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    <title language="eng">The extreme 2016 wheat yield failure in France</title>
    <abstract language="eng">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.</abstract>
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    <author>
      <firstName>Rogério de S.</firstName>
      <lastName>Nóia Júnior</lastName>
    </author>
    <submitter>
      <firstName>Uta</firstName>
      <lastName>Warstat</lastName>
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    <author>
      <firstName>Jean-Charles</firstName>
      <lastName>Deswarte</lastName>
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    <author>
      <firstName>Jean‐Pierre</firstName>
      <lastName>Cohan</lastName>
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    <author>
      <firstName>Pierre</firstName>
      <lastName>Martre</lastName>
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    <author>
      <firstName>Marijn</firstName>
      <lastName>van der Velde</lastName>
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      <firstName>Remi</firstName>
      <lastName>Lecerf</lastName>
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      <firstName>Heidi</firstName>
      <lastName>Webber</lastName>
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    <author>
      <firstName>Frank</firstName>
      <lastName>Ewert</lastName>
    </author>
    <author>
      <firstName>Alex C.</firstName>
      <lastName>Ruane</lastName>
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    <author>
      <firstName>Gustavo A.</firstName>
      <lastName>Slafer</lastName>
    </author>
    <author>
      <firstName>Senthold</firstName>
      <lastName>Asseng</lastName>
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      <value>compounding factors</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>extreme weather</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>food security</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>grain number</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>grain size</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>temporally and multivariate events</value>
    </subject>
    <collection role="institutes" number="2422">FG Integrated Crop Systems Analysis and Modelling</collection>
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    <title language="eng">Enabling modeling of waterlogging impact on wheat</title>
    <abstract language="eng">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.</abstract>
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