[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1251":3,"related-1251":76},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":30,"classification":31,"search_phrases":49,"slug":52,"view_count":53,"doi":54,"paper":55,"created_at":75},1251,"Digital technology and grain industrial chain resilience: mechanisms, heterogeneity, and threshold effects","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1847872","Introduction Against the backdrop of rising global food system vulnerabilities driven by climate shocks, geopolitical disruptions and pandemic aftershocks, enhancing grain industrial chain resilience has become a strategic priority for countries worldwide. Existing literature has widely documented the productivity gains of digital technology in agriculture, yet the causal pathways and boundary conditions through which digitalization shapes grain industrial chain resilience remain under-explored. Methods Drawing on panel data for 285 prefecture-level cities in China spanning 2014 to 2024, this study develops a composite digital technology index and assesses the resilience of the grain industrial chain across three dimensions grounded in resilience theory: resistance, recovery, and transformation capacity. Employing a two-way fixed effects model, instrumental variable estimation, panel threshold model and mediated effects framework, we examine the causal impact, transmission mechanisms and heterogeneous patterns of digital technology on grain industrial chain resilience. Results The results indicate that digital technology exerts a significant positive effect on grain industrial chain resilience, and this finding remains robust after endogeneity adjustment and multiple robustness checks. Mechanism analysis demonstrates that digital technology enhances resilience through two complementary channels: improving resource allocation efficiency and fostering industrial synergy and integration. Heterogeneity analysis shows that the resilience-enhancing effect of digital technology is stronger in eastern regions and plain areas, while dimensional differences appear only as a point-estimate pattern and lack statistical verification. Threshold tests confirm two single-threshold effects, with digital infrastructure and farmers' digital literacy serving as boundary conditions. Further dimensional analysis shows a differentiated constraint pattern. Discussion This study provides empirical evidence and targeted policy references for digital transformation in the grain sector.",null,"Frontiers in Sustainable Food Systems","2026-08-25T00:00:00Z","论文",10,false,79,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,24,19,12,2,1,"基于中国地级市面板数据的实证研究，揭示数字技术提升粮食产业链韧性的机制与边界条件，对政策制定有参考价值。",[24],{"name":9,"url":6},[26,27,28,29],"数字农业","农业信息化","粮食安全","产业链韧性",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":35},"unclassified","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":47,"reason":48},[37,42],{"code":38,"score":20,"supported":13,"title":39,"abstract":40,"tags":41},"I1",[],[],[27,26],{"code":43,"score":21,"supported":13,"title":44,"abstract":45,"tags":46},"A8",[],[],[28],true,"缺少足够的主题证据",[50,51],"产业链韧性 农业信息化 数字农业 粮食安全","产业链韧性 农业信息化","产业链韧性农业信息化数字农业粮食安全-1251",3,"10.3389\u002Ffsufs.2026.1847872",{"doi":54,"openalex_id":56,"authors":57,"venue":9,"cited_by_count":66,"oa_url":67,"card":68,"direction":72,"ingested_from":74},"W7204197128",[58,61,63],{"name":59,"orcid":60},"Hao Li","https:\u002F\u002Forcid.org\u002F0009-0007-0547-5071",{"name":62,"orcid":8},"Yuqiao Ma",{"name":64,"orcid":65},"Zihan Yang","https:\u002F\u002Forcid.org\u002F0000-0003-4305-049X",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1847872\u002Fpdf",{"tldr":69,"method":70,"finding":71,"direction":72,"opportunity":73},"研究数字技术对中国粮食产业链韧性的影响、机制与边界条件。","基于2014-2024年285个城市面板数据，采用双向固定效应、工具变量、面板门","数字技术显著提升粮食产业链韧性，通过优化资源配置和产业协同，且存在基础设施和农民数字素养的门槛效应。","农业人工智能与决策模型","可探索数字技术对粮食产业链韧性的非线性影响，特别是不同维度（如恢复力）的差异化约束机制。","openalex","2026-09-01T04:03:09.775335Z",{"total":77,"page":21,"page_size":77,"items":78},6,[79,139,192,272,318,383],{"id":80,"title":81,"url":82,"summary":83,"summary_zh":8,"content":8,"source_name":84,"source_url":8,"published_at":85,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":86,"score_detail":87,"sources":91,"tags":93,"primary_topic":96,"secondary_topics":101,"classification":102,"search_phrases":125,"slug":128,"view_count":66,"doi":8,"paper":129,"created_at":138},5299,"刘宝涛等《统计与决策》:数字农业与粮食绿色生产效率的耦合协调特征及障碍机制","https:\u002F\u002Fguoyanwang.clcn.net.cn\u002FDRCNet.Mirror.Documents.Web\u002FDocSummary.aspx?DocID=8315929&leafID=18927","吉林农业大学经济管理学院刘宝涛副教授等基于2011—2023年中国31个省份面板数据,运用熵权TOPSIS法、超效率SBM模型测算数字农业发展水平和粮食绿色生产效率;研究期内数字农业发展水平呈\"小幅波动、平稳上升\"时序演化趋势,空间格局呈东南高、西北低、中部均衡;耦合协调度表现为波动上升趋势、由失调衰退向协调发展;粮食产量、科学研究和技术服务业固定资产投资额是主要障碍因子。","统计与决策(2026,11)","2026-10-15T00:00:00Z",78,{"impact":88,"substance":16,"depth":88,"authority":89,"freshness":77,"relevant":21,"comment":90},18,14,"基于31省11年面板数据的实证研究，方法规范、结论有政策参考价值，但属学术论文，公共影响有限，可入精选但非头条。",[92],{"name":84,"url":82},[26,27,28,94,95],"耦合协调","绿色生产",{"code":38,"slug":97,"name":98,"path":99,"parent_code":100},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":103,"locked":13,"version":33,"method":34,"evidence":104},"classified",{"candidates":105,"agricultural_evidence":47},[106,111,115,120],{"code":38,"score":107,"supported":47,"title":108,"abstract":109,"tags":110},13,[26],[26],[27,26],{"code":43,"score":21,"supported":13,"title":112,"abstract":113,"tags":114},[],[],[28],{"code":116,"score":21,"supported":13,"title":117,"abstract":118,"tags":119},"H8",[],[],[95],{"code":121,"score":21,"supported":13,"title":122,"abstract":123,"tags":124},"I4",[],[],[94],[126,127],"刘宝涛 数字农业 粮食绿色生产效率","吉林农业大学 数字农业 耦合协调","刘宝涛数字农业粮食绿色生产效率-5299",{"doi":8,"openalex_id":8,"authors":130,"venue":8,"cited_by_count":66,"oa_url":8,"card":131,"direction":135,"ingested_from":137},[],{"tldr":132,"method":133,"finding":134,"direction":135,"opportunity":136},"测度2011—2023年中国31省数字农业与粮食绿色生产效率的耦合协调及障碍因子。","熵权TOPSIS法、超效率SBM模型，31省面板数据。","耦合协调度波动上升、由失调转向协调；粮食产量与科研投资是主要障碍因子。","农业绿色发展与碳","可深入探究障碍因子的省际异质性与动态传导机制，并检验数字农业对绿色效率的门槛或空间溢出效应。","agent","2026-10-19T00:07:48.451681Z",{"id":140,"title":141,"url":142,"summary":143,"summary_zh":8,"content":8,"source_name":144,"source_url":142,"published_at":145,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":146,"score_detail":147,"sources":150,"tags":152,"primary_topic":155,"secondary_topics":156,"classification":157,"search_phrases":170,"slug":173,"view_count":66,"doi":174,"paper":175,"created_at":191},5579,"A Composite Field-Evaluation Framework for Digital Agriculture Maps","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11283264\u002Fv1","A Composite Field-Evaluation Framework for Digital Agriculture Maps。Research Square","Research Square","2026-10-21T00:00:00Z",38,{"impact":148,"substance":77,"depth":148,"authority":77,"freshness":12,"relevant":21,"comment":149},8,"预印本提出数字农业地图的田间复合评估框架，方法有一定新意，但摘要信息极少、未经同行评审，公共影响有限，暂不宜进入每日精选。",[151],{"name":144,"url":142},[26,27,153,154],"遥感","地图评估",{"code":38,"slug":97,"name":98,"path":99,"parent_code":100},[],{"status":103,"locked":13,"version":33,"method":34,"evidence":158},{"candidates":159,"agricultural_evidence":47},[160,165],{"code":38,"score":107,"supported":47,"title":161,"abstract":163,"tags":164},[162],"digital agriculture",[162],[27,26],{"code":166,"score":21,"supported":13,"title":167,"abstract":168,"tags":169},"I3",[],[],[153],[171,172],"Research Square 数字农业","农业信息化 地图评估 数字农业 遥感","ResearchSquare数字农业-5579","10.21203\u002Frs.3.rs-11283264\u002Fv1",{"doi":174,"openalex_id":176,"authors":177,"venue":144,"cited_by_count":66,"oa_url":142,"card":8,"direction":190,"ingested_from":74},"W7223099987",[178,181,184,187],{"name":179,"orcid":180},"Phillip Lanza","https:\u002F\u002Forcid.org\u002F0000-0002-5545-1744",{"name":182,"orcid":183},"David G. Rossiter","https:\u002F\u002Forcid.org\u002F0000-0003-4558-1286",{"name":185,"orcid":186},"Diana Stuart Sinton","https:\u002F\u002Forcid.org\u002F0000-0001-5828-9001",{"name":188,"orcid":189},"Louis Longchamps","https:\u002F\u002Forcid.org\u002F0000-0002-4761-6094","数字乡村与农业信息化","2026-10-22T11:20:47.463814Z",{"id":193,"title":194,"url":195,"summary":196,"summary_zh":197,"content":8,"source_name":198,"source_url":195,"published_at":199,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":200,"score_detail":201,"sources":206,"tags":208,"primary_topic":212,"secondary_topics":217,"classification":218,"search_phrases":249,"slug":252,"view_count":66,"doi":253,"paper":254,"created_at":271},5491,"Analysis of the Impact of Digital Technology on the Resilience of Anhui’s Agricultural Industry Chain: Threshold Effect of Relative Grain Yield","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010656","Improving the resilience of the agricultural industry chain to frequent external shocks and the intertwined agricultural production risks is critical for ensuring national food security and sustainable agricultural development. Taking the 16 prefecture-level cities in Anhui Province—a major grain-producing region in China—as the research sample, two composite proxy indices were constructed in this study; namely, an agricultural industry chain resilience index built upon aggregate official statistics (rather than direct observed shock-response outcomes) and an index capturing broad rural digital development instead of purely agriculture-specific digital adoption. Based on balanced panel data from 2015 to 2024, two-way fixed-effect, mediation-effect, and panel-threshold models were adopted in this study to examine the association, mediating mechanism, regional heterogeneity, and threshold characteristics conditioned on the relative grain yield. The empirical results show that the rural-oriented digital development index is significantly and robustly positively associated with our composite agricultural industry chain resilience proxy. Although agricultural TFP exhibits a statistically detectable partial mediating pattern in this correlational relationship, the economic magnitude of this indirect channel is extremely modest. As such, this finding indicates a potential theoretical transmission pathway rather than providing definitive empirical evidence for a causal mechanism. The promotion effect exhibits clear regional heterogeneity, with the strongest effect in Northern Anhui, followed by Central Anhui, while the coefficient is positive but insignificant in Southern Anhui. Furthermore, a single-threshold effect of the relative grain yield is identified; that is, the positive association between rural-oriented digital development and resilience proxy cannot be statistically detected when the grain yield remains below the threshold. Only after the agricultural grain yield increases can digital-related rural development release its resilience-related empowering dividends. We note that both composite indices are subject to measurement constraints linked to the local agricultural economic scale and broad rural digital conditions; these limitations are further discussed in the paper. This study provides empirical correlational evidence for the implementation of differentiated digital agriculture policies in major grain-producing provinces.","提升农业产业链对频繁外部冲击与交织叠加的农业生产风险的韧性，对保障国家粮食安全和农业可持续发展至关重要。本研究以中国粮食主产省安徽省的16个地级市为研究样本，构建了两个复合代理指标：一是基于官方统计数据总量（而非直接观测的冲击—响应结果）构建的农业产业链韧性指数，二是刻画农村数字化广泛发展（而非单纯农业领域数字化应用）的指数。基于2015—2024年平衡面板数据，本研究采用双向固定效应模型、中介效应模型和面板门槛模型，考察二者之间的关联、中介机制、区域异质性以及以相对粮食产量为条件变量的门槛特征。实证结果表明，农村导向的数字化发展指数与本研究的农业产业链韧性复合代理指标之间存在显著且稳健的正向关联。尽管农业全要素生产率在这一相关关系中呈现出统计上可检测的部分中介模式，但该间接渠道的经济量级极为有限。因此，这一发现指示的是一种潜在的理论传导路径，而非为因果机制提供确凿的实证证据。其促进效应表现出明显的区域异质性，皖北效应最强，皖中次之，皖南系数为正但不显著。此外，还识别出相对粮食产量的单一门槛效应，即当粮食产量低于门槛值时，农村导向的数字化发展与韧性代理指标之间的正向关联在统计上无法被检测到；只有在农业粮食产量提高之后，数字相关农村发展才能释放其韧性赋能红利。我们注意到，两个复合指数均受到与地方农业经济规模和农村数字化总体状况相关的测度约束；这些局限性在文中进一步讨论。本研究为粮食主产省实施差异化数字农业政策提供了实证相关性证据。","Sustainability","2026-10-20T00:00:00Z",74,{"impact":202,"substance":203,"depth":204,"authority":107,"freshness":148,"relevant":21,"comment":205},16,20,17,"基于安徽16市面板数据的实证研究，方法规范、结论有区域异质性价值，但属省级学术论文，公共影响有限，适合作为主题页聚合素材而非头条精选。",[207],{"name":198,"url":195},[26,209,28,210,211],"数字乡村","农业产业链韧性","安徽农业",{"code":43,"slug":213,"name":214,"path":215,"parent_code":216},"yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[],{"status":103,"locked":13,"version":33,"method":34,"evidence":219},{"candidates":220,"agricultural_evidence":47},[221,227,231,238,244],{"code":43,"score":222,"supported":47,"title":223,"abstract":224,"tags":226},7,[],[28,225],"food security",[28],{"code":38,"score":222,"supported":47,"title":228,"abstract":229,"tags":230},[],[26,162],[26],{"code":232,"score":77,"supported":47,"title":233,"abstract":234,"tags":237},"G",[],[235,236],"农村发展","rural development",[],{"code":239,"score":53,"supported":47,"title":240,"abstract":241,"tags":243},"J1",[],[242],"农业政策",[],{"code":245,"score":21,"supported":13,"title":246,"abstract":247,"tags":248},"I6",[],[],[209],[250,251],"安徽 农业产业链韧性 数字技术","安徽 粮食产量 数字乡村","安徽农业产业链韧性数字技术-5491","10.3390\u002Fsu182010656",{"doi":253,"openalex_id":255,"authors":256,"venue":198,"cited_by_count":66,"oa_url":195,"card":266,"direction":190,"ingested_from":74},"W7223005655",[257,260,262,264],{"name":258,"orcid":259},"Lan Ma","https:\u002F\u002Forcid.org\u002F0000-0003-2468-9917",{"name":261,"orcid":8},"Yuyan Li",{"name":263,"orcid":8},"张国亮",{"name":265,"orcid":8},"Yaoyao Li",{"tldr":267,"method":268,"finding":269,"direction":190,"opportunity":270},"以安徽16市面板数据检验农村数字发展对农业产业链韧性的影响及粮食产量门槛效应。","2015—2024年安徽16市平衡面板，双向固定效应、中介效应与面板门槛模型。","数字发展显著提升产业链韧性，存在粮食相对产量单门槛，且皖北效应最强、皖南不显著。","可细化数字技术指标、引入真实冲击响应韧性测度，并探究门槛形成机制与皖南失效原因。","2026-10-21T23:30:29.219265Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":8,"content":8,"source_name":277,"source_url":275,"published_at":278,"category":11,"cover_url":8,"hotness":279,"is_selected":13,"score":280,"score_detail":281,"sources":284,"tags":288,"primary_topic":291,"secondary_topics":292,"classification":293,"search_phrases":300,"slug":303,"view_count":66,"doi":304,"paper":305,"created_at":317},5396,"Understanding the Digital Agriculture Landscape in Central Africa: 2000-2026 Systematic Review.","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.22601.71520","Understanding the Digital Agriculture Landscape in Central Africa: 2000-2026 Systematic Review.。OpenAlex","OpenAlex","2026-10-19T00:00:00Z",25,37,{"impact":148,"substance":77,"depth":148,"authority":77,"freshness":282,"relevant":21,"comment":283},9,"中非数字农业系统综述，主题相关但摘要信息极少，仅题录级元数据，增量与权威性有限，不建议进入每日精选。",[285,286],{"name":277,"url":275},{"name":277,"url":287},"https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.16146.41922",[26,289,27,290],"智慧农业","中非",{"code":38,"slug":97,"name":98,"path":99,"parent_code":100},[],{"status":103,"locked":13,"version":33,"method":34,"evidence":294},{"candidates":295,"agricultural_evidence":47},[296],{"code":38,"score":89,"supported":47,"title":297,"abstract":298,"tags":299},[162],[162],[27,26,289],[301,302],"中非 数字农业","农业信息化 数字农业 智慧农业 中非","中非数字农业-5396","10.13140\u002Frg.2.2.22601.71520",{"doi":304,"openalex_id":306,"authors":307,"venue":8,"cited_by_count":66,"oa_url":275,"card":8,"direction":190,"ingested_from":74},"W7222930115",[308,310,312,315],{"name":309,"orcid":8},"Anani Ogou",{"name":311,"orcid":8},"Henry Chotangui",{"name":313,"orcid":314},"Honoré Beyegue-Djonko","https:\u002F\u002Forcid.org\u002F0009-0004-6447-5889",{"name":316,"orcid":8},"Christopher Mubeteneh Tankou","2026-10-20T23:30:22.542913Z",{"id":319,"title":320,"url":321,"summary":322,"summary_zh":8,"content":8,"source_name":323,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":279,"is_selected":13,"score":324,"score_detail":325,"sources":328,"tags":332,"primary_topic":336,"secondary_topics":337,"classification":338,"search_phrases":366,"slug":369,"view_count":77,"doi":370,"paper":371,"created_at":382},4969,"Digital technology, agricultural resilience, and food security: A mini review","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.2002109\u002Ffull","Shen在《Frontiers in Sustainable Food Systems》(2026)发表的综述综合了国际数字农业文献与中国'数字经济—农业经济韧性'研究两条主线。提出三点发现:一是数字技术通过不同机制强化食物安全的四个维度——可获得性(生产力与投入效率)、可及性(市场信息与数字金融)、稳定性(溯源、预警与保险)、利用性(溯源、营养信息与移动支付);二是准自然实验证据显示数字基础设施、数字金融和人工智能主要通过社会化服务、土地流转、产业融合、人力资本而非仅'采纳'提升粮食生产与价值链韧性;三是收益分布不均,空间溢出方向取决于吸收能力、市场整合与要素流动性。","Frontiers in Sustainable Food Systems 2026",77,{"impact":16,"substance":326,"depth":88,"authority":89,"freshness":20,"relevant":21,"comment":327},21,"核心期刊综述，系统梳理数字技术强化粮食安全四维度的机制与准自然实验证据，兼具国际文献与中国经验，但发布日期不明、时效性偏弱，适合作为主题页深度聚合素材。",[329,330],{"name":323,"url":321},{"name":9,"url":331},"https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.2002109",[26,28,333,334,335],"农业韧性","空间溢出","数字金融",{"code":43,"slug":213,"name":214,"path":215,"parent_code":216},[],{"status":103,"locked":13,"version":33,"method":34,"evidence":339},{"candidates":340,"agricultural_evidence":47},[341,345,352,357,362],{"code":43,"score":282,"supported":47,"title":342,"abstract":343,"tags":344},[225],[],[28],{"code":346,"score":77,"supported":47,"title":347,"abstract":348,"tags":351},"G2",[],[349,350],"土地流转","社会化服务",[],{"code":38,"score":353,"supported":47,"title":354,"abstract":355,"tags":356},4,[],[26],[26],{"code":358,"score":21,"supported":13,"title":359,"abstract":360,"tags":361},"G3",[],[],[335],{"code":121,"score":21,"supported":13,"title":363,"abstract":364,"tags":365},[],[],[334],[367,368],"Frontiers in Sustainable Food Systems 数字农业","数字技术 农业韧性 粮食安全","FrontiersinSustainableFoodSystems数字农业-4969","10.3389\u002Ffsufs.2026.2002109\u002Ffull",{"doi":370,"openalex_id":372,"authors":373,"venue":9,"cited_by_count":66,"oa_url":331,"card":377,"direction":190,"ingested_from":74},"W7223134273",[374],{"name":375,"orcid":376},"Yang Shen","https:\u002F\u002Forcid.org\u002F0000-0002-6781-6915",{"tldr":378,"method":379,"finding":380,"direction":190,"opportunity":381},"综述数字技术通过多机制强化食物安全四维度并提升农业韧性，但收益分布不均。","综合国际数字农业文献与中国数字经济—农业经济韧性研究两条主线。","数字技术经社会化服务、土地流转等提升韧性，收益不均且空间溢出取决于吸收能力。","可探究数字技术收益不均的成因及吸收能力、市场整合对空间溢出方向的调节机制。","2026-10-15T00:06:08.890950Z",{"id":384,"title":385,"url":386,"summary":387,"summary_zh":388,"content":8,"source_name":389,"source_url":386,"published_at":390,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":391,"score_detail":392,"sources":394,"tags":396,"primary_topic":400,"secondary_topics":401,"classification":402,"search_phrases":433,"slug":436,"view_count":21,"doi":437,"paper":438,"created_at":448},4600,"Optimization model of agricultural economic structure based on LSTM","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-026-09594-x","With the development of artificial intelligence (AI) and agricultural big data, the adjustment of agricultural economic structure has shifted from empirical judgment to data-driven decision-making. This study uses the provincial panel data of agricultural economy in Hubei Province from 2010 to 2023 to construct a comprehensive framework of prediction and optimization. The forecasting layer uses the Long Short-Term Memory (LSTM) model to capture the dynamic relationship among agricultural GDP growth rate, main crop output, rural employment structure, agricultural product price index, financial subsidies and agricultural infrastructure investment. The optimization layer uses the prediction results as parameters. It defines three objectives. overall agricultural benefit, structural deviation and food security risk. It also includes restrictions on cultivated land area, budget, labor supply and food security. Based on these backgrounds, the adjustment scheme of agricultural economic structure was formulated. The results show that the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and r of LSTM model are 0.32, 0.45 and 0.91 respectively. Its prediction performance is better than autoregressive moving average (ARIMA), back propagation neural network (BPNN), support vector regression (SVR) and random forest (RF). These findings show that the combination of multivariate collaborative forecasting and structural optimization constraints can improve the efficiency of agricultural resource allocation. This method also provides quantitative support for rural revitalization, food security and digital agriculture policy design.","随着人工智能（AI）与农业大数据的发展，农业经济结构调整已从经验判断转向数据驱动决策。本研究利用2010—2023年湖北省农业经济省级面板数据，构建了预测与优化的综合框架。预测层采用长短期记忆（LSTM）模型，捕捉农业GDP增长率、主要作物产量、农村就业结构、农产品价格指数、财政补贴与农业基础设施投资之间的动态关系。优化层以预测结果作为参数，设定三个目标：农业整体效益、结构性偏差与粮食安全风险，并纳入耕地面积、预算、劳动力供给与粮食安全等约束条件。在此基础上，制定农业经济结构调整方案。结果表明，LSTM模型的平均绝对误差（MAE）、均方根误差（RMSE）与r分别为0.32、0.45和0.91，其预测性能优于自回归移动平均（ARIMA）、反向传播神经网络（BPNN）、支持向量回归（SVR）与随机森林（RF）。这些发现表明，多变量协同预测与结构优化约束的结合能够提高农业资源配置效率。该方法也为乡村振兴、粮食安全与数字农业政策设计提供了量化支持。","Discover Applied Sciences","2026-10-09T00:00:00Z",75,{"impact":202,"substance":203,"depth":204,"authority":107,"freshness":282,"relevant":21,"comment":393},"基于湖北省级面板数据构建LSTM预测与多目标优化框架，方法对比充分、结论有量化支撑，对数字农业与乡村振兴政策设计具参考价值。",[395],{"name":389,"url":386},[26,397,28,398,399],"农业人工智能","农业经济结构","LSTM预测",{"code":43,"slug":213,"name":214,"path":215,"parent_code":216},[],{"status":103,"locked":13,"version":33,"method":34,"evidence":403},{"candidates":404,"agricultural_evidence":47},[405,409,413,419,424,429],{"code":43,"score":222,"supported":47,"title":406,"abstract":407,"tags":408},[],[28,225],[28],{"code":38,"score":222,"supported":47,"title":410,"abstract":411,"tags":412},[],[26,162],[26],{"code":414,"score":353,"supported":47,"title":415,"abstract":416,"tags":418},"I2",[],[417],"artificial intelligence",[397],{"code":232,"score":53,"supported":47,"title":420,"abstract":421,"tags":423},[],[422],"乡村振兴",[],{"code":245,"score":53,"supported":47,"title":425,"abstract":426,"tags":428},[],[427],"农业大数据",[],{"code":239,"score":53,"supported":47,"title":430,"abstract":431,"tags":432},[],[242],[],[434,435],"湖北 农业经济结构 LSTM","农业经济结构 优化模型","湖北农业经济结构LSTM-4600","10.1007\u002Fs42452-026-09594-x",{"doi":437,"openalex_id":439,"authors":440,"venue":389,"cited_by_count":66,"oa_url":386,"card":443,"direction":190,"ingested_from":74},"W7221088951",[441],{"name":442,"orcid":8},"Xue Gong",{"tldr":444,"method":445,"finding":446,"direction":72,"opportunity":447},"用LSTM预测湖北农业经济指标，并构建多目标优化模型调整农业结构。","LSTM预测2010-2023年湖北面板数据，结合多目标优化与约束条件。","LSTM预测精度优于ARIMA、BPNN等，优化方案可提升农业资源配置效率。","可探索将LSTM与多目标优化耦合框架推广至其他省份或作物类型，并引入实时数据流。","2026-10-10T23:30:34.807708Z"]