Yearly Archives: 2007

114 – Localised vs dispersed natural-resource assets

A key decision for regional NRM managers is the balance of investment between: (a) localized assets: discrete, high-value assets in particular locations (e.g. an important wetland); and (b) dispersed assets: groups of assets that are spread across the region, such as agricultural land, or many small pieces of environmentally valuable land on farms (e.g. remnant native vegetation in Australia).

Why treat localized and dispersed assets differently? The payoff from successfully investing in well chosen localized assets is likely to be high. This means that it may be feasible to use relatively expensive approaches, such as engineering works, or high levels of incentive payments, to protect those assets. The assets selected for funding would be particularly valuable, facing high environmental threat, with high feasibility of protection, and high adoptability of the relevant works needed to protect them.

To compete with investment in localized assets, investment in dispersed assets needs to be relatively low-cost per hectare, and highly effective over large areas. Appropriate responses may include technology development (developing new land-use options that are both sustainable and highly adoptable), extension (where such land-use options already exist but have not yet been adopted), and conservation tenders (which may reveal highly cost-effective interventions).

Weighing up localized and dispersed investment: The different asset types have different strengths and weaknesses as investments (see Table 1). The optimal balance of investment will vary by region, depending on factors such as:

  • the number of threatened iconic assets needing investment in the region;
  • the degree and urgency of the threats to iconic assets;
  • the feasibility of averting those threats;
  • the feasibility of the potential actions for dispersed assets.

Table 1. Main advantages and limitations of investing in different asset types.

Asset type
Main advantage
Main limitation
High confidence of NRM outcomes
Small areas managed
Dispersed (technology development)
Large areas of land-use change attainable
Long time lag
Dispersed (extension)
Engagement of the community
Poor NRM outcomes unless adoptable technologies are available
Dispersed (conservation tenders)
Well targeted investment in dispersed environmental assets
High transaction costs

Ideally, environmental managers would make an explicit decision about the balance of effort between localized and dispersed assets, and the appropriate tools to use in each case. The breakdown for different tools would depend on the local situation. For example, there would be a greater emphasis on technology development where:

  • there is a lack of existing sustainable technologies that are attractive to landholders;
  • there are good opportunities for development of improved technologies that are attractive to landholders;
  • landholders are commercially motivated, rather than lifestyle oriented.

There can be synergies between the two categories. Targeted investment in localized assets does provide some benefits in the form of protection of farmland that is close to the targeted assets. Conversely, the tools suggested for dispersed assets can assist with localized assets as well. For example, technology development can benefit localized assets by reducing the cost of land-use change close to those assets, or by increasing the adoptability of practices.

David Pannell, The University of Western Australia

Further Reading

Pannell, D.J. (2007). Balancing investment in localized and dispersed NRM assets. Two pager (39K) (a bit more detailed than this discussion).

113 – MIDAS model 25th anniversary

My first job out of university back in 1983 was working on the MIDAS model. On October 19 a group of us got together in a workshop to celebrate the 25th anniversary of the model, which is still in active use.

MIDAS is a whole-farm bio-economic model that has been used for analysis of research directions, to advise on agricultural policy and to inform agricultural extension.

In the international world of agricultural computer models, MIDAS is extremely unusual in its longevity. Very few such models last a decade in use, let alone a quarter century. Discussions at the workshop identified several likely reasons for its longevity, including:

  • The continued involvement of some key people over all or most of the model’s life.
  • The quality and usefulness of the models.
  • The transparency of model assumptions, and the responsiveness of the modellers to criticism and advice.
  • Keeping models updated as the farming system have changed.
  • The culture of the agricultural science community in Western Australia in the 1980s, which allowed this sort of interdisciplinary work to succeed.

The first point is particularly important. Three of the of the four original model developers are still involved, not in a hands-on way, but in positions where they can encourage and support MIDAS use (Ross Kingwell, Mike Ewing, and myself). In addition there are two active hands-on model developers/users who have been involved for around 20 years (Andrew Bathgate and John Young).

Most participants in the meeting were from Western Australia, but there is now a large number of MIDAS models for regions of other Australian states: New South Wales, Victoria and South Australia. There seems to be a large new audience for MIDAS who are recognising the model’s ability to integrate science and economics to look at farming issues.

MIDAS shows that you don’t need to use the latest modelling methods to create a very useful tool (it is based on linear programming, which was invented in the 1940s). You do, however, need to do a very good job of both the modelling and the associated communication and collaboration.

Some of the participants in the MIDAS 25th Anniversary meeting. From left: David Falconer, David Morrison, Andrew Bathgate, David Pannell, Michael Renton, Amir Abadi, John Young, Kevin Goss, Ross Kingwell, Jonathon Tocker, Graeme Doole, Rukman Wimalasuriya, Natasha Van Heemst, John Bartle, Anne Hamblin, Steve Schilizzi, Lucy Anderton (obscured), Mike Ewing, Emma Barsden, Steve Robinson, Clinton Revell, Bill Bowden, Mark McHenry, Vilaphonh Xayavong.

David Pannell, The University of Western Australia

Further Reading

Pannell, D.J. (1996). Lessons from a decade of whole-farm modelling in Western Australia. Review of Agricultural Economics 18: 373-383. Full paper (61 K)

112 – Advanced studies in economics and policy for natural resources and the environment

Do you know someone who’d be interested in undertaking PhD studies in economics and policy for natural resources and the environment. Would they like to join an outstanding team at the University of Western Australia?

As part of a major new research effort on resource economics and environmental policy at the University of Western Australia, I am seeking a number of PhD students to join the team. There are a number of scholarships available. Some have a closing date for applications of 31 October 2007, but one will remain open until it is filled.

To complement and be part of the planned program of research, I would like to find students to work on topics from the following possibilities.

  • The optimal balance of public environmental investment between localised and dispersed assets.
  • Understanding the ways that policy program design influences the behaviour of environmental management organizations.
  • Understand the ways that incentive payments for environmental works influence landholder behaviour.
  • Developing and applying detailed bio-economic models of environmental and natural resource management problems, to identify strategies that will achieve the maximum benefits for the available resources.
  • The economics of embedding environmentally friendly practices into farming businesses. How should the farming system be adapted to accommodate them? What are their benefits and costs to landholders? Implications for risk?
  • Other related areas, such as policy design, and policy mechanism choice.

The scholarships are at the top end of the range, and include operating funds. Potential applicants should contact me, ideally before 31 October 2007, to discuss the possibilities.

David Pannell, The University of Western Australia

111 – Tornado politics and abortion politics

In his book “The Honest Broker: Making Sense of Science in Policy and Politics”, Roger Pielke Jr. identifies that some political issues are amenable to solution through scientific input, but some are not.

Pielke gives two extreme example of political issues, for which the roles of science are quite different. Both of the examples are characteristically American, but they do illustrate an important point well. I’ve adapted them a bit in the following descriptions.

1. Tornado politics: The issue here is how to respond in the face of an approaching tornado. For example, you might choose to rush into the basement of the building you are in. In this case you would be reasonably safe, but not completely safe, if the tornado hits. Alternatively, you might choose to attempt to travel to a completely safe tornado shelter. Travelling in the open would be dangerous, and probably lethal, if you got caught by the tornado, but if you made it to the shelter, you would be better off than staying where you are. High quality scientific information about the likely course and timing of the tornado would extremely valuable in making the decision between these two options. The desirable outcome from the decision is clear and uncontroversial: maximum likelihood of personal survival. It is relatively easy for a scientist providing advice about the tornado to remain dispassionate and distant from the decision, and they would probably be more useful to the decision maker if they did so.

2. Abortion politics: The issue is whether a community should allow or ban non-essential abortions. This may be affected by scientific issues to some extent, but mainly by personal morality, emotions, religious positions, and so on. Information is relevant, but not just factual scientific information. For example, the decision of a community might be affected by personal anecdotes, or information about the views of others. In this case, scientific information is not the key driver of the decision. It might, in some circumstances, influence the decision, but most likely the decision will be dominated by moral and religious considerations. If a scientist does attempt to contribute to the debate by providing relevant factual scientific information, the information is likely to be picked up by one side and used to attempt to strengthen their entrenched position in the debate. The desirable outcomes is ambiguous and controversial. It may be difficult for some scientists to prevent their personal moral views from influencing the sort of information and/or advice that they give to the decision makers (i.e. they may behave as stealth issue advocates).

Pielke listed some characteristics of these stylised decision problems, as follows:

Tornado politicsAbortion politics
Information used for evaluation

Information used for rationalisation

Information used to help assess decision alternatives

Information used to help justify existing decisions

Comprehensive analysis desired

Selective analysis desired





The protagonists seek enlightenment

The protagonists seek power


He points out that neither type of politics is necessarily better. They are just inherently different, unavoidably reflecting differences between the natures of the issues.

Sometimes scientists who are trying to engage in an abortion-politics type of issue behave as if the issue were a case of tornado politics. I would nominate climate change as an obvious example.

The bottom line is that science can help resolve uncertainty but not conflicting values.

David Pannell, The University of Western Australia

Further Reading

Pielke, R.A. Jr. (2007). The Honest Broker: Making Sense of Science in Policy and Politics, Cambridge University Press, Cambridge.

110 – Science and policy

I recently read an interesting new book called “The Honest Broker: Making Sense of Science in Policy and Politics”. The author, Roger Pielke Jr., identified four different roles that science can play in the policy process. He also highlighted some traps for scientists attempting to engage with policy.

The four roles are as follows.

1. Pure scientist. The pure scientist stays distant from the decision making process. He or she makes her scientific information available in a passive way, and is unconcerned with how (or even whether) the information is used by policy makers.

2. Science arbiter. The science arbiter does not make specific recommendations to policy makers, but does serve as a resource, providing answers to factual questions that the policy maker asks.

3. Issue advocate. The issue advocate does make specific recommendations, and attempts to make the case for one alternative over another.

4. Honest broker. The honest broker attempts to provide balanced information about the range of decision options facing the policy maker. The approach is to expand or clarify the choices available, while leaving the decision to policy makers based on their own preferences and values.

Pielke points out that there are difficulties to be confronted by scientists in adopting any one of these roles.

For example, a pure scientist who intends to focus only on the science may unwittingly act as a “stealth” issue advocate. Their science may be picked up and used in a very one-sided way in a political debate (for example, think of the climate change debate). Similarly, in attempting to be a science arbiter, it is easy to slip into the role of issue advocate, perhaps unintentionally.

Some scientists are willing to take sides overtly in politically contested debates, and use their status as scientists to promote one side. I would observe that this is not uncommon in environmental science – there seem to be a growing number of environmental scientists making impassioned pleas in the media. This role runs the risk of damaging the the special status of science as a source of independent expertise to the community, especially in complex debates where different scientific experts may adopt opposing advocacy positions.

The honest broker attempts to integrate scientific knowledge with stakeholder concerns, which is an extremely challenging task in many cases.

Pielke argues that, “All four roles are critically important and necessary in a functioning democracy. But scientists do have to choose.” Whether or not scientists are aware of it, whenever their research is relevant to a political issue, they will adopt one of the above four roles. Pielke shows that it is better for scientists to be aware of the issues around each role and to make a conscious choice.

Thinking about my own engagement with policy, I can see that at different times I have played each of the four roles, without hardly being aware that I was shifting between them.

David Pannell, The University of Western Australia

Further Reading

Pielke, R.A. Jr. (2007). The Honest Broker: Making Sense of Science in Policy and Politics, Cambridge University Press, Cambridge.