[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2325":3,"related-2325":137},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":43,"search_phrases":102,"slug":105,"view_count":106,"doi":107,"paper":108,"created_at":136},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",null,"Journal of Advances in Biology & Biotechnology","2026-09-11T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字乡村","智慧农业","遥感监测","碳农业","土壤固碳",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,56,62,70,78,85,92,97],{"code":32,"score":50,"supported":51,"title":52,"abstract":53,"tags":55},15,true,[33],[54,33],"土壤",[30],{"code":39,"score":57,"supported":51,"title":58,"abstract":60,"tags":61},11,[59],"carbon sequestration",[59],[],{"code":63,"score":64,"supported":51,"title":65,"abstract":66,"tags":69},"I3",7,[],[67,68],"遥感","remote sensing",[28],{"code":71,"score":72,"supported":51,"title":73,"abstract":74,"tags":77},"B7",6,[],[75,76],"温室","greenhouse",[],{"code":79,"score":72,"supported":51,"title":80,"abstract":81,"tags":84},"I2",[],[82,83],"机器学习","machine learning",[],{"code":86,"score":87,"supported":51,"title":88,"abstract":89,"tags":91},"A7",3,[],[90],"轮作",[],{"code":93,"score":21,"supported":14,"title":94,"abstract":95,"tags":96},"I1",[],[],[27],{"code":98,"score":21,"supported":14,"title":99,"abstract":100,"tags":101},"I6",[],[],[26],[103,104],"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",0,"10.9734\u002Fjabb\u002F2026\u002Fv29i104418",{"doi":107,"openalex_id":109,"authors":110,"venue":10,"cited_by_count":106,"oa_url":6,"card":128,"direction":134,"ingested_from":135},"W7212265915",[111,114,116,118,120,122,124,126],{"name":112,"orcid":113},"Samarpan Chakraborty","https:\u002F\u002Forcid.org\u002F0000-0002-2740-8722",{"name":115,"orcid":9},"Anusmita Goswami",{"name":117,"orcid":9},"Ritam Dhar",{"name":119,"orcid":9},"Kasturi Mandal",{"name":121,"orcid":9},"Tamalika Mondal",{"name":123,"orcid":9},"Sancharee Paul",{"name":125,"orcid":9},"Priya Sarawgi",{"name":127,"orcid":9},"Sujan Biswas",{"tldr":129,"method":130,"finding":131,"direction":132,"opportunity":133},"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。","农业绿色发展与碳","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。","农业遥感与作物表型","openalex","2026-09-13T23:30:23.245745Z",{"total":72,"page":21,"page_size":72,"items":138},[139,228,339,424,477,533],{"id":140,"title":141,"url":142,"summary":143,"summary_zh":144,"content":9,"source_name":145,"source_url":142,"published_at":146,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":147,"score_detail":148,"sources":152,"tags":154,"primary_topic":159,"secondary_topics":160,"classification":162,"search_phrases":179,"slug":182,"view_count":106,"doi":183,"paper":184,"created_at":227},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。","Frontiers in Sustainable Food Systems","2026-10-20T00:00:00Z",74,{"impact":149,"substance":18,"depth":17,"authority":19,"freshness":150,"relevant":21,"comment":151},12,9,"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。",[153],{"name":145,"url":142},[155,156,157,30,158],"生物炭","农业废弃物","秸秆利用","热解技术",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[161],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":163},{"candidates":164,"agricultural_evidence":51},[165,169,173],{"code":39,"score":57,"supported":51,"title":166,"abstract":167,"tags":168},[59],[59],[],{"code":32,"score":20,"supported":51,"title":170,"abstract":171,"tags":172},[],[54,33],[30,155],{"code":174,"score":175,"supported":51,"title":176,"abstract":177,"tags":178},"H5",5,[],[156],[156,157],[180,181],"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":183,"openalex_id":185,"authors":186,"venue":145,"cited_by_count":106,"oa_url":221,"card":222,"direction":132,"ingested_from":135},"W7223016223",[187,190,192,195,198,200,203,206,208,211,213,216,219],{"name":188,"orcid":189},"Morasanahalli Lokesha Sachin","https:\u002F\u002Forcid.org\u002F0009-0008-8615-0822",{"name":191,"orcid":9},"Yogananda Shivalli Boregowda",{"name":193,"orcid":194},"Matadadoddi Nanjundegowda Thimmegowda","https:\u002F\u002Forcid.org\u002F0000-0001-7632-1036",{"name":196,"orcid":197},"M. A. Ananthakumar","https:\u002F\u002Forcid.org\u002F0000-0003-4376-9541",{"name":199,"orcid":9},"Pudiyanda Shadali Fathima",{"name":201,"orcid":202},"C. M. Sunil","https:\u002F\u002Forcid.org\u002F0000-0002-9625-5686",{"name":204,"orcid":205},"K. M. Pravalika","https:\u002F\u002Forcid.org\u002F0009-0004-5258-392X",{"name":207,"orcid":9},"M.C. Anjali",{"name":209,"orcid":210},"Sathisha Govinakere Sambegowda","https:\u002F\u002Forcid.org\u002F0009-0000-8620-6461",{"name":212,"orcid":9},"B. Divya",{"name":214,"orcid":215},"M. K. Shruthi","https:\u002F\u002Forcid.org\u002F0000-0003-3703-9817",{"name":217,"orcid":218},"Santosh Onte","https:\u002F\u002Forcid.org\u002F0009-0002-8367-1303",{"name":220,"orcid":9},"Sanjeev Kumar","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1966170\u002Fpdf",{"tldr":223,"method":224,"finding":225,"direction":132,"opportunity":226},"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",{"id":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":9,"source_name":145,"source_url":231,"published_at":146,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":234,"score_detail":235,"sources":239,"tags":241,"primary_topic":247,"secondary_topics":248,"classification":255,"search_phrases":312,"slug":315,"view_count":106,"doi":316,"paper":317,"created_at":338},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。",70,{"impact":149,"substance":236,"depth":237,"authority":19,"freshness":150,"relevant":21,"comment":238},20,16,"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。",[240],{"name":145,"url":231},[242,243,244,245,246],"气候智慧农业","碳交易","干旱农业","土壤碳汇","作物选择",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[249,254],{"code":250,"slug":251,"name":252,"path":253,"parent_code":36},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":256},{"candidates":257,"agricultural_evidence":51},[258,263,270,275,282,289,295,301,307],{"code":32,"score":259,"supported":51,"title":260,"abstract":261,"tags":262},14,[33],[54,33],[],{"code":250,"score":13,"supported":51,"title":264,"abstract":265,"tags":269},[],[266,267,268],"气候变化","干旱","climate change",[242],{"code":39,"score":20,"supported":51,"title":271,"abstract":272,"tags":274},[],[273,59],"农业碳",[245,243],{"code":276,"score":72,"supported":51,"title":277,"abstract":278,"tags":281},"A2",[],[279,280],"小麦","wheat",[],{"code":283,"score":72,"supported":51,"title":284,"abstract":285,"tags":288},"B1",[],[286,287],"番茄","黄瓜",[],{"code":290,"score":87,"supported":51,"title":291,"abstract":292,"tags":294},"A5",[],[293],"马铃薯",[],{"code":296,"score":87,"supported":51,"title":297,"abstract":298,"tags":300},"B2",[],[299],"葡萄",[],{"code":302,"score":87,"supported":51,"title":303,"abstract":304,"tags":306},"H6",[],[305],"农业生态",[],{"code":93,"score":87,"supported":51,"title":308,"abstract":309,"tags":311},[],[310],"smart agriculture",[],[313,314],"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":316,"openalex_id":318,"authors":319,"venue":145,"cited_by_count":106,"oa_url":231,"card":332,"direction":337,"ingested_from":135},"W7223064172",[320,322,324,327,329],{"name":321,"orcid":9},"Tasnim Althalaj",{"name":323,"orcid":9},"Ross. M. Thompson",{"name":325,"orcid":326},"Tala H. Qtaishat","https:\u002F\u002Forcid.org\u002F0000-0002-2700-6686",{"name":328,"orcid":9},"Dan Bumblauskas",{"name":330,"orcid":331},"Fayha Al-Shibli","https:\u002F\u002Forcid.org\u002F0000-0002-5628-0975",{"tldr":333,"method":334,"finding":335,"direction":132,"opportunity":336},"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":340,"title":341,"url":342,"summary":343,"summary_zh":344,"content":9,"source_name":345,"source_url":342,"published_at":346,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":347,"score_detail":348,"sources":352,"tags":354,"primary_topic":357,"secondary_topics":360,"classification":367,"search_phrases":401,"slug":404,"view_count":106,"doi":405,"paper":406,"created_at":423},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z",77,{"impact":237,"substance":349,"depth":350,"authority":19,"freshness":13,"relevant":21,"comment":351},21,17,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[353],{"name":345,"url":342},[27,82,28,355,356],"土壤墒情","冬小麦",{"code":276,"slug":280,"name":279,"path":358,"parent_code":359},"\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[361,362],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":79,"slug":363,"name":364,"path":365,"parent_code":366},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":44,"locked":14,"version":45,"method":46,"evidence":368},{"candidates":369,"agricultural_evidence":51},[370,376,380,384,389,396],{"code":276,"score":371,"supported":51,"title":372,"abstract":374,"tags":375},26,[373,280],"winter wheat",[279,373,280],[356],{"code":32,"score":50,"supported":51,"title":377,"abstract":378,"tags":379},[33],[54,33],[355],{"code":79,"score":50,"supported":51,"title":381,"abstract":382,"tags":383},[83],[82,83],[82],{"code":63,"score":13,"supported":51,"title":385,"abstract":386,"tags":388},[],[67,68,387],"multispectral",[28],{"code":390,"score":72,"supported":51,"title":391,"abstract":392,"tags":395},"H3",[],[393,394],"灌溉","irrigation",[],{"code":93,"score":397,"supported":51,"title":398,"abstract":399,"tags":400},4,[],[27],[27],[402,403],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":405,"openalex_id":407,"authors":408,"venue":345,"cited_by_count":106,"oa_url":342,"card":418,"direction":337,"ingested_from":135},"W7223156204",[409,412,414,416],{"name":410,"orcid":411},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":413,"orcid":9},"Yi Zhang",{"name":415,"orcid":9},"WEI Lei",{"name":417,"orcid":9},"Chengcai Zhang",{"tldr":419,"method":420,"finding":421,"direction":134,"opportunity":422},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","2026-10-22T11:20:18.887301Z",{"id":425,"title":426,"url":427,"summary":428,"summary_zh":9,"content":9,"source_name":429,"source_url":9,"published_at":430,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":431,"score_detail":432,"sources":435,"tags":437,"primary_topic":441,"secondary_topics":442,"classification":443,"search_phrases":464,"slug":467,"view_count":21,"doi":9,"paper":468,"created_at":476},5227,"China's farm digitization reduces agricultural carbon emissions but the relationship is nonlinear (U-shaped)","https:\u002F\u002Fcommonplace.workforcefutures.net\u002Fpaper\u002Fopenalex:W7221809067","Gao、Feng、Yuan、Sun 在 OpenAlex 2026-10-16 索引的最新研究,基于中国31个省份2010—2023年平衡面板数据,采用双向固定效应面板与SEM中介模型,发现农业数字技术通过精准管理与间接融资\u002F绿色创新两条路径降低碳排放,但呈U型非线性,转折点为数字化指数0.540,劳动生产率提升可推迟转折点,数字化在区域、规模、排放来源维度呈现异质性。","OpenAlex W7221809067(2026-10-16)","2026-10-16T00:00:00Z",84,{"impact":18,"substance":433,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":434},23,"基于31省2010—2023年面板数据的全国性实证研究，揭示数字化与农业碳排放的U型关系及0.540转折点，结论新颖且具政策参考价值，值得进入每日精选。",[436],{"name":429,"url":427},[26,27,438,439,440],"农业数字化","农业碳排放","绿色创新",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[],{"status":44,"locked":14,"version":45,"method":46,"evidence":444},{"candidates":445,"agricultural_evidence":51},[446,451,456,460],{"code":39,"score":397,"supported":51,"title":447,"abstract":448,"tags":450},[],[449],"碳排放",[439],{"code":93,"score":452,"supported":14,"title":453,"abstract":454,"tags":455},2,[],[],[438,27],{"code":366,"score":21,"supported":14,"title":457,"abstract":458,"tags":459},[],[],[438],{"code":98,"score":21,"supported":14,"title":461,"abstract":462,"tags":463},[],[],[26],[465,466],"农业数字化 碳排放 U型","数字技术 农业碳减排","农业数字化碳排放U型-5227",{"doi":9,"openalex_id":9,"authors":469,"venue":9,"cited_by_count":106,"oa_url":9,"card":470,"direction":132,"ingested_from":475},[],{"tldr":471,"method":472,"finding":473,"direction":132,"opportunity":474},"基于中国31省2010—2023年面板数据，发现农业数字化与碳排放呈U型非线性关系。","双向固定效应面板模型与SEM中介模型，31省平衡面板数据。","数字化通过精准管理和绿色创新降碳，但存在0.540的U型转折点。","可探究转折点后数字化增碳的机制，及劳动生产率等调节因素如何推迟拐点。","agent","2026-10-18T00:05:30.735414Z",{"id":478,"title":479,"url":480,"summary":481,"summary_zh":482,"content":9,"source_name":483,"source_url":480,"published_at":484,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":485,"score_detail":486,"sources":488,"tags":490,"primary_topic":493,"secondary_topics":494,"classification":495,"search_phrases":512,"slug":515,"view_count":397,"doi":516,"paper":517,"created_at":532},4520,"Agricultural Industry Agglomeration and Carbon Emission Intensity: A Systems Perspective on Resilience and Digital Development","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsystems14101263","Decarbonizing agriculture depends not only on the efficiency of individual production activities but also on coordination among production, processing, and service activities. From a systems perspective, this study treats provincial agriculture as an interconnected industrial system and examines the relationship between agricultural industry agglomeration and agricultural carbon emission intensity. Moving beyond conventional measures of production concentration, agricultural industry agglomeration is measured as coordination between horizontal agglomeration in agricultural production and vertical agglomeration across production, processing, and agricultural services. Based on a balanced panel of 390 observations across 30 Chinese provinces from 2011 to 2023, the study uses province and year fixed effects, mediation and moderation analyses, robustness checks, and an endogeneity check. Results show that agricultural industry agglomeration is significantly associated with lower agricultural carbon emission intensity. The mediation results are consistent with a partial intermediary role for agricultural industry resilience in this relationship. The interaction and simple slope results show that the positive association between agricultural industry agglomeration and agricultural industry resilience is larger at higher levels of digital village development. These findings connect subsystem coordination, adaptive capacity, digital conditions, and carbon performance, thereby extending research on agricultural transformation toward lower carbon intensity from a systems perspective.","农业脱碳不仅取决于单个生产活动的效率，还取决于生产、加工和服务活动之间的协调。本研究从系统视角出发，将省级农业视为一个相互关联的产业系统，考察农业产业集聚与农业碳排放强度之间的关系。本研究超越传统的生产集中度衡量方式，将农业产业集聚界定为农业生产横向集聚与生产、加工及农业服务纵向集聚之间的协调。基于2011年至2023年中国30个省份390个观测值的平衡面板数据，本研究采用省份和年份固定效应、中介与调节分析、稳健性检验以及内生性检验。结果表明，农业产业集聚与较低的农业碳排放强度显著相关。中介结果表明，农业产业韧性在这一关系中发挥部分中介作用。交互效应和简单斜率结果表明，在数字乡村发展水平较高时，农业产业集聚与农业产业韧性之间的正向关联更强。这些发现将子系统协调、适应能力、数字条件与碳绩效联系起来，从而从系统视角拓展了农业向低碳强度转型的研究。","Systems","2026-10-08T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":150,"relevant":21,"comment":487},"基于30省2011-2023年面板数据，从系统视角揭示农业产业集聚通过韧性降低碳排放强度，并受数字乡村发展调节，方法规范、结论有政策参考价值。",[489],{"name":483,"url":480},[26,27,491,439,492],"农业韧性","农业产业集聚",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[],{"status":44,"locked":14,"version":45,"method":46,"evidence":496},{"candidates":497,"agricultural_evidence":51},[498,503,508],{"code":39,"score":17,"supported":51,"title":499,"abstract":501,"tags":502},[500],"carbon emission",[449,273,500],[439],{"code":98,"score":64,"supported":51,"title":504,"abstract":505,"tags":507},[],[26,506],"digital village",[26],{"code":93,"score":21,"supported":14,"title":509,"abstract":510,"tags":511},[],[],[27],[513,514],"农业产业集聚 碳排放强度","数字乡村 农业韧性","农业产业集聚碳排放强度-4520","10.3390\u002Fsystems14101263",{"doi":516,"openalex_id":518,"authors":519,"venue":483,"cited_by_count":106,"oa_url":480,"card":526,"direction":531,"ingested_from":135},"W7220924322",[520,522,524],{"name":521,"orcid":9},"Jinghui Hu",{"name":523,"orcid":9},"Jun He",{"name":525,"orcid":9},"Junzhong He",{"tldr":527,"method":528,"finding":529,"direction":132,"opportunity":530},"从系统视角研究中国省级农业产业集聚对农业碳排放强度的影响及韧性与数字化的作用。","30省2011-2023年面板数据，固定效应、中介与调节分析。","农业产业集聚显著降低碳排放强度，产业韧性部分中介，数字乡村发展正向调节。","可探究产业集聚不同模式与数字化协同对碳减排的非线性及空间溢出效应。","数字乡村与农业信息化","2026-10-09T23:30:32.435467Z",{"id":534,"title":535,"url":536,"summary":537,"summary_zh":538,"content":9,"source_name":145,"source_url":536,"published_at":539,"category":12,"cover_url":9,"hotness":540,"is_selected":14,"score":485,"score_detail":541,"sources":543,"tags":548,"primary_topic":551,"secondary_topics":555,"classification":557,"search_phrases":584,"slug":587,"view_count":106,"doi":588,"paper":589,"created_at":606},4416,"Agricultural blockchain readiness environment and carbon emission intensity in maize production: evidence from provincial panel data in major maize-producing areas of China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1942225","China’s major maize-producing regions face the joint challenge of sustaining food production and reducing agricultural emissions. Existing research on digital agriculture often uses broad digitalization measures and therefore does not distinguish the regional conditions that may enable information management, traceability, and verification applications associated with blockchain. This study constructs an Agricultural Blockchain Readiness Environment Index (ABRE Index, block_index) using entropy weighting across digital infrastructure, the agricultural industrial base, and policy support. The index measures regional readiness and enabling conditions rather than realized blockchain adoption. Using 180 province-year observations for 12 major maize-producing provinces from 2010 to 2024, we estimate two-way fixed-effects models with province-clustered inference and conduct supplementary robustness analyses. The ABRE Index is negatively associated with carbon emissions per unit of maize-sown area, with the baseline coefficient statistically significant at the 10% level under conventional clustered inference and supported by supplementary inference procedures. The coefficient remains negative in all 12 leave-one-out specifications, is statistically significant at the 10% level or better in 10 specifications, and the wild cluster bootstrap test yields p = 0.0415. The index is positively correlated with blockchain-related patent activity ( r = 0.1883, p = 0.0114), although this correlation does not establish that the index measures realized adoption. The mechanism analysis is consistent with an organizational pathway through agricultural socialized services: the ABRE Index is positively associated with service_ratio (0.1099, p \u003C 0.01), and agricultural socialized services are negatively associated with ce_area (−2.2765, p \u003C 0.05). The interaction with the high-development indicator is negative but does not reach the conventional 10% significance threshold (−0.1499, p = 0.103). Overall, the evidence supports an associational relationship between blockchain-readiness conditions and maize-production carbon-emission intensity; it does not establish a strictly causal effect of blockchain adoption.","中国主要玉米产区面临保障粮食生产与减少农业排放的双重挑战。现有数字农业研究多采用宽泛的数字化指标，因而未能区分可能支撑与区块链相关的信息管理、追溯和验证应用的区域条件。本研究基于数字基础设施、农业产业基础和政策支持三个维度，采用熵权法构建了农业区块链就绪环境指数（ABRE Index，block_index）。该指数衡量的是区域就绪程度和使能条件，而非已实现的区块链采用。利用2010年至2024年12个主要玉米生产省份的180个省份—年份观测值，我们估计了双向固定效应模型，采用省份聚类推断，并进行了补充稳健性分析。ABRE指数与单位玉米播种面积碳排放呈负相关，基准系数在常规聚类推断下于10%水平上统计显著，并得到补充推断程序的支持。在全部12个逐一剔除（leave-one-out）设定中，该系数均保持为负，其中10个设定在10%或更优水平上统计显著，野生聚类自举检验（wild cluster bootstrap test）得出p = 0.0415。该指数与区块链相关专利活动呈正相关（r = 0.1883，p = 0.0114），尽管这一相关性并不能证明该指数衡量的是已实现的采用。机制分析与通过农业社会化服务发挥作用的组织路径相一致：ABRE指数与service_ratio呈正相关（0.1099，p \u003C 0.01），农业社会化服务与ce_area呈负相关（−2.2765，p \u003C 0.05）。与高发展指标的交互项为负，但未达到常规10%显著性阈值（−0.1499，p = 0.103）。总体而言，证据支持区块链就绪条件与玉米生产碳排放强度之间存在关联关系；但并未确立区块链采用的严格因果效应。","2026-10-07T00:00:00Z",25,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":150,"relevant":21,"comment":542},"基于12个玉米主产省2010—2024年面板数据构建农业区块链就绪环境指数，发现其与玉米碳排放强度负相关，方法规范、数据规模可观，但结论仅为关联性而非因果，属细分领域实证进展。",[544,545],{"name":145,"url":536},{"name":546,"url":547},"Frontiers in Sustainable Food Systems 2026 年 10 月 07 日 Volume 10 Sec. Land, Livelihoods and Food Security","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1942225\u002Ffull",[26,27,549,439,550],"玉米","区块链",{"code":552,"slug":553,"name":549,"path":554,"parent_code":359},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize",[556],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":558},{"candidates":559,"agricultural_evidence":51},[560,564,568,574,580],{"code":552,"score":50,"supported":51,"title":561,"abstract":562,"tags":563},[553],[549,553],[549],{"code":39,"score":149,"supported":51,"title":565,"abstract":566,"tags":567},[500],[449],[439],{"code":93,"score":64,"supported":51,"title":569,"abstract":570,"tags":573},[],[571,572],"数字农业","digital agriculture",[27],{"code":575,"score":87,"supported":51,"title":576,"abstract":577,"tags":579},"G2",[],[578],"社会化服务",[],{"code":98,"score":21,"supported":14,"title":581,"abstract":582,"tags":583},[],[],[26],[585,586],"农业区块链 玉米 碳排放","玉米主产区 区块链 碳强度","农业区块链玉米碳排放-4416","10.3389\u002Ffsufs.2026.1942225",{"doi":588,"openalex_id":590,"authors":591,"venue":145,"cited_by_count":106,"oa_url":600,"card":601,"direction":337,"ingested_from":135},"W7220869193",[592,595,597],{"name":593,"orcid":594},"Pengfei Zhou","https:\u002F\u002Forcid.org\u002F0000-0003-2117-7967",{"name":596,"orcid":9},"Zhiyi Hu",{"name":598,"orcid":599},"Yang Shen","https:\u002F\u002Forcid.org\u002F0000-0002-6781-6915","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1942225\u002Fpdf",{"tldr":602,"method":603,"finding":604,"direction":132,"opportunity":605},"构建农业区块链就绪环境指数，分析其与玉米生产碳排放强度的关联。","熵权法构建ABRE指数，用2010-2024年12省面板数据做双向固定效应模型。","ABRE指数与玉米单位播种面积碳排放负相关，机制可能通过农业社会化服务实现。","可探究区块链就绪环境通过社会化服务减碳的因果机制，并拓展到其他主粮作物或区域。","2026-10-08T23:30:07.668939Z"]