Impact of blockchain technology adoption in farms of FPO members
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Keywords:Adoption, Blockchain technology, Farm income, Impact, Propensity score matching, Supply Chain
BCT adoption remains to be a promising way to achieve food security and safety in many developing countries. This paper explores the impact of blockchain technology adoption on household farm income. Based on a simple random sampling method, a cross sectional survey was conducted in the year 2023 to collect data from 240 sample farmers including 120 BCT adopters and 120 non-adopters in Erode district of Tamil Nadu. The information regarding socio-economic profiles like age, gender, educational status, farming experience, farm size, extension agency contact, training programmes attended, access to technological information were collected from sample farmers through personal interviews. The present research used a treatment effect analysis with propensity score matching approach to assess the impact of blockchain technology adoption on household’s farm income. Results showed a significant increase in farm income as a result of blockchain technology adoption among sample farmers. PSM approach estimated that the blockchain technology adopters earned higher farm income of ₹25829.16 as compared to non-adopters. Hence the findings provide empirical evidence that blockchain technology adoption in agriculture can contribute to improve quality food production and enhance farm income.
Aditya K S, Khan T and Kishore A. 2018. Adoption of crop insurance and impact: Insights from India. Agricultural Economics Research Review 31(2): 163–74. DOI: https://doi.org/10.5958/0974-0279.2018.00034.4
Aweke C S, Hassen J Y, Wordofa M G, Moges D K, Endris G S and Rorisa D T. 2021. Impact assessment of agricultural technologies on household food consumption and dietary diversity in eastern Ethiopia. Journal of Agriculture and Food Research 4: 100141. DOI: https://doi.org/10.1016/j.jafr.2021.100141
Cunguara B and Darnhofer I. 2011. Assessing the impact of improved agricultural technologies on household income in rural Mozambique. Food Policy 36(3): 378–90. DOI: https://doi.org/10.1016/j.foodpol.2011.03.002
Gendzwill A, Kurniewicz A and Swianiewicz P. 2021. The impact of municipal territorial reforms on the economic performance of local governments. A systematic review of quasi-experimental studies. Space and Polity 25(1): 37–56. DOI: https://doi.org/10.1080/13562576.2020.1747420
Gokul Vignesh Udhayakumar, Balaji P, Venkatesa P, Narasimha B and K R Ashok. 2019. A farmers perception on farmer producer organisation (FPO) and extent of its services to farmers: A case of millets. Madras Agricultural Journal 106(10–12): 672–76. DOI: https://doi.org/10.29321/MAJ.2019.000329
Gokul Vignesh Udhayakumar, Balaji P and Sivakumar S D. 2019. Role of factors in farmer’s producer organisations (FPO) based millet value chain. Madras Agriculture Journal 106(spl.): 288–91. DOI: https://doi.org/10.29321/MAJ.2019.000261
Gokul Vignesh Udhayakumar, Balaji P, Venkatesa P and Narasima B. 2020. Farmers Producer Organization Driven Agri-Food Value Chain: Role of Actors and Strategies Book, pp. 1–144. Lap Lambert Academic Publishing, Mauritius. ISBN No 978-6202794718.
Habtemariam L T, Mgeni C P, Mutabazi K D and Sieber S. 2019. The farm income and food security implications of adopting fertilizer micro-dosing and tied-ridge technologies under semi-arid environments in central Tanzania. Journal of Arid Environments 166: 60–67. DOI: https://doi.org/10.1016/j.jaridenv.2019.02.011
Lee B K, Lessler J and Stuart E A. 2010. Improving propensity score weighting using machine learning. Statistics in Medicine 29(3): 337–46. DOI: https://doi.org/10.1002/sim.3782
Leng C, Ma W, Tang J and Zhu Z. 2020. ICT adoption and income diversification among rural households in China. Applied Economics 52(33): 3614–628. DOI: https://doi.org/10.1080/00036846.2020.1715338
Malarkodi M, Sivakumar S D, Balaji P, Divya K, Shantha Sheela M, Vidhyavathi A, Singh P and Kumar A. 2023. Perception and buying behaviour of consumers towards FPOs food products in Tamil Nadu. The Indian Journal of Agricultural Sciences 93(3): 339–41. DOI: https://doi.org/10.56093/ijas.v93i3.132458
Priscilla L and Chauhan A K. 2019. Economic impact of cooperative membership on dairy farmers in Manipur: A propensity score matching approach. Agricultural Economics Research Review 32(1): 117–23. DOI: https://doi.org/10.5958/0974-0279.2019.00010.7
Rosenbaum P R and Rubin D B. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70(1): 41–55. DOI: https://doi.org/10.1093/biomet/70.1.41
Steiner P M and Cook D. 2013. Matching and Propensity Scores. The Oxford Handbook of Quantitative Methods, pp. 237–59, Vol. 1. T D Little (Ed.), Oxford University Press, New Delhi. DOI: https://doi.org/10.1093/oxfordhb/9780199934874.013.0013
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