Individualized livestock productionPhoto: ATB
Research for animal welfare and environmental health
Concepts of livestock farming should incorporate the three pillars of sustainability: environment, society and economy. While there is a growing desire in society for improved animal welfare and enhanced environmental protection, the need for economic survival on the part of farmers must be taken into account at the same time. This requires innovative processes to find their way into practice.
We therefore focus our application-oriented basic research in animal husbandry in line with the One Health approach on improving animal welfare, on husbandry environment, animal and environmental protection as well as on the competitivity of agriculture. Our goals are: objective animal welfare standards, flexible and regionally adapted concepts to solve environmental conflicts, a transparent animal husbandry, consumer acceptance and regional added value.
Our research in this program area is particularly concerned with digital methods for monitoring animal welfare, housing climate and emissions, as well as the evaluation of animal housing systems, especially in open-ventilated and novel housing systems. We develop fundamental knowledge on the generation and dispersion of gaseous emissions and pathogen-laden particles as well as on the ways to minimize the risk of spreading multi-resistant pathogens.
The ATB boundary layer wind tunnel offers extensive possibilities for the investigation of airflow and dispersion processes around agricultural buildings as well as airflow and ventilation processes within the buildings. The detailed understanding at process level feeds into modeling approaches with the goal of estimating emissions from new housing systems and finding opportunities for low emissions in animal-friendly housing.
We consider animal husbandry as a part of circular agricultural systems and integrate our research into the holistic, bioeconomic concept of the Leibniz Innovation Farm, which is currently being established in Groß Kreutz.
We develop and test the use of non-invasive sensors for the animal-specific recording of stress and health. In this context, we analyze animal-individual indicators such as activity, respiration rate, body temperature and performance together with stable climatic parameters in different animal species and husbandry systems, increasingly by using data science, artificial intelligence and sensor fusion. Our aim is to develop automated decision support and control units based on animal-related indicators. Early warning systems are to support farmers in sustainably improving animal health and welfare.
We are developing algorithms to efficiently measure, characterize, and model the dynamics of pollutants that are harmful to the environment, climate, and/or health, and to mitigate infection and antimicrobial resistance (AMR). Within our three-pillar model, we combine measurements in different real-world husbandry systems with physical and mathematical modeling to form an overall system. The solutions developed here are intended to strengthen the resilience of farms and contribute to energy efficiency, moving away from fossil raw materials and reducing emissions.
Our research follows the One Health approach and aims at prevention and control measures against the spread of zoonoses, infectious diseases and antibiotic resistance in the livestock sector. For example, we are investigating the influence of feed additives and improved hygiene measures in pig farming, the effectiveness of different farm manure treatments and parameter variations on the reduction of AMR bacteria in poultry farming, and are developing an early warning model for mastitis in dairy farming.
Our objective for a sustainable livestock production is to close material cycles, to improve nitrogen efficiency, to reduce GHG emissions and to develop individual, regional and flexible concepts for circular agriculture and food systems. To do this, we use methods of modeling, systems analysis, and multi-criteria evaluation, including quantification of trade-offs and synergies. The results are fed into operational decision support systems and proposals for stock improvement as well as into emission models for the analysis of country-specific scenarios and inventories. We also collaborate closely with international bodies at IPCC, UNECE and FAO in this area.
SMP4 – Understanding Antibiotic Use in Livestock Rearing under Infrastructure and Regulatory Constraints: Evidence from DRC ▶
This pilot study will collect first-hand data on antibiotic use in cattle farming and the prevalence of antibiotic resistance in animal food (ASF), humans and the environment in South Kivu province in the eastern Democra…
EmiMod – Weiterentwicklung von Methoden zur Erfassung, Modellierung und Beurteilung des Emissionsgeschehens in Nutztierställen ▶
In the EmiMod joint project, methods for determining emissions from diffuse area sources (e.g. freely ventilated pig and cattle stalls with runs/running yards and other external emission sources such as slurry tanks) are…
ET4D – Development of a practical data management system with embedded sensors for improved environmental management and transparency of dairy farming ▶
The aim of the project is to extend and validate an existing, relatively inexpensive system for monitoring the climate in the barn and emissions (exemplary for dairy cattle). An app or website is to be used to provide ta…
In the WED project, water and effluent management on dairy farms is being investigated and modelled with regard to conditions under climate change. Involving 50 farms, management measures in the barn and in feed will be …
BeLuVa 2.0 – Bestimmung der Luftwechselrate an freibelüfteten Ställen - Validierung von Prognosemodellen (BeLuVa) 2.0 ▶
Mathematical modelling in the BeLuVa (1.0) project (2018-2021) provided initial insights into how boundary conditions influence the air exchange rate (LWR) in individual animal holding areas (TAB) and local concentration…
- Behrens, W.; Kolte, B.; Junker, V.; Frentrup, M.; Dolsdorf, C.; Börger, M.; Jaleta, M.; Kabelitz, T.; Amon, T.; Werner, D.; Nübel, U. (2023): Bacterial genome sequencing tracks housefly-associated dispersal of fluoroquinolone- and cephalosporin-resistant Escherichia coli from a pig farm. Environmental Microbiology. : p. 0. Online: https://doi.org/10.1111/1462-2920.16352 1.0
- Neysari, P.; De Vries, J.; Ogink, N.; Amon, B.; Groot Koerkamp, P. (2023): Reviewing the N-gap in livestock manure systems: Direct and indirect methods for measuring N losses and perspectives for quantifying N2 emission. Biosystems Engineering. (Mai): p. 179-199. Online: https://doi.org/10.1016/j.biosystemseng.2023.03.018 1.0
- Dißmann, L.; Reinhold, P.; Smith, H.; Amon, T.; Sergeeva, A.; Hoffmann, G. (2023): Evaluation of a Respiration Rate Sensor for Recording Tidal Volume in Calves under Field Conditions. Sensors. : p. 0. Online: https://doi.org/10.3390/s23104683 1.0
- Schumacher, B.; Stinner, W.; Strach, K.; Amon, T. (2023): Entwicklung und Test einer Methodik zur Langzeitmessung von Methanemissionen aus Gülle. Biogas Journal. (2): p. 76-85. 1.0
- Cao, M.; Yi, Q.; Wang, K.; Li, J.; Wang, X. (2023): Predicting Ventilation Rate in a Naturally Ventilated Dairy Barn in Wind-Forced Conditions Using Machine Learning Techniques. Agriculture. : p. 0. Online: https://www.mdpi.com/2077-0472/13/4/837 1.0
- Kurras, F.; Gravemeier, L.; Dittmer, A.; Kümper, D.; Jakob, M. (2023): Automatic Monitoring of dairy cows’ lying behaviour using a computer vision system in open barns. Agronomy Research. (2): p. 482-493. Online: https://doi.org/10.15159/AR.23.029 1.0
- Mylostyvyic, G.; Lacetera, N.; Amadori, M.; Sejian, V.; Souza-Junior, J.; Hoffmann, G. (2023): The autumn low milk yield syndrome in Brown Swiss cows in continental climates: hypotheses and facts. Veterinary Research Communications. : p. 0. Online: https://link.springer.com/epdf/10.1007/s11259-023-10203-0?sharing_token=nU0QsYgFRGVqwGQ4fsYrx_e4RwlQNchNByi7wbcMAY50Vv8A11r96FEfstTVqrwM6r-QcLbGluzJHUHxfQ7u8ZtBCAeRIYhisgF7DtxhHrmUKslhR6zbguwtS0KmovwrtEwLuEvqx14_wyxDQtzMr3bW9TJmfVojDuA7mWnJnJQ= 1.0
- Diaz de Otálora Aguirre, X.; Dragoni, F.; Del Prado, A.; Estellés, F.; Wilfart, A.; Krol, D.; Balaine, L.; Anestis, V.; Amon, B. (2022): Identification of representative dairy cattle and fodder crop production typologies at regional scale in Europe. Agronomy for Sustainable Development. (94): p. 0. Online: https://doi.org/10.1007/s13593-022-00830-3 1.0
- Diaz de Otálora Aguirre, X.; del Prado, A.; Dragoni, F.; Estelles, F.; Amon, B. (2022): La sostenibilidad en las explotaciones: desarrollo de un marco de evaluación de modelos a escala de granja. MG Mundo Ganadero. (305): p. 38-41. Online: https://dialnet.unirioja.es/servlet/articulo?codigo=8414206 1.0
- Hassouna, M.; van der Weerden, T.; Beltran, I.; Amon, B.; Alfaro, M.; Anestis, V.; Cinar, G.; Dragoni, F.; Hutchings, N.; Leytem, A.; Maeda, K.; Maragou, A.; Misselbrook, T.; Noble, A.; Rychla, A.; Salazar, F.; Simon, P. (2022): DATAMAN: A global database of methane, nitrous oxide and ammonia emission factors for livestock housing and outdoor storage of manure. Journal of Environmental Quality. : p. 0. Online: https://doi.org/10.1002/jeq2.20430 1.0