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Genetic Information in Agricultural Productivity and Product Development

Author

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  • John A. Miranowski
  • Bruce A. Babcock

Abstract

Two prominent features of recent changes in agriculture have been the advent of precision breeding techniques and an increase in the level of information netputs in production. This article identifies ways in which these features may complement in expanding the variety of processed products, level of productivity, and rate of change in productivity. Using a martingale concept of information, we identify conditions under which information increases the incentives to invest and engage in product differentiation activities. A theory on how genetic uniformity can enhance the rate of learning through process experimentation, and so the rate of technical change, is developed. Copyright 2004, Oxford University Press.

Suggested Citation

  • John A. Miranowski & Bruce A. Babcock, 2004. "Genetic Information in Agricultural Productivity and Product Development," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(1), pages 73-87.
    • Handle: RePEc:oup:ajagec:v:86:y:2004:i:1:p:73-87
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    File URL: http://hdl.handle.net/10.1111/j.0092-5853.2004.00563.x
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    Cited by:

    1. Parcell, Joseph L. & Franken, Jason R.V. & Schafer, Daniel & Patterson, David J. & John, Mike & Kerley, Monty S. & Haden, Kent, 2011. "Coordinating Sire Genetics in a Synchronized AI Program," Journal of the ASFMRA, American Society of Farm Managers and Rural Appraisers, vol. 2011, pages 1-12, June.
    2. Bulut, Harun & Lawrence, John D., 2007. "Meat Slaughter and Processing Plants' Traceability Levels Evidence From Iowa," Staff General Research Papers Archive 12791, Iowa State University, Department of Economics.
    3. Hennessy, David A., 2007. "Informed control over inputs and extent of industrial processing," Economics Letters, Elsevier, vol. 94(3), pages 372-377, March.
    4. Thompson, Nathanael M. & DeVuyst, Eric A. & Brorsen, B. Wade & Lusk, Jayson L., 2014. "Value of Genetic Information for Management and Selection of Feedlot Cattle," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 39(01), pages 1-17, April.
    5. Lambert, David K., 2008. "The expected utility of genetic information in beef cattle production," Agricultural Systems, Elsevier, vol. 99(1), pages 44-52, December.
    6. Hennessy, David A. & Zhang, Jing & Bai, Na, 2019. "Animal health inputs, endogenous risk, general infrastructure, technology adoption and industrialized animal agriculture," Food Policy, Elsevier, vol. 83(C), pages 355-362.
    7. Parcell, Joseph L. & Schaefer, Daniel & Patterson, David J. & John, Mike & Kerley, Monty S. & Haden, Kent, 2008. "Assessing the Value of Coordinated Sire Genetics in a Synchronized AI Program," 2008 Conference, April 21-22, 2008, St. Louis, Missouri 37618, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    8. Antonio Seccia & Antonio Stasi & Luigi Roselli, 2006. "Atteggiamento dei consumatori nei confronti dell'evoluzione del sistema agro-alimentare: l'introduzione di alimenti geneticamente modificati," Economia agro-alimentare, FrancoAngeli Editore, vol. 10(1).
    9. David A. Hennessy, 2005. "Slaughterhouse Rules: Animal Uniformity and Regulating for Food Safety in Meat Packing," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(3), pages 600-609.

    More about this item

    JEL classification:

    • D2 - Microeconomics - - Production and Organizations
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • L0 - Industrial Organization - - General
    • N5 - Economic History - - Agriculture, Natural Resources, Environment and Extractive Industries

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