[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2531":3,"related-2531":102},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":45,"search_phrases":79,"slug":82,"view_count":83,"doi":84,"paper":85,"created_at":101},2531,"Spatial Coupling of Greenhouse Gas Emissions, Vegetation Activity, Groundwater Nitrate Occurrence, and Soil Environments Across Denmark: A 5-km Spatial Association and Soil-Stratified Analysis","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx5rj7d","Integrated environmental assessment increasingly requires the joint analysis of atmospheric emissions, vegetation dynamics, groundwater quality, and soil context rather than isolated thematic mapping. We developed a Denmark-wide 5-km analytical grid in EPSG:25832 containing 1,803 cells and harmonized four continuous environmental indicators: CO2 and CH4 emissions from EDGAR, vegetation activity represented by 2024 MODIS NDVI, and groundwater nitrate occurrence derived from national GEUS observations. Soil information from the GEUS Digital Soil Map was used to stratify environmental associations into five broad environmental groups. Global Pearson, Spearman, and Kendall associations were complemented by global Moran’s I, directional bivariate Moran’s I, local bivariate spatial association, an upper-1% CO2 sensitivity analysis, and soil-stratified statistics. CO2 and NDVI showed the strongest national association (Pearson r = −0.2621), while CH4 was positively associated with CO2 (r = 0.0898) and nitrate occurrence (r = 0.0792). All four continuous variables exhibited significant positive spatial autocorrelation under both Queen and Rook contiguity, with the strongest structure for CH4 and CO2 and the weakest for nitrate. Bivariate Moran analysis supported a negative CO2–NDVI cross-association and positive CH4–CO2 and CH4–nitrate cross-associations. Soil-stratified results showed substantial heterogeneity; the strongest Pearson CO2–NDVI association occurred in Human-affected & Technological environments (r = −0.6208), whereas Sandy & Poor and Sedimentary & Clay-rich environments displayed the clearest positive CH4–CO2 associations. The study is explicitly associative rather than causal. Its principal contribution is a spatially explicit national screening framework that reveals where environmental indicators co-occur, where relationships are spatially organized, and where targeted process-based investigation and monitoring may be most informative.","综合环境评估日益需要对大气排放、植被动态、地下水质量和土壤背景进行联合分析，而非孤立的专题制图。我们构建了一个覆盖丹麦全境、分辨率为5 km的EPSG:25832分析网格，包含1,803个单元，并统一整合了四项连续环境指标：来自EDGAR的CO2和CH4排放、以2024年MODIS NDVI表示的植被活动，以及源自GEUS国家观测数据的地下水硝酸盐赋存情况。来自GEUS数字土壤图的土壤信息被用于将环境关联分层为五个广泛的环境组。在全局Pearson、Spearman和Kendall关联分析的基础上，辅以全局Moran's I、方向性双变量Moran's I、局部双变量空间关联、前1% CO2敏感性分析以及土壤分层统计。CO2与NDVI在全国尺度上表现出最强的关联（Pearson r = −0.2621），而CH4与CO2（r = 0.0898）及硝酸盐赋存（r = 0.0792）呈正相关。四个连续变量在Queen和Rook邻接下均表现出显著的正空间自相关，其中CH4和CO2的空间结构最强，硝酸盐最弱。双变量Moran分析支持CO2–NDVI的负向交叉关联以及CH4–CO2和CH4–硝酸盐的正向交叉关联。土壤分层结果显示出显著异质性；最强的Pearson CO2–NDVI关联出现在人类影响与技术环境中（r = −0.6208），而砂质贫瘠环境和沉积物–黏土丰富环境则表现出最明显的CH4–CO2正相关。本研究明确为关联性而非因果性研究。其主要贡献在于提供了一个空间显式的全国筛查框架，揭示环境指标在何处共现、关系在何处呈现空间组织，以及有针对性的过程性研究与监测在何处可能最具信息价值。",null,"OpenAlex","2026-09-14T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,9,1,"基于全国5公里网格整合温室气体、植被、地下水硝酸盐与土壤环境的空间关联研究，方法系统、数据规模大，对农业环境监测与空间筛查有参考价值，但属关联性分析而非因果结论。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业遥感","温室气体","空间分析","地下水质量","土壤环境",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,58,64,69,74],{"code":40,"score":52,"supported":53,"title":54,"abstract":55,"tags":57},14,true,[41],[56,41],"土壤",[],{"code":33,"score":59,"supported":53,"title":60,"abstract":62,"tags":63},8,[61],"greenhouse",[],[],{"code":65,"score":22,"supported":14,"title":66,"abstract":67,"tags":68},"H4",[],[],[28],{"code":70,"score":22,"supported":14,"title":71,"abstract":72,"tags":73},"I3",[],[],[27],{"code":75,"score":22,"supported":14,"title":76,"abstract":77,"tags":78},"I4",[],[],[29],[80,81],"地下水质量 农业遥感 土壤环境 温室气体","地下水质量 农业遥感","地下水质量农业遥感土壤环境温室气体-2531",3,"10.31223\u002Fx5rj7d",{"doi":84,"openalex_id":86,"authors":87,"venue":9,"cited_by_count":91,"oa_url":92,"card":93,"direction":99,"ingested_from":100},"W7213077215",[88],{"name":89,"orcid":90},"Avideh Asadollahi","https:\u002F\u002Forcid.org\u002F0009-0000-9847-2177",0,"https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F14981\u002Fdownload\u002F26031\u002F",{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"构建丹麦5公里网格，分析温室气体、植被、地下水硝酸盐与土壤的空间耦合关系。","EDGAR排放、MODIS NDVI、GEUS硝酸盐与土壤图，用Moran's ","CO2与NDVI负相关最强，CH4与CO2、硝酸盐正相关，土壤类型显著调节这些关联。","农业绿色发展与碳","可基于该空间筛查框架，在热点区域开展过程模型与监测，探究土壤调节机制及因果路径。","农业遥感与作物表型","openalex","2026-09-15T23:30:20.707747Z",{"total":103,"page":22,"page_size":103,"items":104},6,[105,202,275,352,436,505],{"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":127,"secondary_topics":128,"classification":135,"search_phrases":173,"slug":176,"view_count":22,"doi":177,"paper":178,"created_at":201},3814,"Optimizing biogas slurry and biochar co-application to enhance soil microenvironment, mitigate greenhouse gas emissions, and increase tomato yield in greenhouse systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1866425","To support agricultural carbon peaking and carbon neutrality goals, this study investigated the uncertainties surrounding greenhouse gas emissions and nutrient leaching associated with biogas slurry utilization. It systematically explored the mechanisms and mitigation pathways of co-applying biogas slurry, biochar, and chemical fertilizers for reducing greenhouse gas emissions and nutrient losses under intensive agricultural management characterized by excessive irrigation and fertilizer application. Under equivalent N, P, and K inputs and consistent irrigation levels, different substitution ratios of biogas slurry for chemical fertilizers were evaluated in a point-source underground irrigation system. Treatments included CF, FR, BS25, BS50, BS75, and BS100, with corresponding biochar treatments CF + C, FR + C, BS25 + C, BS50 + C, BS75 + C, and BS100 + C. Tomato yield, soil physical properties, microbial biomass, microbial gene copy numbers, and soil greenhouse gas emissions were measured. Key influencing factors and optimal management strategies were identified using multivariate statistical analysis and a multi-criteria decision approach. The results indicated that biogas slurry combined with biochar significantly improved the soil microenvironment in greenhouse tomato systems by enhancing soil temperature (ST), soil moisture content (SMC), and water-filled pore space (WFPS), especially during the flowering stage in both spring-summer and autumn-winter periods. Biochar also significantly increased microbial biomass carbon (MBC), while microbial gene copy numbers, including AOA, AOB, and mcrA, showed clear seasonal variation. Regarding greenhouse gas emissions, co-application of biogas slurry and biochar significantly affected CO 2 fluxes, particularly from days 8–119, promoting CO 2 emissions. Biochar effectively suppressed N 2 O emissions, promoted CH 4 emissions during days 1–7, and reduced CH 4 fluxes from days 8–119. The BS75 + C treatment significantly increased tomato yield to 151.34 t ha −1 , reduced global warming potential to 9491.43 kg ha −1 , and lowered greenhouse gas emission intensity. Compared with other treatments, BS75 + C consistently showed superior agronomic and environmental performance. Comprehensive analysis showed that ST, SMC, and MBC mainly influenced CO 2 emissions; ST, AOA, and AOB influenced N 2 O emissions; and mcrA, WFPS, and ST influenced CH 4 emissions. Multi-criteria decision analysis identified BS75 + C as the optimal strategy for maximizing yield while minimizing greenhouse gas emissions and improving biogas slurry utilization efficiency in sustainable greenhouse agriculture.","为支撑农业碳达峰与碳中和目标，本研究探讨了沼液利用过程中温室气体排放与养分淋失的不确定性，系统揭示了在过量灌溉与施肥的集约化农业管理条件下，沼液、生物炭与化肥配施减少温室气体排放和养分损失的机理与调控路径。在等氮、磷、钾投入及一致灌溉水平下，采用点源地下灌溉系统，评估了不同沼液替代化肥比例的效果。处理包括CF、FR、BS25、BS50、BS75和BS100，以及相应的生物炭处理CF + C、FR + C、BS25 + C、BS50 + C、BS75 + C和BS100 + C。测定了番茄产量、土壤物理性质、微生物生物量、微生物基因拷贝数及土壤温室气体排放。通过多元统计分析和多准则决策方法，确定了关键影响因素和最优管理策略。结果表明，沼液配施生物炭通过提高土壤温度(ST)、土壤含水量(SMC)和充水孔隙度(WFPS)，显著改善了温室番茄系统的土壤微环境，尤其在春夏季和秋冬季的开花期。生物炭还显著提高了微生物生物量碳(MBC)，而微生物基因拷贝数(包括AOA、AOB和mcrA)则表现出明显的季节性变化。在温室气体排放方面，沼液与生物炭配施显著影响CO₂通量，尤其在8—119天期间促进CO₂排放。生物炭有效抑制了N₂O排放，在1—7天促进CH₄排放，并在8—119天降低CH₄通量。BS75 + C处理显著提高了番茄产量至151.34 t ha⁻¹，将全球增温潜势降至9491.43 kg ha⁻¹，并降低了温室气体排放强度。与其他处理相比，BS75 + C在农艺和环境性能方面始终表现优越。综合分析表明，ST、SMC和MBC主要影响CO₂排放；ST、AOA和AOB影响N₂O排放；mcrA、WFPS和ST影响CH₄排放。多准则决策分析确定BS75 + C为最优策","Frontiers in Sustainable Food Systems","2026-09-29T00:00:00Z",78,{"impact":115,"substance":116,"depth":117,"authority":20,"freshness":21,"relevant":22,"comment":118},16,22,18,"该研究通过沼液与生物炭配施实现温室番茄增产与温室气体减排，方法系统、数据扎实，对农业绿色低碳发展具有参考价值。",[120],{"name":111,"url":108},[122,123,124,125,126],"生物炭","温室番茄","农业碳中和","沼液利用","减排增效",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[129,130],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":131,"slug":132,"name":133,"path":134,"parent_code":37},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":46,"locked":14,"version":47,"method":48,"evidence":136},{"candidates":137,"agricultural_evidence":53},[138,144,148,154,161,168],{"code":33,"score":139,"supported":53,"title":140,"abstract":141,"tags":143},15,[61],[142,61],"温室",[123],{"code":40,"score":139,"supported":53,"title":145,"abstract":146,"tags":147},[41],[56,41],[122],{"code":131,"score":52,"supported":53,"title":149,"abstract":151,"tags":153},[150],"tomato",[152,150],"番茄",[],{"code":155,"score":103,"supported":53,"title":156,"abstract":157,"tags":160},"F3",[],[158,159],"施肥","fertilizer",[],{"code":162,"score":103,"supported":53,"title":163,"abstract":164,"tags":167},"H3",[],[165,166],"灌溉","irrigation",[],{"code":65,"score":83,"supported":53,"title":169,"abstract":170,"tags":172},[],[171],"农业碳",[],[174,175],"沼液 生物炭 温室番茄","BS75 C 温室气体减排","沼液生物炭温室番茄-3814","10.3389\u002Ffsufs.2026.1866425",{"doi":177,"openalex_id":179,"authors":180,"venue":111,"cited_by_count":91,"oa_url":108,"card":196,"direction":97,"ingested_from":100},"W7214739741",[181,184,187,189,192,194],{"name":182,"orcid":183},"Qinglin Sa","https:\u002F\u002Forcid.org\u002F0009-0007-5248-566X",{"name":185,"orcid":186},"Jian Zheng","https:\u002F\u002Forcid.org\u002F0009-0003-5723-1950",{"name":188,"orcid":9},"Ke Zhang",{"name":190,"orcid":191},"Yan Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5950-8904",{"name":193,"orcid":9},"Xuqin FU",{"name":195,"orcid":9},"Shikun Sun",{"tldr":197,"method":198,"finding":199,"direction":97,"opportunity":200},"研究沼液与生物炭配施对温室番茄土壤微环境、温室气体排放及产量的影响。","等氮磷钾滴灌试验，设沼液替代化肥比例与生物炭处理，测产量、土壤及气体。","BS75+C处理番茄产量最高达151.34 t\u002Fha，全球增温潜势最低，减排增产效果最优。","可探究不同气候区或土壤类型下沼液生物炭配施的长期固碳减排效应与微生物机制。","2026-09-30T23:30:07.179554Z",{"id":203,"title":204,"url":205,"summary":206,"summary_zh":207,"content":9,"source_name":208,"source_url":205,"published_at":209,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":210,"score_detail":211,"sources":214,"tags":216,"primary_topic":222,"secondary_topics":223,"classification":225,"search_phrases":251,"slug":254,"view_count":22,"doi":255,"paper":256,"created_at":274},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":117,"substance":116,"depth":212,"authority":20,"freshness":59,"relevant":22,"comment":213},19,"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。",[215],{"name":208,"url":205},[122,217,218,219,220,221],"土壤健康","气候智慧农业","土壤微生物","温室气体减排","养分利用效率",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[224],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":226},{"candidates":227,"agricultural_evidence":53},[228,232,236,242,246],{"code":40,"score":19,"supported":53,"title":229,"abstract":230,"tags":231},[41],[56,41],[217,219,122],{"code":33,"score":52,"supported":53,"title":233,"abstract":234,"tags":235},[61],[142,61],[],{"code":237,"score":59,"supported":53,"title":238,"abstract":240,"tags":241},"I1",[239],"smart agriculture",[],[],{"code":155,"score":22,"supported":14,"title":243,"abstract":244,"tags":245},[],[],[221],{"code":247,"score":22,"supported":14,"title":248,"abstract":249,"tags":250},"H7",[],[],[218],[252,253],"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":255,"openalex_id":257,"authors":258,"venue":208,"cited_by_count":91,"oa_url":205,"card":268,"direction":273,"ingested_from":100},"W7213554252",[259,261,264,266],{"name":260,"orcid":9},"K. G. Rosin",{"name":262,"orcid":263},"Aditya V Machnoor","https:\u002F\u002Forcid.org\u002F0009-0001-5777-9188",{"name":265,"orcid":9},"Rakesh Kokatnoor",{"name":267,"orcid":9},"Arun S. Kalasad",{"tldr":269,"method":270,"finding":271,"direction":97,"opportunity":272},"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。","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。","智慧农业 \u002F 农业物联网","2026-09-19T23:30:11.160909Z",{"id":276,"title":277,"url":278,"summary":279,"summary_zh":280,"content":9,"source_name":281,"source_url":278,"published_at":282,"category":12,"cover_url":9,"hotness":283,"is_selected":14,"score":284,"score_detail":285,"sources":288,"tags":292,"primary_topic":298,"secondary_topics":299,"classification":306,"search_phrases":335,"slug":338,"view_count":22,"doi":339,"paper":340,"created_at":351},2161,"National Carbon Stock and Greenhouse Gas Baseline Assessment for Liberia: Integrating Forest, Blue Carbon, Soil Carbon, and Sectoral Emissions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22685841","This preprint presents an integrated national assessment of carbon stocks and greenhouse gas (GHG) emissions across Liberia, combining forest carbon, blue carbon, soil organic carbon, and major sectoral emission sources within a common spatial framework. The study integrates field measurements, soil and sediment observations, remote sensing, geographic information system (GIS) analysis, spatial modelling, and national activity datasets. Sentinel-2 and Landsat imagery, Digital Earth Africa products, vegetation indices, field-based biomass measurements, and carbon-accounting approaches are used to characterize ecosystem distribution and estimate major carbon pools. The assessment estimates approximately 6.595 million ha of forest and a national forest carbon stock of approximately 2,236 Mt C, equivalent to about 8,200 Mt CO₂e. It also evaluates Liberia's coastal blue-carbon ecosystems and soil organic carbon resources and examines greenhouse gas emissions associated with agriculture, energy, transport, industrial activities, land-use change, and waste. The study provides a spatially integrated scientific framework for strengthening national carbon accounting and Monitoring, Reporting and Verification (MRV), prioritizing ecosystem conservation and restoration, supporting REDD+ and blue-carbon initiatives, and screening potential areas for carbon-market interventions. The manuscript emphasizes the need for continued field measurements, soil and sediment sampling, land-cover validation, explicit uncertainty analysis, and improved sectoral activity data. Author: John Nyemah Natt Jr.Affiliation: Center for Meteorology, Oceanography and Climate Research – LiberiaDocument type: PreprintPublication date: 10 September 2026","本预印本在统一的空间框架内，对利比里亚全国的碳储量与温室气体（GHG）排放进行了综合评估，涵盖森林碳、蓝碳、土壤有机碳以及主要部门排放源。该研究整合了野外实测、土壤与沉积物观测、遥感、地理信息系统（GIS）分析、空间建模以及国家活动数据集。研究采用Sentinel-2与Landsat影像、Digital Earth Africa产品、植被指数、基于野外的生物量测量以及碳核算方法，用以刻画生态系统分布并估算主要碳库。评估估算出森林面积约659.5万公顷，全国森林碳储量约22.36亿吨碳（Mt C），相当于约82亿吨二氧化碳当量（Mt CO₂e）。研究还评估了利比里亚沿海蓝碳生态系统与土壤有机碳资源，并考察了与农业、能源、交通、工业活动、土地利用变化及废弃物相关的温室气体排放。该研究提供了一个空间整合的科学框架，用于加强国家碳核算与监测、报告和核查（MRV），优先开展生态系统保护与恢复，支持REDD+与蓝碳倡议，并筛选适合碳市场干预的潜在区域。本文强调需要持续开展野外测量、土壤与沉积物采样、土地覆盖验证、明确的不确定性分析以及改进的部门活动数据。作者：John Nyemah Natt Jr.所属机构：利比里亚气象、海洋与气候研究中心文献类型：预印本出版日期：2026年9月10日","Zenodo (CERN European Organization for Nuclear Research)","2026-09-10T00:00:00Z",25,84,{"impact":116,"substance":286,"depth":117,"authority":17,"freshness":21,"relevant":22,"comment":287},23,"利比里亚全国碳储量与温室气体基线评估预印本，整合森林、蓝碳、土壤碳与部门排放，方法体系完整、数据规模可观，对农业土地利用与碳汇监测具有参考价值，但属他国案例、非国内三农政策，故未达每日精选门槛。",[289,290],{"name":281,"url":278},{"name":281,"url":291},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22685842",[293,294,295,296,297],"遥感监测","土壤碳","碳汇核算","蓝碳","REDD+",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[300,301],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":75,"slug":302,"name":303,"path":304,"parent_code":305},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":307},{"candidates":308,"agricultural_evidence":53},[309,313,317,324,331],{"code":33,"score":52,"supported":53,"title":310,"abstract":311,"tags":312},[61],[142,61],[],{"code":40,"score":52,"supported":53,"title":314,"abstract":315,"tags":316},[41],[56,41],[],{"code":75,"score":21,"supported":53,"title":318,"abstract":319,"tags":323},[],[320,321,322],"地理信息","GIS","geographic information",[],{"code":70,"score":325,"supported":53,"title":326,"abstract":327,"tags":330},7,[],[328,329],"遥感","remote sensing",[293],{"code":65,"score":22,"supported":14,"title":332,"abstract":333,"tags":334},[],[],[295],[336,337],"碳汇核算 遥感监测 土壤碳 蓝碳","碳汇核算 遥感监测","碳汇核算遥感监测土壤碳蓝碳-2161","10.5281\u002Fzenodo.22685841",{"doi":339,"openalex_id":341,"authors":342,"venue":281,"cited_by_count":91,"oa_url":278,"card":346,"direction":99,"ingested_from":100},"W7212145037",[343],{"name":344,"orcid":345},"JOHN NYEMAH NATT JR","https:\u002F\u002Forcid.org\u002F0000-0003-4084-5916",{"tldr":347,"method":348,"finding":349,"direction":97,"opportunity":350},"整合森林、蓝碳、土壤碳与部门排放，完成利比里亚国家碳储量与温室气体基线评估。","融合野外实测、Sentinel-2\u002FLandsat遥感、GIS与空间建模及国家活","森林约659.5万公顷，碳储量约2236 Mt C（约8200 Mt CO₂e），并核算多部门排放。","可延伸至农业土壤碳汇与部门排放耦合的高分辨率MRV体系及不确定性量化研究。","2026-09-11T23:30:29.631707Z",{"id":353,"title":354,"url":355,"summary":356,"summary_zh":357,"content":9,"source_name":358,"source_url":355,"published_at":359,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":360,"score_detail":361,"sources":364,"tags":366,"primary_topic":371,"secondary_topics":376,"classification":382,"search_phrases":407,"slug":410,"view_count":91,"doi":411,"paper":412,"created_at":435},4898,"An Interpretable Deep Learning Framework for Spatial Prediction of Soil Salinity Using Multi-Source Remote Sensing and Environmental Data: A Case Study of Cangzhou, Hebei Province, China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206465","Soil salinity poses a significant challenge to agricultural productivity and sustainable land management in coastal and semi-arid regions. Soil salinity in coastal agricultural regions is controlled by complex interactions among surface conditions, groundwater processes, soil properties, and terrain characteristics. Accurate spatial prediction requires integrating remote sensing observations with environmental information that represents these processes. This study presents a multi-source deep learning framework that integrates Sentinel-2-derived spectral indices with environmental covariates for regional soil salinity spatial prediction under the environmental conditions of the 2024 sampling period. SHapley Additive exPlanations (SHAP) is employed to quantify the contribution of environmental variables within the prediction framework and improve model interpretability. A convolutional neural network (CNN) incorporating a squeeze-and-excitation (SE) attention mechanism is developed as a nonlinear feature representation framework for integrating heterogeneous predictors, while the Crested Porcupine Optimizer (CPO) is used to optimize model hyperparameters. The proposed framework achieved the highest predictive performance among the benchmark models, with an R2 of 0.785, an RMSE of 0.928, and an MAE of 0.720. Integrating environmental covariates with spectral features further improves the prediction performance, increasing the R2 from 0.701 to 0.785. The results indicate the potential of integrating multi-source environmental information with interpretable deep learning for spatial prediction of near-surface soil salinity under similar coastal agricultural conditions.","土壤盐渍化对沿海和半干旱地区的农业生产力和可持续土地管理构成了重大挑战。沿海农业区域的土壤盐渍化受地表条件、地下水过程、土壤属性和地形特征之间复杂相互作用的控制。准确的空间预测需要将遥感观测与表征这些过程的环境信息相结合。本研究提出了一种多源深度学习框架，该框架将Sentinel-2衍生的光谱指数与环境协变量相结合，用于2024年采样期环境条件下的区域土壤盐渍化空间预测。采用SHapley加法解释（SHAP）方法量化预测框架中环境变量的贡献并提高模型可解释性。开发了一种融合压缩-激励（SE）注意力机制的卷积神经网络（CNN）作为非线性特征表示框架，用于整合异质性预测因子，同时使用冠豪猪优化器（CPO）优化模型超参数。所提出的框架在基准模型中取得了最高的预测性能，R²为0.785，RMSE为0.928，MAE为0.720。将环境协变量与光谱特征相结合进一步提高了预测性能，R²从0.701提升至0.785。结果表明，在类似的沿海农业条件下，将多源环境信息与可解释深度学习相结合用于近地表土壤盐渍化空间预测具有潜力。","Sensors","2026-10-13T00:00:00Z",75,{"impact":115,"substance":362,"depth":19,"authority":20,"freshness":59,"relevant":22,"comment":363},21,"方法新颖、指标可靠，将多源遥感与可解释深度学习结合用于盐渍化空间预测，对沿海农业区土壤治理有参考价值。",[365],{"name":358,"url":355},[367,27,368,369,370],"智慧农业","可解释AI","深度学习","土壤盐渍化",{"code":372,"slug":373,"name":374,"path":375,"parent_code":305},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[377,381],{"code":70,"slug":378,"name":379,"path":380,"parent_code":305},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":383},{"candidates":384,"agricultural_evidence":53},[385,390,394,398,403],{"code":372,"score":115,"supported":53,"title":386,"abstract":388,"tags":389},[387],"deep learning",[369,387],[368,369],{"code":70,"score":139,"supported":53,"title":391,"abstract":392,"tags":393},[329],[328,329],[27],{"code":40,"score":52,"supported":53,"title":395,"abstract":396,"tags":397},[41],[56,41],[],{"code":247,"score":83,"supported":53,"title":399,"abstract":400,"tags":402},[],[401],"干旱",[],{"code":237,"score":22,"supported":14,"title":404,"abstract":405,"tags":406},[],[],[367],[408,409],"沧州 土壤盐渍化 遥感","Sentinel-2 土壤盐分 预测","沧州土壤盐渍化遥感-4898","10.3390\u002Fs26206465",{"doi":411,"openalex_id":413,"authors":414,"venue":358,"cited_by_count":91,"oa_url":355,"card":430,"direction":99,"ingested_from":100},"W7221715654",[415,418,421,423,426,428],{"name":416,"orcid":417},"Mingyuan Ye","https:\u002F\u002Forcid.org\u002F0009-0001-6434-5505",{"name":419,"orcid":420},"Peng Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2549-6765",{"name":422,"orcid":9},"Junchi Ma",{"name":424,"orcid":425},"Bingxia Liu","https:\u002F\u002Forcid.org\u002F0000-0001-7958-7112",{"name":427,"orcid":9},"Jiahao Qi",{"name":429,"orcid":9},"Yuan Jing",{"tldr":431,"method":432,"finding":433,"direction":99,"opportunity":434},"构建融合多源遥感与环境变量的可解释深度学习框架，预测沧州土壤盐分空间分布。","Sentinel-2光谱指数+环境协变量，CNN-SE注意力，CPO优化超参数，","环境协变量与光谱特征结合将R²从0.701提升至0.785，模型性能最优。","可迁移至其他滨海盐渍区，并探索时序动态预测与多深度土壤盐分协同反演。","2026-10-14T23:30:27.313838Z",{"id":437,"title":438,"url":439,"summary":440,"summary_zh":441,"content":9,"source_name":111,"source_url":439,"published_at":442,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":443,"score_detail":444,"sources":446,"tags":448,"primary_topic":452,"secondary_topics":453,"classification":454,"search_phrases":481,"slug":484,"view_count":485,"doi":486,"paper":487,"created_at":504},3874,"Exploring aquaponics systems and greenhouse gas emissions: an agent-based model case study","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1862992","Aquaponics is the combination of aquaculture (i.e., propagation of aquatic species) and hydroponics (i.e., soil-less propagation of plants), which is being investigated as a method for more sustainable food production. Along with ensuring the continued improvement of the technology itself, educating the public about aquaponics using a systems-thinking approach is equally important. Agent-based modeling was used to develop an aquaponics model that will allow any student to have an active learning experience with a generalized operation of the system while also exploring systems-thinking principles. By developing a model, individuals interested in learning are not limited by what their educational institutions can provide, which is often restricted by a lack of resources. The aquaponics agent-based model presented in this study allows the user to change the fish and plant types, climate, and season and display the corresponding greenhouse gas emissions when the theoretical system is operating. The number of plants harvested and the total greenhouse gas emissions are the two stochastic outputs of the model, given potential variations in timelines, system efficiencies, and general human error. Greenhouse gas emissions were compared with those reported in the relevant literature to inform parameter selection. The overall goal of this model is to provide an interactive and broadly accessible educational opportunity.","鱼菜共生（Aquaponics）是水产养殖（即水生物种的繁殖）与水培（即无土栽培植物）的结合，目前正被研究作为一种更可持续的粮食生产方式。在确保该技术本身持续改进的同时，利用系统思维方法向公众普及鱼菜共生知识同样重要。本研究采用基于主体的建模（Agent-based modeling）方法开发了一个鱼菜共生模型，使任何学生都能通过该系统的广义操作获得主动学习体验，同时探索系统思维原则。通过开发这一模型，有兴趣的学习者不再受限于其教育机构所能提供的条件——这些条件往往因资源匮乏而受到制约。本研究提出的鱼菜共生基于主体的模型允许用户更改鱼类和植物类型、气候和季节，并展示理论系统运行时的相应温室气体排放量。考虑到时间线、系统效率和一般人为误差的潜在变化，收获植物数量和温室气体排放总量是该模型的两个随机输出。温室气体排放量已与相关文献中报告的数据进行比较，以指导参数选择。该模型的总体目标是提供一种具有交互性且可广泛获取的教育机会。","2026-09-30T00:00:00Z",61,{"impact":59,"substance":117,"depth":115,"authority":20,"freshness":103,"relevant":22,"comment":445},"该论文提出基于智能体的鱼菜共生教学模型，可模拟鱼类、植物、气候与季节变化并输出温室气体排放，方法新颖且具教育推广价值，但属细分领域学术成果，公共影响有限。",[447],{"name":111,"url":439},[367,449,28,450,451],"设施农业","水产养殖","系统思维",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":455},{"candidates":456,"agricultural_evidence":53},[457,462,469,473,477],{"code":33,"score":19,"supported":53,"title":458,"abstract":459,"tags":461},[61],[142,460,61],"无土栽培",[],{"code":463,"score":325,"supported":53,"title":464,"abstract":465,"tags":468},"D",[],[466,467],"水产","aquaculture",[450],{"code":40,"score":83,"supported":53,"title":470,"abstract":471,"tags":472},[],[41],[],{"code":65,"score":22,"supported":14,"title":474,"abstract":475,"tags":476},[],[],[28],{"code":237,"score":22,"supported":14,"title":478,"abstract":479,"tags":480},[],[],[367],[482,483],"鱼菜共生 温室气体 模型","智慧农业 水产养殖 温室气体 系统思维","鱼菜共生温室气体模型-3874",2,"10.3389\u002Ffsufs.2026.1862992",{"doi":486,"openalex_id":488,"authors":489,"venue":111,"cited_by_count":91,"oa_url":439,"card":499,"direction":97,"ingested_from":100},"W7214975686",[490,493,495,497],{"name":491,"orcid":492},"Marissa Breitenstein","https:\u002F\u002Forcid.org\u002F0009-0005-2148-8812",{"name":494,"orcid":9},"Elisabeth Bautista",{"name":496,"orcid":9},"Alexis Daniels",{"name":498,"orcid":9},"Andrea Hicks",{"tldr":500,"method":501,"finding":502,"direction":97,"opportunity":503},"开发鱼菜共生系统智能体模型，模拟不同鱼、植物、气候和季节下的温室气体排放，用于教学。","基于智能体的建模（ABM），参数参考文献，输出随机性结果。","模型可交互展示鱼菜共生系统的温室气体排放，促进系统思维学习。","可扩展模型纳入更多环境与经济指标，或开发面向公众的在线交互平台。","2026-10-01T23:30:07.298849Z",{"id":506,"title":507,"url":508,"summary":509,"summary_zh":510,"content":9,"source_name":511,"source_url":508,"published_at":512,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":513,"score_detail":514,"sources":516,"tags":518,"primary_topic":522,"secondary_topics":525,"classification":528,"search_phrases":561,"slug":564,"view_count":485,"doi":565,"paper":566,"created_at":580},3804,"Evaluating Reference Evapotranspiration and Soil Moisture as Predictors for Machine Learning-Based Actual Evapotranspiration Estimation: Model Performance and Explainability","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fatmos17100941","Accurate estimation of actual evapotranspiration (ETa) is essential for sustainable water resources management and agricultural planning. This study investigated how the availability of reference evapotranspiration (ET0) and soil moisture (SM) affects ETa prediction performance and predictor importance using Random Forest (RF), Bagging Trees (BT), Least Squares Boosting (LSBoost), Generalized Additive Models (GAM), and Multiple Linear Regression (MLR). Global solar radiation (Rs), land surface temperature (LST), normalized difference vegetation index (NDVI), and SM were evaluated under two primary scenarios, with and without ET0. Models were developed using 80% of the data, with five-fold cross-validation conducted within the training subset, and final performance was evaluated on an independent 20% test subset. Model interpretability was assessed using SHAP, Permutation Feature Importance (PFI), Partial Dependence Plot (PDP), and temporal signed-SHAP analyses, while an additional SM-ablation experiment quantified the incremental predictive contribution of soil moisture. In Scenario I, ET0 was the dominant predictor, and RF and BT achieved the highest independent-test performance (R2 = 0.875; RMSE = 0.398–0.399 mm day−1). Excluding ET0 reduced predictive performance, with RF, LSBoost, and BT achieving R2 values of approximately 0.80 in Scenario II, while the predictor-importance structure shifted primarily toward Rs, followed by SM, LST, and NDVI. Removal of SM further reduced performance in both scenarios, with substantially greater deterioration when ET0 was unavailable: R2 decreased by 0.025–0.044 in Scenario I and by 0.051–0.099 in Scenario II. Under the SM-excluded Scenario I configuration, which more closely aligned the predictor information with previous ANN- and SAFER-based studies, R2 values ranged from 0.820 to 0.832, indicating that differences in ETa prediction performance cannot be attributed solely to model structure but also depend strongly on predictor information content. Overall, ET0 provides substantial atmospheric-demand information for ETa prediction, whereas Rs assumes the dominant predictive role, and SM provides particularly important complementary information when ET0 is unavailable. The combined performance, ablation, and explainability analyses demonstrate the importance of considering predictor composition together with model structure when developing and comparing ETa estimation approaches.","准确估算实际蒸散发（ETa）对于可持续水资源管理和农业规划至关重要。本研究探讨了参考蒸散发（ET0）和土壤水分（SM）的可用性如何影响ETa预测性能及预测因子重要性，采用了随机森林（RF）、Bagging Trees（BT）、最小二乘提升（LSBoost）、广义加性模型（GAM）和多元线性回归（MLR）方法。在全球太阳辐射（Rs）、地表温度（LST）、归一化差异植被指数（NDVI）和SM的条件下，评估了包含和不包含ET0两种主要情景。模型使用80%的数据进行开发，在训练子集内进行五折交叉验证，最终性能在独立的20%测试子集上评估。模型可解释性通过SHAP、排列特征重要性（PFI）、偏依赖图（PDP）和时间符号SHAP分析进行评估，同时通过额外的SM消融实验量化了土壤水分的增量预测贡献。在情景I中，ET0是主导预测因子，RF和BT取得了最高的独立测试性能（R2 = 0.875；RMSE = 0.398–0.399 mm day−1）。排除ET0降低了预测性能，在情景II中RF、LSBoost和BT的R2值约为0.80，同时预测因子重要性结构主要转向Rs，其次是SM、LST和NDVI。去除SM进一步降低了两种情景下的性能，且在ET0不可用时性能下降幅度更大：情景I中R2下降了0.025–0.044，情景II中下降了0.051–0.099。在排除SM的情景I配置下（该配置使预测因子信息更接近此前的ANN和SAFER研究），R2值范围为0.820至0.832，表明ETa预测性能的差异不能仅归因于模型结构，还强烈依赖于预测因子信息含量。总体而言，ET0为ETa预测提供了大量大气需求信息，而Rs承担了主导预测作用，SM在ET0不可用时提供了特别重要的补充信息。综合性能、消融和可解释性分析表明，在开发和比较ETa估算方法时，需将预测因子组成与模型结构一并考虑。","Atmosphere","2026-09-27T00:00:00Z",73,{"impact":17,"substance":116,"depth":117,"authority":20,"freshness":59,"relevant":22,"comment":515},"方法对比与可解释性分析扎实，对农业水资源管理和灌溉决策有参考价值，但属专业论文，公共影响有限。",[517],{"name":511,"url":508},[367,27,519,520,521],"机器学习","土壤墒情","蒸散发估算",{"code":162,"slug":166,"name":523,"path":524,"parent_code":44},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[526,527],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":372,"slug":373,"name":374,"path":375,"parent_code":305},{"status":46,"locked":14,"version":47,"method":48,"evidence":529},{"candidates":530,"agricultural_evidence":53},[531,538,542,547,553,557],{"code":162,"score":19,"supported":53,"title":532,"abstract":534,"tags":537},[533],"evapotranspiration",[535,536,533],"水资源","蒸散发",[],{"code":40,"score":139,"supported":53,"title":539,"abstract":540,"tags":541},[41],[56,41],[520],{"code":372,"score":21,"supported":53,"title":543,"abstract":545,"tags":546},[544],"machine learning",[],[519],{"code":548,"score":83,"supported":53,"title":549,"abstract":550,"tags":552},"J1",[],[551],"农业规划",[],{"code":237,"score":22,"supported":14,"title":554,"abstract":555,"tags":556},[],[],[367],{"code":70,"score":22,"supported":14,"title":558,"abstract":559,"tags":560},[],[],[27],[562,563],"参考蒸散发 土壤水分 机器学习","ET0 实际蒸散发 随机森林","参考蒸散发土壤水分机器学习-3804","10.3390\u002Fatmos17100941",{"doi":565,"openalex_id":567,"authors":568,"venue":511,"cited_by_count":91,"oa_url":508,"card":574,"direction":578,"ingested_from":100},"W7214588993",[569,572],{"name":570,"orcid":571},"Halil Karahan","https:\u002F\u002Forcid.org\u002F0000-0001-5346-5686",{"name":573,"orcid":9},"Devrim Alkaya",{"tldr":575,"method":576,"finding":577,"direction":578,"opportunity":579},"评估ET0与土壤湿度对机器学习估算实际蒸散的影响及可解释性。","用RF、BT、LSBoost、GAM、MLR结合SHAP、PFI、PDP及SM消","ET0主导预测，缺失时Rs主导且SM补充价值显著增大。","农业人工智能与决策模型","可探索缺ET0条件下融合遥感与土壤湿度的可解释ETa估算框架。","2026-09-29T23:30:57.729880Z"]