The objective of this study was to determine the accuracy of prediction of body weight from body measurements in beef cattle. Wither height, chest girth, body length, chest depth, hip width and hip height measurements were obtained from Holstein, Brown Swiss and crossbred (n=140). Determination coefficients (R²) of regression equation that included all body measurements were higher in Brown Swiss and crossbred than Holstein (92.2, 95.0 and 68.2 %, respectively). However, it was found that chest girth was the best parameter of all for prediction of body weight in Brown Swiss (R²=91.1 %) and crossbred cattle (R²=88.8 %) in comparison to Holstein (R²=60.7 %). According to these results, the body weight estimation of Brown Swiss and crossbred cattle using the body measurements produced higher prediction accuracies than Holstein but chest girth was the best parameter to prediction of body weight among all body measurements. However, the prediction accuracy of prediction of body weight from body measurements and also chest girth was decreased when the animals frame size was increased.

The objective of this study was to predict body weight (BW) of pre-slaughtering beef cattle using digital image analysis. Data used in this study were collected from slaughterhouses in Isparta and nearby provinces from 140 animals. Selected body measurements such as body weight (BW), wither height (WH), body length (BL), chest depth (CD), hip width (HW), hip height (HH) and body area (BA) of different breeds of beef cattle were combined and compared by digital image analysis. The body area was included as a different parameter for prediction of BW instead of chest girth. However, regression equation that included only body area gave the lowest R² value for Holstein (18.0%), but the R² value was 43.2 and 51.7% for Brown Swiss and crossbred animals, respectively. The regression equation which included all body traits resulted in R² values 35.3, 85.1, and 79.6% for Holstein, Brown Swiss and crossbred, respectively. The regression equation which included body area and body length showed that prediction ability of digital image analysis was high for prediction of BW in Brown Swiss and crossbred animals compared to Holsteins (R² 82.6, 76.5, and 29.5%, respectively). Results indicated that the prediction ability of digital image analysis was low for prediction of BW. Although possibility of using body area as a parameter in predicting BW is low it can be developed by further and better designed experiments.

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The objective of this study was to predict bodyweight and estimate body measurements of Limousin cattle using digital image analysis (DIA). Body measurements including body length, wither height, chest depth, and hip height of cattle were determined both manually (by measurements stick) and by using DIA. Body area was determined by using DIA. The images of Limousin cattle were taken while cattle were standing in a squeeze chute by a digital camera and analysed by image analysis software to obtain body measurements of each animal. While comparing the actual and predicted body measurements, the accuracy was determined as 98% for wither height, 97% for hip height, 94% for chest depth and 90.6% for body length. Regression analysis between body area and bodyweight yielded an equation with R2 of 61.5%. The regression equation, which included all body traits, resulted in an R2 value of 88.7%. The results indicated that DIA can be used for accurate prediction of body measurements and bodyweight of Limousin cattle.

SUMMARY: The objective of the current study was to determine the accuracy of the prediction of live weight (LW) from body measurements (BMs) by using digital image analysis on female Holstein calves. The calves were measured with a measurement stick and digital image analysis. The following linear parameters were taken: body length (BL), wither height (WH), chest depth (CD), hip height (HH) and hip width (HW). LW and BMs were recorded at birth, at weaning and at 24 weeks of age. Regression coefficients, which included all BMs at birth, gave a low R² value (66·7%), but the R² value was found to be 87·6 and 86·0% at weaning and 24 weeks of age, respectively. A high correlation coefficient was found among LW and CD, HH and HW at weaning (0·90, 0·91 and 090, respectively) and at 24 weeks of age (0·89, 0·90 and 0·91, respectively). The results confirm that for female Holstein calves, digital image analysis is an effective measuring system for the prediction of LW from BMs.