Category Archives: Agriculture

335. Behavioural economics and adoption of agricultural innovations

Behavioural economics has become a thriving field of research over the past decade or so, but research by agricultural economists on farmers’ behaviour when adopting new practices has been going on for over 60 years. What is similar and different between these two fields of research?

I was listening to a presentation by David Zilberman from the University of California, Berkeley at a conference a few years ago, and he made a comment along the lines that agricultural economists had been studying behaviour for a long time before behavioural economics became popular. He was thinking about the large body of research on farmers’ adoption on new practices and technologies.

When I was putting together a recent special issue of Applied Economic Perspectives and Policy on adoption of agricultural innovations, I remembered David’s comment and thought that a paper about this would be a good inclusion. About that time I happened to visit the University of Alberta, and a conversation about this with Maik Kecinski (now at University of Delaware) led to a team of young economists (plus a not-so-young one: me) working together. The resulting paper, “Agricultural Adoption and Behavioral Economics: Bridging the Gap” by Streletskaya et al., is now out and is open access.

To get a brief overview of the paper, you could listen to a conversation with one of the co-authors, Leah Palm-Forster, who happened to be in Perth for the AARES conference last month, which luckily took place just in time to avoid most of the travel shut downs for Covid-19. My conversation with Leah is available as an episode of the AEPP podcast series.

The paper covers a lot of ground. Here I’ll just comment on some of the similarities and differences between the two fields of research. Clearly, both fields are interested in behaviour, both recognize the influence of individual characteristics, preferences, and beliefs on decision-making, and both study economic decisions. But they tend to ask somewhat different questions. Quoting from the paper:

“Agricultural adoption investigates which factors drive or inhibit uptake within a population over time, without necessarily seeking to identify and model behavioral mechanisms that influence adoption. Behavioral economics, on the other hand, seeks to identify and explain behavior that departs from what is predicted by traditional economics through the use of new theories and models of human behavior.”

“Furthermore, agricultural adoption research traditionally has highlighted the role of extrinsic factors such as social, economic, and political context in driving adoption. Behavioral economics, on the other hand, focuses on broadly unpacking the “black box” of human decision making and explores how human cognition and the manner in which tastes and preferences are formed drive decision-making, and how factors such as biases and other social influences impact economic behavior.”

“Agricultural adoption often analyzes field data available at the plot, county, regional and national levels for different types of producers in order to obtain insights about the drivers of new technology diffusion over time and adoption patterns across different farming populations. In the behavioral economics domain, … many laboratory and field experiments rely on smaller samples and consist of cross-sectional data.”

So, there are quite a few differences. But they don’t have to be different. These differences have just evolved organically, largely reflecting the different questions that the researchers were asking.

Researchers working on agricultural adoption are increasingly looking towards behavioural economics for ideas and methods, but I reckon there is also unrealised scope for cross-fertilisation in the other direction. I suspect that many behavioural economists don’t even realise that there is this other big body of research generating knowledge that is relevant to what they do.

Further reading

Streletskaya, N.A., S.D. Bell, M. Kecinski, T. Li, S. Banerjee, L.H. Palm-Forster, and D.J. Pannell. 2020. Agriculture Adoption and Behavioral Economics: Bridging the Gap. Applied Economic Perspectives and Policy 42(1), 54-66.

332. Farmer behaviour and agricultural policy

An understanding of farmers’ adoption of new practices is central to the design of effective and efficient agricultural policies. Aspects of agricultural policy that can be enhanced by good information about adoption include the design of the policy, the targeting of policy effort, and the assessment of additionality. 

In PD330 I advertised a new Special Issue on adoption of agricultural innovations in the journal Applied Economic Perspectives and Policy. There is an audio interview with me about the Special Issue available here.

One of the papers, by Roger Claassen and me, focuses on the relevance to agricultural policy of understanding farmers’ decisions about taking up new practices.

One simple reason for this relevance is that much agricultural policy is concerned with getting farmers to do something they are not already doing or would not otherwise do. Examples of such policies include the following:

  • Programs of agricultural extension to encourage farmers to adopt a new technology that is believed to be more productive than the existing technology farmers are using (e.g., a higher-yielding crop variety);
  • Programs that pay farmers to adopt a practice that generates public benefits (such as protecting or planting vegetation that provides habitat for wildlife);
  • Policies that fund agricultural research, with the intent of generating information or technologies that will be beneficial for farmers or the community; and
  • Policies that use regulations to constrain the behaviour of farmers (such as regulations on clearing of native vegetation).

Since all these policies are about influencing the behaviour of farmers, of course it makes sense that their design and implementation could be enhanced if the designers had a good understanding of what influences the behaviour of farmers. Sometimes policy makers do take this seriously, but not always. I’ve been critical of Australian agri-environmental policies, for example, for often being making overly optimistic assumptions about what farmers will do if we just provide them with some information or pay them a little bit.

In practice, some practices are more attractive to farmers than others. Zero till is used by about 90% of Western Australian farmers, but a practice like variable-rate precision agriculture is used by only a minority. Any one practice is more attractive to some farmers than to others due to varying local conditions, such as rainfall or soil types. Being able to predict variations in adoption would be very helpful to policy makers for targeting their resources. Having a sense of which practices can potentially be adopted, and where, is one of the factors that ought to influence where policies like extension or incentive payments are applied.

Another policy concept that is tangled up with farmer behaviour is additionality. As we say in the paper:

A conservation action (and the resulting environmental gain) that is supported by a payment is additional if the farmer would not have taken the action if he or she had not received the payment. Environmental gains that flow from nonadditional actions cannot be attributed to the incentive program.

If the additionality of a proposed agri-environmental payment scheme is too low, it’s not worth running the scheme. Most farmers were going to adopt the practice anyway, so any incentive payment to them is just a gift, making no difference to environmental outcomes. Policies to promote some practices have high additionality (e.g. filter strips or cover crops in the U.S.) while others have much lower additionality (e.g. conservation tillage in the U.S.).

Assessing additionality is essentially about predicting behaviour. In fact, for programs that aren’t in place yet, assessment of additionality required two predictions about behaviour: what will farmers do if the program does offer them the proposed incentive scheme, and what will they do if there is no incentive payment scheme. The difference between those two predictions tells you the additional change that is attributable to the scheme.

Even assessing an incentive scheme that is already well established requires a sort of prediction. You can observe what farmers are doing with the scheme in place, but to assess additionality you still need to estimate what they would have done in the absence of the scheme.

For all these reasons, an ability to understand and predict farmers’ adoption of new practices is critically important to agricultural policy makers if they want their policies to be effective and efficient.

Further reading

Pannell, D.J. and R. Claassen. 2020. The Roles of Adoption and Behavior Change in Agricultural Policy. Applied Economic Perspectives and Policy 42(1), 31-41.

330. Adoption of agricultural innovations Special Issue

I’m the guest editor for a new Special Issue of the journal Applied Economic Perspectives and Policy. The theme of the issue is “Adoption of Agricultural Innovations” and it includes 11 papers by some of the world’s leading researchers on this topic.

There is an audio interview with me about the Special Issue available here.

The papers are intended to provide reviews or syntheses of key issues related to farmers’ adoption of new practices and technologies. Each paper focuses on a particular aspect of the literature, and the collection as a whole provides an excellent introduction to this enormous body of research.

A particularly nice feature is that all the papers in the issue are open access, meaning that anybody can read them without needing a subscription to the journal. You can access the issue here.

You can hear a brief interview with me providing background and an overview of the Special Issue on the journal’s web page for the issue. As of late Feb 2020, it is the second item in the list under the heading AEPP Podcast.

The first item in that list is another interview related to the Special Issue. In that one I interview Leah Palm-Forster about one of the papers that she and I helped to co-author, called “Agricultural Adoption and Behavioral Economics: Bridging the Gap”. In that paper we talk about the similarities and differences between those two related bodies of research literature, and about possible connections that could be made between them.

Further reading

Here’s a list of all the articles in the issue.

Pannell, D.J. and Zilberman, D. 2020. Understanding adoption of innovations and behavior change to improve agricultural policy. Applied Economic Perspectives and Policy 42(1), 3-7.

Norton, G.W. and J. Alwang. 2020. Changes in Agricultural Extension and Implications for Farmer Adoption of New Practices. Applied Economic Perspectives and Policy 42(1), 8-20.

Heiman, A., Ferguson, J. and D. Zilberman. 2020. Marketing and Technology Adoption. Applied Economic Perspectives and Policy 42(1), 21-30.

Pannell, D.J. and R. Claassen. 2020. The Roles of Adoption and Behavior Change in Agricultural Policy. Applied Economic Perspectives and Policy 42(1), 31-41.

Chavas, J.-P. and C. Nauges. 2020. Uncertainty, Learning and Technology Adoption in Agriculture. Applied Economic Perspectives and Policy 42(1), 42-53.

Streletskaya, N.A., S.D. Bell, M. Kecinski, T. Li, S. Banerjee, L.H. Palm-Forster, and D.J. Pannell. 2020. Agriculture Adoption and Behavioral Economics: Bridging the Gap. Applied Economic Perspectives and Policy 42(1), 54-66.

Weersink, A. and M. Fulton. 2020. Limits to Profit Maximization as a Guide to Behavior Change. Applied Economic Perspectives and Policy 42(1), 67-79.

Montes de Oca Munguia, O. and Llewellyn, R. 2020. The Adopters Versus The Technology: Which Matters More When Predicting or Explaining Adoption? Applied Economic Perspectives and Policy 42(1), 80-91.

Huffman, W.E. 2020. Human Capital and Adoption of Innovations: Policy Implications. Applied Economic Perspectives and Policy 42(1), 92-99.

Llewellyn, R. and B. Brown. 2020. Predicting adoption of innovations by farmers: how is it different in smallholder agriculture? Applied Economic Perspectives and Policy 42(1), 100-112.

Rola-Rubzen, F., T.R. Paris, J. Hawkins and B. Sapkota. 2020. Improving Gender Participation in Agricultural Technology Adoption in Asia: From Rhetoric to Practical Action. Applied Economic Perspectives and Policy 42(1), 113-125.

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

320 – Fixed costs and input rates

Optimal input rates (e.g. of fertilizer to a crop) are not affected by fixed costs. I had an interesting discussion with a Canadian poultry farmer last month, who needed to be convinced of this fact.

In Canada last month I gave a seminar at the Ontario Ministry of Agriculture, Food and Rural Affairs on water pollution from agricultural fertilizers, and how an understanding of the economics of fertilizer application can help identify cost-effective policy strategies for reducing pollution.

One thing I talked about was the economics of applying too much fertilizer (more than would be in the farmer’s own financial interests).

One attendee at the seminar was a poultry farmer (who was also a scientist) who later wanted to talk to me about a reason for increasing input rates that I had not mentioned. The reason he suggested was that, by increasing input rates a farmer can increase his or her production, and even if the resulting increase in revenue is not enough to cover the additional input costs, it helps to dilute the fixed costs of production over a larger value of outputs, making the farmer better off overall.

He said that this is an idea that is common amongst poultry farmers, at least in Ontario. The problem is that it’s completely wrong. There is no way that increasing input rates above the level that maximises the difference between revenue and input costs can make a farmer better off, even if it does mean that the average fixed costs per unit of output is lower.

A simple numerical example will make this clear.

Fixed costs ($/ha)Fertilizer cost ($/ha)Yield (tonne/ha)Revenue ($/ha)Net revenue ($/ha)Average fixed cost ($/tonne)
5001.122017045.45
50201.530023033.33
50401.836027027.77
50602.040029025.00
50802.0541028024.39
501002.0841626624.04
501202.142025023.81

In this example, there is a production cost of $50/ha which is not affected by the rate of fertilizer used. In this sense it is “fixed”.

As the rate of fertilizer applied increases, the input cost goes up, and so does the crop yield, although it increases at a decreasing rate.

The net revenue is the difference between the revenue and the total costs (fixed costs plus fertilizer costs). Given this pattern of revenue and costs, the fertilizer rate that maximises net revenue is the rate corresponding to a cost of $60/ha (the fourth row of numbers in the table), giving a net revenue of $290/ha. This is the fertilizer rate that maximises profit to the farmer.

The last column shows the fixed cost per unit of production. Because the yield keeps increasing at fertilizer rates above the economic optimum, the fixed cost per unit of production keeps falling. The lowest fixed cost per unit of production is in the last row of the table, but this clearly doesn’t have the highest profit.

When you are considering the optimal level of an input, the only costs that matter are the costs that vary as you vary the level of the input. Fixed costs cannot possibly affect the optimal rate of an input because they are fixed. They stay the same at all input rates. The fact that average fixed costs per unit of output might fall at higher input rates is completely irrelevant.

I think I convinced the Canadian poultry farmer. He said he was going to talk to his other farmer friends about it.

Although boosting an input rate loses a farmer money, in many cases the amount lost will be quite small unless the input rate is especially high (due to “flat payoff functions” – see Pannell 2006). That may be why the error has not been detected by the farmer or his friends. The loss may be too small to be noticeable.

To the extent that farmers think that diluting fixed costs is a good idea, explaining to them that it is pointless may help to reduce some farmers’ tendency to apply too much fertilizer. If successful, this may contribute to reducing water pollution.

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