FUTURE DIRECTIONS AND RESEARCH GAPS

Although knowledge about delayed ovulation in cattle has made significant progress, several aspects still require further research to improve reproductive efficiency in the modern livestock industry [1]. To sharpen research focus, future studies should prioritize 2–3 key questions: (i) what physiological or management thresholds distinguish adaptive postpartum ovulatory delay from pathological delayed ovulation; (ii) which combinations of metabolic, inflammatory, and endocrine biomarkers provide the highest predictive accuracy for identifying cows at risk; and (iii) how the timing of interventions, nutritional, hormonal, or environmental, can be optimized to minimize unnecessary treatment while maximizing fertility outcomes.

One promising area of research is the development of predictive biomarkers [209]. Molecular and metabolic biomarkers, including inflammatory mediators, reproductive hormones, and energy metabolites such as NEFA and BHB, have the potential to predict the risk of delayed ovulation before clinical symptoms appear [83]. Future work should focus on validating standardized biomarker panels, establishing practical cutoff values, and assessing their predictive performance under different production systems, parity, physiological stages, and genetic backgrounds. By identifying a panel of sensitive and specific biomarkers, early detection of ovulatory disorders becomes possible, allowing for more timely management and hormonal therapy interventions [210].

Another key avenue is the integration of artificial intelligence (AI) and precision livestock farming (PLF) technologies. AI systems can analyze real-time data on cow behavior, locomotion, body temperature, and feeding patterns to predict estrus and ovulation with greater accuracy than conventional observation-based methods [211, 212]. PLF combines sensors, wearable devices, and AI-based data analysis to provide individualized monitoring of reproductive status [213]. This approach enables tailoring nutritional, hormonal, and environmental management to each cow, reducing estrus cycle variability and the incidence of delayed ovulation.

However, practical implementation challenges remain. High initial costs, infrastructure requirements, and data management complexity limit adoption in low-resource or smallholder farms. Future research should therefore emphasize simplified decision-support tools, low-cost sensor technologies, and management-based indicators that can be applied in extensive or resource-limited production systems [214].

Finally, welfare and ethical considerations must be integrated into future strategies. Excessive or routine hormonal manipulation may increase stress, disrupt endocrine balance, and raise societal concerns regarding sustainable livestock production. Research should evaluate long-term welfare outcomes and prioritize preventive strategies, such as optimized nutrition, environmental management, and early disease detection, before resorting to hormonal interventions [215].

While promising, further studies are needed to refine predictive algorithms, validate biomarkers across diverse management and genetic contexts, evaluate cost–benefit and welfare implications, and integrate these tools effectively into commercial-scale livestock systems [216].