Tropical Plant Research
An International Journal by Society for Tropical Plant Research
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2020, VOLUME 7 ISSUE 1Pages: 6-13
Fitting non-linear models for tree volume estimation in strict nature reserve, South-West, Nigeria
F. E. Adesuyi*, A. S. Akinbowale, O. G. Olugbadieye and K. Jayeola
*Department of Forestry and Wood Technology, Federal University of Technology, Akure, Nigeria
Abstract:
This study tested the efficacy of nonlinear models for tree volume estimation in a complex tropical natural ecosystem. Data were collected from the four permanent sample plots located in Strict Nature Reserve in Akure forest reserve, each plot covering an area of 0.25 ha. All living trees within a diameter range (>10 cm) were measured within all the permanent sample plots. The data were pooled together and sorted according to family: Annonaceae, Meliceae, Sterculiaceae and Ulmaceae. Six non-linear models were fitted using curve expert for the volume models and ranked according to their best of fit using the Akaike’s Information Criteria (AIC), standard error and significance at 5% level of probability. A total of 266 trees were sampled in the four plots but the study made use of 171 trees comprising of 17 species distributed among 4 families. Sterculiaceae had the highest number of species (6 species.) while the most abundant species was Mansonia altissima, followed by Celtis zenkeri. These species have 45 and 42 individual trees, respectively. The assessment criteria using AIC and standard error showed that the entire models are suitable for volume estimation in the study area. The non-linear models showed a reasonable variation depending on family. The result showed that Weibull, Gompertz Relation and Logistic Power models were the most consistent model that gave the best predicted volume when compared with the observed volume for each family in the study area but the Ratkowsky model ranked best of the six models generated when data from each family were combined. The student t-test showed that there were no significant differences between the observed and predicted volume. All the models are very good for tree volume estimation in the study area. Therefore, they are recommended for further use in this ecosystem and similar ones.
This study tested the efficacy of nonlinear models for tree volume estimation in a complex tropical natural ecosystem. Data were collected from the four permanent sample plots located in Strict Nature Reserve in Akure forest reserve, each plot covering an area of 0.25 ha. All living trees within a diameter range (>10 cm) were measured within all the permanent sample plots. The data were pooled together and sorted according to family: Annonaceae, Meliceae, Sterculiaceae and Ulmaceae. Six non-linear models were fitted using curve expert for the volume models and ranked according to their best of fit using the Akaike’s Information Criteria (AIC), standard error and significance at 5% level of probability. A total of 266 trees were sampled in the four plots but the study made use of 171 trees comprising of 17 species distributed among 4 families. Sterculiaceae had the highest number of species (6 species.) while the most abundant species was Mansonia altissima, followed by Celtis zenkeri. These species have 45 and 42 individual trees, respectively. The assessment criteria using AIC and standard error showed that the entire models are suitable for volume estimation in the study area. The non-linear models showed a reasonable variation depending on family. The result showed that Weibull, Gompertz Relation and Logistic Power models were the most consistent model that gave the best predicted volume when compared with the observed volume for each family in the study area but the Ratkowsky model ranked best of the six models generated when data from each family were combined. The student t-test showed that there were no significant differences between the observed and predicted volume. All the models are very good for tree volume estimation in the study area. Therefore, they are recommended for further use in this ecosystem and similar ones.
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