Diverging importance of drought stress for maize and winter wheat in Europe

Abstract

Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO₂ offering no yield benefit in these years.

Fig. 1

Climatic variation in historical national crop yields as captured by crop models. The amount of variation in the observed yields as reported in FAO-stats for the period between 1984 and 2009, as quantified by the coefficient of determination (R²), correlation explained by each of the six simulation sets (black—mean temperature effects only, blue—mean temperature and drought effects, yellow—mean temperature and heat stress with air-temperature effects, magenta—mean temperature and heat stress with canopy-temperature effects, light green—mean temperature, drought, and heat stress with air-temperature effects, and dark green—mean temperature, drought, and heat stress with canopy-temperature effects). Each point represents the mean of the correlation coefficient for the eight winter wheat models and six maize models. The size of the dot indicates the number of models with significant correlations for that simulation set and considered in the respective mean. Gray columns serve as an environmental index indicating the model median average of the ratio of rainfed to irrigated yields for each country. For each plot, countries are ordered by production area in descending order. Note that simulations were only for winter wheat, whereas FAO-stats does not distinguish winter and spring wheat

Fig. 2

Projected changes in European maize and wheat yields for current mix of irrigated and rainfed land use. Relative yield changes were estimated for the period 2040 to 2069 compared to the baseline period (1981–2010) for three representative concentration pathways (RCPs). The boxplots depict 25th to 75th percentile values across the crop models (eight for winter wheat and six for maize) and GCMs (two for RCP2.6 and five for RCPs 4.5 and 8.5). Top (bottom) whiskers extend to the minimum (maximum) of the maximum (minimum) value or the 75th (25th) percentile value plus (minus) 1.5 times the difference between the 75th and 25th percentile values. Circles indicate outliers. Green bars indicate yield changes considering CO₂ fertilization effects and gray bars indicate changes without CO₂ fertilization. Yields at the pixel level were aggregated to EU level considering baseline production areas

Fig. 3

Drivers of yield losses in average and low-yielding years for rainfed systems by country. Drivers of yield change in current baseline climate (1981–2010) and three climate scenarios (RCPs 2.6, 4.5, and 8.5) in the period 2040 to 2069 for a grain maize and b winter wheat. Drivers of yield levels are shown for the average of all years (top row in both panels), and drivers of yields levels for years with yields in the lowest decile (bottom row in both panels). The shape of the symbol indicates the period in which changes are expressed as relative to (triangles are for changes relative to the baseline potential and circles are losses relative to the respective scenario potential), whereas the color indicates the drivers considered (black are mean temperature effects, blue are drought, magenta is heat, and green is the combination of drought and heat). The change in potential yields resulting from the mean temperature effects are shown with black triangles for each scenario relative to the baseline potential. For the other drivers, changes indicated by circles are relative to scenario potential yield. In all cases, the shading indicates if CO₂ fertilization was considered (dark symbols consider CO₂, light symbols do not consider CO₂). Results are shown for the five countries with the largest production area in Europe for each crop, listed by production area

Fig. 4

Changes in drivers of yield losses in average and low-yielding years for European rainfed systems. Baseline refers to the period 1981 to 2010 and the scenario period is 2040 to 2069. a Two measures of change in average yields are presented. The black triangles indicate changes in potential yields due to mean temperature effects relative to potential yields in the baseline period (1981–2010). The second type of change shown is the absolute difference between the scenario and baseline (circles) of drought (blue) or heat (purple) losses relative to potential levels in the respective scenario (circles) for three representative concentration pathways (RCPs). b Difference (% points) between average yield levels and yields in lowest decile of losses for losses from potential yield levels due to mean temperature effects (black), drought limitation (blue), or heat limitation (purple) for the baseline period (1981–2010) and three RCPs (2040–2069). For all panels, maize is shown on the left and winter wheat on the right. Results are shown with (darker shading) and without (lighter shading) CO₂ fertilization effects. Data shown are the EU aggregates with median values across crop model and GCM combinations with error bars indicating the 10th and 90th percentiles

Fig. 5

Change in yield losses due to drought. Change in yield losses due to drought in a grain maize and b winter wheat for 2040–2069 for RCP4.5 relative to the baseline period (1981–2010). Results are presented with (top row) and without (bottom row) consideration of CO₂ fertilization effects and two GCMs: HadGEM2-ES (first column) and MPI-ESM-MR (second column). Results shown are the median response across crop models