Precision farming in crop and livestock productionPhoto: ATB
Researching for a sustainable primary production
The research program is dedicated to site-specific sustainable intensification in the area of primary agricultural production all the way to harvesting.
Our research ranges from a comprehensive online data collection to modelling and process control. Technological and process engineering tasks include sensor-based technologies for precision farming and precision horticulture, the modelling of emissions and of the microclimate in naturally ventilated barn systems as well as the interaction between housing environment and animal welfare. The system assessment is analysing interactions and effects in relation to environment and economy. Here, we concentrate on nitrogen, greenhouse gases and water.
One focus of our research is to develop and apply sensors for assessing the condition of soils, plants and animals. Information on system parameters such as nutrient supply, plant growth, disease pressure, climatic conditions, water demand, respiratory rate, heat stress, fruit ripeness or others can be recorded. Information collected in-situ integrates into the development of complex physiological and physical models. Online analysis is an essential element of individual and flexible process control.
Crop production is of central importance for the bio-economy: plant biomass not only provides food and animal feed, but is also the basis for bio-based materials and energy.
The global sustainability goals outline the current challenges for precision crop production: to increase productivity while using natural resources in a sustainable manner, to preserve biodiversity, ecosystems, soil fertility and natural habitats, to reduce the impact of invasive species and to ensure that chemicals are managed in an environmentally sound manner.
We are thus working on solutions for sensor-supported local resource management in precision agriculture and precision horticulture. The aim of our research is to increase the efficiency of plant production, in particular by means of adaptive process control and the development of technical solutions for individualized plant production, thus reducing consumption of natural resources, the use of chemicals and of emissions.
Innovative sensor technologies are opening up new possibilities in the field of data acquisition (information and communication technology, telemetry and robotics), processing (Big Data) and analysis of data (genomics, phenomics and bioinformatics). They are fundamental tools in the digital transformation process that agriculture is facing. The information obtained is used to develop complex physiological and physical models that enable precise control of production processes in the sense of a sustainable intensification.
Animal husbandry concepts should integrate the three pillars of sustainability - environment, society and economy. Solutions have to be found in the area of conflicting interests. While the public's desire for improved animal welfare and more environmental protection is growing, the economic imperatives for the survival of farmers must also be taken into account to ensure that innovative processes can find their way into practice.
We therefore focus our application-oriented basic research in animal husbandry on the improvement of animal welfare, housing environment, animal and environmental protection and on maintaining economic competitiveness. Our goals are: objective animal welfare standards, concepts for solving environmental conflicts, transparent animal husbandry, consumer acceptance and added value from a regional production.
To the team of the research program 'Precision farming in crop and livestock management'
The smartMILC project aims to explore the establishment of a "digital barn" to support agricultural processes and services with a particular application focus on cattle through the joint use of multi-sensor networks and …
Using AI technologies and unlocking their potential opens up new opportunities to sustainably intensify milk production and use resources efficiently and competitively. AI tools and data-based applications enable a bette…
IRRIWELL – A novel plant-based approach to estimate irrigation water needs and apply optimal deficit strategy - Specific project: Reference data for sensor calibration and implementation of sensor data in physio… ▶
The main goal of IRRIWELL is to test the implementation of a novel approach to estimate water requirements of fruit trees based on stomatal conductance with the aid of plant sensors and mechanistic physiological models. …
weed-AI-seek – Entwicklung eines intelligenten UAV-gestützten Unkrautmonitoringsystems für den selektiven und teilflächenspezifischen Herbizideinsatz ▶
The objective of the "weed-AI-seek" project is to develop an intelligent real-time monitoring and mapping system for the detection of weed distribution in cereal stands. For this purpose, high-resolution aerial image dat…
InnoRind – Innovationsnetzwerk Rind - zukunftsfähige Rinderhaltung in Deutschland unter Berücksichtigung von Tierwohl, Umweltwirkungen und gesellschaftlicher Akzeptanz ▶
The joint project lnnoRind (here: funding phase 1) aims to establish an innovation network for cattle farming that uses the expertise of the project participants to develop innovative approaches for sustainable cattle fa…
More projects within the research program 'Precision farming in crop and livestock production'
- Yu, L.; Shamshiri, R.; Tao, S.; Ren, Y.; Zhang, Y.; Su, G. (2021): Review of research progress on soil moisture sensor technology. International Journal of Agricultural and Biological Engineering. (4): p. 32-42. Online: http://www.ijabe.org/index.php/ijabe/article/view/6404 1.0
- Ashraf, M.; Mahmood, M.; Sultan, M.; Shamshiri, R.; Ibrahim, S. (2021): Investigation of Energy Consumption and Associated CO2 Emissions for Wheat-Rice Crop Rotation Farming. Energies. (16): p. 5094. Online: https://doi.org/10.3390/en14165094 1.0
- de Camargo, T.; Schirrmann, M.; Landwehr, N.; Dammer, K.; Pflanz, M. (2021): Optimized Deep Learning Model as a Basis for Fast UAV Mapping of Weed Species in Winter Wheat Crops. Remote Sensing. (9): p. 1704. Online: https://doi.org/10.3390/rs13091704 1.0
- Kreidenweis, U.; Breier, J.; Herrmann, C.; Libra, J.; Prochnow, A. (2021): Greenhouse gas emissions from broiler manure treatment options are lowest in well-managed biogas production. Journal of Cleaner Production. : p. 124969. Online: https://doi.org/10.1016/j.jclepro.2020.124969 1.0
- Ngaruka, G.; Neema, B.; Mitima, T.; Kishabongo, A.; Kashongwe, O. (2021): Animal source food eating habits of outpatients with antimicrobial resistance in Bukavu, D.R. Congo. Antimicrobial Resistance and Infection Control. (10): p. 124. Online: https://doi.org/10.1186/s13756-021-00991-y 1.0
- Menardo, S.; Berg, W.; Grüneberg, H.; Jakob, M. (2021): Can Green Plants Mitigate Ammonia Concentration in Piglet Barns?. Atmosphere. (9): p. 1150. Online: https://www.mdpi.com/2073-4433/12/9/1150 1.0
- Kaviani Rad, A.; Shamshiri, R.; Azarm, H.; Balasundram, S.; Sultan, M. (2021): Effects of the COVID-19 Pandemic on Food Security and Agriculture in Iran: A Survey. Sustainability. (18): p. 10103. Online: https://doi.org/10.3390/su131810103 1.0
- Bieganowski, A.; Dammer, K.; Siedliska, A.; Bzowska-Bakalarz, M.; Beres, P.; Dabrowska-Zielinska, K.; Pflanz, M.; Schirrmann, M.; Garz, A. (2021): Sensor-based outdoor monitoring of insects in arable crops for their precise control. Pest Management Science. : p. 1109-1114. Online: https://doi.org/10.1002/ps.6098 1.0
- Bernhardt, H.; Bozkurt, M.; Brunsch, R.; Colangelo, E.; Herrmann, A.; Horstmann, J.; Kraft, M.; Marquering, J.; Steckel, T.; Tapken, H.; Weltzien, C.; Westerkamp, C. (2021): Challenges for Agriculture through Industry 4.0. Agronomy. (10): p. 1935. Online: https://doi.org/10.3390/agronomy11101935 1.0
- Cárdenas, A.; Ammon, C.; Schumacher, B.; Stinner, W.; Herrmann, C.; Schneider, M.; Weinrich, S.; Fischer, P.; Amon, T.; Amon, B. (2021): Methane emissions from storage of liquid dairy manure: influence of season, temperature and storage duration. Waste Management. : p. 393-402. Online: https://doi.org/10.1016/j.wasman.2020.12.026 1.0