ECONOMIC ANALYSIS OF MARKETING EFFICIENCY OF PINEAPPLE IN KANO STATE MARKETS, NIGERIA
DOI:
https://doi.org/10.33003/fjs-2020-0402-226Keywords:
Analysis, Efficiency, Pineapple, Marketing, Kano StateAbstract
The study analysed the marketing efficiency of pineapple in selected markets in Kano metropolis. Data was collected with the used of questionnaire and were analysed using descriptive statistics, market margin analysis and market efficiency. Results shows that the majority (33.3%) of the respondents were within the ages of 47-55 years, followed by 23.8% within the age range of 29-37 years and 9.5% of age range of 56-64 years were the minimum. Household size, in the wholesaler’s side category 5-8 size had the highest members with 42.80%, followed by 1-4 and 17-20 with 19.10% each, while 14.30% had 9-12 and the least was households within the range of 13-16 with only 4.8%, while in the retailer’s side category 1-5 size had the highest members with 40.90%, followed by 11-15 with 24.90%, then 22.60% had 6-10 while 9.10% had 16-20 size and the least was household within the range of 21-25 with only 2.30%. However, 31-37years category ranked the least with 4.80%. On the retailers side the result reveals that 13-20 years category ranked the highest with 41.00% and 37-44years category were the least with 6.80% each. The marketing margin analysis indicated that for every 14.4kg pineapple, gross marketing margin of wholesalers N 158.85 was higher than that of retailers N 129.66 whereas net marketing margin of retailers with N 91.24 was higher than that of wholesalers with N 59.09, return on investment of retailers with 1.11 was also higher than that of wholesalers with 1.08, and however, marketing margin of wholesalers
References
Celikoglu HB, Cigizoglu HK. (2007). Public transportation trip flow modeling with generalized
regression neural networks. Adv. Eng. Software 2007;38:71–9.
Cigizoglu HK, Alp M. (2005). Generalized regression neural network in modelling river sediment yield. Adv. Eng. Software 2005;37: 63–8.
Bors, A. G., Gabbouj, G., (1994) “Minimal topology for a radial basis function neural
network for pattern classification,†Digital Signal Processing: a review journal, vol. 4, no. 3,
pp. 173-188.
Kim B, Lee DW, Parka KY, Choi SR, Choi S (2004). Prediction of plasma etching using a
randomized generalized regression neural network. Vacuum 2004; 76:37–43.
Dvořáková, l. and M. Vochozka, (2015). Vykorystannjanejronnychmereždlja
prognozuvannjarozvytkukompaniji. Nacional’naakademijanaukUkrajinyKijiv. (3), 5-12. ISSN 2409-8876.
Vochozka, M. and P. Sheng, (2016). The application of artificial neural networks on the
prediction of the future financial development of transport companies. Komunikácie.18(2),
-67.
Chen W, Fu ZJ, Chen CS (2014). Recent advances in radial basis function collocation
methods. Springer, Berlin.
Gilman S (2010). Oxford American handbook of neurology. Oxford University Press:
Oxford, UK; 2010.
Igor O. Korolev (2014). Alzheimer’s Disease: A Clinical and Basic Science Review,
Medical Student Research Journal, vol. 4 pp. 24-33.
Aram So, DanialHooshyar, Kun Woo Park and HeuiSeok Lim (2017), Early Diagnosis of
Dementia from Clinical Data by Machine Learning Techniques, Appl. Sci. 2017,1-17
Chen, R., Herskovits, E.H (2010). Machine-learning techniques for building a diagnostic
model for very milddementia. Neuroimage 2010, 52, 234–244.
Joshi, S., Shenoy, P.D., Venugopal, K.R., Patnaik L.M (2009), Evaluation of different
stages of dementia employing neuropsychological and machine learning techniques. In
Proceedings of the First International Conference on Advanced Computing, Chennai, India,
–15 December 2009.
Williams, J.A, Weakley, A., Cook, D.J. and Schmitter-Edgecombe M. (2013), Machine
learning techniques for diagnostic differentiation of mild cognitive impairment and
dementia. In Proceedings of the Twenty-Seventh AAAI Conference on Artificial
Intelligence (AAAI-13), Bellevue, WA, USA, 14–18 July 2013.
Cho, P.-C., Chen, W.-H (2012). A double layer dementia diagnosis system using machine
Learning techniques. In Proceedings of the 13th International Conference, (EANN 2012),
London, UK, 20–23, September 2012.
Shanklea, W.R., Mania, S., Dick, M.B., Pazzani, M.J (1998). Simple models for estimating
dementia severity using machine learning. Stud. Health Technol. Inform. 1998,52 Pt 1, 472–476.
Shaikh Abdul Hannan, R. R. Manza, R. J. Ramteke, (2010). Generalized Regression Neural
Network and Radial Basis Function for Heart Disease Diagnosis, International Journal of
Computer Applications, vol. 7 (13) 7-13.
David Oyewola, Danladi Hakimi, Kayode Adeboye, Musa Danjuma Shehu (2016). Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis, International Journal of Engineering Technologies-Ijet, Vol.2, No.4, 142-145.
Published
How to Cite
Issue
Section
FUDMA Journal of Sciences
