AMMI Stability Value Research Articles

Unveiling Phenotypic and Environmental Dynamics: Exploring Genetic Stability and Adaptability of Faba Bean Cultivars in Norwegian Climates

ABSTRACTThis study evaluated 22 spring‐type faba bean cultivars in the main areas for cultivation of faba bean in Norway to assess the variation of 14 faba bean traits due to cultivar (G), environment (E), and their interaction (G × E), and to assess their stability across environments by using the additive main effects and multiplicative interaction (AMMI) analysis and coefficient of variation (CV). Significant G, E, and G × E effects were found for most traits, with environment accounting for much of the variance in yield and the growing degree days (GDD) to different developmental stages. Yield was highly correlated with thousand kernel weight (TKW) and GDD to BBCH 89 (maturation). The stability of the cultivars was studied for yield, TKW, and GDD to BBCH 89. Stability analysis using the AMMI stability value, yield stability index, CV, and the average sum of ranks identified Birgit, Stella, Bobas, and Macho as the most stable high‐yielding cultivars across environments, achieving a mean yield of 6–6.4 tons ha−1. Bobas, Macho, Stella, and Yukon had the most stable TKW (612–699 g) and Bobas, Capri, Trumpet, and Vertigo were the most stable regarding GDD to BBCH 89 (1257°C days, with a base temperature of 5°C). These stable cultivars can be utilized in breeding programs to achieve high and stable faba bean yield in the main growing areas of Norway and other Nordic‐Baltic countries.

Evolution, identification, evaluation, and characterization of a stable salinity tolerant sugarcane variety CoG 7

From the fluff generated during 2005, after the preliminary experiments (2005–2007), a promising clone G2005047 has been identified. It showed moderate resistance to red rot (3.6 on a 9-scale scoring system), less susceptibility to shoot borer (13.25%) and internode borers (25.35%), and resistance to woolly aphid (0%). In the Advanced Yield Trials (2008–2011), it showed advantages over check for cane yield (CY) (11.79%), commercial cane sugar percent (CCSP) (0.35%), and sugar yield (SY) (20.33%). To ascertain its large-scale cultivation suitability, it has experimented under adaptive research trials (2012–2014) at farmers’ fields. It exhibited 18.04%, 1.27%, and 19.55% supremacy over the check Co 86032 for CY, CCSP, and SY respectively. The stability of G2005047 under salinity was ascertained through a multi-environment-based experiment (2015–2017). AMMI (Additive Main-effects and Multiplicative Interactions) and GGE (Genotype × Genotype-Environment interaction) biplots were utilized. ANOVA revealed that the genotypic variation exerted the most significant effect followed by genotype × environment interaction and environment. G2005047 had the highest mean values for yield and quality traits with minimal ASV (AMMI stability value) (2.38:CY; 0.57: CCSP; & 0.58:SY) indicating its good-yielding ability and stability. AMMI I, AMMI II, and GGE biplots confirmed the stability of G2005047. In the jaggery quality assessment trials (2018 and 2019), it yielded 37.1% increased jaggery over the check. Also, the clone G2005047, exhibited moderate resistance to red rot disease, less susceptibility to shoot borer (13.25%) and internode borer (25.35%), and resistance against sugarcane woolly aphid (SWA). Due to supremacy for yield, quality, better performance under salinized situations, and tolerance to disease and pests, the clone G2005047 was released as a variety CoG 7 in 2022.

AMMI and GGE biplot analysis for barley genotype yield performance and stability under multi environment condition in southern Ethiopia

AbstractBarley (Hordeum vulgare L.) is a major grain crop farmed in Ethiopia throughout the long rainy season (Meher) and the short rainy season (Belg) of the year. Barley genotypes were subjected to multi‐environment experiments in six different settings to identify stable genotypes and estimate the impact of genotype × environment interaction (GEI) on grain production. In each area, the field experiment was conducted from mid‐July to January during the primary cropping season of 2021. Three replications of a randomized complete block design were used to set up the trials. According to the additive main effects and multiplicative interaction (AMMI) study, genotype (18.19%), GEI (22.98%), and environment (58.83%) all had an impact on the major treatment sum of squares. The more variance attributed to the environments is a sign of environmental diversity. Given that the two interaction principal component analysis (IPCAs) accounted for 76.94% of the interaction sum of squares, they were sufficient for cross‐validation of the grain yield variance explained by GEI. In contrast to the GGE biplot approaches, which indicated genotypes G12, G3, and G9 as stable and high‐yielding genotypes throughout the environments, the AMMI stability value identified genotypes G3, G12, and G9 as high yielding with stable performance across environments. In general, the GGE biplot and AMMI analysis models demonstrated that genotypes G12, G3, and G9 were stable and yielded well, making G3 acceptable for cultivation in a wider range of environments and G12 and G9 suitable for release.

Grain yield performance of spring maize under different agroecological zones

Maize is an important cereal crop which ranks second in production after rice in Nepal with an increasing demand for livestock and poultry ration but declining in production. This experiment was conducted to screen top- and stable yielding maize hybrids in various agroecological zones of Nepal during the spring season. In this study, nine maize genotypes were evaluated across six environments in randomized complete block design with three replications. The additive main effect and multiplicative interaction (AMMI) ANOVA revealed that environment, genotype, and their interaction had a substantial effect on the grain yield and all five principal components (PCs) were significantly different (P < 0.0001). AMMI stability value revealed that genotypes Rampur composite and Kanchan 101 were the most stable genotypes in all environments. The specific adaptation of genotype as explained by Which-won-where model suggest that Godavari in environment E2, Rajkumar in E4 and E5, and Bisco gold 941 in E1, E3 and E6 were the winning genotypes. Furthermore, the mean-versus-stability model revealed that genotype Kanchan 101 had above average yield with greater stability. In addition, biplot analysis revealed that 78.32% of variation is explained by PC1 and 11.41% by PC2 of the interaction effect. The genotype ranking based on revealed that genotype Kanchan 101 was close to the ideal line and Sano ghogha was at the greatest distance. Conclusively, AMMI and genotype and genotype by environment interaction (GGE) model explicates that genotype Kanchan 101 has both the high yield and stability across all agroecological zones. In future research on multi-year trial with emphasis other agronomic traits to assess the stability and priorities for the development of package of practices for maximizing the grain yield is recommended.

Genotype by Environment Interaction and Stability Analysis for Grain Yield in White Seeded Tef [&amp;lt;i&amp;gt;Eragrostis tef&amp;lt;/i&amp;gt; (zucc.)Trotter] Genotypes in Western Oromia, Ethiopia

Tef [&amp;lt;i&amp;gt;Eragrostis tef (Zucc.) Trotter &amp;lt;/i&amp;gt;L.] is a most important cereal crop in Ethiopia in terms of production, consumption and cash. The study was carried out to investigate grain yield stability and genotype by environment interaction for 18 genotypes conducted in the potential high land areas of Western Oromia, Ethiopia for two consecutive years (2020 to 2021) using Randomized Complete Block Experimental Design with three replications. The study of variance for grain yield using the AMMI model indicated highly significant variation for genotypes, environment, and genotype-environment interactions. Environment accounted for 18.7% of the variance in grain yield, 17.9% for genotypes, and 61.5% for genotypes. The first IPCA component accounted for 47.9% of the interaction effect and revealed the two models were fit. Genotypes G15, G10, G4, G1, and G3 had the lowest AMMI stability value (ASV), indicating stability; genotypes G16, G14, G9, G7, G2, and G5 had the highest ASV value, indicating instability. From over all analysis genotype G1 and G3, showed a high mean grain yield, lowest GSI, ASV and stable compared to other genotypes in the study. As a result, G1 and G3 were identified as the best genotypes for future breeding programs and potential release in Western Oromia, Ethiopia&amp;apos;s highlands.

Evaluation of Yield Stability and Adaptability of Oat Genotypes (Avena sativa L.)

This study’s main objectives were (i) to determine the stability of oat genotypes for the grain yield (t ha-1), (ii) to investigate the relationship of stability parameters with the grain yield in the analyzed set of genotypes, and (iii) to identify the high-yielding, stable, and adaptive oat genotypes for breeding purposes. The study was conducted during the 2015-22 period and involved fourteen winter oat genotypes maintained in the Bulgarian Seed Gene Bank. The trial was laid out in a randomized block design of variants in three replications and an experimental plot size of 10 m2. Sixteen grain yield stability parameters were determined. The AMMI stability value (ASV), the yield stability index (YSI), and the genotype selection index (GSI) were also calculated. A year and genotype x year interaction had an almost equally dominant effect on the grain yield per hectare. The Spearman’s rank correlation coefficients indicated that grain yield had significant positive associations with Thennarasu’s non-parametric statistics—the NP(2), NP(3), and NP(4), Kang’s rank-sum (KR), and the YSI. The yield stability parameters estimated the G11, G12, G13, and the G1 genotypes as the most stable. The ASV identified the G14, G8, and the G12 as the most stable genotypes, while the YSI detected the G14, G12, and G11, respectively. The GSI classified the G14, G12, G8, and the G11 as genotypes with the broadest adaptability to adverse climatic conditions. Thus, they could serve as a source material in winter oat breeding programs.

Genotype-by-environment interaction and stability analysis of grain yield of bread wheat (Triticum aestivum L.) genotypes using AMMI and GGE biplot analyses

Bread wheat is a vital staple crop worldwide; including in Ethiopia, but its production is prone to various environmental constraints and yield reduction associated with adaptation. To identify adaptable genotypes, a total of 12 bread wheat genotypes (G1 to G12) were evaluated for their genotype-environment interaction (GEI) and stability across three different environments for two years using Additive Main Effect and Multiplicative Interaction (AMMI) and genotype main effect plus genotype-by-environment interaction (GGE) biplots analysis. GEI is a common phenomenon in crop improvement and is of significant importance in genotype assessment and recommendation. According to combined analysis of variance, grain yield was considerably impacted by environments, genotypes, and GEI. AMMI and GGE biplots analysis also provided insights into the performance and stability of the genotypes across diverse environmental conditions. Among the 12 genotypes, G6 was selected by AMMI biplot analysis as adaptive and high-yielding genotype; G5 and G7 demonstrated high stability and minimal interaction with the environment, as evidenced by their IPCA1 values. G7 was identified as the most stable and high-yielding genotype. The GGE biplot's polygon view revealed that the highest grain yield was obtained from G6 in environment three (E3). E3 was selected as the ideal environment by the GGE biplot. The top three stable genotypes identified by AMMI stability value (ASV) were G5, G7, and G10, while the most stable genotype determined by Genotype Selection Index (GSI) was G7. Even though G6 was a high yielder, it was found to be unstable according to ASV and ranked third in stability according to GSI. Based on the study's findings, the GGE biplot genotype view for grain yield identified Tay genotype (G6) to be the most ideal genotype due to its high grain yield and stability in diverse environments. G7 showed similar characteristics and was also stable. These findings provide valuable insights to breeders and researchers for selecting high-yielding and stable, as well as high-yielding specifically adapted genotypes.

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.

Genotype by year interaction for selected traits in sweet maize (Zea maize L.) hybrids using AMMI model

This study investigated genotype × environment interactions for the stability of expression of four productivity traits (cobs yield, cobs I class trade share, lend of cobs and fulfilment of cobs) of sweet maize hybrids (Zea mays L.). The additive main effects and multiplicative interaction (AMMI) model was employed to assess genotype × environment interaction. AMMI stability value was used to evaluate both stability and genotype. The genotype selection index was calculated for each hybrid, incorporating both the average trait value and the stability index. Ten sweet maize hybrids were evaluated: Golda, GSS 1453, GSS 3071, GSS 5829, GSS 8529, Overland, Noa, Shinerock, Sindon, and Tessa. Trials were ran conducted over four vegetative seasons at a single location in the Wielkopolska region using replicated field experiments. The AMMI model revealed significant genotypic and environmental effects for all analyzed traits. Based on their superior stability and favorable average trait values, both the Golda cultivar and the GSS 3071 hybrid are recommended for further breeding program inclusion.

Experimental and Biological Approaches for Genotype X Environment Interactions Estimation for Wheat Genotypes Evaluated Under Multi Locational Trials

Genotypes VL907, HS562, HPW484 were ranked as topped three in comparison to the other during the evaluation of nine wheat genotypes at major locations of the north hills zone of the country under rain fed conditions. The least values of AMMI stability measure (ASV) had expressed the desirability of HPW484, HS562, VL2041 genotypes whereas the genotypes HS562, HPW484, VL2041 had been identified by least values of Modified Ammi Stability Value (MASV). The minimum value of simultaneous selection index measure based on the MASV (ssiMASV) had selected HS562, HPW484, VL2041 wheat genotypes while values of ssiWAASB measure found the suitability of HPW484, HS562, HS691 wheat genotypes. The composite non parametric measure NPi (2) had favoured the VL892, HS562 genotypes and values of NPi (3) measure had settled for VL892, HS562 genotypes while VL892, HPW349 wheat genotypes had been pointed by the last composite measure NPi (4). The Ward’s method of Hierarchical Clustering had placed the VL907 genotype in a separate group as compared to others. The shorter rays of measures IPC2, IPC5, IPC3, SD had reflected the less contribution of the joint effects of genotypes and measures in the biplot analysis. Non parametric composite measure NPi (1) had expressed tight direct relation with Si1,Si3,Si4,Si5,Si6,Si7 values. The values of IPC6 &amp; IPC4 had maintained the direct association with BLUP based analytic measures HMGV, RPGV, HMPRVG*Meanb, GAI, Meanb, RPGV*Meanb values. Moreover the values of CV measure had clustered with Si2, Si3, Si4,Si5, Si7 measures of this study.

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.

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.

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.

Analysis of Yield Stability in Diverse Rice Genotypes

Aims/ objectives: This study aims to improve stable and sustainable rice varieties adaptable to changing climatic conditions. It involves assessing genetic variability and Genotype X Environment interaction (G x E) among 186 diverse rice genotypes. The goal is to select genotypes with high breeding value, contributing to the development of rice varieties well-suited to varying climatic conditions. Study Design: The study employed an augmented design in a Randomized Complete Block Design (RCBD). Swarna, Madhuraj-55, Safri-17, Improved Samba Mahsuri, Thavalkannan, and IR64 checks were replicated across environments. Place and Duration of Study: The study took place in College of Agriculture, Raipur, IGKV over two years (wet season-2020 and 2021). Methodology: Analysis of genetic variability and G X E interaction among 186 rice genotypes. Execution of the experiment in an augmented design with a randomized complete block design. Replication of standard rice checks across different environments. Assessment of yield-attributing traits such as plant height, number of effective tillers, panicle length, number of grains per panicle, biological yield per plot, and grain yield. Evaluation of stability was done using univariate (Shukla stability variance, Wricke’s ecovalence, Kang stability statistic) and multivariate (AMMI yield stability index and GGE biplot) stability parameters. Selection of stable genotypes with high yield based on stability analyses. Results: Significant phenotypic variation was observed in yield-attributing traits across seasons. Genetic variability and G x E interaction effect demonstrated by variable genotype performance across environments. Univariate and multivariate stability parameters (S2i, W2i, KSi, AMMI stability value, GGE biplot) were used for stability analyses. Identification of stable genotypes with high yield across environments, including IR13f167, ARC13156, IR93354, F50, Ngalongyi, Giza 178, Arc 10159, Sadajira 19-317, Arith, IR 57920-Ac 25-2-B, Pesagro 102, Mekenzie small, Nasaenge, Kula Karuppan, Vary Gony, MR 69, Kanu Dam, IRRI 123, Sativa IRGC17083-1, Kalia, and Swarna. Conclusion: The study concludes that stability in genotype performance across diverse environments is crucial for the development of sustainable rice varieties. Genotypes with high stability and yield, as identified through stability analyses, hold potential breeding value for developing rice varieties adaptive to climate change. The stable genotypes listed, including IR13f167, ARC13156, IR93354, and others, are recommended for further breeding and development efforts to enhance rice productivity and adaptability.

Stability of grain zinc concentrations across lowland rice environments favors zinc biofortification breeding.

One-third of the human population consumes insufficient zinc (Zn) to sustain a healthy life. Zn deficiency can be relieved by increasing the Zn concentration ([Zn]) in staple food crops through biofortification breeding. Rice is a poor source of Zn, and in countries predominantly relying on rice without sufficient dietary diversification, such as Madagascar, Zn biofortification is a priority. Multi-environmental trials were performed in Madagascar over two years, 2019 and 2020, to screen a total of 28 genotypes including local and imported germplasm. The trials were conducted in the highlands of Ankazomiriotra, Anjiro, and Behenji and in Morovoay, a location representative of the coastal ecosystem. Contributions of genotype (G), environment (E), and G by E interactions (GEIs) were investigated. The grain [Zn] of local Malagasy rice varieties was similar to the internationally established grain [Zn] baseline of 18-20 μg/g for brown rice. While several imported breeding lines reached 50% of our breeding target set at +12 μg/g, only few met farmers' appreciation criteria. Levels of grain [Zn] were stable across E. The G effects accounted for a main fraction of the variation, 76% to 83% of the variation for year 1 and year 2 trials, respectively, while GEI effects were comparatively small, contributing 23% to 9%. This contrasted with dominant E and GEI effects for grain yield. Our results indicate that local varieties tested contained insufficient Zn to alleviate Zn malnutrition, and developing new Zn-biofortified varieties should therefore be a priority. GGE analysis did not distinguish mega-environments for grain [Zn], whereas at least three mega-environments existed for grain yield, differentiated by the presence of limiting environmental conditions and responsiveness to improved soil fertility. Our main conclusion reveals that grain [Zn] seems to be under strong genetic control in the agro-climatic conditions of Madagascar. We could identify several interesting genotypes as potential donors for the breeding program, among those BF156, with a relatively stable grain [Zn] (AMMI stability value (ASV) = 0.89) reaching our target (>26 μg/g). While selection for grain yield, general adaptation, and farmers' appreciation would have to rely on multi-environment testing, selection for grain [Zn] could be centralized in earlier generations.

Analysis of stability for nut yield and ancillary traits in cashew (Anacardium occidentale L.)

Cashew is cultivated in varied agro-ecological regions of India and yield levels vary with regions. Therefore, to identify stable genotype for yield, 18 genotypes were tested in four environments for nut yield and ancillary traits during 2008 to 2018 in randomized block design with two replications. The data of 6th annual harvest and cumulative nut yield of six years was analyzed employing additive main effect and multiplicative interaction (AMMI) and genotype and genotype by environment (GGE) methods. Analysis of variance for 6th annual harvest indicated significant differences (p < 0.01) for eight traits. Environments varied significantly (p < 0.01) for seven traits. Genotype by environment (G × E) interactions were significant (p < 0.01) for all traits. Analysis of variance for cumulative yield revealed significant variations between genotypes, environments, G x E interactions. Interaction principal component analysis (IPCA) 1 (84.39%) and IPCA 2 (10.27%) together captured 95% of variability. Genotypes, environments and G × E interaction were accounted for 16.18%, 4.50% and 77.22% respectively of total variation. The environment Pilicode discriminated better while Vridhachalam was representative. BPP-8 and Vengulra-7 were the winning genotypes in Bhubaneswar while Kanaka and Priyanka in Pilicode, Vengurla-4 in Jhargram and UN-50 in Vridhachalam. Therefore, promoting cultivation of these winning genotypes in the corresponding environments is highly recommended to enhance cashew nut production. As per ASV (AMMI stability value,) K-22-1 was stable genotype followed by Bhubaneswar-1. As per YSI (yield stability index), Bhubaneswar-1 was stable and high yielding followed by K-22-1 and BPP-8. Thus stable genotypes identified in this study viz., K-22-1 and Bhuvaneswar-1 are recommended for cultivation in west and east regions of India which have most cashew growing areas for increasing the cashew nut production.

Multi-environment testing for G×E interactions and identification of high-yielding, stable, medium-duration pigeonpea genotypes employing AMMI, GGE biplot, and YREM analyses.

Pigeonpea [Cajanus cajan (L.) Millspaugh] is a widely grown pulse with high seed protein content that contributes to food and nutritional security in the Indian subcontinent. The majority of pigeonpea varieties cultivated in India are of medium duration (<180 days to maturity), which makes it essential for breeders to focus on the development of stable high-yielding varieties. The diverse agroecological regime in the Indian subcontinent necessitates an efficient multi-environment study by taking into consideration genotype (G) × environment (E) interaction (GEI) that has a significant impact on traits like grain yield (GY) in developing high-yielding and widely adaptable varieties. In the present study, 37 pigeonpea genotypes were evaluated during the 2021 rainy season at ARS Badnapur, ARS Tandur, BAU Ranchi, GKVK Bengaluru, and ICRISAT Patancheru. The GEI was significant on the grain yield (p < 0.01), and hence, genotype + genotype × environment (GGE) and additive main effects and multiplicative interaction (AMMI) biplots along with AMMI stability value (ASV) and yield relative to environmental maximum (YREM) statistics were used to identify stable high-yielding genotypes. The interaction principal component analysis 1 and 2 (IPC1 and IPC2) explained 40.6% and 23.3% variations, respectively. Based on the rankings of genotypes, G37 (ICPL 20205), G35 (ICPL 20203), G8 (ICPL 19404), G17 (ICPL 19415), and G9 (ICPL 19405) were identified as ideal genotypes. Discriminativeness vs. representativeness identified GKVK Bengaluru as an ideal environment for comprehensive evaluation of test genotypes. However, ICPL 19405 was identified as the potentially stable high-yielding genotype for further testing and release across the test environments based on its mean grain yield (1,469.30 kg/ha), least ASV (3.82), and low yield stability index (YSI) of 13.

Identification of soybean (Glycine max (L.) Merrill) varieties suitable for different sowing windows

Seven soybean genotypes were evaluated across three seasons to identify genotypes suitable for different sowing windows. Percent variance contributed by genotypes main effects was more to total genotypic variability than that contributed byseasons, dates of sowing, genotypes × seasons interactions and genotypes × dates of sowing interactions for 50% flowering,number of branches and 100 seed weight. Seasons and dates of sowing main effects contributed more to total genotypic variability than genotypes, GSI and GDOSI for plant height, number of pods and grain yield. DSb 21, DSb 23 and KBS 23 showed lowest estimates of AMMI Stability Value and Stability Index for grain yield hence they are considered as widely adopted genotypes across the seasons.

Identification of soybean (Glycine max (L.) Merrill) varieties suitable for different dates of sowing

Seven soybean genotypes were evaluated across three seasons to identify genotypes suitable for different seasons and dates of sowing. Percent variance contributed by genotypes main effects was more to total genotypic variability than that contributed by seasons, dates of sowing genotypes × seasons interactions and genotypes × dates of sowing interactions for 50% flowering, number of branches and 100 seed weight. Seasons and dates of sowing main effects contributed more to total genotypic variability than genotypes, GSI and GDOSI for plant height, number of pods and grain yield. DSb 21, DSb 23 and KBS 23 showed lowest estimates of AMMI Stability Value and Stability Index for grain yield are widely adopted genotypes.