metan: An R package for multi‐environment trial analysis

Olivoto, Tiago; Lúcio, Alessandro Dal’Col

doi:10.1111/2041-210x.13384

Cited by 539 publications

(308 citation statements)

References 29 publications

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“…Since the SH index requires inverting a phenotypic covariance matrix among traits (equation 11), the presence of highly correlated traits can result in either biased index coefficients –since P is not optimally conditioned– or in an infinite number of solutions if P is not positive definite. Even though non-collinear traits can be selected easily using the function of the R package (Olivoto & Lúcio, 2020) –which would facilitate the implementation of the SH index in future studies– we strongly suggest the use of the MGIDI or even FAI-BLUP indexes to account for the multicollinearity in the data.…”

Section: Discussionmentioning

confidence: 99%

“…Functions to compute the MGIDI, FAI-BLUP, and SH indexes in R software have been elaborated and implemented in the R package (Olivoto & Lúcio, 2020). These functions will make it easier for the implementation of the MGIDI index in future studies.…”

Section: Discussionmentioning

confidence: 99%

“…The mixed model analysis was performed in the R 4.0.0 software using the functions and of the package (Olivoto & Lúcio, 2020). The complete code can be found as Supplementary item S1.3.…”

Section: Methodsmentioning

confidence: 99%

“…The Smith-Hazel (SH) classic index (Smith, 1936; Hazel, 1943), and the recently proposed FAI-BLUP index (Rocha et al, 2018) were computed with the functions and , respectively, of the package (Olivoto & Lúcio, 2020) and were used to validate the potential of the MGIDI index in terms of genetic gains. The selection gains for SH and FAI-BLUP indexes were computed according to equation (9) considering a selection intensity of 15%.…”

Section: Methodsmentioning

confidence: 99%

“…In the second one (SH-2), the multicollinearity-generating traits were excluded and the index was computed with the remaining traits. The traits to be excluded were chosen with the function of the package (Olivoto & Lúcio, 2020) so that there are no VIF great than 10 (Olivoto et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

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MGIDI: towards an effective multivariate selection in biological experiments

Olivoto¹,

Nardino

2020

Preprint

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The efficiency in genotype selection in plant breeding programs could be greater if based on multiple traits, but identifying genotypes that combine high performance across many traits has been a challenger task for breeders.Here, we introduced the theoretical foundations, validated the potential, provided the needed tools for future implementations, and showed how breeders can use the novel multi-trait genotype-ideotype distance index (MGIDI) for genotype selection in plant breeding programs. We illustrated the application of the method using data from a preliminary wheat trial with 14 traits assessed in 44 genotypes.The MGIDI provided negative gains (−2.29% ≤ gains ≤ −0.07%, a total of −5.15%) for all the four traits that wanted to decrease and positive gains (0.46% ≤ gains ≤ 14.28%, a total of 49.64%) for nine of the 10 traits that wanted to increase. The MGIDI should be useful for breeders who aim at the selection of genotypes based on multiple traits because it provides a unique and easy-to-interpret selection process that incorporates the underlying correlation structure of the data and doesn’t require any arbitrary weighting coefficient.The use of the MGIDI will save tremendous time and resources in plant breeding trials of any crop, guaranteeing long-term gains in grain yield without jeopardizing genetic gains in other important traits, thus helping breed of better varieties fast enough to satisfy the growing global population needs for food. A step-by-step guide and an R package containing all the needed functions are provided to facilitate the implementation of the MGIDI index in future studies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…The mixed model analysis was performed in the R 4.0.0 software using the functions and of the package (Olivoto & Lúcio, 2020). The complete code can be found as Supplementary item S1.3.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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MGIDI: towards an effective multivariate selection in biological experiments

Olivoto¹,

Nardino

2020

Preprint

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Grain yield stability of soybean (Glycine Max (L.) Merrill) for different stability models across diverse environments of Ethiopia

Hailemariam

Tesfaye

2023

Agrosystems Geosci & Env

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For grain yield stability analysis, genotype by environment interactions are crucial in properly identifying and discriminating between varieties and locations. Hence, this experiment was conducted with the objectives to evaluate the stability of soybean using additive main effects and multiplicative interaction (AMMI), multi‐trait stability index (MTSI), weighted average absolute scores biplot (WAASB), Eberhart and Russell regression model, and genotype plus genotype by environment interaction (GGE) biplot analysis for grain yield of soybean genotypes and identified stable genotypes in the different soybean agroecologies of Ethiopia. Twenty‐four soybean genotypes were planted at six soybean environments with RCBD in three replications in the 2015/2016 cropping season. Stability measures, namely, AMMI, AMMI stability value, and GGE biplot analysis were used to identify the high‐yielding and stable genotypes across the testing environments. AMMI‐1 biplot showed Pawe as the ideal environment; Bako as a favorable environment; Asosa an average environment; and the rest namely, Dimtu, Jimma, and Metu as unfavorable environments. On the other hand, AMMI‐2 biplot analysis certain genotypes like Prichard, Spry, Delsoy 4710, and Croton 3.9 were identified as stable genotypes. Bako and Metu were identified as the most discriminating environments. Mega environments and the best yielding soybean genotypes on each mega environment were revealed by the GGE biplot analysis model. For other multivariate statistics used for this study, MTSI, WAASB, and regression models, stable and superior varieties for grain yield were revealed. Through the MTSI, the four genotypes, namely, Liu yue mang, SCS‐1, Clarck‐63k, and AFGAT, were found to be stable and superior over the rest tested genotypes. Overall, the genotypes SCS‐1 and AGS‐7‐1 were stable across soybean growing environments and are recommended for mega environment production.

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Experimental plan for tests with pea

et al. 2021

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Correct experimental planning is important to obtain more reliable data with high experimental precision. In this way, the results obtained and the technical recommendations generated are more reliable and representative. Thus, the objective of this work is to estimate the plot and sample sizes and the number of repetitions for the variables (a) number of pods per plant and (b) mass of pods per plant for pea (Pisum sativum L.) cultivation. Uniformity tests were carried in the years 2016, 2017, and 2018 in the experimental area of the Crop Science Department at Federal University of Santa Maria. The cultivar used was Pea Grain 40, which has an indeterminate growth habit, with a cycle of 75-90 d and a cylindrical pod. The plot size for evaluating the number of pods per plant and the mass of pods per plant for pea cultivation is eight and nine plants, respectively. The sample size for evaluating the number of pods per plant and the mass of pods per plant is eight plants in the direction of the line with a half-width of the 20% confidence interval of the mean. For the variables number of pods per plant and pod mass per pea plant, 10 and 12 repetitions are required, respectively, to evaluate up to 20 treatments in a randomized block design and in the incomplete blocks design with up to 100 treatments for significant differences of 35% between treatment averages. INTRODUCTIONThe pea (Pisum sativum L.) is an annual herbaceous legume, with cultivars classified as being of determined and indeterminate growth. Cultivars that show indeterminate growth are used for edible pods production, whereas cultivars with determined growth are used for grain production (Filgueira, 2008). Worldwide, in 2018, the production of dry pea occupied 7.88 million ha of cultivated area, with a production of 13.53 million tons; the largest producers are Europe (38.8%), North and South America (33.6%), Asia (20.5%

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metan: An R package for multi‐environment trial analysis

Cited by 539 publications

References 29 publications

MGIDI: towards an effective multivariate selection in biological experiments

MGIDI: towards an effective multivariate selection in biological experiments

Grain yield stability of soybean (Glycine Max (L.) Merrill) for different stability models across diverse environments of Ethiopia

Experimental plan for tests with pea

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