Metodologia para análise de adaptabilidade e estabilidade por meio de regressão quantílica

Barroso, Laís Mayara Azevedo; Nascimento, Moysés; Nascimento, Ana Carolina Campana; Silva, Fabyano Fonseca e; Cruz, Cosme Damião; Bhering, Leonardo Lopes; Ferreira, Reinaldo de Paula

doi:10.1590/s0100-204x2015000400004

Cited by 10 publications

(12 citation statements)

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“…There are methods based on analysis of variance (Plaisted & Peterson, 1959;Wricke, 1965), regression (Cruz, Torres, & Vencovsky, 1989;Eberhart & Russell, 1966;Finlay & Wilkinson, 1963;Tai, 1971;Barroso et al, 2015), nonparametric methods (Lin & Binns, 1988;Rocha, Muro-Abad, Araújo, & Cruz, 2005;Nascimento et al, 2015), linear mixed models Reml/Blup (Resende, 2004), bayesian methods (Couto et al, 2015), and artificial neural networks (Barroso et al, 2013;.…”

Section: Introductionmentioning

confidence: 99%

<b>Fuzzy control systems for decision-making in cultivars recommendation

et al. 2018

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ABSTRACT. The objective of the present study was to propose fuzzy control systems to support the recommendation of cultivars of different agronomic crops. Grain yield data from 23 lines and 2 cultivars of red bean were used to evaluate the applicability of these controllers. Genotypes were evaluated in nine environments in the Zona da Mata region, Minas Gerais State, Brazil. Using the parameters of Eberhart and Russell analysis, fuzzy controllers were developed with the Mamdani and Sugeno inference systems. Analyses of adaptability and stability were carried out by the method of Eberhart and Russell. The parameters obtained for each genotype were submitted to the respective controllers. There were significant genotypes x environments interaction, which justified the necessity of performing an adaptability and stability analysis. For both controllers (Mamdani and Sugeno), seven lines presented general adaptability, while only one presented adaptability to unfavorable environments. It was also found that both inference systems were useful for developing controllers that had the aim of recommending cultivars. Thus, it was noted that fuzzy control systems have the potential to identify the behavior of bean genotypes.Keywords: common bean; adaptability and stability; computational intelligence.Sistemas de controle fuzzy para tomada de decisão na recomendação de cultivares RESUMO. O presente estudo tem como objetivo propor sistemas de controle fuzzy para auxiliar na recomendação de cultivares de diversas culturas agronômicas. Para avaliar a aplicabilidade desses controladores foram utilizados dados de produtividade de grãos de 23 linhagens e duas cultivares de feijão do grupo comercial vermelho, avaliados em nove ambientes da Zona da Mata do Estado de Minas Gerais, Brasil. A partir dos parâmetros da análise de Eberhart e Russell foram desenvolvidos controladores fuzzy com sistemas de inferência Mamdani e Sugeno. Foram realizadas análises de variância conjunta e de adaptabilidade e estabilidade pelo método de Eberhart e Russell e os parâmetros obtidos para cada linhagem foram submetidos aos respectivos controladores. Verificou-se a ocorrência de interação genótipos por ambientes significativa, existindo a necessidade da realização de análises de adaptabilidade e estabilidade. Em ambos os controladores (Mamdani e Sugeno), sete linhagens (2, 9, 14, 16, 17, 20 e 23) apresentaram adaptabilidade geral, enquanto que somente a linhagem 15 apresentou adaptabilidade a ambientes desfavoráveis. Verificou-se também que ambos os sistemas de inferência adotados, são úteis para desenvolver controladores que visam recomendação de cultivares. Assim, constatou-se potencialidade dos sistemas de controle fuzzy na identificação do comportamento das linhagens de feijoeiro.Palavras-chave: feijão comum; adaptabilidade e estabilidade; inteligência computacional.

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Section: Introductionmentioning

confidence: 99%

<b>Fuzzy control systems for decision-making in cultivars recommendation

et al. 2018

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“…It should be emphasized that the concept of adaptability used in QR models is similar to the one described by EBERHART & RUSSELL (1966); i.e., adaptability refers to the ability of cultivars to benefit from the environment. BARROSO et al, (2015) suggested that the following model of quantile regression is used in studies of adaptability and phenotypic stability:…”

Section: Methodsmentioning

confidence: 99%

“…Although interesting, such studies evaluate the adaptability in average terms, i.e., the correlation between environmental variation (X) and phenotypic response (Y) is explained by conditional expectation E(Y|X). In order to obtain information on different levels of the productivity variable, BARROSO et al (2015) proposed the use of quantile regression (KOENKER & BASSET, 1978) for adaptability and stability studies considering 92 genotypes of alfalfa (Medicago sativa L.) assessed in 20 different environmental conditions. In order to explain the functional relationship between the environmental variation (X) and the phenotypic response (Y), this method, unlike the methods based on linear regression (EBERHART & RUSSELL, 1966) provides a generalized explanation for any quantile of phenotypic values.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the adaptability of black bean cultivars by means of quantile regression

et al. 2019

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The aim of this study was to use quantile regression (QR) to characterize the effect of the adaptability parameter throughout the distribution of the productivity variable on black bean cultivars launched by different national research institutes (research centers) over the last 50 years. For this purpose, 40 cultivars developed by Brazilian genetic improvement programs between 1959 and 2013 were used. Initially, QR models were adjusted considering three quantiles (τ = 0.2, 0.5 and 0.8). Subsequently, with the confidence intervals, quantile models τ = 0.2 and 0.8 (QR0.2 and QR0.8) showed differences regarding the parameter of adaptability and average productivity. Finally, by grouping the cultivars into one of the two groups defined from QR0.2 and QR0.8, it was reported that the younger cultivars were associated to the quantile τ = 0.8, i.e., those with higher yields and more responsive conditions indicating that genetic improvement over the last 50 years resulted in an increase in both the productivity and the adaptability of cultivars.

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“…They differ in their biometric estimation concepts and procedures (Buitrago et al, 2011;Silva, 2012). Among these, we can mention methods that are already consolidated in the literature as those based on analysis of variance (Plaisted and Peterson, 1959;Wricke, 1965;Annicchiarico, 1992), linear regression (Finlay and Wilkinson, 1963;Eberhart and Russell, 1966;Tai, 1971;Cruz et al, 1989), methods non-parametric (Lin and Binns, 1988;Carneiro, 1998;Rocha et al, 2005), Multiple methods centroids (Nascimento et al, 2009;Nascimento et al, 2015), mixed linear models REML/Blup (Resende, 2004), Bayesian methods (Nascimento et al, 2011, Couto et al, 2015, quantile regression (Barroso et al, 2015), Artificial Neural Networks (Barroso et al, 2013), AMMI model (Gauch, 1992) and GGE biplot (Yan et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Research Article Methods of adaptability and stability applied to the improvement of flooded rice

Júnior¹,

Carneiro²,

Santos³

et al. 2020

Genet. Mol. Res.

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Rice (Oryza sativa) is crop that adapts well to diverse soil and climate conditions; breeding programs have generally been committed to identifying and selecting genotypes that are stable and have high productivity in various environments. In this sense, studies of adaptability and stability are of paramount importance to aid in the recommendation of cultivars, since it allows growers to obtain detailed information about the behavior of the genotypes in each region. We evaluatde the adaptability and stability of irrigated rice genotypes grown with continuous flooding, for the selection and recommendation of cultivars for crops or breeding programs. Eighteen genotypes were evaluated for grain yield in four agricultural years at three sites, covering 12 environments. The adaptability and stability were assessed by the methods of Eberhart and Russell, multiple centroids and GGE biplot. Genotypes behaved differently regarding stability and adaptability in the different environments. Both methodologies identified BRA 02691 and MGI 0607-1 as promising to be released as cultivars; however, classification inconsistencies occurred, such as for the line BRA 031001. Multiple centroid and GGE biplot methods demonstrated greater sensitivity ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 19 (3): gmr18434 A.C. Silva Junior et al. 2 than the Eberhart and Russell method. Using the methods simultaneously provides an innovative approach to the interpretation of GxE interactions and is a viable alternative for genotype classification. The genotype MGI 0607-1 showed promising behavior independent of the methodology used.

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Metodologia para análise de adaptabilidade e estabilidade por meio de regressão quantílica

Cited by 10 publications

References 15 publications

<b>Fuzzy control systems for decision-making in cultivars recommendation

<b>Fuzzy control systems for decision-making in cultivars recommendation

Analysis of the adaptability of black bean cultivars by means of quantile regression

Research Article Methods of adaptability and stability applied to the improvement of flooded rice

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