Long-term soil organic carbon and crop yield feedbacks differ between 16 soil-crop models in sub-Saharan Africa

0585152 - ÚVGZ 2025 RIV NL eng J - Článek v odborném periodiku
Couedel, A. - Falconnier, G.N. - Adam, M. - Cardinael, R. - Boote, K. J. - Justes, E. - Smithj, W.N. - Whitbread, A.M. - Affholder, F. - Balkovic, J. - Basso, B. - Bhatia, A. - Chakrabarti, B. - Chikowo, R. - Christina, M. - Faye, B. - Ferchaud, F. - Folberth, C. - Akinseye, F.M. - Gaiser, T. - Galdos, M.V. - Gayler, S. - Gorooei, A. - Grant, B. - Guibert, H. - Hoogenboom, G. - Kamali, B. - Laub, M. - Maureira, F. - Mequanint, F. - Nendel, Claas - Porter, C.H. - Ripoche, D. - Ruane, A.C. - Rusinamhodzi, L. - Sharma, S. - Singh, U. - Six, J. - Srivastava, A. - Vanlauwe, B. - Versini, A. - Vianna, M. - Webber, H. - Weber, T. K. D. - Zhang, C. - Corbeels, M.
Long-term soil organic carbon and crop yield feedbacks differ between 16 soil-crop models in sub-Saharan Africa.
European Journal of Agronomy. Roč. 155, APR (2024), č. článku 127109. ISSN 1161-0301. E-ISSN 1873-7331
Institucionální podpora: RVO:86652079
Klíčová slova: dynamic simulation-model * fertility management * root-growth * tropical environments * maize production * nitrogen * matter * tillage * systems * impacts * Soil-crop simulation * Soil organic matter * Soil-crop feedback * Ensemble modelling * Model intercomparison * Long-term experiments
Obor OECD: Agriculture
Impakt faktor: 5.2, rok: 2022
Způsob publikování: Omezený přístup
https://www.sciencedirect.com/science/article/pii/S1161030124000303?via%3Dihub

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 longterm 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-tocrop 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.
Trvalý link: https://hdl.handle.net/11104/0352892