@misc{NoiaJuniorMartreDeswarteetal., author = {N{\´o}ia-J{\´u}nior, Rog{\´e}rio de S. and Martre, Pierre and Deswarte, Jean-Charles and Cohan, Jean-Pierre and Van der Velde, Marijn and Webber, Heidi and Ewert, Frank and Ruane, Alex C. and Ben-Ari, Tamara and Asseng, Senthold}, title = {Past and future wheat yield losses in France's breadbasket}, series = {Field Crops Research}, volume = {322}, journal = {Field Crops Research}, publisher = {Elsevier BV}, issn = {0378-4290}, doi = {10.1016/j.fcr.2024.109703}, pages = {11}, abstract = {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.}, language = {en} } @misc{Noia JuniorDeswarteCohanetal., author = {N{\´o}ia J{\´u}nior, Rog{\´e}rio de S. and Deswarte, Jean-Charles and Cohan, Jean-Pierre and Martre, Pierre and van der Velde, Marijn and Lecerf, Remi and Webber, Heidi and Ewert, Frank and Ruane, Alex C. and Slafer, Gustavo A. and Asseng, Senthold}, title = {The extreme 2016 wheat yield failure in France}, series = {Global Change Biology}, volume = {29}, journal = {Global Change Biology}, number = {11}, publisher = {Wiley}, issn = {1354-1013}, doi = {10.1111/gcb.16662}, pages = {3130 -- 3146}, abstract = {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.}, language = {en} } @misc{KimWebberAdikuetal., author = {Kim, Yean-Uk and Webber, Heidi and Adiku, Samuel G.K. and N{\´o}ia J{\´u}nior, Rog{\´e}rio de S. and Deswarte, Jean-Charles and Asseng, Senthold and Ewert, Frank}, title = {Mechanisms and modelling approaches for excessive rainfall stress on cereals: waterlogging, submergence, lodging, pests and diseases}, series = {Agricultural and Forest Meteorology}, volume = {344}, journal = {Agricultural and Forest Meteorology}, publisher = {Elsevier BV}, issn = {0168-1923}, doi = {10.1016/j.agrformet.2023.109819}, pages = {13}, abstract = {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.}, language = {en} } @misc{NoiaJuniorStoccaMartreetal., author = {N{\´o}ia-J{\´u}nior, Rog{\´e}rio de S. and Stocca, Valentina and Martre, Pierre and Shelia, Vakhtang and Deswarte, Jean-Charles and Cohan, Jean-Pierre and Piquemal, Beno{\^i}t and Dutertre, Alain and Slafer, Gustavo A. and Zhang, Zhentao and Van Der Velde, Marijn and Kim, Yean-Uk and Webber, Heidi and Ewert, Frank and Palosuo, Taru and Liu, Ke and Harrison, Matthew Tom and Hoogenboom, Gerrit and Asseng, Senthold}, title = {Enabling modeling of waterlogging impact on wheat}, series = {Field crops research}, volume = {333}, journal = {Field crops research}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {0378-4290}, doi = {10.1016/j.fcr.2025.110090}, pages = {1 -- 13}, abstract = {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.}, language = {en} }