Reflexo da interação genótipo x ambiente sobre o melhoramento genético de feijão

Pereira, Tâmara; Schmit, Rodolfo; Haveroth, Eduardo José; Melo, Rita Carolina de; Guidolin, Altamir Frederico; Backes, Rogério Luiz

doi:10.1590/0103-8478cr20130998

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…These results were expected to happen, since grain yield is a quantitative-nature characteristic and, consequently, very environmentally influential. Similar results were reported by PEREIRA et al (2016) andROCHA et al (2017) in studies with different bean species.…”

Section: Resultssupporting

confidence: 90%

Assessing the genotypic performance of carioca beans through mixed models

et al. 2018

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Using genotypes adapted to different regions is one of the main ways to increase Brazilian bean yield. The aim of the present study was to assess the genotypic performance of Carioca beans through mixed models. Fourteen Carioca bean genotypes were assessed in four locations in Pernambuco State (Arcoverde, Caruaru, Belém de São Francisco and São João counties) in 2015. The experiments followed a completely randomized block design, with three repetitions. Genetic parameters were estimated according to the REML/BLUP methodology, whereas genotype selection was based on the harmonic mean of relative performance of genetic values method (MHPRVG). The mean genotype heritability had moderate magnitude, high selective accuracy, besides allowing selection of agronomically superior individuals. Genotypes ‘BRS Notável’, CNFC 15480 and ‘IPR 139’ showed good adaptability and grain yield stability. There was agreement among the statistics μ ̂ + g ̂…, stability (MHVG), adaptability (PRVG), and stability and adaptability of genetic values (MHPRVG) in the discrimination of the most productive genotypes, which presented high adaptability and stability. This outcome indicated that these genotypes can be part of the selection criteria regularly used in bean breeding programs.

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Section: Resultssupporting

confidence: 90%

Assessing the genotypic performance of carioca beans through mixed models

et al. 2018

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“…The grain yield (Vf) was influenced by 25.8% due to the interaction effects (Vint). The interaction (Vint) was determined by 48.6% due to the genetic effects, in this way, the results were concordant to those obtained by Pereira et al (2015) in beans, where 59% of the phenotype was due to the G x E interaction. The influence of the environment on the interaction is due to the nature of the grain yield character, being controlled by many genes and its phenotypic expression is highly dependent on the characteristics of the growing environment (Piepho et al, 2012).…”

Section: Resultssupporting

confidence: 84%

Univariate, multivariate techniques and mixed models applied to the adaptability and stability of wheat in the Rio Grande do Sul State

Szareski¹,

Carvalho²,

Kehl³

et al. 2017

Genet. Mol. Res.

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ABSTRACT. The wheat crop presents sensitivity to the environmental conditions culminating in the genotype x environment interaction, being crucial the use of different methodologies to guide the positioning of genotypes to certain cultivation environments. The objective of this study was to estimate the adaptability and phenotypic stability of wheat genotypes grown in the State of Rio Grande do Sul using univariate and multivariate techniques and mixed models. The yield data of 42 2 V.J. Szareski et al. Genetics and Molecular Research 16 (3): gmr16039735wheat genotypes evaluated in five environments (Cachoeira do Sul, Passo Fundo, Santo Augusto, São Gabriel, and São Luiz Gonzaga) were used in the 2012 and 2013 crop seasons. In each experiment, a randomized complete block design was used, with three replicates. In the evaluation of the genotype x environment interaction, the sum of squares relative to contribution index, the methodology based on the univariate method of Annicchiarico (1992), the multivariate method (AMMI), and the mixed models (REML and MHPRVG) were used. The favorable environments expressed by the univariate method referred to São Gabriel, Cachoeira do Sul, Passo Fundo, Santo Augusto, and São Luiz Gonzaga; for the multivariate method, only Santo Augusto was favorable to the productivity character. The genotypes CD 121 and TBIO Tibagi were adapted and stable for the univariate and multivariate methods. The genotypes TBIO Sinuelo, Quartzo, BRS 327, Mirante, Topázio, Guamirim, TBIO Seleto, Ametista, TBIO Mestre, and BRS Louro were superior through the mixed model approach. The different strategies to estimate the adaptability and phenotypic stability allowed indicating and recommending the best environments and genotypes efficiently to obtain increases in wheat grain yield.

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“…The great challenge of plant breeding is elucidating the participation of this factor in the genotypic performance in order to adjust the genetic constitutions to the environment (Vencovsky, Ramalho, & Toledo, 2012). However, the alternation of genotypes and locations in breeding cycles is often observed, especially in annual and fast-cycle species, such as beans (Pereira et al, 2016). Therefore, locations can affect the performance of certain genetic constitutions, while years may also present superior effects.…”

Section: Union Of the Location And Cultivation Year Factors With A Smentioning

confidence: 99%

Consideration of the appropriate variation sources of the statistical model and their impacts on plant breeding

et al. 2018

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The present work has aimed to assess the consideration of the appropriate variation sources of the statistical model and their impacts on the conclusions plant breeding. The Value for Cultivation and Use test was conducted to assess three common locations (Lages, Ponte Serrada, and Canoinhas) and four non-common locations (Chapecó, Guatambu, Urussanga, and Campos Novos). The grain yields of six bean genotypes were evaluated in order to represent the imbalance between the common and noncommon locations. The statistical analysis considered two situations: i) union of the location factors and cultivation years, with a single variation source called environment and ii) decomposition of the mean square values of the two factors, location and year. According to the simplified analysis (environmental variation source), the F test for the genotype factor was highly significant (p = 0.0006). On the other hand, the hypothesis test for the genotype factor was not significant (p = 0.7370) when the decomposition of mean squares was used. The simplified analysis presents some erroneous points, such as the use of a mean residue to test the hypothesis of the genotype factor, since this factor is composed of several sources of variation, and there is no exact F test. However, approximate F tests can be obtained by constructing linear combinations of average squares. This fact notes the relevance of considering the appropriate sources of variation within the statistical model, with a direct impact on the conclusions and recommendations of cultivars with superior performance.

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Reflexo da interação genótipo x ambiente sobre o melhoramento genético de feijão

Cited by 7 publications

References 19 publications

Assessing the genotypic performance of carioca beans through mixed models

Assessing the genotypic performance of carioca beans through mixed models

Univariate, multivariate techniques and mixed models applied to the adaptability and stability of wheat in the Rio Grande do Sul State

Consideration of the appropriate variation sources of the statistical model and their impacts on plant breeding

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