Through the series, we will cover a number of points about the estimation of benefits from an environmental project. Initially, to keep things simple, I’ll talk about the case where there is a single type of benefit being generated by an environmental project (e.g. a threatened species is being made safer). In later posts I’ll talk about cases with multiple types of benefits from the same project.

This first point is deceptively simple. It is that the benefit of an environmental project is the change in value of the environmental asset as a result of the project. In other words, it is a difference: the difference between the environmental value with the project and without the project.

So, to estimate the benefits of a project, you need two pieces of information: the environmental values with the project and the values without the project. Usually, when we are evaluating a project, the project has not yet been implemented. In that case, both of the required pieces of information have to be predicted. You can’t observe them, because they are in the future.

Note that comparing environmental values “with versus without” the project is not the same as comparing values “before versus after” the project. The reason is that the condition of the asset would probably not be static in the absence of the project. For example, it may be that the asset would degrade in the absence of the project, but its condition would be improved by the project (relative to its current condition). This is illustrated in Figure 1.

The graph illustrates a case where the asset currently has a value of 57 [labelled (1)]. (The 57 is just some measure of value – we’ll discuss values more in later posts.) Without the proposed project, the value is expected to decline steadily, to a score of 37 after 25 years [labelled (3)]. With the project, value would increase to a score of 76 after 25 years [labelled (2)].

Figure 1.

Clearly, in this example, the benefits of the project grow over time (the two lines diverge in Figure 1). Ideally, we would estimate the benefits in each year after the project is implemented and add them up (after allowing for discounting, which we’ll cover in a later post). A practical simplification is to estimate the environmental benefits based on the difference in the asset value with and without the project in a particular future year. For example, we might choose to focus on 25 years in the future, and estimate values at that date with and without the project. In doing this, we need to be careful that we deal appropriately with time (see a later post for details).

Assuming we go with that simplified approach (focusing on benefits at year 25), the relevant measure of project benefits for ranking projects is (2) minus (3). I have seen ranking systems which use (1) minus (3), (2) minus (1), (1) alone or (2) alone, and sometimes more than one of these in the same ranking system, but they are all irrelevant. If you include (2) minus (3) you should not include any of the others listed. To do so will just make the rankings worse.

Because of the “with versus without” principle, a project can generate benefits even if it does not completely prevent degradation of the environmental asset. As long as it slows or reduces degradation, this should be measured as a benefit. Figure 2 shows an illustration of this. In this example, future asset condition with the project (2) is below the initial asset condition (1), but is above future asset condition without the project (3). Since the project benefit is (2) minus (3), the benefit is positive.

Figure 2.

On the other hand, a project that superficially appears to generate large benefits may actually not do so, because those benefits would have been generated even without the project. In other words, the benefits are not ‘additional’ to what would have happened anyway. The without-project line in the graph would be almost as high as the with-project line, so the difference between them (= the benefit of the project) would be minimal (Figure 3).

Figure 3.

For example, suppose that a proposed project encourages farmers to adopt a new type of environmentally beneficial crop, where that crop is much more profitable than farmers’ existing crops. If the private benefits are large enough, it’s a safe bet that the farmers would have adopted the new crop even without the project. It would have been promoted by word of mouth and by private farm business consultants. Adoption of the crop for commercial reasons would have generated environmental benefits as a spin-off.

Making good predictions about the “without project” scenario can be quite difficult, requiring good knowledge of the environment, the relevant management practices and the people whose behaviour matters. Weak thinking about the “without” scenario for environmental projects is a common failing, sometimes leading to exaggerated estimates of the benefits.

Further reading

Pannell, D.J., Roberts, A.M., Park, G. and Alexander, J. (2013). Designing a practical and rigorous framework for comprehensive evaluation and prioritisation of environmental projects, Wildlife Research (forthcoming). Journal web page ♦ Pre-publication version at IDEAS

Suppose you manage an environmental program that has a budget available for spending on environmental projects and there is not enough money to fund every proposed project. You have to decide which projects to fund. How should you do it?

The first principle is that projects should be ranked using a metric (a formula) that consists of a measure of project benefits divided by a measure of project costs. Economists call this metric a Benefit: Cost Ratio (BCR).

There are plenty of project ranking metrics out there in actual use that don’t do this. Some subtract costs instead of dividing them, and some (remarkably) ignore costs entirely. These are mistakes that are costly to the environment.

To illustrate, consider the following three hypothetical projects, with the indicated benefits (B) and costs (C). Because the budget is limited, the first project we should choose is the one with the highest benefits per unit cost (the highest BCR) = project 1. But if we rank according to B – C the top ranked project seems to be project 2, while ranking according to B (ignoring costs) tells us that project 3 is best.

The loss of environmental values from using the wrong metric (i.e., ranking according to B – C or B) depends on how tight the budget is. Assuming that the budget is enough to fund 10% of projects, the loss of environmental benefits is 12% for B-C and 19% for B (based on simulating 1000 funding rounds with 100 potential projects in each).

In other words, fixing up the formula is like increasing the program budget by 14% or 23%. It’s much easier to fix the formula than to increase the budget!

In the examples above, I’ve assumed that we know what the benefits and costs would be for each project. Later posts in this series will deal in detail with how we should estimate the benefits and costs. For now I’ll just make these two observations.

The benefits used in the ranking metric should be the benefits of the proposed intervention or project, not the total benefits of the environmental asset. What difference can be made by the intervention or project, and how important is that difference?

The costs should also represent the costs of the intervention or project. If this project did not go ahead, what level of resources could be diverted to other uses?

p.s. (9 May 2013). A slightly more technical issue: it is sometimes claimed that BCRs are flawed because they can be manipulated by transferring costs from the denominator to the numerator. For example, suppose that a proposed project has benefits of $10m, program costs of $2m (requested from the funding program) and other costs of $1m (from other sources, such as the private sector). We could potentially calculate the BCR as 10/(2+1) = 3.3, or else as (10-1)/2 = 4.5. However, there is no ambiguity about the correct way to do this: what should go into the denominator are the costs that are in limited supply from the perspective of the decision makers in the funding program. They are trying to choose the projects that generate the most net benefits per dollar that they have to allocate. So the correct procedure is to subtract the other costs from the benefits, meaning that the correct BCR for this project would be 4.5.

Things get a bit tricky, however, if projects also require ongoing maintenance funding beyond the current project, and the budget for maintenance funding is expected to be fully allocated. This is realistic for many (probably most) projects. In this case, there are actually two constraints that must be satisfied: the current program budget and the long-term maintenance budget. Strictly, in this situation, projects cannot be ranked using a single formula as a metric. The program would need a mathematical programming model to select which projects deliver the most benefits while satisfying both constraints. In practice, after testing various approaches, I believe that a reasonable approximation is to add up both costs (short-term program costs and long-term maintenance costs) and include the total as the denominator in the single formula.

Further reading

Pannell, D.J., Roberts, A.M., Park, G. and Alexander, J. (2013). Designing a practical and rigorous framework for comprehensive evaluation and prioritisation of environmental projects, Wildlife Research (forthcoming). Journal web page ♦ Pre-publication version at IDEAS

The funding available for environmental projects and policies is a small percentage of the money we would need to deal comprehensively with all environmental problems. As a result, whether we like it or not, we have to choose what we do and don’t protect. Even programs that don’t explicitly prioritise their environmental investments do so implicitly – they just do it in a non-transparent, and usually very poor, way.

In my experience, the difference in potential environmental outcomes between poor prioritisation processes and good ones is enormous.

Doing a good job of ranking the investment options is not that hard if you are aware of a few principles, but it seems to me that most people who are responsible for deciding how environmental funds get allocated are not aware of these principles. Indeed, some of the most commonly used approaches to ranking environmental projects are guaranteed to result in very poor rankings. As a result, we miss easy opportunities to deliver much greater environmental outcomes.

My aim in this series of posts is to outline a set of relevant principles and insights that will help environmental decision makers choose the best projects. My focus is on collecting and analysing the information needed to provide high-quality project rankings. There is another set of issues about how the rules of the program are designed to provide incentives for its participants to behave appropriately (e.g. Pannell and Roberts 2010), but I won’t be covering those here. I’ll be talking about information, calculations and clear thinking – stuff that is easy to get right if you know what you are doing.

My aim is to help with practical decision making. As a result, I’ll be talking about the possibility of cutting corners by simplifying aspects of the process. You’ll see that I’m not averse to well-considered simplifications, but very wary of the risk that some simplifications will sabotage the whole process. For a practical system, simplifications are essential, but bad simplifications are disastrous.

Throughout, I will be assuming that the aim is to provide the most valuable environmental outcomes for the available resources.

What is being ranked?

The first requirement is to be clear about what is being ranked. Sometimes programs set out to rank a set of projects that they might invest in. The projects should define what would be done, to which environmental assets, where, and by whom.

At other times, programs seek to rank a set of environmental assets, with no explicit project activities defined. (I’ll use the term “environmental asset” to refer to any identifiable feature, entity, place, or species that might become a target for investment.) There is a risk here – if you don’t define the project activities for an environmental asset, you cannot rank them on the basis of providing the most valuable outcomes.

The problem is that the environmental value for money depends on the answers to questions like, “what is the technical feasibility of protecting the asset?”, “to what extent would the community cooperate?” and “what would it cost to protect the asset?” However, those questions can only be answered for a particular set of actions or interventions.

To further illustrate the point, various different projects could be defined for the same environmental asset. One potential project might have very ambitious goals, aiming to return the asset to pristine condition, while another might aim for a moderate improvement in its condition. Some of these different projects for the same asset may offer relatively good value for money while others don’t (e.g. Roberts et al. 2012). So you cannot conclude that investing in any particular asset is good or bad without being clear about the project actions that will be undertaken.

If the analysis is limited to environmental assets, not projects, then it is important to be aware of what can and cannot be done with the results. What you can reasonably do is filter the assets to identify ones where it is relatively likely that a well-designed project would deliver worthwhile benefits. This could be done using variables such as:

• the value or significance of the assets,
• the levels of degradation they have already suffered or are likely to suffer in future, and
• the feasibility of managing them (in a loose general sense that doesn’t require specification of particular management actions).

You should not be making final decisions about which assets received funding, because that does require the specification of projects. Rather, you would be concluding that some assets are probably not worth considering further, and so not worth developing projects for.

Even this is not without risks. Because you are not looking at all of the relevant information, there is a chance of excluding some assets that would actually be worth investing in. For example, you might exclude investment in a particular asset because it seems likely to provide only modest benefits, but if the cost of the project is low enough, it could still be worth doing. With this process of filtering assets, you would miss out on cases like that.

However, it still might be worth filtering assets as part of a more comprehensive process. Indeed that is exactly what we do in Step 1 of INFFER (the Investment Framework for Environmental Resources) (Pannell et al. 2012). This is a simplification we use to reduce the cost of the system. If we can knock out some potential investments based on partial information, it takes less work to properly evaluate and rank a reduced set of potential projects.

If you must make final investment decisions based on assets, not projects, you need to imagine a notional project for each asset. Even a rough-and-ready notional project definition would be better than nothing.

Further reading

Pannell, D.J. and Roberts, A.M. (2010). The National Action Plan for Salinity and Water Quality: A retrospective assessment, Australian Journal of Agricultural and Resource Economics54(4): 437-456. Journal web site here ♦ IDEAS page for this paper

Pannell, D.J., Roberts, A.M., Park, G., Alexander, J., Curatolo, A. and Marsh, S. (2012). Integrated assessment of public investment in land-use change to protect environmental assets in Australia, Land Use Policy 29(2): 377-387. Journal web site here ♦ IDEAS page for this paper

Roberts, A.M. Pannell, D.J. Doole, G. and Vigiak, O. (2012). Agricultural land management strategies to reduce phosphorus loads in the Gippsland Lakes, Australia, Agricultural Systems 106(1), 11-22. Journal web site here ♦ IDEAS page for this paper

I organised a small workshop in Brisbane a few weeks ago on estimating the benefits of environmental research. If we could generate this information, it would be useful in several ways. It could be used to make judgements about whether particular research projects are worth doing, to identify priorities from a set of potential projects, and to make the case for continued funding of environmental research. Also, the process of working out the likely benefits could help us understand the ways that research generates benefits, and that might help us to do a better job of generating benefits.

However, as we quickly agreed at the workshop, this is a very difficult thing to do well. For one thing, there are so many different types of environmental research with different possible uses and impacts, and some of them need different thinking and approaches to analysis.

We decided to focus our attention onto the type of research that is least well served by existing tools and frameworks: research that is intended to influence environmental policy. It turns out that this is the most neglected aspect for a reason – it’s the most difficult one to deal with.

You can see why it’s difficult from the following list of stages that one must go through, starting from research and ending up with real-world benefits.

• Funding is allocated to research and research is done
• Something useful is learned – new information is generated (or isn’t)
• The new information influences policy/management (or doesn’t)
• Policy change is implemented by policy makers (or isn’t)
• If the purpose of the policy is to change the behavior of people or businesses, these people respond to the changed policy (or don’t)
• Changes (relative to no research) result in the environment (or not), including unexpected or unintended consequences

To estimate benefits, we need to estimate what happened (or predict what will happen) at each of these stages. If one link in the chain breaks, benefits are not generated. We also need to estimate (or predict) what would have happened in the absence the research – something you can’t actually observe even if the research has been completed and had its impacts.

Research that aims to influence policy is particularly difficult to assess, because the process of policy change is so complex and influenced by numerous factors. It is very difficult to judge what proportion of any particular change may be attributable to the research rather than other factors. This is recognised in the literature as the attribution problem.

Despite all the difficulties, we found that the existing frameworks for research evaluation provided enough of a platform for us to think productively about what we would do for this type of research. A team of us will be working on this challenge over the next while. We aim to work out what would be needed for a comprehensive rigorous framework, and from that produce a set of principles and perhaps rules of thumb that researchers, research funders and policy makers can use when they need to think about the benefits of policy-oriented environmental research.

Further reading

van der Most, F. (2010). Use and non-use of research evaluation: A literature review, Paper no. 2010/16, Circle, Lund University, Sweden. Here

Back in 2002 I published a poem about this in a refereed journal article. I’m pretty pleased with this – you don’t see many poems in refereed journals. This week, somebody told me that my poem had been included (with praise such as, “a beautiful piece of work”) on a web page of econometric poetry. I then did a search and, apart from finding the original paper, I found it reproduced on three other pages (here, here, here), and referred to on several more. Isn’t the web marvelous?

In case you haven’t seen it, here it is.

I’m The Referee
David J. Pannell

You’ve posted in your paper
To a journal of repute
And you’re hoping that the referees
Won’t send you down the chute

You’d better not build up a sense of
False security
I’ve just received your manuscript and
I’m the referee

This power’s a revelation
I’m so glad it’s come to me
I can be a total bastard with
Complete impunity

I used to be a psychopath
But never more will be
I can deal with my frustrations now that
I’m a referee

The poem is therapeutic, as was the paper it was published in (Pannell, 2002), so if you’ve suffered at the hands of referees, you might want to read that too.

Further reading

Pannell, D.J. (2002). Prose, psychopaths and persistence: personal perspectives on publishing. Canadian Journal of Agricultural Economics 50(2), 101–116. Here ♦ IDEAS page for this paper

Our focus is on what should happen in the public sector, on the grounds that it is not helpful to ask what “should” happen in the private sector. The private sector will develop in response to commercial opportunities available to them, irrespective of what we might think should happen.

To set the context, here are some predictions about the environment within which extension will operate. Agriculture will continue to change in response to technology, markets and climate. Cutbacks we have seen in funding for public-sector agricultural extension will not be reversed and may continue. The dismantling of extension infrastructure and capacity in the public sector has gone too far for it to be reversed without major new public investments, and we don’t foresee those occurring. Private sector capacity in extension will continue to grow – including extension provided by purchasers of agricultural products (e.g. dairy, horticulture, sugar), input suppliers (e.g. fertiliser, feeds) and farm management specialists. There will be continuing increases in the average size of farms, and in the number of corporate farms, with resulting growth in the vertical integration of information services (~ “extension”) into farm businesses. There will continue to be growth in the use of advanced information and communication technologies in agriculture, providing information to farmers in novel ways. Falling numbers of graduates from agricultural programs could create a serious challenge to extension services (public and private) to obtain employees with the required knowledge and skills.

In this context, is there a need for ongoing public investment in agricultural extension? We believe that there is. Public-sector agricultural extension can continue to play important roles that address various market failures.

One key role is to foster two-way information flows between researchers and farmers. Information flow from farmers to researchers is needed to ensure that the research conducted will be beneficial to farmers and likely to be adopted by them. Some researchers already have sufficiently strong relationships with their farmer audience not to need this sort of help from extension agents, but many others don’t. The traditional role of extension agents in promoting uptake of beneficial new research results (technologies, systems and practices) should continue. We do not share the negative view of technology transfer that seems to exist among some theorists of extension. We believe that technology transfer and approaches such as participatory research and farmer-to-farmer learning are not mutually exclusive. Indeed, these latter approaches, as part of a broad portfolio of extension methods, can make valuable contributions to the success of technology transfer in appropriate circumstances. Farmer groups and organisations such as the Grower Group Alliance (www.gga.org.au) have key roles to play in this process.

Given that public budgets for extension are unlikely to grow, and may shrink further, it will be crucial for public extension services to take a more business-like approach to prioritising their activities than they have commonly done in the past. Extension efforts should be focused on issues for which there would be substantial benefits to farmers from changing their practices, especially if those new practices would also generate benefits for the broader community (e.g. environmental benefits). Extension would not focus on practices that farmers already have good knowledge about and have decided not to adopt, because non-adoption is a clear signal that the practices do not generate large enough private benefits. The heterogeneity of farms and farmers should be recognised when looking at reasons for non-adoption. This more sophisticated approach to planning extension effort will require greater collection and analysis of information.

As important as social media and other modern communication methods will be, public extension should not rely on them exclusively, but should maintain a level of face-to-face communication. Farming is already socially isolating for some farmers, and with declining farmer numbers this may become a more widespread issue. It is likely that farmers will always put a high value on personal contact in extension.

Finally, we note that, in the past 20 years, public sector extension has been prominent in supporting natural resource management (NRM) policy for agriculture. It has been the go-to policy response of most government NRM programs. Unfortunately, these programs have often funded extension efforts without asking fundamental questions, such as, Are the practices we wish to promote actually adoptable by farmers? A more thoughtful, selective and evidence-based use of extension is needed in this policy context.

 Further reading

Marsh, S.P. and Pannell, D.J. (2000). Agricultural extension policy in Australia: The good, the bad and the misguided. Australian Journal of Agricultural and Resource Economics 44(4): 605-627. Journal web site here ♦ IDEAS page for this paper

Pannell, D.J. and Marsh, S.P. (2013). Public-sector agricultural extension: what should it look like in 10 years? Farm Institute Insights, Vol. 10, No. 1, February 2013. Here

There are more environmental projects available than can be afforded – often many more.

For example, a large national program in Australia recently funded around 5% of proposals received. Of course, proponents have already selected from among the available projects to some degree, so it was probably not more than 1% of potential projects that actually got funded.

Within the set of potential environmental projects, heterogeneity is enormous. They vary widely on every parameter: scale, feasibility, riskiness, importance to the community, likely compliance, costs, time frame, and so on.

As a result, the best projects are much, much better than the rest, meaning that programs can perform much better if they choose their projects well. Unfortunately, the importance of this is under-appreciated by most people involved in managing or participating in environmental programs. Many funded projects are mediocre, while great projects go unfunded or underfunded.

To illustrate how much it matters to focus resources on the best projects, the figure below shows a graph of the benefits and costs of around 7000 potential environmental projects in Australia (from Fuller et al. 2010, courtesy of Richard Fuller). Benefits are measured as the project’s contribution to protecting heavily cleared vegetation types. (See Fuller et al. 2010 for details.) It’s an incomplete measure of benefits, but it’s still useful to illustrate my point.

Note that the benefit and cost axes have been expressed in log scale to allow very small projects to be distinguished on a graph that includes some very large projects. The ranges of benefits and costs are enormous.

If we had enough money to fund 5% of the projects, the set that would generate the largest environmental projects would be the 5% with the highest Benefit: Cost Ratios (BCRs). These are shown as green triangles in the graph.

For this data set, the average BCR of the best 5% is 330 times greater than for the median project. For the best 10%, the ratio is 200 times.

Clearly, if a program faced with these 7000 potential projects fails to fund the best ones, the loss to the environment would be extremely large. From an environmental perspective, it would be well worth putting resources, time and effort into evaluating the projects to identify the best ones.

The quality of information and analysis required to do this successfully is significant. It’s certainly do-able (PD#185), and it’s well worth doing, but it’s rarely done. It requires the sort of analysis that underpins a good conservation tender. I don’t believe that the tender process itself is the key factor – it’s the information and analysis.

Of course, picking cases with large potential BCRs is not enough. Delivering the potential benefits also requires that:

• the project receives sufficient funding (see PD#210),
• the project is well designed with a good internal logic,
• the project uses appropriate delivery mechanisms,
• the project is well implemented, and
• the project is managed in an adaptive way to allow for learning.

But accurately picking the best projects is a great start.

Further reading

Boesso, G. And Kumar, K. (2007). Who or what really counts in a firm’s stakeholder environment: an investigation of stakeholder prioritization and reporting, “Marco Fanno” Working Paper N.51, IDEAS page for this paper

Fuller, R.A., McDonald-Madden, E., Wilson, K.A., Carwardine, J., Grantham, H.S., Watson, J.E.M., Klein, C.J., Green, D.C. & Possingham, H.P. 2010. Replacing underperforming protected areas achieves better conservation outcomes. Nature, 466, 365-367.

Pannell, D.J. (2011). Problems with environmental project prioritisation, Pannell Discussions 185. Here

Pannell, D.J. (2012). Under-estimating the costs of environmental protection, Pannell Discussions 210. Here

Although there was quite substantial climate change in the Western Australia wheatbelt during the 20th century, it had little or no adverse consequences for wheat yields (PD229). Of course, it doesn’t follow that the same will hold during the 21st century. That will depend on the details of how much change occurs, and at what time of year it occurs. These details are highly uncertain.

Even the general pattern of future climate change is inherently uncertain, particularly in the long term. It depends in part on global emissions of greenhouse gases, which in turn depend on economic activity, technology change and climate policy measures over the relevant time frame. Uncertainty about each of these factors is high and increases with the length of time frame. In addition, the world’s climatic system is complex, chaotic and imperfectly understood, so that there is additional uncertainty inherent in the results of global circulation models, the tools used to predict climate.

Uncertainty is greater at the regional scale than at the global scale. And it is greater still when it comes to the within-year timing of changes in rainfall and temperature. We really have little idea about those timings, although they can be crucial in determining the consequences for crop yields (PD229).

Rainfall

Adverse changes in rainfall probably have the largest potential to reduce crop yields. Large reductions in September rainfall would be especially damaging. Unfortunately, rainfall is the factor about which we have the greatest uncertainty. Loosely speaking, regions that are already relatively wet are predicted to get wetter, and regions that are relatively dry (such as the Western Australian wheatbelt) are predicted to get drier, but in truth the models aren’t good enough to give us confidence about what will really happen in any particular place. CSIRO (2007) (somewhat heroically) predicted changes in annual average rainfall for the south-west of -20% to +5% by 2030 and -60% to +10% by 2070 relative to the period 1980-1999.

If the real results do fall within these ranges, 2030 would probably not be catastrophic, unless it includes a large reduction in early-spring rainfall. Clearly, -60% in 2070 would be catastrophic, but it’s the extreme case. Something nearer the midpoint of that range (-20%) would probably be damaging but not catastrophic. The real message about 2070 is that we have huge uncertainty about what rainfall will do – the range of the CSIRO predictions is 70%!

Temperature

By 2050, increases in temperature could perhaps be somewhere in the range of 1 to 3 °C. A positive impact on yield of temperature increases up to 1–3 °C has been reported from relevant crop modelling results when assuming that temperatures increase by the same amount every day across the growing season. Positive yield impacts come from accelerated plant development, leading to avoidance of high maximum temperatures and water stress during grain filling.

On the other hand, if the higher temperatures happen to include an increased frequency of extreme temperatures during grain filling, the result could be very negative. There is a potential for yield reductions of 5% for each day of extreme heat during grain filling.

Again, there is high uncertainty. Future temperature changes could be positive, negative or neutral for crop yields in Western Autralia.

CO₂

CO₂ fertilization of crops is a significant part of the climate/CO₂ story (Attavanich and McCarl, 2012). In future, CO₂ concentrations are likely to increase at about about 4 ppm per year. This is predicted to increase yields of current wheat cultivars in Western Australia by 15–30% over the next 50 years (Asseng and Pannell, 2012). Percentage increases in yield are likely to be greater in dryer agricultural regions, mainly due to increased water-use efficiency. Compared to the changes in rainfall  and temperature, the increases in CO₂ are fairly certain. They are also constant all year, avoiding the tricky problem of predicting the within-year distribution of changes.

Highly adverse changes in rainfall and temperature would be really catastrophic for farmers in the Western Australian wheatbelt, but more likely the overall impacts will be moderate, particularly when the positive yield effects of CO₂ fertilization are factored in.

Reinforcing that judgement, another new paper (Potgieter et al., 2012) concludes that, under a high-emissions scenario, different shires would see changes in crop yields by 2050 of −5% to +6% across most of Western Australia (and Victoria and southern New South Wales), even without allowing for CO₂ fertilization.

I was surprised at how favourable those results are, especially considering that they are for a high-emission scenario. If they are accurate, then any negative impacts would be outweighed by the positive effects of CO₂ fertilization.

I remember reading the original report of the Garnaut review, and being struck by how much it emphasised the risks to agriculture when justifying Australia’s need for a strong policy response to climate change. There certainly are risks to agriculture, but this evidence suggests that they are not as compelling a policy driver as Garnaut indicated. If Potgieter et al. are right, then I would expect that the ongoing decline in public investment in agricultural research will have far worse consequences for agriculture.

This Pannell Discussion draws on part of a paper I’ve recently published with Senthold Asseng, who’s now at the University of Florida (Asseng and Pannell, 2012).

Further reading

Asseng, S. and Pannell, D.J. (2012). Adaptating dryland agriculture to climate change: farming implications and research and development needs in Western Australia, Climatic Change (forthcoming). http://link.springer.com/article/10.1007/s10584-012-0623-1

Attavanich, W. and McCarl, B. (2012). The effect of climate change, CO₂ fertilization, and crop production technology on crop yields and its economic implications on market outcomes and welfare distribution, Agricultural and Applied Economics Association, 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania, IDEAS page for this paper.

Potgieter, A., Meinke, H., Doherty, A., Sadras, V.O., Hammer, G., Crimp, S. and Rodriguez, D. (2012). Spatial impact of projected changes in rainfall and temperature on wheat yields in Australia, Climatic Change (forthcoming). http://link.springer.com/article/10.1007/s10584-012-0543-0

Western Australia is by far the largest grain-crop-producing state of Australia, and wheat is by far its main crop.

The wheatbelt underwent significant climate change during the 20th century, commencing even before climate change was a high-profile issue. The region has had a 20% rainfall decline over the past 110 years, more than any other wheat-growing region in Australia.

There has been a specific pattern to the rainfall decline, with most of it occurring in winter. Figure 1 shows a typical example, for the Mullewa region.

Figure 1. Average monthly rainfall for Mullewa for 1945–1974 (filled bars) and 1975–2008 (open bars)

The other important climatic variable is temperature. Average temperature in the region has increased slightly (0.8 °C) over the past 50 years, but there has been a disproportionate increase in the frequency of hot days during grain filling (Asseng et al. 2011), when wheat yields are adversely affected by high temperatures. Some of that impact may have been off-set by increases in temperature during winter, which helps to increase yields.

Ludwig et al. (2009) used crop modelling to investigate the effects of past climate change on crop yields over the past 60 years, across various locations of the Western Australian wheatbelt. Remarkably, they concluded that there has been no change in wheat yield potential. The reasons they proposed for the lack of impact of reduced rainfall are:

1. Crops have low demand for water during the cool winter months in which the decline has occurred
2. Given the unpredictability of weather, farmers do not apply sufficient inputs (particularly fertilizer) to achieve the higher yields that are theoretically possible in wet years, and
3. Most Western Australian soils have low water-holding capacity, so a large proportion of unused water is lost below the root zone of crops.

A third change, not considered by Ludwig et al. (2009), has been the increase in atmospheric CO₂. Higher CO₂ has presumably contributed to the climatic changes (especially tempterature) but has another effect on crop yields, through so-called “CO₂ fertilization” (Attavanich and McCarl, 2012). Increases in atmospheric CO₂ concentration over the past 50 years have increased wheat yields in Western Australia by approximately 2–8 %.

Overall, I think it’s quite interesting and surprising that, despite really significant changes in climate in the region, these changes have had no significant negative impact on yields, especially when CO₂ fertilization is factored in.

It highlights that the specific details of the climate changes (such as their within-year timing) really matter. Changes that would be damaging at one time of the year can be benign at another. This makes it even harder to accurately predict the impacts of future climate changes, even if we get their average magnitudes right (which is already tough).

At the same time as climate change was having no impact on wheat yields in Western Australia, other things were having big positive impacts, including changes in crop varieties, increased fertiliser use, herbicides, reduced tillage, improved machinery allowing earlier sowing, retention of crop residues, and the use of ‘break’ crops that reduce root diseases. These have combined to increase average wheat yields in the region by around 100% over the past 30 years.

Some scientist have argued that farmers in this region should already be making changes to adapt to climate change. In the light of these results, that advice seems misguided.

This Pannell Discussion is based on part of a paper I’ve recently published with Senthold Asseng, who’s now at the University of Florida (Asseng and Pannell, 2012).

Further reading

Asseng, S. and Pannell, D.J. (2012). Adaptating dryland agriculture to climate change: farming implications and research and development needs in Western Australia, Climatic Change (forthcoming). http://link.springer.com/article/10.1007/s10584-012-0623-1

Attavanich, W. and McCarl, B. (2012). The effect of climate change, CO₂ fertilization, and crop production technology on crop yields and its economic implications on market outcomes and welfare distribution, Agricultural and Applied Economics Association, 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania, IDEAS page for this paper.

Ludwig, F., Milroy, S.P., Asseng, S. (2009). Impacts of recent climate change on wheat production systems in Western Australia. Climatic Change 92: 495–517.

I was at a public seminar this week, at which the leader of a government inquiry was outlining the findings from the resulting report. He made the observation that the great majority of people seemed to agree with the findings, but that there was a small but vocal minority who did not.

Most of the audience seemed to be broadly on side with the speaker, bearing out his claim, but at least one took a very different position. During question time she made a vehement statement that amounted to a denunciation of the entire report.

The speaker responded in kind. He said he thought she probably hadn’t read the report. When she reacted angrily to that, he asserted, “then you didn’t understand it” and glared at her. Later he once again referred to “the small but vocal minority”, this time adding, “who should be ignored”.

It had been quite an aggressive comment, so perhaps the aggressive response was fair enough in a way, but the result was to close off debate. I guess that’s what the speaker wanted but I don’t think it was appropriate, and it didn’t go down well with some of the audience.

While the exchange of fire was exciting, the main thing I’m going to focus on is the speaker’s implication that the views of a group of people must be wrong and should be ignored because they are the views of a small minority. That’s a terrible argument. If evidence and logic is with the minority, it doesn’t matter how few in number they are. As Einstein said in response to a Nazi pamphlet titled 100 authors against Einstein, “If I had been wrong, one would have been enough”.

I’m not saying that the commenter was right. I haven’t read the report in question, so I can’t judge. All I’m saying is that the speaker was wrong to point to the number of people who agreed with the report as an indication that it was sound.

I have personal experience of being in an absolutely miniscule minority on a controversial issue, but eventually being accepted as the one who was correct. The issue was dryland salinity in Australia. My analysis in the early 2000s, drawing together the hydrogeology, economics and sociology of salinity, led me to conclude that the existing policy emphasis on integrated catchment management was misguided in most cases (Pannell, 2001a, 2001b).

Also, salinity management recommendations at the time emphasised the need for all farmers in a catchment to cooperate due to their supposed hydrological inter-dependence. I concluded that this too was misguided, initially for Western Australia (Pannell et al., 2001) and later more generally (Beverley et al., 2012).

For a long time I was the only one who was publicly putting these view, which ran counter to the way that almost everybody was thinking about the problem. An implication of my conclusions was that many millions of dollars were being wasted in public programs to fight salinity.

When I tried to put my position in conferences and meetings, I often generated strong negative reactions, including derision and anger. Many people working in the area said that I didn’t know what I was talking about and rejected my arguments out of hand. One notable incident involved a very public tirade of abuse from a fellow economist following my Presidential Address to the Australian Agricultural and Resource Economics Society in 2001.

Over time, the weight of evidence supporting my position got stronger and stronger (e.g. Barrett-Lennard et al., 2005), and I won people over, or maybe just wore them down. By the time of the Second International Salinity Forum in Adelaide in 2008, my views that had seemed heretical to many in 2001 had become the new orthodoxy.

The point is, it doesn’t matter how small the minority is, they might be right. Logic and evidence is what matters, not weight of numbers.

In fact, whenever there is a really important advance in knowledge that overturns a previous misconception, by definition, the person with the new insight is initially in the small minority.

The salinity experience has made me particularly attuned to the possibility that the majority can be wrong. As a result, I worry about the heavy reliance on scientific consensus as an argument in the climate debate. The consensus might well be right, but the fact that there is a consensus is not, in itself, an argument that should be convincing. There was an almost unanimous consensus about these two aspects of salinity management and policy that turned out to be wrong.

Further reading

Barrett-Lennard, E.G., George, R.J., Hamilton, G., Norman, H.C., Masters, D.G. (2005). Multi-disciplinary approaches suggest profitable and sustainable farming systems for valley floors at risk of salinity, Australian Journal of Experimental Agriculture 45: 1415–1424. Journal web site here

Beverly, C., Roberts, A., Hocking, M., Pannell, D. and Dyson, P. (2011). Using linked surface-groundwater catchment modelling to assess protection options for environmental assets threatened by dryland salinity in southern-eastern Australia, Journal of Hydrology 410: 13-30. Journal web site here

Pannell, D.J. (2001a). Salinity policy: A tale of fallacies, misconceptions and hidden assumptions, Agricultural Science 14(1): 35-37. Here

Pannell, D.J. (2001b). Dryland Salinity: Economic, Scientific, Social and Policy Dimensions, Australian Journal of Agricultural and Resource Economics 45(4): 517-546. Journal web site here ♦ IDEAS page for this paper

Pannell, D.J., McFarlane, D.J. and Ferdowsian, R. (2001). Rethinking the externality issue for dryland salinity in Western Australia, Australian Journal of Agricultural and Resource Economics 45(3): 459-475. Journal web site here ♦ IDEAS page for this paper

Pannell, D.J. and Roberts, A.M. (2010). The National Action Plan for Salinity and Water Quality: A retrospective assessment, Australian Journal of Agricultural and Resource Economics54(4): 437-456. Journal web site here ♦ IDEAS page for this paper