IDEAS home Printed from https://ideas.repec.org/a/ags/remaae/9572.html
   My bibliography  Save this article

The Bulk Constraint and Computer Formulations of Least-Cost Feed Mixes

Author

Listed:
  • Mohr, Graeme M.

Abstract

Conventional linear programming practice in least-cost feed formulation is to introduce a "bulk constraint" row of 100 units and a diet specified in percentage terms. This procedure places an arbitrary density or concentration level on the available nutrients and metabolizable energy within the resultant feedmixes, which is not necessarily consistent with the birds' consumption capacity. In fact, animals and birds have quite flexible consumption patterns and capacities. Growing pullets fed "low-energy" feedmixes can, in pursuit of their energy requirements, consume up to 150 per cent of their "normal" feed intake. Diet specification for growing birds should be in terms of so many ounces of nutrients per day or week rather than in conventional terms of the percentage nutrient composition of the diet.

Suggested Citation

  • Mohr, Graeme M., 1972. "The Bulk Constraint and Computer Formulations of Least-Cost Feed Mixes," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 40(01), pages 1-14, March.
    • Handle: RePEc:ags:remaae:9572
    DOI: 10.22004/ag.econ.9572
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/9572/files/40010015.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.9572?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rosshairy Abd. Rahman & Graham Kendall & Razamin Ramli & Zainoddin Jamari & Ku Ruhana Ku-Mahamud, 2017. "Shrimp Feed Formulation via Evolutionary Algorithm with Power Heuristics for Handling Constraints," Complexity, Hindawi, vol. 2017, pages 1-12, November.

    More about this item

    Keywords

    Livestock Production/Industries;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:remaae:9572. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaresea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.