Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-23T21:59:38.510Z Has data issue: false hasContentIssue false
  • English
  • Français

Economic impacts of regional water scarcity in the São Francisco River Basin, Brazil: an application of a linked hydro-economic model

Published online by Cambridge University Press:  08 November 2011

Marcelo de O. Torres ,
Marco Maneta ,
Richard Howitt ,
Stephen A. Vosti ,
Wesley W. Wallender ,
Luís H. Bassoi  and
Lineu N. Rodrigues
Marcelo de O. Torres
Affiliation:
Department of Economics, University of Brasília, Secretaria da Coordenação de Pós-Graduação em Economia, Campus Darcy Ribeiro, Caixa Postal 4302, 70910-900, Brasília-DF, Brazil. Tel. 55-61-8469-0145. Email: [email protected]
Marco Maneta
Affiliation:
Geosciences Department, University of Montana, USA. Email: [email protected]
Richard Howitt
Affiliation:
Department of Agricultural and Resource Economics, University of California, Davis, USA. Email: [email protected]
Stephen A. Vosti
Affiliation:
Department of Agricultural and Resource Economics, University of California, Davis, USA. Email: [email protected]
Wesley W. Wallender
Affiliation:
Department of Land, Air and Water Resources, University of California, Davis, USA. Email: [email protected]
Luís H. Bassoi
Affiliation:
Embrapa, Semi-Arid Tropics Research Station, Petrolina, Brazil. Email: [email protected]
Lineu N. Rodrigues
Affiliation:
Embrapa, Savannah Research Station, Brasilia, Brazil. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents a linked hydro-economic model and uses it to examine the regional effects of water use regulations and product price changes on the agriculture of the São Francisco River Basin, Brazil. The effects of weather on surface water availability are explicitly addressed using the hydrological model MIKE-Basin. Farmers’ adjustments to changes in precipitation, surface water availability, and other factors are quantified using an economic model based on non-linear programming techniques. The models are externally linked. Results show that regional impacts, at the sub-basin level, vary depending on the location of each sub-basin relative to river flows. The effects of water use regulations and of exogenous price shocks on agriculture depend on weather, location, product mix and production technology. Implications of these results for policies designed to manage agriculture and water use are discussed.

Type
Research Article
Information
Environment and Development Economics , Volume 17 , Issue 2 , April 2012 , pp. 227 - 248
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. (1998), ‘Crop evapotranspiration, guidelines for computing crop water requirements’, FAO Irrigation and Drainage Paper No. 56, Food and Agriculture Organization of the United Nations, Rome, 300 pp.Google Scholar
ANA/GEF/PNUMA/OEA (2004), ‘Projeto de gerenciamento integrado das 1qatividades desenvolvidas em terra na bacia do São Francisco’, Subprojeto 4.5C – Plano decenal de recursos hidricos da bacia hidrografica do Rio San Francisco – PBHSF (2004–2013). 12, Superintendência de Conservação de Água e Solo, Brasília [in Portuguese].Google Scholar
Arfini, F. and Paris, Q. (1995), ‘A positive mathematical programming model for regional analysis of agricultural policies’, in Sotte, E. (ed.), The Regional Dimension in Agricultural Economics and Policies, Proceedings of the 40th Seminar, June 26–28, Ancona: EAAE, pp. 17–35.Google Scholar
Bontemps, C. and Couture, S. (2002), ‘Irrigation water demand for the decision maker’, Environment and Development Economics 7(4): 643–657.CrossRefGoogle Scholar
Braga, B.P.F. and Lotufo, J.G. (2008), ‘Integrated river basin plan in practice: the São Francisco River Basin’, Water Resources Development 24(1): 37–60.CrossRefGoogle Scholar
Cai, X. and Wang, D. (2006), ‘Calibrating holistic water resources – economic models’, Journal of Water Resources Planning and Management 132(6): 414–423.CrossRefGoogle Scholar
Cai, X., McKinney, D.C., and Lasdon, L.S. (2003), ‘Integrated hydrologic-agronomic-economic model for river basin management’, Journal of Water Resources Planning and Management 129(1): 4–17.CrossRefGoogle Scholar
Cai, X., Ringler, C., and You, J.Y. (2008), ‘Substitution between water and other agricultural inputs: implications for water conservation in a river basin context’, Ecological Economics 66(1): 38–50.CrossRefGoogle Scholar
Chatterjee, B., Howitt, R.E., and Sexton, R.J. (1998), ‘The optimal joint provision of water for irrigation and hydropower’, Journal of Environmental Economics and Management 36(3): 295–313.CrossRefGoogle Scholar
Danish Hydraulic Institute (2005), MIKE Basin 2005, User's Guide.Google Scholar
DE/FIH/GRDC and UNESCO/IHP (2001), Annotations for Monthly Discharge Data for World Rivers (excluding former Soviet Union), Boulder, CO: CISL Data Support Section, National Center for Atmospheric Research, [Available at] http://dss.ucar.edu/datasets/ds552.1/.Google Scholar
Draper, A.J., Jenkins, M.W., Kirby, K.W., Lund, J.R., and Howitt, R.E. (2003), ‘Economic-engineering optimization for California Water Management’, Journal of Water Resources Planning and Management May/June, 155–164.CrossRefGoogle Scholar
FAO (2000), Aquastat, Information System on Water and Agriculture, Country Profile: Brazil, Food and Agriculture Organization, [Available at] http://www.fao.org/nr/water/aquastat/countries/brazil/index.stm.Google Scholar
Guan, D. and Hubacek, K. (2007), ‘A new and integrated hydro-economic accounting and analytical framework for water resources: a case study for North China’, Journal of Environmental Management; doi:10.1016/j.jenvman.2007.07.010.Google ScholarPubMed
Heckelei, T. and Britz, W. (2000), ‘Positive mathematical programming with multiple data points: a cross-sectional estimation procedure’, Cahiers d'Economie et Sociologie Rurales 57: 28–50.CrossRefGoogle Scholar
Helming, J.F.M., Peeters, L., and Veendendall, P.J.J. (2001), ‘Assessing the consequences of environmental policy scenarios in Flemish agriculture’, in Heckelei, T., Witzke, H.P. and Henrichsmeyer, W. (eds), Proceedings of the 65th EAAE Seminar: Agricultural Sector Modelling and Policy Information Systems, Bonn University: Vauk Verlag Kiel, pp. 237–245.Google Scholar
House, R.M. (1987), ‘USMP regional agricultural model’, National Economics Division Report No. ERS 30, Washington, DC: USDA.Google Scholar
Howitt, R.E. (1995), ‘A calibration method for agricultural economic production models’, Journal of Agricultural Economics 46: 147–159.CrossRefGoogle Scholar
Howitt, R.E. and Gardner, D.B. (1986), ‘Cropping production and resource interrelationships among California crops in response to the 1985 Food Security Act’, in Impacts of Farm Policy and Technical Change on US and Californian Agriculture, Davis: Issues Center, pp. 271–290.Google Scholar
IBGE (Instituto Brasileiro de Geografia e Estatística) (1998), Agricultural Census 1995/96, Rio de Janeiro: Fundação Instituto Brasileiro de Geografia e Estatística.Google Scholar
IBGE (Instituto Brasileiro de Geografia e Estatística) (2000–2009), Produção Agrícola Municipal, [Available at] http://www.sidra.ibge.gov.br/bda/acervo/acervo2.asp?e=v&p=PA&z=t&o=11.Google Scholar
Kasnakoglu, H. and Bauer, S. (1988), ‘Concept and application of an agricultural sector model for policy analysis in Turkey’, in Bauer, S. and Henrichsmeyer, W. (eds), Agricultural Sector Modelling, Kiel: Wissenschaftsverlag Vauk.Google Scholar
Lance, H.L. and Miller, D. (1998), ‘Estimation of multi-output production functions with incomplete data: a generalized maximum entropy approach’, European Review of Agricultural Economics 25:188–209.CrossRefGoogle Scholar
Maneta, M., Torres, M. de O., Wallender, W., Howitt, R., Vosti, S., Rodrigues, L., and Bassoi, L. (2009a), ‘A spatially distributed hydro-economic model to assess the effects of drought on land use, farm profits, and agricultural employment’, Water Resources Research 45, W11412; doi:10.1029/2008WR007534.CrossRefGoogle Scholar
Maneta, M., Torres, M. de O., Wallender, W., Vosti, S., Kirby, M., Rodrigues, L., and Bassoi, L. (2009b), ‘Water demand and flows in the São Francisco River Basin (Brazil) with increased irrigation’, Agricultural Water Management 96: 1191–1200.CrossRefGoogle Scholar
Marques, G.F., Lund, J.R., Leu, M.R., and Jenkins, M.W. (2006), ‘Economically-driven simulation of regional water systems: Friant-Kern, California’, Journal of Water Resources Planning and Management 132(6): 468–479.CrossRefGoogle Scholar
Mitchell, T.D. and Jones, P.D. (2005), ‘An improved method for constructing a database of monthly climate observations and associated high-resolution grids’, International Journal of Climatology 25: 693–712.CrossRefGoogle Scholar
Paris, Q. and Howitt, R.E. (1998), ‘An analysis of ill-posed production problems using maximum entropy’, American Journal of Agricultural Economics 80: 124–138.CrossRefGoogle Scholar
Petsakos, A. and Rozakis, S. (2009), ‘Critical review and state-of-the-art of PMP models: an application to Greek arable agriculture’, in Rezitis, A. (ed.) Research Topics in Agricultural and Applied Economics Volume 1 (e-book), Bentham Science Publishers, pp. 36–61.Google Scholar
Preckel, P.V., Harrington, D., and Dubman, R. (2002), ‘Primal/dual Positive Math Programming: illustrated through an evaluation of the impacts of market resistance to genetically modified grains’, American Journal of Agricultural Economics 84(3): 679–690.CrossRefGoogle Scholar
Ringler, C., Huy, N.V., and Msangi, S. (2006), ‘Water allocation policy modeling for the Dong Nai River Basin: an integrated perspective’, Journal of the American Water Resources Association 42(6): 1465–1482.CrossRefGoogle Scholar
Röhm, O. and Dabbert, S. (2003), ‘Integrating agri-environmental programs into regional production models: an extension of Positive Mathematical Programming, American Journal of Agricultural Economics 85(1): 254–265.CrossRefGoogle Scholar
Rosegrant, M.W., Ringler, C., McKinney, D.C., Cai, X., Keller, A., and Donoso, G. (2000), ‘Integrated economic-hydrologic water modeling at the basin scale: the Maipo river basin’, Agricultural Economics 24(1): 33–46.Google Scholar
Timmer, C.P. (1988), ‘The agricultural transformation’, in Chenery, H. and Srinivasan, T.N. (eds), Handbook of Development Economics, Vol. I, Amsterdam: Elsevier Science Publishers.Google Scholar
Torres, M. de O., Vosti, S.A., Maneta, M.P., Wallender, W.W., Rodrigues, L.N., Bassoi, L.H., and Young, J.A. (2011), ‘Spatial patterns of rural poverty: an exploratory analysis in the São Francisco River Basin, Brazil’, Nova Economia 21: 45–66.CrossRefGoogle Scholar
Supplementary material: PDF

Torres Supplementary Appendices

Torres Supplementary Appendices

Download Torres Supplementary Appendices(PDF)
PDF 377.5 KB