[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1011":3,"related-1011":76},{"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":33,"secondary_topics":39,"classification":40,"search_phrases":71,"slug":74,"view_count":21,"doi":8,"paper":8,"created_at":75},1011,"妙手回春,酸瘦红壤变良田 浙江省农科院环土所为10万余亩田块'调理'体质 耕地质量显著改善","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7677744831895994886\u002F","浙江新一批耕地土壤改良效果评估新鲜出炉:经浙江省农科院环境资源与土壤肥料研究所'调理',近期有10万余亩田块的土壤'体质'明显增强,耕地质量显著改善。浙江人均耕地仅0.3亩,粮食产量已连续10年保持稳健向上增长,但据环土所调研,浙江部分耕地面临酸瘦、耕层浅薄、养分库容小等困境,其中丘陵盆地尤甚,中低产地占6成。近日,记者跟着环土所所长马军伟团队进入金衢盆地,从取土采样、田间诊脉到实验室检测,体验耕地'调理'全过程。改良后的土壤呈深褐色、酥松,形成水稳性团粒结构,保水保肥又透气;退化板结的对照田则像砖块、孔隙稀少、根系难以下扎。该改良技术为浙江深入实施大面积单产提升行动提供'良田'根基。",null,"潮新闻客户端 记者 来逸晨 实习生 姜景心\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F14fed140422c4be994c4198c7c4c51e2~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788480426&x-signature=6FK%2Fz4gTqh3HISA7UI7AA6YfJUc%3D)\n马军伟（左）带着我们在兰溪香溪镇的稻田间采集土样。通讯员 王雨嫣 摄\n\n浙江新一批耕地土壤改良效果评估新鲜出炉：经省农科院环境资源与土壤肥料研究所（以下简称环土所）“调理”，近期有10万余亩田块的土壤“体质”明显增强，耕地质量显著改善。\n\n浙江人均耕地仅0.3亩，粮食产量已连续10年保持稳健向上增长。为进一步挖掘增产潜力，浙江正深入实施大面积单产提升行动。提单产不仅要良种、良法、良机，更需要良田作根基。然而，据环土所调研，浙江部分耕地面临酸瘦、耕层浅薄、养分库容小等困境，其中丘陵盆地尤甚，中低产地占6成。\n\n助农田“强身健体”有妙招。近日，我们跟着环土所所长马军伟团队进入金衢盆地，从取土采样、田间诊脉到实验室检测，体验耕地“调理”全过程。\n\n**插管入田，给耕地“抽血”取样**\n\n驱车至金华兰溪，车窗外，山坡上的裸土地时隐时现，黄中带红。马军伟告诉我们，这就是金衢盆地典型的“低丘红壤”，“体质”可用三个字形容：酸、瘦、黏。\n\n车停在香溪镇的千亩科技示范区，5年来，这里分两批开展了土壤改良。我们此行的目的，是采集土样、回访改良成效、为剩余未改良的零碎田块“查漏补缺”。\n\n沿着田埂往深处走，马军伟忽然停下脚步，让我们对比两处地块：未培肥的对照田在一处曾经被遗忘的转角，不足半亩，土表呈暗棕色，踩上去不塌陷；田埂另一侧则是连片改良后的示范田，土壤呈深褐色，酥松不少。\n\n“用手感受下。”马军伟招呼道。我们蹲下，手指拂过对照田的土壤，像碰到砂纸一样，硬邦邦的；而捏一把改良后的褐土，轻轻一攥，土粒聚团，手掌张开，它们又自然散落。“这就是水稳性团粒结构”，马军伟解释，优质耕地的土壤团粒就像米花糖，疏松多孔且有韧性，保水保肥又透气；退化板结的土壤则像砖块，孔隙稀少、根系难以下扎。\n\n对比完土质，马军伟给我们递来一套不锈钢取土器和橡胶锤：“分别取样。”\n\n除去杂草，他示意我们一人扶着取土器垂直立于田面，一人拿着橡胶锤瞄准顶端一下下敲击。\n\n5厘米、10厘米、15厘米……取土器连着的金属管像一根针管，慢慢扎入大地的“肌肤”。\n\n“停。”他一声令下，我们立刻停止敲击，合力将取土器垂直拔出。只见一截完整的“土柱”嵌在管内。\n\n“水田土壤采样一般取地表向下0至20厘米的耕作层，它是水稻根系分布最密集、物质交换最活跃的层位，也是土壤调理的目标区域。” 马军伟介绍。在示范田和对照田分别取了样，我们感叹：“整个过程，和人体抽血相似。”\n\n“这个比喻很贴切。”马军伟笑着点头。他带我们轻敲取土器外壁，将土样完整剥离，逐一装入透明样品袋中，标记、封存。和做“血常规”一样，它们将被送进实验室，开展多项检测，为土壤质量精准“画像”。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F3419c8d765cf5b5e34d88ad2f12a0bcd~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1788480426&x-signature=BUE8t%2F1AidcPYWz23kOpXm4fhWo%3D)\n浙江省环土所的工作人员正在检测土壤。\n\n**辨证施治，三剂良方优“体质”**\n\n“马老师，来回访啦！”烈日下，一位皮肤黝黑的汉子自田间走来。他是这片耕地的生产负责人项力通。\n\n他告诉我们，这片土地近年经历了全域土地综合整治，不少区域由建设用地复垦，或是由弃耕失管的碎片化田块重整成片。头年试种，单季稻亩产仅200至300公斤。\n\n“多亏马老师团队‘妙手回春’，土壤改良的第一年，亩产大幅提升，第二年又涨了100公斤！”项力通竖起大拇指。\n\n我们暗自赞叹，同时发出疑问：“底子差的红壤，究竟是如何快速蜕变成良田的？”\n\n团队的王峰博士拎着3个麻袋前来揭秘。马军伟蹲下来，把手伸进1号袋子，掏出一把浅褐色的细颗粒，在掌心摊开：“这是土壤调理剂，内含碱性矿物质，能中和酸度，给土壤‘降火气’。”2号袋子里是深褐色颗粒，凑近能闻到淡淡的腐殖质气息。“这个专用肥能促成水稳性团粒结构，让黏重的土质变得疏松透气。”3号袋子里的颗粒颜色更深，褐中带黑。“这是生物有机肥，补充有机质的同时引入功能菌群，有益菌能分解有机质、释放作物可吸收的速效养分，还能调控土壤微生态、抑制土传病害。”\n\n三个“方子”，三种功效，组成了全套一体化改良方案。马军伟像一位“老中医”，为我们讲解他的开方法则：“土壤调理不是随便撒点石灰、施点肥，只求一时的表面改善。它就像给人调理身体，得先辨明病根，再搭配药方综合施策，以求长效提升。”\n\n随后，项力通带我们来到附近农户的一小块自留地。“我们测过，有机质含量不足2%，低于高标准农田2.6%的基准线。”他对马军伟说，农户也想试试土壤调理。\n\n“先用2号袋子里的专用肥，一周后再用3号袋子里的‘药’。”马军伟迅速“开方”，然后带我们背起撒肥机、手持喷管，将2号袋子里的深褐色颗粒撒向田间。项力通则喊来农户，启动微耕机跟在我们身后，翻动土层，将它们悉数“喂”入土里。\n\n不一会儿，我们就热得满头大汗。我们揩掉脸上的汗水，望着脚下的土地憧憬着：作物根系扎进这样的土里，定能吃得好、喝得饱、长得壮。\n\n**三年蜕变，“体检报告”更健康**\n\n一周后，我们来到位于杭州的省农科院农业装备检测中心环境资源检测室。从兰溪田里取来的土样，已经在这里风干了。\n\n进门后，检测人员首先递来一份“检测清单”——pH值、有机质、全氮、全钾、全磷、有效磷、速效钾、碱解氮。\n\n王峰为我们详解：有机质是土壤肥力的核心基础，决定着土壤的结构稳定性和保肥保水能力；全氮、全磷、全钾是土壤养分的“总储备库”；碱解氮、有效磷、速效钾则是当季作物可直接吸收利用的“速效养分”，对应土壤的即时供肥水平。\n\n走进风干室，只见层层架子上摆满了各式土样。白色托盘垫着牛皮纸，土样被均匀平铺其上，经过自然阴干后变得干燥松散。\n\n“这就是我们从兰溪取来的土样，只剩最后一项有机质含量待测定，一起见证结果吧。”王峰带着我们来到通风橱前。我们跟着他一道戴上口罩和手套，将风干的土块放在研钵里细细研磨一遍，再过两道筛：2毫米大孔径和0.25毫米小孔径筛。\n\n我们将研磨好的土样装入透明的样品袋后，实验人员精密称取0.5克制备好的土样倒入玻璃试管，在高温油浴环境下，用定量的硫酸氧化土壤中的有机碳，再通过一系列换算得出有机质含量。\n\n结果出来了：未改良土壤有机质含量仅13.4g\u002Fkg，改良后的土壤有机质含量达31g\u002Fkg。“经过3年回访，我们可以确定，兰溪香溪镇示范田块的土壤调理成功了。”马军伟欣喜地宣布。\n\n曾经，那里酸化面积占比超55%，有机质平均仅9.5g\u002Fkg，耕地质量等级7.5等（共10等，1等最优）；三年系统改良后，有机质稳定在30g\u002Fkg以上，等级提升至2等及以上，达到优质耕地水平。\n\n目前，环土所已在全省11个设区市累计成功改良了50万余亩田块的土质，这套土壤调理技术正在浙江多地示范推广，从先行示范的试验田走向更多亟待“强身健体”的中低产田。此外，这套技术还走出浙江，在贵州、云南、广西、四川等地得以应用。\n\n“转载请注明出处”","潮新闻 2026-08-25","2026-08-25T00:00:00Z","报道",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},18,20,15,7,1,"报道浙江省农科院土壤改良技术成效，涉及农业科技与耕地质量提升，有具体数据和案例，但属省级科研进展，影响范围有限。",[24],{"name":10,"url":6},[26,27,28,29,30,31,32],"浙江省农科院","红壤改良","环土所","耕地质量","金衢盆地","马军伟","水稳性团粒结构",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":41,"locked":14,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":48},[46,53,60,66],{"code":34,"score":47,"supported":48,"title":49,"abstract":50,"tags":52},4,true,[],[51],"土壤",[29],{"code":54,"score":55,"supported":48,"title":56,"abstract":57,"tags":59},"A8",3,[],[58],"单产提升",[],{"code":61,"score":55,"supported":48,"title":62,"abstract":63,"tags":65},"F3",[],[64],"肥料",[],{"code":67,"score":21,"supported":14,"title":68,"abstract":69,"tags":70},"H1",[],[],[29],[72,73],"水稳性团粒结构 浙江省农科院 红壤改良 耕地质量","水稳性团粒结构 浙江省农科院","水稳性团粒结构浙江省农科院红壤改良耕地质量-1011","2026-08-28T00:06:59.968752Z",{"total":77,"page":21,"page_size":77,"items":78},6,[79,134,183,253,340,423],{"id":80,"title":81,"url":82,"summary":83,"summary_zh":8,"content":84,"source_name":85,"source_url":8,"published_at":86,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":87,"score_detail":88,"sources":93,"tags":95,"primary_topic":100,"secondary_topics":101,"classification":102,"search_phrases":128,"slug":131,"view_count":132,"doi":8,"paper":8,"created_at":133},4943,"江苏农科院资环所技术支撑南京耕地质量提升'四新'项目观摩会召开","https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fxww\u002Fgzdt\u002Fart\u002F2026\u002Fart_76bbacc4963f497da5785a2c05accb7e.html","10月10日,由江苏省农科院资环所技术支撑的南京市耕地质量提升'四新'项目观摩会召开。针对南京地区耕地普遍存在的土壤有机质消耗快、新增耕地有机质含量低、耕层浅薄等瓶颈,通过科学运筹稻麦周年有机物料投入的配比与季节施用量,实现稻麦周年土壤有机质快速提升6.64%,矿物结合态有机碳含量提升15.1%。利用土壤有机质含量变化数据结合多源环境协变量,借助机器学习构建有机质变化评估预测模型。","资环所技术支撑的南京市耕地质量提升“四新”技术集成展示项目现场观摩会顺利召开\n\n文章来源：资环所 发布时间：2026-10-13 18:27 阅读：\n\n10月10日，由江苏省农科院资环所提供技术支撑的南京市耕地质量提升“新产品、新技术、新模式、新装备”集成展示项目（以下简称“四新”项目）现场观摩会顺利召开。全国农业技术推广服务中心研究员汤松、江苏省耕地质量与农业环境保护站副站长张莹、江苏省农业技术推广中心研究员邓建平、南京市农业农村局副局长张震宇、市农业农村局耕地质量处处长左洪林，以及各区农技推广人员、肥料企业代表等共计 60 人参会。会议由南京市耕地质量保护站站长刘胜环主持。\n\n观摩现场，项目技术负责人高岩研究员重点介绍了项目核心技术及其阶段性成效。针对南京地区耕地普遍存在的土壤有机质消耗速率快、新增耕地有机质含量低、耕层浅薄、物理结构差等瓶颈问题，通过科学运筹稻麦周年有机物料投入的配比与季节施用量，结合秸秆快速腐解-适深还田与易氧化有机质长效固持技术，实现稻麦周年土壤有机质快速提升6.64%，矿物结合态有机碳含量提升15.1%。同时，利用土壤有机质含量变化数据结合多源环境协变量，借助机器学习构建了有机质变化评估预测模型；应用光谱技术结合NDRE反演模型开展稻麦季氮肥精准调控，实现田块尺度减量施氮，氮肥偏生产力提升 6%-15%，作物产量同步提高 4%-13%。\n\n南京宁粮环保集团、江苏思农高科技、南京轩凯生物科技、江苏艾萨斯新型肥料工程技术有限公司等合作企业现场展示了高碳有机肥、生物有机肥、秸秆腐解剂、聚谷氨酸类水溶肥、聚合物包膜控释肥等新型肥料产品，为“四新”项目提供了有力支撑。\n\n与会专家充分肯定了项目在中低产田土壤改良方面取得的技术成效，建议总结形成便于农户操作的技术模式和实操手册，加快新技术、新产品推广应用。观摩结束后，会议继续开展技术培训，张震宇副局长部署耕地质量保护相关工作，刘胜环站长作开班动员，全国农业技术推广服务中心汤松研究员、省农业技术推广中心邓建平研究员分别作专题授课。\n\n[![Image 1: 1013.jpg](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002FS569a5bc91a1a4af09168bea31bb9cfc1-750.jpg)](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002F569a5bc91a1a4af09168bea31bb9cfc1.jpg)[](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fxww\u002Fgzdt\u002Fart\u002F2026\u002Fart_76bbacc4963f497da5785a2c05accb7e.html)\n\n[![Image 2: 101301.jpg](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002FS2226cc0a132d4a5088eaa9e3a4a01d41-750.jpg)](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002F2226cc0a132d4a5088eaa9e3a4a01d41.jpg)[](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fxww\u002Fgzdt\u002Fart\u002F2026\u002Fart_76bbacc4963f497da5785a2c05accb7e.html)\n\n[![Image 3: 101302.jpg](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002FSc84ed0ebddce4c9a93c9f8b6aa617296-750.jpg)](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002Fc84ed0ebddce4c9a93c9f8b6aa617296.jpg)[](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fxww\u002Fgzdt\u002Fart\u002F2026\u002Fart_76bbacc4963f497da5785a2c05accb7e.html)\n\n[![Image 4: 101303.jpg](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002FSfc8b07abf4c843c68cbc57aefe18234c-750.jpg)](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fcms_files\u002Ffilemanager\u002F1415708408\u002Fpicture\u002F20268\u002Ffc8b07abf4c843c68cbc57aefe18234c.jpg)[](https:\u002F\u002Fnewzhs.jaas.ac.cn\u002Fxww\u002Fgzdt\u002Fart\u002F2026\u002Fart_76bbacc4963f497da5785a2c05accb7e.html)","江苏省农业科学院农业资源与环境研究所 2026 年 10 月 13 日","2026-10-13T00:00:00Z",69,{"impact":89,"substance":18,"depth":19,"authority":90,"freshness":91,"relevant":21,"comment":92},14,12,8,"省级科研机构支撑的地市级耕地质量提升项目观摩会，含有机质提升6.64%、氮肥偏生产力提升6%-15%等实测数据与多方信源，专业增量较足，但属区域性技术推广动态，公共影响有限。",[94],{"name":85,"url":82},[96,97,29,98,99],"精准施肥","秸秆还田","新型肥料","土壤有机质",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":103},{"candidates":104,"agricultural_evidence":48},[105,109,115,119,123],{"code":34,"score":47,"supported":48,"title":106,"abstract":107,"tags":108},[],[51],[29],{"code":110,"score":55,"supported":48,"title":111,"abstract":112,"tags":114},"I2",[],[113],"机器学习",[],{"code":61,"score":21,"supported":14,"title":116,"abstract":117,"tags":118},[],[],[96],{"code":67,"score":21,"supported":14,"title":120,"abstract":121,"tags":122},[],[],[29],{"code":124,"score":21,"supported":14,"title":125,"abstract":126,"tags":127},"H5",[],[],[97],[129,130],"南京 耕地质量提升 四新项目","江苏农科院 资环所 有机质提升","南京耕地质量提升四新项目-4943",0,"2026-10-15T00:06:02.313598Z",{"id":135,"title":136,"url":137,"summary":138,"summary_zh":8,"content":139,"source_name":140,"source_url":8,"published_at":141,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":142,"score_detail":143,"sources":146,"tags":148,"primary_topic":153,"secondary_topics":154,"classification":155,"search_phrases":178,"slug":181,"view_count":55,"doi":8,"paper":8,"created_at":182},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)| [数字农科院](https:\u002F\u002Fi.caas.cn\u002F)| 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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":144,"substance":17,"depth":89,"authority":89,"freshness":91,"relevant":21,"comment":145},22,"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。",[147],{"name":140,"url":137},[149,150,151,152,29],"黄三角农高区","农业科技创新","院地合作","盐碱地治理",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":156},{"candidates":157,"agricultural_evidence":48},[158,164,169,173],{"code":34,"score":159,"supported":48,"title":160,"abstract":162,"tags":163},13,[161],"盐碱地",[161],[152,29],{"code":165,"score":21,"supported":14,"title":166,"abstract":167,"tags":168},"G1",[],[],[151],{"code":67,"score":21,"supported":14,"title":170,"abstract":171,"tags":172},[],[],[29],{"code":174,"score":21,"supported":14,"title":175,"abstract":176,"tags":177},"J2",[],[],[150],[179,180],"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":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":8,"source_name":189,"source_url":186,"published_at":190,"category":191,"cover_url":8,"hotness":13,"is_selected":14,"score":192,"score_detail":193,"sources":197,"tags":199,"primary_topic":205,"secondary_topics":206,"classification":212,"search_phrases":231,"slug":234,"view_count":132,"doi":235,"paper":236,"created_at":252},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":91,"substance":18,"depth":194,"authority":195,"freshness":13,"relevant":21,"comment":196},17,9,"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。",[198],{"name":189,"url":186},[200,201,202,203,204],"智慧农业","农业人工智能","主动学习","线虫检测","土壤采样",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[207],{"code":110,"slug":208,"name":209,"path":210,"parent_code":211},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":41,"locked":14,"version":42,"method":43,"evidence":213},{"candidates":214,"agricultural_evidence":48},[215,219,224],{"code":34,"score":89,"supported":48,"title":216,"abstract":217,"tags":218},[35],[51,35],[],{"code":110,"score":195,"supported":48,"title":220,"abstract":222,"tags":223},[221],"artificial intelligence",[],[201],{"code":225,"score":20,"supported":48,"title":226,"abstract":227,"tags":230},"I1",[],[228,229],"精准农业","precision agriculture",[200],[232,233],"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":235,"openalex_id":237,"authors":238,"venue":189,"cited_by_count":132,"oa_url":186,"card":245,"direction":249,"ingested_from":251},"W7223082195",[239,241,243],{"name":240,"orcid":8},"Amine Ben Salah",{"name":242,"orcid":8},"Ines Trabelsi",{"name":244,"orcid":8},"Mohamed Aziz Khelifi",{"tldr":246,"method":247,"finding":248,"direction":249,"opportunity":250},"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":254,"title":255,"url":256,"summary":257,"summary_zh":258,"content":8,"source_name":259,"source_url":256,"published_at":260,"category":191,"cover_url":8,"hotness":13,"is_selected":14,"score":261,"score_detail":262,"sources":265,"tags":267,"primary_topic":273,"secondary_topics":274,"classification":275,"search_phrases":301,"slug":304,"view_count":132,"doi":305,"paper":306,"created_at":339},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":263,"substance":144,"depth":17,"authority":89,"freshness":195,"relevant":21,"comment":264},16,"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。",[266],{"name":259,"url":256},[268,269,270,271,272],"遥感监测","莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":276},{"candidates":277,"agricultural_evidence":48},[278,282,289,296],{"code":34,"score":89,"supported":48,"title":279,"abstract":280,"tags":281},[35],[51,35],[],{"code":283,"score":20,"supported":48,"title":284,"abstract":285,"tags":288},"I3",[],[286,287],"遥感","remote sensing",[268],{"code":290,"score":77,"supported":48,"title":291,"abstract":292,"tags":295},"H7",[],[293,294],"气候适应","干旱",[],{"code":297,"score":21,"supported":14,"title":298,"abstract":299,"tags":300},"H6",[],[],[271],[302,303],"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":305,"openalex_id":307,"authors":308,"venue":259,"cited_by_count":132,"oa_url":256,"card":333,"direction":337,"ingested_from":251},"W7223069171",[309,311,313,315,318,320,323,326,328,330],{"name":310,"orcid":8},"Yue Li",{"name":312,"orcid":8},"Mengfei Shi",{"name":314,"orcid":8},"Fei Zhang",{"name":316,"orcid":317},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":319,"orcid":8},"Gowhar Meraj",{"name":321,"orcid":322},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":324,"orcid":325},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":327,"orcid":8},"Yuanfang Chai",{"name":329,"orcid":8},"Hongtao Xu",{"name":331,"orcid":332},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":334,"method":335,"finding":336,"direction":337,"opportunity":338},"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":341,"title":342,"url":343,"summary":344,"summary_zh":345,"content":8,"source_name":346,"source_url":343,"published_at":190,"category":191,"cover_url":8,"hotness":13,"is_selected":14,"score":347,"score_detail":348,"sources":351,"tags":353,"primary_topic":356,"secondary_topics":362,"classification":365,"search_phrases":399,"slug":402,"view_count":132,"doi":403,"paper":404,"created_at":422},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":263,"substance":349,"depth":194,"authority":159,"freshness":13,"relevant":21,"comment":350},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。",[352],{"name":346,"url":343},[200,113,268,354,355],"土壤墒情","冬小麦",{"code":357,"slug":358,"name":359,"path":360,"parent_code":361},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[363,364],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"code":110,"slug":208,"name":209,"path":210,"parent_code":211},{"status":41,"locked":14,"version":42,"method":43,"evidence":366},{"candidates":367,"agricultural_evidence":48},[368,374,378,383,388,395],{"code":357,"score":369,"supported":48,"title":370,"abstract":372,"tags":373},26,[371,358],"winter wheat",[359,371,358],[355],{"code":34,"score":19,"supported":48,"title":375,"abstract":376,"tags":377},[35],[51,35],[354],{"code":110,"score":19,"supported":48,"title":379,"abstract":381,"tags":382},[380],"machine learning",[113,380],[113],{"code":283,"score":13,"supported":48,"title":384,"abstract":385,"tags":387},[],[286,287,386],"multispectral",[268],{"code":389,"score":77,"supported":48,"title":390,"abstract":391,"tags":394},"H3",[],[392,393],"灌溉","irrigation",[],{"code":225,"score":47,"supported":48,"title":396,"abstract":397,"tags":398},[],[200],[200],[400,401],"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":403,"openalex_id":405,"authors":406,"venue":346,"cited_by_count":132,"oa_url":343,"card":416,"direction":421,"ingested_from":251},"W7223156204",[407,410,412,414],{"name":408,"orcid":409},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":411,"orcid":8},"Yi Zhang",{"name":413,"orcid":8},"WEI Lei",{"name":415,"orcid":8},"Chengcai Zhang",{"tldr":417,"method":418,"finding":419,"direction":337,"opportunity":420},"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",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":428,"content":8,"source_name":429,"source_url":426,"published_at":190,"category":191,"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":132,"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。","Frontiers in Sustainable Food Systems",80,{"impact":17,"substance":144,"depth":17,"authority":159,"freshness":195,"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":429,"url":426},[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":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":444},{"candidates":445,"agricultural_evidence":48},[446,450,455,459],{"code":34,"score":77,"supported":48,"title":447,"abstract":448,"tags":449},[],[51,35],[],{"code":451,"score":47,"supported":48,"title":452,"abstract":453,"tags":454},"H4",[],[439],[439],{"code":290,"score":47,"supported":48,"title":456,"abstract":457,"tags":458},[],[293],[437],{"code":110,"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":429,"cited_by_count":132,"oa_url":426,"card":486,"direction":490,"ingested_from":251},"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"]