Body weight prediction using body size measurements in Fleckvieh, Holstein, and Brown Swiss dairy cows in lactation and dry periods
2018
Abstract. The objective of this study was to predict cows'
body weight from body size measurements and other animal data in the
lactation and dry periods. During the whole year 2014, 6306 cows (on
167 commercial Austrian dairy farms) were weighed at each routine performance
recording and body size measurements like heart girth (HG), belly girth (BG),
and body condition score (BCS) were recorded. Data on linear traits like hip
width (HW), stature, and body depth were collected three times a year. Cows
belonged to the genotypes Fleckvieh (and Red Holstein crosses), Holstein, and
Brown Swiss. Body measurements were tested as single predictors and in
multiple regressions according to their prediction accuracy and their
correlations with body weight. For validation, data sets were split randomly
into independent subsets for estimation and validation. Within the prediction
models with a single body measurement, heart girth influenced relationship
with body weight most, with a lowest root mean square error (RMSE) of
39.0 kg, followed by belly girth (39.3 kg) and hip width (49.9 kg). All
other body measurements and BCS resulted in a RMSE of higher than 50.0 kg.
The model with heart and belly girth (Model HG BG ) reduced RMSE to
32.5 kg, and adding HW reduced it further to
30.4 kg (Model HG BG HW ). As RMSE and the coefficient of
determination improved, genotype-specific regression coefficients for body
measurements were introduced in addition to the pooled ones. The most
accurate equations, Model HG BG and Model HG BG HW ,
were validated separately for the lactation and dry periods. Root mean square
prediction error (RMSPE) ranged between 36.5 and 37.0 kg
(Model HG BG HW , Model HG BG , lactation) and 39.9 and
41.3 kg (Model HG BG HW , Model HG BG , dry period).
Accuracy of the predictions was evaluated by decomposing the mean square
prediction error (MSPE) into error due to central tendency, error due to
regression, and error due to disturbance. On average, 99.6 % of the
variance between estimated and observed values was caused by disturbance,
meaning that predictions were valid and without systematic estimation error.
On the one hand, this indicates that the chosen traits sufficiently depicted
factors influencing body weight. On the other hand, the data set was very
heterogeneous and large. To ensure high prediction accuracy, it was necessary
to include body girth traits for body weight estimation.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
22
References
4
Citations
NaN
KQI