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(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 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in Sustainable Food Systems 2026-10-09","2026-10-09T00:00:00Z",80,{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":173,"relevant":455,"comment":635},"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。",[637],{"name":631,"url":629},[639,640,641,642,643],"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":503,"slug":504,"name":505,"path":506,"parent_code":480},[646],{"code":168,"slug":169,"name":170,"path":171,"parent_code":128},{"status":598,"locked":580,"version":4,"method":599,"evidence":648},{"candidates":649,"agricultural_evidence":603},[650,655,659],{"code":503,"score":584,"supported":603,"title":651,"abstract":653,"tags":654},[652],"农业信贷",[652],[641,642],{"code":168,"score":416,"supported":603,"title":656,"abstract":657,"tags":658},[640],[640],[640],{"code":496,"score":400,"supported":603,"title":660,"abstract":661,"tags":662},[],[639],[639],[664,665],"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","10.3389\u002Ffsufs.2026.1948304\u002Ffull",{"doi":667,"openalex_id":11,"authors":669,"venue":11,"cited_by_count":424,"oa_url":11,"card":670,"direction":674,"ingested_from":624},[],{"tldr":671,"method":672,"finding":673,"direction":674,"opportunity":675},"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":678,"title":679,"url":680,"summary":681,"summary_zh":682,"content":11,"source_name":683,"source_url":680,"published_at":684,"category":579,"cover_url":11,"hotness":385,"is_selected":580,"score":685,"score_detail":686,"sources":688,"tags":690,"primary_topic":696,"secondary_topics":697,"classification":698,"search_phrases":726,"slug":729,"view_count":69,"doi":730,"paper":731,"created_at":750},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。","Frontiers in Sustainable Food Systems","2026-10-08T00:00:00Z",81,{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":585,"relevant":455,"comment":687},"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。",[689],{"name":683,"url":680},[691,692,693,694,695],"数字乡村","智慧农业","农村金融","农业社会化服务","气候政策",{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},[],{"status":598,"locked":580,"version":4,"method":599,"evidence":699},{"candidates":700,"agricultural_evidence":603},[701,707,712,716,722],{"code":503,"score":702,"supported":603,"title":703,"abstract":704,"tags":706},7,[],[693,705],"rural finance",[693],{"code":16,"score":702,"supported":603,"title":708,"abstract":709,"tags":711},[],[692,710],"smart agriculture",[692],{"code":496,"score":400,"supported":603,"title":713,"abstract":714,"tags":715},[],[639],[694],{"code":388,"score":717,"supported":603,"title":718,"abstract":719,"tags":721},3,[],[720],"渔业",[],{"code":56,"score":455,"supported":580,"title":723,"abstract":724,"tags":725},[],[],[691],[727,728],"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":730,"openalex_id":732,"authors":733,"venue":683,"cited_by_count":424,"oa_url":680,"card":743,"direction":748,"ingested_from":749},"W7221013071",[734,737,739,741],{"name":735,"orcid":736},"Wenwen Wang","https:\u002F\u002Forcid.org\u002F0000-0003-1547-5714",{"name":738,"orcid":11},"Yuanyi Yang",{"name":740,"orcid":11},"Yanru Yu",{"name":742,"orcid":11},"Yanwei Zhang",{"tldr":744,"method":745,"finding":746,"direction":622,"opportunity":747},"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","openalex","2026-10-09T23:30:06.185798Z",{"id":752,"title":753,"url":754,"summary":755,"summary_zh":756,"content":11,"source_name":683,"source_url":754,"published_at":757,"category":579,"cover_url":11,"hotness":385,"is_selected":580,"score":101,"score_detail":758,"sources":763,"tags":765,"primary_topic":770,"secondary_topics":771,"classification":773,"search_phrases":793,"slug":796,"view_count":717,"doi":797,"paper":798,"created_at":817},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",{"impact":759,"substance":760,"depth":761,"authority":345,"freshness":173,"relevant":455,"comment":762},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。",[764],{"name":683,"url":754},[590,766,767,768,769],"农业保险","农业政策","气候适应","生产韧性",{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},[772],{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"status":598,"locked":580,"version":4,"method":599,"evidence":774},{"candidates":775,"agricultural_evidence":603},[776,782,788],{"code":503,"score":777,"supported":603,"title":778,"abstract":780,"tags":781},15,[779],"agricultural insurance",[766,779],[766],{"code":184,"score":385,"supported":603,"title":783,"abstract":784,"tags":787},[],[785,768,786],"气候变化","climate change",[768],{"code":263,"score":702,"supported":603,"title":789,"abstract":790,"tags":792},[],[590,791],"food security",[590],[794,795],"农业保险改革 粮食生产韧性","完全成本保险 种植收入保险","农业保险改革粮食生产韧性-4289","10.3389\u002Ffsufs.2026.1954205",{"doi":797,"openalex_id":799,"authors":800,"venue":683,"cited_by_count":424,"oa_url":754,"card":812,"direction":674,"ingested_from":749},"W7220367453",[801,804,806,808,810],{"name":802,"orcid":803},"Changzhe Deng","https:\u002F\u002Forcid.org\u002F0009-0006-0852-5997",{"name":805,"orcid":11},"Maimiao Sun",{"name":807,"orcid":11},"Xueyu Lin",{"name":809,"orcid":11},"Chencan Rao",{"name":811,"orcid":11},"Qiuping Guo",{"tldr":813,"method":814,"finding":815,"direction":674,"opportunity":816},"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":819,"title":820,"url":821,"summary":822,"summary_zh":11,"content":823,"source_name":824,"source_url":11,"published_at":825,"category":826,"cover_url":11,"hotness":385,"is_selected":603,"score":245,"score_detail":827,"sources":830,"tags":832,"primary_topic":836,"secondary_topics":837,"classification":838,"search_phrases":853,"slug":856,"view_count":455,"doi":11,"paper":11,"created_at":857},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",{"impact":516,"substance":759,"depth":761,"authority":777,"freshness":828,"relevant":455,"comment":829},6,"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。",[831],{"name":824,"url":821},[590,833,766,834,835],"惠农政策","农业科技投入","风险保障",{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},[],{"status":598,"locked":580,"version":4,"method":599,"evidence":839},{"candidates":840,"agricultural_evidence":603},[841,845,849],{"code":503,"score":416,"supported":603,"title":842,"abstract":843,"tags":844},[766],[766],[766],{"code":263,"score":455,"supported":580,"title":846,"abstract":847,"tags":848},[],[],[590],{"code":549,"score":455,"supported":580,"title":850,"abstract":851,"tags":852},[],[],[834],[854,855],"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":859,"title":860,"url":861,"summary":862,"summary_zh":863,"content":11,"source_name":683,"source_url":861,"published_at":864,"category":579,"cover_url":11,"hotness":385,"is_selected":580,"score":633,"score_detail":865,"sources":867,"tags":869,"primary_topic":875,"secondary_topics":876,"classification":877,"search_phrases":894,"slug":897,"view_count":455,"doi":898,"paper":899,"created_at":913},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。","2026-10-02T00:00:00Z",{"impact":361,"substance":321,"depth":361,"authority":584,"freshness":585,"relevant":455,"comment":866},"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。",[868],{"name":683,"url":861},[870,871,872,873,874],"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":503,"slug":504,"name":505,"path":506,"parent_code":480},[],{"status":598,"locked":580,"version":4,"method":599,"evidence":878},{"candidates":879,"agricultural_evidence":603},[880,885,890],{"code":503,"score":717,"supported":603,"title":881,"abstract":882,"tags":884},[],[883],"agricultural credit",[],{"code":16,"score":717,"supported":603,"title":886,"abstract":887,"tags":889},[],[888],"数字农业",[],{"code":24,"score":455,"supported":580,"title":891,"abstract":892,"tags":893},[],[],[870],[895,896],"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":898,"openalex_id":900,"authors":901,"venue":683,"cited_by_count":424,"oa_url":861,"card":907,"direction":911,"ingested_from":749},"W7216288378",[902,905],{"name":903,"orcid":904},"Ying Wei","https:\u002F\u002Forcid.org\u002F0000-0001-8217-4765",{"name":906,"orcid":11},"Yanjiao Liao",{"tldr":908,"method":909,"finding":910,"direction":911,"opportunity":912},"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。","2026-10-03T23:30:08.106441Z",{"id":915,"title":916,"url":917,"summary":918,"summary_zh":11,"content":11,"source_name":919,"source_url":11,"published_at":920,"category":579,"cover_url":11,"hotness":385,"is_selected":580,"score":921,"score_detail":922,"sources":924,"tags":926,"primary_topic":930,"secondary_topics":931,"classification":933,"search_phrases":954,"slug":957,"view_count":455,"doi":11,"paper":958,"created_at":965},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":361,"substance":321,"depth":361,"authority":345,"freshness":828,"relevant":455,"comment":923},"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。",[925],{"name":919,"url":917},[691,591,927,928,929],"粮食主产区","农业碳排放","省级面板数据",{"code":160,"slug":161,"name":162,"path":163,"parent_code":128},[932],{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},{"status":598,"locked":580,"version":4,"method":599,"evidence":934},{"candidates":935,"agricultural_evidence":603},[936,942,946,950],{"code":160,"score":760,"supported":603,"title":937,"abstract":940,"tags":941},[938,939],"碳排放","农业碳",[938,939],[928],{"code":503,"score":416,"supported":603,"title":943,"abstract":944,"tags":945},[591],[591],[591],{"code":263,"score":455,"supported":580,"title":947,"abstract":948,"tags":949},[],[],[927],{"code":56,"score":455,"supported":580,"title":951,"abstract":952,"tags":953},[],[],[691],[955,956],"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":11,"openalex_id":11,"authors":959,"venue":11,"cited_by_count":424,"oa_url":11,"card":960,"direction":674,"ingested_from":624},[],{"tldr":961,"method":962,"finding":963,"direction":674,"opportunity":964},"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",{"id":967,"title":968,"url":969,"summary":970,"summary_zh":11,"content":971,"source_name":972,"source_url":11,"published_at":973,"category":826,"cover_url":11,"hotness":385,"is_selected":603,"score":974,"score_detail":975,"sources":978,"tags":980,"primary_topic":984,"secondary_topics":985,"classification":987,"search_phrases":1003,"slug":1006,"view_count":400,"doi":11,"paper":11,"created_at":1007},3381,"财政部：2026年农林水支出2.43万亿元，安排2630亿元耕地地力保护补贴、1770亿元常态化帮扶资金","https:\u002F\u002Fczt.xinjiang.gov.cn\u002Fxjczt\u002Fc115020\u002F202609\u002F49f00038d563442fb4cb7d71b4848d93.shtml","国新办'开局起步十五五'系列主题发布会上，财政部表示2026年安排全国一般公共预算农林水支出2.43万亿元、同比增长3.3%。中央财政安排2630亿元稳定实施耕地地力保护补贴、农业保险保费补贴等相关农业补贴政策；常态化帮扶方面已安排下达1770亿元，截至6月底脱贫劳动力就业规模超过3200万人，脱贫县农村居民人均可支配收入增速继续跑赢全国农村平均水平。","## [首页](https:\u002F\u002Fczt.xinjiang.gov.cn\u002Fxjczt\u002Findex.shtml)>[政务公开](https:\u002F\u002Fczt.xinjiang.gov.cn\u002Fxjczt\u002Fc114976\u002Fzwgk.shtml)>[法定主动公开内容](https:\u002F\u002Fczt.xinjiang.gov.cn\u002Fxjczt\u002Fc114979\u002Fredirect_firstChannel.shtml)>[财政改革与业务](https:\u002F\u002Fczt.xinjiang.gov.cn\u002Fxjczt\u002Fc115014\u002Fzfxxgk_lists.shtml)>[农业农村](https:\u002F\u002Fczt.xinjiang.gov.cn\u002Fxjczt\u002Fc115020\u002Fzfxxgk_list.shtml)\n\n*   索 引 号： \nczt000000\u002F2026-01921\n\n*   发文字号： \n〔〕 号\n\n*   发文机关： \n*   成文日期： \n2026-09-20 18:13:23\n\n*   有 效 性： \n*   主题分类： \n\n## 财政部：将继续发力支持推进乡村全面振兴和农业农村现代化\n\n发布日期：**2026-09-20 18:13**来源：**央视新闻**浏览次数：\n\n【字体：_大_ _中_ _小_】 打印 分享到：\n\n目前，中央财政将衔接推进乡村振兴补助资金调整为常态化帮扶资金，在今天国新办举行的“开局起步‘十五五’”系列主题新闻发布会上，财政部有关负责人表示，将继续发力支持推进乡村全面振兴和农业农村现代化。\n\n数据显示，2026年安排全国一般公共预算农林水支出2.43万亿元，同比增长3.3%。其中，在保障国家粮食安全方面，今年，中央财政安排2630亿元，稳定实施耕地地力保护补贴、农业保险保费补贴等相关农业补贴政策，给广大种粮农民吃下“定心丸”。同时，加大力度防止规模性返贫致贫。\n\n财政部综合司司长 李先忠：防止规模性返贫致贫是一项长期任务，今年是常态化帮扶的第一年，中央财政继续保持投入强度，已安排下达 1770亿元。截至6月底，脱贫劳动力就业规模超过3200万人，脱贫县农村居民人均可支配收入的增速继续跑赢全国农村平均水平。\n\n此外，中央财政还将继续支持建设优势特色产业集群、现代农业产业园、农业产业强镇等农业产业融合发展项目，围绕农业农村基础设施建设、城乡基本公共服务均等化等出台一系列措施，继续实施农村公益事业建设财政奖补政策，持续激发农业农村发展活力和内生动力。\n\n责任编辑：\n\n扫一扫在手机打开当前页\n\n*   上一篇：\n*   下一篇：","国新办发布会\u002F新疆维吾尔自治区财政厅 2026-09-23","2026-09-23T00:00:00Z",91,{"impact":516,"substance":760,"depth":976,"authority":777,"freshness":173,"relevant":455,"comment":977},17,"国新办发布会披露2026年中央财政支农资金规模与常态化帮扶首年安排，数据具体、层级高，对三农政策与农业补贴领域具较强参考价值。",[979],{"name":972,"url":969},[981,766,982,591,983],"乡村振兴","常态化帮扶","耕地地力保护补贴",{"code":527,"slug":528,"name":529,"path":530,"parent_code":480},[986],{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},{"status":598,"locked":580,"version":4,"method":599,"evidence":988},{"candidates":989,"agricultural_evidence":603},[990,994,999],{"code":527,"score":416,"supported":603,"title":991,"abstract":992,"tags":993},[982],[982],[982],{"code":503,"score":173,"supported":603,"title":995,"abstract":996,"tags":998},[],[766,997],"农业补贴",[766,591],{"code":480,"score":455,"supported":580,"title":1000,"abstract":1001,"tags":1002},[],[],[981],[1004,1005],"财政部 农林水支出 2.43万亿","耕地地力保护补贴 2630亿","财政部农林水支出2.43万亿-3381","2026-09-25T00:09:27.146137Z",{"id":1009,"title":1010,"url":1011,"summary":1012,"summary_zh":1013,"content":11,"source_name":683,"source_url":1011,"published_at":1014,"category":579,"cover_url":11,"hotness":385,"is_selected":580,"score":1015,"score_detail":1016,"sources":1018,"tags":1020,"primary_topic":1024,"secondary_topics":1025,"classification":1026,"search_phrases":1057,"slug":1060,"view_count":400,"doi":1061,"paper":1062,"created_at":1083},3150,"Climate extremes and grain green productivity in China: buffering effects of agricultural insurance and high-standard farmland","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1918373","Extreme climate shocks are increasingly reshaping the sustainability, resilience, and environmental performance of grain production systems, particularly in countries facing the dual challenge of safeguarding food security and accelerating agricultural green transformation. This study investigates how extreme climate risk affects grain green total factor productivity (GGTFP) in China and whether adaptation-oriented policy instruments can mitigate these adverse effects. Using provincial panel data for 31 Chinese regions from 2001 to 2023, we measure GGTFP with a Super-SBM Global Malmquist–Luenberger index that incorporates grain output as the desirable output and carbon emissions and agricultural non-point source pollution as undesirable outputs. We further construct a composite extreme climate risk index based on four types of extreme events: extreme low temperature, extreme high temperature, extreme rainfall, and extreme drought. Econometric estimates show that China’s GGTFP increased by an average of 1.7% annually during the study period, mainly driven by green technological progress. However, extreme climate risk exhibited an overall upward trend and significantly inhibited GGTFP. Among different climate hazards, extreme rainfall and extreme drought had stronger negative effects than extreme temperature events, suggesting that water-related climate shocks are particularly detrimental to green grain production. The adverse impact of extreme climate risk was more pronounced in high-relief regions and non-major grain-producing areas, revealing substantial spatial heterogeneity in climate vulnerability. Further analysis indicates that agricultural insurance and high-standard farmland development both significantly attenuate the negative effect of extreme climate risk on GGTFP, with interaction coefficients of 0.040 and 0.037, respectively. These findings suggest that risk-sharing mechanisms and climate-resilient farmland infrastructure can help stabilize green productivity under increasing climate uncertainty. This study provides empirical evidence on the green productivity consequences of multi-hazard climate risks and highlights the importance of integrating agricultural insurance, farmland infrastructure improvement, food security, and environmental protection into climate-resilient agricultural development strategies.","极端气候冲击正日益重塑粮食生产系统的可持续性、韧性与环境绩效，对于同时面临保障粮食安全和推进农业绿色转型双重挑战的国家而言尤为如此。本研究考察极端气候风险如何影响中国粮食绿色全要素生产率（GGTFP），以及适应性政策工具能否缓解这些不利影响。基于2001—2023年中国31个地区的省级面板数据，我们采用Super-SBM Global Malmquist–Luenberger指数测算GGTFP，该指数将粮食产出作为期望产出，将碳排放和农业面源污染作为非期望产出。我们进一步基于四类极端事件——极端低温、极端高温、极端降雨和极端干旱——构建了综合极端气候风险指数。计量估计表明，研究期间中国GGTFP年均增长1.7%，主要由绿色技术进步驱动。然而，极端气候风险总体呈上升趋势，并显著抑制了GGTFP。在不同气候灾害中，极端降雨和极端干旱的负面影响强于极端温度事件，表明与水相关的气候冲击对绿色粮食生产尤为不利。极端气候风险的不利影响在地形起伏较大的地区和非粮食主产区更为显著，揭示了气候脆弱性存在显著的空间异质性。进一步分析表明，农业保险和高标准农田建设均显著减弱了极端气候风险对GGTFP的负面影响，交互项系数分别为0.040和0.037。这些发现表明，风险分担机制和气候韧性农田基础设施有助于在气候不确定性加剧的背景下稳定绿色生产率。本研究为多灾种气候风险的绿色生产率后果提供了经验证据，并凸显了将农业保险、农田基础设施改善、粮食安全和环境保护纳入气候韧性农业发展战略的重要性。","2026-09-21T00:00:00Z",86,{"impact":321,"substance":760,"depth":361,"authority":345,"freshness":585,"relevant":455,"comment":1017},"基于31省2001-2023年面板数据的实证研究，量化极端气候对粮食绿色全要素生产率的抑制效应并验证农业保险与高标准农田的缓冲作用，结论可靠且政策含义明确，值得进入每日精选。",[1019],{"name":683,"url":1011},[1021,590,766,1022,1023],"高标准农田","绿色生产力","极端气候",{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},[],{"status":598,"locked":580,"version":4,"method":599,"evidence":1027},{"candidates":1028,"agricultural_evidence":603},[1029,1033,1037,1042,1048,1052],{"code":503,"score":777,"supported":603,"title":1030,"abstract":1031,"tags":1032},[779],[766,779],[766],{"code":263,"score":702,"supported":603,"title":1034,"abstract":1035,"tags":1036},[],[590,791],[590],{"code":136,"score":702,"supported":603,"title":1038,"abstract":1039,"tags":1041},[],[1021,1040],"农田建设",[1021],{"code":184,"score":828,"supported":603,"title":1043,"abstract":1044,"tags":1047},[],[1045,1046],"干旱","drought",[],{"code":160,"score":717,"supported":603,"title":1049,"abstract":1050,"tags":1051},[],[938],[],{"code":168,"score":717,"supported":603,"title":1053,"abstract":1054,"tags":1056},[],[1055],"面源污染",[],[1058,1059],"极端气候 粮食绿色生产率","农业保险 高标准农田 气候适应","极端气候粮食绿色生产率-3150","10.3389\u002Ffsufs.2026.1918373",{"doi":1061,"openalex_id":1063,"authors":1064,"venue":683,"cited_by_count":424,"oa_url":1011,"card":1078,"direction":674,"ingested_from":749},"W7213938600",[1065,1068,1071,1073,1076],{"name":1066,"orcid":1067},"Bin Zheng","https:\u002F\u002Forcid.org\u002F0000-0002-9984-4136",{"name":1069,"orcid":1070},"Shuai Hao","https:\u002F\u002Forcid.org\u002F0000-0001-7856-0495",{"name":1072,"orcid":11},"Junye Zhao",{"name":1074,"orcid":1075},"Liping Liu","https:\u002F\u002Forcid.org\u002F0000-0001-5729-4984",{"name":1077,"orcid":11},"Qian Nie",{"tldr":1079,"method":1080,"finding":1081,"direction":674,"opportunity":1082},"研究极端气候风险对中国粮食绿色全要素生产率的影响及农业保险与高标准农田的缓冲作用。","2001-2023年31省面板数据，Super-SBM GML指数测度GGTFP","极端气候风险显著抑制GGTFP，降雨与干旱影响更强；农业保险和高标准农田均显著缓解该负面效应。","可探究农业保险与高标准农田的协同缓冲机制，及其在不同气候风险类型和区域间的异质性优化配置。","2026-09-22T23:30:08.637590Z",{"id":1085,"title":1086,"url":1087,"summary":1088,"summary_zh":11,"content":1089,"source_name":1090,"source_url":11,"published_at":1091,"category":826,"cover_url":11,"hotness":385,"is_selected":603,"score":245,"score_detail":1092,"sources":1094,"tags":1096,"primary_topic":1100,"secondary_topics":1101,"classification":1102,"search_phrases":1129,"slug":1132,"view_count":408,"doi":11,"paper":11,"created_at":1133},2694,"加快农业保险高质量发展实施方案印发——2030年保险深度1.9%、密度1200元\u002F人","https:\u002F\u002Fjcs.moa.gov.cn\u002Fgzdt\u002F202609\u002Ft20260915_6487691.htm","财政部、农业农村部、金融监管总局、国家林草局联合印发《关于加快农业保险高质量发展的实施方案》,到2030年农业保险深度达到1.9%、密度达到1200元\u002F人、科技投入强度达到1%,稻谷、小麦、玉米三大粮食作物覆盖率稳定在90%左右,综合费用率不高于20%。","各省、自治区、直辖市人民政府，新疆生产建设兵团，国务院有关部委、有关直属机构：\n\n经国务院同意，现将《关于加快农业保险高质量发展的实施方案》印发给你们，请抓好贯彻落实。\n\n财政部 农业农村部\n\n金融监管总局 国家林草局\n\n2026 年 9 月 8 日\n\n**关于加快农业保险高质量发展的实施方案**\n\n为进一步加快农业保险高质量发展，稳定农户收益，服务保障国家粮食安全和农业生产能力，经国务院同意，制定本实施方案。\n\n一、总体要求\n\n坚持以习近平新时代中国特色社会主义思想为指导，深入贯彻党的二十大和二十届历次全会精神，认真落实四中全会部署，全面贯彻习近平总书记关于“三农”工作的重要论述，完整准确全面贯彻新发展理念，加快构建新发展格局，着力推动高质量发展，遵循政府引导、市场运作、自主自愿、协同推进、改革创新的原则，充分发挥农业保险在价格、补贴、保险“三位一体”农业支持政策体系中的重要一环作用。\n\n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，政策更加普惠，发展更加均衡。农业保险深度（保费\u002F第一产业增加值）达到 1.9%，农业保险密度（保费\u002F第一产业就业人数）达到 1200 元\u002F人，农业保险科技投入强度（农业保险机构科技创新投入\u002F保费）达到 1%。稻谷、小麦、玉米三大粮食作物农业保险覆盖率稳定在 90%左右，提升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。农业保险综合费用率不高于 20%。\n\n二、聚焦农业保险发展重点\n\n**（一）健全多层次农业保险体系。**坚持中央与地方共同推动、各部门分工负责，构建复合、多元、多层次的农业保险体系。加强对关系国计民生和粮食安全的大宗农产品以及地方特色农业的保障，稳步推进物化成本保险、完全成本保险、收入保险等险种发展，加强对小农户和农民合作社、家庭农场等新型农业经营主体的保障，强化对防止返贫致贫对象的支持。建立健全投保人、直保端、再保端等多方参与的风险分散链条，发挥好大灾风险准备金作用。\n\n**（二）增强种植业农产品风险保障。**加强主粮、棉花、油料、糖料、橡胶等大宗农产品风险保障，提高区域间农业保险发展均衡性，完善省以下保费分担机制。逐步降低产粮大县农业保险县级保费补贴承担比例，推动扩大稻谷、小麦、玉米、大豆完全成本保险和种植收入保险投保面积。完善对马铃薯、油菜、花生、甜菜、青稞等农产品以及稻谷、小麦、玉米制种的保险支持保障。\n\n**（三）促进养殖业保险规范发展。**完善养殖企业和养殖场（户）养殖保险模式，采用电子耳标、生物识别等技术加强养殖数量监控。动态调整能繁母猪、育肥猪、奶牛等养殖业保险保障水平，鼓励根据不同养殖方式提供差异化风险保障方案。做好牦牛、藏系羊等涉藏特定品种保险支持保障。\n\n**（四）推动林草保险提质增效。**坚持预防与保障并重，与现代林草业发展相适应，推动构建林草综合保险体系。完善公益林保险和商品林保险政策，探索适合相关林业保险险种特点的承保理赔模式。保险机构要合理确定森林保险防灾减损费用计提比例，据实列支。鼓励保险机构开展草原、湿地、国家公园、碳汇等保险试点，丰富林草保险产品。\n\n**（五）支持地方特色发展。**鼓励各地结合实际制定特色农业保险发展政策，加强相关资金统筹，加大支持力度，扩大保险范围，积极发展蔬菜、水果、肉牛、肉羊、禽类、渔业、林下经济等特色农业保险。\n\n**（六）完善产品供给体系。**健全“政策险+商业险”、“基本险+补充险”的农业保险产品供给体系。鼓励各地结合实际在物化成本保险等提供基本保险保障的险种基础上，设计开发相关补充保险，形成更高保障。鼓励各地因地制宜发展农业气象指数保险。支持保险机构发展商业性农业保险，开发便利农户投保的综合型保险保障方案。\n\n**（七）推广“农业保险+”模式。**深化农业保险与信贷、担保、期货等金融工具协同，探索保单质押、联合分担风险等模式。发挥农业保险增信功能，提高投保农户信用等级，促进涉农数据、科技、资产等要素资源融合，引导加大金融资源投入。\n\n**（八）逐步拓宽服务领域。**鼓励保险机构根据农业发展需要以市场化方式提供覆盖农林牧渔、涉农产业上下游、农民人身安全等方面风险的保险产品，探索开展农产品加工、仓储、运输、质量保险及高标准农田工程质量保险，为农业对外合作提供保险服务。鼓励地方和保险机构提供大棚、农房、农机、仓库等农业生产设施设备保险，以及农业绿色发展、智慧农业、民族村寨等领域保险。\n\n三、优化农业保险运行机制\n\n**（九）优化完善承保管理。**财政、发展改革、农业农村、保险监管、林业草原等部门及保险机构加强信息互通，共享农（林）地确权、流转、目标价格补贴面积以及农产品成本调查、畜禽存出栏量、病死猪无害化处理等农业生产经营数据。逐步推行“一户一单”承保模式。保险监管部门指导保险机构制定农业保险精准承保规范，提升可操作性。保险机构应加强农业保险承保精准性管理，开展验标和承保信息审核，集体投保类业务逐步提高抽查验标比例，规模经营主体投保类业务实现全部验标。\n\n**（十）加强财政补贴管理。**强化保费补贴资金管理，加强补贴资金所涉投保信息与农业生产经营数据、历史投保数据的比对核验，提高财政资金使用效益。及时向保险机构拨付保费补贴资金，优化资金拨付方式。鼓励各地实行省级财政部门与保险机构省级分公司“省级对省级”结算等模式。\n\n**（十一）完善查勘定损制度。**农业农村、保险监管、林业草原等部门结合当地种植、养殖、林业生产实际，指导保险机构制定分类或分险种查勘定损操作规范，明确查勘、抽样、定损等工作流程和标准。农业农村、林业草原等部门配合做好灾后查勘和灾损核定工作。\n\n**（十二）提升理赔服务质效。**保险监管部门指导保险机构制定完善农业保险理赔实施规范，提升理赔效率和精准度。保险机构应完善基层服务网络，简化手续、优化流程、赔款到户，建立农业大灾快速理赔响应机制，针对重大自然灾害和事故合理预付部分赔偿金，支持农户恢复生产。保险机构应通过银行转账等非现金方式将保险赔款直接支付给被保险人。\n\n**（十三）鼓励开展风险减量管理。**健全风险减量管理机制，增强事前防灾减灾、事中救灾减损、事后及时赔付相结合的一体化服务能力，推动农业保险从单一事后理赔向全流程风险管理转型。保险机构可与农业农村、林业草原、气象等领域社会化服务机构合作，开展防灾减损工作。\n\n**（十四）健全监督管理机制。**健全农业保险领域常态化监督机制，财政、农业农村、保险监管、林业草原等部门加强联动，加大监督力度。对虚构虚增保险标的、虚假理赔、骗取套取保费补贴等违法违规行为，加大打击和惩戒力度，依法依规予以处理。\n\n四、夯实农业保险发展基础\n\n**（十五）强化信息化支撑。**持续推进全国农业保险数据信息系统、全国农业保险信息管理平台建设。统一农业保险数据标准，加强部门间、中央与地方间农业保险数据信息共享。加快推动农业保险行业数字化转型，促进业务流程智能化、标准化。\n\n**（十六）推进风险区划工作。**推进三大粮食作物、主要畜产品等重要农产品保险风险区划工作，适时发布风险区划结果和保险费率参考。探索开展地方特色农业保险风险区划工作。\n\n**（十七）加强科技应用。**保险机构应加大科技投入强度，合理应用无人机、遥感、物联网、人工智能、区块链、云计算等技术工具，提高保险服务精准度。鼓励保险机构探索开发农业灾害风险模型。制定农业保险科技应用规范或标准。保险机构应开展承保理赔信息实名校验和身份认证。\n\n**（十八）加强遴选管理。**按照适度竞争原则，结合服务能力、风控能力合理确定农业保险承保机构。各省份根据保费规模等因素统筹确定承保机构数量，承保机构一经确定应保持相对稳定，服务有效期一般不少于 3 年。开展承保机构绩效评价，强化绩效评价结果应用，做好承保机构动态调整工作。审慎评估农业保险市场环境和保险机构服务能力，加强经营资格管理，优化准入退出机制。\n\n**（十九）健全政策法规。**适应农业保险发展态势和高质量发展需要，推动适时修订《农业保险条例》，进一步加强农业保险法治建设。\n\n五、健全农业保险大灾风险分散机制\n\n**（二十）完善大灾风险准备金管理。**科学设置农业保险各险种大灾风险准备金计提比例，完善大灾风险准备金触发使用标准。保险机构按照独立运作、因地制宜、分级管理、统筹使用的原则，加强大灾风险准备金使用规范性管理，进一步发挥大灾风险准备金作用。\n\n**（二十一）大力发展农业再保险。**按照约定分保和市场化分保相结合的发展模式，健全农业再保险制度。完善约定分保机制和流程，优化大灾超赔保障。鼓励发展商业性再保险，逐步提高市场化业务比重。推动完善农业再保险机构治理机制，提高运行规范性和发展可持续性。\n\n**（二十二）健全风险分散机制。**研究建立农业保险大灾风险基金，完善农业保险大灾风险分散链条。加强农业保险赔付资金与政府救灾资金协同运用。\n\n六、做好组织实施工作\n\n各地区、各有关部门要高度重视农业保险工作，夯实工作基础，形成工作合力，更好满足“三农”主体风险保障需求。各省级人民政府加强组织领导，统筹推进本行政区域内农业保险规划发展、资源统筹、政策宣传等各项工作，发挥农业保险工作小组作用，组织有关部门和保险机构加强协同配合，确保各项任务落实见效。各有关部门按职责分工做好政策设计、资金安排、监督管理等工作。保险机构切实提高服务质效，可根据业务需要设立协保员，为承保理赔等服务提供便利。","农业农村部计划财务司 2026年9月8日","2026-09-08T00:00:00Z",{"impact":516,"substance":759,"depth":761,"authority":777,"freshness":828,"relevant":455,"comment":1093},"四部门联合印发的全国性农业保险高质量发展实施方案，明确2030年保险深度1.9%、密度1200元\u002F人等量化目标，条款与数据详实，权威性和政策影响力突出，值得进入每日精选。",[1095],{"name":1090,"url":1087},[691,1097,590,766,1098,1099],"农业遥感","风险区划","财政补贴",{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},[],{"status":598,"locked":580,"version":4,"method":599,"evidence":1103},{"candidates":1104,"agricultural_evidence":603},[1105,1109,1113,1117,1121,1125],{"code":503,"score":416,"supported":603,"title":1106,"abstract":1107,"tags":1108},[766],[766],[766],{"code":216,"score":717,"supported":603,"title":1110,"abstract":1111,"tags":1112},[],[218],[],{"code":224,"score":717,"supported":603,"title":1114,"abstract":1115,"tags":1116},[],[226],[],{"code":263,"score":455,"supported":580,"title":1118,"abstract":1119,"tags":1120},[],[],[590],{"code":32,"score":455,"supported":580,"title":1122,"abstract":1123,"tags":1124},[],[],[1097],{"code":56,"score":455,"supported":580,"title":1126,"abstract":1127,"tags":1128},[],[],[691],[1130,1131],"农业保险 农业遥感 数字乡村 粮食安全","农业保险 农业遥感","农业保险农业遥感数字乡村粮食安全-2694","2026-09-17T00:04:35.464456Z",{"id":1135,"title":1136,"url":1137,"summary":1138,"summary_zh":11,"content":1139,"source_name":1140,"source_url":11,"published_at":1141,"category":826,"cover_url":11,"hotness":197,"is_selected":603,"score":974,"score_detail":1142,"sources":1144,"tags":1152,"primary_topic":1157,"secondary_topics":1158,"classification":1159,"search_phrases":1170,"slug":1173,"view_count":717,"doi":11,"paper":11,"created_at":1174},2342,"四部门联合印发《关于加快农业保险高质量发展的实施方案》","https:\u002F\u002Fwww.gov.cn\u002Flianbo\u002F202609\u002Fcontent_7080785.htm","财政部、农业农村部、金融监管总局、国家林草局联合印发《方案》，要求到2030年基本建成多层次农业保险体系，明确八项重点工作并设定量化指标：农业保险深度1.9%、保险密度1200元\u002F人、科技投入强度1%、三大粮食作物覆盖率稳定在90%左右、综合费用率不高于20%。","新华社北京9月11日电（记者申铖）记者9月11日从财政部了解到，财政部等四部门印发《关于加快农业保险高质量发展的实施方案》，要求到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，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\n聚焦农业保险发展重点，方案明确，健全多层次农业保险体系，增强种植业农产品风险保障，促进养殖业保险规范发展，推动林草保险提质增效，支持地方特色发展，完善产品供给体系，推广“农业保险+”模式，逐步拓宽服务领域。同时，方案还要求，优化农业保险运行机制，夯实农业保险发展基础，健全农业保险大灾风险分散机制等。","中国政府网 | 2026-09-11","2026-09-11T00:00:00Z",{"impact":516,"substance":760,"depth":976,"authority":777,"freshness":173,"relevant":455,"comment":1143},"四部门联合印发的全国性农业保险高质量发展方案，明确2030年目标与多层次体系、大灾风险分散等具体条款，政策层级高、信息增量足，值得进入每日精选。",[1145,1146,1149],{"name":1140,"url":1137},{"name":1147,"url":1148},"央广网 2026-09-12","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7685274513470177818",{"name":1150,"url":1151},"人民日报","https:\u002F\u002Fso.html5.qq.com\u002Fpage\u002Freal\u002Fsearch_news?docid=70000021_9886ab0604736152",[1153,766,1154,1155,1156],"十五五","金融支农","多层次保险体系","大灾风险分散",{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},[],{"status":598,"locked":580,"version":4,"method":599,"evidence":1160},{"candidates":1161,"agricultural_evidence":603},[1162,1166],{"code":503,"score":584,"supported":603,"title":1163,"abstract":1164,"tags":1165},[766],[766],[766,1154],{"code":542,"score":455,"supported":580,"title":1167,"abstract":1168,"tags":1169},[],[],[1153],[1171,1172],"多层次保险体系 大灾风险分散 农业保险 金融支农","多层次保险体系 大灾风险分散","多层次保险体系大灾风险分散农业保险金融支农-2342","2026-09-14T00:06:25.382932Z",{"id":1176,"title":1177,"url":1178,"summary":1179,"summary_zh":11,"content":1180,"source_name":1181,"source_url":11,"published_at":1091,"category":826,"cover_url":11,"hotness":447,"is_selected":603,"score":1182,"score_detail":1183,"sources":1186,"tags":1191,"primary_topic":1194,"secondary_topics":1195,"classification":1196,"search_phrases":1223,"slug":1226,"view_count":702,"doi":11,"paper":11,"created_at":1227},2184,"财政部 农业农村部 金融监管总局 国家林草局联合印发《关于加快农业保险高质量发展的实施方案》","https:\u002F\u002Fwww.gov.cn\u002Fzhengce\u002Fzhengceku\u002F202609\u002Fcontent_7080788.htm","财政部、农业农村部、金融监管总局、国家林草局四部门联合印发《关于加快农业保险高质量发展的实施方案》(财金〔2026〕88号),经国务院同意,明确到2030年基本建成多层次农业保险体系,总体达到国际先进水平。具体目标包括农业保险深度达到1.9%、保险密度达到1200元\u002F人、科技投入强度达到1%,稻谷、小麦、玉米三大粮食作物保险覆盖率稳定在90%左右,综合费用率不高于20%。方案部署健全多层次保险体系、扩大特色农产品保险覆盖面、强化保险与产业政策协同等任务。","**关于印发《关于加快农业保险高质量发展的实施方案》的通知**\n\n财金〔2026〕88号\n\n各省、自治区、直辖市人民政府，新疆生产建设兵团，国务院有关部委、有关直属机构：\n\n经国务院同意，现将《关于加快农业保险高质量发展的实施方案》印发给你们，请抓好贯彻落实。\n\n财政部 农业农村部\n\n金融监管总局 国家林草局\n\n2026年9月8日\n\n**关于加快农业保险高质量发展的实施方案**\n\n为进一步加快农业保险高质量发展，稳定农户收益，服务保障国家粮食安全和农业生产能力，经国务院同意，制定本实施方案。\n\n**一、总体要求**\n\n坚持以习近平新时代中国特色社会主义思想为指导，深入贯彻党的二十大和二十届历次全会精神，认真落实四中全会部署，全面贯彻习近平总书记关于“三农”工作的重要论述，完整准确全面贯彻新发展理念，加快构建新发展格局，着力推动高质量发展，遵循政府引导、市场运作、自主自愿、协同推进、改革创新的原则，充分发挥农业保险在价格、补贴、保险“三位一体”农业支持政策体系中的重要一环作用。\n\n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，政策更加普惠，发展更加均衡。农业保险深度（保费\u002F第一产业增加值）达到1.9%，农业保险密度（保费\u002F第一产业就业人数）达到1200元\u002F人，农业保险科技投入强度（农业保险机构科技创新投入\u002F保费）达到1%。稻谷、小麦、玉米三大粮食作物农业保险覆盖率稳定在90%左右，提升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。农业保险综合费用率不高于20%。\n\n**二、聚焦农业保险发展重点**\n\n（一）健全多层次农业保险体系。坚持中央与地方共同推动、各部门分工负责，构建复合、多元、多层次的农业保险体系。加强对关系国计民生和粮食安全的大宗农产品以及地方特色农业的保障，稳步推进物化成本保险、完全成本保险、收入保险等险种发展，加强对小农户和农民合作社、家庭农场等新型农业经营主体的保障，强化对防止返贫致贫对象的支持。建立健全投保人、直保端、再保端等多方参与的风险分散链条，发挥好大灾风险准备金作用。\n\n（二）增强种植业农产品风险保障。加强主粮、棉花、油料、糖料、橡胶等大宗农产品风险保障，提高区域间农业保险发展均衡性，完善省以下保费分担机制。逐步降低产粮大县农业保险县级保费补贴承担比例，推动扩大稻谷、小麦、玉米、大豆完全成本保险和种植收入保险投保面积。完善对马铃薯、油菜、花生、甜菜、青稞等农产品以及稻谷、小麦、玉米制种的保险支持保障。\n\n（三）促进养殖业保险规范发展。完善养殖企业和养殖场（户）养殖保险模式，采用电子耳标、生物识别等技术加强养殖数量监控。动态调整能繁母猪、育肥猪、奶牛等养殖业保险保障水平，鼓励根据不同养殖方式提供差异化风险保障方案。做好牦牛、藏系羊等涉藏特定品种保险支持保障。\n\n（四）推动林草保险提质增效。坚持预防与保障并重，与现代林草业发展相适应，推动构建林草综合保险体系。完善公益林保险和商品林保险政策，探索适合相关林业保险险种特点的承保理赔模式。保险机构要合理确定森林保险防灾减损费用计提比例，据实列支。鼓励保险机构开展草原、湿地、国家公园、碳汇等保险试点，丰富林草保险产品。\n\n（五）支持地方特色发展。鼓励各地结合实际制定特色农业保险发展政策，加强相关资金统筹，加大支持力度，扩大保险范围，积极发展蔬菜、水果、肉牛、肉羊、禽类、渔业、林下经济等特色农业保险。\n\n（六）完善产品供给体系。健全“政策险+商业险”、“基本险+补充险”的农业保险产品供给体系。鼓励各地结合实际在物化成本保险等提供基本保险保障的险种基础上，设计开发相关补充保险，形成更高保障。鼓励各地因地制宜发展农业气象指数保险。支持保险机构发展商业性农业保险，开发便利农户投保的综合型保险保障方案。\n\n（七）推广“农业保险+”模式。深化农业保险与信贷、担保、期货等金融工具协同，探索保单质押、联合分担风险等模式。发挥农业保险增信功能，提高投保农户信用等级，促进涉农数据、科技、资产等要素资源融合，引导加大金融资源投入。\n\n（八）逐步拓宽服务领域。鼓励保险机构根据农业发展需要以市场化方式提供覆盖农林牧渔、涉农产业上下游、农民人身安全等方面风险的保险产品，探索开展农产品加工、仓储、运输、质量保险及高标准农田工程质量保险，为农业对外合作提供保险服务。鼓励地方和保险机构提供大棚、农房、农机、仓库等农业生产设施设备保险，以及农业绿色发展、智慧农业、民族村寨等领域保险。\n\n**三、优化农业保险运行机制**\n\n（九）优化完善承保管理。财政、发展改革、农业农村、保险监管、林业草原等部门及保险机构加强信息互通，共享农（林）地确权、流转、目标价格补贴面积以及农产品成本调查、畜禽存出栏量、病死猪无害化处理等农业生产经营数据。逐步推行“一户一单”承保模式。保险监管部门指导保险机构制定农业保险精准承保规范，提升可操作性。保险机构应加强农业保险承保精准性管理，开展验标和承保信息审核，集体投保类业务逐步提高抽查验标比例，规模经营主体投保类业务实现全部验标。\n\n（十）加强财政补贴管理。强化保费补贴资金管理，加强补贴资金所涉投保信息与农业生产经营数据、历史投保数据的比对核验，提高财政资金使用效益。及时向保险机构拨付保费补贴资金，优化资金拨付方式。鼓励各地实行省级财政部门与保险机构省级分公司“省级对省级”结算等模式。\n\n（十一）完善查勘定损制度。农业农村、保险监管、林业草原等部门结合当地种植、养殖、林业生产实际，指导保险机构制定分类或分险种查勘定损操作规范，明确查勘、抽样、定损等工作流程和标准。农业农村、林业草原等部门配合做好灾后查勘和灾损核定工作。\n\n（十二）提升理赔服务质效。保险监管部门指导保险机构制定完善农业保险理赔实施规范，提升理赔效率和精准度。保险机构应完善基层服务网络，简化手续、优化流程、赔款到户，建立农业大灾快速理赔响应机制，针对重大自然灾害和事故合理预付部分赔偿金，支持农户恢复生产。保险机构应通过银行转账等非现金方式将保险赔款直接支付给被保险人。\n\n（十三）鼓励开展风险减量管理。健全风险减量管理机制，增强事前防灾减灾、事中救灾减损、事后及时赔付相结合的一体化服务能力，推动农业保险从单一事后理赔向全流程风险管理转型。保险机构可与农业农村、林业草原、气象等领域社会化服务机构合作，开展防灾减损工作。\n\n（十四）健全监督管理机制。健全农业保险领域常态化监督机制，财政、农业农村、保险监管、林业草原等部门加强联动，加大监督力度。对虚构虚增保险标的、虚假理赔、骗取套取保费补贴等违法违规行为，加大打击和惩戒力度，依法依规予以处理。\n\n**四、夯实农业保险发展基础**\n\n（十五）强化信息化支撑。持续推进全国农业保险数据信息系统、全国农业保险信息管理平台建设。统一农业保险数据标准，加强部门间、中央与地方间农业保险数据信息共享。加快推动农业保险行业数字化转型，促进业务流程智能化、标准化。\n\n（十六）推进风险区划工作。推进三大粮食作物、主要畜产品等重要农产品保险风险区划工作，适时发布风险区划结果和保险费率参考。探索开展地方特色农业保险风险区划工作。\n\n（十七）加强科技应用。保险机构应加大科技投入强度，合理应用无人机、遥感、物联网、人工智能、区块链、云计算等技术工具，提高保险服务精准度。鼓励保险机构探索开发农业灾害风险模型。制定农业保险科技应用规范或标准。保险机构应开展承保理赔信息实名校验和身份认证。\n\n（十八）加强遴选管理。按照适度竞争原则，结合服务能力、风控能力合理确定农业保险承保机构。各省份根据保费规模等因素统筹确定承保机构数量，承保机构一经确定应保持相对稳定，服务有效期一般不少于3年。开展承保机构绩效评价，强化绩效评价结果应用，做好承保机构动态调整工作。审慎评估农业保险市场环境和保险机构服务能力，加强经营资格管理，优化准入退出机制。\n\n（十九）健全政策法规。适应农业保险发展态势和高质量发展需要，推动适时修订《农业保险条例》，进一步加强农业保险法治建设。\n\n**五、健全农业保险大灾风险分散机制**\n\n（二十）完善大灾风险准备金管理。科学设置农业保险各险种大灾风险准备金计提比例，完善大灾风险准备金触发使用标准。保险机构按照独立运作、因地制宜、分级管理、统筹使用的原则，加强大灾风险准备金使用规范性管理，进一步发挥大灾风险准备金作用。\n\n（二十一）大力发展农业再保险。按照约定分保和市场化分保相结合的发展模式，健全农业再保险制度。完善约定分保机制和流程，优化大灾超赔保障。鼓励发展商业性再保险，逐步提高市场化业务比重。推动完善农业再保险机构治理机制，提高运行规范性和发展可持续性。\n\n（二十二）健全风险分散机制。研究建立农业保险大灾风险基金，完善农业保险大灾风险分散链条。加强农业保险赔付资金与政府救灾资金协同运用。\n\n**六、做好组织实施工作**\n\n各地区、各有关部门要高度重视农业保险工作，夯实工作基础，形成工作合力，更好满足“三农”主体风险保障需求。各省级人民政府加强组织领导，统筹推进本行政区域内农业保险规划发展、资源统筹、政策宣传等各项工作，发挥农业保险工作小组作用，组织有关部门和保险机构加强协同配合，确保各项任务落实见效。各有关部门按职责分工做好政策设计、资金安排、监督管理等工作。保险机构切实提高服务质效，可根据业务需要设立协保员，为承保理赔等服务提供便利。\n\n**关于印发《关于加快农业保险高质量发展的实施方案》的通知**\n\n财金〔2026〕88号\n\n各省、自治区、直辖市人民政府，新疆生产建设兵团，国务院有关部委、有关直属机构：\n\n经国务院同意，现将《关于加快农业保险高质量发展的实施方案》印发给你们，请抓好贯彻落实。\n\n财政部 农业农村部\n\n金融监管总局 国家林草局\n\n2026年9月8日\n\n**关于加快农业保险高质量发展的实施方案**\n\n为进一步加快农业保险高质量发展，稳定农户收益，服务保障国家粮食安全和农业生产能力，经国务院同意，制定本实施方案。\n\n**一、总体要求**\n\n坚持以习近平新时代中国特色社会主义思想为指导，深入贯彻党的二十大和二十届历次全会精神，认真落实四中全会部署，全面贯彻习近平总书记关于“三农”工作的重要论述，完整准确全面贯彻新发展理念，加快构建新发展格局，着力推动高质量发展，遵循政府引导、市场运作、自主自愿、协同推进、改革创新的原则，充分发挥农业保险在价格、补贴、保险“三位一体”农业支持政策体系中的重要一环作用。\n\n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，政策更加普惠，发展更加均衡。农业保险深度（保费\u002F第一产业增加值）达到1.9%，农业保险密度（保费\u002F第一产业就业人数）达到1200元\u002F人，农业保险科技投入强度（农业保险机构科技创新投入\u002F保费）达到1%。稻谷、小麦、玉米三大粮食作物农业保险覆盖率稳定在90%左右，提升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。农业保险综合费用率不高于20%。\n\n**二、聚焦农业保险发展重点**\n\n（一）健全多层次农业保险体系。坚持中央与地方共同推动、各部门分工负责，构建复合、多元、多层次的农业保险体系。加强对关系国计民生和粮食安全的大宗农产品以及地方特色农业的保障，稳步推进物化成本保险、完全成本保险、收入保险等险种发展，加强对小农户和农民合作社、家庭农场等新型农业经营主体的保障，强化对防止返贫致贫对象的支持。建立健全投保人、直保端、再保端等多方参与的风险分散链条，发挥好大灾风险准备金作用。\n\n（二）增强种植业农产品风险保障。加强主粮、棉花、油料、糖料、橡胶等大宗农产品风险保障，提高区域间农业保险发展均衡性，完善省以下保费分担机制。逐步降低产粮大县农业保险县级保费补贴承担比例，推动扩大稻谷、小麦、玉米、大豆完全成本保险和种植收入保险投保面积。完善对马铃薯、油菜、花生、甜菜、青稞等农产品以及稻谷、小麦、玉米制种的保险支持保障。\n\n（三）促进养殖业保险规范发展。完善养殖企业和养殖场（户）养殖保险模式，采用电子耳标、生物识别等技术加强养殖数量监控。动态调整能繁母猪、育肥猪、奶牛等养殖业保险保障水平，鼓励根据不同养殖方式提供差异化风险保障方案。做好牦牛、藏系羊等涉藏特定品种保险支持保障。\n\n（四）推动林草保险提质增效。坚持预防与保障并重，与现代林草业发展相适应，推动构建林草综合保险体系。完善公益林保险和商品林保险政策，探索适合相关林业保险险种特点的承保理赔模式。保险机构要合理确定森林保险防灾减损费用计提比例，据实列支。鼓励保险机构开展草原、湿地、国家公园、碳汇等保险试点，丰富林草保险产品。\n\n（五）支持地方特色发展。鼓励各地结合实际制定特色农业保险发展政策，加强相关资金统筹，加大支持力度，扩大保险范围，积极发展蔬菜、水果、肉牛、肉羊、禽类、渔业、林下经济等特色农业保险。\n\n（六）完善产品供给体系。健全“政策险+商业险”、“基本险+补充险”的农业保险产品供给体系。鼓励各地结合实际在物化成本保险等提供基本保险保障的险种基础上，设计开发相关补充保险，形成更高保障。鼓励各地因地制宜发展农业气象指数保险。支持保险机构发展商业性农业保险，开发便利农户投保的综合型保险保障方案。\n\n（七）推广“农业保险+”模式。深化农业保险与信贷、担保、期货等金融工具协同，探索保单质押、联合分担风险等模式。发挥农业保险增信功能，提高投保农户信用等级，促进涉农数据、科技、资产等要素资源融合，引导加大金融资源投入。\n\n（八）逐步拓宽服务领域。鼓励保险机构根据农业发展需要以市场化方式提供覆盖农林牧渔、涉农产业上下游、农民人身安全等方面风险的保险产品，探索开展农产品加工、仓储、运输、质量保险及高标准农田工程质量保险，为农业对外合作提供保险服务。鼓励地方和保险机构提供大棚、农房、农机、仓库等农业生产设施设备保险，以及农业绿色发展、智慧农业、民族村寨等领域保险。\n\n**三、优化农业保险运行机制**\n\n（九）优化完善承保管理。财政、发展改革、农业农村、保险监管、林业草原等部门及保险机构加强信息互通，共享农（林）地确权、流转、目标价格补贴面积以及农产品成本调查、畜禽存出栏量、病死猪无害化处理等农业生产经营数据。逐步推行“一户一单”承保模式。保险监管部门指导保险机构制定农业保险精准承保规范，提升可操作性。保险机构应加强农业保险承保精准性管理，开展验标和承保信息审核，集体投保类业务逐步提高抽查验标比例，规模经营主体投保类业务实现全部验标。\n\n（十）加强财政补贴管理。强化保费补贴资金管理，加强补贴资金所涉投保信息与农业生产经营数据、历史投保数据的比对核验，提高财政资金使用效益。及时向保险机构拨付保费补贴资金，优化资金拨付方式。鼓励各地实行省级财政部门与保险机构省级分公司“省级对省级”结算等模式。\n\n（十一）完善查勘定损制度。农业农村、保险监管、林业草原等部门结合当地种植、养殖、林业生产实际，指导保险机构制定分类或分险种查勘定损操作规范，明确查勘、抽样、定损等工作流程和标准。农业农村、林业草原等部门配合做好灾后查勘和灾损核定工作。\n\n（十二）提升理赔服务质效。保险监管部门指导保险机构制定完善农业保险理赔实施规范，提升理赔效率和精准度。保险机构应完善基层服务网络，简化手续、优化流程、赔款到户，建立农业大灾快速理赔响应机制，针对重大自然灾害和事故合理预付部分赔偿金，支持农户恢复生产。保险机构应通过银行转账等非现金方式将保险赔款直接支付给被保险人。\n\n（十三）鼓励开展风险减量管理。健全风险减量管理机制，增强事前防灾减灾、事中救灾减损、事后及时赔付相结合的一体化服务能力，推动农业保险从单一事后理赔向全流程风险管理转型。保险机构可与农业农村、林业草原、气象等领域社会化服务机构合作，开展防灾减损工作。\n\n（十四）健全监督管理机制。健全农业保险领域常态化监督机制，财政、农业农村、保险监管、林业草原等部门加强联动，加大监督力度。对虚构虚增保险标的、虚假理赔、骗取套取保费补贴等违法违规行为，加大打击和惩戒力度，依法依规予以处理。\n\n**四、夯实农业保险发展基础**\n\n（十五）强化信息化支撑。持续推进全国农业保险数据信息系统、全国农业保险信息管理平台建设。统一农业保险数据标准，加强部门间、中央与地方间农业保险数据信息共享。加快推动农业保险行业数字化转型，促进业务流程智能化、标准化。\n\n（十六）推进风险区划工作。推进三大粮食作物、主要畜产品等重要农产品保险风险区划工作，适时发布风险区划结果和保险费率参考。探索开展地方特色农业保险风险区划工作。\n\n（十七）加强科技应用。保险机构应加大科技投入强度，合理应用无人机、遥感、物联网、人工智能、区块链、云计算等技术工具，提高保险服务精准度。鼓励保险机构探索开发农业灾害风险模型。制定农业保险科技应用规范或标准。保险机构应开展承保理赔信息实名校验和身份认证。\n\n（十八）加强遴选管理。按照适度竞争原则，结合服务能力、风控能力合理确定农业保险承保机构。各省份根据保费规模等因素统筹确定承保机构数量，承保机构一经确定应保持相对稳定，服务有效期一般不少于3年。开展承保机构绩效评价，强化绩效评价结果应用，做好承保机构动态调整工作。审慎评估农业保险市场环境和保险机构服务能力，加强经营资格管理，优化准入退出机制。\n\n（十九）健全政策法规。适应农业保险发展态势和高质量发展需要，推动适时修订《农业保险条例》，进一步加强农业保险法治建设。\n\n**五、健全农业保险大灾风险分散机制**\n\n（二十）完善大灾风险准备金管理。科学设置农业保险各险种大灾风险准备金计提比例，完善大灾风险准备金触发使用标准。保险机构按照独立运作、因地制宜、分级管理、统筹使用的原则，加强大灾风险准备金使用规范性管理，进一步发挥大灾风险准备金作用。\n\n（二十一）大力发展农业再保险。按照约定分保和市场化分保相结合的发展模式，健全农业再保险制度。完善约定分保机制和流程，优化大灾超赔保障。鼓励发展商业性再保险，逐步提高市场化业务比重。推动完善农业再保险机构治理机制，提高运行规范性和发展可持续性。\n\n（二十二）健全风险分散机制。研究建立农业保险大灾风险基金，完善农业保险大灾风险分散链条。加强农业保险赔付资金与政府救灾资金协同运用。\n\n**六、做好组织实施工作**\n\n各地区、各有关部门要高度重视农业保险工作，夯实工作基础，形成工作合力，更好满足“三农”主体风险保障需求。各省级人民政府加强组织领导，统筹推进本行政区域内农业保险规划发展、资源统筹、政策宣传等各项工作，发挥农业保险工作小组作用，组织有关部门和保险机构加强协同配合，确保各项任务落实见效。各有关部门按职责分工做好政策设计、资金安排、监督管理等工作。保险机构切实提高服务质效，可根据业务需要设立协保员，为承保理赔等服务提供便利。","中国政府网 2026年9月8日",97,{"impact":1184,"substance":447,"depth":508,"authority":777,"freshness":702,"relevant":455,"comment":1185},30,"四部门联合印发的全国性农业保险高质量发展实施方案，明确2030年保险深度1.9%、密度1200元\u002F人等量化目标及承保理赔、大灾风险分散等具体条款，政策层级高、信息增量大，值得进入每日精选。",[1187,1188],{"name":1181,"url":1178},{"name":1189,"url":1190},"经济日报 2026-09-20","https:\u002F\u002Fwww.jingjiribao.cn\u002Fstatic\u002Fdetail.jsp?id=684690",[590,766,1192,1193,1098,1099],"农业数字化","农业再保险",{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},[],{"status":598,"locked":580,"version":4,"method":599,"evidence":1197},{"candidates":1198,"agricultural_evidence":603},[1199,1203,1207,1211,1215,1219],{"code":503,"score":416,"supported":603,"title":1200,"abstract":1201,"tags":1202},[766],[766],[766],{"code":216,"score":717,"supported":603,"title":1204,"abstract":1205,"tags":1206},[],[218],[],{"code":224,"score":717,"supported":603,"title":1208,"abstract":1209,"tags":1210},[],[226],[],{"code":263,"score":455,"supported":580,"title":1212,"abstract":1213,"tags":1214},[],[],[590],{"code":7,"score":455,"supported":580,"title":1216,"abstract":1217,"tags":1218},[],[],[1192],{"code":16,"score":455,"supported":580,"title":1220,"abstract":1221,"tags":1222},[],[],[1192],[1224,1225],"农业再保险 农业数字化 农业保险 粮食安全","农业再保险 农业数字化","农业再保险农业数字化农业保险粮食安全-2184","2026-09-12T00:06:35.317766Z",{"id":1229,"title":1230,"url":1231,"summary":1232,"summary_zh":1233,"content":11,"source_name":683,"source_url":1231,"published_at":1234,"category":579,"cover_url":11,"hotness":385,"is_selected":580,"score":1235,"score_detail":1236,"sources":1239,"tags":1241,"primary_topic":1246,"secondary_topics":1247,"classification":1249,"search_phrases":1270,"slug":1273,"view_count":69,"doi":1274,"paper":1275,"created_at":1297},2128,"The impact of agricultural insurance coverage on farm household income inequality: evidence from corn farmers in Liaoning Province, China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1856386","Introduction As climate variability and production uncertainty increasingly threaten agricultural livelihoods, agricultural insurance has become an important tool for climate risk management and farm income stabilization. However, its distributional effects across farm households remain underexplored. Drawing on Sen’s capability approach, this study investigates the impact of agricultural insurance coverage on income inequality among farm households. Methods Using a two-wave panel dataset of 627 corn-growing households in Liaoning Province, China, from 2021 to 2022, we employ a two-way fixed effects model to estimate the effect of agricultural insurance coverage on farm household income inequality. We further test the robustness of the baseline results through a series of checks, including replacing the dependent variable, controlling for village-level fixed effects, adding village-level controls, clustering bootstrap standard errors at the village level and alternative model specifications. Exploratory analysis and heterogeneity analysis are also conducted. Results The empirical results yield four main findings. First, higher agricultural insurance coverage is positively associated with income inequality among farm households, and this pattern remains robust across multiple tests. Second, the observed association with inequality is concentrated in operating income, with no significant corresponding patterns for wage income, property income, or transfer income. Third, exploratory analysis suggests that agricultural insurance coverage is linked to greater land transfer-in primarily for high-income households, a relationship that is consistent with the observed increased income inequality. Fourth, this association is more pronounced among households with greater realized production loss and among those living in areas without income insurance policies. Discussion This study enriches the literature on agricultural insurance by highlighting its unintended distributional consequences. Although agricultural insurance plays an important role in risk management, its potential to widen inequality implies that policy design should not rely on insurance alone. Greater attention to equity requires complementary redistributive instruments, alongside efforts to make insurance more inclusive for disadvantaged farmers.","引言 随着气候变率和生产不确定性日益威胁农业生计，农业保险已成为气候风险管理和稳定农户收入的重要工具。然而，其对不同农户的分配效应仍缺乏充分探讨。本研究基于森的可行能力方法，考察农业保险覆盖对农户收入不平等的影响。方法 使用2021年至2022年中国辽宁省627个玉米种植户的两期面板数据，采用双向固定效应模型估计农业保险覆盖对农户收入不平等的影响。我们进一步通过一系列检验验证基准结果的稳健性，包括替换因变量、控制村级固定效应、加入村级控制变量、在村级聚类自助法标准误以及替代模型设定。同时进行了探索性分析和异质性分析。结果 实证结果得出四个主要发现。第一，较高的农业保险覆盖与农户收入不平等呈正相关，且这一模式在多项检验中保持稳健。第二，观察到的与不平等的关系集中于经营收入，而工资性收入、财产性收入或转移性收入未呈现显著的相应模式。第三，探索性分析表明，农业保险覆盖与高收入农户更多转入土地相关，这一关系与观察到的收入不平等加剧相一致。第四，这种关联在实际生产损失较大的农户以及居住在无收入保险政策地区的农户中更为明显。讨论 本研究通过揭示农业保险的非预期分配后果，丰富了农业保险文献。尽管农业保险在风险管理中发挥重要作用，但其扩大不平等的潜力意味着政策设计不应仅依赖保险。更加关注公平需要 complementary 再分配工具，同时努力使保险对弱势农户更具包容性。","2026-09-10T00:00:00Z",77,{"impact":1237,"substance":321,"depth":361,"authority":584,"freshness":173,"relevant":455,"comment":1238},16,"基于辽宁627户玉米种植户两期面板数据的实证研究，揭示农业保险覆盖扩大反而加剧农户收入不平等，并指出其通过高收入户土地转入的传导机制，对农业保险政策设计具有重要警示价值。",[1240],{"name":683,"url":1231},[766,1242,1243,1244,1245],"收入不平等","玉米种植","农户收入","土地流转",{"code":224,"slug":225,"name":226,"path":227,"parent_code":200},[1248],{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},{"status":598,"locked":580,"version":4,"method":599,"evidence":1250},{"candidates":1251,"agricultural_evidence":603},[1252,1257,1261,1266],{"code":224,"score":777,"supported":603,"title":1253,"abstract":1255,"tags":1256},[1254],"corn",[226,1254],[1243],{"code":503,"score":777,"supported":603,"title":1258,"abstract":1259,"tags":1260},[779],[766,779],[766],{"code":496,"score":400,"supported":603,"title":1262,"abstract":1263,"tags":1265},[],[1264],"land transfer",[1245],{"code":527,"score":400,"supported":603,"title":1267,"abstract":1268,"tags":1269},[],[1244],[1244],[1271,1272],"收入不平等 农业保险 农户收入 土地流转","收入不平等 农业保险","收入不平等农业保险农户收入土地流转-2128","10.3389\u002Ffsufs.2026.1856386",{"doi":1274,"openalex_id":1276,"authors":1277,"venue":683,"cited_by_count":424,"oa_url":1231,"card":1292,"direction":622,"ingested_from":749},"W7212115989",[1278,1281,1284,1287,1289],{"name":1279,"orcid":1280},"Bo Hu","https:\u002F\u002Forcid.org\u002F0000-0001-8527-3750",{"name":1282,"orcid":1283},"Wenbo Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-4672-1420",{"name":1285,"orcid":1286},"Chunli Wan","https:\u002F\u002Forcid.org\u002F0000-0002-6027-4869",{"name":1288,"orcid":11},"Naihe Jiang",{"name":1290,"orcid":1291},"Fengjie Xie","https:\u002F\u002Forcid.org\u002F0000-0003-0688-3126",{"tldr":1293,"method":1294,"finding":1295,"direction":622,"opportunity":1296},"基于辽宁玉米户面板数据，发现农业保险覆盖扩大反而加剧农户收入不平等。","2021-2022年627户两期面板数据，双向固定效应模型及多种稳健性检验。","保险覆盖与收入不平等正相关，主要来自经营收入，高收入户土地转入更多。","可研究保险与数字信贷、补贴等政策组合对农户收入分配公平性的协同调节机制。","2026-09-11T23:30:07.758358Z",{"id":1299,"title":1300,"url":1301,"summary":1302,"summary_zh":11,"content":11,"source_name":1303,"source_url":11,"published_at":1304,"category":579,"cover_url":11,"hotness":385,"is_selected":580,"score":1235,"score_detail":1305,"sources":1307,"tags":1309,"primary_topic":1311,"secondary_topics":1312,"classification":1313,"search_phrases":1337,"slug":1340,"view_count":400,"doi":11,"paper":1341,"created_at":1348},1508,"金融支持推进乡村全面振兴助力加快农业强国建设——第十九届中国农村金融发展论坛会议综述","https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F475184.html","综述显示第十九届中国农村金融发展论坛围绕数字金融与共同富裕主题形成以下发现：银行机构数字普惠金融水平对共同富裕具有显著促进作用、金融监管在其中发挥了正向调节效应；契约链主要通过促进社会化服务与缓解信贷约束增收、数字链则通过提升数字素养与扩大金融参与增收；农户参与数字经济通过生产智能化、供销平台化、金融普惠化三维协同机制显著促进财富积累。数字普惠金融通过促进非农就业与提升金融可得性不仅增加了农民收入，还推动了低收入群体收入阶层跃迁。","农业信息网","2026-09-01T00:00:00Z",{"impact":508,"substance":508,"depth":361,"authority":416,"freshness":702,"relevant":455,"comment":1306},"论坛综述提炼了数字普惠金融促进共同富裕的实证结论，对农村金融研究有参考价值。",[1308],{"name":1303,"url":1301},[981,1310,592,693],"共同富裕",{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},[],{"status":598,"locked":580,"version":4,"method":599,"evidence":1314},{"candidates":1315,"agricultural_evidence":603},[1316,1320,1325,1329,1333],{"code":503,"score":584,"supported":603,"title":1317,"abstract":1318,"tags":1319},[693],[693],[693,592],{"code":549,"score":173,"supported":603,"title":1321,"abstract":1323,"tags":1324},[1322],"农业强国",[],[],{"code":496,"score":717,"supported":603,"title":1326,"abstract":1327,"tags":1328},[],[639],[],{"code":480,"score":455,"supported":580,"title":1330,"abstract":1331,"tags":1332},[],[],[981],{"code":511,"score":455,"supported":580,"title":1334,"abstract":1335,"tags":1336},[],[],[1310],[1338,1339],"数字普惠金融 乡村振兴 共同富裕 农村金融","数字普惠金融 乡村振兴","数字普惠金融乡村振兴共同富裕农村金融-1508",{"doi":11,"openalex_id":11,"authors":1342,"venue":11,"cited_by_count":424,"oa_url":11,"card":1343,"direction":622,"ingested_from":624},[],{"tldr":1344,"method":1345,"finding":1346,"direction":622,"opportunity":1347},"综述农村金融论坛，探讨数字金融促进共同富裕的机制与效应。","论坛综述，归纳实证研究结论。","数字普惠金融促进共同富裕，金融监管正向调节，多维机制增收。","可深入研究数字普惠金融在农业细分领域的差异化效应及监管适配机制。","2026-09-03T00:06:44.980923Z",{"id":1350,"title":1351,"url":1352,"summary":1353,"summary_zh":1354,"content":11,"source_name":683,"source_url":1352,"published_at":1355,"category":579,"cover_url":11,"hotness":385,"is_selected":580,"score":1356,"score_detail":1357,"sources":1359,"tags":1361,"primary_topic":1364,"secondary_topics":1365,"classification":1366,"search_phrases":1373,"slug":1376,"view_count":455,"doi":1377,"paper":1378,"created_at":1398},1292,"“Striving” or “lying flat”: are government subsidies associated with technological innovation in agricultural firms? A compensation-based correlational study","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1866337","Government subsidies are widely regarded as an important policy instrument for enhancing firms’ technological innovation capabilities. Using data covering Chinese A-share listed agricultural firms from 2007 to 2020, this paper employs OLS regressions with year and industry fixed effects to examine the conditional association between government subsidies and firms’ technological innovation. The findings indicate that higher subsidy intensity is associated with lower subsequent patent output rather than with stronger innovation performance. This pattern remains after replacing the innovation indicator, using patent outcomes at alternative horizons, applying alternative model specifications, and absorbing province-specific annual shocks through province-by-year fixed effects. Mechanism-related association tests show that subsidy intensity is positively associated with executive excess compensation, perk consumption, and wider executive-employee pay gaps, which is consistent with a subsidy-induced incentive distortion referred to in this paper as a “lying-flat” pattern. Heterogeneity analysis further shows that the negative association is more pronounced among non-state-owned firms, firms in eastern China, firms with lower financing constraints, and firms with lower information quality. These findings should be interpreted as correlational evidence, not as causal estimates, and they highlight compensation governance, subsidy supervision, and performance evaluation as important issues in agricultural subsidy policy.","政府补贴被广泛视为提升企业技术创新能力的重要政策工具。本文利用2007年至2020年中国A股上市农业企业数据，采用包含年份和行业固定效应的OLS回归，考察政府补贴与企业技术创新之间的条件关联。研究发现，补贴强度越高，后续专利产出反而越低，而非创新绩效更强。在替换创新指标、采用不同时间跨度的专利结果、应用替代模型设定以及通过省份-年份交互固定效应吸收省级年度冲击后，这一模式依然稳健。机制关联检验表明，补贴强度与高管超额薪酬、在职消费以及高管-员工薪酬差距扩大呈正相关，这与补贴引发的激励扭曲一致，本文将其称为“躺平”模式。异质性分析进一步显示，这种负向关联在非国有企业、东部地区企业、融资约束较低的企业以及信息质量较低的企业中更为显著。上述发现应被解读为相关性证据而非因果估计，并凸显了薪酬治理、补贴监管和绩效评价作为农业补贴政策中的重要议题。","2026-08-31T00:00:00Z",74,{"impact":361,"substance":321,"depth":508,"authority":416,"freshness":69,"relevant":455,"comment":1358},"基于A股农业上市公司数据的实证研究，揭示补贴与创新负相关及激励扭曲机制，对农业补贴政策优化有参考价值。",[1360],{"name":683,"url":1352},[997,1362,1363],"企业创新","政策评估",{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},[],{"status":598,"locked":580,"version":4,"method":599,"evidence":1367},{"candidates":1368,"agricultural_evidence":603},[1369],{"code":503,"score":400,"supported":603,"title":1370,"abstract":1371,"tags":1372},[],[997],[997],[1374,1375],"企业创新 农业补贴 政策评估","企业创新 农业补贴","企业创新农业补贴政策评估-1292","10.3389\u002Ffsufs.2026.1866337",{"doi":1377,"openalex_id":1379,"authors":1380,"venue":683,"cited_by_count":424,"oa_url":1392,"card":1393,"direction":911,"ingested_from":749},"W7204857585",[1381,1383,1386,1389],{"name":1382,"orcid":11},"Anqi Chen",{"name":1384,"orcid":1385},"Xin Song","https:\u002F\u002Forcid.org\u002F0000-0003-3491-5324",{"name":1387,"orcid":1388},"Hui Shang","https:\u002F\u002Forcid.org\u002F0000-0002-7793-4982",{"name":1390,"orcid":1391},"Zhen Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-8057-7624","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1866337\u002Fpdf",{"tldr":1394,"method":1395,"finding":1396,"direction":911,"opportunity":1397},"研究发现政府补贴强度与中国农业上市公司后续专利产出负相关，存在“躺平”效应。","使用2007-2020年中国A股农业上市公司数据，OLS回归及固定效应模型。","补贴强度与专利产出负相关，与高管超额薪酬、在职消费正相关，支持激励扭曲。","可探索补贴监管与高管激励优化机制，或利用机器学习识别补贴滥用模式，提升政策效率。","2026-09-01T23:30:07.813148Z",{"id":1400,"title":1401,"url":1402,"summary":1403,"summary_zh":11,"content":1404,"source_name":1405,"source_url":11,"published_at":1406,"category":826,"cover_url":11,"hotness":385,"is_selected":580,"score":1407,"score_detail":1408,"sources":1410,"tags":1412,"primary_topic":1418,"secondary_topics":1419,"classification":1421,"search_phrases":1433,"slug":1436,"view_count":69,"doi":11,"paper":11,"created_at":1437},751,"农业农村部答复刘琴代表建议:将肉牛纳入农业保险体系,中央财政已累计向安徽拨付奖补资金14.05亿元","https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL4PIDO7S0514BL38.html","农业农村部8月20日答复关于将肉牛纳入农业保险体系的建议:肉牛保险属于地方优势特色农产品保险范畴,目前政策实施范围已覆盖全国,保险机构可结合肉牛主要生产生命周期开发专项保险产品。下一步将会同财政部、金融监管总局引导保险机构提高肉牛保险投保率,构建多层次肉牛保险体系。2022—2025年中央财政累计向安徽省拨付奖补资金14.05亿元,其中2025年为全省3000余户次肉牛养殖户提供风险保障超30亿元。","规范监管体系是促进保险市场有序发展的重要保障。目前，我国已形成由金融监管、财政、审计、农业农村等部门多方协同的保险监管格局，对包括肉牛保险在内的农业保险承保机构开展专项治理工作，重点核查业务真实性、服务实效性、档案规范性等，坚决打击虚假承保、虚假理赔等违规行为。同时，农业农村部持续完善肉牛疫病防控措施，将口蹄疫、布鲁氏菌病、牛结核病等主要牛病纳入国家动物疫病监测计划，对口蹄疫实施国家强制免疫，指导各地按免疫计划开展春秋两季集中免疫和日常补免；推进布鲁氏菌病、牛结核病等疫病净化工作，支持有条件的地区建设无疫区和无疫小区，从源头降低疫病发生风险。下一步，我部将配合有关部门，创新优化肉牛保险监管模式，强化数字化监管能力，推动肉牛信息共享平台建设，促进畜牧、财政、保险等各方数据联动，推进肉牛产业预警体系与防控能力建设，优化肉牛主要疫病监测，切实为肉牛产业高质量发展保驾护航。","农业农村部对人大建议答复 2026-08-20","2026-08-20T06:34:00Z",84,{"impact":447,"substance":508,"depth":777,"authority":777,"freshness":585,"relevant":455,"comment":1409},"农业农村部对人大建议的正式答复，涉及肉牛保险纳入农业保险体系及中央财政奖补资金，政策层级高，信息具体，权威性强，时效性好。",[1411],{"name":1405,"url":1402},[1413,1414,1415,1416,1417],"农业农村部","安徽","肉牛保险","地方优势特色农产品保险","中央财政奖补",{"code":348,"slug":349,"name":350,"path":351,"parent_code":332},[1420],{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},{"status":598,"locked":580,"version":4,"method":599,"evidence":1422},{"candidates":1423,"agricultural_evidence":603},[1424,1429],{"code":348,"score":524,"supported":603,"title":1425,"abstract":1427,"tags":1428},[1426],"肉牛",[1426],[],{"code":503,"score":524,"supported":603,"title":1430,"abstract":1431,"tags":1432},[766],[766],[],[1434,1435],"地方优势特色农产品保险 中央财政奖补 农业农村部 肉牛保险","地方优势特色农产品保险 中央财政奖补","地方优势特色农产品保险中央财政奖补农业农村部肉牛保险-751","2026-08-21T00:05:53.663284Z",{"id":1439,"title":1440,"url":1441,"summary":1442,"summary_zh":11,"content":1443,"source_name":1444,"source_url":11,"published_at":1445,"category":826,"cover_url":11,"hotness":385,"is_selected":580,"score":93,"score_detail":1446,"sources":1448,"tags":1450,"primary_topic":1455,"secondary_topics":1456,"classification":1457,"search_phrases":1484,"slug":1487,"view_count":455,"doi":11,"paper":11,"created_at":1488},524,"四川财政金融互动实施方案出台,农业信贷担保最高补助5000万元\u002F年","https:\u002F\u002Fczt.sc.gov.cn\u002Fscczt\u002Fxzgfxwj\u002F2026\u002F8\u002F10\u002F3d39e4be22cb45d78169fab217f648ce.shtml","四川省财政厅、农业农村厅印发关于进一步深入实施财政金融互动政策加快推进农业农村现代化实施方案(川财规〔2026〕5号),自2026年8月10日施行,有效期5年。对政策性农业担保机构给予最高5000万元\u002F年补助,农业产业化贷款贴息单个项目最高200万元,现代设施农业建设贷款贴息最长5年,涵盖粮食、农机、冷链、种业等领域。","财政、农业农村等部门持续完善工作协调机制，切实履职尽责，加强沟通协作，按照本方案确定的政策内容和年度发布的申报指南，组织做好资金申报和审核，严格绩效监督工作，确保政策到位、精准执行、落实落地。同时，鼓励各级政策性农业担保机构与再担保机构建立多方分险合作机制，提高风险抵御能力。支持省级宜居宜业和美乡村聚居点建设试点县结合群众实际需求，对纳入试点范围的聚居点农民住房建设贷款给予贴息。各地要发挥首创精神，因地制宜探索财政金融互动政策，更好推动农业农村现代化，加强总结提炼，适时推广经验做法。","四川省财政厅","2026-08-09T16:00:00Z",{"impact":361,"substance":777,"depth":416,"authority":416,"freshness":828,"relevant":455,"comment":1447},"省级财政金融互动政策，涉及农业信贷担保补助，有一定信息量，但细节有限。",[1449],{"name":1444,"url":1441},[1451,1452,1453,641,1454],"四川","设施农业","财政金融","贷款贴息",{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},[],{"status":598,"locked":580,"version":4,"method":599,"evidence":1458},{"candidates":1459,"agricultural_evidence":603},[1460,1464,1469,1474,1479],{"code":503,"score":385,"supported":603,"title":1461,"abstract":1462,"tags":1463},[652],[],[641,1453],{"code":458,"score":717,"supported":603,"title":1465,"abstract":1466,"tags":1468},[],[1467],"冷链",[],{"code":80,"score":717,"supported":603,"title":1470,"abstract":1471,"tags":1473},[],[1472],"种业",[],{"code":112,"score":717,"supported":603,"title":1475,"abstract":1476,"tags":1478},[],[1477],"农机",[],{"code":549,"score":717,"supported":603,"title":1480,"abstract":1481,"tags":1483},[],[1482],"农业农村现代化",[],[1485,1486],"农业信贷担保 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农业信贷担保补助最高5000万元\u002F年","https:\u002F\u002Fso.html5.qq.com\u002Fpage\u002Freal\u002Fsearch_news?docid=70000021_1336a7c677009452","四川省财政厅、农业农村厅联合印发《关于进一步深入实施财政金融互动政策加快推进农业农村现代化实施方案》,自2026年8月10日起施行,有效期5年。对不以营利为目的且达一定规模的政策性农业担保机构,给予最高不超过5000万元\u002F年补助。对农业产业化银行贷款实施分类贴息:固定资产贷款按5年期以上LPR减150BP贴息,单个项目最高200万元;生猪产业贷款贴息增加50BP。开展现代设施农业建设贷款贴息试点,贴息比例不高于同期LPR的70%且不得超过2%。","记者从省政府网站获悉，四川省财政厅、农业农村厅联合印发《关于进一步深入实施财政金融互动政策加快推进农业农村现代化实施方案》。该方案聚焦粮食生产经营和现代农业发展，对符合条件的农业经营主体和农村集体经济组织予以信贷担保支持。对不以营利为目的且达到一定规模的政策性农业担保机构，给予最高不超过5000万元\u002F年的补助。方案自2026年8月10日起施行，有效期5年。\n\n方案明确，以粮食生产经营、现代农业发展为重点，以符合条件的农业经营主体和农村集体经济组织为对象的粮食生产经营信贷担保和现代农业发展信贷担保予以支持，农业信贷担保补助范围、标准等根据实际情况，在年度申报指南中予以明确。对不以营利为目的，且达到一定规模的省、市、县级政策性农业担保机构，根据年度农业担保情况，省级财政给予最高不超过5000万元\u002F年补助。补助资金可灵活用于增加注册资本金、风险准备金等，其中用于风险准备金的比例不低于20%。\n\n方案对农业产业化银行贷款实施分类贴息。固定资产贷款按5年期以上LPR减150BP给予贴息，单个项目最高贴息200万元，重点支持农产品初加工、农机具购置、烘干冷链物流、新建规模化养殖场等领域。流动资金贷款按1年期LPR减150BP给予贴息，单个项目最高贴息100万元，重点面向“三大主粮”、油菜、大豆、青稞生产及初加工等。对生猪产业贷款贴息增加50BP，粮食生产和初加工类贷款贴息增加50BP。\n\n此外，方案开展现代设施农业建设贷款贴息试点，对设施种植、畜牧、渔业等领域新建或改建项目贷款予以贴息，贴息比例不高于同期LPR的70%且不得超过2%，单个主体年度贴息不超过200万元。省级财政按县级实际支出贴息资金的15%予以补助。\n\n方案要求规范乡村振兴农业产业发展贷款风险补偿金账户管理，健全管理制度，优化贷款流程和补偿程序。省级将研究制定补助政策，引导地方管好用好风险补偿金资金池，积极发挥增信功能。","四川日报 2026-08-10",{"impact":321,"substance":321,"depth":361,"authority":524,"freshness":702,"relevant":455,"comment":1497},"省级财政金融互动政策，含具体补助与贴息标准，对农业经营主体有实质利好，信息增量高。",[1499],{"name":1495,"url":1492},[1451,1453,641,1501,1502],"LPR","贴息",{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},[],{"status":598,"locked":580,"version":4,"method":599,"evidence":1506},{"candidates":1507,"agricultural_evidence":603},[1508,1512,1516],{"code":503,"score":385,"supported":603,"title":1509,"abstract":1510,"tags":1511},[652],[],[641,1453],{"code":340,"score":717,"supported":603,"title":1513,"abstract":1514,"tags":1515},[],[342],[],{"code":549,"score":717,"supported":603,"title":1517,"abstract":1518,"tags":1519},[],[1482],[],[1521,1522],"农业信贷担保 财政金融 四川 贴息","农业信贷担保 财政金融","农业信贷担保财政金融四川贴息-482","2026-08-13T23:53:44.145233Z",{"id":1526,"title":1527,"url":1528,"summary":1529,"summary_zh":11,"content":1530,"source_name":1531,"source_url":11,"published_at":1532,"category":579,"cover_url":11,"hotness":385,"is_selected":580,"score":921,"score_detail":1533,"sources":1535,"tags":1537,"primary_topic":1542,"secondary_topics":1543,"classification":1544,"search_phrases":1556,"slug":1559,"view_count":828,"doi":11,"paper":11,"created_at":1560},421,"Debt financing and agricultural income among large-scale grain producers: evidence from Jiangsu Province, China","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-8\u002F20260806165737778.htm","发表于《Frontiers in Sustainable Food Systems》。基于江苏省LSGPs(大规模粮食生产者)调查数据,构建债务融资—要素投入—农业收入理论框架,从融资规模与融资结构双视角展开实证。研究核心问题:大规模粮食生产者仍面临组织能力弱、生产成本上升与经营效率低等约束,其根源在于自有资本有限与规模经营资本需求之间的错配严重制约农业收入持续增长。高效农村金融市场通过改善投资能力、促进农业现代化与增强风险韧性缓解资本约束,但中国农村金融市场仍存在发展不均与服务供给不足。论文发表于Frontiers in Sustainable Food Systems。","《Frontiers in Sustainable Food Systems》：Debt financing and agricultural income among large-scale grain producers: evidence from Jiangsu Province, China\n\n**编辑推荐：**\n\n实现大规模粮食生产者（large-scale grain producers, LSGPs）农业收入增长对保障粮食安全与促进农业可持续发展至关重要。基于2022年江苏省645户LSGPs调查数据，研究人员采用普通最小二乘法（ordinary least squ\n\n实现大规模粮食生产者（large-scale grain producers, LSGPs）农业收入增长对保障粮食安全与促进农业可持续发展至关重要。基于2022年江苏省645户LSGPs调查数据，研究人员采用普通最小二乘法（ordinary least squares, OLS）、两阶段最小二乘法（two-stage least squares, 2SLS）与中介效应模型，考察债务融资对农业收入的影响、机制及规模边界。结果显示，总信贷、传统信贷、民间信贷与贸易信贷对农业收入呈倒U形影响，而数字信贷（digital credit）具有显著正向影响。机制分析表明，债务融资主要通过转入耕地规模、机械投资与基础设施投资提升农业收入；传统信贷与数字信贷经全部三条渠道起效，贸易信贷经转入耕地规模与机械投资起效，民间信贷主要经转入耕地规模起效。债务融资对农业收入的影响亦存在教育异质性：相较初中及以下学历农户，高中及以上学历农户的总信贷与贸易信贷拐点更高，且数字信贷的正向收入效应更显著。研究发现，金融政策应有序推进数字信贷发展、将债务融资控制在适度区间、强化对生产性投资的支持，并按农户受教育程度提供差异化金融服务。\n\n研究背景方面，增加农业收入是现代农业发展核心目标，对保障粮食安全与推进乡村振兴具有根本意义。规模化经营成为农业现代化关键路径，但大规模粮食生产者（LSGPs）仍面临组织能力弱、生产成本上升与经营效率低等约束，其根源在于自有资本有限与规模经营资本需求之间的错配严重制约农业收入持续增长。高效农村金融市场通过改善投资能力、促进农业现代化与增强风险韧性缓解资本约束，但中国农村金融市场仍存在发展不均与服务供给不足，LSGPs融资需求未根本解决。既有文献沿农户债务融资行为、债务融资对要素投入决策影响、债务融资对农业收入影响三条线索展开，但对规模经营下债务融资是否存在适度边界、不同渠道异质效应及作用机制缺乏系统检验。为此，研究人员利用江苏省LSGPs调查数据构建债务融资—要素投入—农业收入理论框架，从融资规模与融资结构双视角展开实证，论文发表于《Frontiers in Sustainable Food Systems》。\n\n关键技术方法上，研究人员基于2022年江苏省常州、无锡、南通、泰州、淮安、徐州、宿迁七市分层随机抽取645户有效LSGPs样本（以50亩为大规模经营阈值），以农业净收入对数为主被解释变量，以总信贷及传统信贷、数字信贷、民间信贷、贸易信贷四类渠道为解释变量，以转入耕地规模、机械投资、基础设施投资为中介变量，控制户主年龄、教育年限、健康、家庭人口、非农收入、技术示范户、自有耕地、地权稳定性等变量并加入城市固定效应。估计策略依次采用OLS基准回归（含融资一次项与平方项捕捉倒U形）、以前期信贷及其平方项为工具变量的2SLS处理双向因果与遗漏变量偏误、utest与边际效应分析严格检验倒U形、逐步回归结合bootstrap（500次）的中介效应模型，以及按户主受教育年限以9年为切分的异质性分组回归。\n\n研究结果部分，第4.1节基准回归表明，总信贷对农业收入呈倒U形，拐点约121万元，94.88%样本低于拐点；传统信贷、民间信贷、贸易信贷同样倒U形，拐点分别约65万、52万、53万元；数字信贷线性显著为正。第4.2节内生性与稳健性检验中，2SLS以去年信贷及其平方项为工具变量，Hausman与DWH检验确认内生性，一阶段F均超10，结论与OLS一致；utest显示区间内斜率由正转负且极值落Fieller置信区间；边际效应在P25—P90为正、样本最大值转负；对农业收入1%缩尾及控制其他信贷渠道后结果稳健。第4.3节机制分析显示，转入耕地规模对全部四类渠道均具中介效应；机械投资中介传统、贸易、数字信贷，不中介民间信贷；基础设施投资仅中介传统与数字信贷。第4.4节教育异质性表明，总信贷倒U形在两组的拐点分别为117.57万与122.44万元；贸易信贷拐点由49.54万升至60.34万；数字信贷在初中及以下组不显著，在高中及以上组线性显著为正。\n\n讨论与结论翻译部分，研究人员基于江苏省645户LSGPs田野调查数据，使用OLS、2SLS与中介效应模型考察债务融资对农业收入的影响，发现总信贷呈倒U形，数字信贷正向，传统、民间、贸易信贷倒U形，超90%样本低于拐点。机制上转入耕地规模中介全部渠道，机械投资中介传统、贸易、数字信贷，基础设施投资仅中介传统与数字信贷。教育异质性显示高中及以上组总信贷与贸易信贷拐点更高，数字信贷正效应集中于该组。政策含义包括：信贷供给应控于适度区间；有序推进数字信贷并提升LSGPs使用能力；规范民间与贸易信贷互补作用；优化对转入耕地、机械与基础设施的生产性信贷支持；按教育程度提供差异化金融服务。研究局限为截面数据难以捕捉动态长期效应、工具变量排除限制未完全验证、样本仅限江苏需谨慎外推。","Frontiers in Sustainable Food Systems 2026-08-06","2026-08-06T00:00:00Z",{"impact":361,"substance":759,"depth":508,"authority":345,"freshness":69,"relevant":455,"comment":1534},"基于江苏645户数据，系统考察债务融资对农业收入的影响，方法严谨，结论具有政策参考价值，但时效性稍差。",[1536],{"name":1531,"url":1528},[1538,1539,1540,1541,693],"江苏","债务融资","大规模粮食生产者","农业收入",{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},[],{"status":598,"locked":580,"version":4,"method":599,"evidence":1545},{"candidates":1546,"agricultural_evidence":603},[1547,1551],{"code":503,"score":400,"supported":603,"title":1548,"abstract":1549,"tags":1550},[],[693],[693],{"code":549,"score":717,"supported":603,"title":1552,"abstract":1553,"tags":1555},[],[1554],"农业现代化",[],[1557,1558],"大规模粮食生产者 债务融资 农业收入 农村金融","大规模粮食生产者 债务融资","大规模粮食生产者债务融资农业收入农村金融-421","2026-08-11T23:57:03.342056Z",{"id":1562,"title":1563,"url":1564,"summary":1565,"summary_zh":11,"content":1566,"source_name":1567,"source_url":11,"published_at":1568,"category":579,"cover_url":11,"hotness":385,"is_selected":580,"score":921,"score_detail":1569,"sources":1571,"tags":1573,"primary_topic":1580,"secondary_topics":1581,"classification":1582,"search_phrases":1597,"slug":1600,"view_count":385,"doi":1601,"paper":11,"created_at":1602},269,"Digital agricultural technology innovation and agricultural green total factor productivity: county-level evidence from China","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1898854\u002Fabstract","2026年8月3日Frontiers in Sustainable Food Systems上线的县级面板实证研究,基于2014-2021年中国县级平衡面板数据,结合县级社会经济统计与农业专利记录,开发县级数字农业技术创新(DATI)测量指标,系统考察DATI对农业绿色全要素生产率(AGTFP)的影响。研究发现:第一,DATI显著提升AGTFP,但其效应因区域、数字基础设施禀赋、农业发展条件而异;第二,DATI对AGTFP的影响呈现显著阈值特征,数字基础设施与财政支农力度是关键阈值变量,一旦跨越相关阈值,估计效应由负转正;第三,DATI呈现显著空间依赖性,但其空间溢出效应有限,表明数字农业技术创新所产生的绿色生产率增益主要呈现本地化特征。研究为通过加强农村数字基础设施、深化数字技术在农业生产中的应用、因地制宜推进数字化与绿色化协同转型提供县级证据支持。","## Abstract\n\nDigital agricultural technology innovation (DATI) has become an important driver of agricultural modernization and green transformation. By integrating county-level socioeconomic statistics with agricultural patent records, this study develops a county-level measure of DATI and provides more fine-grained evidence on how digital agricultural technology innovation affects agricultural green total factor productivity in China. Using a balanced panel of Chinese counties data from 2014 to 2021, we examine the effect of DATI on agricultural green total factor productivity (AGTFP). The results reveal three main findings. First, DATI significantly improves AGTFP, although its effects differ across regions, digital infrastructure endowments, and agricultural development conditions. Second, the effect of DATI on AGTFP displays pronounced threshold characteristics, with digital infrastructure and fiscal support for agriculture acting as key threshold variables; once the relevant thresholds are crossed, the estimated effect turns from negative to positive. Third, DATI exhibits significant spatial dependence, but its spatial spillover effects are limited, indicating that the green productivity gains generated by digital agricultural technology innovation remain largely localized. These findings provide fine-grained county-level evidence on the role of DATI in promoting green agricultural development and underscore the need to strengthen rural digital infrastructure, deepen the application of digital technologies in agricultural production, and tailor the coordinated digital and green transformation of agriculture to local conditions.\n\n## 1 Introduction\n\nAgricultural green total factor productivity is widely recognized as a key indicator of agricultural green development and sustainability. Enhancing AGTFP is essential not only for improving resource-use efficiency and environmental performance but also for advancing agricultural modernization and rural revitalization. In recent years, the rapid integration of digital technologies into agricultural production has created new opportunities for agricultural green transformation. Existing studies suggest that digital technologies can improve information acquisition, expand market access, and enhance resource allocation efficiency, thereby promoting agricultural performance and rural development (; ; ). Meanwhile, the proliferation of digital platforms, e-commerce networks, and information and communication technology (ICT) services has significantly expanded market opportunities and improved agricultural productivity, particularly in rural areas where traditional information channels remain limited (; ; ). Digital agricultural technology innovation, represented by big data, artificial intelligence (AI), smart agricultural machinery, and digital management platforms, has emerged as a critical force driving agricultural green development.\n\nThe relationship between digitalization and AGTFP has attracted increasing attention in the agricultural economics. Existing evidence indicates that digital technologies can promote AGTFP through technological innovation, industrial upgrading, and improvements in production efficiency (; ; ). From green development perspective, technological innovation is widely regarded as a fundamental driver of green productivity growth, as it enhances resource-use efficiency, reduces environmental costs, and facilitates environmentally directed technological change (; ). These findings suggest that digital agricultural technology innovation is not only an important manifestation of agricultural digital transformation but also a potential pathway for simultaneously achieving productivity growth and environmental sustainability.\n\nHowever, the effectiveness of digital agricultural technology innovation depends on the successful adoption and diffusion of new technologies. Existing studies emphasize that learning processes, social interactions, and absorptive capacities are crucial in determining whether technological advances can be translated into productivity gains (; ; ). As digital technologies become increasingly embedded in agricultural production systems, digital agricultural technology innovation can accelerate the diffusion of green technologies, improve production efficiency, reduce production risks, and facilitate agricultural modernization (; ). Nevertheless, its impacts are unlikely to be uniform across regions, as they are conditioned by differences in digital infrastructure, institutional environments, and local absorptive capacities. Existing evidence further shows that high-quality innovation can significantly enhance green productivity and strengthen the environmental benefits of technological progress (; ). Moreover, innovation activities often generate knowledge diffusion and interregional learning effects, enabling technological benefits to extend beyond local jurisdictions (; ). Given that knowledge spillovers and factor mobility frequently transcend administrative boundaries, innovation activities tend to exhibit significant spatial dependence and spillover effects (; ). Therefore, digital agricultural technology innovation may not only improve local AGTFP but also promote agricultural green development in neighboring regions through technology diffusion and knowledge spillovers.\n\nOverall, existing studies have highlighted the positive role of digitalization in agricultural green development. However, several important gaps remain. First, most existing studies rely on provincial- or prefecture-level data, while county-level evidence remains relatively scarce despite counties being the primary units for agricultural production and technology adoption in China. In China, counties constitute a key spatial unit for agricultural production, technology extension, infrastructure provision, and rural policy implementation, and a large share of agricultural activities is organized at the county level. Compared with analyses based on provincial or prefecture-level data, a county-level approach can more precisely capture local heterogeneity in agricultural resource endowments, digital infrastructure, technology diffusion capacity, and policy implementation. This finer spatial scale is therefore particularly suitable for examining the effect of digital agricultural technology innovation on agricultural green total factor productivity. By combining county-level panel data with an SBM-based measure of AGTFP that accounts for undesirable outputs, this study provides a more disaggregated empirical assessment of the green productivity effects of digital agricultural technology innovation. In doing so, it extends the literature on digital agriculture and agricultural green development by moving beyond broad measures of digitalization and by identifying the role of agricultural digital innovation at the spatial level where agricultural technologies are adopted and rural policies are implemented.\n\nDigitalization is typically measured using broad indicators such as digital economy indices, internet penet ration rates, or digital finance indicators, whereas relatively few studies directly capture digital agricultural technology innovation from the perspective of agricultural digital innovation and agricultural technology patents. Third, although increasing attention has been paid to threshold effects and spatial dependence, a unified analytical framework integrating digital agricultural technology innovation, threshold effects, spatial spillovers, and AGTFP remains underdeveloped. To address these gaps, this study employs Chinese county-level panel data and incorporates digital agricultural technology innovation, threshold models, and spatial Durbin models into a unified analytical framework to systematically examine the impact of digital agricultural technology innovation on AGTFP and its underlying mechanisms. This study provides new county-level evidence on how digital agriculture contributes to agricultural green transformation and sustainable development.\n\nThis study contributes to the existing literature in three principal respects. First, it shifts the analytical focus from general digitalization to digital agricultural technology innovation. Using county-level agricultural patent information, we capture the creation and accumulation of digital technological knowledge in agriculture and examine how agricultural digital technologies affect county-level AGTFP. Second, the study provides county-level evidence and thereby addresses the relative scarcity of fine-grained empirical evidence in the existing literature. Counties are key spatial units for agricultural production, technology adoption, infrastructure provision, and rural policy implementation in China; thus, a county-level perspective allows for a more precise identification of local heterogeneity in the effect of digital agricultural technology innovation on AGTFP. Third, within an integrated framework linking digital agricultural technology innovation and green productivity, this study examines whether DATI improves AGTFP, thereby deepening our understanding of the mechanisms through which digital agriculture contributes to green agricultural transformation.\n\n## 2 Theoretical analysis and research hypotheses\n\n### 2.1 Digital economy and agricultural green total factor productivity\n\nDigital economy has become an important driver of agricultural green transformation. find that digital inclusive finance significantly improves AGTFP by promoting agricultural technological innovation and industrial structure optimization. emphasize that digital finance promotes AGTFP partly through digital village development. explored the impact of digital transformation on supply chain resilience among Chinese listed agricultural firms. The results demonstrate that digital transformation significantly enhances supply chain resilience. Overall, this strand of literature suggests that the digital economy improves AGTFP through digital infrastructure, financial inclusion, agricultural innovation, industrial upgrading, and rural digital transformation. Recent studies have extended this line of research from the perspectives of digitalization, digital technology adoption, and digital village development. shows that the use of digital technologies can effectively improve agricultural productivity across EU countries. further indicate that ICT adoption contributes to higher land and labor productivity in agriculture. provide recent evidence that agricultural and rural digitalization significantly increases agricultural green total factor productivity (AGTFP), while show that digital village development affects AGTFP through both supply-side and demand-side mechanisms. Taken together, the existing literature suggests that the digital economy can improve AGTFP through digital infrastructure, financial inclusion, agricultural innovation, industrial upgrading, ICT diffusion, and rural digital transformation. Based on the above analysis, we propose research hypothesis 1 as follows:\n\n> _H1_: Digital technology applications significantly promote AGTFP.\n\n### 2.2 Digital technology applications and agricultural green total factor productivity\n\nAnother strand of literature focuses on the application of digital technologies in agricultural production. show that big data in smart farming improves decision-making efficiency across agricultural production and the food supply chain. show that digital agriculture development significantly contributes to AGTFP improvement. demonstrate that deep learning can be applied to crop monitoring, disease detection, yield prediction, and agricultural automation. shows that meaningful participation in digital technologies varies across actors, and that differences in technological perceptions and interpretations influence the diffusion and adoption of digital agricultural technologies. emphasize that farmers’ technology adoption behavior determines whether precision agricultural technologies can effectively improve production efficiency. Therefore, digital technology applications promote AGTFP mainly by improving input-use efficiency, reducing agricultural pollution, strengthening monitoring capacity, and improving production decision-making.\n\nDigital agricultural technologies are not merely general ICT applications, but are embedded in agricultural production systems, farm management, and technology adoption decisions. discuss the opportunities and potential dilemmas associated with agricultural digitalization, suggesting that digital transformation in agriculture should be evaluated in relation to production efficiency, inclusiveness, and sustainability. emphasizes that digital transformation of the agricultural sector can reshape production practices through data collection, monitoring, decision support, and precision input application. further show that the digital revolution in agriculture is driven not only by farmers’ demand but also by supply-side actors, including input firms, software providers, digital service platforms, and technology start-ups. argues that digital innovations can contribute to sustainable and resilient agricultural systems by improving productivity, reducing environmental pressure, and strengthening adaptive capacity. also show that farmers’ preferences and perceived technology attributes are important determinants of precision farming adoption. In addition, agricultural technology innovation and agricultural patents provide an important foundation for understanding DATI. examine China’s agricultural patents and show that agricultural patent value has changed amid the rapid expansion of patent applications, indicating that agricultural patents can reflect important dimensions of agricultural technological innovation. Related studies highlight the roles of environmental regulation, digital technologies, and technological progress in agricultural green development. shows that environmental regulation promotes agricultural green innovation and AGTFP, emphasize the potential of agricultural IoT for monitoring, precision management, and data-driven decision-making. further demonstrate that green innovation, human capital, and infrastructure are important drivers of agricultural green productivity growth.\n\nIn this study, the role of agricultural technological innovation is conceptualized as a transmission mechanism rather than a moderating effect. A moderating effect would imply that agricultural technological innovation changes the strength or direction of the relationship between digital technology applications and AGTFP. However, our theoretical framework emphasizes that digital agricultural technology applications affect AGTFP through specific transmission channels. More specifically, digital agricultural technologies improve agricultural production efficiency, enhance agricultural value creation, facilitate technology adoption and diffusion, and improve rural development conditions. These changes subsequently contribute to the improvement of AGTFP. Therefore, the mechanism is examined through channel variables that capture productivity enhancement and rural development improvement. Based on the above analysis, we propose research hypothesis 2 as follows:\n\n> _H2_: Digital agricultural technology applications promote AGTFP by enhancing agricultural value creation and improving rural development conditions.\n\n### 2.3 Spatial effects of digital agricultural technology innovation on agricultural green total factor productivity\n\nAccording to the theories of new economic geography and spatial spillovers, knowledge, technology, and production factors can flow across regional boundaries, thereby generating significant spatial interactions. Digital agricultural technology innovation is characterized by strong openness, connectivity, and diffusibility. Consequently, it may not only improve local AGTFPbut also influence on neighboring regions through knowledge spillovers, technology diffusion, and factor mobility. Existing studies have documented significant spatial spillover effects of digitalization. found that the digital economy promotes agricultural green development and generates positive effects on neighboring regions through spatial interaction mechanisms. further demonstrated that the digital economy exerts significant spatial spillover effects on green total factor productivity, primarily through green technological innovation and industrial upgrading. showed that carbon trading policies not only improve AGTFP in pilot regions but also generate spillover effects on surrounding areas through technology diffusion and policy transmission. found that the digital economy significantly enhances green total factor productivity and exhibits strong spatial dependence. further confirmed that the digital economy improves green productivity through technological innovation diffusion and cross-regional factor mobility, thereby generating significant spatial spillover effects. Therefore, the knowledge spillover, technology diffusion, and resource allocation effects generated by digital agricultural technology innovation may extend beyond administrative boundaries and influence AGTFP in neighboring regions. Digital agricultural technology innovation may not only promote local agricultural green transformation but also facilitate agricultural green development in surrounding regions through interregional learning and information sharing. Based on the above analysis, the following hypothesis is proposed:\n\n> _H3_: Digital agricultural technology innovation exhibits spatial correlation and generate positive spatial spillover effects on agricultural green total factor productivity.\n\nTo clarify the theoretical basis of [Figure 1](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1898854\u002Fabstract#fig1), this study constructs the analytical framework by integrating technological innovation theory, innovation diffusion theory, green total factor productivity theory, and agricultural sustainable development theory. Digital agricultural technology innovation (DATI) refers to the innovation, diffusion, and application of digital technologies in agricultural production, agricultural resource allocation, rural governance, and agricultural industrial chains. It includes not only the generation of digital agricultural technologies, such as digital patents and intelligent agricultural equipment, but also their practical application in agricultural production and rural development.\n\nFigure 1\n\nBased on this conceptual framework, DATI affect AGTFP through three main pathways. First, the application of digital technologies in agriculture promotes agricultural digitalization and intelligent transformation, improves the allocation and utilization efficiency of agricultural resources, and thereby facilitates the green transformation of agricultural production. Second, DATI accelerates agricultural technology diffusion and innovation, promotes the modernization of agricultural industrial chains, and enhances agricultural technological progress and industrial upgrading. Third, DATI supports rural digitalization and green transformation, improves the rural ecological environment and factor mobility, and thus creates favorable conditions for the growth of AGTFP. [Figure 1](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1898854\u002Fabstract#fig1) illustrates these theoretical mechanisms.\n\n## 3 Data, variables and methodology\n\n### 3.1 Empirical model\n\nThe empirical models are organized according to the research hypotheses. [Equation 1](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1898854\u002Fabstract#E1) is used to estimate the baseline effect of DATI on AGTFP. [Equation 2](https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1898854\u002Fabstract#E2) is used","Frontiers in Sustainable Food Systems 2026-08-03","2026-08-03T00:00:00Z",{"impact":361,"substance":321,"depth":508,"authority":584,"freshness":408,"relevant":455,"comment":1570},"基于县级数据的实证研究，揭示数字农业技术创新对绿色全要素生产率的影响及门槛效应，方法新颖，结论可靠，对政策制定有参考价值。",[1572],{"name":1567,"url":1564},[1574,1575,1576,1577,1578,1579],"Frontiers","空间溢出","阈值效应","农业绿色全要素生产率","数字农业技术创新","县级面板",{"code":503,"slug":504,"name":505,"path":506,"parent_code":480},[],{"status":598,"locked":580,"version":4,"method":599,"evidence":1583},{"candidates":1584,"agricultural_evidence":603},[1585,1589,1593],{"code":503,"score":717,"supported":603,"title":1586,"abstract":1587,"tags":1588},[],[591],[],{"code":16,"score":717,"supported":603,"title":1590,"abstract":1591,"tags":1592},[],[888],[],{"code":40,"score":455,"supported":580,"title":1594,"abstract":1595,"tags":1596},[],[],[1575],[1598,1599],"农业绿色全要素生产率 数字农业技术创新 县级面板 空间溢出","农业绿色全要素生产率 数字农业技术创新","农业绿色全要素生产率数字农业技术创新县级面板空间溢出-269","10.3389\u002Ffsufs.2026.1898854\u002Fabstract","2026-08-07T00:18:31.266403Z",{"technologies":1604,"keywords":1607},[1605,1606],{"name":870,"count":455},{"name":1097,"count":455},[1608,1609,1610,1611,1612,1613,1614,1615,1616,1617,1618,1619],{"name":766,"count":173},{"name":590,"count":828},{"name":641,"count":717},{"name":693,"count":717},{"name":691,"count":717},{"name":591,"count":717},{"name":981,"count":69},{"name":592,"count":69},{"name":1453,"count":69},{"name":1310,"count":455},{"name":1192,"count":455},{"name":928,"count":455}]