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An Evaluation of the Potential to Measure Real-Time Body Weight of Feedlot Cattle
The Professional Animal Scientist 23 (2007):295–299
CPotential S : An Evaluation of the to Measure Real-Time ASE TUDY
Body Weight of Feedlot Cattle W. H. Kolath,*1 C. Huisma,† and M. S. Kerley*2 *Division of Animal Sciences, University of Missouri, Columbia 65211; and †GrowSafe Systems, Ltd., Airdrie, Canada T4B 2A3
ABSTRACT The correlation of front-end BW (partial BW of the animal where only the front 2 legs are weighed) to total BW (BW of the entire animal) and the ability to use front-end BW to forward-project BW were assessed in this study. The objectives of this experiment were to determine if front-end BW could be captured individually in a grouped lot of calves, to determine the correlation of front-end BW to total BW, and to determine if front-end BW could be used to forward-project total BW via regression equations. One hundred sixty-six crossbred heifers (average initial BW 472.4 kg, SD = 36.8) had front-end BW measured for 62 d at a commercial feedlot using an in-pen weighing system located at the water trough. The in-pen weighing system consisted of 6 individual weighing stalls mounted on both sides of the watering trough and was designed to allow only one animal per position at any given time. Animal drinking behavior and BW was measured each time the animal visited the water trough. The majority of animals were weighed by the sys-
1
Current address: GrowSafe Systems, Ltd., 280105 Range Road 22, Airdrie, Canada T4B 2A3. 2 Corresponding author: kerleym@ missouri.edu
tem 4 times/d or less. Less than 10% of the animals were weighed by the system more than 8 times/d. Front-end BW was correlated (r = 0.97) to BW measured using chutes equipped with a scale. Body weight was forward-projected via regression equations using 15, 30, and 45 d of front-end BW data. Analysis showed that 30 d of front-end BW data could be used to predict future BW. In-pen BW was uniformly correlated to total BW and could be used to calculate ADG and forward-project BW of cattle in the feedlot. The potential to quantify and forward-project animal performance and BW could enhance cattle management and marketing. Key words: front-end body weight, body weight prediction, drinking behavior, feedlot
INTRODUCTION Management of feedlot cattle would be enhanced if individual animal performance could be measured in real-time. Marketing of finished cattle could be enhanced if the point of incremental cost of gain could be determined, heavy carcasses prevented, and slaughter weight forward-projected. Technology has made possible real-time individual animal data capture in group-managed environments, which in turn makes possible individual animal management.
These technologies have value when the data captured are reliable, accurate, and improve upon existing production inefficiencies. The use of technology to measure individual animal feed consumption and feeding behavior has identified sickness in animals 4 d prior to conventional methods (Quimby et al., 2001). Likewise, unhealthy animals were also identified by measuring watering behavior in group-managed environments (Basarab et al., 1997). The potential to measure the daily BW of individual animals would have significant benefits. Growth rate could be calculated, BW could be forward-projected based on growth rate, and when measured in combination with feed intake would enable gain efficiency to be computed. The capability to measure these parameters should lead to better animal management, improved diet formulations, and a greater understanding of how management, nutrition, physiology, and genetics influence animal performance. The ability to forward-project cost of BW gain and carcass weight could also be used to enhance cattle marketing decisions. Pilot-scale research (C. Huisma, unpublished data) demonstrated that in-pen weighing equipment (IPW) placed at water troughs captured multiple daily BW measurements and that the equipment was resilient. Further, preliminary data demonstrated
296
that front-end BW was correlated to total BW. Therefore, we hypothesized that the BW of individual animals could be measured daily in a commercial setting and the data then used to forward-project BW. The objectives of this experiment were to evaluate the ability to use front-end weighing platforms at water troughs in a commercial setting to capture daily front-end BW, to determine the relationship of front-end BW to total BW, and to determine the ability to forward-project total BW at a future point in time based on front-end BW data.
MATERIALS AND METHODS Study Design Crossbred continental heifers (n = 166, average initial BW 472.4 kg, SD = 36.8) had individual front-end BW measured each time they visited the water trough. Front end BW is a partial BW measurement in which the front 2 legs of the animal are weighed. The experiment was conducted at a commercial feedlot in Alberta, Canada. The animals used in this study were managed according to standard operating procedures of the feedlot. A barley silage-based diet, formulated to meet NRC requirements (2000) was fed twice daily. Forty-two linear m of feed bunk space and 1.7 m2 of water trough space were provided per pen. All 166 animals were housed in one pen that was 2,730 m2 in size. Body weights of animals were measured on d 1 and 62 of the study by weighing animals in a cattle chute equipped with a scale (Model CC580, Reliable Scale Co., Calgary, Canada). Chute weights were taken in the morning on d 1 and 62, and front-end BW was taken continuously throughout the day. The data from 4 animals was excluded from the analysis due to a missing chute weight. The frequency that a front-end BW was recorded for individual animals was calculated by averaging the number of times each animal was weighed daily throughout the experiment. Using multiple obser-
Kolath et al.
vations per day for each animal, the difference between the greatest and least BW measured by the IPW was calculated. The ability of front-end BW to predict chute-measured BW was determined using a correction factor for IPW to total BW calculated by using the chute BW on d 1. The correction factor was calculated as the ratio of the first chute BW to the measured front-end BW from the same day. This correction factor was necessary to compare the total and frontend BW measurements on d 62. This correction factor was used to correct each daily front-end BW. Either each animal’s individual correction factor or the average correction factor of all animals was used. Finally, the correlation coefficient of IPW-predicted BW and chute-measured BW on d 62 was calculated with IPW measurements from d 0 to 15, 0 to 30, and 0 to 45 used to develop regression equations for increasing BW. Individual animal was considered the experimental unit with each visit to the water trough representing an observation. Analyses were conducted to examine correla-
tions of chute BW and partial BW measurements using the CORR procedure, and regression equations for modeling the partial BW were developed using the REG procedure of SAS (SAS Inst., Inc.; Cary, NC). To test for periodicity of the front-end BW data, the SPECTRA procedure of SAS was utilized using Fisher’s test for white noise.
Front End Weight Data Collection The GrowSafe Beef System (GrowSafe Systems, Ltd.) (Figure 1) was used to collect front-end BW each time the animal visited the water trough. The system consisted of 6 individual weighing stalls (positions) mounted on either side of the watering trough. The individual weighing scales were mounted in a metal frame that was equipped with key holes allowing the individual units to be raised depending on the amount of manure build-up. Each individual weighing unit was equipped with a load cell and analog-to-digital conversion circuitry. The resolution of the
Figure 1. The GrowSafe Beef in-pen weighing equipment (GrowSafe Systems, Ltd.).
CASE STUDY: Real-Time Body Weight Measurement
297
written down to a Microsoft Access database using open database connectivity.
RESULTS AND DISCUSSION
Figure 2. Average number of daily visits to the water trough throughout the experimental period. IPW = in-pen weighing equipment.
samples taken was 25 g. The load cells were sampled 512 times/s, and the average of these measurements were transmitted wirelessly every 2 s to the data acquisition computer. Each individual weighing unit was equipped with an electronic identification (EID) antenna through which the animal has to place its head in order to drink. The system is designed so that only one animal can be present in each position at any given time. The antennas were sampled every 2 s and subsequent EID and address, indicating the location of the individual stall, was transmitted wire-
lessly to the computer. Each system was powered by a 60-W, 12-volts direct current solar cell in combination with a 12-V battery. A standard personal computer running Windows XP was connected to a wireless data receiving station. Every BW sample (collected every 2 s for every individual weighing stall) was written to the hard drive using proprietary software. When an EID was present, the time stamp as well as the location was recorded by the computer. At midnight, the data was compiled to calculate the front-end BW belonging to each EID, and subsequent BW was
Figure 3. Frequency of daily animal visits to the water trough throughout the experimental period. IPW = in-pen weighing equipment.
The average number of visits per day to the water trough is shown in Figure 2. The total number of visits to the water trough was variable ranging from 109 to 577 visits/d. This large range in number of visits was most likely the result of changes in weather as drinking behavior has been correlated to temperature, precipitation, and barometric pressure (Gonyou and Stricklin, 1984). Throughout the study, the greatest percentage of animals had a frequency of visiting the IPW system 2 to 3 times/d (Figure 3). The majority of cattle had a frequency of visiting the IPW system 8 times/d or less. This is similar to other published data in which bulls drank on average 3.4 to 7.8 times/d (Gonyou and Stricklin, 1984) and heifers visited the water trough an average of 6 times/d (Buhman et al., 2000). Front-end BW varied throughout the day with the average range in daily BW being 11.7 kg (Figure 4). Average daily BW range was highly correlated to the total number of visits to the IPW system (r = 0.98). Consecutive 2-d chute BW recorded from different experiments differed on average by 8.7 kg, similar in magnitude to IPW measurements (W. H. Kolath and M. S. Kerley, unpublished data). Multiple chute BW taken throughout the day would be expected to result in a similar or greater range in BW as the observed daily range in front-end BW due to the stress of handling and its effect on gut fill. To determine if there was a pattern to the daily variation in BW, spectral analysis was performed to determine the periodicity of the daily partial BW measurements. This analysis showed a significant period effect of 2 to 4 d. This agrees with previously published data (Golden et al., 2007), which showed that feed intake occurred in a 2- to 4d pattern.
298
Kolath et al.
Figure 6. The correlation of predicted BW based on 15, 30, 45, and 62 d of front-end BW data to measured chute BW on d 62 of the experiment.
Figure 4. Average daily range in BW measured by the in-pen weighing system throughout the experimental period.
To determine an appropriate model to use for future BW projections, linear, quadratic, and cubic models were fit to the data. The linear, quadratic, and cubic models had coefficient of determination values of 0.75, 0.77, and 0.77 when individual animal correction factors were used (Figure 4).
The cubic model did not show any improvement over the quadratic model and was excluded from further analysis. The mean square error of the quadratic model was 472.3 whereas the mean square error of the linear model was reduced to 450.7. The quadratic model mathematically
Figure 5. The relationship of d 62 corrected front-end BW and chute BW. A. Corrected frontend BW was calculated using an individual animal calculated correction factor from the chute BW on d 1 of the experiment and a linear model to predict BW on d 62 (y = 0.89x + 77.33, r2 = 0.75). B. Corrected front-end BW was calculated using the pen’s average correction factor calculated from the chute BW taken on d 1 and a linear model to predict BW on d 62 (y = 0.90x + 73.13, r2 = 0.86). C. Corrected front-end BW was calculated using an individual animal calculated correction factor from the chute BW on d 1 of the experiment and a quadratic model to predict BW on d 62 (y = 0.95x + 49.68, r2 = 0.77). D. Corrected front-end BW was calculated using the pen’s average correction factor calculated from the chute weight taken on d 1 and a quadratic model to predict BW on d 62 (y = 0.91x + 68.50, r2 = 0.86). IPW = inpen weighing equipment.
fit the data better in that the slope was closer to one and the intercept was reduced when the predicted BW was regressed against the measured chute BW. However, the linear model gave a smaller average absolute residual (18.43 kg, SD = 12.77) than the quadratic model (22.09 kg, SD = 14.02). Therefore, based on the smaller mean square error and average absolute residuals, a linear model was used for model prediction of future BW in this study. Larger data sets that encompass longer time periods are needed to determine the optimum model for BW prediction. Total BW based on front-end BW data was highly correlated (r = 0.94) to BW measured in the chute (Figure 5). The use of an average correction factor increased the correlation (r = 0.97) between total BW based on IPW and BW measured in the chute compared with using individual animal correction factors. The use of an average correction factor would be preferred in a commercial setting due to the time and labor required to accurately weigh cattle. Body weight on d 62 was predicted based upon 15, 30, and 45 d of frontend BW data. The correlation between predicted BW and measured chute BW increased from 0.45 to 0.83 when 15 and 30 d of front-end BW data were used to develop the regression equation, respectively (Figure 6). Only slight improvement in correlation coefficients were noted when additional data was used to project BW at d 45. We concluded that 30 d of front-end BW data could be used to forward-project a future weight.
CASE STUDY: Real-Time Body Weight Measurement
These results demonstrated the frequency of drinking events was not affected by the placement of the equipment in front of the water trough. It was also found that front-end BW was highly correlated to total BW and that front-end BW can be used to forward-project a future BW. Further research is warranted to assess the use of the IPW to measure a greater range of BW, determine the ability of front-end BW measurement to detect disease, and the potential to
determine optimal slaughter endpoint and carcass quality.
LITERATURE CITED Basarab, J. A., D. Milligan, R. Hand, and C. Huisma. 1997. Automatic monitoring of watering behavior in feedlot steers: Potential use in early detection of respiratory disease and in predicting growth performance. Can. J. Anim. Sci. 77:554 (Abstr.) Buhman, M. J., L. J. Perino, M. L. Galyean, and R. S. Swingle. 2000. Eating and drinking behaviors of newly received feedlot calves. Prof. Anim. Sci. 16:241.
299
Golden, J. W., M. S. Kerley, and W. H. Kolath. 2007. The relationship of feeding behavior and feed efficiency in Angus steers fed traditional and no roughage diets. J. Anim. Sci. Accepted. Gonyou, H. W., and W. R. Stricklin. 1984. Diurnal behavior patterns of feedlot bulls during winter and spring in northern latitudes. J. Anim. Sci. 58:1075. Quimby, W. F., B. F. Sowell, J. G. P. Bowman, M. E. Branine, M. E. Hubbert, and H. W. Sherwood. 2001. Application of feeding behavior to predict morbidity of newly received calves in a commercial feedlot. Can. J. Anim. Sci. 81:315.
CPotential S : An Evaluation of the to Measure Real-Time ASE TUDY
Body Weight of Feedlot Cattle W. H. Kolath,*1 C. Huisma,† and M. S. Kerley*2 *Division of Animal Sciences, University of Missouri, Columbia 65211; and †GrowSafe Systems, Ltd., Airdrie, Canada T4B 2A3
ABSTRACT The correlation of front-end BW (partial BW of the animal where only the front 2 legs are weighed) to total BW (BW of the entire animal) and the ability to use front-end BW to forward-project BW were assessed in this study. The objectives of this experiment were to determine if front-end BW could be captured individually in a grouped lot of calves, to determine the correlation of front-end BW to total BW, and to determine if front-end BW could be used to forward-project total BW via regression equations. One hundred sixty-six crossbred heifers (average initial BW 472.4 kg, SD = 36.8) had front-end BW measured for 62 d at a commercial feedlot using an in-pen weighing system located at the water trough. The in-pen weighing system consisted of 6 individual weighing stalls mounted on both sides of the watering trough and was designed to allow only one animal per position at any given time. Animal drinking behavior and BW was measured each time the animal visited the water trough. The majority of animals were weighed by the sys-
1
Current address: GrowSafe Systems, Ltd., 280105 Range Road 22, Airdrie, Canada T4B 2A3. 2 Corresponding author: kerleym@ missouri.edu
tem 4 times/d or less. Less than 10% of the animals were weighed by the system more than 8 times/d. Front-end BW was correlated (r = 0.97) to BW measured using chutes equipped with a scale. Body weight was forward-projected via regression equations using 15, 30, and 45 d of front-end BW data. Analysis showed that 30 d of front-end BW data could be used to predict future BW. In-pen BW was uniformly correlated to total BW and could be used to calculate ADG and forward-project BW of cattle in the feedlot. The potential to quantify and forward-project animal performance and BW could enhance cattle management and marketing. Key words: front-end body weight, body weight prediction, drinking behavior, feedlot
INTRODUCTION Management of feedlot cattle would be enhanced if individual animal performance could be measured in real-time. Marketing of finished cattle could be enhanced if the point of incremental cost of gain could be determined, heavy carcasses prevented, and slaughter weight forward-projected. Technology has made possible real-time individual animal data capture in group-managed environments, which in turn makes possible individual animal management.
These technologies have value when the data captured are reliable, accurate, and improve upon existing production inefficiencies. The use of technology to measure individual animal feed consumption and feeding behavior has identified sickness in animals 4 d prior to conventional methods (Quimby et al., 2001). Likewise, unhealthy animals were also identified by measuring watering behavior in group-managed environments (Basarab et al., 1997). The potential to measure the daily BW of individual animals would have significant benefits. Growth rate could be calculated, BW could be forward-projected based on growth rate, and when measured in combination with feed intake would enable gain efficiency to be computed. The capability to measure these parameters should lead to better animal management, improved diet formulations, and a greater understanding of how management, nutrition, physiology, and genetics influence animal performance. The ability to forward-project cost of BW gain and carcass weight could also be used to enhance cattle marketing decisions. Pilot-scale research (C. Huisma, unpublished data) demonstrated that in-pen weighing equipment (IPW) placed at water troughs captured multiple daily BW measurements and that the equipment was resilient. Further, preliminary data demonstrated
296
that front-end BW was correlated to total BW. Therefore, we hypothesized that the BW of individual animals could be measured daily in a commercial setting and the data then used to forward-project BW. The objectives of this experiment were to evaluate the ability to use front-end weighing platforms at water troughs in a commercial setting to capture daily front-end BW, to determine the relationship of front-end BW to total BW, and to determine the ability to forward-project total BW at a future point in time based on front-end BW data.
MATERIALS AND METHODS Study Design Crossbred continental heifers (n = 166, average initial BW 472.4 kg, SD = 36.8) had individual front-end BW measured each time they visited the water trough. Front end BW is a partial BW measurement in which the front 2 legs of the animal are weighed. The experiment was conducted at a commercial feedlot in Alberta, Canada. The animals used in this study were managed according to standard operating procedures of the feedlot. A barley silage-based diet, formulated to meet NRC requirements (2000) was fed twice daily. Forty-two linear m of feed bunk space and 1.7 m2 of water trough space were provided per pen. All 166 animals were housed in one pen that was 2,730 m2 in size. Body weights of animals were measured on d 1 and 62 of the study by weighing animals in a cattle chute equipped with a scale (Model CC580, Reliable Scale Co., Calgary, Canada). Chute weights were taken in the morning on d 1 and 62, and front-end BW was taken continuously throughout the day. The data from 4 animals was excluded from the analysis due to a missing chute weight. The frequency that a front-end BW was recorded for individual animals was calculated by averaging the number of times each animal was weighed daily throughout the experiment. Using multiple obser-
Kolath et al.
vations per day for each animal, the difference between the greatest and least BW measured by the IPW was calculated. The ability of front-end BW to predict chute-measured BW was determined using a correction factor for IPW to total BW calculated by using the chute BW on d 1. The correction factor was calculated as the ratio of the first chute BW to the measured front-end BW from the same day. This correction factor was necessary to compare the total and frontend BW measurements on d 62. This correction factor was used to correct each daily front-end BW. Either each animal’s individual correction factor or the average correction factor of all animals was used. Finally, the correlation coefficient of IPW-predicted BW and chute-measured BW on d 62 was calculated with IPW measurements from d 0 to 15, 0 to 30, and 0 to 45 used to develop regression equations for increasing BW. Individual animal was considered the experimental unit with each visit to the water trough representing an observation. Analyses were conducted to examine correla-
tions of chute BW and partial BW measurements using the CORR procedure, and regression equations for modeling the partial BW were developed using the REG procedure of SAS (SAS Inst., Inc.; Cary, NC). To test for periodicity of the front-end BW data, the SPECTRA procedure of SAS was utilized using Fisher’s test for white noise.
Front End Weight Data Collection The GrowSafe Beef System (GrowSafe Systems, Ltd.) (Figure 1) was used to collect front-end BW each time the animal visited the water trough. The system consisted of 6 individual weighing stalls (positions) mounted on either side of the watering trough. The individual weighing scales were mounted in a metal frame that was equipped with key holes allowing the individual units to be raised depending on the amount of manure build-up. Each individual weighing unit was equipped with a load cell and analog-to-digital conversion circuitry. The resolution of the
Figure 1. The GrowSafe Beef in-pen weighing equipment (GrowSafe Systems, Ltd.).
CASE STUDY: Real-Time Body Weight Measurement
297
written down to a Microsoft Access database using open database connectivity.
RESULTS AND DISCUSSION
Figure 2. Average number of daily visits to the water trough throughout the experimental period. IPW = in-pen weighing equipment.
samples taken was 25 g. The load cells were sampled 512 times/s, and the average of these measurements were transmitted wirelessly every 2 s to the data acquisition computer. Each individual weighing unit was equipped with an electronic identification (EID) antenna through which the animal has to place its head in order to drink. The system is designed so that only one animal can be present in each position at any given time. The antennas were sampled every 2 s and subsequent EID and address, indicating the location of the individual stall, was transmitted wire-
lessly to the computer. Each system was powered by a 60-W, 12-volts direct current solar cell in combination with a 12-V battery. A standard personal computer running Windows XP was connected to a wireless data receiving station. Every BW sample (collected every 2 s for every individual weighing stall) was written to the hard drive using proprietary software. When an EID was present, the time stamp as well as the location was recorded by the computer. At midnight, the data was compiled to calculate the front-end BW belonging to each EID, and subsequent BW was
Figure 3. Frequency of daily animal visits to the water trough throughout the experimental period. IPW = in-pen weighing equipment.
The average number of visits per day to the water trough is shown in Figure 2. The total number of visits to the water trough was variable ranging from 109 to 577 visits/d. This large range in number of visits was most likely the result of changes in weather as drinking behavior has been correlated to temperature, precipitation, and barometric pressure (Gonyou and Stricklin, 1984). Throughout the study, the greatest percentage of animals had a frequency of visiting the IPW system 2 to 3 times/d (Figure 3). The majority of cattle had a frequency of visiting the IPW system 8 times/d or less. This is similar to other published data in which bulls drank on average 3.4 to 7.8 times/d (Gonyou and Stricklin, 1984) and heifers visited the water trough an average of 6 times/d (Buhman et al., 2000). Front-end BW varied throughout the day with the average range in daily BW being 11.7 kg (Figure 4). Average daily BW range was highly correlated to the total number of visits to the IPW system (r = 0.98). Consecutive 2-d chute BW recorded from different experiments differed on average by 8.7 kg, similar in magnitude to IPW measurements (W. H. Kolath and M. S. Kerley, unpublished data). Multiple chute BW taken throughout the day would be expected to result in a similar or greater range in BW as the observed daily range in front-end BW due to the stress of handling and its effect on gut fill. To determine if there was a pattern to the daily variation in BW, spectral analysis was performed to determine the periodicity of the daily partial BW measurements. This analysis showed a significant period effect of 2 to 4 d. This agrees with previously published data (Golden et al., 2007), which showed that feed intake occurred in a 2- to 4d pattern.
298
Kolath et al.
Figure 6. The correlation of predicted BW based on 15, 30, 45, and 62 d of front-end BW data to measured chute BW on d 62 of the experiment.
Figure 4. Average daily range in BW measured by the in-pen weighing system throughout the experimental period.
To determine an appropriate model to use for future BW projections, linear, quadratic, and cubic models were fit to the data. The linear, quadratic, and cubic models had coefficient of determination values of 0.75, 0.77, and 0.77 when individual animal correction factors were used (Figure 4).
The cubic model did not show any improvement over the quadratic model and was excluded from further analysis. The mean square error of the quadratic model was 472.3 whereas the mean square error of the linear model was reduced to 450.7. The quadratic model mathematically
Figure 5. The relationship of d 62 corrected front-end BW and chute BW. A. Corrected frontend BW was calculated using an individual animal calculated correction factor from the chute BW on d 1 of the experiment and a linear model to predict BW on d 62 (y = 0.89x + 77.33, r2 = 0.75). B. Corrected front-end BW was calculated using the pen’s average correction factor calculated from the chute BW taken on d 1 and a linear model to predict BW on d 62 (y = 0.90x + 73.13, r2 = 0.86). C. Corrected front-end BW was calculated using an individual animal calculated correction factor from the chute BW on d 1 of the experiment and a quadratic model to predict BW on d 62 (y = 0.95x + 49.68, r2 = 0.77). D. Corrected front-end BW was calculated using the pen’s average correction factor calculated from the chute weight taken on d 1 and a quadratic model to predict BW on d 62 (y = 0.91x + 68.50, r2 = 0.86). IPW = inpen weighing equipment.
fit the data better in that the slope was closer to one and the intercept was reduced when the predicted BW was regressed against the measured chute BW. However, the linear model gave a smaller average absolute residual (18.43 kg, SD = 12.77) than the quadratic model (22.09 kg, SD = 14.02). Therefore, based on the smaller mean square error and average absolute residuals, a linear model was used for model prediction of future BW in this study. Larger data sets that encompass longer time periods are needed to determine the optimum model for BW prediction. Total BW based on front-end BW data was highly correlated (r = 0.94) to BW measured in the chute (Figure 5). The use of an average correction factor increased the correlation (r = 0.97) between total BW based on IPW and BW measured in the chute compared with using individual animal correction factors. The use of an average correction factor would be preferred in a commercial setting due to the time and labor required to accurately weigh cattle. Body weight on d 62 was predicted based upon 15, 30, and 45 d of frontend BW data. The correlation between predicted BW and measured chute BW increased from 0.45 to 0.83 when 15 and 30 d of front-end BW data were used to develop the regression equation, respectively (Figure 6). Only slight improvement in correlation coefficients were noted when additional data was used to project BW at d 45. We concluded that 30 d of front-end BW data could be used to forward-project a future weight.
CASE STUDY: Real-Time Body Weight Measurement
These results demonstrated the frequency of drinking events was not affected by the placement of the equipment in front of the water trough. It was also found that front-end BW was highly correlated to total BW and that front-end BW can be used to forward-project a future BW. Further research is warranted to assess the use of the IPW to measure a greater range of BW, determine the ability of front-end BW measurement to detect disease, and the potential to
determine optimal slaughter endpoint and carcass quality.
LITERATURE CITED Basarab, J. A., D. Milligan, R. Hand, and C. Huisma. 1997. Automatic monitoring of watering behavior in feedlot steers: Potential use in early detection of respiratory disease and in predicting growth performance. Can. J. Anim. Sci. 77:554 (Abstr.) Buhman, M. J., L. J. Perino, M. L. Galyean, and R. S. Swingle. 2000. Eating and drinking behaviors of newly received feedlot calves. Prof. Anim. Sci. 16:241.
299
Golden, J. W., M. S. Kerley, and W. H. Kolath. 2007. The relationship of feeding behavior and feed efficiency in Angus steers fed traditional and no roughage diets. J. Anim. Sci. Accepted. Gonyou, H. W., and W. R. Stricklin. 1984. Diurnal behavior patterns of feedlot bulls during winter and spring in northern latitudes. J. Anim. Sci. 58:1075. Quimby, W. F., B. F. Sowell, J. G. P. Bowman, M. E. Branine, M. E. Hubbert, and H. W. Sherwood. 2001. Application of feeding behavior to predict morbidity of newly received calves in a commercial feedlot. Can. J. Anim. Sci. 81:315.