262 Precision feeding technologies for health and welfare: practice, promise, and pie-in-the-sky.

Abstract Opportunities to leverage precision technologies to support animal health and welfare (AHW) have been widely reviewed in recent literature. Existing reviews and experimental works focused on precision technology and AHW primarily explore sensor-based monitoring systems using radio frequency identification, global positioning system, accelerometry, camera, microphone, and biosensor-based technologies. A more refined set of studies explore precision feeding systems, or an integration of precision feeding and sensor-based systems. Applying the Iceberg Model for systems thinking, we can recognize that the technologies presented in these existing reviews and experimental studies largely address diverse AHW events, patterns, and trends by modifying the underlying group-focused management structure of animal production systems. Namely, these systems largely achieve advancements in AHW outcomes by allowing access to individual measurements that were previously impractical to obtain in conventional management settings. Although understanding the condition of the individual within the context of group managed livestock is a critical advancement enabled by precision technology, it is also a fairly specific and narrow focus for shifting the structure and assumptions underlying animal management. To explore other potential structural changes that might be enabled by technologies, we examine 3 nutrition-related AHW goals: 1) reduced incidence of metabolic disease; 2) improved matching of dietary nutrients with animal needs; and 3) feeding to support animal happiness. Aligned with the Iceberg framework, we explore a technology development pipeline focused on the sensing, networking, data analytics, and automation advances needed to shift other underlying management structure. For metabolic diseases, we explore the management structure of feeding to maximize group performance and explore technology updates needed to shift toward feeding for optimal individual performance. When matching dietary nutrient supplies to animal needs, we explore the management structure that animals have energy requirements, characterizing a pipeline of technology updates to support shifting toward feeding to support energy-yielding nutrient use efficiency. Finally, in the more challenging scenario of feeding to support animal happiness, we characterize a technology pipeline to support automated monitoring of animal affective state, and how such a pipeline might shift our approach to livestock management. In each of these forward-looking scenarios, key advancements needed in sensing technologies, data analytics, or automation tools are highlighted for future work.