Multivariate analysis methods improve the selection of strawberry genotypes with low cold requirement

Abstract

Methods of multivariate analysis is a powerful approach to assist the initial stages of crops genetic improvement, particularly, because it allows many traits to be evaluated simultaneously. In this study, heat-tolerant genotypes have been selected by analyzing phenotypic diversity, direct and indirect relationships among traits were identified, and four selection indices compared. Diversity was estimated using K-means clustering with the number of clusters determined by the Elbow method, and the relationship among traits was quantified by path analysis. Parametric and non-parametric indices were applied to selected genotypes using the magnitude of genotypic variance, heritability, genotypic coefficient of variance, and assigned economic weight as selection criteria. The variability among materials led to the formation of two non-overlapping clusters containing 40 and 154 genotypes. Strong to moderate correlations were found between traits with direct effect of the number of commercial fruit on the mass of commercial fruit. The Smith and Hazel index showed the greatest total gains for all criteria; however, concerning the biochemical traits, the Mulamba and Mock index showed the highest magnitudes of predicted gains. Overall, the K-means clustering, correlation analysis, and path analysis complement the use of selection indices, allowing for selection of genotypes with better balance among the assessed traits.

Multivariate Contrasts of Seven Strawberry Cultivars in Soilless Cultivation and Greenhouse in Southern Brazil

“…This is because there are no cultivars adapted to the soil and climate conditions in Brazil [41]. However, there are research groups that focus on the development of materials more adapted to tropical and subtropical growing conditions in Brazil [34,42,43].…”

Factors associated with circulatory death after out-of-hospital cardiac arrest: a population-based cluster analysis

“…Third, we optimized within-cluster uniformity by using the k-means method to consolidate the optimal number of clusters. To determine the ideal number of clusters, we employed the elbow method [ 29 , 30 ], identifying the point at which the decrease in within-cluster variability becomes flat. We assessed the reproductivity of the clustering using bootstrap dataset.…”

Promising acerola (Malpighia emarginata Sessé & Moc. ex DC.) genotypes with high fruit quality for different purposes

“…The utilization of selection indices facilitates the enhancement of two or more traits within a breeding program, irrespective of adverse effects such as genetic linkage and pleiotropy (Baker 2020).…”