Modelling Annual Cocoa Production Using ARIMA Time Series Model

Oni O.V.; Oni O.A.; Akanle Y.O.; Ogunleye T.B.

doi:10.52589/AJMSS-0LPATDNK

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Modelling Annual Cocoa Production Using ARIMA Time Series Model

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Publication Date: 2021-11-25

DOI: 10.52589/AJMSS-0LPATDNK

Volume/Issue: Volume 4, Issue 3 (2021)

Page No: 135 - 144

Journal: African Journal of Mathematics and Statistics Studies

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Abstract:

Cocoa is the most valuable tropical agricultural commodity, comes next to oil; a major target in Nigeria’s export diversification strategies. Cocoa production forecasting is important to the Nigerian agricultural transformation agenda. This study attempts to forecast Nigerian cocoa production between 2019 and 2025 using the ARIMA model. The automated analytical procedure implemented in the R software package indicated that ARIMA (0, 1, 1) is the combination with the least Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) and hence, the most appropriate for forecasting. The results revealed that cocoa production would fall by more than 20% in 2025 in comparison with 2018. Thus, to guard against the fall, cocoa farmers in the country should be incentivized through adequate financial and technical assistance.

Keywords:Annual cocoa productionARMAARIMAAIC.

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Authors retain copyright in their articles and have unrestricted rights to reuse, republish, archive, share, and distribute their work after publication.
All articles are published open access under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence, which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, including for commercial purposes, provided appropriate credit is given to the original author(s) and source.

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Copyrights

Authors retain copyright in their articles and have unrestricted rights to reuse, republish, archive, share, and distribute their work after publication.
All articles are published open access under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence, which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, including for commercial purposes, provided appropriate credit is given to the original author(s) and source.