A hierarchical bayesian estimation model for multienvironment plant breeding trials in successive years
In agriculture and plant breeding, multienvironment trials over multiple years are conducted to evaluate and predict genotypic performance under different environmental conditions and to analyze, study, and interpret genotype´ environment interaction (G x E). In this study, we propose a hierarchical Bayesian formulation of a linear–bilinear model, where the conditional conjugate prior for the bilinear (multiplicative) G x E term is the matrix von Mises–Fisher (mVMF) distribution (with environments and sites defined as synonymous). A hierarchical normal structure is assumed for linear effects of sites, and priors for precision parameters are assumed to follow gamma distributions. Bivariate highest posterior density (HPD) regions for the posterior multiplicative components of the interaction are shown within the usual biplots. Simulated and real maize (Zea mays L.) breeding multisite data sets were analyzed. Results showed that the proposed model facilitates identifying groups of genotypes and sites that cause G ´ E across years and within years, since the hierarchical Bayesian structure allows using plant breeding data from different years by borrowing information among them. This model offers the researcher valuable information about G x E patterns not only for each 1-yr period of the breeding trials but also for the general process that originates the response across these periods.
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Crop Science Society of America (CSSA)
2016
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Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, AGRICULTURE, ZEA MAYS, BREEDING, |
Online Access: | https://hdl.handle.net/10883/19675 |
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dig-cimmyt-10883-196752021-02-09T18:24:41Z A hierarchical bayesian estimation model for multienvironment plant breeding trials in successive years Jarquín, D. Pérez-Elizalde, S. Burgueño, J. Crossa, J. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY AGRICULTURE ZEA MAYS BREEDING In agriculture and plant breeding, multienvironment trials over multiple years are conducted to evaluate and predict genotypic performance under different environmental conditions and to analyze, study, and interpret genotype´ environment interaction (G x E). In this study, we propose a hierarchical Bayesian formulation of a linear–bilinear model, where the conditional conjugate prior for the bilinear (multiplicative) G x E term is the matrix von Mises–Fisher (mVMF) distribution (with environments and sites defined as synonymous). A hierarchical normal structure is assumed for linear effects of sites, and priors for precision parameters are assumed to follow gamma distributions. Bivariate highest posterior density (HPD) regions for the posterior multiplicative components of the interaction are shown within the usual biplots. Simulated and real maize (Zea mays L.) breeding multisite data sets were analyzed. Results showed that the proposed model facilitates identifying groups of genotypes and sites that cause G ´ E across years and within years, since the hierarchical Bayesian structure allows using plant breeding data from different years by borrowing information among them. This model offers the researcher valuable information about G x E patterns not only for each 1-yr period of the breeding trials but also for the general process that originates the response across these periods. 2260-2276 2018-11-13T17:18:18Z 2018-11-13T17:18:18Z 2016 Article 0011-183X 1435-0653 https://hdl.handle.net/10883/19675 10.2135/cropsci2015.08.0475 English http://hdl.handle.net/11529/10463 CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose. Open Access PDF United States Crop Science Society of America (CSSA) 5 56 Crop Science |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY AGRICULTURE ZEA MAYS BREEDING AGRICULTURAL SCIENCES AND BIOTECHNOLOGY AGRICULTURE ZEA MAYS BREEDING Jarquín, D. Pérez-Elizalde, S. Burgueño, J. Crossa, J. A hierarchical bayesian estimation model for multienvironment plant breeding trials in successive years |
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In agriculture and plant breeding, multienvironment trials over multiple years are conducted to evaluate and predict genotypic performance under different environmental conditions and to analyze, study, and interpret genotype´ environment interaction (G x E). In this study, we propose a hierarchical Bayesian formulation of a linear–bilinear model, where the conditional conjugate prior for the bilinear (multiplicative) G x E term is the matrix von Mises–Fisher (mVMF) distribution (with environments and sites defined as synonymous). A hierarchical normal structure is assumed for linear effects of sites, and priors for precision parameters are assumed to follow gamma distributions. Bivariate highest posterior density (HPD) regions for the posterior multiplicative components of the interaction are shown within the usual biplots. Simulated and real maize (Zea mays L.) breeding multisite data sets were analyzed. Results showed that the proposed model facilitates identifying groups of genotypes and sites that cause G ´ E across years and within years, since the hierarchical Bayesian structure allows using plant breeding data from different years by borrowing information among them. This model offers the researcher valuable information about G x E patterns not only for each 1-yr period of the breeding trials but also for the general process that originates the response across these periods. |
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Article |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY AGRICULTURE ZEA MAYS BREEDING |
author |
Jarquín, D. Pérez-Elizalde, S. Burgueño, J. Crossa, J. |
author_facet |
Jarquín, D. Pérez-Elizalde, S. Burgueño, J. Crossa, J. |
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Jarquín, D. |
title |
A hierarchical bayesian estimation model for multienvironment plant breeding trials in successive years |
title_short |
A hierarchical bayesian estimation model for multienvironment plant breeding trials in successive years |
title_full |
A hierarchical bayesian estimation model for multienvironment plant breeding trials in successive years |
title_fullStr |
A hierarchical bayesian estimation model for multienvironment plant breeding trials in successive years |
title_full_unstemmed |
A hierarchical bayesian estimation model for multienvironment plant breeding trials in successive years |
title_sort |
hierarchical bayesian estimation model for multienvironment plant breeding trials in successive years |
publisher |
Crop Science Society of America (CSSA) |
publishDate |
2016 |
url |
https://hdl.handle.net/10883/19675 |
work_keys_str_mv |
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