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

intention to encompass a thorough depiction of temperature dynamics and their potential influence on the response variable, motivated by existing studies [10, 25, 42]. For this study, the ten (10) administrative regions that existed between 1992 and 2019 were used. The Statis- tics, Research, and Information Directorate (SRID) of Ghana’s Ministry of Food and Agricul- ture provided the yearly average maize yield in metric tons per hectare (MT/ha) for each district from 1992 to 2019. The average maize yield for each district corresponds to the ratio of the district’s estimated production to the district’s estimated cropped area. Motivated by Preety et al. [43], the LINEST function in Excel is used to handle missing values in the original data before aggregating it further. The specific software used for this interpolation task was Excel because of its user-friendly interface and built-in features for linear interpolation. It is worth noting that although Excel was used for interpolation, the subsequent analysis and modeling were carried out using Python with pandas and scikit-learn. Data preprocessing The data from May to September were aggregated to create the annual data for each climatic variable in the major regions in the Guinea Savannah zone since that is the cropping season for maize in the Upper West, Upper East, and Northern parts of Ghana. From Table 2, the Transitional, Rain Forest, and Coastal Savannah zones have two major cropping seasons for maize (March–August/September and July–December); therefore, the data from March to

December is aggregated to obtain the annual cropping season climate data. To perform this aggregation, the values of the pertinent variables for each month during the time were added up, and the mean value for the entire period was then determined. As a result, each variable for every year of the study had a single value. In addressing the temporal structure of the data, we conducted an Augmented Dickey-Fuller (ADF) test to evaluate the stationarity of the ’maize yield’ time series. Based on the ADF test, it was determined that the series from all regions exhibited nonstationarity, as indicated by p-values exceeding 0.05. Subsequently, detrending was performed using the ‘detrend’ function from ‘scipy.signal’ library in Python, and the detrended variable was further deseasonalized by subtracting its lagged value, assum- ing a yearly seasonality. To achieve stationarity, differencing was applied to the deseasonalized data. This comprehensive approach aimed to mitigate temporal patterns, trends, and seasonal- ity in the ’maize yield’ time series, ensuring that the data is suitable for subsequent analysis and modeling using the fixed-effect regression models. We utilized both the MinMaxScaler and the StandardScaler from the sklearn library in Python to scale the input variables. It became evident that the StandardScaler yielded superior results compared to the MinMaxScaler. Consequently, we opted to standardize the input Table 2. The agro-ecological zones and their maize cultivation calendar in Ghana. Agro-Ecological Zone Administrative Seasons for cultivation of Maize

Planting period-onset length of the cropping cycle Harvesting period-end Coverage areas Guinea Savannah zone Northern region, Upper west Region, Upper East region One season 20-May 90–120 days 10-September Transitional Zone Major part of Brong Ahafo, northern part of Ashanti Region, part of Volta region First season 20-March 90–120 days 20-August Second season 20-July 90–120 days 10-December Rain Forest Zone Eastern region, southern part of Ashanti, and Western region, parts of Volta, Brong Ahafo, Greater Accra, and Central region First season 10-March 90–120 days 10-August Second season 20-July 90–120 days 20-December Coastal zone Parts of Central region, Greater Accra region, Eastern and Volta region First season 20-March 90–120 days 20-September Second season 20-July 90–120 days 20-December https://doi.org/10.1371/journal.pone.0305762.t002 PLOS ONE Impact of climate variability on maize yields in Ghana PLOS ONE | https://doi.org/10.1371/journal.pone.0305762 June 25, 2024 6 / 24