4-9 September 2022, Bonn, Germany
EMS Annual Meeting Abstracts
Vol. 19, EMS2022-115, 2022, updated on 28 Jun 2022
EMS Annual Meeting 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Application of seasonal weather forecasts and climate change scenarios on selected pest and disease algorithms in Austria

Sabina Thaler1,2, Josef Eitzinger1, Gerhard Kubu1, Ahmad Manschadi3, Marlene Palka3, and Stefan Schneider4
Sabina Thaler et al.
  • 1Institute of Meteorology and Climatology, University of Natural Resources and Life Sciences, Vienna, Austria
  • 2CzechGlobe-Global Change Research Institute CAS, Brno, Czech Republic
  • 3Institute of Agronomy, University of Natural Resources and Life Sciences, Vienna, Austria
  • 4Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Vienna, Austria

The Agricultural Risk Information System (ARIS) is a GIS-based model calculating 24 agrometeorological indicators in a high spatial resolution based on 1km gridded weather input over Austrian agricultural areas. These include general or crop specific agroclimatic suitability and risk indicators as well as pest and disease risk indicators based on specific algorithms. Our study, within the framework of the project AGROFORECAST, focuses on the application potential and performance of three insect pest and one disease algorithm (European grapevine moth, grapevine cicada, plum moth and grapevine downy mildew) where the first occurrence of relevant development stages (i.e. adults, nymphs) are calculated for the pests and the start of infection and incubation period of grapevine downy mildew throughout the whole year. To enhance the resilience and sustainability of Austrian agricultural systems under changing climate, the application potential for short-term to seasonal forecasts as well as climate change scenarios were tested for these pest indicators. Tailored information methods of the forecasts and predictions for decision support (e.g. for pest management) and precision agriculture methods were tested.

For the evaluation, different statistical analyses were carried out at selected locations in Austria, using

(a) short-range and seasonal forecast: the current ECMWF long-range ensemble forecast system 5 (SEAS5, in operation since November 2017) with forecasts up to 7 months was used as meteorological input for the ARIS model in combination with ECMWF medium-range forecasts (10-d) and INCA analysis data. Spatial downscaling methods were applied to the forecasts during the harvest season and included downscaled 10-day ECMWF data as well as downscaled 7-month seasonal forecasts of precipitation, global radiation, minimum and maximum temperature on a daily basis with a resolution of 1x1 km. The study period ranged from 2018 to the present;

(b) the Austrian climate change projections ÖKS15 for the long-term assessments, comparing the two time periods present (1981-2010) and near future (2036-2065).

The sensitivity, uncertainties and performance of different prediction ranges for the studied indicators are demonstrated in our study. The results are analyzed in collaboration with stakeholders in regard to performance and potential adaptation needs for crop protection service and pest management applications. In order to determine the long-term changes and possible impacts, these indicators were applied to the Austrian agricultural regions using the different Austrian ÖKS15 climate projections and the two emission scenarios RCP 4.5 and RCP 8.5 for the near future.

How to cite: Thaler, S., Eitzinger, J., Kubu, G., Manschadi, A., Palka, M., and Schneider, S.: Application of seasonal weather forecasts and climate change scenarios on selected pest and disease algorithms in Austria, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-115, https://doi.org/10.5194/ems2022-115, 2022.


Display file

Supporters & sponsors