Prof. Dr. Edna Hillmann

Die Forschung von Prof. Dr. Edna Hillmann konzentriert sich auf die Verbesserung des Tierwohls und die Optimierung von Haltungssystemen in der Nutztierhaltung, insbesondere bei Schweinen, Rindern und Ziegen. Ein zentrales Anliegen ist die Entwicklung und Validierung nicht-invasiver Indikatoren für den emotionalen und gesundheitlichen Zustand von Tieren, darunter akustische Analysen von Lautäußerungen und sensorbasierte Verhaltensüberwachung. Durch den Einsatz von Machine Learning und Datenintegration werden komplexe Verhaltensdaten automatisiert ausgewertet, um Früherkennungssysteme für Krankheiten und Stress zu etablieren. Die Arbeit verbindet empirische Feldstudien mit technologischen Innovationen, um nachhaltige, tiergerechte Produktionsmethoden zu fördern und wissenschaftliche Standards in der Tierwohl-Forschung zu stärken.

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• ProtecTier-CalfLife: Entwicklung eines energieautarken, multiparametrischen Sensorsystems zur kontinuierlichen Überwachung des Verhaltens und des Gesundheitszustandes von Kälbern und Jungrindern – CalfLife Observer

  Quelle ↗

  Förderer: Bundesministerium für Wirtschaft und Energie Zeitraum: 02/2017 - 07/2020 Projektleitung: Prof. Dr. Edna Hillmann

• CowData (Verbesserung des Betriebsmanagements durch Kombination von Stall- und Weidedaten)

  Quelle ↗

  Förderer: Bundesanstalt für Landwirtschaft und Ernährung Zeitraum: 09/2018 - 05/2022 Projektleitung: Prof. Dr. Edna Hillmann

• Was Tiere wollen: ein Pilotversuch mit Ratten

  Quelle ↗

  Zeitraum: 10/2019 - 09/2021 Projektleitung: PD Dr. Lorenz Gygax, Prof. Dr. Edna Hillmann

• Segregation of information about emotional arousal and valence in horse whinnies

  2015

  Scientific Reports · DOI

  Studying vocal correlates of emotions is important to provide a better understanding of the evolution of emotion expression through cross-species comparisons. Emotions are composed of two main dimensions: emotional arousal (calm versus excited) and valence (negative versus positive). These two dimensions could be encoded in different vocal parameters (segregation of information) or in the same parameters, inducing a trade-off between cues indicating emotional arousal and valence. We investigated these two hypotheses in horses. We placed horses in five situations eliciting several arousal levels and positive as well as negative valence. Physiological and behavioral measures collected during the tests suggested the presence of different underlying emotions. First, using detailed vocal analyses, we discovered that all whinnies contained two fundamental frequencies ("F0" and "G0"), which were not harmonically related, suggesting biphonation. Second, we found that F0 and the energy spectrum encoded arousal, while G0 and whinny duration encoded valence. Our results show that cues to emotional arousal and valence are segregated in different, relatively independent parameters of horse whinnies. Most of the emotion-related changes to vocalizations that we observed are similar to those observed in humans and other species, suggesting that vocal expression of emotions has been conserved throughout evolution.

• Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production

  2022

  Scientific Reports · DOI

  Vocal expression of emotions has been observed across species and could provide a non-invasive and reliable means to assess animal emotions. We investigated if pig vocal indicators of emotions revealed in previous studies are valid across call types and contexts, and could potentially be used to develop an automated emotion monitoring tool. We performed an analysis of an extensive and unique dataset of low (LF) and high frequency (HF) calls emitted by pigs across numerous commercial contexts from birth to slaughter (7414 calls from 411 pigs). Our results revealed that the valence attributed to the contexts of production (positive versus negative) affected all investigated parameters in both LF and HF. Similarly, the context category affected all parameters. We then tested two different automated methods for call classification; a neural network revealed much higher classification accuracy compared to a permuted discriminant function analysis (pDFA), both for the valence (neural network: 91.5%; pDFA analysis weighted average across LF and HF (cross-classified): 61.7% with a chance level at 50.5%) and context (neural network: 81.5%; pDFA analysis weighted average across LF and HF (cross-classified): 19.4% with a chance level at 14.3%). These results suggest that an automated recognition system can be developed to monitor pig welfare on-farm.