[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-664":3,"related-664":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":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":62,"slug":65,"view_count":66,"doi":8,"paper":8,"created_at":67},664,"河北省加速旱碱麦品种选育推广 建成规模化旱碱麦繁育基地10.22万亩年产能3.76万吨","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7674437266772886067\u002F","河北省坚持以种适地同以地适种相结合,完善旱碱麦等现代育种技术体系,在全国率先开展省级耐盐碱小麦品种审定,全省已建成规模化旱碱麦繁育基地10.22万亩,年繁种产能达3.76万吨。今年麦收时节沧州中捷产业园区旱碱麦试验田里测产的捷麦20亩产达466.56公斤,创历史新高。捷麦19、捷麦20、沧麦33、泊麦20、吴旱015等品种在沧州运河以东及天津、山东甚至新疆等地的盐碱地扎根。省农林科学院滨海农业研究所建成国内领先的植物耐盐碱精准鉴定平台。",null,"## 河北省加速旱碱麦品种选育推广\n\n2026-08-16 09:36·[金台资讯](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAAL8m7IqT5eR3-VgZjzlMhg66-z57FyRmydYIImMLAsQ4\u002F?source=tuwen_detail)\n\n种子是农业的“芯片”。河北省坚持“以种适地”同“以地适种”相结合，完善旱碱麦等现代育种技术体系，在全国率先开展省级耐盐碱小麦品种审定，全省已建成规模化旱碱麦繁育基地10.22万亩，年繁种产能达3.76万吨，可有效满足旱碱麦大面积推广种植的用种需求。\n\n今年麦收时节，沧州中捷产业园区旱碱麦试验田里，测产的“捷麦20”交出了不错的成绩单：亩产达466.56公斤，创历史新高。\n\n收获，来自一代代育种人的辛勤付出。“‘捷麦20’抗倒伏、抗旱性强、耐盐碱，靠天喝雨水就能生长。”中捷产业园区农科所农技专家张卫军说，为降低盐碱对小麦产量的影响，中捷产业园区不断进行旱碱麦良种选育工作，做强种子“芯片”。\n\n如今，在中捷产业园区诞生的“捷麦19”“捷麦20”旱碱麦良种，已深深扎根在沧州运河以东及天津、山东甚至新疆等地的盐碱地里。\n\n聚焦盐碱地综合利用，河北省加快耐盐碱小麦品种选育进程，旱碱麦品种“群星闪耀”。在捷麦系列之外，“沧麦33”“泊麦20”“吴旱015”等旱碱麦品种也相继由各科研团队选育推出，成为盐碱地上的新希望。\n\n为系统性鉴定筛选适应盐碱地特殊生态环境的优良小麦品种，2023年秋季起河北省启动适宜盐碱地种植小麦品种的展示评价工作。截至目前，已累计种植小麦品种96个批次，所有品种均已在盐碱地环境下完成实地种植与综合性状观测。通过田间表现评估、产量测定等多维度综合评价，筛选出在盐碱地种植综合性状优良的小麦品种16个，为后续品种示范推广及盐碱地粮食产能提升提供了适生品种储备。\n\n选育耐盐碱作物品种、发展盐碱地种业，是破解盐碱地综合开发利用难题的关键。河北省搭建科研平台团队，强化育种技术支撑，整合不同学科背景的科研力量，共同致力于旱碱麦育种与栽培技术的研发与优化。在传统育种基础上，运用基因编辑、诱变育种等现代生物技术，开展耐盐碱小麦新品种培育。\n\n此外，河北省在省农林科学院滨海农业研究所、沧州市农林科学院布局建设耐盐碱鉴定平台。其中，省农林科学院滨海农业研究所已建成国内领先的植物耐盐碱精准鉴定平台，承接国家盐碱地综合利用技术创新中心唐山鉴定实验室职能，同步建成标准化耐盐碱作物种质资源圃(库)。同时，依托国家盐碱地综合利用技术创新中心旱碱麦特色产业创新基地平台，联合省内外科研院所开展旱碱麦等优质盐碱地适生作物的选育推广工作。\n\n河北省农业农村厅相关负责人介绍，2023年以来，河北省旱碱麦种植面积持续增长，现已达到205万亩，充分彰显了优良品种与配套技术的推广成效，旱碱麦种植对保障区域粮食安全、优化农业种植结构发挥了重要作用。(记者郝东伟)","金台资讯 2026-08-16","2026-08-17T00:00:00Z","报道",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},22,20,16,9,1,"省级农业科技进展，数据详实，对盐碱地利用有参考价值。",[24],{"name":10,"url":6},[26,27,28,29],"盐碱地","河北省农科院","旱碱麦","捷麦20",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,49,56],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":48},4,true,[],[26],[26],{"code":50,"score":51,"supported":45,"title":52,"abstract":53,"tags":55},"A2",3,[],[54],"小麦",[],{"code":57,"score":51,"supported":45,"title":58,"abstract":59,"tags":61},"F1",[],[60],"品种审定",[],[63,64],"河北省农科院 旱碱麦 盐碱地 捷麦20","河北省农科院 旱碱麦","河北省农科院旱碱麦盐碱地捷麦20-664",2,"2026-08-17T23:54:54.752151Z",{"total":69,"page":21,"page_size":69,"items":70},6,[71,126,160,201,271,357],{"id":72,"title":73,"url":74,"summary":75,"summary_zh":8,"content":76,"source_name":77,"source_url":8,"published_at":78,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":79,"score_detail":80,"sources":87,"tags":89,"primary_topic":94,"secondary_topics":95,"classification":96,"search_phrases":121,"slug":124,"view_count":66,"doi":8,"paper":8,"created_at":125},1087,"中国工程院院士李洪文、江苏省农科院院长朱艳一行赴南通调研盐碱地综合利用与智慧农业","https:\u002F\u002Fwx.jaas.ac.cn\u002Fxww\u002Fnkyw\u002Fart\u002F2026\u002Fart_9c83c150c7bf435899e2313768931ed0.html","8月21—22日,院长、党委副书记朱艳一行赴南通开展盐碱地综合利用与智慧农业专题调研,中国工程院院士李洪文参加智慧农业调研。调研组先后察看洋口镇国土空间全域治理洋通片区土壤改良项目、国家农业科技重大课题饲草种源中试示范基地、南通智能感知研究院、北京大学长三角光电科学研究院、通州区万亩粮田项目区智能农机作业现场。","8月21—22日，院长、党委副书记朱艳一行赴南通开展盐碱地综合利用与智慧农业专题调研，中国工程院院士李洪文应邀出席智慧农业专题调研并指导工作。院党委委员、副院长孙洪武，党委委员、办公室主任苏国东，市农业农村局局长陈敢、党组成员张进，如东县副县长石晓鹏参加调研。\n\n8月21日，朱艳一行赴如东县调研盐碱地综合改良利用工作。调研组先后察看洋口镇国土空间全域治理洋通片区土壤改良项目、国家农业科技重大课题饲草种源中试示范基地，实地查看田间作物长势，了解盐碱地改良技术路径、耐盐饲草品种筛选、田间排灌设施配套等情况，询问项目推进过程中遇到的现实难题。在苴镇街道虹元村秸秆育秧毯生产工厂，调研组察看生产线运行情况，了解秸秆育秧毯加工工艺与推广应用情况，建议持续优化工艺、拓展应用场景，加快推进绿色农业技术落地推广。在豫东垦区低产田现场，朱艳指出，资环所要联合沿江农科所，主动对接如东豫东现代农业发展有限公司，立足垦区资源禀赋谋划推进低产盐碱地合作，共建沿海盐碱地示范基地，聚焦盐碱地改良、耐盐品种示范、土地提质增效等重点工作，加快科研成果落地转化，切实提升国有土地综合利用效益。\n\n![Image 1](https:\u002F\u002Fwx.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F695893685\u002Fpicture\u002F20268\u002FS82a70a7ccde742e391e3d98983cb326c-700.png)\n\n![Image 2](https:\u002F\u002Fwx.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F695893685\u002Fpicture\u002F20268\u002FSf6c8c2144a864aa1a96848c1e67cce54-700.png)\n\n8月22日，李洪文、朱艳一行深入南通科创平台与农业生产一线调研，先后观摩南通智能感知研究院、北京大学长三角光电科学研究院以及通州区万亩粮田项目区智能农机作业现场。调研组详细了解农业智能感知技术、光电科创成果研发、智能农机装备示范推广等工作进展，围绕智能农机科技创新、产学研深度融合、农业科技成果落地应用等关键内容深入交流研讨，为南通用好高端科创资源，以科技赋能现代农业高质量发展出实招、提思路。\n\n院成果转化处、资环所、信息所、装备所负责人和相关科研骨干参加调研，沿江所主要负责同志陪同调研。\n\n![Image 3](https:\u002F\u002Fwx.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F695893685\u002Fpicture\u002F20268\u002FSca3d24464e3343d0b595a74c0f716d65-700.png)\n\n（文、责任编辑：魏亚凤；审核人：王顺祥）","江苏省农业科学院","2026-08-27T07:00:00Z",53,{"impact":81,"substance":82,"depth":83,"authority":84,"freshness":85,"relevant":21,"comment":86},15,12,8,11,7,"省级农科院领导调研盐碱地综合利用与智慧农业，涉及具体项目与科技应用，有一定信息量，但属常规工作报道，深度一般。",[88],{"name":77,"url":74},[90,26,91,92,93],"智慧农业","江苏省农科院","朱艳","李洪文",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":97},{"candidates":98,"agricultural_evidence":45},[99,104,109,115],{"code":31,"score":81,"supported":45,"title":100,"abstract":101,"tags":103},[26],[102,26],"土壤",[26],{"code":105,"score":82,"supported":45,"title":106,"abstract":107,"tags":108},"I1",[90],[90],[90],{"code":110,"score":51,"supported":45,"title":111,"abstract":112,"tags":114},"C4",[],[113],"饲草",[],{"code":116,"score":51,"supported":45,"title":117,"abstract":118,"tags":120},"F5",[],[119],"农机",[],[122,123],"江苏省农科院 智慧农业 李洪文 盐碱地","江苏省农科院 智慧农业","江苏省农科院智慧农业李洪文盐碱地-1087","2026-08-30T00:11:09.542997Z",{"id":127,"title":128,"url":129,"summary":130,"summary_zh":8,"content":131,"source_name":132,"source_url":8,"published_at":133,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":134,"score_detail":135,"sources":138,"tags":140,"primary_topic":145,"secondary_topics":146,"classification":147,"search_phrases":154,"slug":157,"view_count":158,"doi":8,"paper":8,"created_at":159},834,"[河北省农林科学院]滨海所孟令启团队选育\"曹妃红\"胭脂稻——抢救唐山百年珍稀古稻,实现盐碱地稳产、亩产翻倍","https:\u002F\u002Fwww.163.com\u002Fdy\u002Farticle\u002FL4MMBC1U0514A1T0.html","胭脂稻是唐山独有百年珍稀古稻,原生起源于玉田县,曾为清代御用贡米,但传统原生稻种种性退化严重、耐盐性差、低产难种。河北省农林科学院滨海农业研究所孟令启研究员团队历经多年艰苦攻坚,成功选育出升级版优质耐盐新品种\"曹妃红\",营养更优、产量翻倍、种植稳产、价格亲民。传统种植亩产最高不超过150公斤,产量极不稳定,自然灾害年份极易绝收。\"曹妃红\"让千年皇家贡米走出深宫、扎根盐碱地、飘香百姓餐桌。","今晨，我借骑行之机，随同专家一同前往胭脂稻试验基地实地参观考察。在基地现场，专家细致讲解了胭脂稻的历史短板、育种改良全过程与新品种各项优势，令人收获良多。胭脂稻是唐山独有的百年珍稀古稻，原生起源于玉田县，有着三百余年悠久种植历史，曾为清代御用贡米，文化底蕴深厚、药食价值极高。但传统原生稻种种性退化严重、耐盐性差、低产难种，长期濒临绝迹、价格昂贵，普通百姓难以享用。为抢救珍稀种质、破解盐碱地种稻难题，河北省农林科学院滨海农业研究所孟令启研究员团队历经多年艰苦攻坚，成功选育出升级版优质耐盐新品种“曹妃红”。新品种营养更优、产量翻倍、种植稳产、价格亲民，真正让千年皇家贡米走出深宫、扎根盐碱地、飘香百姓餐桌。","网易\u002F曹妃微故里 2026年8月19日","2026-08-19T00:00:00Z",57,{"impact":136,"substance":19,"depth":13,"authority":69,"freshness":85,"relevant":21,"comment":137},18,"地方科研团队选育耐盐水稻新品种，有具体成果和背景，但信源为自媒体，深度一般。",[139],{"name":132,"url":129},[26,141,142,143,144],"曹妃红","胭脂稻","孟令启","河北省农林科学院滨海所",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":148},{"candidates":149,"agricultural_evidence":45},[150],{"code":31,"score":82,"supported":45,"title":151,"abstract":152,"tags":153},[26],[26],[26],[155,156],"河北省农林科学院滨海所 孟令启 曹妃红 盐碱地","河北省农林科学院滨海所 孟令启","河北省农林科学院滨海所孟令启曹妃红盐碱地-834",5,"2026-08-22T00:05:59.591107Z",{"id":161,"title":162,"url":163,"summary":164,"summary_zh":8,"content":165,"source_name":166,"source_url":8,"published_at":167,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":168,"score_detail":169,"sources":171,"tags":173,"primary_topic":177,"secondary_topics":183,"classification":185,"search_phrases":196,"slug":199,"view_count":158,"doi":8,"paper":8,"created_at":200},442,"河北省滨海水稻全产业链标准体系构建完成——281项标准+数据立标为环渤海盐碱地提供'河北方案'","https:\u002F\u002Fyule.sohu.com\u002Fa\u002F1061503308_121106842","河北省市场监督管理局重点研发项目《河北省滨海水稻全产业链标准体系构建研究》近日顺利通过专家验收,该项目由河北省标准化研究院牵头,联合河北省农林科学院滨海农业研究所、乐亭县农业农村局、秦皇岛市耕地质量监测保护中心及龙头企业等7家单位协同攻关,历时一年半,为河北省滨海盐碱地水稻'量身定制'了一套覆盖全产业链的标准体系。该项目打破传统'经验定标'模式,创新采用'数据立标'方法,系统梳理1000余项现行标准,最终搭建形成包含281项标准的全产业链标准体系,覆盖产地环境、生产种植、加工仓储、品牌管理、休闲农业全链条。唐山、秦皇岛等河北滨海区域水稻种植面积约84万亩,占全省总面积80%以上,平均亩产650-700公斤。","标准体系的价值在于落地应用。目前，项目成果已从“纸面”走向“实践”。一是线上 “一触即达”。河北省滨海水稻全产业链标准专题库已在河北标准图书馆上线，实现单项标准精准匹配与一键链接，经营主体可便捷查询适用标准，解决了“找标准难”的痛点。二是线下 “试点先行”。在唐山、秦皇岛主产区，体系已在产业链相关单位试点应用，通过“有标贯标、无标补标、低标强标”，实现了从选种、育秧、生产到品牌管理全流程操作。\n\n依托覆盖全产业链的标准供给，滨海水稻产业在稳产、增收、融合发展上实现了协同提升。一是在稳产层面，全链条标准贯穿种植、加工、销售各环节，推动单产与品质稳步提升；二是在增收层面，依托标准推动种养结合模式提质扩面，提高土地单位产出效益，稳定农户种植积极性；三是在融合发展层面，以标准为纽带促进种植、加工、观光、研学、体验协同发展，真正实现“一粒米”串起一条“黄金链” 。\n\n河北滨海水稻全产业链标准体系成功构建不仅可以直接服务唐山、秦皇岛稻区，也可推广复制至环渤海其他盐碱地稻区乃至全国特色农产品领域。下一步，河北省标准化研究院将持续发挥省级科研机构的桥梁纽带作用，主动对接农业农村、监管系统等部门及产业龙头企业，推动体系向标准综合体转化；针对特色产区探索打造示范基地，引导更多的经营主体按标生产、依标管理，提升产品溢价能力。同时，加速标准体系的试点推广与迭代升级，为促进农户增收、农区发展，保障粮食安全、推动盐碱地综合利用和乡村振兴贡献标准化价值。（通讯员张学曼 卢佳佳）","河北经济网 2026-08-08","2026-08-08T00:00:00Z",73,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":158,"relevant":21,"comment":170},"省级农业标准化体系构建，具有产业推广价值，信息具体，但时效性稍差。",[172],{"name":166,"url":163},[26,174,175,176],"河北省农林科学院","滨海水稻","标准体系",{"code":178,"slug":179,"name":180,"path":181,"parent_code":182},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[184],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":186},{"candidates":187,"agricultural_evidence":45},[188,192],{"code":31,"score":82,"supported":45,"title":189,"abstract":190,"tags":191},[26],[26],[26],{"code":178,"score":84,"supported":45,"title":193,"abstract":194,"tags":195},[180],[180],[],[197,198],"河北省农林科学院 标准体系 滨海水稻 盐碱地","河北省农林科学院 标准体系","河北省农林科学院标准体系滨海水稻盐碱地-442","2026-08-12T23:57:22.603021Z",{"id":202,"title":203,"url":204,"summary":205,"summary_zh":206,"content":8,"source_name":207,"source_url":204,"published_at":208,"category":209,"cover_url":8,"hotness":13,"is_selected":14,"score":210,"score_detail":211,"sources":214,"tags":216,"primary_topic":221,"secondary_topics":222,"classification":229,"search_phrases":248,"slug":251,"view_count":252,"doi":253,"paper":254,"created_at":270},5583,"Active-Labeling Evaluation for Computer Agriculture Nematode-Cyst Detection Under Sparse Soil-Sample Ground Truth: An Artificial Intelligence Annotation-Efficiency Bound","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544145","Active learning promises to reduce the annotation burden of training nematode-cyst detection models for precision agriculture, but the size of that reduction is not known when ground truth comes from sparse soil samples rather than exhaustive visual inspection. the present analysis derives an annotation-efficiency bound for pool-based active learning under sparse soil-sample ground truth. The detection task is formalised as a two-stage process in which a classifier selects candidate cysts from soil imagery and a laboratory assay confirms a small labelled subset. Under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, the bound is evaluated across a design grid of 8, 16, 24 and 32 clusters and effect sizes of 3, 5 and 8 percentage points, with 2000 replications per cell. The annotation-efficiency gain is bounded by the ratio of label noise to cluster-level variance, and that 52 clusters are required to detect a 5-percentage-point improvement at 80% power. The bound provides a design rule for allocating annotation effort between active selection and confirmatory soil sampling.","主动学习有望减轻精准农业中训练线虫囊检测模型所需的标注负担，但当真实标签来自稀疏土壤样本而非详尽的视觉检查时，这种负担减轻的幅度尚不清楚。本分析推导了在稀疏土壤样本真实标签下基于池的主动学习的标注效率界限。检测任务被形式化为两阶段过程：分类器从土壤图像中筛选候选线虫囊，实验室检测确认一个小的已标注子集。在设定的方差结构下——组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64——该界限在8、16、24和32个簇的设计网格以及3、5和8个百分点的效应量下进行评估，每个单元格重复2000次。标注效率增益受标签噪声与簇级方差之比的约束，并且在80%检验功效下检测5个百分点的改进需要52个簇。该界限为在主动选择与验证性土壤采样之间分配标注工作量提供了设计规则。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z","论文",64,{"impact":83,"substance":18,"depth":212,"authority":20,"freshness":13,"relevant":21,"comment":213},17,"该研究给出稀疏土壤样本下主动学习标注效率的理论边界与聚类设计规则，方法新颖、结论具体，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[215],{"name":207,"url":204},[90,217,218,219,220],"农业人工智能","主动学习","线虫检测","土壤采样",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[223],{"code":224,"slug":225,"name":226,"path":227,"parent_code":228},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":38,"locked":14,"version":39,"method":40,"evidence":230},{"candidates":231,"agricultural_evidence":45},[232,237,242],{"code":31,"score":233,"supported":45,"title":234,"abstract":235,"tags":236},14,[32],[102,32],[],{"code":224,"score":20,"supported":45,"title":238,"abstract":240,"tags":241},[239],"artificial intelligence",[],[217],{"code":105,"score":85,"supported":45,"title":243,"abstract":244,"tags":247},[],[245,246],"精准农业","precision agriculture",[90],[249,250],"线虫孢囊 检测 主动学习","稀疏土壤样本 标注效率","线虫孢囊检测主动学习-5583",0,"10.5281\u002Fzenodo.23544145",{"doi":253,"openalex_id":255,"authors":256,"venue":207,"cited_by_count":252,"oa_url":204,"card":263,"direction":267,"ingested_from":269},"W7223082195",[257,259,261],{"name":258,"orcid":8},"Amine Ben Salah",{"name":260,"orcid":8},"Ines Trabelsi",{"name":262,"orcid":8},"Mohamed Aziz Khelifi",{"tldr":264,"method":265,"finding":266,"direction":267,"opportunity":268},"推导稀疏土壤样本真值下主动学习标注效率的理论上界。","基于方差结构与设计网格的统计模拟，2000次重复。","标注效率增益受标签噪声与簇间方差之比限制，检测5个百分点提升需52簇。","农业人工智能与决策模型","可将该上界推广到其他稀疏真值农业检测任务，并探索主动选择与确认采样的最优分配策略。","openalex","2026-10-22T11:20:58.901747Z",{"id":272,"title":273,"url":274,"summary":275,"summary_zh":276,"content":8,"source_name":277,"source_url":274,"published_at":278,"category":209,"cover_url":8,"hotness":13,"is_selected":14,"score":279,"score_detail":280,"sources":282,"tags":284,"primary_topic":290,"secondary_topics":291,"classification":292,"search_phrases":318,"slug":321,"view_count":252,"doi":322,"paper":323,"created_at":356},5576,"Land cover change and soil salinization in Mozambique: A counterfactual analysis over two decades (2001−2022)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.catena.2026.110691","Soil salinization is a major issue that constrains the sustainable development of agriculture in arid, semi-arid, and coastal lowland areas. It is of great importance for regional land management and climate adaptation to clarify the influence of land cover change on soil salinization. This study aims to quantitatively assess the contribution of land cover change to soil salinization in Mozambique, where both rapid land cover transformation and salinization issues are evident. Firstly, we integrated soil profile electrical conductivity (EC) data, Landsat time-series remote sensing imagery, TerraClimate meteorological data, and the 30 m resolution land cover data, to construct a remote sensing-based EC inversion model for Mozambique. Secondly, we applied a spatially and environmentally constrained counterfactual matching method to quantitatively identify the contributions of different land cover changes on soil salinization trends. Our results reveal that: (1) the spatial distribution of soil salinization in Mozambique exhibits distinct a clear association with proximity to water bodies, with high EC areas primarily concentrated around Lake Malawi in the north and in the coastal regions of Greater Maputo in the south. (2) 26.98% of Mozambique's territory showed a trend of intensifying soil salinization, while 10.88% exhibited a decreasing trend. Areas with intensifying trends are primarily concentrated in the central alluvial plains, localized agricultural development zones, and coastal lowlands. (3) Urbanization was significantly associated with an increasing salinization trend after false discovery rate correction, suggesting that the expansion of impervious surfaces, changes in drainage patterns, and soil compaction may exacerbate localized salt accumulation. Agricultural expansion showed a negative trend difference, consistent with the potential influence of crop cover, tillage, and wet-season leaching, although this association was not significant after false discovery rate correction. The effects of ecological restoration and other factors were not statistically significant. In addition to elucidating the heterogeneous impacts of land cover changes in Mozambique on soil salinization trends, this study also provides methodological guidance and a scientific basis for monitoring\u002Fmanaging soil salinity risks in other coastal lowlands.","土壤盐渍化是制约干旱、半干旱及沿海低地地区农业可持续发展的主要问题。阐明土地覆盖变化对土壤盐渍化的影响，对区域土地管理和气候适应具有重要意义。本研究旨在定量评估土地覆盖变化对莫桑比克土壤盐渍化的贡献，该国土地覆盖转型迅速且盐渍化问题突出。首先，我们整合了土壤剖面电导率（EC）数据、Landsat时间序列遥感影像、TerraClimate气象数据以及30 m分辨率土地覆盖数据，构建了基于遥感的莫桑比克EC反演模型。其次，我们采用空间与环境约束的反事实匹配方法，定量识别不同土地覆盖变化对土壤盐渍化趋势的贡献。研究结果表明：（1）莫桑比克土壤盐渍化的空间分布与水体邻近性呈现明显的关联，高EC区域主要集中于北部的马拉维湖周边以及南部大马普托沿海地区。（2）莫桑比克26.98%的国土面积呈现土壤盐渍化加剧趋势，而10.88%呈现减轻趋势。加剧趋势区域主要集中于中部冲积平原、局部农业开发区及沿海低地。（3）经错误发现率校正后，城市化与盐渍化加剧趋势显著相关，表明不透水面扩张、排水格局改变及土壤压实可能加剧局部盐分累积。农业扩张呈现负向趋势差异，与作物覆盖、耕作及湿季淋洗的潜在影响一致，但该关联经错误发现率校正后不显著。生态恢复及其他因素的影响在统计上不显著。本研究不仅阐明了莫桑比克土地覆盖变化对土壤盐渍化趋势的异质性影响，还为其他沿海低地土壤盐渍化风险的监测与管理提供了方法学指导和科学依据。","CATENA","2026-10-20T00:00:00Z",79,{"impact":19,"substance":17,"depth":136,"authority":233,"freshness":20,"relevant":21,"comment":281},"该研究基于遥感与反事实匹配方法，定量评估土地覆盖变化对盐渍化的贡献，方法新颖、结论可靠，对沿海低地盐渍化监测具有参考价值，但属境外区域研究，国内政策与产业影响有限。",[283],{"name":277,"url":274},[285,286,287,288,289],"遥感监测","莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":293},{"candidates":294,"agricultural_evidence":45},[295,299,306,313],{"code":31,"score":233,"supported":45,"title":296,"abstract":297,"tags":298},[32],[102,32],[],{"code":300,"score":85,"supported":45,"title":301,"abstract":302,"tags":305},"I3",[],[303,304],"遥感","remote sensing",[285],{"code":307,"score":69,"supported":45,"title":308,"abstract":309,"tags":312},"H7",[],[310,311],"气候适应","干旱",[],{"code":314,"score":21,"supported":14,"title":315,"abstract":316,"tags":317},"H6",[],[],[288],[319,320],"莫桑比克 土壤盐渍化 遥感","土地覆盖变化 盐渍化 反事实分析","莫桑比克土壤盐渍化遥感-5576","10.1016\u002Fj.catena.2026.110691",{"doi":322,"openalex_id":324,"authors":325,"venue":277,"cited_by_count":252,"oa_url":274,"card":350,"direction":354,"ingested_from":269},"W7223069171",[326,328,330,332,335,337,340,343,345,347],{"name":327,"orcid":8},"Yue Li",{"name":329,"orcid":8},"Mengfei Shi",{"name":331,"orcid":8},"Fei Zhang",{"name":333,"orcid":334},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":336,"orcid":8},"Gowhar Meraj",{"name":338,"orcid":339},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":341,"orcid":342},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":344,"orcid":8},"Yuanfang Chai",{"name":346,"orcid":8},"Hongtao Xu",{"name":348,"orcid":349},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":351,"method":352,"finding":353,"direction":354,"opportunity":355},"量化莫桑比克2001-2022年土地覆盖变化对土壤盐渍化的贡献。","融合EC剖面、Landsat时序与气候数据反演EC，并用空间环境约束的反事实匹配","26.98%区域盐渍化加剧；城市化显著关联盐渍化上升，农业扩张关联不显著。","农业遥感与作物表型","可将反事实匹配框架迁移至其他滨海低地，并引入作物类型与灌溉制度细化农业扩张的盐渍化效应。","2026-10-22T11:20:42.713789Z",{"id":358,"title":359,"url":360,"summary":361,"summary_zh":362,"content":8,"source_name":363,"source_url":360,"published_at":208,"category":209,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":364,"sources":368,"tags":370,"primary_topic":374,"secondary_topics":377,"classification":380,"search_phrases":414,"slug":417,"view_count":252,"doi":418,"paper":419,"created_at":437},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",{"impact":19,"substance":365,"depth":212,"authority":366,"freshness":13,"relevant":21,"comment":367},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。",[369],{"name":363,"url":360},[90,371,285,372,373],"机器学习","土壤墒情","冬小麦",{"code":50,"slug":375,"name":54,"path":376,"parent_code":182},"wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat",[378,379],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":224,"slug":225,"name":226,"path":227,"parent_code":228},{"status":38,"locked":14,"version":39,"method":40,"evidence":381},{"candidates":382,"agricultural_evidence":45},[383,389,393,398,403,410],{"code":50,"score":384,"supported":45,"title":385,"abstract":387,"tags":388},26,[386,375],"winter wheat",[54,386,375],[373],{"code":31,"score":81,"supported":45,"title":390,"abstract":391,"tags":392},[32],[102,32],[372],{"code":224,"score":81,"supported":45,"title":394,"abstract":396,"tags":397},[395],"machine learning",[371,395],[371],{"code":300,"score":13,"supported":45,"title":399,"abstract":400,"tags":402},[],[303,304,401],"multispectral",[285],{"code":404,"score":69,"supported":45,"title":405,"abstract":406,"tags":409},"H3",[],[407,408],"灌溉","irrigation",[],{"code":105,"score":44,"supported":45,"title":411,"abstract":412,"tags":413},[],[90],[90],[415,416],"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","10.3390\u002Fagronomy16202071",{"doi":418,"openalex_id":420,"authors":421,"venue":363,"cited_by_count":252,"oa_url":360,"card":431,"direction":436,"ingested_from":269},"W7223156204",[422,425,427,429],{"name":423,"orcid":424},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":426,"orcid":8},"Yi Zhang",{"name":428,"orcid":8},"WEI Lei",{"name":430,"orcid":8},"Chengcai Zhang",{"tldr":432,"method":433,"finding":434,"direction":354,"opportunity":435},"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。","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 农业物联网","2026-10-22T11:20:18.887301Z"]