[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-930":3,"related-930":57},{"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":32,"secondary_topics":38,"classification":39,"search_phrases":51,"slug":54,"view_count":55,"doi":8,"paper":8,"created_at":56},930,"山东省农科院作物所小麦遗传育种团队在《Nature Plants》发表小麦品质育种重大研究成果","https:\u002F\u002Fh.mffb.com.cn\u002Fa\u002F526952.html","山东省农业科学院作物研究所小麦遗传育种团队在《Nature Plants》发表小麦品质育种重大研究成果。研究完成济麦44基因组的高质量组装与注释，由中国科学院遗传与发育生物学研究所鲁非研究员、博士生张吉瑾与山东省农科院赵振东院士为共同通讯作者。研究得到国家重点研发计划、国家自然科学基金、山东省泰山学者计划等项目资助，标志山东省农科院在小麦基础研究与应用研究领域实现双突破。",null,"小麦是我国主要口粮作物。随着生活水平的提高，市场对满足不同烘焙和面食加工需求的优质小麦需求持续增加。但在传统育种中，小麦的“高产”与“优质”往往难以兼顾。“济麦44”由山东省农业科学院作物研究所曹新有为首席的小麦遗传育种团队培育，是近年来黄淮麦区表现优异的优质强筋品种之一。累计推广面积达4251万亩，曾连续三年为我国年推广面积第一大强筋小麦品种。连续多年为国家、山东省和山西省小麦的主导品种，是我国首批鉴评的“超强筋小麦品种”之一，亩产三次创全国超强筋小麦单产纪录，最高达808.6公斤\u002F亩，较好地实现了高产与优质的协同。为探明其优良性状背后的遗传基础，研究团队利用前沿测序技术，成功构建了“济麦44”高达14.90 Gb的染色体级别参考基因组。该基因组的质量值达到66.74，单碱基错误率极低，完整解析了小麦基因组中结构极为复杂的面筋基因区域。通过比对分析，研究人员在“济麦44”的一个关键高分子量谷蛋白基因（TaGlu-hmw-1Dx）中精准定位到了一个增加额外半胱氨酸的核苷酸突变。这一突变促进了面筋蛋白网络中二硫键的形成，是赋予面团超强弹性的关键因素之一。","农业信息网 2026-08-23","2026-08-23T00:00:00Z","报道",10,true,92,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},28,24,18,14,8,1,"小麦品质育种重大突破，发表于Nature Plants，兼具科研价值与产业影响。",[25],{"name":10,"url":6},[27,28,29,30,31],"山东省农科院","济麦44","小麦基因组","Nature Plants","赵振东",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":40,"locked":41,"version":42,"method":43,"evidence":44},"classified",false,"2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":14},[46],{"code":33,"score":47,"supported":14,"title":48,"abstract":49,"tags":50},11,[35],[35],[],[52,53],"山东省农科院 小麦基因组 赵振东 济麦44","山东省农科院 小麦基因组","山东省农科院小麦基因组赵振东济麦44-930",5,"2026-08-25T00:03:28.218043Z",{"total":58,"page":22,"page_size":58,"items":59},6,[60,96,201,261,309,352],{"id":61,"title":62,"url":63,"summary":64,"summary_zh":8,"content":65,"source_name":66,"source_url":8,"published_at":67,"category":12,"cover_url":8,"hotness":13,"is_selected":41,"score":68,"score_detail":69,"sources":73,"tags":75,"primary_topic":80,"secondary_topics":81,"classification":82,"search_phrases":90,"slug":93,"view_count":94,"doi":8,"paper":8,"created_at":95},3720,"每亩多打80多斤 山东省农科院实打测产印证九成熟十成收","http:\u002F\u002Fgzylhyzx.com\u002Fdetail.html?newsId=53059676","山东省农业科学院作物研究所先后在淄博临淄、青岛莱西、临沂费县三处试验基地对适度高水分小麦收获干燥一体化技术模式进行了系统实打测产。适度高水分收获较完熟期收获亩增产幅度5.5%至8.9%，平均亩增产超过80斤，机收损失率降低35%至82%。","### 每亩多打80多斤！山东省农科院实打测产印证“九成熟十成收”\n\n2026-09-29 04:36:41\n\n**大众网记者 李兆滨 王冠男 淄博报道**\n\n近日，山东省农业科学院作物研究所先后在淄博临淄、青岛莱西、临沂费县三处试验基地，对“适度高水分小麦收获干燥一体化技术模式”进行了系统的实打测产。三地涵盖了山农28、济麦22、济紫麦4号、垦星8号四个品种，分别在蜡熟末期（籽粒水分22%至27%）和完熟期（水分15%以下）进行收获对比。三个地点、四种品种、不同土壤条件和生态区，数据相互印证，结论高度一致：适度高水分收获较完熟期收获，亩增产幅度在5.5%至8.9%之间，平均亩增产超过80斤；机收损失率降低35%至82%；籽粒破碎率降低33%至51%。\n\n![Image 1](https:\u002F\u002Fdzngimage.dzwww.com\u002F2026\u002F08\u002F06\u002F0f021e4a5ba026f3bf33c94794502725.jpg)\n\n这些数据验证的正是老一辈传下来的一句农谚——“九成熟十成收，十成熟九成收”。意思是小麦在九成熟时收获，能拿到十成的收成；等到十成熟再收，反而要丢一成。道理不难理解：小麦蜡熟末期干物质积累达到峰值，之后进入“养分回流”阶段，部分营养物质反而倒流到秸秆里；同时完熟期穗轴变脆，稍微摆动就容易掉穗掉粒。农谚代代相传，却没有精准的试验数据来支撑。“以前大家都这么说，但到底能差多少，谁也说不出个准数。”一位参与测产的农技人员坦言。如今，山东省农科院用三个地点、四种品种的实打测产，让这句老话有了扎扎实实的数据支撑。\n\n![Image 2](https:\u002F\u002Fdzngimage.dzwww.com\u002F2026\u002F08\u002F06\u002Fd78ad30f3acd924d376b93c9e0c902ec.jpg)\n\n那问题来了：水分那么高收回来，怎么存？传统上农户必须等到麦粒晒干才能入仓，否则就会发热霉变。高水分小麦的干燥和储存，一直是制约“九成熟”收割的瓶颈。\n\n这次试验中，山东长江粮油仓储机械有限公司的智慧干燥储粮仓给出了答案。《小麦入仓通风干燥试验跟踪报告》详细记录了测试的成果。6月8日下午，150吨高水分小麦在淄博试验基地入仓——西仓90吨，初始水分24.5%；东仓60吨，初始水分23.7%。风机间歇式连续通风，干燥周期内东西仓各完成2次倒仓作业。到6月14日取样检测：东仓水分降至13.8%，西仓降至12.8%，5天时间平均降幅达10.8个水分点。\n\n![Image 3](https:\u002F\u002Fdzngimage.dzwww.com\u002F2026\u002F08\u002F06\u002Fa4f8429496c93de0fc26c5db9024de16.jpg)\n\n成本账更是显而易见：整批小麦通风干燥总耗电1785度，以淄博地区农业用电均价0.54元\u002F度核算，总电费963.9元。折算下来，一斤小麦的干燥成本还不到4厘钱。经测算，每吨小麦每降低1个百分点水分仅耗电1.2度，较传统烘干模式优势显著。\n\n![Image 4](https:\u002F\u002Fdzngimage.dzwww.com\u002F2026\u002F08\u002F06\u002Fea93a906f2d67e456a86ae5426940a96.jpg)\n\n桓台县田庄镇大寨村种粮大户吕茂兴是这一技术的受益者之一。他规模种粮已有10余年，今年小麦种植面积达3200多亩。今夏麦收期间阴雨天气偏多，为了抢收抢种，吕茂兴部分地块在含水量接近20%时便进行了收割。今年年初，他联系山东长江粮油仓储机械有限公司，购置了4台第三代智慧干燥储粮仓，并配套建设了储粮仓库。\n\n![Image 5](https:\u002F\u002Fdzngimage.dzwww.com\u002F2026\u002F08\u002F06\u002Fddcfde40c647c3e1f0add75eb9c9e4ea.jpg)\n\n![Image 6](https:\u002F\u002Fdzngimage.dzwww.com\u002F2026\u002F08\u002F06\u002F52a47fd2c67ef7e026e3963b8a180c8d.jpg)\n\n“今年全靠新安装的智慧干燥储粮仓，含水量20%的小麦也敢收回家。不用晾晒，不用烘干塔加热，小麦也不会发热发霉。”吕茂兴表示。据介绍，该粮仓内部安装有多条密布通风小孔的通风管道，粮食入仓后开启鼓风机，干燥空气可通过自下而上和自上而下两种方式快速穿透粮食层，带走籽粒中的水分。第三代产品将平底仓改为锥底，使粮仓内部通风更均匀，同时加装了输送机和提升机，实现了快速倒仓，进一步加快了干燥速度。“含水量在20%的小麦，在智慧干燥储粮仓里经过倒仓和通风，四五天后就能降到安全含水量，运行成本只有烘干塔的三分之一。”\n\n![Image 7](https:\u002F\u002Fdzngimage.dzwww.com\u002F2026\u002F08\u002F06\u002Fd86f81bee251c2c385cce59e1b8e49ba.jpg)\n\n6月18日，吕茂兴将第一批25吨新粮运至收粮点，经检测，水分、杂质等各项指标均合格，新麦顺利售出。\n\n此次实打测产的顺利完成，标志着“适度高水分小麦收获干燥一体化技术模式”在技术路径上已具备可复制、可推广的条件。该模式将农艺措施与仓储装备有机结合，既解决了晾晒难、储粮难的老问题，又实现了增产减损的双重目标。\n\n![Image 8](https:\u002F\u002Fdzngimage.dzwww.com\u002F2026\u002F08\u002F06\u002F7f0122fa85df3709cb9520838a1769fe.jpg)\n\n记者了解到，长江粮机智慧干燥储粮仓采用全封闭设计，防雨、防潮、防渗，自带防虫、防鼠、防鸟结构，全程无需高温烘干，依靠常温气流除湿，小麦色泽、面筋品质不受破坏，避免了露天晾晒带来的泥沙污染和籽粒破碎损耗。仓体配套锥底出料结构，粮食可直接装车出库，省去了装载机转运环节，进一步降低人工投入和粮食破碎损耗。\n\n对种植户而言，这套模式的增收空间不止于增产和减损。提前收割腾茬，可提早夏玉米播种时间，改善玉米生长周期，有助于实现周年粮食增产。高水分小麦收储入仓后，还可择市场高价错峰售粮，进一步拓展种植收益。\n\n当前，受制于晾晒场地不足、烘干设备投入高，农户普遍选择完熟后晚收，不仅收割阶段落粒、破碎损耗加剧，还推迟夏玉米播种窗口期。本套技术模式通过提前收割实现田间“开源”，又依托密闭智能仓储实现储藏“节流”，双向提升种植收益，为粮食收储绿色减损提供了可复制、可推广的解决方案。\n\n  \n免责声明：以上数据来源于天眼查等平台，相关内容仅为传递更多信息之目的，不代表本网观点，亦不代表本网站赞同其观点或证实其内容的真实性。如有侵权请联系0531-85193563。 \n\n查看更多","大众网","2026-09-26T00:00:00Z",75,{"impact":19,"substance":70,"depth":71,"authority":13,"freshness":21,"relevant":22,"comment":72},22,17,"以三地四品种实打测产数据验证农谚，并给出智慧干燥储粮仓的降本增效实证，技术模式可复制推广，具备较高资讯价值。",[74],{"name":66,"url":63},[27,76,77,78,79],"农业科技","小麦收获","智慧仓储","减损增产",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":41,"version":42,"method":43,"evidence":83},{"candidates":84,"agricultural_evidence":14},[85],{"code":33,"score":86,"supported":14,"title":87,"abstract":88,"tags":89},3,[],[35],[],[91,92],"山东省农科院 小麦 实打测产","高水分小麦 智慧干燥储粮仓","山东省农科院小麦实打测产-3720",2,"2026-09-29T00:08:23.247476Z",{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":8,"source_name":102,"source_url":99,"published_at":103,"category":104,"cover_url":8,"hotness":13,"is_selected":41,"score":105,"score_detail":106,"sources":111,"tags":113,"primary_topic":119,"secondary_topics":120,"classification":133,"search_phrases":174,"slug":177,"view_count":178,"doi":179,"paper":180,"created_at":200},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z","论文",77,{"impact":107,"substance":108,"depth":71,"authority":109,"freshness":13,"relevant":22,"comment":110},16,21,13,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[112],{"name":102,"url":99},[114,115,116,117,118],"智慧农业","机器学习","遥感监测","土壤墒情","冬小麦",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[121,127],{"code":122,"slug":123,"name":124,"path":125,"parent_code":126},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":128,"slug":129,"name":130,"path":131,"parent_code":132},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":41,"version":42,"method":43,"evidence":134},{"candidates":135,"agricultural_evidence":14},[136,142,148,153,161,168],{"code":33,"score":137,"supported":14,"title":138,"abstract":140,"tags":141},26,[139,34],"winter 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