Hostname: page-component-848d4c4894-75dct Total loading time: 0 Render date: 2024-06-06T18:20:38.227Z Has data issue: false hasContentIssue false
  • English
  • Français

Hurricanes and Possible Intensity Increases: Effects on and Reactions from U.S. Agriculture

Published online by Cambridge University Press:  26 January 2015

Chi-Chung Chen  and
Bruce McCarl
Chi-Chung Chen
Affiliation:
Department of Applied Economics, National Chung-Hsing University, Taichung, Taiwan
Bruce McCarl
Affiliation:
Department of Agricultural Economics, Texas A&M University, College Station, TX
Get access Rights & Permissions [Opens in a new window]

Abstract

Hurricanes have caused substantial damage in parts of the U.S. Damages are increasing, perhaps as part of a natural cycle or perhaps in part related to global warming. This paper examines the economic damages that hurricanes cause to U.S. agriculture, estimates the increased damage from an increase in hurricane frequency/intensity, and examines the way that sectoral reactions reduce damages. The simulation results show that hurricanes and associated adjustments cause widespread damage and redistribute agricultural welfare. We find that crop mix shifts of vulnerable crops from stricken to nonstricken regions significantly mitigate hurricane damages.

Keywords

crop mixhurricane intensitystochastic agricultural sector modelQ24Q54R14
Type
Research Article
Information
Journal of Agricultural and Applied Economics , Volume 41 , Issue 1 , April 2009 , pp. 125 - 144
Copyright
Copyright © Southern Agricultural Economics Association 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Baumes, H. “A Partial Equilibrium Sector Model of US Agriculture Open to Trade: A Domestic Agricultural and Agricultural Trade Policy Analysis.” Ph.D. dissertation, Purdue University, West Lafayette, IN, 1978.Google Scholar
Becker, N. “A Comparative Analysis of Water Price Support versus Drought Compensation Scheme.” Agricultural Economics 21(1999):81–92.CrossRefGoogle Scholar
Bin, O., and Polasky, S. “Effects of Flood Hazards on Property Values: Evidence Before and After Hurricane Floyd.” Land Economics 80(2004):490–500.Google Scholar
Blake, E.S., Jarrell, J.D., and Rappaport, E.N.. 2006. The Deadliest, Costliest, and Most Intense United States Tropical Cyclones from 1851 to 2005. OAA/NWS/Tropical Prediction Center/National Hurricane Center, Miami, Florida, NOAA Technical Memorandum NWS TPC-4. Internet site: http://www.nhc.noaa.gov/Deadliest_Costliest.shtml.Google Scholar
Blake, E.S., Jarrell, J.D., Rappaport, E.N., and Landsea, C.W.. The Deadliest Costliest, and Most Intense United States Hurricanes from 1851 to 2004. NOAA Technical Memorandum NWS TPC-4, 2005.Google Scholar
Brown, S., Madison, D., Goodwin, H.L., and Clark, D. “The Potential Effects on United States Agriculture of an Avian Influenza Outbreak.” Journal of Agricultural and Applied Economics 39(2007):335–43.Google Scholar
Burras, R.T., Dumas, CF., Farrell, CH., and Hall, W.W. “Impact of Low-Intensity Hurricanes on Regional Economic Activity.” Natural Hazards Review 3(2002):118–25.CrossRefGoogle Scholar
Burton, M.L., and Hicks, M.J. “Hurricane Katrina: Preliminary Estimates of Commercial and Public Sector Damages.” Working Paper, Center of Business and Economic Research, Marshall University, Huntington WV, 2005.Google Scholar
Chang, C.C., McCarl, B.A., Mjelde, J.W., and Richardson, J. “Sectoral Implications of Farm Program Modifications.” American Journal of Agricultural Economics 74(1992):38–49.CrossRefGoogle Scholar
Chen, C.C., and Chang, C.C. “The Impact of Weather on Crop Yield Distribution in Taiwan: Some New Evidence from Panel Data Models and Implications for Crop Insurance.” Journal of Agricultural Economics 33(2005):503–11.CrossRefGoogle Scholar
Chen, C.C., and McCarl, B.A. “The Value of ENSO Information: Consideration of Uncertainty and Trade.” Journal of Agricultural and Resource Economics 25(2000):368–85.Google Scholar
Chen, C.C, McCarl, B.A., and Schimmelpfennig, D.E. “Yield Variability as Influenced by Climate: A Statistical Investigation.” Climatic Change 66(2004):239–61.Google Scholar
Chmielewski, F.M., and Potts, J.M. “The Relationship between Crop Yields from an Experiment in Southern England and Long-Term Climate Variations.” Agricultural and Forest Meteorology 73(1995):43–66.CrossRefGoogle Scholar
Cocks, K.D. “Discrete Stochastic Programming.” Management Science 15(1968):72–79.Google Scholar
Dantzig, G. “Linear Programming Under Uncertainty.” Management Science 1(1955):197–206.Google Scholar
Emanuel, K. “Increasing Destructiveness of Tropical Cyclones over the Past 30 Years.” Nature 436(2005):686–88.Google Scholar
Fellin, L., and Fuller, S. “Effects of Privatizing Mexico's Railroad System on U.S.-Mexico Overland Grain/Oilseed Trade.” Transportation Research Forum 37(1998):46–64.Google Scholar
Guidry, K.M. Assessment of Damage to Louisiana Agricultural, Forestry, and Fisheries Sectors by Hurricane Katrina. Agricultural Center, Research and Extension, Louisiana State University, Baton Rouge, LA, 2005.Google Scholar
Hallstrom, D., and Smith, K. “Market Response to Hurricanes.” Journal of Environmental Economics and Management 50(2005):541–61.Google Scholar
Herndon, C.W., Anderson, J.D., Martin, S.W., and Hood, K.W.. Impact of Hurricane Katrina on Mississippi Agriculture. Extension Service, Mississippi State University, Mississippi State, MS, 2005.Google Scholar
Just, R., and Pope, R.D. “Production Function Estimation and Related Risk Considerations.” American Journal of Agricultural Economics 61(1979):277–84.Google Scholar
Lambert, D.K., McCarl, B.A., He, Q., Kay-len, M.S., Rosenthal, W., Chang, C.C., and Nayda, W.I. “Uncertain Yields in Sectoral Welfare Analysis: An Application to Global Warming.” Journal of Agricultural and Applied Economics 27(1995):423–35.Google Scholar
McCarl, B.A., and Parandvash, G.H. “Irrigation Development versus Hydroelectric Generation: Can Interruptible Irrigation Play a Role?” Western Journal of Agricultural Economics 13(1988):267–76.Google Scholar
McCarl, B.A., and Spreen, T.H. “Price Endogenous Mathematical Programming as a Tool for Sector Analysis.” American Journal of Agricultural Economics 62(1980):87–102.Google Scholar
Naylor, R.L., Falcon, W.P., Rochberg, D., and Wada, N. “Using El Nino/Southern Oscillation Climate Data to Predict Rice Production in Indonesia.” Climatic Change 50(2001):255–65.Google Scholar
NOAA. U.S. Hurricane Strikes by Decade. Internet site: http://www.nhc.noaa.gov/pastdec.shtml.Google Scholar
NOAA. Historical Hurricane Tracks. Internet site: http://maps.csc.noaa.gov/hurricanes/.Google Scholar
Norton, R.D., and Schiefer, G.W. “Agricultural Sector Programming Models: A Review.” European Review of Agriculture Economics 7(1980):229–64.Google Scholar
Paarlberg, P.L., Lee, J.G., and Seitzinger, A.H. “Measuring Welfare Effects of an FMD Outbreak in the United States.” Journal of Agricultural and Applied Economics 35(2003):53–65.Google Scholar
Paarlberg, P.L., Seitzinger, H.A., and Lee, J.G. “Economic Impacts of Regionalization of a Highly Pathogenic Avian Influenza Outbreak in the United States.” Journal of Agricultural and Applied Economics 39(2007):325–33.Google Scholar
Pendell, D.L., Leatherman, J., Schroeder, T.C., and Alward, G.S. “The Economic Impacts of a Foot-And-Mouth Disease Outbreak: A Regional Analysis.” Journal of Agricultural and Applied Economics 39(2007):19–31.CrossRefGoogle Scholar
Pielke, R.A., and Landsea, C.W. “Normalized Hurricane Damage in the United States:1925-95.” Weather and Forecasting 13(1998):621–31.Google Scholar
Pinelli, J.P., Simiu, E., Gurley, K., Subramanian, C., Shang, L., Cope, A., Filliben, J.J., and Hamid, S. “Hurricane Damage Prediction Model for Residential Structures.” Journal of Structural Engineering 130(2004):1685–91.CrossRefGoogle Scholar
Roningen, V. A Static World Policy Simulation (SWOPSIM) Modeling Framework. Staff Rep. No. AGE860265. Washington, DC: US Department of Agriculture, Economic Research Service, 1986.Google Scholar
Saha, A., Havenner, A., and Talpaz, H. “Stochastic Production Function Estimation: Small Sample Properties of ML versus FGLS.” Applied Economics 29(1997):459–69.Google Scholar
Takayama, T., and Judge, G.G.. Spatial and Temporal Price and Allocation Models. Amsterdam: North-Holland Publishing Company, 1971.Google Scholar
Thaysen, K. “An Analysis of Agricultural Risk Implications of United States Policy Change.” PhD dissertation, Texas A&M University, College Station, Texas, 1995.Google Scholar
Tiongco, M., and Dawe, D. “Long-Term Evolution of Productivity in a Sample of Philippine Rice Farm: Implications for Sustainability and Future Research.” World Development 30(2002):891–98.Google Scholar
USDA-NASS. Agricultural Statistics 1951-2005. USDA. A Preliminary Assessment of The Effects of Katrina and Drought on U.S. Agriculture. USDA/OCE, 2005.Google Scholar
U.S. Department of Commerce. Hurricane Katrina Service Assessment Report, 2006.Google Scholar
Webster, P.J., Holland, G.J., Curry, J.A., and Chang, H.-R. “Changes in Tropical Cyclone Number, Duration and Intensity in a Warming Environment.” Science 309(2005):1844–46.CrossRefGoogle Scholar
West, C, and Lenze, D. “Modeling the Regional Impact of Natural Disaster and Recovery: A General Framework and an Application of Hurricane Andrew.” International Regional Science Review 17(1994):121–50.CrossRefGoogle Scholar