Genotype by environment interaction and stability analysis of cowpea [Vigna unguiculata (L.) Walp] genotypes for yield in Ethiopia

Ethiopia is claimed to be a center of diversity for cowpea production. The crop is the most drought tolerant and could help the country overcome the recurrent drought problem; however, the yield is very low due to lack of effort to develop varieties. This research was conducted to evaluate the stability of cowpea genotypes and to estimate the magnitude of genotypes by environment interaction (GEI) effect on grain yield. Sixteen cowpea genotypes were tested at seven environments in an experiment laid out in a 4 × 4 triple lattice design during 2016/17 cropping season. The combined analysis of variance over environments showed significant differences among genotypes and environments, along with significant effect of GEI on grain yield, days to flowering, days to maturity, plant height and pods per plants. Analysis of variance for grain yield from AMMI model indicated the contribution of genotype and environment, with GEI accounting for about 63.3, 5.3 and 29.7% of the total sum of squares, respectively. The result indicated that environments contributed much to the observed variations suggesting the need to test cowpea genotypes in 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 variety TVU 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 first and second highest yield, identified as responsive to both environments but more to favorable environments suggesting the need to further test and develop as varieties. The other two genotypes: 95K-1095-4A and 93K-619-1, identified as unstable and highly responsive to environments suggested to consider the genotypes as candidate varieties where they performed best. Melkassa, Sekota and Jinka were identified as more descrimnating environments, whereas Arbaminch and Kobo were ideal for selecting superior genotypes; however, Babile and Meisso were non descrimnating environments. Key words: Additive main effects and multiplicative interaction (AMMI) stability value, Eberhart and Russell, deviation from regression and triple lattice.