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To investigate the different responses of crops to drought stress under field conditions of Central European Climate for selected crop rotations, a field experiment was conducted at a test site in the Czech Republic from 2014 to 2021. Depending on the crop, rainout shelters were placed in late spring and early summer to study the effects of drought in the final stages of crop development. Due to these rainout shelters and the associated lower water availability for the crops during the summer, a reduction in leaf area index, biomass and yield was observed. For example, a yield decrease of more than 30% was observed for spring barley, winter rape and winter wheat compared to conditions without rainout shelters. The reduction was 25% and 18% for winter rye and silage maize, respectively, under rainout shelters. Soil moisture played a significant role in yield, where a predictive model based on monthly soil moisture explained up to 79% (winter rape) of the yield variance.

In the Czech Republic, soil moisture content during the growing season has been decreasing over the past six decades, and drought events have become significantly more frequent. In 2003, 2015, 2018 and 2019, drought affected almost the entire country, with droughts in 2000, 2004, 2007, 2012, 2014 and 2017 having smaller extents but still severe intensities in some regions. The current methods of visiting cadastral areas (approximately 13,000) to allocate compensation funds for the crop yield losses caused by drought or aggregating the losses to district areas (approximately 1000 km2) based on proxy data are both inappropriate. The former due to the required time and resources, the later due to low resolution, which leads to many falsely negative and falsely positive results. Therefore, the study presents a new method to combine ground survey, remotely sensed and model data for determining crop yield losses. The study shows that it is possible to estimate them at the cadastral area level in the Czech Republic and attribute those losses to drought. This can be done with remotely sensed vegetation, water stress and soil moisture conditions with modeled soil moisture anomalies coupled with near-real-time feedback from reporters and with crop status surveys. The newly developed approach allowed the achievement of a proportion of falsely positive errors of less than 10% (e.g., oat 2%, 8%; spring barley 4%, 3%; sugar beets 2%, 21%; and winter wheat 2%, 6% in years 2017, resp. 2018) and allowed for cutting the loss assessment time from eight months in 2017 to eight weeks in 2018.

To ensure the selection of wheat genotypes that are resilient to future climatic conditions, with drought already being the most significant and acute problem in many areas, twenty winter wheat cultivars were tested for drought stress tolerance from the beginning of stem elongation (DC 30; Zadoks decimal codes) for 49 days (until the stage of grain development, DC 73–75) within an automatic phenotyping platform. The control plants were regularly irrigated to 70% of soil water capacity (SWC), while the drought-stressed plants were subjected to controlled drying until the permanent wilting point (15% of SWC) was reached. Then, the drought-stressed plants were rewatered again to 70% of the maximum SWC. After they recovered, the plants were again exposed to ambient weather conditions. The final yield formation parameters were assessed at the fully ripe stage. Our results showed that the genotypes originating in Western Europe manifested the highest response to the experimentally set drought in the grain number per spike measurement, while the genotypes originating in the warmer regions of southeastern Europe manifested the highest response to the experimental drought mainly in thousand grain weight measurement. Similar response patterns were evident for late- and early-maturing genotypes. The results indicate the potential of selecting genotypes with increased drought resistance even within the existing set of cultivars.

Changes in stomatal conductance and density allow plants to acclimate to changing environmental conditions. In the present paper, the influence of atmospheric CO2 concentration and light intensity on stomata were investigated for two barley genotypes—Barke and Bojos, differing in their sensitivity to oxidative stress and phenolic acid profiles. A novel approach for stomatal density analysis was used—a pair of convolution neural networks were developed to automatically identify and count stomata on epidermal micrographs. Stomatal density in barley was influenced by genotype, as well as by light and CO2 conditions. Low CO2 conditions resulted in increased stomatal density, although differences between ambient and elevated CO2 were not significant. High light intensity increased stomatal density compared to low light intensity in both barley varieties and all CO2 treatments. Changes in stomatal conductance were also measured alongside the accumulation of pentoses, hexoses, disaccharides, and abscisic acid detected by liquid chromatography coupled with mass spectrometry. High light increased the accumulation of all sugars and reduced abscisic acid levels. Abscisic acid was influenced by all factors—light, CO2, and genotype—in combination. Differences were discovered between the two barley varieties: oxidative stress sensitive Barke demonstrated higher stomatal density, but lower conductance and better water use efficiency (WUE) than oxidative stress resistant Bojos at saturating light intensity. Barke also showed greater variability between treatments in measurements of stomatal density, sugar accumulation, and abscisic levels, implying that it may be more responsive to environmental drivers influencing water relations in the plant.