Agriculture Ontology for Meta-analysis (AOM): Livestock Prototype

The Animal Ontology for Livestock Trials (AOM) provides a standardized, interoperable framework for the annotation, integration, and analysis of livestock feed trial data. AOM v2 represents a major expansion and refinement of the original ontology, designed to support more comprehensive metadata representation and cross-database interoperability. Compared to the initial release, AOM v2 incorporates data from over 600 livestock feed trial publications, significantly increasing the breadth and depth of experimental contexts, traits, and interventions represented. This expanded coverage ensures that AOM v2 reflects the current state of livestock nutrition research, especially in diverse agro-ecological systems.

A key advancement in AOM v2 is the systematic alignment of feed ingredient definitions with external reference resources. Every ingredient term in AOM v2 is mapped to an existing unique identifier in one or both of the following internationally recognized feed composition databases, where applicable:

• Feedipedia : an expert-curated online resource for animal feed information
• ILRI Sub-Saharan Africa Feed Composition Database (SSA Feeds) : a regionally focused nutrient composition reference

These mappings allow seamless integration with external datasets, improve consistency in ingredient interpretation, and support cross-study comparability. All feed ingredients in AOM v2 are assigned unique and persistent ontology codes. These codes correspond to matching entries in Feedipedia or the SSA Feeds database when available, facilitating unambiguous identification of ingredients across systems and analytical workflows.

Beyond feed composition, AOM v2 broadens its semantic coverage to include: Physiological stages of animals (e.g., lactation, growth phases), Farming system terminology reflecting production contexts Practice ontologies, including Grazing and pasture management, Pest and disease management and Other common livestock husbandry practices This enriched vocabulary supports detailed characterization of experiment protocols, farm management strategies, and biological conditions encountered in livestock nutrition and production studies. AOM v2 aims to include all essential terms needed to annotate, index, and analyze livestock feed trials with relevance to African research contexts and beyond. The ontology supports FAIR data principles by enabling interoperability between studies and databases, promoting reuse, and improving machine-actionable data linkage.

Methodology:Concepts were harvested from Evidence for Resilience Agriculture (v0.0.1beta) and organized where possible according to existing ontologies.