The compilation of a Body Weight Composite (BWC) to predict body weight in SA Holstein cattle.

The compilation of a Body Weight Composite (BWC) to predict body weight in SA Holstein cattle. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The compilation of a Body Weight Composite (BWC) to predict body weight in SA Holstein cattle. D Jacobus van Niekerk, F.W.C. Neser, M.D. Fair, B.E. Mostert This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7081048/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Apr, 2026 Read the published version in Tropical Animal Health and Production → Version 1 posted 2 You are reading this latest preprint version Abstract This study reviews the possibility of using body measurements to predict body weight by compiling a body weight composite (BWC) for Holstein cattle in intensive management systems. A data set of 701 records from three farms that feed a total mixed ration was used to build a BWC. The BWC included the following traits and attributes with their proportional contributions: Wither height (18%), body depth (8%), angularity (-16%), rump width (11%), chest width (15%), days in milk at classification (18%) and age at classification (14%). A linear regression was fitted for the BWC against the cows’ realised weights. The regression equation was y = 82.377x + 206.11 with an R 2 of 0.617. This regression was used in a verification data set to establish the usefulness of the BWC to predict body weight. The correlation between the predicted and the realised weights was 59%, with the average difference between the predicted and realised weights being 3.2%. It is concluded that the BWC is a useful indicator of body weight for Holstein cows in intensive management systems. Age at classification days in milk linear traits total mixed ration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction In modern dairy management systems body weight recording occurs every time a cow is milked (delaval.com). Unfortunately the capturing of cow weights still does not occur on a regular basis on many farms. Using linear-type traits, for example rump height (RH), chest width (CW), body depth (BD), rump width (RW) and angularity (ANG), a cow size index can be derived as an indicator of cow weight. This was done by VanRaden et al. ( 2018 ) for the US Holstein population. These linear traits were used to define a Body Weight Composite (BWC) that was used in an efficiency index to identify the most efficient cows (VanRaden et al., 2018 ). Several other studies were conducted where the authors used physical measurements of body traits (eg. body length, heart girth, height at the rump or the whither) to predict cow weight, for example in in rural areas in Africa to help small-scale farmers estimate the weight of animals for management purposes (Francis et al. ( 2002 ), Lukuyu et al. ( 2016 ), Comlan et al. ( 2017 ) and Tebug et al. ( 2018 )). These studies measured various body traits (length of body, depth of body, height of the wither, and width of the rump) to predict cow weight. Dairy herds without the means to weigh cows on a regular basis need a way to determine the size of the cow for management and feeding purposes. One of these management purposes is to identify the most effective and profitable cows. Net income over feed cost is one of the most effective measurements of dairy efficiency and can be defined as the cost of total feed consumed during the period subtracted from the total milk income for the same period (Ribeiro et al., 2008 ). To determine the cost of feed, the daily feed intake of each cow should be calculated. Unfortunately, no commercial herd in South Africa measures individual feed intake- it is only calculated per group of animals. To be able to calculate feed intake for individual animals, body weight of the animals should be available. Aim To construct and verify a BWC index for South African Holstein herds with intensive management systems using linear type traits and cow weights from three South African Holstein herds that participate in performance recording. This index will be applied to determine a predicted weight for cows without body weight measurements. Materials and Methods For this study cow weight and linear type traits from three Holstein herds, located in the Northern Provinces of South Africa, were obtained. According to SA Stud Book ( 2023 ) the total number of completed lactations for registered Holstein cows in South Africa, were 4 977. These three herds are milking 1 800 cows, therefore 36% of the national completed lactations for the test year. One of the herds is based in Makhado and is the most northern commercial dairy herd in South Africa. The other two herds are based in Rayton, in Gauteng, and in Davel, in the eastern highveld. All three herds are fed a total mixed ration (TMR) with a base of maize silage. One of the three herds has full-housing for all lactating cows, while the other two herds have full housing for only part of the lactating herd. The herds use predominantly USA semen on their cows and focus in their selection objectives on production, type, fertility and health traits. Data consisted of linear classification records of 701 first lactation cows, as well as the weight of these cows measured on the day of classification. The assessment was performed by the same classifier in the period May 2020 to July 2021. All weights were obtained from electronic farm systems in which the cows are weighed after every milking. These weights are incorporated into the central database of SA Stud Book (Logix) on a weekly basis. In instances where weights on the evaluation date were not available, weights were obtained from the closest weighing date to the evaluation date, with a maximum range of 7 days before or after the assessment. The traits that were used in the analysis were: Rump height - The height of the animal is assessed at the rump on a scale from 1 to 9, where 1 is a very short cow and 9 is a very tall cow. Body depth - The depth of the cow’s body is evaluated in the last rib, where 1 is a very shallow cow and 9 is a very deep cow. Chest width - The width of the cow’s chest is assessed on the floor of the chest between the front legs, where 1 is a very narrow chest and 9 is a very wide chest. Rump width – the width of the cow’s rump is assessed by measuring the distance between the pin bones, where 1 is a very narrow setting and 9 is a wide setting. Angularity is evaluated, the wedge form of the cow – side view and top view – as well as the spring of the rib and the openness of the rib, where 1 is a very squire closed rib cow and 9 is a very wedgy open rib cow. Condition contributes to the assessment and an over-conditioned cow will be penalised towards a 1, while a low condition cow will get credited towards a 9 (World Holstein Friesian Federation, 2005 ). Data analyses were performed using SAS ( 2017 ). Pearson's correlation coefficients were calculated for all traits. A Stepwise regression (SAS, 2017 ) was performed, using a linear regression model that included all traits and attributes that might have an influence on the data. Standardised estimates were obtained for the linear type traits. Data were edited with regards to age at calving and age at classification. All records of animals older than 42 months at calving or at classification were excluded. Furthermore, the classification date should have been later than the calving date, but before the end of the lactation date. This corresponds to the criteria set out in Logix’s editing specifications for the breed’s National Genetic Evaluation (2020 Personal communication SA Stud Book). For a verification set, weights of 334 animals from the same three herds were obtained, excluding the cows that were used in the first data set. These animals were classified between 29 July 2021 and 30 November 2021. All these animals had on-farm weights for the period 7 days prior to or 7 days after linear assessment. Table 7 gives a summary of the linear data obtained. The BWC, constructed according to the weights in Table 5 , was calculated for all cows in the verification data set, to predict the live weight of the cows in the verification data set. This was to determine the applicability of BWC as a predictor of live weight. Results The data used in this study are summarised in Table 1 . Table 1 Average, standard deviation, median, minimum, and maximum linear classification traits for all three Holstein herds. Herd 1 Trait Number Min Max Average Median SD RH 403 5 8 7.02 7 0.60 CW 403 3 8 6.33 7 0.91 BD 403 4 9 6.76 7 0.67 RW 403 4 8 6.53 7 0.79 ANG 403 4 9 6.53 7 0.62 DIM 403 24 348 171.82 170 76.60 Weight 403 416 786 578.84 572 61.78 Age 403 701 1289 896.88 874 117.64 Herd 2 Trait Number Min Max Average Median SD RH 240 5 9 6.83 7 0.73 CW 240 3 8 5.84 6 0.87 BD 240 4 9 6.72 7 0.71 RW 240 3 8 6.08 6 0.87 ANG 240 4 8 6.58 7 0.61 DIM 240 5 313 108.00 111 56.02 Weight 240 436 686 536.73 532 49.95 Age 240 657 1250 862.78 854 78.47 Herd 3 Trait Number Min Max Average Median SD RH 58 3 9 6.71 7 0.85 CW 58 4 7 5.78 6 0.87 BD 58 5 8 6.55 7 0.72 RW 58 5 7 6.19 6 0.73 ANG 58 5 8 6.53 7 0.56 DIM 58 26 395 104.55 84 70.15 Weight 58 458 630 538.53 537 43.40 Age 58 710 1056 811.88 805 59.83 All Herds Trait Number Min Max Average Median SD RH 701 3 9 6.93 7 0.68 CW 701 4 9 6.12 6 0.93 BD 701 3 8 6.73 7 0.69 RW 701 3 8 6.35 6 0.84 ANG 701 4 9 6.55 7 0.61 DIM 701 5 385 144.44 140 76.66 Weight 701 416 786 560.07 552 59.16 Age 701 657 1289 877.9 858 104.82 RH = rump height, CW = chest width, BD = body depth, RW = rump width, ANG = angularity, DIM = days in milk at classification, Weight in kg = weight at classification, Age in days = age at classification Most of the animals in the data set were from Herd 1 (403 animals), followed by Herd 2 (240 animals), with the least of the animals from Herd 3 (58 animals). Herd 3 had only one classification event during this period, while Herds 1 and 2 had two events. The standard deviation for all animals was between 0.61 and 0.93 for linear traits, with CW showing the most variation and ANG the least. No animal received a score of less than 3 for any linear trait, while there were animals that obtained a maximum score of 9 for some of the linear traits, except BD and RW. The maximum classification score for these traits was 8 (Table 1 ). The average classification score for animals in Herd 1 was higher for RH (+ 0.19), CW (0.49), and RW (0.31) compared to the other herds. This was also the case for DIM (+ 67.27 days), weight (40.31 kg), and age (34.1 days). The average classification score for all traits was very similar for Herds 2 and 3. The cows of Herd 1 were on average heavier than those of Herd 2 (12kg) and 3 (18kg). Furthermore, the cows of Herd 1 were also on average older at classification compared to the cows of Herd 2 (+ 68 days) and Herd 3 (+ 51 days) (Table 1 ). Most of the weight variation in classification was observed in Herd 1, where the cow weight ranged from 416 to 786 kg. This explains the high standard deviation for weight in Herd 1 compared to the other herds. The oldest cow in classification (1 289 days) was found in Herd 1, while the youngest cow in classification (657 days) is in Herd 2. The smallest difference between minimum and maximum scores for linear traits was in Herd 3 for RW (ranging from 5 to 7). Figure 1 indicates the distribution of the scores of the classified animals across the classification range for each linear trait. The highest number of animals obtained a score of 6 or 7 for all traits (WH – 83%, CW – 87%, BD – 73%, RW – 80%, ANG – 95% of all animals). The distribution of animals in days of milk (Fig. 2 ) indicates that the largest number of animals was classified between 60 days and 240 days in milk. Only nine animals that were classified were longer than 305 days in milk, while 120 animals were classified before they were 60 days in milk. The distribution of animals throughout the weight range (Fig. 3 ) indicates that 64% weighed between 500 and 600kg on the day of classification. Fifteen animals weighed more than 700kg and nine animals less than 450kg. Fifty-one animals were older than 1 020 days at classification (Fig. 4 ). Most of these (41) were in Herd 1 which is the most northern dairy herd in South Africa, with the distance of travel of the classifier being an issue. Therefore, this herd was not classified according to the schedule, which might cause the animals to be classified at older ages (Personal communication Uys 2021). From Fig. 5 it can be seen that linear scores increased with an increase in cow weight for all linear traits, except ANG. ANG shows a general decline in weight as the angularity score increases (the animals became wedgier and more open-ribbed). As expected, as days in milk increased (Fig. 6 ), the weight of the cows also increased due to classifications being done during the cows’ first lactations when they are still in a growing phase. In Table 2 the Pearson's correlation coefficients are presented between linear type traits, DIM, age at classification, and cow weight across herds. Table 2 Pearson correlation coefficients between linear type traits, DIM, age at classification with cow weight across herds Pearson's correlation Cow weight p RH 0.46 < 0.0001 CW 0.53 < 0.0001 BD 0.37 < 0.0001 RW 0.49 < 0.0001 ANG -0.12 0.0016 DIM 0.57 < 0.0001 Age 0.54 < 0.0001 RH = rump height, CW = chest width, BD = body depth, RW = rump width, ANG = angularity, DIM = days in milk at classification, age = age at classification A stepwise regression was performed using SAS ( 2017 ) to obtain the partial contribution of all independent variables to the dependent variable, body weight, at classification. These independent variables included DIM, CW, RH, RW, ANG, AGE, and BD. The result of the stepwise regression is summarised in Table 3 . Table 3 Summary of stepwise regression indicating the significance of DIM, CW, RH, RW, ANG, and BD on cow weight Step Variable Step Partial R 2 Model R 2 C(p) F-Value Pr > F 1 DIM 1 0.3196 0.3196 532.421 328.29 < .0001 2 CW 2 0.1285 0.4481 302.158 162.58 < .0001 3 RH 3 0.0859 0.5340 149.038 128.40 < .0001 4 RW 4 0.0260 0.5600 104.044 41.14 < .0001 5 ANG 5 0.0240 0.5840 62.6730 40.10 < .0001 6 Classification Age 6 0.0245 0.6085 20.3542 43.48 < .0001 7 BD 7 0.0085 0.6170 7.0233 15.35 < .0001 All variables included in the stepwise regression contributed significantly (P < 0.0001) to the variation observed in cow weight. A Model R 2 of 0.617 was obtained when all variables were included. Standardised estimates were determined to compare the strength of the effect of DIM, age at classification, CW, RH, RW, ANG, AGE, and BD on cow weight. Table 4 Standardised estimates of DIM, age at classification, CW, RH, RW, ANG, AGE, and BD on cow weight Parameter Estimates Variable DF Parameter Estimate Standard Error t Value Pr > |t| Standardized Estimate Intercept 1 205.16728 24.13444 8.50 < .0001 0 RH 1 21.54412 2.24127 9.61 < .0001 0.25 BD 1 9.57191 2.44292 3.92 < .0001 0.11 ANG 1 -20.85782 2.52650 -8.26 < .0001 -0.22 RW 1 11.37175 1.94640 5.84 < .0001 0.16 CW 1 13.08158 1.84287 7.10 < .0001 0.21 DIM 1 0.19558 0.02532 7.73 < .0001 0.25 Age 1 0.11080 0.01886 5.88 < .0001 0.20 To compile the BWC, the results of the standardised estimates were used. In Table 5 the standardised estimates for the linear type traits, as well as their relative contributions with regard to one another, are indicated. These relative contributions were applied as weights for each standardised trait in the BWC compilation. Table 5 Standardised Estimates for the linear traits Variable Standardised Estimate Relative Contribution Weight in BWC RH 0.25 17.76% 18% BD 0.11 8.06% 8% ANG -0.22 -15.54% -16% RW 0.16 11.57% 11% CW 0.21 14.75% 15% dim 0.25 18.21% 18% age 0.20 14.10% 14% The BWC was then fitted to the data set to assess its predictability. The average BWC was 4.30 ± 0.54 and the average cow weight 560 ± 59.16 kg (Table 1 ). The BWC ranged from 2.80 to 6.13. According to the Pearson's correlations in Table 2 , DIM showed the highest correlation (0.57) with cow weight. As indicated in Fig. 6 , cow weight increased with increasing DIM. Similarly, the high correlation (0.54) between age at classification and cow weight confirms that the first lactation cows are still maturing. The highest correlation between linear type traits and cow weight was found to be with CW (0.53), followed by RW (0.49) and RH (0.46). However, there was a low negative correlation between cow weight and ANG (-0.12). This was expected as Fig. 5 indicated that cow weight decreased as the linear score for ANG increased. In Table 4 it can be seen that the largest changes will occur in days in milk (DIM) and RH (0.25 standard deviation units) with each one standard deviation unit change in body weight, followed by ANG (-0.22 standard deviation units), with the smallest change in BD (0.11 standard deviation units). Figure 7 indicates the relationship between the BWC and realised weight over all herds. A linear regression was fitted that resulted in the regression equation of y = 82.377x + 206.11, where y is body weight and x is BWC, with a R 2 value of 0.617. Higher order regressions were also fitted, but it did not increase the goodness of fit. Table 6 Formula for the BWC Trait SA Stud Book and USA This Study WH 0.23 18% CW 0.72 15% BD 0.08 8% RW 0.17 11% ANG -0.47 -16% DIM 18% Age 14% The BWC that is used by US Holstein and SA Stud Book were fitted to the same set of data. Table 6 presents the formula for the BWC used by SA Stud Book and the USA and the BWC constructed in this study. It should, however, be noted that the weightings for the USA and SA Stud Book are applied on breeding values, where DIM and age at calving and classification are adjusted for in the respective Genetic Models. The correlation between the BWC from this study and the BWC used by US Holstein and SA Stud Book applied on this dataset, was 0.78. Figure 8 is the scatter plot for BWC of SA Stud Book & US Holstein and the realised weights of the animals of this study. Table 7 is a summary of the data from the verification dataset. Table 7 Summary of the Verification dataset for validating BWC as predictor of live weight HERD 1 Trait Number Min Max Average Median SD RH 157 5 9 7.05 7 0.73 CW 157 4 8 6.46 7 0.78 BD 157 5 9 6.89 7 0.72 RW 157 5 9 6.42 6 0.82 ANG 157 6 8 6.60 7 0.52 DIM 157 19 206 113.60 110 52.46 Realised Weight 157 423 868 635 630 92.32 Age 157 711 1182 909.40 891 115.28 BWC 157 2.96 5.80 4.37 4.42 0.55 Predicted Weight 157 450 684 566 571 45.01 Difference* 157 27 -184 -69 -59 -47.31 HERD 2 Trait Number Min Max Average Median SD RH 127 5 8 6.72 7 0.60 CW 127 4 7 5.94 6 0.79 BD 127 5 8 6.76 7 0.55 RW 127 5 8 6.13 6 0.72 ANG 127 5 7 6.48 7 0.57 DIM 127 13 204 88.20 88 58.61 Realised Weight 127 415 638 520 514 44.50 Age 127 618 1004 848.45 848 66.25 BWC 127 2.90 5.0 4.01 3.99 0.43 Predicted Weight 127 445 618 536 535 35.61 Difference* 127 30 -20 16 21 -8.89 HERD 3 Trait Number Min Max Average Median SD RH 50 6 8 6.72 7 0.57 CW 50 4 8 5.98 6 0.88 BD 50 6 8 6.92 7 0.86 RW 50 4 8 6.74 6 0.86 ANG 50 5 7 6.58 7 0.53 DIM 50 39 320 113.50 125 57.12 Realised Weight 50 451 678 555 550 48.11 Age 50 737 1312 895.36 855 157.39 BWC 50 3.02 6.07 4.23 4.20 0.62 Predicted Weight 50 455 706 555 552 50.83 Difference* 50 4 28 0 2 2.72 ALL HERDS Trait Number Min Max Average Median SD RH 334 5 9 6.88 7 0.68 CW 334 4 8 6.20 6 0.84 BD 334 5 9 6.85 7 0.66 RW 334 4 9 6.28 6 0.80 ANG 334 5 8 6.55 7 0.54 DIM 334 13 320 107.00 102 54.21 Realised Weight 334 415 868 579 564 84.94 Age 334 618 1312 884.42 868 111.56 BWC 334 2.90 6.07 4.21 4.14 0.54 Predicted Weight 334 445 706 553 547 44.74 Difference* 334 30 -162 -6 -17 -40.2 RH = Rump Height, CW = Chest Width, BD = Body Depth, RW = Rump Width, ANG = Angularity, DIM = Days in milk at classification, Weight in kg = Weight at classification, Age in days = Age at classification, BWC = Body Weight Composite*Difference = Predicted Weight – Realised Weight The Verification Dataset consisted of 334 first lactation cows from the same three herds that were involved in the original data set (Table 1 ). Herd 1 contributed most records (161 records), followed by Herd 2 (127 records), with Herd 3 contributing 50 records. The average animal classified between 6.88 and 6.20 for all five linear traits, with an average weight of 576 kg, 107 days in milk and being 884 days old at classification. This correlates with the original data set (Table 1 ), except for average days in milk that were slightly higher in the original dataset (144 days vs . 107 days). The average BWC on this test-set is 4.04 ± 0.49 (the BWC for the original data set is 4.10 ± 0.54). The average realised body weight based on BWC for the test-set, is 557 kg, which is 3.80% lower than the average body weight (579kg) obtained from the electronic scales on the farm for the cows. The trend for the three herds is the same as in the original data set. Herd 1 had larger and heavier animals and were older at classification. Herd 2 had the lowest average DIM (88 days), while that of Herd 1 and 3 were 113 days. Herd 1 had also the highest BWC (4.19 vs . 3.92 and 3.90, for Herds 2 and 3, respectively). The average of the predicted weights for Herd 1, is 10.87% lower compared to that of the realised weights, while Herd 3’s averages are similar. For Herd 2 the average of the predicted weights was slightly higher compared to that of the realised weights (3.08%). Figure 9 indicates the relationship between the predicted weights against the realised weight obtained from the walk-through scale on the day (+-7 days) of classification across the three herds. The correlation obtained between these two weights is 0.59 with a R 2 = 0.3509. Discussion Studies (Francis et al. 2002 , Vallimont et al. 2010 , Lukuyu et al. 2016 , Comlan et al. 2017 , Tebug et al. 2018 , Gruber et al. 2018) were done on different populations to determine the correlation between body weight (BW) and body measurements. The majority of the studies used linear assessment to determine the contribution of the trait to the estimate of a body weight. The body measurements that were used were heart girth (HG) or chest width (CW), body length (BL) or angularity (ANG), height at the withers (HW), belly girth (BG) or body depth (DB) and the width of the rump (RW). All these studies found the correlation between HG or CW and BW to be the highest. Francis et al. ( 2002 ) studied different cattle breeds in Zimbabwe (Indigenous, Friesian, Brahman, Red Danes and Crossbreds) and found a correlation of r = 0.96 between what and what?. Lukuyu et al. ( 2016 ) reported the correlation between BW and body measurements (length of body, heart girth, width of the rump) in crossbred dairy cattle in Kenya to be 0.84. Comlan et al. ( 2017 ) studied the correlation in Lagune cattle in Benin and found a correlation of 0.96 between body measurments and live weight. Tebug et al. ( 2018 ) also found a correlation of 80% in dairy cattle in Senegal between what and what?. Gruber et al. (2018) studied the correlation in Fleckvieh, Holstein and Brown Swiss cows in Austria and found a correlation of 82% with both HG and BG between what and what?. Vallimont et al. ( 2010 ) found the phenotypic correlations between strength (strength is allocated to an animal based on the measurements of the chest, depth and width, with some attention to bone structure) and body weight in Holstein cows in commercial tie-stall barns in Pennsylvania in the USA, to be 0.42. This was the highest correlation found by Vallimont et al. ( 2010 ) between different body measurements and body weight. Vallimont et al. ( 2010 ) also found the correlation between body weight and stature to be 0.38, 0.32 with BD, 0.23 with RW and − 0.10 with dairy form. The findings in this study correlates therefore with the findings of the abovementioned studies, where the highest correlation with BW is found to be with CW (0.53). The negative correlation between BW and ANG in this study (-0.12) corresponds with the finding of Vallimont et al. ( 2010 ) of -0.10. This trend is also reflected in the BWC compiled by the US Holstein Society (0.72 on CW and − 0.47 on ANG). VanRaden et al. ( 2018 ) estimated the heritability of BWC for the Holstein population in the US to be 40%. He also determined the phenotypic correlations between BWC and production- and secondary traits. The biggest correlation is with productive life and livability (-0.20) and the lowest is with heifer conception rate (0.02). There is no correlation with daughter pregnancy rate. In 2018 SA Stud Book implemented a genetic Efficiency Index that includes a BWC index. The genetic increase of BWC over years of birth increased from − 0.5 in 2002 to 0.44 in 2017. Since 2017 it has decreased to 0.3 in 2019, probably due to breeders that started to penalize bulls that breed large body sizes in their breeding objectives. The aim of the studies done by Lukuyu et al. ( 2016 ), Comlan et al. ( 2017 ), Tebug et al. ( 2018 ) and Gruber et al. (2018) were to determine a model to predict body weight by using body measurements. Lukuyu et al. ( 2016 ) used only HG in the prediction of body weight. The model they used were within 95% of the BW. Comlan et al. ( 2017 ) used BL, HG and HW in the model and were within 94% of body weight. Tebug et al. ( 2018 ) used only HG and the variation from BW for the different breeds varied from − 7.36–11.26%. Gruber et al. (2018) used two models. The first model included HG and BG and obtained a coefficient of determination (R 2 ) of 83.0. A second model included HG, BG and HW and realised a R 2 of 83.5. Costigan et al. ( 2021 ) used body measurements in Irish Holstein Friesian heifers to determine live body weight. The traits they used, were heart girth, body volume and a polynomial of length, girth and height. The model they compiled correlated very highly with live weight (R 2 = 0.97). Wangchuk et al. ( 2017 ) used four methods to determine body weight from body measurements in Brown Swiss and Jersey cross cattle in Bhutan. All four methods included the measurement of girth and body length. The most accurate method deviated 4.7% and 4.84% from live weight measured by a scale for the two breeds, respectively. In this study the average difference between realised weights and predicted weights based on the BWC, was − 3.20%, which is in correspondence to the findings of the above studies. The correlation between the realised and predicted weights (0.59) is, however, lower than that obtained in the other studies. Conclusion A BWC index was constructed based on linear type traits, days in milk and age at classification of three TMR Holstein herds to predict cow weight of first lactation cows. Validation indicates that it is a use full predictor of cow weight in the three herds. By using the linear type data collected over years by SA Holstein for the three herds, a useful prediction of body weight can therefore be obtained for cows with unknown bodyweights to merge with data sets that include cows with measured weights. This can aid in the compilation of an Efficiency Index (EI) for identification of more efficient cows to improve herd economics and therefore improve the sustainability of dairy enterprises. Declarations Competing Interests “The authors have no relevant financial or non-financial interests to disclose.” Author Contributions “All authors contributed to the study conception and design. Data received from SA Stud Book and the SA Holstein Society was edited and analysed by D.J. van Niekerk. The first draft of the manuscript was written by D.J. van Niekerk and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.” Data Availability “The datasets generated during and/or analysed during the current study are not publicly available due to agreements signed with SA Stud Book and the SA Holstein Breed Society, but maybe made available from the corresponding author on reasonable request.].” Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the University of the Free State (Project nr: UFS-AED2024/0061). References Comlan B.G., J. Steve and A.T. Ibrahim. 2017. Use of body measurements to estimate live weight of Lagune cattle in Southern Benin. The Saudi Journal of Life Science. Costigan, H., Delaby, L., Walsh, S., Lahart, B. and Kennedy, E., 2021. The development of equations to predict live-weight from linear body measurements of pasture-based Holstein-Friesian and Jersey dairy heifers. Livestock Science (2021). Doi: https://doi.org/10.1016/j.livsci.2021.104693 Francis J., S. Sibanda and T. Kristensen, 2002. Estimating body weight of cattle using linear body measurements. Zimbabwe Veterinary Journal 33:1. Gruber l., M. Ledinek, F. Steininger, B. Fuerst-Waltl, K. Zottl, M. Royer, K. Krimberger, M. Mayerhofer and C. Egger-Danner. 2018. Body weight prediction using body size measurements in Fleckvieh, Holstein and Brown Swiss dairy cows in lactation and dry periods. Arch. Anim. Breed. 61, 413–424. Lukuyu M.N., J.P. Gibson, D.B. Savage, A.J. Duncan, F.D.N. Mujibi and A.M. Okeyo. 2016. Use of body linear measurements to estimate liveweight of crossbred dairy cattle in smallholder farms in Kenya. Springer Plus 5:63. Ribeiro A. C., A.J. McAllister and S.A. de Queiroz. 2008. Profitability measures of dairy cows. Revista Brasileira de Zootecnia. 37(9): 1607–1616. SA Stud Book, 2023. SA Stud Book Annual Holstein Report, 2022–2023. SAS, 2017. SAS Enterprise Guide, Version 4.3. SAS Institute Inc., Cary, NC, USA. Tebug S.F., A. Missohou, S.S.Sabi, J. Juga, E. J. Poole, M. Tapio and K. Marshall. 2018. Using body measurements to estimate live weight of dairy cattle in low-input systems in Senegal. Journal of applied animal research 46: 1 87–93. Vallimont, J.E., Dechow, C.D., Daubert, J.M., Dekieva, M.W., Blum, J.W., Barlieb, C.M., Liu, W., Varga, G.A., Heinrichs, A.J. and Baumrucker, C.R., 2010. Genetic parameters of feed intake, production, body weight, body condition score, and selected type traits of Holstein cows in commercial tie-stall barns. J. Dairy Sci. 93 : 4892–4901 . VanRaden P. M., Cole J.B, and Parker Gaddis K.L. 2018. Net merit as a measure of lifetime profit: 2018 revision. AIP Research Report NM$7 (5–18) Wangchuk, K., Wangdi, J. and Mindu, M., 2017. Comparison and reliability of techniques to estimate live cattle body weight. J. of applied Anim Research. 46:1, 349–352. World Holstein Friesian Federation, 2005. International type evaluation of dairy cattle. World Holstein Friesian Federation. Cite Share Download PDF Status: Published Journal Publication published 13 Apr, 2026 Read the published version in Tropical Animal Health and Production → Version 1 posted Editor assigned by journal 11 Jul, 2025 First submitted to journal 10 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7081048","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490173135,"identity":"3ec319ce-84d9-4d14-852a-efed9c09b9ac","order_by":0,"name":"D Jacobus van Niekerk","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBADHgYe5gNAWkKGFC1sCSAtPKTYw2MA0UsImLM3H3vws+2ODD/Pmc+vbtRY8DCwHz66AZ8Wy55j6Ya9bc94JHt7t1nnHAM6jCct7QY+LQY3cswkeM4c5jE4z7vNOIcNqEWCx4yAlvxvkn+AWuzP8zwzzvlHlJYcNmmeCqAtvD3Mj3PbiNAC9IuZtAxQi8SZY2bMuX0SPGyE/AIMsWeSbwwO2/P3JD/+nPOtTo6f/fAx/A5DYrNJgEl8ytG1MH8gpHoUjIJRMApGJgAAHU5DfKi/39kAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0002-2854-8804","institution":"UFS: University of the Free State","correspondingAuthor":true,"prefix":"","firstName":"D","middleName":"Jacobus van","lastName":"Niekerk","suffix":""},{"id":490173136,"identity":"e74febc8-e769-4da2-bc16-f1c6693ebc8f","order_by":1,"name":"F.W.C. Neser","email":"","orcid":"","institution":"UFS: University of the Free State","correspondingAuthor":false,"prefix":"","firstName":"F.W.C.","middleName":"","lastName":"Neser","suffix":""},{"id":490173137,"identity":"73a2e211-69b3-4ebc-b4c8-9afcdb956637","order_by":2,"name":"M.D. Fair","email":"","orcid":"","institution":"UFS: University of the Free State","correspondingAuthor":false,"prefix":"","firstName":"M.D.","middleName":"","lastName":"Fair","suffix":""},{"id":490173138,"identity":"84d3fb88-0c0c-441f-8fdb-0e5c65d04bbf","order_by":3,"name":"B.E. Mostert","email":"","orcid":"","institution":"SA Studbook","correspondingAuthor":false,"prefix":"","firstName":"B.E.","middleName":"","lastName":"Mostert","suffix":""}],"badges":[],"createdAt":"2025-07-09 07:27:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7081048/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7081048/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11250-026-05027-4","type":"published","date":"2026-04-13T15:58:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87666710,"identity":"d8a7192f-21f1-469d-a094-1853136ae228","added_by":"auto","created_at":"2025-07-27 11:37:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14193,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the scores of the different linear traits across the range of classification scores over all three herds\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7081048/v1/5475db980bed3bd147efa8bd.png"},{"id":87667135,"identity":"f3d307ba-5d66-4cb7-971f-a2607a25dcf2","added_by":"auto","created_at":"2025-07-27 11:45:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10954,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of classified animals in milk for the three herds.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7081048/v1/3676a58543e853c5a6b3354c.png"},{"id":87666711,"identity":"3ab34d20-5510-431b-bf37-df3bd4f1a487","added_by":"auto","created_at":"2025-07-27 11:37:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9854,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the cows over the weight range of measurement in all three herds\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7081048/v1/63331210456f9703dc302cdf.png"},{"id":87667436,"identity":"cec51b91-ebbc-4e61-843f-85c527631b87","added_by":"auto","created_at":"2025-07-27 11:53:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10942,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the number of animals classified for each month of age at classification\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7081048/v1/3732b663634a0d0c0a75a609.png"},{"id":87666713,"identity":"cd427250-f498-4194-96da-8f02f541ff13","added_by":"auto","created_at":"2025-07-27 11:37:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":33933,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic trends of the scores of the different linear traits compared to body weight.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7081048/v1/2c0ab8bfc7186456e4f0e7b4.png"},{"id":87666722,"identity":"6e7f51dc-f99b-4179-bd42-92edc968b551","added_by":"auto","created_at":"2025-07-27 11:37:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":17237,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic trend of days in milk against body weight\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7081048/v1/109f1f003a31bb1113c070f3.png"},{"id":87667136,"identity":"3dc6717b-b1b9-4d43-ae82-ac833aec78c3","added_by":"auto","created_at":"2025-07-27 11:45:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":29939,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of BWC against realised body weight over all the herds\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7081048/v1/45f83b9fe9d3ffe63ee07190.png"},{"id":87666721,"identity":"c718a9f9-6d98-4676-a7d9-f1533ef9ae18","added_by":"auto","created_at":"2025-07-27 11:37:37","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":43711,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot for BWC (SA Stud Book) and the realised weights in kg of the animals\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7081048/v1/cfc230fcf564e41569bf762e.png"},{"id":87666718,"identity":"6112da2a-e184-4a91-a8dd-4e73f2a295c6","added_by":"auto","created_at":"2025-07-27 11:37:37","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":28147,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted Weight \u003cem\u003eversus\u003c/em\u003e Realised Weights across the three herds\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7081048/v1/267e4cab79de883851ae33ea.png"},{"id":107350851,"identity":"d0b1434d-b284-4880-8015-7771d9c10c4e","added_by":"auto","created_at":"2026-04-20 16:05:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":895165,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7081048/v1/4899fce1-e7a9-49ed-ba79-2745f251ec5d.pdf"}],"financialInterests":"","formattedTitle":"The compilation of a Body Weight Composite (BWC) to predict body weight in SA Holstein cattle.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn modern dairy management systems body weight recording occurs every time a cow is milked (delaval.com). Unfortunately the capturing of cow weights still does not occur on a regular basis on many farms. Using linear-type traits, for example rump height (RH), chest width (CW), body depth (BD), rump width (RW) and angularity (ANG), a cow size index can be derived as an indicator of cow weight. This was done by VanRaden et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for the US Holstein population. These linear traits were used to define a Body Weight Composite (BWC) that was used in an efficiency index to identify the most efficient cows (VanRaden et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Several other studies were conducted where the authors used physical measurements of body traits (eg. body length, heart girth, height at the rump or the whither) to predict cow weight, for example in in rural areas in Africa to help small-scale farmers estimate the weight of animals for management purposes (Francis et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), Lukuyu et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Comlan et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Tebug et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)). These studies measured various body traits (length of body, depth of body, height of the wither, and width of the rump) to predict cow weight.\u003c/p\u003e\u003cp\u003eDairy herds without the means to weigh cows on a regular basis need a way to determine the size of the cow for management and feeding purposes. One of these management purposes is to identify the most effective and profitable cows. Net income over feed cost is one of the most effective measurements of dairy efficiency and can be defined as the cost of total feed consumed during the period subtracted from the total milk income for the same period (Ribeiro et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). To determine the cost of feed, the daily feed intake of each cow should be calculated. Unfortunately, no commercial herd in South Africa measures individual feed intake- it is only calculated per group of animals. To be able to calculate feed intake for individual animals, body weight of the animals should be available.\u003c/p\u003e\u003cp\u003eAim\u003c/p\u003e\u003cp\u003eTo construct and verify a BWC index for South African Holstein herds with intensive management systems using linear type traits and cow weights from three South African Holstein herds that participate in performance recording. This index will be applied to determine a predicted weight for cows without body weight measurements.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eFor this study cow weight and linear type traits from three Holstein herds, located in the Northern Provinces of South Africa, were obtained. According to SA Stud Book (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) the total number of completed lactations for registered Holstein cows in South Africa, were 4 977. These three herds are milking 1 800 cows, therefore 36% of the national completed lactations for the test year. One of the herds is based in Makhado and is the most northern commercial dairy herd in South Africa. The other two herds are based in Rayton, in Gauteng, and in Davel, in the eastern highveld. All three herds are fed a total mixed ration (TMR) with a base of maize silage. One of the three herds has full-housing for all lactating cows, while the other two herds have full housing for only part of the lactating herd. The herds use predominantly USA semen on their cows and focus in their selection objectives on production, type, fertility and health traits.\u003c/p\u003e\u003cp\u003eData consisted of linear classification records of 701 first lactation cows, as well as the weight of these cows measured on the day of classification. The assessment was performed by the same classifier in the period May 2020 to July 2021. All weights were obtained from electronic farm systems in which the cows are weighed after every milking. These weights are incorporated into the central database of SA Stud Book (Logix) on a weekly basis. In instances where weights on the evaluation date were not available, weights were obtained from the closest weighing date to the evaluation date, with a maximum range of 7 days before or after the assessment.\u003c/p\u003e\u003cp\u003eThe traits that were used in the analysis were:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eRump height - The height of the animal is assessed at the rump on a scale from 1 to 9, where 1 is a very short cow and 9 is a very tall cow.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eBody depth - The depth of the cow\u0026rsquo;s body is evaluated in the last rib, where 1 is a very shallow cow and 9 is a very deep cow.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eChest width - The width of the cow\u0026rsquo;s chest is assessed on the floor of the chest between the front legs, where 1 is a very narrow chest and 9 is a very wide chest.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eRump width \u0026ndash; the width of the cow\u0026rsquo;s rump is assessed by measuring the distance between the pin bones, where 1 is a very narrow setting and 9 is a wide setting.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAngularity is evaluated, the wedge form of the cow \u0026ndash; side view and top view \u0026ndash; as well as the spring of the rib and the openness of the rib, where 1 is a very squire closed rib cow and 9 is a very wedgy open rib cow. Condition contributes to the assessment and an over-conditioned cow will be penalised towards a 1, while a low condition cow will get credited towards a 9 (World Holstein Friesian Federation, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eData analyses were performed using SAS (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Pearson's correlation coefficients were calculated for all traits. A Stepwise regression (SAS, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) was performed, using a linear regression model that included all traits and attributes that might have an influence on the data. Standardised estimates were obtained for the linear type traits. Data were edited with regards to age at calving and age at classification. All records of animals older than 42 months at calving or at classification were excluded. Furthermore, the classification date should have been later than the calving date, but before the end of the lactation date. This corresponds to the criteria set out in Logix\u0026rsquo;s editing specifications for the breed\u0026rsquo;s National Genetic Evaluation (2020 Personal communication SA Stud Book).\u003c/p\u003e\u003cp\u003eFor a verification set, weights of 334 animals from the same three herds were obtained, excluding the cows that were used in the first data set. These animals were classified between 29 July 2021 and 30 November 2021. All these animals had on-farm weights for the period 7 days prior to or 7 days after linear assessment. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e gives a summary of the linear data obtained. The BWC, constructed according to the weights in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, was calculated for all cows in the verification data set, to predict the live weight of the cows in the verification data set. This was to determine the applicability of BWC as a predictor of live weight.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe data used in this study are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAverage, standard deviation, median, minimum, and maximum linear classification traits for all three Holstein herds.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eHerd 1\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e171.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e76.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e578.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e61.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e896.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e117.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eHerd 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e108.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e56.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e536.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e49.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e862.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e78.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eHerd 3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e104.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e70.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e538.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e43.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e811.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e59.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eAll Herds\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e144.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e76.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e560.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e59.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e877.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e104.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eRH\u0026thinsp;=\u0026thinsp;rump height, CW\u0026thinsp;=\u0026thinsp;chest width, BD\u0026thinsp;=\u0026thinsp;body depth, RW\u0026thinsp;=\u0026thinsp;rump width, ANG\u0026thinsp;=\u0026thinsp;angularity, DIM\u0026thinsp;=\u0026thinsp;days in milk at classification, Weight in kg\u0026thinsp;=\u0026thinsp;weight at classification, Age in days\u0026thinsp;=\u0026thinsp;age at classification\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e Most of the animals in the data set were from Herd 1 (403 animals), followed by Herd 2 (240 animals), with the least of the animals from Herd 3 (58 animals). Herd 3 had only one classification event during this period, while Herds 1 and 2 had two events.\u003c/p\u003e\u003cp\u003eThe standard deviation for all animals was between 0.61 and 0.93 for linear traits, with CW showing the most variation and ANG the least. No animal received a score of less than 3 for any linear trait, while there were animals that obtained a maximum score of 9 for some of the linear traits, except BD and RW. The maximum classification score for these traits was 8 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe average classification score for animals in Herd 1 was higher for RH (+\u0026thinsp;0.19), CW (0.49), and RW (0.31) compared to the other herds. This was also the case for DIM (+\u0026thinsp;67.27 days), weight (40.31 kg), and age (34.1 days). The average classification score for all traits was very similar for Herds 2 and 3.\u003c/p\u003e\u003cp\u003eThe cows of Herd 1 were on average heavier than those of Herd 2 (12kg) and 3 (18kg). Furthermore, the cows of Herd 1 were also on average older at classification compared to the cows of Herd 2 (+\u0026thinsp;68 days) and Herd 3 (+\u0026thinsp;51 days) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMost of the weight variation in classification was observed in Herd 1, where the cow weight ranged from 416 to 786 kg. This explains the high standard deviation for weight in Herd 1 compared to the other herds. The oldest cow in classification (1 289 days) was found in Herd 1, while the youngest cow in classification (657 days) is in Herd 2. The smallest difference between minimum and maximum scores for linear traits was in Herd 3 for RW (ranging from 5 to 7).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e indicates the distribution of the scores of the classified animals across the classification range for each linear trait. The highest number of animals obtained a score of 6 or 7 for all traits (WH \u0026ndash; 83%, CW \u0026ndash; 87%, BD \u0026ndash; 73%, RW \u0026ndash; 80%, ANG \u0026ndash; 95% of all animals).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe distribution of animals in days of milk (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) indicates that the largest number of animals was classified between 60 days and 240 days in milk. Only nine animals that were classified were longer than 305 days in milk, while 120 animals were classified before they were 60 days in milk.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe distribution of animals throughout the weight range (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicates that 64% weighed between 500 and 600kg on the day of classification. Fifteen animals weighed more than 700kg and nine animals less than 450kg.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFifty-one animals were older than 1 020 days at classification (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Most of these (41) were in Herd 1 which is the most northern dairy herd in South Africa, with the distance of travel of the classifier being an issue. Therefore, this herd was not classified according to the schedule, which might cause the animals to be classified at older ages (Personal communication Uys 2021).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFrom Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e it can be seen that linear scores increased with an increase in cow weight for all linear traits, except ANG. ANG shows a general decline in weight as the angularity score increases (the animals became wedgier and more open-ribbed).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs expected, as days in milk increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), the weight of the cows also increased due to classifications being done during the cows\u0026rsquo; first lactations when they are still in a growing phase.\u003c/p\u003e\u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e the Pearson's correlation coefficients are presented between linear type traits, DIM, age at classification, and cow weight across herds.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePearson correlation coefficients between linear type traits, DIM, age at classification with cow weight across herds\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePearson's correlation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCow weight\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eRH\u0026thinsp;=\u0026thinsp;rump height, CW\u0026thinsp;=\u0026thinsp;chest width, BD\u0026thinsp;=\u0026thinsp;body depth, RW\u0026thinsp;=\u0026thinsp;rump width, ANG\u0026thinsp;=\u0026thinsp;angularity, DIM\u0026thinsp;=\u0026thinsp;days in milk at classification, age\u0026thinsp;=\u0026thinsp;age at classification\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA stepwise regression was performed using SAS (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to obtain the partial contribution of all independent variables to the dependent variable, body weight, at classification. These independent variables included DIM, CW, RH, RW, ANG, AGE, and BD. The result of the stepwise regression is summarised in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of stepwise regression indicating the significance of DIM, CW, RH, RW, ANG, and BD on cow weight\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePartial R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eC(p)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePr\u0026nbsp;\u0026gt;\u0026nbsp;F\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDIM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.3196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e532.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e328.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e302.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e162.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e149.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e128.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e104.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e41.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eANG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e62.6730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e40.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClassification Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20.3542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e43.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.0233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAll variables included in the stepwise regression contributed significantly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) to the variation observed in cow weight. A Model R\u003csup\u003e2\u003c/sup\u003e of 0.617 was obtained when all variables were included.\u003c/p\u003e\u003cp\u003eStandardised estimates were determined to compare the strength of the effect of DIM, age at classification, CW, RH, RW, ANG, AGE, and BD on cow weight.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStandardised estimates of DIM, age at classification, CW, RH, RW, ANG, AGE, and BD on cow weight\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eParameter Estimates\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandard\u003c/p\u003e\u003cp\u003eError\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026nbsp;Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePr\u0026nbsp;\u0026gt;\u0026nbsp;|t|\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eStandardized\u003c/p\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e205.16728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.13444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.54412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.24127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.57191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.44292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-20.85782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.52650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-8.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.37175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.94640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.08158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.84287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.19558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo compile the BWC, the results of the standardised estimates were used. In Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e the standardised estimates for the linear type traits, as well as their relative contributions with regard to one another, are indicated. These relative contributions were applied as weights for each standardised trait in the BWC compilation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStandardised Estimates for the linear traits\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStandardised\u003c/p\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRelative Contribution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWeight in BWC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.76%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.06%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-15.54%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-16%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.57%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.75%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.21%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe BWC was then fitted to the data set to assess its predictability. The average BWC was 4.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54 and the average cow weight 560\u0026thinsp;\u0026plusmn;\u0026thinsp;59.16 kg (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The BWC ranged from 2.80 to 6.13.\u003c/p\u003e\u003cp\u003eAccording to the Pearson's correlations in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, DIM showed the highest correlation (0.57) with cow weight. As indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, cow weight increased with increasing DIM. Similarly, the high correlation (0.54) between age at classification and cow weight confirms that the first lactation cows are still maturing. The highest correlation between linear type traits and cow weight was found to be with CW (0.53), followed by RW (0.49) and RH (0.46). However, there was a low negative correlation between cow weight and ANG (-0.12). This was expected as Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e indicated that cow weight decreased as the linear score for ANG increased.\u003c/p\u003e\u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e it can be seen that the largest changes will occur in days in milk (DIM) and RH (0.25 standard deviation units) with each one standard deviation unit change in body weight, followed by ANG (-0.22 standard deviation units), with the smallest change in BD (0.11 standard deviation units).\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e indicates the relationship between the BWC and realised weight over all herds. A linear regression was fitted that resulted in the regression equation of y\u0026thinsp;=\u0026thinsp;82.377x\u0026thinsp;+\u0026thinsp;206.11, where y is body weight and x is BWC, with a R\u003csup\u003e2\u003c/sup\u003e value of 0.617. Higher order regressions were also fitted, but it did not increase the goodness of fit.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFormula for the BWC\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSA Stud Book and USA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThis Study\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-16%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe BWC that is used by US Holstein and SA Stud Book were fitted to the same set of data. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the formula for the BWC used by SA Stud Book and the USA and the BWC constructed in this study. It should, however, be noted that the weightings for the USA and SA Stud Book are applied on breeding values, where DIM and age at calving and classification are adjusted for in the respective Genetic Models.\u003c/p\u003e\u003cp\u003eThe correlation between the BWC from this study and the BWC used by US Holstein and SA Stud Book applied on this dataset, was 0.78. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e is the scatter plot for BWC of SA Stud Book \u0026amp; US Holstein and the realised weights of the animals of this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e is a summary of the data from the verification dataset.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of the Verification dataset for validating BWC as predictor of live weight\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eHERD 1\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e113.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRealised Weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e92.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e909.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e115.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBWC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted Weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e45.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifference*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-47.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eHERD 2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRealised Weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e44.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e848.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBWC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted Weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifference*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-8.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eHERD 3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e113.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRealised Weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e48.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e895.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e157.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBWC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted Weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e50.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifference*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eALL HERDS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e107.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRealised Weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e84.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e884.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e111.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBWC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredicted Weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e44.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifference*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-40.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eRH\u0026thinsp;=\u0026thinsp;Rump Height, CW\u0026thinsp;=\u0026thinsp;Chest Width, BD\u0026thinsp;=\u0026thinsp;Body Depth, RW\u0026thinsp;=\u0026thinsp;Rump Width, ANG\u0026thinsp;=\u0026thinsp;Angularity, DIM\u0026thinsp;=\u0026thinsp;Days in milk at classification, Weight in kg\u0026thinsp;=\u0026thinsp;Weight at classification, Age in days\u0026thinsp;=\u0026thinsp;Age at classification, BWC\u0026thinsp;=\u0026thinsp;Body Weight Composite*Difference\u0026thinsp;=\u0026thinsp;Predicted Weight \u0026ndash; Realised Weight\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe Verification Dataset consisted of 334 first lactation cows from the same three herds that were involved in the original data set (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Herd 1 contributed most records (161 records), followed by Herd 2 (127 records), with Herd 3 contributing 50 records. The average animal classified between 6.88 and 6.20 for all five linear traits, with an average weight of 576 kg, 107 days in milk and being 884 days old at classification. This correlates with the original data set (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), except for average days in milk that were slightly higher in the original dataset (144 days \u003cem\u003evs\u003c/em\u003e. 107 days). The average BWC on this test-set is 4.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49 (the BWC for the original data set is 4.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54). The average realised body weight based on BWC for the test-set, is 557 kg, which is 3.80% lower than the average body weight (579kg) obtained from the electronic scales on the farm for the cows.\u003c/p\u003e\u003cp\u003eThe trend for the three herds is the same as in the original data set. Herd 1 had larger and heavier animals and were older at classification. Herd 2 had the lowest average DIM (88 days), while that of Herd 1 and 3 were 113 days. Herd 1 had also the highest BWC (4.19 \u003cem\u003evs\u003c/em\u003e. 3.92 and 3.90, for Herds 2 and 3, respectively).\u003c/p\u003e\u003cp\u003eThe average of the predicted weights for Herd 1, is 10.87% lower compared to that of the realised weights, while Herd 3\u0026rsquo;s averages are similar. For Herd 2 the average of the predicted weights was slightly higher compared to that of the realised weights (3.08%). Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e indicates the relationship between the predicted weights against the realised weight obtained from the walk-through scale on the day (+-7 days) of classification across the three herds. The correlation obtained between these two weights is 0.59 with a R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.3509.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eStudies (Francis et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Vallimont et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Lukuyu et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Comlan et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Tebug et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Gruber \u003cem\u003eet al.\u003c/em\u003e 2018) were done on different populations to determine the correlation between body weight (BW) and body measurements. The majority of the studies used linear assessment to determine the contribution of the trait to the estimate of a body weight. The body measurements that were used were heart girth (HG) or chest width (CW), body length (BL) or angularity (ANG), height at the withers (HW), belly girth (BG) or body depth (DB) and the width of the rump (RW). All these studies found the correlation between HG or CW and BW to be the highest. Francis et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) studied different cattle breeds in Zimbabwe (Indigenous, Friesian, Brahman, Red Danes and Crossbreds) and found a correlation of r\u0026thinsp;=\u0026thinsp;0.96 between what and what?. Lukuyu et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported the correlation between BW and body measurements (length of body, heart girth, width of the rump) in crossbred dairy cattle in Kenya to be 0.84. Comlan et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) studied the correlation in Lagune cattle in Benin and found a correlation of 0.96 between body measurments and live weight. Tebug et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also found a correlation of 80% in dairy cattle in Senegal between what and what?. Gruber \u003cem\u003eet al.\u003c/em\u003e (2018) studied the correlation in Fleckvieh, Holstein and Brown Swiss cows in Austria and found a correlation of 82% with both HG and BG between what and what?. Vallimont et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found the phenotypic correlations between strength (strength is allocated to an animal based on the measurements of the chest, depth and width, with some attention to bone structure) and body weight in Holstein cows in commercial tie-stall barns in Pennsylvania in the USA, to be 0.42. This was the highest correlation found by Vallimont et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) between different body measurements and body weight.\u003c/p\u003e\u003cp\u003eVallimont et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) also found the correlation between body weight and stature to be 0.38, 0.32 with BD, 0.23 with RW and \u0026minus;\u0026thinsp;0.10 with dairy form.\u003c/p\u003e\u003cp\u003eThe findings in this study correlates therefore with the findings of the abovementioned studies, where the highest correlation with BW is found to be with CW (0.53). The negative correlation between BW and ANG in this study (-0.12) corresponds with the finding of Vallimont et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) of -0.10. This trend is also reflected in the BWC compiled by the US Holstein Society (0.72 on CW and \u0026minus;\u0026thinsp;0.47 on ANG).\u003c/p\u003e\u003cp\u003eVanRaden et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) estimated the heritability of BWC for the Holstein population in the US to be 40%. He also determined the phenotypic correlations between BWC and production- and secondary traits. The biggest correlation is with productive life and livability (-0.20) and the lowest is with heifer conception rate (0.02). There is no correlation with daughter pregnancy rate.\u003c/p\u003e\u003cp\u003eIn 2018 SA Stud Book implemented a genetic Efficiency Index that includes a BWC index. The genetic increase of BWC over years of birth increased from \u0026minus;\u0026thinsp;0.5 in 2002 to 0.44 in 2017. Since 2017 it has decreased to 0.3 in 2019, probably due to breeders that started to penalize bulls that breed large body sizes in their breeding objectives.\u003c/p\u003e\u003cp\u003eThe aim of the studies done by Lukuyu et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Comlan et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Tebug et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Gruber \u003cem\u003eet al.\u003c/em\u003e (2018) were to determine a model to predict body weight by using body measurements. Lukuyu et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) used only HG in the prediction of body weight. The model they used were within 95% of the BW. Comlan et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) used BL, HG and HW in the model and were within 94% of body weight. Tebug et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) used only HG and the variation from BW for the different breeds varied from \u0026minus;\u0026thinsp;7.36\u0026ndash;11.26%. Gruber \u003cem\u003eet al.\u003c/em\u003e (2018) used two models. The first model included HG and BG and obtained a coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) of 83.0. A second model included HG, BG and HW and realised a R\u003csup\u003e2\u003c/sup\u003e of 83.5. Costigan et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) used body measurements in Irish Holstein Friesian heifers to determine live body weight. The traits they used, were heart girth, body volume and a polynomial of length, girth and height. The model they compiled correlated very highly with live weight (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.97). Wangchuk et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) used four methods to determine body weight from body measurements in Brown Swiss and Jersey cross cattle in Bhutan. All four methods included the measurement of girth and body length. The most accurate method deviated 4.7% and 4.84% from live weight measured by a scale for the two breeds, respectively.\u003c/p\u003e\u003cp\u003eIn this study the average difference between realised weights and predicted weights based on the BWC, was \u0026minus;\u0026thinsp;3.20%, which is in correspondence to the findings of the above studies. The correlation between the realised and predicted weights (0.59) is, however, lower than that obtained in the other studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA BWC index was constructed based on linear type traits, days in milk and age at classification of three TMR Holstein herds to predict cow weight of first lactation cows. Validation indicates that it is a use full predictor of cow weight in the three herds. By using the linear type data collected over years by SA Holstein for the three herds, a useful prediction of body weight can therefore be obtained for cows with unknown bodyweights to merge with data sets that include cows with measured weights. This can aid in the compilation of an Efficiency Index (EI) for identification of more efficient cows to improve herd economics and therefore improve the sustainability of dairy enterprises.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;The authors have no relevant financial or non-financial interests to disclose.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;All authors contributed to the study conception and design. Data received from SA Stud Book and the SA Holstein Society was edited \u0026nbsp;and analysed by D.J. van Niekerk. The first draft of the manuscript was written by D.J. van Niekerk and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;The datasets generated during and/or analysed during the current study are not publicly available due to agreements signed with SA Stud Book and the SA Holstein Breed Society, but maybe made available from the corresponding author on reasonable request.].\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eEthics approval\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the University of the Free State (Project nr: UFS-AED2024/0061).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eComlan B.G., J. Steve and A.T. Ibrahim. 2017. Use of body measurements to estimate live weight of Lagune cattle in Southern Benin. The Saudi Journal of Life Science.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCostigan, H., Delaby, L., Walsh, S., Lahart, B. and Kennedy, E., 2021. The development of equations to predict live-weight from linear body measurements of pasture-based Holstein-Friesian and Jersey dairy heifers. Livestock Science (2021). Doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.livsci.2021.104693\u003c/span\u003e\u003cspan address=\"10.1016/j.livsci.2021.104693\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrancis J., S. Sibanda and T. Kristensen, 2002. Estimating body weight of cattle using linear body measurements. Zimbabwe Veterinary Journal 33:1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGruber l., M. Ledinek, F. Steininger, B. Fuerst-Waltl, K. Zottl, M. Royer, K. Krimberger, M. Mayerhofer and C. Egger-Danner. 2018. Body weight prediction using body size measurements in Fleckvieh, Holstein and Brown Swiss dairy cows in lactation and dry periods. Arch. Anim. Breed. 61, 413\u0026ndash;424.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLukuyu M.N., J.P. Gibson, D.B. Savage, A.J. Duncan, F.D.N. Mujibi and A.M. Okeyo. 2016. Use of body linear measurements to estimate liveweight of crossbred dairy cattle in smallholder farms in Kenya. Springer Plus 5:63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRibeiro A. C., A.J. McAllister and S.A. de Queiroz. 2008. Profitability measures of dairy cows. Revista Brasileira de Zootecnia. 37(9): 1607\u0026ndash;1616.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSA Stud Book, 2023. SA Stud Book Annual Holstein Report, 2022\u0026ndash;2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSAS, 2017. SAS Enterprise Guide, Version 4.3. SAS Institute Inc., Cary, NC, USA.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTebug S.F., A. Missohou, S.S.Sabi, J. Juga, E. J. Poole, M. Tapio and K. Marshall. 2018. Using body measurements to estimate live weight of dairy cattle in low-input systems in Senegal. Journal of applied animal research 46: 1 87\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVallimont, J.E., Dechow, C.D., Daubert, J.M., Dekieva, M.W., Blum, J.W., Barlieb, C.M., Liu, W., Varga, G.A., Heinrichs, A.J. and Baumrucker, C.R., 2010. Genetic parameters of feed intake, production, body weight, body condition score, and selected type traits of Holstein cows in commercial tie-stall barns. \u003cem\u003eJ. Dairy Sci. 93\u003c/em\u003e:\u003cem\u003e4892\u0026ndash;4901\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVanRaden P. M., Cole J.B, and Parker Gaddis K.L. 2018. Net merit as a measure of lifetime profit: 2018 revision. AIP Research Report \u003cem\u003eNM$7 (5\u0026ndash;18)\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWangchuk, K., Wangdi, J. and Mindu, M., 2017. Comparison and reliability of techniques to estimate live cattle body weight. \u003cem\u003eJ. of applied Anim Research. 46:1, 349\u0026ndash;352.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Holstein Friesian Federation, 2005. International type evaluation of dairy cattle. World Holstein Friesian Federation.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Age at classification, days in milk, linear traits, total mixed ration","lastPublishedDoi":"10.21203/rs.3.rs-7081048/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7081048/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study reviews the possibility of using body measurements to predict body weight by compiling a body weight composite (BWC) for Holstein cattle in intensive management systems. A data set of 701 records from three farms that feed a total mixed ration was used to build a BWC. The BWC included the following traits and attributes with their proportional contributions: Wither height (18%), body depth (8%), angularity (-16%), rump width (11%), chest width (15%), days in milk at classification (18%) and age at classification (14%). A linear regression was fitted for the BWC against the cows\u0026rsquo; realised weights. The regression equation was y\u0026thinsp;=\u0026thinsp;82.377x\u0026thinsp;+\u0026thinsp;206.11 with an R\u003csup\u003e2\u003c/sup\u003e of 0.617. This regression was used in a verification data set to establish the usefulness of the BWC to predict body weight. The correlation between the predicted and the realised weights was 59%, with the average difference between the predicted and realised weights being 3.2%. It is concluded that the BWC is a useful indicator of body weight for Holstein cows in intensive management systems.\u003c/p\u003e","manuscriptTitle":"The compilation of a Body Weight Composite (BWC) to predict body weight in SA Holstein cattle.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-27 11:37:33","doi":"10.21203/rs.3.rs-7081048/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2025-07-11T11:12:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Animal Health and Production","date":"2025-07-10T22:59:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f756c2ed-eb6d-429f-aa93-30f5197faec4","owner":[],"postedDate":"July 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:03:04+00:00","versionOfRecord":{"articleIdentity":"rs-7081048","link":"https://doi.org/10.1007/s11250-026-05027-4","journal":{"identity":"tropical-animal-health-and-production","isVorOnly":false,"title":"Tropical Animal Health and Production"},"publishedOn":"2026-04-13 15:58:07","publishedOnDateReadable":"April 13th, 2026"},"versionCreatedAt":"2025-07-27 11:37:33","video":"","vorDoi":"10.1007/s11250-026-05027-4","vorDoiUrl":"https://doi.org/10.1007/s11250-026-05027-4","workflowStages":[]},"version":"v1","identity":"rs-7081048","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7081048","identity":"rs-7081048","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}