GENOTYPE X ENVIRONMENT INTERACTION FOR FRUIT YIELD OF SOME CUCUMBER (Cucumissativus) GENOTYPES.

The present study was performed to analyze the genotype x environment (G×E) interaction for fruit yield of 5 genotypes in four environments; Ikom, Calabar, Obubra and Obudu located at different agro-ecological zones of Cross River State. The cucumber genotypes were grown in randomized complete block design in three replicates in 2015 cropping season. The yield data was analyzed using additive main effect and multiplicative interaction (AMMI) and genotype plus genotype by environment (GGE). Additive main effect and multiplicative interaction (AMMI) analysis of variance showed statistically significant effect of genotypes, environments and the genotype x environment interaction (P < 0.01%). The environment explained 59.59%which showed high differences in variety response to different locations tested. Genotype (G) and genotype x environment interaction (G x E) accounted 15.83% and 11.89% respectively. The first interaction principal component axis (IPCA1) was significant (P < 0.01) except the (IPCA 2) and explained 11.50% and 0.36% of the G X E sum of squares respectively. The Additive main effect and multiplicative interaction stability value (ASV) showed that significant difference existed in the G x E component. Based on the stability parameters, it revealed that none of the genotypes were stable for fruit yield, however according to ASV, and GGE Bi-plot graphical representation, Ashley genotype in relative terms was stable. The genotypes Poinsett (48.43 t ha-1),Ashley(47.49 t ha-1) and Marketer (41.66 t ha-1) were considered to have adaptability to favorable environments, while Market More (MM 13.97t ha-1) and Super Marketer (SM 16.66 t ha-1) adapted to unfavorable conditions for fruit yield. Based on AMMI and GGE bi-plot, ASL had the widest adaptation and was considered as the ideal genotype, whereas P.ST showed specific adaptation. The ideal environments were IKOM (66.85 t ha-1) and OBURA (56.93 t ha-1). Through the GGE bi-plot and AMMI analysis, the superior genotypes identified could serve as references for genotype evaluation and inclusion in further testing in other seasons and environments. [ABSTRACT FROM AUTHOR]