Policies for saving a rare Australian glider: economics and ecology

This paper considers the economics of conserving a species with mainly non-use value, the endangered mahogany glider. Three serial surveys of Brisbane residents provide data on the knowledge of respondents about the mahogany glider. The results supply information about the attitudes of respondents to the mahogany glider, to its conservation and relevant public policies, and about variations in these factors as the knowledge of participants of the mahogany glider alters. Similarly, data are provided and analysed about the willingness to pay of respondents to conserve the mahogany glider and how it changes. Population viability analysis is applied to estimate the required habitat area for a minimum viable population of the mahogany glider to ensure at least a 95% probability of its survival for 100 years. Places are identified in Queensland where the requisite minimum area of critical habitat can be conserved. Using the survey results as a basis, the likely willingness of groups of Australians to pay for the conservation of the mahogany glider is estimated and consequently their willingness to pay for the minimum required area of its habitat. Methods for estimating the cost of protecting this habitat are outlined. Australia-wide benefits are estimated to exceed the costs. Establishing a national park containing the minimum viable population of the mahogany glider is an appealing management option. This would also be beneficial in conserving other endangered wildlife species and ecosystems. Therefore, additional economic benefits to those estimated on account of the mahogany glider itself can be obtained. (C) 2004 Elsevier Ltd. All rights reserved.

Optimising profitability and environmental trade-offs in managing cropping systems using differential evolution

Whilst traditional optimisation techniques based on mathematical programming techniques are in common use, they suffer from their inability to explore the complexity of decision problems addressed using agricultural system models. In these models, the full decision space is usually very large while the solution space is characterized by many local optima. Methods to search such large decision spaces rely on effective sampling of the problem domain. Nevertheless, problem reduction based on insight into agronomic relations and farming practice is necessary to safeguard computational feasibility. Here, we present a global search approach based on an Evolutionary Algorithm (EA). We introduce a multi-objective evaluation technique within this EA framework, linking the optimisation procedure to the APSIM cropping systems model. The approach addresses the issue of system management when faced with a trade-off between economic and ecological consequences.