Image phenotyping of lettuce germplasm with genetically diverse carotenoid levels
2020
Maciel, Gabriel Mascarenhas | Gallis, Rodrigo Bezerra de Araújo | Barbosa, Ricardo Luís | Pereira, Lucas Medeiros | Siquieroli, Ana Carolina Silva | Peixoto, Joicy Vitória Miranda
ABSTRACT Developing biofortified foods such as lettuce is a frequent goal of breeding programs. One obstacle to the success of these efforts is the high temporal and financial cost of determining leaf constituents. Image phenotyping has been increasingly used in crop breeding, but not in lettuce breeding. Until now, the use of image phenotyping to indirectly select carotenoid-rich lettuce inbred lines has not been reported. Therefore, the goal of this study was to use image phenotyping to select lettuce inbred lines with different carotenoid levels. Twenty-two inbred lettuce lines, resulting from the hybridization of the cultivars Pira 72 and Uberlândia 10000 and six successive selfings were evaluated. ‘Grand Rapids’, ‘UFU-Biofort’ and ‘Uberlândia 10000’ were used as controls. The following variables were evaluated: agronomic potential, apparent genetic diversity and image phenotyping. The data were submitted to the ANOVA F test (p ≤ 0.05) and means compared by the Scott–Knott test (p ≤ 0.05). Genetic divergence was represented by dendrograms constructed by UPGMA and the Tocher optimization method. The relative contribution of characters was assessed to identify the most relevant response variable. The genetic diversity of the evaluated germplasm bank was the greatest regarding soil plant analysis development (SPAD)/carotenoid values. Image phenotyping was successfully used to detect different levels of SPAD/carotenoid levels and could be a useful tool for plant breeding. The results of this study can be used to predict the nutritional values of the carotenoid content in lettuce leaves before commercialization.
Show more [+] Less [-]AGROVOC Keywords
Bibliographic information
This bibliographic record has been provided by Scientific Electronic Library Online Brazil