COMPOSITION AND DIVERSITY OF NON-TIMBER FOREST PRODUCTS (NTFPs) IN BATURIYA WETLAND GAME RESERVE, JIGAWA STATE, NIGERIA
DOI:
https://doi.org/10.33003/fjs-2020-0403-402Keywords:
Diversity, floristic composition, Non-timber forest products, Baturiya Game reserveAbstract
Inadequate documentation and information of Non-Timber Forest Products (NTFPs) in the study sites call for the assessment of composition and diversity of the concern products. Therefore, the aim of the study is to assess the Composition and Diversity of Non- Timber Forest Products (NTFPs) in Baturiya Wetland Game Reserve, Jigawa State, Nigeria. A four (4) transects of 1km length was laid at an interval of 400 m. Likewise, in each transect, 4 plots of 100 x 100 m was laid alternately along each transect at 200 m interval. Also four (4) quadrants of 1m x1m in size for small non-timber forest products was randomly laid in each 100 x100m plot for each of the habitats in each plot, plant stocks identified were recorded. Data collected were analysed using Simpson index, Sorenson index and descriptive statistic. Results showed that Upland area had the highest species, number of individual and diversity of Non–Timber Forest Product followed by Swampy area and Fadama area with the values of (94, 1464, D-0.981), (63, 842, D-0.970) and (60, 805, D-0.969) respectively. Upland area and Swampy area had the highest similarity index of 78.5%. The results suggest that the documentation, inventory and management of NTFPs should be addressed in operational plan to enhance its diversity
References
References
Battaglia, M. (2008). Convenience Sampling. In P. J. Lavrakas, Encyclopedia of Survey Research Methods (pp. 149-150). Thousand Oaks, CA: Sage Publications.
Cha, H. J., Kim, Y. S., Park, S. H., Yoon, T. B., Jung, Y. M., & Lee, J. H. (2006). Learning Style Diagnosis Based on User Interface Behavior for the Customization of Learning Interfaces in an Intelligent Tutoring System. In M. Ikeda, K. D. Ashley, & T. W. Chan, Intelligent Tutoring Systems, Springer, Lecture Notes in Computer Science (pp. 513-524).
Chang, Y., Kao, W., Chu, C., & Chiu, C. (2009). A learning style classification mechanism for e-learning . Computers & Education, 53(2), 273–285.
Felder, R., & Silverman, L. (1988). Learning and teaching styles in engineering education. Eng. Educ, 78, 674–681.
GarcÃa, P., Amandi, A., Schiaffino, S., & Campo, M. (2007). Evaluating Bayesian Networks’ Precision for Detecting Students’ Learning Styles. Computers & Education, 49(3), 794-808.
Graf, S. (2007). Adaptivity in Learning Management Systems Focussing on Learning Styles. PhD thesis.Vienna University of Tecnology.
Graf, S., Kinshuk, & Liu, T. (2008). Identifying learning styles in learning management systems by using indications from students’ behaviour. In: ICALT, IEEE. 8th IEEE International Conference on Advanced Learning Technologies, (pp. 482-486).
Hao, Z., Tao, H., Liu, S., Hao, Y., Jia, L., Huali, Y., & Yu, X. (2020). A learning style classification approach based on deep belief network for large-scale online education. J Cloud Comp, 9(26).
Jain, A., & Dubes, R. (1988). Algorithms for Clustering Data. New Jersey: Prentice Hall.
Learn Moodle. (2016, August). Retrieved from Moodle: http://research.moodle.net/158/
Li, L., & Abdul Rahman, S. (2018a, July 25). Students' learning style detection using tree augmented naive Bayes. doi:10.1098/rsos.172108
Li, L., & Abdul Rahman, S. (2018b). Supplementary material from "Students' learning style detection using tree augmented naive Bayes". The Royal Society. Available: https://doi.org/10.6084/m9.figshare.c.4156019.v2.
Morissette, L., & Chartier, S. (2013). The k-means clustering technique: General considerations and implementation in Mathematica. Tutorials in Quantitative Methods for Psychology . 9, pp. 15-24. doi:10.20982/tqmp.09.1.p015
Petchboonmee, P., Phonak, D., & Tiantong, M. (2015, September). A Comparative data mining technique for David Kolb's experiential learning style classification. International Journal of Information and Education Technology, 5(9).
Prabhani Pitigala Liyanage, M., Lasith Gunawardena, K. S., & Hirakawa, M. (2013). International Conference on Advances in ICT for Emerging Regions (ICTer). 261-265.
Published
How to Cite
Issue
Section
FUDMA Journal of Sciences