EconPapers    
Economics at your fingertips  
 

Digital Transition as a Driver for Sustainable Tailor-Made Farm Management: An Up-to-Date Overview on Precision Livestock Farming

Caterina Losacco, Gianluca Pugliese, Lucrezia Forte, Vincenzo Tufarelli (), Aristide Maggiolino and Pasquale De Palo
Additional contact information
Caterina Losacco: Department of Precision and Regenerative Medicine and Jonian Area, Section of Veterinary Science and Animal Production, University of Bari Aldo Moro, Valenzano, 70010 Bari, Italy
Gianluca Pugliese: Department of Precision and Regenerative Medicine and Jonian Area, Section of Veterinary Science and Animal Production, University of Bari Aldo Moro, Valenzano, 70010 Bari, Italy
Lucrezia Forte: Department of Veterinary Medicine, University of Bari Aldo Moro, Valenzano, 70010 Bari, Italy
Vincenzo Tufarelli: Department of Precision and Regenerative Medicine and Jonian Area, Section of Veterinary Science and Animal Production, University of Bari Aldo Moro, Valenzano, 70010 Bari, Italy
Aristide Maggiolino: Department of Veterinary Medicine, University of Bari Aldo Moro, Valenzano, 70010 Bari, Italy
Pasquale De Palo: Department of Veterinary Medicine, University of Bari Aldo Moro, Valenzano, 70010 Bari, Italy

Agriculture, 2025, vol. 15, issue 13, 1-36

Abstract: The increasing integration of sensing devices with smart technologies, deep learning algorithms, and robotics is profoundly transforming the agricultural sector in the context of Farming 4.0. These technological advancements constitute critical enablers for the development of customized, data-driven farming systems, offering potential solutions to the challenges of agricultural intensification while addressing societal concerns associated with the emerging paradigm of “farming by numbers”. The Precision Livestock Farming (PLF) systems enable the continuous, real-time, and individual sensing of livestock in order to detect subtle change in animals’ status and permit timely corrective actions. In addition, smart technology implementation within the housing environment leads the whole farming sector towards enhanced business rentability and food security as well as increased animal health and welfare conditions. Looking to the future, the collection, processing, and analysis of data with advanced statistic methods provide valuable information useful to design predictive models and foster the insight on animal welfare, environmental sustainability, farming productivity, and profitability. This review highlights the significant potential of implementing advanced sensing systems in livestock farming, examining the scientific foundations of PLF and analyzing the main technological applications driving the transition from traditional practices to more modern and efficient farming models.

Keywords: animal health; feeding behavior; precision livestock farming; precision nutrition; sensors (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/13/1383/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/13/1383/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:13:p:1383-:d:1689414

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-07-07
Handle: RePEc:gam:jagris:v:15:y:2025:i:13:p:1383-:d:1689414