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Tools for NRM: Linking investments to outcomes

Citation

Pollino, CA and Lefroy, EC and Jakeman, AJ, Tools for NRM: Linking investments to outcomes, http://www.mssanz.org.au/modsim09/F12/pollino.pdf, 13-17 July 2009, Cairns, Australia, pp. 1-2. (2009) [Non Refereed Conference Paper]

Abstract

Since the emergence of national natural resource management programs, there have been few tools that can assist regional bodies in planning, monitoring and evaluating the success of investments. Tools are needed to assist natural resource managers in better focusing investments, more efficiently allocating scare resources available to regional bodies and demonstrating ongoing improvements in resource condition. Such tools should also be underpinned by robust scientific analysis and promote enhanced understanding of cause and effect through adaptive learning. In order to credibly characterize the links between investments and outcomes, we suggest four key steps: a participatory systems thinking approach is needed to define problems; a strong evidence-base is required to further characterise links between cause and effect (e.g. investments and outcomes); sensitivity assessment is required to simplify relationships to the core controlling variables and identify a suite of interventions likely to achieve the desired outcomes; and the impact of interventions need to be updated through a process of adaptive learning, involving follow up monitoring and modelling review. Clearly, developing such a suite of tools is a substantial exercise. This challenge is the focus of the research hub Landscape Logic (www.landscapelogic.org.au), funded by the Commonwealth Environmental Research Facilities program. In Landscape Logic we are using a suite of tools to link investments to outcomes, through analysis of cause and effect. Our focus issues are water quality and native vegetation condition, linking both social and biophysical processes. We are using conceptual models, retrospective analysis and targeted knowledge collection, to build integration models (Bayesian networks) that sit within a decision support environment. Sensitivity assessment is being used to identify key causality pathways and to simplify complex models. The value of using Bayesian networks lies in their ability to integrate different forms of knowledge across disciplines, identify knowledge gaps and focus new data collection, incorporate the uncertainty inherent in large scale and long term environmental and social processes, and represent knowledge in a form that is useable by decision-makers. In this paper, we outline a process for linking investment to outcomes via a set of tools, and apply these tools to a case study. The focus of the case study is the Black Box (Eucalyptus largiflorens) depression vegetation communities, located on the NSW Murray floodplain. A set of tools were applied to determine the success of a wetland watering program, where a primary outcome was improving the maintenance and regeneration of trees.

Item Details

Item Type:Non Refereed Conference Paper
Keywords:Natural resource management Bayesian networks sensitivity assessment
Research Division:Environmental Sciences
Research Group:Environmental Science and Management
Research Field:Natural Resource Management
Objective Division:Environment
Objective Group:Environmental and Natural Resource Evaluation
Objective Field:Economic Incentives for Environmental Protection
Author:Lefroy, EC (Professor Ted Lefroy)
ID Code:57664
Year Published:2009
Deposited By:Centre for Environment
Deposited On:2009-08-06
Last Modified:2009-08-06
Downloads:0

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