Abstract
The genotype by environment interaction is essential in any plant breeding program. Methodologies allowing the evaluation of nonlinear genotype responses to environmental variation allied to prior beliefs on unknown parameters bring new insights for breeders. In this context, we aimed to propose a Bayesian segmented regression model to infer on phenotypic adaptability and stability of cotton (Gossypium L.) cultivars. The efficiency of using informative and minimally informative prior distributions in the selection of cultivars was also investigated. Randomized complete-block design experiments to study fiber yield (kg/ha) of 16 cotton genotypes were carried out in eight different environments in the State of Mato Grosso, Brazil. The proposed methodology was implemented using the free software R through the rbugs package. Bayesian segmented regression model was able to recommend cotton genotypes for cultivation, allowing to exploit the nonlinear pattern of genotype responses to environmental variation to find out the “ideal” genotype. The use of suitable prior information reduces the ranges of the credibility intervals, implying in higher precision of parameter estimates and, consequently, in reliable genotypes selection.
Similar content being viewed by others
References
Araujo LF, Almeida WS, Bertini CHCM, Neto FCV, Bleicher E (2012) Correlations and path analysis in components of fiber yield in cultivars of upland cotton. Bragantia 71:328–335. https://doi.org/10.1590/S0006-87052012005000036
Avci E (2017) Using informative prior form meta-analysis in Bayesian approach. J data Sci 16:575–588
Carvalho LP, Farias FJC, Morello CL, Teodoro PE (2016) Selection of cotton genotypes for greater length of fibers. Crop Breed Appl Biot 16:340–347. https://doi.org/10.1590/1984-70332016v16n4n50
Casella G, Berger RL (2002) Statistical inference. Duxbury, Pacific Grove
Casella G, George EI (1992) Explaining the Gibbs sampler. The Am Stat 46:167–174. https://doi.org/10.2307/2685208
Cotes JM, Crossa J, Sanches A, Cornelius PL (2006) A Bayesian approach for assessing the stability of genotypes. Crop Sci 46:2654–2665. https://doi.org/10.2135/cropsci2006.04.0227
Couto MF, Nascimento M, do Amaral AT, Silva FF, Viana AP, Vivas M (2014) Eberhart and Russel Bayesian method in the selection of popcorn cultivars. Crop Sci 55:571–577. https://doi.org/10.2135/cropsci2014.07.0498
Crossa J (2012) From genotype × environment interaction to gene × environment interaction. Curr Genom 13:225–244. https://doi.org/10.2174/138920212800543066
Cruz CD, Torres RAA, Vencovsky R (1989) An alternative approach to the stability analysis proposed by Silva e Barreto. Revista Brasileira de Genética 12:567–580
Eberhart SA, Russell WA (1966) Stability parameters for comparing varieties. Crop Sci 6:36–40. https://doi.org/10.2135/cropsci1966.0011183X000600010011x
Evans M, Moshonov H (2006) Checking for prior-data conflict. Bayesian Anal 1:893–914
Farias FJC, Carvalho LP, Silva Filho JL, Teodoro PE (2016) Biplot analysis of phenotypic stability in upland cotton genotypes in Mato Grosso. Genet Mol Res 15:1–8. https://doi.org/10.4238/gmr.15028009
Ferreira DF, Demétrio CGB, Manly BFJ, Machado AA, Vencovsky R (2006) Statistical models in agriculture: biometrical methods for evaluating phenotypic stability in plant breeding. Cerne 12:373–388
Finlay KW, Wilkinson GN (1963) The analysis of adaptation in a plant-breeding programme. Aust J Agr Res 14:742–754. https://doi.org/10.1071/AR9630742
Gauch HG (2006) Statistical analysis of yield trials by AMMI and GGE. Crop Sci 46:1488–1500. https://doi.org/10.2135/cropsci2005.07-0193
Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7:457–511. https://doi.org/10.1214/ss/1177011136
Geweke J (1992) Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments. In: Bernardo LM, Berger J, Dawid AP, Smith AFM (eds) Bayesian statistics, 4th edn. Oxford University Press, Oxford, pp 169–193
Hoogerheide ESS, Farias FJC, Vencovsky R, Freire EC (2007) Estabilidade fenotípica de genótipos de algodoeiro no Estado do Mato Grosso. Pesqui agropecu bras 42:695–698. https://doi.org/10.1590/S0100-204X2007000500012
Kitada S, Hayashi T, Kishino H (2000) Empirical Bayes procedure for estimating genetic distance between populations and effective population size. Genetics 156:2063–2079
Lin CS, Binns MR (1988) A superiority measure of cultivar performance for cultivar x location data. Can J Plant Sci 68:193–198. https://doi.org/10.4141/cjps88-018
Matei G, Woyann LG, Meneguzzi C, Todeschini MH, Trevisan DM, Rosa AC, Benin G (2017) Profiling and genotype × environment interactions of seed sugar contents in Brazilian soybean genotypes. Euphytica 213:203. https://doi.org/10.1007/s10681
Nascimento M, Cruz CD, Campana ACM, Tomaz RS, Salgado CC, Ferreira RP (2009) Alteração no método centroide de avaliação da adaptabilidade genotípica. Pesqui agropecu bras 44:263–269. https://doi.org/10.1590/S0100-204X2009000300007
Nascimento M, Ferreira A, Ferrão RG, Campana ACM, Bhering LL, Cruz CD, Ferrão MAG, Fonseca AFA (2010) Adaptabilidade e estabilidade via regressão não paramétrica em genótipos de café. Pesqui agropecu bras 45:41–48. https://doi.org/10.1590/S0100-204X2010000100006
Nascimento M, Silva FF, Sáfadi T, Nascimento ACC, Ferreira RP, Cruz CD (2011) Abordagem bayesiana para avaliação da adaptabilidade e estabilidade de genótipos de alfafa. Pesqui agropecu bras 46:26–32. https://doi.org/10.1590/S0100-204X2011000100004
Raftery AE, Lewis SM (1992) One long run with diagnostics: implementation strategies for Markov chain Monte Carlo. Stat Sci 7:493–497. https://doi.org/10.1214/ss/1177011143
Setimela PS, Gasura E, Tarekegne AT (2017) Evaluation of grain yield and related agronomic traits of quality protein maize hybrids in Southern Africa. Euphytica 213:289. https://doi.org/10.1007/s10681-017-2082-2
Silva Filho JL, Morello CL, Farias FJC, Lamas FM, Pedrosa MB, Ribeiro JL (2008) Comparação de métodos para avaliar a adaptabilidade e estabilidade produtiva em algodoeiro. Pesqui Agropecu Bras 43:349–355. https://doi.org/10.1590/S0100-204X2008000300009
Smith BJ (2007) boa: an R Package for MCMC Output Convergence Assessment and Posterior Inference. J Stat Softw 21:1–37. https://doi.org/10.18637/jss.v021.i11
Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc B 64:583–639. https://doi.org/10.1111/1467-9868.00353
The R foundation (2010) R: the R project for statistical computing. https://www.r-project.org. Accessed 13 June 2017
Verma MM, Chahal GS, Murty BR (1978) Limitation of conventional regression analysis: a proposed modification. Theor Appl Genet 53:89–91
Wang Q, Wei J, Pan Y, Xu S (2016) An efficient empirical Bayes method for genomewide association studies. J Anim Breed Genet 133:253–263. https://doi.org/10.1111/jbg.12191
Yan J, Prates M (2013) rbugs: Fusing R and OpenBugs and Beyond. R package version 0.5-9. https://CRAN.R-project.org/package=rbugs (Accessed 12 June. 2017)
Yan W, Hunt LA, Sheng Q, Szlavniscs Z (2000) Cultivar evaluation and mega-environment investigation based on the GGE Biplot. Crop Sci 40:597–605. https://doi.org/10.2135/cropsci2000.403597x
Acknowledgements
We are grateful for financial support from Fundação de Apoio à Universidade Federal de Viçosa (FUNARBE), Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG -#PPM-00518-15 and#APQ-00825-14), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq - Universal-#446176/2014-1).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Nascimento, M., Nascimento, A.C.C., e Silva, F.F. et al. Bayesian segmented regression model for adaptability and stability evaluation of cotton genotypes. Euphytica 216, 30 (2020). https://doi.org/10.1007/s10681-020-2564-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10681-020-2564-5