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 - 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 -