Abstract
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.
Highlights
Introduction The biophysical approach proposed byMonteith (1977)[1] states that plant production potential relies on traits associated with plant capacity to intercept solar radiation, to convert this energy into carbohydrates and Coupel-Ledru et al Horticulture Research (2019)6:52 potted trees through the identification of large genotypic variability in architectural and functional traits[4]
The correlation between c_volume and other variables related to tree vigor were lower (R2 = 0.26, for both trunk cross sectional area (TCSA) and pix_num, Fig. 1e and Supplementary Fig. S3)
HT phenotyping methods were combined to study the genotypic variability of variables related to plant architecture (a_volume, c_volume, ci, silhouette to total leaf area ratio (STAR)), functioning (IPL), sensitivity to soil water deficit (WDI, Tsurf – Tair) and “mixed” traits accounting both for architectural and functional characteristics (NDVI, GNDVI, MCARI2)
Summary
Monteith (1977)[1] states that plant production potential relies on traits associated with plant capacity to intercept solar radiation, to convert this energy into carbohydrates and Coupel-Ledru et al Horticulture Research (2019)6:52 potted trees through the identification of large genotypic variability in architectural (plant height, total leaf area) and functional (transpiration, water-use efficiency) traits[4]. These studies were made possible by using new technologies provided in those platforms[5], i.e., Red Green Blue (RGB) images for plant growth estimation, and automated plant transpiration measurements. To the best of our knowledge, TLiDAR has never been deployed in a context of HT phenotyping for genetic studies
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