A Regression Modelling Approach for Stem Volume Estimation of Two Exotic Plantations within Dogo-Kétou Forest Reserve, Benin Republic

Dende Ibrahim Adekanmbi; Adandé Belarmain Fandohan; Marc Aimé Tchoumado; Agossou Bruno Djossa

doi:doi:10.11648/j.ajaf.20231104.17

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A Regression Modelling Approach for Stem Volume Estimation of Two Exotic Plantations within Dogo-Kétou Forest Reserve, Benin Republic

Dende Ibrahim Adekanmbi, Adandé Belarmain Fandohan, Marc Aimé Tchoumado, Agossou Bruno Djossa

Published in American Journal of Agriculture and Forestry (Volume 11, Issue 4)

Received: 21 July 2023 Accepted: 7 August 2023 Published: 15 August 2023

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Abstract

Stem volume models play an important role in forest management, evaluating the economic value of a forest stand and assisting forest managers and other interested parties in determining the optimal strategies for the utilization and conservation of forest resources. Little attention is given to the use of multivariate regression models for plantation species in the study area. This study involved the development of a multivariate regression equation with continuous and categorical independent variables for simultaneous prediction of merchantable volume for Gmelina arborea and Tectona grandis in Dogo-Ketou Forest Reserve. Simple random sampling technique was adopted for plot location from the selected two plantations. Thirty-one temporary plots of dimension 25m by 25m were selected for complete enumeration in all the two plantations of the same age. Tree growth variables measured included diameter at breast height (Dbh) and merchantable height. All data obtained were analyzed using descriptive statistics and multivariate regression analysis. The predictors for the equation were Dbh, merchantable height and tree species type. The results of the analysis revealed that Gmelina arborea exhibited higher average Dbh and height, wider Dbh and height range, more pronounced positive skewness in Dbh distribution, and more negative skewness in height distribution compared to Tectona grandis. Kurtosis values indicated relatively flatter Dbh and height distributions for both species, with Gmelina arborea showing a more peaked height distribution. Gmelina arborea also showed higher mean volume than Tectona grandis. The multivariate regression model developed is: Volume (m³) = -0.467 + 0.024*(Height) + 2.683*(Dbh) + 0.016 (Tree species) with R² of 91.3%. The diameter at breast height (Dbh), height, and tree species were found to be statistically significant predictors for stem volume estimation. The developed model for both plantation species will provide useful basis for yield prediction in the study area.

Published in	American Journal of Agriculture and Forestry (Volume 11, Issue 4)
DOI	10.11648/j.ajaf.20231104.17
Page(s)	169-175
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Exotic Plantations, Volume Estimation Model, Dogo-Kétou Forest Reserve, Benin Republic

References

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[11]	L. B. Gwallameji and M. S. Misau, “Identification and bioactivity of Gmelina species (Gmelina arborea) commonly grown in some selected communities within Toro L. G. A of Bauchi State, Nigeria,” International Journal of Advanced Scientific Research, vol. 4, no. 1, pp. 31–34, Jan. 2019.
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Dende Ibrahim Adekanmbi, Adandé Belarmain Fandohan, Marc Aimé Tchoumado, Agossou Bruno Djossa. (2023). A Regression Modelling Approach for Stem Volume Estimation of Two Exotic Plantations within Dogo-Kétou Forest Reserve, Benin Republic. American Journal of Agriculture and Forestry, 11(4), 169-175. https://doi.org/10.11648/j.ajaf.20231104.17

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ACS Style

Dende Ibrahim Adekanmbi; Adandé Belarmain Fandohan; Marc Aimé Tchoumado; Agossou Bruno Djossa. A Regression Modelling Approach for Stem Volume Estimation of Two Exotic Plantations within Dogo-Kétou Forest Reserve, Benin Republic. Am. J. Agric. For. 2023, 11(4), 169-175. doi: 10.11648/j.ajaf.20231104.17

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AMA Style

Dende Ibrahim Adekanmbi, Adandé Belarmain Fandohan, Marc Aimé Tchoumado, Agossou Bruno Djossa. A Regression Modelling Approach for Stem Volume Estimation of Two Exotic Plantations within Dogo-Kétou Forest Reserve, Benin Republic. Am J Agric For. 2023;11(4):169-175. doi: 10.11648/j.ajaf.20231104.17

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@article{10.11648/j.ajaf.20231104.17,
  author = {Dende Ibrahim Adekanmbi and Adandé Belarmain Fandohan and Marc Aimé Tchoumado and Agossou Bruno Djossa},
  title = {A Regression Modelling Approach for Stem Volume Estimation of Two Exotic Plantations within Dogo-Kétou Forest Reserve, Benin Republic},
  journal = {American Journal of Agriculture and Forestry},
  volume = {11},
  number = {4},
  pages = {169-175},
  doi = {10.11648/j.ajaf.20231104.17},
  url = {https://doi.org/10.11648/j.ajaf.20231104.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajaf.20231104.17},
  abstract = {Stem volume models play an important role in forest management, evaluating the economic value of a forest stand and assisting forest managers and other interested parties in determining the optimal strategies for the utilization and conservation of forest resources. Little attention is given to the use of multivariate regression models for plantation species in the study area. This study involved the development of a multivariate regression equation with continuous and categorical independent variables for simultaneous prediction of merchantable volume for Gmelina arborea and Tectona grandis in Dogo-Ketou Forest Reserve. Simple random sampling technique was adopted for plot location from the selected two plantations. Thirty-one temporary plots of dimension 25m by 25m were selected for complete enumeration in all the two plantations of the same age. Tree growth variables measured included diameter at breast height (Dbh) and merchantable height. All data obtained were analyzed using descriptive statistics and multivariate regression analysis. The predictors for the equation were Dbh, merchantable height and tree species type. The results of the analysis revealed that Gmelina arborea exhibited higher average Dbh and height, wider Dbh and height range, more pronounced positive skewness in Dbh distribution, and more negative skewness in height distribution compared to Tectona grandis. Kurtosis values indicated relatively flatter Dbh and height distributions for both species, with Gmelina arborea showing a more peaked height distribution. Gmelina arborea also showed higher mean volume than Tectona grandis. The multivariate regression model developed is: Volume (m3) = -0.467 + 0.024*(Height) + 2.683*(Dbh) + 0.016 (Tree species) with R2 of 91.3%. The diameter at breast height (Dbh), height, and tree species were found to be statistically significant predictors for stem volume estimation. The developed model for both plantation species will provide useful basis for yield prediction in the study area.},
 year = {2023}
}

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TY - JOUR
T1 - A Regression Modelling Approach for Stem Volume Estimation of Two Exotic Plantations within Dogo-Kétou Forest Reserve, Benin Republic
AU - Dende Ibrahim Adekanmbi
AU - Adandé Belarmain Fandohan
AU - Marc Aimé Tchoumado
AU - Agossou Bruno Djossa
Y1 - 2023/08/15
PY - 2023
N1 - https://doi.org/10.11648/j.ajaf.20231104.17
DO - 10.11648/j.ajaf.20231104.17
T2 - American Journal of Agriculture and Forestry
JF - American Journal of Agriculture and Forestry
JO - American Journal of Agriculture and Forestry
SP - 169
EP - 175
PB - Science Publishing Group
SN - 2330-8591
UR - https://doi.org/10.11648/j.ajaf.20231104.17
AB - Stem volume models play an important role in forest management, evaluating the economic value of a forest stand and assisting forest managers and other interested parties in determining the optimal strategies for the utilization and conservation of forest resources. Little attention is given to the use of multivariate regression models for plantation species in the study area. This study involved the development of a multivariate regression equation with continuous and categorical independent variables for simultaneous prediction of merchantable volume for Gmelina arborea and Tectona grandis in Dogo-Ketou Forest Reserve. Simple random sampling technique was adopted for plot location from the selected two plantations. Thirty-one temporary plots of dimension 25m by 25m were selected for complete enumeration in all the two plantations of the same age. Tree growth variables measured included diameter at breast height (Dbh) and merchantable height. All data obtained were analyzed using descriptive statistics and multivariate regression analysis. The predictors for the equation were Dbh, merchantable height and tree species type. The results of the analysis revealed that Gmelina arborea exhibited higher average Dbh and height, wider Dbh and height range, more pronounced positive skewness in Dbh distribution, and more negative skewness in height distribution compared to Tectona grandis. Kurtosis values indicated relatively flatter Dbh and height distributions for both species, with Gmelina arborea showing a more peaked height distribution. Gmelina arborea also showed higher mean volume than Tectona grandis. The multivariate regression model developed is: Volume (m3) = -0.467 + 0.024*(Height) + 2.683*(Dbh) + 0.016 (Tree species) with R2 of 91.3%. The diameter at breast height (Dbh), height, and tree species were found to be statistically significant predictors for stem volume estimation. The developed model for both plantation species will provide useful basis for yield prediction in the study area.
VL - 11
IS - 4
ER -

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Author Information

Dende Ibrahim Adekanmbi

School of Tropical Forestry, National University of Agriculture, Porto-Novo, Republic of Benin
Adandé Belarmain Fandohan

School of Tropical Forestry, National University of Agriculture, Porto-Novo, Republic of Benin
Marc Aimé Tchoumado

School of Tropical Forestry, National University of Agriculture, Porto-Novo, Republic of Benin
Agossou Bruno Djossa

School of Tropical Forestry, National University of Agriculture, Porto-Novo, Republic of Benin

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Dende Ibrahim Adekanmbi, Adandé Belarmain Fandohan, Marc Aimé Tchoumado, Agossou Bruno Djossa. (2023). A Regression Modelling Approach for Stem Volume Estimation of Two Exotic Plantations within Dogo-Kétou Forest Reserve, Benin Republic. American Journal of Agriculture and Forestry, 11(4), 169-175. https://doi.org/10.11648/j.ajaf.20231104.17

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Dende Ibrahim Adekanmbi; Adandé Belarmain Fandohan; Marc Aimé Tchoumado; Agossou Bruno Djossa. A Regression Modelling Approach for Stem Volume Estimation of Two Exotic Plantations within Dogo-Kétou Forest Reserve, Benin Republic. Am. J. Agric. For. 2023, 11(4), 169-175. doi: 10.11648/j.ajaf.20231104.17

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AMA Style

Dende Ibrahim Adekanmbi, Adandé Belarmain Fandohan, Marc Aimé Tchoumado, Agossou Bruno Djossa. A Regression Modelling Approach for Stem Volume Estimation of Two Exotic Plantations within Dogo-Kétou Forest Reserve, Benin Republic. Am J Agric For. 2023;11(4):169-175. doi: 10.11648/j.ajaf.20231104.17

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@article{10.11648/j.ajaf.20231104.17,
  author = {Dende Ibrahim Adekanmbi and Adandé Belarmain Fandohan and Marc Aimé Tchoumado and Agossou Bruno Djossa},
  title = {A Regression Modelling Approach for Stem Volume Estimation of Two Exotic Plantations within Dogo-Kétou Forest Reserve, Benin Republic},
  journal = {American Journal of Agriculture and Forestry},
  volume = {11},
  number = {4},
  pages = {169-175},
  doi = {10.11648/j.ajaf.20231104.17},
  url = {https://doi.org/10.11648/j.ajaf.20231104.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajaf.20231104.17},
  abstract = {Stem volume models play an important role in forest management, evaluating the economic value of a forest stand and assisting forest managers and other interested parties in determining the optimal strategies for the utilization and conservation of forest resources. Little attention is given to the use of multivariate regression models for plantation species in the study area. This study involved the development of a multivariate regression equation with continuous and categorical independent variables for simultaneous prediction of merchantable volume for Gmelina arborea and Tectona grandis in Dogo-Ketou Forest Reserve. Simple random sampling technique was adopted for plot location from the selected two plantations. Thirty-one temporary plots of dimension 25m by 25m were selected for complete enumeration in all the two plantations of the same age. Tree growth variables measured included diameter at breast height (Dbh) and merchantable height. All data obtained were analyzed using descriptive statistics and multivariate regression analysis. The predictors for the equation were Dbh, merchantable height and tree species type. The results of the analysis revealed that Gmelina arborea exhibited higher average Dbh and height, wider Dbh and height range, more pronounced positive skewness in Dbh distribution, and more negative skewness in height distribution compared to Tectona grandis. Kurtosis values indicated relatively flatter Dbh and height distributions for both species, with Gmelina arborea showing a more peaked height distribution. Gmelina arborea also showed higher mean volume than Tectona grandis. The multivariate regression model developed is: Volume (m3) = -0.467 + 0.024*(Height) + 2.683*(Dbh) + 0.016 (Tree species) with R2 of 91.3%. The diameter at breast height (Dbh), height, and tree species were found to be statistically significant predictors for stem volume estimation. The developed model for both plantation species will provide useful basis for yield prediction in the study area.},
 year = {2023}
}

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