JOURNAL ARTICLE
Satellite-Based Decision Support Tools to Assist Grazing Cattle Production.
Published In: Journal of Animal Science, 2023, v. 101. P. 74 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Fernandes, Marcia H. M. R.; Tedeschi, Luis O. 3 of 3
Abstract
The article focuses on recent advances in modeling and technology applications in animal science, particularly in poultry nutrition, grazing cattle production, and precision livestock farming. It highlights challenges in poultry modeling, including the need for increased education and integration of models with data. Satellite-based decision support tools for grazing cattle utilize remote sensing technologies, such as optical sensors and active sensors like Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LIDAR), combined with machine learning algorithms to monitor grassland biomass and nutritional status for improved pasture management. Additionally, the development of digital twins in precision livestock farming (PLF)—virtual replicas of physical systems created using sensors and cameras—is presented as a method to optimize animal health, productivity, and environmental sustainability through data analytics and big data techniques.
Additional Information
- Source:Journal of Animal Science. 2023/11, Vol. 101, p74
- Document Type:Article
- Subject Area:Earth and Atmospheric Sciences
- Publication Date:2023
- ISSN:0021-8812
- DOI:10.1093/jas/skad281.091
- Accession Number:173680548
- Copyright Statement:Copyright of Journal of Animal Science is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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