TY - GEN A1 - Nóia-Júnior, Rogério de S. A1 - Martre, Pierre A1 - Deswarte, Jean-Charles A1 - Cohan, Jean-Pierre A1 - Van der Velde, Marijn A1 - Webber, Heidi A1 - Ewert, Frank A1 - Ruane, Alex C. A1 - Ben-Ari, Tamara A1 - Asseng, Senthold T1 - Past and future wheat yield losses in France’s breadbasket T2 - Field Crops Research N2 - Context or problem: In recent decades, compounding weather extremes and plant diseases have increased wheat yield variability in France, the largest wheat producer in the European Union. Objective or research question: How these extremes might affect future wheat production remains unclear. Methods: Based on department level wheat yields, disease, and climate indices from 1980 to 2019 in France, we combined an existing disease model with machine learning algorithms to estimate future grain yields. Results: This approach explains about 59% of historical yield variability. Projections from five CMIP6 climate models suggest that extreme low wheat yields, which used to occur once every 20 years, could occur every decade by the end of this century, but elevated CO2 levels might lessen these events. Conclusions: Heatwave-related yield losses are expected to double, while flooding-related yield losses will potentially decline by one third, depending on the representative concentration pathway. Ear blight disease is projected to contribute to 20% of the expected 400 kg ha-1 average yield losses by the end of the century, compared with 12% in the historical baseline period. These projections depend on the timing of anthesis, currently between late May and early June in most departments. Anthesis advancing to early May would shift losses primarily to heavy rainfall and low solar radiation. Implications or significance: French wheat production must adapt to these emerging threats, such as heat stress, which until recently had little impact but may become the primary cause of future yield losses. KW - Compounding factors KW - Extreme weather KW - Machine learning KW - Plant diseases KW - Wheat KW - Yield failure Y1 - 2025 U6 - https://doi.org/10.1016/j.fcr.2024.109703 SN - 0378-4290 VL - 322 PB - Elsevier BV ER - TY - GEN A1 - Schaller, Jörg A1 - Webber, Heidi A1 - Ewert, Frank A1 - Stein, Mathias A1 - Puppe, Daniel T1 - The transformation of agriculture towards a silicon improved sustainable and resilient crop production T2 - npj Sustainable Agriculture N2 - Sustainable and resilient crop production is facing many challenges. The restoration of natural reactive silicon cycles offers an opportunity to improve sustainability through reducing phosphorus fertilizer use and to increase crops’ resilience to drought stress and pests. We therefore call upon farmers, agri-food-researchers, and policymakers to pave the road for transforming agriculture to a silicon-improved sustainable crop production, which represents a promising approach to achieve food security under global change. Y1 - 2024 U6 - https://doi.org/10.1038/s44264-024-00035-z SN - 2731-9202 VL - 2 IS - 1 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Nóia Júnior, Rogério de S. A1 - Deswarte, Jean-Charles A1 - Cohan, Jean‐Pierre A1 - Martre, Pierre A1 - van der Velde, Marijn A1 - Lecerf, Remi A1 - Webber, Heidi A1 - Ewert, Frank A1 - Ruane, Alex C. A1 - Slafer, Gustavo A. A1 - Asseng, Senthold T1 - The extreme 2016 wheat yield failure in France T2 - Global Change Biology N2 - France suffered, in 2016, the most extreme wheat yield decline in recent history, with some districts losing 55% yield. To attribute causes, we combined the largest coherent detailed wheat field experimental dataset with statistical and crop model techniques, climate information, and yield physiology. The 2016 yield was composed of up to 40% fewer grains that were up to 30% lighter than expected across eight research stations in France. The flowering stage was affected by prolonged cloud cover and heavy rainfall when 31% of the loss in grain yield was incurred from reduced solar radiation and 19% from floret damage. Grain filling was also affected as 26% of grain yield loss was caused by soil anoxia, 11% by fungal foliar diseases, and 10% by ear blight. Compounding climate effects caused the extreme yield decline. The likelihood of these compound factors recurring under future climate change is estimated to change with a higher frequency of extremely low wheat yields. KW - compounding factors KW - extreme weather KW - food security KW - grain number KW - grain size KW - temporally and multivariate events Y1 - 2023 U6 - https://doi.org/10.1111/gcb.16662 SN - 1354-1013 VL - 29 IS - 11 SP - 3130 EP - 3146 PB - Wiley 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 - 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 - TY - GEN A1 - Ahrends, Hella Ellen A1 - Piepho, Hans-Peter A1 - Sommer, Michael A1 - Ewert, Frank A1 - Webber, Heidi T1 - Is the volatility of yields for major crops grown in Germany related to spatial diversification at county level? T2 - Environmental Research Letters N2 - Recent evidence suggests a stabilizing effect of crop diversity on agricultural production. However, different methods are used for assessing these effects and there is little systematic quantitative evidence on diversification benefits. The aim of this study was to assess the relationship between volatility of combined crop yields (denoted as standard deviation) and diversity (denoted as Shannon’s Evenness Index SEI) for standardized yield data of major crop species grown in Germany between 1977 and 2018 (winter wheat, winter barley, silage maize and winter rapeseed) at the county level. Portfolio theory was used to estimate the optimal crop area share for minimizing yield volatility. On average, results indicated a weak negative relationship between volatility and the SEI during the past decades for the case of Germany. Optimizing crop area shares for minimizing volatility reduced yield variance on average by 24% but was associated with a decrease in SEI for most counties. This was related to the finding that the stability of individual species, i.e., barley and wheat, was more effective in reducing the volatility of combined yields than the asynchronous variation in annual yields among crops. Future studies might include an increased number of crop species and consider temporal diversification effects for a more realistic assessment of the relation between yield volatility and crop diversity and test the relationship in other regions and production conditions. KW - portfolio theory KW - optimization KW - crop yield KW - NUTS 3 Y1 - 2024 U6 - https://doi.org/10.1088/1748-9326/ad7613 SN - 1748-9326 VL - 19 IS - 10 PB - IOP Publishing ER - TY - GEN A1 - Stella, Tommaso A1 - Webber, Heidi A1 - Rezaei, Ehsan Eyshi A1 - Asseng, Senthold A1 - Martre, Pierre A1 - Dueri, Sibylle A1 - Guarin, Jose Rafael A1 - Pequeno, Diego A1 - Calderini, Daniel A1 - Reynolds, Matthew A1 - Molero, Gemma A1 - Miralles, Daniel A1 - Garcia, Guillermo A1 - Slafer, Gustavo A. A1 - Giunta, Francesco A1 - Kim, Yean-Uk A1 - Wang, Chenzhi A1 - Ruane, Alex C. A1 - Ewert, Frank T1 - Wheat crop traits conferring high yield potential may also improve yield stability under climate change T2 - in silico Plants N2 - Increasing genetic wheat yield potential is considered by many as critical to increasing global wheat yields and production, baring major changes in consumption patterns. Climate change challenges breeding by making target environments less predictable, altering regional productivity and potentially increasing yield variability. Here we used a crop simulation model solution in the SIMPLACE framework to explore yield sensitivity to select trait characteristics (radiation use efficiency [RUE], fruiting efficiency and light extinction coefficient) across 34 locations representing the world’s wheat-producing environments, determining their relationship to increasing yields, yield variability and cultivar performance. The magnitude of the yield increase was trait-dependent and differed between irrigated and rainfed environments. RUE had the most prominent marginal effect on yield, which increased by about 45 % and 33 % in irrigated and rainfed sites, respectively, between the minimum and maximum value of the trait. Altered values of light extinction coefficient had the least effect on yield levels. Higher yields from improved traits were generally associated with increased inter-annual yield variability (measured by standard deviation), but the relative yield variability (as coefficient of variation) remained largely unchanged between base and improved genotypes. This was true under both current and future climate scenarios. In this context, our study suggests higher wheat yields from these traits would not increase climate risk for farmers and the adoption of cultivars with these traits would not be associated with increased yield variability. KW - climate change KW - climate risk KW - genetic yield potential KW - wheat KW - yield variability Y1 - 2023 U6 - https://doi.org/10.1093/insilicoplants/diad013 SN - 2517-5025 VL - 5 IS - 2 PB - Oxford University Press (OUP) ER - TY - GEN A1 - Rezaei, Ehsan Eyshi A1 - Webber, Heidi A1 - Asseng, Senthold A1 - Boote, Kenneth A1 - Durand, Jean Louis A1 - Ewert, Frank A1 - Martre, Pierre A1 - MacCarthy, Dilys Sefakor T1 - Climate change impacts on crop yields T2 - Nature Reviews Earth & Environment Y1 - 2023 U6 - https://doi.org/10.1038/s43017-023-00491-0 SN - 2662-138X VL - 4 IS - 12 SP - 831 EP - 846 PB - Springer Science and Business Media LLC ER -