Mean Performance and Stability in Multi‐Environment Trials I: Combining Features of AMMI and BLUP Techniques

Olivoto, Tiago; Lúcio, Alessandro Dal ́Col; Silva, José Antônio Gonzalez da; Marchioro, Volmir Sérgio; Souza, V. Q. de; Jost, Evandro

doi:10.2134/agronj2019.03.0220

Cited by 241 publications

(390 citation statements)

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“…The genotypic stability of each genotype was quantified by the weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the GEI effects generated by a linear mixed‐effect model (WAASB), estimated as follows:

{WAASB}_{i} = {false\sum}_{k = 1}^{p} | {IPCA}_{i k} \times {EP}_{k} | / {false\sum}_{k = 1}^{p} {EP}_{k}

where WAASB i is the weighted average of absolute scores of the i th genotype, IPCA ik is the score of the i th genotype in the k th interaction principal component axis (IPCA), and EP k is the amount of the variance explained by the k th IPCA. The genotype with the lowest WAASB value is considered the most stable (Olivoto et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2019

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Abbreviations: AMMI, additive main effects and multiplicative interaction; ASV, additive main effects and multiplicative interaction stability value; AUDPC, area under the disease progress curve; BLUP, best linear unbiased prediction; CW, caryopses weight; GEI, genotype × environment interaction; GSI, genotype stability index; GW, grain weight; GWP, grain weight per panicle; GY, grain yield; HI, hulling index; HW, hectoliter weight; IGY, industrial grain yield; IPCA, interaction principal component axis; LMM, linear mixed-effect model; MET, multi-environment trial; MPE, mean performance and stability; MTSI, multi-trait stability index; NEP, number of spikelets per panicle; NG2, number of grains >2 mm; NGP, number of grains per panicle; PL, panicle length; PM, panicle mass; TGW, thousand-grain weight; WAASB, weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the genotype × environment interaction effects generated by an linear mixed-effect model; WAASBY, weighted average of WAASB and response variable. bIoMetrY, ModeLInG, And stAtIstIcsPublished in Agron.

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{WAASB}_{i} = {false\sum}_{k = 1}^{p} | {IPCA}_{i k} \times {EP}_{k} | / {false\sum}_{k = 1}^{p} {EP}_{k}

Section: Methodsmentioning

confidence: 99%

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

et al. 2019

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“…Users will find in a complete framework to implement the most used parametric and non-parametric 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 weighted average of absolute scores from the (T. Olivoto, Lúcio, et al, 2019), the multi-trait stability index (Olivoto et al, 2019), and the implementation of cross-validation procedures for AMMI and BLUP models (Piepho, 1994).…”

Section: Concluding Remarks and Future Improvmentsmentioning

confidence: 99%

“…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 weighted average of absolute scores from the (T. Olivoto, Lúcio, et al, 2019), the multi-trait stability index (Olivoto et al, 2019), and the implementation of cross-validation procedures for AMMI and BLUP models (Piepho, 1994). 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, thus allowing exploiting the maximum of (good or bad) information that a data set can offer.…”

Section: Concluding Remarks and Future Improvmentsmentioning

confidence: 99%

“…For example, between 2013 and 2015, the larger number of papers proposing methods to deal with GEI were related to the Best Linear Unbiased Prediction (BLUP) in LMMs (Eeuwijk, Bustos-Korts, & Malosetti, 2016). Recent advances in this field showed that BLUP is more predictively accurate than AMMI and that the main advantages of these methods can be combined to help researchers to select or recommend stable and high productive genotypes (T. Olivoto, Lúcio, et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…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. Olivoto et al, 2019) in addition to options for computing cross-validation (Piepho, 1994) and BLUP-based stability statistics (Colombari Filho et al, 2013), features frequently used but not yet implemented in any other R package for MET analysis.…”

Section: Introductionmentioning

confidence: 99%

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metan: an R package for multi-environment trial analysis

Olivoto

Lúcio

2020

Preprint

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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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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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Mean Performance and Stability in Multi‐Environment Trials I: Combining Features of AMMI and BLUP Techniques

Cited by 241 publications

References 39 publications

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

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

metan: an R package for multi-environment trial analysis

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

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