TY - GEN A1 - Martre, Pierre A1 - Dueri, Sibylle A1 - Guarin, Jose Rafael A1 - Ewert, Frank A1 - Webber, Heidi A1 - Calderini, Daniel A1 - Molero, Gemma A1 - Reynolds, Matthew A1 - Miralles, Daniel A1 - Garcia, Guillermo A1 - Brown, Hamish A1 - George, Mike A1 - Craigie, Rob A1 - Cohan, Jean‐Pierre A1 - Deswarte, Jean-Charles A1 - Slafer, Gustavo A. A1 - Giunta, Francesco A1 - Cammarano, Davide A1 - Ferrise, Roberto A1 - Gaiser, Thomas A1 - Gao, Yujing A1 - Hochman, Zvi A1 - Hoogenboom, Gerrit A1 - Hunt, Leslie A. A1 - Kersebaum, Kurt C. A1 - Nendel, Claas A1 - Padovan, Gloria A1 - Ruane, Alex C. A1 - Srivastava, Amit Kumar A1 - Stella, Tommaso A1 - Supit, Iwan A1 - Thorburn, Peter A1 - Wang, Enli A1 - Wolf, Joost A1 - Zhao, Chuang A1 - Zhao, Zhigan A1 - Asseng, Senthold T1 - Global needs for nitrogen fertilizer to improve wheat yield under climate change T2 - Nature Plants Y1 - 2024 U6 - https://doi.org/10.1038/s41477-024-01739-3 SN - 2055-0278 VL - 10 IS - 7 SP - 1081 EP - 1090 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Martre, Pierre A1 - Dueri, Sibylle A1 - Brown, Hamish A1 - Asseng, Senthold A1 - Ewert, Frank A1 - Webber, Heidi A1 - George, Mike A1 - Craigie, Rob A1 - Guarin, Jose Rafael A1 - Pequeno, Diego A1 - Stella, Tommaso A1 - Ahmed, Mukhtar A1 - Alderman, Phillip A1 - Basso, Bruno A1 - Berger, Andres A1 - Bracho Mujica, Gennady A1 - Cammarano, Davide A1 - Chen, Yi A1 - Dumont, Benjamin A1 - Rezaei, Ehsan Eyshi A1 - Fereres, Elias A1 - Ferrise, Roberto A1 - Gaiser, Thomas A1 - Gao, Yujing A1 - Garcia-Vila, Margarita A1 - Gayler, Sebastian A1 - Hochman, Zvi A1 - Hoogenboom, Gerrit A1 - Kersebaum, Kurt C. A1 - Nendel, Claas A1 - Olesen, Jørgen A1 - Padovan, Gloria A1 - Palosuo, Taru A1 - Priesack, Eckart A1 - Pullens, Johannes A1 - Rodríguez, Alfredo A1 - Rötter, Reimund P. A1 - Ruiz Ramos, Margarita A1 - Semenov, Mikhail A1 - Senapati, Nimai A1 - Siebert, Stefan A1 - Srivastava, Amit Kumar A1 - Stöckle, Claudio A1 - Supit, Iwan A1 - Tao, Fulu A1 - Thorburn, Peter A1 - Wang, Enli A1 - Weber, Tobias A1 - Xiao, Liujun A1 - Zhao, Chuang A1 - Zhao, Jin A1 - Zhao, Zhigan A1 - Zhu, Yan T1 - Winter wheat experiments to optimize sowing dates and densities in a high-yielding environment in New Zealand: field experiments and AgMIP-Wheat multi-model simulations T2 - Open Data Journal for Agricultural Research N2 - This paper describes the data set that was used to test the accuracy of twenty-nine crop models in simulating the effect of changing sowing dates and sowing densities on wheat productivity for a high-yielding environment in New Zealand. The data includes one winter wheat cultivar (Wakanui) grown during six consecutive years, from 2012-2013 to 2017-2018, at two farms located in Leeston and Wakanui in Canterbury, New Zealand. The simulations were carried out in the framework of the Agricultural Model Intercomparison and Improvement Project for wheat (AgMIP-Wheat). Data include local daily weather data, soil profile characteristics and initial conditions, crop measurements at maturity (grain, stem, chaff and leaf dry weight, ear number and grain number, grain unit dry weight), and at stem elongation and anthesis (total above ground dry biomass, leaf number per stem and leaf area index). Several in-season measurements of the normalized difference vegetation index (NDVI) and the fraction of intercepted photosynthetically active radiation (FIPAR) are also available. The crop model simulations include both daily in-season and end-of-season results from twenty-nine wheat models. KW - field experimental data KW - multi-crop model ensemble KW - sowing date KW - sowing density KW - winter wheat KW - yield potential Y1 - 2024 U6 - https://doi.org/10.18174/odjar.v10i0.18442 SN - 2352-6378 VL - 10 SP - 14 EP - 21 PB - Wageningen University and Research 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 - Nóia-Júnior, Rogério de S. A1 - Stocca, Valentina A1 - Martre, Pierre A1 - Shelia, Vakhtang A1 - Deswarte, Jean-Charles A1 - Cohan, Jean-Pierre A1 - Piquemal, Benoît A1 - Dutertre, Alain A1 - Slafer, Gustavo A. A1 - Zhang, Zhentao A1 - Van Der Velde, Marijn A1 - Kim, Yean-Uk A1 - Webber, Heidi A1 - Ewert, Frank A1 - Palosuo, Taru A1 - Liu, Ke A1 - Harrison, Matthew Tom A1 - Hoogenboom, Gerrit A1 - Asseng, Senthold T1 - Enabling modeling of waterlogging impact on wheat T2 - Field crops research N2 - Most crop simulation models do not consider the effect of waterlogging despite its importance for crop performance. Here, we reviewed the impact of waterlogging during different wheat phenological stages on grain number per unit area, average grain size, and grain yield. Episodes of waterlogging from the onset of tillering to anthesis result in fewer, and during grain filling in lighter grains. To simulate such impacts, we implemented a new waterlogging module into the wheat crop simulation model DSSAT-NWheat, accounting for the effects of waterlogging on wheat root growth, biomass growth, and potential average grain size. The model incorporating the new waterlogging routine was tested using data from a controlled experiment, and it reasonably reproduced wheat yield responses to pre-anthesis waterlogging. A sensitivity analysis showed that the simulated impact of waterlogging on above ground biomass and roots, as well as leaf area index, grain number, and grain yield varied with phenological stages. The simulated crop was most sensitive to pre-anthesis waterlogging, consistent with experimental studies. The new waterlogging-enabled crop model is an initial attempt to consider the impact of excess rainfall and waterlogging on crop growth and final grain yield to reduce model uncertainties when projecting climate change impacts with increasing rainfall intensity. KW - NWheat KW - DSSAT KW - Excess of water KW - Grain number KW - Grain size KW - Wheat crop simulation model Y1 - 2025 U6 - https://doi.org/10.1016/j.fcr.2025.110090 SN - 0378-4290 VL - 333 SP - 1 EP - 13 PB - Elsevier BV CY - Amsterdam ER -