Advertisement

Water Resources Management

, Volume 31, Issue 14, pp 4617–4631 | Cite as

Small and Mid-Sized Farmer Irrigation Adoption in the Context of Public Provision of Hydric Infrastructure in Latin America and Caribbean

  • Gonzalo Villa-CoxEmail author
  • Paul Herrera
  • Ramón Villa-Cox
  • Elvia Merino-Gaibor
Article
  • 173 Downloads

Abstract

According to 2013 statistics from the Economic Commission for Latin America and the Caribbean (EC-LAC 2013), a mere 3.03% of the total agricultural area in ​​the region uses some type of irrigation technology. Thus, there is a high degree of sub-utilization of existing hydric infrastructure given that the supply of irrigation capacity in many countries is greater than the calculated use (see Herrera et al. 2005; IICA 2011; CONAGUA 2014, among others). Nonetheless, there are a limited number of studies that characterize the factors affecting the adoption of irrigation by small and mid-sized farmers in the influence area of irrigation projects. This manuscript presents a novel empirical decision model applicable to irrigation adoption based on exogenous and endogenous factors in the context of LAC countries, which is solved through a binary equation system with latent variables. The main goals are to capture the effect that certain idiosyncratic variables, such as lack of credit access, can have over the decision of irrigation adoption; as well as the costs associated to private goods, financed through credit, which are necessary to access to the benefits of the provision of irrigation as a public good.

Keywords

Irrigation adoption Rural credit and development Recursive bivariate Probit Endogenous linear probability model 

Notes

Acknowledgements

The authors acknowledge the Prefectura de Los Ríos, Ecuador, and the Japan International Cooperation Agency (JICA) for supporting the data collection phase and for allowing its use. Also, collaboration from Juan Carlos Pindo and Maria Fernanda Loor was paramount for the success of this work.

References

  1. Angrist JD, Pischke JS (2008) Mostly harmless econometrics: an empiricist’s companion. Princeton University Press, PrincetonGoogle Scholar
  2. Bartus T (2005) Estimation of marginal effects using margeff. Stata J 5(3):309–329Google Scholar
  3. CAPSERVS (2011) Estudio de Línea Base para el Proyecto de Riego y Drenaje del Río Catarama. Consultora CAPSERVS MediosGoogle Scholar
  4. Chiburis RC, Das J, Lokshin M (2012) A practical comparison of the bivariate probit and linear IV estimators. Econ Lett 117(3):762–766CrossRefGoogle Scholar
  5. Christofides LN, Stengos T, Swidinsky R (1997) On the calculation of marginal effects in the bivariate probit model. Econ Lett 54(3):203–208CrossRefGoogle Scholar
  6. CONAGUA (2014) Estadísticas del Agua en México, Edición 2013. Comisión Nacional del Agua, Secretaría de Medio Ambiente y Recursos Naturales (CONAGUA)Google Scholar
  7. Cremer H, Laffont JJ (2003) Public goods with costly access. J Publ Econ, Elsevier, 87(9):1985–2012Google Scholar
  8. EC-LAC (2013) Anuario Estadístico de América Latina y el Caribe. Website accessed on September 2014 from http://interwp.cepal.org/anuario_estadistico/anuario_2013/default.asp
  9. Escalante R, Catalán H, Basurto S (2013) Determinantes del crédito en el sector agropecuario mexicano: un análisis mediante un modelo Probit. Cuadernos de Desarrollo Rural 10(71):101–124Google Scholar
  10. FAO (2000) Irrigation in Latin America and the Caribbean in Figures (Water Report, 20). Food and Agriculture Organization of the United Nations (FAO). ISBN 92-5-004459-3Google Scholar
  11. FAO (2016) AQUASTAT website. Food and Agriculture Organization of the United Nations (FAO). Website accessed on 2016/05/28 from http://www.fao.org/nr/aquastat/
  12. Fernandez-Cornejo J, McBride WD (2002) Adoption of bioengineered crops. ERS Agricultural Economic Report No. AER810Google Scholar
  13. Green G, Sunding D, Zilberman D, Parker D (1996) Explaining irrigation technology choices: a microparameter approach. Am J Agric Econ 78(4):1064–1072CrossRefGoogle Scholar
  14. Hausman JA (1978) Specification tests in econometrics. Econometrica 46:1251–1271CrossRefGoogle Scholar
  15. Heckman JJ (1978) Dummy endogenous variables in a simultaneous equation system. Econometrica 46:931–959CrossRefGoogle Scholar
  16. Herrera PA, van Huylenbroeck G, Espinel RL (2005) Institutional economic assessment of irrigated agriculture: the case of the peninsula of Santa Elena. ISBN 978-9059-89-072-5Google Scholar
  17. Herrera PA, van Huylenbroeck G, Espinel RL (2006) Asymmetric information on the provision of irrigation through a public infrastructure. Water Resour Manag 20(3):431–447CrossRefGoogle Scholar
  18. Huber PJ (1967) The behavior of maximum likelihood estimates under nonstandard conditions. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. Berkeley, University of California Press, vol 1, pp 221–233Google Scholar
  19. IICA (2011) Boletín informativo mensual AgroAcontecer número 15. Instituto Interamericano de Cooperación para la Agricultura (IICA)Google Scholar
  20. Leiton Soubannier JS (1985) Riego y Drenaje, Año de Edición: 1985. ISBN 978-9977-64-190-4Google Scholar
  21. MAGAP (2011) Plan nacional de riego y drenaje. Ministerio de Agricultura, Ganadería, Acuacultura y Pesca - Subsecretaría de Riego y Drenaje (MAGAP)Google Scholar
  22. Murphy A (2007) Score tests of normality in bivariate probit models. Econ Lett 95(3):374–379CrossRefGoogle Scholar
  23. Negri DH, Brooks DH (1990) Determinants of irrigation technology choice. West J Agric Econ:213–223Google Scholar
  24. Norton RD (2004) Política de desarrollo agrícola. Conceptos y principios. Food and agriculture Organization of the United Nations (FAO). Website accessed on July 2016 from http://agris.fao.org/agris-search/search.do?recordID=XF2015041479
  25. Oluwasola O, Alimi T (2008) Determinants of agricultural credit demand and supply among small-scale farmers in Nigeria. Outlook on Agriculture 37(3):185–193CrossRefGoogle Scholar
  26. White H (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48:817–830CrossRefGoogle Scholar
  27. Wooldridge JM (2010) Econometric analysis of cross section and panel data. MIT press, Cambridge, pp 453–461Google Scholar
  28. Wu D-M (1974) Alternative tests of independence between stochastic regressors and disturbances: finite sample results. Econometrica 42:529–546CrossRefGoogle Scholar
  29. Zeller M, Diagne A, Mataya C (1998) Market access by smallholder farmers in Malawi: implications for technology adoption, agricultural productivity and crop income. Agric Econ 19(1):219–229CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Gonzalo Villa-Cox
    • 1
    • 2
    Email author
  • Paul Herrera
    • 3
  • Ramón Villa-Cox
    • 4
    • 5
  • Elvia Merino-Gaibor
    • 1
  1. 1.Escuela Superior Politécnica del Litoral (ESPOL)Facultad de Ciencias Sociales y HumanísticasGuayaquilEcuador
  2. 2.Department of Agricultural Economics, Faculty of Bioscience EngineeringGhent UniversityGhentBelgium
  3. 3.ESPOL, Centro de Investigaciones RuralesGuayaquilEcuador
  4. 4.School of Computer Science, Institute for Software ResearchCarnegie Mellon UniversityPittsburghUSA
  5. 5.ESPOL, Centro de Investigaciones EconómicasGuayaquilEcuador

Personalised recommendations