Additive Main Effects And Multiplicative Interaction Biplot Research Articles

Response of field pea (Pisum sativum L.) genotypes for grain yield in a multi-environment trial in Southeastern Ethiopia

Field pea (Pisum sativum L.) is a key cool-season food legume grown in Ethiopia, particularly in the Southeastern Arsi Zone. Although there is potential for field pea production in the area, adopting new and improved varieties is challenging because local farmers frequently prioritize cereal crops over field peas. To tackle this issue, a study was conducted to identify and promote high-yielding, improved field pea varieties suitable for the Southeastern farming community and similar agro-ecologies. The study focused on assessing the relationship between genotype and environment as well as the stability pattern of 14 advanced field pea genotypes. The genotypes were evaluated in eight environments in two consecutive cropping seasons (2014–2015) in southeast Ethiopia. The study utilized a randomized complete block design consisting of four replications. Various parametric stability analyses were used, including joint regression, additive main effect and multiplicative interaction (AMMI), and additive main effect and multiplicative interaction stability value (ASV). The grain yield was significantly influenced by genotype, location, and their interactions. The average grain yield ranged from 2809.5 kg ha−1 to 3509.1 kg ha−1. Eberhart's stability analysis identified stable genotypes: G3, G4, and G8, while G14 was unstable. According to additive main effect and multiplicative interaction stability value (ASV) and yield stability index (YSI), genotypes G12 and G13 had very low ASV values, whereas genotype G4 had very low ASV and YSI values. The AMMI and GGE biplot graphs showed that the principal component axes (PC1) and (PC2) accounted for 64.1 % and 61.36 % of the total variation, respectively. The results indicated that genotypes responded differently to environmental conditions and that the environment also influenced genotype performance. Genotypes G4 and G3 consistently performed well and exhibited remarkable stability, making them excellent cultivars for improving field pea productivity.

Unraveling genotype-by-environment interaction in maize using cutting edge statistical tools: Innovative empirical selection for increased yield stability

By prioritizing high yields in ideal conditions, traditional breeding might miss out that genotypes with superior yield and stability. Thus,this study seeks to identify stable genotypes that offer more predictable and reliable yields; under both stress and resourceful environments, leading to not only greater food security but financial stability as well. Incorporating both fixed (AMMI&GGI) and random effects models (BLUP & MTSI), not only leveraged theoretical but also empirical comparisons-weakness of each model is covered by all 4 models-to select elites hybrids Maize. Furthermore, a multi-environment trial assessed 41 hybrids across 4 research stations in 2 winter seasons using RCB design. Likewise, compiled cutting-edge statistical methods for GEI analyses from the “metan” package (R-Software), selected hybrids with consistent performance with meticulous analysis. AMMI(Additive Main effects and Multiplicative Interaction)analysis revealed that of significant (p ≤ 0.05) effects of genotype, environment, & genotype-environment interaction (GEI), explaining yield variance with interaction effects were captured by 3 interaction principal components (IPCs). Moreover,GEI graphics of AMMI biplots elucidates four hybrids(MRM-4062, Super-22, PAC-744, and Gen-4118) with high yield stability and above-average performance. Additionally, eight hybrids (4118, 4558, 5454, NMH1258, PAC 746, PAC 745 Gold, and SUPER 9090) exhibited superior yield, regardless of their stability. Nepalgunj exhibited the lowest GEI value. Conversely, Rampur displayed an above-average GEI, indicating a strong ability to modulate varieties performance. Besides this, GGE biplot revealed two potential mega-environments based on yield similarities. Nine high-yielding hybrids (SUPPER 6768, SUPPER 9396, PAC750, MBS5622, PAC745 Gold, MRM4065, NMH8392, Gen-4118, and Gen-4558) were identified as vertex hybrids, signifying their superior performance across wide environments. Even supposing, mixed effects model, both the best linear unbiased prediction (BLUP) weighted with average absolute score standardized by yields (WAASBY) models, Gen-4118 ranked highest in terms of average yield, followed by NMH-8392, TMH2858, and others, and Multi-Trait Stability Index (MTSI) identified SUPPER-9090 as the most stable and high-yielding genotype, followed by NMH4040, Super-22, Gen-4118, MBS-1144, and NMH1255. In a nutshell, Gen-4118, Super-9090, MRM-4062, NMH1258, NMH-8392, TMH2858, PAC 746, PAC 745 Gold emerge as the highest-yielding and most consistently high-performinghybrids, offering promise for commercial farming across diverse environments. Clearly,this study has potential to mitigate risks for farmers (economic burden, crop failure) and bolster food security/climate resilience. What's more, it paves the way for future research on cold tolerance genes for even greater crop improvement.

Screening of Wheat Genotypes for Water Stress Tolerance Using Soil–Water Relationships and Multivariate Statistical Approaches

Drought stress constricts crop production around the world. Employing high-yielding cultivars with drought tolerance might be the ideal professional approach to coping with its detrimental outcomes. As a result, the current study was performed to investigate the sensitivity and tolerance of nine wheat genotypes to drought stress. In a randomized block design experiment, nine wheat genotypes were subjected to four water treatments: 100%, 85%, 70%, and 55% of the available water (AW). Four water regimes in two growing seasons were counted as eight environmental zones. The leaf’s water relations and photosynthetic pigment were estimated, as well as growth and yield parameters. Univariate and multivariate statistical approaches, including the new method of multi-trait genotype–ideotype distance (MGIDI), were used for evaluation. The analysis of variance revealed that genotype, environment, and their interactions had a highly significant effect on all traits. The same trend was shown by the additive main effects and multiplicative interaction (AMMI) analysis of variance for grain yield across the environments. The AMMI biplot study indicated that the G8 genotype is the most stable in terms of water stress. The G7 genotype can withstand droughts up to 55% of the available water, while the G8 and G3 genotypes can withstand droughts up to 70% of the available water. Based on all examined traits, this index was used to identify the stable genotypes G7, G8, and G3, which can therefore be suggested for cultivation during drought conditions. Furthermore, we found a positive correlation between the MGIDI, ANOVA, and tolerance index results, indicating that the same desirable genotypes of G7 and G8 were identified by these procedures as being highly tolerant and stable across a range of soil moisture conditions. Based on MGIDI analysis, we can recommend that the G7 genotype exhibits higher grain yield and yield-related traits with the best drought-tolerant indices.

Genotype by Environment (G × E) Interaction and Yield Stability of Chickpea (Cicer arietinum L.) Varieties Across Agroecological Regions of Ethiopia

ABSTRACTGenotype by environment (G × E) interaction obstructs breeding by persuading variations in genotype performance. The aim of the present study was to determine the stability and yield performance of Desi and Kabuli chickpea varieties at different agroecological regions of Ethiopia, using different stability parameters. The experiment was laid out in a randomized complete block design (RCBD) with three replications. The additive main effect and multiplicative interaction (AMMI) analysis of variance (ANOVA) indicated highly significant differences (p ≤ 0.01) for environments, genotypes, and importantly G × E interaction. AMMI and GGE biplot, AMMI's stability value (ASV) indicate that the Desi chickpea variety Teketay with mean yield of 2225.6 kg/ha (highest) and the variety Dimtu (1603.9 kg/ha) followed by Natoli with mean yield of 2004.9 kg/ha were found to be stable and adaptable to all environments. Similarly, from the Kabuli chickpea varieties, the variety Koka with mean grain yield of 2257.1 kg/ha (highest) and the variety Ejere with mean yield of 1997.6 kg/ha followed by Shasho (1798.59 kg/ha) were found to be stable and adaptable to all environments and should be promoted for production in chickpea‐growing areas of Ethiopia. In conclusion, identification of stable improved varieties for the different agroecological regions can assist the producers such as the farmers for the effective chickpea production. This leads to sustainable self‐sufficiency of food at the household and country level.

Estimating phenotypic stability for relevant yield and quality traits in French bean (Phaseolus vulgaris L.) using AMMI analysis

The presence of strong G x E (genotype by environment interaction) is a major hurdle for selecting superior genotypes when genotypes are placed into new and unfamiliar production systems. Genotype or cultivar (s) with high yield potential and having less adaptability and stability to particular environment is never a suitable choice for a breeder and farmer particularly. Purposefully, four successive seasons were chosen to enumerate the phenotypic stability of 27 French bean genotypes for yield and quality traits by involving modern statistical tools like AMMI (Additive Main Effect and Multiplicative Interaction), GGE [G + (G x E)] and cluster analysis. AMMI analysis of variance witnessed magnitude of G, E and G x E was 81.94%, 11.58% and 6.48% of the total variation respectively. The IPCA I (Interaction Principal Component Axes) was contributed with 55.44%, 73.60%, 71.81%, 81.69% and 72.16% G x E variations of days to 50% flowering, pod length, number of pods per plant, average pod weight and pod yield respectively. For qualitative traits i.e., protein content (mg/100g FW), total soluble solids (%) and total phenol content (mg GAE/g FW) the involvement of IPCA I to total genotype by environment interaction variations were 89.55%, 96.07% and 66.52% respectively. The AMMI biplot revealed French bean genotypes viz., IC632961, Arka Sukomal, IIHR-PV-29, IIHR-PV-30 having low AMMI stability value and higher mean value for relevant yield and quality traits in both late kharif and rabi as two mega-environments. Multivariate analysis demonstrated significant higher contribution of pod yield associated traits towards total variations and positive correlation between them. The 27 French bean genotypes formed five groups as per Euclidean distance and the clustering revealed the nature of diversity of French bean genotypes viz., IC 632961, IIHR-B-PV-24, Arka Sukomal, Arka Arjun, Ayoka and Phalguni in response to changing environments and can be utilized in future breeding programme. The study revealed pole type French bean genotypes viz., IC 632961, Arka Sukomal and bush type French bean genotypes viz., IIHR-B-PV-29, IIHR-B-PV-30 could be promising for utilization in future breeding programmes for the concerned traits.

AMMI and GGE biplot analysis for genotype × environment interactions affecting the yield and quality characteristics of sugar beet.

Sugar beet, an important sugar crop, contributes significantly to the world's sugar production. However, genotype-environment interactions (GEI) often affect the quality characteristics of sugar beet. Hence, understanding the effects of GEI on sugar beet quality can aid in identifying high-quality genotypes that can adapt to different environments. Traditional variance analysis can only be used to examine the yield of a variety and not its specific adaptability to specific conditions. Therefore, more comprehensive analytical methods are required to evaluate the characteristics of the variety under specific environments. Additive main effects and multiplicative interaction (AMMI) and genotype main effect and genotype × environment interaction (GGE) biplot models can be employed to comprehensively evaluate different varieties and address the drawbacks associated with a single evaluation method. Moreover, these models also allow us to explore new varieties more objectively and comprehensively. In this study, the adaptability and stability of 16 sugar beet varieties, in terms of yield and sugar content, were evaluated using AMMI and GGE biplot analysis in seven pilot projects undertaken in 2022. In the assessment of a small but significant proportion of the total GEI variance for the two qualitative traits (yield and sugar content), 80.58% of the variance was explained by the cumulative contribution of IPC1, IPC2, and IPC3. AMMI and GGE biplots clearly highlighted that KWS4207 (G3) exhibited high and stable quality. They also demonstrated that the experiments in Jalaid Banner (Inner Mongolia) (E7) were the most representative. Together, the results suggested that the comprehensive application of AMMI and GGE biplot analysis allowed for a more comprehensive, scientific, and effective evaluation of sugar beet varieties across different regions. The findings offer a theoretical basis for sugar beet breeding and could guide the rational design of experiments for testing new varieties of sugar beet.

Yield performance and stability analysis of three cultivars of Gayo Arabica coffee across six different environments

Abstract The three cultivars of Gayo Arabica coffee (GAC) are distributed widely in the Gayo Highlands, Aceh Province, Indonesia, between 900 and 1,700 m above sea level (masl). The difference in altitude affects the yield and quality of coffee, and Arabica coffee has biennial bearing characteristics, so there are always annual yield fluctuations. This study aimed to determine the yield performance and stability level of the three GAC cultivars, Gayo 1, Gayo 2, and Gayo 3 (G3), across six different environments. The study used a randomized complete block design with three replications. The six environmental conditions are the combinations of two altitudes (900 and 1,500 masl) and 3 years of production (2019, 2020, and 2021). The estimation of coffee yields is based on the fruit value, which is calculated by the number of productive branches per tree, nodes per branch, berries per node, and the weight of a single cherry. An investigation of the genotype-by-environmental interaction using a combined analysis of variance and the yield performance and stability analysis performed using the Eberhart and Russell method, AMMI (additive main effects and multiplicative interaction) and GGE (genotype + genotype × environment) biplot analysis. Results showed that the yield performance of GAC was significantly affected by the interaction between genotype and environment. G3 was an ideal cultivar because it had high yield performance, was stable, and could adapt to broader environments in Gayo Highland. Therefore, G3 deserves high priority for Arabica coffee development in that region.

Assessment of Genotype X Environment Interaction and Seed Yield Stability in Sesame (Sesamum indicum L.) Using AMMI Analysis

Sesame is considered as an important oilseed crop in terms of nutritional and medicinal properties. Seed yield of any crop is highly labile to environmental factors, which may cause significant reduction in the yield performance. To get substantial stable yield production in crops like sesame, there is urgent need to identify stable genotypes that perform well under different environmental conditions. Thus, the present study has been planned with the aim to assess genotype x environment interaction (GEI) and stability analysis for seed yield per plant using additive main effect and multiplicative interaction (AMMI) model. The experiment involved evaluation of 20 sesame genotypes in randomized complete block design (RCBD) with three replications under three artificially created environments by providing different levels of fertilizers. The environment wise analysis of variance (ANOVA) revealed significant genotypic variance across all the environments. Additionally, AMMI ANOVA revealed significant environment, genotypes and GEI components. GEI component was further classified to interaction principal components (IPC), IPC1 and IPC2, which explain 66.18% and 33.82% of variance due to GEI, respectively. Further, through different stability indices (ASV and GSI) and AMMI biplots, it was revealed that genotype RT-346 and RT-351 are considered as stable genotypes and these can be further recommended to farmer under varying fertility conditions.

Simultaneous evaluation of yield and stability of sugar beet (Beta vulgaris L.) varieties under Egyptian conditions using AMMI and GGE biplot approaches

An experiment was conducted on stability analysis of seven diverse monogram sugar beet (Beta vulgaris L.) varieties for root and sugar yields and their attributes grown under three harvesting dates after sowing during 2021-22 and 2022-23. The data generated from eighteen environments representing the combinations among harvesting dates, locations, and seasons were subjected to additive main effects and multiplicative interaction (AMMI) genotype main effect and genotype x environment interaction (GGE Biplot) analysis. Results obtained from AMMI combined analysis of variance showed that the main effects of sugar beet varieties, environments, and their interaction were highly significant for all studied traits. It is observed that sugar beet plants grown in the Nubaria region and harvested after 210 days, gave the best results for most root and sugar quality traits. The analysis further revealed that Volna and Klara varieties were the elite ones regarding root and sugar yields, and juice quality parameters as an overall mean across the environments. AMMI stability analysis indicated the variety Vangelis was broadly or narrowly stable under different environments and reflected somewhat good performance for most studied traits, while Klara and Volna were considered very promising as above stable ones using GGE Biplot graphs. The findings indicated that GGE Biplot graphs are more accurate and more informative as compared to AMMI stability analysis.

Stability analysis of groundnut (Arachis hypogaea L.) genotypes using AMMI and GGE biplot models and ideal genotype selection indicator

A study on stability analysis was carried out on groundnut (Arachis hypogaea L.) genotypes grown at multilocation over two cropseasons. Based on additive main effects and multiplicative interaction (AMMI) analysis, the first two IPCAs explained 91.93% of the GEIvariation (74.84 and 17.09% for IPCA1 and IPCA2, respectively). Based on AMMI 2, the best genotype for environments RA1 and RA2 was 201. The genotypes, ICG192 and ICG130 were found suitable for environments TA1 and TA2, respectively. ICG178 was better adapted to environment MA2, whereas ICG140 and the control NC2 were the best genotypes for environment RA2. In the GGE biplot, PC1 and PC2 explained 81.22 and 13.33% of the total GGE variance. Based on the ideal genotype selection index (IGSI), the genotypes, ICG115, ICG201, and ICG178 were stable and can be used in the breeding programs to develop new varieties.

Genotype-by-environment interaction analysis for cotton seed yield using various biometrical methods under irrigation regimes in a semi-arid region

ABSTRACT The main objective of this study was to identify high-yielding and stable cotton genotypes under normal irrigation regimes and drought stress conditions using some biometrical methods including combined analysis of variance (ANOVA), joint regression analysis (JRA), the additive main effect and multiplicative interaction (AMMI), and genotype (G) main effect plus genotype-by-environment (GE) interaction (GGE) biplot. Cotton seed yield (CSY) was found to be significantly affected by genotypes, environments, and Genotype-by-environment interaction (GEI) using combined ANOVA, JRA, and AMMI. AMMI was superior, explaining 79% of the total variability caused by GEI under drought stress conditions compared to 66% and 25% for the GGE biplot and JRA, respectively. The CSY was found to be significantly lower under drought stress conditions vs normal irrigation regimes, ranging from 9.03% (G24) to 29.91% (G15) across the five tested environments. The JRA, AMMI, and GGE biplot methods were positively correlated for classifying the genotypes for static stability. The GGE biplot was the most effective and acceptable for identifying stable genotypes and optimal environments in both irrigation regimes. All the methods compared were concordant in separating, ranking, and identifying that the G5 and G20 genotypes were highly stable across the environment as being higher-yielding and drought-tolerant.

Assessment of yield performances for grain sorghum varieties by AMMI and GGE biplot analyses

Grain sorghum is an exceptional source of dietary nutrition with outstanding economic values. Breeding of grain sorghum can be slowed down by the occurrence of genotype × environment interactions (GEI) causing biased estimation of yield performance in multi-environments and therefore complicates direct phenotypic selection of superior genotypes. Multi-environment trials by randomized complete block design with three replications were performed on 13 newly developed grain sorghum varieties at seven test locations across China for two years. Additive main effects and multiplicative interaction (AMMI) and genotype + genotype × environment (GGE) biplot models were adopted to uncover GEI patterns and effectively identify high-yielding genotypes with stable performance across environments. Yield (YLD), plant height (PH), days to maturity (DTM), thousand seed weight (TSW), and panicle length (PL) were measured. Statistical analysis showed that target traits were influenced by significant GEI effects (p < 0.001), that broad-sense heritability estimates for these traits varied from 0.40 to 0.94 within the medium to high range, that AMMI and GGE biplot models captured more than 66.3% of total variance suggesting sufficient applicability of both analytic models, and that two genotypes, G3 (Liaoza No.52) and G10 (Jinza 110), were identified as the superior varieties while one genotype, G11 (Jinza 111), was the locally adapted variety. G3 was the most stable variety with highest yielding potential and G10 was second to G3 in average yield and stability whereas G11 had best adaptation only in one test location. We recommend G3 and G10 for the production in Shenyang, Chaoyang, Jinzhou, Jinzhong, Yulin, and Pingliang, while G11 for Yili.

AMMI Analysis of Genotype × Environment Interaction on Sugar Beet (Beta vulgaris L.) Yield, Sugar Content and Production in Romania

The overall yield and sugar content of sugar beet (Beta vulgaris L.) were determined by the genotype and its interaction with the environment. This study aimed to analyze the interaction of 23 genotypes with different environmental conditions during two growing seasons. To estimate the variance of genotypes, environment, and genotype function of the environment, the R 3.5.1 software package was used. In addition, the multivariate stability method was used to explain the G (genotype) × E (environment) interaction based on the GGE (Genotype plus Genotype-by-Environment) and AMMI (additive main effects and multiplicative interaction) biplots. The AMMI ASV (AMMI stability value) and biplot analysis revealed that only two genotypes (G10 and G11) showed higher values for yield and sugar content and production compared to the other genotypes. The AMMI ASV analysis also showed that the environment significantly influenced the sugar beet yield, sugar content, and sugar production, which were the descriptors for production in this study.

COTTON GERMPLASM CHARACTERIZATION FOR DROUGHT TOLERANCE BASED ON MORPHO-PHYSIOLOGICAL AND FIBER QUALITY PARAMETERS

Drought tolerance is a quantitative trait that is exceedingly challenging to breed, especially for allotetraploids like cotton. The scenario of limited water resources necessitates developing droughttolerant cultivars that conserve significant irrigation water throughout the summer. Therefore, the presented study used a design to statistically analyze the morphological, physiological, and fiber quality parameters linked with drought tolerance, which is a comprehensive method for choosing better genotypes from the available cotton germplasm. Measuring these parameters ensued for plants grown under field conditions. The germplasm comprised 150 cotton genotypes studied at two water regimes, i.e., regular and water-stressed conditions for two consecutive seasons of 2015–2016 and 2016–2017. Data recording ran for different morpho-physiological and fiber quality parameters. Significant differences occurred for all the treatments, genotypes, and Genotype × Environment interaction for all the morphological, physiological, and fiber quality parameters under study. Additive Main effects and Multiplicative Interaction (AMMI) analysis and AMMI biplot analysis helped analyze the results, which revealed that the cotton genotypes FH-900, FH-901, FH-312, AS-1, AS-2, AS-3, RH510, RH-627, AR-2, AR-9, BH-118, BH-175, SLH-74, CIM-1100, CIM-598, and MM-58 were drought tolerant and ranked highest concerning stress condition. Moreover, correlation studies distinguished the relationship between relevant traits concerning drought tolerance.

Selection of High-Yielding and Stable Genotypes of Barley for the Cold Climate in Iran.

The interaction between genotypes and environments plays an important role in selecting superior genotypes for target locations. The main objectives of the present study were to analyze the effect of the genotype-by-environment interaction (GEI) and identify superior, newly developed, and promising barley genotypes for cold regions in Iran. For these purposes, a set of genotypes obtained from breeding programs for cold climates in Iran, along with two reference genotypes, were investigated at eight research stations (Tabriz, Ardabil, Arak, Miandoab, Mashhad, Jolge Rokh, Karaj, and Hamadan) during two consecutive growing seasons (2019-2020 and 2020-2021). The results of the freezing test (LT50) showed that most of the tested genotypes had significant cold tolerance at the seedling stage. Based on the additive main effect and multiplicative interaction (AMMI) analysis, environment (E) and GEI effects explained 49.44% and 16.55% of the total variation in grain yield, respectively. Using AMMI1 and AMMI2 models, G2 and G20 were found to be superior genotypes in terms of grain yield and stability. Moreover, AMMI-based stability parameters considered the G20 genotype to be the ideal genotype. A two-plot analysis of the genotype-by-environment interaction (GGE) biplot showed that the 16 experimental environments were grouped into 2 mega-environments. Of the test environments, ARK1 and KAJ2 had the highest discriminating power and representativeness ability, and these were identified as ideal environments for testing advanced genotypes for yield and stability performance during early barley breeding practices in cold areas in Iran. In conclusion, both AMMI and GGE biplot models identified several superior genotypes, among which G20, with a high average yield relative to the overall average yield and the lowest IPC1 score, was found to have high yield stability and is recommended for inclusion in breeding programs for cold climates in Iran.

AMMI and GGE Biplot Analysis of Yield and Related Traits among Selected Mini-core Pigeonpea (Cajanus cajan L. Millsp.) Accessions

Pigeon pea [Canjanus cajan (L.) Millsp.] is an orphan crop that has remained greatly unimproved in quality and quantity in Africa even though it has great potential of supplying food to the growing population. Its yield is affected by various biotic and abiotic factors and a lack of varieties with broad adaptability to a range of environments. It is therefore important to select accessions that are stable in different environments for yield and yield components. A randomized complete block design layout was used to carry out a multi-locational trial using 107 pigeon pea accessions. Data was taken for the total number of pods per plant at maturity, pod length, seed weight, seeds per pod, and seed yield per plant. Additive main effects and multiplicative interaction (AMMI) with genotype and genotype by environment interaction (GGE) biplot were used to interpret the data. The mean squares for the PCA explained by the first two principal components account for 100% of the G x E interaction. The results revealed that the differences in the yield and yield components across the three locations were mostly due to the genotypes of the pigeon pea and to some extent the genotype by environment interaction. Six accessions were observed to be high-yielding and stable across all three environments.

Stability estimation through multivariate approach among solasodine-rich lines of Solanum khasianum (C.B. Clarke): an important industrial plant.

Solanum khasianum is a medicinally important plant that is a source of steroidal alkaloids 'solasodine.' It has various industrial applications, including oral contraceptives and other pharmaceutical uses. The present study was based on 186 germplasm of S. khasianum, which were analyzed for the stability of economically important traits like solasodine content and fruit yield. The collected germplasm was planted during Kharif 2018, 2019, and 2020 in RCBD with three replications at the experimental farm of CSIR-NEIST, Jorhat, Assam, India. A multivariate approach for stability analysis was applied to identify stable germplasm of S. khasianum for economically important traits. The germplasm was analyzed for additive main effects and multiplicative interaction (AMMI), GGE biplot, multi-trait stability index, and Shukla's variance which were evaluated for three environments. The AMMI ANOVA revealed significant GE interaction for all the studied traits. The stable and high-yielding germplasm was identified from the AMMI biplot, GGE biplot, Shukla's variance value, and MTSI plot analysis. Lines no. 90, 85, 70, 107, and 62 were identified as highly stable fruit yielders while, lines no. 1, 146, and 68 were identified as stable high solasodine lines. However, considering both traits, i.e., high fruit yield and solasodine content, MTSI analysis was performed which showed that lines 1, 85, 70,155, 71, 114, 65, 86, 62, 116, 32, and 182 could be used in a breeding program. Thus, this identified germplasm can be considered for further varietal development and could be used in a breeding program. The findings of the present study would be beneficial for the S. khasianum breeding program.

G×E analysis to Identify the Stable High-yielding Rice Lines among a Set of Selected Germplasm Panel

Rice lines need to be grown and evaluated for yield under different agro-ecological locations to identify stable and high-yielding lines for deployment in breeding programs. With this aim, a set of rice germplasm was evaluated for G×E in four different environments (E1-Dadesuguru-Wet 2020, E2-ICAR–IIRR-Dry 2019, E3-ICAR–IIRR-Wet 2020, E4-ICAR–IIRR-Dry 2020). The experimental trial was laid out in a randomized complete block (RCB) design with three replications at each location for 118 rice lines. Data on yield per plant was analyzed using the Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype, and Genotype × Environment Interaction (GGE) models. The combined analysis of variance (ANOVA) manifested significant variations for tested genotypes, locations, years, genotype × year, and genotype × location interactions revealing the influence of environmental factors on yield traits. All four environments showed discrimination power, whereas E2 and E3 were found as the representative environment as they fall near the Average-Environment axis (AEA). The AMMI biplot PC1 contributed 79.20% variability and PC2 contributed 15.18% variability. From the GGE biplot analysis, the rice lines Phouren, JBB-631-1, and JBB-1325 were found to be the best and most stable. The rice lines Phouren, PUP-229, and TI-112 were stable in the first sub-group Dhadesugur-Wet 2020 (E1). The rice lines Langphou, and NPK-45 were stable in the second sub-group ICAR-IIRR-Wet 2020 (E3). Environment ICAR-IIRR-Dry 2019 (E2) was the third subgroup and the rice lines Moirangphou-Yenthik and TI-3 topped for the same. The ICAR IIRR-Dry 2020 (E4) environment formed the fourth subgroup where Phouren-Amubi, TI-128 and JBB-1325 topped the season. In conclusion, this study revealed that G × E interactions are significant for yield variation, and its AMMI and biplots analysis are efficient tools for visualizing the response of genotypes to different locations.

Yield stability analysis of pearl millet genotypes in arid region of India using AMMI and GGE biplot

Aim: To assess genotypes with ability to sustain yields under arid region and to understand the significance of interactive effects of genotype × environment to develop resilient cultivars for rainfed farming. Methodology: Performances of 12 pearl millet varieties and hybrids released for commercial cultivation in India, were estimated using RBD with three replications during rainy seasons of 2018, 2019 and 2020. The trials were conducted at the Agricultural Research Station, ANGRAU, Ananthapuramu, located in the Scarce Rainfall Zone of Andhra Pradesh, India, with an average annual rainfall of nearly 553 mm and 300 mm to 400 mm during rainy season. Results: The environment and genotype effects were highly significant in the Additive Main Effect and Multiplicative Interaction (AMMI) model, implying that environments are varied and genotypes performed differently in each environment offering a great scope for selecting better adaptive genotypes. Apart from moisture stress, the amount of rainfall received during both the anthesis and grain maturation stages were influenced grain yield through plant height, 1000-grain weight and dry fodder yield. Environment 3 was the best discriminating environment and the hybrids Pratap, 86M86 and NBH 5767 had outperformed the popular open pollinated varieties in ideal conditions. Interpretation: In terms of grain yield,Pusa Comp. 612 and Pratap have proved to be reliable and had a mean higher than the general mean over environments. As a result, Pusa Comp. 612 and Pratap are suggested for large-scale assessment in a wide range of locations in rainfed situations of arid regions. Key words: GT-biplot, Grain yield, Pearl millet, Stability, Trait associations

GENOTYPE BY ENVIRONMENT INTERACTION AND STABILITY ANALYSIS OF ETHIOPIAN COMMERCIAL DURUM WHEAT (Triticum turgidum L.) CULTIVARS IN SOUTHERN ETHIOPIA

<p class="abstrakinggris">The use of improved varieties with wider adaptability and stability is necessary to maximize the productivity of durum wheat. However, due to genotype by environment interaction (GEI) effect, superior genotypes in one environment could be inferior in other environments. Hence, a multi-environmental trial (MET) was commenced to determine the magnitude of GEI effect and identify stable durum wheat genotypes across environments. The MET was conducted using nine durum wheat cultivars in randomized completely block design with three replications across four contrasting locations in 2020 crop seasons. The result of combined analysis of variance showed the presence of significant differences among the main effects; environments, genotypes, and GEI effects for grain yield. The additive main effects and multiplicative interaction (AMMI) combined ANOVA for main effects of environment, genotype, and GEI revealed highly significant differences among cultivars with 66.2%, 26.4%, and 7.3% share of sum square variation, respectively, of the total variation. AMMI and genotype plus genotype by environment (GGE) Bi-plot analysis identified the cultivars Fetan, Denbi, and Mangudo as high yielders and adaptive to the favourable locations. AMMI stability value and yield stability index identified Mangudo as the most stable and adaptive cultivar across locations. The AMMI Bi-plot analysis showed that the first two interaction principal component analysis (IPCAs) captured 90.45% of the total interaction sum of squares (ISS), where IPCA1 took 55.61% and IPCA2 accounted for 34.84% of GEI effects. This study identified Mangudo as the most stable cultivar with acceptable yield while Fetan was the top yielder genotype across locations.</p>