Abstract:
Ethiopia is claimed to be center of diversity for cowpea. The crop being the most drought tolerant, and could serve the country to overcome the recurrent drought problem. However, the yield is very low due to the less attention given to develop varietes. This research was aimed at evaluating the stability of cowpea genotypes and estimating the magnitude of genotype, environment and genotype by environment interaction (GEI) effect on stability of grain yield. Sixteen cowpea genotypes were tested at seven environments in experiment laid out in 4 x 4 triple lattice design during 2016/17 cropping season. The analysis of variance for each environment was conducted in order to check the presence of significance differences among the genotypes to progress to further analyses. The combined analysis of variance over environments showed significant differences among genotypes, environments and significant effect of GEI on grain yield, days to flowering, days to maturity plant height, and pods per plants. Further GEI analyses were conducted using Eberhart and Russell’s (ER’s) and Additive Main Effects and Multiplicative Interaction (AMMI) models, revealed that the environments were diverse, and confirmed the differential performances of genotypes across environments. Analysis of varaiance from AMMI model indicated the contributions of genotype, environment and GEI to the total sum of squares, ranged from 4 to 9.8, 47 to 71 and 16 to 39%, respectively for all traits. Moreover, for grain yield, environment, genotype and GEI accounted for about 63.3%, 5.3% and 29.7% of the total sum of squares, respectively. The result indicated that environment contributed much to the observed variations, suggesting the need to test cowpea genotypes at diverse environments. Considering all stability parmeters viz. deviation from regression (S2di), coefficient of regression (bi) from ER’s model, IPCA1, IPCA2 and AMMI stability value (ASV) from AMMI model, GGE biplot and mean yield, TVU (check variety) was identified as the most stable with mean yield above the mean grain yield of genotypes. Two genotypes, IT-99K-1060a (1398.8 kg/ha) and 86D-378 (1377.1 kg/ha) had the first and the second highest yield with yield advantages of 8.6 and 7.0% over the check variety, respectively, and were identified as responsive to favorable and unfavorable environments suggesting the need to further test them to develop as varieties. Other two genotypes, 95K-1095-4A and 93K-619-1 had mean yield advantages of 2.6 and 0.9%, respectively, over the check variety, and were identified as unstable and highly responsive to environments suggesting the need to consider the genotypes as candidate varieties where they performed best. The GGE biplot identified Melkassa,Sekota and Jinka as more descrimnating environments, Arbaminch and Kobo were ideal for selecting superior genotypes, but Babile and Meisso were not descrimnating environments since provided little or no information about the genotypes’differences. In conclusion, the study identified desirable genotypes for cultivation over environments that could be used as candidate varieties and generated information about the effect of GEI in cowpea genotypes which gave an insight for further study. However tested genotypes have less yield advantage, further studies need to be conducted using more genotypes and diversified environment to understand the effect of GEI on yield and other yield related traits in cowpea