Variability of Maize Prices in Ghana: A Generalized Additive Mixed Model Approach
Variability of Maize Prices in Ghana: A Generalized Additive Mixed Model Approach
Smart Asomaning Sarpong
Kumasi Technical University
Daniel Asare-Kyei
Kumasi Technical University
Felix Addae
Kumasi Technical University
Dora Melanie Yanchira
Department of Statistics, North Carolina State University, Raleigh, NC, USA
Keywords: Generalized Additive Mixed Models; Maize; Price variability; Markets
Abstract
Maize is grown throughout Ghana, and it is one most common cereal on every market; from
A (Ashaiman market) to Z (Zebila Market). In this study, variability in prices of maize has been studied across 35 major markets in all 16 regions of Ghana. Data for this study were compiled weekly for a 5-year duration (2018–2022), except for a week when no maize was grown, i.e., the off-season. The location, week, and year were recorded, along with the price variations, to facilitate analysis. Generalized Additive Mixed Models were used to uncover patterns, trends, and potential factors influencing price. It is hereby established that the maize price increased smoothly from 2018 to 2020, and rapidly from 2020 to 2022. The Kotokura bamarket, Dambai market, Ho Central market, Tarkwa market, and Goaso markets recorded the highest Maize prices within the 5 years. Further analysis reveals that the location of the market contributes more (51.7%) to price variability than the ‘week’. Again, the average trend of maize prices tends to increase from week 1 in January to 3rd week in June and decrease from the last week in June to 3rd week in December. The Government and other agribusiness interest groups may invest in storage facilities to store maize from June to December to help in price stabilization during the off-season from January to early June. Further research will be needed to explain in detail why market location, far or near the farm gate, accounts for more than 50% of price variability.
Author Biographies
Smart Asomaning Sarpong, Kumasi Technical University
Institute of Research, Innovation and Development – IRID. Kumasi Technical University, KsTU
Daniel Asare-Kyei, Kumasi Technical University
Institute of Research, Innovation and Development – IRID. Kumasi Technical University, KsTU
Felix Addae, Kumasi Technical University
Institute of Research, Innovation and Development – IRID. Kumasi Technical University, KsTU
Dora Melanie Yanchira, Department of Statistics, North Carolina State University, Raleigh, NC, USA
Department of Statistics, North Carolina State University, Raleigh, NC, USA
