[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3359":3,"related-3359":56},{"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":28,"search_phrases":35,"slug":38,"view_count":39,"doi":40,"paper":41,"created_at":55},3359,"Agroforestry Policies in 16 European Countries: Current Limitations and a Blueprint for the Future. DigitAF Project - Deliverable 1.6.","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.20312118","Deliverable 1.6 of the EU DigitAF Project (digitaf.eu) is the final deliverable of WorkPackage 1 \"Strengthening agroforestry and carbon farming policies: tools for policymakers\". It analyses agroforestry policies across 14 EU Member States, examining current regulatory limitations, land tenure constraints, and the integration of agricultural trees with broader EU environmental lelgislation. It closes with a blueprint for future agroforestry policy reform. The report highlights severe historical deficiencies in Common Agricultural Policy (CAP) implementation, noting that actual agroforestry adoption has consistently falled short of targets - due to administrative complexities and inadequate data tracking. Land tenancy acts as a major bottleneck; with roughly 46% of EU farmland rented, and short-term lease frameworks which fundamentally conflict with the long-term capital investment required for agroforestry. Beyond the CAP, the scaling of agroforestry is paralyzed by institutional silos and regulatory contradictions. Notably, remote sensing algorithms implemented under the EUDR may bring critical errors, misclassifying traditional anthropogenic silvopastoral systems (like the Spanish Dehesa) as forests, and risking false flags for forest degradation. To resolve these issues, the report presents the EURAF Agroforestry Blueprint (2028–2040). This blueprint includes unified digital infrastructures (LPIS and NFI integration), harmonized definitions recognizing trees as productive assets, and financial de-risking through blended finance like Nature Credits.","欧盟DigitAF项目（digitaf.eu）的可交付成果1.6是工作包1“加强农林业与碳农业政策：政策制定者工具”的最终交付成果。该报告分析了14个欧盟成员国的农林业政策，考察了当前的监管限制、土地权属约束，以及农业树木与更广泛的欧盟环境立法的整合情况。报告最后提出了未来农林业政策改革的蓝图。报告强调了共同农业政策（CAP）实施中严重的历史性缺陷，指出由于行政复杂性及数据追踪不足，农林业的实际采用率始终未能达到目标。土地租赁是主要瓶颈；欧盟约46%的农田为租赁经营，而短期租赁框架与农林业所需的长期资本投资存在根本性冲突。除CAP之外，农林业的规模化还因机构壁垒和监管矛盾而陷入瘫痪。值得注意的是，根据欧盟零毁林法规（EUDR）实施的遥感算法可能带来严重错误，将传统人为林牧系统（如西班牙德埃萨牧场）误分类为森林，并可能错误触发森林退化预警。为解决这些问题，报告提出了EURAF农林业蓝图（2028—2040）。该蓝图包括统一数字基础设施（LPIS与NFI整合）、承认树木为生产性资产的协调定义，以及通过自然信用等混合融资实现金融风险缓释。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-23T00:00:00Z","论文",25,true,87,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},24,23,18,13,9,1,"欧盟DigitAF项目终期报告，系统剖析14国农林业政策瓶颈并提出2028-2040改革蓝图，数据与政策细节扎实，对农业政策与遥感信息化研究有参考价值。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22909703",[29,30,31,32,33,34],"数字基础设施","遥感监测","碳农业","农林业","欧盟CAP","土地权属",[36,37],"DigitAF 农林业政策","EUDR 遥感 Dehesa","DigitAF农林业政策-3359",0,"10.5281\u002Fzenodo.20312118",{"doi":40,"openalex_id":42,"authors":43,"venue":10,"cited_by_count":39,"oa_url":6,"card":47,"direction":53,"ingested_from":54},"W7165358105",[44],{"name":45,"orcid":46},"Gerry LAWSON","https:\u002F\u002Forcid.org\u002F0000-0002-1395-3092",{"tldr":48,"method":49,"finding":50,"direction":51,"opportunity":52},"分析14个欧盟成员国农林业政策局限，提出2028-2040年改革蓝图。","政策文本分析、土地权属与遥感算法评估，结合CAP实施数据。","CAP实施不足、土地租赁短期化及EUDR遥感误判阻碍农林业推广。","农业绿色发展与碳","可研究遥感算法对传统农林系统的误判校正及数字基础设施整合方案。","农业遥感与作物表型","openalex","2026-09-24T23:30:20.637258Z",{"total":57,"page":22,"page_size":57,"items":58},6,[59,111,166,200,244,284],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":9,"source_name":65,"source_url":62,"published_at":66,"category":12,"cover_url":9,"hotness":67,"is_selected":68,"score":69,"score_detail":70,"sources":74,"tags":76,"search_phrases":80,"slug":83,"view_count":39,"doi":84,"paper":85,"created_at":110},2325,"Carbon Farming in the Digital Era: Soil Carbon Sequestration, Permanence, Measurement, Reporting and Verification","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjabb\u002F2026\u002Fv29i104418","Carbon farming is increasingly promoted as a way to rebuild soil organic carbon while creating verifiable climate benefits and new farm income streams. Its credibility, however, depends on a chain of conditions that extends well beyond whether a practice can increase soil carbon at an experimental site. This critical narrative review evaluates the biophysical basis of agricultural soil carbon sequestration, the distinction between molecular persistence and project-level permanence, the measurement of stock change, and the rapidly developing role of digital technologies in measurement, reporting and verification (MRV). Literature published from 2000 to 6 July 2026 was examined, with earlier conceptual material considered only where necessary. Evidence was prioritised from peer-reviewed field studies, meta-analyses, methodological studies and authoritative technical frameworks. The evidence supports the capacity of cover crops, organic amendments, diversified rotations and some biochar applications to raise soil carbon under appropriate conditions, but effect sizes are strongly conditioned by baseline stocks, climate, texture, depth, carbon inputs, management history and system boundaries. Apparent gains can also arise from altered depth distribution or transferred organic matter rather than additional atmospheric carbon removal. Permanence is therefore a management and accounting property, not simply a property of chemically resistant carbon. Direct remeasurement remains the evidentiary anchor for stock-change assessment, yet high spatial variability and slow accumulation make short project periods statistically difficult. Fixed-depth accounting can bias comparisons when bulk density changes; equivalent-soil-mass approaches and explicit uncertainty analysis are more defensible. Remote sensing, digital soil mapping, spectroscopy, machine learning and process-based models can reduce transaction costs and improve stratification, activity monitoring and extrapolation, but they do not remove the need for field calibration, independent validation and periodic soil remeasurement. High-integrity carbon farming consequently requires hybrid MRV, conservative uncertainty treatment, transparent counterfactual baselines, reversal provisions, leakage and non-carbon greenhouse-gas accounting, auditable data lineage and safeguards for equitable participation. Digitalisation can make carbon farming more scalable, but only if it strengthens rather than substitutes for biophysical and governance integrity.","碳农业日益被推广为一种在重建土壤有机碳的同时创造可验证气候效益和新型农业收入来源的途径。然而，其可信度取决于一系列条件，这些条件远不止于某项实践能否在试验点增加土壤碳。本批判性叙事综述评估了农业土壤碳固存的生物物理基础、分子持久性与项目层面永久性之间的区别、碳储量变化的测量，以及数字技术在测量、报告与核查（MRV）中迅速发展的作用。本文考察了2000年至2026年7月6日发表的文献，仅在必要时参考更早的概念性材料。证据优先取自同行评议的田间研究、荟萃分析、方法学研究及权威技术框架。证据支持覆盖作物、有机改良剂、多样化轮作及部分生物炭应用在适当条件下提高土壤碳的能力，但效应量强烈受基线碳储量、气候、质地、深度、碳输入、管理历史和系统边界的影响。表观增益也可能源于深度分布的改变或有机物的转移，而非额外的大气碳去除。因此，永久性是一种管理与核算属性，而不仅仅是化学抗性碳的属性。直接再测量仍是碳储量变化评估的证据锚点，但高空间变异性和缓慢积累使短项目周期在统计上难以奏效。当容重变化时，固定深度核算可能使比较产生偏差；等效土壤质量方法和明确的不确定性分析更具辩护力。遥感、数字土壤制图、光谱学、机器学习和过程模型可以降低交易成本，改善分层、活动监测和外推，但它们并不能消除田间校准、独立验证和定期土壤再测量的需要。因此，高完整性碳农业需要混合MRV、保守的不确定性处理、透明的反事实基线、逆转条款、泄漏和非碳温室气体核算、可审计的数据溯源以及公平参与的保障措施。数字化可以使碳农业更具可扩展性，但前提是它加强而非替代生物物理","Journal of Advances in Biology & Biotechnology","2026-09-11T00:00:00Z",10,false,79,{"impact":19,"substance":71,"depth":19,"authority":20,"freshness":72,"relevant":22,"comment":73},22,8,"系统评述数字技术支撑土壤碳汇MRV的方法与治理条件，对农业碳汇数字化与碳交易机制建设有实质参考价值。",[75],{"name":65,"url":62},[77,78,30,31,79],"数字乡村","智慧农业","土壤固碳",[81,82],"土壤固碳 数字乡村 智慧农业 遥感监测","土壤固碳 数字乡村","土壤固碳数字乡村智慧农业遥感监测-2325","10.9734\u002Fjabb\u002F2026\u002Fv29i104418",{"doi":84,"openalex_id":86,"authors":87,"venue":65,"cited_by_count":39,"oa_url":62,"card":105,"direction":53,"ingested_from":54},"W7212265915",[88,91,93,95,97,99,101,103],{"name":89,"orcid":90},"Samarpan Chakraborty","https:\u002F\u002Forcid.org\u002F0000-0002-2740-8722",{"name":92,"orcid":9},"Anusmita Goswami",{"name":94,"orcid":9},"Ritam Dhar",{"name":96,"orcid":9},"Kasturi Mandal",{"name":98,"orcid":9},"Tamalika Mondal",{"name":100,"orcid":9},"Sancharee Paul",{"name":102,"orcid":9},"Priya Sarawgi",{"name":104,"orcid":9},"Sujan Biswas",{"tldr":106,"method":107,"finding":108,"direction":51,"opportunity":109},"综述数字时代碳农业的土壤固碳、持久性与MRV，强调混合验证与保守核算。","2000-2026年文献批判性综述，聚焦田间研究、荟萃分析与技术框架。","数字技术可降本增效，但无法替代田间校准与定期土壤复测，需混合MRV。","可研究遥感与模型融合的混合MRV框架，量化不确定性并保障小农公平参与。","2026-09-13T23:30:23.245745Z",{"id":112,"title":113,"url":114,"summary":115,"summary_zh":116,"content":9,"source_name":117,"source_url":114,"published_at":11,"category":12,"cover_url":9,"hotness":67,"is_selected":68,"score":118,"score_detail":119,"sources":121,"tags":123,"search_phrases":128,"slug":131,"view_count":39,"doi":132,"paper":133,"created_at":165},3370,"A National Depth-Resolved Soil-Moisture-to-Electromagnetic Proxy Database for Hydrogeophysical Monitoring","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-2026-563","Abstract. Soil moisture is a key control on drought, recharge, crop water availability, agricultural water management, and land-atmosphere exchange, but its depth-resolved spatial variability remains difficult to monitor over large areas. Non-invasive methods such as ground-penetrating radar (GPR), electrical resistivity, and electromagnetic surveys can support soil-moisture estimation, yet their interpretation depends strongly on soil texture and hydraulic-retention properties. This study develops a national six-depth soil-moisture-to-electromagnetic proxy database to support future GPR- and resistivity-based root-zone monitoring and decision support. We used multi-year soil-moisture, porosity, saturation, texture, and hydraulic-retention data from 117 monitoring stations across Hungary at 10, 20, 30, 45, 60, and 75 cm depth. Dielectric permittivity, EM-wave velocity, electrical conductivity, attenuation, and apparent resistivity were derived as proxy variables from observed soil moisture using established petrophysical relationships, including the Topp equation and an Archie-type formulation. The proxy database identified a consistent 30-45 cm buffering zone, where soil moisture increased to 22.25%, dielectric permittivity peaked at 12.29, EM-wave velocity reached a minimum of 0.0935 m ns⁻¹, and apparent resistivity decreased to 545 Ω m. Texture strongly shaped the translated EM response: sandy soils were drier and more resistive, whereas clayey soils retained more water and showed higher dielectric response. Hydraulic-retention variables provided calibration-relevant prior information for explaining why similar EM or resistivity signals may represent different true soil-moisture values across soil types. The framework provides a transferable national calibration layer for drought-monitoring agencies, irrigation planners, precision-agriculture users, and future drone-based geophysical surveys in regions with comparable monitoring and soil databases.","摘要：土壤水分是干旱、补给、作物可用水量、农业水资源管理和陆气交换的关键控制因素，但其深度分辨的空间变异性在大范围区域仍难以监测。探地雷达（GPR）、电阻率和电磁勘探等非侵入式方法可支持土壤水分估算，但其解译在很大程度上取决于土壤质地和水力保持特性。本研究构建了一个全国尺度的六深度土壤水分—电磁代用指标数据库，以支持未来基于GPR和电阻率的根区监测与决策支持。我们使用了匈牙利全国117个监测站多年在10、20、30、45、60和75 cm深度的土壤水分、孔隙度、饱和度、质地和水力保持数据。基于观测土壤水分，利用成熟的地球物理关系（包括Topp方程和Archie型公式），推导出介电常数、电磁波速度、电导率、衰减和视电阻率作为代用变量。该代用指标数据库识别出一致的30–45 cm缓冲带，其中土壤水分增至22.25%，介电常数峰值达12.29，电磁波速度最低为0.0935 m ns⁻¹，视电阻率降至545 Ω m。质地对转换后的电磁响应具有强烈影响：砂质土壤更干、电阻率更高，而黏质土壤持水更多并表现出更高的介电响应。水力保持变量为解释为何相似的电磁或电阻率信号在不同土壤类型中可能代表不同真实土壤水分值提供了与校准相关的先验信息。该框架为干旱监测机构、灌溉规划者、精准农业用户以及未来在具有可比监测和土壤数据库区域开展的无人机地球物理调查提供了一个可迁移的全国校准层。","Earth System Science Data Discussions",80,{"impact":19,"substance":71,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":120},"基于匈牙利117个站点六层深度构建的土壤水分—电磁代理数据库，为根区墒情遥感与精准灌溉决策提供可迁移校准层，方法新颖、数据扎实，对农业信息化有参考价值。",[122],{"name":117,"url":114},[124,125,30,126,127],"农业遥感","精准农业","土壤墒情","智慧灌溉",[129,130],"匈牙利 土壤水分 电磁代理数据库","探地雷达 根区土壤水分 监测","匈牙利土壤水分电磁代理数据库-3370","10.5194\u002Fessd-2026-563",{"doi":132,"openalex_id":134,"authors":135,"venue":117,"cited_by_count":39,"oa_url":114,"card":159,"direction":164,"ingested_from":54},"W7214076150",[136,139,141,143,146,148,150,153,156],{"name":137,"orcid":138},"Diaa Sheishah","https:\u002F\u002Forcid.org\u002F0000-0003-2050-6474",{"name":140,"orcid":9},"Enas Abdelsamei",{"name":142,"orcid":9},"Viktoria Blanka-Vegi",{"name":144,"orcid":145},"Károly Barta","https:\u002F\u002Forcid.org\u002F0000-0001-9450-5277",{"name":147,"orcid":9},"Ahmed M. Ali",{"name":149,"orcid":9},"Khaldoun Abualhin",{"name":151,"orcid":152},"Djamil Al‐Halbouni","https:\u002F\u002Forcid.org\u002F0000-0003-2254-3914",{"name":154,"orcid":155},"Wouter Arnoud Dorigo","https:\u002F\u002Forcid.org\u002F0000-0001-8054-7572",{"name":157,"orcid":158},"György Sípos","https:\u002F\u002Forcid.org\u002F0000-0001-6224-2361",{"tldr":160,"method":161,"finding":162,"direction":53,"opportunity":163},"构建匈牙利全国六深度土壤水分-电磁代理数据库，支持根区水文地球物理监测。","利用117站多年土壤水分、质地与水力参数，经Topp和Archie模型推导电磁代","发现30-45 cm缓冲带水分与介电常数峰值，质地显著影响电磁响应解释。","可扩展至无人机地球物理调查与多区域迁移校准，解决土壤质地导致的电磁信号歧义。","农业人工智能与决策模型","2026-09-24T23:30:36.864649Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":171,"content":9,"source_name":172,"source_url":169,"published_at":11,"category":12,"cover_url":9,"hotness":67,"is_selected":68,"score":173,"score_detail":174,"sources":177,"tags":179,"search_phrases":184,"slug":187,"view_count":39,"doi":188,"paper":189,"created_at":199},3364,"Geographical Analysis of Desertification and Environmental Degradation and the Spatial Variation of Their Manifestations in Parts of the Jifara Plain","https:\u002F\u002Fdoi.org\u002F10.65405\u002Fsjh.2.3.85","This research aims to analyze the spatial variation of desertification and environmental degradation manifestations in the Al-Jfara Plain, identify the degrees, intensity, and geographical distribution of desertification, and determine the main manifestations associated with land degradation. It also seeks to identify the effects of natural and human factors on the spread of desertification and determine the areas most vulnerable to it, thereby contributing to efforts to conserve natural resources and reduce land degradation. The research adopted the analytical approach as the main method for analyzing data related to degraded lands, vegetation cover, rangelands, rainfall, erosion, and agricultural and livestock production. It also employed the descriptive approach to present the natural and human characteristics of the research area, as well as the comparative approach to compare different spatial units and time periods. The research further relied on geographical references, scientific studies, and available data, using percentages and rates of change to compare levels of degradation. The research concluded that there is clear spatial variation in the manifestations of desertification and environmental degradation across the Al-Jfara Plain. Wind erosion, declining vegetation cover, drought, and rainfall variability emerged as the most prominent manifestations of degradation. In addition, overgrazing, excessive exploitation of soil and groundwater resources, and urban expansion contributed to increasing pressure on natural resources. The research recommends reducing overgrazing, rationalizing groundwater use, combating erosion, increasing vegetation cover, regulating land use, and enhancing environmental awareness. It also proposes periodic monitoring of desertification using remote sensing and Geographic Information Systems (GIS), preparing updated maps of land degradation, and implementing projects for the rehabilitation of degraded lands.","本研究旨在分析贾法拉平原荒漠化与环境退化表现的空间差异，识别荒漠化的程度、强度及地理分布，并确定与土地退化相关的主要表现。研究还力求查明自然因素和人为因素对荒漠化蔓延的影响，确定最易受荒漠化影响的区域，从而为保护自然资源和减少土地退化的努力作出贡献。研究采用分析方法作为分析与退化土地、植被覆盖、牧场、降雨、侵蚀以及农牧业生产相关数据的主要方法，同时运用描述性方法呈现研究区域的自然和人文特征，并采用比较方法对不同空间单元和时间段进行比较。研究还依托地理文献、科学研究和现有数据，利用百分比和变化率来比较退化程度。研究得出结论：贾法拉平原各地荒漠化与环境退化表现在明显的空间差异。风蚀、植被覆盖下降、干旱和降雨变率是最突出的退化表现。此外，过度放牧、土壤和地下水资源的过程开发以及城市扩张也加剧了对自然资源的压力。研究建议减少过度放牧、合理利用地下水、防治侵蚀、增加植被覆盖、规范土地利用并增强环境意识。研究还提出利用遥感和地理信息系统（GIS）对荒漠化进行定期监测，编制更新的土地退化地图，并实施退化土地恢复项目。","Shihab Journal of Humanities",55,{"impact":72,"substance":19,"depth":175,"authority":57,"freshness":21,"relevant":22,"comment":176},14,"区域荒漠化空间分析研究，方法常规、结论有参考价值，但属地方性学术成果，公共影响有限。",[178],{"name":172,"url":169},[180,30,181,182,183],"土地退化","GIS","植被覆盖","荒漠化防治",[185,186],"GIS 荒漠化 遥感监测","荒漠化防治 土地退化 植被覆盖 遥感监测","GIS荒漠化遥感监测-3364","10.65405\u002Fsjh.2.3.85",{"doi":188,"openalex_id":190,"authors":191,"venue":172,"cited_by_count":39,"oa_url":169,"card":194,"direction":53,"ingested_from":54},"W7214127171",[192],{"name":193,"orcid":9},"حسنية السني رجب البكوري",{"tldr":195,"method":196,"finding":197,"direction":53,"opportunity":198},"分析吉法拉平原荒漠化与环境退化的空间差异及主要表现，并提出防治建议。","采用分析法、描述法与比较法，结合地理文献和统计数据评估退化程度。","风蚀、植被减少、干旱和降水变率是主要退化表现，过度放牧与地下水超采加剧压力。","可结合遥感与GIS开展荒漠化动态监测，构建多因子退化风险评估模型。","2026-09-24T23:30:21.130938Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":205,"content":9,"source_name":206,"source_url":203,"published_at":11,"category":12,"cover_url":9,"hotness":67,"is_selected":68,"score":207,"score_detail":208,"sources":211,"tags":213,"search_phrases":217,"slug":220,"view_count":39,"doi":221,"paper":222,"created_at":243},3363,"Integrated Assessment of Soil Erosion Drivers Using RUSLE, Remote Sensing, and Scenario-Based Machine Learning in a Data-Scarce Watershed of the Chota Nagpur Plateau, Eastern India","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02576-x","Integrated Assessment of Soil Erosion Drivers Using RUSLE, Remote Sensing, and Scenario-Based Machine Learning in a Data-Scarce Watershed of the Chota Nagpur Plateau, Eastern India。Journal of the Indian Society of Remote Sensing","基于RUSLE、遥感和情景机器学习的土壤侵蚀驱动因素综合评估——以印度东部乔塔纳格普尔高原数据稀缺流域为例。《印度遥感学会杂志》","Journal of the Indian Society of Remote Sensing",63,{"impact":72,"substance":19,"depth":209,"authority":20,"freshness":21,"relevant":22,"comment":210},15,"方法组合有新意但属区域案例研究，影响范围有限，可作为遥感与水土保持主题的补充素材。",[212],{"name":206,"url":203},[214,30,215,216],"机器学习","土壤侵蚀","水土保持",[218,219],"Chota Nagpur Plateau 土壤侵蚀","RUSLE 遥感 机器学习","ChotaNagpurPlateau土壤侵蚀-3363","10.1007\u002Fs12524-026-02576-x",{"doi":221,"openalex_id":223,"authors":224,"venue":206,"cited_by_count":39,"oa_url":9,"card":238,"direction":53,"ingested_from":54},"W7214122637",[225,228,231,233,235],{"name":226,"orcid":227},"Mukesh Kumar Tiwari","https:\u002F\u002Forcid.org\u002F0000-0003-0385-4426",{"name":229,"orcid":230},"Prabhat Kumar Guru","https:\u002F\u002Forcid.org\u002F0000-0002-9294-2091",{"name":232,"orcid":9},"Sanjeet Kumar",{"name":234,"orcid":9},"Yogesh A. Rajwade",{"name":236,"orcid":237},"Narendra Singh Chandel","https:\u002F\u002Forcid.org\u002F0000-0003-4850-4702",{"tldr":239,"method":240,"finding":241,"direction":53,"opportunity":242},"结合RUSLE、遥感与情景机器学习，评估印度Chota Nagpur高原缺数据流域的土壤侵蚀驱动因素","RUSLE模型、遥感数据与情景机器学习集成分析。","在数据稀缺流域识别出土壤侵蚀关键驱动因子并预测不同情景下的侵蚀风险。","可探索缺数据区多源遥感与机器学习融合的土壤侵蚀动态监测与情景预警方法。","2026-09-24T23:30:21.065678Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":249,"content":9,"source_name":250,"source_url":247,"published_at":11,"category":12,"cover_url":9,"hotness":67,"is_selected":68,"score":251,"score_detail":252,"sources":255,"tags":257,"search_phrases":262,"slug":265,"view_count":39,"doi":266,"paper":267,"created_at":283},3361,"Spatiotemporal Patterns and Associations of Ecological Quality and Carbon Storage Across Three Major Agricultural Provinces in Central China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101781","Ecological quality, vegetation productivity, and carbon storage represent different ecosystem properties and may respond differently to land-use change. This study assessed their spatiotemporal patterns and spatial relationships across Hubei, Henan, and Anhui, three major agricultural provinces in central China. Ecological quality was assessed using the Remote Sensing Ecological Index (RSEI), vegetation productivity was estimated as net primary productivity (NPP) using the Carnegie–Ames–Stanford Approach (CASA) model, and carbon storage was estimated using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Spearman correlation, GeoDetector, and a descriptive coordination index were used to compare their spatial relationships. Ecological quality declined from 2000 to 2010 and recovered only modestly thereafter, with persistently lower values in the eastern and northern plains than in the western and southern mountains. From 1990 to 2020, built-up land expanded by 112.03%, while model-estimated carbon storage declined by 34.59 × 106 t. RSEI and NPP were predominantly positively correlated, but correlations varied substantially among provinces, ecological-quality classes, and land-use types. RSEI had the greatest explanatory power for spatial variation in carbon storage among the composite factors (q = 0.420–0.467), and NDVI was the strongest RSEI component. GeoDetector identified bivariate and nonlinear enhancement, indicating that paired factors explained more spatial variation than individual factors. Moderate and primary coordination together accounted for more than 99% of valid pixels, while severe imbalance declined from 0.88% to 0.44%. These findings show that the three indicators are spatially associated but not interchangeable. Together, the results support protecting forested mountain areas, limiting built-up expansion and cropland loss in plains and urban fringes, and using multiple indicators to identify areas requiring local assessment.","生态质量、植被生产力与碳储量表征不同的生态系统属性，可能对土地利用变化作出不同响应。本研究评估了华中三个农业大省——湖北、河南和安徽——上述指标的时空格局及其空间关系。生态质量采用遥感生态指数（RSEI）评估，植被生产力以Carnegie–Ames–Stanford Approach（CASA）模型估算的净初级生产力（NPP）表征，碳储量则采用生态系统服务与权衡综合评估（InVEST）模型估算。运用Spearman相关分析、地理探测器及描述性协调指数比较其空间关系。2000—2010年生态质量下降，此后仅略有恢复，东部和北部平原的值持续低于西部和南部山区。1990—2020年，建设用地扩张112.03%，而模型估算的碳储量下降34.59 × 10⁶ t。RSEI与NPP总体呈正相关，但相关性在各省份、生态质量等级和土地利用类型间差异显著。在复合因子中，RSEI对碳储量空间变异的解释力最强（q = 0.420–0.467），NDVI是RSEI中最强的组分。地理探测器识别出双因子和非线性增强，表明成对因子解释的空间变异多于单因子。中度协调和初级协调合计占有效像元的99%以上，而严重失调从0.88%降至0.44%。研究结果表明，三个指标在空间上相互关联但不可互换。综合而言，研究结果支持保护山地森林区域、限制平原和城市边缘的建设用地扩张与耕地流失，并利用多指标识别需开展局部评估的区域。","Land",78,{"impact":253,"substance":71,"depth":19,"authority":175,"freshness":72,"relevant":22,"comment":254},16,"基于RSEI、CASA与InVEST模型对中部三大农业省生态质量与碳储量的长时序空间关联分析，方法规范、数据扎实，对农业生态保护与国土空间管控有参考价值。",[256],{"name":250,"url":247},[258,30,259,260,261],"农业生态","土地利用变化","生态质量","碳储量",[263,264],"湖北 河南 安徽 农业生态","RSEI 碳储量 遥感","湖北河南安徽农业生态-3361","10.3390\u002Fland15101781",{"doi":266,"openalex_id":268,"authors":269,"venue":250,"cited_by_count":39,"oa_url":247,"card":278,"direction":53,"ingested_from":54},"W7214114544",[270,272,275],{"name":271,"orcid":9},"Xiaohan Liu",{"name":273,"orcid":274},"Lei Wang","https:\u002F\u002Forcid.org\u002F0000-0002-7163-3644",{"name":276,"orcid":277},"Hui Min Zhao","https:\u002F\u002Forcid.org\u002F0000-0001-7587-1116",{"tldr":279,"method":280,"finding":281,"direction":51,"opportunity":282},"评估中部三大农业省生态质量、植被生产力与碳储量的时空格局及空间关联。","用RSEI、CASA模型、InVEST模型结合Spearman相关、GeoDet","生态质量先降后微升，建设用地增112%，碳储减3459万吨；三指标空间关联但不可互换。","可探究不同农业省土地利用变化下生态质量与碳储量的非线性驱动机制及分区管控策略。","2026-09-24T23:30:20.927597Z",{"id":285,"title":286,"url":287,"summary":288,"summary_zh":289,"content":9,"source_name":10,"source_url":287,"published_at":290,"category":12,"cover_url":9,"hotness":13,"is_selected":68,"score":291,"score_detail":292,"sources":294,"tags":298,"search_phrases":302,"slug":305,"view_count":39,"doi":306,"paper":307,"created_at":318},3357,"AI-Driven Precision Agriculture and Crop Resilience: Integrating Artificial Intelligence, IoT and Remote Sensing for Climate-Resilient Indian Agriculture: A Vision for Viksit Bharat 2047","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914538","Abstract Agriculture is central to India's economic development, food security, rural employment, and the achievement of the Viksit Bharat@2047 vision. However, Indian agriculture faces increasingly complex challenges, including climate variability, water scarcity, soil degradation, pest and disease outbreaks, fragmented landholdings, market uncertainty, and unequal access to agricultural knowledge. These challenges require a transition from conventional, input-intensive agriculture towards data-driven, resource-efficient, climate-resilient and farmer-centric production systems. Agriculture in India is increasingly affected by climate variability, water scarcity, soil degradation, pest and disease outbreaks, and unpredictable weather conditions. These challenges threaten crop productivity and food security, particularly among small and marginal farmers. Artificial Intelligence (AI), Internet of Things (IoT), remote sensing, and machine learning offer new opportunities to transform conventional agricultural practices into data-driven precision agriculture systems. This paper presents a conceptual framework for AI-driven precision agriculture aimed at improving crop resilience under changing climatic conditions. Artificial Intelligence (AI), combined with precision agriculture, Internet of Things (IoT), remote sensing, satellite imagery, drones, machine learning, robotics and digital public infrastructure, offers significant opportunities to transform Indian agriculture. AI can support crop and yield prediction, disease and pest identification, weather-based advisories, irrigation optimisation, soil management, crop insurance, market intelligence and early-warning systems. The paper also discusses challenges related to digital inclusion, data governance, affordability, AI reliability, farmer skills, privacy and institutional coordination. It argues that India's objective should not simply be the digitisation of agriculture, but the creation of an intelligent, inclusive and resilient agricultural ecosystem in which technology augments farmer knowledge and decision-making. By 2047, India can aspire to establish globally competitive agriculture that produces more with fewer resources, withstands climate shocks, generates higher and more stable farm incomes, and ensures sustainable food and nutritional security.","摘要 农业对印度的经济发展、粮食安全、农村就业以及“发达印度@2047”愿景的实现至关重要。然而，印度农业面临日益复杂的挑战，包括气候变异性、水资源短缺、土壤退化、病虫害暴发、土地持有碎片化、市场不确定性以及农业知识获取不平等。这些挑战要求从传统的投入密集型农业向数据驱动、资源高效、气候韧性且以农民为中心的生产体系转型。印度农业日益受到气候变异性、水资源短缺、土壤退化、病虫害暴发及不可预测天气条件的影响。这些挑战威胁着作物生产力和粮食安全，尤其是对小农和边缘农民而言。人工智能（AI）、物联网（IoT）、遥感和机器学习为将传统农业实践转变为数据驱动的精准农业系统提供了新机遇。本文提出了一个AI驱动的精准农业概念框架，旨在改善气候变化条件下作物的韧性。人工智能（AI）与精准农业、物联网（IoT）、遥感、卫星影像、无人机、机器学习、机器人技术及数字公共基础设施相结合，为改造印度农业提供了重大机遇。AI可支持作物与产量预测、病虫害识别、基于天气的农事建议、灌溉优化、土壤管理、作物保险、市场情报及预警系统。本文还讨论了与数字包容、数据治理、可负担性、AI可靠性、农民技能、隐私及机构协调相关的挑战。文章认为，印度的目标不应仅仅是农业数字化，而应是创建一个智能、包容且有韧性的农业生态系统，使技术增强农民的知识与决策能力。到2047年，印度有望建立具有全球竞争力的农业，以更少资源生产更多产品，抵御气候冲击，创造更高且更稳定的农业收入，并确保可持续的粮食与营养安全。","2026-09-30T00:00:00Z",69,{"impact":71,"substance":19,"depth":253,"authority":20,"freshness":39,"relevant":22,"comment":293},"概念性框架论文，系统梳理AI、IoT与遥感在印度气候韧性农业中的应用与挑战，有参考价值但无实证数据，且发布日期在未来、时效性缺失，暂不宜进入每日精选。",[295,296],{"name":10,"url":287},{"name":10,"url":297},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914539",[77,78,299,300,301,30],"农业人工智能","农业物联网","气候韧性",[303,304],"印度 精准农业 AI","农业人工智能 农业物联网 数字乡村 智慧农业","印度精准农业AI-3357","10.5281\u002Fzenodo.22914538",{"doi":306,"openalex_id":308,"authors":309,"venue":10,"cited_by_count":39,"oa_url":287,"card":312,"direction":316,"ingested_from":54},"W7214083098",[310],{"name":311,"orcid":9},"Twinkal Prakash Sawant",{"tldr":313,"method":314,"finding":315,"direction":316,"opportunity":317},"提出AI+物联网+遥感驱动的精准农业概念框架，提升印度气候韧性作物生产。","概念框架分析，整合AI、IoT、遥感、卫星、无人机、机器学习与数字公共基础设施。","印度农业应构建智能、包容、有韧性的生态系统，而非仅数字化，以应对气候与资源挑战。","智慧农业 \u002F 农业物联网","可实证检验小农户场景下AI+IoT+遥感集成对作物韧性与收入的实际效果及数字包容机制。","2026-09-24T23:30:13.211525Z"]