it would be useful to know: How pervasive are these different perceptions, how have they changed since the early- to mid-2000s, and how do these perceptions affect resistance management today?

Extension economists often use partial budgeting (that is, returns per acre) to account for growers’ management decisions. However, grower decisions are more complex than that. Decisions depend on nonpecuniary benefits (flexibility, simplicity) to the farmer. Even the direct economic returns are more complex than just a plot level partial budget analysis will show. A model that accounts for the entire household’s well-being provides a better picture of what affects grower decisions, because there are several issues in the farm household at play that are not apparent in a simple budgeting approach. Things to account for include:

  • How flexible is the management system?
  • How much time does the system require?
  • How do regulations and incentives on highly erodible land factor in?
  • What kind of equipment does the farmer have (i.e., is the equipment needed for some BMPs available)?
  • Does the farmer have the knowledge base for a variety of weed control options?
  • Are there management constraints or options related to the ownership of the land?
  • Is there urban development pressure (affecting a farmer’s time horizon)?
  • How long does a farmer plan to farm?
  • What percentage of household income comes from farming?

Furthermore, the benefits of resistance management to an individual grower are uncertain. There may be benefits in the future, and those benefits may be somewhat dependent on what neighbors do, but the costs are certain. Growers have to weigh these uncertain, future benefits that depend on a whole set of contingencies against direct monetary costs of current actions. To deal with that uncertainty, we may have to move away from just looking at short-run profits to something more in the realm of behavioral economics and collective action to understand people’s decisions.

There are useful lessons to be drawn from the Green Revolution about how farmers behave. When the high yield varieties of the Green Revolution became available, they were part of technological packages that depended on complimentary inputs. At the time, it was commonly thought that farmers who did not adopt the new technologies were in some ways “primitive” or “backwards.” Study of the constraints that individual farmers were facing in particular countries or with particular crops revealed that farmers had good reasons for why they were not adopting the technology. Looking at the real constraints that people face, failure to adopt may not result from a lack of understanding. Rather, a farmer’s immediate survival drives what they do. The studies from the Green Revolution also show that adding uncertainty into the analysis improves the explanatory power of economic models. Introducing uncertainty demonstrated the importance of farm size, credit constraints, infrastructure constraints, and the availability of information and opportunities to learn to determine how people were going to adopt technology. It also showed how and what information is provided to farmers is important. It is important to know from what sources farmers are getting their information, how the information source



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