[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1424":3,"related-1424":90},{"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":30,"secondary_topics":35,"classification":42,"search_phrases":60,"slug":63,"view_count":64,"doi":65,"paper":66,"created_at":89},1424,"Unravelling the combined abiotic stress responses of wheat under heat and drought conditions","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.15136","Most of the wheat (Triticum aestivum L.) growing areas of the world experience environmental stresses including drought (moisture stress) and high temperature (heat stress) that adversely affect crop growth, development and yield. Water-deficit often combined with high temperature stress is the main abiotic factor limiting crop growth and development. The present investigation was performed to study the effect on yield and physiological and biochemical traits of different wheat genotypes under individual heat stress (HS) and drought stress (DS) and combined stress (HS + DS) conditions. Distinct from prior studies which predominantly utilised pot experiments, this investigation offers valuable insights derived from field conditions, thereby providing a more accurate assessment of crop performance under HS + DS. The field experiment conducted in plant phenotyping facility, a total of 60 experimental plots was maintained, comprising 5 genotypes, 4 treatments and 3 replications. Grain yield declined by 31.9 % under HS, 61.6 % under DS and 66.6 % under HS + DS relative to the control. Both individual and (HS + DS) stresses also impaired physiological functioning. The HS + DS treatment induced more pronounced damaging effects on root architecture as well than either stress alone, with reductions of 42.8 % in root volume, 27.8 % in root surface area and 32 % in total root length compared with the control. Overall, HS + DS was more damaging than the individual stress, however, the interaction effect might be due to cross adaptation effect. The genotype DBW-14 exhibited a high grain yield (270 g plot-1) under HS, DS and HS + DS conditions, indicating robust cross-adaptation mechanisms. Although genotype 11 HPYT433 demonstrated superior tolerance based on root parameters and relative water content (RWC), DBW-14 significantly outperformed all other evaluated genotypes in grain yield and overall physiological stability under the HS + DS treatment. Consequently, DBW-14 serves as a promising donor line for breeding climate-resilient cultivars.","全球大部分小麦（*Triticum aestivum* L.）种植区都面临包括干旱（水分胁迫）和高温（热胁迫）在内的环境胁迫，这些胁迫对作物生长、发育和产量产生不利影响。水分亏缺常与高温胁迫相伴发生，是限制作物生长发育的主要非生物因素。本研究旨在探讨不同小麦基因型在单独热胁迫（HS）、单独干旱胁迫（DS）以及热旱复合胁迫（HS + DS）条件下对产量及生理生化性状的影响。与以往主要采用盆栽试验的研究不同，本研究基于田间条件提供了有价值的见解，从而能更准确地评估作物在HS + DS条件下的表现。田间试验在植物表型鉴定设施中进行，共设置60个试验小区，包含5个基因型、4个处理和3次重复。与对照相比，籽粒产量在HS条件下下降31.9%，在DS条件下下降61.6%，在HS + DS条件下下降66.6%。单一胁迫和复合胁迫（HS + DS）同样损害了生理功能。HS + DS处理对根系构型的损害效应也较任一单一胁迫更为显著，与对照相比，根体积减少42.8%，根表面积减少27.8%，总根长减少32%。总体而言，HS + DS比单一胁迫更具破坏性，然而，其交互效应可能归因于交叉适应机制。基因型DBW-14在HS、DS和HS + DS条件下均表现出较高的籽粒产量（270克\u002F小区），表明其具有稳健的交叉适应机制。尽管基因型11 HPYT433在根系参数和相对含水量（RWC）方面表现出更优的耐受性，但DBW-14在HS + DS处理下的籽粒产量和整体生理稳定性方面显著优于所有其他供试基因型。因此，DBW-14可作为培育气候韧性品种的有前景的供体材料。",null,"Plant Science Today","2026-09-02T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,12,2,1,"田间条件下小麦热旱复合胁迫响应研究，为气候韧性育种提供候选基因型，数据详实，但时效性较低。",[24],{"name":10,"url":6},[26,27,28,29],"小麦","抗逆育种","气候变化","田间试验",{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[36],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":43,"locked":14,"version":44,"method":45,"evidence":46},"classified","2026-10-22.2","rules",{"candidates":47,"agricultural_evidence":50},[48,54],{"code":31,"score":49,"supported":50,"title":51,"abstract":52,"tags":53},15,true,[32],[26,32],[26],{"code":37,"score":49,"supported":50,"title":55,"abstract":57,"tags":59},[56],"drought",[58,56],"干旱",[28],[61,62],"抗逆育种 气候变化 田间试验 小麦","抗逆育种 气候变化","抗逆育种气候变化田间试验小麦-1424",4,"10.14719\u002Fpst.15136",{"doi":65,"openalex_id":67,"authors":68,"venue":10,"cited_by_count":81,"oa_url":6,"card":82,"direction":86,"ingested_from":88},"W7205009424",[69,72,75,78],{"name":70,"orcid":71},"S K T Muni","https:\u002F\u002Forcid.org\u002F0009-0001-4914-4676",{"name":73,"orcid":74},"Shailesh Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-7303-9489",{"name":76,"orcid":77},"G Swati","https:\u002F\u002Forcid.org\u002F0009-0005-6468-3394",{"name":79,"orcid":80},"Sudipta K. Mishra","https:\u002F\u002Forcid.org\u002F0000-0002-2572-3606",0,{"tldr":83,"method":84,"finding":85,"direction":86,"opportunity":87},"田间条件下研究小麦在热、旱及复合胁迫下的产量和生理响应，发现复合胁迫危害更大，并鉴定出耐性基因型DB","田间表型设施，设置5个基因型、4种处理（对照、热、旱、复合），3次重复，测定产量","复合胁迫比单一胁迫更严重，产量降66.6%；DBW-14在复合胁迫下产量最高，具交叉适应机制，是育种","农业遥感与作物表型","可进一步研究DBW-14的交叉适应分子机制，并利用多组学与表型数据开发耐逆基因型筛选模型，助力精准育种。","openalex","2026-09-02T23:30:23.142840Z",{"total":91,"page":21,"page_size":91,"items":92},6,[93,211,272,323,404,460],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":102,"sources":107,"tags":109,"primary_topic":114,"secondary_topics":115,"classification":122,"search_phrases":179,"slug":182,"view_count":21,"doi":183,"paper":184,"created_at":210},3763,"Improving drought tolerance and water use efficiency in dryland wheat and sorghum in the Texas High Plains","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1952813","Texas High Plains (THP) is one of the most productive agricultural regions in the USA, and the economic value of annual total crop production is nearly $2 billion. The area also serves as a major livestock center for fed cattle and, increasingly, other feeding industries such as dairy. The THP has a semi-arid climate with annual precipitation averaging about 480 mm and the seasonal precipitation only can meet part of the evapotranspiration (ET) requirement for high crop yields. For field crop production, drought is the most common factor limiting yield and water use efficiency (WUE). In this review, we focus on research progress and future challenges of dryland cropping systems in the area. Due to the low growing season precipitation and uneven monthly distributions, dryland crops are largely produced under crop-fallow conditions and wheat-fallow and wheat-sorghum fallow are common. Over the decades, both wheat and grain sorghum yields have increased from the level of 1,000–2,000 kg ha −1 in 1950s to >4,000 kg ha −1 in current time. Although improved management practices (e.g., conservation tillage and residue management) have contributed to increased yield and WUE, genetic improvement of drought tolerance also played an important role, particularly the winter wheat. Multiple-year field studies indicated that selecting cultivars with higher biomass and greater early vigor are beneficial to wheat production under drought. We further found that newer drought tolerant (DT) cultivars used soil water more efficiently than a relative older cultivar. Also, new DT cultivars can extract more water from deeper soil profile and have cooler canopy temperature under drought. In recent years, we are also evaluating remote-sensing tools at both ground and aerial levels (airplane and drone) to characterize wheat genotypes for drought tolerance. We demonstrated that infrared thermometer, thermal imaging, and spectral reflectance data can be used to characterize genotypic variation during growing season.","德克萨斯高平原（THP）是美国最具生产力的农业区之一，年度作物总产量的经济价值接近20亿美元。该地区还是主要的肉牛育肥中心，并且日益成为乳业等其他饲养产业的聚集地。THP属于半干旱气候，年均降水量约480毫米，季节性降水仅能满足高产作物蒸散（ET）需求的一部分。对于大田作物生产而言，干旱是限制作物产量和水分利用效率（WUE）的最常见因素。本文综述了该地区旱作种植制度的研究进展与未来挑战。由于生长季降水少且月分布不均，旱地作物主要在作物-休闲制度下生产，小麦-休闲和小麦-高粱休闲是常见模式。数十年来，小麦和高粱产量均从20世纪50年代的1,000–2,000 kg ha⁻¹提高至当前的4,000 kg ha⁻¹以上。尽管改进的管理措施（如保护性耕作和残茬管理）促进了产量和WUE的提升，但耐旱性的遗传改良也发挥了重要作用，尤其是冬小麦。多年田间研究表明，选择生物量较高和早期长势较强的品种有利于干旱条件下的小麦生产。我们进一步发现，较新的耐旱（DT）品种比相对较老的品种更有效地利用土壤水分。此外，新的DT品种能够从更深土层中提取更多水分，并在干旱条件下具有更低的冠层温度。近年来，我们还在评估地面和航空（飞机和无人机）尺度的遥感工具，以表征小麦基因型的耐旱性。我们证明，红外温度计、热成像和光谱反射数据可用于表征生长季内的基因型变异。","Frontiers in Sustainable Food Systems","2026-09-28T00:00:00Z",80,{"impact":17,"substance":103,"depth":17,"authority":104,"freshness":105,"relevant":21,"comment":106},22,14,8,"综述系统梳理了德州高原旱地小麦高粱抗旱与水分利用效率的研究进展，含多年田间数据与遥感表型新方法，对旱作农业与智慧育种有参考价值。",[108],{"name":99,"url":96},[26,110,111,112,113],"旱作农业","高粱","节水灌溉","遥感育种",{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[116,121],{"code":117,"slug":118,"name":119,"path":120,"parent_code":34},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains",{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":43,"locked":14,"version":44,"method":45,"evidence":123},{"candidates":124,"agricultural_evidence":50},[125,130,135,139,146,153,160,167,173],{"code":31,"score":17,"supported":50,"title":126,"abstract":127,"tags":129},[32],[26,128,32],"winter wheat",[26],{"code":117,"score":49,"supported":50,"title":131,"abstract":133,"tags":134},[132],"sorghum",[111,132],[111],{"code":37,"score":104,"supported":50,"title":136,"abstract":137,"tags":138},[56],[58,56],[],{"code":140,"score":91,"supported":50,"title":141,"abstract":142,"tags":145},"C2",[],[143,144],"肉牛","cattle",[],{"code":147,"score":91,"supported":50,"title":148,"abstract":149,"tags":152},"H2",[],[150,151],"土壤","soil",[],{"code":154,"score":155,"supported":50,"title":156,"abstract":157,"tags":159},"H3",5,[],[158],"evapotranspiration",[110,112],{"code":161,"score":162,"supported":50,"title":163,"abstract":164,"tags":166},"C",3,[],[165],"livestock",[],{"code":168,"score":162,"supported":50,"title":169,"abstract":170,"tags":172},"H8",[],[171],"保护性耕作",[],{"code":174,"score":162,"supported":50,"title":175,"abstract":176,"tags":178},"I3",[],[177],"遥感",[],[180,181],"Texas High Plains 小麦 高粱 抗旱","旱地小麦 水分利用效率 遥感","TexasHighPlains小麦高粱抗旱-3763","10.3389\u002Ffsufs.2026.1952813",{"doi":183,"openalex_id":185,"authors":186,"venue":99,"cited_by_count":81,"oa_url":204,"card":205,"direction":86,"ingested_from":88},"W7214633314",[187,189,192,195,198,200,202],{"name":188,"orcid":9},"薛青武",{"name":190,"orcid":191},"Sushil Thapa","https:\u002F\u002Forcid.org\u002F0000-0002-5589-8124",{"name":193,"orcid":194},"Shuyu Liu","https:\u002F\u002Forcid.org\u002F0000-0003-4748-2900",{"name":196,"orcid":197},"Jackie Rudd","https:\u002F\u002Forcid.org\u002F0000-0001-9186-3386",{"name":199,"orcid":9},"Jourdan Bell",{"name":201,"orcid":9},"Thomas Marek",{"name":203,"orcid":9},"Amir Ibrahim","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1952813\u002Fpdf",{"tldr":206,"method":207,"finding":208,"direction":86,"opportunity":209},"综述美国德州高平原旱地小麦和高粱提高耐旱性与水分利用效率的研究进展与挑战。","多年田间试验、保护性耕作、耐旱品种选育及地面与航空遥感表型工具。","耐旱新品种能更高效利用土壤水、深层取水且冠层温度更低，遥感可表征基因型变异。","可结合无人机热成像与光谱反射构建旱地作物耐旱表型高通量筛选模型，加速品种选育。","2026-09-29T23:30:07.283028Z",{"id":212,"title":213,"url":214,"summary":215,"summary_zh":9,"content":9,"source_name":216,"source_url":9,"published_at":217,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":218,"score_detail":219,"sources":221,"tags":223,"primary_topic":229,"secondary_topics":230,"classification":237,"search_phrases":259,"slug":262,"view_count":21,"doi":9,"paper":263,"created_at":271},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21","2026-10-21T00:00:00Z",82,{"impact":17,"substance":103,"depth":17,"authority":104,"freshness":13,"relevant":21,"comment":220},"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[222],{"name":216,"url":214},[224,225,226,227,228],"智慧农业","农业遥感","作物表型","无人机多光谱","小麦渍害",{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[231,236],{"code":174,"slug":232,"name":233,"path":234,"parent_code":235},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":43,"locked":14,"version":44,"method":45,"evidence":238},{"candidates":239,"agricultural_evidence":50},[240,245,249,254],{"code":31,"score":241,"supported":50,"title":242,"abstract":243,"tags":244},11,[26],[26],[],{"code":174,"score":241,"supported":50,"title":246,"abstract":247,"tags":248},[227],[],[226,225,227],{"code":37,"score":105,"supported":50,"title":250,"abstract":252,"tags":253},[251],"渍害",[],[],{"code":255,"score":21,"supported":14,"title":256,"abstract":257,"tags":258},"I1",[],[],[224],[260,261],"江苏省农科院 小麦渍害 多光谱","VGG21 小麦 硅肥调控","江苏省农科院小麦渍害多光谱-5535",{"doi":9,"openalex_id":9,"authors":264,"venue":9,"cited_by_count":81,"oa_url":9,"card":265,"direction":86,"ingested_from":270},[],{"tldr":266,"method":267,"finding":268,"direction":86,"opportunity":269},"用无人机多光谱图像和VGG21模型识别小麦渍害后硅肥与氨基酸调控效果。","大疆精灵4采集冠层多光谱影像，构建VGG21深度学习分类模型。","VGG21对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","可扩展多生育期、多调控组合与轻量化模型，探索渍害胁迫等级与调控效果的同步识别。","agent","2026-10-22T00:07:10.070916Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":9,"content":9,"source_name":277,"source_url":9,"published_at":278,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":279,"score_detail":280,"sources":285,"tags":287,"primary_topic":288,"secondary_topics":289,"classification":292,"search_phrases":311,"slug":314,"view_count":81,"doi":9,"paper":315,"created_at":322},5296,"雷添杰研究员团队《基于无人机多光谱图像和VGG21模型的小麦渍害调控效果识别》","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-10-16\u002Fdoc-inivmprm8363597.shtml","《智慧农业(中英文)》2026年第8卷第4期刊载梁万杰、刘晓君、雷添杰等论文,在小麦拔节—抽穗和抽穗—灌浆两个生长阶段进行对照、渍水胁迫、硅肥调控、氨基酸调控控制试验;利用大疆精灵4多光谱无人机采集小麦冠层多光谱图像建立数据集,基于VGG21模型构建识别模型,发现硅肥调控样本识别性能最好,识别精度最高,具良好鲁棒性和可扩展性,可为小麦渍害综合防控提供理论与技术支持。","智慧农业(中英文)(2026,8(4))","2026-10-16T00:00:00Z",76,{"impact":281,"substance":282,"depth":283,"authority":104,"freshness":105,"relevant":21,"comment":284},16,21,17,"核心期刊论文，方法新颖、结论可靠，对小麦渍害防控有实用价值，值得进入每日精选。",[286],{"name":277,"url":275},[224,225,226,227,228],{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[290,291],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"code":174,"slug":232,"name":233,"path":234,"parent_code":235},{"status":43,"locked":14,"version":44,"method":45,"evidence":293},{"candidates":294,"agricultural_evidence":50},[295,299,303,307],{"code":31,"score":241,"supported":50,"title":296,"abstract":297,"tags":298},[26],[26],[],{"code":37,"score":241,"supported":50,"title":300,"abstract":301,"tags":302},[251],[251],[],{"code":174,"score":241,"supported":50,"title":304,"abstract":305,"tags":306},[227],[],[226,225,227],{"code":255,"score":64,"supported":50,"title":308,"abstract":309,"tags":310},[],[224],[224],[312,313],"雷添杰 小麦渍害 无人机多光谱","VGG21 小麦渍害 识别模型","雷添杰小麦渍害无人机多光谱-5296",{"doi":9,"openalex_id":9,"authors":316,"venue":9,"cited_by_count":81,"oa_url":9,"card":317,"direction":86,"ingested_from":270},[],{"tldr":318,"method":319,"finding":320,"direction":86,"opportunity":321},"用无人机多光谱图像和VGG21模型识别小麦渍害调控效果。","大疆精灵4采集冠层多光谱图像，构建VGG21识别模型。","硅肥调控样本识别性能最好、精度最高，模型鲁棒可扩展。","可探索多生育期多光谱特征与不同调控措施的耦合机制，并扩展到其他作物渍害监测。","2026-10-19T00:07:48.071055Z",{"id":324,"title":325,"url":326,"summary":327,"summary_zh":328,"content":9,"source_name":99,"source_url":326,"published_at":329,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":330,"score_detail":331,"sources":334,"tags":336,"primary_topic":342,"secondary_topics":343,"classification":350,"search_phrases":377,"slug":380,"view_count":64,"doi":381,"paper":382,"created_at":403},3263,"Identifying drought-responsive candidate genes in root and shoot tissues of wheat (Triticum aestivum L.) through RNA sequencing, cluster analysis, weighted gene co-expression network analysis, and machine learning models","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1952456","Drought stress severely affects the growth, development, and yield formation of wheat. However, the differentiated regulatory mechanisms of wheat roots and shoots during drought adaptation remain to be further analyzed. Therefore, this study used RNA-seq datasets of roots and shoots from multiple wheat varieties, combined with differential expression analysis, Gene Ontology (GO) enrichment analysis, clustering analysis, weighted gene co-expression network analysis (WGCNA), and machine learning were used to characterize drought-responsive transcriptional changes. Differential expression analysis identified 90 and 3,991 differentially expressed genes (DEGs) in roots under DS1 and DS2 respectively, compared with 34 and 1,940 DEGs in shoots. Functional enrichment analysis indicated that root DEGs were mainly involved in root growth, cell wall remodeling, water transport, and osmotic adjustment processes, whereas shoot DEGs are mainly enriched in processes related to stomatal regulation, photosynthesis, lipid metabolism, and oxidative stress responses. WGCNA identified 18 and 19 co-expression modules in roots and shoots, respectively, among which the yellow module was significantly correlated with the response to drought stress in root and shoot tissues. To further screen drought-responsive candidate genes from these modules, we compared multiple machine learning models. The XGBoost model achieved the highest and AUC in both roots and shoots. Combined with SHAP feature contribution analysis, 30 drought-responsive candidate genes with high discriminatory power were identified separately in roots and shoots. Analysis of cis -acting elements in candidate gene promoters revealed abundant drought- and stress-responsive elements, including MYB, G-box, ABRE, MYC, MBS, and LTR. MYB (183 in roots and 164 in shoots), G-box (168 and 98), and ABRE (148 and 86) were particularly abundant, and several candidate genes contained more than five ABRE copies in their promoters, suggesting potential involvement in ABA-mediated drought responses. This study reveals the different transcriptional response processes of wheat roots and shoots under drought stress, and identifies key co-expression modules and core candidate genes through WGCNA combined with XGBoost and SHAP analysis. These findings provide a basis for understanding organ-specific molecular responses to drought stress and offer potential targets for improving drought tolerance in wheat.","干旱胁迫严重影响小麦的生长发育和产量形成。然而，小麦根系和地上部在干旱适应过程中的差异化调控机制仍有待深入解析。因此，本研究利用多个小麦品种根系和地上部的RNA-seq数据集，结合差异表达分析、基因本体（GO）富集分析、聚类分析、加权基因共表达网络分析（WGCNA）和机器学习方法，表征干旱响应转录变化。差异表达分析在DS1和DS2条件下分别鉴定出根系中90个和3,991个差异表达基因（DEGs），而地上部中分别为34个和1,940个DEGs。功能富集分析表明，根系DEGs主要参与根系生长、细胞壁重塑、水分运输和渗透调节过程，而地上部DEGs主要富集于气孔调控、光合作用、脂质代谢和氧化胁迫响应相关过程。WGCNA在根系和地上部中分别鉴定出18个和19个共表达模块，其中黄色模块与根系和地上部组织对干旱胁迫的响应显著相关。为进一步从这些模块中筛选干旱响应候选基因，我们比较了多种机器学习模型。XGBoost模型在根系和地上部中均取得了最高的AUC。结合SHAP特征贡献分析，分别在根系和地上部中鉴定出30个具有高判别力的干旱响应候选基因。候选基因启动子中的顺式作用元件分析揭示了丰富的干旱和胁迫响应元件，包括MYB、G-box、ABRE、MYC、MBS和LTR。其中MYB（根系183个，地上部164个）、G-box（168个和98个）和ABRE（148个和86个）尤为丰富，且多个候选基因的启动子中含有超过5个ABRE拷贝，表明其可能参与ABA介导的干旱响应。本研究揭示了小麦根系和地上部在干旱胁迫下不同的转录响应过程，并通过WGCNA结合XGBoost和SHAP分析鉴定了关键共表达模块和核心候选基因。这些发现为理解干旱胁迫的器官特异性分子响应提供了基础，并为提高小麦耐旱性提供了潜在靶点。","2026-09-23T00:00:00Z",81,{"impact":17,"substance":103,"depth":17,"authority":332,"freshness":13,"relevant":21,"comment":333},13,"该研究结合多组学与机器学习挖掘小麦抗旱候选基因，方法新颖、数据扎实，对分子育种有参考价值，值得进入每日精选。",[335],{"name":99,"url":326},[337,338,339,340,341],"农业人工智能","种业振兴","基因挖掘","作物育种","小麦抗旱",{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[344,349],{"code":345,"slug":346,"name":347,"path":348,"parent_code":235},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":43,"locked":14,"version":44,"method":45,"evidence":351},{"candidates":352,"agricultural_evidence":50},[353,359,363,367,372],{"code":345,"score":49,"supported":50,"title":354,"abstract":356,"tags":358},[355],"machine learning",[357,355],"机器学习",[337],{"code":31,"score":104,"supported":50,"title":360,"abstract":361,"tags":362},[32],[26,32],[],{"code":37,"score":104,"supported":50,"title":364,"abstract":365,"tags":366},[56],[58,56],[],{"code":368,"score":21,"supported":14,"title":369,"abstract":370,"tags":371},"F1",[],[],[338],{"code":373,"score":21,"supported":14,"title":374,"abstract":375,"tags":376},"F2",[],[],[339],[378,379],"小麦 抗旱基因 RNA测序","小麦 根系 地上部 干旱响应","小麦抗旱基因RNA测序-3263","10.3389\u002Ffsufs.2026.1952456",{"doi":381,"openalex_id":383,"authors":384,"venue":99,"cited_by_count":81,"oa_url":326,"card":397,"direction":401,"ingested_from":88},"W7214062209",[385,387,390,392,394],{"name":386,"orcid":9},"Tianle Ji",{"name":388,"orcid":389},"Baoyue Cui","https:\u002F\u002Forcid.org\u002F0009-0000-5881-9682",{"name":391,"orcid":9},"Jinjie Yin",{"name":393,"orcid":9},"Xianyong Meng",{"name":395,"orcid":396},"Jun Yan","https:\u002F\u002Forcid.org\u002F0000-0001-6883-8529",{"tldr":398,"method":399,"finding":400,"direction":401,"opportunity":402},"通过RNA-seq、WGCNA和XGBoost-SHAP分析，鉴定小麦根和茎中响应干旱的候选基因。","RNA-seq、差异表达、WGCNA、XGBoost与SHAP特征分析。","根和茎干旱响应机制不同，各鉴定出30个候选基因，启动子富含MYB、G-box和ABRE元件。","农业人工智能与决策模型","可结合多组学与深度学习构建器官特异性干旱调控网络，并开展候选基因功能验证。","2026-09-23T23:30:07.062126Z",{"id":405,"title":406,"url":407,"summary":408,"summary_zh":9,"content":9,"source_name":409,"source_url":9,"published_at":410,"category":12,"cover_url":9,"hotness":411,"is_selected":14,"score":412,"score_detail":413,"sources":415,"tags":419,"primary_topic":422,"secondary_topics":423,"classification":426,"search_phrases":449,"slug":452,"view_count":162,"doi":9,"paper":453,"created_at":459},2997,"基于无人机多光谱图像和VGG21模型的小麦渍害调控效果识别方法","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7685741381196825088","江苏省农业科学院农业信息研究所梁万杰等联合中国农科院农业环境与可持续发展研究所、湖北粮作所、扬州大学等团队，针对小麦渍害防控提出基于无人机多光谱图像和VGG21模型的快速无损识别方法。在小麦拔节-抽穗和抽穗-灌浆两个阶段开展对照、渍水胁迫、硅肥调控和氨基酸调控4个类别数据集，大疆精灵4多光谱无人机采集小麦冠层多光谱图像，测产评估调控效果。","智慧农业(中英文)2026,8(4):60-69","2026-09-15T12:42:00Z",25,78,{"impact":17,"substance":103,"depth":17,"authority":104,"freshness":91,"relevant":21,"comment":414},"多机构协作提出无人机多光谱结合VGG21的小麦渍害无损识别方法，方法新颖、数据扎实，对智慧农业植保监测有参考价值。",[416,417],{"name":409,"url":407},{"name":418,"url":275},"智慧农业(中英文)8(4) 60-69(2026)",[224,337,420,228,421],"无人机遥感","多光谱成像",{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[424,425],{"code":174,"slug":232,"name":233,"path":234,"parent_code":235},{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":43,"locked":14,"version":44,"method":45,"evidence":427},{"candidates":428,"agricultural_evidence":50},[429,433,437,441,445],{"code":174,"score":332,"supported":50,"title":430,"abstract":431,"tags":432},[227],[227],[421,420],{"code":31,"score":241,"supported":50,"title":434,"abstract":435,"tags":436},[26],[26],[],{"code":37,"score":241,"supported":50,"title":438,"abstract":439,"tags":440},[251],[251],[],{"code":255,"score":21,"supported":14,"title":442,"abstract":443,"tags":444},[],[],[224],{"code":345,"score":21,"supported":14,"title":446,"abstract":447,"tags":448},[],[],[337],[450,451],"江苏省农科院 小麦渍害 无人机多光谱","VGG21 小麦 渍害识别","江苏省农科院小麦渍害无人机多光谱-2997",{"doi":9,"openalex_id":9,"authors":454,"venue":9,"cited_by_count":81,"oa_url":9,"card":455,"direction":86,"ingested_from":270},[],{"tldr":318,"method":456,"finding":457,"direction":86,"opportunity":458},"大疆精灵4多光谱无人机采集冠层图像，构建VGG21分类模型。","该方法可快速无损识别渍害及硅肥、氨基酸调控效果。","可探索多光谱与深度学习结合评估其他逆境调控措施，并迁移至多作物场景。","2026-09-20T00:03:07.858899Z",{"id":461,"title":462,"url":463,"summary":464,"summary_zh":465,"content":9,"source_name":466,"source_url":463,"published_at":217,"category":12,"cover_url":9,"hotness":13,"is_selected":50,"score":467,"score_detail":468,"sources":472,"tags":474,"primary_topic":479,"secondary_topics":484,"classification":486,"search_phrases":518,"slug":521,"view_count":81,"doi":522,"paper":523,"created_at":537},5569,"A four-decadal (1981–2024) quality-assured ground-based soybean phenology dataset from Northeast China: a FAIR-compliant resource for climate impact and agricultural adaptation studies","https:\u002F\u002Fdoi.org\u002F10.1080\u002F20964471.2026.2735138","Although satellite remote sensing has been widely used to monitor land surface phenology, long-term, high-quality ground-based observations are essential for validating and calibrating these products, particularly for understanding agricultural ecosystem responses to climate change. However, such ground-truth datasets remain scarce, limiting the full potential of Earth system data applications. Here we present a comprehensive ground-based soybean phenology dataset for Northeast China, a region critical to national food security. The dataset integrates historical paper-based logs (1980s–1990s) with modern electronic records (2000s onwards) from 43 national agrometeorological stations operated by the China Meteorological Administration (CMA), spanning a 15° latitudinal gradient (38–53°N) across Heilongjiang, Jilin, and Liaoning provinces over 44 years (1981–2024). It provides the timing (day of year, DOY) of eight key phenophases (sowing, emergence, three-leaf, branching, flowering, pod formation, seed filling, maturity) and the durations of six intervening growth intervals. A multi-tiered quality assurance framework combining automated consistency checks, statistical outlier detection, and expert arbitration against agronomic principles and auxiliary climate data ensures high data integrity and completeness (>99%; paper era: 93.8%; electronic era: 98.1%). To facilitate reuse, we provide pre-computed kernel density estimates (probability distributions) and univariate linear regression trends for each station and phenological variable, along with 656 diagnostic plots. The dataset is openly available in the Science Data Bank under a CC BY 4.0 license as primary data tables (XLSX) and diagnostic plots (JPEG, 1200 dpi) (https:\u002F\u002Fdoi.org\u002F10.57760\u002Fsciencedb.37245). As a long-term, spatially explicit, FAIR-compliant ground-truth resource, this dataset is valuable for validating satellite-derived phenology products, calibrating crop modules, and investigating climate-driven impacts on agricultural systems.","尽管卫星遥感已被广泛用于监测地表物候，但长期、高质量的地面观测对于验证和校准这些产品至关重要，尤其是对于理解农业生态系统对气候变化的响应而言。然而，此类地面真值数据集仍然稀缺，限制了地球系统数据应用潜力的充分发挥。本文介绍了一套面向中国东北地区的综合性大豆物候地面数据集，该地区对国家粮食安全至关重要。该数据集整合了中国气象局（CMA）运营的43个国家农业气象站的历史纸质记录（20世纪80—90年代）与现代电子记录（2000年代至今），覆盖黑龙江、吉林和辽宁三省跨越15°纬度梯度（38—53°N）、时间跨度44年（1981—2024年）。数据集提供了8个关键物候期（播种、出苗、三叶、分枝、开花、结荚、鼓粒、成熟）的发生时间（年积日，DOY）以及6个中间生长间隔的持续时间。一套多层质量保证框架结合了自动一致性检查、统计异常值检测以及依据农学原理和辅助气候数据进行的专家仲裁，确保了高数据完整性和完整性（>99%；纸质时代：93.8%；电子时代：98.1%）。为便于重复使用，我们提供了每个站点和物候变量的预计算核密度估计（概率分布）和单变量线性回归趋势，以及656幅诊断图。该数据集以CC BY 4.0许可在Science Data Bank公开提供，包括主数据表（XLSX）和诊断图（JPEG，1200 dpi）（https:\u002F\u002Fdoi.org\u002F10.57760\u002Fsciencedb.37245）。作为一个长期、空间明确、符合FAIR原则的地面真值资源，该数据集对于验证卫星反演的物候产品、校准作物模块以及研究气候驱动对农业系统的影响具有重要价值。","Big Earth Data",89,{"impact":103,"substance":469,"depth":470,"authority":104,"freshness":13,"relevant":21,"comment":471},24,19,"覆盖东北44年、43站的大豆物候地面观测数据集，质控严格且FAIR开放，对遥感验证与气候影响研究有实质价值，值得入选。",[473],{"name":466,"url":463},[475,28,476,477,478],"大豆","东北农业","物候数据","遥感验证",{"code":480,"slug":481,"name":482,"path":483,"parent_code":34},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[485],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":43,"locked":14,"version":44,"method":45,"evidence":487},{"candidates":488,"agricultural_evidence":50},[489,494,500,507,512],{"code":480,"score":49,"supported":50,"title":490,"abstract":492,"tags":493},[491],"soybean",[475,491],[475],{"code":37,"score":13,"supported":50,"title":495,"abstract":496,"tags":499},[],[28,497,498],"农业气象","climate change",[28],{"code":501,"score":91,"supported":50,"title":502,"abstract":503,"tags":506},"A8",[],[504,505],"粮食安全","food security",[],{"code":174,"score":91,"supported":50,"title":508,"abstract":509,"tags":511},[],[177,510],"remote sensing",[],{"code":513,"score":162,"supported":50,"title":514,"abstract":515,"tags":517},"H6",[],[516],"农业生态",[],[519,520],"东北 大豆 物候 数据集","大豆 物候 遥感 验证","东北大豆物候数据集-5569","10.1080\u002F20964471.2026.2735138",{"doi":522,"openalex_id":524,"authors":525,"venue":466,"cited_by_count":81,"oa_url":463,"card":532,"direction":86,"ingested_from":88},"W7223091615",[526,529],{"name":527,"orcid":528},"Quanjun Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-6087-3928",{"name":530,"orcid":531},"Dongli Wu","https:\u002F\u002Forcid.org\u002F0000-0003-1676-2264",{"tldr":533,"method":534,"finding":535,"direction":86,"opportunity":536},"发布东北大豆1981–2024年44年地面物候数据集，含8个物候期与6个间隔期。","整合43个农气站纸质与电子记录，经自动校验、统计离群检测和专家仲裁质控。","数据完整度超99%，并提供核密度分布、线性趋势与656张诊断图。","可基于该地面真值校准遥感物候产品与作物模型，量化气候变化对大豆物候的影响。","2026-10-22T11:20:39.146445Z"]