ReviewVeterinary and animal science2026
Advancing climate-resilient livestock systems: Next-generation emission mitigation strategies and integrated technological innovations.
Review in Veterinary and animal science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Generative artificial intelligence in animal genomics for smart agriculture: Applications, challenges, and future prospects.Veterinary and animal science · 2026Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Livestock production significantly contributes to global greenhouse gas (GHG) emissions, particularly methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂), posing challenges to climate change mitigation and environmental sustainability. This review explores advanced, system-wide approaches to reduce emissions from livestock systems while enhancing productivity, resilience, and resource efficiency. It covers short-term mitigation strategies such as dietary interventions-including methane inhibitors, microbial modulators, and natural compounds-that target enteric fermentation. Long-term solutions involve genetic and breeding innovations, such as microbiome-genome interaction analyses, CRISPR-based editing, and low-methane phenotyping, supported by genomic selection and precision phenotyping tools. The review also assesses advanced manure management technologies like anaerobic digesters and nutrient recovery systems, and examines precision livestock farming tools, including real-time sensors, machine learning models, UAVs, and IoT-based monitoring systems. Emerging digital tools, blockchain, augmented reality, and AI-assisted diagnostics are highlighted for enhancing traceability and decision-making. The potential of integrated energy systems, such as microbial fuel cells, hydrogen electrolysis, algae-based bioenergy, and thermal gasification, is discussed alongside traditional renewables, enabling livestock farms to become clean energy hubs. Circularity is emphasized through silvopasture, algal bioremediation, insect bioconversion, and integrated crop-livestock systems. Environmental assessment tools and the socio-political dimensions of technology adoption, including policy, education, and farmer behavior, are also considered. Future research directions, such as atmospheric methane oxidation, 4D-printed feed additives, and quantum modeling, are proposed. Overall, the review calls for a transdisciplinary, integrated approach to transform livestock systems into climate-smart, low-emission food production networks.
Indexed as
Identifiers
What Socratic 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 Socratic graph.