Author details

[92]. To mitigate the confounding effect of G × E interaction on selection efficiency, plant breeders have devised strategies to ensure progress in selection efficacy. For this reason, genotypes are tested in diverse environments to assess their adaptability and stability [85]. After this sound, analyses are carried out using the appropriate software to assess the extent of G × E interaction effect. Genotypes whose G × E effects are not significant are considered to be

Stability analysis is performed to estimate the performance of genotypes as linear function of the level of productivity in each environment [93]. Eberhart and Russell suggested joint regression analysis to estimate the average performance of a genotype in different environments relative to the mean performance of all genotypes in the same environment [68]. The use of multiplicative models which include the additive main effect and multiplicative interaction (AMMI) model has also been used to assess the stability of other crops [94, 95]. The AMMI model allows fitting of the sum of several multiplicative terms rather than only one multiplicative term in dissecting the performance of genotypes in different environments [93]. Yan also suggested the use of the genotype and genotype × environment interaction (GGE) biplot to graphically visualize genotypic performance across several environments [96]. The use of these strategies will enable the breeder to make informed decisions in where to place which variety

The pounding prominence of tomato as a vegetable is reflected by large volume of research on almost all aspects of the crop. In every crop improvement program, promising genotypes are tested for their performance for some years at a number of sites, to identify genotypes which possess the dual qualities of high-yield sustainability to adverse changes in environment condition. This interplay refers to genotype by environment interaction. A genotype × environment interaction is a change in the relative performance of a character of two or more genotypes measured in two or more environments. Its origin is linked to two concepts: biometric and developmental interaction. Interactions may therefore involve changes in order for genotypes between environments and changes in the absolute and relative magnitude of the genetic, environmental, and phenotypic variances between environments. These can further be classified as no GEI, non-crossover interaction, and crossover interaction. Complex quantitative traits, such as yield, with multiple contributing traits are highly influenced by environment interaction effects. Tomato production, though weather dependent and highly seasonal, can be grown under both field and greenhouse conditions (controlled environment). Researchers perform multilocational trials to evaluate new or improved genotypes across multiple environments (locations and years), before they are promoted for release and commercialization. This organized approach helps increase yield stability of new crop varieties in stress-prone environments. To obtain information on the performance of the genotypes in terms of adaptability and stability, an analysis of the GEI is paramount. Even though several statistical methods have been proposed for analysis and interpretation of GEI, the joint regression analysis method has been widely used; nonetheless, it has numerous limitations. Many other researchers have also found AMMI and GGE biplot efficient for analyzing GEI. A major

stable and therefore selected [62].

84 Recent Advances in Tomato Breeding and Production

15. Conclusion

based on their adaptability for optimum performance.

Michael Kwabena Osei1,3\*, Benjamin Annor1 , Joseph Adjebeng-Danquah<sup>2</sup> , Agyemang Danquah<sup>3</sup> , Eric Danquah<sup>3</sup> , Essie Blay3 and Hans Adu-Dapaah<sup>1</sup>

Address all correspondence to: oranigh@hotmail.com

