Abbreviations: AMMI, additive main effects and multiplicative interaction; ASV, additive main effects and multiplicative interaction stability value; AVRC, index and the ranks of the mean yields; BLUP, best linear unbiased prediction; EV, averages of the squared eigenvector values; GEI, genotype × environment interaction; HMGV, harmonic mean of genotypic values; HMRPGV, harmonic mean of relative performance of genotypic values; IPCA, interaction principal component axis; LMM, linear mixed-effect model; MET, multi-environment trials; NF, no fungicide; RCBD, randomized complete block design; RMSPD, root mean square prediction difference; SPIC, sums of the absolute value of the IPCA scores; SVD, singular value decomposition; WAASB, weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the genotype × environment interaction effects generated by an linear mixedeffect model; WAASBY, weighted average of weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the genotype × environment interaction effects generated by an linear mixed-effect model and response variable; WF, with fungicide; Za, absolute value of the relative contribution of interaction principal component axes to the interaction. bIoMetrY, ModeLInG, And stAtIstIcsPublished in Agron.

The data analyzed in this study showed that the AMMI1 and GGE biplot methods are equivalent to rank genotypes for stability and adaptability.

Resumo -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 método AMMI 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.Termos para indexação: Triticum aestivum, AMMI, GGE biplot, interação genótipo x ambiente, REML/BLUP. Adaptability and stability of wheat cultivars at different sowing dates in the state of Paraná, BrazilAbstract -The objective of this work was to identify the best sowing dates and to evaluate the adaptability and stability of wheat cultivars in two wheat growing regions of the state of Paraná, Brazil. Seven cultivars were evaluated at Guarapuava and nine at Palotina as to grain yield, at four sowing seasons, in 2006, 2007 and 2008. The experimental design was a randomized complete block design, with four and three replicates, for Guarapuava and Palotina, respectively. The methodologies REML/BLUP and genotype main effect and genotype x environment interaction (GGE biplot) were used for adaptability and stability analysis, and the AMMI model was used to identify the best sowing dates. Sowing in July, at Guarapuava, and in April, at Palotina, maximized grain yield. The cultivars Safira, at Guarapuava, and CD 113, at Palotina, are stable, highly adapted and show high grain yield.Index terms: Triticum aestivum, AMMI, GGE biplot, genotype x environment interaction, REML/BLUP. IntroduçãoO trigo (Triticum aestivum L.) é o cereal de maior importância econômica mundial, com mais de 600 milhões de toneladas produzidas anualmente. O Brasil contribui com cerca de seis milhões de toneladas, com destaque para a região Sul, responsável por 90% da produção nacional (Companhia Nacional de Abastecimento, 2010), e para o estado do Paraná, o maior produtor do país.A expressão do potencial de produtividade de grãos depende de fatores genéticos e ambientais, bem como da interação entre ambos, o que resulta em expressivas diferenças no desempenho das cultivares quando cultivadas em diferentes condições ambientais (Yan & Holland, 2010). O termo ambiente (época de semeadura, ano e práticas culturais) pode ser definido como o resultado dos componentes biofísicos que influenciam o desenvolvimento e o crescimento das plantas.O potencial de produtividade de grãos pode ser maximizado pela escolha adequa...

Components of grain yield in wheat and its direct and indirect effects on productivityUsing path analysis it is possible to evaluate the relationships between characters and decompose the correlation into direct and indirect effects. The objective of this study was to estimate genotypic correlations and decompose the direct and indirect effects of yield components on grain yield of wheat cultivars. The experiment was conducted in 2008, in the experimental field of the Cooperativa Central de Pesquisa Agrícola (COODETEC) in Palotina, PR, Brazil. The experiment was arranged in a randomized block design, with three replications. The characteristics evaluated were: ear size, number of spikelets per spike, number of grains per spike, number of spikes per meter, thousand grain weight and grain yield. The results were subjected to analysis of variance and path analysis. The variance analysis showed differences between genotypes, indicating the presence of genetic variability for traits. Indirect selection via number of grains per spike, taking into account the thousand grain weight is the best strategy for obtaining superior genotypes in grain yield.

(1989). a metodologia de lin e binns (1988), de fácil interpretação, foi eficiente em recomendar cultivares de alto rendimento e boa estabilidade, e os materiais mais responsivos, o menor P i e a alta correlação negativa de spearman, entre o rendimento de grãos. Concluiu-se que a metodologia de lin e binns é bastante discriminante e, quando associada ao w i , oferece maior segurança na recomendação de cultivares com maior estabilidade. Palavras-chave:Triticum aestivum L., produção de grãos, adaptabilidade e estabilidade, interação genótipo x ambiente. ABSTRACT meThods For anaLysis oF adapTaBiLiTy and sTaBiLiTy oF wheaT CuLTivars For paraná sTaTe, BraziLThis investigation had the objective to evaluate grain yield adaptability and stability of 17 wheat cultivars (Triticum aestivum L.) for paraná state, Brazil, occording to four different methods. The experiments were carried out in 2007, at different locations of paraná, in complete randomized blocks experimental design, with 4 replicates. The Wricke (1965) methodology indicates stable cultivars, independently of average yield. eberhart and russell (1966) and cruz et al. (1989) methodologies were equally efficient to evaluate stability and indicate cultivars that are stable and also adapted to favorable and unfavorable environments. lin and binns (1988) methodology showed to be of easy interpretation and was efficient to indicate cultivars of high yield and with good stability, where more responsive materials showed a lesser P i , and high negative correlation of spearman between grain yields. it was concluded that LIN and BINNS methodology is very specific and when associated to W i offers more assurance in recommending cultivars for high stability.Key words: Triticum aestivum L., grain yield, adaptability and stability, genotype x environment interaction.

A aptidão tecnológica representa uma oportunidade de agregar valor de mercado ao trigo, principalmente em face do mercado internacional e do setor industrial, o qual busca diferencial de qualidade aos seus produtos. Os fatores genéticos, meteorológicos e de manejo são determinantes para a obtenção da qualidade desejada. Nesse sentido, a adequada escolha da cultivar, o conhecimento das limitações climáticas da região de cultivo e da fertilidade do solo e a execução dos tratos culturais recomendados pela pesquisa podem contribuir substancialmente para a obtenção das características físicas, químicas e biológicas que conferem qualidade à farinha e aos produtos derivados de farinha, conforme abordado nesta revisão.

Wheat (Triticum aestivum) is one of the most important food staples in the south of Brazil. Understanding genetic variability among the assortment of Brazilian wheat is important for breeding. The aim of this work was to molecularly characterize the thirty-six wheat cultivars recommended for various regions of Brazil, and to assess mutual genetic distances, through the use of microsatellite markers. Twenty three polymorphic microsatellite markers (PMM) delineated all 36 of the samples, revealing a total of 74 simple sequence repeat (SSR) alleles, i.e. an average of 3.2 alleles per locus. Polymorphic information content (PIC value) calculated to assess the informativeness of each marker ranged from 0.20 to 0.79, with a mean of 0.49. Genetic distances among the 36 cultivars ranged from 0.10 (between cultivars Ocepar 18 and BRS 207) to 0.88 (between cultivars CD 101 and Fudancep 46), the mean distance being 0.48. Twelve groups were obtained by using the unweighted pair-group method with arithmetic means analysis (UPGMA), and thirteen through the Tocher method. Both methods produced similar clusters, with one to thirteen cultivars per group. The results indicate that these tools may be used to protect intellectual property and for breeding and selection programs.

RESUMOTrinta e dois acessos de feijão (Phaseolus vulgaris L.) foram avaliados em Lages/SC, através da influência de oito caracteres de importância agronômica sobre a produção de grãos por unidade de área. O experimento, em blocos casualizados com quatro repetições, foi conduzido no período de "safrinha" no ano agrícola de 1996/97. Este trabalho teve como objetivo estimar os coeficientes de trilha entre os caracteres primários e secundários. O primeiro grupo de variáveis (primários) foi constituído pelo número de grãos por legume, número de legumes por planta, peso de mil grãos e a população de plantas (POP), e o segundo grupo (secundários), pelo número de dias entre a emergência e o florescimento (FL), número de dias entre a emergência e a maturação de colheita (MC), estatura de planta (EP) e o ponto de inserção do primeiro legume (PIL). Pela análise do coeficiente de trilha, ficaram caracterizados e avaliados os efeitos diretos e indiretos dos caracteres primários NLP e PMG, revelando os maiores efeitos diretos associados à alta correlação. O NLP foi altamente influenciado por plantas de maturação precoce e estatura de planta elevada. Assim, os coeficientes de trilha permitem concluir que os maiores efeitos diretos sobre o rendimento de grãos estão associados, principalmente a NLP, PMG, NGL e ciclo da planta.Palavras-chave: Phaseolus vulgaris L., critérios de seleção, correlações. SUMMARYThirty two bean accesses (Phaseolus vulgaris L.) were evaluated for the influence of eight characters of agronomic importance the production of grains for unit of area, in Lages/SC. The experiment, in randomized blocks with four repetitions, was conducted during the period off-season in the agricultural year of 1996/97. This work aimed estimating the degree of association between grain yield and its secondary components. The first group of variables (primary) was constituted by the number of grains per pod, number of pods per plant, weight of a thousand grains and the population of plants (POP). The second group (secondary), was constituted by the number of days between emergence and flowering (FL), number of days between emergence and harvesting point (MC), plant stature (EP) and the height of first pod insertion (PIL). For the analysis of the trail coefficient, the direct and indirect effects of primary characters NLP and PMG were characterized and evaluated, revealing the large direct effects associated with the high correlation. The NLP was highly influenced by plants of shorter cycle and higher stature. The trail coefficients allow to point out that the largest direct effects on the yield of grains NLP are mainly associated to PMG, NGL and cycle of the plant.