A series of studies are presented on the genetic evaluation of cultivated potato (Solanum tuberosum L.) to improve the accuracy and efficiency of selection at various stages of a breeding programme. The central theme was the use of correlated data, such as relationship information and spatial and across-trial correlations, within a linear mixed modelling framework to enhance the evaluation of candidate genotypes and to improve the genetic response to selection. Analyses focused on several social and economically-important traits for the enhancement of the nutritional value, disease resistance and yield of potato tubers. At the formative stages of a breeding scheme, devising a breeding strategy requires an improved understanding of the genetic control of target traits for selection. To guide a strategy that aims to enhance the micronutrient content of potato tubers (biofortification), univariate and multivariate Bayesian models were developed to estimate genetic parameters for micronutrient tuber content from a breeding population generated from crosses between Andean landrace cultivars. The importance of the additive genetic components and extent of the narrow-sense heritability estimates indicated that genotypic 'individual' recurrent selection based on empirical breeding values rather than family-based selection is likely to be the most effective strategy in this breeding population. The magnitude of genetic correlations also indicated that simultaneous increases in important tuber minerals, iron and zinc, could be achieved. Optimising selection efficiency is an important ambition of plant breeding programmes. Reducing the level of candidate replication in field trials may, under certain circumstances, contribute to this aim. Empirical field data and computer simulations inferred that improved rates of genetic gain with p-rep (partially replicated) testing could be obtained compared with testing in fully replicated trials at the early selection stages, particularly when testing over two locations. P-rep testing was able to increase the intensity of selection and the distribution of candidate entries across locations to account for G×E effects was possible at an earlier stage than is currently practised. On the basis of these results, it was recommended that the full replication of trials (at the first opportunity, when enough planting material is available) at a single location in the early stages of selection should be replaced with the partial replication of selection candidates that are distributed over two locations. Genetic evaluation aims to identify genotypes with high empirical breeding values (EBVs) for selection as parents. Using mixed models, spatial parameters to target greater control of localised field heterogeneity were estimated and variance models to account for across-trial genetic heterogeneity were tested for the evaluation of soil-borne powdery scab disease and tuber yield traits at the early stages of a selection programme. When spatial effects improved model fit, spatial correlations for rows and columns were mostly small for powdery scab, and often small and negative for marketable and total tuber yield suggesting the presence of interplot competition in some years for tuber yield traits. For the evaluation of powdery scab, genetic variance structures were tested using data from 12 years of long-term potato breeding METs (multi-environment trials). A simple homogeneous correlation model for the genetic effects was preferred over a more complex factor analytic (FA) model. Similarly, for the MET evaluation of tuber yield at the early stages, there was little benefit in using more complex FA models, with simple correlation structures generally the most favourable models fitted. The use of less complex models will be more straightforward for routine implementation of potato genetic evaluations in breeding programmes. Evaluations for (marketable) tuber yield were extended to multi-location MET data to characterise both genotypes and environments, allowing a re-evaluation of New Zealand MET selection strategies aimed at broad adaptation. Using a factor analytic mixed model, results indicated that the programme’s two main trial locations in the North and the South Islands optimised differentiation between genotypes in terms of G×E effects. There was reasonable performance stability of genotypes across test locations and evidence was presented for some, but limited, genetic progress of cultivars and advanced clonal selections for tuber marketable yield in New Zealand over recent years. The models and selection strategies investigated and developed in this thesis will allow an improved and more systematic application of genetic evaluations in potato selection schemes. This will provide the basis for well informed decisions to be made on selection candidates for the genetic improvement of potato in breeding programmes.

Statistical analysis is based on an elementary paradigm and the relationship between probabilistic inductive inference, generation and validation of models, and the use of such information in decisions within a specific domain of knowledge. Additionally, techniques can be used to design specific experiments, such as the multi-environmental trials MET, to study the interaction between genotypes and environments. The fitting of probability distributions to data from phenomena allows the knowledge of the behavior of random variables and the later usage of such models in computational simulation. This procedure was carried out in the adjustment of models for maize grains weight, obtained via multi environmental trials. Several methods of adjustment of distribution and mixtures of normal distributions by the EM algorithm were used. The data were obtained through the use of scrapping with software R. Adjusted models were used to simulate, through computational methods implemented in language R, data with behavior known in parametric terms, generating a table that simulates the interaction between genotype and environment factors. Such simulated data were used to verify and compare models based on multivariate analysis, namely AMMI, weighted AMMI and GGE for the study of genotype environment interaction GxE. The results demonstrated the great effectiveness of the models in capturing the properties of the simulated data, contextualizing them as informational tools in the development of new products. / A estatística fundamenta-se em um paradigma elementar, baseado na relação entre a inferência indutiva probabilística, geração e validação de modelos e o uso de tais informações como subsídio em decisões em um domínio específico de conhecimento. Aliado a isso, técnicas podem ser utilizadas para delinear tipos específicos de experimentos, como os ensaios multi ambientais MET para estudos de interação entre genótipos e ambientes. O ajuste de distribuição de probabilidades a dados provenientes de fenômenos permite o conhecimento do comportamento de variáveis aleatórias e posterior uso de tais modelos em simulação computacional. Tal procedimento foi realizado no ajuste de modelos para peso de grãos de genótipos de milho em ensaios multi ambientais, através de diversos métodos de ajuste de distribuição e mixturas de distribuições normais pelo algoritmo EM. Os dados foram obtidos através do uso de scrapping via software R. Por sua vez, os modelos ajustados foram utilizados para simular, através de métodos computacionais implementados em linguagem R, dados com comportamento conhecido em termos paramétricos, através de uma tabela que simula a interação entre os fatores genótipo e ambiente. Tais dados simulados foram utilizados para verificar, e comparar os modelos baseados em análise multivariada de dados, a saber AMMI, AMMI ponderado e GGE, para o estudo da interação genótipo ambiente (GxE). Os resultados demonstraram a grande efetividade dos modelos em captar as propriedades dos dados simulados, contextualizando-os como ferramentas informacionais no desenvolvimento de novos produtos.

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Made available in DSpace on 2014-07-29T14:52:06Z (GMT). No. of bitstreams: 1 Tese Paula Pereira Torga.pdf: 781054 bytes, checksum: 44527a523a51d10dd76cb056750adbc4 (MD5) Previous issue date: 2011-04-14 / Genotypes x environments interaction (GxE) have many implications in a plant breeding program. Its importance becomes most obvious and pronounced in the final evaluation phase of lines for recommendation of new cultivars. At this stage, final tests on networking called value of cultivation and use (VCU) trials are carried out, in different locations, seasons and years, which allows a detailed study of GxE. The interaction can be controlled with a detailed study, and it does not negatively affect in the recommendation, providing a most secure selection and recommendation. There are some ways to mitigate the effect of GxE interaction, among them can be cited: i) the identification of cultivars with greater adaptability and phenotypic stability; ii) environmental stratification; and iii) the decomposition of the interaction to verify which factors (locations, season or years) is more expressive. The aim of this work was to study the GxE interaction in details, using VCU trials of common black bean, analyzing adaptability and phenotypic stability, environmental stratification and decomposition of GxE interaction. It makes possible to have most secure decision in the conduction of tests as the selection and recommendation of cultivars with the purpose to guide the common bean breeding program at Embrapa Rice and Beans. Data for grain yield (kg.ha-1) from the VCU trials of black beans, conducted during the years 2003 and 2004, in 69 environments of Central (43 environments) and Central-South regions (26 environments) from Brazil were analyzed in the following States: Distrito Federal, Goiás, Mato Grosso, Mato Grosso do Sul, Tocantins, Paraná, Santa Catarina, Sao Paulo and Rio Grande do Sul. Complete block design with three replications and plots of four rows with four meters long were used. Each test consisted of 13 genotypes of common black beans: eight elite lines (TB 9409, TB 9713, CNFP 10138, CNFP 7966, CNFP 7972, 7994 CNFP, CNFP 8000, CNFP 9328) and five cultivars (BRS Valente, FT Nobre, Diamante Negro, IPR Uirapuru, FT Soberano). First were done the phenotypic adaptability and stability analysis for region, to identify lines with specific and wide adaptation, using the methodologies of Annicchiarico and AMMI (additive main effects model and multiplicative interaction), with data from 69 trials. The lines with higher specific adaptation in each region were not coincident using the methodologies of Annicchiarico and AMMI. It was not possible identify genotypes with similar patterns of stability specific or broad using methodologies of Annicchiarico and AMMI (MPEA). The methodologies of Annicchiarico and AMMI (MPEAP) presented estimates of broad and specific adaptation very similar. Based on the methods Annicchiarico and AMMI (MPEAP) lines with more specific adaptation were CNFP 8000 and CNFP 7994, respectively, in the Central and Mid-South regions, and CNFP 8000 was more widely adaptated. To check which of the environmental factors (local, season or years) the interaction with common black bean genotypes is more expressive, and identify materials with broad and specific adaptation to sowing seasons, it was performed an analysis of variance with the decomposition of GxE in genotype x year, genotype x season and genotype x location. This analysis was performed by region, using first, 18 trials of Central region and 12 trials of the Central-South region, which allowed a partial isolation of factors and, later, the analysis using eight trials of each region, which allowed a complete isolation of the factors. The results showed that for Central Region was more important to evaluate the genotypes in different seasons and years than at different locations. For Central-South Region were more important evaluations of genotypes in different locations and years than at different seasons. For the Central Region, most genotypes had specific adaptation, but it was possible to identify lines widely adapted. In South-Central region the most genotypes showed widely adapted, but some lines showed strong specific adaptation. The line CNFP 8000 was the most widely adapted when we considered the two sowing season and the two regions. To evaluate the similarity among the locations assessed, it was performed environmental stratification analysis by sowing season for each region separately, using data from 27 trials from Central Region and 24 trials from Central-South Region. We used four different methods: i) traditional, proposed by Lin, complemented by simple fraction analysis of GxE interaction; ii) factor analysis; iii) Pearson correlation estimate; and iv) ecovalence. The results for the Central Region indicated Morrinhos as redundant using all four methodologies and for this reason, it was recommend to remove this local from the network of lines evaluation of Embrapa Rice and Beans. For the Central-South region, it was not detected the presence of similarity between locations and, because of this, all places will remain in the network of lines evaluation. / A interação genótipos x ambientes (GxA) tem inúmeras implicações em um programa de melhoramento de plantas e, na fase de avaliação final das linhagens, para a indicação de novas cultivares, sua importância se torna mais evidente e bastante pronunciada. Nesta fase são conduzidos os ensaios finais em rede, denominados de valor de cultivo e uso (VCU), em diferentes locais, safras e anos, o que permite um estudo detalhado da interação GxA. Com esse detalhamento a interação pode ser controlada, não interferindo negativamente na indicação, proporcionando uma seleção e recomendação mais seguras. Existem algumas maneiras de se atenuar o efeito da interação GxA, entre elas podem-se citar: identificação de cultivares com maior adaptabilidade e estabilidade fenotípica; ii) estratificação de ambientes; e iii) decomposição desta interação, para verificar com qual dos fatores (locais, épocas ou anos) ela é mais expressiva. O objetivo do presente trabalho foi estudar, de forma detalhada, a interação GxA, em ensaios de VCU de feijoeiro-comum com grãos pretos, realizando análises de adaptabilidade e estabilidade fenotípica, de estratificação ambiental e de decomposição da interação GxA, para orientar o programa de melhoramento do feijoeiro-comum da Embrapa Arroz e Feijão e, possibilitar tomadas de decisão mais seguras, tanto na condução dos ensaios, quanto na seleção e indicação de cultivares. Foram utilizados dados de produtividade de grãos (kg ha-1),provenientes dos ensaios de VCU de feijão preto, conduzidos nos anos de 2003 e 2004, em 69 ambientes das Regiões Central (43 ambientes) e Centro-Sul (26 ambientes) do Brasil, nos seguintes Estados: Distrito Federal, Goiás, Mato Grosso, Mato Grosso do Sul, Tocantins, Paraná, Santa Catarina, São Paulo e Rio Grande do Sul. O delineamento experimental utilizado foi o de blocos completos ao acaso, com três repetições e parcelas de quatro linhas com quatro metros de comprimento. Cada ensaio foi constituído por 13 genótipos de feijoeiro-comum com grãos pretos: oito linhagens elite (TB 9409, TB 9713, CNFP 10138, CNFP 7966, CNFP 7972, CNFP 7994, CNFP 8000, CNFP 9328) e cinco cultivares (BRS Valente, FT Nobre, Diamante Negro, IPR Uirapuru, FT Soberano). Primeiramente foram realizadas as análises de adaptabilidade e estabilidade fenotípica por Região, visando identificar linhagens com adaptação específica e ampla, utilizando-se as metodologias de Annicchiarico e AMMI (modelo de efeitos principais aditivos e interação multiplicativa), com os dados dos 69 ensaios. As linhagens com maior adaptação específica em cada região não foram coincidentes utilizando-se as metodologias de Annicchiarico e AMMI. Não foi possível identificar genótipos com padrão de estabilidade específica ou ampla similares utilizando as metodologias de Annicchiarico e AMMI (MPEA). As metodologias de Annicchiarico e AMMI (MPEAP) apresentaram estimativas de adaptação específica e ampla muito semelhantes. Com base nos métodos Annicchiarico e AMMI (MPEAP) as linhagens com maior adaptação específica são CNFP 8000 e CNFP 7994, respectivamente, na Região Central e Centro-Sul, e com maior adaptação ampla foi identificada a CNFP 8000. Para verificar com qual dos fatores ambientais (locais, épocas ou anos) a interação de genótipos de feijoeiro-comum do grupo preto, é mais expressiva, e identificar materiais com adaptação ampla e específica às épocas de semeadura, foi realizada uma análise de variância com a decomposição da interação GxA em genótipos x anos, genótipos x épocas e genótipos x locais. Esta análise foi realizada por Região, utilizando-se primeiramente 18 ensaios da Região Central e 12 da Região Centro-Sul, que permitiram o isolamento parcial dos fatores e, posteriormente, com oito ensaios de cada região, que permitiram o isolamento total dos fatores. Os resultados mostraram que, para a Região Central, é mais importante avaliar os genótipos em diferentes épocas em vários anos do que em diferentes locais. Já para a Região Centro-Sul são mais importantes as avaliações dos genótipos em diferentes locais e anos do que em diferentes épocas. Para a Região Central, a maioria dos genótipos apresentou adaptação específica, mas foi possível identificar linhagens de adaptação ampla. Na Região Centro-Sul a maioria dos genótipos apresentou adaptação ampla, mas alguns mostraram forte adaptação específica. A linhagem CNFP 8000 foi a de maior adaptação ampla quando foram consideradas as duas épocas de semeadura e as duas regiões conjuntamente. Para avaliar a existência de similaridade entre os locais, foram realizadas análises de estratificação ambiental por época de semeadura, para cada região, utilizando-se dados de 27 ensaios da Região Central e de 24 da Região Centro-Sul. Foram utilizadas quatro diferentes metodologias: i) tradicional, proposta por Lin, complementada pela análise da fração simples da interação GxA; ii) análise de fatores; iii) estimativa da correlação de Pearson; e iv) ecovalência. Os resultados obtidos para a Região Central indicaram Morrinhos, como redundante, pelas quatro metodologias utilizadas e, devido a isto, recomendou-se a retirada desse local da rede de avaliação de linhagens da Embrapa Arroz e Feijão. Para a Região Centro-Sul não foi detectada a presença de similaridade entre os locais e, devido a isto, todos permanecerão na rede de avaliação de linhagens.

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