[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5158":3,"related-5158":118},{"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":98,"slug":101,"view_count":102,"doi":103,"paper":104,"created_at":117},5158,"POTATO LATE BLIGHT CAUSED BY PHYTOPHTHORA INFESTANS: BIOLOGY,EPIDEMIOLOGY, CLIMATE CHANGE, AND SUSTAINABLE MANAGEMENT STRATEGIES","https:\u002F\u002Fdoi.org\u002F10.56093\u002Fpotatoj.v53i1.181670","Potato late blight, caused by Phytophthora infestans, remains one of the mostdestructive diseases affecting global potato production, resulting in substantial yield and economiclosses. This review summarizes the pathogen’s biology, disease cycle, epidemiology, host–pathogeninteractions, and current diagnostic approaches, along with major management strategies, includingcultural practices, host resistance, biological control, botanical extracts, chemical control, diseaseforecasting, and integrated disease management. It also highlights the impacts of climate changeon disease distribution and severity, as well as recent advances in molecular breeding, genomeediting, and precision agriculture. Overall, sustainable management of potato late blight requiresintegrating resistant cultivars, environmentally friendly control measures, disease forecasting, andjudicious fungicide use. Continued research on durable resistance, climate-smart management, andemerging biotechnological tools will be essential to enhance potato resilience and ensure futurefood security.","由致病疫霉（Phytophthora infestans）引起的马铃薯晚疫病仍是影响全球马铃薯生产的最具破坏性的病害之一，造成严重的产量和经济损失。本文综述了该病原菌的生物学特性、病害循环、流行病学、寄主—病原互作及当前诊断方法，并总结了主要防控策略，包括栽培措施、寄主抗性、生物防治、植物提取物、化学防治、病害预测及病害综合管理。文章还重点讨论了气候变化对病害分布和严重度的影响，以及分子育种、基因组编辑和精准农业方面的最新进展。总体而言，马铃薯晚疫病的可持续治理需要整合抗病品种、环境友好型防控措施、病害预测和合理使用杀菌剂。持续开展持久抗性、气候智慧型管理及新兴生物技术工具的研究，对于增强马铃薯抗逆性和保障未来粮食安全至关重要。",null,"Potato Journal","2026-10-15T00:00:00Z","论文",10,false,82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,21,18,13,8,1,"核心期刊综述，系统梳理晚疫病生物学、气候变化影响与可持续防控，对马铃薯病害防控与智慧植保具参考价值，但属综述类论文，非突破性成果。",[25],{"name":10,"url":6},[27,28,29,30,31],"马铃薯","绿色防控","分子育种","气候变化","晚疫病",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","A",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,58,64,71,79,86,93],{"code":33,"score":52,"supported":53,"title":54,"abstract":56,"tags":57},15,true,[55],"potato",[27,55],[27],{"code":40,"score":59,"supported":53,"title":60,"abstract":62,"tags":63},12,[61],"climate change",[30],[30],{"code":65,"score":66,"supported":53,"title":67,"abstract":68,"tags":70},"F2",7,[],[29,69],"molecular breeding",[29],{"code":72,"score":73,"supported":53,"title":74,"abstract":75,"tags":78},"I1",6,[],[76,77],"精准农业","precision agriculture",[],{"code":80,"score":81,"supported":53,"title":82,"abstract":83,"tags":85},"F4",4,[],[84],"生物防治",[28],{"code":87,"score":88,"supported":53,"title":89,"abstract":90,"tags":92},"A8",3,[],[91],"粮食安全",[],{"code":94,"score":22,"supported":14,"title":95,"abstract":96,"tags":97},"H8",[],[],[28],[99,100],"马铃薯 晚疫病 致病疫霉","Potato Journal 晚疫病","马铃薯晚疫病致病疫霉-5158",0,"10.56093\u002Fpotatoj.v53i1.181670",{"doi":103,"openalex_id":105,"authors":106,"venue":10,"cited_by_count":102,"oa_url":6,"card":109,"direction":115,"ingested_from":116},"W7222725701",[107],{"name":108,"orcid":9},"Sujata Bashyal",{"tldr":110,"method":111,"finding":112,"direction":113,"opportunity":114},"综述马铃薯晚疫病的生物学、流行学、气候变化影响及可持续防控策略。","文献综述，整合病原生物学、诊断、抗性育种、生物防治与精准农业技术。","可持续防控需整合抗病品种、绿色措施、病害预测与合理用药。","农业人工智能与决策模型","可结合气象与遥感数据构建气候智慧型晚疫病预测模型，支撑精准施药决策。","智慧农业 \u002F 农业物联网","openalex","2026-10-17T23:30:12.547775Z",{"total":73,"page":22,"page_size":73,"items":119},[120,231,337,389,461,521],{"id":121,"title":122,"url":123,"summary":124,"summary_zh":125,"content":9,"source_name":126,"source_url":123,"published_at":127,"category":12,"cover_url":9,"hotness":13,"is_selected":53,"score":15,"score_detail":128,"sources":133,"tags":135,"primary_topic":139,"secondary_topics":140,"classification":142,"search_phrases":207,"slug":210,"view_count":88,"doi":211,"paper":212,"created_at":230},3542,"Drought vulnerability assessment and its severe impact on crop production and livelihood of people: An empirical modelling of northern Ghana","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pclm.0001038","Climate change is intensifying drought vulnerability in northern Ghana, posing serious risks to crop production, food security, and rural livelihoods in semi-arid regions. This study develops an empirical modelling framework to assess drought vulnerability and impacts at three temporal horizons, 2026–2050, 2051–2075, and 2076–2100, under SSP1-2.6 and SSP5-8.5 scenarios. We integrated bias-corrected outputs from five CMIP6 models, MRI-ESM2–0, EC-Earth3, IPSL-CM6A-LR, CESM2, and HadGEM3-GC31-LL, downscaled to 0.25° resolution, with drought indices including the Standardized Precipitation Index SPI, Reconnaissance Drought Index RDI, Normalized Difference Drought Index NDDI, and Soil Moisture Index SMI. These were combined with MODIS-derived Normalized Difference Vegetation Index NDVI, population density, and land use\u002Fland cover using GIS-based tools to generate composite drought vulnerability maps. CMIP6 models were validated against reference datasets and showed strong performance for annual precipitation, r = 0.95, RMSE = 3.4 mm, and temperature, r = 0.99, RMSE = 0.47–0.74°C. Results project increasing drought severity, with mean temperature rises of 2.0–3.9°C and rainfall declines up to 19% by 2100. Crop yields for maize, millet, sorghum, and groundnuts are expected to fall by 25–60% by 2050, with maize showing the strongest negative correlation to drought severity, r = –0.72, p \u003C 0.01, confirmed by Mann-Kendall trends at the 95% confidence level. About 84% of the study area is classified as highly vulnerable due to limited adaptive capacity, over 80% dependence on rain-fed agriculture, more than 49% household food insecurity, less than 23% cropland use, and under 5% water body coverage. Significant uncertainties remain in precipitation projections, with some models showing divergent temperature trends, highlighting challenges in modelling climate impacts. The findings underscore the urgent need for district-level drought preparedness, solar-powered irrigation, index insurance, climate-smart agriculture, and Disaster Risk Reduction aligned with UNDRR and Sendai Framework priorities to build resilience in northern Ghana.","气候变化正在加剧加纳北部的干旱脆弱性，对半干旱地区的作物生产、粮食安全和农村生计构成严重风险。本研究构建了一个经验建模框架，用于评估2026—2050年、2051—2075年和2076—2100年三个时间尺度下SSP1-2.6和SSP5-8.5情景中的干旱脆弱性及其影响。我们将五个CMIP6模型（MRI-ESM2–0、EC-Earth3、IPSL-CM6A-LR、CESM2和HadGEM3-GC31-LL）的偏差校正输出降尺度至0.25°分辨率，并与标准化降水指数（SPI）、侦察干旱指数（RDI）、归一化差值干旱指数（NDDI）和土壤水分指数（SMI）等干旱指数相结合。这些数据与MODIS衍生的归一化差值植被指数（NDVI）、人口密度以及土地利用\u002F土地覆盖数据通过基于GIS的工具相结合，生成综合干旱脆弱性地图。CMIP6模型经参考数据集验证，在年降水量（r = 0.95，RMSE = 3.4 mm）和温度（r = 0.99，RMSE = 0.47–0.74°C）方面表现出色。结果预测干旱严重程度将加剧，到2100年平均气温上升2.0–3.9°C，降雨量下降高达19%。到2050年，玉米、小米、高粱和花生的作物产量预计将下降25–60%，其中玉米与干旱严重程度呈最强负相关（r = –0.72，p \u003C 0.01），并经Mann-Kendall趋势检验在95%置信水平上得到证实。约84%的研究区域被归类为高度脆弱，原因包括适应能力有限、超过80%依赖雨养农业、超过49%的家庭粮食不安全、不足23%的耕地利用率以及低于5%的水体覆盖率。降水预测仍存在显著不确定性，部分模型显示出不同的温度趋势，凸显了气候影响建模的挑战。研究结果强调迫切需要开展县级干旱备灾、太阳能灌溉、指数保险、气候智慧型农业以及与UNDRR和仙台框架优先事项一致的风险减灾工作，以增强加纳北部的韧性。","PLOS Climate","2026-09-25T00:00:00Z",{"impact":19,"substance":129,"depth":19,"authority":130,"freshness":131,"relevant":22,"comment":132},23,14,9,"基于CMIP6多模型与遥感指数的干旱脆弱性实证研究，数据扎实、结论明确，对半干旱区农业防灾与气候适应有参考价值。",[134],{"name":126,"url":123},[91,136,30,137,138],"防灾减灾","遥感监测","干旱风险",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[141],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":143},{"candidates":144,"agricultural_evidence":53},[145,152,159,164,171,178,185,191,196,202],{"code":40,"score":146,"supported":53,"title":147,"abstract":149,"tags":151},19,[148],"drought",[30,150,61,148],"干旱",[30,136],{"code":33,"score":131,"supported":53,"title":153,"abstract":154,"tags":158},[],[155,156,157],"高粱","sorghum","millet",[],{"code":87,"score":66,"supported":53,"title":160,"abstract":161,"tags":163},[],[91,162],"food security",[91],{"code":165,"score":73,"supported":53,"title":166,"abstract":167,"tags":170},"A3",[],[168,169],"玉米","maize",[],{"code":172,"score":73,"supported":53,"title":173,"abstract":174,"tags":177},"H2",[],[175,176],"土壤","soil",[],{"code":179,"score":73,"supported":53,"title":180,"abstract":181,"tags":184},"H3",[],[182,183],"灌溉","irrigation",[],{"code":186,"score":88,"supported":53,"title":187,"abstract":188,"tags":190},"A4",[],[189],"花生",[],{"code":72,"score":88,"supported":53,"title":192,"abstract":193,"tags":195},[],[194],"smart agriculture",[],{"code":197,"score":88,"supported":53,"title":198,"abstract":199,"tags":201},"I4",[],[200],"GIS",[],{"code":203,"score":22,"supported":14,"title":204,"abstract":205,"tags":206},"I3",[],[],[137],[208,209],"加纳北部 干旱 玉米","CMIP6 干旱脆弱性 作物产量","加纳北部干旱玉米-3542","10.1371\u002Fjournal.pclm.0001038",{"doi":211,"openalex_id":213,"authors":214,"venue":126,"cited_by_count":102,"oa_url":123,"card":224,"direction":115,"ingested_from":116},"W7214399028",[215,218,221],{"name":216,"orcid":217},"Theophilus Francis Kofitio","https:\u002F\u002Forcid.org\u002F0009-0000-7147-6581",{"name":219,"orcid":220},"Peace Korshiwor Amoatey","https:\u002F\u002Forcid.org\u002F0000-0002-2794-9721",{"name":222,"orcid":223},"Evans Asenso","https:\u002F\u002Forcid.org\u002F0000-0003-3116-6356",{"tldr":225,"method":226,"finding":227,"direction":228,"opportunity":229},"构建干旱脆弱性经验模型，评估加纳北部未来气候情景下作物与生计影响。","集成5个CMIP6模型、SPI\u002FRDI\u002FNDDI\u002FSMI指数与MODIS NDV","至2100年升温2.0-3.9°C、降水降19%，2050年作物减产25-60%，84%区域高度脆弱","农业遥感与作物表型","可结合遥感干旱指数与作物模型，在数据稀缺区开展多情景脆弱性动态评估与适应策略优化。","2026-09-26T23:30:10.153994Z",{"id":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":9,"source_name":237,"source_url":234,"published_at":238,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":239,"score_detail":240,"sources":242,"tags":244,"primary_topic":249,"secondary_topics":253,"classification":256,"search_phrases":305,"slug":308,"view_count":22,"doi":309,"paper":310,"created_at":336},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":19,"substance":17,"depth":19,"authority":130,"freshness":21,"relevant":22,"comment":241},"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。",[243],{"name":237,"url":234},[245,246,155,247,248],"小麦","旱作农业","节水灌溉","遥感育种",{"code":250,"slug":251,"name":245,"path":252,"parent_code":37},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat",[254,255],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":257},{"candidates":258,"agricultural_evidence":53},[259,264,268,272,279,283,289,295,300],{"code":250,"score":19,"supported":53,"title":260,"abstract":261,"tags":263},[251],[245,262,251],"winter wheat",[245],{"code":33,"score":52,"supported":53,"title":265,"abstract":266,"tags":267},[156],[155,156],[155],{"code":40,"score":130,"supported":53,"title":269,"abstract":270,"tags":271},[148],[150,148],[],{"code":273,"score":73,"supported":53,"title":274,"abstract":275,"tags":278},"C2",[],[276,277],"肉牛","cattle",[],{"code":172,"score":73,"supported":53,"title":280,"abstract":281,"tags":282},[],[175,176],[],{"code":179,"score":284,"supported":53,"title":285,"abstract":286,"tags":288},5,[],[287],"evapotranspiration",[246,247],{"code":290,"score":88,"supported":53,"title":291,"abstract":292,"tags":294},"C",[],[293],"livestock",[],{"code":94,"score":88,"supported":53,"title":296,"abstract":297,"tags":299},[],[298],"保护性耕作",[],{"code":203,"score":88,"supported":53,"title":301,"abstract":302,"tags":304},[],[303],"遥感",[],[306,307],"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":309,"openalex_id":311,"authors":312,"venue":237,"cited_by_count":102,"oa_url":330,"card":331,"direction":228,"ingested_from":116},"W7214633314",[313,315,318,321,324,326,328],{"name":314,"orcid":9},"薛青武",{"name":316,"orcid":317},"Sushil Thapa","https:\u002F\u002Forcid.org\u002F0000-0002-5589-8124",{"name":319,"orcid":320},"Shuyu Liu","https:\u002F\u002Forcid.org\u002F0000-0003-4748-2900",{"name":322,"orcid":323},"Jackie Rudd","https:\u002F\u002Forcid.org\u002F0000-0001-9186-3386",{"name":325,"orcid":9},"Jourdan Bell",{"name":327,"orcid":9},"Thomas Marek",{"name":329,"orcid":9},"Amir Ibrahim","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1952813\u002Fpdf",{"tldr":332,"method":333,"finding":334,"direction":228,"opportunity":335},"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":338,"title":339,"url":340,"summary":341,"summary_zh":9,"content":342,"source_name":343,"source_url":9,"published_at":9,"category":344,"cover_url":9,"hotness":13,"is_selected":14,"score":345,"score_detail":346,"sources":349,"tags":351,"primary_topic":355,"secondary_topics":356,"classification":362,"search_phrases":383,"slug":386,"view_count":387,"doi":9,"paper":9,"created_at":388},5210,"中国农科院西部中心基于RNA干扰研发马铃薯根结线虫绿色防控新技术","https:\u002F\u002Fwri.caas.cn\u002Fmtbd\u002F072be27fc1864e96bd75c1f41598318b.htm","中国农业科学院西部农业研究中心联合国内多家科研单位,成功攻克根结线虫绿色防控关键难题,研发基于RNA干扰的新型防控技术,经纳米载体保护后根结线虫摄取量提升42.3%,基因沉默效果提高1.9倍,幼虫死亡率提升24.2%,温室防治效果提升至78.4%。","近日，中国农业科学院西部农业研究中心西部作物生物安全防控创新团队联合国内多家科研单位，成功攻克根结线虫绿色防控关键难题，研发出基于RNA干扰的新型防控技术，为绿色生物农药落地推广打下坚实基础，也为新疆粮食等作物病虫害治理带来全新的技术方案。\n\n中国农业科学院西部农业研究中心西部作物生物安全防控创新团队科研人员在田间查看根结线虫防控效果。李伦 摄\n\n该中心副主任彭焕介绍，根结线虫是潜藏于土壤中、破坏力极强的“隐形害虫”，可侵染3000余种植物，包括粮食、蔬菜等各类农作物。作物根系被根结线虫侵染取食后，会形成大小不一的根瘤根结，严重削弱作物根系吸收水肥的能力，造成植株生长衰弱、产量锐减。这一虫害也是影响新疆农作物稳产提质的重要原因。\n\n以往防治根结线虫，主要依靠化学类药剂。而长期施用化学药剂，不仅会让根结线虫逐渐产生抗药性，造成药效减退，还会给土壤带来负担。因此农业生产迫切需要高效环保的替代防控技术。RNA干扰技术是极具潜力的绿色防控手段，它能够精准“关闭”该害虫的关键基因，只针对靶标害虫起效，不会伤害其他生物。但该技术的活性物质在土壤中极易被分解，致使这项技术长期停留在实验室阶段，难以应用到大田生产中。\n\n此次科研团队突破技术应用瓶颈，从基因层面筛选出根结线虫的关键靶标基因MEKK-3，该基因只作用于根结线虫，对其他生物安全无害。团队搭建起该活性物质的规模化生产工艺，还采用星形聚阳离子纳米材料为有效成分打造一层“保护外壳”。相当于给药剂穿上防护外衣，使其埋入土壤后不易被分解。经过改良，根结线虫对药剂有效成分摄取量提升42.3%，基因沉默效果提高1.9倍，幼虫死亡率提升24.2%。\n\n温室试验结果显示：单纯施用活性药剂45天后，作物根部根结数量可减少56.6%；将纳米载体与药剂搭配使用，对根结线虫的防治效果提升至78.4%。科研人员探明其中机理：该关键基因被抑制后，根结线虫抵御外界胁迫、维持生存的生理通路遭到破坏，最终无法正常发育，导致死亡。\n\n彭焕介绍，这套新技术打通了从害虫靶点筛选、机理解析、规模化生产到温室试验验证的完整技术链条，证实MEKK-3基因可用于防控多种根结线虫，为研发安全广谱的新型RNA生物农药铺平了道路。他表示，未来该技术落地应用后，能够减少化学农药使用，守护耕地土壤健康，助力新疆农业绿色高质量发展，也为国内土传病虫害的绿色治理提供重要技术储备。","中国农业科学院西部农业研究中心\u002F新疆日报(2026-10)","报道",87,{"impact":347,"substance":129,"depth":19,"authority":130,"freshness":21,"relevant":22,"comment":348},24,"国家级科研机构攻克RNA干扰大田应用瓶颈，数据详实、技术链条完整，具备行业示范价值，值得进入每日精选。",[350],{"name":343,"url":340},[27,352,353,28,354],"RNA干扰","根结线虫","生物农药",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[357],{"code":80,"slug":358,"name":359,"path":360,"parent_code":361},"crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",{"status":46,"locked":14,"version":47,"method":48,"evidence":363},{"candidates":364,"agricultural_evidence":53},[365,369,373,379],{"code":80,"score":130,"supported":53,"title":366,"abstract":367,"tags":368},[28],[28],[352,354,28],{"code":33,"score":131,"supported":53,"title":370,"abstract":371,"tags":372},[27],[],[27],{"code":374,"score":88,"supported":53,"title":375,"abstract":376,"tags":378},"B7",[],[377],"温室",[],{"code":94,"score":22,"supported":14,"title":380,"abstract":381,"tags":382},[],[],[28],[384,385],"中国农科院西部中心 根结线虫 RNA干扰","MEKK-3 纳米载体 生物农药","中国农科院西部中心根结线虫RNA干扰-5210",2,"2026-10-18T00:05:22.944664Z",{"id":390,"title":391,"url":392,"summary":393,"summary_zh":394,"content":9,"source_name":395,"source_url":392,"published_at":396,"category":12,"cover_url":9,"hotness":13,"is_selected":53,"score":397,"score_detail":398,"sources":400,"tags":402,"primary_topic":407,"secondary_topics":411,"classification":413,"search_phrases":441,"slug":444,"view_count":102,"doi":445,"paper":446,"created_at":460},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","2026-10-21T00:00:00Z",89,{"impact":17,"substance":347,"depth":146,"authority":130,"freshness":13,"relevant":22,"comment":399},"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。",[401],{"name":395,"url":392},[403,30,404,405,406],"大豆","东北农业","物候数据","遥感验证",{"code":186,"slug":408,"name":409,"path":410,"parent_code":37},"soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[412],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":414},{"candidates":415,"agricultural_evidence":53},[416,421,426,430,435],{"code":186,"score":52,"supported":53,"title":417,"abstract":419,"tags":420},[418],"soybean",[403,418],[403],{"code":40,"score":13,"supported":53,"title":422,"abstract":423,"tags":425},[],[30,424,61],"农业气象",[30],{"code":87,"score":73,"supported":53,"title":427,"abstract":428,"tags":429},[],[91,162],[],{"code":203,"score":73,"supported":53,"title":431,"abstract":432,"tags":434},[],[303,433],"remote sensing",[],{"code":436,"score":88,"supported":53,"title":437,"abstract":438,"tags":440},"H6",[],[439],"农业生态",[],[442,443],"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":445,"openalex_id":447,"authors":448,"venue":395,"cited_by_count":102,"oa_url":392,"card":455,"direction":228,"ingested_from":116},"W7223091615",[449,452],{"name":450,"orcid":451},"Quanjun Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-6087-3928",{"name":453,"orcid":454},"Dongli Wu","https:\u002F\u002Forcid.org\u002F0000-0003-1676-2264",{"tldr":456,"method":457,"finding":458,"direction":228,"opportunity":459},"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",{"id":462,"title":463,"url":464,"summary":465,"summary_zh":466,"content":9,"source_name":467,"source_url":464,"published_at":468,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":469,"score_detail":470,"sources":473,"tags":475,"primary_topic":479,"secondary_topics":480,"classification":481,"search_phrases":501,"slug":504,"view_count":102,"doi":505,"paper":506,"created_at":520},5561,"Growth Stage and Cultivar Influence the Interpretation of UAV-Derived NDVI for Potato Yield Assessment","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11540-026-10161-5","Abstract Single-date UAV vegetation indices are widely used to assess potato crop performance and estimate yield. However, whether their biological interpretation remains consistent across cultivars and growth stages is rarely examined. This study investigated how the relationships between UAV-derived multispectral and thermal canopy indicators and potato yield changed during crop development under contrasting irrigation in two cultivars with contrasting maturity classes (Anouk and Celandine). UAV surveys were conducted in July and August, and canopy metrics were related to final yield and tuber traits. The relationship between NDVI and yield differed markedly between cultivars and growth stages. In the early-maturing cultivar Anouk, July NDVI showed a strong positive correlation with yield ( r = 0.87, p = 0.002), whereas August NDVI was not informative. In the late-maturing cultivar Celandine, the relationship reversed from a positive correlation in July ( r = 0.56) to a strong negative correlation in August ( r = −0.80, p = 0.009), indicating that greater late-season canopy greenness was associated with lower yield. Exploratory LOOCV Random Forest models achieved the highest yield prediction accuracy using July features for Anouk ( R 2 = 0.72) and August features for Celandine ( R 2 = 0.47), while multi-temporal features provided similar predictive performance. Greater temporal declines in NDVI were associated with higher yield in both cultivars, consistent with the change in canopy greenness acting as a phenological indicator of the transition from vegetative growth to tuber bulking. These results demonstrate that the interpretation of UAV-derived vegetation indices is strongly dependent on cultivar maturity and growth stage. A single UAV acquisition may therefore provide misleading assessments of crop performance when canopy phenology is not considered. Rather than serving as fixed proxies for yield, canopy vegetation indices should be interpreted within cultivar-specific and temporal contexts when used for potato monitoring and agronomic decision-making.","摘要 单期无人机植被指数被广泛用于评估马铃薯作物表现和估算产量。然而，其生物学解释在不同品种和生育期之间是否保持一致，鲜有研究加以检验。本研究探讨了在两种成熟性类别不同的品种（Anouk和Celandine）中，不同灌溉条件下，无人机获取的多光谱与热红外冠层指标与马铃薯产量之间的关系在作物发育过程中如何变化。无人机调查于7月和8月进行，并将冠层指标与最终产量及块茎性状进行关联分析。NDVI与产量之间的关系在品种和生育期之间差异显著。在早熟品种Anouk中，7月NDVI与产量呈强正相关（r = 0.87，p = 0.002），而8月NDVI则不具有指示意义。在晚熟品种Celandine中，该关系由7月的正相关（r = 0.56）转变为8月的强负相关（r = −0.80，p = 0.009），表明后期冠层绿度越高与产量越低相关。探索性留一交叉验证（LOOCV）随机森林模型在使用Anouk的7月特征（R² = 0.72）和Celandine的8月特征（R² = 0.47）时取得了最高的产量预测精度，而多时相特征提供了相似的预测表现。两个品种中NDVI随时间下降幅度越大均与产量越高相关，这与冠层绿度变化作为从营养生长向块茎膨大转变的物候指示相一致。这些结果表明，无人机获取的植被指数的解释强烈依赖于品种成熟性和生育期。因此，在未考虑冠层物候的情况下，单次无人机采集可能对作物表现作出误导性评估。冠层植被指数不应作为产量的固定替代指标，而应在品种特异性和时间背景下加以解释，方可用于马铃薯监测和农艺决策。","Potato Research","2026-10-20T00:00:00Z",79,{"impact":471,"substance":17,"depth":19,"authority":130,"freshness":131,"relevant":22,"comment":472},16,"该研究揭示UAV-NDVI与马铃薯产量的关系随品种熟期和生育阶段而反转，对智慧农业遥感监测具有实质方法学与决策参考价值。",[474],{"name":467,"url":464},[476,27,477,303,478],"智慧农业","产量预测","作物表型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":482},{"candidates":483,"agricultural_evidence":53},[484,488,492,497],{"code":33,"score":52,"supported":53,"title":485,"abstract":486,"tags":487},[55],[27,55],[27],{"code":179,"score":73,"supported":53,"title":489,"abstract":490,"tags":491},[],[182,183],[],{"code":203,"score":73,"supported":53,"title":493,"abstract":494,"tags":496},[],[495],"multispectral",[477,478,303],{"code":72,"score":22,"supported":14,"title":498,"abstract":499,"tags":500},[],[],[476],[502,503],"UAV NDVI 马铃薯 产量","马铃薯 品种 生育期 遥感","UAVNDVI马铃薯产量-5561","10.1007\u002Fs11540-026-10161-5",{"doi":505,"openalex_id":507,"authors":508,"venue":467,"cited_by_count":102,"oa_url":514,"card":515,"direction":115,"ingested_from":116},"W7223067378",[509,512],{"name":510,"orcid":511},"Jiangsan Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-3916-7388",{"name":513,"orcid":9},"Jaroslaw S. Grodek","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs11540-026-10161-5.pdf",{"tldr":516,"method":517,"finding":518,"direction":228,"opportunity":519},"研究不同品种和生育期下无人机NDVI与马铃薯产量的关系差异。","两个熟期品种、多时相无人机多光谱与热红外冠层指标，结合随机森林建模。","NDVI与产量的关系随品种和时期反转，晚熟品种8月NDVI与产量强负相关。","可构建融合品种熟期与冠层物候期的时相自适应产量估算模型，避免单次飞行误判。","2026-10-22T11:20:21.975818Z",{"id":522,"title":523,"url":524,"summary":525,"summary_zh":9,"content":526,"source_name":527,"source_url":9,"published_at":468,"category":344,"cover_url":9,"hotness":13,"is_selected":14,"score":528,"score_detail":529,"sources":532,"tags":534,"primary_topic":539,"secondary_topics":540,"classification":541,"search_phrases":557,"slug":560,"view_count":102,"doi":9,"paper":9,"created_at":561},5526,"甘肃农科院陇薯红1号千亩方实收测产 亩产3033.59公斤商品率92%","https:\u002F\u002Fmlss.gsagr.cn\u002Finfo\u002F1056\u002F2213.htm","10月18日马铃薯研究所科研团队在会宁县大沟镇郭庄村陇薯红1号千亩示范方开展现场测产，实测亩产3033.59公斤、商品率92%。该品种为鲜食与加工兼用型，薯皮光滑呈红色、薯肉红芯黄边富含花青素，抗旱性与综合抗逆性突出，尤宜炸制薯片薯条，已获多家薯片薯条加工企业认可。","测产现场，专家组严格按照马铃薯田间测产技术规程，对自主选育的陇薯红1号进行实收测产，并综合评价其农艺性状、抗逆性与加工品质。经实测，该品种田间长势整齐，丰产性突出，折合亩产3033.59公斤，商品率达92%，抗逆表现良好，块茎外观品质优良，符合加工原料标准。项目负责人、马铃薯所长马丽荣介绍，陇薯红1号是我院马铃薯研究所最新选育的鲜食与加工兼用型品种，尤宜炸制薯片、薯条。该品种薯形规整，薯皮光滑呈红色，薯肉红芯黄边，富含花青素，抗旱性与综合抗逆性突出，炸制的薯片色泽鲜亮、片形均匀、品质稳定。该品种的育成进一步拓展了功能加工型马铃薯的育种方向与应用场景，已获得省内外多家薯片、薯条加工企业的高度评价与广泛认可。","甘肃省农业科学院马铃薯研究所 2026-10-20",73,{"impact":19,"substance":530,"depth":130,"authority":20,"freshness":21,"relevant":22,"comment":531},20,"省级科研机构自主选育的功能加工型马铃薯品种千亩方实收测产，亩产与商品率数据具体，兼具鲜食加工兼用与花青素特色，具备行业参考价值。",[533],{"name":527,"url":524},[27,535,536,537,538],"种业振兴","测产验收","功能加工型","陇薯红1号",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":542},{"candidates":543,"agricultural_evidence":53},[544,548,552],{"code":33,"score":81,"supported":53,"title":545,"abstract":546,"tags":547},[],[27],[27],{"code":87,"score":22,"supported":14,"title":549,"abstract":550,"tags":551},[],[],[536],{"code":553,"score":22,"supported":14,"title":554,"abstract":555,"tags":556},"F1",[],[],[535],[558,559],"甘肃农科院 陇薯红1号 测产","陇薯红1号 亩产3033.59公斤","甘肃农科院陇薯红1号测产-5526","2026-10-22T00:07:04.811596Z"]