Consistency of measurements from an automatic body condition scoring camera C. Hallén Sandgren 1 and U. Emanuelson 2 1 DeLaval International AB, P.O. Box 39, SE-147 21 Tumba, Sweden 2 Dep’t Clinical Sciences, Swedish University of Agricultural Sciences, P.O.Box 7054, SE-750 07 Uppsala, Sweden
[email protected] Abstract The aim of this study was to determine repeatability of body condition score (BCS 1-5) recorded in a technology based on a continuously running 3D camera, linked to a RFID system (DeLaval BCS camera). The study was performed on data over 11 – 25 months from 4 herds in 3 countries. The breed composition of the herds was two with pure Holstein Friesian (HF), one with Simmental and one with 75 % Norwegian Red and 25 % HF crosses. The average daily milk yield varied between 27.1 and 29.6 kg. The monitoring period had a median number BCS scores per day per herd ranging from 57 to 548. An analysis of intraclass correlation (ICC) during week 1-8 (excluding day of calving) between repeated measurements of BCS was conducted with a linear mixed model, where herd and cow within herd were the only variables and included as random effects. Estimated variance components were used to calculate the ICC. A total of 8045 scores were retrieved from the herds and used in the calculations. LS-means for BCS with farm as fixed effect adjusting for season and lactation number in week 1 and week 8 were 3.3/3.0, 3.8/3.5, 3.7/3.4 and 3.2/2.8 respectively in the four herds. The ICC was 0.86 and 0.94 respectively for week 1-4 and 5-8. About 42% of the total variation was explained by cow and 43 % by herd. BCS patterns between herds and lactation numbers during the first 8 weeks confirm earlier findings from literature and the reliability and robustness of the tested technology. The technology is reported to be useful to follow up on dry cow feeding and management, selection of inefficient cows for possible culling and monitoring of finishing of cows for slaughter. Keywords: automatic, body condition scoring, camera Introduction Cows but also herds with unfavorable body condition scores (BCS) over the lactation cycle will display reduced milk production, decreased feed efficiency, impaired reproduction performance and an increased number of post partum diseases with significant negative impact on farm profitability (Roche et al., 2009). To mention only one example; milk yield is greatly influenced by the body condition score, and difference in milk yield between a too lean cow compared to one in an optimal body condition score could be as high as 545 kg during the first 120 days of lactation (Domecq et al., 1997). Although it has been evident for centuries that cows lose and gain condition during the lactation cycle, there was no simple measure of a cow’s stored energy reserves until the 1970s (Stockdale, 2001). Body weight (BW) alone is not a good indicator of body reserves, (Enevoldsen and Kristensen, 1997). Recent work (Thorup et al., 2012) indicates that providing very frequent BW measurements may help overcome some of these issues, but for accurate assessment of adipose and lean tissue, measurements of body condition are needed.
With this as a background, body condition scoring has become the most accepted and used method for assessment of the nutritional status of dairy cows (Roche et al., 2009). However, despite the huge potential in an improved profitability as a result of close management of body condition scores, few farmers score their cows frequently enough, if at all. This is mainly due to lack of either specific knowledge or time, or alternatively hesitance to spend money on a professional scorer. Further on, the subjective nature of manual body condition may result in a too low precision of the method (Ferguson et al., 1997, Kristensen et al., 2006). The purpose of this paper is to describe the recently launched body condition scoring system (DeLaval BCS camera, Tumba Sweden), which overcomes the hurdles hampering a widespread implementation and systematic use of body condition scoring for optimizing herd performance, and to determine the repeatability of the scores during the early part of the lactation. Materials and Methods Description of the BCS recording system The technology is based on a 3D camera, linked to an RFID system which is continuously recording, from above, the rear part of the back from the short ribs to the tail end. Anytime a cow passes under the camera, the system recognizes the movement and selects the best image of the cow in the video sequence. The 3D camera uses light coding technology, which works by projecting a pattern of IR dots on the back of the cow. In a following step the distance between these dots are measured to create an accurate 3D image of the back and an algorithm converting that image information into a body condition score is applied. Depending on the camera location, cows are scored 2 - 10 times per day. As golden standard the scale used to develop algorithms was visual scoring based on the 1 to 5 scale as described in (Elanco Animal Health, 2009) supplemented with assessing the spinous to transverse processes (Edmonson et al 1989) where 1 corresponds to the lowest and 5 to the highest condition score. The system provides daily 7-days rolling average scores, for all cows with at least one, by the system, approved image (the 20 % highest and lowest values per cow removed). Currently there are algorithms available for scoring Holsteins including similar breeds, Simmental and Norwegian Red. The individual BCS scores, comprising 7-day average scores, has been shown to have an average standard deviation (STD) of 0.04, with STD’s ranging from 0.0 to 0.12, and with 99% of the 7-day scores having a STD of the individual scores of ≤0.1 (Forsén, personal information). The accuracy of the 3D camera has been verified showing that the BCS camera scores 95% of the cows within a ± 0.25 interval compared to BCS assessment by a professional scorer (Mazeris, 2015). Herds and data Individual cow data on lactation number, daily milk yield and BCS, and dates of calving and BCS recording, was retrieved from the software program Delpro from 4 herds from 3 countries (2 DE, 1 NL, 1 NO). The breed composition of the herds was two with pure Holstein Friesian (HF), one with Simmental and one with 75 % Norwegian Red and 25 % HF crosses. The monitoring period ranged from between January 2014 to April 2015 until February 2016, with a median number BCS scores per day per herd ranging from 57 to 548.
Statistical methods In order to avoid autocorrelation only one 7-day rolling average BCS per week per animal was used in the statistical analyses. Observations on calving day (days in milk at BCS recording = 0) were excluded and only observations during week 1-8 after calving were included. A linear mixed model, with the random effects of herd and cow within herd included as the only explanatory variables, was used to study the repeatability of BCS. The estimated variance components were used to calculate the intra-class correlation (ICC), as a measure of the repeatability of the observations. The ICC was calculated as:
In addition, a linear mixed model, with the effect of cow included as a random variable, and with herd, week in milk (1-8), lactation number (1, 2, 3+) and season of calving (AprilSeptember vs October-March) as fixed explanatory variables, was used to study the patterns of BCS. The same model was used to study patterns in daily milk yield (kg). Results and Discussion Consistency of measurements Altogether 8045 weekly scorings from the BCS camera, in lactation weeks 1-8 were retrieved from the 4 herds as presented in Table 1. The estimated variance components are presented in Table 2 and shows that, for the whole period, 42% of the variation was explained by differences between individual cows and 43% variation between herds. The ICC was generally high, showing a high repeatability of BCS observations within cow, but the ICC was slightly higher in week 5-8 (94%) as compared with week 1-4 (86%). The ICC increased further in the following 4 week period (data not shown). Table 1. Number of observations per herd and lactation week Herd Week A B C D 441 73 186 1 189 546 79 185 2 201 576 78 177 3 195 577 75 170 4 201 577 70 188 5 199 578 72 175 6 205 571 71 176 7 200 565 69 177 8 203 4431 587 1593 1434 Total Table 2. Variance components and intra class correlations (ICC) for body condition scores in lactation week 1-8 Week 1-8 Week 1-4 Week 5-8 Cow 0.089 0.081 0.102 Herd 0.091 0.080 0.099 Residual 0.032 0.026 0.014 ICC 0.85 0.86 0.94
Patterns for BCS and Milk yield
Least-square means from the linear mixed model showed different BCS patterns in the study herds. Mean BCS in the study herds at the first week after calving varied between 3.2 and 3.8 with the highest in the NRF and Simmental herds and lowest in the HF-herds. In herd B nadir BCS was reached in week 7 whereas steady state during the study period was never reached in the remaining three herds. Moreover, the drop in BCS from week 1 to 8 was 0.41 in herd D as compared to 0.25 in herds B (Figure 1 a). The BSC loss during the study period was 0.39 highest for cows in lactation ≥3 and 0.28 for first and second lactation cows that showed almost identical BCS loss. Cows in second lactation started the lactation with a lower BSC compared to cows in 1st and ≥3rd lactation (Figure 1 b).
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Figure 1 Least-square (LS) means for the effects of the interaction between herd and lactation week (a) and between lactation number and lactation week (b) on body condition score. Moreover, the linear mixed model was also used to study patterns in daily milk yield (kg). Herd D showed a yield curve for the first 8 weeks clearly distinguished from the remaining 3 herds reaching peak yield in week 4 and then starting to decline in week 5 (Figure 2). Difference in milk yield between lactations was 9.1 and 11.0 kg higher per day for 2nd and ≥3rd lactation cows respectively compared to 1st lactation. Hence, the difference in daily milk
yield between lactation 2 and 3 was only 1.9 kg. Lactation curves from cows in lactation 2 showed the weakest persistency starting to decline already in week 8 (data not shown). 45
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Figure 2 Least-square (LS) means for the effects of the interaction between herd and lactation week milk yield We chose to study lactation week 1-8 as this is the time period in the lactation cycle when the body condition score is known to change rapidly and hence the results would be indicative of the validity of the output from the BCS camera. And indeed, we can conclude the technology provides consistent and relevant BCS results also during this period, i.e. a high intra class correlation. The somewhat lower intra class correlation during week 1-4, as compared with 58, is likely to be related to the rapid change in BCS during this period. Other indications of the validity of the scoring system was the clear difference in BCS level and pattern between herds consisting of pure HF cows, A and D, as compared with herd B and C with HF/NRF crossings and Simmental, respectively. The BCS pattern in different lactations compared with milk yield confirms the result from others (Roche et al. 2009) such as the BCS in second lactation cows was lower at the beginning of lactation and 1st lactation cows starting at a high BCS and showing lower loss. The combined evaluation of BCS and milk yield in Herd A indicates a general feeding problem in that herd, which also was confirmed Based on the results and the experience from the farmers and feed advisors using the technology it reported to be very useful in order to make adjustments of dry cow feeding and management. Moreover, BCS camera has been used with great success to select inefficient cows for possible culling, typically cows building up BCS and not responding to feed 60 – 90 days in milk. Also, larger farms have used the technology for efficient monitoring of finishing of cows for slaughter. The technology is still young and it is obvious that an extended number of applications will develop over time. Conclusions It is clear that the automatic body condition scoring camera tested in this study will be a valuable tool to support evaluations of feed efficiency and health.
Based on the obtained ICC values it can be concluded that the measurements conducted with the DeLaval BCS camera show a very high consistency within cow and herd during the most dynamic period of the lactation cycle with respect to BCS and daily milk yield. Also, the presented BCS patterns are in line with earlier reported findings from literature which confirm that the technology provide relevant and robust BCS values. References Domecq, J.J., Skidmore, A.L., Lloyd, J.W., Kaneene, J.B. (1997). Relationship Between Body Condition Scores and Milk Yield in a Large Dairy Herd of High Yielding Holstein Cows Journal of Dairy Science 80:101-112. Edmonson, J., Lean I.J., Weawer, L.D., Farver, T., Webster, G. (1989). A Body Condition Scoring Chart for Holstein Dairy Cows. Journal of Dairy Science 72:68-78. Elanco Animal Health. (2009). The 5-point body condition scoring system. Enevoldsen, C., Kristensen, T. (1997). Estimation of body weight from body size measurements and body condition scores in dairy cows. Journal of Dairy Science 80:1988– 1995. Kristensen, E., Dueholm L., Vink, D., Andersen, J.E., Jakobsen, E.B., Illum-Nielsen, S., Petersen, F.A., Enevoldsen, C. (2006). Within and across-person uniformity of body condition scoring in Danish Holstein cattle. Journal of Dairy Science 89:3721–3728. Mazeris, F. (2015). DeLaval body condition scoring BCS: daily, automatic & consistent scoring of cows. Precision Dairy Conference, June 24-25, 2015. Rochester, MN, USA pp. 47-50. Roche, J.R., Friggens, N.C., Kay, J.K. ,Fisher, M.W., Stafford, K.J., Berry, D.P. (2009). Invited review: Body condition score and its association with dairy cow productivity, health, and welfare. Journal of Dairy Science 92:5769–5801. Stockdale, C.R. (2001). Body condition at calving and the performance of dairy cows in early lactation under Australian conditions: A review. Australian Journal of Experimental Agriculture 41:823–829. Thorup, V.M., Edwards, D., Friggens, N.C. (2012). On-farm estimation of energy balance in dairy cows using only frequent body weight measurements and body condition score. Journal of Dairy Science 95:1784–93.
