Assessing the impact of climate variability on maize yields in the different regions of Ghana—A machine learning perspective

RESEARCH ARTICLE Assessing the impact of climate variability on maize yields in the different regions of Ghana —A machine learning perspective Samuel Asante GyamerahID1*, Clement Asare1, Henry Ofoe Agbi-Kaeser1, Frank Baffour- Ata2 1 Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana, 2 Department of Environmental Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana * saasgyam@gmail.com, asante.gyamerah@knust.edu.gh Abstract Climate variability has become one of the most pressing issues of our time, affecting various aspects of the environment, including the agriculture sector. This study examines the impact of climate variability on Ghana’s maize yield for all agro-ecological zones and administrative regions in Ghana using annual data from 1992 to 2019. The study also employs the stacking ensemble learning model (SELM) in predicting the maize yield in the different regions taking random forest (RF), support vector machine (SVM), gradient boosting (GB), decision tree (DT), and linear regression (LR) as base models. The findings of the study reveal that maize production in the regions of Ghana is inconsistent, with some regions having high variability. All the climate variables considered have positive impact on maize yield, with a lesser vari- ability of temperature in the Guinea savanna zones and a higher temperature variability in the Volta Region. Carbon dioxide (CO2) also plays a significant role in predicting maize yield

across all regions of Ghana. Among the machine learning models utilized, the stacking ensemble model consistently performed better in many regions such as in the Western, Upper East, Upper West, and Greater Accra regions. These findings are important in under- standing the impact of climate variability on the yield of maize in Ghana, highlighting regional disparities in maize yield in the country, and highlighting the need for advanced techniques for forecasting, which are important for further investigation and interventions for agricultural planning and decision-making on food security in Ghana. Introduction Background of the study The variability of the climate has emerged as a critical concern in our era, impacting different facets of our surroundings, such as the agricultural industry. According to Porter et al. [1], cli- mate variability has a greater impact on agricultural production as compared to other factors due to their interaction. The extent of these effects is determined by different climate variables PLOS ONE PLOS ONE | https://doi.org/10.1371/journal.pone.0305762 June 25, 2024 1 / 24 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Gyamerah SA, Asare C, Agbi-Kaeser HO, Baffour-Ata F (2024) Assessing the impact of climate variability on maize yields in the different regions of Ghana—A machine learning perspective. PLoS ONE 19(6): e0305762. https:// doi.org/10.1371/journal.pone.0305762 Editor: Zakari Ali, MRC Unit The Gambia at LSHTM, GAMBIA Received: June 14, 2023 Accepted: June 4, 2024 Published: June 25, 2024

Copyright: © 2024 Gyamerah et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: We confirm that the data used was obtained from a third-party organization (Ghana Meteorological Agency and the Statistics, Research, and Information Directorate (SRID) of Ghana’s Ministry of Food and Agriculture) and hence data is only available upon request. The data underlying the results presented in the study are available from Ministry of Food and Agriculture info@meteo.gov.gh, Ghana and Ghana Meteorological Agency, info@mofa.gov.gh. We confirm that the data used was purchased from a third-party organization (Ghana Meteorological