Salomon Obahoundje

Cocoa production in Ghana and Côte d’Ivoire is threatened by climate variability and extremes, particularly droughts and excessive rainfall. However, quantitative evidence on the impacts of climate change on cocoa yields remains limited, constraining the development of effective climate-smart adaptation strategies. This study assessed future climate impacts on cocoa production using regridded (~5 km resolution) ensemble projections from 12 Global Climate Models under a low (SSP1-2.6) and a high (SSP5-8.5) SSP scenario. Precipitation and temperature data were bias-corrected using five approaches: Delta Change, CDFt, SDM, EQM, and LOCI. Among these, the Delta Change method best preserved intra-annual climate variability, while temperature corrections outperformed precipitation corrections. A Random Forest model, trained on bias-corrected climate data, simulated and projected cocoa yields with an accuracy exceeding 85%, although performance varied across regions. Future changes were assessed for the near future (2026–2055) and far future (2056–2085) relative to a historical baseline (1985–2014). Ensemble projections indicate a drying trend across cocoa-growing areas, with precipitation declining by 5–10% under SSP5-8.5 and increasing modestly (around 5%) under SSP1-2.6. At the same time, temperatures are projected to rise across all regions, exceeding 3.5°C under SSP5-8.5 by the late century, particularly in central and northern zones. Projected yield responses vary spatially. Southern and coastal cocoa-growing areas are expected to experience yield declines of about 5%, with losses reaching up to 20% under severe drought conditions in highly vulnerable regions such as Dix-Huit Montagnes in Côte d’Ivoire under SSP5-8.5. In contrast, some northern and central regions may maintain or slightly increase yields under SSP1-2.6. Vulnerability is shaped by climatic, biophysical, and socio-economic factors, with regions such as Sud-Comoé (Côte d’Ivoire) and Brong Ahafo (Ghana) identified as at risk. These findings highlight the need for targeted adaptation strategies to enhance the resilience of West Africa’s cocoa sector.

Introduction: Rainfall regimes across West Africa have undergone substantial transformations over the past four decades, with profound implications for rainfed agriculture and food security. However, limited understanding persists on how rainfall extremes translate into spatially differentiated crop yield responses across agroclimatic zones. Methods: This study integrates multi-decadal climate datasets with crop yield data from the Global Dataset of Historical Yields (GDHY) to assess the sensitivity of maize, rice, and soybean yields to hydroclimatic extremes across six agroclimatic zones. A sensitivity analysis using an Ordinary Least Squares regression framework quantified crop yield responses to key climatic variations across multi-decadal timescales, agroclimatic zones, and administrative units. Results: Results reveal marked north–south contrasts in rainfall extremes. In the Sahelian and Sudanian zones, seasonal rainfall increased by 5%–25% since the 1990s and up to 80% in the Northern Sahel by 2024, largely driven by more frequent wet days and intensified rainfall events. These trends heighten flood and erosion risks in northern regions. Conversely, the Guinean zones exhibit stagnating or declining rainfall, coupled with over a 90% increase in consecutive dry days in Central Guinea, signaling intensifying drought stress. Crop yield responses associated with these hydroclimatic patterns. Maize yields increased by 40%–50% in the Sahelian, Sudanian, and parts of the Guinean zones, while rice yields declined by 20%–50% in Nigeria’s Sudanian and Eastern Guinea regions. Soybean showed localized yield gains but remained extremely sensitive to rainfall deficits and heat extremes. Discussion: By linking rainfall variability to spatially explicit yield responses, this study provides a novel multi-decadal sensitivity framework for evaluating climate impacts on agriculture in West Africa. The findings highlight the urgent need for region-specific, climate-smart adaptation strategies, emphasizing flood and erosion management in the north and drought resilience in the south, to sustain food production under increasing climatic variability.

This review critically assesses the application of machine learning (ML) algorithms and remote sensing (RS) products in detecting and predicting climate hazards, as well as their impacts on crop yields. Using the PRISMA approach, it examines 177 studies on climate hazards and 197 on RS–ML applications in crop yield modeling. Research is most concentrated in Asia, followed by Africa and the Americas, with agricultural drought emerging as the most frequently studied hazard. Statistical approaches, such as the coefficient of variation, remain the dominant methods for analyzing climate variability. For drought detection, Random Forest (RF) was the most used ML algorithm (17%), followed by Support Vector Machines (SVM, 11%), Artificial Neural Networks (ANN, 8%), Adaptive Neuro-Fuzzy Inference System (ANFIS, 5%), and Extreme Gradient Boosting (XGBoost, 5%). For drought impacts on crop productivity, RF dominated (39%) followed by Least Absolute Shrinkage and Selection Operator (LASSO, 11%), while for climate variability impacts, RF (21%) led alongside SVM (10%), ANN (9%), Long Short-Term Memory (LSTM) (8%), Multiple Linear Regression (MLR) (8%), and Convolutional Neural Network (CNN) (7%). Asia leads in the integration of advanced ML/DL techniques. In contrast, due to infrastructure and data limitations, Africa predominantly employs simpler and more interpretable models. RS products, namely MODIS, TRMM, CHIRPS, and ERA5, are widely used due to their accessibility. However, their limited spatial resolution restricts their effectiveness. The research gaps include a limited investigation at the sub-national level, insufficient ground-truth validation, and inadequate monitoring of complex, compounding hazards like drought–flood–heatwave interactions. Moreover, the research remains skewed toward economically dominant crops (maize, cotton, and soybeans, neglecting marginal crops (cocoa, cashew, cassava, plantain, and coffee) critical to food-insecure regions. The review recommends hybrid modeling frameworks integrating process-based and data-driven methods, broader spatial and crop coverage, standardized protocols, and real-time, microclimate-aware monitoring systems, for improving model reliability and applicability in underrepresented, data-scarce regions such as sub-Saharan Africa, thereby strengthening climate-resilient agriculture and global food security.

To address the growing electricity demand driven by population growth and economic development while mitigating climate change, West and Central African countries are increasingly prioritizing renewable energy as part of their Nationally Determined Contributions (NDCs). This study evaluates the implications of climate change on renewable energy potential using ten downscaled and bias-adjusted CMIP6 models (CDFt method). Key climate variables—temperature, solar radiation, and wind speed—were analyzed and integrated into the Teal-WCA platform to aid in energy resource planning. Projected temperature increases of 0.5–2.7 °C (2040–2069) and 0.7–5.2 °C (2070–2099) relative to 1985–2014 underscore the need for strategies to manage the rising demand for cooling. Solar radiation reductions (~15 W/m²) may lower photovoltaic (PV) efficiency by 1–8.75%, particularly in high-emission scenarios, requiring a focus on system optimization and diversification. Conversely, wind speeds are expected to increase, especially in coastal regions, enhancing wind power potential by 12–50% across most countries and by 25–100% in coastal nations. These findings highlight the necessity of integrating climate-resilient energy policies that leverage wind energy growth while mitigating challenges posed by reduced solar radiation. By providing a nuanced understanding of the renewable energy potential under changing climatic conditions, this study offers actionable insights for sustainable energy planning in West and Central Africa.

This study was carried out in the White Bandama watershed (WBW) in Côte d'Ivoire (West Africa). The objective is to assess the impacts of future climate change (CC) on the hydropower potential (HPP) of the WBW. The methodology is based on coupling the SWAT (Soil and Water Assessment Tool) hydrological model with the Geographic Information System (GIS) QGIS to assess HPP on streams and evaluate the impacts of future CC on HPP of the watershed. Historical and climate projection data (precipitation, minimum and maximum temperature) for a set of three Regional Climate Models (RCM) from CORDEX-AFRICA (CCCma-CanRCM4, CCLM4-8-17 and REMO 2009) under RCP 4.5 were used. The biases of the ensemble mean were corrected by the Delta-change method. The relative change of streamflow discharge and HPP was assessed as the relative difference between the projection periods (2041–2070 and 2071–2100) and the reference period (1976–2005). The results showed a total of 22 future hydropower potential sites in the watershed. These sites were identified, geolocated and classified according to their potential capacity of generation in 82 % as small (1–25 MW potential capacity), 9 % as medium (25–100 MW potential capacity) and 9 % as large (more than 100 MW potential capacity) hydropower. The climate models' ensemble projected an upward trend for both the annual mean discharge of rivers and HPP of the WBW according to RCP 4.5 for the periods 2041–2070 and 2071–2100. On the annual cycle, the months of August and September will record the highest monthly mean flows between 150 and 200 m³ s⁻¹ while the months from November to April will record low monthly mean flows in the WBW.

Climate change (CC) and variability impacts on hydroelectric generation have become critical for hydropower management. The trends of inflow, outflow, reservoir water level, and storage as well as hydraulicity indices of three main dams in Côte d’Ivoire, namely Kossou and Taabo in the Bandama basin and Buyo in the Sassandra basin were examined during 1981–2017 and their impacts on hydropower generation were analyzed. Moreover, the hydropower generation sensitivity to CC of these dams was assessed using statistical analysis. The results reveal that the inflow is highly dependent on rainfall while the water level is highly influenced by the outflow, which is a function of the inflow to the reservoirs and water management policy. Furthermore, the Mann Kendall test revealed that temperature and potential evapotranspiration have increased significantly in all three sub-basins while precipitation shows a significant upward trend only within the Taabo dam catchment area. Meanwhile, inflow to reservoir increased significantly and greatly than precipitation probably due to land use/cover change. Precipitation and inflow show a strong correlation as energy generation is significantly and strongly correlated to outflow (inflow) in all stations (except Kossou). Furthermore, the energy generation at Buyo and Taabo dams is more sensitive to reservoir inflow, while that of Kossou dam is more affected by water level. In addition, the power of a given year is also dependent on the total rainfall of that year and/or the previous year depending on the plant.

Hydropower energy, the main renewable energy source in West Africa, contributes to more than half of the Togo and Benin National electrification. This resource highly depends on water availability in rivers or reservoirs. The water availability heavily relies on climate patterns of the area. In the climate change context, the sustainability of hydropower plants is at risk. This work aims to assess the sensitivity of the Nangbeto hydropower plant to multiyear climate variability using statistical analysis. The results show that energy generation at Nangbeto hydropower is more modulated by four main variables namely inflow to reservoir, water level, rainfall of the actual and the previous year. The energy generation is found to be strongly and significantly correlated to inflow to reservoir, water level, and rainfall. Overall, the Nangbeto hydropower generation is more sensitive to inflow which is controlled by climate variables (rainfall, temperature) and land use/cover change. Therefore, the probable future change in these variables is suggested to be deeply investigated.