Precision Livestock Farming Technologies for Reducing Greenhouse Gas Emissions in Agriculture

Abstract

Precision Livestock Farming (PLF) has emerged as an innovative system to maximize livestock production and minimize greenhouse gas (GHG) emission through the intelligent use of monitoring, automation and data. This review summarizes the latest developments in PLF technologies and how these technologies are helping to advance livestock production in a climate-smart way. It provides an overview of the significant livestock GHG sources, their environmental impacts, and the methane and nitrous oxide sources from manure, as well as the enteric CH4 sources, and energy use. The review also examines the use of sensor-based technologies including wearable devices, Internet of Things (IoT) platforms, smart collars and biometric monitoring systems, for real-time monitoring of animal health, behaviour and emission-related measurements. AI, machine learning, and deep learning analysis of livestock performance and prediction, disease detection and estimation of methane emissions and optimization of resources. In addition, precision health and reproduction management are discussed to achieve better feed efficiency, reduce the waste of resources and increase the sustainability of production. Implementation challenges are also addressed, such as high investments, interoperability, cyber security, data ownership, and skills limitations that hinder broad implementation. In summary, incorporating PLF technologies with AI, advanced sensing, digital agriculture is a viable strategy to create an environmentally sustainable, economically viable, low carbon livestock production system that will contribute to global climate mitigation and resilient agriculture.