Stability analysis is used to evaluate sugar beet genotypes with the goal of maximizing root and white sugar yield

Parviz Fasahat; Javad Rezaei; Heydar Azizi; Hamzeh Hamzeh; Saeed Darabi; Ali Jalilian; Rahim Mohammadian

doi:10.2478/bile-2025-0002

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Stability analysis is used to evaluate sugar beet genotypes with the goal of maximizing root and white sugar yield

Biometrical Letters

Volume 62 (2025): Issue 1 (June 2025)

By: Parviz Fasahat, Javad Rezaei, Heydar Azizi, Hamzeh Hamzeh, Saeed Darabi, Ali Jalilian and Rahim Mohammadian

Open Access

|Jun 2025

Abstract

Sugar beet is regarded as a main source of sugar supply alongside sugarcane. To evaluate new promising hybrids in terms of stability and adaptability, six distinct environments were used to sow 15 different sugar beet genotypes. The results of the combined analysis of variance showed that genotype, environment, and their interaction had a significant impact on root yield and white sugar yield. Among 15 genotypes, using AMMI biplot analysis, G5 and G14 were distinguished as adaptive and high-yielding genotypes for root yield, and G6 for white sugar yield. The interaction between the first two principal components of the AMMI 2 biplot model explained 72.3% of the variation resulting from the G + G × E interaction for root yield and 75.2% for white sugar yield. G5 stands out as the genotype with the greatest stability and yield efficiency. The GGE biplot revealed environment 3 as the ideal environment. Genotypes G7, G6 and G5 were selected as the superior stable genotypes with respect to white sugar yield based on the WAASB stability index, while G5 was determined as the most stable genotype by MSTI. Using the GGE biplot, G5’s high white sugar yield and stability within diverse environments made it the optimum genotype.

References

Alam Z., Akter S., Khan M.A.H., Amin M.N., Karim M.R., Rahman M.H.S., Rashid M.H., Rahman M.M., Mokarroma N., Sabuz A.A., Alam, M.J. (2024): Multivariate analysis of yield and quality traits in sweet potato genotypes (Ipomoea batatas L.). Sci. Hortic. 328: 112901.
Search in Google Scholar Back to article
Amelework A.B., Bairu M.W., Marx R., Laing M., Venter S.L. (2023): Genotype × environment interaction and stability analysis of selected cassava cultivars in South Africa. Plants 12(13): p.2490.
Search in Google Scholar Back to article
Cooke D.A., Scott J.E. (2012): The sugar beet crop. Springer Science & Business Media.
Search in Google Scholar Back to article
Dirim E., Say A., Arslan M. (2024): Assessment of genetic diversity, yield and quality performance of O-Type Sugar Beet (Beta vulgaris L.) breeding lines. Genet Resour Crop Ev. 71(4): 1605-1614.
Search in Google Scholar Back to article
FAO (2023): Crops and livestock products in 2021. Available at: http://www.fao.org/faostat [Accessed 22 January 2024].
Search in Google Scholar Back to article
Fasahat P., Aghaeezadeh M., Jabbari L., Sadeghzadeh Hemayati S., Townson P. (2018): Sucrose accumulation in sugar beet: From fodder beet selection to genomic selection. Sugar Tech. 20: 635-644.
Search in Google Scholar Back to article
Fasahat, P., Hosseinpour, M., Kakueinezhad, M., Hemayati, S.S., Laidig, F. (2023): Effect of latitude on sugar beet bolting occurrence. Poljoprivreda i Sumarstvo 69(2): 181-190.
Search in Google Scholar Back to article
Fasahat P., Hosseinpour M., Kakueinezhad M., Townson P. (2022): Physiological and molecular aspects of sucrose accumulation in sugar beet. In: Misra, V., Srivastava, S., Mall, A.K. eds. Sugar beet cultivation, management and processing. Singapore: Springer Nature Singapore: 27-48.
Search in Google Scholar Back to article
Fasahat P., Muhammad K., Abdullah A., Rahman B.M.A., Siing N.M., Gauch J.H.G., Ratnam W. (2014): Genotype× environment assessment for grain quality traits in rice. Communications in Biometry and Crop Science 9(2): 71-82.
Search in Google Scholar Back to article
Fasahat P., Rajabi A., Rad J.M., Derera J.J.B.B.I.J. (2016): Principles and utilization of combining ability in plant breeding. Biometrics & Biostatistics International Journal 4(1): 1-24.
Search in Google Scholar Back to article
Fasahat P., Rezaei J., Sharifi M., Azizi H., Fotuhi K., Mahdikhani P., Pedram A., Jalilian A., Babaei B. (2024): Analysis of the genotype by environment interactions of sugar beet genotypes under rhizomania contamination. J Agric Sci Tech. 26: 805-817.
Search in Google Scholar Back to article
Galanakis, C.M. (2024): The future of food. Foods 13(4): 506.
Search in Google Scholar Back to article
Gauch H.G., Zobel R.W. (1996): AMMI analysis of yield trials. In: Kang, M.S., Gauch, H.G., eds, Genotype by Environment Interaction. CRC Press: 85–122.
Search in Google Scholar Back to article
Jaggard K.W., Qi A., Ober E.S. (2010): Possible changes to arable crop yields by 2050. Philos T R Soc B. 365: 2835–2851.
Search in Google Scholar Back to article
Khan M.M.H., Rafii M.Y., Ramlee S.I., Jusoh M., Al Mamun M. (2021): AMMI and GGE biplot analysis for yield performance and stability assessment of selected Bambara groundnut (Vigna subterranea L. Verdc.) genotypes under the multi-environmental trials (METs). Sci Rep. 11(1): 22791.
Search in Google Scholar Back to article
Lee S.Y., Lee H.S., Lee C.M., Ha S.K., Park H.M., Lee S.M., Kwon Y., Jeung J.U., Mo Y. (2023): Multi-environment trials and stability analysis for yield-related traits of commercial rice cultivars. Agriculture 13(2): 256.
Search in Google Scholar Back to article
Luterbacher M.C., Asher M.J.C., Beyer W., Mandolino G., Scholten O.E., Frese L., Biancardi E., Stevanato P., Mechelke W., Slyvchenko O. (2005): Sources of resistance to diseases of sugar beet in related Beta germplasm: II. Soil-borne diseases. Euphytica 141: 49–63.
Search in Google Scholar Back to article
Mullualem D., Tsega A., Mengie T., Fentie D., Kassa Z., Fassil A., Wondaferew D., Gelaw T.A., Astatkie T. (2024): Genotype-by-environment interaction and stability analysis of grain yield of bread wheat (Triticum aestivum L.) genotypes using AMMI and GGE biplot analyses. Heliyon 10(12).
Search in Google Scholar Back to article
Murphy S.E., Lee E.A., Woodrow L., Seguin P., Kumar J., Rajcan I., Ablett G.R. (2009): Genotype × Environment interaction and stability for isoflavone content in soybean. Crop Sci. 49: 1313–1321.
Search in Google Scholar Back to article
Oladosu Y., Rafii M.Y., Abdullah N., Magaji U., Miah G., Hussin G., Ramli A. (2017): Genotype × environment interaction and stability analyses of yield and yield components of established and mutant rice genotypes tested in multiple locations in Malaysia. Acta Agr Scand B-S P. 67(7): 590–606.
Search in Google Scholar Back to article
Olivoto T., Lúcio A.D., da Silva J.A., Marchioro V.S., de Souza V.Q., Jost E. (2019): Mean performance and stability in multi‐environment trials I: combining features of AMMI and BLUP techniques. Agron J. 111(6): 2949-2960.
Search in Google Scholar Back to article
Oroian C., Ugruțan F., Mureșan I.C., Oroian I., Odagiu A., Petrescu-Mag I.V., Burduhos P. (2023): AMMI analysis of genotype× environment interaction on sugar beet (Beta vulgaris L.) yield, sugar content and production in Romania. Agronomy. 13(10): 2549.
Search in Google Scholar Back to article
Reinefeld E., Emmerich A., Baumgarten G., Winner C., Beiß U.M. (1974): Zur voraussage des melassezuckers aus rubenanalysen. Zucker. (in German)
Search in Google Scholar Back to article
Sivapalan S., O'brien L., Ortiz-Ferrara G., Hollamby G.J., Barclay I., Martin P.J. (2000): An adaptation analysis of Australian and CIMMYT/ICARDA wheat germplasm in Australian production environments. Crop Pasture Sci. 51: 903–915.
Search in Google Scholar Back to article
Tariku S., Lakew T., Bitew M., Asfaw M. (2013): Genotype by environment interaction and grain yield stability analysis of rice (Oryza sativa L.) genotypes evaluated in north western Ethiopia. Net Journal of Agricultural Science. 1: 10–16.
Search in Google Scholar Back to article
UN/DESA (2017): World population projected to reach 9.8 billion in 2050, and 11.2 billion in 2100. Available at: https://www.un.org/ht/desa/world-population-projected-reach-98-billion-2050-and-112-billion-2100 [accessed 22 January 2024].
Search in Google Scholar Back to article
Wang R., Wang H., Huang S., Zhao Y., Chen E., Li F., Qin L., Yang Y., Guan Y.A., Liu B., Zhang H. (2023): Assessment of yield performances for grain sorghum varieties by AMMI and GGE biplot analyses. Front Plant Sci. 14: 1261323.
Search in Google Scholar Back to article
Yan W., Hunt L.A. (2001): Interpretation of genotype – environment interaction for winter wheat yield in Ontario. Crop Sci. 41: 19-25.
Search in Google Scholar Back to article
Yan W., Kang M.S., Ma B., Woods S., Cornelius P.L. (2007): GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 47: 643–655.
Search in Google Scholar Back to article
Yousefabadi V.A., Mehdikhani P., Nadali F., Sharifi M., Azizi H., Ahmadi M., Fasahat P. (2024): Evaluation of yield and stability of sugar beet (beta vulgaris L.) genotypes using GGE biplot and AMMI analysis. Sci Rep. 14(1): 27384.
Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.2478/bile-2025-0002 | Journal eISSN: 2199-577X | Journal ISSN: 1896-3811

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Language: English

Page range: 29 - 48

Published on: Jun 30, 2025

Published by: Polish Biometric Society

In partnership with: Paradigm Publishing Services

Publication frequency: 2 issues per year

Keywords:

AMMI,

genotype-by-environment interaction,

Related subjects:

Life sciences, other,

Mathematics,

Probability and statistics,

Applied mathematics

© 2025 Parviz Fasahat, Javad Rezaei, Heydar Azizi, Hamzeh Hamzeh, Saeed Darabi, Ali Jalilian, Rahim Mohammadian, published by Polish Biometric Society
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 62 (2025): Issue 1 (June 2025)