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

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), 2023. Published by Science Publishing Group

Keywords

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

References
[1] J. A. Kershaw, T. W. Beers, B. J. Duch, and B. Husch, Forest mensuration. Chichester, Uk; Hoboken, Nj: John Wiley & Sons, 2017.
[2] H. E. Burkhart, Thomas Eugene Avery, and B. P. Bullock, Forest Measurements. Waveland Press, 2018.
[3] D. U. U. Okali, “Sustainable Use of West African Moist Forest Lands,” Biotropica, vol. 24, no. 2, p. 335, Jun. 1992, doi: https://doi.org/10.2307/2388527.
[4] K. Gandji, V. K. Salako, A. E. Assogbadjo, V. O. Orekan, R. L. Glèlè Kakaï, and B. A. Sinsin, “Evaluation of the sustainability of participatory management of forest plantations: the case study of Wari-Maro Forest Reserve, Republic of Benin,” Southern Forests: a Journal of Forest Science, vol. 79, no. 2, pp. 133–142, Feb. 2017, doi: https://doi.org/10.2989/20702620.2016.1255409.
[5] C. Goussanou, S. Guendehou, A. Assogbadjo, M. Kaire, B. Sinsin, and A. Cuni-Sanchez, “Specific and generic stem biomass and volume models of tree species in a West African tropical semi-deciduous forest,” Silva Fennica, vol. 50, no. 2, 2016, doi: https://doi.org/10.14214/sf.1474.
[6] T. A. Nurudeen, J. O. Nwogwugwu, I. B. Chenge, and K. D. Salami, “Non-linear Regression Models for Tree Volume Estimation in a Six-year-old Gmelina arborea (Roxb) Plantation at Oluwa Forest Reserve, South-western, Nigeria,” International Journal of Applied Research and Technology., vol. 6, no. 4, pp. 9–17., Apr. 2017.
[7] F. E. Adesuyi, A. S. Akinbowale, O. G. Olugbadieye, and K. Jayeola, “Fitting non-linear models for tree volume estimation in strict nature reserve, South-West, Nigeria,” Tropical Plant Research, vol. 7, no. 1, pp. 6–13, Apr. 2020, doi: https://doi.org/10.22271/tpr.2020.v7.i1.002.
[8] C. Peng, “Growth and yield models for uneven-aged stands: past, present and future,” Forest Ecology and Management, vol. 132, no. 2–3, pp. 259–279, Jul. 2000, doi: https://doi.org/10.1016/s0378-1127(99)00229-7.
[9] X. Yu, Juha Hyyppä, Mikko Vastaranta, M. Holopainen, and Risto Viitala, “Predicting individual tree attributes from airborne laser point clouds based on the random forests technique,” ISPRS journal of photogrammetry and remote sensing, vol. 66, no. 1, pp. 28–37, Jan. 2011, doi: https://doi.org/10.1016/j.isprsjprs.2010.08.003.
[10] L. G. Houessou, A. Aheco, Y. Adebi, M. H. Yetein, and H. S. Biaou, “Non-Timber Forest Products use in the Gazetted Forest of Dogo-Kétou, Benin (West Africa),” International Journal of Biological and Chemical Sciences, vol. 13, no. 6, pp. 2824–2837., 2019.
[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.
[12] A. Dié, P. Kitin, F. N. Kouamé, J. Van den Bulcke, J. Van Acker, and H. Beeckman, “Fluctuations of cambial activity in relation to precipitation result in annual rings and intra-annual growth zones of xylem and phloem in teak (Tectona grandis) in Ivory Coast,” Annals of Botany, vol. 110, no. 4, pp. 861–873, Jul. 2012, doi: https://doi.org/10.1093/aob/mcs145.
[13] J. K. Vanclay, Modelling Forest Growth and Yield. Oxford University Press, USA, 1994.
[14] A. Agresti, An introduction to categorical data analysis. Hoboken, New Jersey: John Wiley & Sons, Inc., 2018.
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[17] I. Y. Egonmwan and F. N. Ogana, “Application of diameter distribution model for volume estimation in Tectona grandis L. f. stands in the Oluwa forest reserve, Nigeria,” Tropical Plant Research, vol. 7, no. 3, pp. 573–580, Dec. 2020, doi: https://doi.org/10.22271/tpr.2020.v7.i3.070.
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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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    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
  • School of Tropical Forestry, National University of Agriculture, Porto-Novo, Republic of Benin

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

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

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

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