Latest SCI publications

Latest Projects

Research project (§ 26 & § 27)
Duration : 2019-07-01 - 2022-10-31

European cereal production experiences an ongoing intensification on wheat and barley at the expense of minor cereals including einkorn, emmer, oat, rye and spelt. This specialisation leads to continuous loss in agricultural biodiversity and marginalisation of traditional land management systems. A diversification of cropping systems by minor cereals offer benefits with respect to agronomic management, grain processing, nutritional quality, health promotion and numerous ecosystem services. Enhanced plant breeding efforts are of strategic importance to improve the competitiveness of minor cereals in European agriculture. Rye is the only cross-pollinated small-grain cereal, which results in a unique complexity concerning the genetic improvement of rye and underlines the need for rye-specific research concepts. Plant architectural traits are important breeding targets to improve crops yield potential and food security. The overall goal of RYE-SUS is to develop, test and model gibberellin-sensitive semi-dwarf rye genotypes with optimized harvest index, improved lodging resistance, high yield potential and drought tolerance as well as minimised risk of ergot infestation for a sustainable intensification also in marginal production environments. To improve rye competitiveness in European agriculture, RYE-SUS aims to i) make use of hybrid breeding as a cutting edge technology of crop improvement and genome-based precision breeding to increase target-specific selection efficiency and accelerate breeding processes in rye, ii) develop new genotypes leading to improved lodging and drought tolerance, iii) proof the practical potential of genotypes with a novel plant architecture in target environments, which challenge rye cultivation by potentially growth-limiting factors such as drought, frost, or nutrient deficiencies, iv) minimize the risk of extremely toxic ergot alkaloids in the harvest, v) exploit natural genetic diversity in adaptive traits and develop new molecular technologies which support niche range expansion of highly productive rye hybrids in cold climate ecosystems, and vi) develop and exploit a crop model to simulate the growth and development of rye under potentially growth-limiting factors as a tool to support novel integrated pest and crop management methods and practices.
Research project (§ 26 & § 27)
Duration : 2021-08-23 - 2021-12-22

In the planned project, new findings on the quality of a recycled substrate will be researched for a new type of plant cultivator from the company Microgreenbox GmbH, which has a closed climate system. In the course of resource conservation, sustainable use and safeguarding food production, the substrates are to be used several times, so the aim here is to check whether the recycled substrate corresponds to that of the initial substrate.
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