Mean Performance and Stability in Multi‐Environment Trials II: Selection Based on Multiple Traits

Olivoto, Tiago; Lúcio, Alessandro Dal ́Col; Silva, José Antônio Gonzalez da; Sari, Bruno Giacomini; Diel, Maria Inês

doi:10.2134/agronj2019.03.0221

Cited by 155 publications

(238 citation statements)

References 34 publications

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“…Based on the knowledge of an ideal treatment, we reescaled X ij to obtain rX ij as proposed by Olivoto et al (2019).…”

Section: Reescaled Meansmentioning

confidence: 99%

Multivariate analysis of strawberry experiments: where are we now and where can we go?

Olivoto¹,

Diel

Schmidt

et al. 2021

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The multi-trait genotype-ideotype distance index (MGIDI) was used to select superior treatments in experiments with strawberries. Twenty-three productive, qualitative, physiological, and phenological traits with negative and positive desired gains were accessed in 16 treatments, a combination of two cultivars (Albion-neutral days, and Camarosa-short days), two transplants origins (National and Imported), and four organic substrates mixes (Crushed sugarcane bagasse, burnt rice husk, organic substrate, and Carolina commercial substrate). Our results suggest 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 index indicated that the Albion cultivar originated from imported transplants grown in substrates where the main component (70%) is burnt rice husk provides desired values for 20 of a total of 22 traits, which represents a success rate of ~ 91% in selecting traits with desired values. The strengths and weakness view provided by the MGIDI index revealed that the looking for an ideal treatment should direct the efforts on improving the water efficiency use and reducing total acidy of fruits. On the other hand, the strengths of selected treatments are mainly related to productive precocity, total soluble solids, and flesh firmness. The MGIDI index provides a unique, robust, and easy-to-handle process, standing out as a powerful tool to develop better treatment recommendations for experiments with strawberries when multiple traits are assessed.

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“…Based on the knowledge of an ideal treatment, we reescaled X ij to obtain rX ij as proposed by Olivoto et al (2019).…”

Section: Reescaled Meansmentioning

confidence: 99%

Multivariate analysis of strawberry experiments: where are we now and where can we go?

Olivoto¹,

Diel

Schmidt

et al. 2021

Preprint

Self Cite

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“…Users will find in metan a complete framework to implement the most used parametric and nonparametric stability statistics for MET analysis. The package implements stability methods not available in any other R package, including the estimation of BLUP‐based stability statistics (Colombari Filho et al, 2013), newer stability methods such as the WAASB, which is the weighted average of absolute scores from the singular value decomposition of GEI effects matrix obtained in a linear mixed model (Olivoto, Lúcio, Silva, Marchioro, et al, 2019), the multi‐trait stability index (Olivoto, Lúcio, Silva, Sari, et al, 2019) and the implementation of cross‐validation procedures for AMMI and BLUP models (Piepho, 1994). metan can also be useful for to a lot of other researchers since it provides options for implementing worldwide used multivariate statistics, e.g., path analysis, linear, partial and canonical correlations.…”

Section: Concluding Remarks and Future Improvementsmentioning

confidence: 99%

“…This requires a range of different packages to be used, making the coding tedious and difficult to follow. Thus, it seems to be value the creation of an R package that presents an easy workflow, incorporates the most used stability statistics, recently proposed stability methods (Olivoto, Lúcio, Silva, Marchioro, et al, 2019; Olivoto, Lúcio, Silva, Sari, Lúcio, Silva, Sari, & Diel, 2019), options for cross‐validation procedures (Piepho, 1994) and BLUP‐based stability statistics (Colombari Filho et al, 2013). These features are frequently used but are not yet implemented in any other R package for MET analysis.…”

Section: Introductionmentioning

confidence: 99%

metan: An R package for multi‐environment trial analysis

2020

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1. Multi-environment trials (MET) are crucial steps in plant breeding programs that aim at increasing crop productivity to ensure global food security. The analysis of MET data requires the combination of several approaches including data manipulation, visualization and modelling. As new methods are proposed, analysing MET data correctly and completely remains a challenge, often intractable with existing tools. 2. Here we describe the metan R package, a collection of functions that implement a workflow-based approach to (a) check, manipulate and summarize typical MET data; (b) analyse individual environments using both fixed and mixed-effect models; (c) compute parametric and nonparametric stability statistics; (d) implement biometrical models widely used in MET analysis and (e) plot typical MET data quickly.3. In this paper, we present a summary of the functions implemented in metan and how they integrate into a workflow to explore and analyse MET data. We guide the user along a gentle learning curve and show how adding only a few commands or options at a time, powerful analyses can be implemented. 4. metan offers a flexible, intuitive and richly documented working environment with tools that will facilitate the implementation of a complete analysis of MET datasets. K E Y W O R D Sadditive main effect and multiplicative interaction, biometry, genotype-environment interaction, GGE biplot, multi-environment trials, R software, stability, statistics

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“…This requires a range of different packages to be used, making it the coding tedious and difficult to follow. Thus, it seems to be value the creation of an R package that presents an easy workflow, and incorporates the most used stability statistics, as well as recent introduced stability methods (T. Olivoto, Lúcio, et al, 2019; T.…”

Section: Introductionmentioning

confidence: 99%

metan: an R package for multi-environment trial analysis

Olivoto

Lúcio

2020

Preprint

Self Cite

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10 1. Multi-environment trials (MET) are crucial steps in plant breeding programs that aim 11 increasing crop productivity to ensure global food security. The analysis of MET data 12 requires the combination of several approaches including data manipulation, visualization, 13 and modeling. As new methods are proposed, analyzing MET data correctly and 14 completely remains a challenge, often intractable with existing tools. 15 2. Here we describe the metan R package, a collection of functions that implement a 16 workflow-based approach to (a) check, manipulate and summarise typical MET data; (b) 17 analyze individual environments using both fixed and mixed-effect models; (c) compute 18 parametric and non-parametric stability statistics; (c) implement biometrical models 19 widely used in MET analysis; and (d) plot typical MET data quickly.203. In this paper, we present a summary of the functions implemented in metan and how 21 they integrate into a workflow to explore and analyze MET data. We guide the user 22 along a gentle learning curve and show how adding only a few commands or options at 23 a time, powerfull analyzes can be implemented. 24 4. metan offers a flexible, intuitive, and richly documented working environment with tools 25 that will facilitate the implementation of a complete analysis of MET data sets. 26 Key-words: AMMI, biometry, genotype-environment interaction, GGE biplot, multi-27 environment trials, R software, stability, statistics 28 29In 50 years the world average of cereal yields has increased by 64%, from 1.68 to 30 2.76 t ha −1 . In the same period, the total production of cereals has raised from 1.305 × 10 9 to 31 3.6 × 10 9 t, an increase of 175%, while the cultivated area increased by only 7.9% in the same 32 period (FAOSTAT, 2019). These unparallel increases have been possible due to the improved 33 cultivation techniques in combination with superior cultivars. For maize, for example, 50% 34 of the increase in yield was due to breeding (Duvick, 2005). Plant breeding programs have 35 been developing new cultivars for adaptation to new locations, management practices, or 36 growing conditions, in a clear and crucial example of exploitation of genotype-vs-environment 37 interaction (GEI). 38The breeders' desire to modeling the GEI appropriately has led to the development of 39 the so-called stability analyses, which includes ANOVA-based methods (Yates ) and some methods that combines 43 different statistical techniques, such as the Additive Main Effect and Multiplicative Interaction, 44 AMMI, (Gauch, 2013), and Genotype plus Genotype-vs-Environment interaction, GGE, (Yan 45 & Kang, 2003). Then, it is no surprise that scientific production related to multi-environment 46 trial analysis has been growing fast in the last decades. A bibliometric survey in the SCOPUS 47 database revealed that in the last half-century (1969-2019) 6590 documents were published 48 in 902 sources (Journals, books, etc.) by 19.351 authors. In this period, the number of 49 publications has been increased on av...

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Mean Performance and Stability in Multi‐Environment Trials II: Selection Based on Multiple Traits

Cited by 155 publications

References 34 publications

Multivariate analysis of strawberry experiments: where are we now and where can we go?

Multivariate analysis of strawberry experiments: where are we now and where can we go?

metan: An R package for multi‐environment trial analysis

metan: an R package for multi-environment trial analysis

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