Hostname: page-component-848d4c4894-ttngx Total loading time: 0 Render date: 2024-05-03T02:36:24.254Z Has data issue: false hasContentIssue false

Information Value of Climate Forecasts for Rainfall Index Insurance for Pasture, Rangeland, and Forage in the Southeast United States

Published online by Cambridge University Press:  26 January 2015

Denis Nadolnyak
Affiliation:
Department of Agricultural Economics and Rural Sociology, Auburn University, Auburn, Alabama
Dmitry Vedenov
Affiliation:
Department of Agricultural Economics, Texas A&M University, College Station, Texas
Get access

Extract

In this article, possible use of climate forecasts in rainfall index insurance of hay and forage production is considered in a geographical area (southeast United States) relatively heavily impacted by the El Nino Southern Oscillation (ENSO). Analysis of the stochastic properties of rainfall, yields, and the ENSO forecasts using the copula technique shows that the forecast impact depends on the proximity to the Gulf Coast where the impact of the ENSO is more pronounced and earlier in the year. Stochastic modeling shows that the use of skillful long-term climate forecasts by the insured producers creates intertemporal adverse selection that can be precluded by offering forecast conditional premiums. The impacts on the efficiency of the rainfall index insurance and results of sensitivity analysis with respect to model parameters are discussed.

Type
Research Article
Copyright
Copyright © Southern Agricultural Economics Association 2013

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

Agroclimate.org. Internet site: www.agroclimate.org (Accessed July 20, 2012).Google Scholar
American Meteorological Society. Glossary of Meteorology. Internet site: http://amsglossary.allenpress.com/glossary/search?id= skill1 (Accessed July 25, 2012).Google Scholar
Babcock, B.A., Choi, E.K., and Feinerman, E.. “Risk and Probability Premiums for CAM Utility Functions.Journal of Agricultural and Resource Economics 18(1993):1724.Google Scholar
Carriquiry, M.A., and Osgood, D.E.. “Index Insurance, Probabilistic Climate Forecasts, and Production.The Journal of Risk and Insurance 79(2012):287300.10.1111/j.1539-6975.2011.01422.xGoogle Scholar
Gershunov, A.ENSO Influence on Intra-seasonal Extreme Rainfall and Temperature Frequencies in the Contiguous United States: Implications for Long-range Predictability.” Journal of Climate 11(1998):3192–203.10.1175/1520-0442(1998)011<3192:EIOIER>2.0.CO;22.0.CO;2>Google Scholar
Glauber, J.Crop Insurance Reconsidered.American Journal of Agricultural Economics 86(2004):1179–95.10.1111/j.0002-9092.2004.00663.xGoogle Scholar
Halcrow, H.G.Actuarial Structures for Crop Insurance.Journal of Farm Economics 31(1949):418–13.10.2307/1232330Google Scholar
Hansen, J.W., Hodges, A.W., and Jones, J.W.. “ENSO Influences on Agriculture in the Southeastern United States.Journal of Climate 11(1998):404–11.10.1175/1520-0442(1998)011<0404:EIOAIT>2.0.CO;22.0.CO;2>Google Scholar
Mahul, O.Optimum Area Yield Crop Insurance.American Journal of Agricultural Economics 81(1999):7582.10.2307/1244451Google Scholar
Mahul, O.Optimal Insurance against Climatic Experience.American Journal of Agricultural Economics 83(2001):593604.10.1111/0002-9092.00180Google Scholar
Mahul, O., and Wright, B.D.. “Designing Optimal Crop Revenue Insurance.American Journal of Agricultural Economics 85(2003):580–89.10.1111/1467-8276.00457Google Scholar
Mas-Colell, A., Whinston, M.D., and Green, J.R.. Microeconomic Theory. Oxford University Press, USA, 1995.Google Scholar
Miranda, M.J.Area-yield Crop Insurance Reconsidered.American Journal of Agricultural Economics 73(1991):233–12.10.2307/1242708Google Scholar
Murphy, A.H.Skill Scores Based on the Mean Square Error and Their Relationships to the Correlation Coefficient.American Meteorological Society-Monthly Weather Review 116(1988):2417–24.10.1175/1520-0493(1988)116<2417:SSBOTM>2.0.CO;22.0.CO;2>Google Scholar
NASA online database. Internet site: http://gcmd.nasa.gov/records/GCMD_NOAA_NWS_CPC_NINQ34.html (Accessed July 25, 2012).Google Scholar
Nelsen, R.B. An Introduction to Copulas. 2nd ed. Springer, USA, 2006. USDA Risk Management Agency (RMA). Data for Specific Grid Locations. Internet site: http://prfri-rma-map.tamu.edu (Accessed July 27, 2012).Google Scholar
Nelsen, R.B. Rainfall Index Insurance Policy Explanation. Internet site: www.rma.usda.gov/policies/pasturerangeforage (Accessed July 27, 2012).Google Scholar
Rothschild, M., and Stiglitz, J.. “Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information.The Quarterly Journal of Economics 90(1976):629–19.10.2307/1885326Google Scholar
Royce, F.S., Fraisse, C.W., and Baigorria, G.A.. “ENSO Classification Indices and Summer Crop Yields in the Southeastern USA.Journal of Agricultural and Forest Meteorology 151(2011):817–26.10.1016/j.agrformet.2011.01.017Google Scholar
Schnitkey, G.D., Sherrick, B.J., and Irwin, S.H.. “Evaluation of Risk Reductions Associated with Multi-peril Crop Insurance Products.Agricultural Finance Review 63(2003):121.10.1108/00214970380001138Google Scholar
Skees, J.Innovations in Index Insurance for the Poor in Lower Income Countries.Agricultural and Resource Economics Review 37(2008):115.Google Scholar
Tejeda, H.A., and Goodwin, B.K.. “Modeling Crop Prices through a Burr Distribution and Analysis of Correlation between Crop Prices and Yields Using a Copula Method.” Paper presented at the AAEA Annual Meetings, Orlando, FL, July 27-29, 2008.Google Scholar
Vedenov, D.Application of Copulas to Estimation of Joint Crop Yield Distributions.” Paper presented at the 2008 AAEA Meetings, Orlando, FL, July 27-29, 2008.Google Scholar
Wilson, C.A Model of Insurance Markets with Incomplete Information.Journal of Economic Theory 16(1977):167207.10.1016/0022-0531(77)90004-7Google Scholar