GGE biplot analysis of the adaptability and stability of wheat genotypes in Mozambique

O objetivo deste trabalho foi usar o método GGE biplot, para selecionar genótipos de trigo superiores quanto à adaptabilidade e à estabilidade e determinar a produtividade de grãos em Sussundenga, Bárué e Lichinga, em Moçambique, nas safras agrícolas de 2018/2019, 2019/2020 e 2020/2021. Foram avaliados 11 tratamentos, tendo-se utilizado dez genótipos de trigo provenientes do International Maize and Wheat Improvement Center e uma cultivar testemunha, desenvolvida por uma empresa zimbabweana de sementes e usada no programa nacional de trigo do país. A produtividade de grãos foi a principal característica avaliada, por meio de análises individuais e conjuntas de variância, adaptabilidade e estabilidade. Os efeitos dos genótipos e da interação genótipo × ambiente foram significativos. A análise de adaptabilidade e estabilidade pelo método GGE biplot mostrou que os dois primeiros componentes principais explicaram 94,6% da variação total para o efeito ano, e 91,8%, para o efeito localização. Os seguintes genótipos podem ser selecionados para ambientes favoráveis e desfavoráveis: G1, considerado ideal devido sua alta média de produtividade e estabilidade ao longo dos anos; e G4 e G7, por apresentarem, simultaneamente, alta produtividade e estabilidade ao longo dos anos.The objective of this work was to use the GGE biplot method to select superior wheat genotypes for adaptability and stability, and to determine grain yield in Sussundenga, Bárué, and Lichinga, in Mozambique, in the 2018/2019, 2019/2020, and 2020/2021 crop years. Eleven treatments were evaluated, using ten wheat genotypes from International Maize and Wheat Improvement Center and a control cultivar developed by a Zimbabwean seed company and used in the national wheat program of the country. Grain yield was the main trait evaluated through individual and joint analyses of variance, adaptability, and stability. The effects of genotypes and the genotype × environment interaction were significant. The adaptability and stability analysis using the GGE biplot method showed that the first two main components explained 94.6% of the total variation for year effect, and 91.8%, for the location effect. The following genotypes can be selected for favorable and unfavorable environments: G1, considered ideal due to its high mean yield and stability over the years; and G4 and G7, for simultaneously showing a high yield and stability over the years

GGE biplot analysis of the adaptability and stability of wheat genotypes in Mozambique

Abstract The objective of this work was to use the GGE biplot method to select superior wheat genotypes for adaptability and stability, and to determine grain yield in Sussundenga, Bárué, and Lichinga, in Mozambique, in the 2018/2019, 2019/2020, and 2020/2021 crop years. Eleven treatments were evaluated, using ten wheat genotypes from International Maize and Wheat Improvement Center and a control cultivar developed by a Zimbabwean seed company and used in the national wheat program of the country. Grain yield was the main trait evaluated through individual and joint analyses of variance, adaptability, and stability. The effects of genotypes and the genotype × environment interaction were significant. The adaptability and stability analysis using the GGE biplot method showed that the first two main components explained 94.6% of the total variation for year effect, and 91.8%, for the location effect. The following genotypes can be selected for favorable and unfavorable environments: G1, considered ideal due to its high mean yield and stability over the years; and G4 and G7, for simultaneously showing a high yield and stability over the years

Computational softwares for genetic breeding based on image analysis and computational intelligence

O melhoramento vegetal visa desenvolver cultivares altamente produtivas de alta qualidade física e nutricional. Cumprir esse objetivo não é processo simples, uma vez que é necessário reunir, no mesmo genótipo, elevado número de genes favoráveis para uma série de características de interesse, principalmente se considerar que o controle genético desses caracteres apresenta natureza poligênica. Portanto, para tornar o desenvolvimento de novas cultivares mais eficiente é necessário utilizar ferramentas tanto a nível de campo, laboratório e de análise de dados cada vez mais eficientes. Certas áreas tem ganhado elevado destaque no melhoramento genético como a inteligência artificial e a fenômica. A associação dos conhecimentos em fenômica e inteligência artificial podem auxiliar na solução dos principais desafios do melhoramento genético como a influência da interação genótipos por ambientes. Softwares são imprescindíveis para auxiliar nas análises por meio dessas abordagens. Portanto, o objetivo deste trabalho é disponibilizar softwares gratuitos e aplicações em inteligência artificial e fenômica com ênfase em redes neurais artificiais, lógica fuzzy e processamento digital de imagens. Com esse intuito foram desenvolvidos os softwares FENOM e BioFuzzy por meio do software Matlab em integração à linguagem Java. O software FENOM é subdividido em duas áreas de procedimentos: processamento digital de imagens e classificação por meio de redes neurais artificiais. Para processamento de imagens estão disponíveis procedimentos de aquisição, pré-processamento, segmentação e extração de características. Nos procedimentos de classificação estão disponíveis análises por redes neurais artificiais com arquitetura perceptron multicamadas. Já o software BioFuzzy disponibiliza procedimentos de sistemas de decisão fuzzy e de agrupamento fuzzy para auxiliar na recomendação de cultivares. Essas aplicações constituem em importante contribuição para o melhoramento vegetal, principalmente por visar a difusão de tecnologias como inteligência artificial, redes neurais artificiais, sistemas de tomada de decisão fuzzy e fenômica.Plant breeding aims to develop highly productive cultivars of high physical and nutritional quality. This process is not simples, since it is necessary to gather, in the same genotype, a high number of favorable genes for a series of characteristics of interest, especially considering that the genetic control of these characters has polygenic nature. Therefore, to make the development of new cultivars more efficient, it is necessary to use of new tools in field, laboratory and data analysis. Certain areas have gained high prominence in genetic breeding such as artificial intelligence and phenomics. The association of knowledge in phenomics and artificial intelligence can help in solving the main challenges of genetic breeding as the influence of genotypes by environments interaction. Softwares are essential to aid in the analysis by these approaches. Therefore, the objective of this work is to provide free softwares and applications in artificial intelligence and phenomics with emphasis on artificial neural networks, fuzzy logic and digital image processing. With this purpose, FENOM and BioFuzzy software were developed by Matlab software in Java language integration. The FENOM software is subdivided into two areas of procedures: digital image processing and classification by artificial neural networks. For image processing, acquisition, pre-processing, segmentation and feature extraction procedures are available. Artificial neural networks with architecture multilayer perceptron are available in classificatory procedures. BioFuzzy software provides procedures for fuzzy decision systems and fuzzy clustering to aid in the recommendation of cultivars. These applications constitute an important contribution to plant breeding, mainly for the diffusion of technologies such as artificial intelligence, artificial neural networks, fuzzy decision making systems and phenomics.Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorConselho Nacional de Desenvolvimento Científico e Tecnológic

Neural networks and fuzzy logic applied in common bean breeding

Os programas de melhoramento vegetal atualmente utilizam-se de análises estatísticas para auxiliar na identificação de genótipos superiores em diversas etapas do desenvolvimento de um cultivar. Diferentemente dessas análises que são baseadas no paradigma estocástico, a abordagem da inteligência computacional tem sido pouco explorada na área do melhoramento genético. Assim, esse trabalho foi realizado com o objetivo de apresentar técnicas de inteligência computacional como ferramentas auxiliares no melhoramento do feijoeiro. Para demonstrar a aplicabilidade dessa abordagem, foram desenvolvidos dois estudos utilizando dados de avaliação de linhagens de feijão oriundas do Programa Feijão da Universidade Federal de Viçosa. Em um primeiro trabalho o objetivo foi avaliar o potencial das redes neurais artificiais como ferramenta auxiliar no melhoramento da arquitetura de plantas do feijoeiro. Com o intuito de classificar linhagens quanto ao porte, as redes neurais artificiais foram treinadas com dados de repetição de 19 linhagens de feijoeiro avaliadas nas safras de inverno de 2007 e de 2009, quanto a arquitetura de plantas, diâmetro do hipocótilo e altura de plantas. As redes neurais artificias apresentaram elevada capacidade de classificação correta das linhagens avaliadas, de forma que quando utilizado diâmetro do hipocótilo em conjunto com altura média de plantas, as redes neurais artificiais apresentaram melhores resultados do que utilizando somente o diâmetro do hipocótilo. Também observou-se que submeter dados de médias de novas linhagens às redes neurais treinadas com dados de repetição, provê melhores resultados de classificação das linhagens. Em um segundo trabalho o objetivo foi aplicar a Lógica Fuzzy, por meio de controladores, como ferramenta auxiliar na avaliação do comportamento de linhagens de feijão em diferentes ambientes. Para avaliar a aplicabilidade desses controladores foram utilizados dados de produtividade de grãos de 23 linhagens e duas testemunhas de feijão do grupo comercial vermelho, avaliados em nove ambientes da Zona da Mata de Minas Gerais. A partir dos parâmetros da análise de Eberhart e Russell foram desenvolvidos controladores fuzzy com sistemas de inferência Mamdani e Sugeno. Além destes, foi desenvolvido um controlador híbrido do tipo Sugeno baseado nos métodos de Eberhart e Russell e de Lin e Binns modificado. Foram realizadas análises de adaptabilidade e estabilidade pelos métodos de Eberhart e Russell e de Linn e Binns modificado e os respectivos parâmetros e medidas obtidos por meio dessas análises para cada linhagem foram submetidos aos respectivos controladores. Verificou-se que os controladores fuzzy podem ser aplicados para determinar o comportamento das linhagens, sendo o controlar híbrido o mais informativo a respeito da resposta das linhagens frente às variações ambientais. Dentre os sistemas de inferência utilizados, ambos sistemas apresentaram resultados consistentes. Uma vez que os controladores foram desenvolvidos de forma generalizada eles podem ser aplicados na determinação do comportamento de genótipos e na recomendação de cultivares de diferentes culturas agronômicas. Ao observar os resultados obtidos em ambos os trabalhos verificou-se que as técnicas de inteligência computacional apresentam grande potencial para serem empregadas nas diferentes etapas de um programa de melhoramento.Bean breeding programs have currently used statistical analysis in order to help identifying superior genotypes in various stages of a cultivar development. Unlike these analyses that are based on stochastic paradigm, the approach of computational intelligence has been little exploited in breeding. Thus, this study was carried out in order to present computational intelligence techniques as an important tool in bean breeding programs. To demonstrate the applicability of this approach, two studies were carried out using bean lines evaluation data derived from the Bean Breeding Program of the Federal University of Viçosa. In the first study, the objective was to evaluate the potential of artificial neural networks as an auxiliary tool in improving the bean plant architecture. In order to classify lines according to the habit, artificial neural networks were trained with 19 bean lines data from replication collected during the 2007 and 2009 winter crops, regarding plant architecture, hypocotyl diameter and plant height. The artificial neural networks presented high correct classification capability of the evaluated lines. Thus, when the hypocotyl diameter was used together with the mean height of plants, artificial neural networks had better results than when it was used the hypocotyl diameter individually. Also, it was observed that submitting mean data of new lines to neural networks trained with data from replication provides better results for the classification of lines. In the second work, the objective was to apply the fuzzy logic by means of controllers as an auxiliary tool in the evaluation of bean lines behavior in different environments. Grain yield data of 23 lines and two controls of red bean plants (Phaseolus vulgaris L.) were used in order to evaluated the applicability of these controllers. Plants were evaluated in nine environments of Zona da Mata region, Minas Gerais. From the parameters of Eberhart and Russell analysis, the fuzzy controllers were developed with Mamdani and Sugeno inference systems. In addition, Sugeno and Mamdani hybrid controllers were developed based on the methods of Eberhart and Russell and modified Lin and Binns. Adaptability and stability analyses were carried out by the methods of Eberhart and Russell and by the modified method of Lin and Binns, and the respective parameters and measurements obtained by these analyses for each line were submitted to the respective controllers. It was found that fuzzy controllers can be applied to determine the behavior of the lines, and the hybrid controller presented more information regarding the response of lines against the environmental variation. Both inference systems presented consistent results. Since the controllers were developed in a generalized way, they may be widely applied in determining the behavior of genotypes and in recommending cultivars of different crops . By observing the results obtained in both studies, it was found that computational intelligence techniques have great potential to be used in the different stages of a breeding program.Conselho Nacional de Desenvolvimento Científico e Tecnológic

Estabilidade e adaptabilidade de genótipos de algodão de fibra colorida quanto aos caracteres de fibra

O objetivo deste trabalho foi verificar a presença de interação genótipo x ambiente (G x A) e determinar a adaptabilidade e estabilidade fenotípica de linhagens de algodão de fibra marrom, utilizando o modelo de Ebehart & Russell. Foram conduzidos sete experimentos nos estados de CE, GO, MS e RN, em 2010 e 2011, em regime irrigado e de sequeiro. O delineamento utilizado foi blocos casualizados com quatro repetições. Foram avaliados 11 genótipos, segundo sete caracteres relativos à fibra. A interação G x A foi significativa para a maioria dos caracteres. As linhagens 1, 2, 3, 4, 5 e 7 demonstraram capacidade de resposta à melhoria de ambiente, sendo 1 e 5 os genótipos que apresentaram comportamento previsível para todas as características. O índice de fibras curtas mostrou ser uma característica de alta previsibilidade.The objective of this study was to verify the presence of genotype x environment (G x E) interaction and determine the adaptability and phenotypic stability of strains of brown cotton fiber using the model of Ebehart & Russell. Seven experiments were conducted in the states of CE, GO, MS and RN, in 2010 and 2011 under irrigated and rain fed conditions. The experimental design was randomized blocks with four replications. Eleven genotypes were assessed according to seven characters on the fiber. The G x E interaction was significant for most characters. The lines 1, 2, 3, 4, 5 and 7 showed better responsiveness to the environment, being 1 and 5 genotypes showed all predictable behavior characteristics for all characteristics. The content of short fibers proved to be a characteristic of high predictability

Fuzzy control systems for decision-making in cultivars recommendation

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. 

Fuzzy control systems for decision-making in cultivars recommendation

<div><p>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.</p></div

Self-organizing maps in the study of genetic diversity among irrigated rice genotypes

This study presents self-organizing maps (SOM) as an alternative method to evaluate genetic diversity in plant breeding programs. Twenty-five genotypes were evaluated in two environments for 11 phenotypic traits. The genotypes were clustered according to the SOM technique, with variable topology and numbers of neurons. In addition to the SOM analysis, unweighted pair group method with arithmetic mean clustering (UPGMA) was performed to observe the behavior of the clustering when submitted to these techniques and to evaluate their complementarities. Genotype ordering according to SOM was consistent with UPGMA results, evidenced by the basic structure of UPGMA groups being preserved in each group of the maps. Regarding genotype arrangement and the group neighbors, maps involving five neurons presented inferior organization efficiency compared to the six-map arrangements in both environments. It was observed that the organization pattern among the rice genotypes evaluated by the maps was complementary to the UPGMA approach, as observed in all scenarios. It can be concluded that self-organizing maps have the potential to be useful for genetic diversity studies in breeding programs

Genetic potential of common bean parents for plant architecture improvement

In common bean (Phaseolus vulgaris L.) breeding, plant selection that associate erect plant architecture, high yield, and grains with good commercial acceptance has been the choice of breeders. Thus, this study aimed to evaluate potential parents, to obtain promising segregating populations that associate high yield, erect plant architecture and carioca grain type, as well as to obtain information on heterosis, general and specific combining ability of these parents regarding grain yield and traits related to plant architecture. Fourteen common bean lines were crossed under a partial diallel scheme. Group 1 was composed by eight erect plant lines and group 2 by six carioca grain type lines. The F1's plants from the crosses and the 14 parents were evaluated during spring (Mar. sowing) for plant architecture grade, diameter of the hipocotyl, plant mean height, and grain yield. Predominance of additive effects was observed for plant architecture grade and diameter of the hypocotyls. For grain yield and plant mean height, there was a greater contribution of the dominance effects. Thus, selection of erect plants, with a larger diameter of the hypocotyl can be carried out in early generations; while for grain yield and plant mean height, it must be delayed, preferably, to later generations