Calf Birth Weight Predicted Remotely Using Automated in-Paddock Weighing Technology

Area of Science:

• Animal Science
• Reproductive Biology
• Agricultural Technology

Background:

• Accurate calf birth weight (CBW) prediction is crucial for livestock management.
• Traditional methods for monitoring dam and calf weight are labor-intensive.
• Automated weighing systems offer potential for remote data collection.

Purpose of the Study:

• To develop predictive models for calf birth weight (CBW) using remote liveweight (LW) data.
• To assess the utility of an automated in-paddock walk-over-weighing (WOW) scale for this purpose.
• To evaluate the relationship between dam's liveweight change at calving and CBW.

Main Methods:

• Utilized an automated walk-over-weighing (WOW) scale to collect individual cow liveweight (LW) data over five months.
• Manually recorded calf birth date and weight.
• Calculated dam's liveweight change at calving (ΔLWC) and non-foetal weight loss (NFW); employed Pearson's correlation and simple linear regression analysis.

Main Results:

• No significant correlations were found between ΔLWC and pre- or post-calving LW.
• Significant positive associations were observed between ΔLWC and CBW (R2 = 0.56) and NFW (R2 = 0.90).
• Calf birth weight explained 56% of the variation in dam's liveweight change at calving.

Conclusions:

• Dam's liveweight change at calving (ΔLWC) can serve as a reliable indicator of calf birth weight (CBW).
• Remote, in-paddock weighing systems provide timely and accurate LW data for breeding cows.
• This technology can enhance calving management and improve overall herd productivity.