78 Opportunities and Limitations of Modeling and Data Analytics for Precision Livestock Farming

Precision livestock farming (PLF) involves the use of sensors that captures large amounts of real-time information at the building, herd or animal level, which are later processed to control the system. Data processing can be accomplished using mathematical models (MM), artificial intelligence algorithms (AI) or a combination of these and other methods. The choice of the method must be made according to the volume of data to be processed, its nature and the relationship between the available information and the desired control of the system. Several components of PLF such as precision nutrition, early disease detection, animal welfare among others may require sophisticated data processing methods. MM is today the preferred method to estimate nutrient requirements in the precision nutrition component of PLF. Conventional MM estimate average population responses using historical population information. Important limitations of these models are the assumption that all the individuals of the population have the same response to a given nutrient provision and that they have not been developed for real-time estimations using up-to-date available information. Therefore, MM have to be developed specifically for PLF and operate in real-time at individual or small group level, considering the between and within-animal variation. Growth patterns, nutrient utilization and behavior vary among animals and herds. There are opportunities to combine data-driven AI with knowledge-driven MM to control more complex PLF components. AI thrive in large complex datasets, where establishing connections can be otherwise difficult due to data complexity, volume and where flexibility is needed to process real-time data from individuals. In contrast, knowledge-driven MM can simplify complex biological systems based on well-established concepts and information. In both cases, PLF models must be flexible enough to consider changes over time for the same animal or herd, and among animals and herds, acknowledging the method limitation while using its strength.