Abstract
This study investigates the application of random regression models for analyzing multi-harvest data in cacao breeding. The aim was to understand the genetic dynamics over ten harvest years and select high-performing genotypes. The trial was conducted in Ouro Preto D’Oeste, Rondônia, Brazilian Amazon. Twenty biparental cacao crosses were evaluated over ten years using random regression models. Models with different polynomial degrees and covariance structures for the residual effects were compared, and the best model was determined using Akaike Information Criterion. We also compared the genetic gains after selecting using three criteria: breeding values, persistence, and area under genotypic trajectories. The best random regression models differed between traits. Genotype-by-harvest interactions were observed, emphasizing the temporal variability in genotype performance. Genetic correlations across harvests illustrated the dynamic nature of genetic expression. Accuracy and heritability fluctuated over successive harvests, emphasizing the complexity of genotype performance prediction. Non-linear genotypic trajectories revealed the presence of unique genetic attributes associated with each trait, with number of healthy fruits showing a tendency towards standardization and dry bean weight displaying a more complex pattern. Consistency in selecting genotypes based on number of healthy fruits highlights reliable selection. Conversely, the variability in choosing top genotypes for dry bean weight underscores the need for cautious selection strategies, as it is a more complex trait to optimize. Despite these insights, future research should consider specific environmental conditions, management practices, and the integration of genomic information for a more comprehensive understanding of genetic dynamics in cacao breeding.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Code availability
The codes are available from the corresponding authors on reasonable request.
References
Abdulai I, Jassogne L, Graefe S, Asare R, Van Asten P, Läderach P, Vaast P (2018) Characterization of cocoa production, income diversification and shade tree management along a climate gradient in Ghana. PLoS ONE 13(4):e0195777. https://doi.org/10.1371/journal.pone.0195777
Akaike H (1974) A new look at the statistical model identification. IEEE Trans Automat Contr 19:716–723. https://doi.org/10.1109/TAC.1974.1100705
Alves RS, Resende MD, Rocha JR, Peixoto MA, Teodoro PE, Bhering LL, Santos GA (2020) Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in Eucalyptus breeding. Bragantia 79:485–501. https://doi.org/10.1590/1678-4499.20200125
Alves RS, Teodoro PE, de Azevedo Peixoto L, Silva LA, Laviola BG, de Resende MD, Bhering LL (2019) Multiple-trait BLUP in longitudinal data analysis on Jatropha curcas breeding for bioenergy. Ind Crops Prod 130:558–561. https://doi.org/10.1016/j.indcrop.2018.12.019
Amadeu RR, Garcia AA, Munoz PR, Ferrão LF (2023) AGHmatrix: genetic relationship matrices in R. Bioinformatics 39(7):btad445. https://doi.org/10.1093/bioinformatics/btad445
Arnold PA, Kruuk LE, Nicotra AB (2019) How to analyse plant phenotypic plasticity in response to a changing climate. New Phytol 222(3):1235–1241. https://doi.org/10.1111/nph.15656
Bekele F, Bidaisee G, Saravanakumar D (2021) Examining phenotypic diversity and economic value of cacao (Theobroma cacao L.) conserved at the International Cocoa Genebank, Trinidad to support improvement in cocoa yield globally. Tropical Agriculture 97(2). Available at: https://journals.sta.uwi.edu/ojs/index.php/ta/article/view/7970/6904. Accessed on 22/09/2022
Bekele FL, Bidaisee GG, Singh H, Saravanakumar D (2020) Morphological characterisation and evaluation of cacao (Theobroma cacao L.) in Trinidad to facilitate utilisation of Trinitario Cacao globally. Genet Resour Crop Evol 67(3):621–643. https://doi.org/10.1007/s10722-019-00793-7
Bekele F, Phillips-Mora W (2019) Cacao (Theobroma cacao L.) breeding. In: Al-Khayri J, Jain S, Johnson D (eds) Advances in plant breeding strategies: industrial and food crops. Springer, Cham. https://doi.org/10.1007/978-3-030-23265-8_12
Bohmanova J, Miglior F, Jamrozik J, Misztal I, Sullivan PG (2008) Comparison of random regression models with Legendre polynomials and linear splines for production traits and somatic cell score of Canadian holstein cows. J Dairy Sci 91(9):3627–3638. https://doi.org/10.3168/jds.2007-0945
Bornhofen E, Fè D, Lenk I, Greve M, Didion T, Jensen CS, Asp T, Janss L (2022) Leveraging spatiotemporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models. The Plant Genome 15(4):e20255. https://doi.org/10.1002/tpg2.20255
Brien CJ, Demétrio CG (2009) Formulating mixed models for experiments, including longitudinal experiments. J Agric Biol Environ Stat 14:253–280. https://doi.org/10.1198/jabes.2009.08001
Butler DG, Cullis BR, Gilmour AR, Gogel BJ, Thompson R (2018) ASReml-R reference manual version 4.1. VSN International Ltd, Hemel Hempstead
Calvo AM, Botina BL, García MC, Cardona WA, Montenegro AC, Criollo J (2021) Dynamics of cocoa fermentation and its effect on quality. Sci Rep 11(1):16746. https://doi.org/10.1038/s41598-021-95703-2
Campbell M, Momen M, Walia H, Morota G (2019) Leveraging breeding values obtained from random regression models for genetic inference of longitudinal traits. The Plant Genome 12(2):180075. https://doi.org/10.3835/plantgenome2018.10.0075
Carvalho CGP, Cruz CD, Almeida CMVC, Machado PFR (2002) Yield repeatability and evaluation period in hybrid cocoa assessment. Crop Breed Appl Biotechnol 2(1):149–156. https://doi.org/10.12702/1984-7033.v02n01a19
Chaves SFS, Dias LAS, Alves RS, Alves RM, José AR, Almeida CM (2022) Number of harvest years and selection for productivity, witches’ broom resistance, stability, and adaptability in cacao. Agron J 114(6):3234–3245. https://doi.org/10.1002/agj2.21149
Chaves SFS, Evangelista JSPC, Alves RS, Ferreira FM, Dias LAS, Alves RM, Dias KOG, Bhering LL (2022) Application of linear mixed models for multiple harvest/site trial analyses in perennial plant breeding. Tree Genet Genomes 18(6):44. https://doi.org/10.1007/s11295-022-01576-5
Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat 11:381–393. https://doi.org/10.1198/108571106x154443
Dias LAS, Kageyama PY (1998) Repeatability and minimum harvest period of cacao (Theobroma cacao L.) in Southern Bahia. Euphytica 102(1):29–35. https://doi.org/10.1023/A:1018373211196
FAO - Food and Agriculture Organization (2020) Crops and livestock products Food and agriculture data, 2020. Rome: FAO. Available at: https://www.fao.org/faostat/en/#data/QCL. Accessed on 22/09/2022
Farrell AD, Rhiney K, Eitzinger A, Umaharan P (2018) Climate adaptation in a minor crop species: is the cocoa breeding network prepared for climate change? Agroecol Sustain Food Syst 42(7):812–833. https://doi.org/10.1080/21683565.2018.1448924
Faveri JD, Verbyla AP, Cullis BR, Pitchford WS, Thompson R (2017) Residual variance–covariance modelling in analysis of multivariate data from variety selection trials. J Agric Biol Environ Stat 22:1–22. https://doi.org/10.1007/s13253-016-0267-0
Faveri JD, Verbyla AP, Pitchford WS, Venkatanagappa S, Cullis BR (2015) Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials. Crop Pasture Sci 66(9):947–962. https://doi.org/10.1071/CP14312
Faveri JD, Verbyla AP, Rebetzke G (2022) Random regression models for multi-environment, multi-time data from crop breeding selection trials. Crop Pasture Sci 74(4):271–283. https://doi.org/10.1071/CP21732
Gateau-Rey L, Tanner EV, Rapidel B, Marelli JP, Royaert S (2018) Climate change could threaten cocoa production: effects of 2015-16 El Niño-related drought on cocoa agroforests in Bahia, Brazil. PLoS ONE 13(7):e0200454. https://doi.org/10.1371/journal.pone.0200454
Houlahan K, Schenkel FS, Miglior F, Jamrozik J, Stephansen RB, González-Recio O, Charfeddine N, Segelke D, Butty AM, Stratz P, VandeHaar MJ (2023) Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle. J Dairy Sci. https://doi.org/10.3168/jds.2022-23124
Kirkpatrick M, Lofsvold D, Bulmer M (1990) Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124(4):979–993. https://doi.org/10.1093/genetics/124.4.979
Lahive F, Hadley P, Daymond AJ (2019) The physiological responses of cacao to the environment and the implications for climate change resilience. A review. Agron Sustain Dev 39:1–22. https://doi.org/10.1007/s13593-018-0552-0
Lanaud C, Fouet O, Clément D, Boccara M, Risterucci AM, Surujdeo-Maharaj S, Legavre T, Argout X (2009) A meta–QTL analysis of disease resistance traits of Theobroma cacao L. Mol Breed 24(4):361–374. https://doi.org/10.1007/s11032-009-9297-4
Malikouski RG, Alves RS, Peixoto MA, Ferreira FM, Nascimento EF, Morais AL, Zucoloto M, Dias KOG, Bhering LL (2022) Selection index based on random regression model in ‘Tahiti’ acid lime. Euphytica 218:153. https://doi.org/10.1007/s10681-022-03105-w
Moreira FF, Rojas de Oliveira H, Lopez MA, Abughali BJ, Gomes G, Cherkauer KA, Brito LF, Rainey KM (2021) High-throughput phenotyping and random regression models reveal temporal genetic control of soybean biomass production. Front Plant Sci 12:715983. https://doi.org/10.3389/fpls.2021.715983
Mrode RA (2014) Linear models for the prediction of animal breeding values. CAB International, Wallingford
Novomestky F (2022) orthopolynom: collection of functions for orthogonal and orthonormal polynomials. R Packag Vers 1(0–6):1
Oliveira HR, Brito LF, Lourenco DA, Silva FF, Jamrozik J, Schaeffer LR, Schenkel FS (2019) Invited review: advances and applications of random regression models: from quantitative genetics to genomics. J Dairy Sci 102(9):7664–7683. https://doi.org/10.3168/jds.2019-16265
Patterson HD, Thompson R (1971) Recovery of inter-block information when block sizes are unequal. Biometrika 58(3):545–554. https://doi.org/10.1093/biomet/58.3.545
Peixoto MA, Alves RS, Coelho IF, Evangelista JSPC, Resende MDV, Rocha JRASC, Silva FF, Laviola BG, Bhering LL (2020) Random regression for modeling yield genetic trajectories in Jatropha curcas breeding. PLoS ONE 15:e0244021. https://doi.org/10.1371/journal.pone.0244021
Piepho HP, Eckl T (2014) Analysis of series of variety trials with perennial crops. Grass Forage Sci 69(3):431–440. https://doi.org/10.1111/gfs.12054
R Core Team (2021) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/
Rocha JR, Marçal TD, Salvador FV, da Silva AC, Machado JC, Carneiro PC (2018) Genetic insights into elephantgrass persistence for bioenergy purpose. PLoS ONE 13(9):e0203818. https://doi.org/10.1371/journal.pone.0203818
Schaeffer LR (2004) Application of random regression models in animal breeding. Livest Prod Sci 86(1–3):35–45. https://doi.org/10.1016/S0301-6226(03)00151-9
Silva Neto PJ, Matos PGG, Martins ACS, Silva AP (2013) Manual técnico do cacaueiro para a Amazônia brasileira. CEPLAC/SUEPA, Belém
Tahi M, Trebissou C, Ribeyre F, Guiraud BS, da Pokou DN, Cilas C (2019) Variation in yield over time in a cacao factorial mating design: changes in heritability and longitudinal data analyses over 13 consecutive years. Euphytica 215:1–2. https://doi.org/10.1007/s10681-019-2429-y
Wolfinger RD (1996) Heterogeneous variance: covariance structures for repeated measures. J Agric Biol Environ Stat 1:205–230. https://doi.org/10.2307/1400366
Funding
This study had the financial support from Fundação de Amparo a Pesquisa de Minas Gerais (FAPEMIG), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Finance Code 001, and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).
Author information
Authors and Affiliations
Contributions
All authors contributed to the study and conception and design: AA: Writing—Original draft, Visualization; SC: Formal analysis, Methodology and Writing—Review & Editing; MA: Writing—Original draft, Writing—Review & Editing; LD: Supervision, Resources, Writing—Review & Editing; RM: Methodology and Conceptualization; and CA: Writing—Review & Editing and Data curation.
Corresponding author
Ethics declarations
Conflict of interest
There are no conflicts of interests in this article.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Alves, A.K.S., Chaves, S.F.S., Araújo, M.S. et al. Improving multi-harvest data analysis in cacao breeding using random regression. Euphytica 220, 7 (2024). https://doi.org/10.1007/s10681-023-03270-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10681-023-03270-6