Volume 3, Issue 4, July 2018, Page: 65-71
Forecasting of Cashew Area Harvested, Yield and Production Using Trend Analysis
Okeke Daniel Chukwujioke, Department of Agricultural Science Education, Nwafor Orizu College of Education, Nsugbe, Nigeria
Akarue Blessing Okiemute, Department of Agricultural Science Education, College of Education, Warri, Nigeria
Received: Apr. 18, 2018;       Accepted: Jun. 20, 2018;       Published: Jul. 7, 2018
DOI: 10.11648/j.ijae.20180304.11      View  981      Downloads  115
The study was conducted to examine the trend analysis of area, yield and production for Cashew in Nigeria. The findings of the study are based on data from the years (1961 to 2016) and was taken from the database of FAO (2018). Three Models of trend analysis were applied. The models were Linear Trend Model, Quadratic Trend Model, and cubic trend Model. The most appropriate Model for trend analysis of the present study was Cubic Trend Model based on the highest R2 of (95.76 %), (95.76%) and (88.12%) for cashew area harvested, production and yield respectively, coupled with the lowest residual sum square and mean square error. Forecasting of the data was done up to 2026. The forecasted values were area harvested (409459.07ha -486296.12), yield (24272.09hg/ha – 27422.91hg/ha) and production (990382.68tons-1.127E+06). The study presents an insight to national policy makers regarding this essential crop and provides them with a reference range of values in area harvested, yield and production in future so that they may be able to effectively deal with cashew production in Nigeria.
Cashew, Area Harvested, Yield, Production, Cubic Trend Model, RSS, MSE, Nigeria
To cite this article
Okeke Daniel Chukwujioke, Akarue Blessing Okiemute, Forecasting of Cashew Area Harvested, Yield and Production Using Trend Analysis, International Journal of Agricultural Economics. Vol. 3, No. 4, 2018, pp. 65-71. doi: 10.11648/j.ijae.20180304.11
Copyright © 2018 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.
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