TY - GEN A1 - Diancoumba, M. A1 - MacCarthy, Dilys Sefakor A1 - Webber, Heidi A1 - Akinseye, Folorunso M. A1 - Faye, Babacar A1 - Noulèkoun, Florent A1 - Whitbread, Anthony Michael A1 - Corbeels, Marc A1 - Worou, Nadine O. T1 - Scientific agenda for climate risk and impact assessment of West African cropping systems T2 - Global Food Security N2 - Rainfed agriculture is at the centre of many West African economies and a key livelihood strategy in the region. Highly variable rainfall patterns lead to a situation in which farmers’ investments to increase productivity are very risky and will become more risky with climate change. Process-based cropping system models are a key tool to assess the impact of weather variability and climate change, as well as the effect of crop management options on crop yields, soil fertility and farming system resilience and widely used by the West African scientific community. Challenges to use are related to their consideration of the prevailing systems and conditions of West African farms, as well as limited data availability for calibration. We outline here a number of factors need to be considered if they are to contribute to the scientific basis underlying transformation of farming systems towards sustainability. These include: capacity building, improved models, FAIR data, research partnerships and using models in co-development settings. Y1 - 2023 U6 - https://doi.org/10.1016/j.gfs.2023.100710 SN - 2211-9124 VL - 38 PB - Elsevier BV ER - TY - GEN A1 - Rezaei, Ehsan Eyshi A1 - Faye, Babacar A1 - Ewert, Frank A1 - Asseng, Senthold A1 - Martre, Pierre A1 - Webber, Heidi T1 - Impact of coupled input data source-resolution and aggregation on contributions of high-yielding traits to simulated wheat yield T2 - Scientific Reports N2 - High-yielding traits can potentially improve yield performance under climate change. However, data for these traits are limited to specific field sites. Despite this limitation, field-scale calibrated crop models for high-yielding traits are being applied over large scales using gridded weather and soil datasets. This study investigates the implications of this practice. The SIMPLACE modeling platform was applied using field, 1 km, 25 km, and 50 km input data resolution and sources, with 1881 combinations of three traits [radiation use efficiency (RUE), light extinction coefficient (K), and fruiting efficiency (FE)] for the period 2001–2010 across Germany. Simulations at the grid level were aggregated to the administrative units, enabling the quantification of the aggregation effect. The simulated yield increased by between 1.4 and 3.1 t ha− 1 with a maximum RUE trait value, compared to a control cultivar. No significant yield improvement (< 0.4 t ha− 1) was observed with increases in K and FE alone. Utilizing field-scale input data showed the greatest yield improvement per unit increment in RUE. Resolution of water related inputs (soil characteristics and precipitation) had a notably higher impact on simulated yield than of temperature. However, it did not alter the effects of high-yielding traits on yield. Simulated yields were only slightly affected by data aggregation for the different trait combinations. Warm-dry conditions diminished the benefits of high-yielding traits, suggesting that benefits from high-yielding traits depend on environments. The current findings emphasize the critical role of input data resolution and source in quantifying a large-scale impact of high-yielding traits. Y1 - 2024 U6 - https://doi.org/10.1038/s41598-024-74309-4 SN - 2045-2322 VL - 14 IS - 1 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Couëdel, Antoine A1 - Falconnier, Gatien N. A1 - Adam, Myriam A1 - Cardinael, Rémi A1 - Boote, Kenneth A1 - Justes, Eric A1 - Smith, Ward N. A1 - Whitbread, Anthony Michael A1 - Affholder, François A1 - Balkovic, Juraj A1 - Basso, Bruno A1 - Bhatia, Arti A1 - Chakrabarti, Bidisha A1 - Chikowo, Regis A1 - Christina, Mathias A1 - Faye, Babacar A1 - Ferchaud, Fabien A1 - Folberth, Christian A1 - Akinseye, Folorunso M. A1 - Gaiser, Thomas A1 - Galdos, Marcelo V. A1 - Gayler, Sebastian A1 - Gorooei, Aram A1 - Grant, Brian A1 - Guibert, Hervé A1 - Hoogenboom, Gerrit A1 - Kamali, Bahareh A1 - Laub, Moritz A1 - Maureira, Fidel A1 - Mequanint, Fasil A1 - Nendel, Claas A1 - Porter, Cheryl H. A1 - Ripoche, Dominique A1 - Ruane, Alex C. A1 - Rusinamhodzi, Leonard A1 - Sharma, Shikha A1 - Singh, Upendra A1 - Six, Johan A1 - Srivastava, Amit Kumar A1 - Vanlauwe, Bernard A1 - Versini, Antoine A1 - Vianna, Murilo A1 - Webber, Heidi A1 - Weber, Tobias K. D. A1 - Zhang, Congmu A1 - Corbeels, Marc T1 - Long-term soil organic carbon and crop yield feedbacks differ between 16 soil-crop models in sub-Saharan Africa T2 - European Journal of Agronomy N2 - Food insecurity in sub-Saharan Africa is partly due to low staple crop yields, resulting from poor soil fertility and low nutrient inputs. Integrated soil fertility management (ISFM), which includes the combined use of mineral and organic fertilizers, can contribute to increasing yields and sustaining soil organic carbon (SOC) in the long term. Soil-crop simulation models can help assess the performance and trade-offs of a range of crop management practices including ISFM, under current and future climate. Yet, uncertainty in model simulations can be high, resulting from poor model calibration and/or inadequate model structure. Multi-model simulations have been shown to be more robust than those with single models and help understand and reduce modelling uncertainty. In this study, we aim to perform the first multi-model comparison for long-term simulations of crop yield and SOC and their feedbacks in SSA. We evaluated the performance of 16 soil-crop models using data from four long-term maize experiments at sites in SSA with contrasting climates and soils. Each experiment had four treatments: i) no exogenous inputs, ii) addition of mineral nitrogen (N) fertilizer, iii) use of organic amendments, and iv) combined use of mineral and organic inputs. We assessed model performance in two steps: through blind calibration involving a minimum level of experimental data provided to the modeling teams, and subsequently through full calibration, which included a more extensive set of observational data. Model ensemble accuracy was greater with full calibration than blind calibration. Improvement in model accuracy was larger for maize yields (nRMSE 48 vs 18%) than for topsoil SOC (nRMSE 22 vs 14%). Model ensemble uncertainty (defined as the coefficient of variation across the 16 models) increased over the duration of the long-term experiments. Uncertainty of SOC simulations increased when organic amendments were used, whilst uncertainty of yield predictions was largest when no inputs were applied. Our study revealed large discrepancies among the models in simulating i) crop-to-soil feedbacks due to uncertainties in simulated carbon coming from roots, and ii) soil-to-crop feedbacks due to large uncertainties in simulated crop N supply from soil organic matter decomposition. These discrepancies were largest when organic amendments were applied. The results highlight the need for long-term experiments in which root and soil N dynamics are monitored. This will provide the corresponding data to improve and calibrate soil-crop models, which will lead to more robust and reliable simulations of SOC and crop productivity, and their interactions. KW - Soil-crop simulation KW - Soil organic matter KW - Soil-crop feedback KW - Ensemble modelling KW - Model intercomparison KW - Long-term experiments Y1 - 2024 U6 - https://doi.org/10.1016/j.eja.2024.127109 SN - 1161-0301 VL - 155 PB - Elsevier BV ER - TY - GEN A1 - Faye, Babacar A1 - Mbaye, Mamadou Lamine A1 - Webber, Heidi A1 - Dieye, Bounama A1 - Diouf, Diégane A1 - Gaye, Amadou Thierno T1 - Adaptation potential of alternate varieties and fertilization strategies for peanut and maize in Senegal under climate change T2 - Regional environmental change N2 - In Senegal, rising temperatures are projected to reduce maize yields due to a shortened growth duration, while elevated CO2 fertilization may increase peanut yields under climate change. However, there is limited evidence on climate change impacts if crop cultivars change and systems intensify, which is expected to occur in parallel with climate change. For climate-adapted agriculture, the performance of improved agronomy and varieties should be evaluated under current and future climate scenarios. This study assesses the impact of climate change on crop yields of two varieties of peanut and maize at each under current and intensified fertilization. Simulations were performed for mid-century (2045–2074) and end-century (2070–2099) relative to a baseline (1981–2010) using the SIMPLACE modeling framework at 0.5° resolution. Climate projections from nine global climate models (GCMs) were used under SSP2-4.5 and SSP5-8.5 scenarios. Soil data was derived from the Harmonized World Soil Database. The results indicate that the impacts of climate change on crop yields differed by crop. Peanut showed an increase in yield of up to 45% and a decrease for maize of up to 25% by the end of the century. Peanut yield gains were higher under the intensification fertilization case compared to the current fertilization case, whereas for maize, losses were high in the intensification case. Furthermore, yield losses are more substantial in the southern and western parts of the country for both crops. Additionally, for maize, yield losses were higher for the short cycle variety than the long cycle variety; there was little difference between varieties for peanut. KW - Crop yields KW - Intensification KW - Climate change KW - SIMPLACE KW - Senegal Y1 - 2025 U6 - https://doi.org/10.1007/s10113-025-02491-w SN - 1436-3798 SN - 1436-378X VL - 25 IS - 4 SP - 1 EP - 15 PB - Springer Science and Business Media LLC CY - Berlin ; Heidelberg ; New York, NY ER -