[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4445":3,"related-4445":124},{"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":77,"slug":80,"view_count":22,"doi":81,"paper":82,"created_at":123},4445,"Soil intrinsic penetration (SIP) index: decoupling environmental noise from mechanical resistance to improve soybean yield prediction","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75118-7","Soil compaction is a critical global agricultural concern, acting as a severe physical barrier that restricts root system expansion and reduces crop productivity. However, conventional Soil Penetration Resistance (PR) is a transient metric dictated by instantaneous hydraulic states rather than a stable structural property, leading to misleading interpretations in precision agriculture. This study aimed to develop and validate the Moisture-Normalized Soil Intrinsic Penetration (SIP) Index to isolate persistent anthropogenic structural degradation from environmental noise. Data were compiled from five field campaigns (2020–2025) conducted on a Typic Hapludox in Chapadão do Sul, Brazil. Three distinct sensing technologies: electronic (reference); hydraulic; and impact penetrometers, were evaluated across four depth intervals (0.0–0.4 m). The SIP Index was derived using a power-law normalization approach (SIP = PR x SW α x Clay β ), with empirical coefficients optimized via a Differential Evolution algorithm to maximize Spearman correlations with soybean yield. Results consistently identified the 0.10–0.20 m layer as the critical functional compaction barrier (traffic pan) across all sensing systems. While raw PR from continuous-insertion sensors (electronic and hydraulic) showed negligible or counter-intuitive yield correlations (ρ ≈ -0.01 to 0.08), the SIP Index drastically enhanced predictive capacity, reaching ρ = -0.39 for the electronic sensor, representing a substantial shift from the negligible associations observed with raw data, and ρ = -0.41 for the hydraulic system. Impact penetrometers exhibited higher inherent stability against moisture variations, rendering the SIP correction less significant for that device. Ultimately, the SIP framework effectively filters out pedogenic and hydraulic noise, providing a robust anthropogenic signature for delineating management zones and identifying true mechanical constraints to crop performance.","土壤压实是全球农业面临的严峻问题，作为一种严重的物理障碍，限制根系扩展并降低作物生产力。然而，传统的土壤穿透阻力（PR）是一种瞬时指标，受即时水分状态支配，而非稳定的结构属性，这导致在精准农业中产生误导性解读。本研究旨在开发并验证水分归一化土壤固有穿透（SIP）指数，以将持久性人为结构退化与环境噪声分离。数据来源于2020至2025年间在巴西沙帕当杜苏尔一处典型强淋溶老成土上开展的五次田间试验。评估了三种不同的传感技术：电子（参考）、液压和冲击式穿透仪，涵盖四个深度区间（0.0–0.4 m）。SIP指数采用幂律归一化方法（SIP = PR × SW^α × Clay^β）推导，经验系数通过差分进化算法优化，以最大化与大豆产量的Spearman相关性。结果一致表明，在所有传感系统中，0.10–0.20 m土层均为关键功能性压实障碍（机械压实层）。连续插入式传感器（电子和液压）的原始PR与产量相关性可忽略或呈反直觉关系（ρ ≈ -0.01至0.08），而SIP指数大幅提升了预测能力，电子传感器达到ρ = -0.39，相较于原始数据的可忽略关联实现了显著转变，液压系统达到ρ = -0.41。冲击式穿透仪对水分变化表现出更高的固有稳定性，因此SIP校正对该设备意义较小。最终，SIP框架有效滤除了成土和水分噪声，为划定管理分区和识别作物生长的真实机械限制提供了稳健的人为信号。",null,"Scientific Reports","2026-10-06T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,22,18,13,8,1,"提出水分归一化土壤内在穿透指数，用三种传感技术跨五年田间数据验证，显著提升大豆产量预测能力，方法新颖且对精准农业管理分区有实用价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","精准农业","土壤传感","土壤压实","大豆产量",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[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},[51,59,64,70],{"code":33,"score":52,"supported":53,"title":54,"abstract":56,"tags":58},14,true,[55],"soybean",[57,55],"大豆",[],{"code":40,"score":52,"supported":53,"title":60,"abstract":61,"tags":63},[41],[62,41],"土壤",[],{"code":65,"score":21,"supported":53,"title":66,"abstract":67,"tags":69},"I1",[],[28,68],"precision agriculture",[27,28],{"code":71,"score":72,"supported":53,"title":73,"abstract":74,"tags":76},"J3",3,[],[75],"全球农业",[],[78,79],"土壤穿透阻力 大豆产量","SIP 指数 土壤压实","土壤穿透阻力大豆产量-4445","10.1038\u002Fs41598-026-75118-7",{"doi":81,"openalex_id":83,"authors":84,"venue":10,"cited_by_count":113,"oa_url":114,"card":115,"direction":121,"ingested_from":122},"W7220671604",[85,88,90,93,96,99,102,105,107,110],{"name":86,"orcid":87},"Fábio Henrique Rojo Baio","https:\u002F\u002Forcid.org\u002F0000-0002-9522-0342",{"name":89,"orcid":9},"Job Teixeira de Oliveira",{"name":91,"orcid":92},"Cassiano Garcia Roque","https:\u002F\u002Forcid.org\u002F0000-0001-6872-0424",{"name":94,"orcid":95},"Ricardo Gava","https:\u002F\u002Forcid.org\u002F0000-0001-6268-5728",{"name":97,"orcid":98},"Larissa Pereira Ribeiro Teodoro","https:\u002F\u002Forcid.org\u002F0000-0002-8121-0119",{"name":100,"orcid":101},"Cid Naudi Silva Campos","https:\u002F\u002Forcid.org\u002F0000-0001-6810-885X",{"name":103,"orcid":104},"Rafael Felippe Ratke","https:\u002F\u002Forcid.org\u002F0000-0001-6930-3913",{"name":106,"orcid":9},"Octávio Babosa Plaster",{"name":108,"orcid":109},"Dthenifer Cordeiro Santana","https:\u002F\u002Forcid.org\u002F0000-0001-7611-6040",{"name":111,"orcid":112},"Paulo Eduardo Teodoro","https:\u002F\u002Forcid.org\u002F0000-0002-8236-542X",0,"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75118-7_reference.pdf",{"tldr":116,"method":117,"finding":118,"direction":119,"opportunity":120},"提出土壤固有穿透指数SIP，剔除水分等环境噪声，提升大豆产量预测精度。","基于2020-2025年巴西五季田间数据，用幂律归一化与差分进化算法优化系数。","0.10-0.20米为关键压实层，SIP使电子与液压传感器产量相关性提升至-0.39和-0.41。","智慧农业 \u002F 农业物联网","可探索SIP指数在不同土壤类型与作物上的普适性，并集成到实时传感与变量作业决策中。","数字乡村与农业信息化","openalex","2026-10-08T23:30:38.497161Z",{"total":125,"page":22,"page_size":125,"items":126},6,[127,177,226,287,379,456],{"id":128,"title":129,"url":130,"summary":131,"summary_zh":9,"content":132,"source_name":133,"source_url":9,"published_at":134,"category":135,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":141,"tags":143,"primary_topic":149,"secondary_topics":150,"classification":152,"search_phrases":172,"slug":175,"view_count":113,"doi":9,"paper":9,"created_at":176},5045,"山东农科院联合育种团队选育耐盐碱耐密植大豆新品种 在河口盐碱地亩产近300公斤","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7694921927810564648","山东省重点研发计划(农业良种工程)\"耐盐碱耐密植大豆新品种选育\"项目在河口区完成田间现场测产。该项目由渤海圣丰种业牵头,联合山东农业大学、青岛农业大学、山东省农业科学院作物研究所、德州市农业科学院组建跨单位育种团队。在土壤含盐量3‰~5‰的盐碱大豆试验基地,8个参试品系群体密度突破2万株\u002F亩,青农豆密2308亩产297.86公斤、德豆10号289.60公斤。","**爱东营讯** 近日，山东省重点研发计划（农业良种工程）“耐盐碱耐密植大豆新品种选育”项目在河口区完成田间现场测产评估。该项目由渤海圣丰种业科技有限公司牵头实施，联合山东农业大学、青岛农业大学、山东省农业科学院作物研究所、德州市农业科学院组建跨单位育种创新团队，由多名正高级大豆育种专家，对自主选育的8个大豆新品种（系）开展田间产量鉴定。\n\n在土壤含盐量3‰—5‰的河口盐碱大豆试验示范基地，系统评价各参试品系在盐碱胁迫环境下的农艺性状、群体结构与产能潜力。经理论测产核算，全部参试品系群体密度突破2万株\u002F亩，部分品系单株平均粒数超70粒。其中，青农豆密2308综合抗逆与产量性能最优，亩株数达21470.85株，理论亩产297.86公斤；德豆10号表现突出，亩株数20557.20株，理论亩产289.60公斤；其他多个品系也均表现优异。专家组现场研判，本次参试的多个大豆新品系实现耐盐、耐密、高产性状协同聚合。\n\n本次田间测产验收，标志着该省级农业良种攻关项目取得关键性阶段性突破。这批筛选创制的耐盐碱、耐密植、高产稳产大豆新品种（系），可为全省乃至全国盐碱耕地大豆产能提升提供核心种质支撑，对降低我国大豆对外依存度、筑牢种业种源安全屏障具有重要战略价值。\n\n下一步，河口区将持续做好良种攻关项目全流程技术服务，推动各参与单位深化产学研协同创新，加速优良品系多点试验、示范与产业化推广，推动育种科研成果高效转化为盐碱地农业现实生产力。\n\n（记者 李萍 通讯员 施国军）","东营市新媒体中心 爱东营讯 2026年10月10日","2026-10-10T06:27:00Z","报道",71,{"impact":18,"substance":18,"depth":138,"authority":125,"freshness":139,"relevant":22,"comment":140},16,5,"省级良种攻关项目在盐碱地实现耐盐耐密高产协同突破，数据具体、战略价值明确，值得进入每日精选。",[142],{"name":133,"url":130},[144,145,146,147,148],"盐碱地利用","种业振兴","产学研协同","大豆育种","耐盐碱大豆",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[151],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":153},{"candidates":154,"agricultural_evidence":53},[155,160,165],{"code":33,"score":156,"supported":53,"title":157,"abstract":158,"tags":159},12,[57],[57],[147],{"code":40,"score":156,"supported":53,"title":161,"abstract":163,"tags":164},[162],"盐碱地",[62],[144],{"code":166,"score":167,"supported":53,"title":168,"abstract":169,"tags":171},"F1",4,[],[170],"种业",[145],[173,174],"山东农科院 耐盐碱大豆 河口","青农豆密2308 德豆10号 测产","山东农科院耐盐碱大豆河口-5045","2026-10-16T00:13:11.498967Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":9,"content":182,"source_name":183,"source_url":9,"published_at":184,"category":135,"cover_url":9,"hotness":13,"is_selected":14,"score":185,"score_detail":186,"sources":189,"tags":191,"primary_topic":196,"secondary_topics":197,"classification":199,"search_phrases":221,"slug":224,"view_count":125,"doi":9,"paper":9,"created_at":225},3290,"亩产687.5公斤！山东盐碱地花生创下高产新突破——山东省农科院选育花育9131、9135、9136三个高油酸花生新品种","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7687500267012637190\u002F","9月18日，位于省黄三角农高区的山东省农科院东营基地内传来丰收喜讯：依托优质耐盐碱品种与先进栽培技术，示范区种植的三个高油酸花生新品种实现盐碱地高产突破，单品种荚果亩产量均突破600公斤，最高亩产量达687.5公斤。这三个品种是团队在东营盐碱地上历经多年筛选出来的耐盐碱高油酸花生：花育9131、花育9135、花育9136。今年山东气候特殊，花生下果针的7月、荚果充实的8月遭遇持续干旱，可示范田里花生秧蔓虽不算高大，拔起一株根须上挂满密密实实的荚果，捏开外壳果仁饱满厚实。土壤盐分含量2.8‰、PH值7.56。","视频加载中...\n\n9月18日，位于省黄三角农高区的山东省农科院东营基地内传来丰收喜讯，依托优质耐盐碱品种与先进栽培技术，示范区种植的三个高油酸花生新品种实现盐碱地高产突破，单品种荚果亩产量均突破600公斤，最高亩产量达687.5公斤。\n\n秋风掠过的盐碱试验田，泥土裹挟着花生果实的清香扑面而来。田垄之间，测产专家、科研人员、田间工人忙中有序，丈量地块、刨秧甩果、捡拾落果、去杂称重，一场耐盐碱高油酸花生新品种的测产验收正在紧张进行。\n\n脚下这片示范田，为典型滨海盐渍化土壤，土壤盐分含量2.8‰、PH值7.56，种植条件一般。今年山东气候特殊，花生下果针的7月、荚果充实的8月遭遇持续干旱，全省不少地块花生株高受限，出现“果大仁空”现象。可眼前示范田里，花生秧蔓虽不算高大，拔起一株，根须上挂满密密实实的荚果，捏开外壳，果仁饱满厚实。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F7c75be662afd4eddbda3c60cf768a161~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790813093&x-signature=ceo9xNT5tnTa9T4D8DBWDnNp16I%3D)\n“这三个品种，是我们团队在东营盐碱地上历经多年筛选出来的耐盐碱高油酸花生，分别是花育9131、花育9135、花育9136。”国家花生产业技术体系遗传改良研究室主任、山东省花生研究所遗传育种创新团队负责人迟晓元蹲在田垄边，随手捧起一把刚刨出的花生介绍道。迟晓元2008年参加工作便投身花生育种，她们课题组从2013年起扎根滨州、东营开展花生耐盐碱筛选试验，一晃已是十余个年头。\n\n盐碱地育种，绝非一年试验就能下定论。“不同地块盐分分布不均匀，必须常年连续筛选，积累多年试验数据，反复比对，才能甄别真正耐盐碱的好品种。”迟晓元说。试验基地划分两块田地，一块是示范田，另一块筛选圃常年放置多份花生种质材料，不间断开展抗逆鉴定。材料要经过多轮考验，表现稳定后，才进入示范种植环节。\n\n本次测产验收由专业专家组全程核验，专家组由山东省种子管理总站、青岛农业大学、东营市农业综合服务中心等单位的农技推广、作物栽培、种子管理领域专家组成。 测产严格按照相关规范执行：专家组在听取课题组总体情况介绍后，每个品种地块选取3个测产点，每样点面积0.01亩，人工刨秧摘果、去杂后称重。测产结果出炉：按折干率55%计算，花育9131、花育9135和花育9136荚果产量分别为679.25、609.58和687.50公斤\u002F亩。\n\n“三个新品种果实饱满，单株结果数量多，在中度盐碱地叠加当年持续干旱的不利条件下，取得这样的产量，充分展现品种优异的耐盐、抗旱、高产特性，产业化应用前景广阔。”来自青岛农业大学的测产专家柳新伟现场点评。试验采用的一垄四行根域微地形种植模式，形成田间微地形，下雨时水流顺着垄沟排走，垄面保持相对低盐环境，抗涝抑盐，良种良法配套，进一步释放产量潜力。花生属于豆科作物，自身具备固氮能力，无需额外土壤改良，可作为盐碱地改良先锋作物，持续改善地力，为后续其他作物种植打下基础。\n\n记者了解到，花育9131、花育9135、花育9136均为高油酸品种，其中花育9135、花育9136为今年登记的新品种。三个品种已经完成独家品种权转让，分别授权给两家河南企业、一家山东企业，企业拿到授权后正加紧繁育推广，打通了从实验室试验田走向市场的成果转化通道。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fc10dea7011178b18cff9cf5afe81e9e1~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1790813093&x-signature=fa2EWkbEGIGM24I%2BzragFRZZ3J8%3D)\n“高油酸花生有两大优点。”迟晓元介绍，营养价值高、耐储性好。高油酸花生中油酸含量高达75%以上，能有效降低低密度脂蛋白胆固醇水平，有益心血管健康；同时油酸性质稳定，花生及制品不易氧化酸败，货架期更长。与普通花生果相比，高油酸花生果农民增收1元\u002F公斤，榨油企业增收6000-8000元\u002F吨。”\n\n“花生单位面积产量、产油量和种植效益均居国内大宗油料作物首位，且耐瘠薄、抗旱耐盐碱，不与粮食作物争地，盐碱地正是扩种花生的重要阵地。”迟晓元感慨。山东拥有近900万亩盐碱地，仅东营就有约300万亩。落实“扩面积、提单产、提品质”的花生发展方向，把沉睡的盐碱地利用起来，发展耐盐碱花生，正是践行大食物观，实现“以种适地”，提升油料供给保障能力的重要路径。（大众新闻记者 杨珂 李明 通讯员 王敏 宋少双）","大众日报 2026年09月20日","2026-09-20T06:27:00Z",77,{"impact":18,"substance":18,"depth":187,"authority":13,"freshness":125,"relevant":22,"comment":188},17,"山东盐碱地高油酸花生新品种亩产突破687.5公斤，耐盐抗旱数据翔实，成果转化路径清晰，对盐碱地综合利用和油料产能提升有示范意义。",[190],{"name":183,"url":180},[145,192,193,194,195],"山东省农科院","高油酸花生","盐碱地农业","耐盐碱品种",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[198],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":200},{"candidates":201,"agricultural_evidence":53},[202,206,211,217],{"code":40,"score":52,"supported":53,"title":203,"abstract":204,"tags":205},[162],[62,162],[],{"code":33,"score":156,"supported":53,"title":207,"abstract":209,"tags":210},[208],"花生",[208],[193],{"code":212,"score":72,"supported":53,"title":213,"abstract":214,"tags":216},"H7",[],[215],"干旱",[],{"code":166,"score":22,"supported":14,"title":218,"abstract":219,"tags":220},[],[],[145],[222,223],"山东农科院 花育9131 花生","盐碱地 高油酸花生 亩产","山东农科院花育9131花生-3290","2026-09-24T00:03:56.972784Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":9,"content":9,"source_name":231,"source_url":229,"published_at":232,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":233,"score_detail":234,"sources":238,"tags":240,"primary_topic":243,"secondary_topics":247,"classification":249,"search_phrases":270,"slug":273,"view_count":236,"doi":274,"paper":275,"created_at":286},1211,"A map-based decision-support framework for variable-depth seeding of fodder maize integrating proximal soil sensing and multi-temporal satellite NDVI data","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10437-3","A map-based decision-support framework for variable-depth seeding of fodder maize integrating proximal soil sensing and multi-temporal satellite NDVI data。Precision Agriculture","Precision Agriculture","2026-08-25T00:00:00Z",70,{"impact":17,"substance":235,"depth":19,"authority":17,"freshness":236,"relevant":22,"comment":237},20,2,"论文提出结合土壤传感与卫星NDVI的变量播种决策框架，方法新颖，数据可靠，但时效性较低。",[239],{"name":231,"url":229},[27,241,28,242,29],"玉米","遥感",{"code":244,"slug":245,"name":241,"path":246,"parent_code":37},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize",[248],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":250},{"candidates":251,"agricultural_evidence":53},[252,256,261,265],{"code":244,"score":156,"supported":53,"title":253,"abstract":254,"tags":255},[245],[245],[241],{"code":40,"score":257,"supported":53,"title":258,"abstract":259,"tags":260},11,[41],[41],[],{"code":65,"score":139,"supported":53,"title":262,"abstract":263,"tags":264},[],[68],[27,28],{"code":266,"score":22,"supported":14,"title":267,"abstract":268,"tags":269},"I3",[],[],[242],[271,272],"土壤传感 智慧农业 精准农业 玉米","土壤传感 智慧农业","土壤传感智慧农业精准农业玉米-1211","10.1007\u002Fs11119-026-10437-3",{"doi":274,"openalex_id":276,"authors":277,"venue":231,"cited_by_count":113,"oa_url":9,"card":9,"direction":9,"ingested_from":122},"W7204189193",[278,280,283],{"name":279,"orcid":9},"Jialu Sun",{"name":281,"orcid":282},"Manuel Vázquez-Arellano","https:\u002F\u002Forcid.org\u002F0000-0001-7820-5143",{"name":284,"orcid":285},"Abdul Mounem Mouazen","https:\u002F\u002Forcid.org\u002F0000-0002-0354-0067","2026-09-01T04:03:03.736293Z",{"id":288,"title":289,"url":290,"summary":291,"summary_zh":292,"content":9,"source_name":293,"source_url":290,"published_at":294,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":295,"score_detail":296,"sources":298,"tags":300,"primary_topic":303,"secondary_topics":308,"classification":315,"search_phrases":363,"slug":366,"view_count":236,"doi":9,"paper":367,"created_at":378},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":138,"substance":235,"depth":187,"authority":20,"freshness":21,"relevant":22,"comment":297},"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[299],{"name":293,"url":290},[27,28,242,301,302],"盐碱地治理","土壤监测",{"code":266,"slug":304,"name":305,"path":306,"parent_code":307},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[309,314],{"code":310,"slug":311,"name":312,"path":313,"parent_code":307},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":316},{"candidates":317,"agricultural_evidence":53},[318,324,332,336,340,347,353,357],{"code":266,"score":19,"supported":53,"title":319,"abstract":321,"tags":323},[320],"remote sensing",[242,320,322],"multispectral",[242],{"code":310,"score":187,"supported":53,"title":325,"abstract":327,"tags":331},[326],"GIS",[328,329,326,330],"地理信息","空间分析","geographic information",[],{"code":40,"score":17,"supported":53,"title":333,"abstract":334,"tags":335},[41],[62,41],[301],{"code":65,"score":13,"supported":53,"title":337,"abstract":338,"tags":339},[68],[],[27,28],{"code":341,"score":125,"supported":53,"title":342,"abstract":343,"tags":346},"I2",[],[344,345],"机器学习","machine learning",[],{"code":348,"score":72,"supported":53,"title":349,"abstract":350,"tags":352},"H3",[],[351],"灌溉",[],{"code":212,"score":72,"supported":53,"title":354,"abstract":355,"tags":356},[],[215],[],{"code":358,"score":72,"supported":53,"title":359,"abstract":360,"tags":362},"I7",[],[361],"methodological framework",[],[364,365],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",{"doi":9,"openalex_id":368,"authors":369,"venue":293,"cited_by_count":113,"oa_url":290,"card":372,"direction":376,"ingested_from":122},"W7221843179",[370],{"name":371,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":373,"method":374,"finding":375,"direction":376,"opportunity":377},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","农业遥感与作物表型","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z",{"id":380,"title":381,"url":382,"summary":383,"summary_zh":384,"content":9,"source_name":385,"source_url":382,"published_at":294,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":386,"score_detail":387,"sources":391,"tags":393,"primary_topic":396,"secondary_topics":397,"classification":399,"search_phrases":438,"slug":441,"view_count":22,"doi":442,"paper":443,"created_at":455},4896,"Site-Specific Precision Cover Cropping for Soil Health and Environmental Sustainability Using Remote Sensing and Multi-Criteria Analysis","https:\u002F\u002Fdoi.org\u002F10.21663\u002Feeg-d-26-00041","ABSTRACT Cover crops, which are plants or mixtures of plants grown primarily to enhance and protect soil health, are a key strategy in sustainable agriculture. Traditionally, they have been uniformly planted in a field typically during off-season when the main crops are not in competition. However, advancements in precision agriculture, particularly the integration of remote-sensing technology, demand more efficient seeding strategies based on field variability. The aims of this study were to assess the specific needs of a field in Aurora County, SD, and determine the type and optimal combinations of the cover crop mix. The cover crop species considered in this study included legumes, grasses, and brassicas. The remote-sensing data were extracted from Sentinel-2 satellite imagery. Soil samples were collected from 0 to 30 cm depth, and soil properties were measured. Additionally, topographic maps such as slope and aspect were extracted from light detection and ranging (LiDAR) data. The results of precision cover crop analyses showed that 6.5 percent (7.8 ha), 20.6 percent (24.7 ha), 38.1 percent (45.7 ha), 29.6 percent (35.5 ha), and 5.3 percent (6.3 ha) of the studied field should planted in the following mixtures of crops: rye, radish, and vetch; triticale, turnip, and vetch; triticale, radish, and clover; triticale, rapeseed, and pea; and barley, flax, and clover, respectively. The mixtures significantly improved soil properties by reducing compaction, increasing residue C\u002FN ratio, and enhancing weed suppression compared with the control. The resulting map should be interpreted as a decision-support recommendation rather than a fully validated field prescription.","摘要 覆盖作物（cover crops）是指主要为了提升和保护土壤健康而种植的植物或植物混合体，是可持续农业的一项关键策略。传统上，覆盖作物通常在非主作物竞争的季节以均匀方式播种于田间。然而，精准农业的进步，尤其是遥感技术的整合，要求基于田块变异性制定更高效的播种策略。本研究旨在评估南达科他州奥罗拉县某田块的具体需求，并确定覆盖作物混合物的类型及最优组合。本研究考虑的覆盖作物种类包括豆科、禾本科和十字花科植物。遥感数据提取自Sentinel-2卫星影像。土壤样品采集深度为0至30 cm，并测定了土壤性质。此外，从激光雷达（LiDAR）数据中提取了坡度和坡向等地形图。精准覆盖作物分析结果表明，研究田块中分别有6.5%（7.8 ha）、20.6%（24.7 ha）、38.1%（45.7 ha）、29.6%（35.5 ha）和5.3%（6.3 ha）的面积应种植以下作物混合物：黑麦、萝卜和野豌豆；小黑麦、芜菁和野豌豆；小黑麦、萝卜和三叶草；小黑麦、油菜和豌豆；以及大麦、亚麻和三叶草。与对照相比，这些混合物通过降低紧实度、提高残体碳氮比和增强杂草抑制效果，显著改善了土壤性质。所得图谱应被解读为决策支持建议，而非经过充分验证的田间处方。","Environmental and Engineering Geoscience",72,{"impact":156,"substance":388,"depth":187,"authority":20,"freshness":389,"relevant":22,"comment":390},21,9,"该研究将遥感与多准则分析结合用于精准覆盖作物制图，方法新颖、数据具体，对农业信息化与可持续农业有参考价值，但属单点田间案例，影响范围有限。",[392],{"name":385,"url":382},[27,28,242,394,395],"土壤健康","覆盖作物",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[398],{"code":266,"slug":304,"name":305,"path":306,"parent_code":307},{"status":46,"locked":14,"version":47,"method":48,"evidence":400},{"candidates":401,"agricultural_evidence":53},[402,406,410,414,420,427,433],{"code":40,"score":17,"supported":53,"title":403,"abstract":404,"tags":405},[41],[62,41],[394],{"code":266,"score":156,"supported":53,"title":407,"abstract":408,"tags":409},[320],[242],[242],{"code":65,"score":21,"supported":53,"title":411,"abstract":412,"tags":413},[],[28,68],[27,28],{"code":33,"score":125,"supported":53,"title":415,"abstract":416,"tags":419},[],[417,418],"油菜","rapeseed",[],{"code":421,"score":125,"supported":53,"title":422,"abstract":423,"tags":426},"H8",[],[424,425],"可持续农业","sustainable agriculture",[],{"code":428,"score":72,"supported":53,"title":429,"abstract":430,"tags":432},"B1",[],[431],"萝卜",[],{"code":434,"score":22,"supported":14,"title":435,"abstract":436,"tags":437},"A7",[],[],[395],[439,440],"Sentinel-2 遥感 覆盖作物","精准覆盖作物 土壤健康","Sentinel-2遥感覆盖作物-4896","10.21663\u002Feeg-d-26-00041",{"doi":442,"openalex_id":444,"authors":445,"venue":385,"cited_by_count":113,"oa_url":9,"card":450,"direction":376,"ingested_from":122},"W7221669942",[446,448],{"name":447,"orcid":9},"Ali Mirzakhani Nafchi",{"name":449,"orcid":9},"Salman Mirzaee",{"tldr":451,"method":452,"finding":453,"direction":376,"opportunity":454},"利用遥感与多准则分析为南达科他州田块制定精准覆盖作物混播方案。","Sentinel-2影像、LiDAR地形与土壤采样，结合多准则分析。","田块被划分为5个管理区，各推荐不同覆盖作物混播，显著改善土壤紧实度、残体C\u002FN比和抑草效果。","可验证该决策支持地图的田间效果，并融合实时物联网数据优化动态推荐。","2026-10-14T23:30:27.077312Z",{"id":457,"title":458,"url":459,"summary":460,"summary_zh":461,"content":9,"source_name":462,"source_url":459,"published_at":463,"category":12,"cover_url":9,"hotness":13,"is_selected":53,"score":464,"score_detail":465,"sources":467,"tags":469,"primary_topic":472,"secondary_topics":473,"classification":474,"search_phrases":543,"slug":546,"view_count":113,"doi":547,"paper":548,"created_at":577},4832,"Applications and Future Prospects of Plant Multi-Omics Technologies in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms27209034","Against the backdrop of global population growth, intensifying climate change, and limited arable land, ensuring food security and promoting sustainable agriculture have become critical issues. The limitations of conventional agricultural systems are becoming increasingly apparent, creating an urgent need for innovation driven by science and technology. In this context, plant multi-omics technologies serve as the foundational data and technical cornerstone of systems biology and precision agriculture. Integrating multi-level data from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics offers substantial advantages for systematically understanding and improving plants. Unlike previous reviews that mainly catalog omics technologies or focus on individual crops, this review adopts a unified discovery-to-translation framework and critically evaluates the strength of evidence across applications. This review focuses on cutting-edge applications of multi-omics in three key areas: improving the traits of cereal crops (rice, wheat and maize), remediating soil health, and innovating products such as biofertilizers and biostimulants for agriculture. We systematically explore the roles of multi-omics in accelerating breeding processes, deciphering complex agronomic traits, restoring soil ecosystems, and constructing intelligent decision-support systems. The review also examines current challenges, including the complexity of data integration and the difficulties of predicting genotype-by-environment interactions. Furthermore, it outlines future directions involving deeper convergence with artificial intelligence, high-throughput phenomics, and digital twins. In summary, plant multi-omics technology is a vital tool for unravelling the mysteries of plant life and is also a key enabler of intelligent, precise and sustainable agricultural transformation.","在全球人口增长、气候变化加剧和耕地有限的背景下，保障粮食安全和促进可持续农业已成为关键议题。传统农业系统的局限性日益显现，迫切需要由科学和技术驱动的创新。在此背景下，植物多组学技术作为系统生物学和精准农业的基础数据与技术基石，整合基因组学、转录组学、蛋白质组学、代谢组学、表观基因组学和微生物组学等多层次数据，为系统理解和改良植物提供了显著优势。与以往主要罗列组学技术或聚焦单一作物的综述不同，本综述采用统一的“从发现到转化”框架，并批判性评估各应用中的证据强度。本综述聚焦多组学在三个关键领域的前沿应用：改良谷类作物（水稻、小麦和玉米）的性状、修复土壤健康，以及创新生物肥料和生物刺激素等农业产品。我们系统探讨了多组学在加速育种进程、解析复杂农艺性状、恢复土壤生态系统和构建智能决策支持系统中的作用。本综述还审视了当前挑战，包括数据整合的复杂性和预测基因型与环境互作的困难。此外，文章展望了未来方向，涉及与人工智能、高通量表型组学和数字孪生的更深层次融合。总之，植物多组学技术是揭示植物生命奥秘的重要工具，也是智能、精准和可持续农业转型的关键使能技术。","International Journal of Molecular Sciences","2026-10-12T00:00:00Z",83,{"impact":18,"substance":388,"depth":19,"authority":52,"freshness":21,"relevant":22,"comment":466},"核心期刊综述，系统梳理植物多组学在精准农业中的应用与AI、数字孪生等未来方向，专业深度与信源权威度俱佳，值得进入每日精选。",[468],{"name":462,"url":459},[27,470,145,28,394,471],"农业人工智能","多组学",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":475},{"candidates":476,"agricultural_evidence":53},[477,483,488,495,502,506,513,519,523,528,534,538],{"code":65,"score":478,"supported":53,"title":479,"abstract":480,"tags":482},19,[68],[28,481,68],"数字孪生",[27,28],{"code":40,"score":484,"supported":53,"title":485,"abstract":486,"tags":487},7,[],[62,41],[394],{"code":489,"score":125,"supported":53,"title":490,"abstract":491,"tags":494},"A1",[],[492,493],"水稻","rice",[],{"code":496,"score":125,"supported":53,"title":497,"abstract":498,"tags":501},"A2",[],[499,500],"小麦","wheat",[],{"code":244,"score":125,"supported":53,"title":503,"abstract":504,"tags":505},[],[241,245],[],{"code":507,"score":125,"supported":53,"title":508,"abstract":509,"tags":512},"A8",[],[510,511],"粮食安全","food security",[],{"code":212,"score":125,"supported":53,"title":514,"abstract":515,"tags":518},[],[516,517],"气候变化","climate change",[],{"code":421,"score":125,"supported":53,"title":520,"abstract":521,"tags":522},[],[424,425],[],{"code":341,"score":167,"supported":53,"title":524,"abstract":525,"tags":527},[],[526],"artificial intelligence",[470],{"code":529,"score":72,"supported":53,"title":530,"abstract":531,"tags":533},"F3",[],[532],"肥料",[],{"code":166,"score":22,"supported":14,"title":535,"abstract":536,"tags":537},[],[],[145],{"code":539,"score":22,"supported":14,"title":540,"abstract":541,"tags":542},"F2",[],[],[471],[544,545],"植物多组学 精准农业","多组学 智能育种","植物多组学精准农业-4832","10.3390\u002Fijms27209034",{"doi":547,"openalex_id":549,"authors":550,"venue":462,"cited_by_count":113,"oa_url":459,"card":571,"direction":121,"ingested_from":122},"W7221567543",[551,554,556,559,561,563,566,569],{"name":552,"orcid":553},"Tao Tong","https:\u002F\u002Forcid.org\u002F0000-0002-9003-9667",{"name":555,"orcid":9},"Wang Qingxia",{"name":557,"orcid":558},"Fanrong Zeng","https:\u002F\u002Forcid.org\u002F0000-0001-7742-5195",{"name":560,"orcid":9},"Shuzhen Ye",{"name":562,"orcid":9},"Zhijuan Ji",{"name":564,"orcid":565},"Zhong‐Hua Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7531-320X",{"name":567,"orcid":568},"Jiaoyu Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0249-808X",{"name":570,"orcid":9},"Younan Ouyang",{"tldr":572,"method":573,"finding":574,"direction":575,"opportunity":576},"综述植物多组学在精准农业中的应用，涵盖作物改良、土壤修复和生物产品创新。","整合基因组、转录组、蛋白组、代谢组等多组学数据，采用发现到转化框架。","多组学可加速育种、解析复杂农艺性状、修复土壤生态并构建智能决策系统。","农业人工智能与决策模型","多组学与AI、高通量表型、数字孪生融合，解决基因型-环境互作预测难题。","2026-10-13T23:30:29.504594Z"]