Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model
A flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials. Adjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and selection of genotypes. Cur...
| Main Authors: | , , , , , , |
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| Format: | Artículo |
| Language: | Inglés |
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Springer
2020
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| Online Access: | http://hdl.handle.net/20.500.12123/8287 https://link.springer.com/article/10.1007/s00122-017-2894-4 https://doi.org/10.1007/s00122-017-2894-4 |
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| author | Velazco, Julio Gabriel Rodríguez-Álvarez, María Xosé Boer, Martin P. Jordan, David R. Eilers, Paul H. C. Malosetti, Marcos Van Eeuwijk, Fred A. |
| author_browse | Boer, Martin P. Eilers, Paul H. C. Jordan, David R. Malosetti, Marcos Rodríguez-Álvarez, María Xosé Van Eeuwijk, Fred A. Velazco, Julio Gabriel |
| author_facet | Velazco, Julio Gabriel Rodríguez-Álvarez, María Xosé Boer, Martin P. Jordan, David R. Eilers, Paul H. C. Malosetti, Marcos Van Eeuwijk, Fred A. |
| author_sort | Velazco, Julio Gabriel |
| collection | INTA Digital |
| description | A flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials. Adjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and selection of genotypes. Current mixed model methods of spatial analysis are based on a multi-step modelling process where global and local trends are fitted after trying several candidate spatial models. This paper reports the application of a novel spatial method that accounts for all types of continuous field variation in a single modelling step by fitting a smooth surface. The method uses two-dimensional P-splines with anisotropic smoothing formulated in the mixed model framework, referred to as SpATS model. We applied this methodology to a series of large and partially replicated sorghum breeding trials. The new model was assessed in comparison with the more elaborate standard spatial models that use autoregressive correlation of residuals. The improvements in precision and the predictions of genotypic values produced by the SpATS model were equivalent to those obtained using the best fitting standard spatial models for each trial. One advantage of the approach with SpATS is that all patterns of spatial trend and genetic effects were modelled simultaneously by fitting a single model. Furthermore, we used a flexible model to adequately adjust for field trends. This strategy reduces potential parameter identification problems and simplifies the model selection process. Therefore, the new method should be considered as an efficient and easy-to-use alternative for routine analyses of plant breeding trials. |
| format | Artículo |
| id | INTA8287 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | INTA82872020-11-18T16:54:35Z Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model Velazco, Julio Gabriel Rodríguez-Álvarez, María Xosé Boer, Martin P. Jordan, David R. Eilers, Paul H. C. Malosetti, Marcos Van Eeuwijk, Fred A. Sorgo Sorghum Espaciamiento Manejo del Cultivo Ensayo Forrajes Spacing Crop Management Testing Forage A flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials. Adjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and selection of genotypes. Current mixed model methods of spatial analysis are based on a multi-step modelling process where global and local trends are fitted after trying several candidate spatial models. This paper reports the application of a novel spatial method that accounts for all types of continuous field variation in a single modelling step by fitting a smooth surface. The method uses two-dimensional P-splines with anisotropic smoothing formulated in the mixed model framework, referred to as SpATS model. We applied this methodology to a series of large and partially replicated sorghum breeding trials. The new model was assessed in comparison with the more elaborate standard spatial models that use autoregressive correlation of residuals. The improvements in precision and the predictions of genotypic values produced by the SpATS model were equivalent to those obtained using the best fitting standard spatial models for each trial. One advantage of the approach with SpATS is that all patterns of spatial trend and genetic effects were modelled simultaneously by fitting a single model. Furthermore, we used a flexible model to adequately adjust for field trends. This strategy reduces potential parameter identification problems and simplifies the model selection process. Therefore, the new method should be considered as an efficient and easy-to-use alternative for routine analyses of plant breeding trials. EEA Pergamino Fil: Velazco, Julio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Sección Forrajeras; Argentina. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda Fil: Rodríguez-Álvarez, María Xosé. Basque Center for Applied Mathematics(BCAM); España. IKERBASQUE. Basque Foundation for Science. España Fil: Boer, Martin P. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda Fil: Jordan, David R. The University of Queensland. Hermitage Research Facility. Queensland Alliance for Agriculture and Food Innovation; Australia Fil: Eilers, Paul H. C. Erasmus University Medical Centre; Holanda Fil: Malosetti, Marcos. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda Fil: van Eeuwijk, Fred A. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda 2020-11-18T16:49:21Z 2020-11-18T16:49:21Z 2017-04 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/8287 https://link.springer.com/article/10.1007/s00122-017-2894-4 0040-5752 1432-2242 (online) https://doi.org/10.1007/s00122-017-2894-4 eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf Springer Theoretical and Applied Genetics 130 (7) : 1375-1392 (July 2017) |
| spellingShingle | Sorgo Sorghum Espaciamiento Manejo del Cultivo Ensayo Forrajes Spacing Crop Management Testing Forage Velazco, Julio Gabriel Rodríguez-Álvarez, María Xosé Boer, Martin P. Jordan, David R. Eilers, Paul H. C. Malosetti, Marcos Van Eeuwijk, Fred A. Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model |
| title | Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model |
| title_full | Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model |
| title_fullStr | Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model |
| title_full_unstemmed | Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model |
| title_short | Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model |
| title_sort | modelling spatial trends in sorghum breeding field trials using a two dimensional p spline mixed model |
| topic | Sorgo Sorghum Espaciamiento Manejo del Cultivo Ensayo Forrajes Spacing Crop Management Testing Forage |
| url | http://hdl.handle.net/20.500.12123/8287 https://link.springer.com/article/10.1007/s00122-017-2894-4 https://doi.org/10.1007/s00122-017-2894-4 |
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