Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model

• Autores: Velazco, Julio Gabriel; Rodríguez-Álvarez, María Xosé; Boer, Martin P.; Jordan, David R.; Eilers, Paul H. C.; Malosetti, Marcos; Van Eeuwijk, Fred A.
• Año de publicación: 2017
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Theoretical and Applied Genetics 130 (7) : 1375-1392 (July 2017)
• Materia: Sorgo; Sorghum; Espaciamiento; Manejo del Cultivo; Ensayo; Forrajes; Spacing; Crop Management; Testing; Forage
• Institución: Instituto Nacional de Tecnología Agropecuaria

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