ReviewTropical animal health and production2026
Big data and artificial intelligence in animal nutrition: a new era of precision feeding.
Review in Tropical animal health and production, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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4 authors.
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Abstract
The convergence of Big Data and Artificial Intelligence (AI) is redefining animal nutrition by enabling precision feeding systems that are individualized, data-driven, and sustainability-oriented. This review synthesizes recent advances in multi-omics technologies, sensor-based monitoring, and machine learning applications across feed formulation, health surveillance, and production optimization. Precision feeding in pigs has been shown to reduce production costs by more than 8%, decrease protein and phosphorus intake by approximately 25%, lower nutrient excretion by up to 40%, and reduce greenhouse gas (GHGs) emissions by 6%, while maintaining or improving performance. In dairy systems, precision feed management strategies have achieved approximately 9.7% lower dietary crude protein levels, 14% reductions in manure nitrogen excretion, and annual net income gains of USD 137 per cow. AI-driven models have enhanced prediction of milk yield, feed conversion ratio (R² = 0.74), and residual feed intake (R² = 0.76), while enabling 96.26% accuracy in detecting microplastics in poultry feed. Integration of genomic, phenotypic, and sensor-derived datasets supports real-time monitoring, with wearable and IoT technologies transforming livestock management through continuous tracking of feeding behavior, emissions, and welfare indicators. Despite significant progress, current systems remain constrained by data heterogeneity, limited interoperability, and insufficient prescriptive decision-support frameworks. This article identifies methodological, technological, and adoption-related gaps, while highlighting future directions including nutrigenomics- and metagenomics-informed diet design, adaptive precision nutrition, and cost-effective solutions for smallholder systems. Collectively, these innovations establish Big Data and AI-enabled precision nutrition as a cornerstone of sustainable livestock production, advancing food security, climate resilience, and ethical animal management.
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Registered trials
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