Determinants of future crop insurance purchase decisions among pepper growers in the South Hill Zone of Karnataka, India
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DOIhttp://dx.doi.org/10.21511/ins.17(2).2026.06
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Article InfoVolume 17 2026, Issue #2, pp. 76–92
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Type of the article: Research Article
Abstract
The Indian agricultural system is diverse, monsoon-dependent, and smallholder-driven. In this context, crop insurance is a suitable protective measure for farmers, though it has not been widely adopted. This study aims to examine the influence of demographic factors, economic factors, farm-related factors, risk perception factors, insurance-related factors, and institutional factors on future crop insurance purchase decisions among pepper-growing farmers in the south Hill Zone of Karnataka, India. To conduct an empirical investigation, a total of 407 samples were collected using a multistage sampling technique. Logistic regression models were employed to analyze the influence of the considered factors on farmers’ future crop insurance purchase decisions. The results show that all considered variables do not influence farmers’ future crop insurance purchase decisions except the Size of landholding (β = 0.3334, p = 0.034). Among the variables included in the model, size of landholding was the only variable that showed a statistically significant positive association with farmers’ future crop insurance purchase decisions. The findings imply that farmers’ future crop insurance purchase decisions are likely to be affected by structural and resource factors. To improve the effectiveness and inclusiveness of crop insurance, the government should strengthen the scheme with greater emphasis on supporting small and medium-sized landholders.
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JEL Classification (Paper profile tab)G22, Q12, Q18, C25
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References31
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Tables6
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Figures2
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- Figure 1. The study area (South Hill Zone of Karnataka)
- Figure 2. Classification matrix of the binary logistic regression model
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- Table 1. The results of the logit model
- Table 2. Model diagnostic results
- Table 3. Matrix of the binary logistic regression model
- Table A1. Factors influencing crop insurance: impact of agricultural income and indemnity settlements on agricultural sustainability
- Table A2. Influencing factors on crop insurance purchase decision in the future (using a five-point rating scale ranging from very strongly agree to strongly disagree)
- Table A3. Respondents’ results
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- Acchukatla, M., Thogati, R. C., Parle, K. C., Padhy, C., Pattanayak, K. P., & Peter, Y. S. (2025). Assessment of economic viability and production dynamics of chilli cultivation in Guntur District, Andhra Pradesh. Annals of Agri-Bio Research, 30(2), 141-149.
- Agbenyo, W., Jiang, Y., & Ntim-Amo, G. (2022). Impact of crop insurance on cocoa farmers’ income: an empirical analysis from Ghana. Environmental Science and Pollution Research, 29(41), 62371-62381.
- Anjitha, A. C., Hema, M., Prema, A., Franco, D., & Jan, S. (2024). Battling climatic shifts: Vulnerability of coffee-based farm households and resilient practices in coffee farms, Wayanad, Kerala. Indian Journal of Agricultural Economics, 79(3), 420-429.
- Babcock, B. A., & Hart, C. E. (2005). Influence of the premium subsidy on farmers’ crop insurance coverage decisions (Working Paper No. 05-WP 393). Center for Agricultural and Rural Development, Iowa State University.
- Biswal, D., & Bahinipati, C. S. (2025). Demand for crop insurance in India: Evidence from national representative surveys. Indian Economic Journal.
- Bokusheva, R. (2011). Measuring dependence in joint distributions of yield and weather variables. Agricultural Finance Review, 71(1), 120-141.
- Cha, J., Deng, Y., Zheng, S., & Li, F. (2024). Crop insurance, factor allocation, and farmers’ income: evidence from Chinese pear farmers. Frontiers in Sustainable Food Systems, 8, Article 1378382.
- Cole, S., Giné, X., Tobacman, J., Topalova, P., Townsend, R., & Vickery, J. (2013). Barriers to household risk management: Evidence from India. American Economic Journal: Applied Economics, 5(1), 104-135.
- Dercon, S. (2002). Income risk, coping strategies, and safety nets. The World Bank Research Observer, 17(2), 141-166.
- Dragos, C. M., Dragos, S. L., Mare, C., Muresan, G. M., & Purcel, A.-A. (2023). Does risk assessment and specific knowledge impact crop insurance underwriting? Evidence from Romanian farmers. Economic Analysis and Policy, 79, 343-358.
- Enjolras, G., & Sentis, P. (2011). Crop insurance policies and purchases in France. Agricultural Economics, 42(4), 475-486.
- Glaser, L. K. (1996). Crambe: An Economic Assessment of the Feasibility of Providing Multiple-Peril Crop Insurance. Washington, DC: U.S. Department of Agriculture, Economic Research Service for the Risk Management Agency.
- Goodwin, B. K., & Smith, V. H. (2013). What harm is done by subsidizing crop insurance? American Journal of Agricultural Economics, 95(2), 489-497.
- Greatrex, H., Hansen, J., Garvin, S., Diro, R., Le Guen, M., Blakeley, S., Rao, K., & Osgood, D. (2015). Scaling up index insurance for smallholder farmers: Recent evidence and insights (CCAFS Report No. 14). CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS).
- Hasanah, P., Soemarsono, A. R., Susilawati, S., & Azka, M. (2025). Pricing weather index insurance for pepper plantations based on historical burn analysis. AIP Conference Proceedings, 3272(1), 40003.
- Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). Wiley.
- John, D., Hussin, N., Shahibi, M. S., Ahmad, M., Hashim, H., & Ametefe, D. S. (2023). A systematic review on the factors governing precision agriculture adoption among small-scale farmers. Outlook on Agriculture, 52(4), 469-485.
- Kalavakonda, V., & Mahul, O. (2005). Crop insurance in Karnataka (Policy Research Working Paper No. 3654). World Bank.
- Khan, M., Sidhu, K. A., Waraich, Y. M., & Ghardallou, W. (2026). Crop insurance as a climate risk management tool: Evidence from three districts of Punjab, Pakistan. PLOS ONE, 21(3), Article e0344460.
- Madaki, M. Y., Kaechele, H., & Bavorova, M. (2023). Agricultural insurance as a climate risk adaptation strategy in developing countries: A case of Nigeria. Climate Policy, 23(6), 747-762.
- Nivetha, N., Murali, P., Thilagavathi, M., Karthick, V., Selvanayaki, S., Pangayar Selvi, R., & Jagadeshwaran, P. (2025). From Adoption to Impact: A comprehensive analysis of crop insurance in India’s sugarcane sector. Sugar Tech, 27(3), 737-748.
- Oduol, J. B. A., & Tsuji, M. (2005). The effect of farm size on agricultural intensification and resource allocation decisions: Evidence from smallholder farms in Embu District, Kenya. Journal of the Faculty of Agriculture, Kyushu University, 50(2), 727-742.
- Sanz de Acedo Lizárraga, M. L., Sanz de Acedo Baquedano, M. T., & Cardelle-Elawar, M. (2007). Factors that affect decision making: Gender and age differences. International Journal of Psychology and Psychological Therapy, 7(3), 381-391.
- Sherrick, B. J., Barry, P. J., Ellinger, P. N., & Schnitkey, G. D. (2004). Factors influencing farmers’ crop insurance decisions. American Journal of Agricultural Economics, 86(1), 103-114.
- Silong, A. K. F., & Gadanakis, Y. (2020). Credit sources, access and factors influencing credit demand among rural livestock farmers in Nigeria. Agricultural Finance Review, 80(1), 68-90.
- Sinha, S., & Tripathi, N. K. (2016). Assessing the challenges in successful implementation and adoption of crop insurance in Thailand. Sustainability, 8(12), 1306.
- Smith, V. H. (2016). Producer insurance and risk management options for smallholder farmers. The World Bank Research Observer, 31(2), 271-289.
- Teegerstrom, T., Tronstad, R., & Nakamoto, S. (2013). An overview of Risk Management Agency insurance products and Farm Service Agency programs available for Arizona agricultural producers as of December 2012 (Cooperative Extension Publication AZ1567). College of Agriculture and Life Sciences, University of Arizona.
- Vijayakumar, S., Kumar, R. M., Sundaram, R. M., & Balasubramanian, P. (2022). Remote sensing-based transformative crop insurance for rice. Current Science, 123(3), 254.
- Wang, M., Ye, T., & Shi, P. (2016). Factors affecting farmers’ crop insurance participation in China. Revue Canadienne d’agroéconomie [Canadian Journal of Agricultural Economics], 64(3), 479-492.
- Workineh, T. M., Ali, A. C., & Woldearegay, A. G. (2022). Intentions without attention: Challenges in agricultural extension communication in Ethiopia. International Journal of Global Environmental Issues, 21(2-4), 95-112.


