Skip navigation

Portfolio selection theory and wildlife management

Portfolio selection theory and wildlife management

Hearne, J. W., Santika, T. ORCID: 0000-0002-3125-9467 and Goodman, P. (2008) Portfolio selection theory and wildlife management. ORiON, 24 (2). pp. 103-113. ISSN 2224-0004 (Print), 0259-191X (Online) (doi:https://doi.org/10.5784/24-2-62)

[img]
Preview
PDF (Publisher's PDF - Open Access)
28371 SANTIKA_Portfolio_Selection_Theory_and_Wildlife_Management_(OA)_2008.pdf - Published Version
Available under License Creative Commons Attribution.

Download (238kB) | Preview

Abstract

With a strong commercial incentive driving the increase in game ranching in Southern Africa the need has come for more advanced management tools. In this paper the potential of Portfolio Selection Theory to determine the optimal mix of species on game ranches is explored. Land, or the food it produces, is a resource available to invest. We consider species as investment choices. Each species has its own return and risk profile. The question arises as to what proportion of the resource available should be invested in each species. We show that if the objective is to minimise risk for a given return, then the problem is analogous to the Portfolio Selection Problem. The method is then implemented for a typical game ranch. We show that besides risk and return objectives, it is necessary to include an additional objective so as to ensure sufficient species to maintain the character of a game ranch. Some other points of difference from the classical Portfolio Selection problem are also highlighted and discussed.

Item Type: Article
Uncontrolled Keywords: Portfolio selection; multi-objective optimisation; game ranching; wildlife management
Subjects: S Agriculture > S Agriculture (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Agriculture, Health & Environment Department
Last Modified: 07 Jul 2020 17:50
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/28371

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics