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NAAS Score: *5.38 (2020)
[Effective from January 1, 2020]
For more details click here
ICV 2023: 95.56
Index Copernicus ICI Journals Master List 2023 - IJCMAS--ICV 2023: 95.56
For more details click here
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Original Research Articles Volume : 15, Issue : 5, May, 2026

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Int.J.Curr.Microbiol.App.Sci.2026.15(5) : 77-85
DOI : https://doi.org/10.20546/ijcmas.2026.1505.011


Linear and Non-Linear Regression Models for Volume Estimation of Tectona grandis in Thithimathi Forest, Kodagu, India

Sharanappa1, G. T. Thippesha1 and R. Manjula2*
1Department of Silviculture and Agroforestry, College of Forestry, Ponnampet – 571216, India 2Agricultural Statistics, Department of Basic Science and Humanities, College of Forestry, Ponnampet – 571216, India
*Corresponding author
Abstract:

The study investigates linear and non-linear regression models for estimating the volume of Tectona Grandis in the Thithimathi forest, Kodagu district, India. With increasing demand for teak plantations under national afforestation programs, accurate volume estimation is essential for productivity and sustainable forest management. Data were collected from three sample plots, each with 30 trees, measuring diameter at breast height (Dbh) and height to calculate tree volume. Karl Pearson’s correlation analysis revealed a strong positive correlation between volume and Dbh, while the relationship between volume and height was weak across plots. Regression models- linear, quadratic and logarithmic were fitted to the data and evaluated using R2 and RMSE values. Results showed that quadratic models consistently provided the best fit, with R2 value 0.9999 and minimal error, outperforming linear and logarithmic models. Multiple linear regression yields high predictive accuracy (R2 = 0.9821). The findings confirm that quadratic regression models are most suitable for teak volume estimation in the Thithimathi forest, offering reliable tools for forest productivity and management.


Keywords: Volume estimation, correlation, RMSE, Diameter at breast height

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How to cite this article:

Sharanappa, Thippesha G. T. and Manjula R. 2026. Linear and Non-Linear Regression Models for Volume Estimation of Tectona grandis in Thithimathi Forest, Kodagu, India Int.J.Curr.Microbiol.App.Sci. 15(5): 77-85 doi: https://doi.org/10.20546/ijcmas.2026.1505.011
Copyright: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license

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