[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"daily-2026-10-22":3},{"date":4,"title":5,"highlights":6,"content":12,"items":13},"2026-10-22","农业农村日报：秋粮收获过七成，「十五五」规划落地部署",[7,8,9,10,11],"农业农村部在长沙召开部署会，推动「十五五」农业农村发展规划任务落地，要求紧盯约束性指标，守住粮食安全与不发生规模性返贫致贫两条底线，并加强动态监测与中期评估。","全国秋粮收获过七成，东北过六成、黄淮海与西南过八成；冬小麦播种过一成，冬油菜过三成。各类经营主体已收购中晚稻1800万吨。","渔业渔政管理局解读「十五五」增殖放流：确定适宜物种345种、水域428片，2030年年均放流量稳定在200亿尾左右，区域性和珍贵濒危物种占比提至15%。","中国农科院环发所黑土地抗逆技术在吉林大安实收测产，玉米亩产1376.9公斤，较传统垄作增产20.48%，在春旱导致发芽率降低12.9%背景下实现抗旱增产。","前沿研究：农业产业园促进小农户融入数字化供应链的协同机制；无人机多光谱与VGG21模型识别小麦渍害调控效果，硅肥调控识别正确率超91%。","本期聚焦「十五五」农业农村规划落地部署与增殖放流新目标，跟踪秋粮收获收购进度及黑土地抗逆技术测产成效，并收录数字农业、小农户供应链融入、作物表型识别等前沿论文。\n\n---\n*本日报内容整理自公开来源，学术论文元数据来自 OpenAlex 等开放接口；外文资料已译为中文，翻译与摘要仅供参考；引用与决策请以官方原文与正式出版物为准。*",[14,99,189,257,316,375,426,498,545,600,655,712,780,817,865],{"id":15,"title":16,"url":17,"summary":18,"summary_zh":19,"content":20,"source_name":21,"source_url":19,"published_at":22,"category":23,"cover_url":19,"hotness":24,"is_selected":25,"score":26,"score_detail":27,"sources":34,"tags":36,"primary_topic":42,"secondary_topics":48,"classification":49,"search_phrases":93,"slug":96,"view_count":97,"doi":19,"paper":19,"created_at":98},5497,"农业农村部在长沙召开部署会 推动「十五五」农业农村发展规划任务落地","https:\u002F\u002Fjhs.moa.gov.cn\u002Fghgl\u002F202610\u002Ft20261021_6488825.htm","10月21日召开的部署会提出，农业农村领域各专项规划与各省级地方规划已基本印发，全国「一盘棋」总体格局初步形成。会议要求紧盯约束性指标不折不扣完成，聚焦粮食安全与不发生规模性返贫致贫两条底线，谋划储备打基础利长远的重大项目，并加强规划实施动态监测与中期评估。",null,"10月21日，“十五五”农业农村发展规划任务落实部署会在长沙召开，深入学习贯彻习近平总书记关于“三农”工作的重要论述和重要指示批示精神，贯彻落实党中央、国务院决策部署，就落实《加快农业农村现代化“十五五”规划》作出安排，推动重要指标、重点任务、重大工程落地见效，为“十五五”开好局、起好步奠定基础。\n\n会议指出，国务院印发《加快农业农村现代化“十五五”规划》后，各地深入学习贯彻，对标跟进、上下联动、协同发力，有序推进“十五五”农业农村规划编制工作。目前，农业农村领域各专项规划、各省级地方规划已基本印发，全国农业农村“十五五”规划“一盘棋”总体格局初步形成。\n\n会议强调，要准确把握《加快农业农村现代化“十五五”规划》的任务要求和实施重点，强化对农业农村现代化趋势变化和发展规律的认识，进一步提升抓落实促发展的工作质效。要深化认识抓落实，深入学习领会党的二十届四中全会精神，落实规划部署要求，持续加强宣传贯彻，凝聚共识、汇聚众力扎实推进。要紧盯指标抓落实，不折不扣完成好约束性指标，积极推进预期性指标。要聚焦重点抓落实，坚持产量产能、生产生态、增产增收一起抓，提升农业综合生产能力和质量效益，守牢国家粮食安全底线；推进常态化精准帮扶，守牢不发生规模性返贫致贫底线；学习运用“千万工程”经验，扎实推进宜居宜业和美乡村建设，推动基本实现农业现代化、农村基本具备现代生活条件取得重大进展。要谋细工程抓落实，谋划储备一批打基础、利长远的重大项目，做深做实前期工作，推动项目跟着规划走、资金跟着项目走。要跟踪监测抓落实，加强规划实施的动态监测、中期评估和总结评估，推进全过程全环节调度督促，确保如期实现各项目标任务。要加快农业发展全面绿色转型，加强农业绿色发展水平监测评价，扎实开展新一轮农业资源区划，推行绿色生产方式，提升经营主体绿色发展能力，推进农业生态价值实现，持续擦亮农业农村现代化的绿色底色。\n\n各省、自治区、直辖市和新疆生产建设兵团农业农村部门有关负责同志参加会议，农业农村部畜牧兽医局、种业管理司有关负责同志及黑龙江、江苏、浙江、湖南、广东、四川、云南、青海农业农村部门负责同志、湖南省长沙市负责同志作了交流发言。会议期间，与会代表现场考察了长沙县加快农业农村现代化、推进乡村全面振兴的情况。","农业农村部发展规划司 2026-10-21","2026-10-21T00:00:00Z","政策",10,true,91,{"impact":28,"substance":29,"depth":30,"authority":31,"freshness":24,"relevant":32,"comment":33},28,22,16,15,1,"农业农村部召开全国性部署会，明确“十五五”规划落实路径与重点任务，政策层级高、信息增量足，值得进入每日精选。",[35],{"name":21,"url":17},[37,38,39,40,41],"十五五规划","粮食安全","农业农村现代化","农业绿色发展","千万工程",{"code":43,"slug":44,"name":45,"path":46,"parent_code":47},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":54},"classified",false,"2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":25},[56,63,68,73,78,83,88],{"code":57,"score":58,"supported":25,"title":59,"abstract":61,"tags":62},"G",8,[60],"农村发展",[],[],{"code":43,"score":64,"supported":25,"title":65,"abstract":66,"tags":67},4,[],[38],[38],{"code":69,"score":32,"supported":51,"title":70,"abstract":71,"tags":72},"G4",[],[],[41],{"code":74,"score":32,"supported":51,"title":75,"abstract":76,"tags":77},"H4",[],[],[40],{"code":79,"score":32,"supported":51,"title":80,"abstract":81,"tags":82},"H8",[],[],[40],{"code":84,"score":32,"supported":51,"title":85,"abstract":86,"tags":87},"J1",[],[],[37],{"code":89,"score":32,"supported":51,"title":90,"abstract":91,"tags":92},"J2",[],[],[39],[94,95],"农业农村部 长沙 十五五规划","加快农业农村现代化 十五五规划","农业农村部长沙十五五规划-5497",0,"2026-10-22T00:06:57.871828Z",{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":19,"source_name":105,"source_url":102,"published_at":22,"category":106,"cover_url":19,"hotness":24,"is_selected":25,"score":107,"score_detail":108,"sources":113,"tags":115,"primary_topic":121,"secondary_topics":126,"classification":133,"search_phrases":167,"slug":170,"view_count":97,"doi":171,"paper":172,"created_at":188},5569,"A four-decadal (1981–2024) quality-assured ground-based soybean phenology dataset from Northeast China: a FAIR-compliant resource for climate impact and agricultural adaptation studies","https:\u002F\u002Fdoi.org\u002F10.1080\u002F20964471.2026.2735138","Although satellite remote sensing has been widely used to monitor land surface phenology, long-term, high-quality ground-based observations are essential for validating and calibrating these products, particularly for understanding agricultural ecosystem responses to climate change. However, such ground-truth datasets remain scarce, limiting the full potential of Earth system data applications. Here we present a comprehensive ground-based soybean phenology dataset for Northeast China, a region critical to national food security. The dataset integrates historical paper-based logs (1980s–1990s) with modern electronic records (2000s onwards) from 43 national agrometeorological stations operated by the China Meteorological Administration (CMA), spanning a 15° latitudinal gradient (38–53°N) across Heilongjiang, Jilin, and Liaoning provinces over 44 years (1981–2024). It provides the timing (day of year, DOY) of eight key phenophases (sowing, emergence, three-leaf, branching, flowering, pod formation, seed filling, maturity) and the durations of six intervening growth intervals. A multi-tiered quality assurance framework combining automated consistency checks, statistical outlier detection, and expert arbitration against agronomic principles and auxiliary climate data ensures high data integrity and completeness (>99%; paper era: 93.8%; electronic era: 98.1%). To facilitate reuse, we provide pre-computed kernel density estimates (probability distributions) and univariate linear regression trends for each station and phenological variable, along with 656 diagnostic plots. The dataset is openly available in the Science Data Bank under a CC BY 4.0 license as primary data tables (XLSX) and diagnostic plots (JPEG, 1200 dpi) (https:\u002F\u002Fdoi.org\u002F10.57760\u002Fsciencedb.37245). As a long-term, spatially explicit, FAIR-compliant ground-truth resource, this dataset is valuable for validating satellite-derived phenology products, calibrating crop modules, and investigating climate-driven impacts on agricultural systems.","尽管卫星遥感已被广泛用于监测地表物候，但长期、高质量的地面观测对于验证和校准这些产品至关重要，尤其是对于理解农业生态系统对气候变化的响应而言。然而，此类地面真值数据集仍然稀缺，限制了地球系统数据应用潜力的充分发挥。本文介绍了一套面向中国东北地区的综合性大豆物候地面数据集，该地区对国家粮食安全至关重要。该数据集整合了中国气象局（CMA）运营的43个国家农业气象站的历史纸质记录（20世纪80—90年代）与现代电子记录（2000年代至今），覆盖黑龙江、吉林和辽宁三省跨越15°纬度梯度（38—53°N）、时间跨度44年（1981—2024年）。数据集提供了8个关键物候期（播种、出苗、三叶、分枝、开花、结荚、鼓粒、成熟）的发生时间（年积日，DOY）以及6个中间生长间隔的持续时间。一套多层质量保证框架结合了自动一致性检查、统计异常值检测以及依据农学原理和辅助气候数据进行的专家仲裁，确保了高数据完整性和完整性（>99%；纸质时代：93.8%；电子时代：98.1%）。为便于重复使用，我们提供了每个站点和物候变量的预计算核密度估计（概率分布）和单变量线性回归趋势，以及656幅诊断图。该数据集以CC BY 4.0许可在Science Data Bank公开提供，包括主数据表（XLSX）和诊断图（JPEG，1200 dpi）（https:\u002F\u002Fdoi.org\u002F10.57760\u002Fsciencedb.37245）。作为一个长期、空间明确、符合FAIR原则的地面真值资源，该数据集对于验证卫星反演的物候产品、校准作物模块以及研究气候驱动对农业系统的影响具有重要价值。","Big Earth Data","论文",89,{"impact":29,"substance":109,"depth":110,"authority":111,"freshness":24,"relevant":32,"comment":112},24,19,14,"覆盖东北44年、43站的大豆物候地面观测数据集，质控严格且FAIR开放，对遥感验证与气候影响研究有实质价值，值得入选。",[114],{"name":105,"url":102},[116,117,118,119,120],"大豆","气候变化","东北农业","物候数据","遥感验证",{"code":122,"slug":123,"name":124,"path":125,"parent_code":47},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[127],{"code":128,"slug":129,"name":130,"path":131,"parent_code":132},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":50,"locked":51,"version":52,"method":53,"evidence":134},{"candidates":135,"agricultural_evidence":25},[136,141,147,153,160],{"code":122,"score":31,"supported":25,"title":137,"abstract":139,"tags":140},[138],"soybean",[116,138],[116],{"code":128,"score":24,"supported":25,"title":142,"abstract":143,"tags":146},[],[117,144,145],"农业气象","climate change",[117],{"code":43,"score":148,"supported":25,"title":149,"abstract":150,"tags":152},6,[],[38,151],"food security",[],{"code":154,"score":148,"supported":25,"title":155,"abstract":156,"tags":159},"I3",[],[157,158],"遥感","remote sensing",[],{"code":161,"score":162,"supported":25,"title":163,"abstract":164,"tags":166},"H6",3,[],[165],"农业生态",[],[168,169],"东北 大豆 物候 数据集","大豆 物候 遥感 验证","东北大豆物候数据集-5569","10.1080\u002F20964471.2026.2735138",{"doi":171,"openalex_id":173,"authors":174,"venue":105,"cited_by_count":97,"oa_url":102,"card":181,"direction":185,"ingested_from":187},"W7223091615",[175,178],{"name":176,"orcid":177},"Quanjun Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-6087-3928",{"name":179,"orcid":180},"Dongli Wu","https:\u002F\u002Forcid.org\u002F0000-0003-1676-2264",{"tldr":182,"method":183,"finding":184,"direction":185,"opportunity":186},"发布东北大豆1981–2024年44年地面物候数据集，含8个物候期与6个间隔期。","整合43个农气站纸质与电子记录，经自动校验、统计离群检测和专家仲裁质控。","数据完整度超99%，并提供核密度分布、线性趋势与656张诊断图。","农业遥感与作物表型","可基于该地面真值校准遥感物候产品与作物模型，量化气候变化对大豆物候的影响。","openalex","2026-10-22T11:20:39.146445Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":19,"source_name":195,"source_url":192,"published_at":22,"category":106,"cover_url":19,"hotness":24,"is_selected":25,"score":196,"score_detail":197,"sources":201,"tags":203,"primary_topic":209,"secondary_topics":213,"classification":214,"search_phrases":233,"slug":236,"view_count":97,"doi":237,"paper":238,"created_at":256},5551,"The effect of rural tourism on rural prosperity: evidence from China on agricultural labor productivity, incomes, and household food consumption","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1904226","Policy support in China has disproportionately favored rural tourism as a core pillar of rural revitalization. However, its net effect on agricultural labor productivity, rural income, and household consumption remains empirically untested. This study examines how rural tourism expenditure affects these three dimensions of rural prosperity and evaluates alternative policy instruments to maximize developmental benefits. The analysis employs a dynamic autoregressive distributed lag (ARDL) simulation model using annual Chinese data from 1994 to 2023. The findings reveal that an increase in rural tourism expenditure significantly reduces agricultural labor productivity, rural household income, and rural household consumption in both the short run and the long run. A positive shock to rural tourism expenditure similarly generates a negative response across all three outcomes, with the effects persisting over the forecast horizon. These results suggest that the net effect of rural tourism expansion on rural prosperity is consistently negative, challenging the conventional assumption that rural tourism serves as an unconditional driver of rural development. The findings call for conditional tourism subsidies, complementary investments in agricultural capital, and spatially differentiated rural development policies that safeguard agricultural livelihoods while permitting tourism to develop only where it does not displace farming.","中国的政策支持过度倾向于将乡村旅游作为乡村振兴的核心支柱。然而，其对农业劳动生产率、农村收入和家庭消费的净效应尚未得到实证检验。本研究考察乡村旅游支出如何影响农村繁荣的这三个维度，并评估替代性政策工具以最大化发展效益。分析采用动态自回归分布滞后（ARDL）模拟模型，使用1994年至2023年的中国年度数据。研究发现，乡村旅游支出的增加在短期和长期均显著降低农业劳动生产率、农村家庭收入和农村家庭消费。乡村旅游支出的正向冲击同样对上述三项结果产生负向响应，且效应在预测期内持续存在。这些结果表明，乡村旅游扩张对农村繁荣的净效应始终为负，挑战了乡村旅游无条件作为农村发展驱动力的传统假设。研究结果呼吁实施有条件的旅游补贴、对农业资本进行配套投资，以及空间差异化的农村发展政策，在保障农业生计的同时，仅允许旅游业在不挤占农业的地区发展。","Frontiers in Sustainable Food Systems",87,{"impact":29,"substance":198,"depth":199,"authority":111,"freshness":24,"relevant":32,"comment":200},23,18,"基于1994—2023年中国数据的ARDL实证发现，乡村旅游扩张对农业劳动生产率、农户收入与消费的净效应持续为负，对无条件扶持乡村旅游的政策假设构成有力挑战，值得进入每日精选。",[202],{"name":195,"url":192},[204,205,206,207,208],"乡村振兴","农村消费","乡村旅游","农户收入","农业劳动生产率",{"code":69,"slug":210,"name":211,"path":212,"parent_code":57},"rural-industry","乡村产业与县域经济","\u002Ftopic\u002Frural-services\u002Frural-industry",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":215},{"candidates":216,"agricultural_evidence":25},[217,222,227],{"code":69,"score":31,"supported":25,"title":218,"abstract":220,"tags":221},[219],"rural tourism",[206,219],[206],{"code":57,"score":24,"supported":25,"title":223,"abstract":224,"tags":226},[],[204,60,225],"rural development",[204],{"code":228,"score":64,"supported":25,"title":229,"abstract":230,"tags":232},"G6",[],[231],"rural income",[207],[234,235],"乡村旅游 农业劳动生产率 农户收入","乡村旅游 ARDL 农村消费","乡村旅游农业劳动生产率农户收入-5551","10.3389\u002Ffsufs.2026.1904226",{"doi":237,"openalex_id":239,"authors":240,"venue":195,"cited_by_count":97,"oa_url":249,"card":250,"direction":254,"ingested_from":187},"W7223149969",[241,244,246],{"name":242,"orcid":243},"Jin Liu","https:\u002F\u002Forcid.org\u002F0000-0001-7654-255X",{"name":245,"orcid":19},"Betelhemabraham Geremew",{"name":247,"orcid":248},"Meseret C. Abate","https:\u002F\u002Forcid.org\u002F0000-0002-6486-5607","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1904226\u002Fpdf",{"tldr":251,"method":252,"finding":253,"direction":254,"opportunity":255},"用中国1994-2023年数据检验乡村旅游支出对农业劳动生产率、农村收入与消费的净效应。","动态自回归分布滞后（ARDL）模拟模型，1994-2023年中国年度数据。","乡村旅游支出增加在短期和长期均显著降低农业劳动生产率、农村家庭收入与消费。","数字乡村与农业信息化","可结合县域面板与空间异质性，量化旅游与农业的挤出效应并识别有条件补贴的适用边界。","2026-10-22T11:20:16.227845Z",{"id":258,"title":259,"url":260,"summary":261,"summary_zh":19,"content":262,"source_name":263,"source_url":19,"published_at":264,"category":23,"cover_url":19,"hotness":24,"is_selected":25,"score":196,"score_detail":265,"sources":269,"tags":271,"primary_topic":277,"secondary_topics":283,"classification":284,"search_phrases":311,"slug":314,"view_count":97,"doi":19,"paper":19,"created_at":315},5508,"渔业渔政管理局解读「十五五」增殖放流 2030年区域性与濒危物种占比提至15%","https:\u002F\u002Fnynct.hlj.gov.cn\u002Fnynct\u002Fc115383\u002F202610\u002Fc00_31985415.shtml","农业农村部渔业渔政管理局负责人介绍，「十四五」全国累计放流水生生物苗种1500余亿尾。「十五五」确定适宜增殖放流水生物种345种、适宜水域428片，到2030年年均放流量稳定在200亿尾左右，区域性和珍贵濒危物种放流数量占淡水物种放流数量的比例逐步提升至15%，并首次明确社会公众规模性放流的界定标准。","答：当前，增殖放流物种主要有广布型物种、区域性物种和珍贵濒危物种共3类。经过多年组织实施，广布型物种的“量”已达到较高水平，区域性和珍贵濒危物种放流规模有待提高，放流结构、苗种质量、科学放流和效果监管等“质”的方面还需进一步提升。因此，要把提质增效摆在更加突出的位置，推动增殖放流工作向质的有效提升转变。《通知》坚持因地制宜，确定“十五五”适宜增殖放流水生物种345种、适宜放流水域428片，要求各地结合本地区实际，科学进行生态安全风险评估。坚持生态优先，进一步优化放流结构，逐步减少淡水广布型经济物种放流规模，逐步加大区域性和珍贵濒危物种放流数量。坚持科学规范，强化放流过程监管，严查苗种质量。","黑龙江省农业农村厅 2026-10-19","2026-10-19T00:00:00Z",{"impact":266,"substance":29,"depth":199,"authority":267,"freshness":58,"relevant":32,"comment":268},26,13,"部委层面解读十五五增殖放流提质增效目标与345种428片水域数据，政策条款具体，具全国指导意义，值得精选。",[270],{"name":263,"url":260},[272,273,274,275,276],"十五五","生态保护","增殖放流","渔业资源","濒危物种",{"code":278,"slug":279,"name":280,"path":281,"parent_code":282},"D3","integrated-aquaculture","稻渔种养与生态渔业","\u002Ftopic\u002Faquaculture\u002Fintegrated-aquaculture","D",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":285},{"candidates":286,"agricultural_evidence":25},[287,292,298,303,307],{"code":278,"score":288,"supported":25,"title":289,"abstract":290,"tags":291},12,[274],[274],[274],{"code":282,"score":293,"supported":25,"title":294,"abstract":296,"tags":297},11,[295],"渔业",[295],[],{"code":299,"score":32,"supported":51,"title":300,"abstract":301,"tags":302},"D2",[],[],[275],{"code":161,"score":32,"supported":51,"title":304,"abstract":305,"tags":306},[],[],[273],{"code":84,"score":32,"supported":51,"title":308,"abstract":309,"tags":310},[],[],[272],[312,313],"渔业渔政管理局 增殖放流","十五五 增殖放流 物种","渔业渔政管理局增殖放流-5508","2026-10-22T00:06:58.751656Z",{"id":317,"title":318,"url":319,"summary":320,"summary_zh":19,"content":19,"source_name":321,"source_url":319,"published_at":322,"category":106,"cover_url":19,"hotness":24,"is_selected":25,"score":323,"score_detail":324,"sources":326,"tags":328,"primary_topic":334,"secondary_topics":340,"classification":341,"search_phrases":354,"slug":357,"view_count":97,"doi":358,"paper":359,"created_at":374},5540,"A national study of the effectiveness and co-benefits from actions to mitigate water quality impairment and greenhouse gas emissions from dairy farms in New Zealand","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105026","A national study of the effectiveness and co-benefits from actions to mitigate water quality impairment and greenhouse gas emissions from dairy farms in New Zealand。Agricultural Systems","Agricultural Systems","2026-10-22T00:00:00Z",86,{"impact":29,"substance":29,"depth":199,"authority":111,"freshness":24,"relevant":32,"comment":325},"新西兰全国性奶牛场水环境与温室气体协同减排研究，数据规模大、结论有政策参考价值，但属境外研究，对国内读者直接相关性有限。",[327],{"name":321,"url":319},[329,330,331,332,333],"奶业","新西兰","温室气体减排","水质保护","农业环境政策",{"code":335,"slug":336,"name":337,"path":338,"parent_code":339},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":342},{"candidates":343,"agricultural_evidence":25},[344,349],{"code":335,"score":293,"supported":25,"title":345,"abstract":347,"tags":348},[346],"greenhouse",[346],[],{"code":350,"score":32,"supported":51,"title":351,"abstract":352,"tags":353},"C2",[],[],[329],[355,356],"新西兰 奶牛场 温室气体 减排","农业环境政策 温室气体减排 水质保护 新西兰","新西兰奶牛场温室气体减排-5540","10.1016\u002Fj.agsy.2026.105026",{"doi":358,"openalex_id":360,"authors":361,"venue":321,"cited_by_count":97,"oa_url":319,"card":19,"direction":19,"ingested_from":187},"W7223139855",[362,365,368,370,372],{"name":363,"orcid":364},"Richard William McDowell","https:\u002F\u002Forcid.org\u002F0000-0003-3911-4825",{"name":366,"orcid":367},"Katrina Ann Macintosh","https:\u002F\u002Forcid.org\u002F0000-0002-7232-400X",{"name":369,"orcid":19},"A. Wright-Stow",{"name":371,"orcid":19},"P. Astruc",{"name":373,"orcid":19},"C.X.O. Thiange","2026-10-22T11:20:11.497856Z",{"id":376,"title":377,"url":378,"summary":379,"summary_zh":19,"content":380,"source_name":381,"source_url":19,"published_at":264,"category":382,"cover_url":19,"hotness":24,"is_selected":51,"score":383,"score_detail":384,"sources":387,"tags":389,"primary_topic":394,"secondary_topics":399,"classification":400,"search_phrases":421,"slug":424,"view_count":97,"doi":19,"paper":19,"created_at":425},5502,"全国秋粮收获超七成 中晚稻已收购1800万吨","https:\u002F\u002Fwww.peopleweekly.cn\u002Fhtml\u002F2026\u002Fsannong_1019\u002F279469.html","农业农村部最新农情调度显示，全国秋粮收获过七成，其中东北过六成，黄淮海、西南过八成，西北近八成，长江中下游过六成，华南过三成。冬小麦播种过一成，冬油菜播种过三成。国家粮食和物资储备局数据显示，各类经营主体已收购中晚稻1800万吨；秋粮产量占全年粮食产量约四分之三。","本报北京10月18日电  （记者郁静娴）据农业农村部最新农情调度，全国秋粮收获过七成。其中，东北过六成，黄淮海、西南过八成，西北近八成，长江中下游过六成，华南过三成。同时，各地秋冬种有序推进，冬小麦播种过一成，冬油菜播种过三成。\n\n本报北京10月18日电  （记者王东辉）记者从国家粮食和物资储备局获悉：各地秋粮旺季收购正有序展开。初步统计，各类经营主体已收购中晚稻1800万吨。\n\n当前秋粮已开始收获上市，秋粮产量占全年粮食产量的3\u002F4左右，涉及品种多、数量规模大、覆盖区域广。相关部门将加强秋粮收购工作统筹组织，抓好市场化收购和政策性收储，落实落细各项政策举措，全力保障农民售粮渠道顺畅、市场平稳运行。","人民日报 2026-10-19","报道",85,{"impact":266,"substance":29,"depth":31,"authority":31,"freshness":385,"relevant":32,"comment":386},7,"全国性秋粮收获与收购权威数据，信息增量足、信源权威，时效稍滞后但值得进入每日精选。",[388],{"name":381,"url":378},[38,390,391,392,393],"冬小麦","秋粮收购","农情调度","中晚稻",{"code":395,"slug":396,"name":397,"path":398,"parent_code":47},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":401},{"candidates":402,"agricultural_evidence":25},[403,407,412,417],{"code":395,"score":64,"supported":25,"title":404,"abstract":405,"tags":406},[],[397],[390],{"code":122,"score":162,"supported":25,"title":408,"abstract":409,"tags":411},[],[410],"油菜",[],{"code":43,"score":413,"supported":51,"title":414,"abstract":415,"tags":416},2,[],[],[391,38],{"code":154,"score":32,"supported":51,"title":418,"abstract":419,"tags":420},[],[],[392],[422,423],"全国秋粮收获 中晚稻收购","国家粮食和物资储备局 秋粮收购","全国秋粮收获中晚稻收购-5502","2026-10-22T00:06:58.371818Z",{"id":427,"title":428,"url":429,"summary":430,"summary_zh":431,"content":19,"source_name":195,"source_url":429,"published_at":432,"category":106,"cover_url":19,"hotness":24,"is_selected":51,"score":383,"score_detail":433,"sources":436,"tags":438,"primary_topic":444,"secondary_topics":450,"classification":451,"search_phrases":479,"slug":482,"view_count":97,"doi":483,"paper":484,"created_at":497},5469,"How institutional digitalization reshapes entrepreneurial activity in new agricultural business forms: quasi-natural experimental evidence from China's Digital Village Pilot Policy","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1946061","New agricultural business forms are a key driving force for developing countries to overcome the constraints of traditional agriculture and achieve sustainability in food systems. However, it remains unclear whether institutional digitalization can effectively stimulate entrepreneurial vitality in new agricultural business forms. This study develops a theoretical framework of “opportunity creation–opportunity identification–opportunity transformation” and treats China's Digital Village Pilot Policy (DVPP), implemented in 2020, as a quasi-natural experiment. Based on panel data from 1,743 counties in China from 2014 to 2023, this study employs Double Machine Learning (DML) and Spatial Durbin Difference-in-Differences (SDM-DID) models, among other methods, to systematically identify the impact, transmission mechanisms, heterogeneous effects, and spatial spillover effects of institutional digitalization on Entrepreneurial Activity in New Agricultural Business Forms (EANA). The results reveal that: (1) DVPP significantly enhances EANA, and this finding remains robust after a series of tests, including placebo tests, Propensity Score Matching–Difference-in-Differences (PSM-DID), Synthetic Control Method–Difference-in-Differences (SCM-DID), and instrumental variable (IV) estimation; (2) agricultural technological innovation, digital infrastructure development, and digital financial development serve as mediating mechanisms; (3) the policy effects are more pronounced in counties with higher levels of digital access, better-developed transportation infrastructure, and higher levels of digital financial development, exhibiting a pattern of Central > Western > Eastern regions, and are significantly stronger in areas with advantages in characteristic agricultural products; (4) DVPP generates a significant negative spatial spillover effect on EANA in neighboring counties. This study expands theoretical understanding of the relationship between institutional digitalization and entrepreneurship in new agricultural business forms and provides empirical evidence for China and other developing countries to optimize digital rural development and foster new agricultural business forms.","农业新业态是发展中国家突破传统农业约束、实现食物系统可持续发展的关键驱动力。然而，制度数字化能否有效激发农业新业态的创业活力仍不明确。本研究构建了“机会创造—机会识别—机会转化”的理论框架，并将中国2020年实施的数字乡村试点政策（Digital Village Pilot Policy，DVPP）视为准自然实验。基于中国1743个县2014—2023年的面板数据，本研究采用双重机器学习（Double Machine Learning，DML）和空间杜宾双重差分（Spatial Durbin Difference-in-Differences，SDM-DID）等模型，系统识别制度数字化对农业新业态创业活动（Entrepreneurial Activity in New Agricultural Business Forms，EANA）的影响、传导机制、异质性效应和空间溢出效应。结果表明：（1）DVPP显著提升了EANA，且该结论在经过安慰剂检验、倾向得分匹配—双重差分（PSM-DID）、合成控制法—双重差分（SCM-DID）和工具变量（IV）估计等一系列检验后依然稳健；（2）农业技术创新、数字基础设施建设和数字金融发展发挥中介机制作用；（3）政策效应在数字接入水平较高、交通基础设施较完善和数字金融发展水平较高的县更为显著，呈现出中部>西部>东部的格局，且在特色农产品优势区显著更强；（4）DVPP对邻近县的EANA产生显著的负向空间溢出效应。本研究拓展了关于制度数字化与农业新业态创业之间关系的理论认识，为中国及其他发展中国家优化数字乡村发展、培育农业新业态提供了经验证据。","2026-10-20T00:00:00Z",{"impact":29,"substance":198,"depth":199,"authority":267,"freshness":434,"relevant":32,"comment":435},9,"基于1743个县面板数据的准自然实验，方法扎实、结论有政策参考价值，值得进入每日精选。",[437],{"name":195,"url":429},[439,440,441,442,443],"数字农业","数字乡村","县域经济","农业政策","农村创业",{"code":445,"slug":446,"name":447,"path":448,"parent_code":449},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":452},{"candidates":453,"agricultural_evidence":25},[454,459,466,470,474],{"code":445,"score":31,"supported":25,"title":455,"abstract":457,"tags":458},[456],"digital village",[440,456],[440],{"code":460,"score":148,"supported":25,"title":461,"abstract":462,"tags":465},"I2",[],[463,464],"机器学习","machine learning",[],{"code":57,"score":162,"supported":25,"title":467,"abstract":468,"tags":469},[],[225],[],{"code":69,"score":413,"supported":51,"title":471,"abstract":472,"tags":473},[],[],[443,441],{"code":475,"score":32,"supported":51,"title":476,"abstract":477,"tags":478},"I1",[],[],[439],[480,481],"数字乡村试点 新农业业态","DVPP 农村创业","数字乡村试点新农业业态-5469","10.3389\u002Ffsufs.2026.1946061",{"doi":483,"openalex_id":485,"authors":486,"venue":195,"cited_by_count":97,"oa_url":429,"card":492,"direction":254,"ingested_from":187},"W7223041086",[487,489],{"name":488,"orcid":19},"Su Li",{"name":490,"orcid":491},"Changjun Yang","https:\u002F\u002Forcid.org\u002F0009-0000-6236-3489",{"tldr":493,"method":494,"finding":495,"direction":254,"opportunity":496},"用双重机器学习与空间DID评估数字乡村试点政策对新农业业态创业活力的影响。","基于1743县2014-2023面板数据，用DML与SDM-DID做准自然实验。","试点政策显著提升新农业业态创业，经技术创新、数字基建与数字金融中介，并对邻县有负向溢出。","可探究政策负向空间溢出的形成机制及跨县协同激励设计，避免创业资源虹吸。","2026-10-21T23:30:07.205317Z",{"id":499,"title":500,"url":501,"summary":502,"summary_zh":19,"content":503,"source_name":504,"source_url":19,"published_at":22,"category":106,"cover_url":19,"hotness":24,"is_selected":51,"score":505,"score_detail":506,"sources":508,"tags":510,"primary_topic":515,"secondary_topics":520,"classification":521,"search_phrases":532,"slug":535,"view_count":385,"doi":19,"paper":536,"created_at":544},5537,"农业产业园促进小农户融入数字化供应链：协同机制与制度逻辑","https:\u002F\u002Fkyy.nwafu.edu.cn\u002Fkjjz\u002Fxdkjjz\u002F557ffd662c8e4af98e46fbf90f35d91f.htm","西北农林科技大学阮俊虎教授团队联合德国哥廷根大学于晓华教授，以大荔县国家现代农业产业园等为典型案例，从交易成本理论出发围绕技术赋能、组织整合、规则治理与价值分配四个机制开展案例内分析与跨案例比较，揭示农业产业园作为中国特色制度平台促进小农户融入数字化供应链的深层逻辑。","近日，经济管理学院“数智农业运营管理团队”阮俊虎教授联合德国哥廷根大学于晓华教授在《中国农村经济》发表题为“农业产业园促进小农户融入数字化供应链：协同机制与制度逻辑”的研究论文，通过多案例研究揭示农业产业园促进小农户融入数字化供应链的协同机制，并阐释机制形成与运行的制度逻辑。博士研究生刘晨欢为第一作者，阮俊虎教授为通讯作者，王月博士和于晓华教授为合作学者。\n\n数字技术正深刻重构农产品供应链格局，大型电商平台等供应链核心主体加速构建覆盖生产源头至消费终端的数字化供应链体系，该体系不仅体现为数字技术在生产、销售、物流等环节的应用，更强调不同环节与经营主体之间的系统协同与数据贯通，小农户亟须有效融入数字化供应链以应对市场竞争格局的深刻变化。然而，“大国小农”国情农情下，小农户经营规模较小、土地细碎化程度较高，难以独立对接数字化、规模化的现代供应链体系。促进小农户深度融入数字化供应链，进而实现小农户和现代农业发展有机衔接，成为农业现代化进程中值得关注的重要议题。农业产业园作为区域农业发展的综合性制度平台，兼具资源集聚、组织协同与制度保障三重优势，能够为地方政府及园区经营管理主体协同整合制度资源并协调多元主体关系提供平台支撑，但将资源集聚、组织协同和制度供给优势转化为小农户融入数字化供应链的现实能力，其内在机理尚未得到系统性揭示。\n\n本文立足“农业产业园促进小农户融入数字化供应链的内在机理”这一科学问题，从交易成本理论出发，以大荔县国家现代农业产业园等为典型案例，围绕技术赋能、组织整合、规则治理和价值分配四个机制开展案例内分析与跨案例比较分析，揭示不同园区作用机制的共性、差异及情境适配特征，并从制度层面阐释农业产业园作为中国特色制度平台，促进小农户融入数字化供应链的深层逻辑。研究旨在为农业经营呈现小规模、分散化特征的国家和地区促进小农户和现代农业发展有机衔接提供理论参考与实践借鉴。\n\n该研究得到国家社会科学基金重大项目“我国农产品供应链数字化转型的发展战略与实施路径研究”（23&ZD123）的资助。\n\n论文链接：\n\n[https:\u002F\u002Fzgncjj.ajcass.com\u002F?jumpnotice=201606270007#\u002FissueDetail?contentId=123722&year=2026&issue=10&title=%E5%BD%93%E6%9C%9F%E7%9B%AE%E5%BD%95](https:\u002F\u002Fzgncjj.ajcass.com\u002F?jumpnotice=201606270007#\u002FissueDetail?contentId=123722&year=2026&issue=10&title=%E5%BD%93%E6%9C%9F%E7%9B%AE%E5%BD%95)\n\n编辑：张晴 终审：刘玉峰","中国农村经济 2026-10-21",82,{"impact":199,"substance":29,"depth":199,"authority":31,"freshness":434,"relevant":32,"comment":507},"核心期刊论文，多案例揭示农业产业园促进小农户融入数字化供应链的协同机制与制度逻辑，理论新意足、政策参考价值高，值得进入每日精选。",[509],{"name":504,"url":501},[440,511,512,513,514],"小农户","制度逻辑","数字化供应链","农业产业园",{"code":516,"slug":517,"name":518,"path":519,"parent_code":57},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":522},{"candidates":523,"agricultural_evidence":25},[524,528],{"code":516,"score":288,"supported":25,"title":525,"abstract":526,"tags":527},[511],[511],[511],{"code":445,"score":32,"supported":51,"title":529,"abstract":530,"tags":531},[],[],[440],[533,534],"农业产业园 小农户 数字化供应链","大荔县 国家现代农业产业园","农业产业园小农户数字化供应链-5537",{"doi":19,"openalex_id":19,"authors":537,"venue":19,"cited_by_count":97,"oa_url":19,"card":538,"direction":254,"ingested_from":543},[],{"tldr":539,"method":540,"finding":541,"direction":254,"opportunity":542},"以农业产业园为案例，揭示其促进小农户融入数字化供应链的机制与制度逻辑。","交易成本理论，大荔县等典型案例的案例内分析与跨案例比较。","技术赋能、组织整合、规则治理与价值分配四机制共同推动小农户融入数字化供应链。","可量化检验四机制对农户融入绩效的影响，并比较不同产业园模式的适用边界。","agent","2026-10-22T00:07:10.192700Z",{"id":546,"title":547,"url":548,"summary":549,"summary_zh":19,"content":19,"source_name":550,"source_url":19,"published_at":22,"category":106,"cover_url":19,"hotness":24,"is_selected":51,"score":505,"score_detail":551,"sources":553,"tags":555,"primary_topic":561,"secondary_topics":562,"classification":568,"search_phrases":588,"slug":591,"view_count":32,"doi":19,"paper":592,"created_at":599},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21",{"impact":199,"substance":29,"depth":199,"authority":111,"freshness":24,"relevant":32,"comment":552},"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[554],{"name":550,"url":548},[556,557,558,559,560],"智慧农业","农业遥感","作物表型","无人机多光谱","小麦渍害",{"code":395,"slug":396,"name":397,"path":398,"parent_code":47},[563,564],{"code":128,"slug":129,"name":130,"path":131,"parent_code":132},{"code":154,"slug":565,"name":566,"path":567,"parent_code":449},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":50,"locked":51,"version":52,"method":53,"evidence":569},{"candidates":570,"agricultural_evidence":25},[571,575,579,584],{"code":395,"score":293,"supported":25,"title":572,"abstract":573,"tags":574},[397],[397],[],{"code":154,"score":293,"supported":25,"title":576,"abstract":577,"tags":578},[559],[],[558,557,559],{"code":128,"score":58,"supported":25,"title":580,"abstract":582,"tags":583},[581],"渍害",[],[],{"code":475,"score":32,"supported":51,"title":585,"abstract":586,"tags":587},[],[],[556],[589,590],"江苏省农科院 小麦渍害 多光谱","VGG21 小麦 硅肥调控","江苏省农科院小麦渍害多光谱-5535",{"doi":19,"openalex_id":19,"authors":593,"venue":19,"cited_by_count":97,"oa_url":19,"card":594,"direction":185,"ingested_from":543},[],{"tldr":595,"method":596,"finding":597,"direction":185,"opportunity":598},"用无人机多光谱图像和VGG21模型识别小麦渍害后硅肥与氨基酸调控效果。","大疆精灵4采集冠层多光谱影像，构建VGG21深度学习分类模型。","VGG21对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","可扩展多生育期、多调控组合与轻量化模型，探索渍害胁迫等级与调控效果的同步识别。","2026-10-22T00:07:10.070916Z",{"id":601,"title":602,"url":603,"summary":604,"summary_zh":19,"content":605,"source_name":606,"source_url":19,"published_at":264,"category":382,"cover_url":19,"hotness":24,"is_selected":51,"score":505,"score_detail":607,"sources":610,"tags":612,"primary_topic":618,"secondary_topics":623,"classification":624,"search_phrases":650,"slug":653,"view_count":97,"doi":19,"paper":19,"created_at":654},5512,"中国农科院环发所黑土地抗逆技术吉林大安实收测产 玉米亩产1376.9公斤","https:\u002F\u002Fieda.caas.cn\u002Fxwzx\u002Fxzjl\u002F6365a18c2bc34a96863196ccf9e34af0.htm","国家重点研发计划「黑土地抗干旱冷害减灾增效关键技术与示范」项目在吉林大安开展测产观摩，核心示范田玉米产量达13768.8公斤每公顷，较传统垄作增产20.48%。示范区连续三年增产，从2024年12510.7公斤每公顷提升至2026年13768.8公斤每公顷，在春旱导致发芽率降低12.9%的背景下实现抗旱增产。","10月10日，由国家重点研发计划项目“黑土地抗干旱冷害减灾增效关键技术与示范”玉米现场测产与成果展示观摩会在项目示范区之一的吉林省大安市烧锅镇乡举行。经专家现场测产，项目集成技术模式核心示范田的玉米产量达到13768.8公斤\u002F公顷，较传统垄作增产20.48%，验证了研发技术模式应对主粮作物干旱与冷害胁迫实现减灾增效与地力协同提升的可行性。\n\n松嫩平原西部地区农田土壤瘠薄、春播期易遭遇干旱和低温冷害，影响玉米出苗及稳产。针对这一难题，在中国农业科学院农业环境与可持续发展研究所退化及及污染农田修复团队曾希柏研究员的带领下，团队研发集成了瘠薄黑土分层培肥、保墒增温等关键技术，并结合抗旱耐密农作物品种筛选，集成条耕宽窄行秸秆还田、浅埋滴灌水肥一体化、绿色病虫害防控和全程机械化等技术措施，有效破解了播前干旱、低温导致玉米发芽率低，以及耕层变浅、有机质降低、地力逐年下降等导致农作物生长不良和产量低下等问题，显著提升了作物抵御干旱、冷害等逆境胁迫的能力，降低了灾害损失，在2026年春旱严重、玉米发芽率较往年降低12.9%、减产17.1%的前提下，通过项目研发技术的示范应用，实现了作物抗旱增产的目标，为保障松嫩平原粮食主产区稳产丰产、减灾增效提供了有力支撑。\n\n团队依托中国科学院东北地理与农业生态研究所在大安市烧锅镇乡富国村建立了千亩示范区，已连续三年开展定位试验与效果验证。项目组研发的技术模式在近年极端气候条件影响下表现突出： 2024年，示范区种植期雨量充沛，玉米出苗率保持在95%以上，产量达12510.7公斤\u002F公顷；2025年，示范区玉米产量为13124.6公斤\u002F公顷；2026年，面对春旱和低温冷害双重胁迫，玉米产量进一步提升至13768.8公斤\u002F公顷，实现连年增产，同时较当地传统垄作产量增幅超20%，效果显著。\n\n团队与地方政府涉农部门多方联动，围绕示范区建设、现场观摩和技术推广等开展广泛协作。下一步，将继续发挥国家重点研发计划项目的科技引领作用，完善东北黑土区耕地地力提升与减灾增效协同技术体系，扩大示范应用范围，为气候变化下黑土区抗逆增产及地力提升提供技术保障。","中国农科院农业环境与可持续发展研究所 2026-10-19",{"impact":29,"substance":29,"depth":608,"authority":31,"freshness":148,"relevant":32,"comment":609},17,"国家重点研发计划项目在吉林大安连续三年实测增产，数据翔实、信源权威，具备行业参考价值。",[611],{"name":606,"url":603},[613,614,615,616,617],"黑土地保护","保护性耕作","水肥一体化","玉米单产","抗旱技术",{"code":619,"slug":620,"name":621,"path":622,"parent_code":47},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":625},{"candidates":626,"agricultural_evidence":25},[627,631,636,641,646],{"code":619,"score":293,"supported":25,"title":628,"abstract":629,"tags":630},[621],[621],[],{"code":128,"score":162,"supported":25,"title":632,"abstract":633,"tags":635},[],[634],"干旱",[],{"code":637,"score":32,"supported":51,"title":638,"abstract":639,"tags":640},"F3",[],[],[615],{"code":642,"score":32,"supported":51,"title":643,"abstract":644,"tags":645},"H1",[],[],[613],{"code":79,"score":32,"supported":51,"title":647,"abstract":648,"tags":649},[],[],[614],[651,652],"中国农科院 黑土地 玉米 大安","吉林大安 玉米 实收测产","中国农科院黑土地玉米大安-5512","2026-10-22T00:07:03.815140Z",{"id":656,"title":657,"url":658,"summary":659,"summary_zh":660,"content":19,"source_name":661,"source_url":658,"published_at":22,"category":106,"cover_url":19,"hotness":24,"is_selected":51,"score":662,"score_detail":663,"sources":665,"tags":667,"primary_topic":672,"secondary_topics":676,"classification":677,"search_phrases":694,"slug":697,"view_count":97,"doi":698,"paper":699,"created_at":711},5578,"Unlocking digital agriculture through new quality productive forces: nonlinear thresholds and spatial spillovers in China","https:\u002F\u002Fdoi.org\u002F10.1057\u002Fs41599-026-09510-z","New quality productive forces (NQPF) critically enable agricultural digitisation. Using China as a case study, this paper employs panel data from 284 prefecture-level cities in China from 2011 to 2022. The results show that NQPF has a significant promoting effect on agricultural digital transformation and exhibits positive spatial spillover effects, driving not only local transformation but also the development of surrounding regions. Labour skills and innovation capacity play mediating roles in this process. Notably, the enabling effect of NQPF on agricultural digitisation displays significant regional heterogeneity. Threshold analysis indicates that its promoting effect follows a non-linear pattern, characterised by a double threshold effect. Therefore, the development of NQPF should be accelerated, with emphasis placed on regional coordination and talent cultivation, to promote the sustained and in-depth advancement of agricultural digital transformation in accordance with local conditions.","新质生产力（New Quality Productive Forces, NQPF）对农业数字化具有关键赋能作用。本文以中国为案例，采用2011—2022年中国284个地级市的面板数据。结果表明，新质生产力对农业数字化转型具有显著促进作用，并呈现正向空间溢出效应，不仅推动本地转型，也带动周边地区发展。劳动技能与创新能力在这一过程中发挥中介作用。值得注意的是，新质生产力对农业数字化的赋能效应表现出显著的区域异质性。门槛分析表明，其促进作用呈非线性特征，表现为双重门槛效应。因此，应加快新质生产力发展，注重区域协调与人才培养，因地制宜推动农业数字化转型持续深入推进。","Humanities and Social Sciences Communications",81,{"impact":199,"substance":29,"depth":199,"authority":111,"freshness":434,"relevant":32,"comment":664},"基于284个地级市面板数据的实证研究，揭示新质生产力对农业数字化的非线性门槛与空间溢出效应，方法规范、结论有政策参考价值，值得进入每日精选。",[666],{"name":661,"url":658},[439,668,669,670,671],"新质生产力","空间溢出","区域协调","农业数字化转型",{"code":475,"slug":673,"name":674,"path":675,"parent_code":449},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":678},{"candidates":679,"agricultural_evidence":25},[680,685,690],{"code":475,"score":24,"supported":25,"title":681,"abstract":683,"tags":684},[682],"digital agriculture",[],[671,439],{"code":686,"score":32,"supported":51,"title":687,"abstract":688,"tags":689},"I4",[],[],[669],{"code":89,"score":32,"supported":51,"title":691,"abstract":692,"tags":693},[],[],[668],[695,696],"新质生产力 农业数字化","地级市 面板数据","新质生产力农业数字化-5578","10.1057\u002Fs41599-026-09510-z",{"doi":698,"openalex_id":700,"authors":701,"venue":661,"cited_by_count":97,"oa_url":658,"card":706,"direction":254,"ingested_from":187},"W7221723385",[702,704],{"name":703,"orcid":19},"Qingqing Huang",{"name":705,"orcid":19},"Wenjing Guo",{"tldr":707,"method":708,"finding":709,"direction":254,"opportunity":710},"基于中国284个地级市面板数据，检验新质生产力对农业数字化的促进、空间溢出与门槛效应。","2011—2022年284个地级市面板数据，空间计量与门槛回归，中介效应检验。","新质生产力显著促进农业数字化并具正向空间溢出，存在双门槛非线性效应与区域异质性。","可深入探究新质生产力影响农业数字化的微观机制，以及不同区域门槛条件下的差异化政策路径。","2026-10-22T11:20:47.354744Z",{"id":713,"title":714,"url":715,"summary":716,"summary_zh":717,"content":19,"source_name":718,"source_url":715,"published_at":432,"category":106,"cover_url":19,"hotness":24,"is_selected":51,"score":662,"score_detail":719,"sources":721,"tags":723,"primary_topic":728,"secondary_topics":732,"classification":733,"search_phrases":746,"slug":749,"view_count":97,"doi":750,"paper":751,"created_at":779},5562,"pxGPT: A schema-constrained multimodal LLM framework for plant phenotyping","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fbioinformatics\u002Fbtag781","Abstract Summary Deep learning can scale image-based plant phenotyping but typically targets predefined traits, whereas manual phenotypic description is more flexible but difficult to scale. We present Phenotype eXplorer Generative Plant Traits (pxGPT), a Python framework that converts multi-view plant images into structured phenotype records. The workflow combines free-text description, dataset-specific schema generation with human review, and schema-constrained information extraction. We evaluated the extraction stage on three Brassica datasets using 78,973 annotations from ten annotators who had no access to pxGPT predictions. For 38-45 qualitative traits per dataset, pxGPT-human agreement ranged from 0.572 to 0.712, compared with 0.620 to 0.739 agreement among human annotators. All 32,736 required trait fields were present; 93 values were empty, and no qualitative descriptor violations were recorded. For selected morphological structures, pooled false-positive rates were 0.5-8.0%, while sensitivity varied across structure types in one mature-plant dataset (37.5-100%). The curated schemas were applied unchanged with two commercial models and one locally hosted open-weight model, with differences in mean accuracy of 0.03 or less. In the evaluated Brassica datasets, these results support schema-guided extraction of visible plant traits without separate model training for each trait. Human-guided schema development offers a path to extend pxGPT to other crops and imaging conditions, with accuracy requiring validation in each new setting. Availability and Implementation The software is available at https:\u002F\u002Fgithub.com\u002FYaoChengLab\u002Fpxgpt and an archival version of the source code is available on Zenodo (https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22931745). Curated schemas used in this study, annotation guides, phenotype records and human annotations are available at https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22907554. Supplementary information Supplementary data are available at Bioinformatics online.","摘要 深度学习能够扩展基于图像的植物表型分析，但通常针对预定义性状，而人工表型描述更为灵活却难以规模化。我们提出Phenotype eXplorer Generative Plant Traits（pxGPT），一个将多视角植物图像转换为结构化表型记录的Python框架。该工作流程结合了自由文本描述、数据集特定模式生成与人工审核，以及模式约束的信息提取。我们在三个芸薹属（Brassica）数据集上评估了提取阶段，使用了来自十名标注者的78,973条标注，这些标注者无法获取pxGPT的预测结果。对于每个数据集38-45个定性性状，pxGPT与人工标注者之间的一致性范围为0.572至0.712，而人工标注者之间的一致性为0.620至0.739。所有32,736个所需性状字段均已存在；93个值为空，未记录到定性描述符违规。对于选定的形态结构，合并假阳性率为0.5-8.0%，而在一个成熟植株数据集中，灵敏度因结构类型而异（37.5-100%）。经整理的模式在未作修改的情况下应用于两个商业模型和一个本地部署的开放权重模型，平均准确率差异不超过0.03。在所评估的芸薹属数据集中，这些结果支持在无需为每个性状单独训练模型的情况下，对可见植物性状进行模式引导的提取。人工引导的模式开发为将pxGPT扩展至其他作物和成像条件提供了途径，其准确性需在每个新设置中加以验证。可用性与实现 该软件可在https:\u002F\u002Fgithub.com\u002FYaoChengLab\u002Fpxgpt获取，源代码的存档版本可在Zenodo（https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22931745）获取。本研究中使用的经整理模式、标注指南、表型记录和人工标注可在https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22907554获取。补充信息 补充数据可在Bioinformatics在线获取。","Bioinformatics",{"impact":199,"substance":29,"depth":199,"authority":111,"freshness":434,"relevant":32,"comment":720},"提出基于模式约束的多模态大模型植物表型提取框架，在近8万条标注上验证，方法新颖、数据规模可观，对智慧育种与表型自动化有参考价值。",[722],{"name":718,"url":715},[556,724,725,726,727],"农业人工智能","植物表型","多模态大模型","芸薹属",{"code":460,"slug":729,"name":730,"path":731,"parent_code":449},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":734},{"candidates":735,"agricultural_evidence":25},[736,742],{"code":460,"score":58,"supported":25,"title":737,"abstract":738,"tags":741},[],[739,740],"深度学习","deep learning",[724,726],{"code":475,"score":32,"supported":51,"title":743,"abstract":744,"tags":745},[],[],[556],[747,748],"pxGPT 植物表型","Brassica 表型 多模态大模型","pxGPT植物表型-5562","10.1093\u002Fbioinformatics\u002Fbtag781",{"doi":750,"openalex_id":752,"authors":753,"venue":718,"cited_by_count":97,"oa_url":772,"card":773,"direction":778,"ingested_from":187},"W7223127960",[754,757,759,762,765,768,770],{"name":755,"orcid":756},"Po‐Xing Zheng","https:\u002F\u002Forcid.org\u002F0000-0001-5827-5865",{"name":758,"orcid":19},"Jheng-Yan Ou",{"name":760,"orcid":761},"Yu-Lin Wu","https:\u002F\u002Forcid.org\u002F0009-0003-8596-9047",{"name":763,"orcid":764},"Ruei-Shiuan Wang","https:\u002F\u002Forcid.org\u002F0009-0007-5159-1129",{"name":766,"orcid":767},"WU Ting-li","https:\u002F\u002Forcid.org\u002F0000-0001-7996-5515",{"name":769,"orcid":19},"Te-Chang Hsu",{"name":771,"orcid":19},"Yao-Cheng Lin","https:\u002F\u002Facademic.oup.com\u002Fbioinformatics\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fbioinformatics\u002Fbtag781\u002F72201337\u002Fbtag781.pdf",{"tldr":774,"method":775,"finding":776,"direction":185,"opportunity":777},"提出pxGPT框架，用多模态大模型将多视角植物图像转为结构化表型记录。","结合自由文本描述、人工审核的模式生成与模式约束信息抽取，在三个芸薹属数据集上评估","pxGPT与人工标注一致性0.572-0.712，接近人工间一致性，且无需逐性状训练模型。","可探索将模式引导抽取迁移至更多作物与复杂成像条件，并研究自动化模式生成以降低人工审核成本。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:22.173561Z",{"id":781,"title":782,"url":783,"summary":784,"summary_zh":19,"content":785,"source_name":786,"source_url":19,"published_at":264,"category":382,"cover_url":19,"hotness":24,"is_selected":51,"score":662,"score_detail":787,"sources":790,"tags":792,"primary_topic":798,"secondary_topics":804,"classification":805,"search_phrases":812,"slug":815,"view_count":97,"doi":19,"paper":19,"created_at":816},5510,"中国农科院加工所研发3D打印仿生三文鱼 破解老年人吞咽进食难题","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fmtxw\u002Fed0d9b62360a486d96bc32e04531bc1a.htm","农产品加工所食物营养与功能性食品创新团队构建核桃蛋白与蛋清蛋白复合乳液凝胶体系，用挤出式3D打印复刻三文鱼红白纹理。100℃烘烤3分钟为最佳方案，可分别达到国际吞咽障碍食品标准5级和6级，产品蛋白含量达22%，蛋白补充效率较传统产品提高200%。","科研人员对比了蒸制与烘烤两种方式，发现烘烤后的样品中关键风味物质含量明显增加，色泽、香气和吞咽顺滑度都更胜一筹。其中，100℃烘烤3分钟是效果最佳的方案，产品感官评分最高。通过调整烘烤时间，可以精准控制食物的软硬度，分别达到国际吞咽障碍食品标准的5级和6级，适配吞咽能力不同的老人。\n\n经专业测试，这款仿生三文鱼蛋白含量达到22%，质地柔软湿润、易于成团、不易散开，完全符合“可安全吞咽”的食物质地要求。","人民日报海外版 2026-10-19",{"impact":29,"substance":788,"depth":30,"authority":111,"freshness":58,"relevant":32,"comment":789},21,"国家级科研机构在适老食品与3D打印领域取得实质突破，数据与标准等级明确，具备行业示范价值，值得进入每日精选。",[791],{"name":786,"url":783},[793,794,795,796,797],"农产品加工","适老食品","3D打印食品","吞咽障碍食品","仿生食品",{"code":799,"slug":800,"name":801,"path":802,"parent_code":803},"E1","food-processing","加工与食品制造","\u002Ftopic\u002Ffood-supply\u002Ffood-processing","E",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":806},{"candidates":807,"agricultural_evidence":25},[808],{"code":799,"score":64,"supported":25,"title":809,"abstract":810,"tags":811},[],[793],[793],[813,814],"中国农科院 加工所 仿生三文鱼","3D打印 适老食品 吞咽障碍","中国农科院加工所仿生三文鱼-5510","2026-10-22T00:07:03.681391Z",{"id":818,"title":819,"url":820,"summary":821,"summary_zh":19,"content":822,"source_name":823,"source_url":19,"published_at":824,"category":23,"cover_url":19,"hotness":24,"is_selected":51,"score":662,"score_detail":825,"sources":828,"tags":830,"primary_topic":19,"secondary_topics":834,"classification":835,"search_phrases":860,"slug":863,"view_count":97,"doi":19,"paper":19,"created_at":864},5500,"安徽省印发加快数智供应链发展行动方案 部署「数字皖农」农业数智化转型","https:\u002F\u002Fcredit.zhangye.gov.cn\u002Fwcm\u002Fcontent\u002Fdetail\u002F20261016150003_100603.html","《安徽省加快数智供应链发展行动方案（2026—2028年）》提出深入实施「数字皖农」建设行动，加快建设数字智慧农（牧、渔）场，推广智能传感与精准作业技术装备，实施「互联网+」农产品出村进城工程，利用人工智能优化仓储管理和冷链物流调度，探索「前置仓+即时配送」模式，并构建中药材智能化生产与追溯体系。","11．推进数智供应链底层技术研发。贯彻落实数据接口、数据交换、数据管理、产品主数据、电子单证、成熟度模型、服务流程等国家标准、行业标准，完善数智供应链标准体系。支持建设一批精准标注、质量可靠、场景丰富的高质量供应链数据集，为行业大模型训练与应用提供支撑。加快部署新一代高性能、高可靠网络基础设施，为供应链各环节数据采集、传输、处理，提供稳定、安全、高效的算力支撑与网络环境。（责任单位：省发展改革委、省科技厅、省工业和信息化厅、省数据资源局、省通信管理局）\n\n12．提升供应链智能化应用水平。推广“人工智能+”供应链应用，支持企业、高校等建设“人工智能+”供应链实验基地，提供从原型开发、工艺验证到产业孵化全链条服务。支持企业建设数智供应链控制塔，实现全链条实时感知、可视化监控、智能分析与自主响应，提升供应链响应速度和决策水平。（责任单位：省发展改革委、省数据资源局、省工业和信息化厅、省商务厅、省教育厅）\n\n13．探索供应链数据交易。深化数据产权登记试点，扩大数据产权登记规模，探索建立供应链数据产权登记、流通交易、资产化等服务体系。支持企业依法合规参与数据流通交易。鼓励链主企业围绕业务协同、资源优化、服务增值等场景建设运营可信数据空间，赋能中小企业用数创新。（责任单位：省数据资源局、省发展改革委）","安徽省人民政府办公厅 2026-10-16","2026-10-16T00:00:00Z",{"impact":29,"substance":29,"depth":199,"authority":111,"freshness":826,"relevant":32,"comment":827},5,"省级政策文件，条款具体、责任单位明确，含AI+供应链与数据交易等实质举措，但已发布近一周，时效性一般。",[829],{"name":823,"url":820},[556,724,831,832,833],"数字皖农","数智供应链","供应链数据交易",[],{"status":836,"locked":51,"version":52,"method":53,"evidence":837},"needs_review",{"candidates":838,"agricultural_evidence":25,"reason":859},[839,845,851,855],{"code":840,"score":162,"supported":25,"title":841,"abstract":842,"tags":844},"B6",[],[843],"中药材",[],{"code":846,"score":162,"supported":25,"title":847,"abstract":848,"tags":850},"E3",[],[849],"冷链",[],{"code":475,"score":32,"supported":51,"title":852,"abstract":853,"tags":854},[],[],[556],{"code":460,"score":32,"supported":51,"title":856,"abstract":857,"tags":858},[],[],[724],"多个产业证据相当，需要确认主归属",[861,862],"安徽 数字皖农 数智供应链","安徽 供应链数据交易 试点","安徽数字皖农数智供应链-5500","2026-10-22T00:06:58.247037Z",{"id":866,"title":867,"url":868,"summary":869,"summary_zh":870,"content":19,"source_name":871,"source_url":868,"published_at":432,"category":106,"cover_url":19,"hotness":24,"is_selected":51,"score":662,"score_detail":872,"sources":875,"tags":877,"primary_topic":883,"secondary_topics":889,"classification":890,"search_phrases":936,"slug":939,"view_count":97,"doi":940,"paper":941,"created_at":973},5480,"Sustainable Plant Disease Management: Current Advances, Challenges, and Future Directions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010661","Sustainable plant disease management is essential for maintaining agricultural productivity, environmental health, and global food security. Climate change has increased crop vulnerability to emerging and re-emerging plant diseases, posing significant challenges for agricultural sustainability. Although synthetic chemical pesticides have long been effective for disease control, their extensive use is increasingly questioned because of environmental contamination, pathogen resistance, and potential risks to human and ecosystem health. Consequently, plant disease management (PDM) is shifting toward more sustainable and integrated approaches. Future strategies are expected to incorporate multiple disciplines, including: (i) biological control and microbiome engineering to enhance natural plant defense mechanisms; (ii) advanced molecular and genomic technologies, such as CRISPR\u002FCas systems, RNA interference (RNAi), and epigenome editing, to develop durable disease resistance; (iii) smart delivery systems for the precise application of antimicrobial compounds and defense elicitors; (iv) nanotechnology-based platforms for the targeted delivery of nutrients, pesticides, and biocontrol agents, improving efficacy while reducing chemical inputs; (v) digital and precision agriculture technologies, including remote sensing, unmanned aerial vehicles (UAVs), and artificial intelligence (AI)-based diagnostics, to facilitate real-time disease surveillance and targeted interventions; and (vi) agroecological practices such as crop diversification, soil health restoration, and climate-smart farming to enhance agroecosystem resilience. These innovations aim not only to suppress plant pathogens but also to build sustainable, adaptive, and resilient agricultural systems that address future environmental and biological challenges.","可持续的植物病害管理对于维持农业生产力、环境健康和全球粮食安全至关重要。气候变化加剧了作物对新发和再发植物病害的脆弱性，给农业可持续性带来了重大挑战。尽管合成化学农药长期以来在病害防控中行之有效，但其广泛使用正日益受到质疑，原因包括环境污染、病原菌抗药性以及对人类和生态系统健康的潜在风险。因此，植物病害管理（plant disease management, PDM）正转向更加可持续和综合的途径。未来的策略预计将融合多个学科，包括：(i) 生物防治和微生物组工程，以增强植物天然防御机制；(ii) 先进的分子和基因组技术，如CRISPR\u002FCas系统、RNA干扰（RNAi）和表观基因组编辑，以培育持久抗病性；(iii) 用于精准施用抗菌化合物和防御诱导子的智能递送系统；(iv) 基于纳米技术的平台，用于靶向递送养分、农药和生物防治剂，在提高功效的同时减少化学投入；(v) 数字和精准农业技术，包括遥感、无人机（UAVs）和基于人工智能（AI）的诊断，以促进实时病害监测和靶向干预；以及(vi) 农业生态实践，如作物多样化、土壤健康恢复和气候智慧型农业，以增强农业生态系统的韧性。这些创新不仅旨在抑制植物病原菌，还旨在构建可持续、适应性强且具有韧性的农业系统，以应对未来的环境和生物挑战。","Sustainability",{"impact":29,"substance":873,"depth":199,"authority":267,"freshness":58,"relevant":32,"comment":874},20,"系统综述可持续植物病害管理六大技术方向，涵盖AI诊断、纳米递送与基因编辑，对智慧农业与绿色防控具参考价值。",[876],{"name":871,"url":868},[556,878,879,880,881,882],"精准农业","基因编辑","植物病害","生物防治","纳米技术",{"code":884,"slug":885,"name":886,"path":887,"parent_code":888},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",[],{"status":50,"locked":51,"version":52,"method":53,"evidence":891},{"candidates":892,"agricultural_evidence":25},[893,898,903,907,914,918,922,926,931],{"code":475,"score":58,"supported":25,"title":894,"abstract":895,"tags":897},[],[878,896],"precision agriculture",[556,878],{"code":884,"score":385,"supported":25,"title":899,"abstract":900,"tags":902},[],[901,881],"农药",[881],{"code":43,"score":148,"supported":25,"title":904,"abstract":905,"tags":906},[],[38,151],[],{"code":908,"score":148,"supported":25,"title":909,"abstract":910,"tags":913},"H2",[],[911,912],"土壤","soil",[],{"code":128,"score":148,"supported":25,"title":915,"abstract":916,"tags":917},[],[117,145],[],{"code":154,"score":148,"supported":25,"title":919,"abstract":920,"tags":921},[],[157,158],[],{"code":161,"score":162,"supported":25,"title":923,"abstract":924,"tags":925},[],[165],[],{"code":460,"score":162,"supported":25,"title":927,"abstract":928,"tags":930},[],[929],"artificial intelligence",[],{"code":932,"score":32,"supported":51,"title":933,"abstract":934,"tags":935},"F2",[],[],[879],[937,938],"CRISPR RNAi 作物抗病","基因编辑 智慧农业 植物病害 生物防治","CRISPRRNAi作物抗病-5480","10.3390\u002Fsu182010661",{"doi":940,"openalex_id":942,"authors":943,"venue":871,"cited_by_count":97,"oa_url":868,"card":968,"direction":778,"ingested_from":187},"W7223044776",[944,947,950,953,956,959,962,965],{"name":945,"orcid":946},"Karim M. Hassan","https:\u002F\u002Forcid.org\u002F0000-0002-1241-2307",{"name":948,"orcid":949},"Basma T. Abd-Elhalim","https:\u002F\u002Forcid.org\u002F0000-0002-3009-332X",{"name":951,"orcid":952},"Mohamed K. Abou El-Nasr","https:\u002F\u002Forcid.org\u002F0000-0003-1451-833X",{"name":954,"orcid":955},"Essam Yosef Abdul-Hafeez","https:\u002F\u002Forcid.org\u002F0000-0003-4744-4672",{"name":957,"orcid":958},"Ahmed N. Abdelhamid","https:\u002F\u002Forcid.org\u002F0000-0002-1326-7885",{"name":960,"orcid":961},"Mostafa Abdelkader","https:\u002F\u002Forcid.org\u002F0000-0003-2339-5087",{"name":963,"orcid":964},"Mohamed Abd El-Hamed Nasser","https:\u002F\u002Forcid.org\u002F0000-0002-0328-2969",{"name":966,"orcid":967},"Mahmoud A. A. Ali","https:\u002F\u002Forcid.org\u002F0000-0002-9062-0048",{"tldr":969,"method":970,"finding":971,"direction":778,"opportunity":972},"综述植物病害可持续管理的多学科前沿进展、挑战与未来方向。","文献综述，整合生物防治、基因编辑、纳米递送、数字农业与生态农艺。","未来病害管理需融合生物、分子、纳米与数字技术构建韧性农业系统。","遥感、无人机与AI诊断的实时病害监测闭环仍缺田间验证与低成本落地研究。","2026-10-21T23:30:09.242484Z"]