[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-356":3,"related-356":60},{"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":22,"tags":24,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":55,"slug":58,"view_count":50,"doi":8,"paper":8,"created_at":59},356,"品质出众稳增收——豫农269超强筋小麦打造黄淮麦区种植新标杆","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7670812261388124678","由河南科育种业联合河南农业大学选育的豫农269超强筋小麦,适配性特别强,黄淮大部分种植区域都能种,田间长势稳、好管理,农户上手门槛低。最核心的优势是其过硬的超强筋品质,在2020年至2021年连续两年的品质检测中,各项指标全部稳稳达到国家超强筋小麦标准。蛋白质含量高、面筋质量好、面团稳定性佳,加工性能出色,是制作高端面包、精品面食的优质原料。高抗小麦黄花叶病毒病,株型紧凑、茎秆结实,抗倒伏能力强,后期耐高温、灌浆饱满,真正做到了'优质不折产、高产不降质',完美解决了农户种植优质麦的最大顾虑。",null,"**河南经济报记者 郭俊娟 通讯员 赵向婷**\n\n在小麦种植中，既能保住高产、又能长出高品质麦子的品种，一直是广大种植户的首选。由河南科育种业联合河南农业大学选育的豫农269超强筋小麦，完美兼顾了品质、抗性与产量，彻底改变了优质小麦难高产、稳产小麦品质一般的种植现状，成为黄淮麦区表现十分亮眼的优质小麦品种。\n\n豫农269属于半冬性小麦，适配性特别强，黄淮大部分种植区域都能种，田间长势稳、好管理，农户上手门槛低。最核心的优势就是它过硬的超强筋品质，在2020年至2021年连续两年的品质检测中，各项指标全部稳稳达到国家超强筋小麦标准。该品种蛋白质含量高、面筋质量好、面团稳定性佳，加工性能十分出色，是制作高端面包、精品面食的优质原料，相比普通小麦，品质优势十分明显，市场认可度更高。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002Ff2f6d71d7ccd4b918decbd07e04130c5~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1787010895&x-signature=1SBY2sSDObg7%2ByarlC9h9of5ZTY%3D)\n\n不同于市面上很多优质麦娇气、易生病、产量低的问题，豫农269综合田间表现非常均衡。该品种高抗小麦黄花叶病毒病，针对田间高发病害有着天然抵抗力，能帮种植户省去不少管理成本。同时株型紧凑、茎秆结实，抗倒伏能力强，后期耐高温、灌浆饱满、落黄漂亮，几乎不会出现早衰、干瘪的情况。经过多年多地实地种植测试，豫农269亩产表现稳定、丰产潜力大，真正做到了优质不折产、高产不降质，完美解决了农户种植优质麦的最大顾虑。\n\n好品种离不开好企业的用心培育。河南科育种业多年来只专注小麦育种这一件事，踏踏实实做良种、用心服务种植户。企业配备专业的种子加工设备和质检实验室，每一批麦种都会经过精细筛选和严格检测，从纯度、发芽率到籽粒品质层层把关，坚决不让不合格种子流入市场，从源头上保证农户种下去的每一粒种子，性状纯正、长势稳定。\n\n为持续打磨优质麦品种，科育种业长期和河南农业大学深度合作，依托专业科研力量，专攻超强筋小麦品种改良与优化。不盲目跟风扩张，不粗放推广品种，而是沉下心优化品种抗性、品质和丰产性。同时常年深入田间地头，普及科学种植知识，手把手帮助农户调整管理方式，让好品种发挥出更好的效果，用实实在在的行动助力当地小麦品质升级。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-6w9my0ksvp\u002F73a29034859648fcb8b62e4a4976fdfa~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1787010895&x-signature=jRFi3ZgTqKURHUZsCXM9Yi8pzWU%3D)\n\n谈及品种未来和企业发展，科育种业负责人李季更有着朴实且坚定的想法。他表示，种地靠好种，产业靠实干，种业就是服务农户、扎根田间的行业。未来，企业会继续深耕优质小麦育种领域，持续优化豫农269的各项性状，让品种抗病性、适应性、稳定性更强，不断扩大标准化繁育规模，让更多农户种上靠谱、优质、高产的好麦种。同时持续深化科研合作，不断储备新的优质小麦品系，踏踏实实做好良种研发、良种推广，坚持做老百姓认可的好品种，用本土种企力量助力粮食提质增产。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-6w9my0ksvp\u002Fcee1e8c7d98345a59439baf686e9db9b~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1787010895&x-signature=TXhx5wHbTDnDylBJH%2FAbvfld%2By4%3D)\n\n凭借稳定的品质、出色的抗性和均衡的产量表现，豫农269已经在黄淮麦区积累了良好的口碑。未来，河南科育种业将继续坚守初心、深耕育种，以优质良种赋能田间种植，助力区域小麦产业高质量发展，为农户稳产增收保驾护航。","河南经济报 2026-08-06","2026-08-06T00:00:00Z","报道",10,false,32,{"impact":17,"substance":17,"depth":18,"authority":18,"freshness":19,"relevant":20,"comment":21},8,6,4,1,"地方媒体对单一小麦品种的宣传报道，信息增量有限，影响范围较小，时效性一般。",[23],{"name":10,"url":6},[25,26,27,28],"河南农业大学","超强筋小麦","豫农269","黄淮麦区",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,48],{"code":30,"score":43,"supported":44,"title":45,"abstract":46,"tags":47},11,true,[32],[32],[],{"code":49,"score":50,"supported":44,"title":51,"abstract":52,"tags":54},"F1",3,[],[53],"种业",[],[56,57],"河南农业大学 超强筋小麦 黄淮麦区 豫农269","河南农业大学 超强筋小麦","河南农业大学超强筋小麦黄淮麦区豫农269-356","2026-08-10T23:54:42.547501Z",{"total":18,"page":20,"page_size":18,"items":61},[62,167,230,279,321,361],{"id":63,"title":64,"url":65,"summary":66,"summary_zh":67,"content":8,"source_name":68,"source_url":65,"published_at":69,"category":70,"cover_url":8,"hotness":13,"is_selected":14,"score":71,"score_detail":72,"sources":78,"tags":80,"primary_topic":86,"secondary_topics":87,"classification":100,"search_phrases":140,"slug":143,"view_count":144,"doi":145,"paper":146,"created_at":166},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":73,"substance":74,"depth":75,"authority":76,"freshness":13,"relevant":20,"comment":77},16,21,17,13,"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。",[79],{"name":68,"url":65},[81,82,83,84,85],"智慧农业","机器学习","遥感监测","土壤墒情","冬小麦",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[88,94],{"code":89,"slug":90,"name":91,"path":92,"parent_code":93},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":95,"slug":96,"name":97,"path":98,"parent_code":99},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":37,"locked":14,"version":38,"method":39,"evidence":101},{"candidates":102,"agricultural_evidence":44},[103,109,115,120,128,135],{"code":30,"score":104,"supported":44,"title":105,"abstract":107,"tags":108},26,[106,31],"winter wheat",[32,106,31],[85],{"code":89,"score":110,"supported":44,"title":111,"abstract":112,"tags":114},15,[90],[113,90],"土壤",[84],{"code":95,"score":110,"supported":44,"title":116,"abstract":118,"tags":119},[117],"machine learning",[82,117],[82],{"code":121,"score":13,"supported":44,"title":122,"abstract":123,"tags":127},"I3",[],[124,125,126],"遥感","remote sensing","multispectral",[83],{"code":129,"score":18,"supported":44,"title":130,"abstract":131,"tags":134},"H3",[],[132,133],"灌溉","irrigation",[],{"code":136,"score":19,"supported":44,"title":137,"abstract":138,"tags":139},"I1",[],[81],[81],[141,142],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558",0,"10.3390\u002Fagronomy16202071",{"doi":145,"openalex_id":147,"authors":148,"venue":68,"cited_by_count":144,"oa_url":65,"card":158,"direction":164,"ingested_from":165},"W7223156204",[149,152,154,156],{"name":150,"orcid":151},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":153,"orcid":8},"Yi Zhang",{"name":155,"orcid":8},"WEI Lei",{"name":157,"orcid":8},"Chengcai Zhang",{"tldr":159,"method":160,"finding":161,"direction":162,"opportunity":163},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","农业遥感与作物表型","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 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