• KOREAN JOURNAL OF CROP SCIENCE
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• v.60 no.3
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• pp.349-354
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• 2015

The AMMI (additive main effects and multiplicative interaction) and GGE (genotype main effect and genotype by environment interaction) biplot which were accounted for a substantial part of total sum of square in the analysis of variance suggested to be more appropriate models for explaining G $\times$ E interaction. The grain yield of total ten sesame genotypes was significantly affected by environment which explained 61% of total variation, whereas genotype and genotype x environment interaction (G $\times$ E) were explained 16%, 24% respectively. From the results of experiment, three genotypes Miryang49, Koppoom and Ansan were unstable, whereas other three genotypes Kyeongbuk18, Miryang50 and Kanghuk which were shorter projections to AEA ordinate were relatively stable over the environments. Yangbak which was closeness to the mean yield and short projection of the genotype marker lines was regarded as genotype indicating good performance with stability. Ansan, Miryang48 and Yangbaek showed the best performance in the environments of Naju, Suwon, Iksan and Andong. Similarly, genotype Miyrang47 exhibited the best performance in the environments of Chuncheon and Miryang. Andong is the closest to the ideal environment, and therefore, is the most desirable among eight environments.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.4
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• pp.459-466
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• 2019

Objective: This study assessed the possible existence of genotype by environment interactions for milk, fat and protein yields in Holstein cattle raised in one of the most important milk production basins in Brazil. Methods: Changes in the genetic parameters and breeding values were evaluated for 57,967 animals from three distinct regions of southern Brazil, divided according to differences in climate. The genotype by environment interaction was determined by genetic correlations between regions, estimated by the restricted maximum likelihood, considering the animal model. Bull rankings were investigated to verify the ratio of coincident selected animals between regions for each trait. Results: The estimates of heritability coefficients were similar between two regions, but were lower in the third evaluated area, for all traits. Genetic correlations between regions were high, ranging from 0.91 to 0.99 for milk, fat and protein yields, representing the absence of a genotype by environment interaction for productive traits. The percentage of selection error between regions for the top 10% of animals ranged from 0.88% to 2.07% for milk yield, 0.99% to 2.46% for fat yield and 0.59% to 3.15% for protein yield. Conclusion: A slight change in genotype between areas was expected since no significant genotype by environment interactions were identified, facilitating the process of selecting Holstein cattle in southern Brazil.

• Asian-Australasian Journal of Animal Sciences
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• v.12 no.6
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• pp.846-849
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• 1999

Angus postweaning daily gain (PWDG) were analyzed to investigate heterogeneous variance by sex. A set of data (16,239 records) was divided into six sub-data sets according to level of environment. REML estimation was conducted by a multitrait model, where PWDG in each sex was treated as a separate trait. Estimates showed diversity among environmental levels, where the heritability for heifers was high in good environment but low in poor environment. The bull's estimates varied among environmental levels. The largest heterogeneity of phenotypic variance between sexes was estimated in a data set of the poor environment level. The genetic correlations between the heifer's PWDG and the bull's PWDG were high in the good environment and low in the poor environment (-0.17). The results suggest existence of genotype by sex interaction in the poor environment.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.45 no.1
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• pp.38-43
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• 2000

The environment in which a given genotype is grown may influence its grain quality characteristics. When varieties are $\times$ evaluated over numerous environments, a variety environment interaction usually is observed, but the relative magnitude of environmental(E), genetic(G), and G $\times$ E effects on quality is unclear. In order to determine relative contribution of genotype, environment, and G $\times$ E interaction to the variations observed in grain quality characteristics, 18 Korean wheat cultivars and experimental lines were evaluated in two environments in 1998 and 1999. Correlation coefficients between grain quality and agronomic characteristics were also estimated. The analysis of variance for the optical density obtained by reaction bet- ween gliadin and anti-gliadin polyclonal antibody (AGPab) indicated that gliadin content measured by Enzyme-Linked Immunosorbent Assay(ELISA) was significantly in- fluenced by environment and cultivar differences. The significant differences of year and year $\times$ location were also found. The ratio of the variances associated with environmental effects to the variances associated with genetic effect gave relatively greater influence of environmental factor on gliadin content. The different protein content from same genotype grown in different environment might be associated with degree of storage protein accumulations. Significant relationships between ELISA and protein content, yield, ten spike weight, and ten spike number were detected. Polyphenol oxidase (PPO) activity was significantly influenced by year, location, cultivar and year $\times$ location. The variance in grain PPO activities among growing years appeared larger than the variation produced by the cultivar examined. This suggested that the growing environment contributed more to variability in grain PPO concentration.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.252-252
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• 2022

Genotype-by-environment interaction (GEI) refers to the comparative response of genotypes to different environments conditions. Thus, understanding GEI is a fundamental component for selecting superior genotypes for breeding programs. The significance of utilizing early maturing cultivars not only provides flexibility in planting dates, but also serves as an effective strategy to reduce methane emission from the paddy fields. In this study, we conducted multi-environment trials (METs) to evaluate yield-related traits such as culm length, panicle length, panicle number, spikelet per plant, and thousand grain weight. A total of eighty-one Korean commercial rice cultivars categorized as early maturing cultivars, were cultivated in three regions, two planting seasons for two years. The genotype main effect plus genotype-by-environment interaction (GGE) biplot analysis of yield-related traits and grain yield explained 70.02-91.24% of genotype plus GEI variation, and exhibited various patterns of mega-environment delineation, discriminating ability, representativeness, and genotype rankings across the planting seasons and environments. Moreover, simultaneous selection using weighted average of absolute scores from the singular value decomposition (WAASB) and multi-trait stability index (MTSI) revealed six highly recommended genotypes with high stability and crop productivity. The winning genotypes under specific environment can be utilized as useful genetic materials to develop regional specialty cultivars, and recommended genotypes can be used as elite climate-resilient parents to improve yield-potential and reduce methane emission as part to accomplish carbon-neutrality.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.3
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• pp.297-302
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• 2008

Application of genotype by environment ($G\;{\times}\;E$) interaction would be used for identifying optimum test condition of the varietal adaptation in the establishment of breeding purpose. Yield and yield components were used to perform additive main effect and multiplicative interaction (AMMI) analysis. Significant difference for $G\;{\times}\;E$ interaction were observed for all variable examined. For yield, 0.18 of total sum of squares corresponded to $G\;{\times}\;E$ interaction. Correlation analysis was carried out between genotypic scores of the first interaction principal component axis (IPCA 1) for agronomic characters. Significant correlations were observed between IPCA 1 for yield and capsule bearing stem length (CBSL), number of capsule per plant (NOC). The biplot of grain yield means for IPCA1 which accounted for 34% of the variation in total treatment sums of squares showed different reaction according to $G\;{\times}\;E$ interaction, genotypes and environments. Taegu showed relatively lower positive IPCA1 scores, and it also showed smaller coefficient variation of yield mean where it is recommendable as a optimal site for the sesame cultivar adaptation and evaluation trial. In case of variables, Yangbaek and M1 showed relatively lower IPCA1 scores, but the score direction showed opposite each other on the graph. Ansan, Miryang1, Miryang4, and Miryang6 seemed to be similar group in view of yield response against IPCA1 scores. These results will be helpful to select experimental site for sesame in Korea to minimize $G\;{\times}\;E$ interaction for the selection of promising genotype with higher stability.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.62 no.4
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• pp.333-345
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• 2017

The potato tuber is known as a rich source of essential nutrients, used throughout the world. Although potato-breeding programs share some priorities, the major objective is to increase the genetic potential for yield through breeding or to eliminate hazards that reduce yield. Glycoalkaloids, which are considered a serious hazard to human health, accumulate naturally in potatoes during growth, harvesting, transportation, and storage. Here, we used the AMMI (additive main effects and multiplicative interaction) and GGE (Genotype main effect and genotype by environment interaction) biplot model, to evaluate tuber yield stability and glycoalkaloid content in six potato cultivars across three locations during 2012/2013. The environment on tuber yield had the greatest effect and accounted for 33.0% of the total sum squares; genotypes accounted for 3.8% and $G{\times}E$ interaction accounted for 11.1% which is the nest highest contribution. Conversely, the genotype on glycoalkaloid had the greatest effect and accounted for 82.4% of the total sum squares), whereas environment and $G{\times}E$ effects on this trait accounted for only 0.4% and 3.7%, respectively. Furthermore, potato genotype 'Superior', which covers most of the cultivated area, exhibited high yield performance with stability. 'Goun', which showed lower glycoalkaloid content, was the most suitable and desirable genotype. Results showed that, while tuber yield was more affected by the environment, glycoalkaloid content was more dependent on genotype. Further, the use of the AMMI and GGE biplot model generated more interactive visuals, facilitated the identification of superior genotypes, and suggested decisions on a variety of recommendations for specific environments.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.45 no.5
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• pp.316-321
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• 2000

Blast severity data of 39 rice varieties at 11 sites in Korea from 1997 to 1999 were analyzed using AMMI model and pattern analysis. Genotype x Environment (G$\times$E) interaction sum of squares (SS) accounted for 12 % of the total SS. Eight genotype groups and seven location groups were identified based on blast reaction pattern. The data obtained from over 21 sites with 44 test varieties from 1981 to 1996 were also considered. These were compared with the 1997-1999 data using the G$\times$E analysis results. Majority of the variability in the Korean Rice Blast Nursery (KRBN) were attributable to variations due to genotypes. Variations of G$\times$E interaction were maintained though test sites were reduced from 21 to 11 sites. Broadly compatible biological discriminative varieties identified were Nagdongbyeo and Akibare while broadly incompatible biological discriminative varieties identified were Hangangchalbyeo and Seogwangbyeo. Key sites for future evaluation work could be selected from location groups. Each location group should be represented by the site with the strongest interaction pattern. Blast responses in Cheolwon, Gyehwa, Suwon, Iksan, and Icheon showed different patterns from other locations.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.48 no.2
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• pp.65-67
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• 2003

This study was conducted to analyze the effects of genotypes, environments and interaction of G$\times$E on yields of sesame grown in seven different environments by AMMI analysis. Environments accounted for the largest (91 %) proportion of the sums of squares, followed by G$\times$E (8%) and genotypes (1%) Therefore, G$\times$E effects are theoretically eight times as important as G effects. G2 (Yanghukkae) has the largest IPCAI scores indicating higher G$\times$E interaction. G3 (Suwon 171) was near zero score of IPCAI suggesting higher stability than others in yield component. Most of environments except for Iksan area shows different G$\times$E effects by years, which means Iksan is optimal area for multi-environmental adaptation evaluation in sesame breeding programs. According to this experiment, it is concluded that maximization of grain yield through environments can be achieved by specific genotypes in specific environments.

• Asian-Australasian Journal of Animal Sciences
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• v.12 no.6
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• pp.850-853
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• 1999

Angus postweaning daily gain (PWDG) was analyzed to investigate effects of the heterogeneous variance and the genotypes by sex interaction on prediction of EBVs with data sets of various environmental levels. A whole data (16,239 records) was divided into six data sets according to averages of the best linear unbiased estimator (BLUE) of herd environment. The results comparing prediction models showed that single-trait model is adequate for most of the data sets except for the data set of poor environment for both of the bulls and the heifers where the heterogeneity of variance and the genotypes by sex interaction exists. In the prediction with the data set of the low environment level, the bull's EBVs by single-trait models had high product moment correlations with male EBVs of the bulls by the multitrait model. Whereas the heifer's EBVs had moderate correlations with female EBVs by the multitrait model. This moderate correlation seems to be resulted by the heterogeneity of variance and low heritability of the heifer's PWDG. The prediction models with heterogeneity of variance had little effect on the prediction of EBVs for the data sets with moderate to high genetic correlations.