[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-505":3,"related-505":62},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":56,"slug":59,"view_count":60,"doi":8,"paper":8,"created_at":61},505,"\"四良融合\"技术体系破大豆单产瓶颈筑产能根基","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260811\u002F20260811_5\u002Fnmrb_20260811_13374_5_2086928892753449013.html","7月31日,黄淮海(安徽)大豆大面积单产提升技术观摩交流会在安徽省阜阳市太和县淙祥现代农业种植专业合作社召开。本次活动由国家大豆产业技术体系主办,安徽省农业农村厅、安徽省农业科学院、阜阳市农业科学院、太和县人民政府共同承办,聚焦良田、良种、良法、良机\"四良融合\",集中展示国家大豆产业技术体系\"十四五\"期间大豆新品种、新技术、新装备、新产品。国家大豆产业技术体系首席科学家、中国农业科学院作物科学研究所研究员吴存祥表示,大豆大面积增产核心是推动试验田产量转化为大田产量、专家产量转化为农民产量。",null,"农民日报 2026-08-11","2026-08-10T16:00:00Z","报道",10,false,79,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},25,20,15,12,7,1,"聚焦大豆单产提升，展示国家产业技术体系成果，具全国性影响，信息具体，来源权威，时效稍逊。",[24],{"name":9,"url":6},[26,27,28,29,30],"大豆","四良融合","黄淮海","安徽省农科院","太和县",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,49],{"code":32,"score":19,"supported":45,"title":46,"abstract":47,"tags":48},true,[26],[26],[26],{"code":50,"score":51,"supported":45,"title":52,"abstract":53,"tags":55},"A8",3,[],[54],"单产提升",[],[57,58],"安徽省农科院 四良融合 太和县 黄淮海","安徽省农科院 四良融合","安徽省农科院四良融合太和县黄淮海-505",5,"2026-08-13T23:53:46.259657Z",{"total":63,"page":21,"page_size":63,"items":64},6,[65,152,196,244,325,390],{"id":66,"title":67,"url":68,"summary":69,"summary_zh":70,"content":8,"source_name":71,"source_url":68,"published_at":72,"category":73,"cover_url":8,"hotness":12,"is_selected":45,"score":74,"score_detail":75,"sources":81,"tags":83,"primary_topic":88,"secondary_topics":89,"classification":96,"search_phrases":129,"slug":132,"view_count":133,"doi":134,"paper":135,"created_at":151},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","2026-10-21T00:00:00Z","论文",89,{"impact":76,"substance":77,"depth":78,"authority":79,"freshness":12,"relevant":21,"comment":80},22,24,19,14,"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。",[82],{"name":71,"url":68},[26,84,85,86,87],"气候变化","东北农业","物候数据","遥感验证",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[90],{"code":91,"slug":92,"name":93,"path":94,"parent_code":95},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":39,"locked":13,"version":40,"method":41,"evidence":97},{"candidates":98,"agricultural_evidence":45},[99,104,110,116,123],{"code":32,"score":18,"supported":45,"title":100,"abstract":102,"tags":103},[101],"soybean",[26,101],[26],{"code":91,"score":12,"supported":45,"title":105,"abstract":106,"tags":109},[],[84,107,108],"农业气象","climate change",[84],{"code":50,"score":63,"supported":45,"title":111,"abstract":112,"tags":115},[],[113,114],"粮食安全","food security",[],{"code":117,"score":63,"supported":45,"title":118,"abstract":119,"tags":122},"I3",[],[120,121],"遥感","remote sensing",[],{"code":124,"score":51,"supported":45,"title":125,"abstract":126,"tags":128},"H6",[],[127],"农业生态",[],[130,131],"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",0,"10.1080\u002F20964471.2026.2735138",{"doi":134,"openalex_id":136,"authors":137,"venue":71,"cited_by_count":133,"oa_url":68,"card":144,"direction":148,"ingested_from":150},"W7223091615",[138,141],{"name":139,"orcid":140},"Quanjun Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-6087-3928",{"name":142,"orcid":143},"Dongli Wu","https:\u002F\u002Forcid.org\u002F0000-0003-1676-2264",{"tldr":145,"method":146,"finding":147,"direction":148,"opportunity":149},"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":153,"title":154,"url":155,"summary":156,"summary_zh":8,"content":8,"source_name":157,"source_url":8,"published_at":158,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":159,"score_detail":160,"sources":164,"tags":166,"primary_topic":171,"secondary_topics":172,"classification":173,"search_phrases":191,"slug":194,"view_count":133,"doi":8,"paper":8,"created_at":195},5358,"太和县税镇镇皖豆 61 大豆高产示范田实收测产 亩产 313.34 公斤","https:\u002F\u002Fwww.ahnw.cn\u002Fschq\u002Fcontent\u002F2fc36d3d-4806-4599-8673-84505ed00628","2026 年 10 月 12 日,安徽省农科院专家组赴太和县税镇镇姚寨村开展现场实收测产,示范田连片面积 218 亩,前茬为小麦,选用皖豆 61 优良大豆品种。专家组现场选取 20.8 亩田块实收,折算亩产 313.34 公斤,超额实现亩产 600 斤的预定目标。集成应用密植精播、合理群体构建、精准水肥调控、一喷多促等大豆高产关键技术体系。","安徽农网 2026-10-12","2026-10-12T00:00:00Z",56,{"impact":19,"substance":161,"depth":19,"authority":12,"freshness":162,"relevant":21,"comment":163},18,4,"县级示范田实收测产，数据具体、技术体系清晰，但属地方性进展且已过一周，时效与影响层级有限，可作常规资讯而非每日精选。",[165],{"name":157,"url":155},[167,26,168,169,170],"安徽","测产","高产示范","皖豆61",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":174},{"candidates":175,"agricultural_evidence":45},[176,180,186],{"code":32,"score":19,"supported":45,"title":177,"abstract":178,"tags":179},[26],[26],[26],{"code":181,"score":51,"supported":45,"title":182,"abstract":183,"tags":185},"A2",[],[184],"小麦",[],{"code":50,"score":187,"supported":13,"title":188,"abstract":189,"tags":190},2,[],[],[168,169],[192,193],"太和县 皖豆61 测产","安徽农科院 大豆 高产示范","太和县皖豆61测产-5358","2026-10-20T02:47:45.197444Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":8,"content":201,"source_name":202,"source_url":8,"published_at":203,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":204,"score_detail":205,"sources":208,"tags":210,"primary_topic":215,"secondary_topics":216,"classification":217,"search_phrases":239,"slug":242,"view_count":133,"doi":8,"paper":8,"created_at":243},5277,"崖州湾国家实验室+黑龙江省农科院\"崖创豆668\"在吉林梨树测产亩产416.71公斤","https:\u002F\u002Fnews.sciencenet.cn\u002Fsbhtmlnews\u002F2026\u002F10\u002F391613.shtm","崖州湾国家实验室联合国内多个科研团队,近日两款理想株型大豆新品系在山东、吉林两地实收测产丰收:山东东营盐碱地示范区\"国创豆78\"亩产404.85公斤(崖州湾实验室田志喜团队),吉林梨树试验基地\"崖创豆668\"亩产416.71公斤(崖州湾+黑龙江省农科院合作培育);为东北黑土地及同类生态区大豆高产种植提供了优质新品种。","本报讯（记者冯丽妃）近日，崖州湾国家实验室联合国内多个科研团队宣布，两款理想株型大豆新品系在山东、吉林两地实收测产丰收，亩产双双突破400公斤，为提升我国大豆产能、保障国家粮油安全提供了强有力的种源与技术支撑。\n\n此次测产中，山东东营盐碱地示范区的“国创豆78”由崖州湾国家实验室研究员田志喜团队历时8年选育，具有株型紧凑、长分枝、耐盐碱、籽粒大等特点，在盐碱叠加高温干旱的情况下，示范田刨除水分杂质后亩产达404.85公斤。专家组现场勘查后认为，该品系高产、稳产、耐盐性状突出，在同类盐碱生态区具有良好试验示范价值。\n\n与此同时，由崖州湾国家实验室大豆种子创新团队和黑龙江省农科院合作培育的高产耐密新品系“崖创豆668”在吉林梨树试验基地测产时亩产达416.71公斤，丰产潜力优异，为东北黑土地及同类生态区大豆高产种植提供了优质新品种选择。","科学网(2026-10-14)","2026-10-14T00:00:00Z",75,{"impact":76,"substance":17,"depth":79,"authority":206,"freshness":63,"relevant":21,"comment":207},13,"国家级实验室与省农科院联合培育的大豆新品系实收测产突破400公斤，数据具体、信源权威，对东北黑土地大豆产能提升有示范价值，值得进入每日精选。",[209],{"name":202,"url":199},[211,26,212,213,214],"种业振兴","黑土地","粮油安全","耐密品种",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":218},{"candidates":219,"agricultural_evidence":45},[220,224,230,234],{"code":32,"score":162,"supported":45,"title":221,"abstract":222,"tags":223},[],[26],[26],{"code":225,"score":51,"supported":45,"title":226,"abstract":227,"tags":229},"H2",[],[228],"盐碱地",[],{"code":50,"score":21,"supported":13,"title":231,"abstract":232,"tags":233},[],[],[213],{"code":235,"score":21,"supported":13,"title":236,"abstract":237,"tags":238},"F1",[],[],[211],[240,241],"崖创豆668 梨树 测产","崖州湾国家实验室 大豆","崖创豆668梨树测产-5277","2026-10-19T00:07:41.160572Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":249,"content":8,"source_name":250,"source_url":247,"published_at":203,"category":73,"cover_url":8,"hotness":12,"is_selected":13,"score":251,"score_detail":252,"sources":256,"tags":258,"primary_topic":263,"secondary_topics":264,"classification":270,"search_phrases":296,"slug":299,"view_count":162,"doi":300,"paper":301,"created_at":324},4998,"Unmanned aerial vehicle time‐series phenotyping illuminates genotype‐specific responses to off‐target dicamba exposure in soybean","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fppj2.70118","Abstract Dicamba, a synthetic auxin herbicide, is widely used for postemergence weed control. However, the high potential for volatility and spray drift raises concerns about damage to nontarget dicot crops lacking the dicamba monooxygenase transgene, which provides bioengineered resistance. Our objective was to evaluate the practical application of unmanned aerial vehicle (UAV)‐based multispectral imaging for quantifying off‐target dicamba injury, using non‐genetically modified (GM) soybean ( Glycine max (L.) Merr.) genotypes with known tolerant (T) and sensitive (S) responses to provide a reference framework. We conducted field experiments in 2021 and 2022 using varying dicamba rates. Temporal visual injury estimates (0–5 scale) were recorded approximately every 5 days for 6–8 weeks. Significant differences were detected between T and S lines, especially with 1\u002F500 single and weekly repeated treatments, which provided the largest separation. To augment the precision of visual injury assessment, UAV‐based multispectral imagery from each of multiple flights was used to calculate four vegetation indices (VIs): green leaf index (GLI), normalized excess green index (ExG2), normalized green red difference index (NGRDI), and red edge normalized difference vegetation index (RENDVI). These VIs captured differences between T and S lines, indicating dicamba injury can be quantified through remote sensing across contrasting soybean genotypes. Strong negative correlations were observed between visual injury scores and VI scores across treatments at each flight date, with overall correlations for GLI, ExG2, NGRDI, and RENDVI being −0.43, −0.43, −0.44, and −0.39 in 2021 and −0.54, −0.54, −0.64, and −0.57 in 2022. These results support the utility of UAV‐derived VIs for detecting and quantifying off‐target herbicide injury in soybean, facilitating scientific research efforts to develop non‐GM‐tolerant genotypes.","摘要 麦草畏（dicamba）是一种合成生长素类除草剂，广泛用于苗后杂草防除。然而，其高挥发性和喷雾飘移潜力引发了人们对非靶标双子叶作物的担忧，这些作物缺乏提供生物工程抗性的麦草畏单加氧酶（dicamba monooxygenase）转基因。本研究旨在评估基于无人机（UAV）的多光谱成像在量化非靶标麦草畏药害中的实际应用，利用对麦草畏具有已知耐受（T）和敏感（S）响应的非转基因（GM）大豆（Glycine max (L.) Merr.）基因型作为参考框架。我们于2021年和2022年开展了田间试验，使用不同的麦草畏剂量。在约6–8周内，每约5天记录一次时序视觉药害评估（0–5级）。在T和S品系之间检测到显著差异，尤其是在1\u002F500单次和每周重复处理下，差异最为明显。为提高视觉药害评估的精度，利用多次飞行获取的无人机多光谱影像计算了4种植被指数（VIs）：绿叶指数（GLI）、归一化超绿指数（ExG2）、归一化绿红差异指数（NGRDI）和红边归一化差异植被指数（RENDVI）。这些植被指数捕捉到了T和S品系之间的差异，表明麦草畏药害可通过遥感在不同大豆基因型间进行量化。在各飞行日期的所有处理中，视觉药害评分与植被指数评分之间均观察到强负相关，2021年GLI、ExG2、NGRDI和RENDVI的总体相关系数分别为−0.43、−0.43、−0.44和−0.39，2022年分别为−0.54、−0.54、−0.64和−0.57。这些结果支持无人机衍生植被指数在大豆非靶标除草剂药害检测和量化中的实用性，有助于推动开发非转基因耐受基因型的科学研究工作。","The Plant Phenome Journal",77,{"impact":253,"substance":76,"depth":161,"authority":206,"freshness":254,"relevant":21,"comment":255},16,8,"该研究利用无人机多光谱时序表型定量评估大豆非靶标除草剂药害，方法新颖、数据扎实，对智慧农业遥感监测具有参考价值。",[257],{"name":250,"url":247},[259,26,260,261,262],"智慧农业","无人机遥感","植被指数","除草剂药害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[265],{"code":117,"slug":266,"name":267,"path":268,"parent_code":269},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":39,"locked":13,"version":40,"method":41,"evidence":271},{"candidates":272,"agricultural_evidence":45},[273,277,284,291],{"code":32,"score":18,"supported":45,"title":274,"abstract":275,"tags":276},[101],[26,101],[26],{"code":117,"score":278,"supported":45,"title":279,"abstract":280,"tags":283},11,[],[120,281,121,282],"无人机多光谱","multispectral",[260,261],{"code":285,"score":63,"supported":45,"title":286,"abstract":287,"tags":290},"F4",[],[288,289],"除草","weed control",[],{"code":292,"score":21,"supported":13,"title":293,"abstract":294,"tags":295},"I1",[],[],[259],[297,298],"无人机 多光谱 大豆 药害","麦草畏 飘移 大豆 遥感","无人机多光谱大豆药害-4998","10.1002\u002Fppj2.70118",{"doi":300,"openalex_id":302,"authors":303,"venue":250,"cited_by_count":133,"oa_url":247,"card":319,"direction":148,"ingested_from":150},"W7221759072",[304,307,310,313,316],{"name":305,"orcid":306},"Seda Ozer","https:\u002F\u002Forcid.org\u002F0000-0002-1644-2394",{"name":308,"orcid":309},"Brian W. Diers","https:\u002F\u002Forcid.org\u002F0000-0003-3584-5495",{"name":311,"orcid":312},"Dean E. Riechers","https:\u002F\u002Forcid.org\u002F0000-0002-6081-5629",{"name":314,"orcid":315},"Caio Canella Vieira","https:\u002F\u002Forcid.org\u002F0000-0001-5428-3753",{"name":317,"orcid":318},"Alper Adak","https:\u002F\u002Forcid.org\u002F0000-0002-2737-8041",{"tldr":320,"method":321,"finding":322,"direction":148,"opportunity":323},"用无人机多光谱时序表型量化非转基因大豆受除草剂飘移药害的基因型差异。","2021-2022田间试验，无人机多光谱影像计算GLI、ExG2、NGRDI、R","植被指数与目测药害评分显著负相关，能区分耐性与敏感基因型，2022年相关性更强。","可拓展到多作物、多除草剂飘移的无人机早期诊断，并融合机器学习构建药害分级预警模型。","2026-10-15T23:30:20.537887Z",{"id":326,"title":327,"url":328,"summary":329,"summary_zh":8,"content":8,"source_name":330,"source_url":8,"published_at":331,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":332,"score_detail":333,"sources":335,"tags":337,"primary_topic":342,"secondary_topics":343,"classification":348,"search_phrases":385,"slug":388,"view_count":21,"doi":8,"paper":8,"created_at":389},4953,"安徽全省秋粮收获进度已超七成 玉米收获进入尾声 大豆超九成","https:\u002F\u002Fwww.aaas.org.cn\u002F4303153\u002F71484287.html","10月12日,在太和县税镇镇姚寨村,联合收割机在豆田中穿梭,种植大户李超种植的200亩大豆迎来收获,经测产亩产313.34公斤。太和县副县长、安徽省农科院研究员杜祥备介绍,近年来依托安徽省农科院、阜阳市农科院、国家及省大豆产业技术体系等平台,全面推行增密抗逆栽培、'一拌一封一喷'绿色防控、精量播种、抗灾减灾等关键技术。截至10月12日17时,全省已收秋粮4948万亩、进度76%,投入粮食烘干机11953台、烘干粮食41.5万吨。","《安徽日报》转载 安徽省农业科学院 2026 年 10 月 13 日","2026-10-13T00:00:00Z",64,{"impact":161,"substance":253,"depth":19,"authority":12,"freshness":254,"relevant":21,"comment":334},"省级秋收进度与大豆高产实测数据兼具时效与信息增量，但属常规农情通报，深度有限。",[336],{"name":330,"url":328},[338,26,339,340,341],"玉米","粮食烘干","秋粮收获","增密抗逆",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[344],{"code":345,"slug":346,"name":338,"path":347,"parent_code":36},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize",{"status":39,"locked":13,"version":40,"method":41,"evidence":349},{"candidates":350,"agricultural_evidence":45},[351,355,360,365,370,376,380],{"code":32,"score":19,"supported":45,"title":352,"abstract":353,"tags":354},[26],[26],[26],{"code":345,"score":356,"supported":45,"title":357,"abstract":358,"tags":359},9,[338],[],[338],{"code":361,"score":51,"supported":45,"title":362,"abstract":363,"tags":364},"E3",[],[339],[],{"code":285,"score":51,"supported":45,"title":366,"abstract":367,"tags":369},[],[368],"绿色防控",[],{"code":371,"score":51,"supported":45,"title":372,"abstract":373,"tags":375},"F5",[],[374],"收割机",[],{"code":50,"score":21,"supported":13,"title":377,"abstract":378,"tags":379},[],[],[339],{"code":381,"score":21,"supported":13,"title":382,"abstract":383,"tags":384},"J4",[],[],[340],[386,387],"安徽 秋粮 收获进度","太和县 大豆 亩产 313.34公斤","安徽秋粮收获进度-4953","2026-10-15T00:06:03.038238Z",{"id":391,"title":392,"url":393,"summary":394,"summary_zh":8,"content":8,"source_name":395,"source_url":8,"published_at":396,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":332,"score_detail":397,"sources":399,"tags":401,"primary_topic":405,"secondary_topics":406,"classification":407,"search_phrases":424,"slug":427,"view_count":133,"doi":8,"paper":8,"created_at":428},4726,"安徽省农科院在太和举办大豆新品种观摩暨产业化培训","https:\u002F\u002Fwww.aaas.org.cn\u002Fkyjz\u002Fcontent_644111.html","近日,安徽省农科院加工所、安徽省农业技术推广总站联合主办的大豆新品种观摩暨大豆专用品种产业化技术培训交流会在太和县举行,150余名代表参会。项目组已对89个大豆品种开展加工适应性评估,初步筛选出一批适于不同加工用途的专用品种","安徽省农业科学院官网 2026-10-08","2026-10-08T00:00:00Z",{"impact":253,"substance":161,"depth":206,"authority":19,"freshness":60,"relevant":21,"comment":398},"省级科研机构联合推广部门开展的大豆专用品种观摩培训，含89个品种加工适应性评估数据，对种业振兴与产业化有实质参考价值，但属细分行业进展且已过一周时效。",[400],{"name":395,"url":393},[211,26,402,403,404],"农技培训","专用品种","加工适应性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":408},{"candidates":409,"agricultural_evidence":45},[410,414,420],{"code":32,"score":19,"supported":45,"title":411,"abstract":412,"tags":413},[26],[26],[26],{"code":415,"score":162,"supported":45,"title":416,"abstract":417,"tags":419},"G1",[],[418],"技术培训",[402],{"code":235,"score":21,"supported":13,"title":421,"abstract":422,"tags":423},[],[],[211],[425,426],"安徽省农科院 大豆 太和","大豆专用品种 产业化培训","安徽省农科院大豆太和-4726","2026-10-12T00:11:05.556043Z"]