AMMI Analysis Research Articles

Stability Assessment of Groundnut ABLs Using AMMI and GGE Biplot Analysis for Yield Performance under Multi-environments

The current study focused on Identification of stable line among the eight groundnut ABLs and two checks were assessed in three different environments in Karnataka during the 2020-2021 Kharif and Rabi seasons with randomized complete block design (RCBD) with three replications. The analysis of variance revealed significant differences (p≤0.01) among the genotypes, environments, and the genotype by environment interaction (G×E) for kernel yield. The AMMI analysis also showed highly significant differences (p≤0.01) for varieties, environments, and their interaction on kernel yield. The IPCA1 and IPCA2 components explained 72.72% and 25.00% of the total G×E sum of squares, respectively. The variations in kernel yield were attributed to the environment (16.44%), ABLs (81.87%), and ABLs by environment interaction (1.68%). ABLs T65, T77, T81, and T82 were identified as stable across all three environments based on ASV and SI for kernel yield per plant. These stable lines can be used as parents in breeding programs. The AMMI model and GGE biplots were effective tools for evaluating the adaptability and stability of groundnut genotypes in diverse environments.

Dissection of genotype x environment interaction and yield stability analysis in greengram (Vigna radiata L.) using AMMI and GGE biplot

Nineteen green gram [Vigna radiata (L.) Wilczek] genotypes were evaluated for yield stability under field conditions over three seasons. The AMMI analysis revealed that genotype TM 11007 (G3) had wider adaptation for grain yield, performing well across a wide range of environments. The genotypes TM 11038 (G14) in E1 and G3 in E2 and E3 were found to be highly stable and gave the highest yield in their respective mega-environments, while TM 11034 (G11) and TM 11042 (G17) located in the vertex performed better in all the three environments. E2 was the most discriminating test environment for selecting widely adapted genotypes for yield per se, while E3 was the most representative testing environment to provide unbiased information about the performance of genotypes. Based on the average versus stability graph, the genotype TM 11042 stands out because of simultaneous high yields and high stability.

Genotype by environment interaction and performance stability of common bean (Phaseolus vulgaris L.) cultivars grown in Dawuro zone, Southwestern Ethiopia

Abstract The performance of most crop genotypes varies significantly depending on their genotype–environment interactions. Providing information on high-yielding and well-adapted common bean genotypes in a given environment is crucial for small-holder farmers to boost their productivity. A field trial was conducted at the Gendo and Wara locations over two consecutive seasons to understand genotype–environment interactions on grain yield and to identify and select common bean genotypes that were better adapted and had high yield potential for production. Ten genotypes were evaluated for their performance using a randomized complete block design with three replications. The AMMI and GGE models were used to identify superior genotypes regarding their mean grain yield performance. The AMMI analysis of variance results showed that grain yield was significantly (p ≤ 0.05) influenced by genotype–environment interactions. This shows that genotypes varied in grain yield mean performance across environments, indicating the possibility of selecting suitable genotypes with specific and broad adaptability. The sum of squares factor indicated that grain yield variation was contributed by the genotype (61.0%), followed by the environment (29.80%) and the genotype–environment interaction effect (9.20%) of the total variation. The AMMI and GGE biplot analysis results showed that SER-119 and SER-125 were identified as the best genotypes regarding their mean grain yield performance. Additionally, the genotype selection index analysis result indicated that SER-119 and SER-125 were stable and consistently exhibited high mean performance in all evaluated settings. Therefore, SER-119 and SER-125 are prime candidates for production in various agroecologies suitable for common bean cultivation in Dawuro zones. The use of tools like GGE biplots and AMMI analysis helped visualize the genotype–environment interactions, aiding in the selection of appropriate genotypes for both specific and wide adaptability.

Evaluation of sugar beet (Beta vulgaris L.) cultivars for some biochemical and agronomic traits under drought stress

This experiment aimed to evaluate quantitative and qualitative characteristics, tolerance to water deficit, and stability of white sugar in sugar beet cultivars. The experimental design was a split plot based on a randomized complete block design with three replications, where the irrigation levels (normal and water shortage) were assigned to the main plots and 18 sugar beet cultivars were assigned to the subplots. The result revealed that Palma achieved the maximum root and white sugar yield under normal and water deficit; furthermore, the highest indices of YP, YS, MP, STI, HM, YI, DI, REI and MRP belonged to the Palma cultivar. The results of the AMMI analysis based on white sugar yield showed that the additive effects of genotype and environment and the multiplicative effect of G×E accounted for 75.52, 17.05 and 6.76 % of the total data variance. Based on AMMI stability value, the Delta, Pars, Paya and Novodoro cultivars were recognized as stable varieties. Also, the first 2 significant components of the interaction effect (G×E) accounted for 99.12 % of interaction effects variation. Based on the biplot results of the first 2 significant components against white sugar yield, Azare and Merak were the appropriate cultivars. Finally, Based on the multi-trait stability index, Azara, Novodoro and Merak cultivars were selected as stable genotypes. In 2 years and 2 conditions, the Palma cultivar was identified as a cultivar with high yield and drought tolerance and low stability and the Merak cultivar was identified as a cultivar with white sugar yield and acceptable stability.

Estimating yield stability and predicting the response of sesame genotypes to climate change using the SALTMED model

AbstractClimate change (CC) could lead to many crises. Therefore, increasing the number of cultivated varieties represents a low‐cost factor in confronting this problem. The effect of the genotype × environment (G × E) interaction on yield stability was estimated for 28 new sesame lines in the Beni Suwef, El‐Beheira and El‐Menoufia governorates in Egypt across 15 environments from 2019 to 2022 using AMMI analysis. The SALTMED model was used to predict the yield of sesame plants under five increasing air temperature scenarios (CC factor) to obtain future projections of sesame yield to determine the lines that are most genetically stable and facing CC. Variance analysis revealed significant differences in yield between the G and E groups and between the G × E interaction group. Fifteen genotypes yielded better control, and C6.4, C5.8 and C9.6 were selected as genetically stable according to AMMI analysis. The SALTMED model predicted that the yields of lines C3.8 and C6.2 were not affected under the high‐temperature scenarios across the three governorates, moreover lines C1.8, C2.3, and, C6.12 productions were not affected under Beni Suwef and El‐Beheira governorates. of lines C1.8, C2.3 and C6.12 were also not affected by the Beni Suwef or El‐Beheira governorates. It is now possible to establish a hybridization programme in sesame that combines parents with high productivity and high resilience to CC.

Investigation of yield stability of wheat varieties in different locations by AMMI and GGE-biplot analysis

Investigation of yield stability of wheat varieties in different locations by AMMI and GGE-biplot analysis

Stability analysis of promising winter maize (Zea mays L.) hybrids tested across Bihar using GGE biplot and AMMI model approach

Multilocation evaluation facilitates the quantification of genotype-environment interaction (GEI) and the identification of high-yielding, stable hybrids along with partitioning the evaluating location into mega environments. Testing of 24 single cross hybrids and four checks across three environments revealed the significant GEI for all the studied traits. The environment contributed 12.90, 57.10, and 17.59% of the total variation for grain yield, anthesis silking interval, and days to maturity. The study indicated a negative genotypic correlation among grain yield and anthesis silking interval (-0.2244); days to maturity and anthesis silking interval (-0.019), whereas the positive correlation between grain yield and days to maturity (0.067). Location Sabour was found as the most representative environment for testing commonly adapted hybrids. Location Begusarai and Dholi are discriminatory and non-representative environments suitable for selecting location-specific genotypes. Both GGE biplot and AMMI analysis revealed that three hybrids, viz., IMHSB1, IMHSB20, and IMHSB13 were high-yielding with average stability. The identification of superior and stable maize hybrids may contribute to farmers’ income in Bihar.

Genetic and the Environmental Influences on the Concentrations Iron and Zinc in Small Seeded Common Bean (Phaseolus vulgaris L.) Varieties and Advanced Lines From Ethiopia

ABSTRACTCommon bean (Phaseolus vulgaris L.) is a grain legume rich in proteins and micronutrients, particularly iron and zinc. In this study, 30 small‐seeded genotypes were planted in five locations in Ethiopia, following an alpha lattice design with three replications, to determine environmental and genotypic influence on the Fe and Zn concentration. Based on their Fe and Zn contents, bean cultivars were evaluated for adaptability and stability using AMMI analysis. The Fe concentrations of raw bean seed varied from 44.4 to 84.4 μg/g within the panel of small‐seeded genotypes, with an average range of variance of 18 μg/g across environments, and its seed Zn concentrations varied from 19.7 to 32.3 μg/g, with an average range of variance of 12.6 μg/g across environments. The averages bean Fe concentration among the small‐seeded genotypes across sites in Ethiopia was 62.2 and 26.1 μg/g for Zn concentrations. Results from the analysis of variance using the AMMI model indicated that genotypes accounted for 20.53% and 9.49% of the total variance in seed Fe and Zn concentrations, respectively. The environment had a greater impact, affecting 60.92% and 81.52% of total sum of squares for Fe and Zn concentrations, respectively. According to the broad‐sense heritability, there appears to be some genetic control over Fe and Zn concentrations. However, the substantial effects of the environment and genotype‐by‐environment interaction on Fe and Zn concentrations in small‐seeded genotypes indicates breeding for higher amounts of trace minerals in new bean varieties could be a challenging task. This means the notion that beans can be biofortified to have higher concentrations of Fe and Zn might not be achievable in Ethiopia. A shift in breeding strategies that focuses on traits to enhance the bioavailability of Fe and Zn from bean is warranted and could be a solution to enhance the delivery of iron from small‐seeded beans produced in Ethiopia.

Analysis of the Phenotypic Adaptability and Stability of Wheat Genotypes through the Biplot Approach

The present study was conducted during the cropping season during October to April in the year 2022–23. Highly significant effects of environments, genotypes×environments interactions and genotypes had been observed as per the AMMI analysis of twelve wheat genotypes evaluated under advanced varietal trials at eleven major locations and Environments effects had augmented about 49% of the total sum of squares. ASV measure had utilized the 51.7% of the interaction effects had pointed for DBW296, HI1654, PBW899 while MASV measure had exploited nearly 98% of total interaction effects had selected the DBW296, WH1311, UP3111 genotypes as Superiority index measure also the suitability of same genotypes. The simultaneous selection index based on yield and ASV had pointed towards the HI1653, HI1654, HD3369 and genotypes HI1653, DBW397, WH1311 were identified by ssi MASV and genotypes HI1654, HD3369, HI1653 genotypes settled by ssi WAASB measure. Non parametric Si1 had pointed for DBW296, HD3369, NIAW3170 and the values of Si2 and Si3 had pointed towards DBW296, WH1311, HD3369. Composite non parametric measures First measure NPi(1) had favoured the DBW296, HI1653, HD3369 while as per NPi(2) values the DBW296, NIAW3170, PBW899 would be suitable genotypes. Out of the five clusters in the biplot analysis the ASV & WAASB had clustered with NPi(1), Si2, Si3, Si4, Si5, Si6, Si7 non parametric measures as placed in the first quadrant.

Analysis of Pearl Millet's G x E Interaction with the Proposed New Index

Developing cultivars that are stable in a variety of conditions has been a problem for plant breeders. The environment can have an impact on a cultivar's phenotypic performance, or different environments can have different effects on different cultivars. Variance resulting from a combination of an individual's genetic composition and the environment in which they were raised is referred to as the genotype-environment interaction. Reducing genotype-environment interaction through breeding stable genotypes facilitates selection of stable, high-yielding genotypes. In multi-environment cultivar trials, AMMI and GGE biplot analyses are frequently utilized to explain G×E interactions. In order to assess breeding material effectively, India's pearl millet agriculture has been split into three main zones, A1, A, and B, based on climatic circumstances. Using AMMI and GGE biplot analysis, the current study assessed the G×E interaction in pearl millet genotypes from Zone-B in India. Based on normalized grain yield and ASV indices, a new weighted index (WI) has been developed to assess stable and high-yielding genotypes. For this zone, the three interaction principal component axes (IPCA1, IPCA2, and IPCA3) have been found to be important. The indices YSI and WI have been used to identify both the high-yield and most stable genotypes, while the AMMI Stability Value (ASV) and Stability Index have been used to find the most stable genotypes. Based on WI, the genotypes G24 and G13 have been identified stable and high yielding genotypes for zone-B.

Multi Environment Analysis of Soybean Genotypes to Delineate Stability and Adaptability for Yield and Quality Parameters

The present investigation was carried out at three locations of Gujarat under the jurisdiction of Anand Agricultural University (AAU) during kharif-2022. Three diverse environments were selected for conducting an experiment. The experimental material comprised of 40 genotypes of soybean sown in a randomized block design with three replications at all environments. Based on the AMMI analysis for seed yield per plant, E3 was considered as the most suitable environment for seed yield per plant while, E2 was considered as the poor environment. The genotypes G4, G16 and G40 were considered as the most stable genotypes and had general adaptation for seed yield per plant among all the genotypes since they were least affected by the environment. The genotypes G15, G22, G24, G25, G27, G29, G32 and G39 performed well in the poor environment (E2). Genotypes G10, G20 and G33 had specific adaptability in potential environment (E3) Based on the AMMI stability value G24, G25 and G22 were considered as stable genotypes over environment while, G33, G20 and G35 were considered as unstable genotypes. According to the AMMI 2 biplot G15, G16, G22, G24, G25, G27, G32 and G39 were considered as high yielding with general adaptability. The vertex genotypes for E3 were G10, G12, G20 and G33. G31 was for E2 while, vertex genotype for E1 was G26.

Relative Discriminatory Ability of AMMI and GGE Biplot in Analysis of GxE Interaction for Yield and Seed Index in Chickpea (Cicer arietinum L.)

Background: The research focused on investigating the impact of genotype-environment interaction on the yield stability and performance of eight chickpea genotypes across three different locations (Bhatapara, Kawardha and Raipur). Method: The study employed a randomized block design with two replications in each environment in Rabi season during 2021-22. Analysis of variance (ANOVA) revealed significant variations between environments and genotype-environment interaction (G x E) based on AMMI and GGE biplot. Results: To visualize the genotype-environment relationship, a two-dimensional GGE biplot was created using the first two principal components, which accounted for 87.8% and 11.5% of the interaction’s variation. The GGE biplot indicated that genotype g4 (ICCX 161100-B-B-B-B) in environment e2 (Kawardha) and g8 (JG-24) in environment e1 (Bhatapara) exhibited high yield. Genotype g3 (ICCV 201112) in environment e3 (Raipur) also showed high yield, while genotype g5 (JG 2020-10) demonstrated stability across all three environments. Furthermore, the AMMI analysis identified genotype g2 (ICCV 201103) as a superior performer in terms of yield stability across environments. By employing the GGE biplot and AMMI analysis, the study categorized the genotypes into three groups, facilitating simplified visual evaluations. The GGE biplot highlighted that genotype g8 (JG-24) exhibited better seed index in environment e3 (Raipur), while genotype g1 (ICCV 201210) performed well in environment e1 (Bhatapara). Genotype g2 (ICCV 201103) demonstrated a good seed index in all three environments. The AMMI analysis indicated that genotype g3 (ICCV 201112) had a slightly lower seed index but maintained stability across all environments. Overall, the findings of this investigation, as represented by the GGE and AMMI biplots, visually illustrated the relationship between genotypes and environments. The identified genotypes hold potential for improving yield production and further research and breeding efforts can be based on these findings.

Genotype–environment interaction of andrographolide and neo-andrographolide in high therapeutic potential medicinal plants Kalmegh (Andrographis paniculata (Burm. f.) wall. ex nees) by AMMI and GGE biplot analysis

Genotype–environment interaction of andrographolide and neo-andrographolide in high therapeutic potential medicinal plants Kalmegh (Andrographis paniculata (Burm. f.) wall. ex nees) by AMMI and GGE biplot analysis

AMMI and GGE biplot analysis of genotype by environment interaction for yield and yield contributing traits in confectionery groundnut

The global market has a high demand for premium edible grade groundnut, particularly for table use. India, in particular, exhibits significant potential for exporting confectionary grade large seeded groundnut. The environment plays a significant impact in influencing the expression of seed traits, which subsequently affects the confectionary quality of groundnut genotypes. The states of Gujarat and Rajasthan in India are prominent producers of high-quality groundnuts specifically used for confectionary purposes. The current study was conducted with 43 confectionery groundnut genotypes at Junagadh, Gujarat, and Bikaner, Rajasthan, with the goals of understanding genotype-by-environment interaction (GEI) effects and identifying stable, high yielding confectionery quality groundnut genotypes using AMMI and GGE biplot models. Pod yield per plant (PYP), number of pods per plant (NPP), hundred kernel weight (HKW), and shelling percent (SP) were estimated. The interplay between the environment and genotype has had a notable impact on the manifestation of confectionary grade characteristics in peanuts. The results from the Interaction Principal Component Analysis (IPCA) indicate that HKW contributed 76.68% and 18.95% towards the Global Environmental Index (GEI) through IPCA1 and IPCA2, respectively. Similarly, NPP contributed 87.52% and 8.65%, PYP contributed 95.87% and 2.1%, and SP contributed 77.4% and 16.22% towards GEI through IPCA1 and IPCA2, respectively. Based on the ranking of genotypes, the ideal genotypes were PBS 29079B for HKW, PBS 29230 for NPP. The genotypes PBS 29233 and PBS 29230 exhibited superior performance and stability in terms of pod yield, hundred kernel weight, number of pods per plant, and shelling percentage across various sites. These breeding lines have the potential to be developed for the purpose of producing confectionary grade groundnut with larger seeds, in order to fulfil the growing demand for export.

Deciphering G × E Interaction of Photo-insensitive Horsegram [Macrotyloma uniflorum (Lam.) Verdc.] Mutants using AMMI and GGE Biplot Models

Background: Photosensitivity in horsegram restricts horizontal expansion in its cultivable area. Using induced mutagenesis, photo-insensitive mutants of a popular variety PAIYUR 2 were evolved. Methods: The mutants were experimented with across seasons and locations. All the experiments were conducted in the randomized block design with five replications. The AMMI and GGE biplot models were employed to tag the best-yielding and stable genotype(s). Result: The ANOVA indicated significant effects of genotypes (G), environments (E) and their combined genotype × environment interaction (G × E) for all the experimental traits implying a large range of variation. The interaction effect in AMMI has been partitioned into several principal components. Of the six Principal Components (PC), the first PC explained the major variation. It is 60.20% for the number of clusters per plant, 99.60% for days to fifty percent flowering, 92.57% for the number of pods per cluster, 96.63% for the number of pods per plant, 99.66% for days to maturity and 96.34% for yield per hectare. AMMI and GGE biplot analyses helped to identify the best performing and stable photo-insensitive mutants TNAU-HG-DM-001 and TNAU-HG-DM-004 for further exploitation.

Nano Urea Fertilization for More Wheat Yield Under North Western Plains Zone for Irrigated Conditions Assessed by AMMI and BLUP Analysis

Highly significant variations due to treatments, locations and TxL interactions were observed for wheat yield by analysis of variance during field evaluation of nano urea formulations at number of locations in the north western plans zone of the country. First component of AMMI analysis shared of about 51.3% while second accounted for 29.9%. Significant variations due to locations, TxL interactions and treatments effects were observed for thousands grains weight. Around 82.9% of the interaction effects accounted by two significant components while total of significant components were up to 96.9%. Maximum yield was observed at Karnal followed by Hisar more over maximum yield for 56.1 was expressed by T3 treatment. ASV1 & ASV measures based on 81.3% of interaction effects selected T5, T8 and T8, T5 treatments. Average of thousands grains weight found T3, T8, T9 treatments as suitable for maximum realization and as per MASV1 and MASV settled T6, T2 treatments for thousands grains weight. Adaptability measures corresponding to BLUP estimates of yield i.e. PRVG, PRVG*Gu, HMPRVG*Gu, HMPRVG measures found T3, T4, T2 treatments for yield. Superiority index measures considering average thousands grains weight and stability in 65 and 35 ratios for weighted average settled for T3, T7, T8 treatments formulation of nano urea in the study. Biplot analysis observed Pantnagar center had expressed strong bondage with superiority index measures while considering mean, GAI and HM of treatments based on fixed and random effects of treatments. WAASB, W3, W2, W5 had maintained direct association with MASV, MASV1 on right hand side and with ASV1, W1, IPC4 on left side for yield. Treatments T13, T5 and T4 would express unstable yield as compared to T11, T6, T7 placed near to origin of biplot analysis. Thousands grains weight found that Karnal, Hisar centers with IPV2 formed the first cluster while Gurdaspur with Gwalior joined hands with superiority index measures in second cluster of biplot analysis. Next cluster was of adaptability measures PRVG, HMPRVG, PRVG*Gu, HMPRVG*Gu, mean, GAI, HM with Jammu, Delhi and Ludhiana centres. Next cluster of IPC4, IPC6 with Pantnagar observed near to large cluster of adaptability measures PRVG, HMPRVG, PRVG*Gu, HMPRVG*Gu.

Stability yield indices on different sweet corn hybrids based on AMMI analysis.

Currently, sweet corn is considered an important crop due to its high sugar content and low starch content. Important sugars in sweet corn include sucrose, fructose, glucose, and maltose. The purpose of the present study was to use the yield indices of the eight examined sweet corn hybrids and the correlation of the yield indices together. Concentration is important for consumers in terms of yield indices. The research site was located at the Látókép Experimental Station of the University of Debrecen. The small plot experiment had a strip plot design with four replications. The previous crop was sweet corn; the plant density was 64 thousand/ha. The obtained result indicates that Biplot AMMI based on IPCA1 showed that the DB, NO, GS, and GB hybrids had stability and high performance in terms of yield indices. At the same time, fructose and glucose had stable parameters for the hybrids involved in the study. IPCA1 AMMI biplot showed that the ME hybrid had stability and high performance in terms of iron and zinc as well. IPCA2 AMMI biplot showed that DE, GB, and GS hybrids had stability and the highest performance on yield parameters in the scope of the research. Fructose, glucose, and sucrose had stable parameters on hybrids based on IPCA2. The DB and SE hybrids had desirable performance in Lutein and Zeaxanthin based on the biplot. The DE hybrid had a maximum performance on iron and zinc parameters.

Performance Evaluation and Stability of Maize (Zea mays L.) Genotypes for Grain Yield Using AMMI and GGE Biplot

It is a fact that the performance of most crop genotypes varies across environments due to genotype‐environment interactions. Sharing knowledge about maize genotypes that are well‐adapted and high‐yielding in specific environments is essential for smallholder farmers to boost their productivity. A field trial was established in three locations during the 2019 and 2020 growing seasons to identify and select better‐adapted high‐yielding maize genotypes for production. A total of 10 improved varieties were evaluated using a randomized complete block design with three replications. The AMMI analysis of variance showed grain yield significantly (p ≤ 0.001) influenced by genotype‐environment interaction. This suggests that different genotypes showed varying grain yield performance across environments, implying the opportunity to choose effective genotypes suitable for both specific and broad adaptability. Moreover, the combined analysis of variance showed that genotype‐environment interaction significantly influenced plant height, ear height, dry biomass yield, and hundred‐kernel weight. The sum of squares factor explained that grain yield variation was contributed by the genotype (71.22%), followed by the environment (16.54%) and the genotype‐environment interaction effect (12.24%) of the total variation. Genotypes BH‐661, BH‐549, and BH‐546 demonstrated superior performance and stability, surpassing the national maize mean productivity and indicating broad adaptability. The AMMI analysis, GSI, and GGE biplot confirmed their robustness and high yield potential, especially for BH‐661 and BH‐549, making them optimal for variable conditions. Therefore, BH‐661 and BH‐549 are selected as stable genotypes for production in all agroecologies suitable for maize in the Dawuro zone.

Investigating the Stability of Bread Wheat Genotypes under Water Deficit Stress Conditions in Different Cold Climate Regions of using AMMI and GGE-Biplot Analysis

Investigating the Stability of Bread Wheat Genotypes under Water Deficit Stress Conditions in Different Cold Climate Regions of using AMMI and GGE-Biplot Analysis

Performance of Genotype by Environmental Interaction and Stability of Faba Bean (Vicia faba L.) Genotypes in Vertisol Areas of Amhara Region, Ethiopia

Faba beans are leguminous plants that have gained recognition for their significant contributions to sustainable agriculture and climate change mitigation. The experiment was conducted to assess the effect of genotype × environment interaction (GEI) on seed weight stability of faba bean genotypes using 12 genotypes in randomized complete block design with three replications tested at Enewari, Bichena, and Jamma in 2016, 2017, and 2018. The objective of the study is to determine the performance and stability of faba bean genotypes for large seed sizes and comparable seed yield under vertisol areas. The AMMI analyses revealed significant (p < 0.01) differences between genotype and environmental effects as well as genotype‐by‐environment interaction concerning seed yield and seed weight. This indicates the genotypes differently responded to the changes in the test environment or the test environment differentially discriminated the genotypes or both. The analysis of variance for AMMI showed that environment, genotype, and GEI contributed 37%, 26.7%, and 30.4% for seed weight, respectively. The principal components (IPCA 1) and (IPCA 2) explained 54.46% and 20.5% of the interaction, respectively. G4 was identified for their best seed weight with an advantage of 19.9% and 28.1% over the best standard check Hachalu and Dide’a with comparable seed yield. Based on AMMI and ASV stability parameters among the evaluated faba bean genotypes, G4 (EH01078‐4‐3) was the most stable and widely adapted over the studied environments in terms of seed weight and relatively stable and comparable seed yield. Therefore, the identified genotypes G4 (Shewa) were released as new varieties for vertisol areas of the Amhara region and areas with similar agroecology in the country.