ArticleVeterinary and animal science2026
The effects of heat stress on milk yield and composition in dairy cattle: meta-analysis study.
Article in Veterinary and animal science, 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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Abstract
Global climate change has positioned thermal stress as a critical challenge in dairy production. Heat stress is widely documented to involve alterations in both productive traits, and physio-pathological indicators. This meta-analysis was conducted to provide a quantitative synthesis of the observed variations in milk production parameters during thermal stress. Following PRISMA protocols and the PICOS framework, a systematic dataset was compiled from 35 independent studies published between 2010 and 2023. To address the inherent high heterogeneity (I² ≥ 90%) among studies, the Inverse Variance Heterogeneity (IVhet) model was employed for statistical pooling. Effect sizes were interpreted using Cohen's d, and potential sources of variance were explored through THI (Temperature-Humidity Index) based subgroup analyses and meta-regression. The pooled estimators indicate a significant negative association between heat stress and milk yield (d = -0.723). Noticeable reductions were also observed in milk composition parameters, specifically for milk fat (d = -0.633), milk protein (d = -0.555), and milk lactose (d = -0.404). Subgroup analysis indicated that these production declines become more pronounced when the THI exceeds a threshold of 72. However, due to substantial heterogeneity among studies, these pooled estimates should be interpreted as generalized global trends rather than precise universal thresholds. In conclusion, this meta-analysis confirms a consistent alignment between elevated thermal indices and qualitative as well as quantitative production losses. Rather than prescribing specific management interventions, these findings underscore the necessity for future research to account for cumulative exposure duration and localized farm microclimates to better predict production responses.
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