2022
DOI: 10.4025/actasciagron.v44i1.55876
|View full text |Cite
|
Sign up to set email alerts
|

Multi-trait selection of tomato introgression lines under drought-induced conditions at germination and seedling stages

Abstract: To be considered drought-tolerant, a tomato cultivar is required to present some level of tolerance at all developmental stages of plant growth. Since drought tolerance is a stage-specific phenomenon, genotype assessment must be performed separately at all developmental stages. In this study, we used a multi-trait index based on factor analysis and genotype-ideotype distance (FAI-BLUP index) to properly rank 49 tomato genotypes according to their tolerance to drought stress conditions at germination and seedli… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2

Relationship

2
0

Authors

Journals

citations
Cited by 2 publications
(1 citation statement)
references
References 48 publications
0
1
0
Order By: Relevance
“…The FAI-BLUP index (factor analysis and ideotype-design / best linear unbiased predictor) proposed by Rocha, Machado, and Carneiro (2018) combines factorial analyses with genotype-ideotype design for multitrait selection. This index has been successfully used to select the genotypes of several food crops (Silva et al, 2018;Oliveira et al, 2019;Rocha et al, 2019;Woyann et al, 2019;Pessoa et al, 2022). The main advantages of using this index are that there is no need to assign economic weights for each trait and that this index is free from multicollinearity issues.…”
Section: Introductionmentioning
confidence: 99%
“…The FAI-BLUP index (factor analysis and ideotype-design / best linear unbiased predictor) proposed by Rocha, Machado, and Carneiro (2018) combines factorial analyses with genotype-ideotype design for multitrait selection. This index has been successfully used to select the genotypes of several food crops (Silva et al, 2018;Oliveira et al, 2019;Rocha et al, 2019;Woyann et al, 2019;Pessoa et al, 2022). The main advantages of using this index are that there is no need to assign economic weights for each trait and that this index is free from multicollinearity issues.…”
Section: Introductionmentioning
confidence: 99%