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

western and Upper East regions, as well as in the Ashanti region, except for slightly higher MSE in the Eastern region. The Stacking model provides a reliable and accurate approach for predicting maize yield based on climate variables, especially with limited data. There exist certain constraints within this investigation that pave the way for future explo- ration. Primarily, the data at our disposal is constrained, potentially impacting the model’s per- formance. Consequently, future research endeavors could leverage extensive datasets to enhance the accuracy of machine learning models in this field. Moreover, specific machine learning models, such as Linear Regression, are susceptible to multicollinearity. Given that the temperature variables (minimum temperature, maximum temperature, average temperature) may contribute to multicollinearity, alternative regression models should be considered. For instance, one might explore penalized linear regression models like Ridge (L2 regularization), Lasso (L1 regularization), and/or Elastic Net (a combination of L1 and L2 penalties). Author Contributions Conceptualization: Samuel Asante Gyamerah, Frank Baffour-Ata. Data curation: Henry Ofoe Agbi-Kaeser. Formal analysis: Samuel Asante Gyamerah, Clement Asare, Henry Ofoe Agbi-Kaeser. Investigation: Clement Asare. Methodology: Samuel Asante Gyamerah, Clement Asare, Henry Ofoe Agbi-Kaeser. Software: Henry Ofoe Agbi-Kaeser. Supervision: Samuel Asante Gyamerah. Validation: Samuel Asante Gyamerah, Frank Baffour-Ata. Visualization: Samuel Asante Gyamerah.

Writing – original draft: Clement Asare, Henry Ofoe Agbi-Kaeser. Writing – review & editing: Samuel Asante Gyamerah, Frank Baffour-Ata. References 1. J. R. Porter et al., “Food security and food production systems,” 2014. 2. Ahmed M. and Hassan F., “Cumulative effect of temperature and solar radiation on wheat yield,” Not Bot Horti Agrobot Cluj Napoca, vol. 39, pp. 146–152, 2011. 3. Tunde A. M., Usman B. A., and Olawepo V. O., “Effects of climatic variables on crop production in Patigi L. G. A., Kwara State, Nigeria,” Journal of Geography and Regional Planning, vol. 4, no. 14, pp. 695– 700, 2011, [Online]. http://www.academicjournals.org/JGRP 4. Maitah M. et al., “Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia,” Agronomy, vol. 11, no. 11, p. 2344, Nov. 2021. https://doi.org/ 10.3390/agronomy11112344 5. Makowski D., Marajo-Petitzon E., Durand J.-L., and Ben-Ari T., “Quantitative synthesis of temperature, CO2, rainfall, and adaptation effects on global crop yields,” European Journal of Agronomy, vol. 115, p. 126041, Apr. 2020. https://doi.org/10.1016/j.eja.2020.126041 6. Bhat K. A. et al., “Low Temperature Stress Tolerance: An Insight Into the Omics Approaches for Legume Crops,” Front Plant Sci, vol. 13, Jun. 2022. https://doi.org/10.3389/fpls.2022.888710 PMID: 35720588 7. Lizaso J. I. et al., “Impact of high temperatures in maize: Phenology and yield components,” Field Crops Res, vol. 216, pp. 129–140, Feb. 2018. https://doi.org/10.1016/j.fcr.2017.11.013

8. Kaur G., Singh G., Motavalli P. P., Nelson K. A., Orlowski J. M., and Golden B. R., “Impacts and man- agement strategies for crop production in waterlogged or flooded soils: A review,” Agron J, vol. 112, no. 3, pp. 1475–1501, May 2020. https://doi.org/10.1002/agj2.20093 PLOS ONE Impact of climate variability on maize yields in Ghana PLOS ONE | https://doi.org/10.1371/journal.pone.0305762 June 25, 2024 21 / 24