Recognizing Lame Cows Early
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Dr. Proudfoot joins us to discuss early identification of lameness in dairy cattle. Learn about how the prevalence of lameness is often underestimated, how you can improve detection, and some automated tools to aid in early detection that are currently in development. See the full presentation on YouTube at www.youtube.com/watch?v=Ho6wh-Ns6YM
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Recognizing Lame Cows Early
- 1. Recognizing Lame Cows Early Katy Proudfoot, MSc, PhD The Ohio State University College of Veterinary Medicine
- 2. Lameness in dairy cattle • Lameness considered the most important welfare concern • >1,500 articles on lameness in dairy cows since 1990 • Lameness cases are not decreasing 0 20 40 60 80 100 120 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 Numberofjournalarticles Ventura et al., 2015
- 3. What’s the problem? • The research is not getting to the producers? • The research is not practical or applied enough? • Producers don’t see lameness as a problem? • Other problems take more priority (mastitis)? Leach et al., 2010
- 4. What’s the problem? • The research is not getting to the producers? • The research is not practical or applied enough? • Producers don’t see lameness as a problem? • Other problems take more priority (mastitis)? Leach et al., 2010
- 5. How many cows are lame? Producer = 8% lameness Researcher = 25% lameness Espejo et al., 2006
- 6. How many cows are lame? Producer = 7% lameness Researcher = 36% lameness Leach et al., 2010
- 7. How many cows are lame? Producer = 9% lameness Researcher = 22% lameness Fabian et al., 2014
- 8. Why the differences? • How they pick out lame cows (e.g., different definitions) • How they are trained • Where they are looking for the lame cows • Which and how many cows they are looking at
- 9. Why the differences? • How they pick out lame cows (e.g., different definitions) • How they are trained • Where they are looking for the lame cows • Which and how many cows they are looking at
- 10. How do we pick out lame cows? 1. Look for obvious signs of limping
- 11. How do we pick out lame cows? 1. Look for obvious signs of limping 2. Locomotion scoring, more subtle signs Score Category Description 1 Normal Normal gait. Level back posture while walking. 2 Mildly lame Normal gait. Level back posture while standing, but back arched while walking. 3 Moderately lame Gait affected, short striding. Back arched while standing and walking. 4 Lame Back always arched. Only one deliberated step at a time, one or more limbs favored. 5 Severely lame Extreme reluctance to bear weight one or more limbs. Sprecher et al., 1997
- 12. How do we pick out lame cows? 1. Look for obvious signs of limping 2. Locomotion scoring, more subtle signs 3. Automated methods (pedometers, robotic milkers, etc.)
- 14. Locomotion scoring • Scoring systems that categorize cows based on the level of severity of lameness • 3, 4 and 5 point scoring systems • Requires training to learn the scoring system
- 15. HEAD BOB IMPROPER WEIGHT BEARING (‘LIMPING’) ASYMMETRIC STEPS JOINT FLEXION SWINGING IN/ OUT BACK ARCH TRACKING UP WALKING SPEED
- 16. Example 1
- 17. 5-point Scoring System Score Category Description 1 Normal Normal gait. Level back posture while walking. 2 Mildly lame Normal gait. Level back posture while standing, but back arched while walking. 3 Moderately lame Gait affected, short striding. Back arched while standing and walking. 4 Lame Back always arched. Only one deliberated step at a time, one or more limbs favored. 5 Severely lame Extreme reluctance to bear weight one or more limbs. Sprecher et al., 1997
- 18. 3-point Scoring System Score Description 1 Sound with a healthy gait 2 Favors a limb while walking 3 Severely lame, trying to avoid bearing weight on limb National Dairy F.A.R.M. Program
- 19. Example 2
- 20. 5-point Scoring System Score Category Description 1 Normal Normal gait. Level back posture while walking. 2 Mildly lame Normal gait. Level back posture while standing, but back arched while walking. 3 Moderately lame Gait affected, short striding. Back arched while standing and walking. 4 Lame Back always arched. Only one deliberated step at a time, one or more limbs favored. 5 Severely lame Extreme reluctance to bear weight one or more limbs. Sprecher et al., 1997
- 21. 3-point Scoring System National Dairy F.A.R.M. Program Score Description 1 Sound with a healthy gait 2 Favors a limb while walking 3 Severely lame, trying to avoid bearing weight on limb
- 22. Example 3
- 23. 5-point Scoring System Score Category Description 1 Normal Normal gait. Level back posture while walking. 2 Mildly lame Normal gait. Level back posture while standing, but back arched while walking. 3 Moderately lame Gait affected, short striding. Back arched while standing and walking. 4 Lame Back always arched. Only one deliberated step at a time, one or more limbs favored. 5 Severely lame Extreme reluctance to bear weight one or more limbs. Sprecher et al., 1997
- 24. 3-point Scoring System National Dairy F.A.R.M. Program Score Description 1 Sound with a healthy gait 2 Favors a limb while walking 3 Severely lame, trying to avoid bearing weight on limb
- 25. When and Where to Locomotion Score • Flat, non-slip surface • Alleyway where you can see one cow walking at a time • Alleyway as cows come out of the milking parlor (not when they are going into the parlor) • For dry cows, a clean dry alleyway • Ideal to sample the whole herd daily, but other sampling strategies may be more practical for large herds
- 26. Automated Measures of Lameness
- 27. 3D Accelerometers • High lying time may indicate lameness (Ito et al., 2010; Juarez et al., 2003) • Lame cows have longer lying bouts and more variable lying bout duration (Ito et al., 2010) • Low lying time, especially perching, may be a risk factor for lameness (Galindo and Broom, 2000; Proudfoot et al., 2010)
- 28. 3D Accelerometers • Asymmetrical steps (Chapinal et al., 2011) • Walking speed? (Chapinal et al., 2011) • Lame cows lay down ~20 minutes earlier than sound cows after feed is delivered (Yunta et al., 2012) • Lying behavior of lame cows depends on stall surface: lame cows on deep-bedded spend more time lying, those on mattresses do not (Cook et al., 2004; Ito et. al., 2010)
- 29. Robotic Milkers • Lame cows had fewer visits (Borderas et al., 2008) • Load cells under each leg may be able to detect changes in weight bearing (Pastell et al., 2008)
- 30. Other Automated Methods • 3D video detection (Viazzi et al., 2014) A combination of sensors: • Milk yield, neck activity, and ruminating time (Van Hertem et al., 2013) • Milk yield and feeding behavior (Kramer et al., 2009)
- 31. Online Resources
- 32. YouTube Videos • National Dairy F.A.R.M. Program: “How to Locomotion Score a Dairy Cow (2013)” • USDA National Animal Health Monitoring Survey: “NAHMS Lameness Detection Video” • DeLaval: “Do You Know How to Score Locomotion” • DairyNZ: “DairyNZ Healthy Hoof Lameness Scoring” • DairyCo: “DairyCo Mobility Score"
- 33. Apps The Dairyland Initiative http://www.vetmed.wisc.edu/dms/fapm/apps/ls.htm https://www.zinpro.com/lameness/dairy/locomotion-scoring ZINPRO
- 34. Thanks!
Notes de l'éditeur
- Herd manager vs. researcher, 50 freestalls in Minnesota, 5,626 cows. Producers mean = 8%, range 0 to 30%, researchers mean = 25%, range 3.3 to 57.3%
- Not just the 2’s that they were missing - also missing the more severe lame cows because they were not looking in the right place
- If we go back and look at our obviously lame cow, we can see that she is bobbing her head very low – interesting the direction of the head bob can tell you about where she is lame, if she is bobbing her head down (like this cow), that means the lame leg is in the back. If she is bobbing her head upwards, that means the lame leg is more likely in the front leg. A cow may also be lame and not have such an obviously jerky head bob – for example, a cow that is lame in both her front and back legs may carry her head steady despite being lame.
- Add more details to this or keep it about the same as Sprecher?
- 2
- Add more details to this or keep it about the same as Sprecher?
- 3
- Add more details to this or keep it about the same as Sprecher?
- Formulating optimum strategies for monitoring lameness in dairy cattle requires a consideration of the potential benefits and practical realities of different possible approaches. Assessing lameness by viewing the entire milking herd as the cows leave the parlor is a long-established method of lameness detection that ensures minimal disruption (Whay, 2002). Observing the entire herd can be very time consuming if the herd is large.
- Measurement of acceleration while walking as an automated method for gait assessment in dairy cattle Author links open overlay panelN.Chapinal*A.M.de Passillé†M.Pastell‡§L.Hänninen§#L.Munksgaard||J.Rushen† The aims were to determine whether measures of acceleration of the legs and back of dairy cows while they walk could help detect changes in gait or locomotion associated with lameness and differences in the walking surface. In 2 experiments, 12 or 24 multiparous dairy cows were fitted with five 3-dimensional accelerometers, 1 attached to each leg and 1 to the back, and acceleration data were collected while cows walked in a straight line on concrete (experiment 1) or on both concrete and rubber (experiment 2). Cows were video-recorded while walking to assess overall gait, asymmetry of the steps, and walking speed. In experiment 1, cows were selected to maximize the range of gait scores, whereas no clinically lame cows were enrolled in experiment 2. For each accelerometer location, overall acceleration was calculated as the magnitude of the 3-dimensional acceleration vector and the variance of overall acceleration, as well as the asymmetry of variance of acceleration within the front and rear pair of legs. In experiment 1, the asymmetry of variance of acceleration in the front and rear legs was positively correlated with overall gait and the visually assessed asymmetry of the steps (r ≥0.6). Walking speed was negatively correlated with the asymmetry of variance of the rear legs (r = −0.8) and positively correlated with the acceleration and the variance of acceleration of each leg and back (r ≥0.7). In experiment 2, cows had lower gait scores [2.3 vs. 2.6; standard error of the difference (SED) = 0.1, measured on a 5-point scale] and lower scores for asymmetry of the steps (18.0 vs. 23.1; SED = 2.2, measured on a continuous 100-unit scale) when they walked on rubber compared with concrete, and their walking speed increased (1.28 vs. 1.22 m/s; SED = 0.02). The acceleration of the front (1.67 vs. 1.72 g; SED = 0.02) and rear (1.62 vs. 1.67 g; SED = 0.02) legs and the variance of acceleration of the rear legs (0.88 vs. 0.94 g; SED = 0.03) were lower when cows walked on rubber compared with concrete. Despite the improvements in gait score that occurred when cows walked on rubber, the asymmetry of variance of acceleration of the front leg was higher (15.2 vs. 10.4%; SED = 2.0). The difference in walking speed between concrete and rubber correlated with the difference in the mean acceleration and the difference in the variance of acceleration of the legs and back (r ≥0.6). Three-dimensional accelerometers seem to be a promising tool for lameness detection on farm and to study walking surfaces, especially when attached to a leg. Effect of lameness on dairy cows’ visits to automatic milking systems T. F. Borderas, , A. Fournier, , J. Rushen, and , A. M. B. de Passillé Lameness is a major welfare problem for dairy cows and has important economic consequences. On-farm detection of lameness is difficult, and automated methods may be useful for early diagnoses. Lameness may reduce the efficiency of automated milking systems (AMS) if lame cows are less willing to visit the automatic milking unit voluntarily and poor attendance at milking units may help detect lameness. To determine whether a low frequency of visits in an AMS could serve as an indicator of lameness, data on the frequency of visits of 578 cows in 12 AMS on eight farms were collected. From each AMS, 22 cows (from a mean of 54 cows per AMS), were classified as either the 11 highest visitors or the 11 lowest visitors based on the total number of visits to the milking unit. These selected cows (n= 256) were videotaped while walking in a standard test area and their gait scored on a 5-point scale (1 = sound 5 = severely lame). Intra- and inter-observer reliability values between and within observers were high for gait scoring. Significant differences in gait scores between the two groups of cows (P< 0.05) were found in 9 out of 12 AMS: high-visiting cows had better gait scores than low-visiting cows. Four percent of high visitors were classified as slightly lame and 32% of low visitors were classified as either slightly or severely lame. The overall numerical rating score was the most effective in discriminating between high and low visitors, and scoring each individual component of gait did not greatly improve discrimination between the two groups of cows. The frequency that dairy cows visit an AMS is related to their locomotory ability, and data from the AMS may help in the early detection of lameness. Key words: Cattle, lameness, automatic milking systems, behaviour, gait scoring Can automated measures of lying time help assess lameness and leg lesions on tie-stall dairy farms? Author links open overlay panelGemma L.CharltonaVeroniqueBouffardbcJennyGibbonsaElsaVasseurdDerek B.HaleyeDorisPellerinbJeffreyRushenaAnne Mariede Passilléa The time that dairy cows spend lying down is an important measure of their comfort and lameness and injuries to hocks and knees are associated with alterations in lying time. We examined whether automated measures of lying time could identify cows and farms with problems of lameness or leg lesions. Data were collected from 40 lactating Holstein dairy cows from each of 100 tie-stall farms. The occurrence of lameness, hock and knee injuries was recorded and lying times were recorded automatically using accelerometers. There was large variation between individual cows, and between farms in all measures of lying time. At the cow level, there was no relationship (P > 0.10) between being lame and daily duration of lying time. A lower daily duration of lying time was found among cows with hock injuries (mean ± SE: non-injured = 12.79 ± 0.06 h, injured = 12.21 ± 0.06 h; P < 0.001) and cows with knee injuries (mean ± SE: non-injured = 12.54 ± 0.05 h, injured = 12.25 ± 0.06 h; P = 0.04) than those without lesions. The median daily duration of lying time on a farm was negatively correlated with the prevalence of lameness (rp = −0.27, P = 0.006), of hock injuries (rp = −0.35, P = 0.003) and of knee injuries (rp = −0.28, P = 0.004). A canonical discriminant function with canonical coefficients of 0.63 for mean daily duration of lying down, and of 0.54 for mean bout frequency could correctly identify 72% of the farms that were above the median for percent of cows with hock or knee injuries or being lame (linear discriminant function: constant = −95.10, daily duration = 11.39, bout frequency = 4.83) and 68% of the farms below the median (linear discriminant function: constant = −106.95, daily duration = 11.92, bout frequency = 5.30) (Wilks Lambda test P = 0.002). A criterion of a median lying time between 12 h and 13 h alone could identify over 60% of farms above or below the median for lameness, hock and knee lesion prevalence. Automated measures of lying time may be a useful animal-based measure to indicate farms with a high percentage of lame cows or cows with leg lesions.
- Walking speed on its own may not be a good predictor of lameness, but when used with other variables like lying time it may be a predictor. Cook et al. (2004, 2008) found that the type of lying surface affected the behavioral changes due to lameness; lame cows spent more time standing in stalls than nonlame cows, but this difference was greater with mattress stalls compared with sand stalls. These findings show that the resting environment plays an important role in the extent to which behavior is modified by lameness. Lying behavior and heat stress? Time budgets for 14 cows housed in a 3-row free-stall pen were obtained for 4 filming sessions timed to capture different climatic conditions, with a range in mean pen temperature-humidity index from 56.2 to 73.8. Mean lying time decreased from 10.9 to 7.9 h/d from the coolest to the hottest session filmed. This change in behavior occurred predominantly between 0600 h and 1800 h. Time spent standing in the alley increased from 2.6 to 4.5 h/d from the coolest to the hottest session filmed, with changes occurring between 1200 h and 1800 h. There was a negative effect of increasing locomotion score over the summer with higher locomotion scores associated with less time spent standing up in the alley. Time spent drinking increased from 0.3 to 0.5 h/d across the range in temperature-humidity index. Filming session alone did not affect time spent standing in the stall, but the effect of locomotion score was significant, with score 2 and score 3 cows standing in the stall longer than score 1 cows (4.0 and 4.4 compared with 2.9 h/d respectively). Behavioral changes observed and traditionally associated with heat stress were confounded by changes in locomotion score. Increases in claw horn lesion development reported in the late summer may be associated with an increase in total standing time per day. The changes in behavior described were because of mild to moderate heat stress. The finding that activity shifts occur around a temperature-humidity index of 68 supports the use of more aggressive heat-abatement strategies implemented at an activation temperature of around 21°C.
- It is interesting that lame cows stood up 13 min later than nonlame cows relative to the time when the ration was delivered. In addition, lame cows lay down 19 min earlier than nonlame ones after the feed was delivered, which implies that nonlame cows spent more time standing, and probably eating, than did lame cows. It was concluded that lame cows have longer lying bouts than nonlame animals, and that lying behavior around feed delivery time may be an effective proxy to identify moderately lame cows.