Adapting network theory for spatial network externalities in agriculture: A case study on hemp cross‐pollination

Abstract

Growers have increasingly expressed frustration over the negative externalities created by their neighbor's production practices. These spatial agricultural network problems include issues such as cross‐pollination and herbicide drift. We develop novel methods for estimating parameters that allow us to adapt and apply general network diffusion models to these spatial agricultural network problems. Doing so allows us to calculate externality damage within a region and calculate cost‐effective policies for alleviating that externality. We empirically illustrate, motivate, and test this approach by applying it to hemp. We find that network structure is an important factor in externality size and cost‐effective policy response for spatial agricultural network problems. We also find that policies that are implemented early and proactively are more likely to be successful and cost effective than policies implemented retroactively. Finally, we find that in our application of limiting the cross‐pollination damage experienced by growers of feminized hemp from non‐feminized hemp growers, the most cost‐effective policy is to establish a regional quota on non‐feminized production combined with intertemporal cultivar spacing. This policy response will likely change across time and region as economic and network variables evolve.

Hemp pollen dispersal across the United States

“…Furthermore, incorporating the distance between farms would provide a more sophisticated measure of county vulnerability, as was demonstrated theoretically for hemp farms in Kentucky counties 33 . Our vulnerability metric assumes one source of hemp per county, as data for the locations of individual farms are not currently available.…”

Modeling Soybean Planting Decisions with Network Diffusion: Does Herbicide Drift Affect Farmer Profitability and Seed Selection?

“…This requires a framework capable of estimating regional network structure and its effect on OTM damage. Our framework is adapted from a general Spatially based Agricultural Negative Externality Problem diffusion model developed by Young and McCarty (2022), parameterized for the problem of dicamba OTM. We also draw insight from a general network diffusion model developed by Jackson and Yariv (2006).…”