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

variables using the standard scaler. Besides, since the climate variables have different units, the standard scaler become the appropriate choice. The decision for standardization was made to ensure that all variables were aligned on a consistent scale, aiming to fit them within a stan- dardized range. Subsequent to making predictions, an inverse scaling procedure was employed to transform the standardized predictions back to their original scales, maintaining the interpretability of the results in the context of the original data units. The datasets were arbi- trary divided into two subsets, one comprising the test set (20%) for validating and the other containing the training set (80%) for modeling. The entire analysis was conducted using the Python programming language. Model establishment Assessing the impact of climate variability on crop yield is an important task in agricultural research because it provides useful insights into how climate variability affects food security. The goal of this study is to develop a model for measuring the impact of climate variability on maize yield. To accomplish this, we employed a variety of machine learning algorithms, including multiple linear regression, random forest, support vector machines, decision trees, and gradient boosting. These algorithms were used to construct a single model (the stacking ensemble model) that offers high accuracy in assessing the impact of the climate variables on maize. Previous research [35, 40, 44–47] have shown that ensemble learning can perform bet-

ter than individual machine learning methods. The machine learning models under consider- ation are established in this section. The base models establishment Multiple linear regression. A supervised learning strategy designed specifically for regression is linear regression. A linear function is used to depict the relationship between the input and output variables. By minimizing the sum of the squared deviations between the expected and actual output values, a straight line is found. In this study, since our independent variables are more than one, we use the multiple linear regression model. The general equation of a multiple linear regression is given in Eq (1). Yt ¼ β0 þ Xn i¼1 βi � xi;t þ εt ð1Þ Where, Yt is the response variable at time t xi,t is the predictor variable i at time t, βi is the regression coefficients that determines the impact of each climate variables, β0 is the intercept, and εt is the error term at time t. For this studies, Eq (1) can be reformulated as shown in Eq (2) Yt ¼ b0 þ b1x1;t þ b2x2;t þ b3x3;t þ b4x4;t þ b5x5;t þ εt ð2Þ where Yt is the maize yield; x1,t = average temperature; x2,t = maximum temperature; x3,t = minimum temperature; x4,t = precipitation; x5,t = CO2 at time t, εt is the error term at time t or residual representing the difference between the predicted value and the actual value of Yt also known as the unobservable factors affecting Yt; β1, β2, β3, β4, β5 captures respectively the effect of x1,t, x2,t, x3,t, x4,t, x5,t on Yt, and β0 is the intercept of the equation.

Decision trees. A decision tree is a supervised learning method that may be applied to both classification and regression problems. The tree is created by recursively dividing the data according to the values of the input variables. Each leaf node of the tree represents a class label (in the case of classification) or a prediction (in the case of regression), and each internal node of the tree represents a test on an input variable. We formulated the decision tree regression PLOS ONE Impact of climate variability on maize yields in Ghana PLOS ONE | https://doi.org/10.1371/journal.pone.0305762 June 25, 2024 7 / 24