Repeatability of gaseous measurements across consecutive days in sheep using portable accumulation chambers.

Portable accumulation chambers (PACs) enable gaseous emissions from small ruminants to be measured over a 50-min period; to date, however, the repeatability of consecutive days of measurement in the PAC has not been investigated. The objectives of this study were 1) to investigate the repeatability of consecutive days of gaseous measurements in the PAC, 2) to determine the number of days required to achieve precise gaseous measurements, and 3) to develop a prediction equation for gaseous emissions in sheep. A total of 48 ewe lambs (c. 10 to 11 mo of age) were randomly divided into four measurement groups each day, for 17 consecutive days. Gaseous measurements were conducted between 0800 and 1200 hours daily. Animals were removed from perennial ryegrass silage for at least 1 h before measurements in the PAC, and animals were assigned randomly to each of the 12 chambers. Methane (CH4; ppm) concentration, oxygen (O2; %), and carbon dioxide (CO2; %) were measured at three time points (0, 25, and 50 min after entry of the first animal into the first chamber). To quantify the effect of animal and day variation on gaseous emissions, between-animal, between-day, and error variances were calculated for each gaseous measurement using a linear mixed model. The number of days required to gain a certain precision (defined as the 95% confidence interval range) for each gaseous measurement was also calculated. For all three gases, the between-day variance (39% to 40%) accounted for a larger proportion of total variance compared with between-animal variance, while the repeatability of 17 consecutive days of measurement was 0.36, 0.31, and 0.23 for CH4, CO2, and O2, respectively. Correlations between consecutive days of measurement were strong for all three gases; the strongest correlation between day 1 and the remaining days for CH4, CO2, and O2 was 0.71 (days 1 and 6), 0.77 (days 1 and 2), and 0.83 (days 1 and 5), respectively. A high level of precision was achieved when gaseous measurements from PAC were taken over three consecutive days. The prediction equation overestimated gaseous production for all three gases: the correlations between actual and predicted gaseous output ranged from 0.67 to 0.71, with the r2 ranging from 0.45 to 0.71. The results from this study will aid the refinement of the protocol for the measurement of gaseous emissions in sheep using the PAC.

Grazing Cow Behavior's Association with Mild and Moderate Lameness.

Accelerometer-based mobility scoring has focused on cow behaviors such as lying and walking. Accuracy levels as high as 91% have been previously reported. However, there has been limited replication of results. Here, measures previously identified as indicative of mobility, such as lying bouts and walking time, were examined. On a research farm and a commercial farm, 63 grazing cows' behavior was monitored in four trials (16, 16, 16, and 15 cows) using leg-worn accelerometers. Seventeen good mobility (score 0), 23 imperfect mobility (score 1), and 22 mildly impaired mobility (score 2) cows were monitored. Only modest associations with activity, standing, and lying events were found. Thus, behavior monitoring appears to be insufficient to discern mildly and moderately impaired mobility of grazing cows.

Infrared thermography as a tool to detect hoof lesions in sheep.

Lameness has a major negative impact on sheep production. The objective of this study was to 1) quantify the repeatability of sheep hoof temperatures estimated using infrared thermography (IRT); 2) determine the relationship between ambient temperature, sheep hoof temperature, and sheep hoof health status; and 3) validate the use of IRT to detect infection in sheep hooves. Three experiments (a repeatability, exploratory, and validation experiment) were conducted over 10 distinct nonconsecutive days. In the repeatability experiment, 30 replicate thermal images were captured from each of the front and back hooves of nine ewes on a single day. In the exploratory experiment, hoof lesion scores, locomotion scores, and hoof thermal images were recorded every day from the same cohort of 18 healthy ewes in addition to a group of lame ewes, which ranged from one to nine ewes on each day. Hoof lesion and locomotion scores were blindly recorded by three independent operators. In the validation experiment, all of the same procedures from the exploratory experiment were applied to a new cohort of 40 ewes across 2 d. The maximum and average temperature of each hoof was extracted from the thermal images. Repeatability of IRT measurements was assessed by partitioning the variance because of ewe and error using mixed models. The relationship between ambient temperature, hoof temperature, and hoof health status was quantified using mixed models. The percentage of hooves correctly classified as healthy (i.e., specificity) and infected (i.e., sensitivity) was calculated for a range of temperature thresholds. Results showed that a small-to-moderate proportion of the IRT-estimated temperature variability in a given hoof was due to error (1.6% to 20.7%). A large temperature difference (8.5 °C) between healthy and infected hooves was also detected. The maximum temperature of infected hooves was unaffected by ambient temperature (P > 0.05), whereas the temperature of healthy hooves was associated with ambient temperature. The best sensitivity (92%) and specificity (91%) results in the exploratory experiment were observed when infected hooves were defined as having a maximum hoof temperature ≥9 °C above the average of the five coldest hooves in the flock on that day. When the same threshold was applied to the validation dataset, a sensitivity of 77% and specificity of 78% was achieved, indicating that IRT could have the potential to detect infection in sheep hooves.

Investigation of the relationship between udder quarter somatic cell count and udder skin surface temperature of dairy cows measured by infrared thermography.

The objective of the present study was to quantify the relationship between udder skin surface temperature (USST) and somatic cell count (SCC) in lactating dairy cows. Data were recorded on the same 14 Holstein-Friesian cows, at evening (15:00 to 16:00) milking every day over a 2-mo period. Surface temperature measurements of all udders were extracted from thermal images. After imaging, milk was extracted from each quarter and analyzed for SCC. Environmental and cow-related factors (i.e., ambient temperature, humidity, rainfall, wind speed, distance walked to the parlor, number of days since the udder was shaved, parity, and stage of lactation) were recorded on each day of the experiment. A large array of descriptive temperature parameters (DTP) were extracted from every udder image including temperature-based (e.g., maximum, average and minimum USST), pixel count-based, and textural-based DTPs. Several different analytical methods were tested in an attempt to relate any given DTP to SCC; this included investigating the relationship between USST and the log transform of SCC (i.e., somatic cell score; SCS). The temperature range within each udder was also compared with the natural log of the range in SCC of the respective quarters. In a separate analysis, the temperature difference between each DTP and its respective daily baseline (i.e., average of the 5 lowest values of that DTP across the herd) was compared with SCS. Finally, the association between environmental and cow-related factors with each DTP was investigated to create prediction models for each DTP, the residuals of which were compared with SCC. Results from the present study indicate that the correlation between any DTP and SCS was weak (range of -0.16 to 0.19) and so could not be used to identify quarters with high SCC. Although some alternative measures had a significant relationship with SCS, again, the correlation was too weak for practical use on its own. Maximum and average USST could be predicted with a root mean square error of 0.23 and 0.35 °C, respectively, although the residuals from the prediction model could not be used to identify animals with high SCC. This suggests that infrared thermography alone could not be used as a real-time automated tool to detect high SCC for dairy cows in a pasture-based system.