Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-26T03:05:17.548Z Has data issue: false hasContentIssue false

Yielding stability of early maturing potato varieties: Bayesian analysis

Published online by Cambridge University Press:  11 November 2020

M. Przystalski*
Affiliation:
Research Centre for Cultivar Testing, 63-000Słupia Wielka, Poland
T. Lenartowicz
Affiliation:
Research Centre for Cultivar Testing, 63-000Słupia Wielka, Poland
*
Author for correspondence: M. Przystalski, E-mail: [email protected]

Abstract

Field trials conducted in multiple years across several locations play an essential role in plant breeding and variety testing. Usually, the analysis of the series of field trials is performed using a two-stage approach, where each combination of year and site is treated as environment. In variety testing based on the results from the analysis, the best varieties are recommended for cultivation. Under a Bayesian approach, the variety recommendation process can be treated as a formal decision theoretic problem. In the present study, we describe Bayesian counterparts of two stability measures and compare the varieties in terms of the posterior expected utility. Using the described methodology, we identify the most stable and highest tuber yielding varieties in the Polish potato series of field trials conducted from 2016 to 2018. It is shown that variety Arielle was the highest yielding, the third most stable variety and was the second best variety in terms of the posterior expected utility. In the present work, application of the Bayesian approach allowed us to incorporate the prior knowledge about the tested varieties and offered a possibility of treating the variety recommendation process as a formal decision process.

Type
Crops and Soils Research Paper
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bernardo, LAY Júnior, Silva, CP, de Oliveira, LA, Nuvunga, JJ, Pires, LPM, Von Pinho, RG and Balestre, M (2018) AMMI Bayesian models to study stability and adaptability in maize. Agronomy Journal 110, 112.Google Scholar
Butler, D, Cullis, BR, Gilmour, AR and Gogel, BJ (2007) Analysis of Mixed Models for S language Environments: ASRemlR Reference Manual. Brisbane, Australia: Queensland DPI. Available at http://www.vsni.co.uk/resources/doc/asreml-R.pdf.Google Scholar
Caliński, T, Czajka, S, Kaczmarek, Z, Krajewski, P, Pilarczyk, W, Siatkowski, I and Siatkowski, M (2017) On mixed model analysis of multi-environment variety trials: a reconsideration of the one-stage and the two-stage models and analyses. Statistical Papers 58, 433465.10.1007/s00362-015-0706-yCrossRefGoogle Scholar
Cowles, MK and Carlin, BP (1996) Markov chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical Association 91, 833904.CrossRefGoogle Scholar
Crossa, J, Perez-Elizalde, S, Jarquin, D, Cotes, JM, Viele, K, Liu, G and Cornelius, PL (2011) Bayesian Estimation of additive main effects and multiplicative interaction model. Crop Science 51, 14581469.CrossRefGoogle Scholar
Damesa, TM, Möhring, J, Worku, M and Piepho, HP (2017) One step at a time: stage-wise analysis of series of experiments. Agronomy Journal 109, 845857.10.2134/agronj2016.07.0395CrossRefGoogle Scholar
de Oliveira, LA, Silva, CP, Nuvunga, JJ, Silva, AQ and Balestre, M (2016) Bayesian GGE biplot models applied to maize multi-environment trials. Genetics and Molecular Research 15, 121.CrossRefGoogle Scholar
Digby, PGN (1979) Modified joint regression analysis for incomplete variety x environment data. Journal of Agricultural Sciences 93, 8186.Google Scholar
Eberhart, SA and Russell, WA (1966) Stability parameters for comparing varieties. Crop Science 6, 3640.CrossRefGoogle Scholar
Edwards, JW and Jannink, JL (2006) Bayesian modeling of heterogeneous error and genotype × environment interaction variances. Crop Science 46, 820833.CrossRefGoogle Scholar
Edwards, JW and Orellana, M (2015) Increasing selection response by Bayesian modeling of heterogeneous environmental variances. Crop Science 55, 556563.10.2135/cropsci2014.08.0582CrossRefGoogle Scholar
Eskridge, KM and Mumm, RF (1992) Choosing plant cultivars based on the probability of outperforming a check. Theoretical and Applied Genetics 84, 894900.Google ScholarPubMed
Eskrigde, KM (1990) Selection of stable cultivars using a safety first rule. Crop Science 30, 369374.CrossRefGoogle Scholar
Finlay, K and Wilkinson, GN (1963) The analysis of adaptation in a plant breeding programme. Australian Journal of Agricultural Research 14, 742754.10.1071/AR9630742CrossRefGoogle Scholar
Gauch, HG (1988) Model selection and validation for trials with interaction. Biometrics 44, 705715.CrossRefGoogle Scholar
Gelman, A (2006) Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1, 515533.CrossRefGoogle Scholar
Gelman, A and Rubin, DB (1992) Inference from iterative simulation using multiple sequences (with discussion). Statistical Science 7, 457511.CrossRefGoogle Scholar
Gelman, A, Carlin, JB, Stern, HS, Dunson, DB, Vehtari, A and Rubin, DB (2014) Bayesian Data Analysis, 3rd Edn. Boca Raton: Chapman & Hall/CRC.Google Scholar
Gilmour, AR, Gogel, BJ, Cullis, BR, Welham, SJ and Thompson, R (2002) ASReml User Guide Release 1.0. Hemel Hempstead, UK: VSN International Ltd. Available at http://www.VSNIntl.com.Google Scholar
Hadfield, JD (2012) MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. Journal of Statistical Software 33, 122.Google Scholar
Harville, DA (1977) Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association 80, 132138.CrossRefGoogle Scholar
Hu, X, Yan, S and Shen, K (2013) Heterogeneity of error variance and its influence on genotype comparison in multi-location trials. Field Crops Research 149, 322328.10.1016/j.fcr.2013.05.011CrossRefGoogle Scholar
Hu, X, Yan, S and Li, S (2014) The influence of error variance variation on analysis of genotype stability in multi-environment trials. Field Crops Research 156, 8490.CrossRefGoogle Scholar
Josse, J, van Eeuwijk, F, Piepho, HP and Denis, JB (2014) Another look at Bayesian analysis of AMMI models for genotype-environment data. Journal of Agricultural, Biological and Environmental Statistics 19, 240257.Google Scholar
Kang, MS (1988) A rank sum method for selecting high-yielding, stable corn genotypes. Cereal Research Communications 16, 113115.Google Scholar
Lian, L and de los Campos, G (2016) FW: an R package for Finlay–Wilkinson regression that incorporates genomic/pedigree information and covariance structures between environments. G3 Genes, Genomes, Genetics 6, 586597.Google Scholar
Longdon, B, Hadfield, JD, Webster, CL, Obbard, DJ and Jiggins, FM (2011) Host phylogeny determines viral persistence and replication in novel hosts. PLoS Pathology 7, e1002260.CrossRefGoogle ScholarPubMed
Longdon, B, Hadfield, JD, Webster, CL, Obbard, DJ and Jigins, FM (2012) Host phylogeny determines viral persistence and replication in novel hosts. PLoS Pathology 7(9), e1002260.10.1371/journal.ppat.1002260CrossRefGoogle Scholar
Lunn, D, Jackson, C, Best, N, Thomas, A and Spiegelhalter, D (2013) The BUGS Book. A Practical Introduction to Bayesian Analysis. Boca Raton: Chapman& Hall/CRC.Google Scholar
Macholdt, J, Piepho, HP and Honermeier, B (2019) Mineral NPK and manure fertilization affecting the yield stability of winter wheat: results from long-term field experiment. European Journal of Agronomy 102, 1422.CrossRefGoogle Scholar
Malosetti, M, Ribout, JM, Vargas, M, Crossa, J and van Eeuwijk, FA (2008) A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress (Zea mays L). Euphytica 161, 241257.10.1007/s10681-007-9594-0CrossRefGoogle Scholar
Mathew, B, Holand, AM, Koistinen, P, Léon, J and Sillanpää, MJ (2016) Reparametrization-based estimation of genetic parameters in multi-trait animal model using integrated nested Laplace approximation. Theoretical and Applied Genetics 129, 215225.CrossRefGoogle ScholarPubMed
Moore, KJ and Dixon, PM (2015) Analysis of combined experiments revisited. Agronomy Journal 107, 763771.CrossRefGoogle Scholar
Orellana, M, Edwards, JW and Carriquiry, A (2014) Heterogeneous variances in multienvironment yield trials for corn hybrids. Crop Science 54, 10481056.CrossRefGoogle Scholar
Piepho, HP (1996) Simplified procedure for comparing the stability of cropping systems. Biometrics 52, 315320.10.2307/2533168CrossRefGoogle Scholar
Piepho, HP (1998) Methods for comparing the yield stability of cropping systems. Journal of Agronomy and Crop Science 180, 193213.10.1111/j.1439-037X.1998.tb00526.xCrossRefGoogle Scholar
Piepho, HP (1999) Stability analysis using the SAS system. Agronomy Journal 91, 154160.CrossRefGoogle Scholar
Piepho, HP and van Eeuwijk, FA (2002) Stability analysis in crop performance evaluation. In Kang, M (ed). Crop Improvements: Challenges in the Twenty-First Century. Binghampton, New York: Food Products Press, pp. 315351.Google Scholar
Piepho, HP, Möhring, J, Schulz-Streeck, T and Ogutu, JO (2012) A stage-wise approach for analysis of multi-environment trials. Biometrical Journal 54, 844860.CrossRefGoogle ScholarPubMed
Plummer, M, Best, N, Cowles, K and Vines, K (2006) CODA: convergence diagnosis and output analysis for MCMC. RNews 6, 711.Google Scholar
Searle, SR, Casella, G and McCulloch, CE (2006) Variance Components. Hoboken: Wiley.Google Scholar
Shukla, GK (1972) Some statistical aspects of partitioning genotype-environment components of variability. Heredity 29, 237245.CrossRefGoogle Scholar
Smith, AB and Cullis, BR (2018) Plant breeding selection tools built on factor analytic mixed models for multi-environment trial data. Euphytica 214, 143.CrossRefGoogle Scholar
Sorensen, D and Gianola, D (2002) Likelihood, Bayesian and MCMC Methods in Quantitative Genetics. Statistics for Biology and Health. New York: Springer-Verlag.10.1007/b98952CrossRefGoogle Scholar
Speed, TP, Williams, ER and Patterson, HD (1985) A note on the analysis of resolvable block designs. Journal of the Royal Statistical Society Series B 47, 357361.Google Scholar
Spiegelhalter, DJ, Best, NG, Carlin, BP and van der Linde, A (2002) Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society Series B 64, 583689.CrossRefGoogle Scholar
Studnicki, M, Paderewski, J, Piepho, HP and Wójcik-Gront, E (2017) Prediction accuracy and consistency in cultivar ranking for factor-analytic linear mixed models for winter wheat multienvironmental trials. Crop Science 57, 25062516.CrossRefGoogle Scholar
Studnicki, M, Derejko, A, Wójcik-Gont, E and Kosma, M (2019) Adaptation pattern of winter wheat in agro-ecological regions. Scientia Agricola 72, 148156.CrossRefGoogle Scholar
Theobald, CM and Talbot, M (2002) The Bayesian choice of crop variety and fertilizer dose. Journal of the Royal Statistical Society Series C – Applied Statistics 51, 2336.CrossRefGoogle Scholar
Theobald, CM, Talbot, M and Nabugoomu, F (2002) Bayesian approach to regional and local-area prediction from crop variety trials. Journal of Agricultural, Biological and Environmental Statistics 7, 403419.CrossRefGoogle Scholar
Theobald, CM, Roberts, AMI, Talbot, M and Spink, JM (2006) Estimation of economically optimum seed rates for winter wheat from series of trials. Journal of Agricultural Science Cambridge 144, 303316.CrossRefGoogle Scholar
Villemereuil, P (2012) Estimation of biological trait heritability using the animal model: how to use MCMCglmm R package Guide. https://devillemereuil.legtux.org/wpcontent/uploads/2012/tuto_eng.pdf.Google Scholar
Yan, W and Kang, MS (2003) GGE Biplot Analysis: A Graphical Tool for Breeders, Genetists and Agronomists. Boca Raton: CRC Press.Google Scholar
Supplementary material: File

Przystalski and Lenartowicz Supplementary Materials

Przystalski and Lenartowicz Supplementary Materials

Download Przystalski and Lenartowicz Supplementary Materials(File)
File 1.7 MB