Modeling the sensitivity of agricultural water use to price variability and climate change - An application to Swiss maize production

R. Finger

Research output: Contribution to journalArticleAcademicpeer-review

24 Citations (Scopus)

Abstract

We analyze the sensitivity of crop management under current and future climate scenarios to changes in economic boundary conditions. In particular, we focus on the effects of changing price risks. We combine a bio-economic modeling approach and a crop growth model CropSyst with an economic model that represents the decision making process of a risk-averse farmer. We apply the models to irrigated maize production in Switzerland. To analyze the sensitivity of optimal water and nitrogen use to likely future states of several economic variables, we conduct sensitivity analyses with respect to changes in price variability, the price–yield correlation, water and maize prices as well as farmers’ risk preferences. Results show that climate change leads to a strong increase in optimal water use for irrigation, with consequent increases in maize yields. However, our analysis also reveals that the consideration of economic drivers for farmers’ irrigation decisions is indispensable. Strong effects on optimal water use are found for changes in crop (positive) and water (negative) prices. We also find strong implications of risk aversion and price variability on irrigation decisions. A doubling of price variability, which would represent a shift from the current Swiss situation to price variability levels in its neighboring countries, could reduce optimal water use by up to 40%. We conclude that investigations of water demand should consider, beyond expectations on output and input price levels, also the variability of prices.
Original languageEnglish
Pages (from-to)135-143
JournalAgricultural Water Management
Volume109
Issue numberJune
DOIs
Publication statusPublished - 2012

Keywords

  • simulation-model
  • irrigation
  • risk
  • markets
  • uncertainty
  • switzerland
  • strategies
  • experience
  • grassland
  • cropsyst

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