About

Options for developing economically relevant solutions in livestock equipment and precision livestock farming (PLF) are limited today. Animal productivity is close to its biological limits. Reducing ressource use is limited by biological limits for the metabolic efficiency of vertebrates and by entirely justified ethical and societal concerns. At the same time, investment and maintenance costs for PLF solutions that use cameras, sensors and computers to monitor and manage farm animals are high, because of the harsh barn environment and because animals are usually moving targets. Hence, amortisation times are often longer than the average livespan of the devices or there are simply no qualified technicians willing to perform repair and maintenance tasks in a poultry or pig barn.

Incorporating the sensory and cognitive abilities of farm animals can help to overcome this dead centre for example by making the animals come to a sensor instead of equiping all animals with wearables or installing sensors everywhere in the barn. In a similar way, animals can be trained to perform tasks that eliminate the need for motors and other actuators e.g. open a gate or operate a mechanic of a material transport. Animals can control their own living conditions according to their individual needs by learning to use interaction elements of preference tests e.g. to affect the micro-climate. This improves not only their own well-being because acute needs are met. It also provides an information about that this need exists, while the number of interactions or the "work" performed by the animal indicate the urgency of the need. In this way, the animals can communicate their own sensory information and their own evaluation of the environmental conditions on an individual level. This eliminates the need for additional sensors and having to rely on average values for the comfortzone of a species that might be unsuited for the individual or outdated e.g. due to breeding progress.

Precondition for a successful intergration of farm animal into technical systems is a training of signal meanings and the effects of interaction elements. This training must be automated to avoid incurring additional costs. Automating this training still requires sensors and sometimes complex technical setups - however for a limited time and only for newborn animals. Animal - Interaction can help you to design and implement such solutions, even if this requires bridging vendor gaps and data silos.

Personal Details

Dipl. Inf. Dr. sc. agr. Christian Manteuffel

Christian Manteuffel, born in 1978, is a computer scientist from Rostock, Germany. After some years of employment for Gothaer Insurance AG, Rostock University and Mühlbauer TakeID GmbH, he worked since 2008 at the Research Institute for Farm Animal Biology (FBN) nearby Rostock. In 2015, he earned a doctoral degree in agricultural process engineering at Christian Albrechts University in Kiel. From 2019 through 2027, he led the Smart Livestock Farming Unit at FBN. During his time at FBN he (co-) initiated seven third-party funded research and development projects and contributed technical solutions to nine other projects. The successful project proposals written by Dr. Manteuffel have a total volume of 5.04 Mio Euro. His work focuses on IoT sensor/actor systems for process automation and precision livestock farming. Besides, he has devoted a significant portion of his professional career to developing solutions for the automated training of animals and animal-technology interaction.

ORCID: 0000-0003-2525-6232

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