Willingness to pay for improved irrigation water supply reliability: an approach based on probability density functions

M Dolores Guerrero-Baena, AJ Villanueva, Jose A Gomez-Limon, K Glenk

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)
93 Downloads (Pure)

Abstract

In irrigated agricultural systems, a major source of uncertainty relates to water supply, as it significantly affects farm income. This paper investigates farmers’ utility changes associated with shifts in the probability density function of water supply leading to a higher water supply reliability (higher mean and lower variance in annual water allotments). A choice experiment relying on a mean-variance approach is applied to the case study of an irrigation district of the Guadalquivir River Basin (southern Spain). To our knowledge, this is the first study using parameters of these probability density functions of water supply as choice experiment attributes to value water supply reliability. Results show that there are different types of farmers according to their willingness to pay (WTP) for improvements in water supply reliability, with some willing to pay nothing (47.8%) while others have a relatively low (28.0%) or high (24.2%) WTP. A range of factors influencing farmers’ preferences toward water supply reliability are revealed, with those related to risk exposure to water availability being of special importance. The results can be used to assist the design of more efficient policy instruments to improve water supply reliability in Mediterranean and semi-arid climate regions.
Original languageEnglish
Pages (from-to)11-22
Number of pages12
JournalAgricultural Water Management
Volume217
Early online date25 Feb 2019
DOIs
Publication statusPrint publication - 20 May 2019

Keywords

  • Choice experiment
  • Irrigation water availability
  • Mean-variance approach
  • Preference heterogeneity

Fingerprint

Dive into the research topics of 'Willingness to pay for improved irrigation water supply reliability: an approach based on probability density functions'. Together they form a unique fingerprint.

Cite this