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Integrated assessment of four strategies for solving water imbalance in an agricultural landscape

  • Sandrine Allain
  • Gregory Obiang Ndong
  • Romain Lardy
  • Delphine Leenhardt
Research Article

Abstract

Water imbalances are an environmental, social, and economic problem in many agricultural watersheds, including those in temperate climates. Structural changes are recommended because crisis management, through water restrictions, is not sustainable. However, the content of these changes is debated, especially because their impacts concern different sectors and stakeholders and are uncertain. MAELIA is an integrated assessment and modeling platform, which combines a multi-agent model with a geographic information system; it represents fine-scale interactions among water, water management, and agricultural systems, accounting for daily irrigation decisions on each field and effects of the corresponding water withdrawals on water flows. In this article, for the first time, we investigated the effectiveness of some of the most popular strategies aimed at solving water imbalances considering environmental, water management, and agricultural indicators calculated with MAELIA. The alternatives we assessed were (i) reducing the irrigated area, (ii) assisting irrigation with decision-support tools, (iii) implementing crop rotations, and (iv) merging water storage into large reservoirs. Simulations were run for the 2001–2013 period on a case-study area, the downstream Aveyron watershed. We show that, in this area, the decision-support tool and crop-rotation alternatives drastically decreased irrigation withdrawals and required fewer restrictions and flow-support releases. However, those two alternatives had different impacts on the environment and farming systems: decision-support tools cost almost nothing for farming systems and improved environmental indicators slightly, while crop rotations had greater potential for long-term environmental preservation but degraded local and farm economies in the current context. The uniqueness of this study comes from using a fine-scale mechanistic model to assess, in an integrated way, the impacts of politically debated water management strategies that were previously only assessed in terms of potential withdrawal reduction.

Keywords

Integrated assessment and modeling Quantitative water management Irrigated agriculture MAELIA platform 

Notes

Acknowledgements

This work was part of a PhD research and research internship funded by the French Ministry of Higher Education and Research, the French National Agency for Research (project ANR TATA-BOX) and the French Ministry of Agriculture (project CASDAR SIMULTEAU and Joint Technological Unit EAU). We warmly thank Ulrich Eza and Clément Murgue for their support in the simulation process, Michelle and Michael Corson for proofreading this manuscript, and the anonymous reviewers who raised useful comments and questions to improve the quality of this article.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Adour-Garonne Water Agency (2014) Garonne 2050 : Etude prospective sur les besoins et les ressources en eau, à l’échelle du bassin de la Garonne. Agence de l’Eau Adour-Garonne, ToulouseGoogle Scholar
  2. Adour-Garonne Water Agency (2017) Etude pour le renforcement des actions d’économies d’eau en irrigation dans le bassin Adour-Garonne - Tâches 3 et 4. Agence de l’Eau Adour-Garonne, ToulouseGoogle Scholar
  3. Amigues J-P, Debaeke P, Itier B, et al (2006) Adapter l’agriculture à un risque accru de manque d’eau. Expertise scientifique collective, synthèse du rapport, INRA. FranceGoogle Scholar
  4. Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part I: model development. JAWRA J Am Water Resour Assoc 34:73–89CrossRefGoogle Scholar
  5. Bazilian M, Rogner H, Howells M, Hermann S, Arent D, Gielen D, Steduto P, Mueller A, Komor P, Tol RSJ, Yumkella KK (2011) Considering the energy, water and food nexus: towards an integrated modelling approach. Energy Policy 39:7896–7906.  https://doi.org/10.1016/j.enpol.2011.09.039 CrossRefGoogle Scholar
  6. Bergez JE, Leenhardt D, Colomb B, Dury J, Carpani M, Casagrande M, Charron MH, Guillaume S, Therond O, Willaume M (2012) Computer-model tools for a better agricultural water management: tackling managers’ issues at different scales—a contribution from systemic agronomists. Comput Electron Agric 86:89–99.  https://doi.org/10.1016/j.compag.2012.04.005 CrossRefGoogle Scholar
  7. Bizikova L, Roy D, Swanson D et al (2013) The water-energy-food security nexus: towards a practical planning and decision-support framework for landscape investment and risk management. International Institute for Sustainable Development Winnipeg, ManitobaGoogle Scholar
  8. Carluer N, Babut M, Belliard J, Bernez I, Burger-Leenhardt D, Dorioz JM, Douez O, Dufour S, Grimaldi C, Habets F, Le Bissonnais Y, Molénat J, Rollet AJ, Rosset V, Sauvage S, Usseglio-Polatera P, Leblanc B (2016) Joint collective assessment: cumulative impact of reservoirs on the aquatic environment. Summary Report 86 pp + annexesGoogle Scholar
  9. Constantin J, Willaume M, Murgue C, Lacroix B, Therond O (2015) The soil-crop models STICS and AqYield predict yield and soil water content for irrigated crops equally well with limited data. Agric For Meteorol 206:55–68.  https://doi.org/10.1016/j.agrformet.2015.02.011 CrossRefGoogle Scholar
  10. Davies WJ, Zhang J, Yang J, Dodd IC (2011) Novel crop science to improve yield and resource use efficiency in water-limited agriculture. J Agric Sci 149:123–131.  https://doi.org/10.1017/S0021859610001115 CrossRefGoogle Scholar
  11. Debaeke P, Aboudrare A (2004) Adaptation of crop management to water-limited environments. Eur J Agron 21:433–446.  https://doi.org/10.1016/j.eja.2004.07.006 CrossRefGoogle Scholar
  12. DeFries R, Eshleman KN (2004) Land-use change and hydrologic processes: a major focus for the future. Hydrol Process 18:2183–2186.  https://doi.org/10.1002/hyp.5584 CrossRefGoogle Scholar
  13. Duru M, Therond O, Martin G, Martin-Clouaire R, Magne MA, Justes E, Journet EP, Aubertot JN, Savary S, Bergez JE, Sarthou JP (2015) How to implement biodiversity-based agriculture to enhance ecosystem services: a review. Agron Sustain Dev 35:1259–1281.  https://doi.org/10.1007/s13593-015-0306-1 CrossRefGoogle Scholar
  14. Erdlenbruch K, Loubier S, Montginoul M et al (2013) La gestion du manque d’eau structurel et des sécheresses en France. Sci Eaux Territ Numéro 11:78–85Google Scholar
  15. Figureau A-G, Montginoul M, Rinaudo J-D (2015) Policy instruments for decentralized management of agricultural groundwater abstraction: a participatory evaluation. Ecol Econ 119:147–157.  https://doi.org/10.1016/j.ecolecon.2015.08.011 CrossRefGoogle Scholar
  16. Gaudou B, Sibertin-Blanc C, Therond O et al (2013) The MAELIA multi-agent platform for integrated analysis of interactions between agricultural land-use and low-water management strategies. In: International workshop on multi-agent systems and agent-based simulation. Springer, Berlin, pp 85–100Google Scholar
  17. Gordon LJ, Finlayson CM, Falkenmark M (2010) Managing water in agriculture for food production and other ecosystem services. Agric Water Manag 97:512–519.  https://doi.org/10.1016/j.agwat.2009.03.017 CrossRefGoogle Scholar
  18. Guttieri MJ, Ahmad R, Stark JC, Souza E (2000) End-use quality of six hard red spring wheat cultivars at different irrigation levels Univ, of Idaho Agric. Exp. Stn.. manuscript no. 99705. Crop Sci 40:631–635.  https://doi.org/10.2135/cropsci2000.403631x CrossRefGoogle Scholar
  19. Hobbs PR, Sayre K, Gupta R (2008) The role of conservation agriculture in sustainable agriculture. Philos Trans R Soc Lond Ser B Biol Sci 363:543–555CrossRefGoogle Scholar
  20. Lacroix B, Lardy R, Murgue C, et al (2018) SIMULTEAU : un outil pour la gestion collective de la ressource en eau par les Organismes Uniques. Paris, p 7pGoogle Scholar
  21. Lardy R, Mazzega P, Sibertin-Blanc C, et al (2014) Calibration of simulation platforms including highly interweaved processes: the MAELIA multi-agent platform. In: Proceedings of the 7th International Congress on Environmental Modelling and Software, June. pp 15–19Google Scholar
  22. Lardy R, Truche C, Therond O (2016) Modelling small agricultural dams dynamics into the MAELIA multi-agent platformGoogle Scholar
  23. Molden D (2007) Water for food, water for life: a comprehensive assessment of water management in agriculture. Earthscan, RoutledgeGoogle Scholar
  24. Murgue C, Therond O, Leenhardt D (2015) Toward integrated water and agricultural land management: participatory design of agricultural landscapes. Land Use Policy 45:52–63.  https://doi.org/10.1016/j.landusepol.2015.01.011 CrossRefGoogle Scholar
  25. Murgue C, Therond O, Leenhardt D (2016) Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape. Land Use Policy 54:339–354.  https://doi.org/10.1016/j.landusepol.2016.02.020 CrossRefGoogle Scholar
  26. Olesen JE, Bindi M (2002) Consequences of climate change for European agricultural productivity, land use and policy. Eur J Agron 16:239–262.  https://doi.org/10.1016/S1161-0301(02)00004-7 CrossRefGoogle Scholar
  27. Pahl-Wostl C, Sendzimir J, Jeffrey P, Aerts J, Berkamp G, Cross K (2007) Managing change toward adaptive water management through social learning. Ecol Soc 12:. doi:  https://doi.org/10.5751/ES-02147-120230
  28. Pimentel D, Houser J, Preiss E, White O, Fang H, Mesnick L, Barsky T, Tariche S, Schreck J, Alpert S (1997) Water resources: agriculture, the environment, and society. BioScience 47:97–106.  https://doi.org/10.2307/1313020 CrossRefGoogle Scholar
  29. Rey D, Holman IP, Daccache A, Morris J, Weatherhead EK, Knox JW (2016) Modelling and mapping the economic value of supplemental irrigation in a humid climate. Agric Water Manag 173:13–22.  https://doi.org/10.1016/j.agwat.2016.04.017 CrossRefGoogle Scholar
  30. Robert M, Bergez J-E, Thomas A (2018) A stochastic dynamic programming approach to analyze adaptation to climate change—application to groundwater irrigation in India. Eur J Oper Res 265:1033–1045.  https://doi.org/10.1016/j.ejor.2017.08.029 CrossRefGoogle Scholar
  31. Thierfelder C, Wall PC (2010) Rotation in conservation agriculture systems of Zambia: effects on soil quality and water relations. Exp Agric 46:309–325.  https://doi.org/10.1017/S001447971000030X CrossRefGoogle Scholar
  32. Trout TJ (1999) Environmental effects of irrigated agriculture. In: III international symposium on irrigation of horticultural crops 537. pp 605–610Google Scholar
  33. Turral H, Burke JJ, Faurès J-M (2011) Climate change, water and food security. Food and Agriculture Organization of the United Nations Rome, ItalyGoogle Scholar
  34. Ullrich A, Volk M (2009) Application of the soil and water assessment tool (SWAT) to predict the impact of alternative management practices on water quality and quantity. Agric Water Manag 96:1207–1217.  https://doi.org/10.1016/j.agwat.2009.03.010 CrossRefGoogle Scholar
  35. Vidal J-P, Martin E, Franchistéguy L, Baillon M, Soubeyroux JM (2010) A 50-year high-resolution atmospheric reanalysis over France with the Safran system. Int J Climatol 30:1627–1644.  https://doi.org/10.1002/joc.2003 CrossRefGoogle Scholar
  36. Ward FA, Pulido-Velazquez M (2008) Water conservation in irrigation can increase water use. Proc Natl Acad Sci 105:18215–18220.  https://doi.org/10.1073/pnas.0805554105 CrossRefPubMedGoogle Scholar

Copyright information

© INRA and Springer-Verlag France SAS, part of Springer Nature 2018

Authors and Affiliations

  1. 1.AGIR, Toulouse University, INRA, INPT, INP-EI PURPANCastanet Tolosan CedexFrance

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