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

Research output: Contribution to journalArticle

16 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)5-14
Number of pages10
JournalRegional Environmental Change
Volume13
Issue numberSUPPL.1
DOIs
StatePublished - 2013

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technical efficiency
climate
agricultural production
economics
rural development
agriculture
rainfall
forecast
temperature
effect
policy
comparison
index

Keywords

  • Climate bias
  • Production frontier
  • US Agriculture
  • Value of information

ASJC Scopus subject areas

  • Global and Planetary Change

Cite this

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AB - 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.

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