Technical efficiency of Kansas arable crop farms: A local maximum likelihood approach

Bouali Guesmi, Teresa Serra, Allen Featherstone

Research output: Contribution to journalArticlepeer-review


This study uses local maximum likelihood (LML) methods recently proposed by Kumbhakar et al. (2007) to assess the technical efficiency of arable crop Kansas farms. LML techniques overcome the most relevant limitations associated to mainstream parametric stochastic and nonparametric frontier models. LML allows deriving farm-level frontier parameter estimates. The relevance of using localized estimates is evidenced by the observed heterogeneity in production technologies. Technical efficiency scores derived from the LML approach [0.905] are higher than those of the DEA model under CRS [0.808] and SFA [0.804] and close to DEA-VRS [0.917] ratings. Deriving reliable information about farm efficiency performance is relevant to identify inefficient farms and define adequate policy and management strategies. The use of refined methods has thus important implications.

Original languageEnglish (US)
Pages (from-to)703-713
Number of pages11
JournalAgricultural Economics (United Kingdom)
Issue number6
StatePublished - Nov 1 2015


  • Local maximum likelihood approach
  • Nonparametric
  • Technical efficiency

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Economics and Econometrics

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