Optimization of agricultural inputs in all relevant spatial and time scales by means of smart and precision farming options is considered in the project of AGROFORECAST as crucial for ensuring sustainable and resilient production in agriculture, protecting ecosystem services, enhancing biodiversity and food security as well as socio-economic conditions for the farmers. Despite increasing appreciation of the potential value of weather forecasts for agriculture, there is still a gap between what scientists consider as “useful” information and what users (e.g. farmers, advisory services, policy makers) recognize as “usable” in their decision-making processes. 

The main objective of this project was to combine stakeholder-tailored agrometeorological tools/indicators and weather forecasting of different lead times that can be used for optimizing agricultural decision-making in the face of changing climate and climate variability. A focus is laid on efficient use of inputs for crop production (water, fertilizer, agricultural chemicals) needed for smart and precision farming options while protecting (agro-) ecosystem resources and functions in order to identify suitable and, from a stakeholder perspective, acceptable adaptation and mitigation options. Beside the optimization/downscaling of seasonal forecasts, software applications (crop model and GIS-based spatial tool) for forecasting agrometeorological conditions by indicators were further developed and tested (incl. stakeholder involvement) against performance strengths and weaknesses/limitations at various locations and farmers field site. The results show that the performance of the applied tools and indicators depend mainly on a) weather forecast lead time b) regional climate conditions and c) type of impact indicators. Applications for crop management are promising in order to mitigate climate change impacts, cropping risks and negative environmental impacts in crop production (e.g. through reduced N-fertilization). Additionally, results of applications under ÖKS15 climate scenarios for selected case study sites were achieved.

How to cite: Eitzinger, J., Thaler, S., Atencia, A., Hann, P., Kubu, G., Manschadi, A. M., Lalic, B., Palka, M., Schneider, S., Sremac, A. F., Trnka, M., and Trska, C.: Tailored AGROmeteorological FORECAST for improving resilience and sustainability of Austrian farming systems under changing climate, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-213, https://doi.org/10.5194/ems2023-213, 2023.

Meteorological conditions significantly impact the biofix, i.e., the time of the first flight of the first overwintering generation of codling moth (Cydia pomonella). In general, the flight activity of codling moths is influenced by a combination of temperature, humidity, and light duration and intensity, while precipitation and strong wind, particularly wind gusts, can stop insects from flying. Our research also considered the impact of atmospheric pressure on codling moth flight. 

Data describing biofix used in this study come from the codling moth monitoring in the framework of the PIS observing system. For sixteen locations during the 2012-2022 period, only 101 data sets were analyzed for the locations with the same traps and female pheromones. Since the traps were located in production orchards, to avoid the impact of insecticides on possible errors in the first detection of adults, only locations with a high insect population (with over 100 adults per trap during the season) were selected. Counting is performed daily from the beginning of April each year. Meteorological data for the study are provided from two sources: a) the PIS network of automated weather stations located in the orchards (air temperature and humidity, precipitation), and b) the synop reports from the synoptic stations (atmospheric pressure and wind gust).

Our research aims to measure biofix's uncertainty caused by adverse weather conditions (from the codling moth point of view) or events. Starting backward from when an adult is found in the trap, we analyzed the presence of weather conditions that can affect codling moth flight. As a measure of uncertainty, we use the days between trap ketch day and the last day before that when weather conditions allowed adults to fly.

How to cite: Marcic, M. and Lalic, B.: Spatial uncertainty of codling moth biofix caused by adverse weather events, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-566, https://doi.org/10.5194/ems2023-566, 2023.

In Switzerland, as elsewhere in Europe, recurrent drought has been one of the main concerns of the agricultural sector during the last few decades. The dry years of 2003, 2006, 2015, 2018 and again 2022 had serious impacts on forage production. As a result, additional imports were necessary to support in particular the dairy sector. Another consequence of the drought was an increase in the need for irrigation, which, however, could not always be met. Eventually, farmers had to accept restrictions on water withdrawals on several occasions. Climate change projection indicate that such situations could become more common in future.

To increase preparedness and help the agricultural sector, as a whole, better cope with drought under future climatic conditions, quantitative information on drought occurrence at regional to national scale is required. In this contribution, we present a drought assessment for Switzerland that addresses the following questions: (i) Spatial patterns of drought. What was the geography of drought events in the recent pat? Can we expect similar geographic patterns in the future? (ii) Seasonality and persistence of drought. Has there been a shift the temporal characteristics of drought because of climate warming? Are further shifts likely to occur in future? (iii) Irrigation requirements: Has the need for irrigation already increased in the recent past? If yes, where and by how much? What can be expected in this respect for the coming decades?

We base our analysis on high-resolution, gridded meteorological data and climate change projections, and use drought indices to characterize drought events in time and space. We further show how a simple model that relates irrigation requirements to a drought index can be employed to estimate current and future needs across Switzerland. Finally, we discuss how these results can be integrated in initiatives aiming at supporting farmers in the process of adaptation.

How to cite: Calanca, P., Toschini, T., and Wüst-Galley, C.: Spatial patterns and seasonality of drought in Switzerland under present and future climatic conditions, and irrigation water requirements, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-165, https://doi.org/10.5194/ems2023-165, 2023.

Grasslands deliver a range of ecosystem services to society that are potentially threatened by climate change. Assessing climate change impacts on grassland ecosystem services is essential for developing adaptation strategies. This is particularly important for a country like Switzerland, in which grasslands represent about two thirds of its agricultural land. Among grassland ecosystem services, fodder production (provisioning) and soil carbon sequestration (regulating) are most vulnerable to drought and other extreme events. Process-based ecosystem models, such as DayCent, are suitable tools for assessing concomitant impacts of climate change on different ecosystem services. DayCent simulates fluxes of C and N among the atmosphere, vegetation, and soil and allows the prediction of the effect of increasing temperature and CO₂ levels and decreasing rainfall on herbage growth and soil carbon stocks. We have been using DayCent to simulate the dynamics of herbage growth and soil organic carbon pools with varying degree of stability in managed permanent grasslands in Switzerland. For the present study, we selected an experimental grassland site located at Oensingen, Canton of Solothurn. We calibrated DayCent using inverse modeling (PEST tool) and verified the model performance based on several years of field data. We then applied the model to examine how future shifts in climatic conditions are likely to affect yields and carbon stocks, and what are the associated uncertainties. For this exercise, we developed local climate change scenarios using a stochastic weather generator (LARSWG). After reviewing the results, we discuss how process-based modeling can contribute for development of climate-smart agriculture tools, which can support better definition of agricultural policies and payment systems for grassland ecosystem services.

How to cite: dos Reis Martins, M., Ammann, C., and Calanca, P.: Assessing the impact of climate change on grassland ecosystem services using DayCent, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-259, https://doi.org/10.5194/ems2023-259, 2023.

Mediterranean grasslands provide different ecosystem, social and economic services to the Mediterranean basin. Their dynamics are conditioned by the influence of the Mediterranean climate, which results in large inter-annual variability. Gross Primary Production (GPP) represents the carbon (C) uptake of ecosystems through photosynthesis, being the largest flux of the global C balance. Hence, GPP estimations are necessary to assess the dynamics of the global C cycle and to plan a sustainable management of these ecosystems. The study of grasslands GPP must be approached from a dynamic point of view, that is, temporal evolution at different spatial scales.

High frequency satellite data, such as Sentinel-2, have open the door to study ecosystems with a high spatial (10, 20 and 60 m) and temporal resolution (5 days). Although extensive research exists about estimating GPP in grasslands with moderate resolution sensors, such as MODIS, the estimation of GPP in this ecosystem type using Sentinel-2 images is relatively new, especially in Mediterranean sites. The GPP derived from remote sensing data must be evaluated at field scales using eddy covariance (EC) measurements from flux towers. Time series analysis (TSA) represents an excellent tool to analyze high temporal resolution data, such as EC and remote sensing data. Although TSA has been widely used in economics, it is not commonly used for the analysis of environmental variables, such as GPP.

The overall objective of this research is to estimate a GPP model for a Mediterranean grassland in central Spain close to a forest influenced by the Guadarrama mountains (El Escorial, Madrid). This is done using Sentinel-2 vegetation indices and meteorological field information obtained from a GuMNet micrometeorological station where all the surface energy balance (SEB) components (and other meteorological and soil variables) are available for several years. The specific objectives are: (1) To compare different vegetation indices to estimate GPP through the ecosystem light-use efficiency model; (2) To estimate the influence of different meteorological variables on the model; (3) To validate the remote sensing model with the measurements from an EC flux tower (GuMNet-Herrería) in terms of quantity and dynamics through time series analysis.

Some preliminary results indicate how the GPP depends strongly on the vegetation indices and on meteorological and soil variables, being the soil water content a relevant one. This approach allows to model GPP based on the information from this analysis.

How to cite: Cicuéndez, V., Yagüe, C., Inclán, R., Sánchez-Cañete, E. P., Román-Cascón, C., and Sáenz, C.: Modelling Gross Primary Production of a Mediterranean grassland using Sentinel-2 vegetation indices and meteorological field information, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-323, https://doi.org/10.5194/ems2023-323, 2023.

Trees provide cooling services in urban areas in summer. Which tree species are suitable in cities in a future climate remains an open question. One of the criteria for tree species selection is winter hardiness. Winter hardiness is an indicator of the lowest temperatures that plants typically experience in an area. The United States Department of Agriculture (USDA) defines winter hardiness as the average annual minimum temperature. Hence, it is a straightforward and commonly used indicator to predict the likelihood of certain plants to experience frost damage in winter. The most recent winter hardiness map for Europe was made in 1984, and does not include the climate change from the past decades, and neglects the urban heat island effect (UHI) that causes warmer winters in urban areas, impacting winter hardiness. In this study, we developed an updated and downscaled version of the European winter hardiness maps using minimum temperature from the ECA&D E-OBS dataset version 26.0, and the European Local Climate Zone (LCZ) map with a 100x100m resolution.

The new maps represent the winter hardiness for five standard normal periods between 1951 and 2020, and represent the years 2030, 2050, and 2085 for the Netherlands using four climate scenarios. These maps show how hardiness zones have moved to the north and east over the past decades. They show that roughly 60% of Europe is now in a different hardiness zone compared to the 1951-1980 average, potentially allowing new tree species to flourish here. They also indicate that the Netherlands can move up to three hardiness zones between the present and 2085. Furthermore, these maps show that many urban areas are in different winter hardiness zones compared to the rural surroundings, which has additional consequences for the tree species that are able to flourish in urban environments.

How to cite: Hulsman, L., Heusinkveld, B., Ravesloot, M., and Steeneveld, G.-J.: Updated and downscaled European winter hardiness maps including Urban Heat Island effects for urban tree species selection., EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-374, https://doi.org/10.5194/ems2023-374, 2023.

False spring is one of the preconditioned compound climate events that gained a lot of attention in recent years because of its negative impact on agriculture, fruit trees, damage to native forests, and reduction of carbon uptake. The aim of this research is to identify these events in the eastern part of the Baltic Sea region during the spring months from 1950 to 2022 and to assess their frequency, intensity and potential damage to agriculture. To do that, the date of the last spring frost (LSF) as well as the date of the start of the growing season (SGS) were found for each cell of analysed territory for each year. In this study the SGS for a particular year was defined as the first day in a period of six days when the average daily temperature (T_avg) during all these six days was at least +5.0 °C. Meanwhile, the LSF date was determined when the minimum daily air temperature (T_min) in April-June dropped below 0°C for the last time during a particular year. Daily T_avg and T_min data that was needed to determine SGS and LSF dates were obtained from European Centre of Medium-range Weather Forecast ERA-5 reanalysis dataset with a spatial resolution of 0.25° x 0.25°. In this study, a false spring was identified at the corresponding point of the study area if the last spring frost was identified after the start of the growing season. The study showed, that at the end of the analysed period (1950–2022), the growing season started earlier in the entire study area. This change ranged from -0.5 days per decade in the northeastern part of the study area to -2.1 days per decade in the western part of Lithuania. The LSF date during the 73-year study period has also become earlier in almost all study area points. The largest changes (-1.8 days per decade) were observed in the northern and northeastern part of the analysed territory. However, the changes of LSF date were not as rapid as those of the SGS. Therefore, during the study period, the number of false spring cases increased in 73.76% of the study area. The largest growth of such cases was observed in the eastern part of the study area and in coastal regions. There was also an increase in the amount of accumulated heat from the SGS date to the LSF date. Positive changes were found in 82.76% of the study area points, with the largest ones occurring at the border between Lithuania and Kaliningrad and in the eastern part of the study area.

How to cite: Klimavičius, L.: False spring events in the eastern part of the Baltic Sea region, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-438, https://doi.org/10.5194/ems2023-438, 2023.

Agrivoltaic (APV) approaches use PV modules mounted at a sufficient height above the ground to allow synergistic use of agricultural production and energy generation. With this approach, the loss of agricultural land can be minimized. The connections between APV systems and the influence on the micro- and local climate and the yield of the underlying crops have not been sufficiently researched until now. First, APV shading reduces incoming solar radiation, which can lead to a reduction in yield. Second, shading however, also reduces evapotranspiration and can therefore prove to be advantageous, especially during dry periods. The presence of PV panels (by analogy with trees) protects plants from excessive heating and lowers the soil temperature, thereby balancing the microclimate.

In the present study, the influence of different APV designs on the microclimate and plant growth was estimated using model simulations.

The effects of different APV designs (e.g. length, width, inclination, row spacing....) on incident solar radiation under the modules were calculated using a method based on image processing of hemispherical "fisheye" photographs. In this way, global radiation above and below the PV modules was calculated for a period of 10 years.

Simulated global radiation and meteorological data were then used to simulate crop growth of 3 varieties (maize, winter wheat and barley). Simulations were performed using the plant growth model DSSAT. In general, some yield reduction was obtained below the PV modules. Yield reduction was strongly correlated with radiation sums. However, some varieties , especially maize, showed an increase in yield in hot, dry years.

How to cite: Weihs, P., Thaler, S., Eitzinger, J., Zamini, S., Berger, K., and Abdollahi, M.: Influence of PV modules on the incident radiation and the yield of 3 plant varieties., EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-442, https://doi.org/10.5194/ems2023-442, 2023.

Global warming has a significant impact on global food production, including grapes used for winemaking. European vineyards, in particular, are at risk due to changing climatic conditions affecting the grapevine growth cycle, disease outbreaks, and extreme weather conditions.

In the current research, we use the bioclimatic Huglin-index over all 22 wine regions in Hungary to assess the observed heat sums available for grapes, which correlates with sugar content (highly) and yields (significantly). Furthermore, we analyse the spring frosts after the vegetation period started affecting blooming, the heat stress over summer heatwaves affecting grape quality, and other minor climatic conditions as well. For this matter, high-resolution and homogenized daily observations are used from 1971 covering the Carpathian Basin. Results suggest that in the last 50 years, already a 27% increase was detected on average in the heat sums available for grapes, which has already started to have an impact on changing the varieties of grapes in some regions.

For the future, the Euro-CORDEX regional climate simulations offer valuable insights into expected changes in climatic patterns in Europe. These changes of the less-mitigation RCP4.5 and non-mitigation RCP8.5 scenarios are likely to result in earlier ripening, lower acidity, increased alcohol content, and higher susceptibility to pests and diseases, leading to reduced yields and smaller berries due to water stress. With immediate mitigation of RCP2.6 scenario, adaptation strategies could be avoided, such as selecting heat-tolerant grapes, changing of cultivation management, or changing vineyard locations to cooler climates. However, these measures require substantial investment and may not be feasible for all wine producers.

How to cite: Szabó, P., Pongrácz, R., and Bartholy, J.: The impacts of climate change on the Hungarian wines, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-538, https://doi.org/10.5194/ems2023-538, 2023.

Within the R&D project StaPrax-Regio, highly efficient N-stabilized fertilization strategies are identified based on site-specific agrometeorological and soil characteristics. Derived strategies will be transferred to fertilization practice using innovative advisory tools. The aim of the project is a significantly improved utilization of the diverse and complex beneficial effects of N-stabilized fertilization (e.g. reduction of N losses, improved N availability in soil, promotion of root growth, improvement of seedling development) to optimize fertilizer N efficiency. Currently, this has been insufficiently achieved for winter cereals. However, the previous project StaPlaRes demonstrated that a significant increase in N use efficiency can be achieved by an optimized adaptation of N-stabilized fertilization strategies to site-specific soil and weather conditions.

The Deutscher Wetterdienst prepares agro-meteorological analyses for 85 test fields with different winter grain types in Germany: based on long-term measurements such as air temperature and precipitation as well as based on modeling with the agro-meteorological models AMBAV (AgrarMeteorologische Berechnung der aktuellen Verdunstung) and BEKLIMA (BEstandsKLIMA) for e.g. soil moisture and soil temperature. Thus, more differentiated statements about the dependencies of plant development on agro-meteorological conditions are achieved. In addition to the model evaluation with measurements ​during the project, the model data and the site analyses are used to generate regionalized statements on e.g. temperatures, precipitation and phenological development during the main fertilization period. In combination with parallel soil and plant cultivation analyses, highly efficient site-specific fertilization strategies will be identified. For this purpose, existing consulting tools (e.g. BESyD) and GIS-based maps will be used and developed further. In order to take seasonal influences into account, an agro-meteorological model is currently developed in the AMBER environment, which should serve as an additional agro-meteorological decision-making aid at an early stage (if possible a few weeks) before each upcoming fertilization application.

How to cite: Assmann, D., Böttcher, F., Leppelt, T., Kreuter, T., Eißner, F., Thiel, E., Döhler, J., Grunert, M., Pundt, H., and Pleshkanovska, R.: Nitrogen stabilization in fertilization practice: optimization by regionalization based on meteorological and edaphic parameters, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-3, https://doi.org/10.5194/ems2023-3, 2023.

Among natural disasters, droughts cause the most damage and losses to the agricultural sector in Russia. Measures to minimise the damage caused by droughts depend on timely and accurate prediction of droughts. Seasonal prediction of droughts is challenging due to the non-linearity of processes in the coupled atmosphere-land system. For the same reason, it is expected that Artificial Intelligence (AI) methods may be superior to traditionally used statistical methods for drought prediction. The aim of this work is to apply and evaluate different methods of AI for seasonal drought prediction over European Russia. Several indices are used to identify droughts, such as the Standardised Precipitation Index (SPI), the Standardised Precipitation Evapotranspiration Index (SPEI) and the Selyaninov hydro-thermal coefficient (HTC). The latter index is traditionally used in agricultural meteorology in Russia and its relation to the production of various crops is well established. The indices are calculated for the period 1958-2022 using ERA5 reanalysis data. A set of predictors includes several indices characterising the large-scale atmospheric circulation over northern Eurasia (e.g. NAO, AO and others), as well as the ENSO index and the sea surface temperature anomaly over the North Atlantic. Several AI methods are used to predict the values of the drought indices based on the set of predictors for different lead times ranging from two weeks to several months. Prediction is made separately for each of the administrative regions in the European Russia with a special focus on those regions where droughts cause most damage. Another focus of the study is the relationship between droughts and atmospheric blocking events and heat waves. The application of the used AI methods for heat wave prediction is discussed.

How to cite: Chechin, D., Akperov, M., and Timazhev, A.: Seasonal prediction of droughts over European Russia using artificial intelligence methods, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-550, https://doi.org/10.5194/ems2023-550, 2023.