Objective 1. Can pedometers monitor cow activity in a tie-stall facility?
To determine if pedometers can accurately monitor cow activity, pedometer records were compared to 24 hour video surveillance records. A correlation analysis was completed comparing pedometer lying time and lying bouts to video lying time and lying bouts. Results: Lying time R= 0.97, P =<0.0001; Lying bouts R=0.94, P =<0.001 (Figures 1&2).
The results from the correlation test indicate that pedometer and video records are highly correlated; therefore supporting the first hypothesis that pedometers can accurately monitor cow activity in a tie-stall barn.
The results from the correlation test indicate that pedometer and video records are highly correlated; therefore supporting the first hypothesis that pedometers can accurately monitor cow activity in a tie-stall barn.
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Figure 1.
Figure 1. Correlation between video and pedometer lying time.
R = 0.97 P<0.0001
R = 0.97 P<0.0001
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Figure 2.
Figure 2. Correlation between video and pedometer lying bouts.
R = 0.94 P<0.0001
R = 0.94 P<0.0001
Objective 2. Can pedometers predict estrus in tie-stall dairy cows?
Figure 3.
Figure 3 shows the distribution of steps per day. This boxplot displays that steps per day were not normally distributed.
Figure 4.
Figure 4 displays the log 10 transformed data for steps per day using a as.factor(DAYS) formula, as the treatment effects are between days.
Figure 5.
Figure 5 displays is the distribution of lying times per day. This shows that the distribution of steps per day is not normal.
Figure 6.
Figure 6 displays the transformed distribution of lying time per day. The data was transformed using as.factor(DAYS) and (sqrt(0-LYINGTIME+900)).
Figure 7.
Figure 7 displays the distribution of lying bouts per day. The residuals when tested were normally distributed, therefore no transformations were required.
Figure 8.
Figure 8 is the distribution of progesterone concentrations per day after the PGF treatment (Days 0-3). This data is not normally distributed.
Figure 9.
Figure 9 displays the transformed distribution of progesterone concentrations per day after PGF treatment (Days 0-3). The data was transformed using as.factor(DAYS) and log10 equations.