Forecasting Nigeria annual yam production using Grey System Model

  • Lawal Adamu Department of Mathematics, Federal University of Technology, Minna, Nigeria
  • Saidu Daudu Yakubu Department of Mathematical Science, Ibrahim Badamasi Babagida University, Lapai, Nigeria
  • Ndidiamaka Edith Didigwu Department of Mathematics, Enugu State University of Science and Technology, Nigeria
  • Abdullahi Abubakar Department of Mathematics, Faculty of Science, Air Force Institute of Technology, Kaduna, Nigeria
  • Isaac Adaji Department of Mathematics and Statistics, Confluence University of Science and Technology, Osara, Kogi State. Nigeria
  • Mohammed Yahaya Department of Mathematics, Federal University of Technology, Minna, Nigeria
DOI: https://doi.org/10.6084/m9.figshare.20710417
Keywords: Yam, Production, Grey-System, Forecasting, Nigeria

Abstract

Demand for reliable and accurate annual crop production forecast has been on the increase in recent time, because it avails the famers and the government opportunity to plan ahead of time, with regard to import or export in the event of shortfall or surplus to ensure food security of the respective nation. A GM(1,1) system model that forecast the Nigeria annual yam production has been developed and tested on known data. The data used in the research were collected from the archive of Central Bank of Nigeria for a period of Nine years (2010-2018). The fitted result from the model showed 98:97% accuracy, this indicate that, the model is reliable and dependable. Therefore, results from the model could serve as a guide to the yam farmers and
the government to plan strategies for high yam production in the country. 

Published
2022-08-29
How to Cite
Adamu, L., Yakubu, S. D., Didigwu, N. E., Abubakar, A., Adaji, I., & Yahaya, M. (2022). Forecasting Nigeria annual yam production using Grey System Model. International Journal of Mathematical Sciences and Optimization: Theory and Applications, 8(1), 109 - 116. https://doi.org/10.6084/m9.figshare.20710417
Issue
Vol 8 No 1 (2022)
Section
Articles

Copyright (c) 2022 Lawal Adamu, Saidu Daudu Yakubu, Ndidiamaka Edith Didigwu, Abdullahi Abubakar, Isaac Adaji, Mohammed Yahaya

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This work is licensed under a Creative Commons Attribution 4.0 International License.

This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, adaptation, and reproduction in any medium, provided that the original work is properly cited.