Future-Proofing Dairy Farms: Hourly Heat Stress Predictions with Machine Learning

Climate change presents challenges across various facets of life, significantly impacting both human and animal welfare. In agriculture, livestock farming stands out as a sector highly vulnerable to environmental stressors. This vulnerability necessitates the effective assessment and management of climate impacts to ensure the sustainability of agricultural productivity and livelihoods. Dairy farming, a crucial segment of the livestock industry, is notably sensitive to climatic variations. In the United States alone, economic repercussions from heat stress on dairy cattle are estimated to range between $1.5 and $1.7 billion annually.

The susceptibility of dairy cattle to climate conditions is influenced by the nexus of interactions among environmental elements, especially temperature and humidity, and biological parameters. This is compounded by the fact that contemporary breeds have been extensively genetically selected with a focus on maximizing milk production. The Temperature Humidity Index (THI), a straightforward and non-invasive measure, has been developed to estimate the level of thermal stress exerted on cattle by the aggregate impact of temperature and humidity. Calculating THI requires readily accessible climatic data, such as air temperature and relative humidity. The correlation of THI with physiological parameters of cattle has been extensively validated in the scientific literature.

Traditionally, THI values have been estimated using data on a daily basis due to the logistical and computational challenges associated with handling large datasets required for more refined temporal resolutions and the coarse temporal resolution of data from conventional climate models. However, daily-level estimations fall short to accurately capture the dynamic nature of thermal loads within a day or the cumulative effects over successive days, especially when night-time conditions do not facilitate effective heat dissipation.

To overcome these limitations, our study adopts an innovative approach by employing the Extreme Gradient Boost (XGBoost) machine learning algorithm for temporal interpolation (downscaling) daily climate projections to hourly THI values. Utilizing the ERA5 reanalysis dataset for model training, which includes historical hourly data, we applied the model to generate hourly THI projections up to the century’s end. These projections, based on NASA NEX-GDDP-CMIP6 datasets, include twelve climate models and two Shared Socioeconomic Pathways (SSPs): SSP2-45 and SSP5-85, representing moderate and high-emissions scenarios, respectively.

Through our analysis, we identified regions poised to be significantly impacted by climate change, where the implementation of mitigation strategies is critical to safeguarding animal welfare and minimizing economic losses stemming from reduced production and quality deterioration. Our study emphasizes the importance of developing and applying effective measures to reduce the impact of climate change on dairy farming, which is essential for improving resilience and sustainability in agriculture worldwide.