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Identifying Risk Factors Affecting Weather- and Disease-Related Losses in the U.S. Farm-Raised Catfish Industry

Author

Listed:
  • Hanson, Terrill R.
  • Shaik, Saleem
  • Coble, Keith H.
  • Edwards, Seanicaa
  • Miller, J. Corey

Abstract

Two double-limit tobit models are used to identify significant risk factors that most affect farm-raised catfish losses from weather-related events and from disease outbreaks. Results of the weather loss model indicate that the variables for operator education level, number of ponds, pond water depth, production management strategy, past experience with severe losses from low oxygen levels from off-farm power outages, past experience with severe losses from diseases, and being in the South are statistically significant. Results of the disease loss model indicate that the variables for operator experience and pond water depth are significant. De-velopment of models explaining weather and disease losses through observable variables pro-vides a better understanding of the interrelation between the loss perils and explanatory vari-ables so management strategies can be developed to mitigate losses from identified risk factors.

Suggested Citation

  • Hanson, Terrill R. & Shaik, Saleem & Coble, Keith H. & Edwards, Seanicaa & Miller, J. Corey, 2008. "Identifying Risk Factors Affecting Weather- and Disease-Related Losses in the U.S. Farm-Raised Catfish Industry," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 37(1), April.
  • Handle: RePEc:ags:arerjl:44736
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    References listed on IDEAS

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    1. McDonald, John F & Moffitt, Robert A, 1980. "The Uses of Tobit Analysis," The Review of Economics and Statistics, MIT Press, vol. 62(2), pages 318-321, May.
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    Cited by:

    1. Rong Kong & Calum G. Turvey & Guangwen He & Jiujie Ma & Patrick Meagher, 2011. "Factors influencing Shaanxi and Gansu farmers' willingness to purchase weather insurance," China Agricultural Economic Review, Emerald Group Publishing, vol. 3(4), pages 423-440, November.

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