GE Interaction Research Articles

Bayesian approach: an alternative to the additive main effect and multiplicative interaction models for genotypes through environmental interactions

Genotypes of different genetic structures behave differently in various environmental conditions. Genotype-by-environment interaction (GEI) is referred to as differential responses of different genotypes across different environments; GEI is of great importance because of the higher performance of genotypes that GEI assesses. However, the presence of GEI makes analysis more complicated. To up-root these assessment complications, several methods have been proposed, such as Principal Component Analysis (PCA), Cluster Analysis, Additive Main effects, Multiplicative Interaction (AMMI) models, and Genotype plus Genotype by Environment interaction (GGE). These methods neither overcome the problem of overparameterization nor use the prior information. This study aims to use a technique to address these problems; for this purpose, wheat crop data comprised of 30 genotypes tested across 13 different locations of Punjab, Pakistan, for two consecutive years was used. The layout of the experiment was a Randomized Complete Block Design (RCBD). In this study, a comparison was made between Classical methods AMMI, GGE biplot, and Bayesian approach using Von-Mises Fisher distribution as prior. Classical methods showed that genotype V-11098 was the most desirable based on stability and high-yield performance. The Bayesian approach was used for GEI because it simplifies statistical interpretation by relaxing some constraints. It uses the prior information and provides solutions using the Markov Chain Monte Carlo (MCMC) algorithm. Bayesian strategy for analysis of GEI was used to assess the general, specific performance of genotypes and risk related to genotype. Analysis revealed that bi-linear terms 𝜇ଶହ,ଵ for genotype NS-10 genotype and 𝜈ଵଷ,ଵ for environment S13 (Piplan-14) were found significant, indicating that these affect interaction. It was observed that the Bayesian approach could nicely explore GE interaction.

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.

Genotype by environment interaction across water regimes in relation to cropping season response of quinoa (Chenopodium quinoa).

Genotype × environment (GxE) interaction effects are one of the major challenges in identifying cultivars with stable performance across agri-environments. In this study we analysed GE interactions to identify quinoa (Chenopodium quinoa) cultivars with high and stable yields under different soil moisture regimes, representing control conditions, waterlogging and drought. Waterlogging and drought treatments were artificially induced using normoxia, a combination of hypoxia-normoxia, and 10% PEG (Polyethylene glycol) under hydroponic growth conditions, respectively. Both waterlogging and drought conditions significantly reduced the plant height (PH), number of leaves (NoL) and number of branches (NoB), stem diameter (SD), leaf area (LA) and dry weight (DW) of quinoa genotypes. The genotype, water regime, and genotype by water regime effects all significantly affected the measured quinoa traits. Based on the additive main effects and multiplicative interaction (AMMI) model for DW, the genotypes G18, Puno, Q4, 2-Want, Puno, Real1 x Ruy937 and Titicaca were found to exhibit tolerance and were stable across water regimes. A second-stage evaluation was conducted to test genotype × environment interaction effects in crop production field trials, selecting two contrasting seasons based on soil moisture conditions involving a diverse set of genotypes (58 varieties in total). Our results demonstrate significant variations in both growth and yield among the quinoa genotypes across the cropping seasons. The GGE analysis for grain yield indicate that field conditions matched to G × E under hydroponic experimental conditions and the cultivars G18, Q1, Q4, NL-3, G28, 42-Test, Atlas and 59-ALC were classified within a range of high productivity. Our findings provide a basis for understanding the mechanisms of wide adaptation, while identifying germplasm that enhances the water stress tolerance of quinoa cultivars at early growth stages.

Genotype by environment interaction and stability study in bread wheat (Triticum aestivum L.) genotypes in Guji Zone, Southern Ethiopia

Genotype by environment interaction and stability study in bread wheat (Triticum aestivum L.) genotypes in Guji Zone, Southern Ethiopia

Genotype × Environment Interaction for Appraisal of Iron and Zinc Rich Stable Genotypes in Lentil (Lens culinaris Medik.)

Background: Micronutrient malnutrition is a severe peril for wellbeing of humankind, which can be holistically addressed through genetic biofortification. Lentil, proclaim as poor man’s meat can hold a great promise in global biofortification programme. The present study was designed to appraise genetic variability for Fe and Zn content and elucidate the role of Genotype × Environment interaction for delimitation of micronutrient enriched stable lentil genotypes integrating HA-GGE biplot with REML/BLUP. Methods: Grain Fe and Zn content of 44 lentil genotypes grown at three different locations of West Bengal during two consecutive years were estimated for deciphering the G × E interaction combining HA-GGE and REML/BLUP. Result: Results revealed substantial genetic variability for Fe (48.07 to 107.45 mg kg-1) and Zn (38.72 to 60.07 mg kg-1) in 44 lentil genotypes with significant influence of environment and GE interaction. The present study precisely detected ILL-10123 and VL-156 as the ‘ideal’ genotypes for Fe and Zn content, respectively in addition to non-redundant testing location. Identified genotypes and testing location aid in global biofortification programme for upscaling micronutrient concentration in lentil.

Dissection of genetic architecture of nine hazardous component traits of mainstream smoke in tobacco (Nicotiana tabacum L.).

Tobacco (Nicotiana tabacum L.) use is the leading cause of preventable death, due to deleterious chemical components and smoke from tobacco products, and therefore reducing harmful chemical components in tobacco is one of the crucial tobacco breeding targets. However, due to complexity of tobacco smoke and unavailability of high-density genetic maps, the genetic architecture of representative hazardous smoke has not been fully dissected. The present study aimed to explore the genetic architecture of nine hazardous component traits of mainstream smoke through QTL mapping using 271 recombinant inbred lines (RILs) derived from K326 and Y3 in multiple environments. The analysis of genotype and genotype by environment interaction (GE) revealed substantially greater heritability over 95% contributed mostly by GE interaction effects. We also observed strong genetic correlations among most studied hazardous smoke traits, with the highest correlation coefficient of 0.84 between carbon monoxide and crotonaldehyde. Based on a published high-density genetic map, a total of 19 novel QTLs were detected for eight traits using a full QTL model, of which 17 QTLs showed significant additive effects, six showed significant additive-by-environment interaction effects, and one pair showed significant epistasis-by-environment interaction effect. Bioinformatics analysis of sequence in QTL region predicted six genes as candidates for four traits, of which Nt21g04598.1, Nt21g04600.1, and Nt21g04601.1 had pleiotropic effects on PHE and TAR.

Results from Exploratory Work in Li-Rich Regions of the AE-Li-Ge Systems (AE = Ca, Sr, Ba)

The compounds AELi2Ge (AE = Ca, Sr and Ba) were synthesized, and their structures were determined as a part of the exploratory work in the Li-rich regions of the respective ternary systems. The three compounds are isostructural, and their crystal structure is analogous with the orthorhombic structure of BaLi2Si and KLi2As (space group Pmmn). The atomic arrangement can be viewed as an intergrowth of corrugated AEGe layers, alternated with slabs of Li atoms, suggestive of the possible application of these phases as electrode materials for lithium-ion batteries. Both experimental electronic density and calculated electronic structure suggest the existence of Li–Li and Li–Ge interactions with largely covalent character. Despite that, the valence electrons can be partitioned as (AE2+)(Li+)2(Ge4–), i.e., the title compounds can be viewed as valence-precise Zintl phases. The band structure calculations for BaLi2Ge show that a bona fide energy gap in the band structure does not exist and that the expected poor metallic behavior is originated from the AEGe sub-lattice and related to hybridization of Ba5d and Ge3p states in the valence band in proximity of the Fermi level. In addition, electrochemical measurements indicate that Li atoms can be intercalated into CaGe with a maximum capacity of 446 mAh/g, close to the theoretical value of 480 mAh/g of CaLi2Ge, which reveals the possibility of this Li-rich compound to be used as an electrode in Li-ion batteries.

Importance of mega-environments in evaluation and identification of climate resilient maize hybrids (Zea mays L.)

Multi-location experiments on maize were conducted from 2016 to 2019 at ten locations distributed across two agro-climatic zones (ACZ) i.e., ACZ-3 and ACZ-8 of Karnataka, India. Individual analysis of variance for each location-year combination showed significant differences among the hybrids; similarly, combined analysis showed a higher proportion of GE interaction variance than due to genotype. Mega-environments were identified using biplot approaches such as AMMI, GGE, and WAASB methodologies for the years 2016 to 2019. The BLUP method revealed a high correlation between grain yield and stability indices ranging from 0.67 to 1.0. Considering all three methods together, the three location pairs Arabhavi-Belavatagi, Bailhongal-Belavatagi, and Hagari-Sirguppa had three occurrences in the same mega-environment with a value of 0.67, and these location combinations consistently produced winning genotypes. Among the common winning genotypes identified, it was G7 during 2016 and 2017 and G10 during 2018 and 2019, based on WAASBY. The likelihood of Arabhavi-Nippani, Hagari-Mudhol, and Dharwad-Hagari occurring in the same mega-environment is minimal because they did not share the same winning genotype, with the exception of a small number of events. Despite being in the same agro-climatic zone, Arabhavi, Hagari, and Mudhol rarely had a winning genotype in common. An agro-climatic zone is grouped based on climatic and soil conditions which doesn’t consider GE interaction of cultivars thus, releasing the cultivars for commercial cultivation considering mega environments pattern would enhance the yield for the given target region.

Genetic gain and selection of stable genotypes in high zinc rice using AMMI and BLUP based stability methods

Rice is the staple food of almost half of the world’s population, impacting nutrition especially in children, pregnant women, and nursing mothers. Because the traits were quantitatively inherited, they are affected by changes in location and year. A RBD with three replications was used to identify superior and stable high-zinc rice genotypes in Uttar Pradesh, India. Grain zinc content (GZC) is negatively correlated with grain yield using genetic association study. There was a significant G × E interaction (GEI) and V16 and V21 for GYP and V9, V2 and V10 for GZC were identified as stable based on the AMMI model and bi-plot. V11, V5, V21 for grain yield per plant (GYP) and for GZC, V14, and V10 are found to be stable and common in all AMMI stability parameters. V6, V13 and V5 for GYP and V10, V8 and V2 for GZC were identified as stable based on the mean vs. WAASB bi-plot. V21 for GYP and V4 for GZC was the highest yielder and widely adaptable based on WAASBY scores. V13 for GYP and V1 for GZC were all-time winners. V13 and V1 have the highest predicted mean for GYP and GZC, respectively, based on BLUP. V6, V21and V13 were identified as stable and selected based on the multi-trait stability index (MTSI). These selected genotypes selected through BLUP-based stability methods, MTSI, and strength and weakness plots make it easier to evaluate and select genotypes for varietal recommendations and future Zn-fortified rice breeding studies.
 Keywords: GEI, High Zinc Rice, MTSI, Stable genotype

Multi environmental evaluation for selection of stable and high yielding sugarcane (Saccharum officinarum L.) clones based on AMMI and GGE biplot models

The present study was aimed to evaluate the G x E (genotype x environment) interaction using AMMI and GGE biplot analysis to identify sugarcane (Saccharum officinarum L.) genotypes that combine high yield and stability in multi-location trials. Twelve advanced clones generated in the sugarcane breeding programs along with three standards (Co 86032, CoC 671 and Co 09004) were evaluated for cane and sugar yield using randomized complete block design with three replicates over two plant crops and one ratoon during 2019 to 2021 across two years and locations. Data was collected on yield traits and sugar quality characters. Stability parameters were determined. The data was also investigated using GGE biplot and the AMMI models. The results of combined analyses of variance exhibited significant differences among genotypes at the three test environments and when locations were combined. The GE interaction was also significant for all the traits, indicating inconsistency of performance of the genotypes over the locations and seasons. Pooled data analysis of 15 genotypes indicated the superiority of Co 15017 and CoN 15071 for CCS yield across six environments. Two clones, Co 15017 and CoSnk 15102 recorded higher mean cane yield as compared with three standards across the six experimental conditions. Multivariate (AMMI and GGE biplot) statistical analysis indicated that CoSnk 15102 exhibited higher and more stable commercial cane sugar yield and cane yield per hectare, indicating this genotype’s suitability across the test locations. The results of such multi-environmental trials with the elucidation of GE interaction using AMMI and GGE are of great significance in guiding the selection and recommendation of stable and superior varieties in sugarcane production zones.

Stability and Diversity of Elite Lines of Vegetable Cowpea (Vigna unguiculata ssp. unguiculata L.)

Background: The present study was conducted to study the stability and diversity of twenty elite breeding lines of vegetable cowpea developed at ICAR-Indian Institute of Vegetable Research, Varanasi through AMMI analysis and cluster analysis respectively. Methods: All the 20 elite lines were laid out in randomized block design with three replications during three years of sowing viz., Kharif-2018, Kharif- 2019 and Kharif-2021. Each year was considered as onelocation and the dependant variable pod yield per plant was subjected to AMMI analysis for GE interaction study. The mean of all the yield and yield attributes was subjected to cluster analysis for genetic divergence study. Result: AMMI ANOVA showed significant variation for environments (E), genotypes (G) and G×E interactions. The graphical representation of principal component 1 (PC1) vs yield (Y) showed that the genotypes 12,15,11,13,18 were most stable andthe PC1 vs PC2 graph showed that the genotypes 12,11,13,18 were most stable as they were positioned towards the origin. The cluster analysis grouped all the twenty genotypes into four clusters with two genotypes (7,20) in first cluster, five genotypes (17,14,16,8,19) in second cluster, eight genotypes (9,6,1,13,11,12,2,15) in third cluster and five genotypes (5,3,18,4,10) in fourth cluster.

Bonding Analysis of the Ge‐Ge Bonds in the Octagermacubane Ge8(Sit‐butyl2methyl)6

AbstractQuantum chemical calculations have been carried out at the BP86/def2‐SVP level on Ge8(Sit‐butyl2methyl)6(1) and the bonding situation has been analyzed with a variety of methods. The calculated equilibrium geometry of1is in good agreement with the reported x‐ray structure analysis. The D3 correction for dispersion interactions as a sum of pairwise attractions leads to an overestimate of the effect of dispersion forces. Calculations at BP86‐D3(BJ)/def2‐SVP give shorter bonds for Ge(I)−Ge(I) than for Ge(0)−Ge(I), which is in contrast to the experimental values and the BP86/def2‐SVP results. The NBO analysis suggests that the best Lewis structure of1has lone‐pair orbitals at the Ge(0) atoms with occupation numbers of 1.70 e. A lone‐pair character at Ge(0) albeit with less weight is also suggested by the shape of the HOMO, which is an antibonding orbital between the Ge(0) atoms with small contributions from the Ge(I) atoms. The LUMO of1is the corresponding bonding combination of the Ge(0) AOs, which can be explained with the reluctance of the heavier main‐group atoms to s/p hybridization of the valence orbitals. The calculated bond order values suggest significant direct Ge(0)−Ge(0) interactions. This is supported by the shape of the HOMO and by the results of EDA‐NOCV calculations. The deformation densities and the orbitals associated with the pairwise orbital interaction show that there is a direct charge flow between the Ge(0) atoms of the two fragments, but it is not completely separated from the Ge(0)−Ge(I) and Ge(I)−Ge(I) bond formation. The QTAIM calculations suggest that1has a cubic structure with a cage critical point but not a bond critical point for the Ge(0)−Ge(0) interactions. The dispersion interactions of the large substituents in1have a significant influence on the stability of the compound.

Integrating BLUP, AMMI, and GGE Models to Explore GE Interactions for Adaptability and Stability of Winter Lentils (Lens culinaris Medik.).

Lentil yield is a complicated quantitative trait; it is significantly influenced by the environment. It is crucial for improving human health and nutritional security in the country as well as for a sustainable agricultural system. The study was laid out to determine the stable genotype through the collaboration of G × E by AMMI and GGE biplot and to identify the superior genotypes using 33 parametric and non-parametric stability statistics of 10 genotypes across four different conditions. The total G × E effect was divided into two primary components by the AMMI model. For days to flowering, days to maturity, plant height, pods per plant, and hundred seed weight, IPCA1 was significant and accounted for 83%, 75%, 100%, and 62%, respectively. Both IPCA1 and IPCA2 were non-significant for yield per plant and accounted for 62% of the overall G × E interaction. An estimated set of eight stability parameters showed strong positive correlations with mean seed yield, and these measurements can be utilized to choose stable genotypes. The productivity of lentils has varied greatly in the environment, ranging from 786 kg per ha in the MYM environment to 1658 kg per ha in the ISD environment, according to the AMMI biplot. Three genotypes (G8, G7, and G2) were shown to be the most stable based on non-parametric stability scores for grain yield. G8, G7, G2, and G5 were determined as the top lentil genotypes based on grain production using numerical stability metrics such as Francis's coefficient of variation, Shukla stability value (σi2), and Wrick's ecovalence (Wi). Genotypes G7, G10, and G4 were the most stable with the highest yield, according to BLUP-based simultaneous selection stability characteristics. The findings of graphic stability methods such as AMMI and GGE for identifying the high-yielding and stable lentil genotypes were very similar. While the GGE biplot indicated G2, G10, and G7 as the most stable and high-producing genotypes, AMMI analysis identified G2, G9, G10, and G7. These selected genotypes would be used to release a new variety. Considering all the stability models, such as Eberhart and Russell's regression and deviation from regression, additive main effects, multiplicative interactions (AMMI) analysis, and GGE, the genotypes G2, G9, and G7 could be used as well-adapted genotypes with moderate grain yield in all tested environments.

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

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

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.

Analysis of the effect of GE interaction on the grain yield and its related traits in rain-fed Algerian durum wheat (Triticum turgidum L. var. durum) grown in contrasting environments

Selection for higher yield and wider adaptability are the most important tasks in crop breeding programs. (GE) interactions are commonly seen as one of the major barriers in plant breeding. The present work aims to assess the effects of GE interaction for the grain yield of 14 durum wheat varieties grown in rain-fed environments during 2014-2017 cropping seasons, and to analyze the relationships between 15 traits intra and inter-environments. Field trials were carried out in a randomized complete block design with four replicates. Grain yield data were analyzed using AMMI model. The combined analysis of variance showed that the effects of genotype, environment and their interactions were highly significant on the grain yield. Using CV and Pi index, GTA dur was the high yielding (32.5 q ha-1) and most stable variety across all the environments. Based on the inter-character correlation, the indirect selection of grain yield via the number of grains per m2 would be effective. Moreover, the inter-environment correlation of the studied variables confirms there was GE interaction and suggests that the best varieties should be chosen according to their specific adaptation. Cold environments differed from warm and moderate ones in the ranking of varieties. Indeed, Sétif site offers better possibilities for producing the Ofanto variety (39.9 q ha-1). Whereas, GTA dur and Simeto (30.9 q ha-1 and 29.7 q ha-1, respectively) prove to be the most efficient in terms of grain yield at Oued Smar and Khemis Miliana sites together.

Graphical analysis of forage yield stability under high and low potential circumstances in 16 grass pea (Lathyrus sativus L.) genotype

Introducing grass pea genotypes with wide adaptability across diverse environments is important. Dry forage yield of 16 grass pea genotypes, tested in a RCBD design with three replicates across 4 locations over 3 seasons in Iran. The GGE biplot method based on SREG model facilitated a visual evaluation of the best genotypes. The first two principal components accounted for 77 % of the GE interaction and revealed six winning genotypes and four mega-environments. The average location coordinate (ALC) was used to examine both yield performance and stability and indicated IFLA-1913, IFLA-1961, IFLA-1812, and IFLA-2025 were the best genotypes. Based on the ideal-genotype approach, genotype G5 was better than all other genotypes and showed both high forage yield and stability across locations. According to G + GE sources of variations, the genotypes (IFLA-1913, IFLA-1961, IFLA-1812, and IFLA-2025) were the most suitable varieties for the grass pea-producing regions in semi-arid and rain-fed conditions. An ideal location should be both discriminating of the genotypes and representative of the average location, but we could not find such location in this research. Results confirmed that G5 (IFLA-1961) has high stability and high yield performance (4.92 t ha-1), and could introduce as favorable genotype for commercial variety release.

AMMI and GGE Biplot Analyses for Mega Environment Identification and Selection of Some High-Yielding Cassava Genotypes for Multiple Environments

Cassava (Manihot esculenta Crantz) is a staple food and generates income for smallholder farmers in southern Ethiopia. The performance of cassava genotypes varies in different growing environments; thus, the evaluation of genotypes tested in various environments plays an essential role in developing strategies to delineate environments, explore unstable genotypes in target environments, and identify stable genotypes for multiple environments. In this regard, there needs to be more information on the identification of mega-environments and stable genotypes with high yields for wide adaptation. Thus, this study aimed to identify mega-environment and high-yielding cassava genotypes for multiple environments using AMMI and GGE biplots. A total of 25 genotypes were evaluated in six environments using a RCBD during the 2020–2021 cropping season. The AMMI analysis of variances revealed that environments, genotypes, and genotype-environment interaction had a significant ( P ≤ 0.001 ) influence on cassava fresh storage root yield (t·ha−1), showing genetic variability among genotypes by changing environments. The genotype-by-environment interaction showed a 61.36% contribution to the total treatment SS variation, while the environment and genotype effects explained 28.16% and 10.48% of the total treatment SS, respectively. IPCA1 and IPCA2 accounted for 33.42% and 23.5% of the GE interactions SS, respectively. The GGE biplot showed that the six environments used in this study were delineated into three mega-environments, namely, the first (Tarcha and Disa), the second (Wara and Areka), and the third (Jimma and Bonbe). Those mega-environments could be helpful for genotype evaluation and effective breeding. The GGE biplot indicated that the vertex genotypes were G16, G17, and G25. They are regarded as specifically adapted genotypes since they are more responsive to environmental change. The GGE biplot also revealed that Tarcha was ideal, having the most discriminating and representative environment, while G10 was the ideal and the overall winning genotype for the current study. Moreover, the genotypes G10 and G14 were identified as being the most stable, with a higher fresh storage root yield than the grand mean. Thus, G10 and G14 were selected as superior genotypes that could be promoted to advanced yield trials to develop stable cultivars with better storage root yield of cassava.

Environment- and Genotype-Dependent Stability in the Common Wheat Grain Quality (Triticum aestivum L.)

The study was conducted to evaluate the stability of common wheat varieties in four locations with proven different environmental conditions. Three indexes of grain quality were studied: wet gluten content, (WGC); gluten index of grain (GI) and grain sedimentation value (Zeleny). The stability of varieties has been evaluated by many parameters that reflect different aspects of the complete picture for it. The indexes studied are strongly influenced by environmental conditions. The most genetically stable among them is the gluten index (GI), where the genotype has a decisive role of about 70% of the variation, and the most unstable is the wet gluten content (WGC), with only 17% of the effect. As a result of reliable GE, the ranking of the varieties according to the performance of each of the indexes is different in the individual locations. The ranking of varieties in terms of stability according to the ranks of each of the parameters is very different. Even a visual representation of the results, which clears the picture to the maximum extent, shows a different set of stable varieties in each of the quality indexes. Only a few of the varieties (G2, G6, G9, G13, G18, G20, G22) have a good balance between the size and stability of all quality parameters, with a moderate compromise with the grain yield level. The assessment of the stability of the variety in terms of quality can be made according to any of the indexes used. The stability of the variety depends to a large extent on the effect of the environment, which must be considered when selecting a specific index for assessment. The most suitable for this purpose is the gluten index (GI), where the influence of genotype is the strongest, with a significant GE interaction accounting for 25% of all variation. The stability of the variety does not depend on the magnitude of the quality indexes. Stable can be both quality (G2, G6) and varieties with very low grain quality (G18, G20, G22). Stability of quality, at high levels of indexes, is associated with low grain yield and vice versa. From this point of view, combining high yield stability and grain quality at the highest possible levels is a very rare exception (G2, G9).

Yield Stability of Sixteen Rice Genotypes in White Nile Sate, Sudan

The objective of the present investigation was to analyze thepattern of Genotype x Environment (G x E) interaction for grain yield ofsixteen rice genotypes using Additive Main effects And MultiplicativeInteraction (AMMI) model. Genotypes were grown at two locations (Ed-duim and Kosti, White Nile State, Sudan) for two years. Main effects dueto environment (E), genotypes (G) and GxE interaction (GEI) weresignificant (p<0.01), with highest variation (63.3%) accounted for byenvironmental effects. The first Interaction Principal Component Axes(IPCA 1) was significant (p<0.01) and contributed 72.4% of the total GEI.The biplot was identified genotypes and testing environments thatexhibited major sources of GE interaction as well as those that werestable. YUNLU 33 was identified as the best genotype (higher yieldingand stable)