ZAZIkI

Sustainable sugar beet cultivation - Innovations and artificial intelligence
In on-farm research, resource-saving and practicable sugar beet cultivation systems are being developed and evaluated in the Central German region in Saxony-Anhalt. Remote sensing, AI models and Life Cycle Assessment are used.

Duration:

04/2024 – 12/2028

Project team:

Dr. Olga Fishkis,

Dr. Stefan Paulus,

PD Dr. Anna Jacobs

Department:

Cropping Systems & Yield Formation, Sensors & Data Analysis

Funding:

Federal Ministry of Education and Research (BMBF): Model region of the bioeconomy for the digitalization of the plant value chain in the Central German mining area in Saxony-Anhalt (DiP)

Cooperations:

GeoEcology, Martin-Luther-University Halle-Wittenberg
Bioenergy system department, German Biomass Research Center (DBFZ)

The aim of the project is to develop and evaluate resource-saving and practicable sugar beet cultivation systems in the model region "Central German district in Saxony-Anhalt". The new cultivation systems should be adapted to the increasing scarcity of water and the increased risk of erosion in the region due to climate change and also minimize other environmental risks (e.g. greenhouse gas (GHG) emissions, eutrophication, toxicological risks). In cooperation with sugar beet growers' associations from the region and local farm managers, new cultivation methods are tested in practice through on-farm trials. The choice of measure is made according to the individual circumstances. Possible methods include spraying drones and strip tillage. Both traditional methods of data generation in the field and at farm level as well as innovative methods such as autonomous crop monitoring and AI-based analysis of drone and satellite images are used. Based on the data collected, detailed material and energy balances are created for the cultivation systems under consideration. On this basis, a tool for digital decision support for ecological and GHG balancing will be created for users in agricultural practice. The project contributes to the networking of farms and the digitalization of agricultural production in the model region.

Funding

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