Farm Simulator (for nerds)

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I recently received some very good news that a journal article my friend and I coauthored and put under review was finally accepted for publication in the American Journal of Agricultural Economics. The article is titled, "Adapting network theory for spatial network externalities in agriculture: A case study on hemp cross-pollination". Rolls right off the tongue, no?

The idea came to me one night when a group of Murray State ag faculty, local farmers, and hemp processors met for a dinner with the USDA Office of the General Counsel at the Big Apple Grill & Bar in town (highly recommend, if you're ever in Murray, KY). One farmer during the conversation spoke up and said he grew hemp for CBD production in a specific field that year, but was going to switch to growing hemp for fiber or grain in that field the next year. Turns out, a neighboring field was growing hemp for fiber, it shed its pollen, the pollen landed on the flowers of the first farmer's hemp (detrimental to the CBD concentration), and the rest is history. I will add that both farmers were in attendance that night and were perfectly civil, as neighbors.

The next morning, I called my friend, Tanner, and described the problem to him. Immediately, we began brainstorming on how to model this and present it at conferences and maybe even publish it. So, we set to work envisioning an economic framework where farmers weigh their expected costs and benefits to choosing one type of hemp to plant versus another. If one farmer plants the type that sheds pollen, then any one planting the other type is hopefully a safe distance away. Otherwise, if they enjoy growing hemp, may themselves switch to the pollinating type the next year and then become a risk to the next neighbor, and so on until no more of the one type is grown in that immediate area anymore.

Avoiding all the math and such for your sake, reader, I must say this was a fun study to conduct. We got pollen travel data from Cornell University and data on to KY hemp farms by county which we poured into various funnels of our machine, set the dials, and pushed the start button. The results were consistent, robust, and meaningful. Some policies will be more cost effective than others to help mitigate this problem, and we highlight those in the conclusions of the paper. Hopefully any legislators or policy researchers dealing with this problem will take note...

What's especially interesting is the broad array of applications. On one hand, the "pollen" could just be information that spreads through a network of connected individuals, until we have compartmentalization of ideas and practices. Similarly, farmers may wait to adopt a new model of tractor or precision ag technology until the opinion leaders or influencers in their respective networks have tried it or given it a thumbs-up. The question is: what/who is the catalyst that ultimately sets off the cascade of adoption? On the other hand, there's been a lot of unfavorable press about dicamba drifting away from the soybean field it was sprayed on over to another field of non-dicamba resistant soybeans and damaging them. The rational solution for a risk-neutral, profit-maximizing farmer would be to switch over to the resistant variety the next year to avoid any damage. But then if they spray it themselves, they become a risk to their other neighbors who haven't yet adopted that technology, and thus we get mass adoption of one brand of seed in a region.

Whatever the case, this was a fun project, and it has the potential to produce quite a lot of value. There's not only policy prescriptions in our framework of analysis, but there's marketing, management, lending, and a host of other applications that should be of interest to players in the food and agribusiness sector. We're working on at least 3 follow up papers currently, and I'm excited to see where this takes us.

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