A DEA approach for merging dairy farms
2021
Sebastián Lozano | Belarmino Adenso-Díaz
This paper proposes a model for determining the most advantageous merger within a set of dairy farms. It uses data envelopment analysis (DEA) to estimate the total technical efficiency improvement that the merger would produce and for decomposing it into a learning effect and a pure merger effect. A design of experiments has also been carried to test the effects of various factors (the total number of farms, the standard deviation of herd size, the percentage of farms exhibiting increasing returns to scale, the standard deviation of the current technical efficiency of the farms) on different response variables (the percentage of farms involved in the merger, the reduction of herd size and the efficiency improvement obtained by the merger). The results show that the disparity in the herd size of the farms in a region and the percentage of farms that exhibit increasing returns to scale increase the number of farms that enter into the most advantageous merger. The disparity of herd size also increases the number of cows that are not needed after the merger. Finally, the expected efficiency improvement increases with the total number of farms.
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