[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4051":3,"related-4051":84},{"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":38,"search_phrases":62,"slug":65,"view_count":21,"doi":66,"paper":67,"created_at":83},4051,"Maturity-aware credit governance in digital agricultural finance: leakage-free selective fusion for agri-food SME risk assessment","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1946390","Introduction Digital agricultural lenders need risk estimates that remain trustworthy when loan outcomes mature slowly and contextual data become available at different times. This study introduces DAF-XCredit, a maturity-aware selective-fusion framework for agri-food SME credit-risk assessment. Methods DAF-XCredit combines approval-time loan records with outcome-observable historical priors and lagged agricultural context under a fixed 5-year charge-off target. Chronological out-of-fold base scores are calibrated, and a 2014–2015 validation cohort selects the source blocks used in the final fusion model. Experiments use 67,114 public U.S. Small Business Administration 7(a) agri-food loans approved during 2000–2019 and linked USDA data. The untouched 2016–2019 test cohort contains 12,339 loans. Results Across five seeds, the selected loan-plus-agricultural model achieved an AUC of 0.9497 ± 0.0004, recall of 0.6425 ± 0.0080, F1 of 0.4357 ± 0.0036, Brier score of 0.02356 ± 0.00005, and equal-width expected calibration error of 0.00272 ± 0.00022. DAF-XCredit achieved the highest AUC, recall, and F1 and the lowest Brier score and equal-width calibration error among the evaluated models, whereas M1 and M5 led PR-AUC and KS, respectively. A seed-2026 DeLong test found no statistically significant AUC difference from the loan-only model ( p = 0.254). State–year cluster resampling yielded a calibration slope of 0.990 (95% CI 0.897-1.090), and decision curves showed positive net benefit across thresholds of 0.05–0.30. Discussion Maturity-aware source selection, calibration, SHAP analysis, stability resampling, and subgroup auditing provide an auditable framework for agricultural credit governance. The method can support sustainability-oriented financing decisions but does not itself establish sustainability outcomes.","引言 数字农业贷款机构需要这样的风险估计：当贷款结果成熟缓慢、情境数据在不同时间可得时，这些估计仍然可信。本研究提出DAF-XCredit，一个面向农食中小企业信用风险评估的成熟度感知选择性融合框架。方法 DAF-XCredit在固定的5年核销目标下，将审批时的贷款记录与结果可观测的历史先验及滞后的农业情境相结合。对按时间顺序的折外基础分数进行校准，并由2014—2015年验证队列选择最终融合模型所使用的来源模块。实验使用2000—2019年批准的67,114笔美国小企业管理局7(a)农食贷款公开数据及关联的美国农业部数据。未触碰的2016—2019年测试队列包含12,339笔贷款。结果 在五个随机种子下，所选贷款加农业模型达到AUC为0.9497 ± 0.0004，召回率为0.6425 ± 0.0080，F1为0.4357 ± 0.0036，Brier分数为0.02356 ± 0.00005，等宽期望校准误差为0.00272 ± 0.00022。在受评估模型中，DAF-XCredit取得了最高的AUC、召回率和F1，以及最低的Brier分数和等宽校准误差，而M1和M5分别领先PR-AUC和KS。种子2026的DeLong检验发现，与仅贷款模型相比，AUC差异无统计学显著性（p = 0.254）。州—年聚类重抽样得到的校准斜率为0.990（95% CI 0.897-1.090），决策曲线显示在0.05—0.30阈值范围内具有正净收益。讨论 成熟度感知的来源选择、校准、SHAP分析、稳定性重抽样和子组审计为农业信用治理提供了一个可审计框架。该方法可支持面向可持续性的融资决策，但其本身并不确立可持续性结果。",null,"Frontiers in Sustainable Food Systems","2026-10-02T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"方法新颖、数据规模大且校准严谨，对农业数字信贷风控有直接参考价值，值得进入每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业人工智能","农业中小企业","数字农业金融","信用风险评估","农业数据融合",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"G3","rural-finance","农村金融与保险","\u002Ftopic\u002Frural-services\u002Frural-finance","G",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,57],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},3,true,[],[49],"agricultural credit",[],{"code":52,"score":45,"supported":46,"title":53,"abstract":54,"tags":56},"I1",[],[55],"数字农业",[],{"code":58,"score":21,"supported":14,"title":59,"abstract":60,"tags":61},"I2",[],[],[26],[63,64],"DAF-XCredit 农业信贷 风险评估","SBA 7 a 农业贷款 信用模型","DAF-XCredit农业信贷风险评估-4051","10.3389\u002Ffsufs.2026.1946390",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":75,"oa_url":6,"card":76,"direction":80,"ingested_from":82},"W7216288378",[70,73],{"name":71,"orcid":72},"Ying Wei","https:\u002F\u002Forcid.org\u002F0000-0001-8217-4765",{"name":74,"orcid":9},"Yanjiao Liao",0,{"tldr":77,"method":78,"finding":79,"direction":80,"opportunity":81},"提出成熟度感知的选择性融合框架DAF-XCredit，用于农业食品中小企业信贷风险评估。","融合贷款记录、历史先验与滞后农业数据，用SBA 7(a)贷款和USDA数据做时序","所选模型AUC达0.9497，校准误差低，但相对仅贷款模型AUC提升不显著。","农业人工智能与决策模型","可探索成熟度感知融合在动态信贷治理与可持续农业融资中的可解释性与公平性。","openalex","2026-10-03T23:30:08.106441Z",{"total":85,"page":21,"page_size":85,"items":86},6,[87,136,197,270,342,384],{"id":88,"title":89,"url":90,"summary":91,"summary_zh":9,"content":9,"source_name":92,"source_url":9,"published_at":93,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":94,"score_detail":95,"sources":98,"tags":100,"primary_topic":106,"secondary_topics":107,"classification":108,"search_phrases":121,"slug":124,"view_count":75,"doi":125,"paper":126,"created_at":135},5463,"中国粮食生产现代化时空演变特征及影响因素分析 (Spatiotemporal Evolution and Influencing Factors of Grain Production Modernization in China)","http:\u002F\u002Fzgnjhxb.niam.com.cn\u002FCN\u002F10.13733\u002Fj.jcam.issn.2095-5553.2026.10.042","基于2011—2023年省级面板数据，六维构建粮食生产现代化指标，用核密度、空间自相关与GTWR分析。水平稳步提升，呈主产区＞主销区＞产销平衡区梯度；山东、江苏、安徽形成稳定高—高集聚区。数字普惠金融与财政支农正向。","中国农机化学报 2026-10-20","2026-10-20T00:00:00Z",79,{"impact":17,"substance":96,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":97},21,"基于省级面板数据构建六维指标并采用GTWR方法，结论对粮食生产现代化区域差异与数字普惠金融作用有参考价值，但属学术论文，公共影响有限。",[99],{"name":92,"url":90},[101,102,103,104,105],"粮食安全","财政支农","数字普惠金融","粮食生产现代化","空间集聚",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":109},{"candidates":110,"agricultural_evidence":46},[111,116],{"code":32,"score":112,"supported":46,"title":113,"abstract":114,"tags":115},5,[],[102],[103,102],{"code":117,"score":21,"supported":14,"title":118,"abstract":119,"tags":120},"A8",[],[],[101],[122,123],"粮食生产现代化 时空演变","数字普惠金融 粮食生产","粮食生产现代化时空演变-5463","10.13733\u002Fj.jcam.issn.2095-5553.2026.10.042",{"doi":125,"openalex_id":9,"authors":127,"venue":9,"cited_by_count":75,"oa_url":9,"card":128,"direction":132,"ingested_from":134},[],{"tldr":129,"method":130,"finding":131,"direction":132,"opportunity":133},"构建六维指标测度2011—2023年中国粮食生产现代化水平并分析其时空演变与影响因素。","省级面板数据，六维指标体系，核密度、空间自相关与GTWR模型。","现代化水平稳步提升，呈主产区>主销区>产销平衡区梯度，山东、江苏、安徽为稳定高—高集聚区。","数字乡村与农业信息化","可探究数字普惠金融与财政支农的协同机制，并细化到县域尺度检验空间溢出效应。","agent","2026-10-21T00:10:08.522187Z",{"id":137,"title":138,"url":139,"summary":140,"summary_zh":9,"content":9,"source_name":141,"source_url":9,"published_at":142,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":143,"sources":147,"tags":149,"primary_topic":155,"secondary_topics":156,"classification":163,"search_phrases":182,"slug":185,"view_count":186,"doi":187,"paper":188,"created_at":196},4730,"政策型农业信贷担保能否激励农户采纳秸秆还田技术","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1948304\u002Ffull","基于微观数据与变量法,实证评估政策型农业信贷担保对农户采纳秸秆还田技术的影响。研究发现该政策显著促进采纳,主要通过推动社会化服务购买与绿色信贷期限匹配生产周期实现,对数字素养较高、正式社会资本匮乏的农户效应更强","Frontiers in Sustainable Food Systems 2026-10-09","2026-10-09T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":144,"freshness":145,"relevant":21,"comment":146},14,8,"基于微观数据的实证研究，揭示政策型信贷担保通过社会化服务与绿色信贷期限匹配促进秸秆还田，结论具政策参考价值。",[148],{"name":141,"url":139},[150,151,152,153,154],"社会化服务","秸秆还田","农业信贷担保","绿色金融","农户技术采纳",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[157],{"code":158,"slug":159,"name":160,"path":161,"parent_code":162},"H5","waste","面源污染与废弃物利用","\u002Ftopic\u002Fenvironment\u002Fwaste","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":164},{"candidates":165,"agricultural_evidence":46},[166,171,176],{"code":32,"score":19,"supported":46,"title":167,"abstract":169,"tags":170},[168],"农业信贷",[168],[152,153],{"code":158,"score":172,"supported":46,"title":173,"abstract":174,"tags":175},12,[151],[151],[151],{"code":177,"score":178,"supported":46,"title":179,"abstract":180,"tags":181},"G2",4,[],[150],[150],[183,184],"农业信贷担保 秸秆还田","绿色信贷 农户 技术采纳","农业信贷担保秸秆还田-4730",2,"10.3389\u002Ffsufs.2026.1948304\u002Ffull",{"doi":187,"openalex_id":9,"authors":189,"venue":9,"cited_by_count":75,"oa_url":9,"card":190,"direction":194,"ingested_from":134},[],{"tldr":191,"method":192,"finding":193,"direction":194,"opportunity":195},"实证评估政策型农业信贷担保对农户采纳秸秆还田技术的影响。","基于微观数据与变量法进行实证分析。","政策显著促进采纳，通过社会化服务购买与绿色信贷期限匹配实现。","农业绿色发展与碳","可探究数字素养与信贷担保协同机制，及对异质性农户的精准政策设计。","2026-10-12T00:11:14.617567Z",{"id":198,"title":199,"url":200,"summary":201,"summary_zh":202,"content":9,"source_name":10,"source_url":200,"published_at":203,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":204,"score_detail":205,"sources":207,"tags":209,"primary_topic":215,"secondary_topics":216,"classification":217,"search_phrases":246,"slug":249,"view_count":186,"doi":250,"paper":251,"created_at":269},4481,"Climate policy uncertainty and agricultural digital-intelligent transformation in China: evidence from provincial panel data","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1909691","Introduction Climate policy uncertainty can affect long-term investment decisions in agriculture, yet its influence on agricultural digital-intelligent transformation remains insufficiently understood. This study examines how lagged climate policy uncertainty affects agricultural digital-intelligent transformation and whether rural finance and agricultural service policies can moderate this relationship. Methods Using panel data from 31 Chinese provinces from 2013 to 2022, we construct an agricultural digital-intelligent transformation index. A two-way fixed effects model, instrumental variable estimation, heterogeneity analysis, and moderation tests are adopted for empirical analysis. Results The results show that climate policy uncertainty significantly inhibits agricultural digital-intelligent transformation, and this negative effect remains evident after addressing potential endogeneity. Heterogeneity analysis indicates that the inhibitory effect is significant only in regions with moderately developed digital finance and in major grain-consuming areas. Further analysis shows that loans to agriculture, forestry, animal husbandry and fishery industries and the pilot policy for full-process socialized agricultural services positively moderate this relationship and weaken the adverse effect of climate policy uncertainty. Discussion These findings contribute to the literature on climate policy uncertainty and sustainable agricultural transformation by linking policy expectations, agriculture-related loans, and digital-smart agriculture development. The results suggest that stabilizing climate policy expectations, improving rural credit support, and strengthening agricultural service systems are important for promoting climate-resilient and regionally differentiated digital transformation in agriculture.","引言 气候政策不确定性会影响农业的长期投资决策，但其对农业数字化智能化转型的影响尚缺乏充分认识。本研究考察滞后气候政策不确定性如何影响农业数字化智能化转型，以及农村金融和农业服务政策能否调节这一关系。方法 基于2013—2022年中国31个省份的面板数据，构建农业数字化智能化转型指数，采用双向固定效应模型、工具变量估计、异质性分析和调节效应检验进行实证分析。结果 结果表明，气候政策不确定性显著抑制农业数字化智能化转型，且在处理潜在内生性后这一负向效应依然明显。异质性分析表明，该抑制效应仅在数字金融中等发达地区及粮食主销区显著。进一步分析显示，农、林、牧、渔业贷款和农业生产全程社会化服务试点政策对这一关系具有正向调节作用，削弱了气候政策不确定性的不利影响。讨论 这些发现通过将政策预期、涉农贷款与数字智慧农业发展联系起来，为气候政策不确定性与农业可持续转型的文献作出了贡献。结果表明，稳定气候政策预期、完善农村信贷支持、健全农业服务体系，对于促进农业气候韧性型和区域差异化的数字化转型具有重要意义。","2026-10-08T00:00:00Z",81,{"impact":17,"substance":18,"depth":17,"authority":144,"freshness":20,"relevant":21,"comment":206},"基于31省十年面板数据的实证研究，揭示气候政策不确定性对农业数字化智能化转型的抑制效应及农村金融、农业社会化服务的调节作用，方法规范、结论有政策参考价值，值得进入每日精选。",[208],{"name":10,"url":200},[210,211,212,213,214],"数字乡村","智慧农业","农村金融","农业社会化服务","气候政策",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":218},{"candidates":219,"agricultural_evidence":46},[220,226,231,235,241],{"code":32,"score":221,"supported":46,"title":222,"abstract":223,"tags":225},7,[],[212,224],"rural finance",[212],{"code":52,"score":221,"supported":46,"title":227,"abstract":228,"tags":230},[],[211,229],"smart agriculture",[211],{"code":177,"score":178,"supported":46,"title":232,"abstract":233,"tags":234},[],[150],[213],{"code":236,"score":45,"supported":46,"title":237,"abstract":238,"tags":240},"D",[],[239],"渔业",[],{"code":242,"score":21,"supported":14,"title":243,"abstract":244,"tags":245},"I6",[],[],[210],[247,248],"气候政策不确定性 农业数字化 省级面板","农业数字化智能化转型 农村金融","气候政策不确定性农业数字化省级面板-4481","10.3389\u002Ffsufs.2026.1909691",{"doi":250,"openalex_id":252,"authors":253,"venue":10,"cited_by_count":75,"oa_url":200,"card":263,"direction":268,"ingested_from":82},"W7221013071",[254,257,259,261],{"name":255,"orcid":256},"Wenwen Wang","https:\u002F\u002Forcid.org\u002F0000-0003-1547-5714",{"name":258,"orcid":9},"Yuanyi Yang",{"name":260,"orcid":9},"Yanru Yu",{"name":262,"orcid":9},"Yanwei Zhang",{"tldr":264,"method":265,"finding":266,"direction":132,"opportunity":267},"研究气候政策不确定性如何抑制中国农业数字化智能化转型，并检验农村金融与服务政策的调节作用。","2013-2022年31省面板数据，构建农业数字化智能化转型指数，用双向固定效应","气候政策不确定性显著抑制农业数字化智能化转型，该效应仅在数字金融中等发达地区和粮食主销区显著。","可深入探究气候政策预期稳定机制与数字金融、农业社会化服务的协同路径，及区域差异化转型策略。","智慧农业 \u002F 农业物联网","2026-10-09T23:30:06.185798Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":275,"content":9,"source_name":10,"source_url":273,"published_at":276,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":277,"score_detail":278,"sources":283,"tags":285,"primary_topic":290,"secondary_topics":291,"classification":297,"search_phrases":317,"slug":320,"view_count":45,"doi":321,"paper":322,"created_at":341},4289,"When climate risk intensifies, does insurance pay off? A community case study of China's agricultural insurance reform","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1954205","In the context of mounting global climate threats, fostering resilience in food production systems has become a critical imperative for upholding national food security. This paper exploits the staggered implementation, beginning in 2018, of China's agricultural insurance reforms covering full production costs and planting income as a quasi-natural experiment. Drawing on provincial panel data from 2013 to 2024, we first develop a multidimensional composite index to capture food production resilience. A staggered difference-in-differences specification is then adopted to systematically assess the causal effect of these reforms on such resilience and to explore the underlying transmission channels. Moreover, from a novel climate change perspective, the analysis examines the heterogeneous treatment effects across varying degrees of climate risk exposure. The empirical findings reveal that the policy-led agricultural insurance reform significantly and robustly enhances China's food production resilience. Mechanism tests indicate that advances in agricultural technology and the expansion of large-scale farming operations serve as important pathways through which the insurance policy takes effect. Further investigation shows that climate change risk positively moderates the policy impact: as climate risk intensifies, the marginal contribution of insurance to resilience becomes stronger. Heterogeneity analysis also demonstrates that the policy effect is more pronounced in western China and in localities with higher rates of disaster-induced losses. Taken together, these results offer solid empirical backing for broadening the pilot programs of full cost and planting income insurance. They also establish a theoretical basis for positioning publicly supported agricultural insurance as an effective instrument for climate change adaptation, providing valuable guidance on strengthening the climate adaptability and resilience of food production to ensure national food security.","在全球气候威胁日益加剧的背景下，增强粮食生产系统的韧性已成为维护国家粮食安全的关键任务。本文以2018年起分阶段实施的中国农业保险覆盖完全生产成本和种植收入的改革为准自然实验，基于2013—2024年省级面板数据，首先构建多维复合指数以刻画粮食生产韧性，继而采用交错双重差分模型系统评估该改革对粮食生产韧性的因果效应并探究其传导机制。此外，本文从气候变化这一新视角出发，考察了不同气候风险暴露程度下的异质性处理效应。实证结果表明，政策主导的农业保险改革显著且稳健地提升了中国粮食生产韧性。机制检验表明，农业技术进步与规模化经营扩张是保险政策发挥作用的重要路径。进一步研究发现，气候变化风险对政策效应具有正向调节作用：随着气候风险加剧，保险对韧性的边际贡献增强。异质性分析还表明，政策效应在中国西部地区以及灾害损失率较高的地区更为显著。综上，上述结果为扩大完全成本保险和种植收入保险试点提供了坚实的经验依据，也为将公共支持的农业保险定位为气候变化适应的有效工具奠定了理论基础，为增强粮食生产的气候适应性与韧性、保障国家粮食安全提供了有价值的政策启示。","2026-10-05T00:00:00Z",88,{"impact":279,"substance":280,"depth":281,"authority":144,"freshness":145,"relevant":21,"comment":282},24,23,19,"基于2013-2024年省级面板数据的交错DID准自然实验，证实农业保险改革显著提升粮食生产韧性且气候风险具有正向调节作用，方法规范、结论可靠，对完善农业保险试点具有政策参考价值。",[284],{"name":10,"url":273},[101,286,287,288,289],"农业保险","农业政策","气候适应","生产韧性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[292],{"code":293,"slug":294,"name":295,"path":296,"parent_code":162},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":39,"locked":14,"version":40,"method":41,"evidence":298},{"candidates":299,"agricultural_evidence":46},[300,306,312],{"code":32,"score":301,"supported":46,"title":302,"abstract":304,"tags":305},15,[303],"agricultural 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Guo",{"tldr":337,"method":338,"finding":339,"direction":194,"opportunity":340},"基于2013-2024省级面板数据，评估中国农业保险改革对粮食生产韧性的因果影响。","多维度韧性指数+交错双重差分，利用2018年起保险改革试点作准自然实验。","保险改革显著提升粮食生产韧性，气候风险越高其边际贡献越强，西部和灾损高地区效果更明显。","可探究保险改革与农业技术、规模化经营的协同机制，及气候风险下保险产品设计与精准赔付的优化路径。","2026-10-06T23:30:05.886883Z",{"id":343,"title":344,"url":345,"summary":346,"summary_zh":9,"content":347,"source_name":348,"source_url":9,"published_at":349,"category":350,"cover_url":9,"hotness":13,"is_selected":46,"score":351,"score_detail":352,"sources":355,"tags":357,"primary_topic":361,"secondary_topics":362,"classification":363,"search_phrases":379,"slug":382,"view_count":21,"doi":9,"paper":9,"created_at":383},4249,"四部门联合印发加快农业保险高质量发展实施方案","https:\u002F\u002Fwww.fengxin.gov.cn\u002Ffxxrmzf\u002Fzfcgxgxx\u002Fpc\u002Fcontent\u002F2104516031187619840\u002Fcontent_2104516031187619840.html","财政部会同农业农村部、金融监管总局、国家林草局联合印发财金〔2026〕88号《关于加快农业保险高质量发展的实施方案》，是我国农业保险近二十年发展经验的提炼。我国农业保险保费规模从2007年52亿元增长到2025年超1550亿元，每年可为上亿户次农户提供风险保障超5万亿元。","为推动农业保险政策更加普惠、区域发展更加均衡、保障质效持续提升，本次《实施方案》明确了下一阶段发展目标：到2030年，基本建成与农户风险保障需求相契合、与现代农业发展相适应、中央与地方分工负责的多层次农业保险体系，总体达到国际先进水平，实现补贴有效率、产业有保障、农民得实惠、机构可持续的多赢格局。\n\n2019年出台的指导性文件提出了到2022年农业保险深度1%、密度500元\u002F人、三大粮食作物保险覆盖率70%以上等阶段性目标。在此基础上，《实施方案》进一步明确如下发展目标：一是“农业保险深度”，即农业保险保费与第一产业增加值的比值，反映农业保险对农村经济发展的贡献，2030年前达到1.9%。二是“农业保险密度”，即农业保险保费与第一产业就业人员数量的比值，反映农业保险人均惠及保障程度，2030年前达到1200元\u002F人。三是“农业保险科技投入强度”，即农业保险机构科技创新投入与农业保险保费的比值，反映农业保险行业科技创新应用水平，2030年前达到1%。其中，农业保险科技创新投入主要包括农业保险科技工作人员支出、科技服务费用、科技类无形资产和固定资产等项目。四是“三大粮食作物保险覆盖率”，对于关系国计民生和粮食安全的三大粮食作物，全国范围内农业保险覆盖率稳定在90%左右，各省份结合实际制定相关目标。同时，提升地方优势特色农业保险对特色农业产业的保障水平，更好发挥富农强农作用。五是“农业保险综合费用率”，强调不得高于20%，引导农业保险承保机构切实降本增效，将更多资源用于风险保障和理赔服务上。","奉新县人民政府 转载财政部金融司 2026-10-01","2026-10-01T01:00:00Z","政策",92,{"impact":353,"substance":279,"depth":281,"authority":301,"freshness":85,"relevant":21,"comment":354},28,"四部门联合印发的全国性农业保险新政，明确2030年保险深度1.9%、密度1200元\u002F人等量化目标，政策条款与数据增量突出，权威性和专业深度俱佳，值得进入每日精选。",[356],{"name":348,"url":345},[101,358,286,359,360],"惠农政策","农业科技投入","风险保障",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":364},{"candidates":365,"agricultural_evidence":46},[366,370,374],{"code":32,"score":172,"supported":46,"title":367,"abstract":368,"tags":369},[286],[286],[286],{"code":117,"score":21,"supported":14,"title":371,"abstract":372,"tags":373},[],[],[101],{"code":375,"score":21,"supported":14,"title":376,"abstract":377,"tags":378},"J2",[],[],[359],[380,381],"三大粮食作物保险覆盖率 90%","农业科技投入 农业保险 惠农政策 粮食安全","三大粮食作物保险覆盖率90%-4249","2026-10-06T00:04:59.342384Z",{"id":385,"title":386,"url":387,"summary":388,"summary_zh":9,"content":9,"source_name":389,"source_url":9,"published_at":390,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":391,"score_detail":392,"sources":394,"tags":396,"primary_topic":400,"secondary_topics":405,"classification":407,"search_phrases":428,"slug":431,"view_count":21,"doi":9,"paper":432,"created_at":439},3444,"《数字经济和财政支农对农业碳排放双控的影响》——基于2013—2021年30省级面板数据","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F174665.html","基于2013—2021年中国30个省级面板数据（西藏和港、澳、台除外），采用熵值法对农业碳排放双控水平进行评估，随后通过面板Tobit模型探究数字经济对农业碳排放双控的影响，最后分析财政支农在该影响中的调节效应。结果：中国农业碳排放双控水平呈波浪式上升趋势，粮食主销区水平相对较高、主产区相对较低；数字经济对农业碳排放双控具有显著正向影响，财政支农发挥正向调节作用；在粮食主销区促进作用更明显。","《河南农业大学学报》2026年第3期","2026-09-20T00:00:00Z",78,{"impact":17,"substance":18,"depth":17,"authority":144,"freshness":85,"relevant":21,"comment":393},"基于30省面板数据的实证研究，方法规范、结论明确，对数字乡村与农业绿色低碳政策有参考价值，但属学术论文、时效性一般，适合主题聚合而非每日精选头条。",[395],{"name":389,"url":387},[210,102,397,398,399],"粮食主产区","农业碳排放","省级面板数据",{"code":401,"slug":402,"name":403,"path":404,"parent_code":162},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon",[406],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":408},{"candidates":409,"agricultural_evidence":46},[410,416,420,424],{"code":401,"score":280,"supported":46,"title":411,"abstract":414,"tags":415},[412,413],"碳排放","农业碳",[412,413],[398],{"code":32,"score":172,"supported":46,"title":417,"abstract":418,"tags":419},[102],[102],[102],{"code":117,"score":21,"supported":14,"title":421,"abstract":422,"tags":423},[],[],[397],{"code":242,"score":21,"supported":14,"title":425,"abstract":426,"tags":427},[],[],[210],[429,430],"数字经济 农业碳排放 财政支农","河南农业大学学报 农业碳排放双控","数字经济农业碳排放财政支农-3444",{"doi":9,"openalex_id":9,"authors":433,"venue":9,"cited_by_count":75,"oa_url":9,"card":434,"direction":194,"ingested_from":134},[],{"tldr":435,"method":436,"finding":437,"direction":194,"opportunity":438},"基于30省面板数据，研究数字经济与财政支农对农业碳排放双控的影响及调节效应。","熵值法评估碳排放双控水平，面板Tobit模型分析，2013—2021年省级数据。","数字经济显著促进农业碳排放双控，财政支农正向调节，粮食主销区效应更明显。","可探究数字经济影响农业碳排放的空间溢出效应及财政支农的门槛非线性特征。","2026-09-25T00:09:34.446164Z"]