[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"daily-2026-08-25":3},{"date":4,"title":5,"highlights":6,"content":13,"items":14},"2026-08-25","农业科技多点突破，政策护航粮食安全",[7,8,9,10,11,12],"中国农科院揭示钴胺素合成进化时间，山东农科院小麦品质育种成果登《Nature 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农业农村部详解农业植物实质性派生品种制度","https:\u002F\u002Fso.html5.qq.com\u002Fpage\u002Freal\u002Fsearch_news?docid=70000021_6966a877e5244152","农业农村部详解农业植物实质性派生品种EDV制度，介绍首批涵盖水稻、小麦、棉花等10种作物的实施目录，配套判定指南、判定阈值、鉴定机构与专家库提供支撑保障。商业化一个派生品种必须获得原始品种育种人许可并约定收益分享。修订后的制度将让真正具备原始创新能力的育种主体获得更大市场空间，加快推动新品种选育由数量增长向质量提升转变。近3年全国品种审定数量有所回落、审定品种质量明显提升，已撤销问题品种和仿种子品种4000多个。","农业农村部近日举行新闻发布会，解读农业植物实质性派生品种(EDV)制度，介绍首批涵盖水稻、小麦、棉花等10种作物的实施目录，配套判定指南、判定阈值、鉴定机构与专家库提供支撑保障。\n\n什么是实质性派生品种？发布会上举了个例子：有一个水稻品种A，总体表现都很好，但抗稻瘟病能力较弱，其他的育种者对品种A进行修饰改良，提高其抗稻瘟病能力，而其他性状与品种A保持不变，品种B就是品种A的实质性派生品种。\n\n此次制度落地明确：派生品种若要上市销售、规模化推广，必须事先取得原始品种权人许可，双方协商约定收益分成。\n\n这意味着，育种人花费心血培育的好品种，再也不能被随便改一改就拿走，种业创新从此有了“护身符”——\n\n收益分成有法律兜底，原创品种不再被人白拿白用。培育一个农作物原始品种，往往需要8至10年反复持续试验，耗费大量人力、种质资源与资金。有了这项制度，就能够更大力度地保护育种原始创新的积极性，加快突破性新品种的选育推广。\n\n判定规则一目了然，育种人取证维权有了指引。此次新规配套制定发布了判定阈值，就是实质性派生品种判定时参考的品种遗传相似度临界值，水稻、小麦、棉花、谷子、花生及辣椒等作物EDV阈值综合判定设定为90%，油菜、大白菜、蚕豆、桃等作物设定为85%，不同检测技术所设阈值稍有区别，当遗传相似度达到阈值即触发EDV判定举证责任转移。这一设计将直接扭转过去原始品种权人取证难、鉴定难的困境，大幅降低原始品种权人维权门槛。\n\n全程监管一抓到底，让“换马甲式”育种无处遁形。新规并非单打独斗，近年来，农业农村部实施了加强品种全链条管理专项行动，严把试验测试关口，严格品种命名、来源和新颖性审查，撤销问题品种和仿种子品种4000多个。实施实质性派生品种制度，是加强品种全链条管理专项行动的重要举措，激励育种者沉下心来更多地从事育种创新，减少模仿修饰育种。\n\n此次种业知识产权保护新规落地，将重塑种业创新利益分配格局。短期来看，依赖修饰育种的主体面临成本上升和侵权风险，但从长期看，这项制度将让真正具备原始创新能力的育种主体获得更大市场空间，将加快推动新品种选育由“数量增长”向“质量提升”转变。\n\n近3年，全国品种审定数量有所回落，审定品种的质量明显提升，这是种业振兴行动实施以来严管理、提质量、促原创的阶段性成果。随着品种审定标准持续提高、全链条监管不断深化，再叠加实质性派生品种制度的顶层设计协同发力，未来行业将逐渐告别盲目追求品种申报数量的粗放发展模式，育种研发资源逐步向原始创新、重大性状突破集中。良种不断涌现，才能持续挖掘增产潜力，对冲气候灾害、病虫害等风险，把保障粮食安全的种子牢牢攥在自己手中。\n\n农业植物实质性派生品种制度落地，不是对行业的束缚，而是对种业知识产权保护的强化。当每一个原始品种权人都能公平地分享到种业发展红利，“中国种”护佑“中国粮”的根基将更加牢不可破。","人民日报 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时空大模型","呼伦贝尔农垦时空大模型多智能体寒地农业-919","2026-08-25T00:03:26.562463Z",{"id":498,"title":499,"url":500,"summary":501,"summary_zh":20,"content":502,"source_name":503,"source_url":20,"published_at":23,"category":24,"cover_url":20,"hotness":25,"is_selected":52,"score":504,"score_detail":505,"sources":507,"tags":509,"primary_topic":517,"secondary_topics":523,"classification":524,"search_phrases":536,"slug":539,"view_count":58,"doi":20,"paper":20,"created_at":540},928,"江苏省农科院休闲所花卉团队：绣球叶斑病抗性调控研究取得新进展 登《Plant Cell Reports》","https:\u002F\u002Fwww.jaas.ac.cn\u002Fxww\u002Fxsky\u002Fart\u002F2026\u002Fart_b7bd03e25712455daa0b48a8ac049cf2.html","江苏省农科院休闲所花卉团队在国际知名期刊《Plant Cell Reports》(JCR1区，五年影响因子6.1)发表绣球叶斑病抗性调控研究论文。研究人员基于绣球基因组数据鉴定获得164个AP2\u002FERF家族基因，通过对抗病、感病品种在多主棒孢菌侵染前后的转录组分析筛选得到46个病原菌响应型HmaERF基因，并利用瞬时基因功能验证成功挖掘出绣球叶斑病抗性的正调控子HmaERF87。本研究首次系统解析绣球AP2\u002FERF转录因子家族并鉴定关键抗病基因，丰富绣球抵御多主棒孢菌侵染的转录调控理论。","绣球是世界三大花园植物之一，兼具观赏和经济价值。然而，由多主棒孢菌引起的叶斑病在绣球种植中普遍发生，严重影响观赏品质和产业效益。AP2\u002FERF转录因子家族在植物逆境胁迫和病害防御响应中发挥重要作用，但在绣球花中的功能尚不清楚。近日，国家林草科技创新团队、休闲所花卉团队在国际知名期刊《Plant Cell Reports》（JCR1区，五年影响因子6.1）发表了研究论文《Genome\n\nwide AP2\u002FERF analysis identifies HmaERF87 as a positive regulator of Hydrangea macrophylla leaf spot resistance》，为绣球抗叶斑病育种提供了重要的基因资源与理论支撑。\n\n研究人员基于绣球基因组数据，鉴定获得164个AP2\u002FERF家族基因，并系统解析其染色体分布、保守基序、基因结构与进化特征等信息。通过对抗病、感病品种在多主棒孢菌侵染前后的转录组分析，筛选得到46个病原菌响应型HmaERF基因。启动子分析进一步提示，这些上调表达的基因受茉莉酸信号调控。同时，研究人员利用瞬时基因功能验证，成功挖掘出绣球叶斑病抗性的正调控子HmaERF87。本研究首次系统解析了绣球AP2\u002FERF转录因子家族，并鉴定出关键抗病基因，丰富了绣球抵御多主棒孢菌侵染的转录调控理论。研究结果不仅为绣球抗病分子育种提供了重要的靶标基因，也为其他木本观赏植物抗病相关基因挖掘与机制解析研究提供了参考。\n\n论文第一作者为花卉团队与黑龙江大学联合培养硕士研究生陈姝源和花卉团队刘欣童副研究员，团队首席邓衍明研究员与黑龙江大学吴则东教授则为共同通讯作者。本研究得到了江苏省种业振兴“揭榜挂帅”项目、上海市绿化和市容管理局科技项目、国家林草局华东观赏植物生物学重点实验室开放课题等项目的资助。","江苏省农科院 2026-08-24",79,{"impact":30,"substance":228,"depth":30,"authority":280,"freshness":229,"relevant":32,"comment":506},"绣球抗病基因研究取得新进展，为花卉抗病育种提供重要基因资源，属于农业科技细分领域突破，信息详实，来源权威。",[508],{"name":503,"url":500},[510,511,512,513,514,515,516],"江苏省农科院","绣球","AP2","ERF","叶斑病","抗病育种","Plant Cell Reports",{"code":518,"slug":519,"name":520,"path":521,"parent_code":522},"B4","flowers","花卉与观赏植物","\u002Ftopic\u002Fhorticulture\u002Fflowers","B",[],{"status":51,"locked":52,"version":53,"method":54,"evidence":525},{"candidates":526,"agricultural_evidence":26},[527,532],{"code":518,"score":103,"supported":26,"title":528,"abstract":530,"tags":531},[529],"花卉",[529],[],{"code":165,"score":32,"supported":52,"title":533,"abstract":534,"tags":535},[],[],[515],[537,538],"江苏省农科院 抗病育种 叶斑病 绣球","江苏省农科院 抗病育种","江苏省农科院抗病育种叶斑病绣球-928","2026-08-25T00:03:28.145502Z",{"id":542,"title":543,"url":544,"summary":545,"summary_zh":20,"content":546,"source_name":547,"source_url":20,"published_at":548,"category":407,"cover_url":20,"hotness":25,"is_selected":52,"score":549,"score_detail":550,"sources":552,"tags":554,"primary_topic":560,"secondary_topics":561,"classification":562,"search_phrases":573,"slug":576,"view_count":58,"doi":577,"paper":20,"created_at":578},938,"数字乡村建设对粮食生产韧性的影响 基于30个中国省份面板数据的阈值效应分析","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fnutrition\u002Farticles\u002F10.3389\u002Ffnut.2026.1901996\u002Ffull","原文标题：The impact of digital rural construction on grain production resilience。使用2012-2023年30个中国省份面板数据，构建数字乡村建设与粮食生产韧性的综合指标，采用双向固定效应模型、中介模型与面板阈值模型检验直接效应、传导机制与财政支撑边界条件。结果显示数字乡村建设显著提升粮食生产韧性，新质农业生产力通过升级农业劳动力、生产工具与要素配置部分中介该关系；阈值分析揭示两个财政支撑阈值0.0699和0.1236，低于第一阈值时效应最强，两阈值之间效应减弱，高于第二阈值效应不显著。本研究为通过数字乡村发展与精准财政支撑强化粮食与营养安全提供政策证据。","## Abstract\n\nEnsuring resilient grain production is essential for food availability, price stability, and nutrition security under growing climatic, market, and geopolitical uncertainty. However, limited evidence explains how digital rural construction strengthens resilience specifically at the grain-production stage. Using panel data from 30 Chinese provinces from 2012 to 2023, this study constructs composite indices of digital rural construction and grain production resilience and applies a two-way fixed effects model, a mediation model, and a panel threshold model to examine its direct effect, transmission mechanism, and fiscal boundary condition. The results show that digital rural construction significantly improves grain production resilience, and this conclusion remains robust after sample adjustments, alternative variable measurement, and endogeneity tests. Heterogeneity analysis indicates stronger effects in western China, major grain-producing areas, and production-consumption balanced areas. Mechanism analysis shows that new quality agricultural productive forces partially mediate this relationship by upgrading agricultural labor, production tools, and factor allocation. The threshold results reveal two fiscal-support thresholds, 0.0699 and 0.1236: the effect of digital rural construction is strongest below the first threshold, weakens between the two thresholds, and becomes statistically insignificant above the second threshold. These findings provide policy evidence for strengthening food and nutrition security through digital rural development and more efficient fiscal support.\n\n## 1 Introduction\n\nGlobal food and nutrition security is increasingly exposed to multiple, overlapping sources of uncertainty. In recent years, climate change, extreme weather events, geopolitical conflicts, fluctuations in international grain prices, and disruptions to global supply chains have shifted the food security agenda from a narrow concern with aggregate output to a broader concern with stable access to sufficient, safe, and nutritious food. The State of Food Security and Nutrition in the World 2024 indicates that global progress toward ending hunger, food insecurity, and all forms of malnutrition remains insufficient and uneven, and that billions of people still lack reliable access to healthy diets (). For China, a developing country with a population of more than 1.4 billion, resilient grain production is closely related not only to domestic economic stability and basic livelihood protection but also to the affordability and availability of staple foods that underpin dietary energy supply and nutrition security. Unlike ordinary industrial goods, grain production is jointly shaped by natural conditions, resource endowments, factor inputs, technological capacity, and institutional arrangements. It is therefore cyclical, vulnerable, and partly public-good oriented.\n\nAgainst this background, food security can no longer be understood solely as a static question of output sufficiency. It increasingly depends on whether the production system can maintain stable supply, recover rapidly from shocks, adapt continuously, and support long-term access to nutritionally essential foods. Recent evidence from China’s forest carbon sink research further shows that climate-response capacity is shaped not only by ecological endowments but also by research and development intensity, industrial structure upgrading, governance intervention, and regional spillover effects (). This reinforces the need to examine grain production resilience within a broader framework that links climate pressure, ecological governance, technological innovation, and food and nutrition security capacity.\n\nAt the same time, China has continued to promote the application of digital technologies in agricultural and rural modernization. The Action Plan for Digital Rural Development (2022–2025) emphasizes digital infrastructure upgrading, smart agriculture innovation, and digital governance capacity, with the aim of accelerating the digital transformation of agricultural production and management and achieving initial progress in smart agriculture by 2025 (). The 2024 Central No. 1 Document further calls for continued implementation of the digital rural development strategy, the development of smart agriculture, the narrowing of the urban–rural digital divide, the construction of regional big data platforms, and stronger coordination and sharing of agricultural information. The National Smart Agriculture Action Plan (2024–2028) also proposes that, by the end of 2028, information technology should play a visible role in reducing costs, increasing yields, and improving efficiency in grain, oil, and other important agricultural products (). These policy developments suggest that digital rural construction is becoming an important support for risk warning, resource allocation, and system recovery in grain production. The concept of resilience was first used to explain the capacity of ecological systems to maintain structural and functional stability under external disturbance (). It was later introduced into food systems and agricultural economics to analyze the ability of food systems to sustain supply, nutrition security, and social welfare under shocks and stresses (, ). From this perspective, grain production resilience is not a simple extension of output level. Rather, it captures the integrated capacity of a grain production system to ensure stable supply, respond to risks, stabilize food prices, and maintain the production-side foundation for adequate dietary intake in complex and uncertain environments.\n\nExisting studies on grain production resilience have mainly addressed three issues: measurement, spatiotemporal evolution, and determinants. One strand of research focuses on evaluation systems and regional differences, arguing that grain production resilience in China displays clear spatial disparities and is influenced by agricultural resource endowments, technological conditions, stages of economic development, and infrastructure (). A second strand extends the object of analysis from production alone to the broader food system, emphasizing that food security depends not only on production capacity but also on circulation, consumption, governance, and risk regulation (). A third strand examines the formation of food resilience from the perspectives of digital inclusive finance, labor migration, and farmland operation, suggesting that financial access, factor mobility, and scale operation affect the stability of food systems and grain production by improving resource allocation (, ). These studies provide an important foundation for understanding grain production resilience. However, relatively little attention has been paid to how digital rural construction, as a comprehensive digital and institutional arrangement, may strengthen the production-side basis of food availability, price stability, and nutrition security.\n\nDigital rural construction is a concrete extension of the Digital China strategy to agriculture and rural areas and an important pathway for agricultural and rural modernization. Unlike a narrow focus on information infrastructure, digital rural construction encompasses digital infrastructure, rural industrial digitalization, digital finance, rural e-commerce, digital governance, and the development of agricultural data resources. Its essence lies in embedding digital technologies, data elements, and platform-based organizations into the economic and social operation of rural areas (). The integration of the digital economy and agriculture can promote agricultural modernization by improving infrastructure, optimizing the behavior of agricultural operators, and reshaping industrial processes (). Related evidence from Chinese listed firms shows that digital transformation can reduce supply chain concentration by improving information transparency and information acquisition capability, thereby supporting supply-chain diversification and robustness (). Although this evidence is drawn from the corporate sector, it is consistent with the argument that digital technologies reshape inter-organizational relationships and resource coordination, which is relevant to the resilience of agricultural production and circulation systems. The rural digital economy can also increase agricultural total factor productivity through multiple pathways, including production upgrading, management transformation, and industrial improvement, thereby enhancing the quality of agricultural development (). International research similarly shows that smart farming and digital agriculture can improve agricultural efficiency and sustainability through precision management, resource conservation, risk identification, and knowledge sharing (, ). Taken together, these findings indicate that digital technology is moving from an external auxiliary tool to a key input in the agricultural production function, with deeper implications for production, management, governance, and food-system resilience.\n\nFrom the perspective of grain production resilience, digital rural construction may operate through at least three channels that are relevant to nutrition-oriented food security. First, digital infrastructure improves rural information accessibility and alleviates information asymmetry in traditional grain production. This enables agricultural operators to obtain meteorological information, input prices, market demand, and policy support in a more timely manner, thereby improving risk anticipation and production decision-making. More stable grain production can reduce the likelihood of supply disruption and sharp price fluctuations in staple foods, which are important determinants of household food access. Second, industrial digitalization introduces intelligent agricultural machinery, remote sensing, the agricultural Internet of Things, agricultural big data, and digital extension services into grain production. These technologies improve precision in sowing, irrigation, plant protection, harvesting, and post-disaster recovery. Smart agriculture can strengthen the shock resistance and recovery capacity of grain production systems through intelligent forecasting, disaster identification, and factor allocation (). Third, digital finance, rural e-commerce, and digital governance tools can improve farmers’ financing capacity, market connectivity, and access to public services, thereby enhancing the adaptability of grain production systems under market fluctuations and disaster shocks. Previous research has shown that digital rural construction strengthens the resilience of China’s food system, with rural digital finance and digital service platforms playing relatively stable enabling roles (). Incorporating digital rural construction into the analytical framework of grain production resilience therefore helps explain how the upstream production capacity required for food and nutrition security is formed in the era of digital transformation.\n\nIt is also important to recognize that the effect of digital rural construction on grain production resilience may not be purely linear. On the one hand, the extent to which digital technologies empower agriculture depends on whether productive forces can be upgraded. As the concept of new quality productive forces is extended to agriculture, increasing emphasis has been placed on the coordinated advancement of scientific and technological innovation, data elements, intelligent equipment, and human capabilities. New quality agricultural productive forces are dynamic and regionally differentiated, and their formation is closely associated with agricultural technological progress, the upgrading of production tools, and the reorganization of production factors (). The digital economy can enhance grain production resilience through technological innovation capacity and agricultural product marketing capacity, while digital inclusive finance can strengthen grain production resilience through agricultural technological innovation (, ). This suggests that digital rural construction does not merely improve grain production conditions directly. It may also enhance the stable supply capacity and risk response capacity of grain production systems by upgrading agricultural labor, means of production, and objects of labor. At the same time, the role of digital rural construction is constrained by public resource allocation. Fiscal support for agriculture can provide public investment for agricultural infrastructure, extension services, and risk-prevention systems, all of which are closely related to the capacity of local food systems to maintain stable supply under shocks. Yet if fiscal resources are inefficiently allocated or poorly structured, the enabling effect of digitalization may weaken. Local fiscal expenditure on agriculture, forestry, and water affairs is therefore an important condition for digitalization to strengthen agricultural resilience, and the effect of agricultural informatization on agricultural total factor productivity may also exhibit threshold characteristics (, ).\n\nOverall, the existing literature provides a useful foundation for studying digital rural construction and grain production resilience, but three gaps remain. First, studies of digital rural construction have largely focused on high-quality agricultural development, agricultural total factor productivity, farmers’ income, and rural governance, while systematic evidence on its impact on grain production resilience remains limited. Second, research on food resilience often adopts a food-system or supply-chain perspective, leaving the stability, adaptability, and transformative capacity of the production stage underexplored, even though production shocks are transmitted downstream to food availability, affordability, and diet quality. Third, the mechanisms and boundary conditions through which digital rural construction affects grain production resilience have not been sufficiently clarified, particularly the roles of agricultural productivity upgrading and fiscal support for agriculture.\n\nTo address these gaps, this study uses panel data from 30 Chinese provinces from 2012 to 2023 to examine how digital rural construction affects grain production resilience. After constructing composite indices of digital rural construction and grain production resilience, this study applies a two-way fixed effects model, a mediation model, and a panel threshold model to identify the direct effect, transmission mechanism, and fiscal boundary condition of digital rural construction. The study contributes to the literature in three respects. First, while existing research has mainly examined food-system resilience or the broader impact of the digital economy on agriculture, this study focuses specifically on the grain-production stage, thereby clarifying how digital rural construction contributes to the stability, recovery, and adaptive capacity of staple-food production. This provides production-side evidence for understanding nutrition security, especially the availability and affordability of staple foods in developing-country contexts. Second, beyond estimating the average effect of digital rural construction, this study introduces new quality agricultural productive forces as a mechanism variable, providing evidence that rural digital transformation enhances grain production resilience by upgrading agricultural laborers, production tools, and factor allocation. Third, by incorporating fiscal support for agriculture as a threshold variable, this study identifies the public-finance conditions under which digital rural construction is more or less effective, offering policy insights into how fiscal support can be better aligned with digital rural development to strengthen grain production resilience and, ultimately, the upstream foundation of food and nutrition security.\n\n## 2 Theoretical analysis and research hypotheses\n\n### 2.1 Direct effect of digital rural construction on grain production resilience\n\nGrain production resilience refers to the comprehensive capacity of a grain production system to maintain stable supply, recover rapidly, and continue adapting and upgrading under multiple shocks, including natural disasters, market fluctuations, resource constraints, and changes in the institutional environment. Drawing on digital technologies, digital platforms, and data elements, digital rural construction systematically reshapes agricultural production, operation, service provision, and governance. It thus provides new technological and organizational foundations for improving grain production resilience. The key to digitally enabled high-quality agricultural development is to overcome the constraints of inadequate information, inefficient resource allocation, and weak risk response in traditional agriculture through information sharing, technology diffusion, and factor reconfiguration (, ).\n\nFirst, digital rural construction can strengthen the risk-resistance capacity of grain production systems. Grain production is highly dependent on natural conditions and is characterized by strong seasonality. Weather changes, the spread of pests and diseases, fluctuations in input prices, and shifts in market demand can all affect production stability. Traditional agricultural operators often rely on experience-based judgment and have limited access to information, making it difficult to identify external shocks in a timely manner. By expanding rural broadband networks, mobile communication, agricultural information platforms, digital financial services, and digital extension services, digital rural construction improves agricultural operators’ access to meteorological, market, input, policy, and technical information. More efficient information transmission enables grain producers to adjust planting schedules, input use, and risk-prevention strategies more quickly, thereby enhancing system stability under external disturbance.\n\nSecond, digital rural construction can improve the recovery and adjustment capacity of grain production systems. Resilience is reflected not only in pre-shock protection but also in post-shock recovery. The introduction of remote sensing, the agricultural Internet of Things, intelligent machinery, drone-based field inspection, and digital extension services can improve the speed of disaster identification, machinery dispatch, input delivery, and technical support. The development of digital finance, rural e-commerce, and agricultural socialized service platforms can also ease the financial, market, and service constraints faced by agricultural operators during post-disaster recovery. Digital technology-driven high-quality agricultural and rural development is not simply the replacement of one technology by another; rather, it involves the overall upgrading of production organization, public services, and governance capacity (). In this sense, digital rural construction can shorten the time required for a grain production system to move from a shocked state back to a stable state.\n\nThird, digital rural construction can promote structural optimization in grain production systems. A higher level of resilience does not simply mean returning to the original state after a shock; it also implies upgrading production modes under pressure. By incorporating data elements into agricultural production, digital rural construction shifts grain production from experience-based judgment to data-driven decision-making, from extensive input use to precision management, and from single-link optimization to coordinated optimization across production, circulation, finance, and governance. Digital technologies empower agricultural development not only by improving production efficiency but also by strengthening agricultural value-chain coordination and reorganizing business models (). In the grain se","Frontiers in Nutrition 2026-08-05","2026-08-05T00:00:00Z",78,{"impact":228,"substance":80,"depth":306,"authority":280,"freshness":399,"relevant":32,"comment":551},"基于30省面板数据的实证研究，揭示数字乡村建设对粮食生产韧性的影响及财政支持阈值，结论新颖，对政策制定有参考价值，但发表于20天前，时效性不足。",[553],{"name":547,"url":544},[432,555,556,557,558,559],"Frontiers","阈值效应","粮食生产韧性","30省面板","新质农业生产力",{"code":421,"slug":422,"name":423,"path":424,"parent_code":425},[],{"status":51,"locked":52,"version":53,"method":54,"evidence":563},{"candidates":564,"agricultural_evidence":26},[565,569],{"code":421,"score":280,"supported":26,"title":566,"abstract":567,"tags":568},[432],[432],[432],{"code":209,"score":32,"supported":52,"title":570,"abstract":571,"tags":572},[],[],[557],[574,575],"新质农业生产力 粮食生产韧性 数字乡村 阈值效应","新质农业生产力 粮食生产韧性","新质农业生产力粮食生产韧性数字乡村阈值效应-938","10.3389\u002Ffnut.2026.1901996\u002Ffull","2026-08-25T00:03:29.421132Z",{"id":580,"title":581,"url":582,"summary":583,"summary_zh":20,"content":584,"source_name":585,"source_url":20,"published_at":586,"category":120,"cover_url":20,"hotness":25,"is_selected":52,"score":549,"score_detail":587,"sources":589,"tags":591,"primary_topic":595,"secondary_topics":600,"classification":601,"search_phrases":609,"slug":612,"view_count":58,"doi":20,"paper":20,"created_at":613},917,"中华人民共和国农业农村部公告第1054号：认定9家农药登记试验单位","https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-08-19\u002Fdoc-inipmhfp0028927.shtml","根据农药管理条例、农药登记试验管理办法、农药登记试验单位评审规则有关规定，认定北京丰垣科技有限公司、山西农业大学植物保护学院、江苏辉谱检测技术有限公司、福安市青穗农业技术服务有限公司、山东泛谱检测有限公司、烟台市农产品综合质检中心、广东中科英海科技有限公司、南宁绿多邦农业科技有限公司、新疆维吾尔自治区农业科学院植物保护研究所共9家单位为农药登记试验单位，有效期2026年8月19日至2031年8月18日。","*   [新浪首页](http:\u002F\u002Fwww.sina.com.cn\u002F)\n*   [新闻](http:\u002F\u002Fnews.sina.com.cn\u002F)\n*   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>正文\n\n行情\n\n行情\n\n股吧\n\n新闻\n\n外汇\n\n新三板\n\n# 中华人民共和国农业农村部公告 第1054号\n\n中华人民共和国农业农村部公告 第1054号\n\n2026年08月19日 00:00[农业农村部](https:\u002F\u002Fwww.moa.gov.cn\u002Fgovpublic\u002FZZYGLS\u002F202608\u002Ft20260824_6486946.htm)\n\n[新浪财经APP](https:\u002F\u002Ffinance.sina.com.cn\u002Fmobile\u002Fcomfinanceweb.shtml?source=cjzhengwen04 \"语音播报\")[举报](https:\u002F\u002Ffinance.sina.com.cn\u002Fother\u002Fblank\u002Ffeedback2024.shtml?docId=nipmhfp0028927 \"打开举报页\")[缩小字体](javascript:; \"减小字号\")[放大字体](javascript:;)[收藏](javascript:; 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\"分享到QQ\")[QQ空间](http:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=http%3A%2F%2Fcj.sina.com.cn%2Farticle%2Fnormal_detail%3Furl%3Dhttps%253A%252F%252Ffinance.sina.com.cn%252Fjjxw%252F2026-08-19%252Fdoc-inipmhfp0028927.shtml&title=%E4%B8%AD%E5%8D%8E%E4%BA%BA%E6%B0%91%E5%85%B1%E5%92%8C%E5%9B%BD%E5%86%9C%E4%B8%9A%E5%86%9C%E6%9D%91%E9%83%A8%E5%85%AC%E5%91%8A%20%E7%AC%AC1054%E5%8F%B7&desc=&summary=&pics=http%3A%2F%2Fqr.weibo.cn%2Finf%2Fgen%3Fapi_key%3Dbdccdb65d34320c10b65cdf6c3503a53%26data%3Dhttps%253A%252F%252Ffinance.sina.cn%252Fapp%252FQRtoSFA.shtml%253Fsudatrackvalue%253Dcaitou_inQRcode_PcBottom%2526schema%253Dtype%25253D100%2526docid%253Dnipmhfp0028927%2526sinaglobal%253D34.96.49.16_1787616227.986007%26datetime%3Dnocheck%26level%3DH%26output_type%3Dimg%26redirect%3D0%26size%3D100%26type%3Durl%26sign%3Dde3a6b023a630df888be5951aebf6d2a \"分享到QQ空间\")\n\n根据《农药管理条例》、《农药登记试验管理办法》、《农药登记试验单位评审规则》等有关规定，认定北京丰垣科技有限公司等9家单位为农药登记试验单位（详见附件）。特此公告。\n\n农业农村部\n\n2026年8月19日\n\n![Image 8](http:\u002F\u002Fqr.weibo.cn\u002Finf\u002Fgen?api_key=bdccdb65d34320c10b65cdf6c3503a53&data=https%3A%2F%2Ffinance.sina.cn%2Fapp%2FQRtoSFA.shtml%3Fsudatrackvalue%3Dcaitou_inQRcode_PcBottom%26schema%3Dtype%253D100%26docid%3Dnipmhfp0028927%26sinaglobal%3D34.96.49.16_1787616227.986007&datetime=nocheck&level=H&output_type=img&redirect=0&size=100&type=url&sign=de3a6b023a630df888be5951aebf6d2a)\n\n海量资讯、精准解读，尽在新浪财经APP\n\n[![Image 9: 广告](https:\u002F\u002Fn.sinaimg.cn\u002Fnews\u002F42f7389d\u002F20190523\u002FJiJin300x250.jpg)](http:\u002F\u002Fsaxn.sina.com.cn\u002Fclick?type=bottom&t=UERQUzAwMDAwMDA0NDA5OQ%3D%3D&url=https%3A%2F%2Ffinance.sina.com.cn%2Fmobile%2Fcomfinanceweb.shtml%3Fsource%3DPDPS000000044099&sign=e32b776cb8255330)\n\n新浪财经声明：此消息系转载自合作媒体，新浪财经登载此文出于传递更多信息之目的，文章内容仅供参考，不构成投资建议。\n\n[![Image 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[](http:\u002F\u002Flive.finance.sina.com.cn\u002F)[股市直播](http:\u002F\u002Flive.finance.sina.com.cn\u002F)\n\n*   图文直播间\n*   视频直播间\n\n*   [![Image 20: 数字江恩](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002Fac791ff8\u002F20201116\u002FShuZiJiangEn.png\u002Fw120h120f1q80liv.png)### 数字江恩 2026-08-24 23:54:04 自3994回踩以来，本人一直说回踩没错，只是上周三下跌速度太快有问题，需要震荡来减缓下跌速度而已，下跌没错、下跌没完。目前情况便是如此，3860区间的支撑，昨日最低3855。这里难以有强力走势，只是3879后箱体震荡的延续，震荡后，依旧还要再跌一轮。这里的震荡，只要不突破3956都不改变震荡后再跌一轮的判断。而今天来说，不可以再跌破3860，那么还会高位多震荡些时间。支撑不破是可以维持到明日的。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F2342117515)\n*   [![Image 21: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F496\u002Fw248h248\u002F20201127\u002Fc418-kentcvx7740289.jpg\u002Fw120h120f1q80liv.jpg)### 趋势领涨 2026-08-24 23:46:18 【A股限售股解禁一览：25.99亿元市值限售股今日解禁】Wind数据显示，周二（8月25日），共有5家公司限售股解禁，合计解禁量为2.06亿股，按最新收盘价计算，合计解禁市值为25.99亿元。从解禁量来看，苏盐井神（sh603299）、泰凌微（sh688591）、奥来德（sh688378）解禁量居前，解禁股数分别为1.64亿股、2709.11万股、831.87万股。从解禁市值来看，苏盐井神（sh603299）、泰凌微（sh688591）、奥来德（sh688378）解禁市值居前，解禁市值分别为14.7亿元、7.23亿元、3.63亿元。从解禁股数占总股本比例来看，苏盐井神（sh603299）、泰凌微（sh688591）、奥来德（sh688378）解禁比例居前，解禁比例分别为17.14%、11.21%、3.18%。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n*   [![Image 22: 东方红老陈](https:\u002F\u002Ftvax1.sinaimg.cn\u002Fcrop.0.0.600.600.180\u002F6fe7ef8dly8i6y0x364nlj20go0goq3u.jpg)### 东方红老陈 2026-08-24 23:34:07 【情绪热点短线策略】（情绪+趋势+风口）1、CPO光交换机：短期强于科技整体，也强于光通信整体，有短线机会。不要追涨，只适合低吸策略。2、mRNA：作为首款AI参与的抗癌创新医药医疗，具有里程碑意义，短期内，AI+医药，医疗有机会，等待低吸机会。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1877471117)\n*   [![Image 23: 东方红老陈](https:\u002F\u002Ftvax1.sinaimg.cn\u002Fcrop.0.0.600.600.180\u002F6fe7ef8dly8i6y0x364nlj20go0goq3u.jpg)### 东方红老陈 2026-08-24 23:33:58 2026.8.25【中线波段持股策略】（业绩+趋势+风口）1、科技成长方向：中长期继续看好硬科技赛道，AI算力硬件、半导体、人形机器人（sz300024）、商业航天、创新药，固态电池。2、中线标准持仓40%，保守30%，激进50%。3、采用震荡策略，小跌小买，大跌大买，小涨不动，大涨减仓策略。风险第一，赚钱第二。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1877471117)\n*   [![Image 24: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F496\u002Fw248h248\u002F20201127\u002Fc418-kentcvx7740289.jpg\u002Fw120h120f1q80liv.jpg)### 趋势领涨 2026-08-24 23:14:02 【周二（8月25日）重点关注财经事件和经济数据】① 14:00 德国第二季度未季调GDP② 16:00 德国8月IFO商业景气指数③ 20:15 美国至8月8日当周ADP就业人数周度变动④ 21:00 美国6月FHFA房价指数；美国6月S&P\u002FCS20座大城市房价指数⑤ 22:00 美国7月新屋销售总数年化；美国8月谘商会消费者信心指数；美国8月里奇蒙德联储制造业指数（sz399233）⑥ 次日04:30 美国上周API原油库存](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n*   [![Image 25: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F496\u002Fw248h248\u002F20201127\u002Fc418-kentcvx7740289.jpg\u002Fw120h120f1q80liv.jpg)### 趋势领涨 2026-08-24 23:13:15 =加入潜伏擒牛VIP，享四大顶级服务=【1】购买VIP自动加入私密小直播间！【2】每周3-5只超短金股调入调出服务，适合实时看盘的投资者！【3】每周一份高端内部绝密文章：包含近期布局、热点版块、指数预判！【4】每月2~3只高端中线金股服务！（VIP超短、中线个股均有涉足，让上班族也能跟上VIP节奏！）现月课7.5折，1288元！季课6.9折，3558元，续费季度更划算！新朋友可先月课体验！点网址，直接买，订购地址：【更多独家重磅股市观点请点击】【更多独家重磅股市观点请点击】](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n*   [![Image 26: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F496\u002Fw248h248\u002F20201127\u002Fc418-kentcvx7740289.jpg\u002Fw120h120f1q80liv.jpg)### 趋势领涨 2026-08-24 23:12:52 二、美国财政部可动用近1万亿美元的一般账户为债券回购提供资金昨晚，美国财政部公布可能动用规模接近9500亿~1万亿美元的财政部一般账户，为扩大的国债回购计划提供资金。这个资金看起来比前期的40亿美元回购猛得多。但实际情况是，财政部只是资金暂时垫付，只是把发债压力往后挪，并没有消除融资需求。所以，昨晚美国国债利率只是小幅下跌，美股科技股继续大跌。对A股市场来说，科技股占比较大权重，如果今天科技股继续下跌，对大盘走势当然是不利的，大盘再次回踩3850点附近应该是大概率事件，重点就是留意能否再次探底回升。三、有传言称交易所收紧IPO权威人士表示，交易所确有向券商沟通，核心是针对近期申报项目整体质量参差不齐的现状，要求保荐机构切实履行“看门人”职责，避免盲目“一拥而上”的抢报行为，而非收紧IPO或提高亏损企业门槛。现在每周是4只新股，每个月还来一只大盘股，特别是宇树上市的走势基本引发了众怒，从最高价1100元，到昨天最低602元，只用了4天时间，关键是还没有任何见底的迹象，所以，大家还是要注意回避次新股的风险。对A股市场来说，每次救市都是从收紧IPO开始的，昨晚虽然只是传言，但也透露了一个信号，也就是监管导向是从\"数量\"转向\"质量\"，\"带病闯关\"空间被压缩，但科技研发型、战略新兴类企业的上市通道仍然保留。四、其他消息：1、新易盛（sz300502）：上半年净利润同比增长91% 受益于人工智能算力投资的发展销售收入较上年同期增加。上周中际公布业绩大幅预增，但股价跌的连亲妈都不认识，新易盛（sz300502）现在公布业绩也是大幅预增的，关键就是看市场买不买账吧。2、央行：8月25日开展5000亿元MLF操作，期限为1年期。到期是6000亿，相当于资金回笼了1000亿，对股市是利空。3、燧原科技（sh688801）科创板IPO初步询价日为8月28日，8月31日确定发行价格。燧原科技（sh688801）是国产AI芯片“四小龙”之一，这也又要来一只大盘股了！4、美股存储、光通信板块大跌，闪迪、希捷科技跌超6%，美光科技、西部数据跌超5%，SK海力士跌近5%，今天A股市场科技股有承压。大盘短期还有再次回踩3850点附近的趋势，但回踩还是机会。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n*   [![Image 27: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F496\u002Fw248h248\u002F20201127\u002Fc418-kentcvx7740289.jpg\u002Fw120h120f1q80liv.jpg)### 趋势领涨 2026-08-24 23:12:35 科技大跌 IPO收紧 上海放大招前期大盘上涨时，我们反复提示大盘要回踩3850点附近，上周五大盘无量反弹，我们更是直接亮明观点“最迟下周二大盘向下变盘”。结果，昨天大盘开始向下调头，大盘最低回踩3855点附近。有人说我们分析不准，因为你说的是3850点，但最低是3855点，相差5个点！这种人，我们只能说是太菜了，因为在股市，点位只是一个区间，不可能说完全一致的具体点位，不要说是3855点，即使是3860点，3840点也都是正常的！他们也都是在3850点区间之内。大盘最低3855，与我们提示的3850点附近仅差5个点，且探底后迅速企稳回升，留下了较长的下影线。这不是\"不准\"，这是教科书级别的区间预判。那么大盘调整结束了吗？我们认为大盘短期回踩在空间上基本到位，现在就是看能否出现放量反弹，只要放量站上10日线3930点，大盘才算是真正企稳，否则，后期大盘还有反复。当然，股市走势是受到很多消息和环境影响的，如果外围股市连续走弱，最终还是要影响A股的走势，A股跟跌不跟涨，这是多年不变的规律，主要原因就是A股散户多，市场情绪大，量化资金疯狂，容易助涨助跌，放大短期波动，这些都是其它成熟市场不具备的弊端！一、上海放大招：1、推进高性能算力芯片、通用处理器芯片、存储芯片、互联芯片和智能传感器达到国际先进水平；2、全面提升智能计算能力 攻关高性能、低功耗、高效率的智算芯片；3、加快超导带材规模化发展 拓展核聚变、医疗装备磁体、密集型大容量输电等场景。上海明确表态要攻克高性能算力芯片，对算力、AI方向都是利好，现在关键是看市场买不买账，因为现在市场谈到科技就是谈虎色变，科技每次出现利好，都是资金在逢高撤离，抱团瓦解，最终都是一地鸡毛，这点我们在6月底就有反复提示，现在回过头来看，这个提示是非常准确的，所以，科技股只有大跌以后的机会，短期还是建议再等等吧。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n*   [![Image 28: 趋势领涨](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F496\u002Fw248h248\u002F20201127\u002Fc418-kentcvx7740289.jpg\u002Fw120h120f1q80liv.jpg)### 趋势领涨 2026-08-24 23:12:05 大家早上好！趋势为王，做股海的领航者，新的一天，新的战斗，欢迎你来到本直播室！新进的朋友请注意点赞，收藏本直播室，以方便你下次观看，谢谢大家的支持！【更多独家重磅股市观点请点击】](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F1658605145)\n*   [![Image 29: 北京红竹](https:\u002F\u002Fk.sinaimg.cn\u002Fn\u002Ffinance\u002F4ffadfe8\u002F20170407\u002FBeiJingHongZhuTouXiang.jpg\u002Fw120h120f1q80liv.jpg)### 北京红竹 2026-08-24 09:16:25 最后观点必须明确：1、大级别指数这里不悲观了。2、小级别继续等待做加法的位置，还需要耐心等待。3、想要做加法，就要看银行和科技的跷跷板，银行反弹不止，科技下跌不止。4、科技还能起来吗？这种声音还不多，8月中旬科技反弹时，科技还能回落吗？那时这种声音几乎每天我都能收到很多，现在也是要等这种声音。5、逆人性的数据，融资额割肉盘的出现，也是做加法的信号。](http:\u002F\u002Flive.finance.sina.com.cn\u002Fbozhu\u002F2910812187)\n\n[更多>](http:\u002F\u002Flive.finance.sina.com.cn\u002F)\n\n*   [最近访问](http:\u002F\u002Ffinance.sina.com.cn\u002Frealstock\u002Fcompany\u002Fsh000001\u002Fnc.shtml)\n*   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对公业务成扩表主力](https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-08-24\u002Fdoc-inipiuen3320340.shtml)\n*   09\u002F[“精准滴灌”提振消费 多家银行积极响应信用卡分期贴息](https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-08-24\u002Fdoc-inipiueh2260068.shtml)\n*   10\u002F[“Token贷”来了！Token消耗成银行授信新坐标？](https:\u002F\u002Ffinance.sina.com.cn\u002Fjjxw\u002F2026-08-24\u002Fdoc-inipkriy6265940.shtml)\n\n[![Image 30","农业农村部 2026-08-19","2026-08-19T00:00:00Z",{"impact":29,"substance":306,"depth":280,"authority":31,"freshness":143,"relevant":32,"comment":588},"农业农村部认定9家农药登记试验单位，属全国性政策公告，信息权威但细节有限，时效性稍差。",[590],{"name":585,"url":582},[127,592,593,594],"农药管理","1054号","农药登记试验单位",{"code":596,"slug":597,"name":598,"path":599,"parent_code":137},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection",[],{"status":51,"locked":52,"version":53,"method":54,"evidence":602},{"candidates":603,"agricultural_evidence":26},[604],{"code":596,"score":280,"supported":26,"title":605,"abstract":607,"tags":608},[606],"农药",[606],[592],[610,611],"农药登记试验单位 农业农村部 农药管理 1054号","农药登记试验单位 农业农村部","农药登记试验单位农业农村部农药管理1054号-917","2026-08-25T00:03:26.491678Z",{"id":615,"title":616,"url":617,"summary":618,"summary_zh":20,"content":619,"source_name":406,"source_url":20,"published_at":181,"category":407,"cover_url":20,"hotness":25,"is_selected":52,"score":620,"score_detail":621,"sources":623,"tags":625,"primary_topic":629,"secondary_topics":630,"classification":631,"search_phrases":648,"slug":650,"view_count":32,"doi":20,"paper":20,"created_at":651},939,"数字农业试点对城乡土地利用效率差距的影响 基于700个中国县的双重机器学习证据","https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531","原文标题：Smart Agriculture Construction and the Urban-Rural Gap in Land Use Efficiency: Evidence from Digital Agriculture in China。基于2010-2024年700个中国县的面板数据，将数字农业创新与应用基地试点项目作为准自然实验，应用双重机器学习模型检验智慧农业建设对城乡土地利用效率差距的影响。结果显示智慧农业建设显著缩小城乡土地利用效率差距，渠道证据指向农业技术进步、资本配置改善、农业产业升级；政策效应仅在原贫困县、土地流转率较高县、数字基础较强县显著；分解结果显示该计划提升农村土地利用效率及农村资本与劳动边际收益而城市指标统计不变，表明所观察到的趋同主要源于农村追赶而非城市衰退。本研究从要素配置视角阐明智慧农业建设如何再平衡城乡土地利用并识别异质性与渠道证据的统计边界。","## 1. Introduction\n\nChina’s agricultural production is still characterized by the coexistence of smallholder-based household farming and expanding scale operations. Small and fragmented plots remain widespread, while land transfer and the development of commercial family farms have gradually promoted larger-scale and more specialized production [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B1-land-15-01531)]. This production structure creates practical constraints on the transition toward smart agriculture. Fragmented farmland, relatively high technology costs, gaps in digital infrastructure and technical capacity, and uncertainty about investment returns can restrict the adoption of precision and digital technologies [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B1-land-15-01531),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B2-land-15-01531)]. Farmers do not uniformly reject agricultural modernization: evidence from China indicates broad openness to precision-agriculture technologies, but willingness and readiness to adopt them vary substantially with farm scale, perceived economic returns, access to information, financing, and service support [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B1-land-15-01531)]. Against this background, public smart agriculture programs can serve not only as technology-demonstration projects but also as institutional interventions that lower adoption barriers and facilitate the transition from traditional, decentralized production toward more digitalized, standardized, and intensive agricultural production.\n\nLand use efficiency has become an important constraint on high-quality county development in China as extensive spatial expansion becomes less sustainable [[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B3-land-15-01531),[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B4-land-15-01531)]. The issue is relational as well as aggregate: urban land benefits from denser concentrations of capital, technology, and industry, whereas rural land is more often constrained by lower technological intensity, fragmented management, and limited factor inputs [[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B5-land-15-01531),[6](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B6-land-15-01531),[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B7-land-15-01531),[8](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B8-land-15-01531)]. The resulting within-county disparity can impede urban–rural factor integration, making improvement on the rural side central to efficiency-enhancing convergence.\n\nDigital technologies, represented by the Internet of Things, big data, artificial intelligence, remote sensing, and intelligent machinery, provide a potential route for improving the precision of agricultural production and the productivity of rural land [[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B9-land-15-01531),[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B10-land-15-01531)]. Since 2017, the Digital Agriculture Innovation and Application Base program has introduced precision production, digital management, and intelligent equipment through successive county-level pilots. This staggered policy setting makes it possible to examine whether a production-oriented digital intervention narrows the urban–rural land use efficiency gap and, crucially, whether convergence occurs through rural catch-up rather than urban contraction.\n\nThe adoption constraints observed in China have close international parallels. Evidence from European farms shows that precision-agriculture uptake varies with farm scale, expected returns, investment costs, and regional conditions [[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B11-land-15-01531),[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B12-land-15-01531)]; U.S. evidence likewise documents strong scale gradients in adoption and links precision technologies to technical efficiency [[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B13-land-15-01531),[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B14-land-15-01531)]. Research on smallholders in India and Sub-Saharan Africa highlights information quality, connectivity, affordability, skills, and advisory support as recurring adoption constraints [[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B15-land-15-01531),[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B16-land-15-01531),[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B17-land-15-01531)]. These cross-country regularities provide a comparative benchmark for the Chinese case: scale, digital readiness, and complementary services are general boundary conditions, whereas the collective land system and the national pilot designation process are institutionally specific to China.\n\nThis study makes three contributions. First, it shifts attention from the level of agricultural productivity to convergence within the county urban–rural land use system and distinguishes rural catch-up from urban contraction. Second, it treats agricultural technological progress, capital allocation improvement, and agricultural industrial upgrading as complementary transmission channels rather than isolated mechanisms, clarifying their theoretical connections and the limits of comparing their relative magnitudes. Third, it uses DML to flexibly partial out high-dimensional and nonlinear observed confounding while treating the conventional TWFE event study as an auxiliary diagnostic in view of the staggered timing of treatment.\n\n## 2. Literature Review\n\nExisting research on digital agriculture identifies several routes through which technology can affect agricultural and land productivity. One strand emphasizes production efficiency: sensing, data analytics, variable-rate technologies, and other precision-agriculture tools can improve input targeting and technical efficiency [[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B10-land-15-01531),[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B13-land-15-01531),[18](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B18-land-15-01531),[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B19-land-15-01531),[20](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B20-land-15-01531)]. A second strand focuses on information and coordination. Digital advice, data platforms, and agricultural knowledge networks can reduce information frictions, support technology adoption, and strengthen coordination across production and value chains [[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B9-land-15-01531),[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B15-land-15-01531),[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B17-land-15-01531),[21](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B21-land-15-01531),[22](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B22-land-15-01531),[23](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B23-land-15-01531),[24](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B24-land-15-01531)]. A third strand highlights farm scale and fixed costs. Cross-regional evidence from Europe and the United States, together with reviews of small-scale precision farming, shows that farm size and the ability to spread technology costs materially condition adoption [[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B11-land-15-01531),[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B12-land-15-01531),[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B14-land-15-01531),[25](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B25-land-15-01531),[26](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B26-land-15-01531)].\n\nThe same literature also identifies constraints that can limit or unevenly distribute digital agriculture gains. High up-front costs, limited skills, weak connectivity, and insufficient complementary services can slow adoption, especially among smaller farms and resource-constrained producers [[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B12-land-15-01531),[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B15-land-15-01531),[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B16-land-15-01531),[25](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B25-land-15-01531)]. Data governance, trust, advisory capacity, and the organization of agricultural innovation systems can further shape whether digital tools are converted into effective production decisions [[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B9-land-15-01531),[24](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B24-land-15-01531),[27](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B27-land-15-01531),[28](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B28-land-15-01531),[29](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B29-land-15-01531)]. These constraints imply that digitalization can widen rather than narrow performance differences when access to capital, infrastructure, skills, and services is highly unequal, a concern documented particularly in smallholder settings [[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B16-land-15-01531),[30](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B30-land-15-01531)].\n\nComparative evidence therefore points to conditional rather than universal effects. European studies emphasize the roles of farm structure and adoption costs [[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B11-land-15-01531),[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B12-land-15-01531),[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B19-land-15-01531)], U.S. evidence shows that adoption is closely related to farm scale [[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B13-land-15-01531),[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B14-land-15-01531)], and field evidence from India and other developing-country settings shows that digital advisory services depend on trust, information quality, connectivity, and complementary support [[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B17-land-15-01531),[21](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B21-land-15-01531),[22](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B22-land-15-01531)]. Evidence from Sub-Saharan Africa similarly identifies affordability, infrastructure, skills, and institutional support as central barriers for smallholders [[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B15-land-15-01531),[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B16-land-15-01531)]. These results closely mirror the boundary conditions examined later in this study while also showing that the institutional route through which those constraints operate may differ across countries.\n\nAgainst this international literature, the unresolved issue is not an absence of research on digital agriculture itself. Rather, three more specific gaps remain. First, most studies evaluate farm productivity, technology adoption, technical efficiency, income, or environmental performance, while the relative efficiency of urban and rural land within the same local land system is rarely the outcome of interest [[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B9-land-15-01531),[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B11-land-15-01531),[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B13-land-15-01531),[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B19-land-15-01531),[20](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B20-land-15-01531)]. Second, technological progress, factor allocation, and rural industrial integration are often examined separately even though digital transformation can connect these processes through information, investment, and value-chain coordination [[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B9-land-15-01531),[24](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B24-land-15-01531),[31](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B31-land-15-01531),[32](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B32-land-15-01531)]. Third, conventional linear specifications can be restrictive when policy selection and outcomes depend on high-dimensional and nonlinear county characteristics. Accordingly, this study examines whether the staggered Digital Agriculture Innovation and Application Base program changes the within-county urban–rural gap in land use efficiency, how the adjustment is distributed between the rural and urban sides, and which complementary conditions shape the observed response.\n\n## 3. Institutional Background and Theoretical Analysis\n\n### 3.1. Institutional Background\n\nSmart agriculture construction is an important institutional arrangement formed against the background of the Digital China initiative and the in-depth advancement of the rural revitalization strategy. For a long time, Chinese agriculture has been dominated by traditional production methods, characterized by limited precision, low resource efficiency, and short industrial chains; rural land output has long lagged behind urban land, and the urban–rural gap in land use efficiency has been pronounced. To promote the digital transformation of agriculture and accelerate agricultural modernization, the relevant national authorities launched the construction of Digital Agriculture Innovation and Application Bases (pilots) in 2017. Relying on the Internet of Things, big data, artificial intelligence, and intelligent equipment, the bases carry out digital transformation in grain, cash crops, livestock and poultry, and aquaculture; explore models and pathways for smart agriculture; and are designated and announced in successive batches. In terms of policy content, smart agriculture construction is not only an agricultural technology-upgrading project but also carries a functional orientation toward optimizing agricultural factor allocation and promoting agricultural industrial upgrading. On the one hand, by introducing digital technologies and intelligent equipment, the program raises the precision and intensification of agricultural production and improves the allocation of land, water, fertilizer, and labor, providing technological support for higher rural land output. On the other hand, by developing digitalized, standardized, and scaled production, the program promotes the integration of agriculture with processing, logistics, and services and extends agricultural value chains, creating conditions for rural land to shift from inefficient to efficient use.\n\nChina’s land institutions are also central to interpreting the empirical design. Land in urban areas is state-owned, whereas rural and suburban land is generally collectively owned; farm households hold contractual and land management rights, and agricultural operation can be reorganized through lawful transfer or leasing of land management rights without transferring collective ownership [[33](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B33-land-15-01531),[34](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B34-land-15-01531)]. National land survey and cadastral rules distinguish agricultural land from construction land and provide the classification framework from which the study’s rural agricultural land and urban construction land measures are assembled [[35](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B35-land-15-01531)]. The county land-transfer rate is therefore a meaningful indicator of whether fragmented management rights have been consolidated into operating units that are better able to absorb machinery and precision technologies. “Formerly poverty-stricken counties” refers to counties previously included in China’s national poverty-targeting classification [[36](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B36-land-15-01531)].\n\n### 3.2. Theoretical Analysis\n\nThe urban–rural gap in land use efficiency is defined here as a within-county relative disparity in output density between two land use subsystems: urban construction land supporting secondary- and tertiary-industry output, and rural agricultural land supporting primary-industry output. Because the two subsystems produce different outputs on different land categories, the gap is a scale-free relative measure of output density rather than a like-for-like comparison of production technologies; what it captures is how unevenly economic output is generated per unit of land across the urban and rural subsystems of the same county. The formation and the narrowing of the gap can also operate through fundamentally different mechanisms on the two sides: urban land use efficiency is shaped primarily by agglomeration economies, industrial composition, and the management of construction land supply [[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B3-land-15-01531),[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B5-land-15-01531),[6](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B6-land-15-01531),[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B7-land-15-01531),[8](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B8-land-15-01531)], whereas rural land use efficiency depends on agricultural technology, factor allocation, and the scale of operation [[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B11-land-15-01531),[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B13-land-15-01531),[26](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B26-land-15-01531),[37](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B37-land-15-01531)]. The index itself is symmetric: a smaller gap could arise from higher rural efficiency, lower urban efficiency, or simultaneous changes on both sides, and these possibilities have different economic meanings. Because the Digital Agriculture Innovation and Application Base program directly targets agricultural production, its principal adjustment margin is expected to lie on the rural side: urban land already benefits from denser concentrations of capital, technology, and industry, whereas smart agriculture applications can release rural output previously constrained by information, technology, and factor allocation frictions. The theoretically relevant form of convergence is therefore rural catch-up rather than urban decline; [Section 6.2.1](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#sec6dot2dot1-land-15-01531) later separates the two sides empirically so that the side of adjustment is identified rather than assumed.\n\n**Hypothesis****H1.**\n\nSmart agriculture construction narrows the urban–rural gap in land use efficiency primarily through rural catch-up, that is, by increasing rural land use efficiency without requiring a decline in urban land use efficiency.\n\nFrom the perspective of technological progress, the primary driver of rural catch-up in land use efficiency is the upgrading of agricultural technology. Agricultural production in rural areas has long relied on experience and traditional methods, with low technological content and extensive resource use, leaving the output potential of land unrealized. By introducing the Internet of Things, remote-sensing monitoring, big data analytics, and intelligent equipment into agricultural production, smart agriculture construction promotes crop-condition sensing, precision irrigation and fertilization, intelligent pest and disease control, and intelligent machinery operations, shifting agricultural production from extensive to precision modes. Precision production reduces the waste of water, fertilizer, and pesticides and raises resource efficiency on the one hand, while it increases yields and product quality, raising agricultural output per unit of land, on the other hand [[20](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B20-land-15-01531)]. Evidence from precision-agriculture research links these tools to measurable gains in input targeting and technical efficiency [[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B10-land-15-01531),[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B13-land-15-01531),[18](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B18-land-15-01531),[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B19-land-15-01531)], and digital advisory services can substantially improve the quality and reach of production information [[22](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B22-land-15-01531)]. Meanwhile, demonstration projects and agricultural advisory networks can lower the threshold for surrounding farmers to learn and use modern technologies [[23](https:\u002F\u002Fwww.mdpi.com\u002F2073-445X\u002F15\u002F9\u002F1531#B23-land-15-01531)]. Cross-country studies nevertheless show that such gains depend on complementary skills, connectivity, and service support, so a public demonstration program that bundles equipment, data",77,{"impact":30,"substance":228,"depth":30,"authority":280,"freshness":122,"relevant":32,"comment":622},"基于700个县的双重机器学习证据，研究数字农业试点对城乡土地利用效率差距的影响，方法新颖，数据规模大，结论具有政策参考价值。",[624],{"name":406,"url":617},[626,415,627,417,628,418],"智慧农业","数字农业试点","700县",{"code":444,"slug":474,"name":475,"path":476,"parent_code":425},[],{"status":51,"locked":52,"version":53,"method":54,"evidence":632},{"candidates":633,"agricultural_evidence":26},[634,638,642],{"code":436,"score":30,"supported":26,"title":635,"abstract":636,"tags":637},[439],[626,439,440,441],[626],{"code":444,"score":103,"supported":26,"title":639,"abstract":640,"tags":641},[447],[447],[],{"code":643,"score":58,"supported":26,"title":644,"abstract":645,"tags":647},"G2",[],[646],"土地流转",[],[649,451],"城乡土地利用效率差距 双重机器学习 数字农业试点 智慧农业","城乡土地利用效率差距双重机器学习数字农业试点智慧农业-939","2026-08-25T00:03:29.465281Z"]