Log in

Prediction of rectal temperature in Holstein heifers using infrared thermography, respiration frequency, and climatic variables

  • Original Paper
  • Published:
International Journal of Biometeorology Aims and scope Submit manuscript

Abstract

The objective of this study was to develop an equation to predict rectal temperature (RT) using body surface temperatures (BSTs), physiological and climatic variables in pubertal Holstein heifers in an arid region. Two hundred Holstein heifers were used from July to September during two consecutive summers (2019 and 2020). Respiratory frequency (RF) was used as a physiological variable and ambient temperature, relative humidity and temperature-humidity index as climatic variables. For the body surface temperatures, infrared thermography was used considering the following anatomical regions: shoulder, belly, rump, leg, neck, head, forehead, nose, loin, leg, vulva, eye, flank, and lateral area (right side). Initially, a Pearson correlation analysis examined the relationship among variables, and then multiple linear regression analysis was used to develop the prediction equation. Physiological parameters RT and RF were highly correlated with each other (r = 0.73; P˂0.0001), while all BST presented from low to moderate correlations with RT and RF. BST forehead temperature (FH) showed the highest (r = 0.58) correlation with RT. The equation RT = 35.55 + 0.033 (RF) + 0.030 (FH) + ei is considered the best regression equation model to predict RT in Holstein heifers in arid zones. This decision was made on the indicators R2 = 60%, RMSE = 0.25, and AIC = 0.25, which were considered adequate variability indicators.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

RT:

Rectal temperature

BST:

Body surface temperature

RF:

Respiration frequency

HS:

Heat stress

r :

Coefficient of correlation

R 2 :

Coefficient of determination

AIC:

Akaike information criteria

BT:

Body temperature

IRT:

Infrared temperature

NOM:

Norma Oficial Mexicana (Official Mexican Standards)

AT:

Ambient temperature

RH:

Relative humidity

THI:

Temperature-humidity index

FH:

Forehead

RMSE:

Root mean square error

CV:

Coefficient of variation

bpm:

Breaths per minute

MLRE:

Multiple linear regression equations

DW:

Durbin-Watson

DFFITS:

Difference in fits

DFBETAS:

Difference in betas

Q-Q:

Quantile–quantile

PRESS:

Predicted residual error sum of squares

References

Download references

Acknowledgements

The authors appreciate the help and permission for using the animals to Ing. Juan Carlos Reynoso (owner) and Rubén Fregoso (ranch manager), from Corrales San Carlos in Mexicali, B.C., México.

Funding

This research study was supported by the Cuerpo Académico Fisiología y Genética Animal, which belongs to the Instituto de Ciencias Agrícolas of the Universidad Autonóma de Baja California.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Leonel Avendaño-Reyes.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Theusme, C., Avendaño-Reyes, L., Macías-Cruz, U. et al. Prediction of rectal temperature in Holstein heifers using infrared thermography, respiration frequency, and climatic variables. Int J Biometeorol 66, 2489–2500 (2022). https://doi.org/10.1007/s00484-022-02377-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00484-022-02377-0

Keywords

Navigation