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Research project (§ 26 & § 27)
Duration : 2020-04-01 - 2023-03-31

Climate change poses a major threat to ecosystems all over the globe. Rainfall variability results in more frequently occurring drought events and large yield losses. However, current crop management practices are largely based on empirical approaches representing crop demands in “average seasons” under “average price and market conditions”. While these “average” management practices have been relatively successful in the past, rapidly changing production conditions demand a more tailored, site- and season-specific crop management. This project focuses on the development and evaluation of support tools for decision makers of the Austrian agricultural sector through the integration of site-specific crop modelling, spectral sensing and weather forecasting. First, crop models offer cost-efficient tools to generate temporally-dense data on crop growth and yield, soil water and nutrient content as well as crop demand for inputs such as fertilizers. This provides information not only to practitioners on how to best manage crops in the field, but also to researchers to gain a more detailed understanding of the processes that are responsible for e.g. yield formation, crop nutritional status and water demand. Second, spectral sensing via satellites, drones or ground sampling facilitates the collection of data on crop growth and nutritional status at large to very large scales. This data can be used for improving crop management such as N fertilization and irrigation schemes. And third, since decisions and measures taken in crop management are guided by the prevailing weather conditions, accurate weather forecasts can support a more reliable agronomic planning. The combination of all three can be used to provide pre- and in-season crop management support based on projections at field, farm or regional level for a couple of days, weeks or even months in advance. However, there is still a gap between what scientists consider as “useable” information and what users recognise as “useful” in their decision-making processes. This gap prevents the provided support being suitable for actual climate change adaptation in practical terms. Given that agricultural decision/making is a complex and context-specific process, identifying the perceptions and needs of end-users in regard to decision support and how such tools can be tailored to provide “useful” information to end-users will be the second part of this project. The outcomes of this project will be beneficial to different stakeholders, be it farmers, decision makers from the processing industry, policymakers or researchers: farmers can get specific information on optimal soil management, crop selection, sowing date, plant protection, irrigation and fertilization for individual crops at each site. Accurate yield estimations come with major economic advances for the processing industry and agricultural stock trade. Policymakers will be able to take evidence-based decisions when it comes to agricultural compensation payments or spatial planning. And finally research will benefit from an increased accuracy of crop model simulation to draw precise conclusions upon potential agricultural management improvements.
Research project (§ 26 & § 27)
Duration : 2020-09-01 - 2022-08-31

The project objective is to derive new remote sensing indicators of climate-smart agricultural practices that provide enhanced resilience in the face of weather extremes resulting of climate change. We aim to achieve this by combining an extensive time-series field data set containing a range of in-situ reported agricultural practices with remotely sensed time-series measurements obtained from several sensors. The resulting indicators will be openly available for visualization on an online web-portal for farmers, decision makers and research institutions to build upon these and speed up the adoption of resilience enhancing agricultural practices across Europe.
Research project (§ 26 & § 27)
Duration : 2020-06-29 - 2024-06-28

Arable soils are complex ecosystems with a central role in mitigating climate change and adapting agriculture to climate change. Soil organic matter of arable soils can be both, a source and sink for CO2 emissions. As arable soils have lost about 40-60% of their soil organic carbon (SOC) reserves in the course of historical land use changes, it is assumed that arable soils, if managed optimally, also have a corresponding potential to partially compensate for these SOC deficits. The aim of the project is to determine the potentials for SOC storage in pioneer farms and to understand the underlying dynamics of soil organic matter formation. The three main objectives pursued in this project are: - To quantify humus deficits in arable soils and to derive soil type-specific SOC storage potentials in farms with soil ameliorating management. - To determine indicators for monitoring humus formation and carbon sequestration in soils (see Subproject AnIC4Soil). - To combine the analytical indicators with humus models and operational management quantification approaches that allow an action-based estimation of carbon storage potentials.

Supervised Theses and Dissertations