Sensor-data Enabled Indicators in the Investigation of Livestock Resilience

Abstract

Whereas domestication of farm animals has primarily focused on desired productivity traits, the intensification of livestock farming has highlighted the need for improving animal resilience, too. Animal resilience is a complex concept that encompasses the ability for an animal to recover from a particular disturbance. The concept includes resilience to disease, environmental resilience such as extreme and fluctuating climates, but also psychological resilience including stress resilience. Sensor-based data models enable prediction of livestock farming outcomes in response to varying behavioral, physiological, stress and affective states. The quantification of resilience post-disturbance, as well as estimating and predicting resilience pre-disturbance, is challenging. We present a review-based approach in exploring the sensor-data enabled indicators in the investigation of livestock resilience. We assess the intricacies of resilience of farm animals using conceptual, comprehensive, and integrated systems framework. We analyze progress in sensor methods and its possible use to assess various dynamic indicators of livestock resilience. With the rise of sensor-based technologies for livestock farming systems, accurate and sophisticated monitoring systems of animal resilience become more readily available. Wearable sensors, tracking systems, as well as automatic milking systems, provide a way to continuously collect large amounts of quantitative and qualitative data that aid the monitoring of not only health, productivity, and welfare aspects, but also resilience. Sensor-based technologies help breeding goals by contributing to the understanding of the complex, multidimensional framework of livestock resistance. Animal resilience is an essential trait that should be promoted to improve the sustainability of intensive livestock farming. Through digitalization of data collection, farmers can make better livestock management decisions by enhanced understanding of the indicators of environmental, health and psychological resilience, and will be able to predict degrading resilience.