[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1175":3,"related-1175":68},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":62,"slug":65,"view_count":66,"doi":8,"paper":8,"created_at":67},1175,"湖南省农科院柏连阳院士调研江华鹅塘村江永七工岭村乡村振兴帮扶工作 鹅塘七工岭村集体经济增至27万元","https:\u002F\u002Fso.html5.qq.com\u002Fpage\u002Freal\u002Fsearch_news?docid=70000021_5216a904ea156852","湖南日报8月27日报道,25日至26日中国工程院院士、湖南省农科院党委书记柏连阳带队赴江华县鹅塘村、江永县七工岭村实地调研指导驻村帮扶工作。省农科院驻村帮扶工作队自2025年5月入村以来,两村村集体经济由2024年的5万余元增至2025年的27万余元。在鹅塘村,工作队引进市场主体发展订单农业,累计带动近3000人次就近务工。在江永七工岭村,工作队大力推进香芋种质改良、脱毒种苗繁育、水旱轮作示范,有效破解香芋品种退化、病害高发等痛点。在2025年度全省驻村帮扶考核中,鹅塘村、七工岭村两个帮扶队考核获评优秀,6名驻村帮扶队员个人考核全部获优秀等次。",null,"发挥科研主力军优势 省农科院全力帮扶驻点村\n\n![Image 1: 湖南日报](https:\u002F\u002Fqb-profile.image.myqcloud.com\u002Favatar_d8f78cf42dcc6148be98118825b62d87.png)\n\n湖南日报\n\n4天前\n\n湖南日报8月27日讯（全媒体记者 胡盼盼 通讯员 陈俊宇 刘维帅）25日至26日，中国工程院院士、湖南省农科院党委书记柏连阳带队赴江华县鹅塘村、江永县七工岭村实地调研指导驻村帮扶工作。\n\n![Image 2](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-2453784835-098AA502A4E38FF786511F1040FAB9A6\u002F0?fmt=jpg&size=110&h=358&w=640&ppv=1)\n\n调研期间，省农科院院士专家详细了解水稻、香芋等特色产业发展规模、品种改良、市场销路及品牌建设等情况，并走访慰问困难群众，详细了解政策落实成效，摸清群众急难愁盼问题。在鹅塘村、七工岭村，还举行了省农科院定点帮扶村儿童读物捐赠与“好种子”书屋揭牌仪式，并开展农科知识微课堂，旨在为帮扶村儿童播撒知识的“好种子”，以文化力量激发乡村振兴内生动力。\n\n![Image 3](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-118380255-742E7CAEE48D037C6B3A68FE70688D8B\u002F0?fmt=jpg&size=100&h=330&w=640&ppv=1)\n\n据介绍，省农科院驻村帮扶工作队自2025年5月入村以来，聚焦产业发展、基础设施提质、基层治理、民生服务、人才培育等多点发力，两村村集体经济实现明显增长，由2024年的5万余元增至2025年的27万余元。\n\n在鹅塘村，工作队引进市场主体发展订单农业，累计带动近3000人次就近务工，带动脱贫户稳定就业增收。在江永七工岭村，工作队大力推进香芋种质改良、脱毒种苗繁育、水旱轮作示范，有效破解香芋品种退化、病害高发等痛点。在2025年度全省驻村帮扶考核中，鹅塘村、七工岭村两个帮扶队考核获评优秀，6名驻村帮扶队员个人考核全部获优秀等次。\n\n![Image 4](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-2667786742-09C7BEC43EA4DFDC70C927D0AA75411D\u002F0?fmt=jpg&size=130&h=408&w=640&ppv=1)\n\n柏连阳对驻村工作队取得的扎实成效给予充分肯定。他表示，省农科院将持续发挥科技、品种、人才核心优势，扛牢定点帮扶政治责任，做强特色主导产业，用心用情解决群众急难愁盼问题，高质量完成本轮驻村帮扶既定任务，探索建立院地常态化长效合作机制，把试验示范、品种选育、技术服务等留在乡村。\n\n作者：胡盼盼\n\n责编：王亮\n\n一审：王亮\n\n二审：黄利飞\n\n三审：李伟锋","湖南日报 2026-08-27","2026-08-27T14:50:00Z","报道",10,false,60,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,18,12,11,4,1,"省级科研机构帮扶成效报道，数据具体，但时效性一般。",[25],{"name":10,"url":6},[27,28,29,30],"乡村振兴","驻村帮扶","科技助农","集体经济",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,51,57],{"code":45,"score":46,"supported":47,"title":48,"abstract":49,"tags":50},"G",9,true,[27],[],[27],{"code":32,"score":52,"supported":47,"title":53,"abstract":54,"tags":56},3,[],[55],"轮作",[],{"code":58,"score":22,"supported":14,"title":59,"abstract":60,"tags":61},"G1",[],[],[29],[63,64],"乡村振兴 科技助农 集体经济 驻村帮扶","乡村振兴 科技助农","乡村振兴科技助农集体经济驻村帮扶-1175",5,"2026-09-01T00:03:38.261124Z",{"total":69,"page":22,"page_size":69,"items":70},6,[71,180,270,316,366,445],{"id":72,"title":73,"url":74,"summary":75,"summary_zh":76,"content":8,"source_name":77,"source_url":74,"published_at":78,"category":79,"cover_url":8,"hotness":13,"is_selected":14,"score":80,"score_detail":81,"sources":86,"tags":88,"primary_topic":94,"secondary_topics":95,"classification":108,"search_phrases":145,"slug":148,"view_count":149,"doi":150,"paper":151,"created_at":179},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z","论文",80,{"impact":82,"substance":83,"depth":18,"authority":84,"freshness":13,"relevant":22,"comment":85},16,22,14,"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[87],{"name":77,"url":74},[89,90,91,92,93],"智慧农业","农业人工智能","农业遥感","氮素监测","紫花苜蓿",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[96,102],{"code":97,"slug":98,"name":99,"path":100,"parent_code":101},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":103,"slug":104,"name":105,"path":106,"parent_code":107},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":39,"locked":14,"version":40,"method":41,"evidence":109},{"candidates":110,"agricultural_evidence":47},[111,118,124,130,140],{"code":97,"score":18,"supported":47,"title":112,"abstract":114,"tags":117},[113],"hyperspectral",[115,116,113],"遥感","remote sensing",[91],{"code":32,"score":17,"supported":47,"title":119,"abstract":121,"tags":123},[120],"alfalfa",[122,120],"苜蓿",[93],{"code":103,"score":13,"supported":47,"title":125,"abstract":126,"tags":129},[],[127,122,128],"饲草","forage",[93],{"code":131,"score":13,"supported":47,"title":132,"abstract":133,"tags":139},"I2",[],[134,135,136,137,138],"深度学习","机器学习","machine learning","deep learning","decision support",[90],{"code":141,"score":22,"supported":14,"title":142,"abstract":143,"tags":144},"I1",[],[],[89],[146,147],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573",0,"10.1016\u002Fj.eja.2026.128375",{"doi":150,"openalex_id":152,"authors":153,"venue":77,"cited_by_count":149,"oa_url":74,"card":172,"direction":176,"ingested_from":178},"W7223154576",[154,156,158,160,162,165,167,170],{"name":155,"orcid":8},"Shuai Fu",{"name":157,"orcid":8},"Yunhao Li",{"name":159,"orcid":8},"Chunli Miao",{"name":161,"orcid":8},"Jie Liu",{"name":163,"orcid":164},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":166,"orcid":8},"Yongjia Hu",{"name":168,"orcid":169},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":171,"orcid":8},"吴彩霞",{"tldr":173,"method":174,"finding":175,"direction":176,"opportunity":177},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","农业遥感与作物表型","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","openalex","2026-10-22T11:20:39.622844Z",{"id":181,"title":182,"url":183,"summary":184,"summary_zh":185,"content":8,"source_name":186,"source_url":183,"published_at":187,"category":79,"cover_url":8,"hotness":13,"is_selected":14,"score":188,"score_detail":189,"sources":191,"tags":193,"primary_topic":196,"secondary_topics":197,"classification":199,"search_phrases":222,"slug":225,"view_count":149,"doi":226,"paper":227,"created_at":269},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",79,{"impact":18,"substance":83,"depth":18,"authority":19,"freshness":46,"relevant":22,"comment":190},"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[192],{"name":186,"url":183},[89,194,195,115,93],"无人机","育种",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[198],{"code":97,"slug":98,"name":99,"path":100,"parent_code":101},{"status":39,"locked":14,"version":40,"method":41,"evidence":200},{"candidates":201,"agricultural_evidence":47},[202,206,211,218],{"code":32,"score":17,"supported":47,"title":203,"abstract":204,"tags":205},[120],[122,120],[93],{"code":97,"score":19,"supported":47,"title":207,"abstract":209,"tags":210},[208],"multispectral",[208],[115],{"code":141,"score":212,"supported":47,"title":213,"abstract":214,"tags":217},7,[],[215,216],"精准农业","precision agriculture",[89],{"code":103,"score":21,"supported":47,"title":219,"abstract":220,"tags":221},[],[122],[93],[223,224],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567","10.64898\u002F2026.10.14.759249",{"doi":226,"openalex_id":228,"authors":229,"venue":186,"cited_by_count":149,"oa_url":183,"card":264,"direction":176,"ingested_from":178},"W7223083966",[230,233,236,239,242,244,246,249,252,255,258,261],{"name":231,"orcid":232},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":234,"orcid":235},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":237,"orcid":238},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":240,"orcid":241},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":243,"orcid":8},"Julie Hansen",{"name":245,"orcid":8},"Christopher Pierce",{"name":247,"orcid":248},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":250,"orcid":251},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":253,"orcid":254},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":256,"orcid":257},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":259,"orcid":260},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":262,"orcid":263},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":265,"method":266,"finding":267,"direction":176,"opportunity":268},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","2026-10-22T11:20:32.655394Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":8,"content":275,"source_name":276,"source_url":8,"published_at":187,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":277,"score_detail":278,"sources":282,"tags":284,"primary_topic":290,"secondary_topics":291,"classification":298,"search_phrases":311,"slug":314,"view_count":149,"doi":8,"paper":8,"created_at":315},5426,"浙江农科院在景宁发布丘陵梯田稻薯轮作全程机械化与共富模式","http:\u002F\u002Fwww.came.net.cn\u002FsecondLevelPage\u002Finfo\u002F31\u002F227412","浙江省农业科学院联合浙江省自然资源集团、景宁畲族自治县政府在景宁举办丘陵山区多田套合工程示范暨丘陵梯田全程机械化稻薯轮作现场会。项目由省农科院农业装备研究所牵头，核心示范 200 亩、辐射 1000 亩。研发的小土豆移栽机作业效率较人工提升 8 倍以上，「无人机吊运+轨道运输+地面农机」体系下单次吊运 50 公斤以上，山地运输效率提升 10 倍以上、单位运输成本降低 60% 以上；病毒学与生物技术研究所攻关的茎尖脱毒组培苗移栽成活率不低于 90%，脱毒种薯出苗率不低于 98%。","近日，由浙江省农业科学院、浙江省自然资源集团有限公司、景宁畲族自治县人民政府联合主办的丘陵山区多田套合工程示范暨丘陵梯田全程机械化稻薯轮作生产技术集成与共富模式示范现场会在景宁畲族自治县顺利举办。活动系统呈现了省农科院联合省自然资源集团、浙江默梵绿芯种业有限公司等多方主体在景宁探索构建的丘陵山区稻薯轮作全程机械化与产业链共富新模式。\n\n省农科院党委委员、副院长冯冠胜致辞并宣布现场会开幕，他指出，当前浙江省深入推进共同富裕示范区建设，山区是补齐发展短板、激发内生潜力的关键区域。省农科院围绕多田套合、稻薯轮作全程机械化与产业链共富开展协同攻关、集成示范、落地推广，取得了阶段性成果。下一步，将紧贴山区农业产业发展实际需求，充分发挥多良集成优势，持续深化技术攻关、成果熟化、示范推广和落地服务，全力助力景宁农业产业提质增效，系统总结可复制、可推广的丘陵山地产业共富模式，为全国丘陵山区农业高质量发展贡献农科力量。\n\n该项目由省农科院农业装备研究所牵头，联合作物与核技术利用研究所、病毒学与生物技术研究所等共同实施。针对丘陵山区稻薯轮作茬口衔接难、专用适配农机短缺、农产品销路不稳等难题，项目集成土地、科技、产业要素，探索山区产业共富路径：依托省自然资源集团国企资金资源优势，实施“多田套合”土地整治工程，将零散“巴掌田”改造为连片宜机梯田；依托省农科院农机农艺融合科研优势，筛选“薯稻薯”菜用土豆与水稻品种，攻克茎尖脱毒组培与移栽调控茬口技术，研发小土豆移栽装备，构建“无人机吊运+轨道运输+地面农机”协同作业体系；依托浙江默梵绿芯种业有限公司市场品牌优势，推行订单保底收购、品质溢价激励机制，保障农户稳定增收。该模式核心示范200亩、辐射1000亩。\n\n现场展示的丘陵山区稻薯轮作全程机械化生产技术模式，集成精细化整地、机械化种植、轻简化管理、低空吊运+轨道运输、机械化收获等环节。研发适配山地的小土豆移栽机，作业效率较人工提升8倍以上；“无人机吊运+轨道运输+地面农机”协同作业模式中，无人机单次可吊运50公斤以上农机、农资、秧盘，山地运输效率较人工提升10倍以上，单位运输成本降低60%以上。现场还演示了智能灌溉、机械化耕整地、精准植保及收获等成套装备，并展示巡检机器狗、作物无人机监测等智能装备与技术。\n\n在农艺技术方面，省农科院作物与核技术利用研究所开展适宜“薯稻薯”模式的水稻品种筛选，评价耐寒性、早熟性、抗病性及机械化适配性；病毒学与生物技术研究所开展菜用土豆品种筛选与茎尖脱毒组培快繁技术攻关，组培苗移栽成活率不低于90%，可调控茬口5至10天；脱毒种薯出苗率不低于98%，较自留种增产20%至40%。通过品种搭配、播期衔接与密度调控，构建“薯—稻—薯”一年多熟轮作体系，提升复种指数与种薯质量。\n\n产业链运营方面，项目打造“企业+合作社+农户”铁三角模式，统一品种标准、种植技术、品质管控、产品收购全流程标准化管理，推行订单保底收购、品质溢价激励机制，并依托共富工坊、农旅融合等新业态拓宽村集体与农户增收渠道。活动还举行丘陵山地机械化应用合作签约、稻薯轮作种植推广联合体签约及农机农艺融合综合示范基地揭牌仪式。\n\n本次现场会展示的模式实现三大转变，即生产方式从“人力依赖”转向“机械智控”、种植模式从“零散粗放”转向“标准高效”、产业发展从“单一种植”转向“全链共富”，通过稳技术、稳标准、稳销路、稳收益，真正实现山地农业产业提质、农户增收、集体增效的良性共富新格局。\n\n发布时间：2026-10-20提交人：浙江省畜牧技术推广与种畜禽监测总站（浙江省农业机械试验鉴定推广总站） 董玉玲责任编辑：农业农村部农机化总站 信息宣传处","中国农业机械化信息网 2026-10-20",81,{"impact":83,"substance":279,"depth":82,"authority":280,"freshness":46,"relevant":22,"comment":281},21,13,"省级科研机构牵头、多主体协同的丘陵山区稻薯轮作全程机械化与共富模式示范，含具体装备效率与农艺数据，信息增量足，具行业推广参考价值。",[283],{"name":276,"url":273},[285,286,287,288,289],"农机农艺融合","丘陵山区","全程机械化","共富模式","稻薯轮作",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[292],{"code":293,"slug":294,"name":295,"path":296,"parent_code":297},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","F",{"status":39,"locked":14,"version":40,"method":41,"evidence":299},{"candidates":300,"agricultural_evidence":47},[301,307],{"code":293,"score":82,"supported":47,"title":302,"abstract":304,"tags":306},[303],"机械化",[305,303],"农机",[287,285],{"code":32,"score":20,"supported":47,"title":308,"abstract":309,"tags":310},[55],[55],[],[312,313],"浙江农科院 景宁 稻薯轮作","丘陵梯田 全程机械化","浙江农科院景宁稻薯轮作-5426","2026-10-21T00:10:00.508230Z",{"id":317,"title":318,"url":319,"summary":320,"summary_zh":8,"content":321,"source_name":322,"source_url":8,"published_at":323,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":324,"score_detail":325,"sources":329,"tags":331,"primary_topic":334,"secondary_topics":335,"classification":336,"search_phrases":361,"slug":364,"view_count":149,"doi":8,"paper":8,"created_at":365},5211,"省农科院助力景宁破解丘陵山区种田难:多田套合+稻薯轮作新模式","https:\u002F\u002Fwww.jingning.gov.cn\u002Fcol\u002Fcol1376112\u002Fart\u002F2026\u002Fart_de8faa5b86ef48648e94bfbd06c59191.html","2026年10月15日,丘陵山区多田套合工程示范暨丘陵梯田全程机械化稻薯轮作生产技术集成与共富模式示范现场会在浙江景宁举行,浙江省农科院与浙江省自然资源集团合作,通过政企研协同推广\"多田套合+稻薯轮作\"让传统山地面貌焕新。","10月15日，丘陵山区多田套合工程示范暨丘陵梯田全程机械化稻薯轮作生产技术集成与共富模式示范现场会在景宁东坑镇儒章村举行。来自省农业科学院、省自然资源集团以及市县农业农村和自然资源系统的专家、企业代表、种植大户齐聚现场，聚焦丘陵山区土地整治、稻薯轮作技术推广、山地农机装备应用等，共同探索山区农业现代化共富新模式。\n\n开幕仪式上，浙江丽水市自然资源发展有限公司与省农科院农业装备研究所签订丘陵山地机械化应用合作协议；浙江默梵绿芯种业有限公司与景宁种植大户代表签订稻薯轮作种植推广联合体合作协议；现场为“农机农艺融合综合示范基地”揭牌。\n\n仪式结束后，与会人员前往东坑镇儒章村多田套合核心示范区开展现场观摩。连片整治后的梯田错落铺开，一改过去碎片化、零散化的面貌。现场，多田套合土地整治工程成果、优质稻薯新品种、轻简化种植模式逐一展示。\n\n无人旋耕机、巡检机器狗、微耕机、无人机吊运小农机、水稻割晒打捆机、小土豆移栽机等一批适配山区小地块的农机装备轮番作业演示。轨道自动化运输设备穿梭梯田之间，破解山地物资转运难题，过去梯田“人工作业、肩挑背扛”的传统劳作场景被现代化农机替代，让“农机上山”从设想变为现实。\n\n本次现场会由浙江省农业科学院、浙江省自然资源集团、景宁畲族自治县人民政府联合主办，聚焦丘陵山区现实短板，集成土地整治、优良品种、山地农机、稻薯轮作、共富经营整套模式。通过政企研协同发力，把科研院所实验室里的技术成果，落地到畲乡的丘陵梯田，在保障粮食生产的基础上，挖掘土特产产业链价值，拓宽山区群众增收渠道，为全省丘陵山区农业现代化提供景宁实践样本。","景宁县政府(2026-10-16)","2026-10-16T00:00:00Z",66,{"impact":18,"substance":326,"depth":84,"authority":46,"freshness":327,"relevant":22,"comment":328},17,8,"省级科研机构与地方政府共建丘陵山区全程机械化稻薯轮作示范，含实质技术集成与多方合作，具行业参考价值。",[330],{"name":322,"url":319},[89,332,286,333,289],"农机装备","土地整治",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":337},{"candidates":338,"agricultural_evidence":47},[339,343,347,352,357],{"code":32,"score":20,"supported":47,"title":340,"abstract":341,"tags":342},[55],[55],[],{"code":293,"score":21,"supported":47,"title":344,"abstract":345,"tags":346},[],[303],[332],{"code":348,"score":22,"supported":14,"title":349,"abstract":350,"tags":351},"G2",[],[],[333],{"code":353,"score":22,"supported":14,"title":354,"abstract":355,"tags":356},"H1",[],[],[333],{"code":141,"score":22,"supported":14,"title":358,"abstract":359,"tags":360},[],[],[89],[362,363],"景宁 稻薯轮作 丘陵山区","浙江省农科院 山地农机","景宁稻薯轮作丘陵山区-5211","2026-10-18T00:05:23.005537Z",{"id":367,"title":368,"url":369,"summary":370,"summary_zh":8,"content":8,"source_name":371,"source_url":369,"published_at":372,"category":79,"cover_url":8,"hotness":13,"is_selected":14,"score":373,"score_detail":374,"sources":376,"tags":378,"primary_topic":384,"secondary_topics":385,"classification":396,"search_phrases":413,"slug":416,"view_count":149,"doi":417,"paper":418,"created_at":444},4652,"Linking land use advantage and temporal stability for row configuration design in maize\u002Fsoybean intercropping under water-limited dryland conditions in northeastern China","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.105005","Linking land use advantage and temporal stability for row configuration design in maize\u002Fsoybean intercropping under water-limited dryland conditions in northeastern China。Agricultural Systems","Agricultural Systems","2026-10-10T00:00:00Z",74,{"impact":19,"substance":279,"depth":18,"authority":84,"freshness":46,"relevant":22,"comment":375},"核心期刊论文，方法新颖、结论可靠，但属细分领域研究，产业影响有限，可作为专业精选。",[377],{"name":371,"url":369},[379,380,381,382,383],"间作","玉米大豆带状复合种植","东北旱作","水分利用","作物行配置",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[386,391],{"code":387,"slug":388,"name":389,"path":390,"parent_code":36},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize",{"code":392,"slug":393,"name":394,"path":395,"parent_code":36},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",{"status":39,"locked":14,"version":40,"method":41,"evidence":397},{"candidates":398,"agricultural_evidence":47},[399,404,408],{"code":32,"score":19,"supported":47,"title":400,"abstract":402,"tags":403},[401],"intercropping",[401],[379],{"code":387,"score":20,"supported":47,"title":405,"abstract":406,"tags":407},[388],[388],[],{"code":392,"score":20,"supported":47,"title":409,"abstract":411,"tags":412},[410],"soybean",[410],[],[414,415],"玉米大豆间作 行配置 东北","旱地 间作 产量稳定性","玉米大豆间作行配置东北-4652","10.1016\u002Fj.agsy.2026.105005",{"doi":417,"openalex_id":419,"authors":420,"venue":371,"cited_by_count":149,"oa_url":369,"card":8,"direction":8,"ingested_from":178},"W7221334648",[421,424,427,429,431,433,436,438,440,442],{"name":422,"orcid":423},"Yue Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0506-409X",{"name":425,"orcid":426},"Xiaoyi Wang","https:\u002F\u002Forcid.org\u002F0000-0003-3843-2434",{"name":428,"orcid":8},"Shixiang Xi",{"name":430,"orcid":8},"Hongjun Du",{"name":432,"orcid":8},"Chen Feng",{"name":434,"orcid":435},"Zhe Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-4579-7709",{"name":437,"orcid":8},"Erjing Guo",{"name":439,"orcid":8},"Wei Bai",{"name":441,"orcid":8},"Lizhen Zhang",{"name":443,"orcid":8},"Zhanxiang Sun","2026-10-11T23:30:03.973971Z",{"id":446,"title":447,"url":448,"summary":449,"summary_zh":450,"content":8,"source_name":451,"source_url":448,"published_at":452,"category":79,"cover_url":8,"hotness":13,"is_selected":14,"score":453,"score_detail":454,"sources":456,"tags":458,"primary_topic":464,"secondary_topics":470,"classification":478,"search_phrases":523,"slug":526,"view_count":149,"doi":527,"paper":528,"created_at":552},4570,"Agroecological practices, perceived benefits, and determinants of adoption in the Yayu Coffee Forest Biosphere Reserve, Southwest Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01113-4","Agroecology offers a promising pathway for reconciling forest conservation with rural livelihoods in biodiversity-rich tropical landscapes, yet empirical evidence on the breadth, intensity, and determinants of adoption remains limited, particularly in UNESCO biosphere reserves. This study investigates agroecological practices, perceived contributions, and determinants of adoption in the Yayu Coffee Forest Biosphere Reserve (YCFBR), Southwest Ethiopia. A cross-sectional concurrent mixed-methods design was employed, combining 293 randomly selected households, complemented by key informant interviews, focus group discussions, and field observations. Binary logistic regression identified determinants of adoption (extensive margin), while ordinal logistic regression examined adoption intensity using the Agroecological Adoption Index (AAI) (intensive margin). Number of Practices Adopted (NPA), Weighted Adoption Intensity (WAI), and Duration of Adoption (DUR) provided complementary insights into adoption breadth, intensity, and persistence. Qualitative data were analyzed thematically to contextualize quantitative findings. Results showed that 72% of households adopted at least one agroecological practice, including coffee agroforestry, homegarden agroforestry, soil and water conservation, climate-smart agriculture, intercropping, crop rotation, mixed tree planting, afforestation and reforestation, organic soil fertility management, and biological pest and disease management. Adoption intensity varied: 34.6% low, 41.2% moderate, and 24.2% high. Practices were perceived to improve soil fertility, biodiversity conservation, climate resilience, forest management, and livelihood diversification while reducing pressure on natural forests and strengthening ecological connectivity and adaptive capacity. Sex, education, farm size, extension services, training, credit access, and cooperative membership significantly determined adoption and intensity, whereas age, household size, farming experience, and distance to forest were non-significant. Agroecology provides a multifunctional, context-specific pathway for advancing sustainable forest management, biodiversity conservation, and livelihood resilience. Strengthening agroecology-oriented extension, gender-responsive training, access to finance, indigenous knowledge integration, and policy mainstreaming are essential for long-term environmental sustainability and community resilience in the YCFBR and comparable forest-dependent landscapes.","农业生态学为在生物多样性丰富的热带景观中协调森林保护与农村生计提供了一条有前景的路径，但关于其采用广度、强度及决定因素的实证证据仍然有限，尤其是在联合国教科文组织生物圈保护区中。本研究调查了埃塞俄比亚西南部亚尤咖啡森林生物圈保护区（YCFBR）的农业生态实践、感知贡献及采用决定因素。研究采用横断面同步混合方法设计，结合293户随机选取的家庭，并辅以关键知情人访谈、焦点小组讨论和实地观察。二元逻辑回归识别了采用的决定因素（外延边际），而有序逻辑回归则使用农业生态采用指数（AAI）考察了采用强度（内涵边际）。采用实践数量（NPA）、加权采用强度（WAI）和采用持续时间（DUR）为采用广度、强度和持续性提供了补充性见解。定性数据采用主题分析法，以对定量发现进行情境化解释。结果表明，72%的家庭至少采用了一项农业生态实践，包括咖啡农林复合、家庭园圃农林复合、水土保持、气候智能型农业、间作、轮作、混合植树、植树造林和再造林、有机土壤肥力管理以及生物病虫害管理。采用强度存在差异：低度34.6%、中度41.2%、高度24.2%。受访者认为这些实践改善了土壤肥力、生物多样性保护、气候韧性、森林管理和生计多样化，同时减轻了对天然森林的压力，并增强了生态连通性和适应能力。性别、教育程度、农场规模、推广服务、培训、信贷获取和合作社成员资格显著决定了采用及采用强度，而年龄、家庭规模、耕作经验和距森林距离则不显著。农业生态学为推进可持续森林管理、生物多样性保护和生计韧性提供了一条多功能、因地制宜的路径。加强面向农业生态学的推广、性别响应型培训、融资渠道、本土知识整合和政策主流化，对于YCFBR及类似依赖森林的地区实现长期环境可持续性和社区韧性至关重要。","Discover Environment","2026-10-08T00:00:00Z",73,{"impact":19,"substance":83,"depth":326,"authority":280,"freshness":46,"relevant":22,"comment":455},"基于293户调查的实证研究，数据扎实、结论具体，对热带林区农业生态化与咖啡产业可持续发展有参考价值，但属区域性案例研究，公共影响层级有限。",[457],{"name":451,"url":448},[459,460,461,462,463],"可持续生计","咖啡产业","生态农业","农林复合经营","生物多样性保护",{"code":465,"slug":466,"name":467,"path":468,"parent_code":469},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","B",[471,472],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":473,"slug":474,"name":475,"path":476,"parent_code":477},"H6","biodiversity","生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":479},{"candidates":480,"agricultural_evidence":47},[481,487,492,500,507,513,518],{"code":465,"score":17,"supported":47,"title":482,"abstract":484,"tags":486},[483],"coffee",[485,483],"咖啡",[460],{"code":32,"score":46,"supported":47,"title":488,"abstract":489,"tags":491},[],[55,379,490,401],"crop rotation",[],{"code":473,"score":46,"supported":47,"title":493,"abstract":494,"tags":499},[],[495,496,497,474,498],"生物多样性","农业生态","水土保持","agroecology",[],{"code":501,"score":69,"supported":47,"title":502,"abstract":503,"tags":506},"H2",[],[504,505],"土壤","soil",[],{"code":508,"score":52,"supported":47,"title":509,"abstract":510,"tags":512},"H7",[],[511],"climate resilience",[],{"code":141,"score":52,"supported":47,"title":514,"abstract":515,"tags":517},[],[516],"smart agriculture",[],{"code":519,"score":22,"supported":14,"title":520,"abstract":521,"tags":522},"H8",[],[],[461],[524,525],"埃塞俄比亚 亚尤咖啡林 生物圈保护区","农林复合经营 采纳影响因素","埃塞俄比亚亚尤咖啡林生物圈保护区-4570","10.1007\u002Fs44274-026-01113-4",{"doi":527,"openalex_id":529,"authors":530,"venue":451,"cited_by_count":149,"oa_url":544,"card":545,"direction":551,"ingested_from":178},"W7221057925",[531,534,537,540,542],{"name":532,"orcid":533},"Alemayehu Abera","https:\u002F\u002Forcid.org\u002F0000-0002-6225-9304",{"name":535,"orcid":536},"Samuel Taye","https:\u002F\u002Forcid.org\u002F0000-0001-9662-3939",{"name":538,"orcid":539},"Adanech Asfaw","https:\u002F\u002Forcid.org\u002F0009-0007-2586-0559",{"name":541,"orcid":8},"Chaluma Kicha",{"name":543,"orcid":8},"Endashew Mogessie","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01113-4.pdf",{"tldr":546,"method":547,"finding":548,"direction":549,"opportunity":550},"研究埃塞俄比亚亚尤咖啡森林生物圈保护区农户采用农业生态实践的程度、感知效益及影响因素。","混合方法：293户问卷+访谈+焦点小组，二元与有序logistic回归分析采用广","72%农户至少采用一项实践，采用强度分低中高；性别、教育、推广、信贷等显著影响采用。","农业绿色发展与碳","可延伸研究农业生态实践采用对森林保护与生计韧性的长期因果效应及数字化推广干预。","智慧农业 \u002F 农业物联网","2026-10-10T23:30:11.538537Z"]