Farmers’ Preferences for Climate-Smart Agriculture—An Assessment in the Indo-Gangetic Plain



This study was undertaken to assess farmers’ preferences and willingness to pay (WTP) for various climate-smart interventions in the Indo-Gangetic Plain. The research output will be helpful in integrating farmers’ choices with government programs in the selected regions. The Indo-Gangetic Plain (IGP) was selected because it is highly vulnerable to climate change, which could adversely affect the sustainability of the rice-wheat production system and the food security of the region. Climate-smart agriculture (CSA) can mitigate the negative effects of climate change and improve the efficiency of the rice-wheat-based production system. To assess farmers’ choices and their WTP for the potential climate-smart technologies and other interventions, scoring and bidding protocols were implemented through focus group meetings. Laser land leveling (LLL), crop insurance, and weather advisory services were found to be the preferred interventions in the Eastern IGP, whereas, in the Western IGP, farmers preferred LLL, direct seeding, zero tillage, irrigation scheduling, and crop insurance. Through the bidding approach, farmers implicitly expressed their WTP for new technologies that could transform current agricultural practices into relatively low-carbon impact and more productive farming methods. But actual large-scale adoption of the preferred climate-smart technologies and other interventions would require access to funding as well as capacity building among technology promoters and users.


Smallholders: contingent valuation Scoring Preference Technology 


  1. Aggarwal, P. K., Joshi, P. K., Ingram, J. S., & Gupta, R. K. (2004). Adapting food systems of the Indo-Gangetic plains to global environmental change: Key information needs to improve policy formulation. Environmental Science & Policy, 7(6), 487–498.CrossRefGoogle Scholar
  2. Aggarwal, P. K., Palanisami, K., Khanna, M., & Kakumanu, K. R. (2012). Climate change and food security of India: Adaptation strategies in the irrigation sector. World Agriculture, 3, 20–26.Google Scholar
  3. Ahmad, J. K., Goldar, B. N., Jakariya, M., & Misra, S. (2002). Willingness to pay for arsenic-free, safe drinking water in Rural Bangladesh: Methodology and results. Field Note, Water and Sanitation Program—South Asia. New Delhi: World Bank.
  4. Altaf, M. A., Whittington, D., Jamal, H., & Smith, V. K. (1993). Rethinking rural water supply policy in the Punjab, Pakistan. Water Resources Research, 29(7), 1943–1954.CrossRefGoogle Scholar
  5. Breidert, C., Hahsler, M., & Reutterer, T. (2006). A review of methods for measuring willingness to pay. Innovative Marketing, 4(2), 8–32.Google Scholar
  6. Cohen, D. R., & Zilberman, D. (1997). Actual versus stated willingness to pay: A comment. Agricultural and Resource Economics, 22(2), 376–381.Google Scholar
  7. de Chaisemartin, C., & Mahe, T. (2009). How to understand our willingness to pay to fight climate change? A choice experiment approach. Palaiseau, France: Department d’Economie, Ecole Polymethine, Centre National de la Recherche Scientifique.Google Scholar
  8. Food and Agriculture Organization (FAO). (2010). Climate-smart agriculture: Policies, practices and financing for food security, adaptation and mitigation. Rome: FAO.
  9. Garrido, A. (2005). Using good economic principles to make irrigators true partners of water and environment policies. Paper presented at OECD Workshop on Agriculture and Water Sustainability, Markets and Policies, Adelaide, Australia, November 14–18.Google Scholar
  10. Haba, S. (2004). Factors influencing the willingness to pay for agricultural information delivery technologies by cooperative-oriented agribusinesses in Rwanda: Evidence from the Abahuzamugambi coffee growers cooperative of Maraba-Butare, Rwanda. M.Sc. dissertation, Texas A&M University, College Station, TX, USA.Google Scholar
  11. Horna, J. D., Smale, M., & von Oppen, M. (2005). Farmer willingness to pay for seed-related information: Rice varieties in Nigeria and Benin (IFPRI Discussion Paper 142). Washington, DC: International Food Policy Research Institute.Google Scholar
  12. Indian Council of Agricultural Research (ICAR). (2009). Handbook of agriculture. New Delhi: ICAR.Google Scholar
  13. Intergovernmental Panel on Climate Change (IPCC). (2007). IPCC fourth assessment report: Climate change 2007 (AR4). Geneva: IPCC.CrossRefGoogle Scholar
  14. Marbeau, Y. (1987). What value pricing research today? Market Research Society, 29(2), 153–182.Google Scholar
  15. Mehla, R. S., Verma, J. K., Hobbs, P. R., Gupta, R. K. (2000). Stagnation in the productivity of wheat in the Indo-Gangetic plains: Zero-till-seed-cum-fertilizer drill as an integrated solution. RWC Paper Series 8 (9 p.). New Delhi, India: Rice-Wheat Consortium for the Indo-Gangetic Plains.Google Scholar
  16. Merino-Castelló, A. (2003). Eliciting consumers’ preferences using stated preference discrete choice models: Contingent ranking versus choice experiment. UPF Economic and Business Working Paper No. 705. Barcelona, Spain: Universitat Pompea Fabra.
  17. Mishra, S. (2006). Farmers’ suicides in Maharashtra. Economic and Political Weekly, 41(16), 1538–1545.Google Scholar
  18. Mitchell, R., & Carson, R. (1989). Using surveys to value public goods: The contingent valuation method. Resources for the future. Washington, DC: Johns Hopkins University Press.Google Scholar
  19. Mwangi, G. J. (1998). The linkages in the transfer and adoption of agricultural technologies. International Agriculture and Extension Education, 5(1), 63–68.Google Scholar
  20. Seth, R., Ansari, V. A., & Datta, M. (2009). Weather-risk hedging by farmers: An empirical study of willingness-to-pay in Rajasthan, India. Risk Finance, 10(1), 54–66.CrossRefGoogle Scholar
  21. Sharma, A. K., & Vashistha, A. (2007). Weather derivatives: Risk hedging prospectus in agriculture and power sector in India. Risk and Finance, 8(2), 112–132.CrossRefGoogle Scholar
  22. Singh, G. (2010). Crop insurance in India (Working Paper No. 2010-06-01). Ahmedabad, India: Indian Institute of Management.Google Scholar
  23. Tyagi, N. K. (1984). Effect of land surface uniformity on irrigation quality and economic parameters in sodic soils under reclamation. Irrigation Science, 5(3), 151–166.CrossRefGoogle Scholar
  24. Ulimwengu, J., & Sanyal, P. (2011). Joint estimation of farmers’ stated willingness to pay for agricultural services (IFPRI Discussion Paper 01070). Washington, DC: International Food Policy Research Institute.Google Scholar
  25. World Bank. (2010). World development report 2010: Development and climate change. Washington, DC: World Bank.

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.International Food Policy Research InstituteNew DelhiIndia
  2. 2.Borlaug Institute for South Asia, CIMMYTNew DelhiIndia
  3. 3.Formerly, ICAR-Agricultural Scientists Recruitment BoardNew DelhiIndia

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