Is the Irrigation Water Demand Really Convex?
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We estimate the irrigation water demand for a season under uncertainty using a dynamic programming model and a crop-growth simulation model (EPIC-PHASE) linked to a CRRA utility function representing the farmer's objective function. This model is used to generate the data allowing the estimation of irrigation water demand by a nonparametric procedure. An application shows that demand functions present four main areas: for very small quantities, the demand is inelastic. In the second area, where water is no more an essential input and is not yet a risk reducing input, the demand is elastic. But, we find a third, non-intuitive, area for larger quantities where the water is a risk reducing input and the demand becomes inelastic again. In the last area the water demand is obviously elastic for important total water quantities. This result is of great importance to analyse a regulation policy.
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Process-based crop simulation models are now regarded by economists as a better alternative to traditional regression-based models, because the former simulates the biological and physical processes related to plant growth with better precision.
“Process-based crop simulation models are now regarded by economists as a better alternative to the traditional regression based models, because the former simulates the biological and physical process related to the plant growth with better precision (Bontemps et al., 2001).”
From Development and Application of Process-based Simulation Models for Cotton Production: A Review of Past, Present, and Future Directions (2014) · cites this paper as Bontemps et al., 2001
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Experimental — accuracy not yet measuredWhen irrigation water availability is too low to have a risk-reducing impact but high enough for normal crop growth, farmers are responsive to changes in water price.
“Bontemps et al. (2001) linked the data generated by EPIC to an economic model and showed that when irrigation water availability is too low to have risk-reducing impact, but high enough for normal crop growth, the farmers are responsive to changes in water price.”
From Development and Application of Process-based Simulation Models for Cotton Production: A Review of Past, Present, and Future Directions (2014) · cites this paper as Bontemps et al. (2001)
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Research works printed but not linked: 12
- Bontems P.; A. Thomas [2000]. "Information value and risk premium in agricultural production under risk: the case of split nitrogen application for corn". American Journal of Agricultural Economics, V ol.82, pp. 59-70.
- Bouzit A.M. [1996]. Modélisation du comportement des agriculteurs face au risque: investigations de la théorie de l'utilité dépendant des rangs. PH'D dissertation. ENS Cachan.
- Chavas J.P.; M.T. Holt [1996]. "Economic behavior under uncertainty: a joint analysis of risk preferences and technology". Review of Economics and Statistics 78: 329-335.
- Couture S. [2000]. Aspects dynamiques et aléatoires de la demande en eau d'irrigation. PH'D dissertation. Université de Toulouse I.
- Garrido A.; C. Varela-Ortega; J.M. Sumpsi [1997]. "The interaction of agricultural pric- ing policies and water districts' modernization programs: a question with unexpected answers." Paper presented a t the Eighth Conference of the European Association of En- vironmental and Resource Economists, Tilburg, The Netherlands, june 26-28.
- Härdle W. [1990]. Applied nonparametric regression. Econometric society monographs. Cambridge University Press.
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- Michalland B. [1995]. Approche économique de la gestion de la ressoruce en eau pour l'usage de l'irrigation. PH'D dissertation. Université de Bordeaux.
- Moore M.R.; D.H. Negri "A multicrop production model of irrigated agriculture, applied to water allocation policy of the bureau of reclamation." Journal of Agricultural and resources Economics 17: 30-43.
- Moore M.R.; N.R. Gollehon; M.B. Carrey [1994]. "Multicrop production decisions in western irrigated agriculture: the role of water price." American Journal of Agricultural Economics: 359-74.
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Thorp, K. R., Ale, S., Bange, M. P., Barnes, E. M., Hoogenboom, G., Lascano, R. J., McCarthy, A. C., Nair, S., Paz, J. O., Rajan, N., et al. (2014) · Journal of Cotton Science · doi:10.56454/oovc6073 · Cited by 96 · see citing works
- Authors
- Bontemps, C., Couture, S., & Favard, P.
- Affiliations
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
- Université de Tours (FR)
- Corresponding author
- Bontemps, Christophe
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2001
- Pages
- 263-278
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W3122913045
- Cited by
- 5
- References
- 0
- Citations by year
- 2021: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- AgEcon Search (University of Minnesota, USA) (submittedVersion, cc-by-nc-nd)
- Econstor (Econstor) (submittedVersion, other-oa)
- HAL (submittedVersion)
- AgEcon Search (University of Minnesota, USA)
- Funders
- Università degli Studi di Padova
- Institut National de la Recherche Agronomique
- Topics
- Physical Sciences › Engineering › Ocean Engineering › Water resources management and optimization
- Life Sciences › Agricultural and Biological Sciences › Soil Science › Irrigation Practices and Water Management
- Life Sciences › Agricultural and Biological Sciences › Soil Science › Agricultural risk and resilience
- Keywords
- irrigation water demand, water demand elasticity, CRRA utility, crop growth simulation, dynamic programming, nonparametric estimation, agricultural water management
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