MGIDI: a powerful tool to analyze plant multivariate data

Abstract

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.

Multi-Trait Selection of Tropical Maize Genotypes Under Optimum and Acidic Soil Conditions

“…This method performs selection using genotype and ideotype distances formulated by the breeder, which can be a solution to weighting coefficients for economic traits or multicollinearity issues in other selection methods (Olivoto and Nardino, 2021). Olivoto et al (2022) reported that this selection method has advantages in terms of easy genotype ranking, strengths and weaknesses view, and the option to weigh each trait in genotype ranking. Several previous studies have reported the utility of this method in selecting superior genotypes under abiotic stress and mega-environments (Maranna et al, 2021;Singamsetti et al, 2023).…”

Identification of Wheat Ideotype under Multiple Abiotic Stresses and Complex Environmental Interplays by Multivariate Analysis Techniques

“…So, the multi-environment trials (METs) model the magnificent efforts in modern breeding programs for the valid and reliable selection of genotypes. The reliability of prediction (the expected value is close to the visible value) is crucial for an appropriate genotype recommendation and delimitation of mega-environments [12,15,35]. According to Gauch and Zobel [55], to increase the accuracy of prediction in METs, researchers must utilize statistical models that have high prediction abilities [35].…”

“…The agronomic trials' purpose was to examine the impact of factor level/s on plant characteristics to describe, understand, and analyze natural processes under study [13][14][15]. Toward the end of the trial, the scholars often have more columns (one for each trait), which need analysis to come up with and make inferences on the factor (rows) performance.…”

“…Multivariate techniques such as the coefficients of stepwise multiple linear regression (SMLR) analysis, cluster analysis (CA), principal component analysis (PCA), and LDA have been extensively used in plant trials. Despite SMLR and CA's importance, one weakness is the collinearity often observed across a range of assessed traits leading to unfavorable results or bias if not handled correctly [15,28,29]. PCA and LDA have been thoroughly used for dimensionality minimization and visual convergence of a two-way table combining treatments and traits [30,31].…”

“…Therefore, it is a very powerful tool to select the donors' parents in future hybridization programs to obtain new recombination by integrating all traits into an ideotype. Using the MGIDI in studies evaluating stability can lead to avoiding unnecessary accounts and better strategic decisions, which facilitates making recommendations for excellent cultivars [15,20,29].…”