Environment, Development and Sustainability

, Volume 18, Issue 2, pp 315–346 | Cite as

Modelling livelihoods and household resilience to droughts using Bayesian networks

  • Wendy S. Merritt
  • Brendan Patch
  • V. Ratna Reddy
  • Geoffrey J. Syme
Article
  • 546 Downloads

Abstract

Over the last four decades, the Indian government has been investing heavily in watershed development (WSD) programmes that are intended to improve the livelihoods of rural agrarian communities and maintain or improve natural resource condition. Given the massive investment in WSD in India, and the recent shift from micro-scale programmes (<500 ha) to meso-scale (~5000 ha) clusters, robust methodological frameworks are needed to measure and analyse impacts of interventions across landscapes as well as between and within communities. In this paper, the sustainable livelihoods framework is implemented using Bayesian networks (BNs) to develop models of drought resilience and household livelihoods. Analysis of the natural capital component model provides little evidence that watershed development has influenced household resilience to drought and indicators of natural capital, beyond an increased area of irrigation due to greater access to groundwater. BNs have proved a valuable tool for implementing the sustainable livelihoods framework in a retrospective evaluation of implemented WSD programmes. Many of the challenges of evaluating watershed interventions using BNs are the same as for other analytical approaches. These are reliance on retrospective studies, identification and measurement of relevant indicators and isolating intervention impacts from contemporaneous events. The establishment of core biophysical and socio-economic indicators measured through longitudinal household surveys and monitoring programmes will be critical to the success of BNs as an evaluation tool for meso-scale WSD.

Keywords

Bayesian networks (BN) Watershed development (WSD) Sustainable livelihoods Natural capital Drought resilience 

Notes

Acknowledgments

The Australian Centre for International Agricultural Research (ACIAR) funded the ‘Impacts of meso-scale Watershed Development in Andhra Pradesh (India) and their implications for designing and implementing improved WSD policies and programs’ project (LWR/2006/072) under which this research was undertaken. The authors would like to thank the households who kindly gave up their time to answer the comprehensive surveys, the two anonymous reviewers for their comments on earlier versions of this paper and Clive Hilliker for his assistance in the preparation of the figures in this manuscript.

References

  1. Adato, M., Meizen-Dick, R., Hazell, P., & Haddad, L. (2007). Integrating social and economic analyses to study impacts on livelihoods and poverty: conceptual frameworks and research methods. In M. Adato & R. Meizen-Dick (Eds.) Agricultural research, livelihoods, and poverty: Studies of economic and social impacts in six countries (pp. 22–55).  Baltimore: The International Food Policy Research Institute, The Johns Hopkins University Press.Google Scholar
  2. Adger, N. W. (2006). Vulnerability. Global Environmental Change, 16, 268–281.CrossRefGoogle Scholar
  3. Baker, J. L. (2000). Evaluating the impact of development projects on poverty: a handbook for practitioners. Washington, DC: World Bank Publications.CrossRefGoogle Scholar
  4. Bamberger, M., Rugh, J., Church, M., & Fort, L. (2004). Shoestring evaluation: Designing impact evaluations under budget, time and data constraints. American Journal of Evaluation, 25, 5–37.CrossRefGoogle Scholar
  5. Barron, J., & Noel, S. (2011). Valuing soft components in agricultural water management interventions in meso-scale watersheds: A review and synthesis. Water Alternatives, 4, 145–154.Google Scholar
  6. Batchelor, C. H., Rao, M. S. R. M., & Rao, S. M. (2003). Watershed development: A solution to water shortages in semi-arid India or part of the problem? Land Use and Water Resources Research, 3, 1–10.Google Scholar
  7. Baumann, P. (2000). Sustainable livelihoods and political capital: arguments and evidence from decentralisation and natural resource management in India. London: Overseas Development Institute.Google Scholar
  8. Binder, C. R., Hinkel, J., Bots, P. W. G., & Pahl-Wostl, C. (2013). Comparison of frameworks for analyzing social-ecological systems. Ecology and Society, 18, 26. doi:10.5751/ES-05551-180426.CrossRefGoogle Scholar
  9. Bouma, J., van Soest, D., & Bulte, E. (2007). How sustainable is participatory watershed development in India? Agricultural Economics, 36, 13–22.CrossRefGoogle Scholar
  10. Bromley, J., Jackson, N. A., Clymer, O. J., Giacomello, A. M., & Jensen, F. V. (2005). The use of Hugin® to develop Bayesian networks as an aid to integrated water resource planning. Environmental Modelling and Software, 20, 231–242.CrossRefGoogle Scholar
  11. Cain, J., Batchelor, C., & Waughray, D. (1999). Belief networks: a framework for the participatory development of natural resources management strategies. Environment, Development and Sustainability, 1, 123–133.CrossRefGoogle Scholar
  12. Calder, I., Gosain, A., Rao, M. S. R. M., Batchelor, C., Garratt, J., & Bishop, E. (2008a). Watershed development in India. 2. New approaches for managing externalities and meeting sustainability requirements. Environment, Development and Sustainability, 10, 427–440.CrossRefGoogle Scholar
  13. Calder, I., Gosain, A., Rao, M. S. R. M., Batchelor, C., Snehalatha, M., & Bishop, E. (2008b). Watershed development in India. 1. Biophysical and societal impacts. Environment, Development and Sustainability, 10, 537–557.CrossRefGoogle Scholar
  14. Castelletti, A., & Soncini-Sessa, R. (2007). Bayesian networks and participatory modelling in water resource management. Environmental Modelling and Software, 22, 1075–1088.CrossRefGoogle Scholar
  15. Chan, T., Ross, H., Hoverman, S., & Powell, B. (2010). Participatory development of a Bayesian network model for catchment-based water resource management. Water Resources Research, 46, W07544. doi:10.1029/2009WR008848.CrossRefGoogle Scholar
  16. Chen, S. H., & Pollino, C. A. (2012). Good practice in Bayesian network modelling. Environmental Modelling and Software, 37, 134–145.CrossRefGoogle Scholar
  17. Croke, B., Herron, N., Pavelic, P., Ahmed, S., Reddy, V. R., Ranjan, R., et al. (2012). Impacts of meso-scale watershed development in Andhra Pradesh (India) and their implications for designing and implementing improved WSD policies and programs. Water Practice and Technology,. doi:10.2166/wpt.2012.025.Google Scholar
  18. De Nicola, F., & Giné, X. (2014). How accurate are recall data? Evidence from coastal India. Journal of Development Economics, 106, 52–65.CrossRefGoogle Scholar
  19. Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of Research Statistics Society, 39, 323–356.Google Scholar
  20. Dhanagare, D. N. (1987). Green revolution and social inequalities in rural India. Economic and Political Weekly, 22, AN-137 - AN-144.Google Scholar
  21. EEA. (1999). Environmental indicators: Typology and overview. Copenhagen: European Environment Agency.Google Scholar
  22. Ellis, F. (2000). Livelihoods and diversity in developing countries. London: Oxford University Press.Google Scholar
  23. Ellis, F., & Biggs, S. (2001). Evolving themes in rural development 1950s-2000s. Development Policy Review, 19, 437–448.CrossRefGoogle Scholar
  24. Fan, S., Hazell, P., & Haque, T. (2000). Targeting public investments by agro-ecological zone to achieve growth and poverty alleviation goals in rural India. Food Policy, 25, 411–428.CrossRefGoogle Scholar
  25. FAO. (2006). The new generation of watershed management programmes and projects. Rome: Food and Agriculture Organisation of the United Nations.Google Scholar
  26. Farrington, J., Turton, C., & James, A. J. (Eds.). (1999). Participatory watershed development: Challenges for the twenty-first century. New Delhi: Oxford University Press.Google Scholar
  27. Freeman, H. A., Shiferaw, B., & Swinton, S. M. (2005). In B. Shiferaw, H. A. Freeman, & S. M. Swinton (Eds.), Natural resources management in agriculture: methods for assessing economic and environmental impacts (p. 2004). Wallingford: CABI Publishing.Google Scholar
  28. GoI (2011). Common guidelines for watershed development projects. (59 pp). New. Delhi: Government of India.Google Scholar
  29. Hanjra, M. A., & Qureshi, M. E. (2010). Global water crisis and future food security in an era of climate change. Food Policy, 35, 365–377.CrossRefGoogle Scholar
  30. Henriksen, H. J., Rasmussen, P., Brandt, G., von Bu¨low, D., & Jensen, F. V. (2007). Public participation modelling using Bayesian networks in management of groundwater contamination. Environmental Modelling and Software, 22, 1101–1113.CrossRefGoogle Scholar
  31. Hope, R. A. (2007). Evaluating social impacts of watershed development in India. World Development, 35, 1436–1449.CrossRefGoogle Scholar
  32. Jakeman, A. J., Letcher, R. A., & Norton, J. P. (2006). Ten iterative steps in development and evaluation of environmental models. Environmental Modelling and Software, 21, 602–614.CrossRefGoogle Scholar
  33. Joshi P. K, Jha, A. K, Wani S. P., Sreedevi T. K. & Shaheen, F. A. (2008). Impact of watershed program and conditions for success: a meta-analysis approach. International Crops Research Institute, Global Theme on Agroecosystems. Report no. 46, Andhra Pradesh, India.Google Scholar
  34. Joshi, P. K., Pangare, V., Shiferaw, B., Wani, S. P., Bouma, J., & Scott, C. (2004). Watershed development in India: synthesis of past experiences and needs for future research. Indian Journal of Agricultural Economics, 59, 303–320.Google Scholar
  35. Kelly (Letcher), R. A., Jakeman, A. J., Barreteau, O., Borsuk, M. E., El Sawah, S., Hamilton, S. H., et al. (2013). Selecting among five common modelling approaches for integrated environmental assessment and management. Environmental Modelling and Software, 47, 159–181.CrossRefGoogle Scholar
  36. Kemp-Benedict, E., Bharwani, S., de la Rosa, E., Krittasudthacheewa, C., & Matin, N. (2009). Assessing water-related poverty using the sustainable livelihoods framework. (25 pp). Stockholm: Stockholm Environment Institute.Google Scholar
  37. Kerr, J. (2002). Watershed development, environmental services, and poverty alleviation in India. World Development, 30, 1387–1400.CrossRefGoogle Scholar
  38. Kerr, J. (2007). Watershed management: lessons from common property theory. International Journal of the Commons, 1, 89–109.CrossRefGoogle Scholar
  39. Kerr, J. M., Pangare, G., & Pangare, V. L. (2002). Watershed development in India: An evaluation. Washington, DC: International Food Policy Research Institute.Google Scholar
  40. Kjaerulff, U. B., & Madsen, A. L. (2008). Bayesian networks and influence diagrams: A guide to construction and analysis (Information science and statistics). New York: Springer.CrossRefGoogle Scholar
  41. Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. Cambridge: Massachusetts Institute of Technology.Google Scholar
  42. Korb, K. B., & Nicholson, A. E. (2011). Bayesian artificial intelligence (Computer science and data analysis series). Philadelphia: Taylor and Francis Group.Google Scholar
  43. LaFlamme, M. (2007). Developing a shared model for sustainable Aboriginal livelihoods in natural-cultural resource management. In Paper presented at the MODSIM 2007 international congress on modelling and simulation Christchurch, New Zealand, December 2007.Google Scholar
  44. Lauritzen, S. L., & Spiegelhalter, D. J. (1990). Local computations with probabilities on graphical structures and their application to expert systems. In G. Shafer & J. Pearl (Eds.), Readings in uncertain reasoning (pp. 415–458). Burlington, MA: Morgan Kaufmann.Google Scholar
  45. Marcot, B. G., Steventon, J. D., Sutherland, G. D., & McCann, R. K. (2006). Guidelines for developing and updating Bayesian belief networks for ecological modeling. Canadian Journal of Forest Research, 36, 3063–3074.CrossRefGoogle Scholar
  46. Nedumaran, S., Shiferaw, B., Bantilan, M. C. S., Palanisami, K., & Wani, S. P. (2013). Bioeconomic modeling of farm household decisions for ex-ante impact assessment of integrated watershed development programs in semi-arid India. Environment, Development and Sustainability, 16, 257–286.CrossRefGoogle Scholar
  47. Newton, A. C., Marshall, E., Schreckenberg, K., Golicher, D., te Velde, D. W., Edouard, F., et al. (2006). Use of a Bayesian belief network to predict the impacts of commercializing non-timber forest products on livelihoods. Ecology and Society, 11, art24.Google Scholar
  48. Palanisami, K., & Kumar, S. D. (2009). Impacts of watershed development programmes: Experiences and evidences from Tamil Nadu. Agricultural Economics Research Review, 22, 387–396.Google Scholar
  49. Palanisami, K., Kumar, S. D., & Wani, S. P. (2009). A manual on impact assessment of watersheds. Global theme on agroecosystems. (Vol. Report No. 53, pp. 56). Patancheru 502 324, Andhra Pradesh, India: International Crops Research Institute for Semi-Arid Tropics.Google Scholar
  50. Plummer, R., & Armitage, D. (2007). A resilience-based framework for evaluating adaptive co-management: Linking ecology, economics and society in a complex world. Ecological Economics, 61, 62–74.CrossRefGoogle Scholar
  51. Prescott-Allen, R. (2001). The wellbeing of nations: A country-by country index of quality of life and the environment. Washington, DC: Island Press.Google Scholar
  52. Puskur, R., & Thorpe, W. (2005). Crop and non-crop productivity gains: Livestock in water scarce watersheds. In B. R. Sharma, J. S. Samra, C. A. Scott, & S. P. Wani (Eds.), Watershed management challenges: Improving productivity, resources and livelihoods (pp. 95–115). Colombo: International Water Management Institute.Google Scholar
  53. Rao, C. H. H. (2000). Watershed development in India—Recent experience and emerging issues. Economic and Political Weekly, 35, 3943–3947.Google Scholar
  54. Reddy, V. R., Chiranjeevi, T., Rout, S. K., & Reddy, S. M. (2014). Assessing livelihood impacts of watersheds at scale. In V. R. Reddy & G. J. Syme (Eds.), Integrated assessment of scale impacts of watershed interventions. Waltham, MA: Elsevier.Google Scholar
  55. Reddy, V. R., Gopinath Reddy, M., Galab, S., Soussan, J., & Springate-Baginski, O. (2004). Participatory watershed development in India: Can it sustain rural livelihoods? Development and Change, 35, 297–326.CrossRefGoogle Scholar
  56. Reddy, V. R., Gopinath Reddy, M., & Soussan, J. (2010). Political economy of watershed management: Policies, institutions, implementation and livelihoods. Jaipur: Rawat Publishers.Google Scholar
  57. Reddy, V. R., & Syme, G. J. (2014). Integrated assessment of scale impacts of watershed interventions. Amsterdam: Elsevier.Google Scholar
  58. Reed, M., Fraser, E. D. G., Morse, S., & A.J., D. (2005). Integrating methods for developing sustainability indicators to facilitate learning and action. Ecology and Society, 10, r3. [online].Google Scholar
  59. Scoones, I. (1998). Sustainable rural livelihoods: a framework for analysis. Brighton: Institute of Development Studies.Google Scholar
  60. Sreedevi, P. D., Sarah, S., Alam, F., Ahmed, S., Chandra, S., & Pavelic, P. (2014). Investigating geophysical and hydrogeological variabilities and their impact on water resources in the context of meso-watersheds. In V. R. Reddy & G. J. Syme (Eds.), Integrated assessment of scale impacts of watershed interventions. Waltham, MA: Elsevier.Google Scholar
  61. Syme, G. J., Reddy, V. R., Pavelic, P., Croke, B. F. W., & Ranjan, R. (2012). Confronting scale in watershed development in India. Hydrogeology Journal, 20, 985–993.CrossRefGoogle Scholar
  62. Ticehurst, J. L., Curtis, A., & Merritt, W. S. (2011). Using Bayesian Networks to complement conventional analyses to explore landholder management of native vegetation. Environmental Modelling and Software, 26, 52–65.CrossRefGoogle Scholar
  63. Turton, C. (2000). Enhancing Livelihoods Through Participatory watershed Development in India. Working Paper 131. Overseas Development institute, London, UK. Development. London: Routledge.Google Scholar
  64. Walker, B. H., & Salt, D. (2006). Resilience thinking: Sustaining ecosystems and people in a changing world. Washington DC: Island Press.Google Scholar
  65. Walmsley, J. J. (2002). Framework for measuring sustainable development in catchment systems. Environmental Management, 2–9, 195–206.CrossRefGoogle Scholar
  66. Wani, S. P., Joshi, P. K., Raju, K. V., Sreedevi, T. K., Wilson, M. J., Shah, A., et al. (2008). Community watershed as growth engine for development of dryland areas—Executive summary: A comprehensive assessment of watershed programs in India. Patancheru 502 324, Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics, 36 pp.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Wendy S. Merritt
    • 1
  • Brendan Patch
    • 2
    • 3
  • V. Ratna Reddy
    • 4
  • Geoffrey J. Syme
    • 5
  1. 1.Fenner School of Environment and SocietyThe Australian National UniversityCanberraAustralia
  2. 2.School of Mathematics and PhysicsThe University of QueenslandSt LuciaAustralia
  3. 3.Korteweg-de Vries Institute for MathematicsUniversity of AmsterdamAmsterdamThe Netherlands
  4. 4.Livelihoods and Natural Resource Management Institute (LNRMI)HyderabadIndia
  5. 5.Centre for PlanningEdith Cowan UniversityJoondalupAustralia

Personalised recommendations