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Regional Environmental Change

, Volume 13, Supplement 1, pp 5–14 | Cite as

Assessing the value of climate information and forecasts for the agricultural sector in the Southeastern United States: multi-output stochastic frontier approach

  • Daniel Solís
  • David Letson
Original Article

Abstract

A multi-output/input stochastic distance frontier model is used to analyze the effect of interannual climatic variability on agricultural production and to assess the impact of climate forecasts on the economic performance of this sector in the Southeastern United States. The results show that the omission of climatic conditions when estimating regional agricultural production models could lead to biased technical efficiency (TE) estimates. This climate bias may significantly affect the effectiveness of rural development policies based on regional economic performance comparisons. We also found that seasonal rainfall and temperature forecasts have a positive effect on economic performance of agriculture. However, the effectiveness of climate forecasts on improving TE is sensitive to the type of climate index used. Policy implications stemming from the results are also presented.

Keywords

US Agriculture Climate bias Value of information Production frontier 

Notes

Acknowledgments

We would like to thank two anonymous referees and seminar participants at 2011 Southern Agricultural Economics Association annual meeting, and the Southeast Climate Consortium Program Review for comments and suggestions. Research support grants from the Office of Science and Technology, and the Office of Climate, Water and Weather Services at The National Oceanic and Atmospheric Administration—National Weather Service, and from USDA/NIFA grant No. 2011-0828 are gratefully acknowledged.

References

  1. Aigner D, Lovell C, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econom 6:21–37CrossRefGoogle Scholar
  2. Alvarez A, del Corral J, Solís D, Pérez J (2008) Does intensification improve the economic efficiency of dairy farms? J Dairy Sci 91:3699–3709CrossRefGoogle Scholar
  3. Ball E, Gollop F, Kelly-Hawke A, Swinand G (1999) Patterns of productivity growth in the U.S. farm sector: linking state and aggregate models. Am J Agric Econ 81:164–179CrossRefGoogle Scholar
  4. Battese G, Coelli T (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir Econom 20:325–332CrossRefGoogle Scholar
  5. Bravo-Ureta B, Solís D, Manipani J, Moreira V, Thiam A, Rivas T (2007) Technical efficiency in farming: a metaregression analysis. J Prod Anal 27:57–72CrossRefGoogle Scholar
  6. Breuer N, Cabrera V, Ingram K, Broad K, Hildebrand P (2008) AgClimate: a case study in participatory decision support system development. Climatic Change 87:385–403Google Scholar
  7. Cabrera V, Letson D, Podesta G (2007) The value of climate information when farm programs matter. Agric Syst 93:25–42CrossRefGoogle Scholar
  8. Cabrera V, Solís D, Letson D (2009) Optimal crop insurance under climate variability: contrasting insurer and farmer interests. Transact ASABE 52:623–631Google Scholar
  9. Chen C, McCarl B (2000) The value of ENSO information to agriculture: consideration of event strength and trade. J Agric Res Econom 25:368–385Google Scholar
  10. Coelli T, Perelman S (1999) A comparison of parametric and non-parametric distance functions: with application to European railways. Eur J Oper Res 117:326–339CrossRefGoogle Scholar
  11. Demir N, Mahmud S (2002) Agro-climatic conditions and regional technical inefficiencies in agriculture. Can J Agric Econom 50:269–280CrossRefGoogle Scholar
  12. Fuglie K, MacDonald J, Ball E (2007) Productivity growth in U.S. agriculture. Economic brief number 9. USDA, ERS, Washington, DCGoogle Scholar
  13. Haim D, Shechter M, Berliner P (2008) Assessing the impact of climate change on representative field crops in Israeli agriculture: a case study of wheat and cotton. Climatic Change 86:425–440CrossRefGoogle Scholar
  14. Hansen J (2002) Realizing the potential benefits of climate prediction to agriculture: issues, approaches, challenges. Agric Syst 74:309–330CrossRefGoogle Scholar
  15. Jagtap S, Jones J, Hildebrand P, Letson D, O’Brien J, Podestá G, Zierden D, Zazueta F (2002) Responding to stakeholder’s demands for climate information: from research to applications in Florida. Agric Syst 74:415–430Google Scholar
  16. Jondrow J, Lovell K, Materov I, Schmidt P (1982) On the estimation of technical inefficiency in the stochastic frontier production function model. J Econom 19:233–238CrossRefGoogle Scholar
  17. Jones J, Hansen J, Royce F, Messina C (2000) Potential benefits of climate forecasting to agriculture. Agric Ecosyst Environ 82:169–184CrossRefGoogle Scholar
  18. Kumbhakar S, Lovell C (2000) Stochastic frontier analysis. Cambridge University Press, Cambridge, MACrossRefGoogle Scholar
  19. Kumbhakar S, Orea L, Rodríguez-Alvarez A, Tizonas E (2007) Do we estimate an input or an output distance function? An application of the mixture approach to European railways. J Prod Anal 27:87–100CrossRefGoogle Scholar
  20. Lazo J, Lawson M, Larsen P, Waldman D (2011) U.S. economic sensitivity to weather variability. Bull Am Meteorol Soc 92:709–720CrossRefGoogle Scholar
  21. Letson D, Podesta G, Messina C, Ferreyra A (2005) The uncertain value of perfect ENSO phase forecasts: stochastic agricultural prices and intra-phase climatic variations. Climatic Change 9:163–196CrossRefGoogle Scholar
  22. Liu J, Men C, Cabrera V, Uryasev S, Fraisse C (2009) Optimizing crop insurance under climate variability. J Appl Meteorol Climatol 47:2572–2580CrossRefGoogle Scholar
  23. Mavromatis T, Jagtap S, Jones J (2002) El Nino-Southern Oscillation effects on peanut yield and nitrogen leaching. Clim Res 22:129–140Google Scholar
  24. Meza F, Hansen JJ, Osgood D (2008) Economic value of seasonal climate forecasts for agriculture: review of ex-ante assessments and recommendations for future research. J Appl Meteorol Climatol 47:1269–1286CrossRefGoogle Scholar
  25. Msangi S, Rosegrant M, You L (2006) Ex post assessment methods of climate forecast impacts. Climate Res 33:67–79CrossRefGoogle Scholar
  26. Murphy A, Katz R (2005) Economic value of weather and climate forecasts. Cambridge University Press, Cambridge, MAGoogle Scholar
  27. National Oceanic and Atmospheric Administration (2012) Chart the future—NOAA’s next generation strategic plan. NOAA, Silver Spring, MDGoogle Scholar
  28. Shao B, Lin W (2001) Measuring the value of information technology in technical efficiency with stochastic production frontiers. Inf Softw Technol 43:447–456CrossRefGoogle Scholar
  29. Solís D, Bravo-Ureta B, Quiroga R (2009) Technical efficiency among peasant farmers participating in natural resource management programs in Central America. J Agric Econ 60:202–219CrossRefGoogle Scholar
  30. St-Pierre N, Cobanov B, Schnitkey G (2003) Economic losses from heat stress by U.S. livestock industries. J Dairy Sci 86(E Suppl):E52–E77Google Scholar
  31. Wang H (2002) Heteroscedasticity and non-monotonic efficiency effects of a stochastic frontier model. J Prod Anal 18:241–253CrossRefGoogle Scholar
  32. Westerling A, Hidalgo H, Cayan D, Swetnam T (2006) Warming and earlier spring increases Western US forest wildfire activity. Science 313:940–943Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Division of Marine Affairs and Policy (MAF), Rosenstiel School of Marine and Atmospheric Science (RSMAS)University of MiamiMiamiUSA

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