PROFITABILITY AND CONSTRAINTS OF AGRICULTURAL COMMERCIALIZATION AMONG SMALLHOLDER RICE FARMERS IN NASARAWA STATE, NIGERIA
DOI:
https://doi.org/10.33003/fjs-2024-0806-3075Keywords:
Profitability, Smallholders, Rice, Farmers, Costs, ReturnAbstract
This study analysed the profitability of smallholder rice farmers in Nasarawa State, Nigeria. Multistage sampling technique was used to select 300 rice farmers for the study. The primary data utilized for the study were collected using structured questionnaires. The data were analysed using descriptive statistics and farm budgetary techniques. The result of the study revealed that rice production in the study area is profitable with gross margin of N103, 876.17/ha. The most important problems identified were inadequate supportive institutions, poor access to credit, and poor rural infrastructure. Based on the findings of the study, it was recommended that provision of adequate and improved agricultural supportive institutions such as research, financial and marketing as well as extension services should be strengthened in order to improve smallholder rice profitability in the study area.
References
Adjabi, I., Ouahabi, A., Benzaoui, A. and Taleb-Ahmed, A., (2020) Past, present, and future of face recognition: A review. Electronics, 9(8), p.1188.
Barney, N. (n.d.). Face detection. SearchEnterpriseAI. Retrieved from https://www.techtarget.com/searchenterpriseai/definition/face-detection.
DeepInsight. 2024. InsightFace. GitHub repository. Available at: https://github.com/deepinsight/insightface.
Faruqe, M. O., & Hasan, M. A. M. (2009, August). Face recognition using PCA and SVM. In 2009 3rd international conference on anti-counterfeiting, security, and identification in communication (pp. 97-101). IEEE.
Google Cloud. (n.d.). What is artificial intelligence? Google Cloud. https://cloud.google.com/learn/what-is-artificial-intelligence.
Isaac, S., Ayodeji, D. K., Luqman, Y., Karma, S. M., & Aminu, J. (2024). CYBER SECURITY ATTACK DETECTION MODEL USING SEMI-SUPERVISED LEARNING. FUDMA JOURNAL OF SCIENCES, 8(2), 92-100.
Jayaswal, R., & Dixit, M. (2020, April). Comparative analysis of human face recognition by traditional methods and deep learning in real-time environment. In 2020 IEEE 9th international conference on communication systems and network technologies (CSNT) (pp. 66-71). IEEE.
Kaspersky. (n.d.). What is facial recognition? Kaspersky. Retrieved November 6, 2024, from https://www.kaspersky.com/resource-center/definitions/what-is-facial-recognition.
Khairuddin, M. H., Shahbudin, S., & Kassim, M. (2021). A smart building security system with intelligent face detection and recognition. In IOP conference series: Materials science and engineering (Vol. 1176, No. 1, p. 012030). IOP Publishing.
Kortli Y., Jridi M., Al Falou A., & Atri M. (2020). Face Recognition Systems: A Survey. Sensors, 20(2), 342. https://doi.org/10.3390/s20020342.
Lin, S. H. (2000). An introduction to face recognition technology. Informing Sci. Int. J. an Emerg. Transdiscipl., 3, 1-7.
Milossi, M. (2021). Remote biometric identification systems and ethical challenges: The case of facial recognition. In 2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) (pp. 1-6). IEEE.Paper 12.
Porter, G., & Doran, G. (2000). An anatomical and photographic technique for forensic facial identification. Forensic Science International, 114(2), 97-105. https://doi.org/10.1016/S0379-0738(00)00290-5.
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