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In the context of understanding lameness and injury from slipping, our objective was to characterize hoof impact and slide of 5 cows walking on 6 flooring surfaces commonly used in Ontario dairy farms: diamond-grooved concrete (DC), sanded epoxy-covered concrete (EC), grooved rubber mat (GR), high-profile rubber mat (HR), low-profile rubber mat (LR), and turf grass (TG; Kentucky bluegrass/fescue mix). Surface hardness was measured on each surface using a Clegg Impact Soil Tester. Five trained lactating Holstein cows were each walked over all 6 surfaces sequentially in a randomized order. Walking speeds were determined from 60-fps videos. A 3-axis accelerometer attached to the lateral claw of each hindfoot captured continuous horizontal (aH), vertical (aV), lateral (aTLat), and medial (aTMed) accelerations at 2,500 Hz during each trial, from which peak values were identified. Data from 45°-rosette strain gauges glued to the dorsal surface of both medial and lateral hooves allowed for the calculation of principal strains (ε1 and ε2). From continuous data, several data points were extracted from 3 to 6 stances/trial: peak values of aH, aV, and aT for the impact phase of the stance; midstance values of ε1 and ε2 as proxies for force on the foot; magnitudes of normal (i.e., consistent and repeatable) sliding on the surface during the support phase; and 3 timing events to capture the cadence of the motion. All aH and aV signals were inspected onscreen to identify irregularities between the end of impact and beginning of breakover that indicated hoof slipping, which was observed on all surfaces. The effects on all measured variables of surface, cow, speed, and hoof (and all significant higher-order factors) were assessed by ANOVA in SAS 9.4 (SAS Institute Inc.), after verifying data normality. Values of aHmax, indicating grip on the surface from highest to lowest, ranked the surfaces in this order: LR, DC, HR, GR, EC, and TG. Ranking on aVmax, indicating most to least cushioning of the hoof on impact, ranked the surfaces in this order: DC, HR, GR, EC, LR, and TG. Differences in ranking among these and other significant impact variables indicate that future studies of lameness on different surfaces need to include all significant variables identified here. We detected no surface and strain interactions in either the ε1 or ε2 strain, indicating that the surfaces do not affect the overall loads on the foot at midstance. Additionally, lateral and medial hooves may have different roles in a stance. The results highlight the capacity to evaluate flooring types with this technology, and the study provides a tool for future work to examine the role of flooring types in the causation of lameness.
Dairy cattle lameness has been identified as a consistent problem worldwide and it is one of the most significant productivity and welfare issues in dairy farming (
Short communication: Herd-level reproductive performance and its relationship with lameness and leg injuries in freestall dairy herds in the northeastern United States.
), and flooring choice in today's freestall barns can increase the cows' risk of slipping and falling, which can increase the risk for lameness and hoof disorders, and therefore affect mobility (
). Hoof slip is thought to be a risk factor in the pathogenesis of white line disease, a claw horn lesion, due to uneven loading of the hoof and increased pressure on the sole (
). These claw horn lesions have also been demonstrated to reduce the digital cushion of the hoof and predispose animals to additional future claw horn lesions (
A history of lameness and low body condition score is associated with reduced digital cushion volume, measured by magnetic resonance imaging, in dairy cattle.
). Hoof slip is multifactorial, with cow behavior, handling, and environmental factors such as friction coefficient of the floor, presence of manure on the floor, floor dryness, floor softness, and rapid movement of the animal all affecting hoof slip (
). Therefore, there is a need to examine different hoof–surface interactions in a controlled study to further understand how slip-related injuries and changes to footfall may be influenced by intrinsic features of the flooring type.
Numerous designs of flooring are available, with varying degrees of cushioning to reduce impact loading on the foot and surface relief to generate friction and prevent slipping. Some horizontal sliding of the foot is normal at the beginning of each stance and, to a lesser extent, during it. Impeding these motions on a surface that has too much grip is potentially injurious. On the other hand, if grip (surface friction) is too low, slipping of the hoof through the middle, weightbearing phase of the stance can also be injurious because of the higher forces on the limb at this time.
Therefore, the aims of the study were to assess the utility of the study methodology for examining hoof slippage, to characterize the prevalence of slippage on the surfaces examined, and to provide baseline data on the 6 surfaces studied. The primary objective was to describe and compare the kinetic interactions throughout each phase of the stance of the hind hooves of walking dairy cattle on 6 common flooring types in dairy barns in Ontario, Canada. A suite of measurements was recorded to describe each stage of the stance, using 3-dimensional accelerometers to characterize motions and foil strain gauges to assess loading of the hoof capsule. Surfaces were ranked on values of maximum horizontal and vertical slide to compare their characteristics of grip and cushioning, respectively. The null hypothesis was that none of these variables differed between surfaces, with the expectation that they would show different patterns of variation according to surface type. The second objective was to identify the presence or absence of abnormal slipping of the hoof in the plane of each surface during the midstance phase of the stance. This is when the hoof should show minimal, repeatable movement.
MATERIALS AND METHODS
Cow Selection and Training
The study took place at the Ontario Dairy Research and Innovation Centre (ODRIC, Elora, ON, Canada). Animal and housing data collection and study design were approved by the University of Guelph Animal Care Committee (AUP#3711). All animal management complied with the guidelines of the Canadian Council on Animal Care (
principle of reducing animal use. Cows were selected in consultation with staff from the facility based on animal availability, normal gait, absence of detectable lameness, and good temperament. Six cows were trained for 6 wk to walk calmly on lead by one trained handler (JEF) across different surfaces found in the Dairy Centre; one animal was removed from the trial based on temperament. Five cows were then introduced, before being walked each time, to a handling chute and a dummy backpack. The backpack was an equine saddlepad, attached with elastic straps, and modified to later carry the recording equipment. Training was deemed complete when each animal remained quiet and manageable during the entire process.
Surface Selection
Surfaces at the ODRIC are representative of those commonly found in commercial dairies in Ontario, Canada. Six were included in this study: diamond-grooved concrete (DC; Grandview Concrete Grooving), sanded epoxy-covered concrete (EC; SP3 Concrete Services Inc.), grooved rubber mat (GR; Dairy Grip-Grooved Belt; Legend Rubber Inc.), high-profile rubber mat (HR; Aggressive Agrimat; Legend Rubber Inc.), low-profile rubber mat (LR; Smooth Agrimat; Legend Rubber Inc.), and turf grass (TG).
All surfaces were installed in 2015 and example photographs are shown in Figure 1. The DC surface was created with 32 MPa concrete that was grooved in a diamond pattern with grooves 19.05 mm wide, 7.94 mm deep, and 10.5 cm apart. The EC surface was made with 32 MPa concrete as the base, with a 15-mm-thick coat of epoxy applied. While the layer of epoxy was setting, an aggregate (24 grit) was applied to rejection. When the EC surface was fully cured, excess aggregate was swept away before a topcoat of Sikafloor 261 (Sika AG) was applied. The GR surface was 19 mm thick and had a smooth bottom. It had parallel grooves running in the same direction of the cows walking that were 23 mm wide and set 154 mm apart from each other and cut 3.2 mm deep. In between each pair of parallel grooves, there was a zigzag groove in the rubber also 23 mm wide and 3.2 mm deep, with each segment of the zigzag groove being 100 to 115 mm long and coming to 23 mm from each straight parallel groove at its closest points. The HR surface was 19 mm thick with a rubber studded bottom of 5-mm hemisphere studs set 15 mm apart; the top surface had a raised repeating pattern (29 mm long by 10 mm at the widest points) approximately 2.5 mm high (Figure 1). The LR surface was 19 mm thick, also with a studded bottom like HR, and a raised repeating pattern (24 mm long by 8 mm at the widest points) approximately 1 mm high (Figure 1). The TG surface was adjacent to the barn. It consisted of Kentucky bluegrass (Poa pratensis) and fescue (Festuca spp.) and had not previously received cow foot traffic.
Figure 1Example photographs of the 6 surfaces used in the experiment, shown with a 30-cm ruler for scale. (A) diamond-grooved concrete (DC) with 2-cm-wide grooves; (B) sanded epoxy-covered concrete (EC); (C) grooved rubber mat (GR); (D) high-profile rubber mat with 3-mm-high pattern (HR); (E) low-profile rubber mat with 1-mm-high pattern (LR); (F) turf grass (TG).
All indoor surfaces are pressure washed regularly and were sprayed with a hose before each test day to wet the surface and wash away obvious manure.
Surface Hardness
Surface hardness of each of the 6 surfaces in the trial was assessed using a 10-kg Clegg Impact Soil Tester (CIST: Lafayette Instrument Co.), an instrument that has been used previously to quantify the hardness of stall surfaces (
Short communication: Use of the Clegg hammer measure as an indicator of stall-surface compressibility in tie-stall housing and its relationship with stall comfort.
). The tester has a scale of impact values (IV) from 1 to 100, where higher values indicate a less compressible (harder) surface. The CIST was used on 4 randomly selected locations, along the length of each walking surface, chosen sporadically by one observer (JEF). Impact values were recorded for the 4 trials per surface with averages and standard deviations for each surface computed with these data. Surfaces inside the building were tested on 1 d before the commencement of the in vivo testing schedule, but the outside TG surface was tested on each of the 3 experimental days, in case of weather-related changes. Additionally, one of the sporadically chosen locations for the GR, HR, and LR surfaces was selected for further testing. A CIST was conducted 4 times on the same location, with IV used to calculate average and standard deviation for that single location.
Sensor Attachment and Positioning
Experimentation took place over 3 d in July 2019. Each trained animal was restrained in a headlock, where the lightweight backpack containing the data logger was secured to the animal with wide Velcro straps (Figure 2A). The animal was then restrained in the handling chute, where the dorsal surface of each hind hoof and the outer surface of the lateral hooves were gently rasped with a hoof-trimming rasp to make small flat areas for the sensors, and were cleaned with alcohol on a gauze swab. Sensor attachment was noninvasive, involving only the surface of each hoof, using procedures modified from
Figure 2Overview of the study apparatus, including the data collection backpack, the wiring, vet wrap, strain gauges (on both the lateral and the medial hoof), and the 3-axis accelerometer on the lateral hoof alone (partly masked by protective tape). All cows were walked across the surfaces by the single trained researcher.
Strain gauges (Showa Strain Gauge model N32-FA-2-350-11) were attached to the dorsal site with a cyanoacrylate adhesive (Loctite 411, Henkel Corp.), approximately midway from coronary band to distal border of the hoof, straddling the dorsal boundary of the hoof curvature (Figure 2B). These gauges have 3 separate foils at 45°, which change in electrical resistance when they are deformed (strained). Each gauge completes a Wheatstone bridge in an amplifier, resulting in a voltage output that is proportional to the strain in the material under load. The strains themselves are in proportion to the applied load inducing deformations in the hoof wall. Surface strain is quantified as minimum (ε1) and maximum (ε2) principal strains, which are perpendicular to each other, but of variable orientation within the surface. Strain ε2 is the most compressive strain, and ε1 is the most tensile (or sometimes least compressive) strain. The use of 3 foils allows ε1 and ε2 to be determined even if none of the foils are aligned with the strains (
). Strain has no unit, but is normally reported as microstrain (µε) in experiments such as this to acknowledge the minute degree of deformation in the hoof material.
A 3-axis accelerometer (model 356B21 Triaxial Accelerometer, PCB Piezotronics Inc.) was attached with epoxy (Equithane Superfast Adhesive, Vettec Inc.) to the lateral hoof of each hind foot and aligned to capture continuous horizontal (aH), vertical (aV), and transverse (aT) accelerations (Figure 2B).
The cow was then removed from the chute and transferred to a headlock, where wires from the sensors were led up the rear legs of the cows to an amplifying and datalogging system (SLICE Micro, Diversified Technical Systems Inc.) located in the backpack. The wires were held to the legs with VetRap (VetRap Bandaging Tape, 3M).
Surface Accelerometer and Strain Recording
Following sensor attachment, an individual cow was led to each of the 6 surfaces in a predetermined order randomized for that cow. On reaching each surface, the cow was allowed to acclimate while researchers wetted the surface immediately before data collection to standardize the surfaces. The data collection system was triggered, and the animal was led across the surface with a loosely held halter at an unforced walking speed, while being recorded on video at 60 frames per second (fps), with the camera (model GC-PX100BU, JVC Kenwood Corp.) situated 10 m back from the midpoint of the test region of the surface. Linear dimensions on the film were calibrated with respect to measurable dimensions in the immediate background (within 1 m of the path of the cow), such as the width of gates and door openings. Data from the hoof sensors were sampled at 2,500 Hz. A trial was considered acceptable if the cow walked steadily, with no hesitation or jump-steps. After each cow had walked across all the surfaces, once each, it was considered to have completed the study. After the experiment, the cow was put back into the handling chute, where the data were downloaded from the data collector using DTS SliceWare software (Diversified Technical Systems), and the strain gauges and accelerometers were removed. The cow was then transferred back to the headlock to remove the wiring and backpack, before being returned to the pen.
Data Preparation
Walking speed (in m/s) was calculated for all trials from the 60-fps videos using a custom program in MATLAB (version 2019a, The MathWorks Inc.), using the number of frames needed to cross between 2 markers of known separation in the background.
Strain and acceleration data were analyzed using custom-written programs (by JJT) in MATLAB. Raw strains collected by the rosette gauges were zeroed to their values at mid-swing, when the hoof was in the air, and converted to principal strain magnitudes and angles with respect to the axis of the digit. Accelerations were zeroed to their value at midstance, when the hoof was assumed to be stationary. Footfalls that did not have a distinct zero phase that lasted for most of the mid-part of the stance were not included.
Some footfalls showed single large spikes of acceleration during the mid-part of the stance, when the foot is normally stationary on the ground. The spikes were taken to be indicative of slips (Figure 3). Their presence was recorded, and 2 of the variables extracted from the acceleration data (and described below) tested their statistical significance in relation to surface properties.
Figure 3Identification of a slip and the corresponding hoof positions matched to events on strain and acceleration graphs for one stance. Points 1 to 4 are described in the text under Data Synthesis.
Approximately 5 m of data was examined, ignoring the beginning and end of a 10- to 12-m walk. Between 3 and 6 zeroed stances for each hoof on each surface were analyzed. Four registration time points on each stance were hand-digitized using a program in MATLAB (Figure 3):
(1)
an initial spike in acceleration and strain, signaling primary impact;
(2)
a flattening of the acceleration trace, signaling the beginning of the support phase;
(3)
a distinct increase in acceleration, signaling the start of breakover as the foot begins to lift from the surface;
(4)
a point of inflection in the acceleration trace, signaling the hoof leaving the ground at the end of breakover.
The program extracted the values of recognizable events on the acceleration and strain traces occurring in the intervals between these points, as well as the time the foot was on the ground and the duration of the whole stride, as listed and described in Table 1.
Table 1Descriptive summary of variables extracted from sampling the stances and how they relate to the whole stance or a specific section of the stance: initial spike (signaling primary impact), flattening of acceleration (signaling secondary impact), the second spike in acceleration (signaled the end of the support phase and into the breakover phase), and a point of inflection (signaling the end of break-over and the stance;
Measurement variables include peak vertical deceleration on first impact (aVmax); peak horizontal deceleration (aHmax) as the forward motion of the animal tends to push the foot forward during secondary impact; hoof loading at midstance (expressed as principal strains in the capsule horn, Tmse1 and Tmse2), and duration of the stance and support and breakover phases (standur, breakdur). Two variables captured values of the normal horizontal (craniocaudal; slideH) and transverse (slideT) motion of the hoof on the surface during the stance. Speed of walking and surface hardness were recorded as covariates, the latter with a CIST.
Statistical Methods
Statistical analyses were performed using SAS (version 9.4; SAS Institute Inc.) with cow treated as the experimental unit. Before analysis, all data were screened for normality by assessing the distribution and the presence of any outliers using the UNIVARIATE procedure. Non-normal variables were subjected to several transformations iteratively, until each variable was normalized (Table 2). The normally distributed data were summarized using the MEANS and SUMMARY procedures. Data were summarized by cow, surface, hoof, CIST results, and speed, and MIXED models were analyzed with cow as the random effect. Fixed effects included hoof, categorized speed, and surface, with 2- and 3-way interactions in the initial model, which were removed if P > 0.1. Three categories of speed were established: slow for speed <0.3 m/s; medium for 0.3 m/s ≤ speed ≤0.65 m/s, and fast for speed >0.65 m/s. The model statement included options to output the full model and to use the kenwardroger2 method (
) to calculate the degrees of freedom. Following model determination, the residuals were examined for normality with the UNIVARIATE procedure and plotted against the class and random variables. All residuals were normally distributed and did not require further adjustment. All 2- and 3-way interactions were omitted from the final model. The LSMEANS option was added to the final model to examine the confidence limits along with the P-values for differences between model variables, allowing for multiple comparisons.
Table 2Description of the transformations performed on the different outcome variables (and the associated unit of that variable; see Table 1) used for the analysis
Surface hardness results are reported in Table 3. The hardest surface was the sanded EC (IV = 72.9), followed by DC (IV = 49.4), GR (IV = 17.8), HR (IV = 12.0), LR (IV = 11.7), and then TG (IV = 6.9; Table 3). Hardness of TG varied between 6.2 and 7.9 among the 3 testing days (Table 3). The CIST data clearly separated the concrete from rubber surfaces along expected lines, and also distinguished surfaces within the 2 material types. Turf grass was softer than all of the rubber surfaces, but showed daily variability.
Table 3Summary of the average Clegg Impact Soil Tester (CIST) results representing common flooring types in Ontario, Canada (n = 6)
CIST measures were repeated at least 4 times either in different locations or in the same location, with measures (mean ± SD) reported as impact values.
CIST measures were repeated 4 times on different locations for each test day to account for daily variation in turfgrass hardness.
Day 1
6.2 ± 0.81
—
Day 2
6.7 ± 0.66
—
Day 3
7.9 ± 1.43
—
1 CIST measures were repeated at least 4 times either in different locations or in the same location, with measures (mean ± SD) reported as impact values.
2 CIST measures were repeated 4 times on the same location to account for the elastic nature of rubber.
3 CIST measures were repeated 4 times on different locations for each test day to account for daily variation in turfgrass hardness.
Spikes in acceleration data during midstance, when the hoof should have been planted firmly on the substrate, indicate slipping (Figure 3) and were recorded for all but 3 of the trials (Table 4). There was never more than one slip per trial, although some slips consisted of a short series of slip-and-stop events. The exceptions were that cows 1 and 5 did not slip on the EC surface, and cow 3 did not slip on the GC surface (Table 4). These data are of concern, in that they demonstrate that slipping is a regular occurrence on each of these commonly used surfaces, even at a moderate walk, and could be a factor in causing lameness.
Table 4Presence of slips in acceleration traces on each surface for each cow (where × denotes slipping and – denotes absence of a slip); in most cases, only one stance in each trace showed slipping
Summary statistics are reported in Table 5. During the primary impact phase, aVmax was 17.8 ± 16.8 m/s2 (mean ± SD; Table 5). In the secondary impact phase, average aHmax was 9.1 ± 6.6 m/s2, the average magnitude of peak transverse lateral deceleration spanning primary and secondary impact (aTmaxLat) was 3.4 ± 3.5 m/s2, and the average magnitude of peak transverse medial deceleration spanning primary and secondary impact (aTmaxMed) was 8.2 ± 7.1 m/s2 (Table 5). In the support phase, average Tmse1 was 3,097 ± 949 με, average Tmse2 was 2,395 ± 1,101 με, average angle of principal strain at midstance for the toe (Tmsth) was 43.0 ± 27.9°, and average time to peak ε2 strain for toe (Te2t) was 0.7 ± 0.2 s (Table 5). The slideH and slideT movements of the hoof during secondary impact were 18.0 ± 3.8 mm and 9.8 ± 1.2 mm, respectively (Table 5). The variables Tmsth, slideH, and slideT were bimodal or otherwise nontransformable, and there was no way to separate these variables based on any of the class variables used in the model. The mean time to the start of breakover was 0.7 ± 0.2 s, whereas the duration of breakover (breakdur) was 0.1 ± 0.1 s. Due to the tight interval (<0.1 s) for breakdur, the variable was not included in subsequent modeling (Table 5).
Table 5Descriptive summary of the variables collected from the study (see Table 1)
Both speed (P = 0.03) and surface (P < 0.001) showed significant fixed effects on aVmax (Table 6). Values of aVmax did not distinguish slow speeds from either medium (P = 0.48) or fast (P = 0.16; Table 6) speeds but did distinguish medium from fast speeds (P = 0.008). The least squares means for aVmax were 9.5 m/s2 at medium speeds and 16.6 m/s2 at fast speeds (Figure 4A). A significant increase in hoof deceleration with speed is expected: the hoof makes contact with the ground at vertical velocities, which increase with speed, but comes to a halt in approximately the same time at all gaits. The fact that slow speeds are not distinguishable from the faster speeds is due to their greater variability.
Table 6Summary of the type 3 tests of fixed effects results for all models with cow as the random effect (significance declared at P < 0.05)
Figure 4(A) Predicted probabilities of the magnitude of peak vertical deceleration at primary impact (aVmax; m/s2) for the different speeds of slow, medium, and fast. (B) Predicted probabilities of the magnitude of peak vertical deceleration at primary impact (aVmax; m/s2) for the different surfaces of low-profile rubber mat (LR), sanded epoxy-covered concrete (EC), high-profile rubber mat (HR), grooved rubber mat (GR), diamond-grooved concrete (DC), and turf grass (TG). Bars representing confidence intervals with different letters (a–c) differ significantly at P < 0.05.
Surface type significantly affected aVmax (P < 0.001; Table 6, Figure 4B). Surface types DC and HR had the highest aVmax magnitudes at 24.2 and 21.8 m/s2, respectively. These values are not significantly different from each other (P = 0.71), but both DC and HR differed from GR (P = 0.02 and P = 0.05, respectively), EC (P = 0. 0.002 and P = 0.008, respectively), and LR (P = 0.003 and P = 0.004, respectively) in term of Vmax (Figure 4B). Turf grass was significantly different from DC (P < 0.001), HR (P < 0.001), GR (P = 0.003), and EC (P = 0.04), with the lowest value of aVmax: 5.77 m/s2 (Figure 4B).
There was some concordance between the rankings of surfaces on aVmax (DC, HR, GR, EC, LR, TG) and on hardness values from the CIST test (EC, DC, GR, HR, LR, TG). Surface EC is the clear exception: it is the hardest surface but hoof deceleration on this surface is in the lower half of the ranking. This discordance raises the possibility that the cows can selectively adjust their rate of footfall onto the surface based not just on perception of hardness. If hardness were the only factor affecting impact, there are 2 potential extremes of response. One is that the velocity of the hoof before impact is adjusted to achieve constant deceleration (i.e., the shock of impact is surface independent). The other is that impact velocity is itself constant, in which case the deceleration of the hoof on contact would correlate closely with surface hardness, as it does generally in the present data. On surface EC, there appears to be some assertion of control over the impact velocity, possibly related to properties of the surface other than hardness. For example, if the cows perceived it as being more slippery, they might be more tentative in their footing. The present data do not address that suggestion.
Factors Affecting Secondary Impact
Secondary impact was assessed by aHmax (anteroposterior deceleration) and aTmaxLat and aTmaxMed (side-to-side decelerations). Speed had a significant effect on all 3 outcome variables relating to the secondary impact phase (Table 6): aHmax (P = 0.03), aTmaxLat (P = 0.01), and aTmaxMed (P = 0.04). Least squares means estimates for aHmax were similar to those for aVmax, with faster speeds resulting in higher aHmax magnitudes (10.4 m/s2) than medium speed stances (5.8 m/s2; P = 0.008; Figure 5A). This trend was also observed with aTmaxLat and aTmaxMed, as both medium and fast stances were significantly different from one another in both models (P = 0.003 and P = 0.01, respectively; Figures 5B and 5C). These results indicate that speed has an effect on variables relating to accelerations during secondary impact. Such a finding was to be expected, but the curvilinear relationship of the measured variables should be noted with caution, because not all cows walked at all 3 speeds.
Figure 5Predicted probabilities of the magnitude of (A) peak horizontal deceleration spanning primary and secondary impact (aHmax; m/s2), (B) peak transverse lateral deceleration spanning primary and secondary impact (aTmaxLat; m/s2), and (C) peak transverse medial deceleration spanning primary and secondary impact (aTmaxMed; m/s2) for the different speeds: slow, medium, and fast. (D) Predicted probabilities of the magnitude of peak transverse medial deceleration spanning primary and secondary impact (aTmaxMed; m/s2) for the different hooves: right lateral and left lateral. (E, F) Predicted probabilities of the magnitude of (E) peak horizontal deceleration spanning primary and secondary impact (aHmax; m/s2) and (F) peak transverse medial deceleration spanning primary and secondary impact (aTmaxMed; m/s2) for the different surfaces: low-profile rubber mat (LR), sanded epoxy-covered concrete (EC), high-profile rubber mat (HR), grooved rubber mat (GR), diamond-grooved concrete (DC), and turf grass (TG). Bars representing confidence intervals that have different letters (a–d) differ significantly at P < 0.05.
The right lateral hoof (8.6 m/s2) and the left lateral hoof (5.6 m/s2; Figure 5D) differed from each other in aTmaxMed. This measurement indicates a medial motion of short duration as the foot settles on the ground, and the reasons for the asymmetry are not clear. A possible factor was that the trained handler walking the cow was always on the left side of the cow (mainly due to the way halters are designed), so the alignment of the cow may have been at a slight angle to the line of its forward progress.
Surface type had a significant effect on aHmax of the hoof during secondary impact, when the leg momentarily slows the forward momentum of the animal. There was no significant difference among any of the artificial surfaces or clear separation of the concrete from the rubber surfaces (Figure 5E), but the values for TG were significantly lower than for any of the artificial surfaces (P = 0.01). The lack of separation between rubber and concrete surfaces was unexpected because a clear difference in rates of hoof wear have been shown for asphalt and rubber surfaces (
). However, this result may signify that no surface tested in this study compared to the benefit of TG, further pointing to the need to examine further developments in flooring types.
The magnitude of peak transverse medial deceleration spanning primary and secondary impact (aTmaxMed) showed significant effects of surface (P = 0.005) and hoof (P = 0.01) (Table 6). Higher values of aTmaxMed indicate that sliding is halted more rapidly and may be associated with greater horizontal forces on the foot. Surfaces were ranked on aTmaxMed values from high to low as follows: EC (10.9 m/s2), LR (8.6 m/s2), HR (8.5 m/s2), GR (6.4 m/s2), DC (5.9 m/s2), and TG (3.4 m/s2; Figure 5F); TG was the lowest and significantly different from all other surfaces except for DC (P = 0.09; Figure 5F). The results suggest that TG and DC surfaces bring the hoof to a halt more slowly, thereby lowering transverse medial accelerations and potentially resulting in fewer slips or splays. Nevertheless, even these surfaces showed some slipping (Table 4).
The higher value for EC was expected, because this surface does not provide adequate slip prevention (
). The results for the rubber surface types (LR, HR, and GR; Figure 5F) were also predictable, because these surfaces are primarily focused on cushioning rather than preventing secondary movement of the hoof (
Loading on the 4 hooves at midstance of the support phase was quantified by Tmse1 and Tmse2, representing the principal strains ε1 and ε2 (which are perpendicular to each other), and Tmsth, which describes the orientation of ε2 with respect to the craniocaudal axis of the hoof. Together, these 3 variables indicate that each hoof is being twisted relative to its craniocaudal axis, because ε1 and ε2 are much closer in absolute value than for simple axial compressive loading (Figure 6A) and are oriented at approximately 45° to the axis. This compares to the loading on the sides of the equine hoof but is very different from the primarily compressive loading at its toe (
). The fact that ε1 and ε2 are subequal in bovine hooves indicates that, although torsion is the primary loading regimen, the hooves also experience compressive forces because of weightbearing.
Figure 6(A) Predicted probabilities of the ε1 (Tmse1; top) and ε2 (Tmse2; bottom) principal strains at midstance for the toe for the different hooves: right medial, right lateral, left medial, and left. (B) Predicted probabilities of the time to peak ε2 strain for toe (Te2t) for the different hooves. (C) Predicted probabilities of time to peak ε2 strain for toe (Te2t) for the different speeds: slow, medium, and fast. Bars representing confidence intervals that have different letters (a–c) differ significantly at P < 0.05.
Considering ε1 and ε2 together, the material of the medial hooves is more strongly strained than the lateral hooves at midstance, and the right medial is more strained than the left medial. These differences were not statistically significant: hoof was the only significant fixed effect for both Tmse1 (P < 0.001) and Tmse2 (P < 0.001; Table 6), but it is appropriate to consider the mechanical significance of strain differences.
found that peak and midstance forces were higher on the lateral hooves than on the medial hooves, which appears to contradict the present data. But there are not straightforward relationships between the forces on the hooves, their variability over time, the distribution of pressure on the undersurface of the hooves, and the strains induced in the materials of each hoof. The fact that more-or-less vertical force vectors translate into strains representing torsion in the material of the hooves underscores the complexity of the transduction from force to strain and stress (as was demonstrated by finite-element computer analysis of stress and strain in the hooves;
). A possible implication of torsion in the hoof wall is that loading of the laminar junction is uneven around the circumference of the hooves, which could have consequences for the causation and effects of lesions.
There was no significant effect of surface on principal strains at midstance: Tmse1 (P = 0.93) or Tmse2 (P = 0.97), which demonstrates that once the hoof is planted on each surface, the mechanical properties of the surface that are significant upon impact no longer come into play.
Variable Te2t represents the time to peak ε2 strain for the toe and had speed (P < 0.001) and hoof (P < 0.001) as significant fixed effects; Te2t inversely correlates with speed: slow (0.93 s), medium (0.68 s), and fast (0.59 s; Figure 6B). Expressing Te2t as a percentage of stance duration (standur), peak strain occurred later in the stance for slow speeds (84.5%) than at moderate (69.8%) and fast (66.1%) speeds.
Hoof was also a significant fixed effect for Te2t (P < 0.001; Table 6). The right lateral (0.82 s) and left lateral (0.81 s) hooves had higher times to peak ε2 strain than either the left medial hoof (0.64 s) or the right medial hoof (0.67; Figure 6B). This finding correlates with changes in force on each hoof during the stance (
) and emphasizes the difference in weight bearing and stability roles between the 2 hooves.
Factors Affecting the Breakover Phase
The breakover variable quantified the time from foot on surface to start of breakover and was significantly related to the 2 fixed effects of speed (P = 0.005; Figure 7A) and surface (P = 0.02; Table 6). The significance of speed was not surprising, because standur was also significantly related to the fixed effect of speed (Figure 7B).
Figure 7(A) Predicted probabilities of the time from foot on surface to start of breakover (breakover) for the different speeds of slow, medium, and fast. (B) Predicted probabilities of the duration of the stance, when the foot is on the ground (standur) for the different speeds of slow, medium, and fast. (C) Predicted probabilities of breakover time on each surface. (D) Predicted probabilities of the duration of the stride (striddur) for the different speeds of slow, medium, and fast. (E) Predicted probabilities of standur on each surface. (F) Predicted probabilities of striddur on each surface. Surfaces: LR = low-profile rubber mat, EC = sanded epoxy-covered concrete, HR = high-profile rubber mat, GR = grooved rubber mat, DC = diamond-grooved concrete, TG = turf grass. Bars representing confidence intervals that have different letters (a–c) differ significantly at P < 0.05.
The order of longest to shortest breakover time did not clearly segregate concrete from rubber surfaces: HR (0.93 s), TG (0.87 s), DC (0.76 s), GR (0.76 s), EC (0.73 s), and LR (0.73 s; Figure 7C). Out of these times, the times for HR and TG were not significantly different from each other (P = 0.38); however, HR was significantly different from DC (P = 0.03), GR (P = 0.02), EC (P = 0.01), and LR (P = 0.008; Figure 7C), and TG was significantly different from LR (P = 0.05) and EC (P = 0.03;Figure 7C). We hypothesize that these results highlight the cow's comfort of walking on certain surfaces, as a shorter breakover time would likely result from cows that were unsure of footing on a surface. In this case, what we see is that HR provides cushioning to the hoof and a surface that is comparable to TG. Additionally, we see that hypothesized slippery surfaces have shorter breakover times, whereas surfaces that provide more sure footing have longer breakover times. However, more research needs to be done in this area as the coefficient of friction between hoof and the surfaces examined has yet to be investigated.
Factors Affecting the Whole Stride and Stance
Speed and surface both had significant effects on variable striddur (the duration of strides from one footfall to the next) and standur (the time from first to last contact of the hoof with the surface in each stance). Both interactions were significant, with P < 0.001 for both variables (Table 6). The relationship to speed is intuitive (Figure 7B and 7D). Surfaces TG and HR showed significantly longer stride durations (TG, 1.58 s; HR, 1.60 s) and stance durations (TG, 1.09 s; HR, 1.15 s. Figure 7E and 7F). We hypothesize that these longer times are related to greater confidence of the animal while walking on them. Speed (P < 0.001) and surface (P < 0.001) were also significant for striddur—the time taken for one stride from one footfall to the next.
Effects of Surface Hardness
The CIST data in Table 3 allow the surfaces to be ranked by hardness and compared with the significant measured kinematic variables ranked on their own values (Figure 8). The significant variables described the deceleration along 3 axes of the hoof on impact and the duration of the footfall. The CIST data ranked the 2 concrete surfaces as the hardest, with rubber surfaces next, and turf grass as the softest.
Figure 8Ranking of surfaces on significant hoof variables compared with ranking of hardness measurements from the CIST. Variables: CIST = Clegg Impact Soil Tester; aVmax = magnitude of peak vertical deceleration at primary impact; aHmax = magnitude of peak horizontal deceleration spanning primary and secondary impact; aTmaxMed = magnitude of peak transverse medial deceleration spanning primary and secondary impact; Breakover = time from foot on surface to start of breakover; standur = duration of the portion of each stance when the foot is on the ground. Surfaces: DC = diamond-grooved concrete with 2-cm-wide grooves; EC = sanded epoxy-covered concrete; GR = grooved rubber mat; HR = high-profile rubber mat with 3-mm-high pattern; LR = low-profile rubber mat with 1-mm-high pattern; TG = turf grass.
Vertical deceleration on hoof impact (aVmax) presented a similar ranking (with EC as the exception), with the interpretation that the cows did not try to adjust the rate of descent of their hooves onto each surface. The low ranking of both aVmax and aHmax on concrete surface EC suggests the cows placed their feet relatively gently on this surface. A possible explanation is that the cows found this surface to be slippery. The implication is that the cows placed their feet more carefully or softly onto this surface, which is in accord with the low ranking of EC on horizontal deceleration at impact (aHmax). Rubber surface LR ranked highest on aHmax, indicating it had the most grip on impact. If EC and LR are taken out of consideration, the other 4 surfaces showed similar rankings for aVmax, aHmax, and CIST values, indicating that vertical and craniocaudal hoof deceleration correlate with the hardness of each surface.
Medially directed deceleration of the hoof immediately following impact (aTmaxMed) was of similar magnitudes to those of craniocaudal deceleration (aHmax; Table 5). aTmaxMed ranked the surfaces in similar order to the CIST data, indicating that this deceleration varies with hardness. The exception was concrete surface DC, on which the hoof decelerated medially at a lower rate than concrete or rubber, with no obvious explanation.
The duration of the stance (standur) ranked the surfaces in a similar order to the CIST values: longer on concrete, intermediate on rubber, and least on turf, with some minor reorganization (Figure 8). This contrasts with the timing of the beginning of breakover, for which the sequence of surfaces is more or less randomized. It remains to be determined whether this change in kinematics during the stance is of mechanical significance.
CONCLUSIONS
Measurements of vertical impact (aVmax) generally corresponded with surface hardness, except for the epoxy concrete, on which impact was lower than for 2 of the rubber surfaces. Slipping occurred for some strides on all surfaces, which engenders some concern and highlights the importance of evaluating new flooring types for their capacity to improve comfort, gait, and reduction of slips using technologies that account for real world use of surfaces by cows. Principal strains ε1 and ε2 showed a complex transduction from force to strain, indicating substantial torsion in the material at the toe of each hoof. Surface type affected the cadence of limb motion, as shown by significant effects on time to breakover, stride duration, and stance duration. The results highlight the capacity to evaluate the mechanical interaction of cattle hooves flooring types with this technology, and to identify with considerable sensitivity the prevalence of slipping on each type.
ACKNOWLEDGMENTS
The authors thank the Ontario Ministry of Food, Agriculture and Rural Affairs (Guelph, ON, Canada) Summer Employment Opportunity Program for financially supporting the primary author (JF) in completing this study. The study received no additional external funding. The authors thank the staff of the Ontario Dairy Research Centre (Elora, ON, Canada) for their help with conducting the trial and Christoph Wand (OMAFRA, Guelph, ON, Canada) and Danielle Halucha (University of Guelph, Guelph, ON, Canada) for their help with data collection. The authors have not stated any conflicts of interest.
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