[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3767":3,"related-3767":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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":108,"slug":111,"view_count":21,"doi":112,"paper":113,"created_at":138},3767,"Alfalfa-mediated phytoremediation and soil restoration for sustainable cereal and fruit crop production: a review of roles, detoxification mechanisms, nitrogen enhancement, and agroecological integration","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1963157","Soil contamination by heavy metals, petroleum hydrocarbons, and excessive agrochemical inputs, coupled with declining soil fertility, poses a major threat to sustainable cereal and fruit production and global food security. This review synthesizes evidence from peer-reviewed studies published between 2000 and 2026 to evaluate the multifunctional role of alfalfa ( Medicago sativa L.) as a biological soil engineer for phytoremediation, soil restoration, and sustainable agricultural intensification. The reviewed evidence indicates that alfalfa can contribute to the remediation of contaminated soils through phytoextraction, phytostabilization, and rhizosphere-mediated degradation of organic pollutants, while simultaneously improving soil fertility through biological nitrogen fixation (BNF). Its deep and extensive root system can enhance soil structure, aggregation, water-holding capacity, and soil organic carbon, while promoting beneficial microbial communities, nutrient cycling, and suppression of soil-borne pathogens. In cereal-based systems, these processes may reduce contaminant bioavailability, decrease dependence on synthetic fertilizers, and improve the productivity and resilience of subsequent crops. In fruit-based systems, including orchards and perennial cropping systems, alfalfa can provide additional benefits through intercropping or cover-cropping, including nitrogen inputs, improved soil moisture conservation, enhanced soil biological activity, and potential economic returns. However, the accumulation of contaminants in alfalfa biomass remains an important food- and feed-safety concern, highlighting the need for rigorous contaminant monitoring and appropriate biomass management. Emerging approaches, including targeted microbial inoculation, biochar amendments, intercropping strategies, and gene-editing technologies, may further improve remediation efficiency and agroecosystem sustainability. Overall, integrating alfalfa into cereal- and fruit-based production systems represents a promising nature-based approach for restoring degraded soils, improving soil resilience, reducing external inputs, and supporting sustainable crop production. Future research should prioritize long-term, multi-site field studies to evaluate remediation performance, contaminant mass balances, plant–microbe interactions, biomass safety, and the long-term productivity of cereal and fruit production systems.","重金属、石油烃和过量农用化学品投入造成的土壤污染，加之土壤肥力下降，对可持续的谷类与水果生产及全球粮食安全构成重大威胁。本综述综合了2000年至2026年间发表的同行评审研究证据，评估紫花苜蓿（Medicago sativa L.）作为生物土壤工程师在植物修复、土壤恢复和可持续农业集约化中的多功能作用。所综述的证据表明，紫花苜蓿可通过植物提取、植物稳定和根际介导的有机污染物降解促进污染土壤的修复，同时通过生物固氮（BNF）提高土壤肥力。其深而广泛的根系能够改善土壤结构、团聚体、持水能力和土壤有机碳，同时促进有益微生物群落、养分循环并抑制土传病原菌。在谷类作物系统中，这些过程可降低污染物的生物有效性，减少对合成肥料的依赖，并提高后茬作物的生产力和韧性。在水果系统中，包括果园和多年生种植系统，紫花苜蓿可通过间作或覆盖作物提供额外效益，包括氮输入、改善土壤保水、增强土壤生物活性以及潜在的经济回报。然而，紫花苜蓿生物量中污染物的积累仍是一个重要的食品和饲料安全问题，凸显了严格污染物监测和适当生物量管理的必要性。新兴方法，包括定向微生物接种、生物炭改良、间作策略和基因编辑技术，可能进一步提高修复效率和农业生态系统可持续性。总体而言，将紫花苜蓿纳入谷类和水果生产系统，代表了一种有前景的基于自然的途径，用于恢复退化土壤、提高土壤韧性、减少外部投入并支持可持续作物生产。未来研究应优先开展长期、多地点田间研究，以评估修复性能、污染物质量平衡、植物–微生物相互作用、生物量安全性以及谷类和水果生产系统的长期生产力。",null,"Frontiers in Sustainable Food Systems","2026-09-28T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,21,14,9,1,"系统综述紫花苜蓿在污染土壤修复与农田可持续生产中的多功能作用，对绿色农业与土壤治理有参考价值，但属综述类论文，非重大突破。",[24],{"name":10,"url":6},[26,27,28,29,30],"绿色农业","生物固氮","紫花苜蓿","土壤修复","重金属污染",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,59,67,74,82,89,95,102],{"code":39,"score":51,"supported":52,"title":53,"abstract":54,"tags":58},19,true,[40],[55,56,40,57],"土壤","根际","rhizosphere",[29,30],{"code":32,"score":17,"supported":52,"title":60,"abstract":62,"tags":66},[61],"alfalfa",[63,64,65,61],"间作","苜蓿","intercropping",[28],{"code":68,"score":69,"supported":52,"title":70,"abstract":71,"tags":73},"C4",7,[],[72,64],"饲料",[28],{"code":75,"score":76,"supported":52,"title":77,"abstract":78,"tags":81},"A8",6,[],[79,80],"粮食安全","food security",[],{"code":83,"score":84,"supported":52,"title":85,"abstract":86,"tags":88},"F3",4,[],[87],"肥料",[27],{"code":90,"score":84,"supported":52,"title":91,"abstract":92,"tags":94},"H8",[],[93],"可持续农业",[26],{"code":96,"score":97,"supported":52,"title":98,"abstract":99,"tags":101},"F2",3,[],[100],"基因编辑",[],{"code":103,"score":97,"supported":52,"title":104,"abstract":105,"tags":107},"H6",[],[106],"农业生态",[],[109,110],"紫花苜蓿 土壤修复 重金属","苜蓿 间作 果园 固氮","紫花苜蓿土壤修复重金属-3767","10.3389\u002Ffsufs.2026.1963157",{"doi":112,"openalex_id":114,"authors":115,"venue":10,"cited_by_count":130,"oa_url":6,"card":131,"direction":135,"ingested_from":137},"W7214674081",[116,118,120,122,124,126,128],{"name":117,"orcid":9},"E. Igwe",{"name":119,"orcid":9},"S. Diakite",{"name":121,"orcid":9},"B. Y. Konyannik",{"name":123,"orcid":9},"E. Tarvids",{"name":125,"orcid":9},"N. Y. Rebouh",{"name":127,"orcid":9},"D. O. Kapralova",{"name":129,"orcid":9},"P. E. Norman",0,{"tldr":132,"method":133,"finding":134,"direction":135,"opportunity":136},"综述紫花苜蓿通过植物修复与土壤恢复支撑谷类与果树可持续生产的多功能作用。","综述2000–2026年同行评议文献，聚焦植物提取、根际降解、生物固氮与间作。","苜蓿可修复污染土壤、固氮增碳并改善结构，但生物量污染物累积带来食饲安全风险。","农业绿色发展与碳","可研究苜蓿-微生物-生物炭联合修复的长期田间效果及污染物质量平衡与生物量安全处置。","openalex","2026-09-29T23:30:07.577120Z",{"total":76,"page":21,"page_size":76,"items":140},[141,211,283,336,420,509],{"id":142,"title":143,"url":144,"summary":145,"summary_zh":9,"content":9,"source_name":146,"source_url":144,"published_at":147,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":148,"score_detail":149,"sources":154,"tags":156,"primary_topic":162,"secondary_topics":163,"classification":171,"search_phrases":198,"slug":201,"view_count":21,"doi":202,"paper":203,"created_at":210},4084,"Integrating Crop Rotation and Remote Sensing to Assess Soil and Water Conservation in Organic Soils of the Everglades Agricultural Area","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.31332.74887","Integrating Crop Rotation and Remote Sensing to Assess Soil and Water Conservation in Organic Soils of the Everglades Agricultural Area。OpenAlex","OpenAlex","2026-10-02T00:00:00Z",68,{"impact":150,"substance":17,"depth":151,"authority":152,"freshness":20,"relevant":21,"comment":153},12,16,13,"该论文将轮作与遥感结合评估有机土壤水土保持，方法有创新且数据可靠，但属区域研究，影响范围有限，时效性强，值得关注。",[155],{"name":146,"url":144},[157,158,159,160,161],"智慧农业","轮作","遥感","水土保持","有机土壤",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[164,170],{"code":165,"slug":166,"name":167,"path":168,"parent_code":169},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":172},{"candidates":173,"agricultural_evidence":52},[174,179,184,189,193],{"code":32,"score":150,"supported":52,"title":175,"abstract":177,"tags":178},[176],"crop rotation",[176],[158],{"code":165,"score":150,"supported":52,"title":180,"abstract":182,"tags":183},[181],"remote sensing",[181],[159],{"code":39,"score":185,"supported":52,"title":186,"abstract":187,"tags":188},11,[40],[40],[],{"code":103,"score":21,"supported":14,"title":190,"abstract":191,"tags":192},[],[],[160],{"code":194,"score":21,"supported":14,"title":195,"abstract":196,"tags":197},"I1",[],[],[157],[199,200],"Everglades 农业区 遥感 轮作","有机土壤 水土保持 遥感","Everglades农业区遥感轮作-4084","10.13140\u002Frg.2.2.31332.74887",{"doi":202,"openalex_id":204,"authors":205,"venue":9,"cited_by_count":130,"oa_url":144,"card":9,"direction":209,"ingested_from":137},"W7216417358",[206],{"name":207,"orcid":208},"Bivek Bhusal","https:\u002F\u002Forcid.org\u002F0009-0001-6408-0033","农业遥感与作物表型","2026-10-03T23:30:32.356375Z",{"id":212,"title":213,"url":214,"summary":215,"summary_zh":216,"content":9,"source_name":10,"source_url":214,"published_at":217,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":218,"score_detail":219,"sources":223,"tags":225,"primary_topic":231,"secondary_topics":232,"classification":234,"search_phrases":255,"slug":258,"view_count":84,"doi":259,"paper":260,"created_at":282},2916,"Synergistic effects of seedbed methods and legume intercropping on soil moisture conservation and productivity of groundnut in rainfed Alfisols","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1870854","Field experiments were conducted during the moisture-limited north-east monsoon ( rabi ) seasons of 2020–2022 to evaluate the effects of seedbed methods and legume-based intercropping systems on groundnut growth, yield, soil moisture conservation, rainwater use efficiency (RWUE), groundnut equivalent yield (GEY), and post-harvest soil fertility under rainfed Alfisol conditions. The experiment was laid out in a strip-plot design with three seedbed methods as main plots (compartmental bunding, raised bed, and flatbed) and four intercropping systems as subplots (groundnut with black gram, cowpea, red gram, or castor in a 4:1 row ratio). The raised bed produced the numerically highest mean pod yield (528 kg ha −1 ) and RWUE (4.45 kg ha −1 mm −1 ). For both variables, it was statistically similar to compartmental bunding but superior to flatbed. Groundnut + cowpea produced the numerically highest mean pod yield among intercrops (499 kg ha −1 ). GEY and economic rankings were descriptive; raised bed + black gram had the highest GEY (803.17 kg ha −1 ) and net return (Rs. 16,190 ha −1 ) under the stated prices. The tables report treatment responses averaged over 3 years. A retrospective combined ANOVA with year as a fixed factor revealed highly significant year × treatment interactions for pod yield, biomass and RWUE (Year × Seedbed: F 4, 12 = 206.66, p \u003C 0.0001; Year × Intercrop: F 6, 18 = 1,980.42, p \u003C 0.0001; Year × Seedbed × Intercrop: F 12, 36 = 4,668.10, p \u003C 0.0001), indicating that treatment rankings were not stable across the contrasting rainfall seasons (166–264 mm). Consequently, the results apply to the pooled 3-year average only and should be interpreted as conditional on comparable moisture-limited rabi conditions.","在2020—2022年水分受限的东北季风（rabi）季节开展了田间试验，以评估在雨养阿尔菲索尔（Alfisol）条件下，苗床方式与基于豆科作物的间作体系对花生生长、产量、土壤保水、雨水利用效率（RWUE）、花生当量产量（GEY）以及收获后土壤肥力的影响。试验采用条带区设计，以3种苗床方式为主区（隔畦、高畦和平畦），以4种间作体系为副区（花生与黑豆、豇豆、红豇豆或蓖麻按4:1行比间作）。高畦的荚果平均产量（528 kg ha⁻¹）和RWUE（4.45 kg ha⁻¹ mm⁻¹）在数值上最高。对于这两个变量，高畦与隔畦在统计上相似，但优于平畦。在间作处理中，花生+豇豆的荚果平均产量在数值上最高（499 kg ha⁻¹）。GEY和经济排名为描述性结果；在所述价格下，高畦+黑豆的GEY最高（803.17 kg ha⁻¹），净收益也最高（16,190卢比 ha⁻¹）。表中报告的是3年平均的处理响应。以年份为固定因素进行的回顾性联合方差分析显示，荚果产量、生物量和RWUE的年份×处理互作均极显著（年份×苗床：F₄,₁₂=206.66，p\u003C0.0001；年份×间作：F₆,₁₈=1,980.42，p\u003C0.0001；年份×苗床×间作：F₁₂,₃₆=4,668.10，p\u003C0.0001），表明在降雨量差异较大的季节间（166—264 mm），处理排名并不稳定。因此，结果仅适用于3年合并平均值，并应解释为以可比的限水rabi条件为条件。","2026-09-18T00:00:00Z",71,{"impact":150,"substance":18,"depth":220,"authority":152,"freshness":221,"relevant":21,"comment":222},17,8,"三年田间试验数据扎实、结论有明确适用条件，但属细分作物栽培研究，公共影响有限，适合作为专业参考而非每日精选头条。",[224],{"name":10,"url":214},[226,227,228,229,230],"花生","旱作农业","间作套种","土壤保墒","雨水利用",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[233],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":235},{"candidates":236,"agricultural_evidence":52},[237,241,245,250],{"code":32,"score":19,"supported":52,"title":238,"abstract":239,"tags":240},[65],[63,65],[],{"code":39,"score":19,"supported":52,"title":242,"abstract":243,"tags":244},[40],[55,40],[],{"code":246,"score":84,"supported":52,"title":247,"abstract":248,"tags":249},"A4",[],[226],[226],{"code":251,"score":21,"supported":14,"title":252,"abstract":253,"tags":254},"H3",[],[],[227],[256,257],"花生 豆科间作 土壤水分","雨养 Alfisols 花生 产量","花生豆科间作土壤水分-2916","10.3389\u002Ffsufs.2026.1870854",{"doi":259,"openalex_id":261,"authors":262,"venue":10,"cited_by_count":130,"oa_url":214,"card":277,"direction":135,"ingested_from":137},"W7213544012",[263,265,267,270,272,274],{"name":264,"orcid":9},"Kannan Pandian",{"name":266,"orcid":9},"Vijayakumar Shanmugam",{"name":268,"orcid":269},"Surya Teja Varanasi","https:\u002F\u002Forcid.org\u002F0009-0002-3885-7205",{"name":271,"orcid":9},"Mohamed Roshan Abu Firnass Mustaffa",{"name":273,"orcid":9},"Ratnadeep Kar",{"name":275,"orcid":276},"Anbarasu Mariyappillai","https:\u002F\u002Forcid.org\u002F0000-0001-7970-4454",{"tldr":278,"method":279,"finding":280,"direction":135,"opportunity":281},"评估雨养条件下不同苗床与豆科间作对花生土壤保水及产量的协同效应。","2020-2022年雨养Alfisols田间试验，裂区设计，3种苗床与4种间作。","高垄与分室垄保水及产量优于平作，花生+豇豆产量最高，但处理排序随年份降雨变化。","可探究不同降雨年型下苗床与间作组合的稳定性，并引入物联网监测土壤水分动态。","2026-09-19T23:30:08.227243Z",{"id":284,"title":285,"url":286,"summary":287,"summary_zh":9,"content":9,"source_name":288,"source_url":9,"published_at":289,"category":290,"cover_url":9,"hotness":13,"is_selected":14,"score":291,"score_detail":292,"sources":296,"tags":298,"primary_topic":304,"secondary_topics":309,"classification":312,"search_phrases":330,"slug":333,"view_count":334,"doi":9,"paper":9,"created_at":335},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":17,"substance":293,"depth":294,"authority":150,"freshness":221,"relevant":21,"comment":295},20,15,"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。",[297],{"name":288,"url":286},[299,300,301,302,303],"地方标准","苜蓿青贮","盐碱地改良","梨栽培","农业技术",{"code":68,"slug":305,"name":306,"path":307,"parent_code":308},"feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",[310,311],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":313},{"candidates":314,"agricultural_evidence":52},[315,321,326],{"code":68,"score":316,"supported":52,"title":317,"abstract":319,"tags":320},22,[318,64],"青贮",[318,64],[],{"code":39,"score":294,"supported":52,"title":322,"abstract":324,"tags":325},[323],"盐碱地",[55,323],[301],{"code":32,"score":185,"supported":52,"title":327,"abstract":328,"tags":329},[64],[64],[],[331,332],"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",2,"2026-09-05T00:04:35.934739Z",{"id":337,"title":338,"url":339,"summary":340,"summary_zh":341,"content":9,"source_name":342,"source_url":339,"published_at":343,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":344,"sources":346,"tags":348,"primary_topic":352,"secondary_topics":353,"classification":356,"search_phrases":388,"slug":391,"view_count":130,"doi":392,"paper":393,"created_at":419},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z",{"impact":151,"substance":316,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":345},"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[347],{"name":342,"url":339},[157,349,350,351,28],"农业人工智能","农业遥感","氮素监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[354,355],{"code":165,"slug":166,"name":167,"path":168,"parent_code":169},{"code":68,"slug":305,"name":306,"path":307,"parent_code":308},{"status":45,"locked":14,"version":46,"method":47,"evidence":357},{"candidates":358,"agricultural_evidence":52},[359,364,368,374,384],{"code":165,"score":17,"supported":52,"title":360,"abstract":362,"tags":363},[361],"hyperspectral",[159,181,361],[350],{"code":32,"score":294,"supported":52,"title":365,"abstract":366,"tags":367},[61],[64,61],[28],{"code":68,"score":13,"supported":52,"title":369,"abstract":370,"tags":373},[],[371,64,372],"饲草","forage",[28],{"code":375,"score":13,"supported":52,"title":376,"abstract":377,"tags":383},"I2",[],[378,379,380,381,382],"深度学习","机器学习","machine learning","deep learning","decision support",[349],{"code":194,"score":21,"supported":14,"title":385,"abstract":386,"tags":387},[],[],[157],[389,390],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":392,"openalex_id":394,"authors":395,"venue":342,"cited_by_count":130,"oa_url":339,"card":414,"direction":209,"ingested_from":137},"W7223154576",[396,398,400,402,404,407,409,412],{"name":397,"orcid":9},"Shuai Fu",{"name":399,"orcid":9},"Yunhao Li",{"name":401,"orcid":9},"Chunli Miao",{"name":403,"orcid":9},"Jie Liu",{"name":405,"orcid":406},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":408,"orcid":9},"Yongjia Hu",{"name":410,"orcid":411},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":413,"orcid":9},"吴彩霞",{"tldr":415,"method":416,"finding":417,"direction":209,"opportunity":418},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":421,"title":422,"url":423,"summary":424,"summary_zh":425,"content":9,"source_name":426,"source_url":423,"published_at":427,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":428,"score_detail":429,"sources":431,"tags":433,"primary_topic":436,"secondary_topics":437,"classification":439,"search_phrases":461,"slug":464,"view_count":130,"doi":465,"paper":466,"created_at":508},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",79,{"impact":17,"substance":316,"depth":17,"authority":150,"freshness":20,"relevant":21,"comment":430},"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[432],{"name":426,"url":423},[157,434,435,159,28],"无人机","育种",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[438],{"code":165,"slug":166,"name":167,"path":168,"parent_code":169},{"status":45,"locked":14,"version":46,"method":47,"evidence":440},{"candidates":441,"agricultural_evidence":52},[442,446,451,457],{"code":32,"score":294,"supported":52,"title":443,"abstract":444,"tags":445},[61],[64,61],[28],{"code":165,"score":150,"supported":52,"title":447,"abstract":449,"tags":450},[448],"multispectral",[448],[159],{"code":194,"score":69,"supported":52,"title":452,"abstract":453,"tags":456},[],[454,455],"精准农业","precision agriculture",[157],{"code":68,"score":84,"supported":52,"title":458,"abstract":459,"tags":460},[],[64],[28],[462,463],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567","10.64898\u002F2026.10.14.759249",{"doi":465,"openalex_id":467,"authors":468,"venue":426,"cited_by_count":130,"oa_url":423,"card":503,"direction":209,"ingested_from":137},"W7223083966",[469,472,475,478,481,483,485,488,491,494,497,500],{"name":470,"orcid":471},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":473,"orcid":474},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":476,"orcid":477},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":479,"orcid":480},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":482,"orcid":9},"Julie Hansen",{"name":484,"orcid":9},"Christopher Pierce",{"name":486,"orcid":487},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":489,"orcid":490},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":492,"orcid":493},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":495,"orcid":496},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":498,"orcid":499},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":501,"orcid":502},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":504,"method":505,"finding":506,"direction":209,"opportunity":507},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","2026-10-22T11:20:32.655394Z",{"id":510,"title":511,"url":512,"summary":513,"summary_zh":514,"content":9,"source_name":515,"source_url":512,"published_at":516,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":517,"score_detail":518,"sources":520,"tags":522,"primary_topic":527,"secondary_topics":528,"classification":529,"search_phrases":552,"slug":555,"view_count":130,"doi":556,"paper":557,"created_at":577},5490,"Biochar for post-war land remediation in Ukraine: A critical review supported by bibliometric analysis","https:\u002F\u002Fdoi.org\u002F10.1093\u002Finteam\u002Fvjag167","This critical review analyzes the current state of scientific research on biochar in Europe and examines its potential use for post-war restoration and improvement of soil fertility in Ukraine. A bibliometric analysis of scientific publications in the Scopus and Web of Science databases for the period 2014-2023 was conducted using VOSviewer visualization and data processing methods. The analysis identified the main areas of research and gaps in knowledge that are relevant to the future application of this technology in Ukraine. Moreover, remote sensing methodologies were employed to identify potential remediation requirements in the Ukrainian territory. This involved the analysis of changes in the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) of a part of Ukraine's agricultural territory situated along the frontline, prior to and following a period of three years during a full-scale war. The results revealed changes in vegetation and moisture levels, indicating land degradation in the border area under study. However, these observations do not constitute direct evidence of soil contamination or the effectiveness of biochar; rather, they indicate areas where further field studies and an assessment of restoration measures are needed. A strengths, weaknesses, opportunities, and threats (SWOT) analysis was used as a strategic discussion tool to identify the opportunities and challenges associated with the implementation of biochar in Ukraine. This review also identifies the main scientific uncertainties, barriers, economic aspects, and research priorities that should be considered before the large-scale implementation of biochar-based technologies in Ukraine.","这篇批判性综述分析了欧洲生物炭（biochar）科学研究的现状，并探讨其在乌克兰战后恢复与土壤肥力提升中的潜在应用。研究采用VOSviewer可视化与数据处理方法，对Scopus和Web of Science数据库中2014—2023年的科学出版物进行了文献计量分析。分析识别了与该项技术在乌克兰未来应用相关的主要研究领域和知识空白。此外，研究采用遥感方法识别乌克兰境内潜在的修复需求，具体包括对乌克兰沿前线部分农业区域在全面战争前后三年期间的归一化植被指数（NDVI）和归一化水分指数（NDMI）变化进行分析。结果显示植被与水分水平发生变化，表明所研究边境地区存在土地退化。然而，这些观测结果并不构成土壤污染或生物炭有效性的直接证据，而是指示了需要进一步开展田间研究和评估修复措施的区域。研究采用优势、劣势、机会与威胁（SWOT）分析作为战略讨论工具，识别在乌克兰实施生物炭相关的机遇与挑战。本综述还明确了在乌克兰大规模实施基于生物炭的技术之前应考虑的主要科学不确定性、障碍、经济因素及研究优先事项。","Integrated Environmental Assessment and Management","2026-10-19T00:00:00Z",77,{"impact":151,"substance":18,"depth":17,"authority":19,"freshness":221,"relevant":21,"comment":519},"该综述结合文献计量与遥感NDVI\u002FNDMI分析，评估生物炭用于乌克兰战后农田修复的潜力与不确定性，方法新颖、结论审慎，对农业遥感与土壤治理具参考价值，但属区域性研究，未达重大突破层级。",[521],{"name":515,"url":512},[523,524,525,29,526],"生物炭","土地退化","遥感监测","乌克兰农业",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":530},{"candidates":531,"agricultural_evidence":52},[532,539,543,547],{"code":533,"score":19,"supported":52,"title":534,"abstract":536,"tags":538},"I7",[535],"bibliometric",[537,535],"文献计量",[],{"code":39,"score":221,"supported":52,"title":540,"abstract":541,"tags":542},[],[55,40],[29,523],{"code":165,"score":69,"supported":52,"title":544,"abstract":545,"tags":546},[],[159,181],[525],{"code":548,"score":21,"supported":14,"title":549,"abstract":550,"tags":551},"H1",[],[],[524],[553,554],"生物炭 战后土壤修复 乌克兰","NDVI NDMI 遥感 土地退化","生物炭战后土壤修复乌克兰-5490","10.1093\u002Finteam\u002Fvjag167",{"doi":556,"openalex_id":558,"authors":559,"venue":515,"cited_by_count":130,"oa_url":9,"card":572,"direction":209,"ingested_from":137},"W7223053668",[560,563,566,569],{"name":561,"orcid":562},"Olha Shomko","https:\u002F\u002Forcid.org\u002F0000-0003-2418-0985",{"name":564,"orcid":565},"Iryna Davydova","https:\u002F\u002Forcid.org\u002F0000-0001-6535-3948",{"name":567,"orcid":568},"Marco Bartoli","https:\u002F\u002Forcid.org\u002F0000-0002-5022-6206",{"name":570,"orcid":571},"Elena Maestri","https:\u002F\u002Forcid.org\u002F0000-0002-8745-9564",{"tldr":573,"method":574,"finding":575,"direction":135,"opportunity":576},"综述生物炭用于乌克兰战后土地修复，结合文献计量与遥感分析。","Scopus\u002FWoS文献计量、VOSviewer、NDVI\u002FNDMI遥感与SWO","前线农区植被与湿度变化显示土地退化，但需田间验证生物炭效果。","可研究战区土地退化的遥感快速诊断与生物炭修复效果的田间验证体系。","2026-10-21T23:30:25.223030Z"]