RESUMO
BACKGROUND: Commonly, several traits are assessed in agronomic experiments to better understand the factors under study. However, it is also common to see that even when several traits are available, researchers opt to follow the easiest way by applying univariate analyses and post-hoc tests for mean comparison for each trait, which arouses the hypothesis that the benefits of a multi-trait framework analysis may have not been fully exploited in this area. RESULTS: In this paper, we extended the theoretical foundations of the multi-trait genotype-ideotype distance index (MGIDI) to analyze multivariate data either in simple experiments (e.g., one-way layout with few treatments and traits) or complex experiments (e.g., with a factorial treatment structure). We proposed an optional weighting process that makes the ranking of treatments that stands out in traits with higher weights more likely. Its application is illustrated using (1) simulated data and (2) real data from a strawberry experiment that aims to select better factor combinations (namely, cultivar, transplant origin, and substrate mixture) based on the desired performance of 22 phenological, productive, physiological, and qualitative traits. Our results show that most of the strawberry traits are influenced by the cultivar, transplant origin, cultivation substrates, as well as by the interaction between cultivar and transplant origin. The MGIDI ranked the Albion cultivar originated from Imported transplants and the Camarosa cultivar originated from National transplants as the better factor combinations. The substrates with burned rice husk as the main component (70%) showed satisfactory physical proprieties, providing higher water use efficiency. The strengths and weakness view provided by the MGIDI revealed that looking for an ideal treatment should direct the efforts on increasing fruit production of Albion transplants from Imported origin. On the other hand, this treatment has strengths related to productive precocity, total soluble solids, and flesh firmness. CONCLUSIONS: Overall, this study opens the door to the use of MGIDI beyond the plant breeding context, providing a unique, practical, robust, and easy-to-handle multi-trait-based framework to analyze multivariate data. There is an exciting possibility for this to open up new avenues of research, mainly because using the MGIDI in future studies will dramatically reduce the number of tables/figures needed, serving as a powerful tool to guide researchers toward better treatment recommendations.
RESUMO
This study aimed to assess cassava root and stem yield at different spacings and four transplanting seasons of seedlings produced by an adaptation of the rapid multiplication method. The experimental design was a randomized block design with three replications in a factorial (4 × 5) arranged in strips. Treatments consisted of the combinations of growing seasons (November 9 and 24 and December 3 and 26) and spacings (0.6 × 0.6, 0.8 × 0.8, 0.8 × 0.6, 1.0 × 0.6, and 1.0 × 0.8 m). The percentage of survival, main stem branching height, number of branches, base, middle, and upper diameter, mean diameter, stem length, number of buds per stem and hectare, root length and diameter, root fresh matter per plant and hectare, and number of roots per plant were measured at harvest. Seedling transplanting performed up to November 24 favored the expression of stem and root yield. The spacing of 1.0 × 0.6 m was the most suitable for stem yield traits.