Advertisement

Regional Environmental Change

, Volume 18, Issue 7, pp 1943–1955 | Cite as

The impact of global change on economic values of water for Public Irrigation Schemes at the São Francisco River Basin in Brazil

  • Márcia Maria Guedes Alcoforado de MoraesEmail author
  • Anne Biewald
  • Ana Cristina Guimarães Carneiro
  • Gerald Norbert Souza da Silva
  • Alexander Popp
  • Hermann Lotze-Campen
Original Article

Abstract

Economic values of water for the main Public Irrigation Schemes in the sub-middle region of the São Francisco River Basin, in northeastern Brazil, are determined in this study using an integration of a global agro-economic land and water use (MAgPIE) with a local economic model (Positive Mathematical Programming). As in the latter, the water values depend on the crops grown, and as Brazilian agriculture is strongly influenced by the global market, we used a regionalized version of the global model adapted to the region in order to simulate the crop land use, which is in turn determined by changes in global demand, trade barriers, and climate. The allocation of sugarcane and fruit crops projected with climate change by the global model, showed an impact on the average yields and on the water costs in the main schemes resulting in changes in the water values locally. The economic values for all schemes in the baseline year were higher than the water prices established for agricultural use in the basin. In the future, these water values will be higher in all the schemes. The highest water values currently and in the future were identified in municipalities with a significant proportion of area growing irrigated sugarcane. Being aware of current water values of each user in a baseline year and in a projected future under global climate and socioeconomic changes, decision makers should improve water allocation policies at local scale, in order to avoid conflicts and unsustainable development in the future.

Keywords

Economic value of water Water pricing São Francisco River Basin Semi-arid region Positive mathematical programming Global model 

Notes

Funding information

The first and the third author are sponsored by CNPQ—Conselho Nacional de Desenvolvimento Científico e Tecnológico—and CT-HIDRO, Brazilian government agency and fund. The PhD student is sponsored by CNPQ and all the authors are participants of the Innovate project, which was funded by the German Federal Ministry of Education and Research (BMBF) and CNPq/CAPES—Coordenação de Aperfeiçoamento do Pessoal de Ensino Superior (Brazil).

Supplementary material

10113_2018_1291_MOESM1_ESM.pdf (420 kb)
ESM 1 (PDF 419 kb).
10113_2018_1291_MOESM2_ESM.pdf (761 kb)
ESM 2 (PDF 761 kb).

References

  1. Alcoforado de Moraes M, Ringler C, Cai X (2011) Policies and instruments affecting water use for bioenergy production. Biofuels Bioprod Biorefin 5(4):431–444.  https://doi.org/10.1061/41173(414)345 CrossRefGoogle Scholar
  2. Alcoforado de Moraes MMG, Carneiro ACG, Souza da Silva GN, Marques GF (2015) Technical coefficients of direct use of water in monetary terms for agriculture and urban water use. Water Sci Technol Water Supply 15(5):1123–1132.  https://doi.org/10.2166/ws.2015.075 CrossRefGoogle Scholar
  3. Alcoforado de Moraes MMG, Ribeiro MR, Watkins DW, Figueiredo LN, Viana JN, da Silva GS, Carneiro AG (2016) Integrated economic models to support decisions on water pricing in biofuel production river basins: three case studies from Brazil. Biofuels Bioprod Biorefin 10(3):255–269.  https://doi.org/10.1002/bbb.1581 CrossRefGoogle Scholar
  4. Amaral FS, Coelho MR, Teixeira WG, Caldeirano SB, Gregoris G (2012) Avaliação do sistema radicular de cana-de-açúcar cultivada em vertissolos no município de Juazeiro - BA. Emprapa Solos, Rio de Janeiro, Brasil. ISSN 1517–2627Google Scholar
  5. ANA (2009) Cojuntura dos Recursos Hídricos no Brasil / Agência Nacional de Águas. Brasilia-DF, Brazil.Google Scholar
  6. ANA (2011) Resolution 461 - 27th of June of 2011. Brasilia-DF, Brazil.  http://arquivos.ana.gov.br/resolucoes/2011/461-2011.pdf
  7. ANA (2012) Water resources planning in Brazil. Special Issue – Brasília-DF, Brazil.Google Scholar
  8. Assad ED, Pinto HS, Zullo J Jr (2008) Aquecimento Global e a Nova Geografia da Produção Agrícola no Brasil. EMBRAPA, CampinasGoogle Scholar
  9. Beck F (2013) Modelling land use change in the catchment área of the São Francisco River in Brazil. Humboldt University of Berlin, BerlinGoogle Scholar
  10. Biewald A, Rolinski S, Lotze-Campen H, Schmitz C, Dietrich JP (2014) Valuing the impact of trade on local blue water. Ecol Econ 101:43–53.  https://doi.org/10.1016/j.ecolecon.2014.02.003 CrossRefGoogle Scholar
  11. Biewald A, Lehtonen H, Lotze-Campen H, Bodirsky B, Dietrich JP, Humpenöder F, Popp A (2015) Cereals and oilseed production in Finland under different socioeconomic scenarios until 2050: an analysis with models of two different scales. In: International conference of agricultural economists, MilanGoogle Scholar
  12. Bodirsky BL, Rolinski S, Biewald A, Weindl I, Popp A, Lotze-Campen H (2015) Global food demand scenarios for the 21st century. PLoS One 10(11):e0139201.  https://doi.org/10.1371/journal.pone.0139201 CrossRefGoogle Scholar
  13. Bondeau A, Smith PC, Zaehle S, Schaphoff S, Lucht W, Cramer W, Gerten D, Lotze-Campen H, Müller C, Reichstein M, Smith B (2007) Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob Chang Biol 13(3):679–709.  https://doi.org/10.1111/j.1365-2486.2006.01305.x CrossRefGoogle Scholar
  14. Cai X, Ringler C, You JY (2008) Substitution between water and other agricultural inputs: implications for water conservation in a river basin context. Ecol Econ 66(1):38–50.  https://doi.org/10.1016/j.ecolecon.2008.02.010 CrossRefGoogle Scholar
  15. Carneiro AG (2014) Uma Análise Econômica de Mudanças no uso da Terra e Produção de matéria-prima de biocombustíveis no Brasil: O Papel da disponibilidade de água para o setor de Irrigação. Thesis - UFPE, Recife, Brazil.Google Scholar
  16. Carneiro AG, Nunez HM, Onal H, Alcoforado de Moraes M (2014) An economic analysis of land use changes and sugarcane production in Brazil: the role of irrigation water. In: World Congress of Environmental and Resource Economists, Istanbul.  https://doi.org/10.13140/2.1.1318.3362
  17. CBHSF (2004) Plano Decenal de Recursos Hídricos da Bacia Hidrográfica do Rio São Francisco - Comitê da Bacia Hidrográfica do Rio São Francisco. Salvador, Brazil.Google Scholar
  18. CODEVASF (2006) Relatório de Gestão - Companhia de Desenvolvimento do Vale do. São Francisco o e do Parnaíba, Brasilia-DFGoogle Scholar
  19. Dietrich JP, Popp A, Lotze-Campen H (2013) Reducing the loss of information and gaining accuracy with clustering methods in a global land-use model. Ecol Model 263:233–243.  https://doi.org/10.1016/j.ecolmodel.2013.05.009 CrossRefGoogle Scholar
  20. EMPRAPA (2008) Soils of the submiddle of the São Francisco valley (Solos do submédio do vale do São Francisco). Embrapa Semi-árido, PetrolinaGoogle Scholar
  21. EPA (2010) Renewable fuel standard program (RFS2) regulatory impact analysis. U.S. Environmental Protecion Agency, Washington, DCGoogle Scholar
  22. FAO (2016) Food and Agriculture Organization of the United Nations (FAO). Accessed 24/06 2016Google Scholar
  23. Figueiredo LEN (2015) A demanda da água para irrigação: uma aplicação da programação matemática positiva para os perímetros irrigados do submédio do Rio São Francisco. Dissertation, Federal University of Pernambuco, Recife, Pernambuco, BrasilGoogle Scholar
  24. FUNARBE (2011) Desenvolvimento da Matriz de Coeficientes técnicos para recursos hídricos no Brasil. MMA, BrasiliaGoogle Scholar
  25. Graziano da Silva J (1989) A Irrigação e a problemática fundiária do Nordeste. Instituto de Economia da Unicamp/ PRONI, CampinasGoogle Scholar
  26. Hagel H, Hoffmann C, Doluschitz R (2014) Mathematical programming models to increase land and water use efficiency in semi-arid NE Brazil. Int J Food Syst Dyn 4:173–181.  https://doi.org/10.18461/ijfsd.v5i4.542 CrossRefGoogle Scholar
  27. Harou JJ, Pulido-Velazquez M, Rosenberg DE, Medellin-Azuara J, Lund JR, Howitt RE (2009) Hydro-economic models: Concepts, design, applications, and future prospects. J Hydrol 375:627–643.  https://doi.org/10.1016/j.jhydrol.2009.06.037
  28. House RM (1987) USMP Regional Agricultural Model. National Economics Division Report. USDA, Washington DCGoogle Scholar
  29. Howitt RE (1995) Positive mathematical programming. Am J Agric Econ 77(2):329–342.  https://doi.org/10.2307/1243543 CrossRefGoogle Scholar
  30. Howitt RE, Gardner DB (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, p 271–290Google Scholar
  31. Howitt R, Medellín-Azuara J, MacEwan DL, Lund JR (2012) Calibrating disaggregate economic models of agricultural production and water management. Environ Model Softw 38:244–258.  https://doi.org/10.1016/j.envsoft.2012.06.013 CrossRefGoogle Scholar
  32. IBGE (2006) Census (agriculture) 2006. https://sidra.ibge.gov.br/
  33. Justice C, Townshend JR, Vermote E, Masuoka E, Wolfe R, Saleous N, Roy D, Morisette J (2002) An overview of MODIS land data processing and product status. Remote Sens Environ 83(1-2):3–15.  https://doi.org/10.1016/S0034-4257(02)00084-6 CrossRefGoogle Scholar
  34. Kasnakoglu H, Bauer S (1988) Concept and application of an agricultural sector model for policy analysis in Turkey. In: Bauer S, Henrichsmeyer (eds) Agricultural sector modelling. Proceedings of the 16th Symposium of the EAAE, Wissenschaftsverlag Vauk, Kiel, p 71–84Google Scholar
  35. Kölling K (2014) Analyzing model results of climate and socioeconomic changes on the agricultural production in the catchment area of the Rio São Francisco in North-Eastern Brazil. Humboldt University of Berlin, BerlinGoogle Scholar
  36. Krause M, Lotze-Campen H, Popp A, Dietrich JP, Bonsch M (2013) Conservation of undisturbed natural forests and economic impacts on agriculture. Land Use Policy 30(1):344–354.  https://doi.org/10.1016/j.landusepol.2012.03.020 CrossRefGoogle Scholar
  37. Lima JP, Miranda EA (2000) Fruticultura irrigada: os casos das regiões de Petrolina-Juazeiro e norte de Minas Gerais. Banco do Nordeste do Brasil, FortalezaGoogle Scholar
  38. Lotze-Campen H, Müller C, Bondeau A, Rost S, Popp A, Lucht W (2008) Global food demand, productivity growth, and the scarcity of land and water resources: a spatially explicit mathematical programming approach. Agric Econ 39:325–338.  https://doi.org/10.1111/j.1574-0862.2008.00336.x CrossRefGoogle Scholar
  39. Maneta M, Torres MDO, Wallender W, Vosti R, Howitt R, Rodrigues L, Bassoi L (2009) A spatially distributed hydro-economic model to assess the effects of drought on land use, farm profits, and agricultural employment. Water Resour Res.  https://doi.org/10.1029/2008WR007534
  40. Medellin-Azuara J, Howitt RE, Waller-Barrera C, Mendoza-Espinosa LG, Lund JR, Taylor JE (2009) A calibrated agricultural water demand model for three regions in northern Baja California. Agrociencia 43:83–96Google Scholar
  41. Medellín-Azuara J, Harou J, Howitt RE (2010) Estimating economic value of agricultural water under changing conditions and the effects of spatial aggregation. Science of the Total Environment 408:5639–5648.  https://doi.org/10.1016/j.scitotenv.2009.08.013 CrossRefGoogle Scholar
  42. MIN (2005) Strategic plan for the sustainable development of the semi-arid region (Plano Estratégico de Desenvolvimento Sustentável do Semi-Árido) - Ministerio da Integração Nacional. Cidade Gráfica e Editora LTDA, BrasiliaGoogle Scholar
  43. Nakicenovic N, Lempert RJ, Janetos AC (2014) A framework for the development of new socio-economic scenarios for climate change research: introductory essay: a forthcoming special issue of climatic change. Clim Chang 122(3):351–361.  https://doi.org/10.1007/s10584-013-0982-2 CrossRefGoogle Scholar
  44. Oliveira AC, Souza HR, Vergolino JR, Galvão OA, Almeida J, Melo A (1991) Impactos Econômicos da Irrigação Sobre o Polo Petrolina/Juazeiro. Ed. Universitária, RecifeGoogle Scholar
  45. PAM (2012) Municipal agricultural production (Produção Agrícola Municipal - PAM). IBGE. https://sidra.ibge.gov.br/pesquisa/pam/tabelas
  46. Popp A, Humpenöder F, Weindl I, Bodirsky B, Bonsch M, Lotze-Campen H, Müller C, Biewald A, Rolinski S, Stevanovic M, Dietrich JP (2014) Land use protection for climate change mitigation. Nat Clim Chang 4(12):1095–1098.  https://doi.org/10.1038/nclimate2444 CrossRefGoogle Scholar
  47. Portmann FT, Siebert S, Döll P (2010) MIRCA2000—global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Glob Biogeochem Cycles (1).  https://doi.org/10.1029/2008GB003435
  48. Sampaio E, Sampaio Y (2004) Ensaios sobre a economia da fruticultura irrigada. Banco do Nordeste do Brasil, FortalezaGoogle Scholar
  49. Schmitz C, Biewald A, Lotze-Campen H, Popp A, Dietrich JP, Bodirsky B, Krause M, Weindl I (2012) Trading more food: implications for land use, greenhouse gas emissions, and the food system. Glob Environ Change 22(1):189–209.  https://doi.org/10.1016/j.gloenvcha.2011.09.013 CrossRefGoogle Scholar
  50. Silva FBR, Riché GR, Tonneau JP, Sousa Neto NC, Brito LTL, Coreia RC, Cavalcanti AC, Silva FHBB, Silva AB, Araújo Filho JC (1993) Zoneamento Agroecológico do Nordeste: diagnóstico do quadro natural e agrossocioeconômico. UEP, RecifeGoogle Scholar
  51. Silva GS, Figueiredo LE, Alcoforado de Moraes M (2015) Demand curves for water resources of the main water users in sub-middle São Francisco basin. RBCIAMB 36(36):45–59.  https://doi.org/10.5327/Z2176-947820151004 CrossRefGoogle Scholar
  52. Sohngen B, Tennity C, Hnytka M (2009) Global Forestry Data for the economic modeling of land use. Economic analysis of and use in global climate change policy. Routledge, New YorkGoogle Scholar
  53. Souza da Silva GN, Alcoforado de Moraes MMG (2018 (under review)) Economic water management decisions: trade-offs between conflicting objectives in the Sub-Middle region of the São Francisco watershed. Reg Environ ChangGoogle Scholar
  54. SUDENE (1979) Serviço Nacional de Levantamento e Conservação de Solos. Levantamento exploratório-reconhecimento de solos da margem direita do rio São Francisco, Estado da Bahia. SUDENE, RecifeGoogle Scholar
  55. Torres MO, Maneta M, Howitt R, Vosti SA, Wallender WW, Bassoi LH, Rodrigues LN (2012) Economic impacts of regional water scarcity in the São Francisco River Basin, Brazil: an application of a linked hydro-economic model. Environ Dev Econ 17(02):227–248.  https://doi.org/10.1017/S1355770X11000362 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Márcia Maria Guedes Alcoforado de Moraes
    • 1
    Email author
  • Anne Biewald
    • 2
  • Ana Cristina Guimarães Carneiro
    • 3
  • Gerald Norbert Souza da Silva
    • 4
  • Alexander Popp
    • 2
  • Hermann Lotze-Campen
    • 2
    • 5
  1. 1.Department of Economics and Graduate Program at Federal University of PernambucoRecifeBrazil
  2. 2.Potsdam Institute for Climate Impact Research–PIKPotsdamGermany
  3. 3.Federal University of PernambucoRecifeBrazil
  4. 4.Graduate Program in Civil EngineeringFederal University of PernambucoRecifeBrazil
  5. 5.Humboldt-Universtität zu BerlinBerlinGermany

Personalised recommendations