Abstract
Pressures on resources and climate change are likely to strongly impact the availability of water, which directly affects agricultural systems. To estimate these impacts, we develop a prospective approach combining an agricultural supply side economic model and a crop model. We extend previous work by incorporating water resource constraints and apply our model to a large part of the French agricultural sector under three climate scenarios over 2010–2010. Results indicate that, at a given water price, potential change in irrigation water demand would differ strongly according to the region concerned and the scenario applied. In France as a whole, irrigation increases in all scenarios, by 60% under the intermediate scenario, by 40% under the least extreme scenario, and by 20% under the toughest scenario. Differentiating the northern and southern regions, the relative increase is more pronounced in the north, while demand in the south significantly increases under the intermediate scenario and decreases under the toughest scenario. When considering autonomous adaptation of farming systems to climate change, agricultural income in northern regions is likely to be negatively affected to a greater extent than in southern regions.
Similar content being viewed by others
Notes
Gross margin is commonly defined as the difference between farmers’ revenues and their variable costs.
References
Eau France. (2012). Les prélèvements en eau en 2009 et leurs évolutions depuis dix ans. Retrieved August 10, 2020, from https://www.eaufrance.fr/publications/les-prelevements-en-eau-en-2009-et-leurs-evolutions-depuis-10-ans.
Ministère de l’environnement, de l’énergie et de la mer. (2017). Les prélèvements d’eau douce en France: les grands usages en 2013 et leur évolution depuis 20 ans. Retrieved 10 August 2020, from https://www.statistiques.developpement-durable.gouv.fr/sites/default/files/2018-10/datalab-prelevement-eau-mise-en-ligne.pdf.
Alexandratos, N., & Bruinsma, J. (2012). “World Agriculture towards 2030/2050—The 2012 Revision.” ESA Working Paper No. 12-03. Agricultural Development Economics Division, FAO. Retrieved 10 August 2020, from http://www.fao.org/3/ap106e/ap106e.pdf.
International Water Management Institute. (2007). Water for food, water for life: A comprehensive assessment of water Management in Agriculture. Colombo, Sri Lanka. Retrieved 10 August 2020, from https://www.iwmi.cgiar.org/assessment/files_new/synthesis/Summary_SynthesisBook.pdf.
Mukherjee, M., & Schwabe, K. (2015). Irrigated agricultural adaptation to water and climate variability: the economic value of a water portfolio. American Journal of Agricultural Economics, 97(3), 809–832.
Strzepek, K., & Boehlert, B. (2010). Competition for water for the food system. Philosophical. Transanctions.of the Royal. Society. B, 365, 2927–2940.
Intergovernmental Panel on Climate Change. (2013). Fifth Assessment Report—Climate Change 2013. Retrieved 7 February 2017, from https://www.ipcc.ch/report/ar5/wg1/index_fr.shtml.
Foster, T., & Brozović, N. (2018). Simulating crop-water production functions using crop growth models to support water policy assessments. Ecological Economics 152:9–21.
Oberdorff, T., Pont, D., Hugueny, B., & Porcher, J.-P. (2002). Development and validation of a fish-based index for the assessment of river health in France. Freshwater Biology, 47, 1720–1734.
Supit, I., Van Diepen, C. A., Boogaard, H. L., Ludwig, F., & Baruth, B. (2010). Trend analysis of the water requirements, consumption and deficit of field crops in Europe. Agricultural and Forest Meteorology, 150(1), 77–88.
Van der Velde, M., Wriedt, G., & Bouraoui, F. (2010). Estimating irrigation use and effects on maize yield during the 2003 heatwave in France. Agriculture, Ecosystems & Environment, 135(1–2), 90–97.
Mendelsohn, R., & Nordhaus, W. (1999). The impact of global warming on agriculture: a Ricardian analysis: Reply. The American Economic Review, 89(4), 1046–1048.
Ay, J., Chakir, R., Doyen, L., Jiguet, F., & Leadley, P. (2014). Integrated models, scenarios and dynamics of climate, land use and common birds. Climatic Change, 126, 13–30. https://doi.org/10.1007/s10584-014-1202-4.
Cortignani, R., & Severini, S. (2009). Modeling farm-level adoption of deficit irrigation using positive mathematical programming. Agricultural Water Management, 96(12), 1785–1791.
Kampas, A., Petsakos, A., & Rozakis, S. (2012). Price induced irrigation water saving: unraveling conflicts and synergies between European agricultural and water policies for a Greek Water District. Agricultural Systems, 113, 28–38.
Graveline, N., Loubier, S., Gleyses, G., & Rinaudo, J.-D. (2012). Impact of farming on water resources: assessing uncertainty with Monte Carlo simulations in a global change context. Agricultural Systems, 108, 29–41.
Janssen, S., & van Ittersum, M. K. (2007). Assessing farm innovations and responses to policies: a review of bio-economic farm models. Agricultural Systems, 94, 622–636.
Döll, P. (2002). Impact of climate change and variability on irrigation requirements: a global perspective. Climatic Change, 54, 269–293.
Levis, S., Badger, A., Drewniak, B., Nevison, C., & Xiaolin, R. (2016). CLMcrop yields and water requirements: avoided impacts by choosing RCP 4.5 over 8.5. Climatic Change, 146, 501.
Yoo, J., Simonit, S., Kinzig, A. P., & Perrings, C. (2014). Estimating the price elasticity of residential water demand: the case of Phoenix, Arizona. Applied Economic Perspectives and Policy, 36, 333–350.
CGAAER. (2017). “Eau, agriculture et changement climatique: Statu quo ou anticipation ?” Ministère de l’agriculture et de l’alimentation. Rapport n° 16072. Retrieved 10 August 2020, from https://agriculture.gouv.fr/sites/minagri/files/cgaaer_16072_2017_rapport.pdf.
Pagé, C., & Terray, L. (2010). Nouvelles projections climatiques à échelle fine sur la France pour le 21ème siècle: les scénarii SCRATCH2010. In Technical Report for the CERFACS (Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique), Toulouse, France. Retrieved 10 August 2020, from https://www.cerfacs.fr/~page/publications/report_cerfacs_regional_scenarii_scratch2010.pdf.
Pagé, C., Terray, L., & Boé, J. (2010). dsclim: a software package to downscale climate scenarios at regional scale using a weather-typing based statistical methodology. In Technical Report for the CERFACS (Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique), Toulouse, France. Retrieved 10 August 2020, from https://www.cerfacs.fr/~page/dsclim/dsclim_doc-latest.pdf.
Zhao, G., Webber, H., Hoffmann, H., Wolf, J., Siebert, S., & Ewert, F. (2015). The implication of irrigation in climate change impact assessment: a European-wide study. Global change biology, 21, 4031–4048.
Godard, C., Roger-Estrade, J., Jayet, P. A., Brisson, N., & Le Bas, C. (2008). Use of available information at a European level to construct crop nitrogen response curves for the regions of the EU. Agricultural Systems, 97(1–2), 68–82.
Leclère, D., Jayet, P.-A., & de Noblet-Ducoudré, N. (2013). Farm-level autonomous adaptation of European agricultural supply to climate change. Ecological Economics, 87, 1–14.
Humblot, P., Jayet, P.-A., & Petsakos, A. (2017). Farm-level bio-economic modeling of water and nitrogen use: calibrating yield response functions with limited data. Agricultural Systems, 151, 47–60.
Jayet, P.A. & et al. (2018a). “The European agro-economic model AROPAj”. Retrieved 10 August 2020, from https://www6.versailles-grignon.inra.fr/economie_publique/Media/fichiers/ArticlAROPAj.
Jayet, P. A., Barberis, D., Humblot, P., & Lungarska, A. (2018b). Spatialisation de la demande en eau agricole en France par l’intégration de l’eau d’irrigation dans un modèle bioéconomique. Revue Internationale de Géomatique, 28(4), 485–503.
Aghajanzadeh-Darzi, P., Jayet, P. A., & Petsakos, A. (2017). Improvement of a bio-economic mathematical programming model in the case of non-marketed outputs. Journal of Quantitative Economics, 15(3), 489–508. https://doi.org/10.1007/s40953-016-0058-z.
Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., Zimmer, D., Sierra, J., Bertuzzi, P., Burger, P., Bussière, F., Cabidoche, Y.-M., Cellier, P., Debaeke, P., Gaudillère, J. P., Hénault, C., Maraux, F., Seguin, B., & Sinoquet, H. (2003). An overview of the crop model stics. European Journal of Agronomy, 18(3–4), 309–332.
Déqué, M., Dreveton, C., Braun, A., & Cariolle, D. (1994). The ARPEGE/IFS atmosphere model: a contribution to the French community climate modeling. Climate Dynamics, 10(4–5), 249–266.
Panagos, P., Van Liedekerke, M., Jones, A., & Montanarella, L. (2012). European Soil Data Centre: response to European policy support and public data requirements. Land Use Policy, 29(2), 329–338.
Bourgeois, C., Ben Fradj, N., & Jayet, P. A. (2014). How cost-effective is a mixed policy targeting the management of three agricultural N-pollutants? Environmental Modeling and Assessment, 19(5), 389–405. https://doi.org/10.1007/s10666-014-9401-y.
Marshall, E., Aillery, M., Malcolm, S., & Williams, R. (2015). Agricultural production under climate change: the potential impacts of shifting regional water balances in the United States. American Journal of Agricultural Economics, 97(2), 568–588.
Chakir, R. (2009). Spatial downscaling of agricultural land-use data: an econometric approach using cross entropy. Land Economics, 85(2), 238–251.
Cantelaube, P., Jayet, P. A., Carré, F., Bamps, C., & Zakharov, P. (2012). Geographical downscaling of outputs provided by an economic farm model calibrated at the regional level. Land Use Policy, 29(1), 35–44.
Observation and Statistics Department. (n.d.) Geography and indicators related to sustainable development. Retrieved August 10, 2020, from http://geoidd.developpement-durable.gouv.fr/geoclip_stats_o3/#l=fr;v=map1.
Acknowledgments
Public institutions associated with the PIREN-Seine program, the French Ministry of Research and Ecole Normale Supérieure de Lyon are gratefully acknowledged for their financial support. This work is part of the “Investissements d’Avenir” program overseen by the French National Research Agency (ANR) (LabEx BASC; ANR-11-LABX-0034). The authors thank Michael Westlake for his copy-editing and Jeffrey Norville for helpful comments on the manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Rights and permissions
About this article
Cite this article
Barberis, D., Chiadmi, I., Humblot, P. et al. Climate Change and Irrigation Water: Should the North/South Hierarchy of Impacts on Agricultural Systems Be Reconsidered?. Environ Model Assess 26, 13–36 (2021). https://doi.org/10.1007/s10666-020-09724-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10666-020-09724-8