Abstract
A reliable phenotyping and a thorough investigation of the experimental data via accurate statistical methods are key requirements for attaining selection gain. Coffee bean yield data are provided from annual harvests. The data analysis is generally performed based on total phenotypic data of entire period or in biennia using a split-plot-in-time model. An essential aspect of these data is the covariance associated with some random factors of the statistical model. The aim of this work was to evaluate different covariance matrix structures in coffee progenies bean yield modeling and their implications for prediction accuracy of progenies genotypic values and selection under different harvest data grouping strategies. We evaluated 21 S0:1 Coffea arabica L. progenies during eight harvests. The analyses were conducted considering all the harvests (annual or biennia) and focusing only on the high yield or low yield years. In each case, we modeled the residual covariance matrix (R) and the genetic covariance matrix over harvests (G). We noticed that some models are more suitable in explaining the coffee yield pattern. There were alterations in parameter estimates, prediction error variance of genotypic values, rankings and coincidence index in selecting the best progenies. The model involving annual harvests gave more information regarding the coffee progenies yield behavior in comparison to biennia.
Similar content being viewed by others
References
Apiolaza LA, Gilmour AR, Garrick DJ (2000) Variance modelling of longitudinal height data from a Pinus radiata progeny test. Can J For Res 30:645–654. doi:10.1139/x99-246
Bernardo R (2010) Breeding for quantitative traits in plants. Stemma Press, Woodbury
Bonomo P, Cruz CD, Viana JMS, Pereira AA, Oliveira VR, Carneiro PCS (2004) Evaluation of coffee progenies from crosses of Catuai Vermelho and Catuai Amarelo with “Hibrido de Timor” descents. (In Portuguese, with English abstract.). Bragantia 63:207–219. doi:10.1590/S0006-87052004000200006
Botelho CE, Rezende JC, Carvalho GR, Carvalho AM, Andrade VT, Barbosa CR (2010) Adaptability and phenotype stability of Arabica coffee cultivars in Minas Gerais, Brazil. (In Portuguese, with English abstract.). Pesqui Agropecu Bras 45:1404–1411. doi:10.1590/S0100-204X2010001200010
Carvalho A, Krug CA, Mendes JET, Antunes Filho H, Morais H, Aloisi Sobrinho J et al (1952) Breeding of coffee plant IV: Mundo Novo coffee cultivar. (In Portuguese, with English abstract.). Bragantia 12:97–129. doi:10.1590/S0006-87051952000200001
Carvalho GR, Mendes ANG, Bartholo GF, Cereda GJ (2006) Mundo novo coffee (Coffea arabica L.) cultivar progenies evaluation. (In Portuguese, with English abstract.). Cienc Agrotec 30:853–860. doi:10.1590/S1413-70542006000500005
Carvalho SP, Custódio TN, Baliza DP, Rezende TT (2012) Meta-analysis for heritability estimates of vegetative and reproductive traits of Coffea arabica L. (In Portuguese, with English abstract.). Semin-Cienc Agrar 33:1291–1298. doi:10.5433/1679-0359.2012v33n4p1291
Cecon PR, Silva FF, Ferreira A, Ferrão RG, Carneiro APS, Detmann E et al (2008) Repeated measure analysis in the clonal evaluation in ‘Conilon’ coffee. (In Portuguese, with English abstract.). Pesqui Agropecu Bras 43:1171–1176. doi:10.1590/S0100-204X2008000900011
Cheng J, Edwards LJ, Maldonado-Molina MM, Komro KA, Muller KE (2010) Real longitudinal data analysis for real people: building a good enough mixed model. Stat Med 29:504–520. doi:10.1002/sim.3775
Cilas C, Montagnon C, Bar-Hen A (2011) Yield stability in clones of Coffea canephora in the short and medium term: longitudinal data analyses and measures of stability over time. Tree Genet Genome 7:421–429. doi:10.1007/s11295-010-0344-4
Everitt BS (1999) Analysis of longitudinal data: beyond MANOVA. Br J Psychiatry 172:7–10. doi:10.1192/bjp.172.1.7
Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics. Longmans Green, Harlow, Essex
Hamblin J, Zimmerman MJO (1986) Breeding common bean for yield mixtures. Plant Breed Rev 4:245–272. doi:10.1002/9781118061015.ch8
Hu X, Spilke J (2011) Variance–covariance structure and its influence on variety assessment in regional crop trials. Field Crop Res 120:1–8. doi:10.1016/j.fcr.2010.09.015
Keselman HJ, Algina J, Kowalchuk RK (2001) The analysis of repeated measures design: a review. Br J Math Stat Psychol 54:1–20. doi:10.1348/000711001159357
Knafl GJ, Beeber L, Schwartz TA (2012) A strategy for selecting among alternative models for continuous longitudinal data. Res Nurs Health 35:647–658. doi:10.1002/nur.21508
Littell RC, Pendergast J, Ranjini N (2000) Modeling covariance structure in the analysis of repeated measures data. Stat Med 19:1793–1819. doi:10.1002/1097-0258(20000715)19:13<1793:AID-SIM482>3.0.CO;2-Q
Littell RC, Littell RC, Milliken GA, Stroup WW, Wolfinger RD, Schabenberger O (2006) SAS for mixed models. SAS Institute, Cary
Liu S, Rovine MJ, Molenaar CM (2012) Selecting a linear mixed model for longitudinal data: repeated measures analysis of variance, covariance pattern model, and growth curve approaches. Psychol Methods 17:15–30. doi:10.1037/a0026971
Mariguele KH, Resende MDV, Viana JMS, Silva FF, Silva PSL, Knop FC (2011) Methods of longitudinal data analysis for the genetic improvement of sugar apple. (In Portuguese, with English abstract.). Pesqui Agropecu Bras 46:1657–1664. doi:10.1590/S0100-204X2011001200011
Medina Filho HP, Bordignon R, Guerreiro Filho O, Maluf MP, Fazuoli LC (2007) Breeding of Arabica coffee at IAC, Brazil: objectives, problems and prospects. Acta Hortic 745:393–408. doi:10.17660/ActaHortic.2007.745.25
Mistro JC, Fazuoli LC, Guerreiro Filho O, Silvarolla MB, Toma-Braghini M (2007) Determination of number of years in Arabica coffee progenies selection through repeatability. Crop Breed Appl Biotechnol 8:79–84. doi:10.12702/1984-7033.v08n01a11
Oliveira ACB, Pereira AA, Silva FL, Rezende JC, Botelho CE, Carvalho GR (2011) Prediction of genetic gains from selection in Arabica coffee progenies. Crop Breed Appl Biotechnol 11:106–113. doi:10.1590/S1984-70332011000200002
Piepho HP, Eckl T (2014) Analysis of series of variety trials with perennial crops. Grass Forage Sci 69:431–440. doi:10.1111/gfs.12054
Piepho HP, Mohring J (2007) Computing heritability and selection response from unbalanced plant breeding trials. Genetics 177:1881–1888. doi:10.1534/genetics.107.074229
Piepho HP, Buchse A, Richter C (2004) A mixed modelling approach for randomized experiments with repeated measures. J Agron Crop Sci 190:230–247. doi:10.1111/j.1439-037X.2004.00097.x
Pinto LRM, Sanches CL, Dias CT, Loguercio LL (2013) Advantages of multivariate analysis of profiles for studies with temporal variation of treatment effects in plants. Int J Plant Sci 174:85–96. doi:10.1086/668218
Resende MDV (2007) Matemática e estatística na análise de experimentos e no melhoramento genético (Mathematics and statistics in the experiment analysis and genetic inprovenment-In Portuguese.) Embrapa Florestas, Colombo
Resende RMS, Resende MDV, do Valle CB, Jank L, Torres Júnior RAA, Cançado LJ (2007) Selection efficiency in Brachiaria hybrids using a posteriori blocking. Crop Breed Appl Biotechnol 7:296–303. doi:10.12702/1984-7033.v07n03a09
Resende RMS, Casler MD, Resende MDV (2013) Selection methods in forage breeding: a quantitative appraisal. Crop Sci 53:1925–1936. doi:10.2135/cropsci2013.03.0143
SAS Institute (2009) User’s guide: statistics. SAS Institute, Cary
Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464. doi:10.2307/2958889
Smith AB, Cullis BR, Thompson R (2005) The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. J Agric Sci 143:449–462. doi:10.1017/S0021859605005587
Smith AB, Stringer JK, Wei X, Cullis BR (2007) Varietal selection for perennial crops where data relate to multiple harvests from a series of field trials. Euphytica 157:253–266. doi:10.1007/s10681-007-9418-2
White TL, Hodge GR (1988) Best linear prediction of breeding values in a forest tree improvement program. Theor Appl Genet 76:719–727. doi:10.1007/BF00303518
Wolfinger RD (1996) Heterogeneous variance: covariance structures for repeated measures. J Agric Biol Environ Stat 1:205–230
Acknowledgments
We are grateful to anonymous reviewer for helpful comments. Professor Julio Sílvio de Sousa Bueno Filho—Universidade Federal de Lavras (UFLA), Departamento de Ciências Exatas- owing heritability estimator suggestion. Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). Consórcio Pesquisa Café.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Andrade, V.T., Gonçalves, F.M.A., Nunes, J.A.R. et al. Statistical modeling implications for coffee progenies selection. Euphytica 207, 177–189 (2016). https://doi.org/10.1007/s10681-015-1561-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10681-015-1561-6