[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3200":3,"related-3200":58},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":57},3200,"Timing Matters: Optimising the Early Blight Spraying Strategy in Potatoes in the Netherlands","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11540-026-10146-4","Abstract Potato early blight is a fungal disease in potato, caused by the fungus Alternaria solani . We analysed 25 experiments (2009–2023) in different locations with different cultivars to assess effects of the disease on yield, disease progression curves and effects of fungicides. Results showed large unexplained variation in the yield losses versus AUDPC, which varied between 0 and −20% and 0 to 20 ton fresh\u002Fha. Disease control with fungicides increased yields by on average 2–3 ton\u002Fha fresh weight. We observed large interannual variation in onset of the disease (first symptoms) which implies that in a year with late onset, earliest sprayings are probably too early to be effective and can be skipped without any yield loss. Results showed fungicides delay onset of the disease, but not the growth rate of the disease severity. Once well established, the disease grows equally strong with and without fungicide use. This finding implies that once well established, one could just as well stop using fungicides, as they are at that stage no longer effective, which means latest spraying(s) can be skipped. A Pdays based model was tested to predict onset of the disease. The model had some skill in predicting the onset of the disease, although large uncertainty remains. The model was implemented in a decision support system where it advises farmers from which day onwards to start spraying. Our analysis suggests scope for reducing fungicide by up to ~50% without yield penalty, by skipping the first one or two sprayings in years where early blight comes at normal or late date and skipping the last one or two sprayings in years where early blight comes at early or normal date.","摘要 马铃薯早疫病是一种由茄链格孢菌(Alternaria solani)引起的真菌性病害。我们分析了2009至2023年间在不同地点、不同品种上开展的25项试验，以评估该病害对产量、病害进展曲线的影响以及杀菌剂的效果。结果表明，产量损失与AUDPC之间的关系存在较大的无法解释的变异，产量损失变化范围为0至−20%，即0至20吨鲜重\u002F公顷。使用杀菌剂进行病害防治使产量平均增加2至3吨\u002F公顷鲜重。我们观察到病害始发期(首次出现症状)存在较大的年际变异，这意味着在始发期较晚的年份，最早的喷药可能因过早而无效，可以跳过而不造成任何产量损失。结果表明，杀菌剂延迟了病害的始发，但不影响病害严重度的增长率。一旦病害充分建立，无论是否使用杀菌剂，其增长强度相同。这一发现意味着，一旦病害充分建立，就可以停止使用杀菌剂，因为在该阶段杀菌剂已不再有效，这意味着最后的几次喷药可以跳过。我们测试了一个基于Pdays的模型来预测病害始发期。该模型在预测病害始发期方面具有一定的能力，但仍存在较大的不确定性。该模型已被整合到一个决策支持系统中，用于建议农民从哪一天开始喷药。我们的分析表明，通过在不来或晚来早疫病的年份跳过前一到两次喷药，以及在早来或正常来早疫病的年份跳过后一到两次喷药，有将杀菌剂使用量减少约50%而不造成产量损失的空间。",null,"Potato Research","2026-09-19T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"基于25个试验的新结论显示可减少约50%杀菌剂用量而不减产，对精准施药与智慧植保具参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"马铃薯","决策支持系统","农药减量","智慧植保","早疫病",[32,33],"马铃薯 早疫病 杀菌剂 减量","荷兰 马铃薯 早疫病 预测模型","马铃薯早疫病杀菌剂减量-3200",0,"10.1007\u002Fs11540-026-10146-4",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":49,"card":50,"direction":54,"ingested_from":56},"W7213645010",[40,43,46],{"name":41,"orcid":42},"P.A.J. van Oort","https:\u002F\u002Forcid.org\u002F0000-0001-7617-5382",{"name":44,"orcid":45},"Bert Evenhuis","https:\u002F\u002Forcid.org\u002F0000-0002-6895-1190",{"name":47,"orcid":48},"Geert J. T. Kessel","https:\u002F\u002Forcid.org\u002F0000-0003-4559-4896","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs11540-026-10146-4.pdf",{"tldr":51,"method":52,"finding":53,"direction":54,"opportunity":55},"基于25年试验分析马铃薯早疫病发病时间与产量损失，提出可减少约50%杀菌剂用量的精准喷施策略。","分析2009–2023年25个田间试验，构建Pdays模型预测发病起点并集成决策","杀菌剂仅延迟发病不降低病害增长率，晚发年可跳过早期喷施、早发年可跳过末期喷施而不减产。","农业人工智能与决策模型","可结合气象与遥感数据提升Pdays模型预测精度，并开发实时变量喷施决策系统。","openalex","2026-09-22T23:30:50.511266Z",{"total":59,"page":21,"page_size":59,"items":60},6,[61,98,146,175,198,220],{"id":62,"title":63,"url":64,"summary":65,"summary_zh":66,"content":9,"source_name":67,"source_url":64,"published_at":68,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":69,"score_detail":70,"sources":75,"tags":77,"search_phrases":82,"slug":85,"view_count":35,"doi":86,"paper":87,"created_at":97},3197,"YieldVision: Model-Driven Decision Support System for Crop Yield Prediction","https:\u002F\u002Fdoi.org\u002F10.56201\u002Frjpst.vol.9.no1.2026.pg176.194","Agriculture, a cornerstone of global food security, faces unprecedented challenges in achieving sustainable productivity. Crop yield prediction, a crucial aspect of agricultural planning, suffers from inefficiencies rooted in technological and methodological gaps. While traditional approaches rely on historical data and empirical estimations, they often fail to address the complexity of environmental interactions because they lack real-time data integration and limited access to accurate data, leading to inaccuracies and resource mismanagement. Addressing these issues requires the development of advanced Machine Learning (ML) models-based system that can predict crop yield with high accuracy in dynamic environments. This paper presents YieldVision, a decision support system driven by advanced ML models for Crop Yield Prediction based on environmental, soil, and crop-specific factors. In this work, we proposed three crop prediction models: Random Forest (RF), Long Short Time Memory (LSTM) and Hybrid (RF LSTM). RF regressor effectively captured the complex, nonlinear relationships among soil nutrients, fertilizer application, and temperature. The LSTM network learned the way short-term weather fluctuations impact crop development over time, and the hybrid model combined LSTM for environmental-related factors, and RF for soil-related factors. Experimental results on the Crop Yield Prediction Dataset containing soil and weather parameters showed that RF has the highest accuracy 𝑅2 = 99.10% and outperforms both LSTM and hybrid which have 87.50% and 97.30% accuracy respectively, confirming its suitability for the dataset. RF has the lowest RMSE and MAE compared to LSTM and hybrid models. The models are integrated into a decision support system that is deployed to operationalize the models in real-world settings with user interfaces accessible via mobile or web-delivered real-time alerts and yield forecasts to farmers and agronomists.","农业作为全球粮食安全的基石，在实现可持续生产力方面面临着前所未有的挑战。作物产量预测作为农业规划的关键环节，因技术和方法上的不足而效率低下。传统方法依赖历史数据和经验估算，但由于缺乏实时数据集成且难以获取准确数据，往往无法应对环境相互作用的复杂性，导致预测不准确和资源管理不善。解决这些问题需要开发基于先进机器学习（ML）模型的系统，能够在动态环境中高精度地预测作物产量。本文提出了YieldVision，一个由先进机器学习模型驱动的决策支持系统，用于基于环境、土壤和作物特定因素的作物产量预测。在本研究中，我们提出了三种作物预测模型：随机森林（RF）、长短期记忆网络（LSTM）和混合模型（RF-LSTM）。随机森林回归器有效捕捉了土壤养分、施肥量和温度之间复杂的非线性关系。LSTM网络学习了短期天气波动如何随时间影响作物发育，而混合模型将LSTM用于环境相关因素，RF用于土壤相关因素。在包含土壤和天气参数的作物产量预测数据集上的实验结果表明，随机森林具有最高的准确率𝑅2 = 99.10%，优于LSTM和混合模型，后两者的准确率分别为87.50%和97.30%，证实了随机森林对该数据集的适用性。与LSTM和混合模型相比，随机森林具有最低的RMSE和MAE。这些模型被集成到一个决策支持系统中，该系统已部署用于在实际环境中运行这些模型，其用户界面可通过移动端或网页访问，向农民和农艺师提供实时警报和产量预测。","RESEARCH JOURNAL OF PURE SCIENCE AND TECHNOLOGY","2026-09-21T00:00:00Z",68,{"impact":71,"substance":72,"depth":73,"authority":71,"freshness":20,"relevant":21,"comment":74},12,20,16,"提出RF、LSTM及混合模型并集成决策支持系统，方法对比与精度数据扎实，对智慧农业产量预测有参考价值，但属单篇论文且来源期刊影响力有限。",[76],{"name":67,"url":64},[78,79,80,81,27],"智慧农业","农业人工智能","产量预测","机器学习",[83,84],"YieldVision 作物产量预测","RF LSTM 混合模型 产量预测","YieldVision作物产量预测-3197","10.56201\u002Frjpst.vol.9.no1.2026.pg176.194",{"doi":86,"openalex_id":88,"authors":89,"venue":67,"cited_by_count":35,"oa_url":9,"card":92,"direction":54,"ingested_from":56},"W7213883348",[90],{"name":91,"orcid":9},"D.J.S. Sako",{"tldr":93,"method":94,"finding":95,"direction":54,"opportunity":96},"提出YieldVision决策支持系统，用RF、LSTM及混合模型预测作物产量并部署应用。","基于土壤、环境与作物数据，构建RF、LSTM和RF-LSTM混合模型并集成到We","随机森林表现最佳，R²达99.10%，优于LSTM（87.50%）和混合模型（97.30%）。","可探索多源实时数据融合与模型可解释性，提升跨区域泛化能力并降低对高精度历史数据的依赖。","2026-09-22T23:30:43.591303Z",{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":9,"source_name":104,"source_url":101,"published_at":105,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":107,"sources":110,"tags":112,"search_phrases":116,"slug":119,"view_count":35,"doi":120,"paper":121,"created_at":145},3057,"Recent Horizons in Pyridine-Based Agrochemicals (2020-2026): From Molecular Design to Ecological Safety","https:\u002F\u002Fdoi.org\u002F10.1021\u002Facsagscitech.6c00690","Abstract Pyridine derivatives have emerged as indispensable pharmacophoric scaffolds in contemporary agrochemistry, driving breakthroughs across next-generation insecticides, plant growth regulators (PGRs), micronutrient chelators, and biostimulants. This review provides a comprehensive, critical synthesis of structural innovations, advanced formulations, and ecological safety profiles of pyridine-based agrochemicals reported from 2020 to 2026. We systematically evaluate how structure-activity relationship (SAR) optimizations and density functional theory (DFT) quantum modeling have enabled the precise engineering of legacy neonicotinoids and targeted pyridine platforms to maximize entomological target-site affinity such as within insect nicotinic acetylcholine and ryanodine receptors while proactively decoupling insecticidal potency from non-target ecotoxicity toward vital pollinators like honeybees (Apis mellifera). Beyond molecular architecture, this work highlights the pioneering integration of green chemistry paradigms and smart formulation technologies, focusing on biodegradable polymer networks (e.g., carboxymethyl cellulose\u002Fpoly(4-vinylpyridine) (CMC\u002FP4VP) hydrogels) engineered to stabilize micronutrient chelation, trigger biostimulatory foliage responses, assist in soil remediation, and mitigate environmental leaching via controlled diffusion mechanisms. Furthermore, we examine the deployment of advanced analytical tools, including surface-enhanced Raman spectroscopy (SERS), for ultra-sensitive trace monitoring of pyridinic residues in agro-ecosystems. By bridging fundamental chemical engineering with stringent ecological compliance and future artificial intelligence-driven molecular design, this review delivers essential, actionable perspectives for researchers and policymakers advancing sustainable, climate-smart agriculture.","吡啶衍生物已成为当代农业化学中不可或缺的药效团骨架，推动了新一代杀虫剂、植物生长调节剂（PGRs）、微量营养元素螯合剂及生物刺激素等领域的突破性进展。本综述对2020至2026年间报道的吡啶类农用化学品的结构创新、先进制剂技术及生态安全性进行了全面而批判性的综合评述。我们系统评估了构效关系（SAR）优化与密度泛函理论（DFT）量子建模如何实现对传统新烟碱类及靶向吡啶平台的精准设计，以最大化昆虫靶标位点亲和力（如昆虫烟碱型乙酰胆碱受体和兰尼碱受体），同时主动将杀虫效力与非靶标生态毒性解耦，尤其是对蜜蜂（Apis mellifera）等重要传粉昆虫的毒性。在分子结构之外，本工作重点介绍了绿色化学范式与智能制剂技术的前沿融合，聚焦于可生物降解聚合物网络（如羧甲基纤维素\u002F聚（4-乙烯基吡啶）（CMC\u002FP4VP）水凝胶）的设计，旨在稳定微量营养元素螯合、触发叶片生物刺激响应、辅助土壤修复，并通过控释扩散机制减少环境淋溶。此外，我们还探讨了先进分析工具（包括表面增强拉曼光谱（SERS））在农业生态系统中吡啶类残留超灵敏痕量监测方面的应用。通过将基础化学工程与严格的生态合规要求及未来人工智能驱动的分子设计相衔接，本综述为推进可持续、气候智慧型农业的研究人员与政策制定者提供了重要且可操作的见解。","ACS Agricultural Science & Technology","2026-09-20T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":108,"freshness":20,"relevant":21,"comment":109},14,"系统综述吡啶类农药2020-2026年分子设计与生态安全进展，方法新颖、结论可靠，对绿色农药研发有参考价值，但属综述类论文，产业影响有限。",[111],{"name":104,"url":101},[78,28,113,114,115],"生态安全","绿色农药","人工智能育种",[117,118],"吡啶类农药 分子设计","CMC P4VP 水凝胶 缓释","吡啶类农药分子设计-3057","10.1021\u002Facsagscitech.6c00690",{"doi":120,"openalex_id":122,"authors":123,"venue":104,"cited_by_count":35,"oa_url":9,"card":138,"direction":144,"ingested_from":56},"W7213761670",[124,127,130,133,136],{"name":125,"orcid":126},"Hamdy Khamees Thabet","https:\u002F\u002Forcid.org\u002F0000-0001-8387-0404",{"name":128,"orcid":129},"Ahmed Abdou O. Abeed","https:\u002F\u002Forcid.org\u002F0000-0002-7284-4750",{"name":131,"orcid":132},"Mohamed R. Fouad","https:\u002F\u002Forcid.org\u002F0000-0002-4102-5111",{"name":134,"orcid":135},"Gameel A. M. Elhagali","https:\u002F\u002Forcid.org\u002F0000-0002-8053-9203",{"name":137,"orcid":9},"Mohamed S. A. El-Gaby",{"tldr":139,"method":140,"finding":141,"direction":142,"opportunity":143},"综述2020-2026年吡啶类农药从分子设计到生态安全的进展。","综述SAR优化、DFT建模、绿色制剂与SERS残留监测。","可精准设计高靶标活性且对蜜蜂低毒的吡啶农药，并实现可控释放。","农业绿色发展与碳","可探索AI驱动的吡啶分子设计及其对传粉昆虫的生态风险预测。","智慧农业 \u002F 农业物联网","2026-09-21T23:30:09.112694Z",{"id":147,"title":148,"url":149,"summary":150,"summary_zh":9,"content":9,"source_name":151,"source_url":9,"published_at":105,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":69,"score_detail":152,"sources":155,"tags":157,"search_phrases":162,"slug":165,"view_count":35,"doi":9,"paper":166,"created_at":174},3049,"土壤压实与灌溉管理：对精准农业中土壤水力变化的启示","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1853","意大利帕多瓦大学A.C.与L.B.评估土壤压实通过改变土壤水力特性对精准农业灌溉管理的综合影响。研究维护土壤结构作为维持土壤水力功能、提升灌溉效率与农业系统长期可持续性最有效途径，使用HYPROP水力特性分析仪测定田间持水量（FC）、永久萎蔫点（PWP）、饱和水力传导度（Ksat）等关键参数，结合无人机遥感（UAV）与决策支持系统（DSS）实现精准灌溉调度。研究获SOILWAT（BIRD 2026）项目资助，为精准农业管理决策提供可量化水力参数基础。","MDPI Agronomy 16(18):1853",{"impact":71,"substance":17,"depth":73,"authority":19,"freshness":153,"relevant":21,"comment":154},9,"学术论文，方法结合HYPROP与无人机遥感，对精准灌溉有参考价值，但属细分领域研究，公共影响有限。",[156],{"name":151,"url":149},[27,158,159,160,161],"精准农业","无人机遥感","智慧灌溉","土壤压实",[163,164],"帕多瓦大学 土壤压实 灌溉","HYPROP 水力特性 精准灌溉","帕多瓦大学土壤压实灌溉-3049",{"doi":9,"openalex_id":9,"authors":167,"venue":9,"cited_by_count":35,"oa_url":9,"card":168,"direction":144,"ingested_from":173},[],{"tldr":169,"method":170,"finding":171,"direction":144,"opportunity":172},"评估土壤压实改变水力特性对精准灌溉管理的影响，并提出维护土壤结构的对策。","用HYPROP测FC、PWP、Ksat，结合无人机遥感与决策支持系统调度灌溉。","维护土壤结构是保持水力功能、提升灌溉效率与长期可持续性的最有效途径。","可探索压实-水力参数-遥感反演耦合模型，实现压实风险与灌溉调度的实时协同优化。","agent","2026-09-21T00:04:39.395594Z",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":9,"content":180,"source_name":181,"source_url":9,"published_at":11,"category":182,"cover_url":9,"hotness":13,"is_selected":14,"score":183,"score_detail":184,"sources":186,"tags":188,"search_phrases":193,"slug":196,"view_count":35,"doi":9,"paper":9,"created_at":197},2979,"广西农业农村厅：2026\"蒙科聚\"农牧领域19项科技成果集中发布 3项现场签约","https:\u002F\u002Fszb.northnews.cn\u002Fbfxb\u002Fresfile\u002F2026-03-25\u002F02\u002Fbfxb2026032502.pdf","2026年\"蒙科聚\"第4期专题发布会暨农牧领域科技成果发布活动近日在包头分中心举行。包头市农牧业科学研究院7位专家集中发布\"马铃薯新品种包薯4号\"\"朱顶红新品种霞光青黛\"\"加工辣椒全程机械化生产技术\"等19项自主研发成果，含15个优质新品种、4项实用技术。\"肉羊鲜精常温保存\"等7项核心成果备受关注，3项现场签订产业化合作协议。","2026 年 3 月 25 日 本版主编： 郝小军 版式策划： 兰 峰责任校对： 绍 文\n\n# 2\n\n记者 3 月 24 日从内蒙古自治 区 水 利 厅 获 悉 ： 近 日 ， 水 利 部 公 布 2025 年 度 大 禹 水 利 科 学 技 术 奖 获 奖 名 单 ， 内 蒙 古 自 治 区 水 旱 灾 害 防 御 技 术 中 心 作 为 主 要 完 成 单 位 参 与 完 成 的 “ 北 方 典 型 地 区 山 洪 灾 害 防 御 关 键 技 术 研 究 及 应 用 ” “ 牧 区 草 原 干 旱 监 测 预 警 与 旱 灾 综 合 风 险 评 估 关 键 技 术 ” 两 项 成 果 荣 获 科 技 进 步 奖 二 等奖。 作为水利行业最高科技奖， 大禹水利科学技术奖由国家科学技 术奖励工作办公室批准设立， 是中 国水利领域首个社会科技奖项。 本次大禹水利科学技术奖共收到提 名 成 果 207 项 ， 通 过 形 式 审 查 206 项， 最终获奖 80 项。 内蒙古自治区水利厅将继续 坚持创新驱动、 融合共享， 加快培 育发展水利新质生产力， 积极推进 水利科研转型， 为推动全区水利高 质量发展提供有力科技支撑。 \n\n（据草原云） \n\n# 喜报！ 内蒙古两项成果荣获大禹水利科学技术奖 \n\n## 打卡幸福出版社 \n\n3 月 24 日， 呼和浩特市民正在打卡坐落于呼和浩特市新城区幸福巷 的红庐 ·幸福出版社。 据了解， 该建筑由中国工程院院士张鹏举团队匠 心设计， 依托 1947 年建成的原内蒙古人民出版社旧址保护性修缮焕新， 既完整留存老院落的历史肌理与城市文脉， 又融入阅读、 文创、 艺术展 陈等多元现代功能， 项目目前处于试运行阶段， 将于 5 月 1 日正式开放， 成为市民感受人文之美、 畅享幸福生活的城市文化新地标。 \n\n摄影\u002F草原云 ·北方新报首席记者 牛天甲 \n\n## 内蒙古集中销毁 15 吨侵权假冒伪劣商品 涉及食品汽配等 14 类 \n\n新报讯 （草原云 ·北方新报记者 刘睿睿） 3 月 24 日上午， 内蒙 古自治区市场监督管理局联合呼和浩特市市场监督管理局、 市公安 局、 呼和浩特白塔国际机场海关等多部门， 在呼和浩特市举办打击侵 权假冒伪劣商品集中统一销毁行动， 以实际行动彰显监管执法部门严 厉打击侵权假冒违法行为、 筑牢市场安全防线的坚定决心。 本次行动依法对查获的侵权假冒伪劣商品进行集中无害化处置， 涵盖食品、 烟酒、 电动自行车、 服装鞋帽、 汽车配件、 日用百货等 14 大 类商品， 总量达 15 吨， 货值近 213 万元。 销毁过程严格遵循环保标准， 采用碾压、 拆解、 破碎等无害化处理方式。 今年以来， 内蒙古自治区市场监督管理局制定印发 《2026 年开展 侵权假冒伪劣商品集中统一销毁工作方案》 ，在全区范围内统筹推进 集中销毁工作。 此次集中行动既是对前期打击侵权假冒工作成果的集 中展示， 也对各类侵权假冒违法分子形成强力震慑， 切实维护了市场 秩序与广大消费者的合法权益。 下一步， 自治区市场监管局将坚守依 法治理、 打建结合、 统筹协作、 社会共治的工作原则， 紧盯群众反映强 烈、 社会舆论高度关注、 侵权假冒问题多发的重点领域与重点区域， 聚焦重点行业、 特色农畜产品和重点市场， 持续凝聚执法合力， 深入开展 打击侵权假冒专项整治行动， 不断净化市场环境， 全力营造安全放心、 安心舒心的消费环境， 全方位守护人民群众消费安全。 \n\n## 种在心里的光” ：内蒙古创新打造生活中的思政课 \n\n新报讯 （草原云 ·北方新报首席记者 王树天） 3 月 23 日， 由内蒙古自治 区教育厅指导、 呼和浩特市教育局主办的 “种在心里的光” —— “生活中的思政 课” 教育成果展示活动在内蒙古艺术剧院举行。 活动以校园一日德育为主线， 将课堂教学、 课间活动、 研学实践等育人场景搬上舞台， 打造沉浸式思政课堂。 活动在交响乐 《花儿与少年》 中拉开帷幕。 苏虎街实验小学的班会课 《梦想 的种子》 在童真对话中让思政种子悄然扎根； 新城区光华小学等多校师生联袂 呈现国旗下演讲， 合唱 《生长吧》 ，实现个人梦想与家国情怀的同频共振； 呼和 浩特市第二中学演绎 《我愿以身许国》 ，将航天精神与思政内涵深度融合； 包头 市少年宫的研学音乐剧 《小燕子》 再现走进包钢的研学之旅， 感悟奋斗精神。 活动在主题朗诵与情景合唱 《育人之路》 中推向高潮。 这场 “生活中的思政课” 打破了传统课堂边界， 让思政教育变得可感、 可触、 可学， 实现润物无声、 入脑入心。 近年来， 内蒙古自治区教育厅以铸牢中华民族共同体意识为主线， 全面推 进“大思政课” 建设， 打造 “行走的思政课” “剧场里的思政课” “生活中的思政 课” 育人矩阵， 推动思政 “小课堂” 与社会 “大课堂” 深度融合。 下一步， 全区教 育系统将持续深化全员全过程全方位育人， 推进大中小学、 家校社、 知信行一 体化建设， 把思政之光照进每一个青少年心里， 培养担当民族复兴重任的时代 新人。 \n\n## 内蒙古 19 项农牧领域科技成果发布 3 项现场签约 \n\n新报讯 （草原云 ·北方新报记者 郝少英） 记者从内蒙古自治区 科技厅了解到， 2026 年 “蒙科聚” 第 4 期专题发布会专题暨农牧领域科 技成果发布活动近日在 “蒙科聚” 包头分中心举行， 集中展示了农牧 领域科技创新突破， 力促科技成果与市场需求靶向对接， 推动产学研 深度融合从 “协同” 向“联动” 迈进。 发布会上， 包头市农牧业科学研究院 7 位专家集中发布了 “马铃 薯新品种： 包薯 4 号” “朱顶红新品种： 霞光青黛” “加工辣椒全程机械 化生产技术” 等 19 项自主研发成果 （含 15 个优质新品种、 4 项实用技 术） ，涵盖果蔬、 作物、 畜禽、 花卉等多个品类， 特别是 “肉羊鲜精常温 保存” 等 7 项核心成果备受企业、 合作社和种植大户关注。 参会企业、 合作社针对稗草新品种产量和市场前景、 番茄与草莓科技成果转化模 式等话题与相关专家进行了深入探讨。 包头市农牧科学研究院与北京 益沃德农业生物科技有限公司签订 “玉米新品种： 包玉 406” 产业化合 作与技术转让协议； 与昆明立深新园生物科技有限公司签署 “宝珠草 莓” 种植与果品销售合约； 与内蒙古绿海浩康园林景观生态有限责任 公司签订农牧领域产学研战略合作协议。 此次发布的农牧领域科技成果均通过试验示范验证， 适应性强、 实用性突出， 可精准破解本地农牧生产关键技术难题， 有效实现丰富 农产品供给、 带动农户增收。","北方新报","报道",64,{"impact":73,"substance":17,"depth":71,"authority":153,"freshness":153,"relevant":21,"comment":185},"省级农牧科技成果集中发布与现场签约，含具体品种与技术，具行业参考价值，但属常规发布会通稿，深度有限。",[187],{"name":181,"url":178},[26,189,190,191,192],"种业振兴","成果转化","产学研合作","农牧科技成果",[194,195],"蒙科聚 农牧科技成果 包头","包头农科院 包薯4号 朱顶红","蒙科聚农牧科技成果包头-2979","2026-09-20T00:03:00.827614Z",{"id":199,"title":200,"url":201,"summary":202,"summary_zh":9,"content":203,"source_name":204,"source_url":9,"published_at":205,"category":182,"cover_url":9,"hotness":13,"is_selected":14,"score":206,"score_detail":207,"sources":210,"tags":212,"search_phrases":215,"slug":218,"view_count":35,"doi":9,"paper":9,"created_at":219},2724,"高淀粉马铃薯新品种展示观摩和研讨会在云南宣威举办——云薯108淀粉含量17.06%","http:\u002F\u002Fyn.xinhuanet.com\u002F20260913\u002F1fc2273502cd409aa3de16ab34412d68\u002Fc.html","高淀粉马铃薯新品种展示观摩和研讨会在云南宣威召开,集中展示全国18家马铃薯育种科研单位及企业最新选育的46个优质高淀粉马铃薯新品种。核心示范主推品种云薯108由云南省农科院经济作物研究所牵头选育,鲜薯淀粉含量达17.06%,具备黄皮黄肉、口感优良、休眠期长、耐储运等优势。","9 月 10 日至12 日，由云南省马铃薯技术创新中心（筹）联合国家马铃薯产业技术体系抗晚疫病品种改良岗位、省内马铃薯淀粉加工龙头企业共同主办的“高淀粉马铃薯新品种展示观摩和研讨会”在曲靖市宣威市召开。本次活动汇聚全国马铃薯育种科研力量，集中展示优异新品种，研讨产业关键技术，搭建起产学研企深度融合的交流合作平台。\n\n![Image 1](http:\u002F\u002Fyn.xinhuanet.com\u002F20260913\u002F1fc2273502cd409aa3de16ab34412d68\u002F202609131fc2273502cd409aa3de16ab34412d68_202609133c9d3efccb614ed7b4c51363c6685a56.jpg)\n\n高淀粉马铃薯新品种展示观摩和研讨会现场（9月11日摄）\n\n会上，国家马铃薯产业技术体系岗位科学家贺苗苗、李建武和吕黄珍，云南云淀淀粉有限公司董事长林英辉等4位参会嘉宾作专题分享。专家们结合产业发展现状与技术前沿，围绕高淀粉马铃薯种质资源筛选与功能基因挖掘、高淀粉马铃薯优良品种选育及配套高效栽培技术、马铃薯多元化加工技术与智能装备研发进展三大核心技术方向展开详细讲解，并结合产业发展实际，系统介绍了云南省马铃薯技术创新中心（筹）这一省级创新平台的筹建进度、建设定位、核心功能及未来产学研赋能规划。\n\n![Image 2](http:\u002F\u002Fyn.xinhuanet.com\u002F20260913\u002F1fc2273502cd409aa3de16ab34412d68\u002F202609131fc2273502cd409aa3de16ab34412d68_202609130cff89eef70f4989895af39adbb61d57.jpg)\n\n专家组测评现场（9月11日摄）\n\n活动期间，参会专家及代表前往宣威市龙场镇勺姑村，实地观摩云南云淀淀粉有限公司马铃薯种质资源示范基地田间收获现场，对集中展示的全国18家马铃薯育种科研单位及企业最新选育的46个优质高淀粉马铃薯新品种，进行抗病性、丰产性、商品品质及淀粉含量等核心指标对比研判，为企业生产经营建言献策，提升马铃薯淀粉加工产能与经济效益。\n\n经专家组测评，此次示范品种中，小区产量前五的品种分别为定薯3号、晋W3、鄂薯16号、青薯15号、昭薯12号；淀粉含量前五的品种为云薯108、垦薯3号、W1、陇薯14号、青薯15号。专家组一致认为，此次展示的高产高淀粉马铃薯新品种综合性状优良，适配本地气候与种植条件，具备大面积示范推广和产业化应用的良好前景。\n\n![Image 3](http:\u002F\u002Fyn.xinhuanet.com\u002F20260913\u002F1fc2273502cd409aa3de16ab34412d68\u002F202609131fc2273502cd409aa3de16ab34412d68_20260913f89b53ac7f65474180d8f588941e21f1.jpg)\n\n参会人员在马铃薯种质资源示范基地田间观摩合影（9月11日摄）\n\n此次观摩核心示范主推品种为云薯108，该品种由云南省农业科学院经济作物研究所牵头，联合昭通市农业科学院、昆明云薯农业科技有限公司历经十余年协同选育而成，是适配云南及西南产区的突破性优质马铃薯品种。具备黄皮黄肉、口感优良、休眠期长、耐储运等突出优势，属于鲜食、加工两用型优质品种，鲜薯淀粉含量达17.06%。该品种淀粉品质优异、晚疫病抗性强、适应性表现突出、产业化价值高，为云南省马铃薯产业提档升级、全链条高质量发展提供有力的品种支撑。\n\n下一步，主办方将不断完善良种良法配套栽培技术体系，持续做强马铃薯精深加工、鲜薯商品化销售等多元产业业态，通过持续夯实马铃薯种业创新根基，推动云南省马铃薯特色产业朝着规模化、标准化、优质化、高附加值方向稳步升级。（完）","新华网云南 2026年9月13日","2026-09-13T00:00:00Z",71,{"impact":17,"substance":72,"depth":208,"authority":71,"freshness":59,"relevant":21,"comment":209},15,"省级马铃薯育种平台集中展示46个新品种并给出淀粉含量17.06%等实测数据，产学研企融合信息扎实，具备行业参考价值。",[211],{"name":204,"url":201},[26,189,191,213,214],"高淀粉品种","精深加工",[216,217],"产学研合作 高淀粉品种 种业振兴 精深加工","产学研合作 高淀粉品种","产学研合作高淀粉品种种业振兴精深加工-2724","2026-09-17T00:04:38.989997Z",{"id":221,"title":222,"url":223,"summary":224,"summary_zh":225,"content":9,"source_name":226,"source_url":223,"published_at":227,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":228,"score_detail":229,"sources":232,"tags":234,"search_phrases":237,"slug":240,"view_count":35,"doi":241,"paper":242,"created_at":252},2682,"Machine Learning-Based Decision Support System for Greenhouse Crop Management Under Mite Infestation Conditions","https:\u002F\u002Fdoi.org\u002F10.36099\u002Fjess.v1i3.001","This paper presents an integrated machine learning-based Decision Support System (DSS) for greenhouse crop management under mite infestation conditions, specifically designed for Sri Lankan agricultural contexts. The research addresses critical challenges faced by greenhouse farmers regarding pest management and crop productivity optimization through a comprehensive system combining NetLogo simulation for synthetic data generation, ensemble machine learning models, and a web-based interface. Field research with Sri Lankan greenhouse farmers revealed that mite infestations cause up to 40% crop losses, driving panic-induced pesticide overuse and knowledge gaps in pest management timing. The system integrates environmental monitoring, pest prediction, and crop yield forecasting to provide actionable recommendations for farmers. The mite infestation prediction model achieved 86% accuracy, while the system successfully addresses data scarcity challenges through agent-based modeling. The DSS demonstrates potential for transforming reactive farming practices into predictive, data-driven approaches while accommodating the technological constraints of developing agricultural contexts.","本文提出了一种基于机器学习的集成决策支持系统（DSS），用于螨虫侵染条件下的温室作物管理，专为斯里兰卡农业情境设计。该研究针对温室农户在害虫管理和作物生产力优化方面面临的关键挑战，通过一个综合系统加以解决，该系统结合了用于合成数据生成的NetLogo仿真、集成机器学习模型以及基于网络的界面。针对斯里兰卡温室农户的实地研究表明，螨虫侵染可导致高达40%的作物损失，进而引发恐慌性农药过度使用以及害虫管理时机方面的知识缺口。该系统整合了环境监测、害虫预测和作物产量预测，为农户提供可操作的推荐建议。螨虫侵染预测模型达到了86%的准确率，同时该系统通过基于智能体的建模成功应对了数据稀缺的挑战。该决策支持系统展现出将被动应对式耕作实践转变为预测性、数据驱动方法的潜力，同时兼顾了发展中农业情境的技术约束。","Journal of Environmental and Sustainability Science","2026-09-16T00:00:00Z",74,{"impact":208,"substance":72,"depth":230,"authority":71,"freshness":13,"relevant":21,"comment":231},17,"将机器学习与智能体仿真结合用于温室螨害预测与决策支持，方法新颖、数据翔实，对设施农业植保信息化有参考价值。",[233],{"name":226,"url":223},[78,79,235,27,236],"设施农业","病虫害预警",[238,239],"农业人工智能 决策支持系统 病虫害预警 智慧农业","农业人工智能 决策支持系统","农业人工智能决策支持系统病虫害预警智慧农业-2682","10.36099\u002Fjess.v1i3.001",{"doi":241,"openalex_id":243,"authors":244,"venue":226,"cited_by_count":35,"oa_url":223,"card":247,"direction":54,"ingested_from":56},"W7213235680",[245],{"name":246,"orcid":9},"S. Nasiketha",{"tldr":248,"method":249,"finding":250,"direction":54,"opportunity":251},"为斯里兰卡温室农户开发基于机器学习的决策支持系统，预测螨害并优化作物管理。","NetLogo仿真生成合成数据，集成机器学习模型与网页界面。","螨害预测准确率达86%，可缓解数据稀缺并减少农药滥用。","可探索小样本下合成数据与迁移学习结合，提升发展中国家温室病虫害预测泛化能力。","2026-09-16T23:30:51.573509Z"]