Agricultural policies in Kenya aim to improve farmers’ livelihoods. With projected climate change, these policies are short of mechanisms that promote farmers’ adaptation. As a result, smallholders are confronted with a variety of challenges including climate change, which hinders their agricultural production. Local knowledge can be instrumental in assisting smallholders to cope with climate change and variability. In this paper, we present empirical evidence that demonstrates local knowledge, perceptions and adaptations to climate change and variability amongst smallholders of Laikipia district of Kenya. A Palmer Drought Severity Index (PDSI) calculated for one station is compared with smallholders’ perceptions. Data was collected using qualitative and quantitative methods in Umande and Muhonia sub-locations. Qualitative data included 46 transcripts from focus group discussions and key informant interviews. Quantitative data is derived from 206 interviewees. We analyzed qualitative and quantitative data using Atlas-ti and SPSS respectively. According to smallholders’ perceptions, climatic variability is increasingly changing. Local perceptions include decreasing rainfalls, increasing temperatures, increasing frosts and increasing hunger. The PDSI shows a trend towards severe droughts in the last four decades, which is in accordance with farmers’ perceptions. Smallholders use a combination of coping and adaptation strategies to respond to variability, including, among others, diversification of crop varieties, migration and sale of livestock. Significant relationships exist between drought perceptions and some adaptations such as migration and sale of livestock. Farmers have an in-depth knowledge of climatic variability, which they use to inform their coping and adaptation strategies. Knowledge of climatic perceptions and adaptations are vital entry points for decision makers and policy makers to learn how and where to enhance the adaptive capacity of smallholders in rainy and drought periods.

The awareness of drought and its impacts on Central Europe increased after the significant drought episodes in 2000, 2003, 2012 and 2015, which were all estimated to have caused over 500 million Euro in damage in the Czech Republic alone. These events indicated the need for timely and high‐resolution monitoring tools that would enable analysing, monitoring and forecasting of drought events. Monitoring soil water availability in near real time and at high‐resolution (up to 0.5 × 0.5 km for some products) helps farmers and water managers to mitigate impacts of these extreme events. The Czech Drought Monitor was developed between 2012 and 2014 and has since been operational as an online platform. It uses an operational modelling system that consists of four pillars: (a) weekly soil moisture estimates based on spaceborne Advanced Scatterometer sensor measurements; (b) the daily SoilClim soil moisture model, which runs based on high‐density network data from the Czech Hydrometeorological Institute with a 55‐year reference period; (c) weekly reports on vegetation condition, which is deduced from satellite‐based vegetation indices and early warnings of imminent drought impacts; and (d) weekly reports of soil moisture, especially after drought impacts, which are provided by dozens of experts. Since 2016 the drought forecast (+9 days) has been released daily based on an ensemble of five numerical weather prediction models combined with a weekly drought outlook (+2 months). The analysis of four recent episodes (2000, 2003, 2012 and 2015) clearly showed that both large‐scale and regionally restrained drought episodes posed serious risks in terms of their impacts and damage. Comparisons with historical droughts showed that these events, especially the 2000, 2003 and 2015 events, were among the top five drought episodes in the June–August period observed in the Czech Republic since 1961 in terms of spatial extent, magnitude and duration.

SUMMARY: The objective of the present study was to compare the performance of seven different, widely applied crop models in predicting heat and drought stress effects. The study was part of a recent suite of model inter-comparisons initiated at European level and constitutes a component that has been lacking in the analysis of sources of uncertainties in crop models used to study the impacts of climate change. There was a specific focus on the sensitivity of models for winter wheat and maize to extreme weather conditions (heat and drought) during the short but critical period of 2 weeks after the start of flowering. Two locations in Austria, representing different agro-climatic zones and soil conditions, were included in the simulations over 2 years, 2003 and 2004, exhibiting contrasting weather conditions. In addition, soil management was modified at both sites by following either ploughing or minimum tillage. Since no comprehensive field experimental data sets were available, a relative comparison of simulated grain yields and soil moisture contents under defined weather scenarios with modified temperatures and precipitation was performed for a 2-week period after flowering. The results may help to reduce the uncertainty of simulated crop yields to extreme weather conditions through better understanding of the models’ behaviour. Although the crop models considered (DSSAT, EPIC, WOFOST, AQUACROP, FASSET, HERMES and CROPSYST) mostly showed similar trends in simulated grain yields for the different weather scenarios, it was obvious that heat and drought stress caused by changes in temperature and/or precipitation for a short period of 2 weeks resulted in different grain yields simulated by different models. The present study also revealed that the models responded differently to changes in soil tillage practices, which affected soil water storage capacity.

SUMMARY: The present study investigates regional climate change impacts on agricultural crop production in Central and Eastern Europe, including local case studies with different focuses in Austria, the Czech Republic and Slovakia. The area studied experiences a continental European climate and is characterized by strong climatic gradients, which may foster regional differences or trends in the impacts of climate change on agriculture. To study the regional aspects and variabilities of climate change impacts on agriculture, the effect of climate change on selected future agroclimatic conditions, crop yield and variability (including the effect of higher ambient CO₂ concentrations) and the most important yield limiting factors, such as water availability, nitrogen balance and the infestation risks posed by selected pests were studied. In general, the results predicted significant agroclimatic changes over the entire area during the 21st century, affecting agricultural crop production through various pathways. Simulated crop yield trends confirmed past regional studies but also revealed that yield-limiting factors may change from region to region. For example, pest pressures, as demonstrated by examining two pests, are likely to increase due to warmer conditions. In general, higher potentials for cereal yield increase are seen for wetter and cooler regions (i.e. uplands) than for the drier and warmer lowlands, where yield potentials will be increasingly limited by decreasing crop water availability and heat under most scenarios. In addition, yield variability will increase during the coming decades, but this may decrease towards the end of the 21st century. The present study contributes to the interpretation of previously conducted climate change impact and adaptation studies for agriculture and may prove useful in proposing future research in this field.

During the last decade, satellite-based precipitation products have been believed to be a potential source for forcing inputs in hydro-meteorological and agricultural models, which are essential especially over the mountains area or in basins where ground gauges are generally sparse or nonexistent. This study comprehensively evaluates several newly released precipitation products, i.e., MSWEP-V2.2, IMERG-V05B, IMERG-V06A, IMERG-V05-RT, ERA5, and SM2RAIN-ASCAT, at daily and monthly time-scales over Austria. We show that all the examined products are able to reproduce the spatial precipitation distribution over the country. MSWEP, followed by IMERG-V05B and -V06A, show the strongest agreement with in situ observations and perform better than other products with respect to spatial patterns and statistical metrics. Both IMERG and ERA5 products seem to have systematic precipitation overestimation at the monthly time-scale. IMERG-V06A performs slightly better than IMERG-V05B. With respect to heavy precipitation (P > 10 mm/day), MSWEP compare to other products demonstrate advantages in detecting precipitation events with a higher spatial average of probability of detection (POD) and lower false alarm ratio (FAR) scores skill (0.74 and 0.28), while SM2RAIN and ERA5 reveal lower POD (0.35) and higher FAR (0.56) in this precipitation range in comparison with other products. However, the ERA5 and MSWEP indicate robust average POD and FAR values with respect to light/moderate precipitation (10 mm > P ≥ 0.1 mm) with 0.94 and 0.11, respectively. Such robustness of MSWEP may be rooted in applying the daily rain gauges in calibration processes. Moreover, although all products accurately map the spatial precipitation distribution they still have difficulty capturing the effects of topography on precipitation. The overall performance of the precipitation products was lower in the peripheries of the study area where most stations are situated in the mountainous area and was higher over the low altitude regions. However, according to our analysis of the considered products, MSWEP-V2.2, followed by IMERG-V06S and -V05B, are the most suitable for driving hydro-meteorological, agricultural, and other models over mountainous terrain.