Habitability for a connected, unequal and changing world - Harald Sterly; Marion Borderon; Patrick Sakdapolrak; Neil Adger; Ayansina Ayanlade; Alassane Bah; Julia Blocher; Suzy Blondin; Sidy Boly; Timothée Brochier; Loïc Brüning; Simon Bunchuay-Peth; et al. (Global Environmental Change, 2025)

The function of love: A signaling-to-alternatives account of the commitment device hypothesis - Benjamin Gelbart; Kathryn V. Walter; Daniel Conroy-Beam; Casey Estorque; David M. Buss; Kelly Asao; Agnieszka Sorokowska; Piotr Sorokowski; Toivo Aavik; Grace Akello; Mohammad Madallh Alhabahba; Charlotte Alm; et al. (Evolution and Human Behavior, 2025)

Sustainable intensification of sorghum-based cropping systems in semi-arid sub-Saharan Africa: The role of improved varieties, mineral fertilizer, and legume integration - Amadou Traore; Gatien N. Falconnier; Antoine Couëdel; Benjamin Sultan; Vimbayi G.P. Chimonyo; Myriam Adam; François Affholder (Field Crops Research, 2023)

Co-production opportunities seized and missed in decision-support frameworks for climate-change adaptation in agriculture – How do we practice the “best practice”? - Aleksandra Dolinska; Emeline Hassenforder; Ana Maria Loboguerrero; Benjamin Sultan; Jérôme Bossuet; Jeanne Cottenceau; Michelle Bonatti; Jon Hellin; Insaf Mekki; Alexis Drogoul; Vincent Vadez (Agricultural Systems, 2023)

Predicting crop yields in Senegal using machine learning methods - Alioune Badara Sarr; Benjamin Sultan (International Journal of Climatology, 2023)
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.

Millet and sorghum yield simulations under climate change scenarios in Senegal - Adama Faye; Ibrahima Camara; Mbaye Diop; Amadou OuryDiallo; Bassirou Sine; Mélinda Noblet; Babacar Faye; Dimitri Defrance; Benjamin Sultan; Ndeye Yacine Badiane Ndour (Regional Environmental Change, 2022)

North Atlantic centers of action and seasonal to subseasonal temperature variability in Europe and eastern North America - Mahmoud Osman; Benjamin Zaitchik; Hamada Badr; Sultan Hameed (International Journal of Climatology, 2021)
The North Atlantic Oscillation (NAO) is one of the most prominent patterns of atmospheric circulation variability in the Northern Hemisphere. Its climate impacts have been identified for regions extending from North America to northern Asia and the Middle East, with implications for sub‐seasonal variability and, in some cases, meteorological trends over multiple years to decades. The NAO itself, however, is the combined expression of two pressure centers: the Icelandic Low and the Azores High. Previous work has shown that when these “centers of action” (CoA) are separated out and studied they can often provide better insight and explanatory power than the NAO does on its own. Here, we analyse the influence that the CoA have on daily minimum air temperature in winter and daily maximum temperature in summer in eastern North America and Europe. The paper has two goals: (a) to characterize CoA associations with temperature on a range of timescales, including sub‐seasonal analyses that reflect short‐term dynamics of the CoA and that are frequently ignored in NAO teleconnection studies, and (b) to evaluate NAO and CoA influence on temperature across seasons (winter and summer) and in upstream and downstream regions (eastern North America and Europe) using a consistent combination of datasets and methods, in order to contextualize results from prior studies that targeted a single region or season. We find that associations with summertime daily maximum and wintertime daily minimum temperatures are strongest for seasonal averages, but select CoA indices inform explanation of temperature variability at subseasonal timescales. The strength of the Icelandic Low and the location of both CoA show strong associations in winter, while in summer the locations of the CoA show temperature associations that are distinct from and sometimes stronger than those found for CoA strength or NAO indices.

Climate change in Africa: costs of mitigating heat stress - Ben Parkes; Jennifer Cronin; Olivier Dessens; Benjamin Sultan (Climatic Change, 2019)

Direct and indirect effects of CO 2 increase on crop yield in West Africa - Benjamin Sultan; Ben Parkes; Marco Gaetani (International Journal of Climatology, 2019)
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 CO 2 increase and in the response of the cultivated crop to this altered climate with more CO 2 in the atmosphere. Here, we analyse a set of idealized climate simulations to investigate the effect of CO 2 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 CO 2 concentration are analysed to study the atmospheric response to direct radiative forcing induced by increasing CO 2 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 CO 2 radiative forcing. We show that if CO 2 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.

The impact of future climate change and potential adaptation methods on Maize yields in West Africa - Ben Parkes; Benjamin Sultan; Philippe Ciais (Climatic Change, 2018)

Impact of ET 0 method on the simulation of historical and future crop yields: a case study of millet growth in Senegal - Johanna Ramarohetra; Benjamin Sultan (International Journal of Climatology, 2018)
The reference evapotranspiration (ET 0 ) 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 ET 0 equations on crop yield prediction has been little studied. The present study proposes a sensitivity analysis of the impact of the choice of the ET 0 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 ET 0 and yield over the major part of Senegal, Hargreaves equation being more appropriated under dry climates. The choice of ET 0 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 ET 0 equation is increasingly important, with local temperature changes out to 4 °C, while extreme changes above 6 °C depend less on the ET 0 equation.

Perceptions of recent rainfall changes in Niger: a comparison between climate-sensitive and non-climate sensitive households - Frédéric Kosmowski; Antoine Leblois; Benjamin Sultan (Climatic Change, 2016)

Effect of climate change, CO 2 trends, nitrogen addition, and land‐cover and management intensity changes on the carbon balance of European grasslands - Jinfeng Chang; Philippe Ciais; Nicolas Viovy; Nicolas Vuichard; Mario Herrero; Petr Havlík; Xuhui Wang; Benjamin Sultan; Jean‐François Soussana (Global Change Biology, 2016)
Several lines of evidence point to European managed grassland ecosystems being a sink of carbon. In this study, we apply ORCHIDEE ‐ GM a process‐based carbon cycle model that describes specific management practices of pastures and the dynamics of carbon cycling in response to changes in climatic and biogeochemical drivers. The model is used to simulate changes in the carbon balance [i.e., net biome production ( NBP )] of European grasslands over 1991–2010 on a 25 km × 25 km grid. The modeled average trend in NBP is 1.8–2.0 g C m −2 yr −2 during the past two decades. Attribution of this trend suggests management intensity as the dominant driver explaining NBP trends in the model (36–43% of the trend due to all drivers). A major change in grassland management intensity has occurred across Europe resulting from reduced livestock numbers. This change has ‘inadvertently’ enhanced soil C sequestration and reduced N 2 O and CH 4 emissions by 1.2–1.5 Gt CO 2 ‐equivalent, offsetting more than 7% of greenhouse gas emissions in the whole European agricultural sector during the period 1991–2010. Land‐cover change, climate change and rising CO 2 also make positive and moderate contributions to the NBP trend (between 24% and 31% of the trend due to all drivers). Changes in nitrogen addition (including fertilization and atmospheric deposition) are found to have only marginal net effect on NBP trends. However, this may not reflect reality because our model has only a very simple parameterization of nitrogen effects on photosynthesis. The sum of NBP trends from each driver is larger than the trend obtained when all drivers are varied together, leaving a residual – nonattributed – term (22–26% of the trend due to all drivers) indicating negative interactions between drivers.

The greenhouse gas balance of European grasslands - Jinfeng Chang; Philippe Ciais; Nicolas Viovy; Nicolas Vuichard; Benjamin Sultan; Jean‐François Soussana (Global Change Biology, 2015)
The greenhouse gas ( GHG ) balance of European grasslands ( EU ‐28 plus Norway and Switzerland), including CO 2 , CH 4 and N 2 O, is estimated using the new process‐based biogeochemical model ORCHIDEE ‐ GM over the period 1961–2010. The model includes the following: (1) a mechanistic representation of the spatial distribution of management practice; (2) management intensity, going from intensively to extensively managed; (3) gridded simulation of the carbon balance at ecosystem and farm scale; and (4) gridded simulation of N 2 O and CH 4 emissions by fertilized grassland soils and livestock. The external drivers of the model are changing animal numbers, nitrogen fertilization and deposition, land‐use change, and variable CO 2 and climate. The carbon balance of European grassland ( NBP ) is estimated to be a net sink of 15 ± 7 g C m −2 year −1 during 1961–2010, equivalent to a 50‐year continental cumulative soil carbon sequestration of 1.0 ± 0.4 Pg C. At the farm scale, which includes both ecosystem CO 2 fluxes and CO 2 emissions from the digestion of harvested forage, the net C balance is roughly halved, down to a small sink, or nearly neutral flux of 8 g C m −2 year −1 . Adding CH 4 and N 2 O emissions to net ecosystem exchange to define the ecosystem‐scale GHG balance, we found that grasslands remain a net GHG sink of 19 ± 10 g C‐ CO 2 equiv. m −2 year −1 , because the CO 2 sink offsets N 2 O and grazing animal CH 4 emissions. However, when considering the farm scale, the GHG balance ( NGB ) becomes a net GHG source of −50 g C‐ CO 2 equiv. m −2 year −1 . ORCHIDEE ‐ GM simulated an increase in European grassland NBP during the last five decades. This enhanced NBP reflects the combination of a positive trend of net primary production due to CO 2 , climate and nitrogen fertilization and the diminishing requirement for grass forage due to the Europe‐wide reduction in livestock numbers.

Weather regimes over Senegal during the summer monsoon season using self-organizing maps and hierarchical ascendant classification. Part II: interannual time scale - A. K. Guèye; Serge Janicot; A. Niang; S. Sawadogo; Benjamin Sultan; A. Diongue-Niang; S. Thiria (Climate Dynamics, 2012)

Including tropical croplands in a terrestrial biosphere model: application to West Africa - Alexis Berg; Benjamin Sultan; Nathalie de Noblet-Ducoudré (Climatic Change, 2011)

Weather regimes over Senegal during the summer monsoon season using self-organizing maps and hierarchical ascendant classification. Part I: synoptic time scale - A. K. Guèye; Serge Janicot; A. Niang; S. Sawadogo; Benjamin Sultan; A. Diongue-Niang; S. Thiria (Climate Dynamics, 2011)

Multi‐scales and multi‐sites analyses of the role of rainfall in cotton yields in West Africa - Benjamin Sultan; Marthe Bella‐Medjo; Alexis Berg; Philippe Quirion; Serge Janicot (International Journal of Climatology, 2010)
Cotton is the main tradable crop of West and Central African countries, representing for some countries the main exported agricultural product. Cotton is then of major concern since it represents an important source of income, accounting for more than a tenth of total exports. Moreover, the subsector as a whole is essential for rural poverty reduction. Since cotton is a rainfed crop in these countries, its yield is closely related to climate, in particular to rainfall variability. The objective of this study is to point out the role of rainfall variability in cotton yields. Our approach consists in taking two completely different sites in the analysis of the climate‐yields relationships, i.e. an experimental plot in Mali with a long‐term historical yield‐survey and farmers' yields in 28 administrative units in Cameroon. We found that the same rainfall parameters (rainy season onset and length) are major drivers for the year‐to‐year and the spatial distribution of cotton productivity, even if the role of rainfall variability is strongly reduced in farmers' exploitations where other non‐climatic factors such as human management, biotic stresses, pests, etc., impact crop productivity. The link between rainfall and cotton yields seems to depend on the mean climate since the driest cotton areas in Cameroon are the most sensitive to climate variability. The coherence of the results from the two very different situations gives us some confidence in the generalization of our findings to the whole West and Central Africa. Our study shows also that the aggregation of yield data from the local scale to the national scale tends to smooth the non‐climatic variability and highlight the role of climate in the year‐to‐year variability of cotton yields. These results are important in working towards the predictability of crop yields using rainfall information, in particular to highlight the most salient rainfall parameters that are needed in a forecast system. Copyright © 2009 Royal Meteorological Society