Agricultural Information Transfer among Tribal Farmers of Malkangiri Using Machine Learning

SANJU SAHA; Anita Patra; M. Devender  Reddy; Ajay Kumar Prusty; Peddi Naga Harsha  Vardhan

doi:10.48165/IJEE.2026.62325

Authors

Sanju Saha CENTURION UNIVERSITY OF TECHNOLOGY AND MANAGEMENT
Anita Patra CENTURION UNIVERSITY OF TECHNOLOGY AND MANAGEMENT https://orcid.org/0000-0003-1174-1755
M. Devender Reddy Dr D Rama Naidu Vignana Jyothi Institute of Rural Development https://orcid.org/0000-0003-4263-3845
Ajay Kumar Prusty CENTURION UNIVERSITY OF TECHNOLOGY AND MANAGEMENT https://orcid.org/0000-0003-3670-802X
Peddi Naga Harsha Vardhan CENTURION UNIVERSITY OF TECHNOLOGY AND MANAGEMENT

https://doi.org/10.48165/IJEE.2026.62325

Keywords:

Agricultural Information Transfer, Machine Learning, Random Forest, SHAP Values, Tribal Farmers

Abstract

The study was carried out in 2026 to examine the extent of agricultural information transfer and the determinants of agricultural information transfer among tribal farmers of Malkangiri district of Odisha. Four blocks were selected purposively, and 30 farmers were randomly selected from each block through a multistage random sampling technique, constituting a total sample of 120 respondents. Primary data were collected using a pre-tested and structured interview schedule. Data were analysed using descriptive statistics random forest regression algorithm in Google Colaboratory, with SHapley Additive exPlanations values computed for explainable feature-level attribution. The agricultural information transfer was low to moderate across all five channels, ranging from the highest mean score for personal locality contact (1.96) to the lowest for private sector engagement (1.18; gap = 60.76%). Experience on farms and age were the most significant predictors, together explaining 66.77 per cent of the model's feature importance, with education having little influence. The training performance of the random forest model was good (R² = 0.9159), while it acknowledges the 0.4877 gap and attributes it to sample size and RF's sensitivity.

References

Anamica, M., & Sujeetha, T. N. (2017). Migration behaviour index – A measure to quantify the migration behaviour of rural youth. International Journal of Current Microbiology and Applied Sciences, 6(11), 3044–3057. https://doi.org/10.20546/ijcmas.2017.611.357

Bernzen, A., Jenkins, J. C., & Braun, B. (2019). Climate change-induced migration in coastal Bangladesh? A critical assessment of migration drivers in rural households under economic and environmental stress. Geosciences, 9(1), Article 51. https://doi.org/10.3390/geosciences9010051

Bhattamishra, R. (2020). Distress migration and employment in indigenous Odisha, India: Evidence from migrant-sending households. World Development, 136, Article 105047. https://doi.org/10.1016/j.worlddev.2020.105047

Borozan, Đ., Bojanić, I. B., & Leko Šimić, M. (2024). Exploring return intentions from the young migrant's point of view. Ekonomski vjesnik: Review of Contemporary Entrepreneurship, Business, and Economic Issues, 37(1), 93–107. https://doi.org/10.51680/ev.37.1.7

Gaikwad, A. B., Bhamare, M. G., & Ban, S. H. (2020). Migration behaviour of tribal youth. Gujarat Journal of Extension Education, 31(1), 190–196.

Ghosal, J., Bal, M., Das, A., Panda, B., Ranjit, M., Behera, M. R., Kar, S., Satpathy, S. K., Dutta, A., & Pati, S. (2024). To leave no one behind: Assessing utilization of maternal newborn and child health services by all the 13 particularly vulnerable tribal groups (PVTGs) of Odisha, India. Health Research Policy and Systems, 22(1), Article 12. https://doi.org/10.1186/s12961-023-01101-7

Ghosh, S., Kumar, A., Prusty, A. K., Naik, A., & Padhy, C. (2025a). Modelling livelihood security of tribal farmers in South Odisha using machine learning. Indian Journal of Extension Education, 61(4), 141–147. https://doi.org/10.48165/IJEE.2025.61423

Ghosh, S., Sarkar, A., Chakraborty, S., Mondal, K., & Malitha, A. B. (2025b). Agricultural extension approaches and rural youth engagement in West Bengal: A comprehensive review. NG Agricultural Sciences, 1(4), 33–49. https://doi.org/10.66132/ngas010404

Haq, F., Shutkin, T. Y., Afreen, M., & Mark, B. G. (2025). Cryo-social dynamics: The interplay of glacial dynamics and socioeconomic conditions in the Shigar Valley, Karakoram, Pakistan. GeoJournal, 90(1), Article 37. https://doi.org/10.1007/s10708-025-11289-6

Koirala, S., & Bashyal, S. (2025). The land left behind: A systematic review of transnational migration-induced change and its implication for rural sustainability in Nepal. Humanities and Social Sciences Communications, 12(1), Article 38. https://doi.org/10.1057/s41599-024-04180-1

Kujur, R., & Minz, S. K. (2021). Proliferation of tribal migrants and repercussion: Case study from the tribal areas of Sundargarh District, Odisha (India). Current Research Journal of Social Sciences and Humanities, 4(1), 27–44. http://dx.doi.org/10.12944/CRJSSH.4.1.04

Maurya, A. S., Bhavesh, M., A., & Malik, J. S. (2022). Migration behaviour of rural youth in Haryana. Indian Journal of Extension Education, 58(3), 93–98. https://epubs.icar.org.in/index.php/IJEE/article/view/125104

Mohanty, P. R., Sahoo, M., & Ghosh, S. (2025). Empowered by enterprise: Evaluating livelihood status improvement through attracting and retaining youth in agriculture (ARYA). Indian Journal of Extension Education, 61(2), 56–61. https://doi.org/10.48165/IJEE.2025.61211

Mohanty, S. S. (2024). Distress labour migration from Western Odisha – An overview. International Journal for Multidisciplinary Research, 6(1), Article 14139. https://doi.org/10.36948/ijfmr.2024.v06i01.14139

Nallala, S., Ghosh, U., Desaraju, S., Kadam, S. M., Kadarpeta, R. R., & Van Belle, S. (2023). Why are they "unreached"? Macro and meso determinants of health care access in hard to reach areas of Odisha, India. International Journal for Equity in Health, 22(1), Article 2. https://doi.org/10.1186/s12939-022-01817-y

Ngadi, N., Zaelany, A. A., Latifa, A., Harfina, D., Asiati, D., Setiawan, B., Ibnu, F., Triyono, T., & Rajagukguk, Z. (2023). Challenge of agriculture development in Indonesia: Rural youth mobility and aging workers in agriculture sector. Sustainability, 15(2), Article 922. https://doi.org/10.3390/su15020922

Pal P. K., Bhutia Phubu Tshering, Das L., Lepcha Norden & Nain M.S. (2017). Livelihood diversity in family farming in selected hill areas of West Bengal, India. Journal of Journal of Community Mobilization and Sustainable Development. 12(2), 172-178.

Pandey, D. K., De, H. K., Kumar, P., & Dubey, S. K. (2019). Agrarian change and well-being status of Mara tribe in Mizoram. Indian Journal of Agricultural Sciences, 89(11), 1828–1831. https://doi.org/10.56093/ijas.v89i11.95302

Roy, D., Acharya, S. K., Haque, M., Banerjee, A., Sarkar, A. K., & Mandal, T. K. (2022). Migration attributes in adaptation and its correlates during pandemic: The socio-ecological interpretation. Indian Journal of Extension Education, 59(1), 75–80. https://epubs.icar.org.in/index.php/IJEE/article/view/131868

Sabar, J. (2018). Socio demographic profile of respondents in Gajapati district: A study. Journal of Management Research and Analysis, 5(1), 29–33. https://doi.org/10.18231/2394-2770.2018.0006

Sandesh, V. V. M., Kumar, A. A., & Smitha, K. P. (2020). Impact of Migration on the Livelihoods of Tribes People. International Journal of Current Microbiology and Applied Sciences, 9(11), 37203730. https://doi.org/10.20546/ijcmas.2020.911.446

Sai, M., Prusty, A. K., Padhy, C., & Reddy, I. C. (2024). Migration behavior of rural youth from agriculture in North Coastal Andhra Pradesh. Indian Journal of Extension Education, 60(4), 30–34. https://doi.org/10.48165/IJEE.2024.60406

Selod, H., & Shilpi, F. (2021). Rural-urban migration in developing countries: Lessons from the literature. Regional Science and Urban Economics, 91, Article 103713. https://doi.org/10.1016/j.regsciurbeco.2021.103713