Evaluation of a lentil collection (Lens culinaris Medik) using morphological traits and digital phenotyping

The objective of this work was to evaluate 81 lentil cultivars using morphological traits and seed characteristics using digital phenotyping. Caliber (C) and the color traits luminosity (L), color coordinates a and b, and color index (CI) were measured and analyzed with appropriate software; al...

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Bibliographic Details
Main Authors: María Andrea Espósito, Ileana Gatti, Carolina Julieta Bermejo, Enrique Luis Cointry
Format: Article
Language:English
Published: Facultad de Ciencias Agrarias. Universidad Nacional de Cuyo 2020-06-01
Series:Revista de la Facultad de Ciencias Agrarias
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Online Access:https://revistas.uncu.edu.ar/ojs3/index.php/RFCA/article/view/3055
Description
Summary:The objective of this work was to evaluate 81 lentil cultivars using morphological traits and seed characteristics using digital phenotyping. Caliber (C) and the color traits luminosity (L), color coordinates a and b, and color index (CI) were measured and analyzed with appropriate software; also yield (Y), plant height (PH) and days to flowering (DF) were measured. Highly significant differences between cultivars were present for all traits and high heritability in broad sense heritability (H2B) for C (97%), CI (94%), a (93%) and L and b (83%) were found, indicating high genetic variability for these traits. Digital phenotyping showed to be a powerful tool for germplasm characterization along with field evaluation of agronomical traits. Principal Component Analysis and Cluster Analysis allows de identification of differentiated groups of cultivars with similar characteristics, leading to a more efficient use of the germplasm available as commercial cultivars or as parents in a breeding program. Among these groups, group 1 had 32 cultivars with highest C and group 2 had 21 cultivars with higher Y. Highlights Digital phenotyping showed to be a powerful tool for germplasm characterization along with field evaluation of agronomical traits. Principal Component Analysis and Cluster Analysis allows the identification of differentiated groups of cultivars with similar characteristics. Cultivar groups with similar characteristics allow more efficient use of germplasm.
ISSN:0370-4661
1853-8665