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品种推广","上海市农科院品种推广申怡草莓-700","2026-08-18T23:59:16.931679Z",{"id":224,"title":225,"url":226,"summary":227,"summary_zh":8,"content":228,"source_name":229,"source_url":8,"published_at":230,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":231,"score_detail":232,"sources":234,"tags":236,"primary_topic":240,"secondary_topics":241,"classification":242,"search_phrases":259,"slug":262,"view_count":104,"doi":8,"paper":8,"created_at":263},439,"江苏省农科院中丹饲料产业研究院召开2026年项目进展总结会——二甲酸钠获批新饲料添加剂证书","https:\u002F\u002Fnewias.jaas.ac.cn\u002Fxww\u002Fxwzx\u002Fart\u002F2026\u002Fart_4dcd68452edc4579bdecb97c2501210b.html","8月7日,江苏省农科院中丹饲料产业研究院组织召开2026年项目进展总结会,研究院全体技术骨干参会。会议由中丹化工研发部总经理李宗付主持,畜牧研究所闫俊书、宦海琳及中丹化工陈秋楠分别就二甲酸钠、抗氧化剂两大核心项目的阶段性进展作专题汇报。目前,二甲酸钠新型饲料添加剂已成功获批新饲料添加剂证书,标志着核心技术研发的关键性突破。下一步研究院将重点推进二甲酸钠多场景养殖应用试验,持续拓宽畜禽养殖适配场景,优化配套饲料配方体系,完善产业化应用大数据支撑。","为系统梳理重点项目阶段性成果，明晰后续产品研发与产业发展路径，助力绿色饲料添加剂产业提质增效，8月7日，江苏省农科院中丹饲料产业研究院组织召开2026年项目进展总结会。会议由中丹化工研发部总经理李宗付主持，研究院全体技术骨干参会。\n\n会上，畜牧研究所闫俊书、宦海琳及中丹化工陈秋楠分别就二甲酸钠、抗氧化剂两大核心项目的阶段性进展作专题汇报。目前，二甲酸钠新型饲料添加剂已成功获批新饲料添加剂证书，标志着核心技术研发的关键性突破，为产品市场化、产业化落地奠定了坚实基础。结合当前行业发展趋势与产业化实际需求，参会人员围绕技术工艺优化、应用场景拓展、市场推广落地等议题展开深入研讨与交流，精准梳理现存短板，明确下一阶段重点工作。后续，研究院将重点推进二甲酸钠多场景养殖应用试验，持续拓宽畜禽养殖适配场景，优化配套饲料配方体系，完善产业化应用大数据支撑，稳步推进产品系列化升级，加速实现多元化场景规模化落地应用。\n\n本次总结会系统梳理了研究院重点项目阶段性成效与不足，进一步厘清了科研创新与产业落地的工作思路。下一步，江苏省农科院中丹饲料产业研究院将持续依托院地协同合作优势，深耕绿色饲料添加剂研发领域，持续加快技术迭代升级与成果转化落地，不断夯实产品核心竞争力，为江苏饲料产业绿色转型升级和高质量发展赋能增效。","江苏省农科院 2026-08-07","2026-08-07T00:00:00Z",57,{"impact":16,"substance":16,"depth":12,"authority":85,"freshness":172,"relevant":20,"comment":233},"省级科研机构在绿色饲料添加剂领域的项目进展，涉及新证书获批，有一定行业价值，但内容偏通稿，时效性一般。",[235],{"name":229,"url":226},[237,29,238,239],"江苏省农科院","中丹研究院","绿色养殖",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":45,"locked":13,"version":46,"method":47,"evidence":243},{"candidates":244,"agricultural_evidence":52},[245,249,254],{"code":32,"score":51,"supported":52,"title":246,"abstract":247,"tags":248},[59],[59],[],{"code":36,"score":19,"supported":52,"title":250,"abstract":251,"tags":253},[],[54,252],"畜禽",[],{"code":255,"score":20,"supported":13,"title":256,"abstract":257,"tags":258},"C6",[],[],[239],[260,261],"江苏省农科院 中丹研究院 饲料添加剂 绿色养殖","江苏省农科院 中丹研究院","江苏省农科院中丹研究院饲料添加剂绿色养殖-439","2026-08-12T23:57:22.509835Z",{"id":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":8,"source_name":270,"source_url":267,"published_at":271,"category":272,"cover_url":8,"hotness":12,"is_selected":13,"score":273,"score_detail":274,"sources":277,"tags":279,"primary_topic":285,"secondary_topics":291,"classification":299,"search_phrases":336,"slug":339,"view_count":340,"doi":341,"paper":342,"created_at":370},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":184,"substance":275,"depth":17,"authority":18,"freshness":12,"relevant":20,"comment":276},22,"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。",[278],{"name":270,"url":267},[280,281,282,283,284],"智慧农业","农业人工智能","农业遥感","氮素监测","紫花苜蓿",{"code":286,"slug":287,"name":288,"path":289,"parent_code":290},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[292,298],{"code":293,"slug":294,"name":295,"path":296,"parent_code":297},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":13,"version":46,"method":47,"evidence":300},{"candidates":301,"agricultural_evidence":52},[302,309,315,321,331],{"code":293,"score":17,"supported":52,"title":303,"abstract":305,"tags":308},[304],"hyperspectral",[306,307,304],"遥感","remote sensing",[282],{"code":286,"score":16,"supported":52,"title":310,"abstract":312,"tags":314},[311],"alfalfa",[313,311],"苜蓿",[284],{"code":32,"score":12,"supported":52,"title":316,"abstract":317,"tags":320},[],[318,313,319],"饲草","forage",[284],{"code":322,"score":12,"supported":52,"title":323,"abstract":324,"tags":330},"I2",[],[325,326,327,328,329],"深度学习","机器学习","machine learning","deep learning","decision support",[281],{"code":332,"score":20,"supported":13,"title":333,"abstract":334,"tags":335},"I1",[],[],[280],[337,338],"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":341,"openalex_id":343,"authors":344,"venue":270,"cited_by_count":340,"oa_url":267,"card":363,"direction":367,"ingested_from":369},"W7223154576",[345,347,349,351,353,356,358,361],{"name":346,"orcid":8},"Shuai Fu",{"name":348,"orcid":8},"Yunhao Li",{"name":350,"orcid":8},"Chunli Miao",{"name":352,"orcid":8},"Jie Liu",{"name":354,"orcid":355},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":357,"orcid":8},"Yongjia Hu",{"name":359,"orcid":360},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":362,"orcid":8},"吴彩霞",{"tldr":364,"method":365,"finding":366,"direction":367,"opportunity":368},"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":372,"title":373,"url":374,"summary":375,"summary_zh":8,"content":376,"source_name":377,"source_url":8,"published_at":378,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":379,"score_detail":380,"sources":383,"tags":385,"primary_topic":390,"secondary_topics":391,"classification":404,"search_phrases":429,"slug":432,"view_count":340,"doi":8,"paper":8,"created_at":433},5519,"四川农科院资环所赴简阳调研 推进粗饲料加工与种养循环落地","https:\u002F\u002Fwww.chinawestagr.com\u002Ftszl\u002Fshowts.asp?id=67504","10月9日院资环所（草业所）副所长朱永群一行赴简阳市平泉街道调研座谈，汇报「天府农科」四川省平原丘陵区粗饲料加工利用研究院项目建设进展。三方就共建饲草料加工示范点、推进种草养猪等绿色循环项目达成共识，强调立足实际选准主导品种与技术模式，形成「饲料—养殖—粪肥—农田」闭环，把示范基地建成可学可复制的样板。","λԺʻ                ʱ䣺2026\u002F10\u002F16331\n\nΪ᳹ʵҹƶDzݲҵչľ߲𣬼ӿƼɹת2026109գԺʻҵȺоԱһиƽȪ̸ֵƽȪֵίֵ֣ίǡ´¼ֵũҵۺϷġͩйظͬ־пũƼ޹˾³¡ܾˣԺʻҵƹܿƿƳ޸㸱оԱҵ̷оʿȲμӡ\n\nϣ㱨“‘츮ũ’ĴʡƽԭϼӹоԺĿ”Ľ豳岼ֺ½չ¶³˹˾ҵͺϣDzϼӹʾ㣬ӿƽֲɫѭĿ˽ֵԴͲҵʾֵոԴḻֳϺãDzֲϻôַţϣڼ˲ϵõԺֵ֧֣õءùϡ\n\nȺڽָҪʵѡ׼ƷֺͼģʽḻDzϲƷߣͻ“Բݴ”ѭγ“—ֳ——ũ”ջעر׼ƶƷʾؽɿѧɸƵ塣","四川省农业科学院 2026-10-20","2026-10-20T00:00:00Z",49,{"impact":85,"substance":12,"depth":63,"authority":381,"freshness":19,"relevant":20,"comment":382},13,"省级科研机构赴地方推进粗饲料加工与种养循环落地的调研通稿，有具体合作与示范意图，但属区域性进展，信息增量有限，不宜进入每日精选。",[384],{"name":377,"url":374},[386,387,388,389],"院企合作","科技成果转化","种养循环","粗饲料加工",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[392,398],{"code":393,"slug":394,"name":395,"path":396,"parent_code":397},"E1","food-processing","加工与食品制造","\u002Ftopic\u002Ffood-supply\u002Ffood-processing","E",{"code":399,"slug":400,"name":401,"path":402,"parent_code":403},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",{"status":45,"locked":13,"version":46,"method":47,"evidence":405},{"candidates":406,"agricultural_evidence":52},[407,411,415,419,425],{"code":32,"score":18,"supported":52,"title":408,"abstract":409,"tags":410},[59],[59,318],[],{"code":393,"score":51,"supported":52,"title":412,"abstract":413,"tags":414},[389],[389],[],{"code":399,"score":152,"supported":52,"title":416,"abstract":417,"tags":418},[388],[],[388],{"code":420,"score":158,"supported":52,"title":421,"abstract":422,"tags":424},"C1",[],[423],"养猪",[],{"code":39,"score":72,"supported":13,"title":426,"abstract":427,"tags":428},[],[],[387,386],[430,431],"四川省农科院 简阳 粗饲料","简阳 种养循环 示范基地","四川省农科院简阳粗饲料-5519","2026-10-22T00:07:04.243033Z"]