[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3304":3,"related-3304":66},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":61,"slug":64,"view_count":45,"doi":8,"paper":8,"created_at":65},3304,"粤黔协作再深化 科技赋能山地农业——广东省农科院设施农业研究所与黔南民族职业技术学院签署农业科技合作框架协议","https:\u002F\u002Fqnkj.qiannan.gov.cn\u002Fxwzx_102\u002Ftpxw\u002F202609\u002Ft20260921_90923552.html","9月16日下午，广东省农业科学院设施农业研究所与黔南民族职业技术学院举行农业科技合作框架协议签约仪式。州委常委、副州长荣彦，广东省农业科学院设施农业研究所副所长黄琳，贵州省农科院党委委员、副院长李武，黔南民族职业技术学院党委副书记、院长刘荣鹏等出席签约仪式。双方将结合黔南气候土壤条件开展设施栽培、水肥高效利用、良种培育等技术集成示范，联合申报科研及产业示范项目，共建人才培养与成果示范基地，建立科技信息共享机制。",null,"**粤黔协作再深化 科技赋能山地农业**\n\n**——广东省农科院设施农业研究所与黔南民族职业技术学院签署农业科技合作框架协议**\n\n为持续深化粤黔东西部协作，黔南州科技局积极推动科研院所与职业院校资源协同联动，助力黔南现代山地特色高效农业高质量发展，9月16日下午，广东省农业科学院设施农业研究所与黔南民族职业技术学院举行农业科技合作框架协议签约仪式。\n\n![Image 1: 59F5FCCB2C628E3D892B37E119E4C666.jpg](https:\u002F\u002Fqnkj.qiannan.gov.cn\u002Fxwzx_102\u002Ftpxw\u002F202609\u002FW020260921399386613210.jpg)\n\n州委常委、副州长荣彦，广东省农业科学院设施农业研究所副所长黄琳，贵州省农科院党委委员、副院长李武，黔南民族职业技术学院党委副书记、院长刘荣鹏等领导专家出席签约仪式。\n\n会上，参会人员围绕设施农业技术攻关、产业示范、人才培育、科技成果转化等内容交流研讨。荣彦同志指出，希望双方以此次签约为契机，紧扣黔南山地农业发展需求，围绕现代设施农业技术研发应用、各级科研产业项目联合申报、人才交流培养、科技成果转化及产业服务等重点内容开展合作，推动广东先进设施农业科研成果本土化转化应用，扎实开展联合科研攻关、实训平台共建、人才联合培养，着力打造“科研院所+职业院校”协同发展样板。州直有关部门要强化服务保障，充分用好粤黔协作资源，为各项合作落地见效营造良好环境。\n\n此次签约，打通了科研、教学、产业的联动通道，是粤黔协作在农业科技领域的创新举措。双方将结合黔南气候土壤条件开展设施栽培、水肥高效利用、良种培育等技术集成示范，联合申报科研及产业示范项目，共建人才培养与成果示范基地，建立科技信息共享机制，面向农业经营主体做好技术咨询、标准制定、品牌提升等服务，把广东科研优势、黔南产业资源、职院育人平台有机结合，为黔南乡村全面振兴注入科技动能。","黔南州科学技术局 2026年09月21日","2026-09-21T00:00:00Z","报道",10,false,52,{"impact":17,"substance":18,"depth":13,"authority":19,"freshness":20,"relevant":21,"comment":22},12,14,9,7,1,"地市级院地合作签约通稿，有实质合作方向但缺乏新政策条款与数据，属区域协作动态，可作备选而非每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"设施农业","人才培养","院地合作","山地农业","粤黔协作",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,56],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},3,true,[],[49],"土壤",[],{"code":52,"score":21,"supported":14,"title":53,"abstract":54,"tags":55},"G1",[],[],[28],{"code":57,"score":21,"supported":14,"title":58,"abstract":59,"tags":60},"G5",[],[],[27],[62,63],"广东省农科院 设施农业研究所 黔南","黔南民族职业技术学院 设施农业 合作","广东省农科院设施农业研究所黔南-3304","2026-09-24T00:03:59.648963Z",{"total":67,"page":21,"page_size":67,"items":68},6,[69,120,189,256,341,424],{"id":70,"title":71,"url":72,"summary":73,"summary_zh":8,"content":74,"source_name":75,"source_url":8,"published_at":76,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":77,"score_detail":78,"sources":83,"tags":85,"primary_topic":90,"secondary_topics":91,"classification":92,"search_phrases":115,"slug":118,"view_count":45,"doi":8,"paper":8,"created_at":119},2819,"中国农科院组团全面入驻国家盐碱地中心:10支创新团队布局黄三角农高区","https:\u002F\u002Fiarrp.caas.cn\u002Fysdt\u002Fmtbd\u002F362cd263199245d7a9bc94847e372f4c.htm","9月11日,中国农业科学院在山东省黄三角农高区牵头召开盐碱地综合利用国家技术创新中心建设推进会议。国家盐碱地中心总部基地已全面建成投用,中国农科院10支创新团队入驻,常驻科研人员和研究生数量持续增加,打造轻度、中度、重度全类型盐碱地示范场景超1700亩。资划所所长吴文斌介绍,作为国家盐碱地中心和中国农科院盐碱地农业中心建设牵头单位,已初步形成优势互补、联动发展的良好创新格局。","[中国农科院](http:\u002F\u002Fwww.caas.cn\u002F)| [English](http:\u002F\u002Fiarrp.caas.cn\u002Fen)| [全重网站](https:\u002F\u002Falrkl.iarrp.cn\u002F)| 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[身边榜样](https:\u002F\u002Fiarrp.caas.cn\u002Fdqyd\u002Fdqcxwh\u002Fgrb\u002Findex.htm)\n\n        *   ### [荣誉称号](https:\u002F\u002Fiarrp.caas.cn\u002Fdqyd\u002Fdqcxwh\u002Frych\u002Findex.htm)\n\n        *   ### [文化展示](https:\u002F\u002Fiarrp.caas.cn\u002Fdqyd\u002Fdqcxwh\u002Fwhzs\u002Findex.htm)\n\n    *   ### [学习园地](https:\u002F\u002Fiarrp.caas.cn\u002Fdqyd\u002Fdqxxyd\u002Findex.htm)\n\n*   ### [学术期刊](https:\u002F\u002Fiarrp.caas.cn\u002Fxzqk\u002Findex.htm)\n\n您当前所在位置： [首页](https:\u002F\u002Fiarrp.caas.cn\u002Findex.htm)» [院所新闻](https:\u002F\u002Fiarrp.caas.cn\u002Fysdt\u002Findex.htm)» 媒体报道 \n# 【科技日报】中国农业科学院组团全面入驻国家盐碱地中心\n\n发布者：管理员 发布时间：2026-09-15 作者：马爱平 来源：科技日报 点击量：\n\n9月11日，中国农业科学院（以下简称“中国农科院”）在山东省黄三角农高区牵头召开盐碱地综合利用国家技术创新中心建设推进会议。\n\n![Image 6](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F14\u002F580580_5d055b35-6a23-46e6-bbfc-020ed463472dcopy.jpg)\n\n黄三角农高区党工委副书记、管委会常务副主任朱英坤表示，国家盐碱地中心总部基地已全面建成投用，中国农科院10支创新团队入驻，农高区设立科教产发展专项资金，布局“一基地四园区”产业载体，从根本上解决了“有地方、有人、有钱搞科研”的问题。农高区将全力当好科研工作“勤务员”，为科研团队营造最优创新创业环境，与中国农科院同心同向，共同抓好国家盐碱地中心考核评估、2026国际盐碱地农业大会等重点任务，为保障国家粮食安全、推进黄河流域生态保护和高质量发展作出新贡献。\n\n中国农科院副院长刘现武表示，国家盐碱地中心自2025年6月正式揭牌并实体化运行以来，10支院级创新团队陆续入驻，常驻科研人员和研究生数量持续增加，打造轻度、中度、重度全类型盐碱地示范场景超1700亩，各项工作取得实实在在的阶段性成效。各入驻团队要真驻实驻，进一步聚焦研究方向，加强团队建设，聚力核心攻关，共同打造国内领先、国际一流的盐碱地科技创新高地。\n\n中国农科院农业资源与农业区划研究所（以下简称“资划所”）所长吴文斌表示，作为国家盐碱地中心和中国农科院盐碱地农业中心建设牵头单位，资划所秉持开放合作、互利共赢的理念，与各共建单位开展了扎实有效的合作，初步形成了优势互补、联动发展的良好创新格局，为国家盐碱地中心常态化运行、高质量发展筑牢了坚实基础。下一步，资划所将继续与各兄弟单位同心携手，坚持“平台共建、资源共享、攻坚协同”，深耕共建共享、协同协作的创新发展模式，以规范化、制度化保障多方协作高效运转，全力将盐碱地中心打造成为国内领先、国际一流的盐碱地科技创新高地。\n\n![Image 7](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-09\u002F14\u002F580580_56636f40-5fbb-41f3-a258-e9586bd6d0f1copy.jpg)\n\n中国农科院盐碱地农业中心和中国农科院10支创新团队在现场进行了情况介绍与成果推介。会上还举行了合作项目签约仪式，4支团队和合作企业围绕盐碱地综合治理、特色农产品加工等科技成果转化与产业培育达成合作协议。\n\n（资划所供图）\n\n  \n\n『[打印](https:\u002F\u002Fiarrp.caas.cn\u002Fysdt\u002Fmtbd\u002F362cd263199245d7a9bc94847e372f4c.htm#)』『[关闭](https:\u002F\u002Fiarrp.caas.cn\u002Fysdt\u002Fmtbd\u002F362cd263199245d7a9bc94847e372f4c.htm#)』\n\n[设为首页](http:\u002F\u002Fiarrp.caas.cn\u002F)∣[加入收藏](javascript:window.external.AddFavorite('http:\u002F\u002Fiarrp.caas.cn\u002F', '%E5%86%9C%E4%B8%9A%E8%B5%84%E6%BA%90%E4%B8%8E%E5%86%9C%E4%B8%9A%E5%8C%BA%E5%88%92%E7%A0%94%E7%A9%B6%E6%89%80'))∣[联系我们](https:\u002F\u002Fiarrp.caas.cn\u002Flxwm\u002Findex.htm)\n\n  \n Copyright©2012-2021 中国农业科学院农业资源与农业区划研究所版权所有  \n 地址：北京市海淀区中关村南大街12号 邮编：100081 电话：010-82109640  \niarrp.caas.cn ([京ICP备14003094号-1](https:\u002F\u002Fbeian.miit.gov.cn\u002F))京公网安备 11010802028641号 技术支持：中国农业科学院农业信息研究所","中国农业科学院农业资源与农业区划研究所 \u002F 科技日报","2026-09-15T00:00:00Z",76,{"impact":79,"substance":80,"depth":18,"authority":18,"freshness":81,"relevant":21,"comment":82},22,18,8,"国家级科研机构成建制入驻盐碱地国家技术创新中心，属盐碱地综合利用领域的实质性布局进展，具备聚合价值。",[84],{"name":75,"url":72},[86,87,28,88,89],"黄三角农高区","农业科技创新","盐碱地治理","耕地质量",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":93},{"candidates":94,"agricultural_evidence":46},[95,101,105,110],{"code":32,"score":96,"supported":46,"title":97,"abstract":99,"tags":100},13,[98],"盐碱地",[98],[88,89],{"code":52,"score":21,"supported":14,"title":102,"abstract":103,"tags":104},[],[],[28],{"code":106,"score":21,"supported":14,"title":107,"abstract":108,"tags":109},"H1",[],[],[89],{"code":111,"score":21,"supported":14,"title":112,"abstract":113,"tags":114},"J2",[],[],[87],[116,117],"农业科技创新 黄三角农高区 盐碱地治理 耕地质量","农业科技创新 黄三角农高区","农业科技创新黄三角农高区盐碱地治理耕地质量-2819","2026-09-18T00:03:26.848170Z",{"id":121,"title":122,"url":123,"summary":124,"summary_zh":8,"content":8,"source_name":125,"source_url":123,"published_at":126,"category":127,"cover_url":8,"hotness":13,"is_selected":14,"score":128,"score_detail":129,"sources":133,"tags":135,"primary_topic":139,"secondary_topics":140,"classification":141,"search_phrases":165,"slug":168,"view_count":21,"doi":169,"paper":170,"created_at":188},1250,"Rain-shelter soil cultivation as a rainfall risk adaptation option for sustainable lemon balm production in a Korean highland field assessment","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1929768","Sustainable crop production requires strategies that stabilize the supply of high-value herb raw materials under rainfall and climate variability. Lemon balm ( Melissa officinalis L.) is a medicinal, culinary, and aromatic herb whose commercial value depends largely on stable leaf biomass. This exploratory, single-season, fixed-site field assessment compared adjacent plastic-house rain-shelter (PH) and open-field (PN) soil cultivation in the Pyeongchang highland area, Republic of Korea, during 0–62 days after transplanting (DAT). PH excluded direct rainfall and received 44.0 mm of controlled irrigation, whereas PN received 527.2 mm of gross water input, including 385.5 mm of July rainfall. PH had lower cumulative total shortwave radiation, slightly higher mean temperature, lower period-level relative humidity, and a higher calculated mean vapor pressure deficit than PN. At 62 DAT, PH showed greater shoot fresh weight and shoot, leaf, and root dry matter, while leaf fraction remained similar between systems. Area-based leaf dry matter, root-to-shoot ratio, and shoot dry-matter percentage also supported greater whole-plant vigor in PH. However, the cultivation systems were not independently replicated, baseline soil properties were unavailable, and soil-water status, leaf wetness, disease incidence, gas exchange, leaf area, and phytochemical quality were not measured. The observed contrast is therefore interpreted as an integrated system-level association rather than a causal effect of rainfall exclusion or any single microclimatic factor. Rain-shelter soil cultivation warrants independently replicated, soil-characterized, multi-year evaluation as a rainfall-risk adaptation option for monsoon-exposed highland herb production.","Frontiers in Sustainable Food Systems","2026-08-25T00:00:00Z","论文",58,{"impact":13,"substance":80,"depth":130,"authority":17,"freshness":131,"relevant":21,"comment":132},16,2,"针对高海拔地区柠檬香蜂草在降雨风险下的设施栽培研究，提供初步数据，但需多年验证。",[134],{"name":125,"url":123},[136,26,137,138],"智慧农业","柠檬香蜂草","气候变化适应",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":142},{"candidates":143,"agricultural_evidence":46},[144,149,155,160],{"code":32,"score":145,"supported":46,"title":146,"abstract":147,"tags":148},11,[33],[33],[],{"code":150,"score":45,"supported":46,"title":151,"abstract":152,"tags":154},"H3",[],[153],"irrigation",[],{"code":156,"score":21,"supported":14,"title":157,"abstract":158,"tags":159},"H7",[],[],[138],{"code":161,"score":21,"supported":14,"title":162,"abstract":163,"tags":164},"I1",[],[],[136],[166,167],"气候变化适应 柠檬香蜂草 智慧农业 设施农业","气候变化适应 柠檬香蜂草","气候变化适应柠檬香蜂草智慧农业设施农业-1250","10.3389\u002Ffsufs.2026.1929768",{"doi":169,"openalex_id":171,"authors":172,"venue":125,"cited_by_count":179,"oa_url":180,"card":181,"direction":185,"ingested_from":187},"W7204194163",[173,176],{"name":174,"orcid":175},"Ju Young Lee","https:\u002F\u002Forcid.org\u002F0000-0002-5531-5523",{"name":177,"orcid":178},"Jai-Eok Park","https:\u002F\u002Forcid.org\u002F0000-0001-5625-9386",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1929768\u002Fpdf",{"tldr":182,"method":183,"finding":184,"direction":185,"opportunity":186},"评估韩国高地柠檬香蜂草在雨棚与露地栽培下的生长差异，雨棚栽培提高生物量。","单季固定田间对比试验，比较塑料雨棚与露地栽培，监测微气候与生长指标。","雨棚栽培显著增加地上部鲜重和干物质，但因果性需进一步验证。","智慧农业 \u002F 农业物联网","可延伸研究雨棚栽培对药用植物次生代谢物及品质的影响，并开展多年多点随机区组试验。","openalex","2026-09-01T04:03:09.694080Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":8,"source_name":195,"source_url":192,"published_at":196,"category":127,"cover_url":8,"hotness":13,"is_selected":14,"score":197,"score_detail":198,"sources":202,"tags":204,"primary_topic":209,"secondary_topics":210,"classification":217,"search_phrases":235,"slug":238,"view_count":179,"doi":239,"paper":240,"created_at":255},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",64,{"impact":81,"substance":199,"depth":200,"authority":19,"freshness":13,"relevant":21,"comment":201},20,17,"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。",[203],{"name":195,"url":192},[136,205,206,207,208],"农业人工智能","主动学习","线虫检测","土壤采样",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[211],{"code":212,"slug":213,"name":214,"path":215,"parent_code":216},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":218},{"candidates":219,"agricultural_evidence":46},[220,224,229],{"code":32,"score":18,"supported":46,"title":221,"abstract":222,"tags":223},[33],[49,33],[],{"code":212,"score":19,"supported":46,"title":225,"abstract":227,"tags":228},[226],"artificial intelligence",[],[205],{"code":161,"score":20,"supported":46,"title":230,"abstract":231,"tags":234},[],[232,233],"精准农业","precision agriculture",[136],[236,237],"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","10.5281\u002Fzenodo.23544145",{"doi":239,"openalex_id":241,"authors":242,"venue":195,"cited_by_count":179,"oa_url":192,"card":249,"direction":253,"ingested_from":187},"W7223082195",[243,245,247],{"name":244,"orcid":8},"Amine Ben Salah",{"name":246,"orcid":8},"Ines Trabelsi",{"name":248,"orcid":8},"Mohamed Aziz Khelifi",{"tldr":250,"method":251,"finding":252,"direction":253,"opportunity":254},"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。","2026-10-22T11:20:58.901747Z",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":261,"content":8,"source_name":262,"source_url":259,"published_at":263,"category":127,"cover_url":8,"hotness":13,"is_selected":14,"score":264,"score_detail":265,"sources":267,"tags":269,"primary_topic":275,"secondary_topics":276,"classification":277,"search_phrases":302,"slug":305,"view_count":179,"doi":306,"paper":307,"created_at":340},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":130,"substance":79,"depth":80,"authority":18,"freshness":19,"relevant":21,"comment":266},"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。",[268],{"name":262,"url":259},[270,271,272,273,274],"遥感监测","莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":278},{"candidates":279,"agricultural_evidence":46},[280,284,291,297],{"code":32,"score":18,"supported":46,"title":281,"abstract":282,"tags":283},[33],[49,33],[],{"code":285,"score":20,"supported":46,"title":286,"abstract":287,"tags":290},"I3",[],[288,289],"遥感","remote sensing",[270],{"code":156,"score":67,"supported":46,"title":292,"abstract":293,"tags":296},[],[294,295],"气候适应","干旱",[],{"code":298,"score":21,"supported":14,"title":299,"abstract":300,"tags":301},"H6",[],[],[273],[303,304],"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":306,"openalex_id":308,"authors":309,"venue":262,"cited_by_count":179,"oa_url":259,"card":334,"direction":338,"ingested_from":187},"W7223069171",[310,312,314,316,319,321,324,327,329,331],{"name":311,"orcid":8},"Yue Li",{"name":313,"orcid":8},"Mengfei Shi",{"name":315,"orcid":8},"Fei Zhang",{"name":317,"orcid":318},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":320,"orcid":8},"Gowhar Meraj",{"name":322,"orcid":323},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":325,"orcid":326},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":328,"orcid":8},"Yuanfang Chai",{"name":330,"orcid":8},"Hongtao Xu",{"name":332,"orcid":333},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":335,"method":336,"finding":337,"direction":338,"opportunity":339},"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":342,"title":343,"url":344,"summary":345,"summary_zh":346,"content":8,"source_name":347,"source_url":344,"published_at":196,"category":127,"cover_url":8,"hotness":13,"is_selected":14,"score":348,"score_detail":349,"sources":352,"tags":354,"primary_topic":358,"secondary_topics":364,"classification":367,"search_phrases":401,"slug":404,"view_count":179,"doi":405,"paper":406,"created_at":423},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",77,{"impact":130,"substance":350,"depth":200,"authority":96,"freshness":13,"relevant":21,"comment":351},21,"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。",[353],{"name":347,"url":344},[136,355,270,356,357],"机器学习","土壤墒情","冬小麦",{"code":359,"slug":360,"name":361,"path":362,"parent_code":363},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[365,366],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":212,"slug":213,"name":214,"path":215,"parent_code":216},{"status":39,"locked":14,"version":40,"method":41,"evidence":368},{"candidates":369,"agricultural_evidence":46},[370,376,381,386,391,396],{"code":359,"score":371,"supported":46,"title":372,"abstract":374,"tags":375},26,[373,360],"winter wheat",[361,373,360],[357],{"code":32,"score":377,"supported":46,"title":378,"abstract":379,"tags":380},15,[33],[49,33],[356],{"code":212,"score":377,"supported":46,"title":382,"abstract":384,"tags":385},[383],"machine learning",[355,383],[355],{"code":285,"score":13,"supported":46,"title":387,"abstract":388,"tags":390},[],[288,289,389],"multispectral",[270],{"code":150,"score":67,"supported":46,"title":392,"abstract":393,"tags":395},[],[394,153],"灌溉",[],{"code":161,"score":397,"supported":46,"title":398,"abstract":399,"tags":400},4,[],[136],[136],[402,403],"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":405,"openalex_id":407,"authors":408,"venue":347,"cited_by_count":179,"oa_url":344,"card":418,"direction":185,"ingested_from":187},"W7223156204",[409,412,414,416],{"name":410,"orcid":411},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":413,"orcid":8},"Yi Zhang",{"name":415,"orcid":8},"WEI Lei",{"name":417,"orcid":8},"Chengcai Zhang",{"tldr":419,"method":420,"finding":421,"direction":338,"opportunity":422},"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。","2026-10-22T11:20:18.887301Z",{"id":425,"title":426,"url":427,"summary":428,"summary_zh":429,"content":8,"source_name":125,"source_url":427,"published_at":196,"category":127,"cover_url":8,"hotness":13,"is_selected":14,"score":430,"score_detail":431,"sources":433,"tags":435,"primary_topic":441,"secondary_topics":442,"classification":443,"search_phrases":463,"slug":466,"view_count":179,"doi":467,"paper":468,"created_at":492},5549,"Characterising climate mitigation and adaptation performance in oil palm plantation: evidence from Indonesia","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1871877","Despite growing demand for ‘climate-friendly’ palm oil, little is known about how existing soil and management conditions align with climate mitigation and adaptation goals. This study characterised the climate mitigation and adaptation performance of oil palm (OP) production across different soil and management conditions based on simulated carbon balance and productivity. The Agricultural Production Systems SIMulator (APSIM)-OilPalm model was used to simulate productivity and carbon balance in 25 industrial OP sites on mineral soils in North Sumatra, Indonesia. K-means clustering was used to group similarity patterns between simulated carbon balance and productivity with observed soil and management conditions data. The model has good skill in simulating OP productivity at sites with standard plant density, achieving coefficient of determination (R 2 ) yield up to 0.79, though performance is poor at plantations with plant density outside the standard range or very low observed yields. Simulated OP productivity exhibits moderate variability from 15.38 to 28.67 t fresh fruit bunch (FFB) ha −1 yr. −1 . Simulated carbon balance, which is comparable to measured values in the region, indicates that all observed OP sites serve as a carbon sink with low variability from −2.09 to −3.86 t C eq. ha −1 yr. −1 , driven by uniformity in climate factors and plant density. Eleven of 25 sites have good mitigation and adaptation performance, indicated by high carbon sinks, yield and soil organic carbon (SOC) increment. These sites are characterised by greater plant density and moderate levels of N fertilisation, sand content and Ganoderma boninense (G. boninense) infection, with low carbon nitrogen (CN) ratio. Over half of sites face climate adaptation challenges, indicated by either low productivity or SOC increment and characterised by low plant density and nitrogen fertiliser with high G. boninense infection and sandy soils. This study contributes information about mitigation and adaptation in determining the management of OP and supports the tracking of climate-friendly palm oil within broader supply chains.","尽管对“气候友好型”棕榈油的需求日益增长，但人们对现有土壤和管理条件如何与气候减缓及适应目标相协调知之甚少。本研究基于模拟碳平衡和生产力，表征了油棕（OP）生产在不同土壤和管理条件下的气候减缓与适应表现。研究采用农业生产系统模拟器（APSIM）-OilPalm模型，模拟了印度尼西亚北苏门答腊25个矿质土壤工业油棕样点的生产力和碳平衡。运用K均值聚类方法，将模拟碳平衡与生产力之间的相似性模式与观测到的土壤和管理条件数据进行分组。该模型在标准种植密度样点的油棕生产力模拟中具有良好能力，决定系数（R²）最高达0.79，但在种植密度超出标准范围或观测产量极低的种植园中表现较差。模拟油棕生产力呈现中等变异性，范围为15.38至28.67 t鲜果串（FFB）ha⁻¹ yr.⁻¹。模拟碳平衡与该地区实测值相当，表明所有观测油棕样点均为碳汇，变异性较低，范围为−2.09至−3.86 t C eq. ha⁻¹ yr.⁻¹，这归因于气候因子和种植密度的一致性。25个样点中有11个具有良好的减缓和适应表现，表现为高碳汇、产量和土壤有机碳（SOC）增量。这些样点的特征是种植密度较大，氮肥施用量、砂含量和灵芝（Ganoderma boninense，G. boninense）感染程度适中，碳氮（CN）比低。超过半数样点面临气候适应挑战，表现为生产力或SOC增量较低，其特征是种植密度和氮肥用量低、灵芝感染率高及砂质土壤。本研究为确定油棕管理中的减缓和适应提供了信息，并支持在更广泛供应链中追踪气候友好型棕榈油。",80,{"impact":80,"substance":79,"depth":80,"authority":96,"freshness":19,"relevant":21,"comment":432},"基于APSIM模型对印尼25个油棕园碳平衡与生产力的模拟研究，方法清晰、数据具体，对气候友好型油棕管理与供应链追踪有参考价值，但属区域性论文，影响范围有限。",[434],{"name":125,"url":427},[436,437,438,439,440],"作物模型","气候智慧农业","油棕","碳汇","可持续种植",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":444},{"candidates":445,"agricultural_evidence":46},[446,450,455,459],{"code":32,"score":67,"supported":46,"title":447,"abstract":448,"tags":449},[],[49,33],[],{"code":451,"score":397,"supported":46,"title":452,"abstract":453,"tags":454},"H4",[],[439],[439],{"code":156,"score":397,"supported":46,"title":456,"abstract":457,"tags":458},[],[294],[437],{"code":212,"score":21,"supported":14,"title":460,"abstract":461,"tags":462},[],[],[436],[464,465],"APSIM-OilPalm 油棕 碳平衡","印尼 油棕 气候适应","APSIM-OilPalm油棕碳平衡-5549","10.3389\u002Ffsufs.2026.1871877",{"doi":467,"openalex_id":469,"authors":470,"venue":125,"cited_by_count":179,"oa_url":427,"card":486,"direction":490,"ingested_from":187},"W7223112249",[471,474,477,480,483],{"name":472,"orcid":473},"Lisma Safitri","https:\u002F\u002Forcid.org\u002F0000-0002-7188-1058",{"name":475,"orcid":476},"Marcelo Valadares Galdos","https:\u002F\u002Forcid.org\u002F0000-0002-6080-0726",{"name":478,"orcid":479},"Iput Pradiko","https:\u002F\u002Forcid.org\u002F0000-0001-7877-8400",{"name":481,"orcid":482},"Alexis Comber","https:\u002F\u002Forcid.org\u002F0000-0002-3652-7846",{"name":484,"orcid":485},"Andrew Juan Challinor","https:\u002F\u002Forcid.org\u002F0000-0002-8551-6617",{"tldr":487,"method":488,"finding":489,"direction":490,"opportunity":491},"用APSIM模型模拟印尼油棕园碳平衡与产量，评估其气候减缓与适应表现。","APSIM-OilPalm模型模拟25个站点，结合K-means聚类分析土壤与管","所有站点均为碳汇，11个站点减缓与适应表现良好，过半站点面临适应挑战。","农业绿色发展与碳","可扩展至不同气候带与泥炭地油棕园，结合遥感数据构建区域尺度气候友好型油棕认证体系。","2026-10-22T11:20:15.883770Z"]