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The Use of Seemingly Unrelated Regression (SUR) to Predict the Carcass Composition of Lambs

Author

Listed:
  • Vasco A. P. Cadavez

    (Mountain Research Center (CIMO), ESA - Instituto Politécnico de Bragança (Portugal))

  • Arne Henningsen

    (Institute of Food and Resource Economics, University of Copenhagen)

Abstract

The aim of this study was to develop and evaluate models for predicting the carcass composition of lambs. Forty male lambs of two different breeds were included in our analysis. The lambs were slaughtered and their hot carcass weight was obtained. After cooling for 24 hours, the subcutaneous fat thickness was measured between the 12th and 13th rib and the total breast bone tissue thickness was taken in the middle of the second sternebrae. The left side of all carcasses was dissected into five components and the proportions of lean meat, subcutaneous fat, intermuscular fat, kidney and knob channel fat, and bone plus remainder were otained. Our models for carcass composition were fitted using the SUR estimator which is novel in this area. The results were compared to OLS estimates and evaluated by several statistical measures. As the models are intended to predict carcass composition, we particularly focussed on the PRESS statistic, because it assesses the precision of the model in predicting carcass composition. Our results showed that the SUR estimator performed better in predicting LMP and IFP than the OLS estimator. Although objective carcass classification systems could be improved by using the SUR estimator, it has never been used before for predicting carcass composition.

Suggested Citation

  • Vasco A. P. Cadavez & Arne Henningsen, 2011. "The Use of Seemingly Unrelated Regression (SUR) to Predict the Carcass Composition of Lambs," IFRO Working Paper 2011/12, University of Copenhagen, Department of Food and Resource Economics.
    • Handle: RePEc:foi:wpaper:2011_12
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    File URL: http://okonomi.foi.dk/workingpapers/WPpdf/WP2011/WP_2011_12_carcass_composition_lambs.pdf
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    References listed on IDEAS

    as
    1. Henningsen, Arne & Hamann, Jeff D., 2007. "systemfit: A Package for Estimating Systems of Simultaneous Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i04).
    Full references (including those not matched with items on IDEAS)

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    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Seemingly unrelated regressions and lamb carcasses
      by Economic Logician in Economic Logic on 2011-10-26 19:01:00

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    2. Nasiru Inuwa & Sagir Adamu & Mohammed Bello Sani & Abubakar Muhammad Saidu, 2022. "Resource Curse Hypothesis in GCC Member Countries: Evidence from Seemingly Unrelated Regression," Biophysical Economics and Resource Quality, Springer, vol. 7(4), pages 1-10, December.
    3. Kotamraju, Nirmal Ravi Kumar & Babu, Suresh Chandra & Maligireddy, Jagan Mohan Reddy, 2021. "Impact of Market Reforms on Technology Adoption and Profitability: The Case of Tomato Farmers in India," Asian Journal of Applied Economics, Kasetsart University, Center for Applied Economics Research, vol. 28(2).
    4. Sitienei, Isaac & Gillespie, Jeffrey & Scaglia, Guillermo, 2016. "Goal Structure of U.S. Grass-Fed Beef Producers," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 229565, Southern Agricultural Economics Association.
    5. Siwarat Kuson & Songsak Sriboonchitta & Peter Calkins, 2012. "Household determinants of poverty in Savannakhet, Laos: Binary choice model approach," The Empirical Econometrics and Quantitative Economics Letters, Faculty of Economics, Chiang Mai University, vol. 1(3), pages 33-52, September.

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    More about this item

    Keywords

    Carcass; Quality; Ordinary least squares; Seemingly unrelated regression;
    All these keywords.

    JEL classification:

    • Q19 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Other
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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