Evaluation of animal-based parameters of welfare in two Hungarian dairy farms using principal component analysis
DOI:
https://doi.org/10.17205/SZIE.AWETH.2020.2.177Keywords:
Holstein Friesian cow, HRV parameters, milk production, body conditionAbstract
The aim of this study was to give information about the characterization of parameters of heart rate variability (HRV) and some traditional parameters on two Holstein Friesian farms (farm 1, farm 2). Both were small-scale farms. The average daily milk yield, body condition of the animals, locomotion and temperament tests and cardiac activity measurements were made in autumn 2013. The physical activity of the animals was also recorded (lying, lying and ruminating, standing, standing and ruminating, feeding). Principal component analysis (PCA) was performed using the SPSS 20 (SPSS Inc., Chicago, IL) statistical software. Rotation of factors was done by the Oblimin method. The latent variables were calculated from the correlation matrix of the parameters. In the interpretation of PCs, only loadings greater than 0.6 were considered practically significant. The daily milk yield of cows was 25.5±4.41 kg and 23.5±8.29 kg on farm 1 and 2, respectively. Means for body condition score, temperament, heart rate, RMSSD, HF, LF/HF were recorded: farm 1: 3.1 score; 1.3 score; 73.42 beat/min; 14.19 ms; 23.52; 9.95 ratio; and farm 2: 2.7 score; 1.7 score; 74.05 beat/min; 18.75 ms; 28.53; 6.47 ratio respectively. Parameters recorded for the two farms were similar. On farm 1, the following four significant PCs (explain together over 80% of the total variance) were involved in the analysis: traits of HRV (1), body condition-temperament score (2), daily milk production (3), RMSDD (4). The first principal component (36.0% of total variance) can be interpreted as a linear combination of HF, HR and LF/HF examined parameters. On farm 2, the following four principal components (explain together 81.2% of the total variance) were defined: traits of HRV (1), body condition and daily milk production (2), temperament (3), RMSDD (4). The first principal component in the case of farm 2 corresponds to a group of three parameters: LF/HF, HF and HR. This principal component explained 29.5% of the all variance.
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