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

in temperature in this region is small indicating how the temperature in the regions vary less over time. The region with the highest variability is the Volta region which experiences signifi- cant variations in temperatures. In all regions of Ghana, carbon dioxide (CO2) also plays a sig- nificant role in predicting maize production. In some areas, additional climate factors like precipitation and minimum temperature are also quite crucial. For instance, the Brong Ahafo, Greater Accra, and Upper East regions place a significant emphasis on precipitation. When compared to the Guinea savanna and Rainforest zones, the relevance of lowest temperature is more obvious in the transitional zone (such as Brong Ahafo). Policy implications Our research focuses on maize yield variability in all the regions in Ghana and its contributing components (climate variables), with the employment of machine learning algorithms. We employed machine learning algorithms because of the abilities in handling nonlinear data. Pol- icymakers should enhance agricultural technologies, farming techniques, and assistance pro- grams to maintain maize production. They should also be aware of the factors influencing maize output in Greater Accra and investigate climate-resilient farming methods. As maize yields increase across the country, these initiatives will also help to mitigate Ghana’s agricul- ture sector’s low average yield and significant yield variability. Overproduction and market sales that arise from this produce economic opportunities and food self-sufficiency, but it’s

important to take sustainability and climate change into account. Although greater production is a good thing, it is crucial to prioritize sustainable agriculture and the reduction of green- house gases, ensuring that climate-smart agriculture practices reduce the sector’s carbon foot- print and boost climate resilience. The information highlights the need to modify agricultural practices to local weather patterns to improve crop output and highlights the complexity of connections between climate and maize yield. Since Ghanaian maize yield assessments provide information on regional variability, yield determinants, and climate variables, this emphasizes the value of localized agriculture that considers regional weather variations and crop growth. The findings of these analyses show the importance of implementing targeted programs and regulations to address local issues, giving Ghana the chance to boost maize production, food security, economic development, and environmental sustainability. This study’s conclusions are based on analysis and facts based on our data span. To further understand Ghanaian maize yield patterns, new data and studies may require additional analy- sis. This study lays the groundwork for evidence-based decision-making and measures to boost maize production and agricultural resilience in the country employing the stacking ensemble learning model as an optimal predictive model. Conclusion This study sought to assess the impact of climate variability on all the regions in Ghana’s maize

yield and to develop an accurate prediction model by employing five machine learning tech- niques (SVM, GB, RF, DT and LR) to establish a high-precision prediction model using the Stacking Ensemble Learning model. Our model is optimized using hyperparametric optimiza- tion and validated using 5-fold cross-validation to improve its generalizability. We found that CO2 is a crucial factor in predicting maize yield in all regions of Ghana. Precipitation is impor- tant for maize yield in the Greater Accra, Brong Ahafo, and Upper East regions. Minimum temperature is less significant in the Guinea savanna and Rain Forest zones shows a greater impact in the transitional zone, particularly in the Brong Ahafo region. The Stacking model showed higher accuracy and robustness compared to SVM and linear regression models, as confirmed by MSE and MAE evaluation metrics. Stacking outperformed other models in the PLOS ONE Impact of climate variability on maize yields in Ghana PLOS ONE | https://doi.org/10.1371/journal.pone.0305762 June 25, 2024 20 / 24