Adoption and Challenges of Agricultural Technology and Machinery: Insights from Farmers in Lupao, Nueva Ecija
Published 12/30/2025
Keywords
- Agricultural Technology,
- Technology Adoption,
- farming,
- Sustainability practices
How to Cite
Copyright (c) 2026 The QUEST: Journal of Multidisciplinary Research and Development

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Abstract
This study explores the adoption and challenges of agricultural technology and machinery among 100 smallholder farmers in Lupao, Nueva Ecija. A descriptive survey design was employed, utilizing a structured questionnaire to gather data on respondents’ demographic profiles, levels of awareness, extent of technology use, and the socio-economic, institutional, technological, environmental, and cultural factors influencing adoption. Findings reveal that most respondents are male, over 45 years old, with at least a high school education and extensive farming experience. Farms are generally under three hectares, and rice remains the predominant crop. Results further indicate that farmers exhibit substantial awareness of modern machinery, sustainable practices, and government support programs. Many integrate traditional and modern methods, often owning or renting machinery to complement their operations. Adoption of agricultural technology is shaped by factors such as cost, farm size, age, institutional support, ease of use, and climate variability. However, farmers continue to face barriers, including high investment costs, limited access to credit, low technical knowledge, inadequate facilities, policy constraints, and sociocultural resistance. These challenges restrict the full and efficient adoption of innovations. The study highlights the importance of strengthening agricultural extension services, creating accessible financial schemes, providing hands-on training, and improving rural infrastructure. It also emphasizes the role of inclusive policy-making and active community involvement in promoting technology adoption. It is recommended that government programs focus on targeted capacity-building and sustained support to enable smallholder farmers to maximize the benefits of modern agricultural technologies for resilient and sustainable farming.
References
- Abbass, H. A. (2019). Social integration of artificial intelligence: functions, automation allocation logic and human-autonomy trust. Cognitive Computation, 11(2), 159-171. https://link.springer.com/article/10.1007/s12559-018-9619-0
- Aitchison, S., & McNeill, P. (2019). Authorship in the age of artificial intelligence. Educational Philosophy and Theory, 51(12), 1229-1242. of Writing Studies, 9(2), 30-42. https://doi.org/10.1093/ijws/ijw023
- Binns, C. (2023). AI in academic writing: Enhancements and challenges. International Journal of Writing Studies, 9(2), 30–42. https://doi.org/10.1093/ijws/
- Calvo, R. A., O'Rourke, S. T., Jones, J., Yacef, K., & Reimann, P. (2010). Collaborative writing support tools on the cloud. IEEE Transactions on Learning Technologies, 4(1), 88-97. https://ieeexplore.ieee.org/abstract/document/5669252/
- Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443-3463. https://link.springer.com/article/10.1007/s10639-020-10159-7
- Chen, P. (2023). Artificial intelligence and the future of writing. New Media & Society, 25(2),568-588. http://www.doi.org/10.1177/14614448211022493.
- Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in education and teaching international, 61(2), 228-239. https://www.tandfonline.com/doi/full/10.1080/14703297.2023.2190148
- Esteban, A. P. (2019). Algri: A programming model and algorithm for building a smart agricultural production system. Agricultural Research & Technology: Open Access Journal, 22(5), 195-208.
- Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2024). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460-474.
- Gayed, J. M., Carlon, M. K. J., Oriola, A. M., & Cross, J. S. (2022). Exploring an AI-based writing Assistant's impact on English language learners. Computers and Education: Artificial Intelligence, 3, 100055.
- Hu, W.(2023). A review of artificial intelligence writing tools for language learning. Computer Assisted Language Learning, 36(2), 189–212.
- Jin, X., Jiang, Q., Xiong, W., Feng, Y., & Zhao, W. (2024). Effects of student engagement in peer feedback on writing performance in higher education. Interactive Learning Environments, 32(1), 128-143.
- Lee, J. Y., & Ryu, S. (2021). The potential and challenges of artificial intelligence (AI) writing Assistants in second language writing. RELC Journal, 52(2), 263-282.
- Niemann, T. (2022). The role of AI in academic writing: An overview. Computers & Education, 45(2), 204-215. https://doi.org/10.1016/j.compedu.2022.01.007.
- Sofi, B.B.M.A. (2024), "Artificial intelligence-powered tools and academic writing: to use or not to use ChatGPT", Saudi Journal of Language Studies, Vol. 4 No. 3, pp. 145-161. https://doi.org/10.1108/SJLS-06-2024-0029
- Swartz, S., Luck, S. and Sharma, S. (2025), "Global virtual teams projects and developing AI literacy: a mixed-methods study on preparing students for the international technology-infused workplace", Higher Education, Skills and Work-Based Learning, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/HESWBL-09-2024-0266
- Tyson, M.M. and Sauers, N.J. (2021), "School leaders' adoption and implementation of artificial intelligence", Journal of Educational Administration, Vol. 59 No. 3, pp. 271-285. https://doi.org/10.1108/JEA-10-2020-0221
- Zhang, H., & Kim, J. (2023). The impact of AI writing assistants on student Learning Outcomes. Journal of Educational Computing Research, 61(2), 215-233. https://doi.org/10.1177/07356331231122814.