Sensitivity of Genotype by Environment Interaction Models to Outlying Observations

Main Article Content

S. Oluwafemi Oyamakin
M. Olalekan Durojaiye

Abstract

Plant breeding program depends on its ability to provide farmers with genotypes with guaranteed superior performance (phenotype) in terms of yield and/or quality across a range of environmental conditions. To achieve this aim, it is necessary to have an understanding of the model suitable for or leading to a good phenotype. In this study, two cases of scenarios were considered to have a clearer view of the performance of Genotype by Environment Interaction on the following four models; Additive Main Effect and Multiplicative Interaction (AMMI), Finlay Wilkinson (FW), Genotype and Genotype by Environment Interaction (GGE) and Mixed Model. We experiment the inference behind the violation of the assumption of normal distribution by observing the data contamination of two case scenarios (Lowest and Highest outlying observations). It was observed on the two data Types of Balance and Unbalance designs with different Levels of generations. We achieved that by comparative performance of the data contamination techniques under the two case scenarios; Case I scenario was done for Lowest Outlying Observations where 50%, 100% and 500% data contamination on the First Quarter (P1), Mid quarter (P2) and Last Quarter (P3). We then deduced from the result of the model evaluation that, at each levels of data contamination for Balance and Unbalance design, Mixed model was the ideal model for  interaction. Case II scenario was done for Highest Outlying Observations where 50%, 100% and 500% data contamination on the First Quarter (P1), Mid quarter (P2) and Last Quarter (P3) were examined on each levels of generations. We then observed from the result of the model evaluation that, at each levels of data contamination for Balance and Unbalance design, Mixed model also outperformed the other three models.

Keywords:
Plant breeding, genotype-by-environment interaction, AMMI, FW, GGE and mixed model, simulation.

Article Details

How to Cite
Oyamakin, S. O., & Durojaiye, M. O. (2020). Sensitivity of Genotype by Environment Interaction Models to Outlying Observations. Asian Journal of Research in Agriculture and Forestry, 5(2), 29-45. https://doi.org/10.9734/ajraf/2020/v5i230081
Section
Review Article

References

Borron S. Building resilience for an unpredictable future: How organic agriculture can help farmers adapt to climate change. Food and Agriculture Organization of the United Nations, Rome; 2006.

Kumar S, Bourai VA. Economic analysis of pulses production, their benefits and constraints: A case study of sample villages of Assan Valley of Uttarakhand, India. IOSR Journal of Humanities and Social Science. 2012;41-53.

Keim P, Pearson T, Okinaka AR. Microbial Forensics: DNA fingerprinting of Bacillus anthracis (anthrax). Analytical Chemistry. 2008;80(13):4791-4800.

Tumuhimbise R, Melis R, Shanahan P, Kawuki R. Genotype environment interaction effects on early fresh storage root yield and related traits in cassava. The Crop Journal. 2014;2(5): 329–337.

View at: Publisher Site | Google Scholar.

Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157(4):1819-1829.

Cobb JN, Declerck G, Greenberg A, Clark R, McCouch S. Nextgeneration phenotyping: Requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement. Theor. Appl. Genet. 2013;126(4):867-887.
DOI: 10.1007/s00122-013-2066-0

Cooper M, Messina CD, Podlich D, Totir LR, Baumgarten A, Hausmann NJ, Wright D, Graham G. Predicting the future of plant breeding: Complementing empirical evaluation with genetic prediction. Crop; 2014b.

Dawson JC, Endelman JB, Heslot N, Crossa J, Poland J, Dreisigacker S, Manes Y, Sorrells ME, Jannink J-L. The use of unbalanced historical data for genomic selection in an international wheat breeding program. Field Crops Res. 2013; 154:12-22.
DOI:10.1016/j.fcr.2013.07.020PastureSci.65(4):311-336
DOI:10.1071/CP14007

Heslot N, Akdemir D, Sorrells M, Jannink J-L. Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theor. Appl. Genet. 2013;127:1-18.

Jarquin D, Crossa J, Lacaze X, Cheyron P, Daucourt J, Lorgeou J, Piraux F, Guerreiro L, Perez P, Calus M, Burgue~no J, Campos G. A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor. Appl. Genet. 2013;3:1-13.

Bustos-Korts D, Malosetti M, Chapman S, FA van Eeuwijk. Modelling of genotype by environment interaction and prediction of complex traits across multiple environments as a synthesis of crop growth modelling, genetics and statistics. Springer, New York. 2016;55-82.
DOI:10.1007/978-3-319-20562-552

Malosetti M, Bustos-Korts D, Boer MP, van Eeuwijk FA. Multi environment genomic prediction: Issues in relation to genotype by environment interaction. Crop Sci; 2016.
DOI:10.2135/cropsci2015.05.0311

Hammami H, Rekik B, Gengler N. Genotype by Environment Interaction in Dairy Cattle. Biotechnologie, Agronomie, Societeet Environnement. 2009;13(1).

Finlay KW, Wilkinson GN. The analysis of adaptation in a plant breeding program. 1963;742-54.

Yan W, Tinker NA. Biplot Analysis of Multi-Environment Trial Data: Principles and Applications. Canadian Journal of Plant Science. 2006;86:623-645.
DOI: http://dx.doi.org/10.4141/P05-169

Gauch HG. Model selection and validation for yield trials with interaction. Biometrics. 1988;705-715.

Purchase JL, Hatting H, Van Deventer CS. Genotype environment interaction of wheat in South Africa: Stability analysis of yield performance. South African Journal of Plant and Soil. 2000;17(3):101–107.

View at: Publisher Site | Google Scholar.

Farshadfar E, Mahmodi N, Yaghotipoor A. AMMI stability value and simultaneous estimation of yield and yield stability in bread wheat (Triticum aestivum L.). Australian Journal of Crop Science. 2011; 5(13):1837–1844.
View at: Google Scholar

Bose LK, Jambhulkar NN, Pande K, Singh ON. Use of AMMI and other stability statistics in the simultaneous selection of rice genotypes for yield and stability under direct-seeded conditions, Chilean Journal of Agricultural Research. 2014;74(1):1–9.
View at: Publisher Site | Google Scholar

Piepho H-P. Stability analysis using the SAS system. Agron. J. 1999;91:154-160.
Crossref

SAS Institute. Table 46.7 in the online document; 2015.
Available:http:==support:sas:com=onlinedoc=913=getDoc=en=statug:hlp=mixedsect19:htm [Accessed 11 June 2015].