No abstract

Twelve field pea genotypes were evaluated in seven environments in Eastern Amhara in main production season (2010-2012). The objective of this trial was to identify stable and high yielding field pea genotype for production in Eastern Amhara. The trial was conducted using randomized complete block design with three replications. Combined analysis of variance for grain yield revealed that genotypes, environments, and genotype by environment interaction effect were highly significant (P ≤ 0.01). The environments, GEI, and genotypes were accounted for 77.47%, 13.83%, and 4.37%, of the total sum squares, respectively, indicating that field pea grain yield was significantly affected by the changes in the environment, followed by GEI and genotypic effect. The candidate genotype, EH-03-002, showed 14.42% and 44.87% yield advantage over the standard and local checks, respectively. Considering the seven environments data and field performance evaluation during the variety verification trial, the National Variety Releasing Committee has approved the official release of EH-03-002 with the vernacular name of “Yewaginesh” for moisture deficit areas of Wag Lasta and similar agroecologies.

Cowpea is one of the most important grain legumes for human consumption and animal feeding. Despite this importance, its production is hampered by biotic and abiotic constraints. Genotype by environment interaction study was performed to identify the most stable cowpea genotype(s) and the desirable environment(s) for cowpea research in Ethiopia. Twenty‐four cowpea landraces and one standard check were evaluated for grain yield and yield‐related traits at six locations (Sekota, Kobo, Sirinka, Melkassa, Mieso, and Babile) using 5 × 5 triple lattice during 2019. Combined analysis of variance showed that grain yield was significantly affected by environments, genotypes, and GE interactions. AMMI analysis revealed the contribution of environment, genotype, and GEI for 29.79%, 15.6%, and 42.06% of variation on grain yield. The first two principal components explained 57.97% of the total GEI variance. AMMI model selected G24 as 1st and 2nd best genotype at five environments. The polygon view of the GGE biplot identified three mega‐environments (ME1, ME2, and ME3) with winning genotypes: G24, G3, and G16, respectively. The highest productive (2528.8 kg ha−1) environment, miesso has been identified as the most; discriminating and representative testing environment whereas the lowest productive (1676.1 kg ha−1) Sirinka was the least discriminating and representative. The highest yielder G24 (2632 kg ha−1) was identified as the “ideal” and the most stable genotype followed by G16 (2290 kg ha−1) while the least stable and low yielder was G11. Therefore, genotypes G24 and G16 were recommended for verification and commercial production in most cowpea growing areas of Ethiopia.

The study was conducted to estimate the effects of genotype, environment, and genotype × environment interaction on grain yield and yield-related traits and to identify stability genotype. At six environments, twenty-four cowpea landraces and one check were evaluated in a 5 × 5 triple lattice during the 2019 cropping season. Data were collected on yield and yield-related traits. The analysis of variance for each environment and across environments showed significant differences among genotypes, environments, and GEI for most traits including yield. Environment, genotype, and GEI showed 27.45%, 20.9%, and 49.55% contribution to the total sum of squares, respectively, for grain yield. This indicated that the environments were diverse and most of the variation in grain yield was caused due to interaction and environmental means. G24 (2632 kg ha−1) and G16 (2290 kg ha−1) were the highest yielder and stable genotypes with mean grain yields above the grand mean (2049.28 kg ha−1) and standard check (2273 kg ha−1). G24 and G16 were the most stable genotypes according to cultivar superiority, Wricke’s ecovalence, regression coefficient, and devotion from regression stability models.