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<p>Measuring individual cows’ response to heat stress at large-scale is challenging because physiological traits are not recorded routinely, and production traits are unspecific and require environmental data for interpretation. Milk mid-infrared (MIR) spectra, already recorded in routine, offer a potential alternative, as heat stress affects milk composition and is therefore expected to be reflected in MIR spectra. This study thus aimed to develop a MIR prediction equation for individual heat stress response. Surface temperature and milk traits from 399 cows were recorded to develop a combined heat stress response phenotype. This phenotype resulted from two equations: one predicting surface body temperature (R<sup>2</sup> = 0.67; RMSE = 0.64&#xa0;°C) and one classifying records into three heat stress response classes based on surface temperature and milk composition (accuracy = 61%). The final prediction was applied to historical milk recording data associated with weather information to assess external validity. A mixed model was also fitted to identify cow characteristics associated with stronger predicted heat stress responses. As reported in the literature, multiparous cows, in early lactation, with the highest 24&#xa0;h milk yield tended to be more affected. Overall, the prediction developed in this study shows strong potential for routine heat stress detection.</p>

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Prediction of heat stress response in dairy cows using milk mid-infrared spectra

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月19日

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  • Pauline Lemal,
  • Clément Grelet,
  • Frédéric Dehareng,
  • Hélène Soyeurt,
  • Martine Schroyen,
  • Nicolas Gengler

摘要

Measuring individual cows’ response to heat stress at large-scale is challenging because physiological traits are not recorded routinely, and production traits are unspecific and require environmental data for interpretation. Milk mid-infrared (MIR) spectra, already recorded in routine, offer a potential alternative, as heat stress affects milk composition and is therefore expected to be reflected in MIR spectra. This study thus aimed to develop a MIR prediction equation for individual heat stress response. Surface temperature and milk traits from 399 cows were recorded to develop a combined heat stress response phenotype. This phenotype resulted from two equations: one predicting surface body temperature (R2 = 0.67; RMSE = 0.64 °C) and one classifying records into three heat stress response classes based on surface temperature and milk composition (accuracy = 61%). The final prediction was applied to historical milk recording data associated with weather information to assess external validity. A mixed model was also fitted to identify cow characteristics associated with stronger predicted heat stress responses. As reported in the literature, multiparous cows, in early lactation, with the highest 24 h milk yield tended to be more affected. Overall, the prediction developed in this study shows strong potential for routine heat stress detection.