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Multi-scale high-throughput phenotyping of apple architectural and functional traits in orchard reveals genotypic variability under contrasted watering regimes

机译:果园苹果结构和功能性状的多尺度高通量表型揭示了不同灌溉方式下的基因型变异

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摘要

Despite previous reports on the genotypic variation of architectural and functional traits in fruit trees, phenotyping large populations in the field remains challenging. In this study, we used high-throughput phenotyping methods on an apple tree core-collection (1000 individuals) grown under contrasted watering regimes. First, architectural phenotyping was achieved using T-LiDAR scans for estimating convex and alpha hull volumes and the silhouette to total leaf area ratio (STAR). Second, a semi-empirical index (IPL) was computed from chlorophyll fluorescence measurements, as a proxy for leaf photosynthesis. Last, thermal infrared and multispectral airborne imaging was used for computing canopy temperature variations, water deficit, and vegetation indices. All traits estimated by these methods were compared to low-throughput in planta measurements. Vegetation indices and alpha hull volumes were significantly correlated with tree leaf area and trunk cross sectional area, while IPL values showed strong correlations with photosynthesis measurements collected on an independent leaf dataset. By contrast, correlations between stomatal conductance and canopy temperature estimated from airborne images were lower, emphasizing discrepancies across measurement scales. High heritability values were obtained for almost all the traits except leaf photosynthesis, likely due to large intra-tree variation. Genotypic means were used in a clustering procedure that defined six classes of architectural and functional combinations. Differences between groups showed several combinations between architectural and functional traits, suggesting independent genetic controls. This study demonstrates the feasibility and relevance of combining multi-scale high-throughput methods and paves the way to explore the genetic bases of architectural and functional variations in woody crops in field conditions.
机译:尽管先前有报道说果树的结构和功能性状具有基因型变异,但在田间对大量种群进行表型鉴定仍具有挑战性。在这项研究中,我们对在不同灌溉方式下生长的苹果树核心集合(1000个个体)使用了高通量表型分析方法。首先,使用T-LiDAR扫描实现建筑表型分析,以估算凸壳和alpha壳的体积以及轮廓与总叶面积比(STAR)。第二,从叶绿素荧光测量结果计算出半经验指数(IPL),作为叶片光合作用的代名词。最后,热红外和多光谱机载成像用于计算冠层温度变化,水分亏缺和植被指数。将这些方法估计的所有性状与植物测得的低通量进行比较。植被指数和阿尔法壳体积与树叶面积和树干横截面积显着相关,而IPL值与独立叶数据集上收集的光合作用测量值显示出很强的相关性。相比之下,从机载图像估计的气孔导度与冠层温度之间的相关性较低,强调了整个测量尺度之间的差异。除叶片光合作用外,几乎所有性状均获得较高的遗传力值,这可能是由于树内变异较大所致。在聚类过程中使用了基因型手段,该过程定义了六类建筑和功能组合。群体之间的差异显示了建筑和功能性状之间的几种组合,表明了独立的遗传控制。这项研究证明了结合多尺度高通量方法的可行性和相关性,并为探索田间木本作物的结构和功能变异的遗传基础铺平了道路。

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