Erratum to "Multi-trait stability index for identification of stable green gram (Vigna radiata (L.) Wilczek) genotypes with MYMV resistance" [Heliyon Volume , Issue 12, June 2024, Article e32763].

[This corrects the article DOI: 10.1016/j.heliyon.

Multi-trait stability index for identification of stable green gram ( (L.) Wilczek) genotypes with MYMV resistance.

Multi-environment trials (MET) are crucial for selecting genotypes that are well-suited to different environmental conditions. Incorporating multiple traits in the analysis can provide more reliable recommendations for selecting genotypes with desirable traits, including resistance to the Mungbean Yellow Mosaic Virus (MYMV) and high yield potential. The use of a Multi-Trait Stability Index (MTSI) is a good approach for analyzing the stability of genotypes across multiple traits under MYMV stress.

Multi-trait selection in multi-environments for performance and stability in cassava genotypes.

Genotype-environment interaction (GEI) presents challenges when aiming to select optimal cassava genotypes, often due to biased genetic estimates. Various strategies have been proposed to address the need for simultaneous improvements in multiple traits, while accounting for performance and yield stability. Among these methods are mean performance and stability (MPS) and the multi-trait mean performance and stability index (MTMPS), both utilizing linear mixed models.

RGB image-based method for phenotyping rust disease progress in pea leaves using R.

Background: Rust is a damaging disease affecting vital crops, including pea, and identifying highly resistant genotypes remains a challenge. Accurate measurement of infection levels in large germplasm collections is crucial for finding new resistance sources. Current evaluation methods rely on visual estimation of disease severity and infection type under field or controlled conditions.

Genetic gains in tropical maize hybrids across moisture regimes with multi-trait-based index selection.

Unpredictable weather vagaries in the Asian tropics often increase the risk of a series of abiotic stresses in maize-growing areas, hindering the efforts to reach the projected demands. Breeding climate-resilient maize hybrids with a cross-tolerance to drought and waterlogging is necessary yet challenging because of the presence of genotype-by-environment interaction (GEI) and the lack of an efficient multi-trait-based selection technique. The present study aimed at estimating the variance components, genetic parameters, inter-trait relations, and expected selection gains (SGs) across the soil moisture regimes through genotype selection obtained based on the novel multi-trait genotype-ideotype distance index (MGIDI) for a set of 75 tropical pre-released maize hybrids.

Multi-trait selection for mean performance and stability of maize hybrids in mega-environments delineated using envirotyping techniques.

Under global climate changes, understanding climate variables that are most associated with environmental kinships can contribute to improving the success of hybrid selection, mainly in environments with high climate variations. The main goal of this study is to integrate envirotyping techniques and multi-trait selection for mean performance and the stability of maize genotypes growing in the Huanghuaihai plain in China. A panel of 26 maize hybrids growing in 10 locations in two crop seasons was evaluated for 9 traits.

MGIDI: a powerful tool to analyze plant multivariate data.

Background: Commonly, several traits are assessed in agronomic experiments to better understand the factors under study. However, it is also common to see that even when several traits are available, researchers opt to follow the easiest way by applying univariate analyses and post-hoc tests for mean comparison for each trait, which arouses the hypothesis that the benefits of a multi-trait framework analysis may have not been fully exploited in this area.

Results: In this paper, we extended the theoretical foundations of the multi-trait genotype-ideotype distance index (MGIDI) to analyze multivariate data either in simple experiments (e.

Stability Indices to Deciphering the Genotype-by-Environment Interaction (GEI) Effect: An Applicable Review for Use in Plant Breeding Programs.

Experiments measuring the interaction between genotypes and environments measure the spatial (e.g., locations) and temporal (e.

MGIDI: toward an effective multivariate selection in biological experiments.

Motivation: Multivariate data are common in biological experiments and using the information on multiple traits is crucial to make better decisions for treatment recommendations or genotype selection. However, identifying genotypes/treatments that combine high performance across many traits has been a challenger task. Classical linear multi-trait selection indexes are available, but the presence of multicollinearity and the arbitrary choosing of weighting coefficients may erode the genetic gains.

Multivariate diallel analysis by factor analysis for establish mega-traits.

In plant breeding, the dialelic models univariate have aided the selection of parents for hybridization. Multivariate analyses allow combining and associating the multiple pieces of information of the genetic relationships between traits. Therefore, multivariate analyses might refine the discrimination and selection of the parents with greater potential to meet the goals of a plant breeding program.

Phenotypic variance of black oat growing in crop seasons reveals genetic effects predominance.

This work aimed to determine variance components and genetic parameters, as well as phenotypic, genetic and environmental correlations among black oat (Avena strigosa) families grown in different crop season. Seventy-six black oat families and three controls (BRS Madrugada, BRS Centauro, BRS 139 Neblina) were evaluated in two crop seasons (2016 and 2017), using families with intercalary controls experimental design. The results reveled high potential of black oat families to compose a breeding program, due to families and controls variance were similar, variance components expressed greater genetic variance origin for crop season.