Prof. Dr. Edna Hillmann

Profil

Zusammenfassung

Prof. Dr. Edna Hillmann erforscht das Wohlbefinden von Nutztieren durch Verhaltensbeobachtung und nicht-invasive Messmethoden. Sie entwickelt Systeme zur Erfassung von Tiergesundheit und emotionalem Zustand — etwa durch Vokalisationsanalyse, Sensortechnik und Verhaltensmonitoring — und nutzt diese Erkenntnisse, um Haltungsbedingungen tiergerechter zu gestalten.

Skills

Name

Prof. Dr. Edna Hillmann

Titel

Prof. Dr.

Fakultät

Lebenswissenschaftliche Fakultät

Institut

Albrecht Daniel Thaer-Institut für Agrar- und Gartenbauwissenschaften

E-Mail

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HU-FIS-Profil

Quelle ↗

Zuletzt gescrapt

28.6.2026, 01:06:43

• 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

• Entwicklung der Grundlagen für ein nationales Monitoring des Tierwohls - Teilprojekt 8

  Quelle ↗

  Förderer: Bundesanstalt für Landwirtschaft und Ernährung Zeitraum: 06/2020 - 07/2023 Projektleitung: Prof. Dr. Edna Hillmann

• Maternales Verhalten von Hennen beim Zweinutzungshuhn

  Quelle ↗

  Förderer: Alexander von Humboldt-Stiftung: Forschungskostenzuschuss Zeitraum: 03/2022 - 08/2024 Projektleitung: Prof. Dr. Edna Hillmann

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

  2015

  Scientific Reports · 111 Zitationen · 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.

• Gentle touching in early life reduces avoidance distance and slaughter stress in beef cattle

  2012

  Applied Animal Behaviour Science · 104 Zitationen · DOI

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

  2022

  Scientific Reports · 102 Zitationen · 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.

• Agroscope

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

  other

• Friedrich-Löffler-Institut

  Tiergerechte Ernährung und Lämmeraufzucht in der ökologischen Milchziegenhaltung

  other

• Georg-August-Universität Göttingen

  Tiergerechte Ernährung und Lämmeraufzucht in der ökologischen Milchziegenhaltung

  university