的问题：定制原料、热解条件和合成后修饰是否能在养分利用效率上产生可重复的增益；伴随而来的土壤微生物组变化在功能上是否有意义，还是主要停留在描述层面；以及在短期实验中观察到的温室气体效益能否在田间管理下持续存在，并经受住碳核算的审视。这篇批判性叙述综述考察了将生物炭性质与土壤微生物响应、养分保持与转化，以及农业土壤中氧化亚氮、甲烷和二氧化碳减排联系起来的证据。文献通过结构化检索开放学术索引、农业和政府间知识库以及引文追踪来识别，所有保留的来源均对照权威书目记录进行了核实。综合结果表明，最强且最可重复的效应是土壤气候性的，而非材料特异性的：响应集中于酸性、粗质地、养分贫瘠和热带土壤，而温带肥沃系统往往显示可忽略的农艺增益。微生物组研究一致报告组成上的更替而α多样性变化甚微，且对这些变化的功能解读在很大程度上依赖于标记基因丰度，而非已证实的过程速率。氧化亚氮抑制在总体上得到较好支持，但不同综合研究之间相差可达五倍，且近期田间工作表明，新施用的高用量生物炭可能增加排放。淹水系统中的甲烷结果取决于水分管理和施用历史。碳永久性仍存争议，因为用于信用核算的主要替代指标所反映的是碳化程度，而非田间风化条件下的土壤内驻留时间。置信度最高的是石灰效应、阳离子保持和短期氮素保全，最低的是景观尺度上的长期净气候效益。优先事项包括：将工程化材料与过程水平测量相结合的多年度析因田间试验、生物炭性质的标准化报告，以及基于风化而非","Asian Journal of Soil Science and Plant Nutrition","2026-09-18T00:00:00Z",80,{"impact":17,"substance":18,"depth":253,"authority":152,"freshness":181,"relevant":21,"comment":546},"系统评述设计型生物炭在土壤微生物、养分效率与温室气体减排上的证据强度与不确定性，学术增量扎实，对农业绿色低碳与土壤健康方向有参考价值。",[548],{"name":542,"url":539},[550,28,29,551,552,553],"生物炭","土壤微生物","温室气体减排","养分利用效率",{"code":555,"slug":556,"name":557,"path":558,"parent_code":559},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[561],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":563},{"candidates":564,"agricultural_evidence":48},[565,569,575,579,583],{"code":33,"score":151,"supported":48,"title":566,"abstract":567,"tags":568},[34],[57,34],[28,551,550],{"code":555,"score":19,"supported":48,"title":570,"abstract":572,"tags":574},[571],"greenhouse",[573,571],"温室",[],{"code":46,"score":181,"supported":48,"title":576,"abstract":577,"tags":578},[50],[],[],{"code":309,"score":21,"supported":14,"title":580,"abstract":581,"tags":582},[],[],[553],{"code":67,"score":21,"supported":14,"title":584,"abstract":585,"tags":586},[],[],[29],[588,589],"生物炭 温室气体 减排","养分利用效率 气候智慧农业 温室气体减排 土壤微生物","生物炭温室气体减排-2928","10.9734\u002Fajsspn\u002F2026\u002Fv12i4764",{"doi":591,"openalex_id":593,"authors":594,"venue":542,"cited_by_count":126,"oa_url":539,"card":604,"direction":133,"ingested_from":134},"W7213554252",[595,597,600,602],{"name":596,"orcid":9},"K. G. Rosin",{"name":598,"orcid":599},"Aditya V Machnoor","https:\u002F\u002Forcid.org\u002F0009-0001-5777-9188",{"name":601,"orcid":9},"Rakesh Kokatnoor",{"name":603,"orcid":9},"Arun S. Kalasad",{"tldr":605,"method":606,"finding":607,"direction":608,"opportunity":609},"综述设计型生物炭在气候智慧农业中的效果，指出其农艺与减排效益高度依赖土壤气候条件。","结构化文献检索与引文追踪，整合生物炭性质、土壤微生物、养分与温室气体证据。","最强效应集中于酸性贫瘠热带土壤，温带肥沃土壤增益有限；氧化亚氮抑制变异大，碳持久性存疑。","农业绿色发展与碳","需多年多因子田间试验，结合过程速率测量与风化态碳核算，验证设计型生物炭的长期净气候效益。","2026-09-19T23:30:11.160909Z"]