Increasing Sustainability in Livestock Production Systems through High-Throughput Phenotyping Approaches

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About this Research Topic

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Background

Key goals of sustainable animal production systems are reducing environmental impact, improving social acceptability and increasing economic profitability while sustaining local communities. These goals can be achieved by making efficient use of animal feed sources, guaranteeing food security through improved animal health, improving animal welfare, and human health, and ensuring economic and societal relevance of animal production systems to local communities, among others. Achievement of the listed goals and objectives requires: a) measuring and analyzing inputs and outputs of animal production systems, including animal-specific traits and environmental variables, and b) making data-informed management and selection decisions, aligned with breeding and economic goals of the farm.

For the measurement and analysis of the different animal production variables, the emergence of novel tools for on-farm data recording, as in the case of Precision Livestock Farming (PLF), has revolutionized phenotype recording systems. Nowadays, novel technologies enable phenotyping a large number of animals, thus, allowing to define new traits or indicators and accessing real-time data to make more informed decisions on the sustainability of the production system. Also, PLF constitutes a novel approach to study traits that are difficult to measure or define, such as those related to animal fitness, health and welfare, fertility, feed efficiency, disease resistance, and adaptability. However, the use of data generated by PLF sensors and its integration with other available information at the animal level such as genomic data remains a challenge. In that sense, new methodologies and prediction algorithms have been proposed, using approaches such as machine learning. 

Besides innovations targeting large-scale phenotyping, a deep understanding of the genome biology of new and routinely measured traits is a path to sustainable livestock production. A common example of the potential implementation of genomic technology to improve sustainability is genomic selection for feed efficiency in cattle. Nonetheless, the available methods to record individual feed intake are expensive, limiting their broad application, which highlights the need for new cost-effective indicators or correlated measures. Other examples are integrating phenotypic, genotypic, and environmental information to select animals less affected by heat stress; and addressing the genetic and epigenetic mechanisms of adaptation to local and regional conditions to optimize breeding and management strategies. The relevance of adaptability relies on the premise that selecting more adapted animals represents a reduction in production cost and improvement of welfare in temperate and tropical climate conditions with outdoor systems. 

This topic will encompass research projects and results obtained at the academy and industry levels with focus on PLF applications, methods and algorithm development, challenges on the integration of information and joint initiatives to use and share data, and efficient genomic technologies to improve aspects related to sustainable animal production. We welcome applications of original research, methodology, and review articles on the following themes, but not limited to: 

• PLF initiatives, use of sensors/cameras for capturing novel phenotypes aiming for sustainable animal production. 
• Development of methodologies and algorithms used to analyze data captured from sensors/cameras toward a more sustainable animal production, and that integrate or aim to integrate this information with genomics and other ‘omics’. 
• Initiatives or joint projects for collaboration on the use of data generated through PLF for sustainable animal systems; 
• Tools that can be implemented to record novel phenotypes such as accelerometers and data loggers to increase sustainability in livestock production; 
• New indicators to be used as breeding goals in livestock production systems, aiming for efficiency in selection and sustainability; 
• Genetics of thermal stress, immunity response, disease resistance, fitness, welfare, and behavior in different species: estimation of genetic parameters, correlations, and genetic and regulatory mechanisms. 
• Novel methodologies to characterize and manage local heritage and help local breeds to find their role in the current market demand.

Keywords: genomic selection, Phenomics, precision livestock, sensors, deep learning, data analysis

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