Development of allometric equation for the prediction of young Makila (Litsea angulata BI) biomass for handling climate change
Vol 6, Issue 2, 2023
VIEWS - 11 (Abstract) 6 (PDF)
Abstract
Every plant is significantly important in tackling climate change, including Makila (Litsea angulata BI) an endemic wood species found in the forest of Moluccas Provinces. Therefore, this research aimed to examine the role of the Makila plant in tackling climate change by measuring biomass content using constructing an allometric equation. The method used was a destructive sampling, where 40 units of Makila plant at the sampling level were felled, and sorted according to root, stem, branch, rating, and leaf segments. Each segment was weighed both at wet and after drying, followed by a classical assumption test in data processing, and the formulation of an allometric equation. The regression model was examined for normality and suitability in predicting independent variables, ensuring there were no issues with multicollinearity, heteroscedasticity, and autocorrelation. The results yielded a multiple linear regression, namely: Y = −1131.146 + 684.799X1 + 4.276X2, where Y is biomass, X1 is the diameter, and X2 is the tree height. Based on the results of the t-test: variable X1 partially affected Y while variable X2 partially had no effect on Y. The F-test indicated that variables X1 and X2 jointly affected Y with R Square: 0.919 or 91.9% and the rest was influenced by other unexplored factors. To simplify biomass prediction and field measurement, a regression equation that used only 1 independent variable, namely tree diameter, was used for the experiment. Allometric equation only used 1 variable, Y = −1,084,626 + 675,090X1, where X1 = tree diameter, Y = Total biomass with R = 0.957, and R2 = 0.915. Considering the potential for time, cost, and energy savings, as well as ease of measurement in the field, the biomass of young Makila trees was simply predicted by measuring the tree diameter and avoiding the height. This method used the strong relationship between biomass, plant diameter, and height to facilitate the estimation of biomass content accurately by entering the results of field measurements.
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1. Siruru H, Titarsole J. Volume IX Nomor 1. Makila. Available online: http://akademik.faperta.unpatti.ac.id/wp-content/uploads/2016/06/Makila-Volume-IX-Nomor-1-2015-siruru-titarsole.pdf (accessed on 3 May 2023).
2. Fitriyanti F, Qalbiyah S, Sayakti P. Identification of Kalangkala (Litsea Angulata Bi) Bark Macroscopically, Microscopically, and Phytochemical Screening (Indoneisan). Parapemikir: Jurnal Ilmiah Farmasi. 2020; 9(2): 1-9. doi: 10.30591/pjif.v9i2.1832
3. Hassler M. World Plants. Synonymic Checklist and Distribution of the World Flora. Available online: https://www.worldplants.de/world-plants-complete-list/complete-plant-list/?name=Litsea-glutinosa#plantUid-24811 (accessed on 3 May 2023).
4. Kamle M, Mahato DK, Lee KE, et al. Ethnopharmacological Properties and Medicinal Uses of Litsea cubeba. Plants. 2019; 8(6): 150. doi: 10.3390/plants8060150
5. Putri AS. Toxicity, antioxidant ability and inhibition of oral pathogens by monoterpene-rich essential oil of Litsea angulata Blume. Agric Nat Resour. Available from: https://www.researchgate.net/publication/343889302_Toxicity_antioxidant_ability_and_inhibition_of_oral_pathogens_by_monoterpene-rich_essential_oil_of_Litsea_angulata_Blume
6. Almurdani M, Hendra R, Teruna HY. Antioxidant activities of medang lendir (Litsea glutinosa) stem bark. Pharm Educ. 2023;23(2):122–5.
7. Ramana KV, Raju AJS. Traditional and commercial uses of Litsea glutinosa (Lour) C. B. Robinson (Lauraceae). J Med Plants Stud. 2017; 5(3):89-91.
8. Fern K. Tropical Plants Database. Available online: https://tropical.theferns.info/viewtropical.php?id=Litsea+glutinosa (accessed on 3 May 2023).
9. Kassim, AR, Sabri MDM, Kamarudin MF, Birigazzi L. Inventory of Tree Biomass and Volume Allometric Equations in Southeast Asia Available online: https://www.un-redd.org/sites/default/files/2021-10/MRV-19.pdf (accessed on 3 May 2023).
10. Fajriansyah F, Diana R, Matius P. Tree Profile of Litsea spp. in the University Forest (Indonesian). Makila: Jurnal Penelitian Kehutanan. 2019; 13(2): 85-100. doi: 10.30598/makila.v13i2.2435
11. Abdillah MH, Aldi M. Application of crumb rubber solid waste in yellow red podzolic soil on macro nutrient availability and improvement of soil physical properties (Indonesian). EnviroScienteae. 2021; 16(2): 264. doi: 10.20527/es.v16i2.9658
12. Bevans R. Multiple Linear Regression |A Quick Guide (Examples). Scribbr. Available online: https://www.scribbr.com/statistics/multiple-linear-regression/ (accessed on 3 May 2023).
13. Chicco D, Warrens MJ, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science. 2021; 7: e623. doi: 10.7717/peerj-cs.623
14. Cahyandari R, Hisani N. Multiple linear regression model using robust m-estimator regression parameter estimator (Case Study: Rice Production in West Java Province in 2009) (Indonesian). Available online: file:///C:/Users/Gun Mardiatmoko/OneDrive/JOURNAL SUSTAINABLE FORESTRY/REFERENSI MENDELEY/MODEL REGRESI LINIER BERGANDA- 291-541-1-SM.pdf (accessed on 3 May 2023).
15. Padilah TN, Adam RI. Multiple linear regression analysis in estimating rice productivity in Karawang district (Indonesian). Fibonacci: Jurnal Pendidikan Matematika dan Matematika. 2019; 5(2): 117. doi: 10.24853/fbc.5.2.117-128
16. Chandra YI, Kosdiana K, Riastuti M. Application of Statistical Calculation of Linear Correlation Regression Coefficient for Oil and Gas Production (Indonesian). Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer. 2022; 6(2): 19-25. doi: 10.55886/infokom.v6i2.500
17. Uyanık GK, Güler N. A Study on Multiple Linear Regression Analysis. Procedia—Social and Behavioral Sciences. 2013; 106: 234-240. doi: 10.1016/j.sbspro.2013.12.027
18. Zach. What Are Standardized Residuals? Available online: https://www.statology.org/standardized-residuals/ (accessed on 3 May 2023).
19. Sapra RL. Using R2 with caution. Current Medicine Research and Practice. 2014; 4(3): 130-134. doi: 10.1016/j.cmrp.2014.06.002
20. Mardiatmoko G. Allometric equations for predicting above and below-ground biomass of young rain tree [Albizia saman (Jacq.) merr.] to handle climate change. Available online: https://www.researchgate.net/publication/314256342_Allometric_equations_for_predicting_above_and_below-ground_biomass_of_young_rain_tree_Albizia_Saman_Jacq_Merr_to_handle_climate_change (accessed on 3 May 2023).
21. Mardiatmoko G. Allometric Equations For Predicting Biomass Of Young Canarium Tree (Canarium indicum L) To Handle Climate Change. Available online: https://www.shin-norinco.com/article/allometric-equations-for-predicting-biomass-of-young-canarium-tree-canarium-indicum-l-to-handle-climate-change (accessed on 3 May 2023).
DOI: https://doi.org/10.24294/sf.v6i2.2860
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