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

[25] highlighted that climate factors, such as rainfall, only contribute to a minor portion (about 4.2% of the fluctuations in annual maize output in Ghana). Their findings do indicate that climate factors account for 40.8% of maize yield under drier-than-normal rainfall condi- tions, indicating that rainfall does have a significant impact on crop productivity in Ghana. The effect of specific climate factors (temperature and rainfall) on maize production in the Ejura-Sekyedumase Municipality, Ghana, was examined by Cudjoe et al. [12] utilizing trend analysis, regression analysis, and chi-square analysis. The research’s conclusions show that there is clear climate variability in the study area and that it significantly affects crop produc- tivity. Another study by Baffour-Ata et al. [28] investigated how climate factors, such as yearly rainfall, its onset and cessation, the number of dry days, and temperature, affect the growth of a few different crops, including millet, sorghum, rice, groundnuts, and maize in northern Ghana. They discovered that number of dry days, temperature, onset, yearly precipitation, and cessation explained about 25% of the variation in maize production using the Mann-Kendall trend test and multiple regression analysis. Evidently, there is a lack of research addressing the impact of climate variables on maize yield across all agro-ecological zones and administrative regions in Ghana. Consequently, it is essential to fill this gap, and this study aims to do so.

Given the necessity of assessing the impact of climate variability on crop yield, various tech- niques have been developed that enable the analysis of complex interactions between climate variables and crop yield. These techniques involve the use of statistical models, machine learn- ing algorithms, and remote sensing data, among others. Lobell and Burke [29] predicted the possible impacts of climate variabilities on crop yield using three statistical models (time series, panel, and cross-sectional models). They discovered that depending on the climate variable and spatial scale being considered, the performance of statistical models differs. The responses to temperature change were more accurately predicted by panel and cross-sectional models than the responses to precipitation change, which only used data from a single site. The histor- ical period utilized for training was less important for the models based on numerous sites, and performance improved when individual sites were averaged across countries. However, according to Gyamerah et al. [30], it can be difficult to precisely quantify and estimate the impact of climate variability on crop output using statistical techniques because of the com- plexity and non-linear relationship between crop yield and climate variables. Crane-Droesch [31] utilized a semiparametric deep neural network variant, employed as a machine learning technique to account for the nonlinear interactions in high-dimensional datasets as well as known parametric structure and undiscovered cross-sectional variability. According to their

study, the machine learning technique outperforms the classical statistical methods in forecast- ing the yields of years withheld during model training, according to data on maize yields from the US Midwest, which they used to demonstrate the efficacy of their methodology. In addi- tion, their findings revealed significant adverse impact of climate variability on maize yield, while these effects are less severe than those predicted using conventional statistical techniques. They did this by employing scenarios from a variety of climate models. Appiah-Badu et al. [32] used a methodical and exacting approach to evaluate the effectiveness of several classification algorithms in predicting rainfall in various ecological zones of Ghana. The study employed five well-known machine learning techniques for classification: multilayer perceptron, deci- sion tree, extreme gradient boosting, random forest, and K-nearest neighbor. In their investi- gation, MLP, XGB, and RF all performed well at the three training-to-testing ratios of 90:10, 80:20, and 70:30 whereas KNN did poorly in all zones. Cedric et al. [33] also suggested a machine learning-based prediction method to forecast the yield of six crops at the national level in West African countries throughout the course of the year. The prediction system was developed using a decision tree, k-nearest neighbor, and multivariate logistic regression mod- els. They combine agricultural yields, chemical data, climate, and weather data in their PLOS ONE Impact of climate variability on maize yields in Ghana

PLOS ONE | https://doi.org/10.1371/journal.pone.0305762 June 25, 2024 3 / 24