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In sub-Saharan Africa, temperatures are generally conducive to malaria transmission, and rainfall provides mosquitoes with optimal breeding conditions. The objective of this study is to assess the impact of future climate change on malaria transmission in West Africa using community-based vector-borne disease models, TRIeste (VECTRI). This VECTRI model, based on bias-corrected data from the Phase 6 Coupled Model Intercomparison Project (CMIP6), was used to simulate malaria parameters, such as the entomological inoculation rate (EIR). Due to the lack of data on confirmed malaria cases throughout West Africa, we first validated the forced VECTRI model with CMIP6 data in Senegal. This comparative study between observed malaria data from the National Malaria Control Program in Senegal (Programme National de Lutte contre le Paludisme, PNLP, PNLP) and malaria simulation data with the VECTRI (EIR) model has shown the ability of the biological model to simulate malaria transmission in Senegal. We then used the VECTRI model to reproduce the historical characteristics of malaria in West Africa and quantify the projected changes with two Shared Socio-economic Pathways (SSPs). The method adopted consists of first studying the climate in West Africa for a historical period (1950–2014), then evaluating the performance of VECTRI to simulate malaria over the same period (1950–2014), and finally studying the impact of projected climate change on malaria in a future period (2015–2100) according to the ssp245 ssp585 scenario. The results showed that low-latitude (southern) regions with abundant rainfall are the areas most affected by malaria transmission. Two transmission peaks are observed in June and October, with a period of high transmission extending from May to November. In contrast to regions with high latitudes in the north, semi-arid zones experience a relatively brief transmission period that occurs between August, September, and October, with the peak observed in September. Regarding projections based on the ssp585 scenario, the results indicate that, in general, malaria prevalence will gradually decrease in West Africa in the distant future. But the period of high transmission will tend to expand in the future. In addition, the shift of malaria prevalence from already affected areas to more suitable areas due to climate change is observed. Similar results were also observed with the ssp245 scenario regarding the projection of malaria prevalence. In contrast, the ssp245 scenario predicts an increase in malaria prevalence in the distant future, while the ssp585 scenario predicts a decrease. These findings are valuable for decision makers in developing public health initiatives in West Africa to mitigate the impact of this disease in the region in the context of climate change. [ABSTRACT FROM AUTHOR]

Agriculture plays an important role in Senegalese economy and annual early warning predictions of crop yields are highly relevant in the context of climate change. In this study, we used three main machine learning methods (support vector machine, random forest, neural network) and one multiple linear regression method, namely Least Absolute Shrinkage and Selection Operator (LASSO), to predict yields of the main food staple crops (peanut, maize, millet and sorghum) in 24 departments of Senegal. Three combination of predictors (climate data, vegetation data or a combination of both) are used to compare the respective contribution of statistical methods and inputs in the predictive skill. Our results showed that the combination of climate and vegetation with the machine learning methods gives the best performance. The best prediction skill is obtained for peanut yield likely due to its high sensitivity to interannual climate variability. Although more research is needed to integrate the results of this study into an operational framework, this paper provides evidence of the promising performance machine learning methods. The development and operationalization of such prediction and their integration into operational early warning systems could increase resilience of Senegal to climate change and contribute to food security. [ABSTRACT FROM AUTHOR]

The Sahelian Great Green Wall (SGGW) is an influential project to combat desertification and promote sustainable land management on a large scale, involving 11 countries in the Sahel region of Africa. The UNCCD's 2020 progress report showed a mixed picture concerning the meeting of the initial targets. At the One Planet Summit in 2021, announcements were made to consolidate the implementation of the SGGW, most notably with the creation of the Great Green Wall Accelerator. In this context, our paper sets out to review the scientific work conducted with regard to the SGGW. We have thus carried out a bibliometric analysis of the literature on SGGW. Although the initiative involves 11 countries and covers a large spectrum of scientific disciplines, our results show the predominance of ecological studies in the SGGW literature and a concentration of studies in certain geographies of interest, such as northern Senegal. Moreover, based on a secondary analysis of publications on land restoration and sustainable ecosystem management in Sahelian countries, we show that the literature relevant to SGGW topics is richer and fills in the information gaps we have identified at thematic and geographical levels. By showing that SGGW studies are overly focused on certain topics and geographical areas, our paper argues for a better interdisciplinary mobilization of researchers working on GGW-related topics. The scientific and operational success of the project depends on stronger networking between the different research teams and themes, both in Africa and internationally. [ABSTRACT FROM AUTHOR]

Abstract Background Every year, West Africa is afflicted with Meningococcal Meningitis (MCM) disease outbreaks. Although the seasonal and spatial patterns of disease cases have been shown to be linked to climate, the mechanisms responsible for these patterns are still not well identified. Results A statistical analysis of annual incidence of MCM and climatic variables has been performed to highlight the relationships between climate and MCM for two highly afflicted countries: Niger and Burkina Faso. We found that disease resurgence in Niger and in Burkina Faso is likely to be partly controlled by the winter climate through enhanced Harmattan winds. Statistical models based only on climate indexes work well in Niger showing that 25% of the disease variance from year-to-year in this country can be explained by the winter climate but fail to represent accurately the disease dynamics in Burkina Faso. Conclusion This study is an exploratory attempt to predict meningitis incidence by using only climate information. Although it points out significant statistical results it also stresses the difficulty of relating climate to interannual variability in meningitis outbreaks.

Abstract Background Meningococcal meningitis (MM) represents an important public health problem especially in the "meningitis belt" in Africa. Although seasonality of epidemics is well known with outbreaks usually starting in the dry season, pluri-annual cycles are still less understood and even studied. In this context, we aimed at study MM cases time series across 9 sahelo-sudanian countries to detect pluri-annual periodicity and determine or not synchrony between dynamics. This global and comparative approach allows a better understanding of MM evolution in time and space in the long-term. Results We used the most adapted mathematical tool to time series analyses, the wavelet method. We showed that, despite a strong consensus on the existence of a global pluri-annual cycle of MM epidemics, it is not the case. Indeed, even if a clear cycle is detected in all countries, these cycles are not as permanent and regular as generally admitted since many years. Moreover, no global synchrony was detected although many countries seemed correlated. Conclusion These results of the first large-scale study of MM dynamics highlight the strong interest and the necessity of a global survey of MM in order to be able to predict and prevent large epidemics by adapted vaccination strategy. International cooperation in Public Health and cross-disciplines studies are highly recommended to hope controlling this infectious disease.

Climate change is already affecting agro-ecosystems and threatening food security by reducing crop productivity and increasing harvest uncertainty. Mobilizing crop diversity could be an efficient way to mitigate its impact. We test this hypothesis in pearl millet, a nutritious staple cereal cultivated in arid and low-fertility soils in sub-Saharan Africa. We analyze the genomic diversity of 173 landraces collected in West Africa together with an extensive climate dataset composed of metrics of agronomic importance. Mapping the pearl millet genomic vulnerability at the 2050 horizon based on the current genomic-climate relationships, we identify the northern edge of the current areas of cultivation of both early and late flowering varieties as being the most vulnerable to climate change. We predict that the most vulnerable areas will benefit from using landraces that already grow in equivalent climate conditions today. However, such seed-exchange scenarios will require long distance and trans-frontier assisted migrations. Leveraging genetic diversity as a climate mitigation strategy in West Africa will thus require regional collaboration. Replacement of local crops with alternative varieties adapted to future conditions may improve food security under climate change. Here the authors apply landscape genomics and ensemble climate modelling to pearl millet in West Africa, supporting the potential of transfrontier assisted seed exchange. [ABSTRACT FROM AUTHOR]

Climate change directly threatens food security in West Africa through a negative impact on productivity of the main staple food crops. However, providing consistent future crop yield projections in the region remains challenging because of uncertainty in the response of the regional climate to the CO2 increase and in the response of the cultivated crop to this altered climate with more CO2 in the atmosphere. Here, we analyse a set of idealized climate simulations to investigate the effect of CO2 concentration increase on the West African monsoon and potential impacts on crop yields of maize. On the one hand, simulations with prescribed SST and quadrupled CO2 concentration are analysed to study the atmospheric response to direct radiative forcing induced by increasing CO2 concentration, not mediated by ocean heat capacity. On the other hand, simulations with prescribed SST augmented by 4 K are analysed to study the atmospheric response to the global ocean warming expected as a consequence of the increasing CO2 radiative forcing. We show that if CO2 concentration increase has a positive impact on crop yield due to the fertilization effect, it also has a direct effect on the monsoon which acts to increase (decrease) rainfall in the eastern (western) part of the Sahel and increase (decrease) crop yields consequently. Finally, we show that SST warming acts to reduce rainfall and increase local temperatures leading to strong reduction of crop yield. The reduction of crop yield is more important in the eastern part of the Sahel where the warming is more intense than in the western part of the Sahel. Overall, positive effects are weaker and more uncertain than the negative effects in the analysed simulations. Crop yield response to rainfall and temperature variations in the GLAM model. Pixel by pixel difference against the domain average for mean yield and total growing season rainfall (left) and mean temperature (right). Values are then averaged over the 30 years of the control experiment to give more than 400 values expressed in percentage. [ABSTRACT FROM AUTHOR]

ABSTRACT: The reference evapotranspiration (ET0) is an integrated climatic variable from which many crop models derive simulated crop yields. In most of these models, different equations are parameterized leaving the choice of the equation to the user. However, the impact of the choice of the ET0 equations on crop yield prediction has been little studied. The present study proposes a sensitivity analysis of the impact of the choice of the ET0 equation on simulated millet yields using SARRA‐H crop model over 12 Senegalese stations representative of the Sudano‐Sahelian climate conditions of West Africa. Priestley‐Taylor, a modified Priestley‐Taylor and Hargreaves equations lead to simulated yields up to 19% than those calculated using the Penman‐Monteith equation. Despite high biases in wind speed, among the tested methods, the Penman‐Monteith method remains the most robust to derive ET0 and yield over the major part of Senegal, Hargreaves equation being more appropriated under dry climates. The choice of ET0 formulation introduces uncertainties representing 8% of baseline yield regardless of precipitation changes; for wet conditions these uncertainties approach 30% of the overall climate change impact. The choice of ET0 equation is increasingly important, with local temperature changes out to 4 °C, while extreme changes above 6 °C depend less on the ET0 equation. [ABSTRACT FROM AUTHOR]

The ability of a region to feed itself in the upcoming decades is an important issue. TheWest African population is expected to increase significantly in the next 30 years. The responses of crops to short-term climate change is critical to the population and the decision makers tasked with food security. This leads to three questions: how will crop yields change in the near future? What influence will climate change have on crop failures? Which adaptation methods should be employed to ameliorate undesirable changes. An ensemble of near-term climate projections are used to simulate maize, millet and sorghum in West Africa in the recent historic period (1986-2005) and a near-term future when global temperatures are 1.5K above preindustrial levels to assess the change in yield, yield variability and crop failure rate. Four crop models were used to simulate maize, millet and sorghum in West Africa in the historic and future climates. Across the majority of West Africa the maize, millet and sorghum yields are shown to fall. In the regions where yields increase, the variability also increases. This increase in variability increases the likelihood of crop failures, which are defined as yield negative anomalies beyond 1 standard deviation during the historic period. The increasing variability increases the frequency of crop failures across West Africa. The return time of crop failures falls from 8.8, 9.7 and 10.1 years to 5.2, 6.3 and 5.8 years for maize, millet and sorghum respectively. The adoption of heat-resistant cultivars and the use of captured rainwater have been investigated using one crop model as an idealized sensitivity test. The generalized doption of a cultivar resistant to high-temperature stress during flowering is shown to be more beneficial than using rainwater harvesting. [ABSTRACT FROM AUTHOR]