Genetic and Genotype × Environment Interaction Effects for Appearance Quality of Rice

This study was conducted to generate genetic information in rice varieties based on a complete diallel crosses over two years. The results indicated that genotype effect was significant for all traits. Genotype × environment interaction effects were significant only for cooked grain length （CGL） and cooked grain shape （CGSH）. General combining ability （GCA） and specific combining ability （SCA） effects were significant for entire traits, which indicated the important roles of both additive and non-additive gene actions. GCA x environment interaction effects were significant for CGL, CGSH and grain elongation index （GEI）. In the controlling of the inheritance of milled grain shape （GSH）, milled grain width （MGW）, GEI, milled grain length （MGL）, CGSH and cooked grain width （CGW）, the additive gene effects were more important than non-additive one. The average degree of dominance was within the range of partial dominance for all of the traits. The narrow-sense heritability was ranged from 0.65 （GSH） to 0.36 （CGL）. GCA effects were significant for all of the parents in milled grain length and it was significant for some of the parents in other traits. The crosses of Deilmani × IRFAON-215 exhibited significant SCA for GEI. The positive mean of heterosis was observed for CGW. The highest maximum values of heterosis were revealed in GEI, flowed by GSH, MGW and CGW. GCA and MPV were significantly and positively correlated together for all traits.

Evaluation of Genotype × Environment Interaction in Rice Based on AMMI Model in Iran

Identification of high-yielding stable promising rice lines and determination of suitable areas for rice lines would be done by additive main effects and multiplicative interaction(AMMI) model. Seven promising rice genotypes plus two check varieties Shiroudi and 843 were analyzed using a randomized complete block design with three replications in three consecutive years(2012, 2013 and 2014). Homogenous error variance was indicated in the nine environments for grain yield. The combined analysis of variance indicated significant effects of environment, genotype and genotype × environment(GE) interactions on grain yield. The significant effect of GE interaction reflected on the differential response of genotypes in various environments and demonstrated that GE interaction had remarkable effect on genotypic performance in different environments. The application of AMMI model for partitioning the GE interaction effects showed that only the first two terms of AMMI were significant based on Gollob's Ftest. The lowest AMMI-1 was observed for G7, G2 and G6. G7 and G6 had higher grain yield. According to the first eigenvalue, which benefits only the first interaction principal component scores, G1, G6, G2 and G9 were the most stable genotypes. The values of the sum of first two interaction principal component scores could be useful in identifying genotype stability, and G6, G5 and G2 were the most dynamic stable genotypes. AMMI stability value introduced G6 as the most stable one. According to AMMI biplot view, G6 was high yielding and highly stable genotype. In conclusion, this study revealed that GE interactions were an important source of rice yield variation, and its AMMI biplots were forceful for visualizing the response of genotypes to environments.

Genotype × Environment Interaction for Iron Concentration of Rice in Central Java of Indonesia

To explore the effect of genotype and genotype x environment interaction on Fe concentration in rice grains, Fe concentrations of 10 genotypes were analyzed across eight paddy field environments during 2007-2008 using the AMMI-biplot method. Experiments were conducted using a randomized completely block design with three replications in eight environments. Results indicated that environment （E）, genotype （G） and genotype x environment interaction （GE） significantly affected Fe concentration in rice grains. Environment explained 74.43 % of total （G＋E＋GE） variation, whereas G and GE captured 5.60% and 19.67%, respectively. Rice genotype Barumun was desirable in terms of the highest ability and stability for Fe concentration in rice grains. Environment in genotype Cilongok was the best representative of the overall environments and the most powerful to discriminate rice genotypes.

Effects of erect panicle genotype and environment interactions on rice yield and yield components

The dense and erect panicle (EP) genotype conferred by DEP1 has been widely used in the breeding of high-yield Chinese japonica rice varieties.However,the breeding value of the EP genotype has rarely been determined at the plant population level.Therefore,the effects of the interaction of EP genotype and the environment at different locations and times on rice yield and its various components were investigated in this study.Two sets of near-isogenic lines (NILs)of EP and non-EP (NEP) genotypes with Liaojing 5 (LG5) and Akitakomachi (AKI) backgrounds were grown in the field in 2016 and 2017 in Shenyang,China,and Kyoto,Japan.In 2018,these sets were grown only in Kyoto,Japan.The average yields of the EP and NEP genotypes were 6.67 and 6.13 t ha^(-1)for the AKI background,and 6.66 and 6.58 t ha^(-1)for the LG5 background,respectively.The EP genotype positively affected panicle number (PN) and grain number per square meter (GNPM),mostly resulting in a positive effect on harvest index (HI).In contrast,the EP genotype exerted a negative effect on thousand-grain weight (KGW).The ratio of the performance of the EP genotype relative to the NEP genotype in terms of yield and total biomass correlated positively with mean daily solar radiation during a 40-day period around heading.These results indicate that the effectiveness of the EP genotype depends on the availability of solar radiation,and the effect of this genotype is consistently positive for sink formation,conditional in terms of source capacity,and positive in a high-radiation environment.

Effects of virtual agents on interaction efficiency and environmental immersion in MR environments

Background Physical entity interactions in mixed reality(MR)environments aim to harness human capabilities in manipulating physical objects,thereby enhancing virtual environment(VEs)functionality.In MR,a common strategy is to use virtual agents as substitutes for physical entities,balancing interaction efficiency with environmental immersion.However,the impact of virtual agent size and form on interaction performance remains unclear.Methods Two experiments were conducted to explore how virtual agent size and form affect interaction performance,immersion,and preference in MR environments.The first experiment assessed five virtual agent sizes(25%,50%,75%,100%,and 125%of physical size).The second experiment tested four types of frames(no frame,consistent frame,half frame,and surrounding frame)across all agent sizes.Participants,utilizing a head mounted display,performed tasks involving moving cups,typing words,and using a mouse.They completed questionnaires assessing aspects such as the virtual environment effects,interaction effects,collision concerns,and preferences.Results Results from the first experiment revealed that agents matching physical object size produced the best overall performance.The second experiment demonstrated that consistent framing notably enhances interaction accuracy and speed but reduces immersion.To balance efficiency and immersion,frameless agents matching physical object sizes were deemed optimal.Conclusions Virtual agents matching physical entity sizes enhance user experience and interaction performance.Conversely,familiar frames from 2D interfaces detrimentally affect interaction and immersion in virtual spaces.This study provides valuable insights for the future development of MR systems.

A Study on the Effects of Different Interaction Combinations and Language Levels on Continuous Writing in Online Environments

This study was conducted with non-English sophomore students,aiming to explore the effects of different interaction combinations and language levels on continuous writing in an online environment,and compare the differences in lexical alignments and composition quality of learners with different interaction combinations and language levels in the same continuous writing task through experiments.The results show that the mean values of the word-phrase alignment of the paired group were higher than those of the individual group in different interaction combinations,and the two groups showed significant differences;in terms of composition quality,the individual group was better than the paired group,but there was no significant difference between the two groups in terms of task continuation.Secondly,the word-phrase alignment and composition scores of the different language-level groups were higher than those of the same language-level groups,and there was a significant difference between the two groups in terms of word-phrase alignments,but not in terms of composition scores.The results of this study can be useful and informative for second language teachers in future continuous teaching in online environments.

Analysis of Commuting Modal Shift in Consideration of Social Interaction of Consciousness for Environment

It is the matter for achievement of the low carbon transport system that the excessive use of private vehicles can be controlled appropriately.Not only improvement of service level of modes except private vehicle,but also consciousness for environmental problem of individual trip maker is important for eco-commuting promotion.On the other hand,consciousness for environment would be changed by influence of other person.Accordingly,it is aimed in the study that the structure of decision-making process for modal shift to the eco-commuting mode in the local city is described considering environmental consciousness and social interaction.For the purpose,the consciousness for the environment problem and the travel behavior of the commuter at the suburban area in the local city are investigated by the questionnaire survey.The covariance structure about the eco-consciousness is analyzed with the database of the questionnaire survey by structural equation modeling.As the result,it can be confirmed with the structural equation model that the individual environmental consciousness is strongly related with the intention of self-sacrifice and is influenced with the local interaction of the individual connections.On the other hand,the intention of modal shift for the commuting mode is analyzed with the database of the questionnaire survey.It can be found out that the environmental consciousness is not statistically significant for commuting mode choice with the present poor level of service of public transport.However,the intention of self-sacrifice for the prevention of the global warming is statistically confirmed as the factor of modal shift with the operation of eco-commuting bus service with the RP/SP integrated estimation method.As the result,the multi-agent simulation system with social interaction model for eco consciousness is developed to measure the effect of the eco-commuting promotion.For the purpose,the carbon dioxide emission is estimated based on traffic demand and road network condition in the traffic environment model.On the other hand,the relation between agents is defined based on the small world network.The proposed multi-agent simulation is applied to measure the effect of the eco-commuting promotion such as improvement of level of service on the public transport or education of eco-consciousness.The effect of the promotion plan can be observed with the proposed multi-agent system.Finally,it can be concluded that the proposed multi-agent simulation with social interaction for eco-consciousness is useful for planning of eco-commuting promotion.

Genotype-by-environment interaction for grain yield among novel cowpea(Vigna unguiculata L.) selections derived by gamma irradiation

This study determined the effects of genotype-by-environment(G × E) interaction and stability of yield among elite cowpea(Vigna unguiculata L.) selections derived by gamma irradiation. The study was conducted in Namibia at three selected sites: Bagani, Mannheim,and Omahenene, during 2014/2015 and 2015/2016. Thirty-four newly developed mutant genotypes and three local checks were evaluated using a randomized complete block design with three replications. Grain yield data were analyzed using the additive main effects and multiplicative interaction(AMMI) and the genotype main effect plus genotype-by-environment interaction(GGE) biplot methods. The AMMI and GGE biplot models explained 77.49% and 75.57% of total observed genotypic variation, respectively.Bagani and Omahenene were the environments best discriminating the test genotypes during 2014/2015 and 2015/2016, respectively. Four promising mutant genotypes: G9(Sh L3 P74), G10(Sh R3 P4), G12(Sh R9 P5), and G4(Sh L2 P4), showed wide adaptation and grain yields of 2.83, 2.06, 1.99, and 1.95 t ha^(-1), respectively. The novel mutant lines are useful genetic resources for production or future cowpea breeding programs in Namibia or similar environments.

Mapping epistasis and environment × QTX interaction based on four-omics genotypes for the detected QTX loci controlling complex traits in tobacco

Using newly developed methods and software, association mapping was conducted for chromium content and total sugar in tobacco leaf, based on four-omics datasets. Our objective was to collect data on genotype and phenotype for 60 leaf samples at four developmental stages, from three plant architectural positions and for three cultivars that were grown in two locations. Association mapping was conducted to detect genetic variants at quantitative trait SNP(QTS) loci, quantitative trait transcript(QTT) differences,quantitative trait protein(QTP) variability, and quantitative trait metabolite(QTM) changes,which can be summarized as QTX locus variation. The total heritabilities of the four-omics loci for both traits tested were 23.60% for epistasis and 15.26% for treatment interaction.Epistasis and environment × treatment interaction had important impacts on complex traits at all-omics levels. For decreasing chromium content and increasing total sugar in tobacco leaf, six methylated loci can be directly used for marker-assisted selection, and expression of ten QTTs, seven QTPs and six QTMs can be modified by selection or cultivation.

Interaction of genotype and environment effects on important traits of cassava(Manihot esculenta Crantz)

General and specific environmental adaptation of genotypes is the main goal of breeders.However, genotype-by-environment(G x E) interaction complicates the identification of genotypes for release. This study aimed at analyzing the effects of G x E interaction on the expression of important cassava traits using two multivariate analyses: additive main effects and multiplicative interaction(AMMI) and genotype stability index(GSI). Total carotene content(TCC), postharvest physiological deterioration(PPD), and reaction to viral diseases were significantly affected by G x E interaction effects. The low percent(%)variation due to genotype for cassava brown streak disease(GBSD) explained the influence of environment on CBSD expression. The % variation due to genotype for TCC was higher(96%) than variation due to environment(1.7%) and G x E interaction(2.4%) indicating a low interaction effect of environment on TCC accumulation. The % variation due to genotype was higher than % variation due to environment for all traits but CBSD root necrosis and CBSD on stems, indicating the influence of environment on the severity of the viral diseases. These findings indicate that screening for disease resistance requires multi-environment trials, whereas a single-environment trial suffices to screen for total carotene content.

Genotype and Environment Interactions of Yields and Yield Components of Tea (Camellia sinensis) Cultivars in Kenya

The tea crop provides income and employment to rural populations in many countries. In Kenya, tea, which is the leading export commodity crop, is grown in highlands east and west of the Rift Valley at altitudes ranging from 1300 m to 2700 m above mean sea level. Variable responses of tea genotypes to different environments have been demonstrated. This affects the growth, productivity, and quality of tea. However, most tea husbandry practices are uniform across tea growing regions leading to variations in yields and quality in the different environments. Understanding causes of variations in tea growth parameters and yields to varying environments is vital to optimizing husbandry practices for maximization of productivity. The responses in growth and yield parameters of clonal tea to locations of production and their contribution to yields were compared. A genotype × environment trial was conducted in three sites (Kangaita, Timbilil and Kipkebe). At each site, a trial comprising 20 cultivars was laid in a randomized complete design replicated 3 times. Yields, yield components and climatic data were collected then subjected to analysis of variance and regression analysis. There were significant (p ≤ 0.05) yield variations between clones and locations. Yields ranged from 5162 kg mt/ha on clone TRFK 303/577 at Kipkebe to 935 kg mt/ha/year on clone TRFK 7/3 in Kangaita, surpassing the maximum variation possible postulated in earlier studies. The responses of the tea yield components to weather parameters varied with genotypes and environments. Shoot growth rates in Timbilil (r = 0.476)) and shoot density (Kangaita (r = 0.652) significantly (p ≤ 0.05)) correlated with yields. Yield components and weather parameters contribution to the total yield also varied with locations. The variations demonstrated that not all yield components can be used universally as yield indicators for clonal selection in different locations. For optimal production, selected tea clones should therefore be tested before adoption for commercial planting in other locations.

Genotype by Environment Interaction Analysis for Tuber Yield of Potato (<i>Solanum tuberosum</i>L.) Using a GGE Biplot Method in Amhara Region, Ethiopia

Potato is one of the important crops grown in mid and high altitude areas of Ethiopia. Several potato genotypes have been introduced in different parts of this region. However, the stability and performance of these genotypes are not yet assessed. Therefore, the objective of this study was to determine the effect of genotype, environment and their interaction for tuber yield and identify stable potato genotypes. The study was conducted using eight potato genotypes in rainfed production season of years 2010 and 2011 at five potato growing locations in the region. Randomized complete block design with three replications was used for the experiment. Among the testing locations, the superior mean tuber yield (25.43 t/ha) was obtained at Adet while the inferior (13.89 t/ha) was at Injibara. Similarly, among the genotypes, CIP-396004.337 gave the highest mean tuber yield (25.66 t/ha), while CIP-395011.2 gave the lowest (17.78 t/ha). Combined ANOVA indicated that the main effects due to environments, genotypes and genotype by environment interaction were highly significant. The contribution of E, G and GEI to the total variation in tuber yield was about 47.11%, 8.83% and 44.07%, respectively. The GEI was further partitioned using GGE biplot model. The first two principal components obtained by singular value decomposition of the centered data of tuber yield explained 71.26% of the total variability caused by (G + GE). Out of these variations, PC1 and PC2 accounted 51.24% and 20.02% variability, respectively. GGE biplot view of this study identified Serinka as ideal testing location and CIP-396004.337 as ideal genotype for Amhara region in Ethiopia.

Mixed Model, AMMI and Eberhart-Russel Comparison via Simulation on Genotype ×Environment Interaction Study in Sugarcane

Brazil is the world leader in sugarcane production and the largest sugar exporter. Developing new varieties is one of the main factors that contribute to yield increase. In order to select the best genotypes, during the final selection stage, varieties are tested in different environments (locations and years), and breeders need to estimate the phenotypic performance for main traits such as tons of cane yield per hectare (TCH) considering the genotype × environment interaction (GEI) effect. Geneticists and biometricians have used different methods and there is no clear consensus of the best method. In this study, we present a comparison of three methods, viz. Eberhart-Russel (ER), additive main effects and multiplicative interaction (AMMI) and mixed model (REML/BLUP), in a simulation study performed in the R computing environment to verify the effectiveness of each method in detecting GEI, and assess the particularities of each method from a statistical standpoint. In total, 63 cases representing different conditions were simulated, generating more than 34 million data points for analysis by each of the three methods. The results show that each method detects GEI differently in a different way, and each has some limitations. All three methods detected GEI effectively, but the mixed model showed higher sensitivity. When applying the GEI analysis, firstly it is important to verify the assumptions inherent in each method and these limitations should be taken into account when choosing the method to be used.

Temporal and Spatial Factors Affecting the Nature of Genotype x Environment Interaction in Sugarcane (<i>Saccharum officinarum L.</i>) under Ethiopian Agro-Climatic Conditions: An Integrated Approach

Analytical approaches are important for identification the causes of Genotype x environment interaction (GEI) in multi-environment trials (MET). The objectives of this investigation were to explore the nature and causes of the GEI in sugarcane under Ethiopian agro ecological conditions. Data of Cane yield, recoverable sucrose% and sugar yield obtained from 13 test environments were subjected to Additive Main and Multiplicative Interaction (AMMI2) for empirical study. For environmental characterization, mean values of twenty seven temporal and spatial factors were subjected to Principal Component Analysis (PCA). Moreover, to identify the environmental factors affecting the patterns of GEI and its components, mean values of these factors were correlated with environment AMMI2 IPCA scores of the yield traits studied. Results of the present study indicated that the GEI accounted for larger variation than the genotypic effects which suggested its importance and complexity. The PCA bi-plot successfully identified the environmental factors that most discriminated the test environments (crop years and locations). The correlation analysis between the environmental factors and environmental AMMI2 IPCA scores adequately identified the environmental factors affecting GEI and its components. Soil organic matter% and soil clay%, pan evaporation at establishment, relative humidity at growth stage and harvesting month were the major factors that substantially affect the GEI interaction patterns in cane yield while maximum relative humidity recorded during the growing season, all temperature regimes recorded during the entire growing season and at different crop stages were the major factors that affect GEI in recoverable sucrose%. Moreover, the pattern of GEI in sugar yield was significantly affected by harvest age, percent of clay in soil, altitude, relative humidity at harvest and pan evaporation at establishment. For efficient environmental selection and networks, genotype evaluations and formulation of appropriate sugarcane breeding strategy, f METs should adopt the inclusion of these environmental factors identified as major factors influencing the patterns of GEI. Moreover, more information will be generated if other physiological and soil moisture stress indices are included in future GEI studies in sugarcane.

RGxE: An R Program for Genotype x Environment Interaction Analysis

Genotype x environmental interaction (GxE) can lead to differences in performance of genotypes over environments. GxE analysis can be used to analyze the stability of genotypes and the value of test locations. We developed an Rlanguage program (RGxE) that computes univariate stability statistics, descriptive statistics, pooled ANOVA, genotype F ratio across location and environment, cluster analysis for location, and location correlation with average location performance. Univariate stability statistics calculated are regression slope (bi), deviation from regression (S2d), Shukla’s variance (σi2), S square Wricke’s ecovalence (Wi), and Kang’s yield stability (YSi). RGxE is free and intended for use by scientists studying performance of polygenic or quantitative traits over multiple environments. In the present paper we provide the RGxE program and its components along with an example input data and outputs. Additionally, the RGxE program along with associated files is also available on GitHub at https://github.com/mahendra1/RGxE, http://cucurbitbreeding.com/todd-wehner/publications/software-sas-r-project/? and http://cuke.hort.ncsu.edu/cucurbit/wehner/software.html.

Genotype × environmental interactions for analyzing adaptability and stability in different clones of Dalbergia sissoo Roxb.

In total 36 superior clones of Dalbergia sissoo Roxb., screened from 300 selections conducted in natural and growing range of India and Nepal, were multiplied using single nodal cuttings and estab- lished to evaluate genotypexenvironmental interactions for adaptability and stability at the age of 30 months in three geographical locations in the state of Punjab, India. Clone 124 had maximum adaptability and stability （bi = 1.04） to perform exceedingly well over the locations. Clones 36 and 1 were stable with mean regression coefficient of 0.84 and 1.22, respectively. Nonetheless, clone 4 1 performed exceedingly well for all the characters to attain maximum population mean, and the perform- ance varied substantially across the locations. Therefore, clone 41 was considered as productive but non-adaptive clone. Though some of the clones were sensitive to sites, 14 clones for height, 16 for collar diameter, 12 for DBH and 7 for volume were relatively un-sensitive with higher regression coefficient. Nonetheless, clone 124 was the most Stable with average bi value of 1.04 and productive, which could play an important role in future breeding and commercial deployment of stable and produc- tive planting stock of Dalbergia sissoo.

Multi-Environment Evaluation and Genotype ×Environment Interaction Analysis of Sorghum [<i>Sorghum bicolor</i>(L.) Moench] Genotypes in Highland Areas of Ethiopia

Sorghum [Sorghum bicolor (L.) Moench] is a high-yielding, nutrient-use efficient, and drought tolerant crop that can be cultivated on over 80 per cent of the world’s agricultural land. However, a number of biotic and abiotic factors are limiting grain yield increase. Diseases (leaf and grain) are considered as one of the major biotic factors hindering sorghum productivity in the highland and intermediate altitude sorghum growing areas of Ethiopia. In addition, the yield performance of crop varieties is highly influenced by genotype × environment (G × E) interaction which is the major focus of researchers while generating improved varieties. In Ethiopia, high yielding and stable varieties that withstand biotic stress in the highland areas are limited. In line with this, the yield performance of 21 sorghum genotypes and one standard check were evaluated across 14 environments with the objectives of estimating magnitude G × E interaction for grain yield and to identify high yielder and stable genotypes across environments. The experiment was laid out using Randomized Complete Block Design with three replications in all environments. The combined analysis of variance across environments revealed highly significant differences among environments, genotypes and G × E interactions of grain yield suggesting further analysis of the G × E interaction. The results of the combined AMMI analysis of variance indicated that the total variation in grain yield was attributed to environments effects 71.21%, genotypes effects 4.52% and G × E interactions effects 24.27% indicating the major sources of variation. Genotypes 2006AN7010 and 2006AN7011 were high yielder and they were stable across environments and one variety has been released for commercial production and can be used as parental lines for genetic improvement in the sorghum improvement program. In general, this research study revealed the importance of evaluating sorghum genotypes for their yield and stability across diverse highland areas of Ethiopia before releasing for commercial production.

Genotype-by-Environment Interaction and Yield Stability Analysis in Finger Millet (<i>Elucine coracana</i>L. Gaertn) in Ethiopia

Finger millet is one of the most neglected and underutilized crops worldwide, yet an important food cereal for millions of poor farmers in Africa. An experiment was carried out to determine adaptation range of diverse set of finger millet accessions and identify superior types with excellent yield potential for use as cultivar or as germplasm source for future breeding endeavors. A total of 44 indigenous accessions selected in previous evaluations and two check varieties were tested in two sets (mixed and colored) each containing 22 entries in a total of 11 environments between 2004 and 2008 seasons. Data were collected on grain yield, days to flowering, and plant height. The result showed that 2.5%, 79.1% and 18.3% of the total sum of squares in the mixed set and 2.1%, 86.9% and 11.0% in the colored set was attributed to genotype, environment, and genotype × environment interaction (GEI) effects, respectively. Furthermore, 54.6% and 46.19% of the GEI sum of squares in the mixed and in the colored set, respectively, were contributed by the first two interaction principal component axes (IPCA1 and IPCA2). A white seed accession (Acc. 203572) from the mixed set and three other accessions (Acc. 229469, Acc. 203410 and Acc. 203539) from the colored set were most stable and also had above average mean grain yield across environment and thus are recommended for release as cultivars to improve finger millet production in these environments.

Additive Main Effect and Multiplicative Interaction Stability Analysis of Grain Yield Performance in Cowpea Genotypes across Locations

Crops are largely influenced by climatic conditions during the growing season and therefore, minor deviation from optimal conditions can seriously threaten yield. In view of this, knowledge on the effect of environmental factors on crop growth and development could reduce the possibilities of significant yield loss. There have been statistical methods which have been developed in respect to characterizing crops but the additive main effect and multiplicative interaction (AMMI) method integrates analysis of variance and principal components analysis into a unified approach. AMMI has been used in the analysis of G × E interaction with greater precision in many crops. The objective of this study was to assess the extent of genotype x environment interaction and to select the stable cowpea genotypes in Ghanaian environments over seasons using AMMI model. Eight genotypes of cowpea released by Crops Research Institute of Ghana over two decades were selected for evaluation in two locations and two seasons using RCBD with 3 replications in forest and transitional zones of Ghana. When the mean yields of various genotypes were subjected to the AMMI model, the results showed that, a highly significant (P 0.001) genotype by location and by year interaction effects for cowpea grain yield was recorded with 63.1% of the total variation attributable to environmental effects. The AMMI Bi-plot of PC1 and GGE Bi-plot gave 80.8% and 89.3% respectively. Genotype Asontem (G3) had the highest yield and was adapted to all the environments and seasons. Genotypes Asetenapa (G1) and Soronko (G6) were however not stable with consistently low yield across all the environments. It is recommended that farmers in Forest and transitional zones of Ghana should cultivate the highly stable cowpea genotypes in order to get stable yields across environments due to climatic change.