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Performance of alternative spatial models in empirical Douglas-fir and simulated datasets

Based on an empirical dataset originating from the French Douglas-fir breeding program, we showed that the bidimensional autoregressive and the two-dimensional P-spline regression spatial models clearly outperformed the classical block model, in terms of both goodness of fit and predicting ability....

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Main Authors: Cappa, Eduardo Pablo, Muñoz, Facundo, Sánchez, Leopoldo
Format: info:ar-repo/semantics/artículo
Language:Inglés
Published: Springer 2019
Subjects:
Forest Genetic Resources
Mathematical Models
Recursos Genéticos Forestales
Pseudotsuga menziesii
Modelos Matemáticos
Global and Local Spatial Trends
Autoregressive Residual
Two-dimensional P-splines
Douglas Fir
Tendencias Espaciales Globales y Locales
Residual Autorregresivo
Splines P bidimensionales
Pino Oregón
Abeto de Douglas
Online Access:https://link.springer.com/article/10.1007%2Fs13595-019-0836-9
http://hdl.handle.net/20.500.12123/6222
https://doi.org/10.1007/s13595-019-0836-9
Summary:Based on an empirical dataset originating from the French Douglas-fir breeding program, we showed that the bidimensional autoregressive and the two-dimensional P-spline regression spatial models clearly outperformed the classical block model, in terms of both goodness of fit and predicting ability. In contrast, the differences between both spatial models were relatively small. In general, results from simulated data were well in agreement with those from empirical data.

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