Rumen Microbiota-Based Machine Learning Approach for Predicting Heat Stress and Identifying Associated Microbes.

ABSTRACT: Heat stress poses a significant global challenge to sustainable livestock production, leading to detrimental impacts on animal production and welfare. Reduced appetite and increased body temperature further disrupt the gastrointestinal microbial ecosystem of heat-stressed animals, altering nutrient digestion and affecting host production. However, reported heat-stress-associated microbes have varied across studies, partly due to inconsistencies in microbiota analysis pipelines and taxonomic levels reported. In this study, to identify consistent rumen microbial taxa influenced by heat stress and evaluate potential of rumen microbiota in heat stress prediction, we collected publicly available raw 16S rRNA gene amplicon sequencing data of rumen fluid samples from lactating Holstein cattle hou

SUBMITTER: Joshi H 

PROVIDER: S-EPMC12769712 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature