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Research project (§ 26 & § 27)
Duration : 2021-05-01 - 2024-04-30

Drought stress is the most important global challenge for plant production. Also in Austria, uncertainty in water supply is an increasing challenge for agriculture. Thus, there is high need for novel approaches to sustain farmers in better adapting to and managing climate change related stresses like drought and heat in both annual as well as perennial crops such as grapevine. Improving plant resistance to drought requires improving physiological defence mechanisms that help plants to avoid damage during stress periods. To do so, however improved measurement methods to better understand, monitor and predict plant response to drought are required. Current physiological methods are mostly limited to measure single plants and are often hardly applicable under field conditions. The application of imaging sensors in plant sciences could provide improved solutions to advance towards monitoring plant physiology. This dissertation aims to develop a novel multimodal imaging approach combining different wavelength spectra – visible, near infrared, shortwave infrared and thermal – to capture distinctive physiological traits of grapevine. The thesis will first develop imaging-based models to capture the key drought response function at the single leaf scale. These models will then be applied to monitor leaf canopies of grapevine during drought stress and recovery in the climate chamber to test early stress detection via high spatial resolution datasets. Finally, the approach is used in the field to identify rootstocks with superior physiological defence against drought and to assist in irrigation scheduling via image-based stress monitoring. It can be expected that the innovative multimodal imaging setup combined with advanced data-analytical methods will provide an important advance for plant phenotyping, drought monitoring and contribute to the adaptation of grapevine/crop production to climate change.
Research project (§ 26 & § 27)
Duration : 2021-05-01 - 2024-04-30

Climate change challenges crop production due to increasing drought and heat stress. In Austria, sugar beet is an important crop that is mostly grown in Eastern Austria where water is the main yield-limiting factor. Therefore, sustainable sugar beet production in Austria requires efficient strategies to optimize water use and increase plant resistance to drought. The aim of this dissertation project is to use isotope analysis as a tool for breeding and management approaches to water-efficient sugar beet production. Using a combination of stable isotope analyses for water and carbon, we will establish a method to quantify the efficiency of root water uptake from subsoil and its impact on mitigating the impact of drought on sugar beet. The developed method will be first applied to identify sugar beet cultivars with superior capacity of soil water extraction to improve growth under water-limited conditions. Thereafter, we will use the stable isotope approach to evaluate sugar beet production systems in the field, focussing on the impact of limiting evaporation water losses by soil surface coverage. We expect that this project will provide an improved isotope-based approach for crop breeding and management to support the design of effective climate change adaptation measures in agriculture.
Research project (§ 26 & § 27)
Duration : 2021-01-01 - 2022-06-30

The aim of this project is the development of methods for satellite, model and AI-based yield forecasting of agricultural crops in the context of Austrian agriculture. In order to achieve the project goal, the following technologies and methods will be combined or further developed: - Sentinel 2 spectral data (satellite images) will be used as input for the AI component, the reflection model PROSAIL and the crop growth model iCrop. - The AI component will be trained to recognize agricultural crops with manually marked and already available training data from the Austria and USA. The trained model should be able to correctly classify the crop plants visible on the satellite images to 90%. With the reflection model PROSAIL, possible reflection values are calculated by discrete variation of the input values and stored in a database. The reflection values shown in the satellite images are then compared with the values in the database in order to be able to draw conclusions about possible input value combinations and crop plants. - The results of the AI component and the reflection model will be transferred to the fuzzy logic classifier in order to finally determine the crop species. - The identified crop species and growth parameters derived from the spectral data (e.g. Leaf Area Index) are used to calibrate the iCrop model to generate yield forecasts (as well as harvest time, phenology, fertilizer and water requirements).

Supervised Theses and Dissertations