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 (NCF) on the mass of commercial fruit (MCF). 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.

Productivity and analysis of morphological characters of experimental strawberry genotypes

“…These cultivars are reported to be superior and have good characteristics of commercial interests such as flavor (aroma, taste, and texture), as well as greater resistance to biotic and abiotic factors (Zeist & Resende, 2019). However, more recently, the plants presented phytosanitary problems and clonal soma variation (Barth et al, 2022). Being these genotypes improved BRANDT, GQ; SILVA, LFL; SOUZA, DC; RESENDE, LV; NUNES, NS.…”

Maintaining and Improving Peas (Pisum sativum L.) CV. Master Pea (Local Cultivar) Using UPOV Descriptor and Multivariate Analysis in the Vegetable Seed Production Field

“…Thus, the quantity of peas seed that can be produced will depend on the ability of these seeds to express the characteristics of the variety well [3]. Methods of multivariate analysis are an effective strategy to aid in the early phases of crop genetic improvement, in part because it enables the simultaneous evaluation of numerous attributes [4].…”