Genotype by Environment Interaction Effects on the Crop of Sugar Beet (Beta vulgaris L.) Using Multivariate Analysis
Asian Journal of Research in Crop Science,
Evaluation of genotypes under Egyptian desert conditions comes in the first order for the Plant Breeding and Conservation Program of the Desert Research Center (DRC). The objective of this study was to analyze the effect of the genotype by environment interaction of sugar beet across various locations using multivariate models. Data for studied traits of sugar beet were obtained from experiments at three regions: Saint Catherine, South Sinai Governorate (E1); Baloza station, North Sinai Governorate (E2); and East El- Qantra station, El-Ismailia Governorate (E3) in Egypt. All examined traits were significantly impacted (p <0.05 or 0.01) by environment (E), genotypes (G), and their interaction (GEI) using the AMMI model, with the exception of root length/plant by the environments as well as leaves weight/plant and total soluble solids percentage % traits by the genotypes. GEI was partitioned into two principal components (PCs), which were significant for all studied traits (P < 0.05 and P < 0.01). The highest variability from the total variance was recorded by environmental influences for leaves weight/plant and total soluble solids percentage % traits, as well as by genotype effects for the other studied traits. The environmental index showed that some environments were favorable and some environments were unfavorable for the two traits. The highest root weight/plant and most studied traits were noticed in the E2 environment. Based on the GGE model for root weight/plant, the test environments E1 and E2 are more representative and have the greatest ability to discriminate genotypes, thus favoring the selection of superior genotypes. The genotypes G2, G5, and G6 perform best in the E1 and E2 environments as well as are the most productive and stable compared with the other genotypes. According to PCA and cluster analysis, the genotypes G5 and G6 showed the best performance in response to environments and positive association with root weight/plant and most studied traits. Based on the results of statistical methods used in this study, G5 and G6 genotypes should be used in future sugar beet breeding in an effort to improve productivity and sustainable production of sugar beet in Egypt.
- Sugar beet
- environmental index
- multivariate analysis
How to Cite
Al Jbawi Ea, Al Raei F, Al Ali A, Al Zubi H. Genotype – environment interaction study in sugar beet (Beta vulgaris l.). International Journal of Environment. 2016; (5)3:74-86.
Sorour Ma, Mehanni Ae, Mahmoud Ea, Gaber Noha F. Sugar beet quality and juice purity of some sugar beet varieties (Beta vulgaris l.) Grown in toshka region, egypt as effected by harvesting ages and storage conditions. Archives of Agriculture Sciences Journal. 2020;3(3):64-81.
Anar Mj Lin Z, Hoogenboom G, Shelia V, Batchelor Wd, Teboh Jm, Ostlie M, Schatz Bg, Khan M. Modeling growth, development and yield of sugar beet using dssat. Agric. Syst. 2019;169:58-70.
Faostat. Food and agriculture organization of the United Nations; 2022.
(Access on 18 Aug 2022).
El-hashash Ef, Hassan Ae, Agwa Am. Genotype by environment interaction effects and evaluation of drought tolerance indices under normal and severe stress conditions in barley. Int J PLant Breed Crop Sci. 2018;5:300-316.
Falconer Ds, Mackay Tfc. Introduction to quantitative genetics, 4th edition longman, New York. 1996;132-133.
Yan W, Hunt La, Sheng Q, Szlavnics Z. Cultivar evaluation and mega-environment investigation based on gge biplot. Crop sci. 2000;40:596-605.
Björnsson J. Stability analysis towards understanding genotype x environment interaction.. Plant agriculture department of university of guelph, ontario, Canada; 2002.
(Access on 18 Aug 2022)
Ssemakula G, Dixon A. Genotype × environment interaction, stability and agronomic performance of carotenoid-rich cassava clones. Sci res essays 2007;2: 390–399. Available:https://cgspace.cgiar.org/handle/10568/92828
Hoffmann Cm, Huijbregts T, Van Swaaij N, Jansen R. Impact of different environments in europe on yield and quality of sugar beet genotypes. European Journal of Agronomy. 2009;30:17-26.
Gauch Hg, Piepho Hp, Annicchiarico P. Statistical analysis of yield trials by ammi and gge: further considerations. Crop Sci. 2008;48:866–889.
Zobel Rw, Wright Mg. Gauch Hg. Statistical analysis of yield trial. Agron. J. 1988;80(3):388-393. Available:https://doi.org/10.2134/agronj1988.00021962008000030002x
Gabriel Kr. The biplot graphic display of matrices with applications to principal component analysis. Biometrics. 1971;58: 453-467.
Yan W, Tinker Na. Biplot analysis of multi-environment trial data: principles and applications. Can J Plant Sci. 2006;86: 623-645.
Curcic Z, Danojević D, Bojan M, Ciric M, Taski-Ajdukovic K, Nagl N. Gge biplot analysis of sugar beet multi-environment trials. Povrtarstvo. 2017; 54(2):61-67.
Curcic Z, Ciric M, Nagl N, Taski-Ajdukovic K. Effect of sugar beet genotype, planting and harvesting dates and their interaction on sugar yield. Front. Plant Sci. 2018; 9:1041.
Hassani M, Heidari B, Dadkhodaie A, Stevanato P. Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris l.). Euphytica. 2018;214:79.
Studnicki M, Lenartowicz T, Noras K, Wójcik-Gront E, Wyszyński Z. Assessment of stability and adaptation patterns of white sugar yield from sugar beet cultivars in temperate climate environments. Agronomy. 2019;9(7):405.
Bocianowski J, Wielkopolan B, Jakubowska M. Ammi analysis of the effects of different insecticidal treatments against agrotis spp. On the technological yield from sugar beet. Agriculture. 2022a; 12:157.
Škrbić B, Ðurišić-Mladenović N, Mačvanin N. Determination of metal contents in sugar beet (Beta vulgaris) and its products: Empirical and chemometrical approach. Food science and technology research. 2010;16(2):123-134.
Hu X-H, Zhou J-C, Yang H-Z (2019). Comprehensive evaluation of different sugar beet varieties by using principal component and cluster analyses. Iop Conf. Series: Journal of Physics: Conf. Series. 2019;117:1-14.
Alami l, Terouzi W, Otmani M, Abdelkhalek O, Salmaoui S, Mbarki M. Effect of sugar beet harvest date on its technological quality parameters by exploratory analysis". Journal of Food Quality. 2021;8.
Islam Mj, Uddin Mj, Hossain Ma, Henry R, Begum Mk, Sohel M, Mou Ma, Ahn J, Cheong Ej, Lim Ys. Exogenous putrescine attenuates the negative impact of drought stress by modulating physio-biochemical traits and gene expression in sugar beet (Beta vulgaris l.). Plos One 2022;17(1): e0262099.
Kleuker G, Hoffmann Cm. Causes of different tissue strength, changes during storage and effect on the storability of sugar beet genotypes. Postharvest Biology and Technology 2022;183:111744.
Majumdar R, Strausbaugh Ca, Vincill Ed, Eujayl I, Galewski Pj. Leaf bacteriome in sugar beet shows differential response against beet curly top virus during resistant and susceptible interactions. International Journal of Molecular Sciences. 2022; 23(15):8073. Available:https://doi.org/10.3390/ijms23158073
Steel Rgd, Torrie Jh. Principles and procedures of statistics. 2nd edition. Mcgraw hill book company inc., New York; 1980.
Basford Ke, Cooper M. Genotype × environmental interactions and some considerations of their implications for wheat breeding in australia. Australian Journal of Agricultural Research. 1998; 49(2):153-174.
Mather K, Jinks Jl. Introduction to biometrical genetics, chapman and hall limited, london; 1971.
El-hashash Ef, Agwa Am. Comparison of parametric stability statistics for grain yield in barley under different drought stress severities. Merit Research Journal of Agricultural Science and Soil Sciences. 2018;6(7):098-111.
Bayomi Kem, El-hashash Ef. Moustafa Esa. Comparison of genetic parameters in non-segregating and segregating populations of sugar beet in egypt. Asian Journal of Research in Crop Science. 2019;3(4):1-12.
Yan W, Hunt La. Interpretation of genotype × environment interaction for winter wheat yield in ontario. Crop sci. 2001;41(1):19-25.
Thillainathan M, Fernandez Gcj. A novel approach to plant genotypic classification in multi-site evaluation. Hort. Sci. 2002; 37(5):793-798.
El-Hashash Ef, Km El-absy Km. Genotype x environment interaction, environmental indices and stability analyses forsome selected genotypes of barley. Journal of Applied Sciences Research. 2013;9(7): 4371-4377.
Yan W, Kang Ms, Ma, Bl, Woods S, Cornelius Pl. Gge biplot vs. Ammi analysis of genotype-by-environment data. Crop sci. 2007;47(2):643-655.
Yan w, rajcan I. Biplot analysis of test sites and trait relations of soybean in ontario. Crop sci. 2002;2(1):11-20.
Yan W, Kang Ms. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists (1st Ed.). Crc press. 2002.
Yan W. Singular-value partition for biplot analysis of multi-environment trial data. Agronomy J., 2002;94(5):990-996.
Yan W. Ggebiplot-a windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agron. J. 2001;93(5):1111-1118.
Mehareb Em, El-bakary Hmy, Abo Elenen Ffm. Comprehensive evaluation of sugar beet genotypes for yield and relative traits by multivariate analysis. Svu-international Journal of Agricultural Sciences. 2021;3(1): 96-111.
Bocianowski J, Jakubowska M, Kowalska J. The interaction of different abiotic conditions on the value of the component traits of the technological yield of sugar beet. Euphytica. 2022b;218: 110.
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