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]
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