North Sumatra's Food Availability Model: Technology Adoption, Farmer Organization Support, and Geographic Variability
DOI:
https://doi.org/10.35877/454RI.qems4476Keywords:
Agricultural productivity, Food availability, Geographic variation, Institutional support, Technology adoptionagricultural productivityAbstract
Abstract
Food availability is a strategic issue affecting economic stability and community welfare, particularly in agrarian regions such as North Sumatra Province. This study aims to analyze factors influencing food availability, including institutional support, geographic variation, and the adoption of agricultural technology. Data were collected through a survey of 100 rice farmers in Percut District, Deli Serdang Regency, using a structured questionnaire. Analysis was conducted using Structural Equation Modeling (SEM) with Smart PLS software to evaluate validity, reliability, and relationships among variables. The results indicate that institutional support and geographic variation significantly affect food availability, while the adoption of modern agricultural technology does not show a significant effect. Institutional support, including training, extension services, and production facilitation, enhances farmers’ productivity, whereas physical environmental conditions, such as soil quality, topography, and rainfall, are primary determinants of production success. These findings emphasize the importance of strengthening institutional capacity and regional management as policy priorities, while technology implementation should be adapted to local needs and farmers’ capacities. Limitations include a restricted geographic scope and technology variables that do not fully capture modern agricultural complexity. This study provides a conceptual foundation for strategies aimed at enhancing food security and guiding local policy interventions.
Keywords:. Agricultural productivity; Food availability; Geographic variation; Institutional support; Technology adoptionagricultural productivity
References
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Anwarudin, A., Andriyani, W., Dp, B. P., & Kristomo, D. (2022). The Prediction on the Students’ Graduation Timeliness Using Naive Bayes Classification and K-Nearest Neighbor. Journal of Intelligent Software Systems, 1(1), 75. https:/doi.org/10.26798/jiss.v1i1.597
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Bahua, M. I. (2016). Assessing professional competencies of agricultural extension workers: A case study of Indonesian’s agribusiness sector. International Journal of Agriculture Innovations and Research, 4(4), 743–746.
Basir-Cyio, M., Isrun-Baso, M., Nakazawa, K., Mahfudz-Muchtar, T., Napitupulu, M., Anshary, A., Rauf, R. A., & Laude, S. (2020). The effect of traditional gold mining to land degradation mercury contamination and decreasing of agricultural productivity. Bulgarian Journal of Agricultural Science, 26(3), 612–621.
Blanch, J., Walter, T., & Enge, P. (2017). Protection levels after fault exclusion for advanced RAIM. Navigation Journal of the Institute of Navigation, 64(4), 505–513. https:/doi.org/10.1002/navi.210
BPS Sumatra Utara. (2014). Sumatra Utara dalam Angka 2014. https:/sumut.bps.go.id/publication/2014/10/30/7f2d6341e46e31b4bd79b377/sumatera-utara-dalam-angka-2014.html
Chammem, N., Issaoui, M. D. E., Almeida, A. I. D., & Delgado, A. M. (2018). Food crises and food safety incidents in European Union, United States and Maghreb Area: Current risk communication strategies and new approaches. Journal of AOAC International, 101(4), 923–938. https:/doi.org/10.5740/jaoacint.17-0446
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Ferroni, M., & Zhou, Y. (2017). The private sector and India’s agricultural transformation. Global Journal of Emerging Market Economies, 9(1–3), 28–37. https:/doi.org/10.1177/0974910117716406
Glendenning, C. J., Chandra, B. S., & Asenso-Okyere, K. (2019). Extension through entrepreneurial approach: Evaluating the agriclinics program. In S. C. Babu & P. K. Joshi (Eds.), Agricultural Extension Reforms in South Asia (pp. 201–234). Academic Press. https:/doi.org/10.1016/b978-0-12-818752-4.00011-4
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook. Springer International Publishing. https:/doi.org/10.1007/978-3-030-80519-7
Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd, Ed.). Sage Publications.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https:/doi.org/10.1108/EBR-11-2018-0203
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https:/doi.org/10.1007/s11747-014-0403-8
Lubis, M. M., Bakti, D., Ginting, R., & Ayu, S. F. (2022). Production and demand forecasting analysis of rice in North Sumatra in 2030. IOP Conference Series: Earth and Environmental Science, 977(1), 012060. https:/doi.org/10.1088/1755-1315/977/1/012060
Mansour, T. G. I., Abdelazez, M. A., Eleshmawi, K. H., & Abd El-Ghani, S. S. (2019). Environmental SWOT analysis for agricultural extension in North Sinai governorate Egypt. Turkish Journal of Agriculture - Food Science and Technology, 7(10), 1503–1508. https:/doi.org/10.24925/turjaf.v7i10.1503-1508.2216
Murray, C. J. L., Callender, C. S. K. H., Kulikoff, X. R., Srinivasan, V., Abate, D., Abate, K. H., & Lim, S. S. (2018). Population and fertility by age and sex for 195 countries and territories 1950–2017: A systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 392(10159), 1995–2051. https:/doi.org/10.1016/S0140-6736(18)32278-5
Pawlak, K., & Ko?odziejczak, M. (2020). The role of agriculture in ensuring food security in developing countries: Considerations in the context of sustainable food production. Sustainability, 12(13), 5488. https:/doi.org/10.3390/su12135488
Prastyono, A., Gautama, B. H., & Zhafranianto, I. (2023). Penggunaan Chatbot Artificial Intelligence dan Pembangunan Karakter Mahasiswa: Sebuah Studi Empiris. 12.
Priefer, C., Jörissen, J., & Bräutigam, K. R. (2016). Food waste prevention in Europe: A cause-driven approach to identify leverage points for action. Resources Conservation and Recycling, 109, 155–165. https:/doi.org/10.1016/j.resconrec.2016.03.004
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. SmartPLS GmbH.
Sabir, S. S., Sukesi, K., & Yuliati, Y. (2018). The performance of agricultural extension workers in utilizing cyber extension in Malang Raya Region. Journal of Socioeconomics and Development, 1(2), 113–120. https:/doi.org/10.31328/jsed.v1i2.772
Saleh, H., Surya, B., Ahmad, D. N. A., & Manda, D. (2020). The role of natural and human resources on economic growth and regional development. Journal of Open Innovation: Technology Market and Complexity, 6(4), 103. https:/doi.org/10.3390/joitmc6040103
Yang, T., Qian, K., Lo, D. C.-T., Xie, Y., Shi, Y., & Tao, L. (2016, April). Improve the Prediction Accuracy of Naïve Bayes Classifier with Association Rule Mining. 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS). 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), New York, NY, USA. https:/doi.org/10.1109/bigdatasecurity-hpsc-ids.2016.38
Published
2025-12-07
How to Cite
Lubis, M. M. (2025). North Sumatra’s Food Availability Model: Technology Adoption, Farmer Organization Support, and Geographic Variability. Quantitative Economics and Management Studies, 6(6). https://doi.org/10.35877/454RI.qems4476
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