# An Integrated Approach Using Temperature–Humidity Index, Productivity, and Welfare Indicators for Herd-Level Heat Stress Assessment in Dairy Cows

## TL;DR

This paper introduces a new method to assess heat stress in dairy cows by combining environmental data with productivity and health indicators.

## Contribution

A five-step analytical framework integrating THI with productivity and welfare indicators for herd-level heat stress assessment.

## Key findings

- Maximum daily THI is strongly associated with milk composition and dry matter intake.
- Cumulative heat load and elevated night-time THI increase mastitis and lameness.
- Including welfare indicators improves the accuracy of heat stress models.

## Abstract

Dairy cows are highly sensitive to heat stress, particularly in naturally ventilated barns where indoor conditions depend on the external climate. While the temperature–humidity index (THI) is widely used to estimate thermal load, traditional approaches often rely on average or maximum values alone, overlooking daily patterns and delayed physiological responses. This study presents a five-step analytical approach that combines THI data with herd-level records on milk production, feed intake, mastitis, and lameness to evaluate the overall impact of heat stress. By integrating environmental and clinical indicators, the method improves the detection of heat-related risks and provides a more accurate assessment of cumulative effects on productivity and welfare. This approach supports practical decision-making in dairy systems and may serve as a foundation for predictive models and real-time monitoring strategies.

The temperature–humidity index (THI) remains one of the most widely used tools for assessing heat stress in dairy farming; however, its application is often limited by methodological inconsistencies and insufficient integration with welfare indicators. This study proposes a unified analytical framework for evaluating thermal load at the herd level by combining daily THI values with productivity, feed intake, and clinical indicators such as mastitis and lameness. The analysis was based on two years of herd-level data from a commercial dairy farm with naturally ventilated barns. General linear models (GLM) were applied to assess both direct and delayed effects of heat stress and to compare model reproducibility across years. The results confirmed that maximum daily THI had the strongest association with milk composition and dry matter intake, while cumulative heat load and elevated night-time THI contributed to increased mastitis and lameness incidence. The inclusion of welfare indicators substantially improved the explanatory power of THI-based models, providing a more biologically relevant assessment of heat stress. The proposed framework enhances the accuracy of herd-level monitoring and supports the development of predictive models for welfare-oriented management in dairy systems.