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Linear Approximation Using MOTAD and Separable Programming: Should It Be Done?

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  • Bruce A. McCarl
  • Hayri Önal

Abstract

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.

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  • Bruce A. McCarl & Hayri Önal, 1989. "Linear Approximation Using MOTAD and Separable Programming: Should It Be Done?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(1), pages 158-166.
    • Handle: RePEc:oup:ajagec:v:71:y:1989:i:1:p:158-166.
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    Cited by:

    1. Rosa, Franco, 2014. "Evaluation of risk in farm planning: a case study," 2014 Third Congress, June 25-27, 2014, Alghero, Italy 173126, Italian Association of Agricultural and Applied Economics (AIEAA).
    2. El-Shazly, Fawzy A. & Mansour, Mahmoud E. E. & Ahmed, Mousa A. & Shehata, Emad A., 2009. "التركيب المحصولى المصرى فى ظل المخاطرة والمتغيرات المحلية والدولية [Egyptian Cropping Pattern Under Risk and Varying Domestic and International Circumstances]," MPRA Paper 42634, University Library of Munich, Germany, revised 04 Oct 2009.
    3. L.D. Tamini & M. Doyon & M.M. Zan, 2014. "Investment behavior of Canadian Egg Producers: Analyzing the Impacts of Risk Aversion and Variability of Prices and Costs of Production," Cahiers de recherche CREATE 2014-2, CREATE.
    4. Nieuwoudt, W. L., 1992. "Agricultural Policy Research: Some Thoughts Concerning Methodology And Technology," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 31(1), March.
    5. Apland, Jeffrey & Hauer, Grant, 1993. "Discrete Stochastic Programming: Concepts, Examples And A Review Of Empirical Applications," Staff Papers 13793, University of Minnesota, Department of Applied Economics.
    6. Yang, Wanhong & Khanna, Madhu & Farnsworth, Richard & Onal, Hayri, 2003. "Integrating economic, environmental and GIS modeling to target cost effective land retirement in multiple watersheds," Ecological Economics, Elsevier, vol. 46(2), pages 249-267, September.
    7. Zhang, Jianzhong & Xu, Chengxian, 2010. "Inverse optimization for linearly constrained convex separable programming problems," European Journal of Operational Research, Elsevier, vol. 200(3), pages 671-679, February.
    8. Petrick, Martin & Ditges, C. Markus, 2000. "Risk in agriculture as impediment to rural lending: the case of North-Western Kazakhstan," IAMO Discussion Papers 24, Leibniz Institute of Agricultural Development in Transition Economies (IAMO).
    9. Franco Rosa & Mario Taverna & Federico Nassivera & Luca Iseppi, 2019. "Farm/crop portfolio simulations under variable risk: a case study from Italy," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 7(1), pages 1-15, December.
    10. Doole, Graeme & Pannell, David J., 2011. "Evaluating environmental policies under uncertainty through application of robust nonlinear programming," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 55(4), pages 1-18.
    11. Ridier, Aude & Chaib, Karim & Roussy, Caroline, 2016. "A Dynamic Stochastic Programming model of crop rotation choice to test the adoption of long rotation under price and production risks," European Journal of Operational Research, Elsevier, vol. 252(1), pages 270-279.
    12. Lufumpa, Judith Nanyangwe, 1991. "Determination of optimal crop combinations on Zambian farms: incorporation of certainty and risk considerations in mathematical programming models," ISU General Staff Papers 1991010108000017625, Iowa State University, Department of Economics.
    13. Arata, Linda & Donati, Michele & Sckokai, Paolo & Arfini, Filippo, 2014. "Incorporating risk in a positive mathematical programming framework: a new methodological approach," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182659, European Association of Agricultural Economists.
    14. Ahodo, Kwadjo & Freckleton, Robert P. & Oglethorpe, David, 2015. "Investigation of factors affecting arable farming profit, crop complexity and risk under the single farm payment policy," 89th Annual Conference, April 13-15, 2015, Warwick University, Coventry, UK 204231, Agricultural Economics Society.
    15. Herr, Galen G., 1989. "Two articles employing risk programming techniques to determine optimal enterprise combinations on southern Iowa farms," ISU General Staff Papers 1989010108000017597, Iowa State University, Department of Economics.

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