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Open access Jul 2026

Multi-environment Evaluation of Bread Wheat (Triticum aestivum L.) Genotypes in Mid-altitude of Ethiopia using Advanced Mixed Models

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. · 0 citations
Open access Sep 2026

Genetic Variability, Broad-Sense Heritability, and Trait Associations of Bread Wheat (Triticum aestivum L.) Across Multiple Environments in Kenya

Aims: This study evaluated phenotypic variation and genotypic differences, estimated broad-sense heritability for grain yield, and examined associations among agronomic traits in five bread wheat (Triticum aestivum L.) genotypes comprising two parental cultivars and three mutation-derived lines. Study Design: Randomised complete block design with three replications. Place and Duration of Study: The genotypes were evaluated at Kitale, Eldoret, and Njoro, Kenya, during the 2021 and 2022 long-rain seasons, representing six environments. Methodology: Seven agronomic traits were analysed using descriptive statistics, combined analysis of variance, restricted maximum likelihood estimation of variance components, entry-mean broad-sense heritability, and Pearson correlation analysis. Results: Grain yield ranged from 1.80 to 3.50 t ha⁻¹, with a mean of 2.70 t ha⁻¹. Genotype significantly affected all evaluated traits, demonstrating differentiation among the wheat materials, while environmental effects varied among traits. Grain yield was significantly influenced by genotype, location, and season, with a significant genotype × location interaction indicating differential genotypic responses across locations. For grain yield, genotypic variance (σ²G = 0.018) exceeded residual variance (σ²e = 0.012) and genotype × environment interaction variance (σ²GE = 0.006). Entry-mean broad-sense heritability was high (H² = 0.91), indicating strong repeatability of differences among genotype means under the multi-environment testing framework. Grain yield was moderately and positively associated with thousand-kernel weight (r = 0.42), while days to maturity was moderately and negatively associated with thousand-kernel weight (r = −0.53). Days to heading and days to maturity were moderately and positively correlated (r = 0.42). Conclusion: The relatively large genotypic variance and high entry-mean heritability indicate that the evaluated materials could be reliably differentiated for grain yield across the testing framework, although environmental and interaction effects remained relevant. Integrating replicated grain-yield performance with genetic parameters and complementary agronomic traits, particularly thousand-kernel weight, provides a sound basis for identifying promising parental and mutation-derived materials for advancement in bread wheat improvement.

Ego Amos Kibiwott, Kinyua Miriam Gacieri, Kiplagat Oliver Kipchoge · 0 citations
Open access Sep 2026

Genotype × Environment Interaction and GGE Biplot Analysis for Grain Yield and Agronomic Traits in Bread Wheat (Triticum aestivum L.)

This study aimed to identify high-yielding and stable bread wheat genotypes through genotype × environment interaction analysis using GGE biplot methodology. A total of 160 genotypes were evaluated during the 2023 and 2024 cropping seasons at Dabat and Adet in northwestern Ethiopia using an alpha lattice design with two replications. Significant effects of genotype, environment, and their interactions (p ≤ 0.0001) were observed across all eleven agronomic traits studied. The analysis revealed that genotypes G10, G36, G52, G81, and G28 consistently combined superior grain yield with high stability across environments. Among the test sites, Dabat 2024 emerged as an ideal environment for evaluating grain yield performance. Environmental clustering further delineated two distinct mega-environments, providing valuable insights for breeders in selecting genotypes with either specific or broad adaptation. These findings highlight the utility of GGE biplot analysis in guiding wheat breeding programs toward improved yield stability and targeted genotype deployment.

Workineh Fenta, Assefa Sintayehu, Tesfaye Alemu et al. · 0 citations
Open access Sep 2026

Multi-Environment Evaluation of Bread Wheat Genotypes for Sustainable Production Using AMMI, GGE Biplot and Multi-Trait Stability Index Analyses

Climate change and environmental variability pose major challenges to sustainable wheat production, highlighting the need for stable and high-yielding cultivars. This study evaluated fifteen bread wheat (Triticum aestivum L.) genotypes across twelve environments during two consecutive growing seasons using a randomized complete block design with three replications. Stability and performance were assessed using Additive Main Effects and Multiplicative Interaction (AMMI), Genotype main plus Genotype × Environment (GGE) biplot, and multi-trait stability index (MTSI). AMMI combined ANOVA indicated that both grain yield per plot and falling number were significantly affected by genotype, environment and their interaction (GEI). The GGE biplot revealed that the first two principal components together explained 86.92% of the total variation in grain yield per plot and 88.27% for falling number, demonstrating the reliability of the model in interpreting GEI patterns. AMMI and GGE biplot analyses consistently identified Sakha 95 (G1), Misr 4 (G3), Sakha Line#1 (G6), Sakha Line#2 (G7), Gemmeiza Line#2 (G14), and Gemmeiza Line#3 (G15) as high-yielding and stable genotypes across environments. In contrast, Sakha Line#4 (G9), Sakha Line#6 (G11), Sakha Line#7 (G12), and Gemmeiza Line#1 (G13) showed poor adaptation and low stability. MTSI further refined selection by integrating yield and quality traits, identifying Gemmeiza Line#3, Sakha Line#3, Sakha Line#2, and Giza 171 as superior genotypes at 25% selection intensity, characterized by low MTSI values. The identification of stable, high-performing genotypes with desirable grain quality can contribute to sustainable wheat production by improving yield reliability under diverse environmental conditions and supporting more efficient cultivar selection for climate-resilient wheat production. Overall, integrating AMMI, GGE biplot, and MTSI provided a robust framework for identifying stable and high-performing wheat genotypes, supporting selection decisions in multi-environment breeding programs.

M. Genedy, Mahmoud A. Hussein, A. R. Ibrahim et al. · 0 citations
Open access Jul 2026

Selection of Stable High-yielding Fine Grain Rice (Oryza sativa L.) Genotypes Using AMMI, WAASB, and Multi-Trait Stability Indices

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. · 0 citations
Open access Aug 2026

Evaluation of bread wheat (Triticum aestivum L.) genotypes in Ethiopia through multi-environment trial analysis using factor analytic mixed models

Bread wheat is a vital crop for Ethiopia’s food security, with expanding cultivation driven by variety development efforts and strategic national initiatives. This study presents a multi-environment trial (MET) analysis to evaluate the genetic performance of 140 unique bread wheat genotypes, using data from 20 trials conducted across Ethiopia’s major wheat-growing regions. Each trial was designed using a row-column design (RCD) arranged in a rectangular array of plots. Four traits —grain yield (GYLD), hectoliter weight (HLW), days to heading (DTH), and thousand kernel weight (TKW) were considered in the analysis. We fitted a factor analytic linear mixed model (FAMM) based on a one-stage approach. Spatial models were fitted to account for field trends, while factor analytic (FA) model was used to model genotype-by-environment (G × E) effects. Model comparisons showed that the FA model consistently outperformed the diagonal (DIAG) model across all traits, offering greater flexibility capturing the variation in G × E effects. Heritability estimates were generally high, especially for DTH, which also showed strong genetic correlations across environments highlighting its reliability as a selection trait. In contrast, yield-related traits exhibited complex G × E patterns and variable heritability, reflecting their polygenic nature and environmental sensitivity. While the highest-yielding check variety, Balcha, retained its lead in GYLD, three genotypes—EBW202104, EBW202110, and EBW193155—demonstrated stable and competitive performance across environments for multiple traits, particularly grain quality and maturity. This makes them strong candidates for varietal release and registration for commercial production in Ethiopia. Future research should integrate genomic and environmental data.

N. Geleta, Tarekegn Argaw, Bayisa Asefa et al. · 0 citations

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