[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2423":3,"related-2423":103},{"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":39,"search_phrases":78,"slug":81,"view_count":62,"doi":82,"paper":83,"created_at":102},2423,"Spatio-temporal evolution and driving force analysis of carbon storage coupled with PLUS-InVEST-GeoDetector in the Fen River Basin of China","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71784-9","Abstract Against the backdrop of China’s “Dual Carbon” (carbon peak and carbon neutrality) strategic initiative, terrestrial ecosystem carbon sequestration has become a core pillar of climate mitigation and ecological governance. This study systematically quantifies the spatiotemporal distribution of carbon storage and and projects long-term carbon sink trajectories under divergent socioecological development pathways across the Fen River Basin—a representative ecologically fragile tributary catchment of the Yellow River. Four scenarios based on the basin’s ecological landscape planning were established: Natural Development, Economic Priority, Cropland Protection, and Ecological Protection. By coupling the PLUS and InVEST models, an analytical framework linking land use change with ecosystem carbon storage was constructed to simulate and predict the spatial distribution and dynamics of carbon storage under different scenarios. The Geodetector was used to identify the key driving factors. The results indicate that: (1) From 2005 to 2020, cropland remained the dominant land use type but showed a continuous decreasing trend alongside grassland degradation. Forest land area initially decreased before recovering slightly, while built-land expanded consistently. (2) The total carbon storage in the basin showed a declining trend, decreasing from 700.32 Tg in 2005 to 683.48 Tg in 2020, with a cumulative loss of 16.84 Tg. Soil organic carbon was the major carbon pool. Spatially, areas with high carbon storage were primarily distributed in the eastern and western parts of the basin, while the central-southern regions generally had lower carbon storage. Carbon storage exhibited significant positive spatial autocorrelation, with High-High clusters (hot spots) being more extensive than Low-Low clusters (cold spots). The area experiencing carbon loss was substantially larger than the area with carbon gain. (3) Multi-scenario simulations revealed that different development pathways significantly impact carbon storage. The Ecological Protection scenario yielded the highest carbon storage (683.57 Tg), highlighting the effectiveness of ecological protection in enhancing regional carbon sink capacity. (4) NDVI was the dominant factor influencing the spatial heterogeneity of carbon storage, possessing strong independent explanatory power. Its interactive effects with other factors further enhanced the ability to explain variations in carbon storage. This study elucidates the coupling mechanism between land use change and carbon storage, providing a scientific reference for ecological protection, land structure optimization, and carbon sink enhancement in the Fen River Basin.","在中国“双碳”（碳达峰与碳中和）战略倡议背景下，陆地生态系统碳汇已成为气候减缓与生态治理的核心支柱。本研究系统量化了汾河流域——黄河具有代表性的生态脆弱支流流域——碳储量的时空分布，并预测了不同社会生态发展路径下的长期碳汇轨迹。基于流域生态景观规划，设立了四种情景：自然发展、经济优先、耕地保护和生态保护。通过耦合PLUS与InVEST模型，构建了土地利用变化与生态系统碳储量关联的分析框架，以模拟和预测不同情景下碳储量的空间分布与动态变化。运用地理探测器识别关键驱动因子。结果表明：（1）2005年至2020年，耕地仍为主要土地利用类型，但呈持续下降趋势，草地亦退化。林地面积先减少后略有恢复，建设用地持续扩张。（2）流域碳储量总量呈下降趋势，从2005年的700.32 Tg降至2020年的683.48 Tg，累计损失16.84 Tg。土壤有机碳为主要碳库。空间上，高碳储量区主要分布于流域东西部，中南部地区碳储量普遍较低。碳储量表现出显著的正空间自相关，高-高集聚区（热点）范围大于低-低集聚区（冷点）。碳损失面积显著大于碳增益面积。（3）多情景模拟揭示，不同发展路径对碳储量影响显著。生态保护情景碳储量最高（683.57 Tg），凸显了生态保护在提升区域碳汇能力方面的有效性。（4）NDVI是影响碳储量空间分异的主导因子，具有较强独立解释力。其与其他因子的交互作用进一步增强了对碳储量变化的解释能力。本研究阐明了",null,"Scientific Reports","2026-09-12T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,14,8,1,"耦合PLUS-InVEST-GeoDetector量化汾河流域碳储时空演变并模拟多情景碳汇路径，方法新颖、数据扎实，对黄河流域生态保护与土地结构优化有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感","生态保护","黄河流域","土地利用","碳汇",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,60,67,73],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},7,true,[],[31,50],"carbon sequestration",[31],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":59},"H2",6,[],[57,58],"土壤","soil",[],{"code":61,"score":62,"supported":47,"title":63,"abstract":64,"tags":66},"H1",3,[],[65],"耕地保护",[],{"code":68,"score":69,"supported":14,"title":70,"abstract":71,"tags":72},"H6",2,[],[],[30,28],{"code":74,"score":22,"supported":14,"title":75,"abstract":76,"tags":77},"I3",[],[],[27],[79,80],"土地利用 生态保护 黄河流域 碳汇","土地利用 生态保护","土地利用生态保护黄河流域碳汇-2423","10.1038\u002Fs41598-026-71784-9",{"doi":82,"openalex_id":84,"authors":85,"venue":10,"cited_by_count":92,"oa_url":93,"card":94,"direction":100,"ingested_from":101},"W7212371101",[86,88,90],{"name":87,"orcid":9},"Jie Chen",{"name":89,"orcid":9},"Pengxiang Gao",{"name":91,"orcid":9},"Yi Hou",0,"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71784-9_reference.pdf",{"tldr":95,"method":96,"finding":97,"direction":98,"opportunity":99},"耦合PLUS-InVEST-GeoDetector模拟汾河流域多情景碳储量演变并识别驱动因子。","PLUS与InVEST耦合模拟四情景土地利用，GeoDetector识别驱动因子","2005-2020年碳储量下降16.84 Tg，生态保护情景碳储量最高，NDVI为主导驱动因子。","农业绿色发展与碳","可引入农业管理措施与政策情景，细化耕地保护对碳汇的贡献机制研究。","农业遥感与作物表型","openalex","2026-09-14T23:30:24.574426Z",{"total":54,"page":22,"page_size":54,"items":104},[105,171,259,336,408,516],{"id":106,"title":107,"url":108,"summary":109,"summary_zh":110,"content":9,"source_name":111,"source_url":108,"published_at":112,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":113,"score_detail":114,"sources":119,"tags":121,"primary_topic":125,"secondary_topics":126,"classification":127,"search_phrases":147,"slug":150,"view_count":92,"doi":151,"paper":152,"created_at":170},4835,"Spatiotemporal Patterns of Carbon Storage and Their Driving Mechanisms Under Multiple Land-Use Scenarios: A Case Study of the Henan Section of the South-to-North Water Diversion Project","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101928","The water source area of the Henan Reach for the South-to-North Water Diversion Project is an important ecological protection zone, where variations in land-use patterns can adversely affect the region’s carbon sink capacity and long-term ecological stability. To examine the spatiotemporal distribution of carbon sinks and clarify the underlying factors driving their changes, an analytical framework combining PLUS, InVEST, and GeoDetector was applied. Xichuan County, Xixia County, Neixiang County, and Dengzhou City—four administrative units in Nanyang City that together form the Henan water source zone of the Middle Route—were selected as the research areas. Land-use records from 2002 to 2023 were analyzed to identify changes in the production–living–ecological (PLE) land-use structure and carbon storage dynamics through 2030 under four development scenarios: natural development, ecological protection, cultivated land protection, and engineering safety. The results indicated a gradual expansion of ecological land, while cultivated land and general ecological land exhibited distinct development trajectories. Spatially, carbon storage showed a pronounced west–high\u002Feast–low pattern, with high-density zones concentrated in the northwestern mountainous and hilly terrain under key ecological land and low-density zones prevailing in the eastern plains, urban built-up areas, and the Danjiangkou Reservoir surface. During the observation period, carbon gain patches proliferated across the northwestern mountains and reservoir-adjacent ecological restoration belts, whereas carbon loss areas gradually expanded from scattered hotspots toward the eastern plains and river valleys. Regional carbon sink initially decreased slightly but subsequently showed a gradual increase. Ecological land functioned as the primary carbon sink, whereas cultivated land contributed the largest share of carbon emissions. Across the simulated trajectories, the ecological conservation scenario resulted in the highest carbon sequestration, followed by the cultivated land protection scenario, while the natural development and engineering safety scenarios showed relatively limited carbon sequestration effects. Under the four scenarios, the projected 2030 spatial layout preserved the prevailing west–high\u002Feast–low configuration but diverged notably in the distribution of gains and losses: the ecological protection scenario generated connected carbon gain patches over the northwestern mountains and reservoir restoration belts, the cropland protection scenario triggered dispersed carbon loss zones around urban peripheries and river valley agricultural belts, and the engineering safety scenario maintained the most stable spatial pattern. GeoDetector analysis identified slope, elevation, and NDVI as the principal factors associated with spatial variations in carbon sink. Furthermore, the interaction between slope and NDVI produced the highest explanatory power, indicating that their combined influence on the phenomenon was significantly stronger than the effect of either factor alone. Based on these findings, adjustments to the land-use plan are proposed to enhance carbon sequestration and strengthen ecological security in the Henan section of the South-to-North Water Diversion Project.","南水北调工程河南段水源区是重要的生态保护区域，土地利用格局的变化会对区域碳汇能力和长期生态稳定性产生不利影响。为探究碳汇的时空分布特征并厘清其变化的驱动因素，本研究构建了PLUS、InVEST与GeoDetector相结合的分析框架。选取南阳市西川县、西峡县、内乡县和邓州市四个行政区作为研究区，四者共同构成中线工程河南水源区。基于2002—2023年土地利用记录，分析了生产—生活—生态（PLE）土地利用结构的变化，并在自然发展、生态保护、耕地保护和工程安全四种发展情景下模拟了至2030年的碳储量动态。结果表明，生态用地逐步扩张，而耕地和一般生态用地呈现出不同的发展轨迹。空间上，碳储量表现出显著的西高东低格局，高密度区集中于西北部山地丘陵地带的重点生态用地，低密度区则主要分布在东部平原、城市建成区和丹江口水库水面。观测期内，碳增益斑块在西北部山区和水库周边生态修复带不断扩展，而碳损失区域则从零星热点逐步向东部平原和河谷蔓延。区域碳汇初期略有下降，随后呈逐步增加趋势。生态用地是主要的碳汇来源，而耕地贡献了最大的碳排放份额。在各模拟情景中，生态保护情景的碳固持效果最佳，其次是耕地保护情景，而自然发展和工程安全情景的碳固持效果相对有限。四种情景下，2030年预测空间布局均保持了西高东低的基本格局，但在增益与损失的分布上存在显著差异：生态保护情景在西北部山区形成了连片的碳增益斑块，并","Land","2026-10-10T00:00:00Z",78,{"impact":17,"substance":115,"depth":19,"authority":116,"freshness":117,"relevant":22,"comment":118},22,13,9,"核心期刊论文，方法组合新颖、情景模拟数据扎实，对水源区生态保护与土地利用规划有参考价值，但属区域案例研究，公共影响层级有限。",[120],{"name":111,"url":108},[28,122,30,123,124],"遥感监测","碳储量","南水北调",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":128},{"candidates":129,"agricultural_evidence":47},[130,135,139,143],{"code":33,"score":13,"supported":47,"title":131,"abstract":132,"tags":134},[],[133,31,50],"碳排放",[123],{"code":61,"score":62,"supported":47,"title":136,"abstract":137,"tags":138},[],[65],[],{"code":68,"score":69,"supported":14,"title":140,"abstract":141,"tags":142},[],[],[30,28],{"code":74,"score":22,"supported":14,"title":144,"abstract":145,"tags":146},[],[],[122],[148,149],"南水北调 河南水源区 碳储量","PLUS InVEST GeoDetector 土地利用","南水北调河南水源区碳储量-4835","10.3390\u002Fland15101928",{"doi":151,"openalex_id":153,"authors":154,"venue":111,"cited_by_count":92,"oa_url":108,"card":165,"direction":98,"ingested_from":101},"W7221508293",[155,157,160,162],{"name":156,"orcid":9},"Xiaoyang Li",{"name":158,"orcid":159},"Wei Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6322-2597",{"name":161,"orcid":9},"Qi Jia",{"name":163,"orcid":164},"Lei Liu","https:\u002F\u002Forcid.org\u002F0000-0002-4966-5914",{"tldr":166,"method":167,"finding":168,"direction":98,"opportunity":169},"结合PLUS、InVEST与地理探测器，模拟南水北调河南水源区多情景土地利用与碳储时空演变及驱动机制","PLUS-InVEST-GeoDetector耦合框架，2002-2023年土地","碳储呈西高东低格局，生态保护情景固碳最优，生态用地为主要碳汇、耕地为主要排放源。","可引入多目标优化与政策成本约束，量化水源区生态保护与耕地保护的碳汇权衡协同路径。","2026-10-13T23:30:53.020186Z",{"id":172,"title":173,"url":174,"summary":175,"summary_zh":176,"content":9,"source_name":177,"source_url":174,"published_at":178,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":179,"score_detail":180,"sources":183,"tags":185,"primary_topic":189,"secondary_topics":194,"classification":196,"search_phrases":235,"slug":238,"view_count":239,"doi":240,"paper":241,"created_at":258},3554,"Spatial Estimation of Carbon Sequestration Using Remote Sensing and Its Application in Carbon-credit Assessment: A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91125","Carbon sequestration is fundamental to climate-change mitigation because terrestrial and aquatic ecosystems store carbon in vegetation, biomass, soils, and the Earth’s crust. Reliable estimation of carbon stocks and their temporal and spatial changes is essential for the formulation of climate policy, ecosystem management, carbon accounting, and carbon-credit assessment. Conventional field measurements provide detailed information but are constrained by sampling requirements, cost, labour, spatial heterogeneity, and limitations in continuous monitoring. Remote sensing provides a complementary approach by enabling spatially continuous observations of vegetation characteristics, land-cover change, biomass, and other carbon-related variables across different spatial and temporal scales. Advances in multispectral, hyperspectral, synthetic aperture radar, LiDAR, thermal, and high-resolution satellite observations have extended the capability for carbon estimation. Their integration with geographic information systems, field observations, statistical techniques, and machine-learning algorithms enables precise spatial assessment of above-ground biomass, below-ground biomass, soil organic carbon, forest carbon, agricultural carbon, mangrove carbon, and blue-carbon resources. However, uncertainties associated with sensor characteristics, field-data quality, model selection, spatial variability, temporal dynamics, and scaling remain acknowledged limitations. Converting spatial carbon estimates into carbon credits requires consideration of baseline conditions, additionality, permanence, leakage, uncertainty, verification, and measurement, reporting, and verification requirements. This review critically examines remote sensing technologies, carbon-estimation models, spatial mapping approaches, machine-learning techniques, carbon-accounting frameworks, and their integration into carbon-credit assessment. It places particular emphasis on multisource data fusion, emerging Earth-observation technologies, artificial intelligence, uncertainty assessment, and digital MRV systems. Major methodological, technological, and policy gaps are identified, along with opportunities for developing transparent, spatially explicit, scientifically robust, and scalable carbon-credit assessment frameworks.","碳固存是减缓气候变化的基础，因为陆地和水生生态系统将碳储存在植被、生物量、土壤和地壳中。可靠估算碳储量及其时空变化，对于制定气候政策、生态系统管理、碳核算和碳信用评估至关重要。传统野外测量可提供详细信息，但受采样要求、成本、劳动力、空间异质性以及连续监测局限性等因素制约。遥感提供了一种互补方法，能够在不同时空尺度上对植被特征、土地覆盖变化、生物量及其他碳相关变量进行空间连续观测。多光谱、高光谱、合成孔径雷达、激光雷达、热红外和高分辨率卫星观测的进步，拓展了碳估算能力。这些技术与地理信息系统、野外观测、统计技术和机器学习算法的集成，使得对地上生物量、地下生物量、土壤有机碳、森林碳、农业碳、红树林碳和蓝碳资源进行精确空间评估成为可能。然而，与传感器特性、野外数据质量、模型选择、空间变异性、时间动态和尺度转换相关的不确定性仍是公认的局限。将空间碳估算转化为碳信用，需要考虑基线条件、额外性、永久性、泄漏、不确定性、核查以及测量、报告和核查要求。本综述批判性地审视了遥感技术、碳估算模型、空间制图方法、机器学习技术、碳核算框架及其在碳信用评估中的集成。特别强调了多源数据融合、新兴地球观测技术、人工智能、不确定性评估和数字化MRV系统。识别了主要的方法学、技术和政策差距，以及开发透明、空间明确、科学稳健且可扩展的碳信用评估框架的机遇。","Journal of Geography Environment and Earth Science International","2026-09-25T00:00:00Z",67,{"impact":181,"substance":19,"depth":17,"authority":181,"freshness":117,"relevant":22,"comment":182},12,"系统综述遥感碳汇估算与碳信用评估的方法、不确定性与数字MRV进展，对农业碳汇与碳交易信息化有参考价值，但属综述类论文，公共影响有限。",[184],{"name":177,"url":174},[186,27,31,187,188],"机器学习","碳信用","数字MRV",{"code":74,"slug":190,"name":191,"path":192,"parent_code":193},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[195],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":197},{"candidates":198,"agricultural_evidence":47},[199,206,212,218,222,229],{"code":74,"score":19,"supported":47,"title":200,"abstract":202,"tags":205},[201],"remote sensing",[27,201,203,204],"multispectral","hyperspectral",[27],{"code":33,"score":207,"supported":47,"title":208,"abstract":209,"tags":211},15,[50],[210,50],"农业碳",[31],{"code":213,"score":46,"supported":47,"title":214,"abstract":215,"tags":217},"I2",[],[186,216],"artificial intelligence",[186],{"code":53,"score":54,"supported":47,"title":219,"abstract":220,"tags":221},[],[57,58],[],{"code":223,"score":54,"supported":47,"title":224,"abstract":225,"tags":228},"I4",[],[226,227],"地理信息","geographic information",[],{"code":230,"score":62,"supported":47,"title":231,"abstract":232,"tags":234},"H7",[],[233],"气候变化",[],[236,237],"遥感 碳汇 估算","碳信用 评估 遥感","遥感碳汇估算-3554",4,"10.9734\u002Fjgeesi\u002F2026\u002Fv30i91125",{"doi":240,"openalex_id":242,"authors":243,"venue":177,"cited_by_count":92,"oa_url":174,"card":253,"direction":100,"ingested_from":101},"W7214392294",[244,246,249,251],{"name":245,"orcid":9},"Aishwarya Desai",{"name":247,"orcid":248},"Himalaya Ganachari","https:\u002F\u002Forcid.org\u002F0000-0002-6157-9242",{"name":250,"orcid":9},"Sangita Shinde",{"name":252,"orcid":9},"Sachinkumar Nandgude",{"tldr":254,"method":255,"finding":256,"direction":98,"opportunity":257},"综述遥感碳储量估算技术及其在碳信用评估中的应用与挑战。","综述多光谱、高光谱、SAR、LiDAR等遥感与GIS、机器学习融合方法。","遥感可空间连续估算多类碳库，但不确定性及MRV要求制约碳信用转化。","可研究多源数据融合与AI驱动的数字MRV，降低碳信用评估不确定性。","2026-09-26T23:30:30.002197Z",{"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":14,"score":267,"score_detail":268,"sources":272,"tags":274,"primary_topic":279,"secondary_topics":280,"classification":281,"search_phrases":305,"slug":308,"view_count":92,"doi":309,"paper":310,"created_at":335},4510,"Organic carbon sequestration potential in alluvial agricultural terraces: A case study of a lama-bordo system in Oaxaca, Mexico","https:\u002F\u002Fdoi.org\u002F10.4067\u002Fs0718-58392026000600930","Soil is the primary C reservoir in terrestrial ecosystems and provides a variety of ecosystem services.Therefore, quantifying soil organic C (SOC) storage is essential for conservation technologies that ensure the sustainability of this non-renewable resource.In this context, the objective of this study was to estimate the SOC content within a pre-Hispanic agricultural terrace system, known locally as lama-bordo, in the Mixteca Alta region of Oaxaca, Mexico, using soil sampling, remote sensing, and geographic information systems.Based on the organic matter content, the SOC was determined in each soil sample from the surface layer (0-30 cm).The total SOC content in the lama-bordo system (LBS) was estimated using the average depth of each section and a highresolution digital elevation model.The results indicate that the LBS is capable of storing between 254.88 and 449.68 Mg ha -1 SOC, which can prevent the emission of C into the atmosphere through its conservation.These findings highlight the need for further research to generate more comprehensive knowledge of the functions of alluvial agricultural terraces.","土壤是陆地生态系统中主要的碳库，并提供多种生态系统服务。因此，量化土壤有机碳（SOC）储量对于确保这一不可再生资源可持续性的保护技术至关重要。在此背景下，本研究旨在利用土壤采样、遥感（remote sensing）和地理信息系统（geographic information systems），估算墨西哥瓦哈卡州米斯特卡阿尔塔地区一处前西班牙时期的农业梯田系统（当地称为lama-bordo）中的SOC含量。基于有机质含量，测定了每个表层（0-30 cm）土壤样品的SOC。利用每个断面的平均深度和高分辨率数字高程模型（digital elevation model），估算了lama-bordo系统（LBS）的SOC总含量。结果表明，LBS能够储存254.88至449.68 Mg ha⁻¹的SOC，通过对其进行保护可防止碳释放到大气中。这些发现凸显了进一步研究的必要性，以更全面地认识冲积农业梯田的功能。","Chilean journal of agricultural research","2026-10-07T00:00:00Z",66,{"impact":21,"substance":269,"depth":270,"authority":116,"freshness":21,"relevant":22,"comment":271},20,17,"基于遥感与GIS量化前西班牙时期梯田系统固碳潜力，方法新颖数据扎实，但属区域案例研究，公共影响有限。",[273],{"name":265,"url":262},[27,275,276,277,278],"土壤固碳","地理信息系统","墨西哥","农业梯田",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":282},{"candidates":283,"agricultural_evidence":47},[284,288,292,296,300],{"code":33,"score":21,"supported":47,"title":285,"abstract":286,"tags":287},[50],[],[],{"code":53,"score":46,"supported":47,"title":289,"abstract":290,"tags":291},[],[57,58],[275],{"code":74,"score":46,"supported":47,"title":293,"abstract":294,"tags":295},[],[27,201],[27],{"code":223,"score":46,"supported":47,"title":297,"abstract":298,"tags":299},[],[226,227],[276],{"code":68,"score":62,"supported":47,"title":301,"abstract":302,"tags":304},[],[303],"生态系统服务",[],[306,307],"Oaxaca lama-bordo 梯田 固碳","墨西哥 梯田 土壤有机碳","Oaxacalama-bordo梯田固碳-4510","10.4067\u002Fs0718-58392026000600930",{"doi":309,"openalex_id":311,"authors":312,"venue":265,"cited_by_count":92,"oa_url":329,"card":330,"direction":100,"ingested_from":101},"W7220837836",[313,315,318,321,323,326],{"name":314,"orcid":9},"B. Estela Santiago-Mejía",{"name":316,"orcid":317},"Demetrio S. Fernández-Reynoso","https:\u002F\u002Forcid.org\u002F0000-0002-1734-3152",{"name":319,"orcid":320},"Gerardo Colín-García","https:\u002F\u002Forcid.org\u002F0000-0002-3155-6699",{"name":322,"orcid":9},"M. Magdalena Nevárez-Favela",{"name":324,"orcid":325},"Moisés Márquez-Velázquez","https:\u002F\u002Forcid.org\u002F0009-0004-9341-628X",{"name":327,"orcid":328},"Madai Sánchez-Galindo","https:\u002F\u002Forcid.org\u002F0000-0002-4371-0817","http:\u002F\u002Fwww.scielo.cl\u002Fpdf\u002Fchiljar\u002Fv86n6\u002F0718-5839-chiljar-86-06-930.pdf",{"tldr":331,"method":332,"finding":333,"direction":98,"opportunity":334},"估算墨西哥瓦哈卡前西班牙时期梯田系统的土壤有机碳储量。","土壤采样、遥感与GIS结合，基于DEM估算0-30cm SOC。","梯田系统可储存254.88-449.68 Mg\u002Fha SOC，保护可防止碳释放。","可延伸研究不同梯田类型或区域的碳汇潜力及保护措施的固碳效益。","2026-10-09T23:30:26.302393Z",{"id":337,"title":338,"url":339,"summary":340,"summary_zh":9,"content":341,"source_name":342,"source_url":9,"published_at":343,"category":344,"cover_url":9,"hotness":13,"is_selected":14,"score":345,"score_detail":346,"sources":349,"tags":351,"primary_topic":357,"secondary_topics":358,"classification":359,"search_phrases":403,"slug":406,"view_count":92,"doi":9,"paper":9,"created_at":407},5498,"江西省「十五五」农业农村现代化规划印发 部署智慧农业与四季接续","https:\u002F\u002Fjx.ifeng.com\u002Fc\u002F8xPB4YCdgGF","《江西省「十五五」农业农村现代化规划》（赣府发〔2026〕16号）围绕32项重点任务展开，明确推进信息技术与农业深度融合，加快农业人工智能技术创新与场景拓展，建设农业农村大数据平台。规划还部署井冈山国家农业科技园区升建国家农高区，推广「村播」「田播」电商直播带货，并开展农产品碳足迹管理与农业碳交易。","（一）提升粮食等重要农产品供给保障水平。坚持产量产能、生产生态、增产增收一起抓，深入实施藏粮于地、藏粮于技战略，落实新一轮粮食产能提升行动，实施粮油作物大面积单产提升行动，建设吨粮片、高产片，促进良田良种良机良法集成增效。优化农产品种植结构，稳定水稻、油菜生产，大力发展油茶产业，提升大豆产能。优化生猪产能综合调控机制，推进牛羊产业扩量提质，促进畜禽养殖节本提质增效。推动农产品品种培优和品质提升，增加营养、健康、风味农产品供给。完善价格、补贴、保险“三位一体”的种粮农民收益保障机制，建立健全与周边省份粮食产销协作机制，强化粮食产购储加销协同保障。\n\n（二）构建多元化食物供给体系。践行大食物观，坚持农林牧渔并举，全方位、多用途开发食物资源。提升“菜篮子”产业质量和效益，推动蔬菜、水果、茶叶、中药材产业优质发展。丰富畜产品供给，发展蜂、兔、肉鸽、鹌鹑等特色养殖。积极发展大水面生态渔业，科学推进稻渔综合种养。发展林下经济，壮大林药和森林食品。发展富硒食品与功能食品，开发新型食品资源。\n\n（三）加强耕地保护与质量提升。严格落实耕地保护党政同责，严守耕地保护红线。统筹农用地布局优化，严格耕地占补平衡管理，规范补充耕地质量验收。科学开展红壤改良利用，推进酸化耕地治理，分阶段实施耕地有机质提升行动。分区分类高质量推进高标准农田建设，对新建和改造提升实行差异化投入，优先在鄱阳湖平原、赣抚平原、吉泰盆地等区域推进整区域建设，逐步把具备条件的永久基本农田建成高标准农田。完善高标准农田立项、建设、验收和管护机制，编制高标准农田建设全流程工作规范，统一建设标准、施工工艺、管理流程，压实管护责任。开展高标准农田整治提升行动，按照“集中修复一批、改造提升一批、疏通灌溉一批、置换退出一批”分类施策，逐步补齐设施短板、消除存量问题。健全完善问题发现响应处置机制。实施大中型灌区现代化建设与改造，推动井冈灌区、赣抚平原灌区等项目建设，强化高标准农田渠系与灌区骨干工程衔接。\n\n（四）提升农业防灾减灾救灾能力。发展韧性农业，完善农业气象、水文、地质灾害等监测预警机制。推进防洪排涝抗旱设施建设，持续开展农田沟渠修复整治、中小河流和平原涝区治理。完善农机应急救灾设施装备条件,健全和落实救灾机具配置、救灾备荒种子储备和应急调用机制，加快补齐烘干、仓储等设施装备短板。强化病虫疫情和重大动物疫病监测预警、联防联控和应急处置，建强县乡动植物疫病防控专业队伍。","凤凰网江西 2026-10-21","2026-10-21T00:00:00Z","政策",80,{"impact":115,"substance":347,"depth":19,"authority":21,"freshness":117,"relevant":22,"comment":348},23,"省级「十五五」农业农村现代化规划全文，条款与数字细节充实，涉及智慧农业、高标准农田、粮食产能等实质部署，值得进入每日精选。",[350],{"name":342,"url":339},[352,353,354,355,356],"智慧农业","高标准农田","十五五规划","大食物观","粮食产能",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":360},{"candidates":361,"agricultural_evidence":47},[362,369,374,379,384,389,394,398],{"code":363,"score":364,"supported":47,"title":365,"abstract":367,"tags":368},"J2",11,[366],"农业农村现代化",[366],[],{"code":370,"score":117,"supported":47,"title":371,"abstract":372,"tags":373},"I1",[352],[],[352],{"code":33,"score":54,"supported":47,"title":375,"abstract":376,"tags":378},[],[210,377],"碳足迹",[],{"code":213,"score":62,"supported":47,"title":380,"abstract":381,"tags":383},[],[382],"农业人工智能",[],{"code":385,"score":22,"supported":14,"title":386,"abstract":387,"tags":388},"A8",[],[],[356],{"code":390,"score":22,"supported":14,"title":391,"abstract":392,"tags":393},"E2",[],[],[355],{"code":61,"score":22,"supported":14,"title":395,"abstract":396,"tags":397},[],[],[353],{"code":399,"score":22,"supported":14,"title":400,"abstract":401,"tags":402},"J1",[],[],[354],[404,405],"江西 十五五 农业农村现代化规划","江西 高标准农田 鄱阳湖平原","江西十五五农业农村现代化规划-5498","2026-10-22T00:06:58.106992Z",{"id":409,"title":410,"url":411,"summary":412,"summary_zh":413,"content":9,"source_name":414,"source_url":411,"published_at":415,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":416,"score_detail":417,"sources":419,"tags":421,"primary_topic":427,"secondary_topics":430,"classification":436,"search_phrases":489,"slug":492,"view_count":92,"doi":493,"paper":494,"created_at":515},5472,"Turning dirt into gold: the business case of soil carbon for agrarian pathways in arid climate","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1966265","Introduction Agricultural carbon sequestration offers considerable potential for mitigating anthropogenic climate change, particularly in the low-organic-carbon soils of the Middle East. This study presents a foundational scenario-based assessment of soil carbon sequestration and economic feasibility. Materials and methods Crop production and market datasets from Jordanian statistics (1998–2023) were used to estimate potential soil carbon sequestration and economic performance. Using benchmarks from literature in comparable agroecological regions, four soil carbon management practices were evaluated across multiple crops for their carbon storage potential using Monte Carlo uncertainty and sensitivity analysis. Sensitivity of economic performance was assessed under optimistic\u002Fpessimistic scenarios. Results Among the evaluated practices, the highest carbon sequestration rate was achieved through increased Soil Organic Carbon (SOC), reaching a maximum of 27.44 metric tons of CO₂ per hectare per year (tCO₂\u002Fha\u002Fyear) under the high scenario for cucumbers. In contrast, barley showed the highest sequestration rate at 0.45 tCO₂\u002Fha\u002Fyear under increased SOC in the high scenario. Sensitivity analysis showed that ±20% uncertainty in the management coefficients resulted in practice-specific changes of 1.11 tCO₂\u002Fha\u002Fyear for increased SOC and 0.46 tCO₂\u002Fha\u002Fyear for improved cropland management. Horticultural crops, particularly cucumbers and lemons, exhibited the highest sequestration rates and strongest responses to management interventions. Field crops such as wheat, barley, and olives demonstrated lower, more stable sequestration rates. Economic analysis revealed that cucumbers, grapes, potatoes, and Open-field farming (OFF) tomatoes achieved the highest tri-pillar performance (net and adjusted profits and break-even cost) alongside effective carbon sequestration. These findings highlight that crop choice is a primary determinant of both crop profitability and sequestrated carbon potential and feasibility, with significant implications for climate-smart agriculture, carbon credit mechanisms, and intensification strategies in data-scarce regions. Study limitations While empirical field validation remains a future priority, this model-based study provides a screening-level theoretical baseline to guide targeted research and investment in arid agricultural systems.","引言 农业碳封存为缓解人为气候变化提供了可观潜力，尤其是在中东低有机碳土壤中。本研究提出了一项基于情景的土壤碳封存与经济学可行性基础评估。材料与方法 利用约旦统计数据（1998—2023年）中的作物生产和市场数据集，估算潜在的土壤碳封存及经济表现。参考可比农业生态区域文献中的基准值，采用蒙特卡洛不确定性分析和敏感性分析，评估了四种土壤碳管理措施在多种作物上的碳储存潜力。在乐观\u002F悲观情景下评估了经济表现的敏感性。结果 在评估的各项措施中，通过增加土壤有机碳（SOC）实现的碳封存速率最高，在黄瓜的高情景下达到最高27.44公吨二氧化碳\u002F公顷\u002F年（tCO₂\u002Fha\u002Fyear）。相比之下，大麦在增加SOC的高情景下表现出最高的封存速率，为0.45 tCO₂\u002Fha\u002Fyear。敏感性分析表明，管理系数±20%的不确定性导致增加SOC和改善农田管理分别产生1.11 tCO₂\u002Fha\u002Fyear和0.46 tCO₂\u002Fha\u002Fyear的措施特异性变化。园艺作物，尤其是黄瓜和柠檬，表现出最高的封存速率和对管理干预的最强响应。小麦、大麦和橄榄等大田作物表现出较低但更稳定的封存速率。经济分析显示，黄瓜、葡萄、马铃薯和露地栽培（OFF）番茄在有效碳封存的同时实现了最高的三支柱表现（净利润、调整后利润和盈亏平衡成本）。这些发现突出表明，作物选择是作物盈利能力和封存碳潜力及可行性的主要决定因素，对气候智慧型农业、碳信用机制和数据稀缺地区的集约化策略具有重要意义。研究局限性 尽管田间实证验证仍是未来的优先事项，但这项基于模型的研究提供了筛选层面的理论基线，以指导干旱农业系统中的针对性研究和投资。","Frontiers in Sustainable Food Systems","2026-10-20T00:00:00Z",70,{"impact":181,"substance":269,"depth":17,"authority":116,"freshness":117,"relevant":22,"comment":418},"基于约旦1998–2023年统计数据的模型情景研究，量化了不同作物固碳速率与经济收益，对干旱区农业碳汇与碳信用机制有参考价值，但属区域性模拟研究，尚未经田间验证。",[420],{"name":414,"url":411},[422,423,424,425,426],"气候智慧农业","碳交易","干旱农业","土壤碳汇","作物选择",{"code":53,"slug":58,"name":428,"path":429,"parent_code":37},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[431,435],{"code":230,"slug":432,"name":433,"path":434,"parent_code":37},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"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":47},[439,443,449,453,460,467,473,479,484],{"code":53,"score":20,"supported":47,"title":440,"abstract":441,"tags":442},[58],[57,58],[],{"code":230,"score":13,"supported":47,"title":444,"abstract":445,"tags":448},[],[233,446,447],"干旱","climate change",[422],{"code":33,"score":21,"supported":47,"title":450,"abstract":451,"tags":452},[],[210,50],[425,423],{"code":454,"score":54,"supported":47,"title":455,"abstract":456,"tags":459},"A2",[],[457,458],"小麦","wheat",[],{"code":461,"score":54,"supported":47,"title":462,"abstract":463,"tags":466},"B1",[],[464,465],"番茄","黄瓜",[],{"code":468,"score":62,"supported":47,"title":469,"abstract":470,"tags":472},"A5",[],[471],"马铃薯",[],{"code":474,"score":62,"supported":47,"title":475,"abstract":476,"tags":478},"B2",[],[477],"葡萄",[],{"code":68,"score":62,"supported":47,"title":480,"abstract":481,"tags":483},[],[482],"农业生态",[],{"code":370,"score":62,"supported":47,"title":485,"abstract":486,"tags":488},[],[487],"smart agriculture",[],[490,491],"约旦 土壤碳汇 黄瓜","土壤固碳 经济可行性","约旦土壤碳汇黄瓜-5472","10.3389\u002Ffsufs.2026.1966265",{"doi":493,"openalex_id":495,"authors":496,"venue":414,"cited_by_count":92,"oa_url":411,"card":509,"direction":514,"ingested_from":101},"W7223064172",[497,499,501,504,506],{"name":498,"orcid":9},"Tasnim Althalaj",{"name":500,"orcid":9},"Ross. M. Thompson",{"name":502,"orcid":503},"Tala H. Qtaishat","https:\u002F\u002Forcid.org\u002F0000-0002-2700-6686",{"name":505,"orcid":9},"Dan Bumblauskas",{"name":507,"orcid":508},"Fayha Al-Shibli","https:\u002F\u002Forcid.org\u002F0000-0002-5628-0975",{"tldr":510,"method":511,"finding":512,"direction":98,"opportunity":513},"基于约旦1998–2023年数据，评估干旱区土壤固碳潜力与经济可行性。","用蒙特卡洛不确定性与敏感性分析，评估四种土壤碳管理措施在多作物下的表现。","黄瓜等园艺作物固碳率最高，作物选择是固碳与盈利的关键决定因素。","数据稀缺干旱区可结合田间验证与碳信用机制，研究作物级固碳经济激励设计。","智慧农业 \u002F 农业物联网","2026-10-21T23:30:07.383006Z",{"id":517,"title":518,"url":519,"summary":520,"summary_zh":521,"content":9,"source_name":414,"source_url":519,"published_at":415,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":522,"score_detail":523,"sources":525,"tags":527,"primary_topic":532,"secondary_topics":533,"classification":535,"search_phrases":552,"slug":555,"view_count":92,"doi":556,"paper":557,"created_at":600},5467,"Temperature-directed valorisation of coconut frond through pyrolysis for carbon sequestration and sustainable agricultural residue management","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1966170","Coconut frond, an abundantly generated yet underutilized agricultural residue, was pyrolysed at 350 °C, 450 °C, and 550 °C alongside traditionally produced biochar (BC Trad) to evaluate the effects of pyrolysis temperature on physicochemical characteristics, fuel properties, carbon stability, and residue-valorisation potential. Comprehensive characterization using proximate and ultimate analyses, BET\u002FBJH, FTIR, XRD, Raman spectroscopy, SEM-EDS, total pyrogenic carbon (TPC), estimated CO 2 -equivalent stable-carbon retention potential, fuel and energy indices, and 90-day carbon mineralization incubation revealed pronounced temperature-dependent transformations. Biochar carbon recovery was highest at 350 °C (78.91%), demonstrating greater retention of feedstock carbon at lower pyrolysis temperature, whereas increasing temperature progressively enhanced carbon concentration, fixed carbon, thermostable carbon fraction, porosity, and surface area. BET surface area increased from 4.45 m 2 g −1 in BC Trad to 16.67 m 2 g −1 in BC550, accompanied by progressive pore refinement and depletion of oxygen-containing functional groups. Raman spectroscopy showed a decline in ID\u002FIG from 1.28 to 0.80, consistent with increasing structural ordering and aromatic condensation at higher temperatures. Fuel quality also improved with increasing temperature, with higher heating value increasing from 20.72 to 25.56 MJ kg −1 and fuel ratio from 1.49 to 4.26. TPC and estimated CO 2 -equivalent stable-carbon retention potential were highest at BC350 (0.29 kg C kg −1 feedstock and 0.86 kg CO 2 kg −1 feedstock, respectively), whereas BC550 exhibited the lowest apparent net soil-system carbon mineralization among the biochars during the 90-day incubation. Separately, its lower H\u002FC and O\u002FC ratios corresponded to a modelled BC +100 stability index of 0.720 and an estimated mean residence time of 347.61 years, providing model-derived indications of longer-term carbon persistence. Overall, the findings demonstrate a clear temperature-dependent trade-off between carbon recovery and carbon stability, with BC350 providing greater structural recalcitrance and apparent short-term mineralization resistance; the optimal pyrolysis temperature therefore depends on the intended end use rather than a single treatment being universally superior. Together, the short-term mineralization results and model-derived stability estimates indicate distinct dimensions of carbon persistence that should be considered when optimizing coconut-frond pyrolysis for sustainable agricultural-residue management, durable soil-carbon sequestration, and circular bioeconomy applications.","椰子叶是一种大量产生但未充分利用的农业废弃物，其在350 °C、450 °C和550 °C下热解，并与传统生产的生物炭（BC Trad）进行对比，以评估热解温度对理化特性、燃料性质、碳稳定性及残余物增值潜力的影响。采用工业分析和元素分析、BET\u002FBJH、FTIR、XRD、拉曼光谱、SEM-EDS、总热解碳（TPC）、估算CO₂当量稳定碳保留潜力、燃料与能量指标以及90天碳矿化培养进行综合表征，揭示了显著的温度依赖性变化。生物炭碳回收率在350 °C时最高（78.91%），表明较低热解温度下原料碳的保留更多，而升高温度则逐步提高碳浓度、固定碳、热稳定碳组分、孔隙率和比表面积。BET比表面积从BC Trad的4.45 m² g⁻¹增至BC550的16.67 m² g⁻¹，同时伴随孔隙细化和含氧官能团的消耗。拉曼光谱显示ID\u002FIG从1.28降至0.80，与较高温度下结构有序性和芳香缩合度增加相一致。燃料品质亦随温度升高而改善，高位热值从20.72增至25.56 MJ kg⁻¹，燃料比从1.49增至4.26。TPC和估算CO₂当量稳定碳保留潜力在BC350时最高（分别为0.29 kg C kg⁻¹原料和0.86 kg CO₂ kg⁻¹原料），而BC550在90天培养期间表现出生物炭中最低的表观净土壤系统碳矿化。此外，其较低的H\u002FC和O\u002FC比对应模拟BC +100稳定性指数为0.720，估算平均停留时间为347.61年，提供了模型推导的较长时期碳持久性指示。总体而言，研究结果揭示了碳回收与碳稳定性之间明显的温度依赖性权衡，BC350表现出更高的结构惰性和表观短期抗矿化性；因此，最佳热解温度取决于预期最终用途，而非单一处理普遍优越。短期矿化结果与模型推导的稳定性估算共同表明，碳持久性具有不同维度，应予考虑。",74,{"impact":181,"substance":115,"depth":19,"authority":116,"freshness":117,"relevant":22,"comment":524},"该研究系统考察了热解温度对椰叶生物炭碳回收与碳稳定性的权衡关系，数据翔实、结论可靠，对农业废弃物资源化与土壤固碳具有参考价值，但属于细分领域基础研究，公共影响有限。",[526],{"name":414,"url":519},[528,529,530,275,531],"生物炭","农业废弃物","秸秆利用","热解技术",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[534],{"code":53,"slug":58,"name":428,"path":429,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":536},{"candidates":537,"agricultural_evidence":47},[538,542,546],{"code":33,"score":364,"supported":47,"title":539,"abstract":540,"tags":541},[50],[50],[],{"code":53,"score":21,"supported":47,"title":543,"abstract":544,"tags":545},[],[57,58],[275,528],{"code":547,"score":548,"supported":47,"title":549,"abstract":550,"tags":551},"H5",5,[],[529],[529,530],[553,554],"椰叶 热解 生物炭","生物炭 碳封存 农业废弃物","椰叶热解生物炭-5467","10.3389\u002Ffsufs.2026.1966170",{"doi":556,"openalex_id":558,"authors":559,"venue":414,"cited_by_count":92,"oa_url":594,"card":595,"direction":98,"ingested_from":101},"W7223016223",[560,563,565,568,571,573,576,579,581,584,586,589,592],{"name":561,"orcid":562},"Morasanahalli Lokesha Sachin","https:\u002F\u002Forcid.org\u002F0009-0008-8615-0822",{"name":564,"orcid":9},"Yogananda Shivalli Boregowda",{"name":566,"orcid":567},"Matadadoddi Nanjundegowda Thimmegowda","https:\u002F\u002Forcid.org\u002F0000-0001-7632-1036",{"name":569,"orcid":570},"M. A. Ananthakumar","https:\u002F\u002Forcid.org\u002F0000-0003-4376-9541",{"name":572,"orcid":9},"Pudiyanda Shadali Fathima",{"name":574,"orcid":575},"C. M. Sunil","https:\u002F\u002Forcid.org\u002F0000-0002-9625-5686",{"name":577,"orcid":578},"K. M. Pravalika","https:\u002F\u002Forcid.org\u002F0009-0004-5258-392X",{"name":580,"orcid":9},"M.C. Anjali",{"name":582,"orcid":583},"Sathisha Govinakere Sambegowda","https:\u002F\u002Forcid.org\u002F0009-0000-8620-6461",{"name":585,"orcid":9},"B. Divya",{"name":587,"orcid":588},"M. K. Shruthi","https:\u002F\u002Forcid.org\u002F0000-0003-3703-9817",{"name":590,"orcid":591},"Santosh Onte","https:\u002F\u002Forcid.org\u002F0009-0002-8367-1303",{"name":593,"orcid":9},"Sanjeev Kumar","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1966170\u002Fpdf",{"tldr":596,"method":597,"finding":598,"direction":98,"opportunity":599},"研究不同热解温度对椰叶生物炭碳固存与稳定性的影响。","350\u002F450\u002F550°C热解椰叶，多手段表征并做90天矿化培养。","碳回收率与碳稳定性随温度呈权衡，最佳温度取决于用途。","可探索热解温度与还田情景耦合的长期碳固存模型及经济性评估。","2026-10-21T23:30:07.083785Z"]