Agreement analysis and predictive modeling of rectal temperature using thermographic surface temperatures in young and adult dairy goats

Abstract

Abstract: The objective of this study was to evaluate the use of thermographic imaging to estimate and predict rectal temperature in dairy goats. A total of 98 goats, comprising 45 young and 53 adults, were used. Rectal temperature was measured using a digital thermometer, and a thermal image was captured immediately afterward. The anatomical regions evaluated in the thermographic images were the eye, subocular region, hindquarters, shoulder, and head. Data analysis included Pearson correlation and Bland–Altman agreement analyses. For prediction, simple linear regression and decision-tree models were applied. Low correlations (<0.40) were observed between rectal temperature and the surface temperatures of the eye, subocular region, hindquarters, shoulder, and head regions in both young and adult goats. In the agreement analysis, the subocular region showed the lowest bias, with values of 5.57°C and 4.89°C for young and adult goats, respectively. For prediction, the decision-tree models outperformed the simple linear regression models, achieving R² values of 0.1441 and a mean absolute error of 0.2415 for young goats, and an R² of 0.38 and a mean absolute error of 0.2513 for adult goats. It was concluded that thermal imaging can be used to estimate and predict rectal temperature following correction for systematic bias.
Keywords: dairy goats, infrared thermography, precision livestock farming, animal welfare, body temperature.