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Evaluation of sugarcane genotypes with respect to sucrose yield across three crop cycles using GGE biplot analysis

Published online by Cambridge University Press:  04 August 2021

Aliya Momotaz*
USDA-ARS, Sugarcane Field Station, 12990 US Hwy. 441 N, Canal Point, FL33438, USA
Per H. McCord
Irrigated Agriculture Research and Extension Center, Washington State Univ., 24106 N. Bunn Road, Prosser, WA99350, USA
R. Wayne Davidson
Florida Sugar Cane League, Inc., P.O. Box 1208, Clewiston, FL33440, USA
Duli Zhao
USDA-ARS, Sugarcane Field Station, 12990 US Hwy. 441 N, Canal Point, FL33438, USA
Miguel Baltazar
Florida Sugar Cane League, Inc., P.O. Box 1208, Clewiston, FL33440, USA
Orlando Coto Arbelo
USDA-ARS, Sugarcane Field Station, 12990 US Hwy. 441 N, Canal Point, FL33438, USA
Hardev S. Sandhu
University of Florida, Everglades Research and Education Center, 3200 East Palm Beach Rd., Belle Glade, FL33430, USA
*Corresponding author. Email:


The experiment was carried out in three crop cycles as plant cane, first ratoon, and second ratoon at five locations on Florida muck soils (histosols) to evaluate the genotypes, test locations, and identify the superior and stable sugarcane genotypes. There were 13 sugarcane genotypes along with three commercial cultivars as checks included in this study. Five locations were considered as environments to analyze genotype-by-environment interaction (GEI) in 13 genotypes in three crop cycles. The sugarcane genotypes were planted in a randomized complete block design with six replications at each location. Performance was measured by the traits of sucrose yield tons per hectare (SY) and commercial recoverable sugar (CRS) in kilograms of sugar per ton of cane. The data were subjected to genotype main effects and genotype × environment interaction (GGE) analyses. The results showed significant effects for genotype (G), locations (E), and G × E (genotype × environment interaction) with respect to both traits. The GGE biplot analysis showed that the sugarcane genotype CP 12-1417 was high yielding and stable in terms of sucrose yield. The most discriminating and non-representative locations were Knight Farm (KN) for both SY and CRS. For sucrose yield only, the most discriminating and non-representative locations were Knight Farm (KN), Duda and Sons, Inc. USSC, Area 5 (A5), and Okeelanta (OK).

Research Article
© The Author(s), 2021. Published by Cambridge University Press

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Ahmadi, J., Vaezi, B. and Fotokian, M.H. (2012). Graphical analysis of multi-environment trials for barley yield using AMMI and GGE-Biplot under rain-fed conditions. Journal of Plant Physiology and Breeding 291, 4354.Google Scholar
Crossa, J., Gauch, H.G. and Zobel, R.W. (1990). Additive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Science 30, 493500.CrossRefGoogle Scholar
Daniels, J. and Roach, B.T. (1987). Taxonomy and evolution. Chapter 2. In Heinz, DJ (Ed), Sugarcane improvement through breeding, Volume 11. Amsterdam, Netherlands: Elsevier, pp. 784.CrossRefGoogle Scholar
Davidson, R.W., Scott, A.W., Hernandez, E., Gordon, V.S., McCord, P., Sandhu, H.S., Zhao, D., Comstock, J.C., Sood, S., Singh, M.P., Islam, M.S., Baltazar, M. and McCorkle, K. (2019). Registration of ‘CP 08–1968’ Sugarcane. Journal of Plant Registrations 178186.CrossRefGoogle Scholar
Duma, S.W, Shimelis, H., Ramburan, S. and Shayanowako Admire, I.T. (2019). Genotype-by region interactions of released sugarcane varieties for cane yield in the South African sugar industry. Journal of Crop Improvement 33, 478504.CrossRefGoogle Scholar
Edme, S.J., Tai, P.Y.P., Glaz, B., Gilbert, R.A., Miller, J.D., Davidson, J.O., Dunckelman, J.W. and Comstock, J. (2004). Registration of ‘CP 96–1252’ Sugarcane. Crop Science 45, 423.Google Scholar
Frutos, E., Galindo, M.P. and Leiva, V. (2014). An interactive biplot implementation in R for modeling genotype-by-environment interaction. Stoch Environ Res Risk Assess 28, 16291641.CrossRefGoogle Scholar
Gilbert, R.A., Comstock, J.C., Glaz, B., Edme, S.J., Davidson, R.W., Glynn, N.C., Miller, J.D. and Tai, Y.P. (2008). Registration of ‘CP 00–1101’ Sugarcane. Journal of Plant Registration 2, 95101.CrossRefGoogle Scholar
Gilbert, R.A., Shine, J.M. Jr., Miller, J.D., Rice, R.W. and Rainbolt, C.R. (2006). The effect of genotype, environment and time of harvest on sugarcane yields in Florida, USA. Field Crop Research 95, 156170.CrossRefGoogle Scholar
Glaz, B. and Kang, M.S. (2008). Location contributions determined via GGE Biplot analysis of multi environment sugarcane genotype-performance trials. Crop Science 48, 941950.CrossRefGoogle Scholar
Jackson, P. and McRae, T.A. (2001). Selection of sugarcane genotypes in small plots. Effects of plot size and selection criteria. Crop Science 41, 315322.CrossRefGoogle Scholar
Jamshidmoghaddam, M. and Pourdad, S.S. (2013). Genotype × Environment interactions for seed yield in rainfed winter safflower (Carthamus tinctorius L.) multi-environment trials in Iran. Euphytica 190, 357369.CrossRefGoogle Scholar
Khan, I.A., Seema, N., Raza, S., Yasmine, S. and Bibi, S. (2013). Environmental interactions of sugarcane genotypes and yield stability analysis of sugarcane. Pakistan Journal of Botany 45, 16171622.Google Scholar
Kimbeng, C.A., Zhou, M. M. and da Silva, J. A. (2009). Genotype x Environment Interactions and Resource Allocation in Sugarcane Yield Trials in the Rio Grande Valley Region of Texas. Journal of American Society of Sugarcane Technology 29, 1124.Google Scholar
Legendre, B.L. (1992). The core/press method for predicting the sugar yield from cane for use in cane payment. Sugar Journal 54, 27 Google Scholar
Lingle, S.E. (1997). Seasonal internode development and sugar metabolism in sugarcane. Crop Science 37, 12221227.CrossRefGoogle Scholar
Lingle, S.E., Johnson, R.M., Tew, T.L. and Viator, R.P. (2010). Changes in juice quality and sugarcane yield with recurrent selection for sucrose. Field Research 118, 152157.CrossRefGoogle Scholar
Milligan, S. (1994). Test site allocation within and among stages of a sugarcane breeding program. Crop Science 34, 11841190.CrossRefGoogle Scholar
Milligan, S.B., Gravois, K.A., Bischoff, K.P. and Martin, F.A. (1990). Crop effects on genetic relationships among sugarcane traits. Crop Science 30, 927931.CrossRefGoogle Scholar
Momotaz, A., Davidson, R.W., Zhao, D., McCord, P.H., Sandhu, H.S., Baltazar, M., Islam, M.S. and Coto Arbelo, O. (2021). Genotype-by-environment interaction analysis across three crop cycles in sugarcane. Journal of Crop Improvement 35, 276290. DOI: 10.1080/15427528.2020.1817220 CrossRefGoogle Scholar
Naseri, A.A., Jafari, S. and Alimohammadi, M. (2007). Soil compaction due to sugarcane (Saccharum officinarum) mechanical harvesting and the effects of subsoiling in the improvement of soil physical properties. Journal Applied Science 7, 36393648.CrossRefGoogle Scholar
Odero, D.C., Rainbolt, C.R., Gilbert, R.A. and Dusky, J.A. (2013). Sugarcane ripeners in Florida. In Sugarcane Cultural Practices (Sugarcane Handbook) SS-AGR-215/SC015.Google Scholar
Orgeron, A.J., Gravois, K. A. and Bischoff, K.P. (2007). Planting rate effects on sugarcane yield trials. Journal of American Society for Sugarcane Technology 27, 1234.Google Scholar
Pacheco, Á., Mateo, V., Gregorio, A., Francisco, R., José, C. and Juan, B. (2015). GEA-R (Genotype x Environment Analysis with R for Windows) Version 4.1, hdl:11529/10203, CIMMYT Research Data and Software Repository Network, V16.Google Scholar
Papini-Terzi, F.S, Rocha, F.R., Vêncio, R.Z.N., Felix, J. M., Branco, D.S., Waclawovsky, A.J, Del Bem, L.E. V., Lembke, C.G., Costa, M.D.L., Nishiyama, M.Y., Vicentini, R. Jr, Vincentz, M.G.A., Ulian, E.C., Menossi, M. and Souza, G.M. (2009). Sugarcane genes associated with sucrose content. BMC Genomics 10, 120.CrossRefGoogle ScholarPubMed
Ramburan, S., Wettergreen, T., Berry, S.D. and Shongwe, B. (2013). Genetic, environmental and management contributions to ratoon decline in sugarcane. Field Crops Research 146, 105112. CrossRefGoogle Scholar
Rea, R, De Sousa-Vieira, O, Díaz, A, Ramón, M., Briceño, R., George, J, Niño, M. and Demey, J. (2015). Assessment of yield stability in sugarcane genotypes using non-parametric methods. Agronomía Colombiana 33, 131138. Doi 10.15446/agron.colomb.v33n2.49324 CrossRefGoogle Scholar
Rice, R.W., Gilbert, R.A. and Daroub, S.H. (2002). Application of the soil taxonomy key to the organic soils of the Everglades Agricultural Area. Available at (verify ed 14 Mar. 2008). SS-AGR-246. Univ. of Florida, Inst. of Food and Agril. Sci., Gainesville.Google Scholar
Salassi, M.E. and Milligan, S.B. (2013). Economic analysis of sugarcane variety selection, crop yield patterns, and ratoon crop plow out decisions. Journal of Production Agriculture 10, 539545.CrossRefGoogle Scholar
SAS Institute. (2017). SAS for windows, Version 9.4 Service Pack 4, Cary NC, USA.Google Scholar
Silveira, L.C.I., Kist, V., Paula, T.O.M., Barbosa, M.H.P., Peternelli, L.A. and Daros, E. (2013). Ammi analysis to evaluate the adaptibility and phenotypc stability of sugarcane genotypes. Scientia Agricola 70, 2732.CrossRefGoogle Scholar
Tai, P.Y.P, Miller, J.D., Glaz, B., Deren, C.W. and Shine, J.M. (1991). Registration of ‘CP 78–1628’ Sugarcane. Crop Science 31, 236.CrossRefGoogle Scholar
Yan, W. (2001). GGE Biplot-A Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agronomy Journal 93, 11111118.CrossRefGoogle Scholar
Yan, W. and Hunt, L.A. (2001). Interpretation of Genotype 3 Environment Interaction for Winter Wheat Yield in Ontario. Crop Science 41, 1925.CrossRefGoogle Scholar
Yan, W. and Kang, M.S. (2003). GGE Biplot analysis: a graphical tool for breeders, geneticists, and agronomists. Boca Raton, Florida: CRC Press.Google Scholar
Yan, W. and Tinker, N.A. (2006). Biplot analysis of multienvironment trial data: Principles and applications. Canadian Journal of Plant Science 86, 623664.CrossRefGoogle Scholar
Yan, W., Kang, M.S., Ma, B., Woods, S. and Cornelius, P.L. (2007). GGE biplot vs. AMMI analysis of genotype -by-environment data. Crop Science 47, 641653.CrossRefGoogle Scholar
Zhang, J., Arro, J., Chen, Y. and Ming, R. (2013). Haplotype analysis of sucrose synthase gene family in three Saccharum species. BMC Genomics 14, 314. doi: 10.1186/1471-2164-14-314.CrossRefGoogle ScholarPubMed
Zhao, D. and Yang-Rui, L. (2015). Climate Change and Sugarcane Production: Potential Impact and Mitigation Strategies. International Journal of Agronomy. Hindawi Publishers.CrossRefGoogle Scholar
Zhao, D., Zhu, K., and Momotaz, A. (2020). Determination of Sugarcane Growth, Physiology, and Yield Traits and Their Relationships across Genotypes. ASA, CSSA, SSSA International Annual Meeting. Virtual Convention, USA. 2020_Poster.Google Scholar
Zobel, R.W., Wright, M.J. and Gauch, H.G. (1988). Statistical analysis of a yield trial. Agronomy Journal 80, 38.CrossRefGoogle Scholar