Multivariate analysis as a tool in the initial selections of golden flax (Linum usitatissimum L.)

Abstract

The genetic variability in plant populations can be estimated through multivariate analysis, which allows to analyze the genotypes based on a set of traits to identify the traits with the greatest influence for the divergence and the correlation between them. In this sense, the objective of the present work was to estimate the genetic divergence between golden flax lines using multivariate analysis for initial plant selections. For this purpose, 73 lines, in addition to the control, were tested in a randomized complete block design, with three replications, and traits of cycle, stature, and yield were measured. Twelve groups were obtained based on the Mahalanobis distance estimate and Tocher cluster, and the technical length was the most important trait for the dissimilarity. The line of group XI was promising, with early maturation and satisfactory seed productivity. The graphic of dispersion of the canonical variables showed the most divergent lines, and the greatest divergence was observed between groups III and IV. Multivariate analysis was an important tool for the initial choice of superior golden flax.

Agronomic performance and yield stability of yellow flax genotypes in the state of Santa Catarina, Brazil

“…Yield gains can be achieved by the selection of genotypes with a greater yield stability and adaptation to local environmental conditions. Since flax shows a high variability regarding agronomic traits such as plant height, flowering and maturity stages, number of capsules per plant, yield per plant, and yield per area (Kumar et al, 2021;Fioreze et al, 2022), a strategy used by genetic improvement programs is the estimation of genetic variability.…”