Optimal Model to Estimate Biomass and Carbon Stock at Agroforestry Stand Cempaka (Elmerillia Sp) in Minahasa District
Yohanes Andreas Robert Langi1, Altien Jonatan Rindengan2, Charles Eferaim Mongi3, Rinancy Tumilaar4, Martina Langi5
1Yohanes Andreas Robert Langi, Department of Mathematics, Sam Ratulangi University, Manado, Indonesia.
2Altien Jonatan Rindengan, Department of Mathematics, Sam Ratulangi University, Manado, Indonesia.
3Charles Eferaim Mongi, Department of Mathematics, Sam Ratulangi University, Manado, Indonesia.
4Rinancy Tumilaar, Department of Mathematics, Sam Ratulangi University, Manado, Indonesia.
5Martina Langi, Department of Forestry, Sam Ratulangi University, Manado, Indonesia.
Manuscript received on 03 August 2019 | Revised Manuscript received on 26 August 2019 | Manuscript Published on 05 September 2019 | PP: 169-172 | Volume-8 Issue-2S7 July 2019 | Retrieval Number: B10410782S719/2019©BEIESP | DOI: 10.35940/ijrte.B1041.0782S719
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Management of stands in community forests such as agroforestry and stands outside forest areas can reduce greenhouse gas emissions. The agroforestry system is a good choices in reducing climate change compared to other options in terrestrial ecosystems. This study aimed at obtaining the most optimal model to estimating the biomass of Cempaka tree (Elmerillia Sp) in community forest stands in Minahasa Regency. The sample was selected through stratified random sampling from two locations. The first location represents the stand of the Cempaka tree community forest, and the second location represents mixed community forest stands. 35 trees were selected for felling, and measurements of wet weight and biomass were carried out based on tree parts. The model to be developed is an allometric regression model of 35 selected trees, and a previously published model. The estimation model obtained is the Cempaka tree biomass estimator model according to tree dimensions such as stems, branches, twigs, leaves, and roots. The results showed that the allometric regression model in the form of logarithmic regression with one independent variable, i.e. diameter of the tree, was quite good in predicting the Cempaka tree biomass. The accuracy of the estimator model for total tree biomass shows R2 of 99.5% with MSE 0.0023 in pure cempaka tree stands. At the second location the coefficient R2 is 98.3% with MSE 0.0038. The predictive results show that the cempaka tree in the community forest stands has a biomass content of 62% – 72%, and the stem part is the largest content.
Keywords: Allometric Regression, Biomass, Cempaka Tree, Estimator Model.
Scope of the Article: Bio-Science and Bio-Technology