Influence of Agricultural Credit, Financial Inclusion, and Technology Adoption on Farmers' Economic Performance: A Structural Equation Modeling Approach.
DOI:
https://doi.org/10.64882/ijrt.v14.i3.1621Keywords:
Agricultural Credit; Financial Inclusion; Technology Adoption; Farmers' Economic Performance; Structural Equation Modeling (SEM); Smallholder Farmers; Rural Development; Agricultural Finance; Precision Agriculture; Sustainable Agriculture.Abstract
Agricultural modernization has become increasingly dependent on farmers' access to institutional finance and their ability to adopt innovative agricultural technologies. However, limited financial resources continue to constrain technology adoption among smallholder farmers, thereby affecting agricultural productivity and economic performance. The present study investigates the structural relationships among Agricultural Credit, Technology Adoption, and Farmers' Economic Performance using a Structural Equation Modeling (SEM) approach. A quantitative cross-sectional research design was employed, and primary data were collected from 384 smallholder farmers using a structured questionnaire. The sample size was determined using Cochran's formula with a 95% confidence level and a 5% margin of error. The proposed structural model was estimated using Maximum Likelihood Estimation (MLE). The empirical findings reveal that Agricultural Credit has a significant positive effect on Technology Adoption (β = 0.46, t = 9.20, p < 0.001), indicating that improved access to institutional finance enables farmers to invest in modern agricultural technologies. Furthermore, Technology Adoption exerts a strong positive influence on Farmers' Economic Performance (β = 0.58, t = 9.66, p < 0.001), demonstrating that the adoption of improved farming technologies enhances productivity, profitability, resource-use efficiency, and farm income. The results suggest that technology adoption serves as the primary mechanism through which agricultural credit is transformed into measurable economic benefits. The study concludes that integrated rural development policies combining accessible agricultural finance, technology dissemination, extension services, and financial inclusion can substantially improve agricultural productivity and strengthen rural livelihoods. The findings contribute to the literature on agricultural finance and rural development by providing empirical evidence supporting the sequential relationship between agricultural credit, technological innovation, and farmers' economic performance. The study also offers practical implications for policymakers, financial institutions, agricultural extension agencies, and development organizations seeking to promote sustainable agricultural growth and inclusive rural economic development.
References
Abdulai, A., & Huffman, W. E. (2014). The adoption and impact of soil and water conservation technology: An endogenous switching regression application. Land Economics, 90(1), 26–43. https://doi.org/10.3368/le.90.1.26
Feder, G., Just, R. E., & Zilberman, D. (1985). Adoption of agricultural innovations in developing countries: A survey. Economic Development and Cultural Change, 33(2), 255–298. https://doi.org/10.1086/451461
Food and Agriculture Organization. (2022). The state of food and agriculture 2022: Leveraging automation in agriculture for transforming agrifood systems. FAO.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Hayami, Y., & Ruttan, V. W. (1985). Agricultural development: An international perspective (Rev. ed.). Johns Hopkins University Press.
Karlan, D., Osei, R., Osei-Akoto, I., & Udry, C. (2014). Agricultural decisions after relaxing credit and risk constraints. The Quarterly Journal of Economics, 129(2), 597–652. https://doi.org/10.1093/qje/qju002
Levine, R. (2005). Finance and growth: Theory and evidence. In P. Aghion & S. Durlauf (Eds.), Handbook of Economic Growth (Vol. 1A, pp. 865–934). Elsevier.
OECD. (2023). Agricultural policy monitoring and evaluation 2023. OECD Publishing.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Schultz, T. W. (1964). Transforming traditional agriculture. Yale University Press.
Scoones, I. (1998). Sustainable rural livelihoods: A framework for analysis. IDS Working Paper No. 72. Institute of Development Studies.
United Nations. (2023). The Sustainable Development Goals report 2023. United Nations.
World Bank. (2022). World development report 2022: Finance for an equitable recovery. World Bank.
Adebayo, S. A., & Olagunju, K. O. (2022). Agricultural credit accessibility and farm productivity among smallholder farmers. Heliyon, 8(11), e11453.
Khanal, A. R., Mishra, A. K., & Mohanty, S. (2018). Impact of precision agriculture on farm performance: Evidence from developing economies. Agricultural Economics, 49(6), 745–758.
Mwangi, M., & Kariuki, S. (2015). Factors determining adoption of new agricultural technology by smallholder farmers in developing countries. Journal of Economics and Sustainable Development, 6(5), 208–216.
Rahman, S. (2021). Financial inclusion and agricultural productivity: Evidence from rural households in South Asia. Journal of Rural Studies, 82, 151–162.
Tey, Y. S., & Brindal, M. (2012). Factors influencing the adoption of precision agricultural technologies: A review. Precision Agriculture, 13(6), 713–730.
World Bank. (2023). Digital agriculture for food security and rural transformation. World Bank.
Zhang, X., & Fan, S. (2022). Agricultural innovation, technology adoption, and rural economic development: Evidence from developing countries. Food Policy, 108, 102236.
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