Jharkhand Journal of Development and Management Studies

(A Quarterly Review of Development and Management Trends)

A National Journal Indexed in the UGC-CARE List

Implications of Digital Transformation on Human Resource Management in Agriculture and its Impact on Farmers' Credit Access

Priti Jha1 and Aviral Pandey2

 1Scholar at the Division of Economics and Agricultural Economics, A. N. Sinha Institute of Social Studies and Aryabhatta Knowledge University, Patna. Email: pritipritijha5@gmail.com 

2Assistant Professor in the Division of Economics and Agricultural Economics at A. N. Sinha Institute of Social Studies, Patna. Email: aviralpandey.ansiss@gmail.com

ABSTRACT:

Digital transformation, driven by artificial intelligence, holds the potential to revolutionize human resource management in agriculture and address credit access issues faced by farming households. In India, a significant rural population relies on agriculture, but uncertainties create challenges in securing financing, leading to improper human resource management and a credit gap. Smallholder farmers, who lack access to credit, struggle to adopt advanced methods and make efficient use of their human resources. Technology and artificial intelligence offer promising solutions by enhancing human resource management and providing data for informed lending decisions. This study examines the impact of digital transformation on overall agriculture, farmers' credit accessibility, associated implementation complexities, and offers policy recommendations. The methodology involves a systematic literature review. The article is divided into four sections: methodology, findings, discussion, and recommendations, with the overarching goal of improving credit access for farming households in India.


Keywords: Agricultural credit, Artificial intelligence, Digital transformation, Smallholder farmers

DOI:


FULL TEXT:


REFERENCES:

Abuhusain, M. (2020). The role of artificial intelligence and big data on loan decisions. Accounting,  6, 1291–96.

Autio, E. (2017). Digitalisation, ecosystems, entrepreneurship and policy. Perspectives into topical issues is society and ways to support political decision making. Government’s Analysis, Assessment and Research Activities (Policy Brief 20/17).

Brooks, S., & Loevinsohn, M. (2011). Shaping agricultural innovation systems responsive to food insecurity and climate change. Natural Resources Forum, 35(3), 185-200.

Bukchin, S., & Kerret, D. (2020). The role of self-control, hope and information in technology adoption by smallholder farmers–A moderation model. Journal of Rural Studies, 74, 160–168.

Chelladurai, J. C. (2020). The role of new media towards sustainable agricultural development among farmers of Kancheepuram district, Tamilnadu. International Journal of Social Research Methodology, 4(4), 29–34.

Diao, X., Silver, J., & Takeshima, H. (2016). Agricultural mechanization and agricultural transformation (Vol. 1527). IFPRI Discussion Paper 01527.

Fairooz, H. M. M., & Wickramasinghe, C. N. (2019). Innovation and development of digital finance: A review on digital transformation in banking & financial sector of Sri Lanka. Asian Journal of Economics, Finance and Management, 105, 69–78.

FAO (2019). Digital agriculture transformation and digital innovation. Food and Agriculture Orgation of the United Nations, FAO IT Division Rome.

Garcia-Blanco, T., Davalos, A., & Visioli, F. (2017). Tea, cocoa, coffee, and affective disorders: Vicious or virtuous cycle?. Journal of Affective Disorders, 224, 61–68.

Goh, R. Y., Lee, L. S., Seow, Hsin-Vonn., & Gopal, K. (2020). Hybrid harmony search-Artificial intelligence models in credit scoring. Entropy22(9), 989.

Groher, T., Heitkämper, K., & Umstätter, C. (2020). Digital technology adoption in livestock production with a special focus on ruminant farming. Animal, 14(11), 2404–2413.

Guo, J. L., Cui, K., Qian, J. F., et al. (2013). Technology innovation and stability of agricultural industrial Chain. Journal of Agricultural Science & Technology, 15(4), 84–87.

Harkness, C., Areal, F. J., Semenov, M. A., et al. (2021). Stability of farm income: The role of agricultural diversity and agri-environment scheme payments. Agricultural Systems, 187, 103009.

Hellmuth, M. E., Osgood, D. E., Hess, U., Moorhead, A., & Bhojwani, H. (2009). Index insurance and climate risk: Prospects for development and disaster management. Climate and society, No. 2. International Research Institute for Climate and Society (IRI), Columbia University, New York, USA.

Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of artificial intelligence in agriculture sector. Advanced Agrochem, 2(1), 15-30.

Jiang, S., Zhou, J., & Qiu, S. (2022). Digital agriculture and urbanization: Mechanism and empirical eesearch. Technological Forecasting and Social Change, 180, 121724.

Khan, F., & Ali, Y. (2022). Moving towards a sustainable circular bio-economy in the agriculture sector of a developing country. Ecological Economics, 196, 107402.

Klein, M. G., Verter, V., & Moses, B. G. (2020). Designing a rural network of dialysis facilities. European Journal of Operational Research, 282(3), 1088–1100.

Kumar, A., Sharma, S., & Mahdavi, M. (2021). Machine learning (Ml) technologies for digital credit scoring in rural finance: A literature review. Risks, 9(11), 192.

Kumar, J., Murali-Baskaran, R. K., Jain, S. K., et al. (2021). Emerging and re-emerging biotic stresses of agricultural crops in India and novel tools for their better management. Current Science, 21(1), 26–36.

Lin, S., Lin, J., Han, F., & Luo, X. R. (2022). How big data analytics enables the alliance relationship stability of contract farming in the age of digital transformation. Information & Management59(6), 103680.

Liu, Y., Liu, J., & Zhou, Y. (2017). Spatio-temporal patterns of rural poverty in China and targeted poverty alleviation strategies. Journal of Rural Studies, 52, 66–75.

Martin, S. P. (2003). Is the digital divide really closing? A critique of inequality measurement in a nation online. Society, 1(4), 1–13.

Mhlanga, D. (2020). Industry 4.0 in finance: The impact of artificial intelligence (AI) on digital financial inclusion. International Journal of Financial Studies8(3), 45.

Mokyr, J., & Strotz, R. H. (1998). The second industrial revolution, 1870-1914. Storia dell’economia Mondiale, 21945(1).

Ozdogan, B., Gacar, A., & Aktas, H. (2017). Digital agriculture practices in the context of agriculture 4.0. Journal of Economics Finance and Accounting, 4(2), 186–193.

Patel, R. (2013). The long green revolution. The Journal of Peasant Studies, 40(1), 1-63.

Pratama, M. F., Rauf, R. A., Antara, M., et al. (2019). Factors influencing the efficiency of cocoa farms: A study to increase income in rural Indonesia. PLoS ONE, 14(4).

Rose, D. C., & Chilvers, J. (2018). Agriculture 4.0: Broadening responsible innovation in an era of smart farming. Frontiers in Sustainable Food Systems2, 87.

Rosegrant, M. W., & Hazell, P. B. (2000). Transforming the rural Asian economy: The unfinished revolution (Vol. 1). Hong Kong: Oxford University Press.

Santos, D., & Ferreira, J. C. (2019). IoT Power Monitoring System for Smart Environments. Sustainability, 11(19), 5355.

Sarr, M., & Swanson, T. (2017). Will technological change save the world? The rebound effect in international transfers of technology. Environmental and Resource Economics, 66, 577–604.

Talaviya, T., Shah, D., Patel, N., Yagnik, H., & Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture4, 58-73.

Tilson, D., Lyytinen, K., & Sørensen, C. (2010). Research commentary—digital infrastructures: The missing IS research agendaInformation Systems Research21(4), 748-759.

Ucum, I., Gulcubuk, B., Berk, A., et al (2018). Clustering approach, the development of agriculture in rural areas. National Agricultural Economics Congress.

Varangis, P., Kioko, M., Spahr, M., Hishigsuren, G., & Miller, H. (2014). Access to finance for smallholder farmers: Learning from the experiences of microfinance institutions in Latin America.  International Finance Corporation, World Bank.

Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems,  28(2), 118-144.

Wang, Z., Li, J., Liu, J., & Shuai, C. (2020). Is the photovoltaic poverty alleviation project the best way for the poor to escape poverty? A DEA and GRA analysis of different projects in rural China. Energy Policy, 137, 111105.

WEF (2021). Artificial intelligence for agriculture innovation. Community paper. World Economic Forum.

Xie, M., Irfan, M., Razzaq, A., et al. (2022). Forest and mineral volatility and economic performance: Evidence from frequency domain causality approach for global data. Resources Policy, 76, 102685.

Yang, C., Sun, Z. (2020). Data management system based on blockchain technology for agricultural supply Chain. International Conference on Data Mining Workshops (ICDMW).

Zhang, X., & Fan, D. (2023). Can agricultural digital transformation help farmers increase income? An empirical study based on thousands of farmers in Hubei province. Environment, Development and Sustainability, 1-27.

Zhang, Y. J., &  Pan, X. (2021). Does the risk aversion of crude oil market investors have directional predictability for the precious metal and agricultural markets?. China Agricultural Economic Review, 13(4), 894–911.

Zhou, C. (2017). Structural dividends in agricultural department: Based on the research of growth and volatility effects. Economic Survey.

User

image view

Please wait....

Login to verify subscription