Skip to main content

Advertisement

Log in

Climate Change and Irrigation Water: Should the North/South Hierarchy of Impacts on Agricultural Systems Be Reconsidered?

  • Published:
Environmental Modeling & Assessment Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. http://www.gesteau.fr/document/bilan-du-projet-explore-2070-eau-et-changement-climatique

  2. Gross margin is commonly defined as the difference between farmers’ revenues and their variable costs.

References

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

    Article  Google Scholar 

  6. Strzepek, K., & Boehlert, B. (2010). Competition for water for the food system. Philosophical. Transanctions.of the Royal. Society. B, 365, 2927–2940.

    Article  Google Scholar 

  7. 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.

  8. Foster, T., & Brozović, N. (2018). Simulating crop-water production functions using crop growth models to support water policy assessments. Ecological Economics 152:9–21.

  9. 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.

    Article  Google Scholar 

  10. 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.

    Article  Google Scholar 

  11. 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.

    Article  Google Scholar 

  12. Mendelsohn, R., & Nordhaus, W. (1999). The impact of global warming on agriculture: a Ricardian analysis: Reply. The American Economic Review, 89(4), 1046–1048.

    Article  Google Scholar 

  13. 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.

    Article  CAS  Google Scholar 

  14. Cortignani, R., & Severini, S. (2009). Modeling farm-level adoption of deficit irrigation using positive mathematical programming. Agricultural Water Management, 96(12), 1785–1791.

    Article  Google Scholar 

  15. 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.

    Article  Google Scholar 

  16. 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.

  17. 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.

    Article  Google Scholar 

  18. Döll, P. (2002). Impact of climate change and variability on irrigation requirements: a global perspective. Climatic Change, 54, 269–293.

    Article  Google Scholar 

  19. 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.

    Article  Google Scholar 

  20. 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.

    Article  Google Scholar 

  21. 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.

  22. 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.

  23. 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.

  24. 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.

    Article  Google Scholar 

  25. 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.

    Article  Google Scholar 

  26. 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.

    Article  Google Scholar 

  27. 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.

    Article  Google Scholar 

  28. 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.

  29. 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.

    Article  Google Scholar 

  30. 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.

    Article  Google Scholar 

  31. 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.

    Article  Google Scholar 

  32. 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.

    Article  Google Scholar 

  33. 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.

    Article  Google Scholar 

  34. 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.

    Article  Google Scholar 

  35. 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.

    Article  Google Scholar 

  36. Chakir, R. (2009). Spatial downscaling of agricultural land-use data: an econometric approach using cross entropy. Land Economics, 85(2), 238–251.

    Article  Google Scholar 

  37. 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.

    Article  Google Scholar 

  38. 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.

Download references

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

Authors

Corresponding author

Correspondence to Pierre-Alain Jayet.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Fig. 9
figure 9

Summary indicators for precipitations derived from the ARPEGE simulations for the A1B SRES scenario of the IPCC [22, 23]

Fig. 10
figure 10

Summary indicators for temperatures derived from the ARPEGE simulations for the A1B SRES scenario of the IPCC [22, 23]

Fig. 11
figure 11

Evolution of water demand in the north “fra1” (on the left) and south (on the right) of France “fra2” for the A1b scenario over period from 2010 to 2100 (averages per decade) by region for the A1B CC scenario. Legend: 121—Île-de-France; 131—Champagne-Ardenne; 132—Picardie; 133—Haute-Normandie; 134—Centre; 135—Basse-Normandie; 136—Bourgogne; 141—Nord-Pas-de-Calais; 151—Lorraine; 152—Alsace; 153—Franche-Comté; 162—Pays de la Loire; 163—Bretagne; 164—Poitou-Charentes; 182—Aquitaine; 183—Midi-Pyrénées; 184—Limousin; 192—Rhône-Alpes; 193—Auvergne; 201—Languedoc-Roussillon; 203—Provence-Alpes-Côte d’Azur; 204—Corse

Fig. 12
figure 12

Impact of changes in water price by increasing level from 1 up to 6, respectively on gross margin (top row, billion euros), demand for irrigation water (middle row, billion cubic meters), and demand for N-mineral fertilizer (bottom row, million metric tons), distributed over the period 2010–2100 for France as a whole and for three climate projections (B1, A1B, and A2). Vertical red lines refer to the benchmark (AROPAj baseline results for 2009)

Fig. 13
figure 13

Impact of changes in water price by increasing level from 1 up to 6, on wheat activity, respectively with regard to area (million hectares), marketed and on-farm quantity (million tons), and demand for irrigation water (billion cubic meters), from the left column to the right column, distributed over the period 2010–2100 for southern France (top row), northern France (middle row), and the whole of France (bottom row) for the A1B climate projections. Vertical red lines refer to the benchmark (AROPAj baseline results for 2009)

Table 1 Regional impact of water price change on demand for irrigation, for three decades in the A1B CC scenario, 2011–2020, 2051–2060, and 2091–2100, expressed as a % compared with demand with 0-change water prices (on gray lines—absolute value averaged per decade of required irrigation in million cubic meters). Baseline prices are from 2009

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10666-020-09724-8

Keywords

Navigation