Omics and precision livestock farming for heat stress resilience: integrating molecular biomarkers with real-time phenotyping for climate-smart livestock systems

Heat stress represents a major constraint to sustainable livestock production under accelerating climate change, compromising animal welfare, productivity, reproductive efficiency, and metabolic stability. Conventional mitigation strategies remain mainly reactive and are insufficient to address the multifactorial and dynamic nature of thermal stress. Recent advances in omics technologies and precision livestock farming (PLF) provide opportunities to transition toward predictive, resilience-oriented livestock systems. Multi-omics approaches (genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics) have explained interconnected regulatory networks underlying thermotolerance, through oxidative balance, immune modulation, metabolic reprogramming, and gut integrity. The PLF technologies enable continuous, real-time monitoring of physiological, behavioral, and environmental indicators, which includes core body temperature, rumination dynamics, respiration patterns, and heat-load indices. Despite these advances, a critical gap remains, omics studies are often disconnected from field-based phenotypes, while PLF systems lack mechanistic biological decision. Therefore, the integration of molecular and sensor-derived datasets represents a key frontier for advancing heat stress management. As such integration enables predictive modeling, early detection of thermal strain, and targeted intervention strategies, including precision nutrition, microbiome-informed supplementation, adaptive cooling systems, and resilience-oriented breeding. Nevertheless, several challenges limit large-scale implementation, which includes limited biomarker standardization, insufficient longitudinal field-scale datasets, and incomplete understanding of epigenetic and transgenerational mechanisms. This review synthesizes current advances in omics-driven characterization of heat stress responses and critically evaluates the translational potential of integrating omics with PLF. The authors propose that scalable, systems-level frameworks linking molecular biomarkers with real-time phenotyping are essential for enabling predictive, data-driven decision support, thereby enhancing productivity, safeguarding animal welfare, and improving sustainability under increasingly variable thermal environments.