Young Farmers' Utilization of Internet for Agricultural Purposes: Evidence from Chiang Mai Province, Thailand

Taveechai Khamtavee

Program in Agricultural Extension and Rural Development, Department of Agricultural Development and Economy, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand

Juthathip Chalermphol

Department of Agricultural Development and Economy, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand

Sukit Kanjina

Department of Agricultural Development and Economy, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand

Ruth Sirisunyaluck

Department of Agricultural Development and Economy, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand


Received: 2 May 2024; Received in revised form:14 June 2024; Accepted: 15 June 2024; Published: 30 June 2024

Copyright © 2024 Taveechai Khamtavee, Juthathip Chalermphol, Sukit Kanjina, Ruth Sirisunyaluck. Published by Nan Yang Academy of Sciences Pte. Ltd.

Creative Commons LicenseThis is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.


Despite Thailand ranking high among Asia-Pacific countries in The Network Readiness Index 2022 and the increasing Internet access, rural communities in Thailand still face significant barriers to fully utilizing the Internet for agriculture-related activities. This gap in effective digital connection hinders the transfer of crucial information and communication necessary to support and enhance agricultural practices. Hence, this paper’s objective aims to explore utilization of the Internet by young farmers and the factors affecting this utilization. To achieve this, 369 young farmers in Mae Chaem district, Chiang Mai province, were surveyed. Data collected was analyzed using descriptive statistics (e.g., frequency and percentage) and relationships were analyzed using Ordered logistic regression analysis. Research results show that all farmers had Internet access and used it on their smartphones (100%). The participants used smartphone applications related to agriculture, with most utilizing LINE and Facebook to contact their fellow farmers (90.0% and 89.2%, respectively). Most participants search for agricultural information through YouTube (86.2%) and search engines (e.g., Google) (61.3%) to find information on plant and animal varieties and methods for crop planting and raising animals. Seven factors were found to influence Internet utilization for agricultural purposes: age, education, contact with agricultural extension officers, agricultural organization membership, use of home or cable Internet, and use of government-provided Internet were statistically significant in affecting Internet use (p < 0.01). Based on these results following policy recommendations are provided to encourage farmers to use the Internet for agricultural purposes: Relevant government agencies should set directional policies to create more contact channels for farmers, develop and disseminate educational materials online via social media platforms such as Facebook, LINE, and YouTube, and improve the Internet network and services.

Keywords: Internet utilization; Agriculture; Young farmer; Thailand


[1] Sharma, Y.K., Mangla, S.K., Patil, P.P., et al., 2018. Sustainable food supply chain management implementation using DEMATEL approach. Advances in Health and Environment Safety. 13, 115–125. DOI:

[2] Rural Population, Percent—Country Rankings [Internet] [cited 20 May 2024]. Available from:

[3] Digital Farmer Profiles: Reimagining Smallholder Agriculture [Internet] [cited 15 May 2024]. Available from:

[4] Trendov, M., Varas, S., Zeng, M., 2019. Digital technologies in agriculture and rural areas: Status report. Food and Agriculture Organization of the United Nations: Rome, Italy.

[5] Pender, J., Gebremedhin, B., 2008. Determinants of agricultural and land management practices and impacts on crop production and household income in the highlands of Tigray, Ethiopia. Journal of African Economies. 17(3), 395–450. DOI:

[6] Khan, N., Siddiqui, B.N., Khan, N., et al., 2020. Analyzing mobile phone usage in agricultural modernization and rural development. International Journal Agricultural Extension. 8(2),139–147. DOI:

[7] Qin, T., Wang, L., Zhou, Y., et al., 2022. Digital technology and-services-driven sustainable transformation of agriculture: Cases of China and the EU. Agriculture. 12(2), 297. DOI:

[8] Ministry of Agriculture and Cooperatives. Operational plan of the Ministry of Agriculture and Cooperatives: 2020-2022. Bangkok, Thailand: Ministry of Agriculture and Cooperatives; 2020.

[9] Siriruchatapong, P., 2016. Thailand digital economy and society development plan. Available from:

[10] The Role of Information and Communication Technologies in Ghana’s Rural Development [Internet] [cited 20 May 2024]. Available from:,the%20rural%20areas%20of%20Ghana

[11] Salemink, K., Strijker, D., Bosworth, G., 2017. Rural development in the digital age: A systematic literature review on unequal ICT availability, adoption, and use in rural areas. Journal of Rural Studies. 54, 360–371. DOI:

[12] Evans, O., 2018. Repositioning for increased digital dividends: Internet usage and economic wellbeing in Sub-Saharan Africa. Journal of Global Information Technology Management. 21(2), 94–114. DOI:

[13] The Importance of Icts in the Provision of Information for Improving Agricultural Productivity and Rural Incomes in Africa [Internet] [cited 13 May 2024]. Available from:

[14] Erlangga, Wihardi, Y., Nugraha, E. (editors), 2020. Development mobile learning for vegetable farming in Indonesia based on mobile cloud computing. Proceedings of the 6th International Conference on Science in Information Technology (Icsitech). IEEE: USA. Palu, Indonesia; 2020 Oct 21–22. p. 6–10. DOI:

[15] Burke, K., Sewake K., 2008. Adoption of computers and Internet technology in small firm agriculture: A study of flower growers in Hawaii. Journal of Extension. 46, 5–19.

[16] The Network Readiness Index 2022 [Internet] [cited 12 May 2024]. Available from:

[17] National Statistical Office, 2022. Survey of the Use of Information and Communication Technology in Households 2022. Available from:

[18] Rogers, E.M., 2003. Diffusion of innovations, 5th ed. Free Press: New York, USA.

[19] Digital Economy and Society Development Driven Center, Office of the Permanent Secretary, 2020. The Internet Use of Net Pracharat Villages Report.. Available from:

[20] Ministry of Digital Economy and Society. Statistics on the results of surveys on the behavior of Internet users in Thailand 2022. Bangkok, Thailand: Ministry of Digital Economy and Society; 2022. Available from:

[21] Department of Agriculture Extension. Farmers' Registry, 2022. Chiang Mai: Chiang Mai Provincial Agricultural Office. Available from:

[22] Kanjina, S., 2021. Farmers’ use of social media and its implications for agricultural extension: Evidence from Thailand. Asian Journal Agriculture Rural Development.11(4), 302–310. DOI:

[23] Manalili, N.M., Capiña, X.G.B., 2023. Thailand’s Young Smart Farmer (YSF) Program.

[24] Yamane, T., 1973. Statistics: An introductory analysis, 3rd ed. Harper & Row: New York, USA.

[25] Liu, X., 2009. Ordinal regression analysis: fitting the proportional odds model using Stata, SAS and SPSS. Journal of Modern Applied Statistical Methods. 8(2).

[26] Darshan, N.P., Meena, B.S., 2017. Constraints in the use of social media as perceived by researchers and extension personnel in Karnal district of Haryana, India. International Journal of Current Microbiology and Applied Sciences. 6(10), 3239–3243. DOI:

[27] Rahman, S., Sarkar Mithun, M.N.A., 2021. Effect of social media use on academic performance among university students in Bangladesh. Asian Journal of Education and Social Studies. 20(3),1–12. DOI:

[28] Michels, M., von Hobe C.F., Weller von Ahlefeld, P.J., et al., 2021. The adoption of drones in German agriculture: A structural equation model. Precision Agriculture. 22(5),1728–1748. DOI:

[29] Vanich Bancha, K., Wanich Bancha, T., 2015. Using SPSS for windows in data analysis, 21st ed. Samlada Printing House: Bangkok.

[30] Stevens, J., 1996. Applied multivariate statistics for the social sciences. Lawrence Erlbaum Associates: Mahwah, USA.

[31] Sebatta, C., Mugisha, J., Katungi, E., 2014. Smallholder farmers’ decision and level of participation in the potato market in Uganda. Modern Economy. 5, 895–906. DOI:

[32] Reimers, M., Klasen, S., 2013. Revisiting the role of education for agricultural productivity. American Journal of Agricultural Economics. 95(1), 131-152. DOI:

[33] Ali, J., 2012. Factors affecting the adoption of infomation and communication technologies (ICTs) for farming decisions. Journal of Agricultural & Food Information. 13, 78-96. DOI:

[34] Mehta, P., Solanki, R., Patel, V., 2020. Factors affecting adoption of digital technologies among farmers: Evidence from Gujarat, India. Journal of Agribusiness in Developing and Emerging Economies. 10(3), 293–314.

[35] Michailidis, A., Partalidou, M., Nastis, S.A., et al., 2011. Who goes online? Evidence of Internet use patterns from rural Greece. Telecommun Policy. 35(4), 333–343. DOI:

[36] Orisakwe, O.E., Agomuo, E.E., 2011. Adoption of improved agroforestry technologies among contact farmers in Imo State, Nigeria. Asian Journal of Agriculture and Rural Development. 2(1), 1–9.

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