Volume 6, Issue 6, December 2020, Page: 163-169
Post-Harvest Loss Modeling of Maize Produce in Kenya
Julius Sang, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Wanjoya, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Antony Ngunyi, Department of Statistics and Actuarial Science, Dedan Kimathi University of Technology, Nyeri, Kenya
Received: Oct. 6, 2020;       Accepted: Oct. 21, 2020;       Published: Oct. 30, 2020
DOI: 10.11648/j.ijdsa.20200606.11      View  59      Downloads  36
Abstract
The classical linear model is commonly used to model the relationship between a response variable and a set of explanatory variables. The normality assumption is usually required so as to ease the hypothesis testing for the various linear regression models but it can be misleading for a proportional response variable that is bounded. This makes the ordinary least squares regression inappropriate for a regression model with a bounded dependent variable. This research proposes the fractional beta regression model as an alternative to help examine the determinants of post-harvest loss management of maize produce for farmers in Kenya. The response variable (Post-Harvest Loss Coefficient (PHLC)) is assumed to have a mixed continuous-discrete distribution with probability mass between zero and one. The fractional beta distribution is used to describe the continuous component of the model, since its density has a wide range of different shapes depending on the values of the two parameters that index the distribution. The study uses a suitable parameterization of the beta law in terms of its mean and a precision parameter, the parameters of the mixture distribution shall be modeled as functions of regression parameters. The considered parameters are Agriculture, Storage, Education, Fumigation and Transport. Inference on parameters, model diagnostics and model selection tools for the fractional beta regression is also be provided. Data used for this research was purely primary data which was collected from Uasin Gishu County, Kenya maize farmers through administration of a research questionnaire.
Keywords
Fractional Beta Regression, Post-Harvest Losses, Maize Produce
To cite this article
Julius Sang, Anthony Wanjoya, Antony Ngunyi, Post-Harvest Loss Modeling of Maize Produce in Kenya, International Journal of Data Science and Analysis. Vol. 6, No. 6, 2020, pp. 163-169. doi: 10.11648/j.ijdsa.20200606.11
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Unlu, H. & Aktas, S. (2017). Beta regression for the Indicator values of well-being Index for provinces in Turkey. Journal of Engineering Technology and Applied Sciences 2 (2); 101-111. doi: 10.30931/jetas.321165.
[2]
Rojas, O. (2019). Operational maize yield model development and validation based on remote sensing and agro-meteorological data in Kenya. International Journal of Remote sensing, (28) 17; 3775-3793. doi: 10.1080/01431160601075608.
[3]
Balgah, S. N., Tata, E. S. & Mojoko F. M. (2017). Effects or rainfall and temperature oscillations on maize yields in Buea-Division, Cameroon. Journal of Agricultural science, (9) 2; 63-72. doi: 10.5539/jas.v9n2p63.
[4]
Pangayar, R. S., Muraligopal, S., Vasanthi, R. & Swaminathan, B. (2015). Statistical Analysis of Trends in the maize area, production and productivity in India. Annals of plant and soil research 17 (special issue): 233-237.
[5]
Adesoji & Babatunde (2013). Applying artificial neural network modeling for predicting post-harvest loss in some common agrifood commodities. Nigerian stored products research institute, Nigeria. Proceedings of the International Food Operations and Processing Simulation Workshop 978-88-97999-83-6; Bruzzone, Longo, Piera and Vignali Eds.
[6]
Hugo, P. K. (2016). Yield and technical efficiency of maize production in Busia County, Kenya. Moi university-Department of Economics.
[7]
Jadhav, V., Chinnappa, B. V. & Gaddi, G. M. (2017). Application of ARIMA model for forecasting Agricultural prices. Journal of Agricultural Science and Technology 19 (5); 981-992.
[8]
Gurmu, M. Y., Meyer, F. & Hassan, R. (2017). Modeling price formation and dynamics in the Ethiopian maize market. Journal of Agricultural Science and Technology 19 (5); 1439-1452.
[9]
Short, C., Mulinge, W., & Witwer, M. (2012). Analysis of incentives and disincentives for maize in Kenya. Technical notes series, Rome: MAFAP, FAO.
[10]
Chen, X., Wu, L., Shan, L. & Zang, Q. (2018). Main Factors Affecting Post-Harvest Grain Loss during the Sales Process: A Survey in Nine Provinces of China. Sustainability, 2018 (10); 661. doi: 10.3390/su10030661.
[11]
Rembold, F., Hodges, R., Bernard, M., Knipschild, H. & Leo, O. (2011). An Innovative Framework to Analyze and Compute Quantitative Post-Harvest Losses for Cereals under different farming and environmental conditions in East and Southern Africa. The African Post-Harvest Losses Information Systems (APHLIS). doi: 10.2788/40345.
[12]
Koskei, P., Bii, C. C., Musotsi, P. & Muturi, S. K. (2020). Post-Harvest Storage Practices of Maize in Rift Valley and Lower Eastern Regions of Kenya: A Cross-Sectional Study. International Journal of Microbiology, 2020. doi: 10.11/2020/6109214.
[13]
Parfitt, J., Barthel, M. & Macnaughton, S. (2010). Foodwaste within food supply chains. A quantification and potential for change to 2050, Philosophical Transaction of the Royal Society Biological sciences, (365) 2; 3065-3081. doi: 10.1098/rstb.2010.0126.
[14]
Ayieko, P. O., Nelson, W., Wawire, H. & Ombuki, C. (2013). The response of maize production in Kenya to economic incentives. International Journal of Development and Sustainability.
[15]
Mwanjele, S. M., Waiganjo, P. W., Moturi, A. C. & Muthoni, M. (2014). Intelligent System For Predicting Agricultural Drought For Maize Crop. International Journal of Technology Enhancements and Emerging Engineering Research, (2) 4; 2347-4289.
Browse journals by subject