Estimate the above ground biomass in Brant’s oak (Quercus brantii Lindl. ) (Case Study: Region Melah-Shbanan Khorramabad)

Document Type : Complete scientific research article

Authors

Assistant Professor in Forestry & Forest Biometry, Forestry DepartmentFaculty of Agriculture Lorestan University

Abstract

Abstract
Background and objectives: Today, the use of ecological indicators for understanding ecosystem condition and monitoring and evaluating changes over time, has been common in developed countries. There is well evident that it is lack of information in ecological indices, such as knowledge of the production potential of forest ecosystem biomass and its species. Allometric equations are useful tools estimate the biomass of trees.
Materials and methods: For this purpose 28 trees of Quercus Persica species were selected randomly (without replacement) in the region Melah‌Shbanan in Khorramabad. Knee diameter, diameter at breast height, crown diameter and height of standing trees were measured and then these trees were cut. In order to determine the ratio of dry weight to fresh weight the different parts of trees such as trunks and branches were separated and weighed, then discs (samples) of different parts of trees were taken and send to the laboratory. With this ratio, the dry weight of the crown, trunk and eventually woody aboveground biomass was calculated.
Results: The 28 trees of the study, 26 trees were selected with good distribution. Using Stepwise, linear, Quadratic, power and exponential regression models, allometric equations with a high coefficient of determination (p<0/05) were achieved. Results of Stepwise regression models for estimating biomass trees if you use a stepwise model that includes variables crown diameter and Knee diameter as an independent variables, Suitable model, y=39.856X1+3.946X-121.236 (R2=0.8). Results of linear, Quadratic, power and exponential regression models for estimating biomass trees if you use that includes variables crown diameter, Suitable model, Quadratic models (R2=0.927).
Conclusion: The results showed that generally between the independent variables, crown diameter with indices of modeling was produced better equations (0/927). Also, the Quadratic regression regression model was better than other regression models. Results: The 28 trees of the study, 26 trees were selected with good distribution. Using Stepwise, linear, Quadratic, power and exponential regression models, allometric equations with a high coefficient of determination (p<0/05) were achieved. Results of Stepwise regression models for estimating biomass trees if you use a stepwise model that includes variables crown diameter and Knee diameter as an independent variables, Suitable model, y=39.856X1+3.946X-121.236 (R2=0.8). Results of linear, Quadratic, power and exponential regression models for estimating biomass trees if you use that includes variables crown diameter, Suitable model, Quadratic models (R2=0.927).
Conclusion: The results showed that generally between the independent variables, crown diameter with indices of modeling was produced better equations (0/927). Also, the Quadratic regression regression model was better than other regression models.

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References

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Journal of Wood and Forest Science and Technology
Volume 24, Issue 4
March 2018
Pages 148-172

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