GGE Biplot Analysis Research Articles

Registration of “Ikhulule’’ Finger Millet (Eleusine coracana (<I>L</I>.) Gaertn) Variety

Although finger millet is grown extensively in Ethiopia, the national average yield is much less than the crop's genetic potential. This is due to a lack of stable, high-yielding, and disease-tolerant finger millet varieties. Thus, the current study was conducted to find stable, high-yielding, and disease-tolerant genotypes for increasing production. A randomized complete block design was used to evaluate twelve finger millet genotypes under a regional variety trial at Mechara and Habro for three consecutive years (2017 to 2019) against standard checks (Maba, Addis01, and Axum). The tested genotypes were brought from the Melkasa Agricultural Research Center. Further, some of these genotypes were collected from different areas of Oromia and the remaining were introduced from Kenya including Ikulule through Melkasa Agricultural Research Center. At the multi-environment evaluation, the yield advantage of the new variety is 18% higher than the best-performed standard check Maba (5.8tha<sup>-1</sup>). Additionally, the GGE biplot analysis showed that the Ikulule variety is high-yielding across locations and years. Also at the variety verification trial, the overall mean grain yield of (Ikulule) at on station and farmers' fields is 3.81 t tha<sup>-1</sup> whereas the standard check kumusa is 2.91 tha<sup>-1</sup>. The new variety is not only high-yield but also resistant to blast rust and shoot flies relative to standard check. As a result, this new variety of Ikulule was released for the west Hararghe zone districts and similar agroecologies.

Unravelling Yield and Yield-Related Traits in Soybean Using GGE Biplot and Path Analysis

Soybean (Glycine max) is a vital crop for food, animal feed, and industrial products. However, its yield performance is significantly affected by genotype-by-environment interaction (GEI), which complicates the selection of high-yielding, stable varieties. This study aimed to evaluate the yield performance and stability of 12 elite soybean varieties across five major production areas in Uganda using GGE biplot and path analysis. The varieties were planted in a randomized complete block design with three replications over two consecutive seasons. Results revealed significant differences in grain yield among the varieties, locations, and their interactions (p < 0.001). The highest-yielding varieties were Maksoy 5N (979 kg ha−1), Maksoy 4N (978 kg ha−1), Maksoy 3N (930 kg ha−1), and Signal (930 kg ha−1). GGE biplot analysis grouped the locations into two mega-environments, with the Maksoy varieties exhibiting greater yield stability compared to Seed Co. varieties. Path analysis showed that traits such as the number of lower internodes, central internode length, and filled pods had the highest positive direct effects on grain yield. This study provides insights into soybean breeding in tropical environments, highlighting traits that can be targeted to improve yield and stability. The findings offer a framework for breeding programs in Uganda and similar agro-ecological regions, promoting more resilient and productive soybean varieties. This study also illustrated the potential advantages of employing more complex mathematical techniques like path analysis to uncover yield and yield-related traits in soybean breeding programs.

Enhancing Fruit Retention and Juice Quality in ‘Kinnow’ (Citrus reticulata) Through the Combined Foliar Application of Potassium, Zinc, and Plant Growth Regulators

Improving fruit quality and reducing pre-harvest fruit drop are critical goals for Citrus reticulata production in Pakistan, where climatic and nutritional challenges affect yield and juice quality. This study evaluated the combined effects of plant growth regulators (salicylic acid and indole acetic acid) and nutrients (potassium and zinc) on fruit drop and juice volume in Citrus reticulata L. Field trials were conducted at three locations in Punjab, Pakistan (Layyah, Faisalabad, and Sargodha) using a randomized complete block design (RCBD) with five replications per treatment. Nutrients (K and Zn at 100 mg/L each) and growth regulators (SA at 100 mg/L and IAA at 5 mg/L) were applied individually or in combination at three growth stages. Statistical analyses, including PCA, ANOVA, and GGE biplot, were used to identify the most effective treatments for improving fruit juice quality and reducing fruit drop. The combined foliar application of SA + K + Zn was the most effective across all parameters, except fruit drop, juice citric acid contents, and juice pH, which were negatively affected. The highest juice potassium content was observed with K application. The PCA and GGE biplot analysis indicated that the Sargodha orchard performed best, with the SA + K treatment being the most effective there, while SA + K + Zn showed the best results at Layyah and Faisalabad for reducing fruit drop, enhancing juice volume, and improving fruit quality. However, individual fruit, juice, and juice nutrient contents traits analyses revealed that the most significant improvements in fruit and juice quality were observed at the Sargodha site instead of Layyah and Faisalabad. The treatment SA + K + Zn proved to be the most stable and consistent in enhancing citrus fruit and juice quality across all three selected locations. The findings suggest that adopting the SA + K + Zn treatment could be a practical approach for citrus farmers aiming to enhance crop yield and fruit quality, thereby supporting agricultural productivity and export potential in Pakistan.

Evaluation of yield and stability of sugar beet (beta vulgaris L.) genotypes using GGE biplot and AMMI analysis

Rhizomania is the most destructive sugar beet disease in the world, and in recent years, it has widespread in most of the sugar beet growing areas in Iran. Since the control of this soil-borne disease is a difficult task, the use of resistant genotypes is known as the best measure against the disease. The ultimate goal of sugar beet breeders is to produce genotypes that can be used in both infected and non-infected fields without any reduction in terms of yield and quality. Twenty-one sugar beet genotypes along with four controls were evaluated in randomized complete block design with four replications in fields with natural infection to rhizomania in five research stations. Important sugar beet traits including root yield, sugar yield, sugar content, and white sugar yield were evaluated for two years (2021 and 2022). For all traits, location was the main source of variation that spanned 33 to 55% of the total sum of the square followed by the location×year×genotype accounted for 3–40% of the variation. Based on the results of analysis of variance, multivariate stability parameters were computed to evaluate the genotypes’ stability. The first two principal components (IPCA1 and IPCA2) generated by GGE biplot contributed for 31.3 and 17.5% difference in genotype×environment interaction for root yield, respectively. According to the GGE biplot, genotypes RM-11 and RM-12 were identified as the winning genotypes across environments for both root yield and white sugar yield traits whereas AMMI model identified RM-14 and RM-9 (for root yield) and RM-1 (for white sugar yield) as best genotypes. Based on the ideal genotype ranking, RM-11 and RM-10 were the best performer with a high mean yield as well as stability in the studied environments. The biplot rendered using the weighted average of absolute scores (WAASB) and root yield and white sugar yield identified RM-11 and RM-9 as superior genotypes in terms of yield and stability. The selected genotypes can be used in breeding programs to transfer the disease resistance and cultivar development.

Exploring Stable Low Soil Phosphorous Stress Tolerance in Rice Using Novel Allele Recombination From Oryza rufipogon

ABSTRACTAdvent of changing climatic conditions along with nutrient deficient soils adversely affects the environment for the rice production. Wild introgression lines derived from KMR3 and Oryza rufipogon population were evaluated in six environments, including optimum and low phosphorus stress condition. Significant differences among the introgression lines were observed for plant height, tiller number, biomass, grain yield per plant, days to 50% flowering and harvest index across the environments. Based on grain yield observed under optimum phosphorus and limited phosphorus (P stress condition), eight stress tolerance indices were calculated and found STI and GMP are the better indices to discriminate among tolerant and susceptible genotypes, and correlation studies also confirmed the significant association between STI and GMP. Cluster analysis based on stress tolerance indices revealed three different clusters distinguishing genotypes based on their stable performance on yield related traits. AMMI and GGE biplot analysis to identify the stable performance across environments revealed NSR60, NSR101, NSR105, NSR85 and NSR86 as high grain yielders, whereas NSR135, NSR5 and NSR88 as stable performers. WAASBY‐based stability analysis on multiple traits (MTSI) showed NSR135, NSR79 and NSR18 with lowest MTSI, indicating their high stability and high mean performance compared with parent KMR3. Further genotyping for low P tolerance gene (PSTOL1) and grain yield genes (Gn1a, SPIKE, TGW6, DEP1 and OsSPL14) using allele specific markers showed that the desirable alleles of SPIKE, Gn1a and TGW6 were derived from wild parent O. rufipogon. Low P tolerance allele PSTOL1 was absent in recurrent parent KMR3; however, the introgression lines harboured desirable alleles, which were derived from O. rufipogon. Further mapping studies will help to identify a significant potential QTLs/gene for low P tolerance from O. rufipogon. Wild introgression lines, NSR85, NSR124, NSR80, NSR54, NSR86 and NSR88, were found as the high yielding and nutrient stress tolerant genotypes, which can be used as potential donors in future breeding programmes for low P stress tolerance.

Application of univariate, multivariate, and mixed models to the stability analysis of Ethiopian tetraploid wheat cultivars under irrigation condition

AbstractThe testing of durum wheat (Triticum turgidum subsp. durum) varieties in different irrigated environments is critical for determining the stability of their performance and adaptation. In this study, 12 popular and newly developed durum wheat varieties were examined in eight irrigated locations with the purpose of investigating genotype–environment interaction and their effect on Ethiopian tetraploid wheat yield stability across diverse environments. The field experiment has two replications with a randomized complete block design. Multivariate (analysis of variance, additive main effect and multiplicative interaction [AMMI], and genetics, genetics × environment [GGE] biplot) and univariate (bi [regression coefficient], S2d [deviation from regression], σi2 [Shukla's stability variance], Wi2 [Wricke's ecovalence], YSi [yield stability statistic], and CVi [coefficient of variance]) analysis methods were used to identify stable varieties. The AMMI analysis showed that the genetic × environmental interaction was highly significant (p > 0.01), while the genotype and environment variation were not significant. The first two principal component axes (IPCA1 and IPCA2) were highly significant (p > 0.01) and contributed 79% of the total GE interaction. Univariate statistical models indicate that Bulala is a stable, high‐yielding variety that can adapt to various environments. GGE biplot analysis revealed that the eight test environments were clustered into three mega‐environments, ranked Bulala as the most stable variety across diverse environments. The results of the combined mean analysis, multivariate and univariate models revealed that Bulala is a high yielder (3.46 tons ha−1) and stable variety across all test environments, while Mukiye variety has a high yield (3.43 tons ha−1) but is not as stable or adaptive to multiple locations. As a result, Bulala was recommended for further demonstration and popularization in test locations and places with similar agroecologies under irrigation.

Graphical Analysis of Multi-environmental Trials for Bread Wheat (Triticum aestivum L.) Grain Yield Based on GGE Bi-Plot Analysis

Bread wheat (Triticum aestivum L.) is a crucial crop in Ethiopia, and breeders test newly developed elite lines for superiority to existing cultivars to boost national productivity. The study was undertaken during the 2021–22 to 2022–23 cropping seasons at seven environments in optimum moisture areas of Ethiopian using 36 diverse and advanced bread wheat genotypes to evaluate the GEI by the graphical method of GGE biplot and to identify the genotypes with high mean yield performance and stability. Field experiments were conducted at the Adet, Asasa, Kulumsa, and Sinana research centers in Ethiopia. The experiments were planted in an alpha lattice design replicated three times in six rows of 2.5m long. Row-to-row distance and distance between blocks were 0.2m and 1.5m, respectively. The analysis of variance revealed that genotype, environment, and their interaction showed a highly significant effect on the yield as reflected in the GGE model and the GGE model indicated the suitability of the genotypes EBW202136 (33), Boru (1), and EBW202172 (12), with high mean yield and stability, whereas the genotypes EBW202185 (16) and Deka (36) produced high mean yield, but unstable. Likewise, the genotypes EBW202164 (27) and EBW202192 (29) produced low mean yield and unstable. The AMMI analysis of variance for grain yield across the environments showed that 17.26% of the total variation was attributed to genotypic effects, 64.03% to environmental effects, and 18.71% to GEI effects. Two mega environments were identified based on GGE biplot analysis and the which-won–model indicated the adaptation of genotypes Boru (1), EBW202159 (4), EBW202172 (12), EBW202171 (19), and EBW202136 (33) to first mega-environment and genotypes EBW202157 (3), EBW202166 (5), EBW202160 (6), EBW202162 (9), EBW202185 (16), Dursa (17) and Deka (36) in the second. These approaches allowed the identification of stable and high-yielding genotypes (EBW202136 (33) and EBW202172 (12)) which can be included in the national verification program, with a plan to release a new variety, and other genotypes with high yield could be utilized in breeding programs to further improve grain yield in bread wheat.

Genotype by Environment Interaction Using AMMI, GGE Biplot and Multivariate Analysis of Nepalese Aromatic Rice Landraces

Aromatic group of rice comprises fine, medium, long and extra-long grain that has rich in agro-biodiversity, carries historical and cultural significance in Nepalese society. The objective was to identify stable, high-yielding aromatic rice landraces for Nepal Thirty aromatic rice landraces including two released varieties as standard checks were evaluated at three locations during 2020 and 2021. The experiment was laid out in a Randomized Complete Block Design with three replications. The combined analysis of variance indicated differences among genotypes and genotype-environment interactions for grain yield. The additive main effects and multiplicative interaction (AMMI) analysis revealed that genotypic effects for 69%, genotype × environment interaction effects for 28% and environmental effects accounted for 3% of the total sum of squares. Similarly, GGE biplot analysis showed that the principal components (PC1 and PC2) accounted for 53% and 47% of the GGE sum of squares, respectively. Based on AMMI1, AMMI2 biplots, and GGE biplots, the genotypes Samba Masuli Sub-1 and Sugandhit Dhan-1 were identified as the high yielding and stable fine and aromatic rice landraces. Other promising genotypes are Damari Dhan, Tilki, Hiupuri, Kalo Basmati, Gouriya, and Brahmamusi Dhan. Specifically, Samba Masuli Sub-1 demonstrated the highest grain yield performance at the NRRP, Hardinath, while Sunaulo Sugandha and Balamsari Dhan excelled in the Tarahara and Khajura environments, respectively. In PCA analysis, four PCs showed significant eigen value >1.0 contributing more than 84% of total variance. Biplot analysis reflected that 1000 grain weight, panicle length, heading and maturity days, and grain yield showed greater variation among the landraces. The results provide a robust basis for the further evaluation and verification of this findings in advance stage trials. The superior and outstanding landraces with higher yields and aroma are potential future varieties that can be either use directly as varieties after selection or utilize in future breeding programs for the development of new aromatic rice varieties.

Identification of foxtail millet (Seteria italica (L.) P. Beauv.) genotypes for multi-season adaptability using GGE biplot analysis in Foothills of Nagaland

Foxtail millet is a main millets crop of Northeastern region of India. The genotype-by-environment analyses assist in understanding the potential performance of the genotypes over environments. The present study aimed to investigate the 30 foxtail genotypes over the four different environments to identify the genotypes with stable yield in this region. The investigation was carried out during July 2022 to May 2023 for four different dates of sowing with fifty-five days interval. Two environments maintained under rainfed condition and the remaining two environments are maintained under irrigated condition with seven days interval. The experiment was conducted in randomized complete block design with three replications in all environments. Genotype-environment interactions significantly influenced grain yield across four environments, while replicates were non-significant. Pooled analysis revealed significant genotypic effects and seasonal impacts. The “discriminating power vs. representativeness” GGE biplot showed that E4 (showing dates in January 2ndfort night) is the most ideal test environment for foxtail millet elite line selection based on discriminative ability and representativeness. The accessions that performed best in each environment based on “which-won-where” polygon biplot, genotypes G19 (FOX 4392) and G27 (FOX 4420) showed superior and stable performance in E1, however G25 (FOX 4341) and G1 (ELS 20) excelled in E2, E3, and E4.Mean vs stability biplots exposed G1 (ELS 20) is stable and high yielding performance. Ranking genotypes GGE biplot identify the G25 (FOX 4341) is an ideal genotype due to its higher yield and stability compared to the other genotypes. Keywords: GGE Biplots, Foxtail millet, Tukey's Honest Significant Difference (HSD) Test

AMMI and GGE Biplot Analysis for Selection of Some High Yielding Terminal Heat Stress Tolerant Wheat (Triticum aestivum) Genotypes in Bangladesh

AMMI and GGE Biplot Analysis for Selection of Some High Yielding Terminal Heat Stress Tolerant Wheat (Triticum aestivum) Genotypes in Bangladesh

Identification of high-yielding and stable cultivars of wheat under different sowing dates: Comparison of AMMI and GGE-biplot analyses

Evaluating the adaptability and yield stability of wheat varieties across different sowing dates is crucial for the success of breeding programs. In this study, we used Additive Main Effects and Multiplicative Interactions (AMMI) and Genotype × Genotype–Environment (GGE) biplot analyses to evaluate the performance of 13 wheat varieties across eight sowing dates. The main objective was to compare varieties and sowing dates to identify the most stable varieties for cultivation. Both AMMI and GGE biplot analyses showed significant effects of genotype, environment and genotype-environment (GE) interaction on grain yield. The interaction patterns identified by AMMI and GGE-biplots categorized Mihan, Oroom and Pishgam as the most stable, high yielding and early maturing wheat varieties. Furthermore, while the GGE biplot method proved to be more efficient than the AMMI model for stability analysis, our results indicate considerable overlap between the results of these two approaches. These results provide a valuable basis for optimizing genetic improvement strategies in wheat breeding programs.

GGE biplot analysis for genotype × environment interactions affecting the seed shattering of Psathyrostachys juncea

The Psathyrostachys juncea (Fisch.) Nevski. Is an important forage grass in cold and arid regions. However, its high seed shattering (SS) traits affects seed yield. The SS phenomenon arises from the intricate interplay between its genotype and environment (GE). To gain a comprehensive understanding of SS variations in different Psathyrostachys juncea (Fisch.) Nevski materials under the influence of GE interactions, a two-year experiment was conducted on 300 individual plants at two locations. Utilizing the GGE biplot method, the current research aimed to assess the adaptability and stability of Psathyrostachys juncea (Fisch.) Nevski on SS trait, with a focus on identifying genotype materials conducive to breeding. The analysis of variance indicated that genotype, environment, year, and their interactions exhibited extremely significant effects on SS. Among these factors, the GE interaction showed as the primary contributor to variability in the SS trait of Psathyrostachys juncea (Fisch.) Nevski. The GGE biplot illustrated that Hohhot and Baotou emerged as representative locations with strong discriminative power for the materials. Genotypes 18 and 120 exhibited high SS rates in Hohhot, while 118 and 111 showed high SS rates in Baotou. Genotypes 132 and 177 were identified as ideal genotypes with both high and stable SS rates. These findings serve as valuable references for applications in grazing pastures and provide genotype materials for the genetic improvement and mechanistic analysis of SS traits in Psathyrostachys juncea (Fisch.) Nevski.

Multi-environment Analysis of Yield and Quality Traits in Sugarcane (Saccharum sp.) through AMMI and GGE Biplot Analysis

Multi-environment Analysis of Yield and Quality Traits in Sugarcane (Saccharum sp.) through AMMI and GGE Biplot Analysis

Stability assessment of selected clones of Neolamarckia cadamba under the multi environmental trials using AMMI and GGE biplot analysis

Stability assessment of selected clones of Neolamarckia cadamba under the multi environmental trials using AMMI and GGE biplot analysis

Genotype x environment interaction and yield stability of soybean (Glycine max l.) genotypes in multi-environment trials (METs) in Nigeria

Genotype × environment interaction (GEI) poses a critical challenge to plant breeders by complicating the identification of stable variety (ies) for performance across diverse environments. GGE biplot and AMMI analyses have been identified as the most effective and appropriate statistical techniques for identifying stable and high-performing genotypes across diverse environments. The objective of this study was to identify widely adapted and high-yielding soybean genotypes from Multi-Locational Trials (MLTs) using GGE and AMMI biplot analyses. Fifteen IITA-bred elite soybean lines and three standard checks were evaluated for stability of performance in a 3 × 6 alpha lattice design with three replications across seven locations in Nigeria. Significant (p < 0.001) differences were detected among genotypes, environments, and GEI for grain yield, which ranged between 979.8 kg ha−1 and 3645 kg ha−1 with a mean of 2324 kg ha−1. To assess the stability of genotypes, analyses were conducted using the general linear method, GGE, and the Additive Main Effect and Multiplicative Interaction (AMMI) approach, as well as WAAS and ASV rank indices. In the GGE biplot model, the first two principal components accounted for 67.4 % of the total variation, while in the AMMI model, the first two Interaction Principal Component Axes (IPCA1 and IPCA2) explained 73.20 % and 11.40 % of the variation attributed to genotype by environment interaction, respectively. GGE biplot identified G10 and G16 as the most stable and productive genotypes, while WAASB index revealed G16, G10, G9, G4 and G2 as the most adaptive, stable and productive genotypes across locations, and ASV identified G9, G13, G4, G14 and G10 as the most stable and productive.Consequently, genotypes G2, G4, G9, G10 and G16 displayed outstanding and stable grain yield performance across the test locations and are, therefore, recommended for release as new soybean varieties suitable for cultivation in the respective mega environment where they performed best. More importantly, the two genotypes are recommended for recycling as sources of high-yield and yield stability genes, and as parental lines for high-yield and stable performance for future breeding and genomic selection.

Multi‐Environment Analysis of Nutritional and Grain Quality Traits in Relation to Grain Yield Under Drought and Terminal Heat Stress in Bread Wheat and Durum Wheat

ABSTRACTHeat and drought are two important constraints to global wheat productivity; understanding the genotypic responses for quality parameters under harsh production conditions (drought and heat) is very important for developing nutrient‐dense wheat varieties. A set of 15 modern bread wheat (Triticum aestivum L. subsp. aestivum) and durum wheat (Triticum turgidum subsp. durum) cultivars were tested in nine environments, including three different production conditions (normal, heat and drought) during 2020–21. Genotype stability performance for yield, nutrition and quality parameters is assessed using multienvironment trials through AMMI and GGE Biplot analysis. We discovered intriguing stress dynamics in grain zinc content (Zn) and grain iron content (Fe). Under heat stress, zinc concentration increases but decreases under drought stress, while iron does the opposite. Selecting zinc, starch and kernel weight under terminal heat stress can boost yield. Protein content and yield are inversely related, making it difficult for breeders to optimise both traits. G × E interactions and stability indices across all environments have found genotypes with high‐yielding stable genotypes, G12 (MP1358) (42.09 ppm) and G5 (HI1544) (42.41 ppm) have high Fe content. G12 (MP1358) (14.98%) ranked highest in protein concentration. Meanwhile, for Zn content, G11 (MACS 4058) (45.23 ppm) and G15 (WH730) (42.44 ppm) were top performers across environments. G7 (HI 1636) and G12 (MP1358) stand out as a win‐win genotype for their high potential and stability in yield, protein, Zn and Fe content. Our study shows the complex relationships and possible suggestions for targeted breeding programmes under heat and drought stress conditions to improve wheat grain quality and micronutrient profiles without yield loss.

Genotype by environment interaction effect and fresh root yield stability of cassava genotypes under contrasting nitrogen regimes

Nitrogen (N) is an important nutrient element needed by cassava for optimum yield and it is a vital component of nucleotides (nucleic acids), enzymes, amino acids (proteins), chlorophyll molecules and hormones, among other essential compounds required for growth and development of cassava. Nitrogen stress is a major cassava production constraint, the study aimed to examine genotype by environment interaction (GEI) effects and fresh root yield stability of 203 diverse cassava clones to identify genotypes with stable performance under low and optimum nitrogen regimes across environments using AMMI and GGE biplot analysis. Experiments were conducted using an augmented block design with three replications for two years in three locations in Nigeria. There were significant differences (p < 0.001) in the genotype’s mean performances as well as significant differences (p < 0.001) in the environment’s mean performances for all the traits measured in both nitrogen regimes. The AMMI analysis of variance showed significant effects (p < 0.001) for genotypes, environments and the interactions for fresh root yield in both nitrogen regimes. The biplot analysis showed that for fresh root yield in the optimum nitrogen regime, the principal component accounted for 81.54% of the G + GE (Genotype plus and Genotype by Environment) variation. The G + GE for fresh root yield in the low nitrogen regime accounted for a total of 71.64% of the variation. Ten genotypes were identified as the best genotypes under the optimum nitrogen regime, while eleven genotypes were the best under the low nitrogen regime. Three genotypes under optimum nitrogen regimes were high-yielding. Still, they were unstable in their fresh root yield performance across the environments and can be recommended as specifically adapted to the environments they performed best. Three other genotypes were high-yielding genotypes under low nitrogen but were highly unstable in their fresh root yield mean performance across the environments. The environments Otobi_YR1, Igbariam_YR2, and Umudike_YR1 were identified as the most discriminatory among the test environments. The environments Umudike_YR2 and Igbariam_YR1 were identified as the most representative of the test environments and can represent a mega-environment. The best 21 genotypes that performed above the grand mean for fresh root yield in both nitrogen regimes can be further evaluated on the farmer’s field for possible advancement.

Analysis of genotype-by-environment interaction effect in barley genotypes using AMMI and GGE biplot methods

Genotype-by-environment interaction (GEI) analysis play a key role in any breeding program involving the development of new varieties for cultivation across various environments or in a specific region. The additive main effects and multiplicative interaction (AMMI) method and the GGE biplot are the two main statistical tools that have emerged to analyze GEI in multi-environment trials (METs). The main goal of the present study was to identify the best-performing and stable barley genotypes for the warm regions of Iran. For this purpose, 18 new advanced barley genotypes were investigated in five warm locations in Iran during two cropping seasons (2021–2023). In all experiments, test genotypes were evaluated in a randomized complete block design (RCBD) with three replications. Based on results, grain yield was significantly dependent on environments (E), genotypes (G), and GEI. The GEI effect was further divided into three principal component axes (IPCAs). The AMMI method identified genotypes G3, G9, G10, and G14 as ideal genotypes due to their low IPCA scores and high performances. In the GGE biplot analysis, the initial two PCAs accounted for 49.36 % of the total variation of grain yield, including both G and GEI effects. Based on averaged two-year data, genotypes G3, G4, G10, and G14 showed particular adaptability in the Zabol and Moghan regions. Moreover, the ranking of test environments showed good discriminatory and representative abilities for the Zabol and Moghan regions, so these environments constituted a mega-environment in Iran's warm climate. The genotype ranking indicated G3, G10 and G14 genotypes as the superior genotypes with the highest grain yield and stability in different test environments. Moreover, these results were confirmed by the results obtained by WAASB and WAASBY biplots. In conclusion, genotypes G3, G10 and G14 can be suggested for commercial usage and cultivation in various regions in Iran's warm climate.

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.

Evaluation of Recently Released Finger Millet Varieties for Their Adaptability in West Haraghe Zone, Eastern Ethiopia

Finger millet is a major grain crop in the west hararghe zone. However, due to major constraints like lack of improved varieties and drought, the productivity is by far lower than the genetic potential of a crop in the study areas. Thus, current study initiated to obtain high-yielding and stable varieties. The study was conducted in districts of Habro, Mechara, and Gamachis of the west hararghe zone, using eight improved and one standard check finger millet varieties at 2020 main cropping seasons. The experiment was laid down in a randomized completely block design with three replications. Analysis of variance for grain yield across locations showed significant differences at p&amp;lt; 0.05. Further analysis of AMMI indicated that environments, varieties, and their interaction effects were significantly different. Even if, tested materials showed a significantly different grain yield across locations nevertheless, the GGE bi-plot analyses implied relatively high yielding and consistent across environments for varieties Bako-09, Gudetu, and Addis-01. Therefore, these varieties of finger millet were recommended for further evaluation at the farmer’s field.