Estimation of shrub biomass: development and evaluation of allometric models leading to innovative teaching methods

Maraseni, Tek Narayan and Cockfield, Geoff and Apan, Armando and Mathers, Nicole (2005) Estimation of shrub biomass: development and evaluation of allometric models leading to innovative teaching methods. International Journal of Business and Management Education, Specia. pp. 17-32. ISSN 1832-0236

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Abstract

Accurate estimation of biomass is becoming vital for selling carbon into national and international markets. Being a dry continent, Australia’s natural forest has several shrub species. However, because of limited availability of methodology and difficulty in estimation they are unaccounted for in many cases. This paper has three objectives: (a) to address the major problem in multiple regressions, (b) to develop the best allometric equation for the biomass estimation of a popular shrub species, wild raspberry (Rubus probus) and (c) to prepare a teaching tool, by following systematic and logical steps, for biomass estimation using ForecastXTM software. We identified the possible explanatory variables, by discussing with experts and citing literature, for shrub biomass and then measured them by destructive sampling at Taabinga, near Kingaroy, Queensland. Our research suggests that careful analysis of correlation matrices gives very important clues to which variables we should select and which we should not for the models. High multicollinearity among the independent variables is a major problem in multiple regressions. This study shows that this problem could easily be solved by using basic scientific formula and applying a single variable instead of applying many highly correlated variables in the model. Unlike most statistical books, our analysis does not suggest to reject that variable from the model whose coefficient is not statistically significantly different from zero as it could be highly influential in another set of combination. Similarly, we recommend using the 'intercept' even if its value is not significantly different with zero as it does not cost extra money to be included but it does help the predictive power of the model. Although we developed a range of biomass prediction models (for wild raspberry) that can be used in different circumstances, our first recommendation is for the model which is based on girth and crown volume. Where cost is the major issues, we prefer the model which employs girth and crown area, as it gives a good result and needs only three variables to be measured. These findings can be helpful in teaching the practical applications of multiple regression in courses such as Data Analysis and Business Forecasting.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Authors retain copyright (USQ publication). This is the published version.
Depositing User: Mr Tek Maraseni
Faculty / Department / School: Historic - Faculty of Business - No Department
Date Deposited: 12 Oct 2007 04:55
Last Modified: 02 Jul 2013 22:50
Uncontrolled Keywords: shrub; wild raspberry; biomass; multiple regression; multicollinearity
Fields of Research (FOR2008): 01 Mathematical Sciences > 0104 Statistics > 010401 Applied Statistics
05 Environmental Sciences > 0503 Soil Sciences > 050301 Carbon Sequestration Science
07 Agricultural and Veterinary Sciences > 0705 Forestry Sciences > 070502 Forestry Biomass and Bioproducts
Socio-Economic Objective (SEO2008): B Economic Development > 85 Energy > 8505 Renewable Energy > 850501 Biofuel (Biomass) Energy
URI: http://eprints.usq.edu.au/id/eprint/3213

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