[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2937":3,"related-2937":139},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":45,"search_phrases":85,"slug":88,"view_count":89,"doi":90,"paper":91,"created_at":138},2937,"Modeling the effects of livestock rotation frequency on forage production and soil carbon using a spatially explicit grazing distribution","https:\u002F\u002Fdoi.org\u002F10.1080\u002F17583004.2026.2732593","Livestock grazing strongly influences terrestrial ecosystems and carbon (C) dynamics, but outcomes depend on grazing management. High-frequency rotation (HFR) grazing may reduce the uneven grazing distribution commonly associated with low-frequency rotation (LFR), which can alter forage production and soil organic carbon (SOC). However, most ecosystem models do not explicitly represent uneven grazing distribution, limiting their ability to evaluate management effects. We enhanced the MEMS ecosystem model by incorporating discrete spatial units to simulate grazing distribution driven by key environmental drivers, including forage availability and quality, and distance to water. Using remote sensing-derived enhanced vegetation index (EVI), we verified the simulated grazing distribution at an experimental rangeland site in Oklahoma. We tested the model’s sensitivity to rotation frequency under different management (stocking rate, timing, and duration) and weather (typical, dry, and wet) scenarios. Simulated light and moderate grazing generally sustained forage production and SOC regardless of rotation frequency. However, under heavy grazing, the 30-year projection showed HFR reduced localized overgrazing and sustained higher pasture average forage productivity and SOC stocks than LFR. Long-term experimental data are needed to validate these results. Our study offers guidance for designing grazing management experiments to address this critical knowledge gap and advance C management strategies.","牲畜放牧对陆地生态系统和碳（C）动态具有强烈影响，但其结果取决于放牧管理方式。高频轮牧（HFR）可能减少通常与低频轮牧（LFR）相关的不均匀放牧分布，从而改变饲草产量和土壤有机碳（SOC）。然而，大多数生态系统模型并未明确表征不均匀放牧分布，限制了其评估管理效果的能力。我们通过引入离散空间单元增强了MEMS生态系统模型，以模拟由关键环境驱动因素（包括饲草可获得性和质量，以及与水源的距离）驱动的放牧分布。利用遥感反演的增强植被指数（EVI），我们在俄克拉荷马州一个实验性牧场验证了模拟的放牧分布。我们测试了模型在不同管理（载畜率、时间和持续时间）和天气（典型、干旱和湿润）情景下对轮牧频率的敏感性。模拟的轻度与中度放牧无论轮牧频率如何，通常均能维持饲草生产和SOC。然而，在重度放牧下，30年预测显示HFR减少了局部过度放牧，并比LFR维持了更高的牧场平均饲草生产力和SOC储量。需要长期实验数据来验证这些结果。我们的研究为设计放牧管理实验提供了指导，以弥补这一关键知识空白并推进碳管理策略。",null,"Carbon Management","2026-09-17T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,14,9,1,"改进MEMS模型显式模拟放牧空间分布，揭示高频轮牧在重度放牧下维持牧草生产与土壤碳的优势，方法新颖但尚缺长期实测验证，属细分领域有价值研究。",[25],{"name":10,"url":6},[27,28,29,30,31],"土壤有机碳","遥感监测","草地碳汇","轮牧管理","生态模型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,58,64,70,78],{"code":40,"score":52,"supported":53,"title":54,"abstract":55,"tags":57},15,true,[41],[56,41],"土壤",[27],{"code":33,"score":20,"supported":53,"title":59,"abstract":61,"tags":63},[60],"forage",[62,60],"饲草",[],{"code":37,"score":65,"supported":53,"title":66,"abstract":68,"tags":69},11,[67],"livestock",[67],[],{"code":71,"score":72,"supported":53,"title":73,"abstract":74,"tags":77},"I3",7,[],[75,76],"遥感","remote sensing",[28],{"code":79,"score":80,"supported":53,"title":81,"abstract":82,"tags":84},"H7",3,[],[83],"干旱",[],[86,87],"MEMS模型 放牧分布","俄克拉荷马 轮牧 土壤碳","MEMS模型放牧分布-2937",2,"10.1080\u002F17583004.2026.2732593",{"doi":90,"openalex_id":92,"authors":93,"venue":10,"cited_by_count":128,"oa_url":129,"card":130,"direction":136,"ingested_from":137},"W7213466883",[94,96,99,101,104,106,109,111,114,116,118,120,122,124,126],{"name":95,"orcid":9},"Yao Zhang",{"name":97,"orcid":98},"Rafael S. Santos","https:\u002F\u002Forcid.org\u002F0000-0003-4829-0644",{"name":100,"orcid":9},"Emma Hamilton",{"name":102,"orcid":103},"Paige Stanley","https:\u002F\u002Forcid.org\u002F0000-0002-4444-3351",{"name":105,"orcid":9},"Hao Yang",{"name":107,"orcid":108},"Keith Paustian","https:\u002F\u002Forcid.org\u002F0000-0001-5262-5596",{"name":110,"orcid":9},"Erica L. Patterson",{"name":112,"orcid":113},"Stephen M. Ogle","https:\u002F\u002Forcid.org\u002F0000-0003-1899-7446",{"name":115,"orcid":9},"Isabella C.F. Maciel",{"name":117,"orcid":9},"Guilhermo F. S. Congio",{"name":119,"orcid":9},"Hugh Aljoe",{"name":121,"orcid":9},"Alejandro Romero-Ruiz",{"name":123,"orcid":9},"Jeff J. Goodwin",{"name":125,"orcid":9},"Jason Rowntree",{"name":127,"orcid":9},"M. Francesca Cotrufo",0,"https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17583004.2026.2732593?needAccess=true",{"tldr":131,"method":132,"finding":133,"direction":134,"opportunity":135},"改进MEMS模型模拟放牧分布，评估轮牧频率对牧草生产和土壤碳的影响。","增强MEMS模型空间离散单元，用EVI遥感验证，模拟不同放牧和天气情景。","重度放牧下高频轮牧减少局部过牧，维持更高牧草生产力和土壤有机碳。","农业绿色发展与碳","需长期实验验证模型结果，并探索不同气候和放牧管理下土壤碳动态的普适性机制。","智慧农业 \u002F 农业物联网","openalex","2026-09-19T23:30:13.860699Z",{"total":140,"page":22,"page_size":140,"items":141},6,[142,207,255,345,422,499],{"id":143,"title":144,"url":145,"summary":146,"summary_zh":9,"content":147,"source_name":148,"source_url":9,"published_at":149,"category":150,"cover_url":9,"hotness":13,"is_selected":14,"score":151,"score_detail":152,"sources":156,"tags":158,"primary_topic":164,"secondary_topics":165,"classification":173,"search_phrases":202,"slug":205,"view_count":22,"doi":9,"paper":9,"created_at":206},5123,"盐碱地里青贮玉米「争气」了 黑龙江省农科院「龙育15」「龙育17」松嫩平原盐碱旱田亩产全株三四吨","https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-10\u002F16\u002Fcontent_597377.html","黑龙江省农业科学院草业研究所承担的青贮玉米新品种示范推广及产业化应用项目,围绕松嫩平原西部盐碱化旱田选育的「龙育15」「龙育17」两个品种,在盐碱地块上平均增产超一成,亩产全株三四吨,粗淀粉含量达32%。项目组围绕保全苗、稳产量、提品质给种植企业做全流程服务,留茬30公分平衡产量与品质。项目建成1.2万亩高产优质栽培示范,每年在安达市推广3万亩以上,还走进内蒙古等周边饲草产区。千亩套种示范田里玉米垄沟套种燕麦,青贮玉米产量持平,每亩多收600多公斤燕麦,亩增收300多元,燕麦蛋白含量高,混进青贮补齐蛋白短板。","![Image 3](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Fshare_logo.jpg)\n\n[![Image 4](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Flogo.png)](http:\u002F\u002Fwww.stdaily.com\u002F)\n\n*   [时政](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fszxw\u002Fnode_357.html)\n*   [热点](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Frdxw\u002Fnode_327.html)\n*   [政务](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fzwxw\u002Fnode_362.html)\n*   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5](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Ffdj.png)](https:\u002F\u002Fsearch.stdaily.com:8888\u002Ffounder\u002FNewSearchServlet.do?siteID=1)[登录](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html?url=https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-10\u002F16\u002Fcontent_597377.html)[-](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html)\n\n 所在位置： [中国科技网首页](https:\u002F\u002Fwww.stdaily.com\u002Findex.html)>[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)> 正文 \n\n# 盐碱地里，青贮玉米“争气”了\n\n 2026-10-16 15:09:54 来源: 科技日报 点击数：\n\n![Image 6](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimg\u002F20240301dzv.png)0\n\n[](javascript:; \"新浪微博\")[](javascript:; \"微信\")\n\n**科技日报记者 朱虹**\n\n暮秋的黑龙江省安达市天高云淡，一垄垄盐碱旱田刚翻过土，泛着浅浅的白霜。10月13日，记者跟随黑龙江省农科院草业研究所研究员林红，穿过连片田块，走进安达绿生源现代养殖场。还没进牛舍，一股青贮特有的酸香就扑面而来。黑白花奶牛次第低头，把脑袋埋进食槽，咀嚼声此起彼伏。饲养员抄起一捧青贮料，深绿的茎叶裹着饱满的籽粒，“这就是咱们的‘龙育15’‘龙育17’。”\n\n![Image 7](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-10\u002F16\u002F597377_a90e9d31-3cb7-4c4d-b464-ab39a784e19ccopy.jpg)\n\n安达素有“中国牛城”之名，可松嫩平原西部这片土地，盐碱是绕不开的坎。土壤碱化重、地力弱，青贮玉米一度种不出像样的品质，干物质、淀粉指标上不去，优质饲草供不应求，牛越养越“挑食”。能不能在盐碱地里种出合格的青贮玉米，成了当地草畜产业躲不开的考题。而由黑龙江省农科院草业研究所承担的黑龙江省农业科技创新跨越工程项目——青贮玉米新品种示范推广及产业化应用，正是冲着这道题来的。\n\n“高产、优质，适合青贮饲料用途，这是我们选育最核心的标尺。”站在牛舍边，林红介绍，青贮玉米拼的是高淀粉、高干物质，“龙育15”“龙育17”的培育目标就是“产量和品质两头都要”。它们在盐碱地块上，平均增产超一成，亩产全株三四吨，粗淀粉含量能到32%，肉牛吃、长肉快，奶牛吃、奶量高。\n\n良种到手，技术指导一路跟到地头。项目组围绕保全苗、稳产量、提品质，给种植企业做全流程服务。“青贮玉米每亩达不到5000株，高产就没有前提。”林红说，从整地保墒到精准播种，再到田间管护，环环盯紧。\n\n![Image 8](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-10\u002F16\u002F597377_a95e303c-16eb-4ad2-bd50-703dac8cdb0acopy.jpg)\n\n最难拿捏的，是收割那一刀。收矮了，产量看着高，可地不平，土跟着卷进饲料，营养也打了折扣；收高了，品质上去了，种植户又心疼收入。此前绿生源养殖场总经理周现平就为这一刀找过项目组。林红最终给出折中方案——留茬30公分，“既保产量，又守品质，现在牧场都认这个数”。牛舍里的青贮窖已经装得满满当当，新收的料压得瓷实，来年开窖，又是一窖好饭。\n\n品种在田里扎根，推广也在向外铺开。“龙育15”“龙育17”两个品种，项目建成1.2万亩高产优质栽培示范，每年在安达市推广3万亩以上，还走进内蒙古等周边饲草产区，带动松嫩平原盐碱化旱田地力提升、绿色高效增收。千亩套种示范田里，项目组在玉米垄沟里套种燕麦，青贮玉米产量和常规种植持平，每亩却多收600多公斤燕麦，亩增收300多元；燕麦蛋白含量高，混进青贮正好补齐蛋白短板，饲料品质和喂饲效果都跟着上了台阶。\n\n一方面，项目把良种良法送进地里；另一方面，企业把产业链拉得更长。绿生源依托自有的一万多亩基地，从种到收，再到加工销售，一气呵成，牛吃不完的青贮还能外销；养殖产生的粪污经处理后还田，反哺盐碱地，把“种好草、养好牛、肥好地”串成了闭环。\n\n夕阳把草料场镀成金色，青贮裹包在田埂边排开，“龙育15”“龙育17”在这扎下根。牛槽里多了料，田里多了收成，松嫩平原的青贮玉米，在这片曾经的薄地上“争气”了。\n\n（科技日报记者 朱虹 摄）\n\n责任编辑：王倩\n\n相关稿件：\n\n![Image 9](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Fpinglun.png)网友评论\n\n评论\n\n最热评论\n\n没有更多评论了\n\n[热点](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Frdxw\u002Fnode_327.html)\n\n[![Image 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日","2026-10-16T00:00:00Z","报道",77,{"impact":19,"substance":153,"depth":154,"authority":20,"freshness":21,"relevant":22,"comment":155},20,16,"省级科研机构在盐碱旱田推出青贮玉米新品种并配套栽培技术，数据具体、多方信源，具行业推广价值。",[157],{"name":148,"url":145},[159,160,161,162,163],"种业振兴","盐碱地治理","饲草产业","良种良法","青贮玉米",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[166,172],{"code":167,"slug":168,"name":169,"path":170,"parent_code":171},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":174},{"candidates":175,"agricultural_evidence":53},[176,181,185,190,197],{"code":33,"score":154,"supported":53,"title":177,"abstract":179,"tags":180},[178],"青贮",[62,178],[163,161],{"code":167,"score":17,"supported":53,"title":182,"abstract":183,"tags":184},[169],[169],[163],{"code":40,"score":17,"supported":53,"title":186,"abstract":188,"tags":189},[187],"盐碱地",[187],[160],{"code":191,"score":192,"supported":53,"title":193,"abstract":194,"tags":196},"A7",4,[],[195],"套种",[161],{"code":198,"score":89,"supported":14,"title":199,"abstract":200,"tags":201},"F1",[],[],[159,162],[203,204],"黑龙江农科院 龙育15 龙育17","松嫩平原 盐碱地 青贮玉米","黑龙江农科院龙育15龙育17-5123","2026-10-17T00:13:40.216554Z",{"id":208,"title":209,"url":210,"summary":211,"summary_zh":9,"content":9,"source_name":212,"source_url":9,"published_at":213,"category":214,"cover_url":9,"hotness":13,"is_selected":14,"score":215,"score_detail":216,"sources":219,"tags":221,"primary_topic":227,"secondary_topics":228,"classification":234,"search_phrases":250,"slug":253,"view_count":89,"doi":9,"paper":9,"created_at":254},1668,"沧州盐碱地\"种\"出两项省地方标准：盐碱地梨栽培技术、紫花苜蓿种植与袋式青贮技术","https:\u002F\u002Fwww.cangzhou.gov.cn\u002Fcangzhou\u002Fc100002\u002F202609\u002F6c25653abc2a4318ba939c6772c6df40.shtml","河北省市场监督管理局发布《科技成果权威性竞争性评价规范》等 40 项地方标准。其中沧州市的《盐碱地梨栽培技术规程》和《滨海盐碱地紫花苜蓿种植与袋式青贮技术规程》两项标准入选。规程分别由海兴瑞农农业发展有限公司、河北省农林科学院农业资源环境研究所等多家单位起草，聚焦盐碱地梨园建设、土壤改良、苗木选育、水肥管控等关键环节，破解盐碱地苜蓿出苗难、产量不稳等难题。","沧州市人民政府","2026-09-04T00:00:00Z","政策",73,{"impact":19,"substance":153,"depth":52,"authority":17,"freshness":217,"relevant":22,"comment":218},8,"沧州两项盐碱地农业技术规程入选省地方标准，具有省级推广价值，技术细节详实。",[220],{"name":212,"url":210},[222,223,224,225,226],"地方标准","苜蓿青贮","盐碱地改良","梨栽培","农业技术",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[229,230],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":191,"slug":231,"name":232,"path":233,"parent_code":171},"forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping",{"status":46,"locked":14,"version":47,"method":48,"evidence":235},{"candidates":236,"agricultural_evidence":53},[237,242,246],{"code":33,"score":18,"supported":53,"title":238,"abstract":240,"tags":241},[178,239],"苜蓿",[178,239],[],{"code":40,"score":52,"supported":53,"title":243,"abstract":244,"tags":245},[187],[56,187],[224],{"code":191,"score":65,"supported":53,"title":247,"abstract":248,"tags":249},[239],[239],[],[251,252],"盐碱地改良 农业技术 地方标准 苜蓿青贮","盐碱地改良 农业技术","盐碱地改良农业技术地方标准苜蓿青贮-1668","2026-09-05T00:04:35.934739Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":260,"content":9,"source_name":261,"source_url":258,"published_at":262,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":263,"score_detail":264,"sources":266,"tags":268,"primary_topic":272,"secondary_topics":273,"classification":284,"search_phrases":311,"slug":314,"view_count":89,"doi":315,"paper":316,"created_at":344},4172,"Machine learning reveals distinct drivers of soil organic carbon across the mountain-oasis-desert system of northwest China","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13021-026-00522-5","The mountain-oasis-desert system (MODS) is a distinctive landscape pattern shaped by interactions between geomorphological configurations and hydrological processes in arid regions. However, most soil organic carbon (SOC) mapping studies treat arid landscapes as homogeneous units, leaving limited quantitative evidence on how SOC patterns and environmental associations differ among MODS subsystems. As a critical ecosystem highly sensitive to climate change and human activities, understanding these responses is essential for regional soil carbon management and ecological restoration. In this study, we developed an ecosystem-based modeling framework in the Hexi Region (HXR) to construct separate SOC prediction models for different subsystems, aiming to investigate the spatiotemporal dynamics of SOC stocks and identify their key driving factors across MODS. The results show that incorporating remote sensing (RS) variables significantly improved model performance, increasing the mean R 2 from 0.51 to 0.58. Among the tested models, XGBoost achieved the best performance, providing an effective tool for SOC mapping in arid regions. Shapley additive explanations (SHAP) analysis of the best-performing XGBoost models identified distinct leading predictors across the MODS, including surface shortwave radiation (SRAD) in mountain and desert ecosystems and the difference vegetation index (DVI) in oasis ecosystems. Furthermore, the partial least squares path model (PLS-PM) revealed contrasting influences of climate change and human activities across ecological subsystems. Climatic factors exerted the strongest influence in the mountainous subsystem, whereas in the oasis subsystem intensive human management became the dominant driver of soil carbon cycling. From 2001 to 2023, SOC stocks in oasis regions increased significantly at a rate of 0.17 t C ha⁻ 1 year⁻ 1 , potentially associated with land-use change and agricultural management. Approximately 19.3% of the study area experienced significant SOC stocks declines. By resolving subsystem-specific SOC patterns and environmental associations, this study advances ecosystem-aware SOC assessment in arid regions. The resulting framework also provides a basis for differentiated carbon management across MODS.","山地-绿洲-荒漠系统（MODS）是干旱区地貌格局与水文过程相互作用形成的独特景观格局。然而，大多数土壤有机碳（SOC）制图研究将干旱区景观视为均质单元，关于MODS各子系统间SOC格局及其环境关联差异的定量证据仍然有限。作为对气候变化和人类活动高度敏感的关键生态系统，理解这些响应对于区域土壤碳管理和生态恢复至关重要。本研究在河西地区（HXR）构建了基于生态系统的建模框架，针对不同子系统分别建立SOC预测模型，旨在探究MODS中SOC储量的时空动态并识别其关键驱动因子。结果表明，引入遥感（RS）变量显著提升了模型性能，平均R²从0.51提高至0.58。在测试的模型中，XGBoost表现最佳，为干旱区SOC制图提供了有效工具。对最优XGBoost模型的SHAP（Shapley加法解释）分析识别出MODS各子系统的不同主导预测因子，其中山地和荒漠生态系统为地表短波辐射（SRAD），绿洲生态系统为差值植被指数（DVI）。此外，偏最小二乘路径模型（PLS-PM）揭示了气候变化和人类活动在各生态子系统中的差异化影响。气候因子在山地子系统中影响最强，而在绿洲子系统中，集约化人类管理成为土壤碳循环的主导驱动因素。2001年至2023年，绿洲区域SOC储量以0.17 t C ha⁻¹ year⁻¹的速率显著增加，这可能与土地利用变化和农业管理有关。约19.3%的研究区域经历了SOC储量的显著下降。通过解析子系统特定的SOC格局及其环境关联，本研究推进了干旱区生态系统感知型SOC评估，所构建的框架也为MODS差异化碳管理提供了依据。","Carbon Balance and Management","2026-10-03T00:00:00Z",79,{"impact":154,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":265},"基于XGBoost与SHAP的干旱区SOC分异驱动研究，方法新颖、数据翔实，对区域碳管理与生态修复有参考价值。",[267],{"name":261,"url":258},[269,27,270,28,271],"机器学习","生态修复","干旱区农业",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[274,278],{"code":79,"slug":275,"name":276,"path":277,"parent_code":44},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":279,"slug":280,"name":281,"path":282,"parent_code":283},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":285},{"candidates":286,"agricultural_evidence":53},[287,291,297,302,306],{"code":40,"score":52,"supported":53,"title":288,"abstract":289,"tags":290},[41],[56,41],[27],{"code":79,"score":13,"supported":53,"title":292,"abstract":293,"tags":296},[],[294,83,295],"气候变化","climate change",[271],{"code":279,"score":21,"supported":53,"title":298,"abstract":300,"tags":301},[299],"machine learning",[],[269],{"code":71,"score":72,"supported":53,"title":303,"abstract":304,"tags":305},[],[75,76],[28],{"code":307,"score":22,"supported":14,"title":308,"abstract":309,"tags":310},"H6",[],[],[270],[312,313],"河西地区 土壤有机碳 机器学习","山地-绿洲-荒漠 遥感 碳储量","河西地区土壤有机碳机器学习-4172","10.1186\u002Fs13021-026-00522-5",{"doi":315,"openalex_id":317,"authors":318,"venue":261,"cited_by_count":128,"oa_url":258,"card":338,"direction":342,"ingested_from":137},"W7219075005",[319,321,324,326,329,331,334,336],{"name":320,"orcid":9},"Y. Chen",{"name":322,"orcid":323},"Haiyang Xi","https:\u002F\u002Forcid.org\u002F0000-0003-3158-8161",{"name":325,"orcid":9},"Meng Zhu",{"name":327,"orcid":328},"Wenju Cheng","https:\u002F\u002Forcid.org\u002F0000-0002-1822-5668",{"name":330,"orcid":9},"Yulian Hao",{"name":332,"orcid":333},"Linbo Qu","https:\u002F\u002Forcid.org\u002F0009-0007-4669-8407",{"name":335,"orcid":9},"Zhehui Wang",{"name":337,"orcid":9},"Bin Wang",{"tldr":339,"method":340,"finding":341,"direction":342,"opportunity":343},"构建基于生态系统的机器学习框架，预测西北荒漠-绿洲-山地系统土壤有机碳并识别各子系统驱动因子。","XGBoost、SHAP、PLS-PM，结合遥感变量与2001-2023年多源数","遥感变量提升模型R²至0.58；山地和沙漠由辐射主导，绿洲由人类管理主导，绿洲碳储量年增0.17 t","农业遥感与作物表型","可延伸研究绿洲农业管理措施对土壤碳的长期影响，并开发子系统自适应的碳监测与决策模型。","2026-10-04T23:30:24.631460Z",{"id":346,"title":347,"url":348,"summary":349,"summary_zh":350,"content":9,"source_name":351,"source_url":348,"published_at":352,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":353,"score_detail":354,"sources":357,"tags":359,"primary_topic":363,"secondary_topics":368,"classification":374,"search_phrases":394,"slug":397,"view_count":89,"doi":398,"paper":399,"created_at":421},3792,"Estimation of soil organic carbon under rice-fallow system using Sentinel-2 derivatives","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.11182","The evaluation of soil properties under a century-old traditional rice-fallow practice in highly fragile topography (i.e., hill slopes and lowlands) of North-east (NE) India in the Eastern Himalaya faces multiple challenges in conventional studies of soil properties. Alternatively, remote sensing offers a viable solution. An attempt was made to study the soil organic carbon (SOC) under century old traditional rice-fallow practice in highly fragile topography (i.e., hill slopes and lowlands) of Bhoirymbong, Meghalaya, NE India in Eastern Himalaya using remote sensing indices derived from Sentinel-2. The objective was to develop a suitable regression model for rice-fallow systems across different hill slopes in northeastern India in the Eastern Himalaya. A total of 100 composite soil samples from the 0–15 cm depth were collected from rice-fallow areas on various slopes (nearly level: 0–3 %, gentle: 3–8 %, moderate: 8–15 %, steep: 15–30 % and very steep: > 30 %) during November 2020. Each composite sample was prepared from 10 randomly collected soil samples within a 10 × 10 m area and analysed for SOC using standard protocols in the analytical laboratory of School of Natural Resource Management, College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal. Nearly synchronised, cloud-free (\u003C 10 % cloud cover) Sentinel-2 data consistent with soil sampling period were accessed from the US Geological Survey (USGS) official website to derive 15 indices. Results showed that brightness index (BI), BI2 and the soil bareness index (SBI) were the most influential indices for SOC. The highest and lowest SOC values were found in gently undulating topography (2–5 % slope) and rolling topography (10–15 % slope), at 2.86 % and 1.17 %, respectively. After rice crop harvesting, the red-NIR-based indices did not show a significant difference, whereas the green-NIR-based indices were significantly different at p \u003C 0.05. Hence, it is concluded that green-NIR-based indices are the most suitable for SOC prediction in NE India in the Eastern Himalaya.","在印度东北部东喜马拉雅地区高度脆弱的地形（即山坡和低地）下，对百年传统水稻休耕实践下的土壤性质进行评估，在常规土壤性质研究中面临多重挑战。遥感则提供了一种可行的替代方案。本研究尝试利用Sentinel-2衍生的遥感指数，研究印度东北部东喜马拉雅地区梅加拉亚邦Bhoirymbong在高度脆弱地形（即山坡和低地）下百年传统水稻休耕实践中的土壤有机碳（SOC）。目标是建立一个适用于东喜马拉雅地区印度东北部不同山坡水稻休耕系统的合适回归模型。2020年11月，在不同坡度（近水平：0–3%、缓坡：3–8%、中坡：8–15%、陡坡：15–30%和极陡坡：>30%）的水稻休耕区采集了100个0–15 cm深度的复合土壤样品。每个复合样品由10 × 10 m区域内随机采集的10个土壤样品制备而成，并在Central Agricultural University, Imphal农业科学研究生院自然资源管理学院分析实验室采用标准方法分析SOC。从美国地质调查局（USGS）官方网站获取与土壤采样期基本同步、无云（云量\u003C10%）的Sentinel-2数据，以衍生15个指数。结果表明，亮度指数（BI）、BI2和土壤裸露指数（SBI）是对SOC影响最大的指数。SOC最高值和最低值分别出现在缓起伏地形（2–5%坡度）和波状地形（10–15%坡度），分别为2.86%和1.17%。水稻收获后，基于红-近红外的指数未显示出显著差异，而基于绿-近红外的指数在p \u003C 0.05水平上差异显著。因此，得出结论：基于绿-近红外的指数最适合东喜马拉雅地区印度东北部的SOC预测。","Plant Science Today","2026-09-28T00:00:00Z",69,{"impact":17,"substance":153,"depth":154,"authority":355,"freshness":217,"relevant":22,"comment":356},13,"基于Sentinel-2遥感指数估算稻作休耕系统土壤有机碳，方法有新意但属区域案例研究，对国内智慧农业遥感应用有参考价值。",[358],{"name":351,"url":348},[360,361,27,28,362],"数字农业","Sentinel-2","水稻休耕",{"code":364,"slug":365,"name":366,"path":367,"parent_code":171},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice",[369,370],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":71,"slug":371,"name":372,"path":373,"parent_code":283},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":46,"locked":14,"version":47,"method":48,"evidence":375},{"candidates":376,"agricultural_evidence":53},[377,381,385,389],{"code":40,"score":52,"supported":53,"title":378,"abstract":379,"tags":380},[41],[56,41],[27],{"code":364,"score":20,"supported":53,"title":382,"abstract":383,"tags":384},[365],[366,365],[],{"code":71,"score":217,"supported":53,"title":386,"abstract":387,"tags":388},[],[75,76],[361,28],{"code":390,"score":22,"supported":14,"title":391,"abstract":392,"tags":393},"I1",[],[],[360],[395,396],"印度东北部 水稻休耕 土壤有机碳","Sentinel-2 遥感 土壤有机碳","印度东北部水稻休耕土壤有机碳-3792","10.14719\u002Fpst.11182",{"doi":398,"openalex_id":400,"authors":401,"venue":351,"cited_by_count":128,"oa_url":348,"card":416,"direction":342,"ingested_from":137},"W7214617000",[402,405,407,410,413],{"name":403,"orcid":404},"Janaki Singh Naorem","https:\u002F\u002Forcid.org\u002F0000-0001-9534-0339",{"name":406,"orcid":9},"D Priya",{"name":408,"orcid":409},"Deepak Kumar Dwivedi","https:\u002F\u002Forcid.org\u002F0009-0003-5408-887X",{"name":411,"orcid":412},"J S Rajkumar","https:\u002F\u002Forcid.org\u002F0009-0003-7863-277X",{"name":414,"orcid":415},"Tamang Basant","https:\u002F\u002Forcid.org\u002F0000-0001-6856-7298",{"tldr":417,"method":418,"finding":419,"direction":342,"opportunity":420},"用Sentinel-2指数估算印度东北部稻作休耕系统不同坡度的土壤有机碳。","采集100个0-15cm土样，结合Sentinel-2的15个遥感指数建立回归模","绿光-近红外指数最适合预测SOC，缓坡SOC最高（2.86%），陡坡最低（1.17%）。","可探索多时相Sentinel-2与地形因子耦合，提升复杂地形区SOC制图精度与普适性。","2026-09-29T23:30:28.690009Z",{"id":423,"title":424,"url":425,"summary":426,"summary_zh":427,"content":9,"source_name":428,"source_url":425,"published_at":429,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":263,"score_detail":430,"sources":432,"tags":434,"primary_topic":439,"secondary_topics":440,"classification":441,"search_phrases":461,"slug":464,"view_count":128,"doi":465,"paper":466,"created_at":498},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",{"impact":154,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":431},"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。",[433],{"name":428,"url":425},[28,435,436,437,438],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":442},{"candidates":443,"agricultural_evidence":53},[444,448,452,457],{"code":40,"score":20,"supported":53,"title":445,"abstract":446,"tags":447},[41],[56,41],[],{"code":71,"score":72,"supported":53,"title":449,"abstract":450,"tags":451},[],[75,76],[28],{"code":79,"score":140,"supported":53,"title":453,"abstract":454,"tags":456},[],[455,83],"气候适应",[],{"code":307,"score":22,"supported":14,"title":458,"abstract":459,"tags":460},[],[],[437],[462,463],"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":465,"openalex_id":467,"authors":468,"venue":428,"cited_by_count":128,"oa_url":425,"card":493,"direction":342,"ingested_from":137},"W7223069171",[469,471,473,475,478,480,483,486,488,490],{"name":470,"orcid":9},"Yue Li",{"name":472,"orcid":9},"Mengfei Shi",{"name":474,"orcid":9},"Fei Zhang",{"name":476,"orcid":477},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":479,"orcid":9},"Gowhar Meraj",{"name":481,"orcid":482},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":484,"orcid":485},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":487,"orcid":9},"Yuanfang Chai",{"name":489,"orcid":9},"Hongtao Xu",{"name":491,"orcid":492},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":494,"method":495,"finding":496,"direction":342,"opportunity":497},"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。","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":500,"title":501,"url":502,"summary":503,"summary_zh":504,"content":9,"source_name":505,"source_url":502,"published_at":506,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":151,"score_detail":507,"sources":511,"tags":513,"primary_topic":517,"secondary_topics":522,"classification":525,"search_phrases":558,"slug":561,"view_count":128,"doi":562,"paper":563,"created_at":580},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",{"impact":154,"substance":508,"depth":509,"authority":355,"freshness":13,"relevant":22,"comment":510},21,17,"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。",[512],{"name":505,"url":502},[514,269,28,515,516],"智慧农业","土壤墒情","冬小麦",{"code":518,"slug":519,"name":520,"path":521,"parent_code":171},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[523,524],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":279,"slug":280,"name":281,"path":282,"parent_code":283},{"status":46,"locked":14,"version":47,"method":48,"evidence":526},{"candidates":527,"agricultural_evidence":53},[528,534,538,542,547,554],{"code":518,"score":529,"supported":53,"title":530,"abstract":532,"tags":533},26,[531,519],"winter wheat",[520,531,519],[516],{"code":40,"score":52,"supported":53,"title":535,"abstract":536,"tags":537},[41],[56,41],[515],{"code":279,"score":52,"supported":53,"title":539,"abstract":540,"tags":541},[299],[269,299],[269],{"code":71,"score":13,"supported":53,"title":543,"abstract":544,"tags":546},[],[75,76,545],"multispectral",[28],{"code":548,"score":140,"supported":53,"title":549,"abstract":550,"tags":553},"H3",[],[551,552],"灌溉","irrigation",[],{"code":390,"score":192,"supported":53,"title":555,"abstract":556,"tags":557},[],[514],[514],[559,560],"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":562,"openalex_id":564,"authors":565,"venue":505,"cited_by_count":128,"oa_url":502,"card":575,"direction":136,"ingested_from":137},"W7223156204",[566,569,571,573],{"name":567,"orcid":568},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":570,"orcid":9},"Yi Zhang",{"name":572,"orcid":9},"WEI Lei",{"name":574,"orcid":9},"Chengcai Zhang",{"tldr":576,"method":577,"finding":578,"direction":342,"opportunity":579},"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"]