Environment Interaction Analysis Research Articles

Genotype by environment interaction and stability analysis for harvest date in sugar beet cultivars

This research assessed the quantitative and qualitative reactions of commercially grown sugar beets to four different harvest dates and their yield stability. The study followed a split-plot design within a randomized complete block design over 3 years. The main plot involved 10 sugar beet cultivars, while the subplot involved four harvest dates: August 13 (HD1), September 7 (HD2), October 3 (HD3), and November 12 (HD4). The study found that environmental conditions, genotypes, and harvest dates significantly affected various traits of sugar beet. Yearly environmental variations and their interactions with genotypes and harvest dates had substantial impacts on all measured traits at the 1% probability level. Additive main effect and multiplicative interaction analysis based on white sugar yield indicated that genotype and environment's additive effects, as well as the genotype–environment interaction, were significant at 1% probability level. Shokoufa and Arya, which exhibit high white sugar yield (WSY) and low first interaction principal component (IPC1) values, are identified as desirable due to their stability across different environments. Among the harvest dates in different years, the fourth and third dates showed a higher yield than the total average. Perfekta and Ekbatan exhibited high specific adaptability. According to the multi-trait stability index, Arta, Arya and Sina were recognized as stable and superior across all measured traits.

Application of AMMI and GGE biplot for genotype by environment interaction and yield stability analysis in potato genotypes grown in Dawuro zone, Ethiopia

The performance of most crop genotypes is greatly influenced by the interactions between genotype and environment. Providing information on well-adapted and high-yielding potato genotypes in a given environment is paramount for small-holder farmers to enhance their productivity. The study aimed to understand GEI effect on tuber yield, and identify and select widely or specifically adapted high-yielding potato genotypes for production. Eleven genotypes were evaluated using a randomized complete block design with three replications. The results of the AMMI analysis of variance showed that tuber yield significantly (p ≤ 0.05) influenced genotype-environment interaction. This reveals that genotypes exhibit varying mean performance in tuber yield across different environments. The sum squared results showed that genotype (62.40 %) and environment (26.73 %) were the main contributors to tuber yield variation, while the genotype-environment interaction effect (10.87 %) contributed least to then tuber yield total variation. The AMMI, GGE biplot, and GSI analysis revealed Gudanie and Gorebella as superior genotypes in tuber yield, demonstrating high mean performance across tested environments. Thus, Gudanie and Gorebella have been chosen as the most broadly adaptable genotypes for production in all potato-growing agroecologies in the Dawuro zone. The GGE and AMMI biplot provided the genotype-by-environment interaction association among environments and genotypes.

Comprehensive evaluation of potato cooking quality of advanced lines based on the genotype-by-storage environment interaction

Potato cooking quality directly affects consumers' willingness to purchase potato products. The influence of storage environment on cooking quality is often ignored. In this study, the cooking qualities of ten potato lines stored at 25 °C and 4 °C for 0, 30, 60 and 90 days were investigated, respectively. The ratio of chlorogenic acid to citric acid content of raw potatoes stored at 25 °C remained stable, whereas significantly increased after 60 days of 4 °C storage. The lightness of cooked potatoes was significantly decreased after 90 days of 4 °C storage, whereas the ACD (after-cooking darkening) degree was generally increased at 4 °C storage. The hardness of cooked potatoes was generally decreased at 25 °C storage, whereas significantly decreased until 60 days of 4 °C storage. The sensory evaluation score of cooked potatoes was still higher after 90 days of 4 °C storage compared to 25 °C. Combined with the genotype-by-storage environment interaction and grey correlation analysis, potato line G1 was screened as an ideal material with excellent and stable performance in cooking quality under different storage conditions. This study established a comprehensive evaluation method to evaluate the impact of genotype-by-storage environment interaction on potato cooking quality, which would help develop strategies for breeding potato cooking processing-type cultivars.

Dissection of genotype x environment interaction and yield stability analysis in greengram (Vigna radiata L.) using AMMI and GGE biplot

Nineteen green gram [Vigna radiata (L.) Wilczek] genotypes were evaluated for yield stability under field conditions over three seasons. The AMMI analysis revealed that genotype TM 11007 (G3) had wider adaptation for grain yield, performing well across a wide range of environments. The genotypes TM 11038 (G14) in E1 and G3 in E2 and E3 were found to be highly stable and gave the highest yield in their respective mega-environments, while TM 11034 (G11) and TM 11042 (G17) located in the vertex performed better in all the three environments. E2 was the most discriminating test environment for selecting widely adapted genotypes for yield per se, while E3 was the most representative testing environment to provide unbiased information about the performance of genotypes. Based on the average versus stability graph, the genotype TM 11042 stands out because of simultaneous high yields and high stability.

Genotype by environment interaction and stability analysis of three agronomic traits in Kersting's groundnut (Macrotyloma geocarpum) using factor analytic modeling and environmental covariates

AbstractUnderstanding genotype by environment interaction (GEI) represents a challenge in Kersting's groundnut [Macrotyloma geocarpum (Harms) Maréchal and Baudet] breeding for selecting high‐performing and stable lines across environments. Here, we investigated GEI and stability in Kersting's groundnut using factor analytic (FA) based linear mixed models and environmental covariates. A total of 375 accessions were evaluated across 3 years (2017, 2018, and 2019) and two locations (Sékou and Savè) in Benin, generating five environments (E1, E2, E3, E4, and E5). The traits measured included days to 50% flowering (DFF), grain yield (YLD), and 100‐seed weight (HSW). The study generated multi‐environment values for grain yield and its components in Kersting's groundnut. The genetic correlations between pairs of environments ranged from −0.71 to 0.99. The genetic correlations between YLD and HSW indicated positive and moderate to high correlations in all environments. The FA analysis revealed that FA2 structure accounted for 93.9% of the genetic variability in DFF with factor 1 accounting for more than 90% of the environments variations. Two factors explained 87% of the genetic variance in grain yield, and 70% of the environments variability were clustered by factor 1. For HSW, two factors explained 85% of the genetic variance of the environments, and factor 1 accounted for 72.7%. Combining environmental covariates to FA models revealed that precipitation, temperature, and growth cycle duration were highly correlated to the environmental loadings of factor 1. Relative humidity and solar radiation showed moderate to high correlations with factor 2 loadings. Those covariates explained the high GEI among environments clustered by a given factor. Precipitations and temperatures affected the variations in grain yield. Finally, based on latent regression analysis, the accessions AF202, AF221, AF223, AF225, and AF256 were identified as accessions combining best performance for grain yield, early flowering, and 100‐seed weight, showing adaptability across environments and stability to some environments.

Genotype by Environment Interaction and Stability Analysis for Yield of Chili Pepper (Capsicum annuum L.) in East Hararghe, Ethiopia

Genotype by Environment Interaction and Stability Analysis for Yield of Chili Pepper (Capsicum annuum L.) in East Hararghe, Ethiopia

Genotype x Environment Interaction and Grain Yield Stability Analysis of Soybean (Glycine max (L.) Merrill) Genotypes in Western Ethiopia

Genotype x Environment Interaction and Grain Yield Stability Analysis of Soybean (Glycine max (L.) Merrill) Genotypes in Western Ethiopia

Genotype x Environment Interaction and Grain Yield Stability Analysis of Soybean (Glycine max (L.) Merrill) Genotypes in Western Ethiopia

Genotype x Environment Interaction and Grain Yield Stability Analysis of Soybean (Glycine max (L.) Merrill) Genotypes in Western Ethiopia

Investigating the effects of genotype and environment interaction (GEI) and stability analysis on short-duration rapeseed yield and oil content under different environmental conditions.

Investigating the effects of genotype and environment interaction (GEI) and stability analysis on short-duration rapeseed yield and oil content under different environmental conditions.

Genotype x Environment Interaction and Stability Analysis for Seed Yield in Desi Chickpea (Cicer arietinum L.) under Irrigated Condition

Background: Chickpea is mainly grown on marginal lands and encounter the problem of erratic rainfall that causes lack of water availability especially at terminal growth stages. Exploration of yield stability and defining the adaptation domains of advance genotypes in different ecosystem is highly imperative for crop genetic improvement programs. Development of variety with high yield potential and stable under irrigated condition is one of the major objectives of chickpea improvement programme, which can be achieved by improving selection efficiency with estimating stability parameters. Ten chickpea genotypes as advance breeding lines and popular varieties were evaluated to assess seed yield stability under irrigated condition during rabi 2021-22. Methods: Experiments were conducted in randomized block design with three replications at five different locations of Karnataka i.e. Bidar, Kalaburagi, Bheemarayanagudi, Raichur and Hagari. The interaction analysis (AMMI ANOVA) indicates large variations for seed yield due to highly significant (p less than 0.01) environment (65.42%) and significant (p less than 0.05) genotype (10.03%), thus the genotypes were diverse with large sum of squares for seed yield and environment. Result: The differential response of chickpea genotypes for seed yield across the environments was observed. IPCA1 and IPCA2 together captured 12.97 per cent of the interaction mean squares RG-2016-134 and KCD-2019-05 showed higher mean yields and adaptable to favourable environments. Based on AMMI bi-plot analysis, genotypes (KCD-2019-05 and Super Annigeri-1) were stable and (DC-17-1111 and RG-2016-134) were unstable. Similarly, best performer genotypes and three mega environments were noticed through the analysis. Three environments i.e., (Bidar, Kalaburagi and Raichur) were found with most selective for seed yield estimation. The genotypes (RGV-203, KCD-2019-02 and Super Annigeri-1) were the best among all the ten tested genotypes in terms of seed yield stability.

Genotype by environment interaction, AMMI, GGE biplot, and mega environment analysis of elite Sorghum bicolor (L.) Moench genotypes in humid lowland areas of Ethiopia

This study aimed to evaluate high-yielding, stable sorghum genotypes and determine the ideal (representative and discriminating) testing environments for genotypes in the humid lowlands of Ethiopia. A total of forty-two sorghum genotypes were used for a field trial conducted in six different environments using a randomized complete block design. Yield stability, Additive main effect, multiplicative interaction (AMMI), and genotype and genotype by environment interaction (GGE) were computed. The AMMI analysis explained 62.85% of the G×E variance. The AMMI1 biplot revealed that (G4; Mok079 and (G16; Ba066) genotypes had higher grain yields. AMMI2 biplot suggested that genotypes (G18; Y0470),(G23;100620), (G29; PML981475), and (G11; ETSC300373-4) show higher sensitivity to environmental changes because of their strong genotype-by-environment interactions. The GGE captured 79.46% of the GGE variance, and the GGE biplot identified genotypes (G4; Mok079), (G10; Sl081) and (G16; Ba066) were the most stable genotypes whereas(G39; ETSC120051-3) was the least stable genotypes. The GGE biplot identified Assosa (AS20) as a suitable environment, whereas PW20 and JM20 were the most discriminating and non-representative environments. The GGE biplot was found to identify three main mega-environments for sorghum growing in the humid lowlands of Ethiopia., both the AMMI and GGE biplots revealed (G4; Mok079) had the highest level of adaptability to all tested environments and was approved by the National Variety Release Committee for release in 2022.

Assessment of Genotype X Environment Interaction and Seed Yield Stability in Sesame (Sesamum indicum L.) Using AMMI Analysis

Sesame is considered as an important oilseed crop in terms of nutritional and medicinal properties. Seed yield of any crop is highly labile to environmental factors, which may cause significant reduction in the yield performance. To get substantial stable yield production in crops like sesame, there is urgent need to identify stable genotypes that perform well under different environmental conditions. Thus, the present study has been planned with the aim to assess genotype x environment interaction (GEI) and stability analysis for seed yield per plant using additive main effect and multiplicative interaction (AMMI) model. The experiment involved evaluation of 20 sesame genotypes in randomized complete block design (RCBD) with three replications under three artificially created environments by providing different levels of fertilizers. The environment wise analysis of variance (ANOVA) revealed significant genotypic variance across all the environments. Additionally, AMMI ANOVA revealed significant environment, genotypes and GEI components. GEI component was further classified to interaction principal components (IPC), IPC1 and IPC2, which explain 66.18% and 33.82% of variance due to GEI, respectively. Further, through different stability indices (ASV and GSI) and AMMI biplots, it was revealed that genotype RT-346 and RT-351 are considered as stable genotypes and these can be further recommended to farmer under varying fertility conditions.

Erratum to: Genotype–environment interaction and yield stability analysis of Colombian yellow diploid potato cultivars using mixed linear models and environmental covariates

Erratum to: Genotype–environment interaction and yield stability analysis of Colombian yellow diploid potato cultivars using mixed linear models and environmental covariates

Genotype x environment interaction and stability analysis in vegetable Amaranth (Amaranthus spp.) under temperate conditions of Kashmir

Genotype x environment interaction and stability analysis in vegetable Amaranth (Amaranthus spp.) under temperate conditions of Kashmir

Genotype x Environment Interaction and Yield Stability Analysis of Early Maturing Sorghum [Sorghum bicolor] Genotypes in East Hararghe Zone, Ethiopia

Genotype x Environment Interaction and Yield Stability Analysis of Early Maturing Sorghum [Sorghum bicolor] Genotypes in East Hararghe Zone, Ethiopia

Genotype by environment interaction and stability analyses of durum wheat elite lines evaluated in Ethiopia

Genotype by environment interaction and stability analyses of durum wheat elite lines evaluated in Ethiopia

Simultaneous evaluation of yield and stability of sugar beet (Beta vulgaris L.) varieties under Egyptian conditions using AMMI and GGE biplot approaches

An experiment was conducted on stability analysis of seven diverse monogram sugar beet (Beta vulgaris L.) varieties for root and sugar yields and their attributes grown under three harvesting dates after sowing during 2021-22 and 2022-23. The data generated from eighteen environments representing the combinations among harvesting dates, locations, and seasons were subjected to additive main effects and multiplicative interaction (AMMI) genotype main effect and genotype x environment interaction (GGE Biplot) analysis. Results obtained from AMMI combined analysis of variance showed that the main effects of sugar beet varieties, environments, and their interaction were highly significant for all studied traits. It is observed that sugar beet plants grown in the Nubaria region and harvested after 210 days, gave the best results for most root and sugar quality traits. The analysis further revealed that Volna and Klara varieties were the elite ones regarding root and sugar yields, and juice quality parameters as an overall mean across the environments. AMMI stability analysis indicated the variety Vangelis was broadly or narrowly stable under different environments and reflected somewhat good performance for most studied traits, while Klara and Volna were considered very promising as above stable ones using GGE Biplot graphs. The findings indicated that GGE Biplot graphs are more accurate and more informative as compared to AMMI stability analysis.

Genotype by environment (G × E) interaction analysis for seed yield and other contributing traits in linseed (Linum usitatissimum L.) across conventional and zero budget natural farming production systems in north-western Himalayas

Development of high-yielding and stable linseed (Linum usitatissimum L.) genotypes in Himachal Pradesh under zero budget naturalfarming system (ZBNF) is an absolute need. A study was conducted to evaluate 30 elite genotypes of linseed for yield and stability across sixteen different environments over two years using Additive main effects and a multiplicative interaction model. For seed yield the environment, genotype and genotype x environment interaction (GEI) effects were highly significant (p <0.001), with contributions to total observed variation of 89.74, 0.81 and 8.08%, respectively. Out of the two production systems, ZBNF was observed to show consistently poor mean yields as compared to the conventional system across all locations. However, locations Kangra and Dhaulakaun performed better under the ZBNF system than Palampur and Bajaura. ZBNF was also identified as less discriminating with weak interactive forces. As per Eberhart and Russell model, the most stable and high-yielding genotype was Surbhi whereas, as per AMMI model, the ASV and GSI values indicated Giza-7 (G18), KL-285 (G16), KL-311 (G1) and Surbhi (G28) as stable and high in grain yield. For yield attributing traits such as primary branches per plant genotypes Janki (G27) and KL-314 (G4), for secondary branches per plant genotypes KL-263 (G10) and Janki (G27), for seeds per capsule genotypes KL-279 (G13), Binwa (G30) and Him Alsi-2 (G19), for trait biological yield per plant Jeewan (G22), for harvest index KL-236 (G6) and Him Alsi-1 (G26) and for 1000 seed weight genotypes Him Alsi-1 (G26) and KL-285 (G16) showed, high stability along with high mean performance and therefore could be selected. Genotype KL-284 (G15) showed specific adaptation under ZBNF system of Bajaura and is therefore recommended for production under respective environments after further evaluation

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

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

Genotype–environment interaction and yield stability analysis of Colombian yellow diploid potato cultivars using mixed linear models and environmental covariates

AbstractSeveral statistical methods have been developed to decompose the phenotypic response based mainly on the general means, the effect of the genotype, and its interaction with the environment, treating the environmental variance as a confounding factor. However, the estimation of the environmental effect from the traits evaluated would establish a dependency on the genotype, resulting in a lower fit and predictive potential of the model. The integration of environmental covariates into the models that associate edaphoclimatic characteristics with the traits of interest has been proposed, with the purpose of increasing the predictive potential and the variance contained by the models. The aim of this study was to evaluate the sensitivity of the traits of interest in yellow diploid potato (Solanum tuberosum L. Group: Phureja) crops to environmental covariates, selecting the most relevant covariates as parameters in empirical multiple regression models based on environmental variance. The results showed high yield variability due to covariates of the water component, while the quality traits were mainly affected by traits of the energetic and physicochemical components of the soil. The adjusted models explained the variance intrinsically due to the environment, reaching adjustments greater than 20%. Therefore, it is concluded that the traits have a high phenotypic sensitivity, and the incorporation of environmental covariates into the genotype‐by‐environment interaction analysis models could improve the understanding of the stability and adaptability of cultivars from the data obtained in multi‐environment trials.