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Technical Efficiency of Sow Farms: A Parametric and Non-parametric Approach

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Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 224))

Abstract

Pig production is very important in Spain and increasing competition has led the pig industry to look for ways of improving the efficiency of the production process. This chapter presents the analysis of technical efficiency in sow farms vertically integrated into the same company comparing parametric and non-parametric approaches. Empirical data from 96 Spanish sow farms classified into 2 groups depending on the final product, that is, farms producing weaned piglets (FPP) or feeder pigs (FPFP) were available. The results for the stochastic frontier production function for feeder pigs and weaned piglets exhibit problems related to multicolinearity. Even though, the observed trends of technical efficiencies calculated from both approaches were consistent. The results revealed considerable efficiencies in this study being FPP more efficient that FPFP (0.99 vs. 0.87 with the parametric approach, and 0.93 vs. 0.91 with VRS-DEA model). Scale efficiency was also very high showing that 58 % of FPP and 45 % of FPFP are small farms in which efficiency gains would be expected by increasing the size. In addition, farm-specific factors affecting productive inefficiencies from CRS–DEA and VRS–DEA models were explored using a Tobit model. The output, number of sow, feed consumed, and artificial insemination were the variables showing significant coefficients at the 5 % level. Finally, the efficiency measures presented in this study are similar to other European studies and demonstrate the higher technical efficiency of the pig sector in Spain.

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Correspondence to Xavier Ezcurra .

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Ezcurra, X., Plà-Aragonés, L.M. (2015). Technical Efficiency of Sow Farms: A Parametric and Non-parametric Approach. In: Plà-Aragonés, L. (eds) Handbook of Operations Research in Agriculture and the Agri-Food Industry. International Series in Operations Research & Management Science, vol 224. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2483-7_14

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