Developing and validating a model to predict the dry matter intake of grazing lactating beef cows

Area of Science:

• Animal Science
• Agricultural Engineering
• Nutritional Physiology

Background:

• Current methods for measuring dry matter intake (DMI) in grazing lactating beef cows are impractical for commercial settings due to being invasive, time-consuming, and expensive.
• Accurate DMI estimation is crucial for evaluating feed efficiency in beef cattle production.

Purpose of the Study:

• To develop and validate a non-invasive model for predicting the DMI of grazing lactating beef cows.
• To enable practical application on commercial farms by utilizing easily obtainable animal measurements.

Main Methods:

• A calibration dataset of 94 measurements from 106 beef or beef-dairy crossbred cows was used.
• Multivariable and backwards linear regression analyses (SAS PROC REG) were employed, incorporating body measurements, type scores, grazing behavior, and thermal imaging.
• The model included variables such as milk yield, body weight (BW), parity, calving day, maternal origin, width at pins, full body depth, ruminating mastications, central ligament, and rump width score.

Main Results:

• A multivariable regression model was constructed, identifying 32 variables associated with DMI (P < 0.25).
• Five specific body and behavior variables, along with milk yield, BW, parity, calving day, and maternal origin, were retained in the final model (P < 0.05).
• The developed model increased DMI predictability by 0.23 (R² = 0.68) in the calibration dataset and achieved an R² of 0.59 in an independent validation dataset.

Conclusions:

• The study successfully developed and validated a non-invasive model to predict DMI in grazing lactating beef cows.
• This model offers a more accessible and less invasive tool for estimating DMI in large populations of commercial beef cows, aiding in feed efficiency assessments.
• While acknowledging applicability challenges, the findings support the use of this model for practical on-farm applications.