To enhance table olive tree authentication, relationship, and productivity, we consider the analysis of 18 worldwide table olive cultivars (Olea europaea L.) based on morphological, biological, and physicochemical markers analyzed by bioinformatic and biostatistic tools. Accordingly, we assess the relationships between the studied varieties, on the one hand, and the potential productivity-quantitative parameter links on the other hand. The bioinformatic analysis based on the graphical representation of the matrix of Euclidean distances, the principal components analysis, unweighted pair group method with arithmetic mean, and principal coordinate analysis (PCoA) revealed three major clusters which were not correlated with the geographic origin. The statistical analysis based on Kendall's and Spearman correlation coefficients suggests two highly significant associations with both fruit color and pollinization and the productivity character. These results are confirmed by the multiple linear regression prediction models. In fact, based on the coefficient of determination (R 2) value, the best model demonstrated the power of the pollinization on the tree productivity (R 2 = 0.846). Moreover, the derived directed acyclic graph showed that only two direct influences are detected: effect of tolerance on fruit and stone symmetry on side and effect of tolerance on stone form and oil content on the other side. This work provides better understanding of the diversity available in worldwide table olive cultivars and supplies an important contribution for olive breeding and authenticity.
Keywords: Directed acyclic graph; Principal components analysis; Principal coordinate analysis; Productivity; Table olive; Unweighted pair group method with arithmetic mean.