Adaptability and yield stability of winter barley (Hordeum vulgare) varieties: Bayesian analysis

Marcin Przystalski

doi:10.1017/S0021859625100233

Adaptability and yield stability of winter barley (Hordeum vulgare) varieties: Bayesian analysis

Published online by Cambridge University Press: 22 August 2025

Marcin Przystalski

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Marcin Przystalski*: Affiliation:
Research Centre for Cultivar Testing, Słupia Wielka 34, 63-022 Słupia Wielka, Poland
*: Corresponding author: Marcin Przystalski; Email: marprzyst@gmail.com

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Abstract

Winter barley is mainly grown in Europe. Before new varieties are recommended for cultivation, they undergo evaluation in breeding and variety trials. Based on the results of these trials, the stability and adaptability of promising new lines or varieties are assessed. In the present study, based on results from post-registration trials, we compared varieties grown in the 2020/21, 2021/22 and 2022/23 seasons. We fitted two Bayesian mixed models and assessed the stability of the varieties using the posterior estimates of variance components from the preferred model. We also used Bayesian probabilistic methods to recommend the best varieties. Using the probabilistic methods, we identified the varieties that were the most stable and had the highest yield in the barley post-registration trials. The varieties Melia, Mirabelle and Zenek were shown to be the three most stable and highest yielding. Furthermore, these three varieties had the highest joint probability of superior performance and stability. This study demonstrates that probabilistic methods within a Bayesian framework are a powerful tool for recommending the best winter barley varieties. The R-codes for both models are provided in a Supplement.

Keywords

Hierarchical model joint probability of superior performance and stability series of field trials two-stage approach

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Type: Crops and Soils Research Paper
Information: The Journal of Agricultural Science , First View , pp. 1 - 14

DOI: https://doi.org/10.1017/S0021859625100233 [Opens in a new window]
Copyright: © The Author(s), 2025. Published by Cambridge University Press

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Footnotes

Mention of trade names or commercial products in this article is solely for the purpose of providing scientific information and does not imply recommendation or endorsement by the Research Centre for Cultivar Testing.

References

Banaś, K, Przystalski, M and Łacka, A (2023) Stability analysis of spring oat genotypes in south-west Poland. Biometrical Letters 60, 97−108.Google Scholar

Banner, KM, Irvine, KM and Rodhouse, TJ (2020) The use of Bayesian priors in ecology: The good, the bad and the not great. Methods in Ecology and Evolution 11, 882−889.Google Scholar

Becker, HC and Léon, J (1988) Stability analysis in plant breeding. Plant Breeding 101, 1−23.Google Scholar

Brooks, ME, Kristensen, K, van Benthem, KJ, Magnusson, A, Berg, CW, Nielsen, A, Skaug, HJ, Maechler, M and Bolker, BM (2017) glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal, 9, 378–400. (Accessed 12 December 2024)Google Scholar

Caliński, T, Czajka, S and Kaczmarek, Z (1987a) A model for the analysis of a series of experiments repeated at several places over a period of years: I Theory. Biuletyn Oceny Odmian 12, 7−33.Google Scholar

Caliński, T, Czajka, S and Kaczmarek, Z (1987b) A model for the analysis of a series of experiments repeated at several places over a period of years: II Example. Biuletyn Oceny Odmian 12, 35−71.Google Scholar

Carpenter, B (2023) Prior choice recommendations. Available at https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations (Accessed 16 February 2025).Google Scholar

Chaves, SFS, Krause, MD, Dias, LAS, Garcia, AAF and Dias, KOG (2024) ProbBreed: A novel tool for calculating the risk of cultivar recommendation in multi-environment trials. Genes, Genomes, Genetics 14, jkae013.Google Scholar

Congdon, P (2020) Bayesian Hierarchical Models with Applications using R, 2nd Edition. Boca Raton FL: CRC Press.Google Scholar

Craine, EB, Choi, H, Schroeder, KL, Brueggeman, R, Esser, A and Murphy, KM (2023) Spring barley malt quality in eastern Washington and northern Idaho. Crop Science 63, 1148−1168.Google Scholar

Czajka, S (1995) Statistical methods for the analysis of a series of multiple experiments carried out in incomplete designs. Biometrical Letters 32, 101−129 (in Polish).Google Scholar

de Valpine, P, Paciorek, C, Turek, D, Michaud, N, Anderson-Bergman, C, Obermeyer, F, Wehrhahn Cortes, C, Rodrìguez, A, Lang, DT and Paganin, S (2022) NIMBLE User Manual. R package manual version 0.12.2. Available at https://r-nimble.org (Accessed 9 June 2022).Google Scholar

de Valpine, P, Turek, D, Paciorek, CJ, Anderson-Bergman, C, Lang, DT and Bodik, R (2017) Programming with models: Writing statistical algorithms for general model structures with NIMBLE. Journal of Computational and Graphical Statistics 26, 403−413.Google Scholar

DeGroot, MH (1970) Optimal Statistical Decisions. New York: McGraw-Hill.Google Scholar

Dias, KOG, dos Santos, JPR, Krause, MD, Piepho, HP, Guimarães, LJM, Pastina, MM and Garcia, AAF (2022) Leveraging probability concepts for cultivar recommendation in multi-environment trials. Theoretical and Applied Genetics 135, 1385−1399.Google Scholar

Eberhart, SA and Russell, WA (1966) Stability parameters for comparing varieties. Crop Science 6, 36–40.Google Scholar

Eskridge, KM and Mumm, RF (1992) Choosing plant cultivars based on the probability of outperforming a check. Theoretical and Applied Genetics 84, 494−500.Google Scholar

Figueroa-Zúñiga, JI, Arellano-Valle, RB and Ferrari, SLP (2013) Mixed beta regression: A Bayesian perspective. Computational Statistics and Data Analysis 61, 137–147.Google Scholar

Finlay, K and Wilkinson, G (1963) The analysis of adaptation in a plant breeding programme. Australian Journal of Agricultural Research 14, 742−754.Google Scholar

Gauch, HG (1992) Statistical Analysis of Regional Yield Trials. AMMI Analysis of Factorial Designs. New York: Elsevier.Google Scholar

Gelman, A and Rubin, DB (1992) Inference from iterative simulation using multiple sequences (with discussion). Statistical Science 7, 457−511.Google Scholar

Hedges, LV and Olkin, I (1985) Statistical Methods for Meta-Analysis. Orlando: Academic Press.Google Scholar

Kurasiak-Popowska, D, Katańska-Kaczmarek, A, Matysik, P, Stefański, P, Przybył, P, Skotarczak, E and Nawracała, J (2024) Stability of early maturing soybean genotypes in Poland. Agriculture 14, 2202.Google Scholar

Laidig, F, Drobek, T and Meyer, U (2008) Genotypic and environmental variability of yield for cultivars from 30 different crops in German official variety trials. Plant Breeding 127, 541−547.Google Scholar

Lemoine, NP (2019) Moving beyond noninformative: why and how to choose weakly informative priors in Bayesian analyses. Oikos 128, 912–928.Google Scholar

Mead, R, Riley, J, Dear, K and Singh, SP (1986) Stability comparison of intercropping and monocropping systems. Biometrics 42, 253–266.Google Scholar

Miranda, IR, Dias, KOG, Júnior, JDP, Carneiro, PCS, Carneiro, JES, Carneiro, VQ, Souza, EA, Melo, LC, Pereira, HS, Vieira, RF and Martins, FAD (2024) Use of Bayesian probabilistic model approach in common bean varietal recommendation. Crop Science 64, 3163−3173.Google Scholar

Najewski, A, Madajska, K, Skrzypek, A and Szarzyńska, J (2023) Results of post-registration variety trials. Winter Cereals 2022. (in Polish). https://coboru.gov.pl/Publikacje_COBORU/Wyniki_PDO/WPDO_Zbozaozime_2022.pdf Google Scholar

Najewski, A, Madajska, K, Skrzypek, A and Szarzyńska, J (2024) Results of post-registration variety trials. Winter Cereals 2024. (in Polish). https://coboru.gov.pl/Publikacje_COBORU/Wyniki_PDO/WPDO_201_zboza_ozime_2023.pdf Google Scholar

Nonyane, BAS and Theobald, CM (2008) Multiplicative models for combining information from several sensory experiments: A Bayesian analysis. Food Quality and Preference 19, 260−266.Google Scholar

Pérez, P, de los Campos, G, Crossa, J and Gianola, D (2010) Genomic-enabled prediction based on molecular markers and pedigree using Bayesian Linear Regression Package in R. The Plant Genome 3, 106–119.Google Scholar

Piepho, HP (1994) Partitioning genotype-environmental interaction in regional yield trials via a generalized stability variance. Crop Science, 34, 1682−1685.Google Scholar

Piepho, HP (1996) Simplified procedure for comparing the stability of cropping systems. Biometrics 52, 315−320.Google Scholar

Piepho, HP (1999) Stability analysis using the SAS system. Agronomy Journal 91, 154–160.Google Scholar

Piepho, HP and van Eeuwijk, FA (2002) Stability analysis in crop performance evaluation. In Kang, M. (ed), Crop Improvement: Challenges in the Twenty-First Century. New York: Haworth Press, pp. 315−351.Google Scholar

Pilarczyk, W (2009) The extent and prevailing shape of spatial relationships in Polish variety testing trials in wheat. Plant Breeding 128, 411−415.Google Scholar

Plummer, M, Best, N, Cowles, K and Vines, K (2006) CODA: Convergence diagnosis and output analysis for MCMC. RNews 6, 7–11.Google Scholar

Pour-Aboughadareh, A., Khalili, M., Poczai, P., Olivoto, T. (2022) Stability indices to deciphering the genotype-by-environment interaction (GEI) effect: An applicable review for use in plant breeding programs. Plants 11, article 414.Google Scholar

Przystalski, M and Lenartowicz, T (2017) Comparing the resistance of mid-maturing maize varieties to European corn borer (Ostrinia nubilalis Hbn.)-Results from the Polish VCU registration trials. Plant Breeding. 136, 498–508.Google Scholar

Przystalski, M and Lenartowicz, T (2020) Yielding stability of early maturing potato varieties: Bayesian analysis. The Journal of Agricultural Science 158, 564−573.Google Scholar

Przystalski, M and Lenartowicz, T (2023) Organic system vs. conventional−A Bayesian analysis of Polish potato post-registration trials. The Journal of Agricultural Science 161, 97−108.Google Scholar

Rodrigues-Motta, M and Forkman, J (2022) Bayesian analysis of nonnegative data using dependency-extended two-part models. Journal of Agricultural, Biological and Environmental Statistics 27, 201−221.Google Scholar

Sandro, P, Kucek, LK, Sorrels, ME, Dawson, JC and Gutierrez, L (2022) Developing high-quality value-added cereals for organic systems in the US Upper Midwest: Hard red winter wheat (Triticum aestivum L.) breeding. Theoretical and Applied Genetics 135, 4005−4027.Google Scholar

Shukla, GK (1972) Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29, 237–245.Google Scholar

Silva, FF, Viana, JMS, Faria, VR and de Resende, M (2013) Bayesian inference of mixed models in quantitative genetics of crop species. Theoretical and Applied Genetics 126, 1749–1761.Google Scholar

Singh, M, Al-Yassin, A and Omer, SO (2015) Bayesian estimation of genotypes means, precision, and genetic gain due to selection from routinely used barley trials. Crop Science 55, 501−513.Google Scholar

Skovbjerg, CK, Knudsen, JN, Füchtbauer, W, Stougaard, J, Stoddard, FL, Janss, L and Andersen, SU (2020) Evaluation of yield, yield stability and yield-protein relationship in 17 commercial faba bean cultivars. Legume Science 2, e39.Google Scholar

Sorensen, D and Gianola, D (2002) Likelihood, Bayesian and MCMC Methods in Quantitative Genetics . Statistics for Biology and Health. New York: Springer-Verlag.Google Scholar

Sorensen, D and Waagepetersen, R (2003). Normal linear models with genetically structured residual variance heterogeneity: A case study. Genetical Research 82, 207−222.Google Scholar

Stan Development Team (2024). RStan: the R interface to Stan. R package version 2.32.6. https://mc-stan.org/.Google 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, 2506−2516.Google Scholar

Studnicki, M and Piepho, HP (2024) Hierarchical modelling of variance components makes analysis of resolvable incomplete block designs more efficient. Theoretical and Applied Genetics 137, article number 134.Google Scholar

Theobald, CM, Roberts, AMI, Talbot, M and Spink, JM (2006) Estimation of economically optimum seed rates for winter wheat from series of trials The Journal of Agricultural Science 144, 303−316.Google Scholar

Theobald, CM, Talbot, M and Nabugoomu, F (2002) A Bayesian approach to regional and local-area prediction from crop variety trials. Journal of Agricultural, Biological and Environmental Statistics 7, 403−419.Google Scholar

Tigerstedt, PMA (1994) Adaptation, variation and selection in marginal areas. Euphytica, 77, 171−174.Google Scholar

Valenzuela-Antelo, JL, Benitez-Riquelme, I, Vargas-Hernandez, M, Huerta-Espino, J, Bentley, AR, Villasenor-Mir, HE. and Pinera-Chavez, FJ (2023) Multi-location trials identify stable high-yielding spring bread and durum wheat cultivars in Mexico. Crop Science 63,2103–2114.Google Scholar

Viechtbauer, W (2010) Conducting meta-analyses in R with the metafor package. Journal of Statistical Software 36, 1–48. (Accessed 10 June 2022)Google Scholar

Villar-Hernández, BJ, Pérez-Elizalde, S, Crossa, J, Pérez-Rodriguez, P, Toledo, FH and Burgueño, J (2018) A Bayesian decision theory approach for genomic selection. G3 Genes, Genomes, Genetics 8, 3019−3037.Google Scholar

Villar-Hernández, BJ, Pérez-Elizalde, S, Martini, JWR, Toledo, F, Perez-Rodriguez, P, Krause, M, Garcia-Calvillo, ID, Covarrubias-Pazaran, G and Crossa, J (2021) Application of multi-trait Bayesian decision theory for parental genomic selection. G3 Genes, Genomes, Genetics 11, jkab012.Google Scholar

Watanabe, S (2010) Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research 11, 3571–3594.Google Scholar

Wricke, G (1962) Über eine Methdode zur Erfassung der ökologischen Streubreite in Feldversuchen. Zeitschrift für Pflanzenzüchtung 47, 92−96.Google Scholar

Yang, Y, Schön, CC and Sorensen, D (2012) The genetics of environmental variation of dry matter grain yield in maize. Genetics Research 94, 113–119.Google Scholar

Youngflesh, C (2018) MCMCvis: tools to visualize, manipulate, and summarize MCMC output. Journal of Open Source Software 3, 640.Google Scholar

Zawieja, B, Slebioda, L and Mikulski, T (2023) Progress in plant tolerance to the fungal disease Sclerotinia in breeding experiments on winter oilseed rape. Biometrical Letters 60, 23−35.Google Scholar