SUMMARY It is becoming increasingly evident that a plant-pathogen interaction may be compared to an open warfare, whose major weapons are proteins synthesized by both organisms. These weapons were gradually developed in what must have been a multimillion-year evolutionary game of ping-pong. The outcome of each battle results in the establishment of resistance or pathogenesis. The plethora of resistance mechanisms exhibited by plants may be grouped into constitutive and inducible, and range from morphological to structural and chemical defences. Most of these mechanisms are defensive, exhibiting a passive role, but some are highly active against pathogens, using as major targets the fungal cell wall, the plasma membrane or intracellular targets. A considerable overlap exists between pathogenesis-related (PR) proteins and antifungal proteins. However, many of the now considered 17 families of PR proteins do not present any known role as antipathogen activity, whereas among the 13 classes of antifungal proteins, most are not PR proteins. Discovery of novel antifungal proteins and peptides continues at a rapid pace. In their long coevolution with plants, phytopathogens have evolved ways to avoid or circumvent the plant defence weaponry. These include protection of fungal structures from plant defence reactions, inhibition of elicitor-induced plant defence responses and suppression of plant defences. A detailed understanding of the molecular events that take place during a plant-pathogen interaction is an essential goal for disease control in the future.

O objetivo deste trabalho foi avaliar os métodos estatísticos de análise da interação de genótipos com ambientes (GxA), enfatizando a adaptabilidade e a estabilidade fenotípica. Utilizaram-se dados de produtividade de grãos de soja de sete experimentos em Goiás, testando 28 genótipos, dos quais quatro cultivares comerciais. Avaliaram-se os métodos Tradicional, Plaisted & Peterson, Wricke, Finlay & Wilkinson, Eberhart & Russell, Verma, Chahal & Murty, Toler, AMMI (additive main effect and multiplicative interaction), Hühn, Annicchiarico e Lin & Binns. Avaliou-se a associação entre os métodos pela correlação de Spearman. Observou-se forte associação entre os de Plaisted & Peterson e Wricke, cujo uso concomitante foi contra-indicado. A mesma conclusão é atribuída aos métodos Annicchiarico e Lin & Binns, também fortemente associados, o que implica em classificações fenotípicas muito semelhantes. O uso de um deles, entretanto, é recomendado. Métodos baseados, exclusivamente, em coeficientes de regressão, devem ser utilizados em associação com outro, fundamentado na variância da interação GxA, ou em medidas estatísticas como a variância dos desvios da regressão. O uso combinado do método de Eberhart & Russell e AMMI é outra indicação, em razão de suas correlações significativas com a maioria dos outros métodos e uma associação relativamente fraca entre eles.

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Resumo -O objetivo deste trabalho foi avaliar a influência da interação de genótipos com ambientes (GxA) na produtividade de grãos de um conjunto de linhagens de soja (Glycine max L.). Foram utilizados dados de 11 experimentos (ambientes) realizados no Estado de Goiás. Em cada experimento foram avaliados 18 genótipos, sendo quatro cultivares comerciais como testemunhas. O método de análise da interação foi o procedimento AMMI (modelo de efeitos principais aditivos e interação multiplicativa). O padrão significativo das interações GxA foi captado apenas pelo primeiro eixo principal AMMI, o qual explicou 36% da soma de quadrados GxA original, sugerindo contaminação da matriz de interações clássica por ruídos que prejudicam a qualidade das predições de respostas fenotípicas obtidas pelos métodos tradicionais. Quanto à estabilidade de comportamento, a maioria das linhagens experimentais destacou-se (com menores interações com ambientes) em relação às cultivares testemunhas. Estas, no entanto, foram relativamente mais produtivas, sobretudo a cultivar Conquista. Entre as novas linhagens, os genótipos L-16, L-13 e L-14 mostraram ser os mais promissores para fins de recomendação como cultivares.Termos para indexação: Glycine max, progênie, interação genótipo-ambiente, adaptação, biplot. Application of AMMI analysis in the assessment of yield stability in soybeanAbstract -The objective of this work was to evaluate the influence of the genotype environment (GE) interaction on the grain yield of a soybean (Glycine max L.) lines group. Yield data from 11 trials (environments) conducted in the State of Goiás, Brazil were used. In each trial 18 genotypes were tested, from which four were commercial cultivars as checks. The statistical method was the AMMI analysis (additive main effect and multiplicative interaction analysis). A significant GE interaction pattern was captured only for the first principal AMMI axis, which explained 36% of the original square sum of the GE interaction, suggesting contamination of the classic GE interaction matrix by noise arising from unpredictable factors, assuring that AMMI analysis provides a better prediction of phenotypic responses than traditional methodologies. About yield stability, most experimental lines outstands (with low GE interaction) over checks. However, the checks, mainly the cultivar Conquista, showed higher yield averages. Among the experimental lines, the genotypes L-16, L-13 and L-14 appeared to be the most promising for cultivars recommendation.Index terms: Glycine max, progeny, genotype environment interaction, adaptation, biplot.(1) Aceito para publicação em 17 de dezembro de 2002. IntroduçãoAs interações de genótipos com ambientes (GxA) trazem aos melhoristas dificuldades na identificação de genótipos superiores, seja por ocasião da seleção, seja no momento da recomendação de cultivares. A presença dessas interações indica que o comportamento relativo dos genótipos nos testes depende, fundamentalmente, das condições ambientais a que estão submetidos. Desta forma, a res...

Phenotypic yield stability is a trait of special interest for plant breeders. Many statistical procedures are available for stability analysis, each of them allowing for different interpretations. The objective of the present study was to determine the degree of correlation among the 13 statistical parameters that can be used for the analysis of phenotypic stability. Such correlations could be used to assess the extent to which these 13 parameters identify unique genetic effects. Yield data were obtained from 12 yield trials involving 76 common bean (Phaseolus vulgaris L.) genotypes and 12 location‐year production environments in Brazil. The stability statistics were divided in four groups according to the structure from which they were derived. On the basis of rank correlation, it was concluded that (i) there were highly significant correlations between many of the stability statistics (among and within groups) indicating that several of the statistics probably measure similar aspects of phenotypic stability; (ii) mean yields were positively correlated with many of the stability statistics; (iii) there was an association between the Group A statistics (variances and ranges) and the Group C statistics (regression and determination coefficients), and a similar association between the Group B (ecovalence) and Group D (variance of deviations from regression) statistics; (iv) the segmented linear regression coefficient (b1i) was overall the most independent parameter, indicating that the other stability statistics do not satisfactorily reflect genotypic responses in poor environments; (v) the strong correlation between the regression coefficients and the coefficients of determination indicates that the latter are not needed to measure the predictability of the estimated genotypic response; and (vi) the variance of the deviations from regression can provide assessment of the relative contribution of the genotype to the genotype × environment interaction as well as its biological stability.