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 - 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 - Kim, Yean-Uk A1 - Webber, Heidi A1 - Adiku, Samuel G.K. A1 - Nóia Júnior, Rogério de S. A1 - Deswarte, Jean-Charles A1 - Asseng, Senthold A1 - Ewert, Frank T1 - Mechanisms and modelling approaches for excessive rainfall stress on cereals: waterlogging, submergence, lodging, pests and diseases T2 - Agricultural and Forest Meteorology N2 - As the intensity and frequency of extreme weather events are projected to increase under climate change, assessing their impact on cropping systems and exploring feasible adaptation options is increasingly critical. Process-based crop models (PBCMs), which are widely used in climate change impact assessments, have improved in simulating the impacts of major extreme weather events such as heatwaves and droughts but still fail to reproduce low crop yields under wet conditions. Here, we provide an overview of yield-loss mechanisms of excessive rainfall in cereals (i.e., waterlogging, submergence, lodging, pests and diseases) and associated modelling approaches with the aim of guiding PBCM improvements. Some PBCMs simulate waterlogging and ponding environments, but few capture aeration stresses on crop growth. Lodging is often neglected by PBCMs; however, some stand-alone mechanistic lodging models exist, which can potentially be incorporated into PBCMs. Some frameworks link process-based epidemic and crop models with consideration of different damage mechanisms. However, the lack of data to calibrate and evaluate these model functions limit the use of such frameworks. In order to generate data for model improvement and close knowledge gaps, targeted experiments on damage mechanisms of waterlogging, submergence, pests and diseases are required. However, consideration of all damage mechanisms in PBCM may result in excessively complex models with a large number of parameters, increasing model uncertainty. Modular frameworks could assist in selecting necessary mechanisms and lead to appropriate model structures and complexity that fit a specific research question. Lastly, there are potential synergies between PBCMs, statistical models, and remotely sensed data that could improve the prediction accuracy and understanding of current PBCMs' shortcomings. KW - Excess rain KW - Yield loss mechanisms KW - Process-based crop model KW - Model improvement Y1 - 2024 U6 - https://doi.org/10.1016/j.agrformet.2023.109819 SN - 0168-1923 VL - 344 PB - Elsevier BV 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 -