Linear Approximation Using Motad And Separable Programming: Should It Be Done?

Bruce A. McCarl, Hayri Onal

Research output: Contribution to journalArticlepeer-review


Linear approximation techniques have often been applied to nonlinear mathematical programming models for computational efficiency reasons. Price-endogenous agricultural sector models and risk models have found numerous applications. This article addresses the issue of approximation efficiency. Based on computational experience with a series of moderate and large-scale sector and risk models, it is concluded that direct nonlinear solution is more efficient than using linear approximations for sector and risk models having objective function nonlinearities. On the other hand, the experimental results indicate that approximation should continue in case of models with nonlinearities in their constraints.

Original languageEnglish (US)
Pages (from-to)158-166
Number of pages9
JournalAmerican Journal of Agricultural Economics
Issue number1
StatePublished - Feb 1989
Externally publishedYes


  • Agricultural sector models
  • Linear approximation
  • Risk programming
  • Solution efficiency

ASJC Scopus subject areas

  • Agricultural and Biological Sciences (miscellaneous)
  • Economics and Econometrics


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