Survival prognosis in plantations of Pinus caribaea Morelet var. caribaea Barrett & Golfari

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Published: Jan 31, 2018
Keywords:
Forest plantations, regular mortality, nonlinear regression, Artificial Neural Networks (ANNs).

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Ouorou Ganni Mariel Guera
Universidade Federal Rural de Pernambuco (UFRPE), Departamento de Ciência Florestal (DCFL), Laboratório de Biometria e Manejo Florestal (LBMF)
José Antônio Aleixo da Silva
Universidade Federal Rural de Pernambuco (UFRPE), Departamento de Ciência Florestal (DCFL), Laboratório de Biometria e Manejo Florestal (LBMF)
Rinaldo Luiz Caraciolo Ferreira
Universidade Federal Rural de Pernambuco (UFRPE), Departamento de Ciência Florestal (DCFL), Laboratório de Biometria e Manejo Florestal (LBMF)
Daniel Álvarez Lazo
Universidad de Pinar del Río "Hermanos Saiz Montes de Oca" Facultad de Ciencias Forestales y Agropecuaria Departamento Forestal
Héctor Barrero Medel
Universidad de Pinar del Río "Hermanos Saiz Montes de Oca", Facultad de Ciencias Forestales y Agropecuaria Departamento Forestal

Abstract

The present study was carried out with the objective of obtaining regression equations and Artificial Neural Networks (ANNs) for the prognosis of Pinus caribaea var. caribaea survival in Macurije Forest Company, province of Pinar del Río - Cuba. The data used in the modeling comes from the measurement of the variables age (years) and survival (density) in circular permanent plots of 500 m² established in P. caribaea var. caribaea plantations. The study was divided into three stages: i) Adjustment of survival traditional regression models; ii) Training of ANNs for survival prognosis, including categorical variables «site» and «Basic Units of Forest Production»; iii) Comparison of regression equations performance with those of ANNs in survival prognosis. The best models and ANNs were selected based on: adjusted determination coefficient - R2aj (%), square root of the mean square error - RMSE (%) and residue distribution analysis. The evaluation of the models goodness of fit also included the verification of the assumptions of normality, homocedasticity and absence of serial autocorrelation in the residues by Kolmogorov-Smirnov, White and Durbin-Watson tests, respectively. The model of Pienaar and Shiver (1981) turned out to be the best fit in survival prognosis. The ANN MLP 13-10-1 was the one with the best generalization capacity and presented a performance similar to that of Pienaar and Shiver equation.

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How to Cite
Guera, O. G. M., Silva, J. A. A. da, Ferreira, R. L. C., Álvarez Lazo, D., & Barrero Medel, H. (2018). Survival prognosis in plantations of Pinus caribaea Morelet var. caribaea Barrett & Golfari. Cuban Journal of Forest Science, 6(1), 15–30. Retrieved from https://cfores.upr.edu.cu/index.php/cfores/article/view/299
Issue
Vol. 6 No. 1 (2018): january-april
Section
Scientific articles
Author Biographies

Ouorou Ganni Mariel Guera, Universidade Federal Rural de Pernambuco (UFRPE), Departamento de Ciência Florestal (DCFL), Laboratório de Biometria e Manejo Florestal (LBMF)

Ingeniero Forestal, Doctor en Ciencias Forestales

José Antônio Aleixo da Silva, Universidade Federal Rural de Pernambuco (UFRPE), Departamento de Ciência Florestal (DCFL), Laboratório de Biometria e Manejo Florestal (LBMF)

Ingeniero Forestal

Rinaldo Luiz Caraciolo Ferreira, Universidade Federal Rural de Pernambuco (UFRPE), Departamento de Ciência Florestal (DCFL), Laboratório de Biometria e Manejo Florestal (LBMF)

Ingeniero Forestal

Daniel Álvarez Lazo, Universidad de Pinar del Río "Hermanos Saiz Montes de Oca" Facultad de Ciencias Forestales y Agropecuaria Departamento Forestal

Ingeniero Forestal, Doctor en Ciencias Forestales

Héctor Barrero Medel, Universidad de Pinar del Río "Hermanos Saiz Montes de Oca", Facultad de Ciencias Forestales y Agropecuaria Departamento Forestal

Ingeniero Forestal, Doctor en Ciencias Forestales

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