Repeatability of associations between analytical methods of adaptability, stability, and productivity in soybean

Woyann, Leomar Guilherme; Milioli, Anderson Simionato; Bozi, Antonio Henrique; Dalló, Samuel Cristian; Matei, Gilvani; Storck, Lindolfo; Benin, Giovani

doi:10.1590/s0100-204x2018000100007

Cited by 9 publications

(11 citation statements)

References 21 publications

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“…The increasing demand requires the development of high yielding genotypes, as well as improved strategies for crop management (Benin et al, 2012;Szareski et al, 2016). In this context, the importance of reliably recommending a particular genotype for a specific growing condition is undeniable, since the main traits of agronomic interest and seed yield are determined by genetic and environmental effects, and also by the combined action of genotype by environment (G x E) interactions (Carvalho et al, 2016;Kehl et al, 2016;Woyann et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Research Article Phenotypic and predicted genetic approaches for genotype ranking of wheat seed yield in Brazil

Szareski¹,

Carvalho²,

Kehl³

et al. 2018

Genet. Mol. Res.

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This work was aimed at determining stability and adaptability through Additive Main Effects and Multiplicative Interaction (AMMI) and Genotype Main Effects and Genotype Environment Interaction (GGE) methodologies, as well as to estimate and predict Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP) parameters and employ them in multivariate models using wheat genotypes grown in the major wheat regions of Brazil. The trials were conducted during the 2017growing seasnon across 12 regions of Brazil, with nine wheat genotypes, arranged in three replicates. When there were significant G x E interactions, the AMMI and GGE methods were applied. The scores were represented in biplot graphs through multivariate methodology of the principal components. REML/BLUP estimates and predictions were employed in the GGE multivariate method to obtain inferences based on genetic effects, which was denominated predicted genetic GGE approach. The predicted genetic approach was superior to a phenotypic comparison to explain the effects of genotypes x ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 17 (3): gmr18026 V.J. Szareski et al 2 environments interaction for wheat seed yield in Brazil. Specific adaptability for seed yield was established through phenotypic and genetic predicted approaches for genotypes BRS 331 and Marfimin the environment Itapeva, SP, as well as the genotype FPS Certerotoin the environment Cascavel. PR, and BRS 327 in the environment Cruz Alta, RS. The use of multivariate biometric methodologies along with the new predicted genetic approach enables reliable positioning of wheat genotypes for seed production across the main wheat regions of Brazil.

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Section: Introductionmentioning

confidence: 99%

Research Article Phenotypic and predicted genetic approaches for genotype ranking of wheat seed yield in Brazil

Szareski¹,

Carvalho²,

Kehl³

et al. 2018

Genet. Mol. Res.

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“…Therefore, when a genotype is located inside the macro environment, it is considered as of specific adaptability for those growing conditions (Yan and Kang, 2003). The definition of macro environment is attributed through the relation between edaphoclimatic characteristics, managements and similar response of the genotype to variations imposed by the environment (Woyann et al, 2018). The macro environment corresponding to environments of Santa Rosa -RS (E1) and Tenente Portela -RS (E2) defined that the genotypes BRS Tordilha RR (G1), Fepagro 36 RR (G5), FPS Netuno RR (G6), FPS Júpiter RR (G9), BMX Magna RR (G11) and A 6411 RG (G12) are of specific adaptability to this macro environment.…”

Section: Resultsmentioning

confidence: 99%

Multivariate index of soybean seed vigor: a new biometric approach applied to the effects of genotypes and environments

et al. 2018

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: The objective of this work was to apply a multivariate index of vigor in soybean seeds to identify the differential effects of the genotypes x environments interaction on the quality of the seeds produced in the 2016/2017 harvest season. The experiment was conducted in a randomized block design, organized in the factorial scheme, with five seed production environments x 20 soybean genotypes, arranged in four replications. The seed production environments of Tenente Portela - RS and Santa Rosa - RS soybeans are characterized as the environments that express the highest magnitude for seed vigor index, however biometrically Sarandi - RS has been defined as the optimal environment for the production of high vigor seeds according to the tested genotypes. High seed vigor index were expressed for the genotypes TMG 7161 RR, AMS Tibagi RR, BMX Magna RR, Fepagro 37 RR and NA 5909 RG. The differential effects of the genotypes x production environments interaction of soybean seeds influenced by more than 68% the vigor index of the seeds produced.

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“…Users who research the associations between stability indexes (e.g. Bornhofen et al, 2017; Freiria et al, 2018; Shahbazi, 2019; Woyann et al, 2018) often find difficulties in computing the set of statistics and binding them into a ‘ ready‐to‐read ’ file. metan provides an efficient solution for doing that.…”

Section: The Metan Packagementioning

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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Repeatability of associations between analytical methods of adaptability, stability, and productivity in soybean

Cited by 9 publications

References 21 publications

Research Article Phenotypic and predicted genetic approaches for genotype ranking of wheat seed yield in Brazil

Research Article Phenotypic and predicted genetic approaches for genotype ranking of wheat seed yield in Brazil

Multivariate index of soybean seed vigor: a new biometric approach applied to the effects of genotypes and environments

metan: An R package for multi‐environment trial analysis

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