Logistic Regression Analysis of Factors Influencing Mobile Application Adoption in Smallholder Livestock Farming: A Case Study from Northern Thailand
Abstract
This study examines the factors affecting the adoption of mobile farming management applications by smallholder livestock farmers in Northern Thailand. Data from 300 farmers were analyzed using binary logistic regression to evaluate 14 independent variables and their influence on application use. Four significant factors were identified: education level, participation in training programs, extension support, and membership in farmer associations. Education and participation in training programs were highly significant (p<0.001), whereas extension support and membership in farmer associations were both significantly associated with mobile application use (p<0.01 and p<0.05). Our findings indicate that educational initiatives, training programs, and strong extension support are crucial in enhancing mobile application adoption. Farmer associations also play a vital role in promoting technology use through peer influence and social networks. These insights highlight the importance of targeted strategies to improve mobile application adoption, thereby contributing to more efficient livestock management practices. To create practical and long-term digital solutions specifically designed to meet smallholder farmers’ requirements, it is essential to gain an in-depth understanding of these elements. The findings highlight the importance of addressing educational gaps, promoting training programs, and enhancing extension services to encourage technology adoption among smallholder farmers. By focusing on these critical factors, farmers can increase their adoption of mobile applications, thereby improving livestock management efficiency and enhancing the adaptability of smallholder farming systems in rural areas.
References
Baba, S., Dagong, M. I. A., Sohrah, S., & Utamy, R. F. (2019). Factors affecting the adoption of agricultural by-products as feed by beef cattle farmers in Maros regency of South Sulawesi, Indonesia. Tropical Animal Science Journal, 42(1), 76–80. https://doi.org/10.5398/tasj.2019.42.1.76
Balehegn, M., Duncan, A., Tolera, A., Ayantunde, A. A., Issa, S., Karimou, M., Zampaligré, N., André, K., Gnanda, I., Varijakshapanicker, P., & Kebreab, E. (2020). Improving adoption of technologies and interventions for increasing supply of quality livestock feed in low- and middle-income countries. Global Food Security, 26, 100372. https://doi.org/10.1016/j.gfs.2020.100372
Barrios, D., Olivera-Angel, M., & Palacio, L. G. (2023). Factors associated with the adoption of mobile applications (Apps) for the management of dairy herds. Revista de Economia e Sociologia Rural, 61(4), 264382. https://doi.org/10.1590/1806-9479.2022.264382
Baseca, C. C., Sendra, S., Lloret, J., & Tomas, J. (2019). A smart decision system for digital farming. Agronomy 9(5), 216. https://doi.org/10.3390/agronomy9050216
Batla, A. B., Kikani, Y. B., Joshi, D. G., & Patel, K. (2023). Real time cattle health monitoring using IoT, ThingSpeak, and a mobile application. Journal of Ethology & Animal Science, 5(1), 1–7. https://doi.org/10.23880/jeasc-16000131
Chandrarathna, R. M. D. S. M., Weerasinghe, T. W. M. S. A., Madhuranga, N. S., Thennakoon, T. M. L. S., Gamage, A., & Lakmali, E. (2022). “The Taurus”: Cattle breeds and diseases identification mobile application using machine learning. International Journal of Engineering Management Research, 12(6), 198–205. https://doi.org/10.31033/ijemr.12.6.27
Dhraief, M. Z., Bedhiaf, S., Dhehibi, B., Oueslati-Zlaoui, M., Jebali, O., & Ben-Youssef, S. (2019). Factors affecting innovative technologies adoption by livestock holders in arid areas of Tunisia. New Medit, 18(4), 3–18. https://doi.org/10.30682/nm1904a
Ifeoma, L. S. W., Adelabu, A. W., & Olayemi, S. S. (2021). Technology adoption capabilities of small farm dairy cattle holders in Gwagwalada, Abuja: Effects of asymmetric information and extension approaches. International Journal of Agricultural Economics, 6(6), 320–328. https://doi.org/10.11648/j.ijae.20210606.20
Junior, S. L. C., Balthazar, G. R., & Silva, I. J. O. (2021). Development and validation of a mobile app for the diagnosis of heat stress in livestock animals. International Journal of Agricultural, Environmental and Bioresearch, 6(3), 209–222. https://doi.org/10.35410/IJAEB.2021.5639
Kambale, P. D. R., Patil, D., & Ganavi, N. R. (2024). Mobile technology for farmers: An overview of agricultural apps. Asian Journal of Agricultural Extension, Economics & Sociology, 42(9), 75–81. https://doi.org/10.9734/ajaees/2024/v42i92543
Kenny, U., & Regan, A. (2021). Co-designing a smartphone app for and with farmers: Empathising with end-users’ values and needs. Journal of Rural Studies, 82, 148–160. https://doi.org/10.1016/j.jrurstud.2020.12.009
Louta, M., Panagiotis, K., Vasiliki, P., Sotiria, V., Evangelos, T., Stergios, P., Georgia, K., Alexandros, T., Socratis, D., & Georgios, A. (2023). FarmDain, A decision support system for dairy sheep and goat production. Animals, 13(9), 1495. https://doi.org/10.3390/ani13091495
Ma, W., Owusu-Sekyere, E., Zheng, H., & Owusu, V. (2023). Factors influencing smartphone usage of rural farmers: Empirical analysis of five selected provinces in China. Information Development, 1-14. https://doi.org/10.1177/02666669231201828
Mansour, T. (2022). Factors affecting mobile phone usage by farmers as a source of agricultural information in Sharqia governorate, Egypt. Journal of Tekirdag Agriculture Faculty, 19(2), 412–425. https://doi.org/10.33462/jotaf.1013886
Michels, M., Bonke, V., & Musshoff, O. (2019). Understanding the adoption of smartphone apps in dairy herd management. Journal of Dairy Science, 102(10), 9422–9434. https://doi.org/10.3168/jds.2019-16489
Mohanty, A. K., Rao, T. K., Harisha, K. S., Agme, R., Gogoi, C., & Velu, C. M. (2024). IoT applications for livestock management and health monitoring in modern farming. Educational Administration: Theory and Practice, 30(4), 2141–2153.
Nasirahmadi, A., & Hensel, O. (2022). Toward the next generation of digitalization in agriculture based on digital twin paradigm. Sensors, 22(2), 498. https://doi.org/10.3390/s22020498
Navulur, S., Sastry, A. S. C. S., & Prasad, M. N. G. (2017). Agricultural management through wireless sensors and Internet of Things. International Journal of Electrical and Computer Engineering, 7(6), 3492–3499. https://doi.org/10.11591/ijece.v7i6.pp3492-3499
Okoroji, V., Lees, N. J., & Lucock, X. (2021). Factors affecting the adoption of mobile applications by farmers: An empirical investigation. African Journal of Agricultural Research, 17(1), 19–29. https://doi.org/10.5897/AJAR2020.14909
Palacpac, E. P., Balingit, K. A. M. P., Bonifacio, A. A. D., Villanueva, M. A., Tolentino, R. B., Uy-DeGuia, M. R. D. L., Llantada, P. L. T., Castillo, C. I., Brul, B. J., Rubio, H. C. A., & Abes, M. M. (2024). Evaluating the usability, perceived performance, and perceived effects of KBGAN iHealth© and KBGAN iFeed© mobile apps for buffalo management in selected municipalities in the Philippines. Journal of Buffalo Science, 13, 31–45. https://doi.org/10.6000/1927-520X.2024.13.04
Saengwong, S., Intawicha, P., & Phuwisaranakom, P. (2021). Assisting knowledge dissemination of postpartum beef cows management using smartphone-based technology. Walailak Journal of Science and Technology, 18(11), 10695. https://doi.org/10.48048/wjst.2021.10695
Schulz, P., Prior, J., Kahn, L., & Hinch, G. (2022). Exploring the role of smartphone apps for livestock farmers: Data management, extension and informed decision making. Journal of Agricultural Education and Extension, 28(1), 93–114. https://doi.org/10.1080/1389224X.2021.1910524
Sennuga, S. O., Ujoyi, S. A., Bamidele, J., Onjewu, S. S., Lai-Solarin, W. I., & Omole, A. A. (2023). Exploring the role of smartphone apps for livestock farmers’ data management, extension, and informed decision making in Nigeria. International Journal of Probiotics and Dictetics, 3(2), 46–53. https://doi.org/10.33140/IJPD.03.02.01
Shanka, D. S., & Genale, A. H. (2022). Mobile application-based expert system for cattle disease diagnosis and treatment in Afan Oromo language. International Journal of Information Systems and Informatics, 3(3), 131–149. https://doi.org/10.47747/ijisi.v3i3.856
Szafraniec-Siluta, E., Zawadzka, D., & Strzelecka, A. (2022). Application of the logistic regression model to assess the likelihood of making tangible investments by agricultural enterprises. Procedia Computer Science, 207, 3894–3903. https://doi.org/10.1016/j.procs.2022.09.451
Thar, S. P., Ramilan, T., Farquharson, R. J., Pang, A., & Chen, D. (2021). An empirical analysis of the use of agricultural mobile applications among smallholder farmers in Myanmar. Electronic Journal of Information Systems in Developing Countries, 87(2), e12159. https://doi.org/10.1002/isd2.12159
The World Bank. (2020). Thailand rural income diagnostic: Challenges and opportunities for rural farmers. Retrieved May 5, 2024, from https://documents.worldbank.org/en/publication/documents-reports
Triatmojo, A., Muzayyanah, M. A. U., Syahlani, S. P., & Guntoro, B. (2024). Demographic targeting of users in mobile applications for livestock digital marketing among smallholder cattle farmers. Agrisocionomics: Jurnal Sosial Ekonomi Pertanian, 8(2), 602–613. https://doi.org/10.14710/agrisocionomics.v8i2.22722
Yuniarsih, E. T., Salam, M., Jamil, M. H., & Tenriawaru, A. N. (2024). Determinants determining the adoption of technological innovation of urban farming: Employing binary logistic regression model in examining Rogers’ framework. Journal of Open Innovation: Technology, Market, and Complexity, 10(2), 100307. https://doi.org/10.1016/j.joitmc.2024.100307
Authors
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors submitting manuscripts should understand and agree that copyright of manuscripts of the article shall be assigned/transferred to Tropical Animal Science Journal. The statement to release the copyright to Tropical Animal Science Journal is stated in Form A. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA) where Authors and Readers can copy and redistribute the material in any medium or format, as well as remix, transform, and build upon the material for any purpose, but they must give appropriate credit (cite to the article or content), provide a link to the license, and indicate if changes were made. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
Article Details
