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This paper uses network analysis to study the geo-localization decisions of new organic dairy farm operations in the USA between 2002 and 2015. Given a dataset of organic dairy certifications we simulated spatio-temporal networks based on the location of existing and new organic dairy farming operations. The simulations were performed with different probabilities of connecting with existing or incoming organic farmer operations, to overcome the lack of data describing actual connections between farmers. Calculated network statistics on the simulated networks included the average degree, average shortest path, closeness (centrality), clustering coefficients, and the relative size of the largest cluster, to demonstrate how the networks evolved over time. The findings revealed that new organic dairy operations cluster around existing ones, reflecting the role of networks in the conversion into organic production. The contributions of this paper are twofold. First, we contribute to the literature on clustering, information sharing, and market development in the agri-food industry by analyzing the potential implications of social networking in the development of a relatively new agriculture market. Second, we add to the literature on empirical social networks by using a new dataset with information on actors not previously studied analytically.

The Role of Clustering in the Adoption of Organic Dairy: A Longitudinal Networks Analysis between 2002 and 2015