Testes multiambientais na seleção de genótipos de arroz utilizando o modelo de regressão nos sítios ou locais

A análise da interação genótipo x ambiente utilizda no melhoramento de plantas tem sofrido mudanças na última década, melhorando a sua eficiência quanto à seleção dos genótipos sob diferentes condições ambientais. O objetivo deste trabalho foi analisar a produtividade e estabilidade de 12 genótipos de arroz em oito ambientes, durante os anos 2005 e 2006, na Colômbia. O delineamento utilizado foi o de blocos ao acaso com quatro repetições. Os parâmetros de estabilidade fenotípica e o agrupamento dos ambientes foram estimados pelo estudo da interação genótipo x ambiente, segundo o método SREG (Regressão nos sítios ou locais) e seu gráfico biplot (GGE). As análises estatísticas indicaram diferenças significativas (com 5% de probabilidade de erro) entre genótipos e entre ambientes e significância (com 5% de probabilidade de erro) da interação genótipo x ambiente, sugerindo uma resposta diferente dos genótipos nos vários ambientes. No método SREG, os dois primeiros componentes principais da interação explicaram 75,29% da interação. Os genótipos 400094, 350361 e a variedade Fedearroz 50 foram considerados os de maior produtividade. Segundo o gráfico biplot GGE, os ambientes La Libertad e Escobal foram os mais favoráveis para o cultivo do arroz.

Seleção de genótipos de trigo para rendimento de grãos e qualidade de panificação em ensaios multiambientes

O objetivo deste estudo foi selecionar linhagens de trigo para qualidade de panificação e para rendimento de grãos (RG), em ensaios multiambientes, e identificar ambientes de teste ideais para a seleção. Foram avaliados 39 genótipos de trigo, sendo 18 linhagens e nove cultivares testemunhas, em 2010, e 11 linhagens e quatro cultivares testemunhas, em 2011, no Estado do Paraná. Algumas linhagens e locais não foram repetidos entre os dois anos. O delineamento experimental utilizado foi o de blocos casualizados, com três repetições. A metodologia, em gráfico biplot GGE (Genotype and Genotype-by-Environment), foi utilizada para a avaliação de genótipos e ambientes de teste. Na safra 2010, os locais de Nova Fátima, Pitangueiras e Ventania foram discriminantes (maximizaram a expressão do RG e diferenciação entre genótipos) e representativos do conjunto de ambientes avaliados. Em 2011, Rolândia e Astorga destacaram-se como ambientes ideais para seleção simultânea para RG e qualidade de panificação. As linhagens BIO-08528 e BIO-08228 foram classificadas como genótipos ideais para o RG e a concentração proteica dos grãos (CPG), respectivamente. Na safra 2011, as linhagens BIO-10161 e BIO-10141 foram superiores para RG e para qualidade de panificação, respectivamente. O teste de sedimentação SDS e a CPG correlacionaram-se entre si (r = 0,61**) e foram moderadamente associados com a força de glúten (r = 0,49** e 0,74**), indicando que podem ser utilizados na seleção indireta para qualidade de panificação.

Identificación de mega‑ambientes para potenciar el uso de genotipos superiores de arroz en Panamá

El objetivo de este trabajo fue evaluar tres métodos para identificar mega‑ambientes, para optimizar el uso del potencial genético de los cultivares de arroz, durante el proceso de selección, y para hacer recomendaciones sobre siembras comerciales en Panamá. Los datos experimentales fueron obtenidos de los ensayos de productividad de cultivares precoces realizados entre 2006 y 2008. Para lograr la estratificación de los ambientes y definir los mega‑ambientes, se utilizaron los métodos del genotipo vencedor mediante el modelo AMMI1, el modelo biplot GGE y el de conglomerado por el método de Ward, complementado con el biplot GGE. Los tres métodos utilizados identificaron dos mega‑ambientes, donde los cultivares sobresalientes fueron Fedearroz 473 e Idiap 145‑05. Hubo una coincidencia de 100% en el agrupamiento del conglomerado x el biplot GGE, mientras que entre conglomerado x AMMI1 y biplot GGE x AMMI1 fue de 95,2%. El genotipo más estable, en ambos mega‑ambientes, fue el cultivar Idiap 145‑05, lo que indica capacidad de adaptación amplia y específica. La capacidad adaptativa de los genotipos superiores y no las condiciones agroclimáticas de las localidades evaluadas fue responsable de la definición de los mega‑ambientes.

Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

Abstract:The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty-nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.

GGE biplot analysis of multi-environment yield trials of rice produced in a temperate climate

Low temperature is one of the main environmental constraints for rice ( Oryza sativa L.) grain production yield. It is known that multi-environment studies play a critical role in the sustainability of rice production across diverse environments. However, there are few studies based on multi-environment studies of rice in temperate climates. The aim was to study the performance of rice plants in cold environments. Four experimental lines and six cultivars were evaluated at three locations during three seasons. The grain yield data were analyzed with ANOVA, mixed models based on the best linear unbiased predictors (BLUPs), and genotype plus Genotype × Environment interaction (GGE) biplot. High genotype contribution (> 25%) was observed in grain yield and the interaction between genotype and locations was not very important. Results also showed that ‘Quila 241319’ was the best experimental line with the highest grain yield (11.3 t ha-1) and grain yield stability across the environments; commercial cultivars were classified as medium grain yield genotypes.

Identification of sorghum hybrids with high phenotypic stability using GGE biplot methodology.

The aim of this study was to identify sorghum hybrids that have both high yield and phenotypic stability in Brazilian environments. Seven trials were conducted between February and March 2011. The experimental design was a randomized complete block with 25 treatments and three replicates...

Selection of mango rosa genotypes in a breeding population using the multivariate-biplot method.

Mango (Mangifera indica L.) trees stand out among the main fruit trees cultivated in Brazil. The mango rosa fruit is a very popular local variety (landrace), especially because of their superior technological characteristics such as high contents of Vitamin C and soluble solids (SS), as well as attractive taste and color. The objective of this study was to select a breeding population of mango rosa (polyclonal variety; ≥5 individuals) that can simultaneously meet the fresh and processed fruit Vmarkets, using the multivariate method of principal components and the biplot graphic.

Biplot analysis of strawberry genotypes recommended for the State of Espírito Santo.

Most strawberry genotypes grown commercially in Brazil originate from breeding programs in the United States, and are therefore not adapted to the various soil and climatic conditions found in Brazil. Thus, quantifying the magnitude of genotype x environment (GE) interactions serves as a primary means for increasing average Brazilian strawberry yields, and helps provide specific recommendations for farmers on which genotypes meet high yield and phenotypic stability thresholds. The aim of this study was to use AMMI (additive main effects and multiplicative interaction) and GGE biplot (genotype main effects + genotype x environment interaction) analyses to identify high-yield, stable strawberry genotypes grown at three locations in Espírito Santo for two agricultural years. We evaluated seven strawberry genotypes (Dover, Camino Real, Ventana, Camarosa, Seascape, Diamante, and Aromas) at three locations (Domingos Martins, Iúna, and Muniz Freire) in agricultural years 2006 and 2007, totaling six study environments. Joint analysis of variance was calculated using yield data (t/ha), and AMMI and GGE biplot analysis was conducted following the detection of a significant genotypes x agricultural years x locations (G x A x L) interaction. During the two agricultural years, evaluated locations were allocated to different regions on biplot graphics using both methods, indicating distinctions among them. Based on the results obtained from the two methods used in this study to investigate the G x A x L interaction, we recommend growing the Camarosa genotype for production at the three locations assessed due to the high frequency of favorable alleles, which were expressed in all localities evaluated regardless of the agricultural year.

A new graphic format to facilitate the understanding of technological innovation models: the seesaw of competitiveness

Many models exist in the literature to explain the success of technological innovation. However, no studies have been made regarding graphic formats representing the technological innovation models and their impact, or on the understanding of these models by non-specialists in technology management. Thus, the main objective of this paper is to propose a new graphic configuration to represent the technological innovation management. Based on the literature, the innovation model is presented in the traditional format. Next, the same model is designed in the graphic format - named `the see-saw of competitiveness` - showing the interfaces among the identified factors. The two graphic formats were compared by a group of graduate students in terms of the ease in understanding the conceptual model of innovation. The statistical analysis shows that the seesaw of competitiveness is preferred.

Teaching drawing: an interdisciplinary pedagogical experience in a graphic design course

Various authors have written about the importance of drawing in design methodology. Their general conclusion points drawing as an essential tool for design research, as it allows investigation of several alternative solutions in design process (Cross, 2007). The recent profound changes in design nature (Norman, 2011), justify a discussion on the purpose of drawing in design courses. As a consequence of this new reality, the educational institutions face the challenge of the definition of their curricular structures and teaching methodologies. Among others, concepts such as collaboration and multidisciplinary design approaches have been discussed as strategies for design education (Heller and Talarico, 2011, pp. 82-85). In this context, and using our teaching activity experience in Drawing and Design areas, the authors discuss:  how can drawing methods be included in the current design teaching?  can drawing be considered as an interdisciplinary approach?  what contributions can these methodologies provide to the educational/learning process? Based on these concerns, we developed an interdisciplinary project in the Graphic Design Course with two curricular units: Drawing 1 and Aesthetic and Design Theory 1. In this article the authors present the aims and process developed, and discuss the outcomes of this pedagogical experience.

Nostalgia Studio. A game engine for creating collaborative multiplayer online graphic adventure games

No decorrer do projeto SELEAG foi desenvolvido um jogo de aventura gráfica educativo com o propósito de ensinar história, cultura e relações sociais aos alunos. Este jogo foi avaliado em contexto de sala de aula em diversos países, obtendo resultados positivos. No entanto, por motivos técnicos, alguns dos objetivos propostos pelo projeto não puderam ser devidamente explorados, como permitir que o jogo fosse extensível por outros educadores ou suportar a colaboração online entre os jogadores. Nomeadamente, as ferramentas utilizadas para desenvolver o jogo eram demasiado complicadas para serem utilizadas fora da equipa de desenvolvimento, o que limitou a extensibilidade do projeto, e tornou impossível que educadores sem conhecimentos de programação fossem também capazes de traduzir os seus conteúdos educativos para este formato. Além disso, apesar do jogo possuir algumas funcionalidades de colaboração online, toda a interação era efetuada externamente ao jogo, através de um fórum de mensagens, o que demonstrou ser pouco motivante para os jogadores, pois muitos deles nem se aperceberam que havia uma componente de colaboração no jogo. O objetivo desta tese incide sobre estes dois problemas, e consistiu em desenvolver um editor e motor de jogo com uma interface simples de utilizar, que não necessita de conhecimentos prévios de programação, e que permite criar jogos de aventura gráfica com uma componente de colaboração online verdadeiramente embebida na jogabilidade. A aplicação desenvolvida foi testada por um conjunto de utilizadores de diversas áreas, tendo-se obtido resultados que demonstram a acessibilidade e simplicidade da mesma, independentemente do nível de experiência prévio de programação do utilizador. A componente de colaboração online foi também muito bem recebida pelos utilizadores, os quais demonstraram bastante interesse em ver jogos de aventura gráfica com componente de colaboração online serem desenvolvidos no futuro.

The Standard Biplot

Biplots are graphical displays of data matrices based on the decomposition of a matrix as the product of two matrices. Elements of these two matrices are used as coordinates for the rows and columns of the data matrix, with an interpretation of the joint presentation that relies on the properties of the scalar product. Because the decomposition is not unique, there are several alternative ways to scale the row and column points of the biplot, which can cause confusion amongst users, especially when software packages are not united in their approach to this issue. We propose a new scaling of the solution, called the standard biplot, which applies equally well to a wide variety of analyses such as correspondence analysis, principal component analysis, log-ratio analysis and the graphical results of a discriminant analysis/MANOVA, in fact to any method based on the singular-value decomposition. The standard biplot also handles data matrices with widely different levels of inherent variance. Two concepts taken from correspondence analysis are important to this idea: the weighting of row and column points, and the contributions made by the points to the solution. In the standard biplot one set of points, usually the rows of the data matrix, optimally represent the positions of the cases or sample units, which are weighted and usually standardized in some way unless the matrix contains values that are comparable in their raw form. The other set of points, usually the columns, is represented in accordance with their contributions to the low-dimensional solution. As for any biplot, the projections of the row points onto vectors defined by the column points approximate the centred and (optionally) standardized data. The method is illustrated with several examples to demonstrate how the standard biplot copes in different situations to give a joint map which needs only one common scale on the principal axes, thus avoiding the problem of enlarging or contracting the scale of one set of points to make the biplot readable. The proposal also solves the problem in correspondence analysis of low-frequency categories that are located on the periphery of the map, giving the false impression that they are important.

Multivariate analyses of genotype x environment interaction of popcorn

The objectives of this work were to evaluate the genotype x environment (GxE) interaction for popcorn and to compare two multivariate analyses methods. Nine popcorn cultivars were sown on four dates one month apart during each of the agricultural years 1998/1999 and 1999/2000. The experiments were carried out using randomized block designs, with four replicates. The cv. Zélia contributed the least to the GxE interaction. The cv. Viçosa performed similarly to cv. Rosa-claro. Optimization of GxE was obtained for cv. CMS 42 for a favorable mega-environment, and for cv. CMS 43 for an unfavorable environment. Multivariate analysis supported the results from the method of Eberhart & Russell. The graphic analysis of the Additive Main effects and Multiplicative Interaction (AMMI) model was simple, allowing conclusions to be made about stability, genotypic performance, genetic divergence between cultivars, and the environments that optimize cultivar performance. The graphic analysis of the Genotype main effects and Genotype x Environment interaction (GGE) method added to AMMI information on environmental stratification, defining mega-environments and the cultivars that optimized performance in those mega-environments. Both methods are adequate to explain the genotype x environment interactions.

Adaptabilidade e estabilidade de cultivares de trigo em diferentes épocas de semeadura, no Paraná

O objetivo deste trabalho foi identificar as melhores épocas de semeadura e avaliar a adaptabilidade e a estabilidade de cultivares de trigo, em duas regiões tritícolas do Paraná. Avaliou-se a produtividade de grãos de sete cultivares, em Guarapuava, e de nove, em Palotina, em quatro épocas de semeadura, em 2006, 2007 e 2008. Utilizou-se o delineamento experimental de blocos ao acaso, com quatro repetições em Guarapuava, e três em Palotina. Foram utilizadas a metodologia REML/BLUP e a dos efeitos principais dos genótipos e da interação genótipo x ambiente (GGE biplot) para a avaliação da adaptabilidade e da estabilidade das cultivares, e o métodoAMMI para a identificação das melhores épocas de semeadura. Semeaduras em julho, em Guarapuava, e em abril, em Palotina, maximizam a produtividade de grãos. As cultivares Safira, em Guarapuava, e CD 113, em Palotina, são estáveis, amplamente adaptadas e apresentam alta produtividade de grãos.

Épocas de semeadura e desempenho agronômico de cultivares de soja

O objetivo deste trabalho foi avaliar o efeito de épocas de semeadura no desempenho agronômico de cultivares de soja em São Domingos, SC, e indicar as cultivares mais estáveis e adaptadas a cada época. O experimento foi conduzido durante dois anos agrícolas (2008/2009 e 2009/2010), com seis cultivares e quatro épocas de semeadura (15/10, 15/11, 15/12 e 15/1), em delineamento experimental de blocos ao acaso, com três repetições e parcelas com área útil de 3,6 m². A metodologia AMMI (modelos de efeitos principais aditivos com interação multiplicativa) foi utilizada para avaliar o desempenho produtivo das cultivares, e a GGE (genótipo e interação genótipo x ambiente) para avaliar a adaptabilidade e a estabilidade das cultivares nas diferentes épocas de semeadura. Em ambos os anos agrícolas, as semeaduras em 15/10 e 15/11 maximizaram o número de vagens por planta, o número de grãos por vagem, a estatura das plantas, o número de ramos, a massa de mil sementes e, consequentemente, a produtividade de grãos. As cultivares de ciclo médio ou precoce com porte elevado são mais adequadas para semeaduras tardias.