Additive Main Effects And Multiplicative Interaction Analysis Research Articles

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.

Characterization of phenotypic traits associated with anthracnose resistance in selected common bean (Phaseolus vulgaris L.) breeding material

Anthracnose caused by Colletotrichum lindemuthianum is the major common bean disease worldwide causing complete yield loss under favourable disease conditions. This study aimed to determine phenotypic traits associated with anthracnose resistance for future use in breeding programmes. Twenty-two common bean varieties (CBVs) were selected basing on susceptibility to anthracnose, advanced breeding lines, improved variety resembling advanced breeding lines and the farmer variety widely grown in Tanzania. Selected varieties were planted in anthracnose hotspot fields and the same CBVs were planted in a screen house to validate resistance to anthracnose. Anthracnose infection score, leaf length, leaf width, length of fifth internode, length of petiole, plant vigour, canopy height and canopy width were recorded. Data on number of plants emerging; days to flowering; days to maturity; plant stands at harvest; and grain yield were also collected and analysed using R software. Phenotypic traits evaluated differed significantly among genotypes, environment and genotype by environment interaction. Seventy-five percent of phenotypic traits evaluated were positively correlated to anthracnose resistance.Highly-strong correlations to anthracnose were observed on number of days to maturity, plant stands at harvest, plant vigour and grain yield. Leaf length, leaf width, length of fifth internode, length of petiole and number of stands emerging were strongly correlated to anthracnose resistance. Additive main effects and multiplicative interaction analysis (AMMI) revealed highest contribution of environment on anthracnose infection-58.9% and grain yield −84.9% compared to genotype effects on anthracnose infection −32.7% and grain yield-15.7%. Based on these results, four traits – plant vigour, number of days to maturity, number of plant stands at harvest and grain yield – are recommended for selecting anthracnose-resistant varieties. NUA 48, NUA 64 and RWR 2154 were superior varieties, resistant to anthracnose and high yielding, while Sweet Violet and VTT 923-23-10 were most stable varieties across environments. Further on-farm research is suggested to assess their performance and identify traits preferred by farmers.

Selection of Durum Wheat and SSR Markers for Organic Farming in Central Italy Using AMMI Analysis

Durum wheat is one of the main crops in the Mediterranean region, which is characterized as the hotspot of climate change, with large year-to-year weather fluctuations. Although chemical input reduction in agriculture is strongly demanded, as well as healthy food, there is still a lack of stable and high-yielding crop varieties specifically adapted for organic conditions. This study evaluates the performance of fifteen durum wheat varieties in terms of suitability for organic farming in central Italy and assesses the impact of the genotype–environment interaction (GEI) on productive and quality traits. Variety performance was evaluated in field experiments over four successive seasons. In addition, a genotypic diversity analysis of 38 microsatellites associated with traits important for organic farming was performed. The AMMI (additive main effects and multiplicative interaction) stability analysis revealed that the best and most stable genotype regarding quality traits, such as thousand-kernel weight, protein content, and test weight was the ancient variety, Senatore Cappelli. The most stable and high yield was determined for the Fuego, Iride, and Mv-Pelsodur genotypes. Moreover, SSR markers that could be used for plant breeding, targeting organic farming systems based on molecular markers and GEI results, were identified.

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.

Genotype and environment interactions of yield contributing characters of field mustard (Brassica rapa L.)

Genotype-environment interaction is the norm of reciprocal action that determines the relationship between gene and environmental factors. Gene and environment interaction takes place when different environments affect the genotypes and vice versa. The investigation portrays the interaction between genes and environments of yield contributing characteristics of eleven genotypes of field mustard. Three seeding-time environments, viz., early, late, and very late of eleven mustard genotypes were laid out with three replications in a randomized complete block design. Among the three environments, which one was favorable and which was not was determined by their significant differences through a combined analysis of variance. The IPCA 1 (first interaction principal component axis) score of genotypes in the AMMI (additive main effects and multiplicative interaction analysis) of G×E interactions was estimated as an indication of the ability to respond to the environments and the performance with changing environments of the genotypes. Considering all the scores, Sonali Sharisha (SS-75), and BINA Sharisha-10 were found to be highly stable genotypes, while among the three environments, the environment-1 (early sowing) was found the best sowing time for raising and harvesting a good mustard crop. Bangladesh J. Bot. 52(4): 1055-1065, 2023 (December)

Assessment of G × E interaction and stability parameters for quality, root yield and its associating traits in ashwagandha [Withania somnifera (L.) Dunal] germplasm lines

Withania somnifera (L.) Dunal holds significant pharmaceutical and nutritional benefits. The stability and superior root yield of ashwangandha genotype is a crucial parameter for long-term development and raw material supply for pharma-industries. The impact of genotype × environment (G × E) interaction on economic traits (root yield) stability on ninety-six genotypes in four different environments was evaluated in this study. Using two models of stability analysis namely Eberhart - Russell and Additive Main Effects and Multiplicative Interaction (AMMI) analysis models, current study was focused on adaptability and phenotypic stability of ashwangandha genotypes. Field trail was conducted in a randomized complete block design with three replications. Phenotypic data were collected on eleven important traits including four commercially important traits viz total soluble sugars (TSS), starch content, withanolides content and crude fiber in the main and late-season in 2020 and 2021. Analysis of variance exhibited consistency between both models. These results depicated G × E interaction, revealing subtle variations in response to environmental conditions. Eberhart and Russell model provided valuable trait performance insights, while AMMI offered genotype and environment insights. Interestingly, Eberhart and Russell model identified Env 3 as superior while Env 4 as a challenging environment. Genotypes viz. AGP-84, AGP-16, AGP-79, AGP-95, AGP-57 and AGP-14 exhibited stability and consistent performance for root yield across environments. AMMI analysis captured 52.7 % variance in the first principal component and 26.3 % in the second. Genotypes namely AGP 18, AGP 22, and AGP 57 were found best and stabile with high mean dry root yield in both models. Env 1 stood out as the most distinct, and Env 2 was representative for selecting the best genotypes.

Characterization of heterogeneity in popular rice landrace through field and molecular evaluation

ContextIdentification and purification of the landrace Chittimuthyalu using stability and molecular heterozygosity can be useful for rice diversity. ObjectivesThe present study reports the characterization of 12 genotypic variants of Chittimuthyalu in terms of (i) variation and correlation among agromorphological, grain quality and nutrient traits, viz., Zn and Fe; (ii) stability across years; and (iii) characterization and heterozygosity. MethodsA total of twelve genotypic variants were identified based on field-level agromorphological observations in the Chittimuthyalu landrace. For identification of a stable genotypic variant for grain Zn, genotype by environment (G × E) interaction was analyzed across three years using additive main effects and multiplicative interaction (AMMI) model and genotype main effects plus G × E (GGE) model for multienvironment data analysis along with principal component analysis (PCA). Correspondingly, twenty-five high polymorphism information content (PIC) RM markers were used for the identification of molecular heterozygosity in landrace. ResultsStepwise regression analysis of phenotype revealed the interdependence of zinc in polished rice (ZPR), iron in polished rice (IPR), and single plant yield (SPY) and the positive association of grain Zn with IPR and SPY. Based on GGE and AMMI analyses of the variants, MCM3 was found to be stable across the environments for ZPR. Out of 46% polymorphic RM markers, seven markers (RM10149, RM13347, RM21749, RM13263, RM10361, RM12276 and RM474) were heterozygous. Heterozygous alleles were observed in eight variants (MCM2, MCM3, MCM5, MCM6 MCM7, MCM8, MCM10 and MCM12). The polymorphic information content (PIC) of the markers ranged from 0.58 to 0.88, with an average of 0.64. The percentage of the most common allele frequencies ranged from 4.2 to 95.8 across the markers. RM13347 showed nine markertrait associations with P ≤ 0.05 and R2 ranging from 2.3% to 14.3% with a mean of 7.74%. ConclusionsThe present study has revealed the inherent heterogeneity in landraces in terms of their agromorphological, yield-related and nutritional quality traits. A promising genetic stock (MCM3) was identified as a donor for high grain ZPR and was registered as genetic stock. ImplicationsThe identified genetic stock is being shared with breeders in the breeding programme and farmers for the multiplication of high grain Zn rice.

Genotype × environment interaction and stability analysis for seed yield and yield components in sesame (Sesamum indicum L.) in Benin Republic using AMMI, GGE biplot and MTSI

Increasing production requires the development of high yielding cultivars adapted to various environments. Multi-environment trials (MET) remains the best approach to assess the performance of accessions across environments. The objective of this study was to select the best performing and stable accessions of sesame across different environments in Benin Republic. Nineteen sesame accessions were evaluated across eight environments using a randomized complete block design with four replicates. The accessions were evaluated for three traits: days to 50% flowering (D50F), thousand-seed weight (TSW), and seed yield (SY) during 2020 growing season. The stable and top-performing accessions across environments were determined using AMMI (Additive main effects and multiplicative interaction), GGE (Genotype main effect and genotype × environment interaction), and MTSI (Multi-trait stability index). AMMI analysis of variance showed a significant difference across environments for the three traits. The accessions were affected by environmental conditions for the three traits. The broad-sense heritability estimates were high (>0.60) for all the traits, indicating the improvement is achievable through selection. AMMI1 and AMMI2 biplots identified G10 and G13 as high seed yielding accessions adapted to environments E1, E2. The GGE biplot showed two mega-environments for TSW and three mega-environments for D50F and SY. For SY, G11 and G13 were the best accessions in the first mega-environment, G10 the best accession in the second mega-environment; G3 and G8 were the best accessions in the third mega-environment. AMMI and GGE analyses identified G10, G5, G12 as high seed yielding and stable accessions across environments. GGE biplot revealed that E1 and E2 were the most suitable environments for multi-location trials based on their discriminating ability and representativeness. MTSI indicated G10, G13, G19 as promising germplasm to be recommended for breeding program.

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.

Genotype x Environment Interaction and Oil yield Stability of Linseed (Linum usitatissimum L.) Genotypes in North, Central and Southeast Ethiopia

Linseed (Linum usitatissimum L.) is one of the most prominent industrial oilseed crops cultivated for both seed and fiber. Lack of stable genotypes across the linseed production area is one of the problems. Thirteen linseed genotypes were planted in randomized complete block design with three replications at six linseed major growing agro-ecologies of North, central and Southeastern Ethiopia (Werabe, D\Markos, Welkite, Holeta, Kulumsa and Adiet) in 2021/2022 cropping season. With the objectives of determining the effects of GEI, on oil yield of linseed and identifying better performing and well adapted linseed genotypes than the control variety, and to prepare for registration and release of selected high oil yielding genotypes in the different linseed agro-environment conditions of Ethiopia. The oil yield subjected to the combined analysis of variance showed a highly significant (p<0.01) effect of genotype, location, and genotype x location interactions (GLI). Similarly the combined AMMI ANOVA for oil yield revealed that there were highly significant differences among genotypes, locations and genotype by location interactions and accounted 22.11%, 31.40% and 46.49% of the total variations respectively. The highest percentages of environmental variations are an indication that environment is the major factor that influences the yield performance of linseed oil yield in Ethiopia. In addition, the first two IPCAs were significant and accounted for 80.77% of the total interactions sum squares. Six stability measures viz Eberhart and Russell analysis (bi and S2di), Additive Main Effect and Multiplicative Interaction (AMMI) model, AMMI Stability Value (ASV),Yield Stability Index (YSI),Genotype Main Effect and Genotype by Environment Interaction Effect (GGE) bi plot analysis Model were used to evaluate the stable genotypes across the testing locations. Genotypes 10097(G2), 10103 (G3) and 239716 (G7) were more stable by Eberhart and Russell analysis and AMMI Stability Value. Genotypes 10103 (G3) and 208360 (G8) were more stable by Yield Stability Index. Genotypes 208360 (G8) and 10103 (G3) were selected as better genotypes that appeared in the five and four locations by AMMI analysis, respectively. According to one-year data, the six locations are grouped into one mega environment for linseed production with one winning genotype and genotype 208360 (G8) was an ideal genotype, while location A diet was an ideal environment by GGE analysis. Genotypes 208360 (G8), 234005 (G4)and 10103 (G3) are the three of the best performing genotypes than the other genotypes and control variety (Berene) in oil yield across locations. Therefore, those three highest oil yielder genotypes have a potential to be registered in Ethiopia. However, this trail need to be repeated for one more season, and or three of the best performing genotypes will be verified along with the check on farmers' fields for release.

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.

A comparative study on single and multiple trait selections of equatorial grown maize hybrids

Maize (Zea mays L.) production in tropical equatorial regions faces significant challenges due to agroclimatic and soil fertility variability, necessitating the evaluation of maize hybrid adaptability and phenotypic stability across diverse agroecosystems. This study compares the effectiveness of the additive main effects and multiplicative interaction (AMMI) and multi-trait genotype-ideotype distance (MGIDI) models for identifying superior maize hybrids well-suited to the equatorial climate. Fifteen genotypes, including 13 hybrid candidates and two popular commercial varieties (BISI 2 and NASA 29), were analyzed in 10 distinct environments in Indonesia over three consecutive years (2018–2020). The ANOVA method used in the AMMI model analyzed variance into three major components, with PCA analysis indicating that environments (E), genotypes (G), and their interaction (G × E) had a highly significant effect on yield (p < 0.001). Two hybrids, HM04 (CI301032/G102612) and HM02 (CI272022/G102612), displayed high adaptability and stability across various environments, with significantly higher yields than the grand mean by AMMI analysis. Additionally, HM10 (MAL03/CLYN231) and HM09 (G102612/CLYN231) were narrowly adapted to the ME-1 and ME-2 mega-environments, indicating they are best suited for these specific environments. Similar to AMMI, the MGIDI model suggested HM04 (MGIDI index = 1.74) and HM02 (MGIDI index = 1.76) as the two highest-performing hybrids, determined by their yield and nine other traits. Using the multiple trait combination index as a tool to assess the performance of these hybrids enabled researchers to determine the most effective traits for each genotype. The two models are recommended and may be integrated for comprehensive data interaction analysis, which simplifies the process of delineating genotypes with the environment and enables stakeholders to select desired traits while considering their strengths and weaknesses.

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.

Genotype by environment interaction and stability analysis of sweet potato (Ipomoea batatas L.) genotypes in West Hararghe zone, Eastern Ethiopia

Sweet potato is an important food crop in Eastern Ethiopia, including the West Hararghe Zone. However, the area is still far behind in attaining the required optimum productivity of Sweet potato, this is due to biotic and abiotic factors, inappropriate Agronomic practices and lack of improved variety, to tackle these problems; variety improvement research is the major one. Therefore, the objectives of the study were to estimate the magnitudes of genotype, environment, and genotype by environment interaction effects on Sweet potato genotypes and to identify the high yielder and stable genotypes for wide adaptability in West Hararghe Zone, Eastern Ethiopia. The experiment was carried out at two locations (Mechara on station and Habro district) in three consecutive years (2018–2020) on 20 Sweet potato genotypes using a randomized complete block design (RCBD) with three replications. Additive main effects and multiplicative interactions analysis (AMMI) indicated that the yield performances of genotypes were under the major environmental effects of genotype by environmental interactions. The first two principal component axes (PCA 1 and 2) were significant (p≤0.01) and cumulatively contributed to 73% of the total genotype by environment interaction. In GGE bi-plot analysis using genotypic and environmental scores of the first PCA 1 and lower PCA 2 scores gave high yields (stable genotypes), and genotypes with lower PCA 1 and larger PCA 2 scores had low yields (unstable genotypes), as in the sites tested. Besides, genotypes G3 and G5 were stable across tested locations and gave higher total root yields (43.94t ha-1 and 49.34 t ha-1), respectively). However, G5 was recommended for possible release for wide adaptability in West Hararghe Zone and similar agro-ecology in the country. Res. Agric. Livest. Fish. 10(1): 43-52

Evaluation of Indian ginseng [Withania somnifera (L.) Dunal] breeding lines and genotype-by-environment interaction across production environments in western India.

In order to find location-specific and broadly adapted genotypes for total root alkaloid content and dry root yield along with additive main effects and multiplicative interactions (AMMI) and genotype (G) main effects plus genotype × environment (E) interaction in Indian ginseng (Withania somnifera (L.) Dunal), (GGE) biplot analyses were used in the current study. Trials were carried out in a randomized complete block design (RCBD) over three succeeding years viz., 2016-2017, 2017-2018 and 2018-2019 at three different locations (S. K. Nagar, Bhiloda and Jagudan). Analysis of variance (ANOVA) for AMMI for dry root yield revealed that the environment, genotype, and GE interaction, respectively, accounted for significant sums of squares of 35.31%, 24.89%, and 32.96%. For total root alkaloid content, a significance of 27.59% of total sum of squares was justified by environment, 17.72% by genotype and 43.13% by GEI. Nine experimental trials in total were taken into consideration as contexts for the GEI analysis in 16 genotypes, including one check. AMMI analysis showed that genotypes, SKA-11, SKA-27, SKA-23 and SKA-10 were superior for mean dry root yield and SKA-11, SKA-27 and SKA-21 had better performance for total root alkaloid content across environment. The GGE biplot analysis showed genotypes SKA-11, SKA-27, SKA-10 desirable for dry root yield and SKA-26, SKA-27, SKA-11 for total root alkaloid content. As a result of the GGE and AMMI biplot techniques, SKA-11 and SKA-27 were determined to be the most desired genotypes for both total root alkaloid content and dry root yield. Further, simultaneous stability index or SSI statistics identified SKA-6, SKA-10, SKA-27, SKA-11 and AWS-1 for higher dry root yield, whilst SKA-25, SKA-6, SKA-11, SKA-12 and AWS-1 for total alkaloid content from root. Based on trait variation, GGE biplot analysis identified two mega-environments for dry root yield and a total of four for total root alkaloid content. Additionally, two representative and discriminating environments-one for dry root production and the other for total root alkaloid content were found. Location-specific and breeding for broad adaptation could be advocated for improvement and release of varieties for Indian ginseng.

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.

STABILITY IN YIELD PERFORMANCE EVALUATION OF SELECTED COTTON (Gossypium hirsutum) GENOTYPES IN VARIABLE AGRO-ENVIRONMENTS

Yield constancy is a crucial characteristic for a variety to become popular among growers. To study this aspect, the present trial was carried out at seven locations in the cotton belt of Punjab during 2020-21. Twenty-five upland cotton strains from different breeding stations were tested along with the standard variety CIM-602. The main objective was to choose super-yielding plus stable strains. Maximum variability due to environments (65%) followed by GEI (22.8%) was observed. The first two interaction principal components (IPC) were squeezed with 72.3% of cumulative variability due to GEI. The analysis of additive main effects and multiplicative interaction (AMMI) diagnosed AMMI5 as an appropriate model. Strain CKC-5 gave maximum mean yield (1812kg ha-1) and winner in all AMMI models. Test sites were split into two mega environments (ME). ENT7 (CRS Faisalabad) site was bearing the highest mean seed cotton yield of (2532kg ha-1) with the biggest (52.18) IPC1 score. The correlation between sites and IPC1 scores was (0.68) as recorded by AMMI analysis. AMMI1 ranks depicted that (PCI2) CIM-875 bears yield advantage of (29.09%) at ENT6 (Vehari) site over (trial winner strain CKC-5) due to micro adaptations. Genotype Selection Index (GSI) discriminated strain BS-J5 as yielder cum stable one with the least GSI value. Approval of this strain for general cultivation from the respective forum may boost cotton production in the province.

Additive Main Effect and Multiplicative Interaction Analysis for Grain Yield of Early Maturing Sorghum [Sorghum Bicolor (L.) Moench] Varieties in Drought Prone Areas of Temben, North Ethiopia.

Shortage of widely adapted and high yielding variety is one of the major bottlenecks for production and productivity of sorghum in dry lowlands of Tigray region, Northern Ethiopia. A field experiment was conducted during the main seasons of 2018 and 2019 at four environments using randomized complete block design with three replications to evaluate the performance of ten early maturing sorghum varieties for grain yield using AMMI (Additive Main Effects and Multiplicative Interaction) model. The combined analysis of variance revealed highly significant (P≤0.01) genotype (G), environment (E) and genotype × environment interaction (GEI). The significant genotype by environment interaction effects were further partitioned in to two significant interaction principal components by using AMMI model. The AMMI analysis of variance showed that the genotype, environment and interaction sum squares contributed 41.55 %, 28.67 % and 29.78 % to the treatment sum squares for grain yield respectively. In addition, the first two IPCAs and interaction residual were significant. The first two IPCAs accounted for a total of 82.20 % of the interaction sum square. The results revealed that the observed yield variation among genotypes were due to genetic potential of genotypes and interaction rather than location differences. The highest yield was obtained from Melkam (3276 kg ha-1), while the lowest was from Grana-1 (2094 kg ha-1) and the average grain yield of genotypes was 2462 kg ha-1. Therefore, Melkam is selected as the best stable variety with consistent yielding performance across the testing environments in dry lowland areas of Temben and similar agro-ecologies in Tigray region, Northern Ethiopia.