Abstract
Cane yield and juice quality traits are essential and important in release of sugarcane variety for commercial cultivation. Twenty elite sugarcane clones along with two popular standards (Co 86032 and PI 1110) were evaluated at Sakthi sugars, Sivaganga, in two plant crops and one ratoon crop for 2 years to identify a variety suitable for this location. Cane yield (t/ha), commercial cane sugar (CCS) yield (t/ha), CCS % at harvest, juice sucrose %, juice brix %, juice purity %, single cane weight (kg), stalk height (cm) and stalk diameter (cm) at harvest were considered for the study. Within environment analysis of variance (ANOVA) indicated significant differences among genotypes for almost all the traits in all three environments (two plant and one ratoon crops). Combined ANOVA results displayed non-significant genotype environment interaction for all the traits except height and diameter. Cane yield and sugar yield were significant higher in II (second) plant crop compared to I (first) plant and ratoon crops, whereas no significance was observed for other traits. Heritability estimates were higher for juice quality traits as compared to cane traits. Single cane weight recorded highest estimates for repeatability and heritability of means over harvests. Best linear unbiased predictions were estimated for all the traits through restricted maximum likelihood method for individual crops and pooled data. The clone Co 09004 recorded high estimates for quality traits, and clone Co 11015 was found to be superior for both yield and quality traits.
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References
Almeida, L.M., A.P. Viana, G.M. Gonçalves, and G.C. Entringer. 2014. Selection of sugar cane full-sib families using mixed models and ISSR markers. Genetics and Molecular Research 13(4): 9202–9212.
Balzarini, M. 2000. Biometrical Models for Predicting Future Performance in Plant Breeding. A Ph.D. Dissertation, LSU, LA, USA. UMI catalog.
Ben, H., and F. Michael. 2018. Metrics: Evaluation Metrics for Machine Learning. R package version 0.1.4.
Bernardo, R. 1996. Best linear unbiased prediction of maize single-cross performance. Crop Science 36: 872–876.
Bernardo, R. 1999. Best Linear Unbiased Predictor Analysis. In: The Genetics and Exploitation of Heterosis in Crops. ASSA-CSSA-SSSA, Madison, USA, pp. 269–276.
Boake, C.R.B. 1989. Repeatability: Its role in evolutionary studies of mating behavior. Evolutionary Ecology 3: 173–182.
Bozdogan, H. 1987. Model selection and Akaike’s information criterion (AIC): The general theory and its analytical extensions. Psychometrika 52: 345–370. https://doi.org/10.1007/BF02294361.
Braz, T.G.S., D.M. Fonseca, L. Jank, M.D.V. Resende, J.Á. Martuscello, and R.M.I. Simeão. 2013. Genetic parameters of agronomic characters in Panicum maximum hybrids. Revista Brasileira De Zootecnia 42(4): 231–237. https://doi.org/10.1590/S1516-35982013000400001.
Douglas, B., M. Martin, B. Ben, and W. Steve. 2015. Fitting Linear Mixed-Effects Models using lme4. Journal of Statistical Software 67(1): 1–48.
Furlani, R.C.M., M.L.T. Moraes, M.D.V. Resende, J.E. Furlani, P.S. Goncalves, F.M.V. Valério, and J.R. Paiva. 2005. Estimation of variance components and prediction of breeding values in rubber tree breeding using the REML/BLUP procedure. Genetics and Molecular Biology 28(2): 271–276.
Henderson, C.R. 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics 31: 423–447.
Hothorn, T., F. Bretz, and P. Westfall. 2008. Simultaneous Inference in General Parametric Models. Biometrical Journal 50(3): 346–363.
Leite, M.S.O., L.A. Peternelli, M.H.P. Barbosa, P.R. Cecon, and C.D. Cruz. 2009. Sample size for full-sib family evaluation in sugarcane. Pesquisa Agropecuaria Brasileria 44: 1562–1574.
Olivoto, T., and A.D. Lúcio. 2020. Metan: An R package for multi-environment trial analysis. Methods Ecology Evolution 11: 783–789. https://doi.org/10.1111/2041-210X.13384.
Olivoto, T., and M. Nardino. 2020. MGIDI: toward an effective multivariate selection in biological experiments. Bioinformatics btaa981. https://doi.org/10.1093/bioinformatics/btaa981.
Panter, D.M., and F.L. Allen. 1995. Using best linear unbiased predictions to enhance breeding for yield in soybean: I. Choosing parents. Crop Science 35: 397–405.
Pedrozo, C.A., M.H.P. Barbosa, F.L. Silva, M.D.V. Resende, and L.A. Peternelli. 2011. Repeatability of full-sib sugarcane families across harvests and the efficiency of early selection. Euphytica 182: 423–430.
Piepho, H.P., J. Möhring, A.E. Melchinger, and A. Büchse. 2008. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161: 209–228.
R Core Team. 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Resende, M.D.V. 2002. Genética, biométrica e estatística: no melhoramento de plantas perenes. Brasília: Embrapa Informação Tecnológica.
Smith, A.B., and B.R. Cullis. 2018. Plant breeding selection tools built on factor analytic mixed models for multi-environment trial data. Euphytica 214: 143. https://doi.org/10.1007/s10681-018-2220-5.
Walker, C. A., F. Pita, and K.G. Campbell. 2011. Comparison of linear mixed models for multiple environment plant breeding trials. In Annual Conference on Applied Statistics in Agriculture. http://newprairiepress.org/agstatconference/2011/proceedings/13. Accessed June 2020
Acknowledgements
Authors gratefully acknowledge South Indian Sugar Mills Association (SISMA) for collaboration with ICAR-Sugarcane Breeding Institute, and cane personnel at Sakthi sugars private limited, Sivaganga, for taking good care of the crop and supporting in conduct of field trials. We express our gratitude to Director, ICAR-Sugarcane Breeding Institute, and Head, Division of crop improvement for providing adequate support to conduct this trial.
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Pathy, T.L., Mohanraj, K. Estimating Best Linear Unbiased Predictions (BLUP) for Yield and Quality Traits in Sugarcane. Sugar Tech 23, 1295–1305 (2021). https://doi.org/10.1007/s12355-021-01011-4
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DOI: https://doi.org/10.1007/s12355-021-01011-4