crucial to these investigations. Literature Cited Acuna, T.L.B. Lafitte, H.R. Wade, L.J. 2008 Genotype x environment interactions for grain yield of upland rice backcros lines in diverse hydrological environments Field Crops Res. 108 117 125 Adalid, A
its extreme sensitivity to environmental variations and genotype-by-environment (G × E) interactions ( Dhakare and More, 2008 ; Yadav and Ram, 2010 ). In field evaluation trials, the performance of a genotype is determined by the genotypic main effect
, but these attempts have achieved only mixed success with selected clones often having unpredictable and/or unstable yields ( George et al., 2011 ). Sweetpotatoes commonly exhibit a genotype × environment (G × E) interaction ( Collins et al., 1987
Tibbitts, 2000 ; Cox et al., 1976 ; Thibodeau and Minotti, 1969 ; Yanagi et al., 1983 ). Yield of lettuce, especially in hot conditions, is controlled by genotype and environment but the influence by the interaction of both is still not well understood
linear mixed model with REML procedure was used to estimate the additive genetic ( σ A 2 ), nonadditive genetic ( σ d 2 ), and G×E ( σ g × e 2 ) variances for all traits. In the linear mixed model, the genotypes and genotype-environment interaction were
genetic control and genotype × environment interactions associated with geosmin concentration and TDS in table beet, to facilitate breeding for desired table beet flavor. The earthy flavor in table beet is conferred by geosmin ( trans -1,10-dimethyl- trans
Genotype × environment interaction for resistance to the twospotted spider mite (Tetranychus urticae Koch) of eleven clones of Fragaria L. sp. (strawberries) grown in six environments throughout the United States was examined using two multivariate analysis techniques, principal coordinate analysis (PCA) and additive main effect and multiplicative interaction (AMMI). Both techniques provided useful and interesting ways of investigating genotype × environment interaction. PCA analysis indicated that clones X-11 and E-15 were stable across both low and high environments for the number of spider mites per leaflet. The initial AMMI analysis showed that the main effects of genotype, environment, and their first-order interaction were highly significant, with genotype × environment interaction due mainly to cultivar `Totem' and environment FL94. A second AMMI analysis, which excluded `Totem' and FL94, showed that the main effects of the remaining genotypes, environments, and genotype × environment interaction were also highly significant. AMMI biplot analysis revealed that FL93 and GH93 were unstable environments, but with opposite interaction patterns; and GCL-8 and WSU2198 were unstable genotypes with similar interactions that were opposite those of WSU 2202.
Improved methods of breeding, selection, and testing for yield can be developed with information on the magnitude and nature of genotype–environment interactions. Cultivar trials of processing tomatoes (Lycopersicon esculentum Mill.) grown in Ontario for 2 years at 5 locations each year were studied for genotype–environment interactions. Cultivars were evaluated for phenotypic stability and desirability using regression coefficients, mean square deviations from linear regression, and t test comparisons of genotype means with environment means. Genotype-environment interactions were significant for yield of marketable fruit each year and in a combined analysis across years. Regression analysis indicated that low-yielding genotypes had above-average yield stability across environments, while several high-yielding genotypes were unstable. Several cultivars were found to be desirable because they had a high mean yield and did not have lower yields than the test mean in any of the 5 environments. Regression analysis alone could result in misleading conclusions about the performance of high-yielding tomato genotypes. Large genotype-environment interaction variances relative to genotype variances were detected. The interaction variance components involving year were large relative to the genotype-location interaction variance, indicating the need for multiyear evaluation and selection for stability even when breeding for a limited geographic region.
Genotype (G) x environment (E) interactions were measured in sweet potatoes (Ipomoea batatas L.) for yield (seven genotypes, six locations, 3 years) and selected quality factors (nine genotypes, six locations, 2 years). Yield of all grades of roots and all quality factors tested were affected significantly by genotype, environment, and G × E interactions. Quality factors were less affected by G × E interactions than yield factors. Broad-sense heritability estimates ranged from 75% to 92% for yield factors and 94% to 99% for quality factors. Estimates of variances of clonal means with varying years, locations, and replications suggest that 2 years, four locations, and four replications would provide reliable test data for yield and quality factors.
Genotype by environment (G × E) effects in Regional Cooperative Southernpea trials for the southeastern United States were investigated to characterize the extent, pattern, and potential impact of G × E on seed yield of southernpea [Vigna unguiculata (L.) Walp] genotypes. The structure of G × E effects was investigated using the Additive Main Effect and Multiplicative Interaction (AMMI) method. AMMI analyses revealed a highly significant genotype × environment interaction, most of which was partitioned into a genotype × location component of variance. AMMI first principal component axis scores stratified environments into two groups that minimized variation within groups. Biological interpretation of groupings and visual assessment of the AMMI biplot, revealed high-yielding genotypes interacting positively with one group of environments and conversely, low-yielding genotypes interacting positively with the other group. There were some significant rank changes of genotypes as yield potential varied across environments. Some environments showed similar main effects and interaction patterns indicating that most of the G × E effects could be captured with fewer testing sites, and consequently redundancy of some testing environments over years.