Development of the laser remote caliper as a method to estimate surface area and body weight in beef cattle

Transcription

1 Development of the laser remote caliper as a method to estimate surface area and body weight in beef cattle Sarah Core, Stephen Miller, and Matthew Kelly This manuscript was prepared under the supervision of Profs. Stephen Miller and Matthew Kelly, Department of Animal and Poultry Science, Ontario Agricultural College. Linear measurements combined with surface area and volume calculations were used to develop formulas to estimate body weight (BW) in beef cattle. These measurements were evaluated directly or estimated using a laser remote caliper (LRC) and digital imaging software. Seventy-two dry, late gestation beef cows aged 3-13 years were measured and weighed Six measurements for each cow were taken; the cattle were weighed, a body condition score (BCS) was assigned, heart girth (HG), hip width (HW), and hip height (HH) were measured directly and 3 digital pictures were taken. The digital pictures portrayed three different views; side view (restrained), rear view (restrained), and side view (free-stall). Body length, HW, HH, surface area and volume were indirectly calculated from the digital images. For each view a complete (C-) formula (direct and indirect measures) and remote (R-) formula (only indirect measures) to estimate BW was developed. The R-squared values , , , , 0.611, were attained for C-side view free-stall, C-side view (restrained), C-rear view (restrained), R-side view free-stall, R-side view (restrained), and R-rear view (restrained). The accuracy of these formulas was 81% on average. BCS, HG and HW were the most significant factors when developing a formula for BW (p-value < 0.001). Side view (restrained) image measurements were most accurate in estimating BW. These measurements were highly correlated with the direct measurements and digital linear body measurements were not distorted (due to poor posture/positioning) as seen with the other views. The results for this study show that linear measurements collected by digital imaging methods can be a useful tool for estimating BW. B ody weight estimation for production animals is a crucial measurement in order to develop many other protocols for animal production. This study aims to evaluate multiple means to estimate BW as well as to develop a new indirect and noninvasive method of assessment that could be used for cattle as well as other species such as large African or Asian animals that are kept in zoos. Other animals, such as large African or Asian zoo species, often provide challenges in determining an accurate BW due to their size as well as handling restrictions. 15, 16 The welfare and handling of these animals could also benefit from an indirect BW measurement. Estimation of BWs can impact animal nutrition which is an essential aspect of animal production. Creating a well balanced diet that will result in high growth and performance rates is often one of the most costly portions of animal rearing. 8 Feed additives such as protein or vitamins and minerals are often the most expensive portion of the feeding regimen and should not be wasted. Expensive nutrients can be formulated more economically if the diets are created for specific BWs. Formulation of diets for different weights should follow the guidelines that are outlined in the Nutrient Requirements of Beef Cattle. 9 BW records are also a very useful tool for developing breeding programs. If growth rates, as well as birth weight and mature weights are closely monitored, selection for animals with high growth rates or with moderate mature weights will increase the efficiency of later generations within the herd. 4 Other types of descriptive measurements such as udder conformation or body condition score (BCS) would also be useful in breeding programs if accurate records were kept. These types of descriptive measurements are often subject to bias, so if records were kept as digital image files, analysis could be more uniformly performed and they could become very useful in selection and breeding. 10 CURRENT METHODS FOR ESTIMATING BODY WEIGHT The electronic scale is the most direct method to estimate BW and is widely used among producers. This method of measurement is one of the most accurate methods for determining BW. Most of the variance that occurs when using an electronic scale has to do with daily fluxes in gut fill 57

2 and the amount of mammary secretions, as well as weight shifting by the cow. 2 Some of the major drawbacks of this method are that it requires confinement and close contact with the animals and can be time consuming, which is why not all producers use these scales. Currently there are not many indirect methods that can be used to accurately predict the BW of cattle quickly and affordably. Methods that are available are often only designed for experimental use due to costs of construction and the complex analyses that are needed. Some current methods that are used for research include digital imaging techniques to assess body condition, as well as computer vision systems. Both methods require expensive digital imaging equipment, very specific housing conditions and indepth image analysis. 3, 17 Other techniques that are used to estimate BW include the Heart Girth Measurement as well as the Hipometer method. Both are straightforward techniques but require close contact with restrained animals and uniform placement of the measuring devices for an accurate calculation. 6, 1 This study developed methods to estimate BW in beef cattle while overcoming some of the shortcomings of the techniques mentioned above. This new method was compared and contrasted to other techniques for accuracy as well as for simplicity. The goal was to determine a method that will be easy to use and will promote record keeping for future use. OBJECTIVE AND HYPOTHESIS The objective of this study was to determine an inexpensive and non-invasive method to accurately estimate BW of beef cattle in order to increase the efficiency of many aspects of production. A digital imaging device was developed in order to digitally measure linear body dimensions and to calculate surface area, volume, and BW. It was hypothesized that BW in cattle can be extrapolated from surface area and volume measurements that are collected using digital photography and reference points created by laser beams. METHODS AND MATERIALS The Laser Remote Caliper (LRC) is a digital imaging device containing two parallel laser beams that were used as markers for digital image analysis (Figure 1). Since the distance between the two markers was known, then surface area and volume could be extrapolated from any image (Figure 3). A calibration device could also be created to maintain the distance between the markers (Figure 2). From this information, a formula was derived to predict BW. For this study multiple images were taken from different views and then analyzed in order to conclude which images are necessary to accurately extrapolate BW. Figure 1: The Laser Remote Caliper Figure 2: Calibration Device Seventy-two cows in late gestation from the Elora Beef Research Center ranging in age from 3-13 years were utilized in order to compare the electronic scale measurement, the Hipometer, hip height (HH) and the heart girth (HG) measurement to the LRC (Figures 4 and 5). All of the cows were housed in groups of 6 in an opensided pole barn at the Elora Beef Research Center. The measurements were collected by running the animals through a squeeze chute. This allowed the cows to be weighed twice, and for measurements of HG, HH and hip width (HW) to be collected (See Figures 23-26). The HG and Hipometer measurements followed the protocols from the original experiments. 1,6 While the cows were in the chute both sideview and rear-view pictures were taken. This helped ensure that the picture was taken perpendicular to the cows and decreased estimation errors. A third picture was taken in a free-stall located at the end of the chute. A bucket of feed was placed in the corner to encourage cows to stand perpendicular to the photographer. Each cow was also assigned three BCSs (1-5), by three independent scorers, which were used with the other methods of estimation to increase accuracy. Three data sets were created from the two sets of image analysis: rear-view data, side-view data and free-stall data. These data sets were then split into two categories: Remote (measurable by LRC only) and Complete (combination of LRC and direct measurements). Once all of the images were collected, they were input and analyzed in the Sigmascan Pro 5.0 program. 14 The digital image analysis program allowed the comparison of the width of the marker points and the width and height of the cow. These measurements were taken twice for each cow in order to determine repeatability. From the digital measurements surface area, volume and other body measurements (such as HH and HW) were estimated and a BW formula was derived. The results of this formula were then compared to the values collected from the other means of measurement to calculate variances and correlations. The formulas were derived using 58

3 Figure 3: Dimensional Surface Area Calculation of a Digital Image ANOVA tables to select variables that were significantly (p-value <0.1) correlated with BW. Standard Error and Incremental R-squared values were also calculated. The incremental R-squared values were created in order to observe the amount that each coefficient contributed to the formula. These values were calculated by taking one coefficient out of the formula at a time and then subtracting the new R-squared value from the initial R-squared. The R- squared values can also be used to estimate accuracy by taking the square root of them. The formulas that were created to estimate BW only included terms that were statistically significant (p-value < 0.1) ANOVA tables were also used in order to observe the variance between measurements that were collected using the different methods. These measurements were also compared with BW in order to determine significant correlations. All of the statistical analysis was conducted in R Version Multiple methods of surface area calculation were also compared. The first method used the tracing mechanism in Sigmascan Pro The cow s body (torso and legs) was traced along the perimeter and surface area was automatically calculated. The next method required estimation of HH and body length (BL) to calculate surface area. These estimations were comparable to the measurements that were collected to determine accuracy of estimation. This method was also used for the rear-view pictures (HW x HH = surface area) as well as for the sideview and free-stall views (HH x BL) and could either be combined with side-view or rear-view measurements respectively to determine a volume. HG multiplied with BL could also be used to calculate volume. RESULTS AND DISCUSSION Average Measurements The average and standard deviations (SD) of the direct and indirect measurements were calculated in order to develop a standard for the herd. These averages and SDs from the direct measurements (BW, BCS, HG, HH and HW) are found in Table 1 and the coefficients of variation (CV) are in Table 3. The cows that were measured were a fairly uniform group of cows which may have provided bias when developing the formula to predict BW. Enevoldsen and Kristensen 2 found that different models were needed for different types of diets as well as for different stages of production and age. All of the cows that were measured in this study were pregnant, but were on three different diets and were of different ages. The different diets were not significantly correlated with varying BWs in this study therefore diet was not considered in the analysis. A good indicator of herd uniformity is consistent BCSs. The average BCS for this set of cows was 3.05 with a SD of and a CV of Height and width were also uniform throughout the herd with SD of cm and cm and CVs of and and respectively. These numbers show a very small variance in herd body type diversity. Table 2 shows the average for indirect measurements such as LRC HH, LRC HW, LRC BL, and area calculations from each picture type. From the results of Table 2 the side-view pictures were the most precise when measuring BL or area because the standard deviation was much lower for the sideview versus the free-stall view. The free-stall average for HH ( cm) is closer to the direct measurement of height 59

4 Table 1: Summary of live Body Size Measurements from Dry Pregnant Beef cows Traits Average SD BW (kg) BCS (1-5) Heart Girth (cm) Height (cm) Hip Width (cm) Age (years) (SD = standard deviation, BW = body weight, BCS = body condition score) ( cm) but this may be due to a larger SD than sideview and did not indicate that free-stall height calculations were more accurate. The SD of HH is higher than that of the side-view and the directly measured height but it was much lower than the rear-view height. The rear-view was the most distorted in terms of HH but HW is accurate. This result may have occurred because of the proximity of the LRC to the cow in the rear-view. The camera was not perpendicular to the floor when these pictures were taken in order to capture the entire height of the cow (Figures 19, 22). Therefore the height was distorted but not the width in the pictures. This was shown in Table 4 where rear-view HH was not significantly correlated with any of the other HHs. HH measurements could also have been misjudged by the cow stretching her legs out when the picture were taken (Figure 21). This increased the estimated value of HH. In future data collection, this measurement should be repeated at a distance that allows a picture to be taken so that the camera is parallel to the cow and perpendicular to the floor. Also, rear-view pictures should be taken in a free-stall environment to capture a more natural body posture. This could be achieved by taking pictures while cattle are at the feed bunk. It was observed that HH may also be skewed when the cows were in the head locking mechanism because they were often arching their backs. Height Correlations Table 4 shows the correlations between all of the HH measurements (direct and indirect) as well as their correlations with BW. Since each indirect measurement was collected twice an average was calculated. The averages for all of the data (HH, HW, and BL) were significantly correlated with both of the remote measurements, so only the averages were used in further analysis. The two measurements for side-view height were only 66% correlated. This value was much lower than the expected 90% repeatability value. Figure 12 was an example of good posture and the estimated HH for this picture is within 2 SD s of the measured height. Some variance in HH may be due to decreased visibility especially around the hooves (as in Figure 14). Figure 11 was another case where height estimation may have been skewed. In this picture the cow had an abnormal posture and the points that were used to estimate HH were not clearly defined. This was also the case in Figure 13 where the cow was not standing straight and her body was curved. These variances could be corrected in future studies by correcting positioning and using more freestall images which could help capture cows in more natural postures. BW was only significantly correlated with directly measured HH (p-value <0.001) and the second remote measurement (height2) in free-stall (p value<0.05). This indicates that estimated indirect height by itself is not a good indicator of BW because overall it was not significant. If the LRC values could be calculated more accurately, they may be more significantly correlated with BW. Table 4 showed that side-view HH was the most correlated with actual HH (P-value <0.05). This may be because all of the cows were perpendicular to the camera when the pictures were collected. For a large proportion of the free-stall pictures the cows were perpendicular to the camera because feed was provided in the corner of the free-stall and therefore were aligned so that they were perpendicular to the camera. However, as the amount of feed decreased, the cows became less interested, and some cows were not perpendicular to the camera which caused variance in the data set and the accuracy of the LRC measurements decreased. If the LRC measurement accuracy was increased, estimated HH could be a significant factor for calculating BW. Length Correlations BL was only calculated from the free-stall and side-view pictures. For further analysis, direct BLs should be measured for comparison purposes such as HH was in this study. Both the side-view and free-stall BLs were correlated with each other, but the side-view measurements were the most Figure 4: Body Length and Height Dimensions that are calculated 60

5 Table 2: Summary of Laser Remote Caliper measurements from Digital Images representing different views of Dry, Pregnant Beef Cows Side-View Rear-View Free-Stall Traits Average SD Average SD Average SD LRC Height (cm) LRC Length (cm) N/A N/A LRC Width (cm) N/A N/A N/A N/A Height x Width (cm 2 ) N/A N/A N/A N/A Height x Length (cm 2 ) N/A N/A LRC Area (cm 2 ) Table 3: Summary of Variations of live and remote image measurements of Dry, Pregnant Beef Cows Coefficient of Variation (side-view) Coefficient of Variation (rear-view) Coefficient of Variation (freestall) Traditional Traits Coefficient of Variation LRC Traits BW (kg) LRC Height (cm) BCS LRC Length (cm) N/A Heart Girth (cm) LRC Width (cm) N/A N/A Height (cm) Height x Width (cm 2 ) N/A N/A Hip Width (cm) Height x Length (cm 2 ) N/A Age LRC Area (cm 2 ) Table 4: Significant Correlations between Different Methods of Height Estimation and their Correlation to Body Weight of Dry Pregnant Beef Cattle Height FreeH1 FreeH2 RearH1 RearH2 SideH1 SideH2 FreeAve RearAve SideAve BW *** 0.45 *0.3 Height ** 0.36** 0.38*** FreeH1 n/a *** 0.29* 0.47*** 0.93*** 0.36** FreeH2 n/a 0.73*** 1 0.2* 0.43*** n/a 0.93*** n/a 0.22** RearH1 n/a 0.29* 0.2* *** n/a n/a n/a 0.74*** n/a RearH2 n/a n/a n/a 0.74*** 1 n/a n/a n/a 1.00*** n/a SideH1 0.34** 0.47*** 0.43*** n/a n/a *** 0.49*** 0.2* 0.9*** SideH2 0.36** n/a n/a n/a n/a 0.66*** 1 0.2* n/a 0.92*** FreeAve n/a 0.93*** 0.93*** n/a n/a 0.49*** 0.2* 1 n/a 0.37** RearAve n/a n/a n/a 0.74*** 1.00*** 0.2* n/a n/a 1 n/a SideAve 0.38*** 0.36** 0.22** n/a n/a 0.9*** 0.92*** 0.37** n/a 1 Signif. codes: 0 '***' '**' 0.01 '*' 0.05, not significant n/a (BW = body weight, FreeH1/FreeH2 = Freestall Height, RearH1/RearH2 = rearview height, SideH1/SideH2 = sideview height, FreeAve = average of FreeH1/FreeH2, RearAve= average of RearH1/RearH2, SideAve = average of SideH1 and SideH2) 61

6 Table 5: Significant Correlations between Different Methods of Length Estimation and their Correlation to Body Weight of Dry Pregnant Beef Cattle Length Correlations FreeL1 FreeL2 SideL1 SideL2 FreeAve SideAve BW *0.25 n/a ***0.42 ***0.47 n/a ***0.45 FreeL *** 0.27* 0.29* 0.96*** 0.3* FreeL2 0.85*** * 0.36** 0.96*** 0.34** SideL1 0.27* 0.29* *** 0.29* 0.94*** SideL2 0.29* 0.36** 0.77*** ** 0.94*** FreeAve 0.96*** 0.96*** 0.29* 0.34** ** SideAve 0.3* 0.34** 0.94*** 0.94*** 0.33** 1 Signif. codes: 0 '***' '**' 0.01 '*' 0.05, Not Significant n/a (BW = body weight, FreeL1/FreeL2 = Freestall Length, SideL1/SideL2 = sideview length, FreeAve = average of FreeL1/FreeL2, SideAve = average of SideL1 and SideL2) significantly correlated (p-value<0.001) with BW (Table 5). These results occurred for similar reasons as the above mentioned height variance within the free-stall. When the cow was standing on an angle in relation to the camera, BL becomes distorted (Figures 16, 18). Pictures are also sometimes distorted because of increased movement in the free-stall. Figure 17 shows that the lines can become blurred which may lead to some variation. The use of feed in freestall as a distracter should be maximized in any further studies. The side-view lengths 1 and 2 also had a correlation that was lower than expected (0.77). This variation may be attributed to the bars located at the front of the crate, because the scapula and humerus outlines were not as visible as in the free-stall (Figure 11-4 versus Figure 15-8) which was a more repeatable measure (correlation between free-stall BL 1 and 2 was 0.85). Further research could be done including the head and neck within the measurement to see if there are any significant differences between using the full length of the cow versus just the length of the torso. Heinrichs et al. 6 found that BL is significantly correlated with BW (R-squared = 0.936, P < 0.01) where the correlation for BL and BW in this study was only 0.2. These results show that there may be large difference between dairy and beef cattle in linear measurements. Also Heinrich et al. did not state averages and CVs for the herd that they collected their data from. 6 The herd may not have had much variance in BW and this would artificially inflate the correlation values. Hip Width Correlations The results from Table 6 were collected from the rear-view pictures exclusively. BW was highly correlated with both the actual HW and the LRC HW. The repeatability for the LRC measurement of HW was statistically significant (p-value <0.001) and these measurements were correlated with the actual HW. Many other studies have also found that HW is highly correlated with BW. Dingwell et al. found that HW (measured with a Hipometer) was significantly correlated with BW (0.94) and Enevoldsen and Kristensen found that HW was the strongest single predictor of BW. HW was one of the most accurate and precise (CV 0.10) measurements because it is a skeletal measurement which does not alter significantly with different postures. 1,2 Area Correlations The difference between HH x HW and LRC area was the air space below the cow s torso. The traced area was a more accurate calculation but was also more time consuming (See LRC Image Analysis Step 3). Table 7 showed that the traced area was more significantly correlated with BW than the HH x HW calculation. This is because the variance in the HH x HW area calculations was not consistent for all cows. Taller cows had a greater distance between the torso and the ground which did not increase BW significantly. Further research should be done to determine if height of the torso x HW would be more correlated with BW and LRC area. BW is highly correlated (p-value < 0.001) with the sideview traced area calculations and somewhat correlated (pvalue < 0.05) with side-view HH x HW area calculations (Table 7). This was due to the high level of accuracy of height and length estimations from this view as well as the consistency of cow orientation to the camera. BW was also correlated with the free-stall and rear-view traced area measurements, but the correlation was not as strong. Free-stall and side-view areas are somewhat correlated and this correlation has potential to increase if more cows in the free-stall pictures were positioned perpendicular to the 62

7 camera. The correlation between traced area and HH x HW is significant for each type of picture; therefore traced area should be used to predict BW because it is more significantly correlated with BW (0.62 vs 0.34). Body Weight The BW data that was collected from two different scales on the same day was analyzed to ensure that the data was accurate. The measures on the two scales had a correlation of 1.00 (p-value <0.001). Body Weight Formulations From the nine formulas that were derived to estimate BW (Tables 9 17), complete side-view and rear-view formulas were the most accurate because of the high R-squared values. The most significant measurements that contributed to BW were BCS, HG and HH x HW. HG was the only value that could not be estimated from data collected from the LRC. More analysis could be completed to analyze HG correlations. For example, HG may be estimated from torso height. Other studies have also found that HG is significantly correlated (0.94) with BW. 1 Free-stall The free-stall complete analysis had the most significant variables included in the formula. Table 9 portrayed the coefficients and the statistical analysis for this view. Height was only slightly significant (p-value < 0.1) in this formula, where the other variables were strongly significant p-value < 0.001). This model had a high R-squared value and therefore had a low amount of variability that was left unexplained by this model. BCS, HG and HW contribute significantly to this model (high incremental R-squared) and were important for predicting BW as observed by Table 6: Correlations between Different Methods of Hip Width Estimation and their Correlation to Body Weight of Dry Pregnant Beef Cattle LRC Hip1 LRC Hip2 Hip Width Hip Ave BW **0.37 **0.33 ***0.72 **0.35 LRC Hip *** 0.23* 1.00*** LRC Hip2 1.0*** * 1.00*** Hip Width 0.23* 0.23* * Hip Ave 1.00*** 1.00*** 0.23* 1 Signif. codes: 0 '***' '**' 0.01 '*' 0.05 BW = body weight, LRC Hip1/LRC Hip 2 = rearview hip width, Hip width = traditionally measured, Hip ave = average LRC Hip1/LRC Hip 2) Figure 5: Hip Width Measurement Enevoldson and Kristensen. 2 Since these variables were direct measurements and were not available in the remote calculations, the Multiple R-squared value decreased when these values were removed. Table 10 portrayed the statistical analysis and formula coefficients for remote measures available in the free-stall. Breed was a significant factor for this analysis and it appeared that it was only significant when HG was not present. The standard error for Continental and Piedmontese breeds was calculated in comparison to British breeds. Figure 6 showed that breeds such as Piedmontese have a more uniform and smaller HG, so breed may be an indicator of HG, which is a significant factor when calculating BW. From the coefficients in Table 10, Continental and Piedmontese breeds were lighter in BW than British breeds. Further investigation could determine the strength of this correlation. BCS was also a very important estimator of BW, especially extreme body scores/weights as observed in Figure 7. BCS is also correlated with HG. When HG is not used, BCS becomes more significant. Figure 8 shows the relationship between HG and BCS. Side-View The length coefficient in the complete side-view was a positive value while in the free-stall it appeared as a negative value. This might have been due to other coefficients in the model such as the HH x BL coefficient (free-stall: side view: ). All of the other coefficients were similar to the free-stall values, except that height was not significant. Side-view height was much lower than the directly measured height and free-stall height (Table 2). The R-squared value was also high for the complete side-view (Table 11). R-squared decreased to in the remote side-view (Table 12) but this value was higher than the remote freestall (R-squared = ). Breed was not significant in this view. BL, traced area and BCS were all positively correlated with BW, while HH x BL was negatively correlated. 63

8 HEART.GIRTH BW HEART.GIRTH B C P breed BCS BCS (Breed C = Continental Breed, Breed P = Piedmontese Breed, Breed B = British Breeds) Figure 6: Distribution of Heart Girth (cm) in Dry Pregnant Beef Cattle of 3 Breed Types Figure 7: Distribution of Body Weight (kg) in Dry Pregnant Beef Cattle for different body conditions Figure 8: Relationship between Body Condition Score and Heart Girth (cm) in Dry Pregnant Beef Cattle Table 7: Significant Correlations between Different Methods of Area Estimation and their Correlation to Body Weight of Dry Pregnant Beef Cattle free TA1 free TA2 free HW1 free HW2 side TA1 side TA2 side HW1 side HW2 rear TA1 rear TA2 rear HW1 rear HW2 BW 0.37* 0.38* n/a n/a ***0.51 ***0.62 **0.3 **0.34 *0.25 *0.27 *0.2 n/a freeta *** 0.73*** n/a 0.3* 0.42*** 0.34** 0.2* n/a n/a n/a n/a freeta2 0.75*** *** 0.47*** n/a 0.36*** n/a 0.27* n/a n/a n/a n/a freehw1 0.73*** 0.57** 1 n/a n/a 0.34** 0.4*** 0.24* n/a n/a n/a n/a freehw2 n/a 0.47*** n/a 1 n/a n/a n/a n/a n/a n/a n/a n/a sideta1 0.3* n/a n/a n/a *** 0.69*** 0.46*** n/a n/a n/a n/a sideta2 0.42*** 0.36*** 0.34** n/a 0.66*** *** 0.57*** n/a n/a n/a n/a sidehw1 0.34** n/a 0.4*** n/a 0.69*** 0.42*** *** n/a n/a n/a n/a sidehw2 0.2* 0.27* 0.24* n/a 0.46*** 0.57*** 0.57*** 1 n/a n/a n/a n/a rearta1 n/a n/a n/a n/a n/a n/a n/a n/a *** 0.94*** 0.69*** rearta2 n/a n/a n/a n/a n/a n/a n/a n/a 0.85*** *** 0.79*** rearhw1 n/a n/a n/a n/a n/a n/a n/a n/a 0.94*** 0.84*** *** rearhw2 n/a n/a n/a n/a n/a n/a n/a n/a 0.69*** 0.79*** 0.70*** 1 Signif. codes: 0 '***' '**' 0.01 '*' 0.05, Not Significant n/a (BW = body weight, FreeTA1/FreeTA2 = Freestall Traced Area, FreeHW1/FreeHW2 = Freestall Height x Length Area, RearTA1/RearTA2 = rearview Traced Area, RearHW1/RearHW2 = rearview Height x Width Area, SideTA1/SideTA2 = sideview traced area, SideHW1/SideHW2 = sideview height x length area) 64

9 Trace.vol L.x.HG HW.vol Figure 9: The Relationship Between (Height x length x Width) Volume (cm 3 ) and Traced Volume (cm 3 ) in Dry Pregnant Beef Cattle BW Figure 10: The Relationship Between (length x Heart Girth) Volume (cm3) and Body Weight (kg) in Dry Pregnant Beef Cattle Table 8: Correlations between Electronic Scale Measures of Body Weight in Beef Cattle BW1 BW2 BW *** BW2 1.00*** 1 (BW1/BW2 = body weight) From this it was hypothesized that HH was also negatively correlated with BW because there was more space under taller cows which does not contribute to BW. Rear-view Table 13 portrays the complete rear-view coefficients and statistical analysis. Directly measured height was significant for this view. This showed that it was important to collect more pictures to determine if it was possible to estimate rearview height more accurately. LRC HW was more significant than measured HW. This could be due to a higher variance in the LRC width measurements which allowed for a greater array of values to compare to BW. The rear-view coefficients were all positively correlated with BW. The surface area coefficients were very different from the other views because width x height was used to calculate surface area rather than length x height. Rear-view had a high R- squared value (0.8078). This value dropped to for remote rear-view (Table 14). Since the complete rear-view had the highest R-squared value more research should be focused on rear-view traits in order to predict BW. The remote value could be increased significantly if height could accurately be estimated. The Piedmontese breed was significantly different from the British breeds in the remote rear-view which could be attributed to different HGs as discussed earlier. LRC height was significant for this formula; however it was not significantly correlated with BW or any of the other heights (Table 4). Volume Volume was calculated using three different methods. The first method was calculated by multiplying the traced surface areas from the side-view and the rear-view. The second method multiplied the direct HH with BL and direct HW. The third calculation multiplied HG by BL. Volume was a good tool for estimating BW. The R- squared values were and and for traced and HH x BL x HW and HG x BL respectively. Tables show the coefficients and statistical analysis for the volume calculations. All of the volume calculations were significantly correlated with each other (0.89) and had similar Multiple and partial R-squared values, p-values and SE. The high correlation may be due to the correlation between HH x BL x HW and traced volume (Figure 9). The R-squared values were high when comparing with some of the other formulas for BW estimation. The Partial R-squared value for HG was high, so in order to make volume a remote calculation, HG would need to be estimated from a LRC measurable trait in order to maintain a high R-squared value. Since the three methods for estimating volume are similar in accuracy, HH x HW x BL may be a more useful calculation because it was much easier and quicker to calculate than the traced volume and could be completed 65

10 Table 9: Estimation of Body Weight Formula and Statistics for the Freestall Complete View of Dry Pregnant Beef Cattle intercept BreedC BreedP Area (cm 2 ) BCS coefficients p-value * ** *** SE Incremental R Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Multiple R-squared: (Breed C = Continental Breed, Breed P = Piedmontese Breed breed difference compared to British breeds) Table 10: Estimation of Body Weight Formula and Statistics for the Freestall Remote View of Dry Pregnant Beef Cattle intercept BreedC BreedP Area (cm 2 ) BCS coefficients p-value * ** *** SE Incremental R Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Multiple R-squared: (Breed C = Continental Breed, Breed P = Piedmontese Breed breed difference compared to British breeds) Table 11: Estimation of Body Weight Formula and Statistics for the Side Complete View of Dry Pregnant Beef Cattle Intercept LRC Length (cm) trace area (cm 2 ) height x length (cm 2 ) BCS Heart Girth (cm) Hip Width (cm) coefficients p-value *** *** ** *** *** *** SE Incremental R Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Multiple R-squared:

11 Table 12: Estimation of Body Weight Formula and Statistics for the Side Remote View of Dry Pregnant Beef Cattle Intercept LRC Length (cm) traced area (cm 2 ) height x length (cm 2 ) BCS coefficients p-value *** *** * *** SE Incremental R Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Multiple R-squared: Table 13: Estimation of Body Weight Formula and Statistics for the Rear Complete View of Dry Pregnant Beef Cattle Intercept Height (cm) LRC Hip Width (cm) Actual Hip Width (cm) Traced Area (cm 2 ) BCS Heart Girth (cm) coefficients p-value *** *** ** *** *** *** SE Incremental R Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1; Multiple R-squared: Table 14: Estimation of Body Weight Formula and Statistics for the Rear Remote View of Dry Pregnant Beef Cattle intercept breedc breedp Height (cm) height x width (cm 2 ) BCS coefficients p-value *** * *** *** *** SE Incremental R Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Multiple R-squared: (Breed C = Continental Breed, Breed P = Piedmontese Breed breed variance compared to British breeds) Table 15: Estimation of Body Weight Formula and Statistics for Traced Volume (side-view area x rear-view area) of Dry Pregnant Beef Cattle Intercept Breed C Breed P BCS Heart Girth (cm) Volume (traced) (cm 3 ) coefficients E-03 p-value * *** *** *** SE E-05 Incremental R Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1; Multiple R-squared: (Breed C = Continental Breed, Breed P = Piedmontese Breed breed variance compared to British breeds) 67

12 Table 16: Estimation of Body Weight Formula and Statistics for Volume (Height x LRC Length x Hip width) of Dry Pregnant Beef Cattle Intercept Breed C Breed P BCS Heart Girth (cm) Volume (HxLxW) (cm 3 ) coefficients E-04 p-value * *** *** ** SE E-05 Incremental R Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1; Multiple R-squared: (Breed C = Continental Breed, Breed P = Piedmontese Breed breed variance compared to British breeds) Table 17: Estimation of Body Weight Formula and Statistics for Volume (LRC Length x Heart Girth) of Dry Pregnant Beef Cattle Intercept Breed C Breed P BCS Volume ( L x HG) coefficients E+00 p-value * *** *** SE Incremental R Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1; Multiple R-squared: remotely versus the HG x BL calculation. If HG was available then HG x BL may be a useful formula. It had the highest R-squared value for volume calculations (0.6806). BCS was very significant in this calculation with a partial R- squared value of This was a simple subjective score that did not need many variables and was still very accurate. Future study of remote volume could analyze the correlation between chest depth x BL to see if the correlation to BW is similar. GENERAL DISCUSSION The above mentioned results proved that it was possible to estimate BW and linear body dimensions remotely. This had many positive implications such as increasing intensity of selection on farms especially if no method of BW estimation is currently in place due to time or financial constraints. From the R-squared values BW could be estimated remotely with 75% accuracy. Even if cows were weighed regularly it might be more efficient to use the LRC rather than running the entire herd through a chute and using traditional methods for weight estimation. The LRC also provided other advantages such as providing linear body dimensions. Northcutt et al. 10 found that the heritability of HH is 0.83+/- 0.11, while the heritability for BW is only 0.48+/ Since HH is significantly correlated with BW, if HH is selected for breeding, than genetic change for BW will improve. Genetic change would also increase significantly if repeatability increased for measuring HH remotely since accuracy is directly proportional to genetic change. The heritabilities for the other linear measurements, such as HW, BL and HG, are much lower: 0.2 +/ -0.1, /- 0.12, / respectively. 5 Following this study, there is much room for further research. From the pictures that are currently available, more linear measurements could determine if HG could be estimated or if there are other measurements that may be significant in estimating BW. If more digital images were collected, there could be many alterations to the methods of picture collection. A set of standards should be created in order to regulate the quality of the pictures. These standards might include cow posture and positioning, which was variable in most of the pictures that were analyzed. Some other standards could also regulate the distance that the picture was taken from the cow (in order to ensure proper angles). Also a LRC could be created with three lasers that 68

13 Core, Miller, and Kelly are set up in a triangular formation. This would allow measurements in order to decrease the significance of body positioning. The lasers should form an equilateral triangle and if they were skewed in the image, than the rest of the measurements could be altered in order to increase the accuracy. Further pictures could also be taken in the free-stall in order to collect rear images and to see if the accuracy would increase or if it would be possible to collect rear-view pictures while the animals are unrestrained. If the accuracy and consistency could be increased on traits such as HH then these measures might become more significant when estimating BW. This effect could also be observed if the outliers were removed and the data was re-analyzed. CONCLUSIONS There are many different methods to indirectly calculate BW, each with varying levels of accuracy. BCS, HG and HW were factors that significantly contributed to successful estimation throughout all of the different views and methods. In order to successfully calculate BW by a remote device, a method for HG estimation needs to be developed. If pictures were taken accurately so that they were orientated perpendicular to the cows, the LRC could be a useful tool for indirect estimation of BW. Side View Figure 11 Figure 12 Figure 13 Figure 16 Figure 17 Figure 14 Free Stall Figure Figure 18

14 Core, Miller, and Kelly Rear View Figure 19 Figure 20 Figure 21 Figure 22 LRC LRC P PICTURE ICTURE A ANALYSIS NALYSIS (FIGURES 23 26) Step 1: Laser Distance Estimation Cow # Picture # View 1 pic 1 Side laser Laser Laser A laser B distance Distance Position Position (Pixels ) (Inches ) laser Distance (inches ^2) Step 2: Height/Length Estimation Las er Dis tanc e (Inc hes ) Height A Height B 1400 Height (inc hes ) Length A (pix els ) 636 Length B (pix els ) 2212 Length (inc hes ) 62.91

15 Step 3: Traced Area Las er Dis tanc e (Inc hes ) las er Dis tanc e (inc hes ^2) Height (inc hes ) Length (inc hes ) Trac e P erim eter Trac e P erim eter (Inc hes ) Trac e A rea Trac e area (inc hes ) Height x Length A rea Step 4: Rear View Analysis la s e r d is t a n c e (P ix e ls ) L a s e r D is t a n c e (In c h e s ) la s e r D is t a n c e (in c h e s ^2 ) L a s e r A la s e r B C o w # P ic t u re # V ie w P o s it io n P o s it io n 1 p ic 2 R e a r

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