Socio-Technical Analysis of Smart Farming Adoption in Sustainable Agricultural Systems in Indonesia
Keywords:
Smart Farming, Socio-Technical Analysis, Sustainable Agriculture, Digital Agriculture, Smallholder FarmersAbstract
This study aims to analyze smart farming adoption in sustainable agricultural systems in Indonesia through a socio-technical perspective. The study responds to the growing need to understand why digital agricultural technologies are not always adopted evenly by farmers, despite their potential to improve efficiency, productivity, and environmental sustainability. This research used a qualitative multi-site case study design in three agricultural contexts that represent different stages of smart farming adoption. Data were collected through semi-structured interviews, field observation, and documentation involving 20 to 25 participants, including farmers, farmer group leaders, agricultural extension workers, village officers, technology facilitators, and local program managers. Data were analyzed using thematic analysis supported by data condensation, data display, and conclusion drawing. The findings reveal five main themes: technological readiness, farmer learning and digital literacy, trust in digital recommendations, institutional support, and adaptation to local farming practices. The study shows that smart farming adoption is not only a technical matter, but also a social process shaped by trust, collective learning, infrastructure, and institutional mediation. Farmers adopt smart farming when they see clear benefits, understand the tool, receive continuous support, and can adjust the technology to local farming needs. The study contributes to socio-technical theory by explaining how digital agriculture operates in smallholder farming contexts. It also offers practical and policy implications for designing inclusive, affordable, and locally relevant smart farming programs in Indonesia.
References
Adeoye-Olatunde, O. A., & Olenik, N. L. (2021). Research and scholarly methods: Semi-structured interviews. Journal of the American College of Clinical Pharmacy, 4(10), 1358–1367. https://doi.org/10.1002/jac5.1441
Bahn, R. A., Yehya, A. A. K., & Zurayk, R. (2021). Digitalization for sustainable agri-food systems: Potential, status, and risks for the MENA region. Sustainability, 13(6), 3223. https://doi.org/10.3390/su13063223
Braun, V., & Clarke, V. (2021). Can I use TA? Should I use TA? Should I not use TA? Comparing reflexive thematic analysis and other pattern-based qualitative analytic approaches. Counselling and Psychotherapy Research, 21(1), 37–47. https://doi.org/10.1002/capr.12360
Carrer, M. J., Filho, H. M. de S., Vinholis, M. de M. B., & Mozambani, C. I. (2022). Precision agriculture adoption and technical efficiency: An analysis of sugarcane farms in Brazil. Technological Forecasting and Social Change, 177, 121510. https://doi.org/10.1016/j.techfore.2022.121510
Charmaz, K., & Thornberg, R. (2021). The pursuit of quality in grounded theory. Qualitative Research in Psychology, 18(3), 305–327. https://doi.org/10.1080/14780887.2020.1780357
Choruma, D. J., Dirwai, T. L., Mutenje, M. J., Mustafa, M., Chimonyo, V. G. P., Jacobs-Mata, I., & Mabhaudhi, T. (2024). Digitalisation in agriculture: A scoping review of technologies in practice, challenges, and opportunities for smallholder farmers in Sub-Saharan Africa. Journal of Agriculture and Food Research, 18, 101286. https://doi.org/10.1016/j.jafr.2024.101286
DeLay, N. D., Thompson, N. M., & Mintert, J. R. (2022). Precision agriculture technology adoption and technical efficiency. Journal of Agricultural Economics, 73(1), 195–219. https://doi.org/10.1111/1477-9552.12440
Dhanaraju, M., Chenniappan, P., Ramalingam, K., Pazhanivelan, S., & Kaliaperumal, R. (2022). Smart farming: Internet of Things (IoT)-based sustainable agriculture. Agriculture, 12(10), 1745. https://doi.org/10.3390/agriculture12101745
Dibbern, T., Romani, L. A. S., & Massruhá, S. M. F. S. (2024). Main drivers and barriers to the adoption of digital agriculture technologies. Smart Agricultural Technology, 8, 100459. https://doi.org/10.1016/j.atech.2024.100459
Friha, O., Ferrag, M. A., Shu, L., Maglaras, L., & Wang, X. (2021). Internet of Things for the future of smart agriculture: A comprehensive survey of emerging technologies. IEEE/CAA Journal of Automatica Sinica, 8(4), 718–752. https://doi.org/10.1109/JAS.2021.1003925
Getahun, S., Kefale, H., & Gelaye, Y. (2024). Application of precision agriculture technologies for sustainable crop production and environmental sustainability: A systematic review. The Scientific World Journal, 2024, 2126734. https://doi.org/10.1155/2024/2126734
Giua, C., Materia, V. C., & Camanzi, L. (2022). Smart farming technologies adoption: Which factors play a role in the digital transition? Technology in Society, 68, 101869. https://doi.org/10.1016/j.techsoc.2022.101869
Hoang, H. G., & Tran, H. D. (2023). Smallholder farmers’ perception and adoption of digital agricultural technologies: An empirical evidence from Vietnam. Outlook on Agriculture, 52(4), 457–468. https://doi.org/10.1177/00307270231197825
John, D., Hussin, N., Shahibi, M. S., Ahmad, M., Hashim, H., & Ametefe, D. S. (2023). A systematic review on the factors governing precision agriculture adoption among small-scale farmers. Outlook on Agriculture, 52(4), 469–485. https://doi.org/10.1177/00307270231205640
Johnson, J. L., Adkins, D., & Chauvin, S. (2020). A review of the quality indicators of rigor in qualitative research. American Journal of Pharmaceutical Education, 84(1), 7120. https://doi.org/10.5688/ajpe7120
Karunathilake, E. M. B. M., Le, A. T., Heo, S., Chung, Y. S., & Mansoor, S. (2023). The path to smart farming: Innovations and opportunities in precision agriculture. Agriculture, 13(8), 1593. https://doi.org/10.3390/agriculture13081593
Lajoie-O’Malley, A., Bronson, K., van der Burg, S., & Klerkx, L. (2020). The future(s) of digital agriculture and sustainable food systems: An analysis of high-level policy documents. Ecosystem Services, 45, 101183. https://doi.org/10.1016/j.ecoser.2020.101183
Li, W., Clark, B., Taylor, J. A., Kendall, H., Jones, G., Li, Z., Jin, S., Zhao, C., Yang, G., Shuai, C., Cheng, X., Chen, J., Yang, H., & Frewer, L. J. (2020). A hybrid modelling approach to understanding adoption of precision agriculture technologies in Chinese cropping systems. Computers and Electronics in Agriculture, 172, 105305. https://doi.org/10.1016/j.compag.2020.105305
Lim, W. M. (2025). What is qualitative research? An overview and guidelines. Australasian Marketing Journal, 33(2), 199–229. https://doi.org/10.1177/14413582241264619
MacPherson, J., Voglhuber-Slavinsky, A., Olbrisch, M., Schöbel, P., Dönitz, E., Mouratiadou, I., & Helming, K. (2022). Future agricultural systems and the role of digitalization for achieving sustainability goals: A review. Agronomy for Sustainable Development, 42, 70. https://doi.org/10.1007/s13593-022-00792-6
Manzoor, F., Wei, L., Siraj, M., Lu, X., & Qiyang, G. (2025). Digital agriculture technology adoption in low- and middle-income countries: A review of contemporary literature. Frontiers in Sustainable Food Systems, 9, 1621851. https://doi.org/10.3389/fsufs.2025.1621851
Miine, L. K., Akorsu, A. D., Boampong, O., & Bukari, S. (2023). Drivers and intensity of adoption of digital agricultural services by smallholder farmers in Ghana. Heliyon, 9(12), e23023. https://doi.org/10.1016/j.heliyon.2023.e23023
Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., & Martynenko, A. (2022). IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet of Things Journal, 9(9), 6305–6324. https://doi.org/10.1109/JIOT.2020.2998584
Osrof, H. Y., Tan, C. L., Angappa, G., Yeo, S. F., & Tan, K. H. (2023). Adoption of smart farming technologies in field operations: A systematic review and future research agenda. Technology in Society, 75, 102400. https://doi.org/10.1016/j.techsoc.2023.102400
Qazi, S., Khawaja, B. A., & Farooq, Q. U. (2022). IoT-equipped and AI-enabled next generation smart agriculture: A critical review, current challenges and future trends. IEEE Access, 10, 21219–21235. https://doi.org/10.1109/ACCESS.2022.3152544
Ronaghi, M. H., & Forouharfar, A. (2020). A contextualized study of the usage of the Internet of Things (IoTs) in smart farming in a typical Middle Eastern country within the context of Unified Theory of Acceptance and Use of Technology model (UTAUT). Technology in Society, 63, 101415. https://doi.org/10.1016/j.techsoc.2020.101415
Satria, D., Maghraby, W., & Setyanti, A. M. (2025). Digital agricultural technology for smallholder farmers: Barriers and opportunities in Indonesia. SOCA: Jurnal Sosial Ekonomi Pertanian, 18(3), 267–281. https://doi.org/10.24843/SOCA.2024.v18.i03.p01
Thomas, R. J., O’Hare, G., & Coyle, D. (2023). Understanding technology acceptance in smart agriculture: A systematic review of empirical research in crop production. Technological Forecasting and Social Change, 189, 122374. https://doi.org/10.1016/j.techfore.2023.122374
Yadav, D. (2022). Criteria for good qualitative research: A comprehensive review. The Asia-Pacific Education Researcher, 31(6), 679–689. https://doi.org/10.1007/s40299-021-00619-0
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Bayu Rohman, Sekar Iskandar, Ridwan Yulianto (Author)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.




