NDLI: Development of growth models for spruce based on mixed-effects models

Content Provider	IEEE Xplore Digital Library
Author	Zhao, J. Kang, X. Zhang, H. Li, Y. Zhang, Q.
Copyright Year	2010
Description	Author affiliation: The Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, China, 100083 (Zhao, J.; Kang, X.) \|\| Liaoning Academy of Forest Science, Shenyang, 110032, China (Zhang, H.) \|\| College of science, Beijing Forestry University, China, 100083 (Li, Y.; Zhang, Q.)
Abstract	The study of growth model is a basic research in forest growth and yield modeling. Most of growth models were developed using ordinary regression method. It is assumed that the observations were independent and obey Gauss distribution. Those models reflect the average growth across different plots, but neglect the correlation and variance between individuals and plots. However, mixed-effects models which include both fixed and random parameters could solve the problem very well. Our objective is to develop growth models for main tree species spruce in Changbai Mountains by using mixed-effects models. 14 clear cutting plots were investigated in Changbai Mountain area, China. Data of 619 individual trees including age, height, diameter, and volume were used in this study to calibrate the growth models. Richards model and its mixed-effects model were used to develop growth models by using PROC NLIN and PROC NLMIXED in SAS. Decision coefficient $(R^{2}),$ root mean square error (RMSE), and mean absolute difference (MAD) were used to evaluate the accuracy of the two models. Compared with the basic model, the $R^{2}$ of the mixed-effects model which included random-effect parameter increased 37%–82%, RMSE and MAD decreased 12%–30% and 13–28%, respectively. In conclusion, the mixed-effects model was suitable to calibrate growth models.
Starting Page	2138
Ending Page	2142
File Size	272571
Page Count	5
File Format	PDF
ISBN	9781424459315
e-ISBN	9781424459346
DOI	10.1109/FSKD.2010.5569739
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Solid modeling random-effect parameter Biological system modeling Fitting Predictive models growth model accuracy Data models Mathematical model Synthetic aperture sonar fixed-effect parameter
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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