Multivariate analysis revealed genetic divergence and promising traits for indirect selection in black oat

Meira, Daniela; Meier, Carine; Olivoto, Tiago; Follmann, Diego Nicolau; Rigatti, Alexsander; Lunkes, Andrei; Marchioro, Volmir Sérgio; Souza, Velci Queiróz de

doi:10.5039/agraria.v14i4a6514

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…With the increase in the number of seeds (NS), there is an increase in the seed mass (SM). Studies by Meira et al (2019a), report that the increase in productivity is highly correlated with the increase in the number of grains per plant and grain weight. By increasing the seed mass (SM), there are increases in TSM.…”

Section: Resultsmentioning

confidence: 99%

Akaike criteria and selection of physiological multi-character indexes for the production of black oat seeds

Moura¹,

Carvalho²,

Silva³

et al. 2021

Commun. Plant. Sci.

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The selection of characters provides the identification of complex characteristics, the objective of the work was to use the Akaike criteria and multiple regressions to prove the multi character selection and the interrelationships based on the Phenotypic, Standardized, Willians, Multiplicative, Mulamba and Mock indexes for the selection of black oat genotypes with high physiological potential of seeds. The study was conducted in Fortaleza dos Valos -RS in an experimental design used in randomized blocks with three replications. The trends for the selection of various Step Wise variables and the Akaike criteria are similar for most selection indices. For selection indexes, the phenotypic index and the standardized index correlate with the morphological characteristics, being mainly for the number of tillers. The genetic and multiplicative indices correlate with the characteristics of the seed. The phenotypic selection index was the one that stood out the most, regardless of the criterion used. This index correlates positively with the number of tillers. To select plants that present superior quality seeds, individuals with characteristics such as smaller panicles and with less spikelet ramifications and less panicle mass are sought. Highlighted Conclusions1. The phenotypic selection index was the one that stood out the most. 2. To obtain quality seeds, plants with smaller panicles, less spiky branching and less panicle mass are sought.

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Section: Resultsmentioning

confidence: 99%

Akaike criteria and selection of physiological multi-character indexes for the production of black oat seeds

Moura¹,

Carvalho²,

Silva³

et al. 2021

Commun. Plant. Sci.

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“…Author details 1 Department of Plant Science, Federal University of Santa Catarina, Florianópolis, SC 88034-000, Brazil. 2 Departament of Plant Science, Federal University of Pampa, Itaqui, RS 97650-000, Brazil.…”

Section: Supplementary Informationmentioning

confidence: 99%

“…Agronomic experiments aim to test the effects of levels or combinations of factor levels on plant traits to understand the phenomena under study [1][2][3]. At the end of an experiment, the researchers often have a spreadsheet with dozens of columns (one for each trait), that need to be analyzed to make inferences on the treatment (rows) performance.…”

Section: Introductionmentioning

confidence: 99%

MGIDI: a powerful tool to analyze plant multivariate data

et al. 2022

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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.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Al-Ashkar,

Sallam,

Ibrahim

et al. 2023

Plants

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Multiple abiotic stresses negatively impact wheat production all over the world. We need to increase productivity by 60% to provide food security to the world population of 9.6 billion by 2050; it is surely time to develop stress-tolerant genotypes with a thorough comprehension of the genetic basis and the plant’s capacity to tolerate these stresses and complex environmental reactions. To approach these goals, we used multivariate analysis techniques, the additive main effects and multiplicative interaction (AMMI) model for prediction, linear discriminant analysis (LDA) to enhance the reliability of the classification, multi-trait genotype-ideotype distance index (MGIDI) to detect the ideotype, and the weighted average of absolute scores (WAASB) index to recognize genotypes with stability that are highly productive. Six tolerance multi-indices were used to test twenty wheat genotypes grown under multiple abiotic stresses. The AMMI model showed varying differences with performance indices, which disagreed with the trait and genotype differences used. The G01, G12, G16, and G02 were selected as the appropriate and stable genotypes using the MGIDI with the six tolerance multi-indices. The biplot features the genotypes (G01, G03, G11, G16, G17, G18, and G20) that were most stable and had high tolerance across the environments. The pooled analyses (LDA, MGIDI, and WAASB) showed genotype G01 as the most stable candidate. The genotype (G01) is considered a novel genetic resource for improving productivity and stabilizing wheat programs under multiple abiotic stresses. Hence, these techniques, if used in an integrated manner, strongly support the plant breeders in multi-environment trials.

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Multivariate analysis revealed genetic divergence and promising traits for indirect selection in black oat

Cited by 5 publications

References 13 publications

Akaike criteria and selection of physiological multi-character indexes for the production of black oat seeds

Akaike criteria and selection of physiological multi-character indexes for the production of black oat seeds

MGIDI: a powerful tool to analyze plant multivariate data

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

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