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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
A review of the literature on the economic value of climate forecasts can be found in Katz and Murphy (2005).
In this study, we consider the following states: Alabama (AL), Florida (FL), Georgia (GA), North Carolina (NC) and South Carolina (SC). These states were selected because they display climate variability patterns in temperature and precipitation associated with ENSO events.
In an ISDF, the RTS corresponds to the inverse of the sum of output elasticities (Coelli and Perelman 1999).
The input elasticities for purchased inputs (the variable used to normalize the ISDF) were estimated by homogeneity conditions. The elasticities for purchased inputs are 0.270 and 0.486 for the models with and without climate variables, respectively.
The official ranking can be found at http://www.ers.usda.gov/data/agproductivity/.
This change in the ranking positions could be explained by the following reasons: (a) SC has proportionally more land under irrigation than AL, and farmers with irrigation may be better able to respond to climatic fluctuations; and (b) controlling for climate variations may have a larger impact on SC than on AL, since the former has a greater exposure to the coast, making it more sensitive to variations associated with ENSO. However, this is an area that deserves further research.
References
Aigner D, Lovell C, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econom 6:21–37
Alvarez A, del Corral J, Solís D, Pérez J (2008) Does intensification improve the economic efficiency of dairy farms? J Dairy Sci 91:3699–3709
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 81:164–179
Battese G, Coelli T (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir Econom 20:325–332
Bravo-Ureta B, Solís D, Manipani J, Moreira V, Thiam A, Rivas T (2007) Technical efficiency in farming: a metaregression analysis. J Prod Anal 27:57–72
Breuer N, Cabrera V, Ingram K, Broad K, Hildebrand P (2008) AgClimate: a case study in participatory decision support system development. Climatic Change 87:385–403
Cabrera V, Letson D, Podesta G (2007) The value of climate information when farm programs matter. Agric Syst 93:25–42
Cabrera V, Solís D, Letson D (2009) Optimal crop insurance under climate variability: contrasting insurer and farmer interests. Transact ASABE 52:623–631
Chen C, McCarl B (2000) The value of ENSO information to agriculture: consideration of event strength and trade. J Agric Res Econom 25:368–385
Coelli T, Perelman S (1999) A comparison of parametric and non-parametric distance functions: with application to European railways. Eur J Oper Res 117:326–339
Demir N, Mahmud S (2002) Agro-climatic conditions and regional technical inefficiencies in agriculture. Can J Agric Econom 50:269–280
Fuglie K, MacDonald J, Ball E (2007) Productivity growth in U.S. agriculture. Economic brief number 9. USDA, ERS, Washington, DC
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 86:425–440
Hansen J (2002) Realizing the potential benefits of climate prediction to agriculture: issues, approaches, challenges. Agric Syst 74:309–330
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 74:415–430
Jondrow J, Lovell K, Materov I, Schmidt P (1982) On the estimation of technical inefficiency in the stochastic frontier production function model. J Econom 19:233–238
Jones J, Hansen J, Royce F, Messina C (2000) Potential benefits of climate forecasting to agriculture. Agric Ecosyst Environ 82:169–184
Kumbhakar S, Lovell C (2000) Stochastic frontier analysis. Cambridge University Press, Cambridge, MA
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 27:87–100
Lazo J, Lawson M, Larsen P, Waldman D (2011) U.S. economic sensitivity to weather variability. Bull Am Meteorol Soc 92:709–720
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 9:163–196
Liu J, Men C, Cabrera V, Uryasev S, Fraisse C (2009) Optimizing crop insurance under climate variability. J Appl Meteorol Climatol 47:2572–2580
Mavromatis T, Jagtap S, Jones J (2002) El Nino-Southern Oscillation effects on peanut yield and nitrogen leaching. Clim Res 22:129–140
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 47:1269–1286
Msangi S, Rosegrant M, You L (2006) Ex post assessment methods of climate forecast impacts. Climate Res 33:67–79
Murphy A, Katz R (2005) Economic value of weather and climate forecasts. Cambridge University Press, Cambridge, MA
National Oceanic and Atmospheric Administration (2012) Chart the future—NOAA’s next generation strategic plan. NOAA, Silver Spring, MD
Shao B, Lin W (2001) Measuring the value of information technology in technical efficiency with stochastic production frontiers. Inf Softw Technol 43:447–456
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 60:202–219
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–E77
Wang H (2002) Heteroscedasticity and non-monotonic efficiency effects of a stochastic frontier model. J Prod Anal 18:241–253
Westerling A, Hidalgo H, Cayan D, Swetnam T (2006) Warming and earlier spring increases Western US forest wildfire activity. Science 313:940–943
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Solís, D., Letson, D. Assessing the value of climate information and forecasts for the agricultural sector in the Southeastern United States: multi-output stochastic frontier approach. Reg Environ Change 13 (Suppl 1), 5–14 (2013). https://doi.org/10.1007/s10113-012-0354-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10113-012-0354-x