Best linear unbiased prediction in combination with path analysis in processing grapes

Abstract

The knowledge of correlations between multiple characteristics in plant breeding leads to more effective selection strategies. The path analysis allows refining these correlations and partitioning them into direct and indirect effects on the main variable. The path analysis becomes more effective when based on predicted genotypic values rather than phenotypic values. The objective was to evaluate correlations between the main agronomic characteristics of grapevine cultivation and their direct and indirect effects on yield per plant to improve selection strategies to reach superior progenies. A randomized complete block design was installed using four cultivars and two rootstocks, five repetitions, and plots of four plants. Data from three crop seasons were analyzed from a mixed model and genetic correlations were subject to the path analysis. A high and positive significant correlation was found between average fruit production and the number of clusters per plant. On the other hand, the average production per plant showed a low correlation to cluster width and height per grapevine. Wider and higher berries tend to increase berry fresh mass and therefore increase the contents of soluble solids and reducing sugars. Among the features, the number of clusters per plant has the strongest direct effect on fruit production in grape cultivars. Berry fresh mass, berry length, and berry width were indirectly influenced by the number of clusters and showed high heritability compared to yield and number of clusters. These characteristics could be used in indirect selection.