Study on Dominant Height Growth of Fir Plantations Based on a Nonlinear Mixed Modeling Approach for Longitudinal Data

LI Chun-ming

The improvement on the dominant height growth implies in better productivity estimation due to the forest height growth is directly related with the site characteristics and forest productivity. A modified Richards growth model with nonlinear mixed effects is simulated used SAS software for modeling fir plantation dominant height growth in conjunction with different plantation density in Dagangshan Experiment Bureau of Jiangxi Province. Akaike Information Criterion(AIC) and Bayesian Information Criterion(BIC) were used in model performance evaluation. Within-plot time series error autocorrelation of dominant height growth data and different plantation density expressed with dummy variable were taken into account in mixed model. Finally, the precision of mixed models was compared with the precision of conventional nonlinear ordinary regression analysis method based on validation data. The result showed that the precision of modified Richards forms nonlinear mixed effect model which takes into account plot’s random effect was better than that of conventional regression model. Increasing the number of random effect parameter can increase the precision of model. First-order autoregressive error model in explaining time series error autocorrelation of dominant height growth not only improved simulated precision, but also can describe error distribution of sequence observation data; The precision of mixed model considering plot random effects, time series error autocorrelation and different plantation density at one time is better than that of ordinary regression analysis method.

1. Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

Received Date: 2010-03-20

Abstract: The improvement on the dominant height growth implies in better productivity estimation due to the forest height growth is directly related with the site characteristics and forest productivity. A modified Richards growth model with nonlinear mixed effects is simulated used SAS software for modeling fir plantation dominant height growth in conjunction with different plantation density in Dagangshan Experiment Bureau of Jiangxi Province. Akaike Information Criterion(AIC) and Bayesian Information Criterion(BIC) were used in model performance evaluation. Within-plot time series error autocorrelation of dominant height growth data and different plantation density expressed with dummy variable were taken into account in mixed model. Finally, the precision of mixed models was compared with the precision of conventional nonlinear ordinary regression analysis method based on validation data. The result showed that the precision of modified Richards forms nonlinear mixed effect model which takes into account plot’s random effect was better than that of conventional regression model. Increasing the number of random effect parameter can increase the precision of model. First-order autoregressive error model in explaining time series error autocorrelation of dominant height growth not only improved simulated precision, but also can describe error distribution of sequence observation data; The precision of mixed model considering plot random effects, time series error autocorrelation and different plantation density at one time is better than that of ordinary regression analysis method.

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Reference (12)

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Study on Dominant Height Growth of Fir Plantations Based on a Nonlinear Mixed Modeling Approach for Longitudinal Data

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