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Precision livestock farming (PLF) concerns the management of livestock using the principles and technologies of process engineering. Precision nutrition (PN) is part of the PLF approach and involves the use of feeding techniques that allow the proper amount of feed with the suitable composition to be supplied in a timely manner to individual animals or groups of animals. Automatic data collection, data processing, and control actions are required activities for PN applications. Despite the benefits that PN offers to producers, few systems have been successfully implemented so far. Besides the economical and logistical challenges, there are conceptual limitations and pitfalls that threaten the widespread adoption of PN. Developers have to avoid the temptation of looking for the application of available sensors and instead concentrate on identifying the most appropriate and relevant information needed for the optimal functioning of PN applications. Efficient PN applications are obtained by controlling the nutrient requirement variations occurring between animals and over time. The utilization of feedback control algorithms for the automatic determination of optimal nutrient supply is not recommended. Mathematical models are the preferred data processing method for PN, but these models have to be designed to operate in real time using up-to-date information. These models are therefore structurally different than traditional nutrition or growth models. Combining knowledge- and data-driven models using machine learning and deep learning algorithms will enhance our ability to use real-time farm data, thus opening up new opportunities for PN. To facilitate the implementation of PN in farms, different experts and stakeholders should be involved in the development of the fully integrated and automatic PLF system. Precision livestock farming and PN should not be seen as just being a question of technology, but a successful marriage between knowledge and technology.
Pomar et al. (Mon,) studied this question.