Extant research on cluster mapping has excluded agriculture because agricultural employment data is elusive. This paper aims to define agricultural clusters in Mexico that complement the set of clusters proposed by Delgado et al. (2015).
This paper proposes an algorithm that uses the footprint of production data from Mexico to define agricultural clusters based on the patterns of crop colocation.
The authors find that there are 12 clusters of agricultural commodities in Mexico that represent 34.1% of agricultural production in the country. The authors also find that only a few crops are geographically concentrated, and that these crops tend to have higher land productivity that the rest of agriculture.
This paper is constraint to Mexico. However, the cluster definitions might be relevant to other countries.
The authors find large land productivity differences between clusters and dispersed crops. The successful development of clusters of traded agricultural commodities can significantly impact a region’s prosperity.
To the best of the authors’ knowledge, this study is the first cluster-mapping effort of agricultural activity.
