[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2417":3,"related-2417":120},{"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":39,"search_phrases":86,"slug":89,"view_count":22,"doi":90,"paper":91,"created_at":119},2417,"Climate-smart but economically vulnerable: livelihood strategy choices among millet-producing households in Central Tanzania","https:\u002F\u002Fdoi.org\u002F10.1080\u002F14735903.2026.2728319","The irony of climate-smart agriculture is particularly evident in central Tanzania, where farmers cultivating millet, a crop promoted as climate change solutions, face the most difficult livelihood choices. This study examines the livelihood strategies of millet farmers in the Manyoni and Bahi districts of central Tanzania, including the factors influencing these choices. A triangulated clustering analysis based on the activity approach classified the 417 farming households into five distinct clusters, where income levels, commercialization indices, and food security scores were compared. A multinomial logit model examined the drivers of livelihood strategy choices. The results identify five distinct livelihood strategies, with the majority of farmers engaging in multiple economic activities, such as off-farm activities, livestock keeping, and cultivating commercial crops. Cluster 1 represents 28% of households, with the highest income, food security, and market participation, primarily due to finger millet cultivation. In contrast, Cluster 4, comprising farmers who grow only pearl millet, is the poorest, with low income and limited market participation. The key drivers of cluster choices included education, age, extension services, and geographical location. Findings highlight that livelihood strategies among millet-producing households yield distinct welfare outcomes; thus, policies and interventions should prioritize livelihood combinations that result in improved food security and economic resilience.","气候智能型农业的讽刺之处在坦桑尼亚中部尤为明显：种植小米——一种被宣传为气候变化解决方案的作物——的农民却面临着最艰难的生计选择。本研究考察了坦桑尼亚中部马尼奥尼区和巴希区小米种植户的生计策略，包括影响这些选择的因素。基于活动方法的三角聚类分析将417个农户划分为五个不同的集群，并对收入水平、商业化指数和粮食安全得分进行了比较。多项Logit模型检验了生计策略选择的驱动因素。结果识别出五种不同的生计策略，大多数农民从事多种经济活动，如非农活动、畜牧养殖和种植经济作物。集群1占农户的28%，拥有最高的收入、粮食安全和市场参与度，主要归因于指状粟的种植。相比之下，集群4由仅种植珍珠粟的农民组成，是最贫困的群体，收入低且市场参与有限。集群选择的关键驱动因素包括教育、年龄、推广服务和地理位置。研究结果表明，小米种植户的生计策略产生了不同的福利结果；因此，政策和干预措施应优先考虑能够改善粮食安全和经济韧性的生计组合。",null,"International Journal of Agricultural Sustainability","2026-09-11T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,9,1,"基于417户聚类与多项Logit模型的实证研究，揭示气候智慧型作物种植户的生计分化与福利差异，对同类地区政策设计有参考价值，但属区域性研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"粮食安全","气候智慧农业","小农户增收","小米种植","农户生计",{"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",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,59,66,74,81],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},11,true,[49],"millet",[49],[],{"code":53,"score":54,"supported":47,"title":55,"abstract":56,"tags":58},"A8",7,[],[27,57],"food security",[27],{"code":60,"score":54,"supported":47,"title":61,"abstract":62,"tags":65},"H7",[],[63,64],"气候变化","climate change",[28],{"code":67,"score":68,"supported":47,"title":69,"abstract":70,"tags":73},"C",6,[],[71,72],"畜牧","livestock",[],{"code":75,"score":76,"supported":47,"title":77,"abstract":78,"tags":80},"I1",3,[],[79],"smart agriculture",[],{"code":82,"score":22,"supported":14,"title":83,"abstract":84,"tags":85},"G6",[],[],[31],[87,88],"气候智慧农业 小农户增收 农户生计 小米种植","气候智慧农业 小农户增收","气候智慧农业小农户增收农户生计小米种植-2417","10.1080\u002F14735903.2026.2728319",{"doi":90,"openalex_id":92,"authors":93,"venue":10,"cited_by_count":110,"oa_url":6,"card":111,"direction":117,"ingested_from":118},"W7212316876",[94,96,99,102,105,107],{"name":95,"orcid":9},"Prisca E. Kimaro",{"name":97,"orcid":98},"Niloofar Khalili","https:\u002F\u002Forcid.org\u002F0000-0002-9079-518X",{"name":100,"orcid":101},"Constance Rybak","https:\u002F\u002Forcid.org\u002F0000-0002-7581-8021",{"name":103,"orcid":104},"Stefan Sieber","https:\u002F\u002Forcid.org\u002F0000-0002-4849-7277",{"name":106,"orcid":9},"Daniel Ndyetabula",{"name":108,"orcid":109},"Dagmar Mithöfer","https:\u002F\u002Forcid.org\u002F0000-0002-9347-682X",0,{"tldr":112,"method":113,"finding":114,"direction":115,"opportunity":116},"研究坦桑尼亚中部小米种植户的生计策略选择及其影响因素。","对417户聚类分析，并用多项Logit模型分析生计策略驱动因素。","多数农户采取多种经营活动，仅种珍珠粟的农户最贫困，教育、年龄、推广服务和地理位置是关键驱动因素。","数字乡村与农业信息化","可探索数字推广服务如何帮助气候智慧型作物种植户优化生计组合、提升经济韧性。","智慧农业 \u002F 农业物联网","openalex","2026-09-14T23:30:11.425655Z",{"total":68,"page":22,"page_size":68,"items":121},[122,225,328,391,435,480],{"id":123,"title":124,"url":125,"summary":126,"summary_zh":127,"content":9,"source_name":128,"source_url":125,"published_at":129,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":130,"score_detail":131,"sources":135,"tags":137,"primary_topic":142,"secondary_topics":143,"classification":144,"search_phrases":206,"slug":209,"view_count":110,"doi":210,"paper":211,"created_at":224},4359,"A longitudinal analysis of diet quality, groundwater depletion and glacier thicknesses with implications for the sustainable development goal 2","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2976-601x\u002Faea4b9","Abstract The United Nations Sustainable Development Goal 2 (SDG) underscores the need for eliminating hunger, improving nutrition, and promoting sustainable agriculture. For achieving SDG2, it is critical to address several factors such as agricultural production, groundwater depletion, and glacier decline under climate change. Using longitudinal district-level data from India from 2000–2016, a framework is developed for analyzing the data on agricultural outputs, well-depths (distance from ground to water tables), and in situ and remote sensing data on glacier thicknesses for the sustainability of food systems. First, the analysis of data on the production of rice, wheat, maize, sorghum, and pearl millet for 310 districts in India showed that greater rice output was significantly associated with higher well depths. Second, milk production was positively associated with well depths, indicating that improving diet quality entails higher water utilization. Third, an analysis of remote sensing data on changes in glacier heights for 0.5° × 0.5° grids in the Northern Hemisphere indicated large and significant declines over time; glacier thickness reductions were generally small using in situ data. Overall, the empirical results will facilitate the formulation of evidence-based policies for tackling ‘zero hunger’ and diet quality goals, cultivating water-efficient crops, recharging groundwater, and monitoring the dynamics of glaciers for long-term sustainability.","摘要 联合国可持续发展目标2（SDG2）强调消除饥饿、改善营养和促进可持续农业的必要性。为实现SDG2，必须应对气候变化下的农业生产、地下水枯竭和冰川退缩等若干关键因素。本研究利用印度2000—2016年地区级纵向数据，构建了一个分析框架，用于整合农业产出数据、井深（地面至地下水位之间的距离）数据以及冰川厚度的实地观测与遥感数据，以评估粮食系统的可持续性。首先，对印度310个地区水稻、小麦、玉米、高粱和珍珠粟产量数据的分析表明，水稻产量越高与井深越大显著相关。其次，牛奶产量与井深呈正相关，表明改善膳食质量伴随着更高的水资源利用。第三，对北半球0.5°×0.5°网格冰川高度变化的遥感数据分析表明，冰川高度随时间推移出现大幅且显著的下降；而基于实地数据的冰川厚度减少幅度总体较小。总体而言，这些实证结果将有助于制定循证政策，以应对“零饥饿”和膳食质量目标、培育节水作物、回补地下水以及监测冰川动态，从而实现长期可持续发展。","Environmental Research Food Systems","2026-10-06T00:00:00Z",80,{"impact":132,"substance":133,"depth":132,"authority":20,"freshness":21,"relevant":22,"comment":134},18,22,"基于印度310个区2000—2016年面板数据，将膳食质量、地下水枯竭与冰川消融纳入SDG2分析框架，数据规模与方法均有新意，对农业可持续性政策有参考价值。",[136],{"name":128,"url":125},[27,138,139,140,141],"可持续农业","遥感监测","地下水超采","膳食营养",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":145},{"candidates":146,"agricultural_evidence":47},[147,153,159,166,173,180,187,191,197,201],{"code":33,"score":21,"supported":47,"title":148,"abstract":149,"tags":152},[],[150,151,49],"高粱","sorghum",[],{"code":154,"score":54,"supported":47,"title":155,"abstract":156,"tags":158},"H8",[],[138,157],"sustainable agriculture",[138],{"code":160,"score":54,"supported":47,"title":161,"abstract":162,"tags":165},"I3",[],[163,164],"遥感","remote sensing",[139],{"code":167,"score":68,"supported":47,"title":168,"abstract":169,"tags":172},"A1",[],[170,171],"水稻","rice",[],{"code":174,"score":68,"supported":47,"title":175,"abstract":176,"tags":179},"A2",[],[177,178],"小麦","wheat",[],{"code":181,"score":68,"supported":47,"title":182,"abstract":183,"tags":186},"A3",[],[184,185],"玉米","maize",[],{"code":60,"score":68,"supported":47,"title":188,"abstract":189,"tags":190},[],[63,64],[],{"code":192,"score":76,"supported":47,"title":193,"abstract":194,"tags":196},"H3",[],[195],"水资源",[],{"code":53,"score":22,"supported":14,"title":198,"abstract":199,"tags":200},[],[],[27],{"code":202,"score":22,"supported":14,"title":203,"abstract":204,"tags":205},"E2",[],[],[141],[207,208],"印度 水稻 地下水 井深","冰川厚度 遥感 粮食安全","印度水稻地下水井深-4359","10.1088\u002F2976-601x\u002Faea4b9",{"doi":210,"openalex_id":212,"authors":213,"venue":128,"cited_by_count":110,"oa_url":125,"card":217,"direction":223,"ingested_from":118},"W7220726463",[214],{"name":215,"orcid":216},"Alok Bhargava","https:\u002F\u002Forcid.org\u002F0000-0002-0399-0949",{"tldr":218,"method":219,"finding":220,"direction":221,"opportunity":222},"基于印度2000–2016年区级数据，分析饮食质量、地下水消耗与冰川消融对SDG2可持续性的影响。","印度310区作物产量、井深纵向数据及遥感冰川厚度数据，构建分析框架。","水稻产量越高井越深，奶产量与井深正相关，北半球冰川厚度显著下降。","农业绿色发展与碳","可结合多源遥感与农户调查，量化饮食结构变化对地下水与冰川的跨尺度反馈机制。","农业遥感与作物表型","2026-10-07T23:30:26.146010Z",{"id":226,"title":227,"url":228,"summary":229,"summary_zh":230,"content":9,"source_name":231,"source_url":228,"published_at":232,"category":12,"cover_url":9,"hotness":13,"is_selected":47,"score":233,"score_detail":234,"sources":238,"tags":240,"primary_topic":243,"secondary_topics":248,"classification":250,"search_phrases":305,"slug":308,"view_count":76,"doi":309,"paper":310,"created_at":327},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",82,{"impact":132,"substance":235,"depth":132,"authority":236,"freshness":21,"relevant":22,"comment":237},23,14,"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。",[239],{"name":231,"url":228},[27,241,63,139,242],"防灾减灾","干旱风险",{"code":60,"slug":244,"name":245,"path":246,"parent_code":247},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[249],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":251},{"candidates":252,"agricultural_evidence":47},[253,260,264,268,272,279,285,291,295,301],{"code":60,"score":254,"supported":47,"title":255,"abstract":257,"tags":259},19,[256],"drought",[63,258,64,256],"干旱",[63,241],{"code":33,"score":21,"supported":47,"title":261,"abstract":262,"tags":263},[],[150,151,49],[],{"code":53,"score":54,"supported":47,"title":265,"abstract":266,"tags":267},[],[27,57],[27],{"code":181,"score":68,"supported":47,"title":269,"abstract":270,"tags":271},[],[184,185],[],{"code":273,"score":68,"supported":47,"title":274,"abstract":275,"tags":278},"H2",[],[276,277],"土壤","soil",[],{"code":192,"score":68,"supported":47,"title":280,"abstract":281,"tags":284},[],[282,283],"灌溉","irrigation",[],{"code":286,"score":76,"supported":47,"title":287,"abstract":288,"tags":290},"A4",[],[289],"花生",[],{"code":75,"score":76,"supported":47,"title":292,"abstract":293,"tags":294},[],[79],[],{"code":296,"score":76,"supported":47,"title":297,"abstract":298,"tags":300},"I4",[],[299],"GIS",[],{"code":160,"score":22,"supported":14,"title":302,"abstract":303,"tags":304},[],[],[139],[306,307],"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":309,"openalex_id":311,"authors":312,"venue":231,"cited_by_count":110,"oa_url":228,"card":322,"direction":117,"ingested_from":118},"W7214399028",[313,316,319],{"name":314,"orcid":315},"Theophilus Francis Kofitio","https:\u002F\u002Forcid.org\u002F0009-0000-7147-6581",{"name":317,"orcid":318},"Peace Korshiwor Amoatey","https:\u002F\u002Forcid.org\u002F0000-0002-2794-9721",{"name":320,"orcid":321},"Evans Asenso","https:\u002F\u002Forcid.org\u002F0000-0003-3116-6356",{"tldr":323,"method":324,"finding":325,"direction":223,"opportunity":326},"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","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":329,"title":330,"url":331,"summary":332,"summary_zh":333,"content":9,"source_name":334,"source_url":331,"published_at":335,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":336,"score_detail":337,"sources":340,"tags":342,"primary_topic":347,"secondary_topics":348,"classification":349,"search_phrases":371,"slug":374,"view_count":110,"doi":375,"paper":376,"created_at":390},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":338,"substance":133,"depth":132,"authority":236,"freshness":21,"relevant":22,"comment":339},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。",[341],{"name":334,"url":331},[343,344,345,163,346],"智慧农业","马铃薯","产量预测","作物表型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":350},{"candidates":351,"agricultural_evidence":47},[352,358,362,367],{"code":33,"score":353,"supported":47,"title":354,"abstract":356,"tags":357},15,[355],"potato",[344,355],[344],{"code":192,"score":68,"supported":47,"title":359,"abstract":360,"tags":361},[],[282,283],[],{"code":160,"score":68,"supported":47,"title":363,"abstract":364,"tags":366},[],[365],"multispectral",[345,346,163],{"code":75,"score":22,"supported":14,"title":368,"abstract":369,"tags":370},[],[],[343],[372,373],"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":375,"openalex_id":377,"authors":378,"venue":334,"cited_by_count":110,"oa_url":384,"card":385,"direction":117,"ingested_from":118},"W7223067378",[379,382],{"name":380,"orcid":381},"Jiangsan Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-3916-7388",{"name":383,"orcid":9},"Jaroslaw S. 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