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

analysis. According to the study, all three machine learning models produced favorable accu- racy, with the decision tree model performing best with 95.3% accuracy. To prevent overfitting during cross-validation, they additionally used a hyper-parameter tuning strategy. The predic- tion of the K-Nearest Neighbor model and the decision tree model were shown to relate to the expected data when the authors examine the relationship between the predicted results and the expected outcomes, suggesting that the models function as expected. However, using clus- ters for small datasets may result in overfitting when using forecasting models [34]. To over- come this issue, they used the stacking ensemble learning method (SELM), which helped them increase their generalization capacity. Li et al. [35] also used the Stacking Ensemble Learning framework to integrate three effective machine learning algorithms: support vector machine, random forest, and K-nearest neighbors as base-models. Over the course of 34 years, the study collected daily weather information as well as information on soybean output from 173 county-level administrative districts and meteorological stations in China’s two primary soy- bean-growing regions. Their findings suggested that the stacking model provides more accu- racy and versatility. They also discovered that the stacking prediction model of soybean meteorological yield provides a novel method for successfully predicting soybean production with a MAPE of less than 5%. Based on the findings presented above, the stacking ensemble

learning model has demonstrated promising results for reliably predicting maize yield using meteorological data and machine learning techniques. With our data spanning 27 years, the stacking ensemble model can be used to develop a high-precision and reliable model to analyze the impact of climate variabilities on maize yield prediction. This study considers all the regions in Ghana. This fills a large information void by includ- ing all the four ecological zones in Ghana, which encompass all the regions in the country, as opposed to prior studies that only focused on only one region [12, 28, 36], only one zone [27, 37], or selected zones [38, 39]. The study provides a more thorough assessment of the effect of climate change on maize yields in Ghana by considering all ecological zones and all the regions within each zone, making its conclusions reliable and applicable to the entire nation. Even though Atiah et al. [25] found that weather variables significantly influence maize yield vari- ability, the effects of climatic variability on crop production are complex and poorly under- stood. Machine learning algorithms have the capacity to examine intricate connections between climatic and weather variables and crop productivity. Machine learning algorithms for predicting agricultural output based on numerous meteorological and climatic elements have a better level of accuracy than the current time-consuming statistical procedures since cli- mate variables are nonlinear [33]. Even though machine learning (ML) approaches have a

high level of accuracy, their potential applications in agricultural yield prediction have not been realized due to a lack of more reliable methods [40]. By including different model types and architectural characteristics across the models, ensemble learning model forecasts can increase generalization and prediction accuracy [41]. To address the problem of agricultural yield prediction, it is advantageous to introduce an ensemble learning method [35]. Based on the review of related literature, SSA nations, including Ghana, have paucity of ensemble-based ML algorithms in analyzing the impact of climate variability on crop yields. Additionally, due to its superior prediction accuracy, the stacking ensemble model algorithm is suggested in this paper to combine the algorithms of the LR, SVM, DT, RF, and GB to assess the impact of cli- mate variables on maize yield and to fill the previously mentioned study gap. Our study aims at achieving the following goals: (i) to identify the key climatic variables that have the most significant impact on maize yield in each region; (ii) to assess the impacts of climate variability on the yield of maize in each region of Ghana; (iii) to develop a reliable pre- dictive model using machine learning algorithms that can capture the volatile, non-stationary, and non-linear nature of the climate variables and accurately forecast maize yield under PLOS ONE Impact of climate variability on maize yields in Ghana PLOS ONE | https://doi.org/10.1371/journal.pone.0305762 June 25, 2024 4 / 24