Category Archives: Natural resource management

327 – Heterogeneity of farmers

Farmers are highly heterogeneous. Even farmers growing the same crops in the same region are highly variable. This is often not well recognised by policy makers, researchers or extension agents.

The variation between farmers occurs on many dimensions. A random sample of farmers will have quite different soils, rainfall, machinery, access to water for irrigation, wealth, access to credit, farm area, social networks, intelligence, education, skills, family size, non-family labour, history of farm management choices, preferences for various outcomes, and so on, and so on. There is variation amongst the farmers themselves (after all, they are human), their farms, and the farming context.

This variation has consequences. For example, it means that different farmers given the same information, the same technology choices, or facing the same government policy, can easily respond quite differently, and they often do.

Discussions about farmers often seem to be based on an assumption that farmers are a fairly uniform group, with similar attitudes, similar costs and similar profits from the same practices. For example, it is common to read discussions of costs and benefits of adopting a new farming practice, as if the costs and the benefits are the same across all farmers. In my view, understanding the heterogeneity of farm economics is just as important as understanding the average.

Understanding the heterogeneity helps you have realistic expectations about how many farmers are likely to respond in particular ways to information, technologies or policies. Or about how the cost of a policy program would vary depending on the target outcomes of the program.

We explore some of these issues in a paper recently published in Agricultural Systems (Van Grieken et al. 2019). It looks at the heterogeneity of 400 sugarcane farmers in an area of the wet tropics in Queensland (the Tully–Murray catchment). These farms are a focus of policy because nutrients and sediment sourced from them are likely to be affecting the Great Barrier Reef. “Within the vicinity of the Tully-Murray flood plume there are 37 coral reefs and 13 seagrass meadows”.

Our findings include the following.

  • Different farmers are likely to respond differently to incentive payments provided by government to encourage uptake of practices that would reduce losses of nutrients and sediment.
  • Specific information about this can help governments target their policy to particular farmers, and result in the program being more cost-effective.
  • As the target level of pollution abatement increases, the cost of achieving that target would not increase linearly. Rather, the cost would increase exponentially, reflecting that a minority of farmers have particularly high costs of abatement. This is actually the result that economists would generally expect (see PD182).

Further reading

Van Grieken, M., Webster, A., Whitten, S., Poggio, M., Roebeling, P., Bohnet, I. and Pannell, D. (2019). Adoption of agricultural management for Great Barrier Reef water quality improvement in heterogeneous farming communities, Agricultural Systems 170, 1-8. Journal web page * IDEAS page

325 – Ranking projects based on cost-effectiveness

Where organisations are unable or unwilling to quantify project benefits in monetary or monetary-equivalent terms, a common approach is to rank potential projects on the basis of cost-effectiveness. Just like ranking projects based on Benefit: Cost Ratio (BCR), this approach works in some cases but not others.

To rank projects based on cost-effectiveness, you choose the metric you will use to measure project benefits, estimate that metric for each project, estimate the cost of each project, and divide the benefit metric by the cost. You end up with a cost-effectiveness number for each potential project, and you use these numbers to rank the projects.

An advantage of this approach is that it sidesteps the challenges of having to measure all the benefits in monetary or monetary-equivalent terms, which is what you have to do calculate a BCR. A disadvantage is that it only works to compare projects that generate similar types of benefits, which can all be measured with the same metric.

Assuming that we are satisfied with your benefits metric and that the projects to be ranked are similar enough, the question is, in what circumstances is it appropriate to rank projects based on cost-effectiveness? (Assuming that the objective is to maximise the overall benefits across all the projects that get funded.) It is logical to ask this given that cost-effectiveness is closely related to the BCR (it has the same structure – it’s just that benefits are measured differently), and we’ve seen in PD322, PD323 and PD324 that ranking projects by BCR works in some situations but not others.

It turns out that the circumstances where it is logical to use cost-effectiveness to rank projects are equivalent to the circumstances where it is logical to rank projects using BCR.

(i) If you are ranking separate, unrelated projects, doing so on the basis of cost-effectiveness is appropriate. Ranking projects by cost-effectiveness implies that there is a limited budget available and you are aiming to allocate it to the best projects.

(ii) If you are ranking mutually exclusive projects (e.g. different versions of the same project), ranking on the basis of cost-effectiveness can be highly misleading. If there are increasing marginal costs and/or decreasing marginal benefits (which are normal), ranking by cost-effectiveness will bias you towards smaller project versions. In PD323, I said to rank such projects by NPV and choose the highest NPV you can afford with the available budget. If we are not monetising the benefits, there is no equivalent to the NPV — you cannot subtract the costs from a non-monetary version of the benefits. This means that, strictly speaking, you cannot rank projects in this situation (mutually exclusive projects) without monetising the benefits. If you absolutely will not or cannot monetise the benefits, what I suggest you do instead is identify the set of project versions that can be afforded with the available budget, and choose the project version from that set that has the highest value for the benefit metric. (Theoretically it should be the project version with the greatest net benefit (benefits – costs) but that is not an option here because in Cost-Effectiveness Analysis the benefits and costs are measured in different units.)

You don’t divide by the costs, but you do use the costs to determine which project versions you can afford. This is a fudge that only makes sense if you adopt the unrealistic assumption that any unspent money will not be available to spend on anything else, but it seems to me to be the best way to go, if monetising the benefits is not an option.

(iii) If you are ranking separate, unrelated projects, and there are multiple versions available for at least one of those projects, then cost-effectiveness does not work and the rule about choosing the highest-value benefit metric does not work either. Instead, you should build an integer programming model to simultaneously weigh up both problems: which project(s) and which project version(s). There is a brief video showing you how to do this in Excel in PD324. In the video, the benefits are measured in monetary terms, but the approach will work if you use non-monetary measures of the benefits.

There are a number of tools available for ranking projects based on cost-effectiveness (e.g. Joseph et al. 2009) but it is important to be clear that the approach only works in certain cases.

Even if you are using cost-effectiveness in the right circumstances (case (i) above), it has a couple of limitations relative to using BCR. One is that you cannot use it to rank projects with distinctly different types of benefits that cannot all be measured with the same metric. Another limitation is that cost-effectiveness provides no evidence about whether any of the projects would generate sufficient benefits to outweigh its costs.

Further reading

Joseph, L.N., Maloney, R.F. and Possingham, H.P. (2009). Optimal allocation of resources among threatened species: a project prioritization protocol. Conservation Biology, 23, 328-338.  Journal web site

Pannell, D.J. (2015). Ranking environmental projects revisited. Pannell Discussions 281. Here * IDEAS page

319 – Reducing water pollution from agricultural fertilizers

I gave a talk to the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA) on July 16, 2019, exploring ways to reduce water pollution from agricultural fertilizers.

Many methods have been proposed to reduce water pollution from agricultural fertilizers. The list includes use of nitrification inhibitors, land retirement, vegetation buffer strips along waterways, flood-plain restoration, constructed wetlands, bioreactors, cover crops, zero till and getting farmers to reduce their fertilizer application rates.

Last year, while I was at the University of Minnesota for several months, I reviewed the literature on these options and came to the conclusion that the option with the best prospects for success is reducing fertilizer application rates. It’s the only one of these options that is likely to be both effective and cheap.

In my talk, I made the case for agencies who are trying to reduce pollution to focus on reducing fertilizer rates.

In brief, I identified three key reasons why there are untapped opportunities to reduce fertilizer rates.

1. Some farmers apply more fertilizer than is in their own best interests. Surveys in the US suggest that something like 20 to 30% of American farmers could make more profit if they reduced their rates. If it was possible to identify these farmers and convince them of this, it would be a rare win-win for farmers and the environment.

2. Even those farmers who currently apply fertilizer close to the rates that would maximize their profits could cut their rates without sacrificing much profit. Within the region of the economically optimal rate, the relationship between fertilizer rate and profit is remarkably flat. New estimates by Yaun Chai (University of Minnesota) of this relationship for corn after corn in Iowa indicate that farmers could cut their rates by 30% below the profit-maximizing rate and only lose 5% of their profits from that crop. For corn after soybeans, the equivalent opportunity is for a 45% cut!

3. Some farmers believe that applying an extra-high rate of fertilizer provides them with a level of insurance. They think it reduces their risk of getting a low yield. However, the empirical evidence indicates exactly the opposite. When you weigh up the chances of an above-average yield and a below-average yield, higher fertilizer rates are actually more risky than lower rates. In addition, price risk interacts with yield risk to further increase the riskiness of high rates.

I think there is a real opportunity to explore these three factors in more depth and try to come up with policy approaches that could deliver reduced fertilizer usage in a highly cost-effective way. Some of it would just be about effective communication (e.g. the design of “nudges”, as popularised in behavioural economics) while some might require a modest financial commitment from government or industry. One idea is to offer something like a money-back guarantee to those farmers who agree to reduce their rates by a specified amount. If they lose money as a result, they get compensation. Because of the flatness of the fertilizer-profit relationship, the payments required would usually be very small.

I recorded the presentation to OMAFRA, and it’s available here.

Further reading

Pannell, D.J. (2006). Flat-earth economics: The far-reaching consequences of flat payoff functions in economic decision making, Review of Agricultural Economics 28(4), 553-566. Journal web page * Prepublication version here (44K). * IDEAS page

Pannell, D.J. (2017). Economic perspectives on nitrogen in farming systems: managing trade-offs between production, risk and the environment, Soil Research 55, 473-478. Journal web page

313 – Joining the dots versus growing the blobs

For the recent AARES conference in Adelaide, Maksym Polyakov did a wonderfully creative poster presenting our research on optimal targeting of ecological restoration.

There is a small image of the poster below, but if you want to see the details, go here. (Scroll down when you get there to see the poster.)

Not surprisingly, it won the prize for the best poster at the conference.

Abstract

The primary causes of biodiversity decline worldwide are habitat destruction, alteration and fragmentation resulting from human economic activities such as agriculture or property development. Public- and private-sector organizations allocate considerable resources to slow down biodiversity decline by developing conservation networks that preserve the remaining habitat. In this study we use simulation to compare several strategies to spatially target ecological restoration effort to create conservation networks, on private lands in a fragmented agricultural landscape. The evaluated targeting strategies are aggregation, connectivity and representativeness. The effectiveness of these targeting strategies is compared to the effectiveness of ecological restoration without targeting. We allow for heterogeneity of landowners’ willingness to participate in restoration projects and explicitly assume that that not all parcels within target areas will be restored. We model the probability of participation in restoration projects as a function of the private benefits of ecological restoration captured by the landowner. The results of the simulation are analyzed using regression analysis. Our results suggest that effectiveness of the targeting strategies depends on landscape characteristics (level of fragmentation) and species characteristics (habitat requirements and area of home range). On average, when uncertainty about whether landowners will participate is considered, for most analyzed species, the aggregation strategy outperforms the connectivity strategy with the representativeness strategy performing worst. This is contrary to the findings of previous studies and Government policy, that connectivity is the most effective strategy in fragmented landscapes. Accounting for the landowners’ behavior through a private benefits function improves the biodiversity outcome for most species.

312 – The economics of nitrogen in agriculture

The global challenge of feeding seven billion people would be more difficult without nitrogen fertilizer, but it causes pollution of rivers, lakes and coastal waters around the world, and it contributes to emissions of greenhouse gases. It increases the profitability of individual farmers, but it is over-applied in many cases, wasting money and needlessly worsening environmental problems.

These are, in large part, economic issues. In a recent paper I attempted to summarise the large and diverse research literatures on the economics of nitrogen in agriculture. Here are some of the key points.

At the farm level

The production function for nitrogen (N) fertilizer (the relationship between yield and the rate of nitrogen fertilizer) always exhibits diminishing marginal returns – it flattens out at higher fertilizer rates. In dry conditions, yield may even fall at high N rates.

The rate of nitrogen fertilizer that maximises expected profit is less than the rate that maximises expected yield, sometimes much less.

Here’s a really neat tool that shows the relationships between N, yield and profit for corn in the US. http://cnrc.agron.iastate.edu/

Visual effect of nitrogen fertilizer on corn

Risk

N fertilizer affects the riskiness of cropping. For two reasons, higher N rates are more risky (i.e. profits are more variable at higher N rates). One reason is that the grain price is itself risky. Since profit depends on grain price times yield, and yield usually increases with increasing N rate, the more N you apply, the more variable your profit will be. In addition, yield also tends to be slightly more variable at higher N rates.

Flat payoff functions

There always exists a range of fertilizer rates that are only slightly less profitable than the profit-maximising rate (i.e. a range where the payoff function is relatively flat), and in most cases, that flat range is wide. This means that the farmer has flexibility in choosing the fertilizer rate. If a lower rate would better satisfy another objective (e.g. risk reduction), the farmer can choose that rate with little sacrifice of profit. If regulators require a moderate reduction in fertilizer rate below the farmer’s economic optimum, the cost to the farmer will be small. Flat payoff functions also mean that the benefits of precision-agriculture technologies that spatially adjust fertilizer rates within a field will usually be small.

Nitrogen pollution

Typically, the marginal cost to farmers of nitrogen emissions abatement is low for low levels of abatement but increases at an increasing rate as the required level of abatement increases. As a result, modest targets for abatement can often be achieved at low cost, but ambitious targets can be extremely costly.

Spatial targeting of abatement effort (both at the regional and international scales) can generate much larger benefits than untargeted policies, although these additional benefits are likely to be offset to some degree by increased costs required to run a targeted program (costs of information and administration).

Policies intended to increase farmers’ incomes can have the unintended consequence of increasing nitrogen pollution by increasing the incentive to apply fertilizer.

Further reading

Pannell, D.J. (2017). Economic perspectives on nitrogen in farming systems: managing trade-offs between production, risk and the environment, Soil Research 55, 473-478. Journal web page

Gandorfer, M., Pannell, D.J. and Meyer-Aurich, A. (2011). Analyzing the Effects of Risk and Uncertainty on Optimal Tillage and Nitrogen Fertilizer Intensity for field crops in Germany, Agricultural Systems 104(8), 615-622. Journal web page ♦ IDEAS page

Schilizzi, S. and Pannell, D.J. (2001). The economics of nitrogen fixation, Agronomie 21(6/7), 527-538.

Pannell, D.J. and Falconer, D.A. (1988). The relative contributions to profit of fixed and applied nitrogen in a crop‑livestock farm system, Agricultural Systems 26(1), 1‑17. Journal web page ♦ IDEAS page

Pannell, D.J. (2006). Flat-earth economics: The far-reaching consequences of flat payoff functions in economic decision making, Review of Agricultural Economics 28(4), 553-566. Journal web page ♦ IDEAS page