Jul 2026· American Journal of Bioscience and Bioengineering· 0 citations· 4 references
Abstract
Sorghum (Sorghum bicolor (L.) Moench) is a critical staple cereal in semi-arid tropics, yet its productivity is highly constrained by genotype × environment interaction (GEI), which complicates variety selection and recommendation. This study aimed to estimate the magnitude of GEI, evaluate grain yield performance, and identify stable, high-yielding and early-maturing sorghum genotypes for potential release in East Hararghe, Ethiopia. Fourteen sorghum genotypes alongside two standard checks (Fadis 01 and Melkam) were tested across six environments, combining two locations (Fadis and Erer) over three consecutive main cropping seasons (2022–2024) using a randomized complete block design with three replications. Data on grain yield and agronomic traits were subjected to combined analysis of variance, Additive Main Effects and Multiplicative Interaction (AMMI) analysis, and Genotype Main Effect plus GEI (GGE) biplot analysis. Combined ANOVA revealed highly significant (P < 0.001) effects for genotype, environment, and GEI, confirming differential genotypic responses across testing environments. AMMI analysis partitioned the total grain yield variation, attributing 18.54% to genotype, 25.15% to environment, and 28.86% to GEI, indicating that environmental factors and their interaction with genotypes were the dominant sources of variation. The first two interaction principal component axes (IPCA1 and IPCA2) jointly explained 75.56% of the GEI variation, with IPCA1 contributing 52.6% and IPCA2 contributing 22.96%. Genotype G6 (ETSC14576-5-1) recorded the highest mean grain yield (4265 kg ha⁻¹) and demonstrated exceptional stability across environments, as evidenced by its proximity to the IPCA zero line in the AMMI1 biplot, favorable AMMI stability value, and low genotype selection index. GGE biplot analysis further ranked G6 closest to the ideal genotype, confirming its superior mean performance and stability. Polygon view identified three mega-environments, with G6 emerging as the winning genotype in one of them. Based on the integrated assessment using mean yield, AMMI parameters, and GGE biplot outputs, genotype ETSC14576-5-1 (G6) is identified as the most stable and high-yielding genotype across the tested environments. Therefore, this genotype is recommended for variety verification and subsequent release for cultivation in East Hararghe and similar agro-ecologies.
Wheat is one of Ethiopia’s most imperative food safety plants. Demand for wheat in Ethiopia has been growing over the years. But, wheat imports improved during the last five years. The know-how of the interaction between genotypes and the environments with yield and its components is a number one aspect of effective selection in crop improvement. Therefore, this looks at aimed to perceive bread wheat genotypes with high grain yield ranges, yield stability and disease tolerance across locations. The study used 20 bread wheat genotypes inclusive of one local and one standard check (Sanate) at fitche agricultural research center (FiARC) within the 2022-2023 essential cropping seasons. 8 agronomic traits and 4 economically essential disease reaction data were evaluated. Analysis of variance detected significant differences among genotypes in both separated and combined evaluation of variance (ANOVA). The mixed ANOVA and the additive main effects and multiplicative interactions (AMMI) evaluation for grain yield across check environments exhibited, vast outcomes via environments, explained 12.5% of the entire variation. The genotype and genotype environmental interplay (GEI) had been great and accounted for 36.5% and 35.5%, respectively. Essential additives (PCA1 and PCA2) accounted for 16.9% and 11.6% of the GEI respectively, with 28.5% of the whole version. Normally, G124 and G127 had been recognized as ideal genotypes in-terms of yield, stability, and tolerance to the respective disease reactions and can be encouraged for use as parental line within the future breeding packages.
G. Negash, Alemayehu Birr· American Journal of Plant Bi...· 0 citations
Understanding genotype-environment interaction is crucial for optimizing cotton hybrid performance, facilitating consistent yield and stability in semi-arid regions. To address this challenge, a total of 45 hybrids were evaluated across three kharif seasons (2021–2023) at Punjab Agricultural University Regional Research Station, Abohar. The analysis integrated combined analysis of variance (ANOVA), additive main effects and multiplicative interaction (AMMI), and genotype + genotype × environment (GGE) biplot approaches, supplemented with stability indices such as AMMI stability value (ASV), genotype stability index (GSI), and the weighted average of absolute scores (WAASBY) index, to identify high-performing and stable hybrids. Seed-cotton yield ranged from 2 122 to 3 168 kg·ha−1, with environmental effects explaining the largest proportion of phenotypic variation. The significant genotype × environment interaction (GEI) indicated differential hybrid performance across seasons. AMMI and GGE analyses identified hybrids exhibiting both broad and specific adaptation. Stability indices (ASV, GSI, and WAASBY) consistently identified G7, G8, G17, G22, and G45 as both stable and high-yielding, with mean seed cotton yield (SCY) ranging from 2 625 to 2 996 kg·ha−1. For yield component traits, G35 and G21 showed the highest sympodia per plant, G20 and G41 exhibited superior boll weight, and G38 had the highest bolls per plant. GGE biplot analysis indicated that E1 was both discriminative and representative for SCY, whereas E2 and E3 were most informative for BW and BPP, respectively, reflecting pronounced seasonal variations. Overall, the integrated analytic pipeline (ANOVA → AMMI → GGE → ASV/GSI/WAASBY) effectively partitioned and interpreted complex GEI into robust selection decisions. This approach facilitated the identification of a refined set of hybrids exhibiting temporal stability, high yield potential, and suitability for both wide and season-specific adaptation.
Sunayana Punia, Manpreet Singh, S. Yadav· Journal of Cotton Research· 0 citations
Food barley is a staple crop in the Ethiopian highlands, yet productivity remains low due to the use of low-yielding local varieties. This study evaluated the performance and adaptability of 11 food barley varieties (Abdane, Adoshe, Biftu, Guta, Robera, Cross#41/98, EH-1493, Harbu, HB-1965, HB-1966, and one local check) across six environments (Gurawa, Jarso, and Meta districts over 2019 and 2021 main cropping seasons). The experiment was conducted using a randomized complete block design with three replications. Combined analysis of variance revealed highly significant differences (p < 0.01) among varieties, environments, and their interactions for all traits. AMMI analysis indicated that environment explained 62.71% of total variation, genotype 5.74%, and genotype-by-environment interaction (GEI) 14.13%. The first two principal components of GEI accounted for 77.37% of the interaction variation. Varieties Abdane (4.81 t ha
-1
) and Robera (4.41 t ha
-1
) recorded the highest grain yields, outperforming the local check by 31.4% and 25.2%, respectively. GGE biplot analysis identified Abdane and Robera as the most stable and high-yielding varieties across environments, while Guta, Harbu, Cross#41/98, and the local check were unstable. Therefore, Abdane and Robera are recommended for further demonstration and production in East Hararghe highlands and similar agro-ecologies.
Zeleke Legesse, Jifara Gudeta, Fikadu Tadesse et al.· American Journal of Bioscien...· 0 citations
The experiment was conducted during May to December, 2024–2025 main seasons at mid-altitude bread wheat growing agro-ecologies of Ethiopia to study the agronomic performance, genotype-by-environment (G×E) interactions, heritability, cluster and correlation of bread wheat breeding genotypes across 11 multi-environment trials (METs).Utilizing an alpha lattice and partially replicated design, the study employed Factor Analytic Mixed Models (FAMM) and GGE biplot analysis to partition variance, GxE and identify drivers of yield stability. Grain yield ranged significantly from 1.60 to 6.50 t ha-1, underscoring substantial phenotypic plasticity across the environments. Results indicated that 55.80% of genotypes exceeded the grand mean yield, 77% yielded above 4.5 t ha-1, and 20% outperformed the standard check “Melka.” While grain yield exhibited high heritability (H2) ranged 16.48%–92.19%), traits like days to heading (DTH) and plant height (PHT) remained genetically stable (H2 > 85%). GGE biplot analysis partitioned testing sites into distinct mega-environments, identifying locations 25BWOPNMKU, 24BWPNMAB and 25BWOPNMAA as the most discriminative testing sites for selection. Although genotype EBW190004 achieved the highest mean yield (5.52 t ha-1), it showed high environmental sensitivity. Conversely, EBW190128 and EBW190063 were the most stable genotypes. Notably, EBW222059 appeared as the promising candidate for regional release, balancing high productivity (5.44 t ha-1) with exceptional resilience in moisture-constrained environments. Furthermore, the study confirmed high potential for genetic gain and demonstrated that the FAMM-based MET analysis provided a robust framework for identifying superior genotypes in bread wheat breeding programs across the mid-altitude regions of Ethiopia.
Bayisa Asefa, Berhanu Sime, Habtemriam Zegeye et al.· International Journal of Bio...· 0 citations
Cowpea [Vigna unguiculata (L.) Walp.] is a vital food and fodder crop in Niger, but its productivity is constrained by drought, poor soils, striga and pest pressures. Developing extra-early dual-purpose varieties is essential for strengthening food and feed security in these fragile production zones. This study evaluated the effects of genotype, environment, and their interaction on the stability of grain and fodder yields in 21 cowpea genotypes tested across six environments using an alpha-lattice design with three replications. Stability was assessed through Shukla’s stability variance, the cultivar superiority index, Wricke’s ecovalence, GGE biplot, and the multi-trait genotype–ideotype distance index (MGIDI). Results revealed significant differences among genotypes, environments, and genotype × environment interactions for all traits; environmental effects had the highest mean squares for all traits (p < 0.001). Broad-sense heritability ranged from 0.77 (fodder yield) to 0.88 (days to 50% flowering), and genetic advance as percentage of mean was high for yield traits (GAM = 78.67% for pod yield, 81.80% for grain yield, 99.59% for fodder yield), indicating strong selection potential. Mean grain yield across all genotypes and environments was 1,394.32 kg ha ⁻ ¹, and the coefficient of variation ranged from 26.59% (GY) to 40.63% (FY). Notably, ten extra-early lines—BM22, BP02, BP15, MM142, 65B5080, BP18, BM21, BM13, MM141, and BM12—were identified as combining early maturity with high and stable grain and fodder yields. These lines should be further evaluated across diverse agroecological zones to confirm their adaptability and alignment with farmer preferences, prior to deployment as released varieties or as parents in cowpea improvement programs targeting climate-resilient, dual-purpose productivity in the Sahel.
Fine-grain rice is highly preferred by consumers due to its superior cooking quality and higher market price. Consequently, the Bangladesh Rice Research Institute (BRRI) has developed several fine-grain rice genotypes through its breeding programs. This study evaluated the yield performance and stability of nine advanced fine-grain rice lines along with two local cultivars and five released varieties across ten agro-ecological zones during the 2023–24 Boro season. Genotype–environment interaction (GEI) for grain yield (GY) and yield-related traits was analyzed using the Additive Main Effects and Multiplicative Interaction (AMMI) model, the Weighted Average of Absolute Scores and Yield (WAASBY) index, and the Multi-Trait Stability Index (MTSI). Combined analysis of variance revealed that genotype, environment, and GEI significantly influenced grain yield and its components. Most traits exhibited moderate to high heritability (0.74–0.97) with selection accuracy above 80%, indicating strong genetic control. Among the tested genotypes, BR17-23-8-2-7B (G6) recorded the highest mean yield, while Bhanga, Faridpur (E3) was identified as the most favorable environment. Stability analyses indicated that G6 ranked highest based on WAASBY (91.45), whereas BRH11-7-17-10B (G7) and BRH9-3-2B (G8) were identified as the most stable genotypes according to MTSI. Overall, G6, G7, and G8 showed superior performance compared with the local and released varieties, suggesting their potential for cultivation and use in breeding programs targeting diverse agro-ecological environments.
Bangladesh Rice J.29(1): 1-17, 2025
M. M. Hasan, M. H. R. Mukul, Shamsunnaher et al.· Bangladesh Rice Journal· 0 citations
