Abstract
Climate services that can anticipate crop yields can potentially increase the resilience of food security in the face of climate change. These services are based on our understanding of how crop yield anomalies are related to climate anomalies, yet the share of global crop yield variability explained directly by climate factors is largely variable between regions. In Europe, France has been a major crop producer since the beginning of the 20th Century. Process based and statistical approaches to model crop yields driven by observed climate have proven highly challenging in France. This is especially due to a high regional diversity in climate but also due to environmental and agro-management factors. An additional level of uncertainty is introduced if these models are driven by seasonal-to-decadal surface climate predictions due to their low performances before the harvesting months of both wheat and maize in western Europe. On the other hand, large scale circulation patterns can possibly be better predicted than the regional surface climate, which offers the opportunity to rely on large scale circulation patterns as an alternative to local climate variables. This method assumes a certain degree of stationarity in the relationships between large scale patterns, surface climate variables, and crop yield anomalies. However, such an assumption was never tested, because of the lack of suitable long-term data. This study uses a unique dataset of subnational crop yields in France covering more than a century. By calibrating and comparing statistical models linking large scale circulation patterns and observed surface climate variables to crop yield anomalies, we can demonstrate that the relationship between large scale patterns and surface variables relevant for crop yields is not stationary. Therefore, large scale circulation pattern based crop yield forecasting methods can be adopted for seasonal predictions provided that regression parameters are constantly updated. However, the statistical crop yield models based on large-scale circulation patterns are not suitable for decadal predictions or climate change impact assessments at even longer time-scales.
Highlights
Global agricultural crop areas have been increasingly exposed to unfavourable weather conditions, significantly deteriorating yields of the main agricultural crops (Lesk et al 2016, Zampieri et al 2017a)
As the location and magnitude of surface weather anomalies are difficult to predict beyond a two weeks horizon, understanding the relationship between large-scale atmospheric circulation patterns and surface climate variability might provide a more effective way to predict the potential impact on crops
Winter East Atlantic-West Russian (EAWR) and spring East Atlantic (EA) patterns influenced the highest share of soft wheat cropland during the first half of the 20th Century; but their influence gradually decreased during the second half of the 20th Century
Summary
Global agricultural crop areas have been increasingly exposed to unfavourable weather conditions, significantly deteriorating yields of the main agricultural crops (Lesk et al 2016, Zampieri et al 2017a). Climate change is expected to exacerbate the frequency, severity, and spatial extent of extreme events (Zhu and Troy 2018, Ceglar et al 2019, Zampieri et al 2019) thereby changing the risk of simultaneous breadbaskets failures (Gaupp et al 2020). Preventing adverse effects of unfavourable climate events on crop yields to avoid crop failures requires timely and effectively planned agronomical decisions, which can be put in practice if the future weather evolution is known early enough in the growing season. Relevant dynamical processes on sub-seasonal to seasonal time scale include features such as ElNino (Cane et al 1994, Iizumi et al 2014), Madden and Julian oscillation (Andersson et al 2020), stratospheric circulation (Byrne et al 2017), stationary Rossby waves (Kornhuber et al 2019), and large-scale atmospheric circulation patterns (Ceglar et al 2017).
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