ReviewJournal of animal science2025
ASAS-NANP Symposium: mathematical modeling in animal nutrition: harnessing real-time data and digital twins for precision livestock farming.
Review in Journal of animal science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Review
- Magnetometry for Agriculture and Animal Systems: From Classical Sensors to Quantum-Enabled Biosensing.Biosensors · 2026Review
- ASAS-NANP symposium: mathematical modeling in animal nutrition: training the future generation in data and predictive analytics for sustainable development. A summary of the 2024 symposium.Journal of animal science · 2026Article
- Video-based cattle behaviour detection for digital twin development in precision dairy systems.npj veterinary sciences · 2026Article
- Animal digital twins: systems architecture for climate-smart protein production.npj veterinary sciences · 2026Review
- From machine learning to digital twin integration for livestock production and research.Frontiers in veterinary science · 2026Review
- ASAS-NANP SYMPOSIUM: Mathematical Modeling in Animal Nutrition: Training the Future Generation in Data and Predictive Analytics for Sustainable Development. A Summary of the 2023 Symposium.Journal of animal science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing population and urbanization have intensified livestock production, raising concerns about sustainability, animal welfare, and disease transmission. There is a need to improve the sustainability of animal production. Precision Livestock Farming (PLF) emerges as a promising solution, utilizing sensors, Internet of Things, and data analytics to enhance animal management. Digital twins create virtual replicas of physical entities such as animals, buildings, and overall farm operations. This paper highlights the transformative potential of PLF and digital twin technology. The integration of real-time data with digital twins represents a frontier of technological innovation with the potential to transform PLF. The future of PLF looks promising with advancements in artificial intelligence, machine learning, blockchain, and augmented reality, which are expected to drive further innovations and enhance the capabilities of digital twins. In this review, we provide a brief overview of the current state-of-the-art developments in the field of precision livestock agriculture and highlight the potential for digital twin technologies to transform the agricultural sector.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.