Precision Feeding, Digital Twins and Decision-Support Systems for Livestock Production

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Livestock Farming Technology".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 666

Editor


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Guest Editor
Texas A&M AgriLife Research, College Station, TX, USA
Interests: precision livestock farming; beef cattle efficiency; help decision-making of ranchers; environmental impacts on livestock production

Special Issue Information

Dear Colleagues,

The rapid evolution of sensing technologies, data analytics, and modeling approaches is transforming livestock production systems toward more precise, efficient, and sustainable management. Precision feeding systems, supported by advances in digital agriculture and agricultural engineering, now enable the integration of animal-level information with nutritional, environmental, and management data to optimize resource use and animal performance under increasingly variable production conditions.

The focus of this Special Issue is on innovative technologies and engineering-based approaches that support precision feeding and decision-making in livestock systems, with particular emphasis on ruminant production. Contributions are encouraged that combine biological understanding with technological development, including sensing, data integration, and analytical frameworks.

The scope of the Special Issue covers, but is not limited to, the following: precision feeding systems; sensor-based monitoring of intake, behavior, physiology, and environment; smart feeding and watering infrastructure; digital twins and decision-support tools; mechanistic, statistical, and machine learning models for nutrient utilization and performance prediction; computer vision applications; and the integration of animal, feed, and environmental data across spatial and temporal scales. Both experimental and applied studies, as well as methodological and review papers, are welcome.

The purpose of this Special Issue is to bridge the gap between engineering innovation and animal science by highlighting research that translates technological advances into biologically meaningful and practically applicable feeding strategies. Emphasis is placed on systems that improve feed efficiency, reduce environmental impacts, enhance animal welfare, and support adaptive management under climatic and operational uncertainty.

The existing literature on precision livestock farming often focuses on individual technologies or isolated performance metrics, while precision nutrition studies frequently rely on simplified or static assumptions regarding animal behavior and environment. This Special Issue aims to supplement and extend the current literature by promoting integrative approaches that link sensing technologies, feeding systems, and analytical models within coherent decision-support frameworks. By bringing together engineering-driven innovations with physiological and nutritional perspectives, this Issue will contribute to a more holistic understanding of how precision feeding systems can function under real-world conditions. The Special Issue will serve as a platform for advancing reproducible, scalable, and biologically informed solutions that move beyond proof-of-concept studies toward operational implementation in modern livestock systems.

Dr. Pablo Guarnido-lopez
Guest Editor

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Keywords

  • precision feeding
  • precision livestock farming
  • smart feeding systems
  • livestock sensors
  • digital twins
  • decision-support systems
  • animal nutrition modeling
  • feed efficiency
  • machine learning in livestock
  • sustainable animal production

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Published Papers (1 paper)

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23 pages, 1956 KB  
Article
A Hybrid Multi-Agent Control Architecture for Interoperable and Deterministic IoT-Based Swine Precision Feeding
by Vicente López-Sacanell and Lluís Miquel Plà-Aragonés
AgriEngineering 2026, 8(6), 242; https://doi.org/10.3390/agriengineering8060242 - 13 Jun 2026
Viewed by 334
Abstract
Precision Livestock Farming (PLF) requires real-time control systems that connect high-level Decision Support Systems with resource-constrained edge devices. This paper presents a hybrid Multi-Agent System (MAS) architecture for swine precision feeding designed to address the trade-off between semantic interoperability and real-time operational efficiency. [...] Read more.
Precision Livestock Farming (PLF) requires real-time control systems that connect high-level Decision Support Systems with resource-constrained edge devices. This paper presents a hybrid Multi-Agent System (MAS) architecture for swine precision feeding designed to address the trade-off between semantic interoperability and real-time operational efficiency. The proposed Controlling Module uses a dual-layer communication strategy: a lightweight character-delimited TCP/IP protocol ensures deterministic performance for embedded controllers, while an XML-serialized format that maps to the FIPA Agent Communication Language preserves semantic interoperability. A custom serialization/deserialization algorithm was developed to process this XML structure within LabVIEW while avoiding the overhead typically associated with generic DOM/SAX parsers. The architecture was validated in a 120 h laboratory test that combined a Digital Twin simulation of 50 virtual feeders with Hardware-in-the-Loop testing of key sensing components. Under these test conditions, no communication failures were observed, all simulated network interruptions were recovered from, and the system operated with a modest resource footprint, including an average CPU use of 15% and a peak memory use of 350 MB. The platform also processed 2590 consumption events without reported data loss during the validation period. These results indicate that the proposed hybrid MAS architecture is a feasible solution for integrating interoperable decision support and deterministic edge control in PLF applications. Full article
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