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We present work using two long-term climate datasets to show that nitrogen fertiliser is an important aspect of yield projection for three major crops. The ability of linear models using climate variables as predictors to accurately project the yield of maize, rice and wheat over multi-decadal scales is improved with the addition of fertiliser as an input. Highly productive nations including Argentina, India, Poland and South Africa show significant improvement in yield simulations and show that fertiliser use should not be discounted when estimating yield variability. The use of nitrogen fertiliser in the generalised linear models improves yield forecast by 18% using the Princeton climate dataset and 23% using the WFDEI climate dataset. This work therefore supports the use of additional predictors than climate for improving the ability of statistical models to reconstruct yield variability. [ABSTRACT FROM AUTHOR]

Abstract: In West Africa, agriculture, mainly rainfed, is a major economic sector and the one most vulnerable to climate change. A meta-database of future crop yields, built up from 16 recent studies, is used to provide an overall assessment of the potential impact of climate change on yields, and to analyze sources of uncertainty. Despite a large dispersion of yield changes ranging from −50% to +90%, the median is a yield loss near −11%. This negative impact is assessed by both empirical and process-based crop models whereas the Ricardian approach gives very contrasted results, even within a single study. The predicted impact is larger in northern West Africa (Sudano-Sahelian countries, −18% median response) than in southern West Africa (Guinean countries, −13%) which is likely due to drier and warmer projections in the northern part of West Africa. Moreover, negative impacts on crop productivity increase in severity as warming intensifies, with a median yield loss near −15% with most intense warming, highlighting the importance of global warming mitigation. The consistently negative impact of climate change results mainly from the temperature whose increase projected by climate models is much larger relative to precipitation change. However, rainfall changes, still uncertain in climate projections, have the potential to exacerbate or mitigate this impact depending on whether rainfall decreases or increases. Finally, results highlight the pivotal role that the carbon fertilization effect may have on the sign and amplitude of change in crop yields. This effect is particularly strong for a high carbon dioxide concentration scenario and for C3 crops (e.g. soybean, cassava). As staple crops are mainly C4 (e.g. maize, millet, sorghum) in WA, this positive effect is less significant for the region. [Copyright &y& Elsevier]

• Unprecedented report on 23 precipitation datasets reliability across West Africa. • Large discrepancies are observed between the precipitation datasets reliability. • The datasets reliability differ in space and according to the considered time step. • MSWEP is the most accurate dataset to represent the daily precipitation dynamic. • CHIRPS is the most accurate dataset to represent the monthly precipitation dynamic. This study aims reporting on 23 gridded precipitation datasets (P-datasets) reliability across West Africa through direct comparisons with rain gauges measurement at the daily and monthly time scales over a 4 years period (2000–2003). All P-datasets reliability vary in space and time. The most efficient P-dataset in term of Kling–Gupta Efficiency (KGE) changes at the local scale and the P-dataset performance is sensitive to seasonal effects. Satellite-based P-datasets performed better during the wet than the dry season whereas the opposite is observed for reanalysis P-datasets. The best overall performance was obtained for MSWEP v.2.2 and CHIRPS v.2 for daily and monthly time-step, respectively. Part of the differences in P-dataset performance at daily and monthly time step comes from the time step used to proceed the gauges adjustment (i.e day or month) and from a mismatch between gauge and satellite reporting times. In comparison to the others P-datasets, TMPA-Adj v.7 reliability is stable and reach the second highest KGE value at both daily and monthly time step. Reanalysis P-datasets (WFDEI, MERRA-2, JRA-55, ERA-Interim) present among the lowest statistical scores at the daily time step, which drastically increased at the monthly time step for WFDEI and MERRA-2. The non-adjusted P-datasets were the less efficient, but, their near-real time availability should be helpful for risk forecast studies (i.e. GSMaP-RT v.6). The results of this study give important elements to select the most adapted P-dataset for specific application across West Africa. [ABSTRACT FROM AUTHOR]

To contribute to building sustainable and effective climate change adaptation solutions avoiding usability gap, it is largely recommended to engage in the process of co-production, integrating expertise and knowledge from various academic and non-academic actors. We want to learn if and how co-production, believed to effectively link knowledge and decision-making, and thus suggested as the best practice in building decision-support frameworks, is really applied in the frameworks that are being implemented on the ground. A literature review allowed us to identify integrated decision-support frameworks for climate-change adaptation in agriculture developed and used over the period of the last 10 years and involving non-academic stakeholders. To analyse them, we chose as an assessment tool the four co-production principles proposed by Norström and colleagues: context-based, pluralistic, goal-oriented and interactive. The useful entry points for incorporating co-production in the design of decision-support that we found in the reviewed frameworks include among the others adequate participants selection strategy, building on existing interaction spaces, developing a theory of change with the participants, and involving participants in the design of different elements of the method. The architectures of the analyzed frameworks contained more elements that responded to pluralistic and interactive principles than to context-based and goal oriented principles, we have also identified gaps in the design, such as taking into account the personal characteristics of researchers that could strengthen a framework's implementation and its impact, or attempts at bridging different levels of decision making, to cover the triad of science, policy and practice. A detailed look at the decision-frameworks that are actually being applied allows for a critical reflection whether and how we as researchers use what we preach as an effective way of responding to sustainability challenges in agriculture. Co-production principles turn out to be a useful tool for analysis and we suggest they can be used as a check-list when designing decision-support frameworks for climate-change adaptation. This papers offers useful examples of how to shift the research-led processes of decision-support towards more co-production with non-academic actors, to increase chances of bridging the gaps between science, policy and practice. [Display omitted] • Knowledge co-production is largely suggested as the best practice to bridge the gaps between science, policy and practice. • Evidence is lacking for the actual use of co-production in decision-support for climate-change adaptation in agriculture. • We identify useful entry points for applying co-production principles from already used decision-support frameworks. • Researchers are rarely selected to build decision-support frameworks based on their ability to support co-production. • When designing decision-support frameworks co-production principles can be used as a guiding tool. [ABSTRACT FROM AUTHOR]

Intercropping is a key entry point for sustainable intensification of cropping systems in sub-Saharan Africa where variable rainfall conditions prevail. Crop simulation models can complement field experiments to assess the agronomic and environmental performances of intercropping systems under diverse climatic conditions, including hypothetical future climate. So far, crop models that can handle intercropping, such as STICS, have not often been extensively evaluated for tropical conditions and for species grown by farmers in sub-Saharan Africa. The objective of this study was to evaluate the performance of the calibrated STICS model to simulate sorghum-cowpea intercropping systems in rainfed conditions in West Africa. We used data from field experiments conducted at the N'Tarla Agronomic Station in Mali in 2017 and 2018. Two varieties of sorghum (local and improved) with contrasting photoperiod sensitivities were grown as sole crop and intercropped with cowpea, with additive design. Two sowing dates and two levels of mineral fertilization were also investigated. Model simulations were evaluated with observed data for phenology, leaf area index (LAI), aboveground biomass, grain yield and in-season soil moisture. Large variations in aboveground biomass of sorghum and cowpea was observed in the experiment (i.e. 3.5 – 9.6 t/ha for sorghum and 0.4–2.5 t/ha for cowpea), owing to the treatments (i.e. sole vs intercrop, early vs late sowing, no fertilizer input vs fertilizer input). Such variations were satisfactorily reproduced by the model, with EF of 0.81 in calibration and 0.58 in evaluation (with relative rRMSE of 23 % and 43 %) across crops. Sorghum AGB simulations were more accurate (rRMSE of 21 % and EF of 0.54) than cowpea AGB simulations (rRMSE of 25 % and EF of −0.09). The two main observed features of the intercropping system were well reproduced by the model. Firstly, cowpea and sorghum aboveground biomass decreased with intercropping compared with sole cropping, and the decrease in cowpea biomass was greater than the decrease in sorghum biomass. Secondly, despite a reduction in sorghum and cowpea yield, Land Equivalent Ratio of the intercropping for aboveground biomass was always above one. With regard to grain yield, observed LER was above one only in the non-fertilized treatment. The model failed at reproducing this behavior, probably because of insufficiently accurate calibration of the process leading to grain yield formation: rRMSE for grain yield was 49 % in calibration and 41 % in evaluation. Based on these findings, we discuss avenues to improve model calibration and use the model to explore options for sustainable intensification in land constrained sub-Saharan Africa. • Accurate STICS simulations of intercropped sorghum and cowpea aboveground biomass. • Inaccurate STICS simulations of intercropped sorghum and cowpea grain yield. • The model can be used to start exploring inter-annual performance of intercropping. • Experiments looking at N dynamics in intercropping needed to refine calibration. [ABSTRACT FROM AUTHOR]

Summary: Objectives: Meningococcal meningitis (MM) is still a huge threat in the African meningitis belt. To fight against epidemics, a strengthened health information system, based upon weekly collected data, was set up in Mali. We aimed to study the spatio-temporal dynamics of MM in this country between 1992 and 2003. Methods: We were first interested in the impact of population size on the disease persistence. We then used cross-correlation analysis to study the spread of the disease on three different spatial scales, i.e., inter-region (global) and inter-district and intra-district (local) levels. Results: We found no persistence of MM at district level in Mali during the whole of the study period. However, we found persistence on a nationwide scale after the 1997 big epidemics, as opposed to the 1992–1996 time periods. In terms of spread, two main regions seem to lead MM dynamics in Mali, even if on a local scale the ‘cities–villages’ diffusion pattern was not systematically observed. Conclusions: This study improves knowledge on the spread and persistence of MM in Mali in recent years. It constitutes a first spatial study describing persistence and spread of MM in an African meningitis belt country. The next step should be the integration of vaccination and genetic variability data to clarify the route of spread of the disease in the human population. [Copyright &y& Elsevier]

Climate change studies in recent decades have been based on Global Climate Models (GCMs), and the changes in the distribution of climatic regions over time extracted from these models can be represented using the Köppen climatic classification system, which predicts the global distribution of biomes based on monthly precipitation and average temperatures. In this study, the Köppen classification is used to evaluate the impacts of the melting of the Greenland and Antarctic Ice Sheets on GCM simulation results, on regional and global scales. To assess the impacts of accelerated ice sheet melting, an approach is utilized which is based on numerical simulations from the IPSL-CM5A-LR GCM; here, freshwater is introduced near the ice sheets and is superimposed on the RCP8.5 scenario. The changes in the distribution of the Köppen climatic regions under various scenarios (a historical run from observations, RCP8.5, and various examples of polar ice sheet melting) and comparisons between them reveal that major changes will occur on the global scale during the period 2041–2060. The analysis of group shifts within the Köppen classification system reveals that when freshwater from Greenland or Antarctica is introduced into the ocean, the inter-tropical belt undergoes greater change than it does under the RCP8.5 scenario. A focus on sub-group shifts within the Koppen classification system shows that changes in precipitation have major impacts on the climate in the Southern Hemisphere. Further, the changes are more drastic if the freshwater originates from Greenland than from Antarctica or from both locations. However, changes in temperature strongly impact the climate in the Northern Hemisphere and are significantly affected by the melting of the Greenland Ice Sheet. This study highlights the importance of considering ice sheet melting in the modeling of future global climate. • Assess impacts of ice sheets melting on GCM simulations using Koppen classification. • Freshwater from Greenland/Antarctica influences changes already induced by RCP8.5 • Precipitation changes have major impacts on the climate in the Southern Hemisphere. • Temperature changes strongly impact the climate in the Northern Hemisphere. • Importance of ice sheet meltings in climate projections causing model uncertainty. [ABSTRACT FROM AUTHOR]