
==== Front
J Anim Sci
J Anim Sci
jansci
Journal of Animal Science
0021-8812
1525-3163
Oxford University Press US

39212666
10.1093/jas/skae236
skae236
Animal Health and Well Being
AcademicSubjects/SCI00960
Behavioral activity patterns but not hair cortisol concentrations explain steers’ transition-related stress in the first 6 wk in the feedlot
https://orcid.org/0009-0008-3470-9284
Mijar Sanjaya Faculty of Veterinary Medicine, University of Calgary, Calgary, Alberta, Canada

van der Meer Frank Faculty of Veterinary Medicine, University of Calgary, Calgary, Alberta, Canada

Hodder Abigail Faculty of Veterinary Medicine, University of Calgary, Calgary, Alberta, Canada

Pajor Ed Faculty of Veterinary Medicine, University of Calgary, Calgary, Alberta, Canada

Orsel Karin Faculty of Veterinary Medicine, University of Calgary, Calgary, Alberta, Canada

Corresponding author: karin.orsel@ucalgary.ca
2024
30 8 2024
30 8 2024
102 skae23612 1 2024
28 8 2024
14 9 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

Stress during the transition of beef steers from ranch to feedlot may depend on steer source and preconditioning. The interplay between physiological and behavioral patterns of preconditioned (PC) and auction-derived (AD) steers, particularly after commingling, is poorly understood. Our objective was to evaluate whether hair cortisol (HC) concentrations were related to the health and performance of PC and AD steers and study behavioral activities after commingling over 6 wk in a feedlot. Steers, sourced either from ranch (PC, n = 250) or local auction (AD, n = 250), were assigned into 1 of 5 pens, 100% PC (100PC); 75% PC 25% AD (75PC); 50% PC 50% AD (50PC); 25% PC 75% AD (25PC), and 100% AD (0PC), each pen containing 100 steers. Pen was the experimental unit and individual steers were the observational unit where physiological and behavioral changes were measured. The study subsampled 225 steers (PC = 113 and AD = 112) which were equipped with CowManager ear tags to record behaviors. On day 40, hair samples from each steer were collected by clipping hair close to the skin. Data were analyzed using multiple linear, logistic regression, or multilevel negative binomial regression models depending on the outcomes. There was no difference in HC concentrations (day 40) between PC and AD steers (P = 0.66), and no association with Bovine Respiratory Disease (BRD)-related morbidity (P = 0.08) or average daily gain (ADG) (P = 0.44). After adjusting for source and commingling effects, HC concentrations did not affect time spent eating (P = 0.83), ruminating (P = 0.20), active (P = 0.89), or non-active (P = 0.32). PC steers spent more time eating and ruminating over weeks 1 to 4 (P < 0.01) and weeks 1 to 3, respectively (P < 0.05), and more time being active over weeks 1 and 2 compared to AD steers (P < 0.001), but less time being non-active than AD steers on weeks 1 to 3 (P < 0.001). Steers in 100PC and 50PC pens spent more time eating than steers in 0PC (P < 0.001), whereas steers in 25PC spent less time eating than steers in 0PC (P < 0.001). Steers in 0PC spent the most time being not active (P < 0.01). In conclusion, preconditioned steers spent more time eating, ruminating, and being active and less time being not active over the first 3 wk in the feedlot, regardless of commingling. The HC concentrations did not identify potentially lower stress related to ranch transfer and were neither associated with BRD-related morbidity nor ADG.

Preconditioned steers spend more time eating, ruminating, and being active than auction-derived steers, regardless of commingling in the feedlot.

auction market steers
behavioral activities
feedlot
hair cortisol
preconditioning
transition
Beef Cattle Research Council 10.13039/501100005019 10028475
==== Body
pmcIntroduction

Preconditioning aims to reduce stress associated with the transition of steers from a ranch to a feedlot. Stressors include weaning, transportation, environment acclimatization, and mixing with steers from multiple sources (Step et al., 2008; Buddle et al., 2018). However, auction-derived (AD) steers are generally exposed to these stressors over a short period (Wilson et al., 2017). In contrast, preconditioned (PC) steers undergo a more gradual exposure, using strategies to mitigate the associated stress and thereby reduce the overall impact. Furthermore, commingling of steers is common in feedlots to promote homogeneity (Peden et al., 2018; Hubbard et al., 2021). However, commingling can disrupt social hierarchy (Loerch and Fluharty, 1999), acting as an additional stressor for steers until a new hierarchy is established (Grant and Albright, 2001).

The response to stress, especially when exposed repeatedly, suppresses immune function (Salak-Johnson and McGlone, 2007), making steers more susceptible to diseases such as Bovine Respiratory Disease (BRD) and reducing feedlot productivity (Coetzee et al., 2012; Moya et al., 2013). The transition of steers from the ranch to the feedlot induces prolonged stress, which is experienced for days to weeks and can result in changes in the hypothalamic-pituitary-adrenal (HPA) axis activity over extended intervals (Minton, 1994; Salak-Johnson and McGlone, 2007). Hair cortisol (HC) is an efficient diagnostic tool for assessing prolonged HPA axis activity (Macbeth et al., 2010; Ashley et al., 2011; Russell et al., 2012) and has been validated in beef cattle (Moya et al., 2013). For instance, cattle that underwent castration without pain relief and those with clinical disease had higher HC concentrations compared to cattle that were castrated and given analgesics or that were clinically healthy (Comin et al., 2013; Burnett et al., 2015; Creutzinger et al., 2017).

In addition to impacts on steer health, stress can also have a profound role in steers’ behaviors (Loerch and Fluharty, 1999; Hulbert and Moisá, 2016). For instance, cattle that were clinically affected for an average of 2 wk had higher HC concentrations and were reported with less time eating and ruminating (Burnett et al., 2015; Stangaferro et al., 2016; Braun et al., 2017).

Currently, there is a lack of knowledge regarding physiological and behavioral activity patterns in low-stressed steers, when commingled with various proportions of AD steers which are expected to have experienced higher stress. Furthermore, the association between behavioral activity and source, including effects of commingling, remains unexplored. Our objectives were to 1) study the behavioral activities of PC and AD steers and their responses after commingling at varying percentages (75%, 50%, and 25%) over 6 wk in a feedlot to elucidate differences in health outcomes; and 2) evaluate whether HC concentrations are associated with the health and performance of PC and AD steers.

Methodology

The protocol was approved by the University of Calgary Veterinary Sciences Animal Care Committee (Animal Care Protocol AC20-0041) and adhered to the Canadian Council of Animal Care guidelines for humane animal use. Steers’ BRD-related morbidity and ADG data, previously reported in our publication (Mijar et al., 2023), were further used in this study to determine their association with HC concentrations and behavioral activities. The study design was described in detail by Hodder et al. (2023) and is briefly summarized below.

Steers

Angus crossbred steers (n = 500) were acquired from 2 sources. Steers sourced from a single ranch (W. A. Ranches at the University of Calgary) were PC (n = 250) and the rest were purchased from a local auction market (AD steers) in Olds, AB (n = 250). Of these steers, PC (n = 113) and AD (n = 112) were selected based on systematic sampling where every second steer that entered the chute during processing upon their arrival at the feedlot were CowManager (Agis Automatisering BV, Harmelen, the Netherlands) ear tagged, monitored, and used for this study.

Preconditioned steers

All PC steers (n = 250) were vaccinated intranasally against bovine herpesvirus-1 (BHV-1), parainfluenza-3 (PI3), and bovine respiratory syncytial virus (BRSV) (Inforce 3, Zoetis, Parsipanny, NJ, USA) at birth. At processing at approximately 60 d of age, all steers were vaccinated against BHV-1, PI3, and BRSV (Inforce 3, Zoetis, parsipanny, NJ, USA), bovine viral diarrhea virus (BVDV) 1, 2 and Mannheimia hemolytica (Bovi-Shield Gold One Shot, Zoetis, Parsipanny, NJ, USA), and clostridial pathogens (Ultrabac 7/Somubac, Zoetis, Parsipanny NJ, USA), surgically castrated, followed by oral administration of meloxicam (3 mL/45 kg), and a growth-promoting implant Synovex C (Zoetis, Parsipanny NJ, USA). Steers were fence-line weaned for 5 d at 3.5 to 5.5 mo of age with only auditory and visual but no physical contact between steers and dams. Booster doses of vaccines were administered during this weaning period. After 5 d, steers were transported in a standard cattle liner to a pasture pen located 5 km away. They were provided with feed and water in a pasture pen for 45 d. Finally, when the steers were aged 5 to 7 mo, they were transported in standard cattle liners directly to the feedlot (65 km away) (Hodder et al., 2023).

AD steers

The AD steers (n = 250) matched to PC steers for frame and weight were purchased from a local auction market and transported to the feedlot approximately 1 km from the auction market. About 100 of the AD steers stayed one extra day at the auction with access to feed and water. Steer origin, vaccination, and timing and strategies of weaning of the AD steers were unknown.

Arriving at the feedlot

On arrival at the feedlot (D0), all PC and AD steers received the same vaccines as during processing with the addition of Ivomec (Boehringer Ingelheim, Ingelheim, Germany), and a Synovex C (Zoetis, Parsipanny NJ, USA) growth-promoting implant, in accordance with industry standards. No routine antimicrobial was provided on arrival. Steers were screened for clinical signs of BRD, such as rectal temperature of 40 °C or higher, nasal discharge, and/or difficulty breathing during 6 wk in the feedlot. Those diagnosed with BRD were treated with Florfenicol and Flunixin Meglumine (Resflor Gold, Merck, Madison, NJ, USA), Enrofloxacin (Baytril 100, Bayer HealthCare LLC, Shawnee Mission, Kansas, USA), and Trimethoprim-Sulfadoxine (Trimidox, Vetoquinol N.-A. Inc., Lavaltrie, Quebec, Canada) for first, second, and third pulled steers respectively during the 6-wk period. The steers were weighed on arrival and the average daily gain (ADG) over 6 wk was calculated based on the weight in and weight out (ADG = weight out − weight in/days on feed (DOF)) in kg/d.

Pen allocation at the feedlot

On D0, 500 steers were assigned to 1 of 5 pens, 100 steers per pen. On November 13th, 150 PC steers arrived at the feedlot, of which the first 100 that entered the chute, irrespective of age or weight, were placed in the pen 100% PC (100PC) and 50 were placed in 50% PC 50% AD (50PC). Additionally, 150 AD steers arrived, of which 100 were placed in 100% AD (0PC) and 50 were placed in 50PC. On November 14th, 100 PC steers arrived, of which 75 were placed in 75% PC 25% AD (75PC) and 25 were placed in 25% PC 75% AD (25PC). Of the 100 AD steers that arrived the next day, 75 were placed in 25PC and the remaining 25 were placed in 75PC. All the steers in each pen were fed in a bunk. The amount of feed to the steers in each pen was adjusted based on whether the bunk was empty or not. Each pen contained windbreakers and provided a bunk space of 45 cm per head with a bunk allowance for 65% of the steers. There was unlimited access to water for the steers.

CowManager ear tag placement for recording behavioral activities

On D0, of 500 steers, 225 (PC = 113 and AD = 112) received a CowManager ear tag (Agis Automatisering BV), validated for use in beef steers (Wolfger et al., 2015b) and were assigned to 1 of 5 pens, 45 steers per pen. The CowManager ear tag consists of a 3-dimensional accelerometer sensory system that captures ear movement patterns to calculate eating, ruminating, activity (highly active and active), and resting times (not active). As per the definition by CowManager, eating was described as chewing, licking and/or swallowing feed, ruminating was chewing the cud either standing or lying, inactivity was doing nothing for 60 s, which could be lying or standing still, and activity was defined as non-of-the above, all other activities like walking and drinking water. Reported “Highly active” and “Active” were merged as active in our study as the former is validated only to detect estrus in heifers. The accelerometer in the ear tags recorded the behavioral data performed by steers each minute through the detection of ear movement. Recorded data were transmitted to a router and then to a computer for further analyses.

Hair sampling

On D40, patches of hair (approximately 5 × 5 cm) were collected from the right hip, located over the femur–ischium junction, of all 500 steers by clipping with an electric razor close to the skin (Moya et al., 2013). Hair samples were collected to capture steers’ long-term stress since birth, while on ranch, till they were transported to the feedlot and commingled for up to 6 wk at the feedlot. The collected samples were dried at room temperature for several days and stored in a paper envelope at room temperature pending processing and analysis. However, hair samples from only 225 steers (CowManager ear tagged), together with the highest performing PC (n = 26) and least performing AD steers (n = 25), regardless of BRD incidence, were analyzed for cortisol extraction. The performance of steers was determined by ADG.

HC extraction

HC was extracted from collected hair samples, as described (Koren et al., 2002) and modified (Accorsi et al., 2008). Briefly, 80 to 100 mg of hair was washed twice with 30 mL of distilled water for 3 min, followed by washing with 20 to 30 mL of high-performance liquid chromatography (HPLC) grade isopropanol for 2 min. The hair was dried in a fume hood for 24 h at room temperature. Washed hair was pulverized using a Retch Ball Mill (Mixer Mill Type MM200, Burlington, ON, Canada) and 1.5 mL of HPLC grade methanol was added to 40 mg of pulverized hair samples and sonicated in a water bath for 30 min at room temperature. Sonicated samples were incubated on a shaking incubator at 160 rpm, 50 °C for 18 h. After incubation, samples were collected in a centrifuge tube and centrifuged at 17,000 × g, at 4 °C for 20 min. Then, 1.5 mL supernatant was transferred into a new 13 × 100 mm borosilicate glass tube. The supernatant was evaporated in a shaking incubator at 160 rpm, at 50 °C for at least 8 h. Finally, tubes containing dried extracts were stored at −20 °C until analyzed.

HC enzyme-linked immunosorbent assay (ELISA) and validation

Stored cortisol extracts were reconstituted in 200 μL of phosphate-buffered saline, vortexed for 2 min, and centrifuged at 16,000 × g for 10 min at 4 °C. The supernatant was collected and processed using a commercial ELISA kit (The SALIMETRICS LLC, State College, PA, USA), as described in the manufacturer’s manual. The concentration of cortisol from hair samples was determined from the standard curve by a 4-parameter logistic curve using a graph pad, first in microgram/deciliter (μg/dL), and then transformed into pg/mg of hair.

The technique was validated as described (Moya et al., 2013) with slight modifications. Serial dilution and spike-and-recovery tests were performed to validate and assess the accuracy of the commercial ELISA when measuring cortisol from hair samples. For the serial dilution test, a pooled sample was resuspended in ELISA buffer with 5% ethanol, and serial dilution in assay buffer was performed as undiluted, 2×, 4×, 8×, 16×, and 32× and evaluated based on the dilution-adjusted concentration and dilution percentage recovery (observed/expected × 100). In our experiment, analysis of serial diluted hair samples had a mean recovery rate of 99.19 ± 7.82%. A recovery rate of 80% to 120% is desirable, as it implies the flexibility to assay samples with varying cortisol concentrations (Moya et al., 2013).

The spike-and-recovery test was performed to determine the interference of other constituents of the sample matrix on cortisol detection. Briefly, 40 mg of pulverized hair was spiked with 80 μL of each cortisol standard (high: 1 μg/dL; medium: 0.333 μg/dL; and low: 0.111 μg/dL) before extraction of cortisol by incubation. After 18 h of incubation, cortisol was measured. The recovery rate was expressed in percentages and calculated by dividing the samples spiked before extraction by the sample spiked after extraction and multiplied by 100. Recovery rates < 80% or > 120% represent interference of other constituents of the sample matrix (Moya et al., 2013). In our experiment, mean recovery rate was 108.75 ± 7.46%.

The intra-assay coefficient of variation (CV) from all the duplicate samples was calculated to assess precision within the test. The inter-assay CV was determined by evaluating low and high control samples in >3 consecutive assay runs. In our experiment, the intra- and inter-assay CV were 2.40% and 11.50%, respectively, which was within the desired range (<10% and <15%, respectively).

Statistical analyses

CowManager data were recorded as minutes of each behavior performed each hour for 24 h/d. The data were obtained as a percentage per day, by adding the number of minutes for each behavior performed per day divided by the total number of minutes per day (1,440). The sum of time spent on each behavior during the first 6 wk at the feedlot was analyzed.

The experimental design consisted of the same steers and pen allocation as described (Hodder et al. 2023). In this experiment, the experimental unit (EU) was pen where treatment (commingling) was independently assigned. The observational unit (OU) was steer where outcomes (behavioral activities) were measured. The use of EU and OU in our study was to compare outcomes between all PC (n = 113) and all AD (n = 112) irrespective of commingling; hence, steer was used as an OU.

Statistical analysis was performed using STATA (Ver. 16.1; StataCorp LLC, College Station, TX, USA). Univariate analysis for variables with P > 0.2 was used as a cutoff and was not included in the final model. The HC concentrations collected from hair samples on day 40 at the feedlot and association with steers’ source (PC vs. AD) in both commingled and non-commingled pens were modeled using a linear regression model. Similarly, the linear regression model was used to model ADG as an outcome and HC concentrations as an explanatory variable. For the outcome of BRD-related morbidity, the logistic regression model was used to determine if morbidity was explained by HC concentrations. The effects of HC concentrations or BRD status on behavioral activities (time spent eating, ruminating, active, and not being active) were modeled using independent negative binomial regression models by including blocking effect of source and commingling. Independent linear regression models were used to evaluate the impact of ADG on behavioral activities over 6 wk in the feedlot. For the objective of studying the patterns of behavioral activities by source of steers and commingling, a hierarchical data structure was used. Repeated measurements over weeks 1 to 6 for behavioral activities were analyzed at steer level. Each week consisted of 7 d, except week 6 (5 d), which was adjusted by the offset of log number of days in a week in the model.

Mixed multilevel modeling was used to accommodate data with this hierarchical structure. Pen, preconditioning status, and week were used as fixed effects, whereas steer registration identification number nested within the pen was used as a random effect for repeated measurements over weeks.

Results

Comparison of steer’s behavioral activities at a pen level during 6 wk in a feedlot

Source significantly modified time spent eating in steers among pens over weeks (Table 1). Temporal patterns of time spent eating by PC and AD steers within 75PC, 50PC, and 25PC pens are provided in Table 2 and temporal patterns of time spent eating by PC and AD steers within 100PC and 0PC are shown in Figure 1. Adjusted for confounding effects of source, time spent eating by steers differed at a pen level, where steers in 100PC and 50PC spent more time eating compared to steers in 0PC (P < 0.001). However, steers in 25PC spent less time eating than steers in 0PC (P < 0.001). Time spent eating by steers did not differ between 75PC and 0PC.

Table 1. Final multilevel mixed effect negative binomial regression of time spent eating for preconditioned (n = 113) and AD (n = 112) steers in the first 6 wk after the arrival in the feedlot

Predictor	IRR	95% CI	P value	
Intercept	164.27	146.36 ± 184.38	<0.001	
Pen	
 0PC1	Referent			
 100PC	1.35	1.09 ± 1.67	0.006	
 75PC	1.05	0.87 ± 1.26	0.639	
 50PC	1.45	1.22 ± 1.72	<0.001	
 25PC	0.71	0.60 ± 0.83	<0.001	
Week	
 1	Referent			
 2	1.00	0.96 ± 1.06	0.74	
 3	0.95	0.90 ± 1.00	0.07	
 4	1.16	1.09 ± 1.23	<0.001	
 5	1.37	1.28 ± 1.47	<0.001	
 6	1.36	1.28 ± 1.47	<0.001	
Source of steers	
 Auction-derived	Referent			
 Preconditioned	2.34	2.02 ± 2.72	<0.001	
Source × week	0.85	0.83 ± 0.87	<0.001	
1PC, preconditioned.

Table 2. Time spent eating (minutes per week), mean ± SD by steers in commingled pens during weeks 1 to 6 in the feedlot

Pen	PC	AD	
Weeks	Weeks	
n	1	2	3	4	5	6	n	1	2	3	4	5	6	
75PC	34	2,332.53 ± 774.81*	2,385.25 ± 748.70*	1,812.78 ± 584.98*	1,844.66 ± 582.29*	1,852.31 ± 488.24	1,706.34 ± 460.03	11	1,154.17 ± 667.85	1,140.83 ± 789.62	1,271.58 ± 854.91	1,680.83 ± 919.87	1,993.50 ± 922.78	1,826.65 ± 862.17	
50PC	23	2,990.71 ± 1,008.75*	2,683.38 ± 793.84*	1,998.04 ± 622.03	2,006.88 ± 536.07	2,051.96 ± 628.51	1,836.22 ± 30.10	22	2,116.46 ± 949.46	2,179.96 ± 982.59	1,944.27 ± 744.87	2,145.18 ± 874.28	2,149.96 ± 813.99	1,855.21 ± 750.02	
25PC	11	2,004 ± 1,184.86*	2,133.64 ± 968.85*	1,446.91 ± 750.91*	1,392.73 ± 613.78	1,365.18 ± 382.20	1,348.24 ± 18.24	34	894.35 ± 417.78	742.21 ± 485.60	778.15 ± 415.97	1,037.62 ± 483.74	1,308 ± 537.37	1,297.38 ± 531.67	
PC, preconditioned steers; AD, auction-derived steers.

*Difference (P < 0.001) between PC and AD steers within pen in respective weeks.

Figure 1. Time spent eating (in minutes per week) by AD and PC steers during weeks 1 to 6 in the feedlot in 0PC-pen and 100PC-pen, respectively. The line represents the mean time spent eating (in minutes per week) by steers during weeks 1 to 6 in the feedlot. AD, auction-derived steers; PC, preconditioned steers. *Difference (P < 0.001).

Time spent ruminating by steers among pens did not differ (P = 0.76). Steers in weeks 3, 4, 5, and 6 spent less time ruminating compared to week 1 (P < 0.001). However, there was no difference in time spent ruminating by steers in weeks 1 and 2 (P = 0.41).

Steers in 100PC, 75PC, 50PC, and 25PC spent less time active than steers in 0PC (P < 0.001, P < 0.001, and P < 0.01, and P < 0.001, respectively). The source of steers in a pen affected the time spent being active by steers over weeks on the feedlot (P < 0.001).

While comparing pens, steers in 50PC spent less time non-active (Incidence rate ratio (IRR) = 0.91, P = 0.03), whereas steers in 25PC spent more time non-active than steers in 0PC (IRR = 1.09, P = 0.03). The source of steers differed in time spent non-active by steers over weeks (P < 0.001).

Comparison of behavioral activities between PC and AD steers during 6 wk in a feedlot

Mean percentage of time spent eating, ruminating, active, or not active of PC steers in 100PC and AD steers in 0PC, and PC and AD steers commingled within 75PC, 50PC, and 25PC-pen during 6 wk in the feedlot are provided in Figure 2. Time spent eating by source of steers over the first 6 wk in the feedlot is provided in Figure 3. While comparing all PC (n = 113) vs. AD steers (n = 112) irrespective of placement, PC steers spent more time eating than AD steers over weeks 1 to 4 (IRR = 1.95, 2.00, 1.45, P < 0.001 and IRR = 1.14, P < 0.01, respectively). Similarly, PC steers spent more time ruminating on weeks 1 and 2 compared to AD steers (IRR = 1.09 and 1.10, P < 0.01, respectively).

Figure 2. Mean percentage time spent on eating, ruminating, active, and not active by steers summarized over 6 wk in the feedlot. (A) Steers within 0PC-pen and 100PC-pen. (B) Steers within 75PC-pen along with pen average. 75% PC consists of 75 PC, whereas 25% AD consists of 25 AD steers within 75PC-pen. (C) Steers within 50PC-pen along with a pen average. 50% PC consists of 50 PC, whereas 50% AD consists of 50 AD steers within 50PC-pen. (D) Steers within 25PC pen. 25% PC consists of 25 PC, whereas 75% AD consists of 75 AD steers within 25PC-pen. Each pen consists of 100 steers.

Figure 3. Time spent eating (minutes per week) for PC and AD steers during weeks 1 to 6 in the feedlot. AD, auction-derived steers; PC, preconditioned steers. ***Difference (P < 0.001); **Difference (P < 0.01).

The PC steers also spent more time active over weeks 1 and 2 compared to AD steers (IRR = 1.16 and 1.12, P < 0.001, respectively). Time spent is not active in minutes by PC and AD steers during the first 6 wk in the feedlot is provided in Figure 4. The PC steers spent less time being non-active over weeks 1 to 3 in a feedlot than AD steers (IRR = 0.66, 0.68, and 0.86, P < 0.001, respectively).

Figure 4. Time spent not active (minutes per week) for preconditioned and auction-derived steers during weeks 1 to 6 in the feedlot. AD, auction-derived steers; PC, preconditioned steers. *Difference (P < 0.001).

Comparison of steer’s behavioral activities by BRD status and ADG over 6 wk in the feedlot

The temporal pattern of time spent eating by BRD non-affected vs. BRD-affected steers during 6 wk in the feedlot is provided in Figure 5. Steers not affected by BRD spent more time eating than BRD-affected steers (IRR = 1.12, P < 0.001). Time spent ruminating by BRD non-affected steers was greater whereas time spent by BRD non-affected steers being non-active was lesser compared to BRD-affected steers (IRR = 1.06 and 0.92, P < 0.001, respectively). Time spent being active between BRD-affected vs. BRD non-affected steers was not significant.

Figure 5. Time spent eating (in minutes per week) by BRD non-affected and affected steers during weeks 1 to 6 in the feedlot. BRD, Bovine Respiratory Disease. ***Difference (P < 0.001); *Difference (P < 0.05).

The steers’ ADG did not significantly affect time spent eating (P = 0.25), but affected time spent ruminating (IRR = 1.04, P = 0.02), active (IRR = 1.06, P < 0.001) and not active (IRR = 0.95, P < 0.001) over 6 wk in the feedlot. Preconditioned steers with higher ADG spent more time eating and active (P = 0.01 and P = 0.03, respectively) but less time being not active (P < 0.01). Similarly, AD steers with higher ADG spent more time eating, ruminating, and active (P < 0.01) but less time being not active (P < 0.001).

Comparison of HC concentrations between single source and commingled pens

The HC concentrations were not different between PC steers in 100PC and PC steers with various commingling ratios (75PC, 50PC, and 25PC) (P = 0.06, P = 0.22, and P = 0.93, respectively) or combined (P = 0.07). In a similar analysis focused on AD steers compared to commingled AD steers, there was no significant difference in HC concentrations.

HC concentrations between PC and AD steers

The HC concentration of steers ranged from 2.51 to 20.57 pg/mg. Upon comparing between CowManager ear-tagged PC (n = 113) and AD steers (n = 112), irrespective of their placement in the study, there was no difference in HC concentrations (11.39 ± 2.91 vs. 11.46 ± 2.43, P = 0.66). Upon comparison of 2 sources in non-commingled pens, HC concentrations between PC (n = 45) in the 100PC-pen and AD (n = 45) in the 0PC-pen did not differ (10.93 ± 3.22 vs. 11.75 ± 2.45, P = 0.57). Additionally, when the best-performing PC (n = 26) and worst-performing AD steers (n = 25) or PC steers with BRD and PC steers without BRD were compared, HC concentrations did not differ (P = 0.92).

Impact of HC on health, performance, and behavioral activities of steers

Source had a confounding effect on HC concentrations for morbidity related to BRD; however, this association was not significant (P = 0.08). Similarly, there was no effect of HC on ADG of steers (P = 0.44). After accounting for the influencing factors of source and commingling, no effect of HC was observed on time spent eating by steers (P = 0.83). Similarly, HC did not significantly affect time spent ruminating (P = 0.20), active (P = 0.89), or non-active (P = 0.32).

Discussion

In the present study, HC concentrations were not significantly different between PC and AD steers. Changes in HC concentrations due to compromised living, housing, and management conditions were reported in cows and heifers (Silva et al., 2016; Schubach et al., 2017) and bulls (Creutzinger et al., 2017). Stressful events are mitigated during preconditioning which can have a crucial role in fostering resilience in steers. Possible reasons for the absence of differences in HC concentrations between PC and AD steers could be that the potential weaning stress experienced by AD steers during the short interval after weaning might not have increased HC concentrations enough to make them detectably different from PC steers. Moreover, other factors at the ranch like health status, pain procedures like castration, and vaccination history of AD steers were unknown.

Similarly, there were no significant differences in HC concentrations associated with BRD-related morbidity or ADG. In our previous publication, we reported that out of 500 steers, 66 PC and 124 AD steers were diagnosed with BRD over 40 d in the feedlot (Mijar et al., 2023); however, disease occurred over a short interval, as the majority of BRD diagnosed steers had clinical cures in response to antibiotic treatment at first pull. The HC concentration depends on the duration of disease, with higher concentrations in chronically ill compared to acutely ill or healthy cattle (Braun et al., 2017), which may explain the lack of significant difference in HC concentrations in our study.

Although several studies reported associations between higher HC concentrations and adverse performance, e.g., suboptimal growth rates (Vesel et al., 2020), it is essential to acknowledge that the ADG in steers can be affected by a combination of various factors. Importantly, it does not inherently imply that stressed animals will consistently have a lower ADG or vice versa.

We did not detect significant differences in HC concentrations among commingled pens. Although HC concentrations can reflect stress due to repeated exposure over relatively short intervals, e.g., 3 wk (Burnett et al., 2014), steers in our study may not have sufficiently elicited stress responses due to commingling. Furthermore, hair samples collected on D40 at the feedlot to quantify HC concentrations in our study may not distinctly isolate the stress caused by commingling but more so of the ranch phase before feedlot entry. However, hair samples collected in our study enabled quantification of cortisol deposition for as long as 6 wk after arrival at the feedlot, thereby capturing long-term stress experienced by steers.

Since we could not identify long-term stress in steers through HC concentrations, we shifted our focus to behavioral responses. Our 6-wk time series analysis in the feedlot detected patterns in eating, ruminating, and activity. We hypothesized that PC steers would exhibit more favorable behaviors, especially in eating and ruminating, during the initial week compared to AD steers.

The PC steers spent more time eating than AD steers over the first 6 wk in the feedlot in our study; this confirmed the direct observations in the first week of arrival in the feedlot that PC spent more time eating compared to AD steers (Hodder et al. 2023). The PC steers were trained to eat from a bunk on the ranch to facilitate adaptation to the new feedlot environment. Additionally, as the AD group had a higher incidence of BRD, illness may have negatively impacted their willingness to eat, as reported (Wolfger et al., 2015a; Knauer et al., 2017; Conboy et al., 2021). Our study also supported the fact that BRD-affected steers spent less time eating over the first few weeks in the feedlot.

Although PC steers spent more time eating than AD steers over weeks 1 to 6 in our study, the median time spent eating by PC steers declined whereas that of AD steers inclined over weeks 1 to 3 and remained stable over weeks 4 to 6 for both groups of steers. The incline in time spent eating over weeks by AD steers may be due to steers getting familiar periodically with the feedlot environment. However, the decreasing trend of time spent eating by PC steers may be due to experienced social dominance.

In our study, we found that BRD-affected steers spent more time being non-active than BRD non-affected steers, consistent with findings from other studies (Theurer et al., 2013; Wolfger et al., 2015a; Toaff-Rosenstein et al., 2016). However, there was no difference in active time between these 2 groups. We do not have a clear explanation for the lack of difference in activity levels, but it could be due to prompt treatment and care of diseased steers, which could have mitigated the impact of the disease on their activity. Another possible explanation could be the CowManager system’s time budgeting of behaviors. For instance, in our study, steers affected by BRD spent less time eating and ruminating when they were more non-active, resulting in no difference in overall activity levels. Furthermore, healthy steers might not have felt the need to walk more, as they spent more time eating. Additionally, a lower sensitivity of CowManager ear tag to monitor activity levels, was reported with Pearson correlation and concordance correlation coefficient of 0.73 and 0.95, respectively, for activity levels between the sensor and visual monitoring of behaviors (Bikker et al., 2014), might also have influenced these findings.

In our study, time spent eating by steers in 100PC and 50PC was higher compared to steers in 0PC-pen. However, the increase in time spent eating by steers in pens did not follow with the higher percentages of PC steers placed within a pen. Many factors such as dominance, aggression or temperament, social hierarchy or rank, and ability to cope with competitive situations of steers may have important roles on time spent eating by steers in a commingled pen (Nkrumah et al., 2007; González et al., 2008; Llonch et al., 2018; Whalin et al., 2021).

Our study did not assess social rank, hierarchy, social learning, and temperament of steers placed within pens; therefore, we cannot appraise impacts of these factors on time steers spent eating among pens. Further research is warranted to comprehensively understand impacts of commingling proportions on steer behaviors, with due consideration of social dynamics. Additionally, our study had a limitation of absence of replicated pens and therefore the results oriented through pen-level comparison may not be generalized. Replication of the study design for each commingling percentage of PC and AD steers is warranted to better understand the impacts of commingling on steer behavioral performance.

Conclusions

Regardless of commingling, PC steers spent more time eating, ruminating, and spent less time being not active compared to AD steers during the initial few weeks at the feedlot. However, the behavioral findings at the pen level may not be generalized because of lack of pen replications. HC did not identify potentially lower stress associated with ranch transfer in steers. Additionally, the measurements of HC concentrations could not detect BRD-related morbidity or ADG.

Acknowledgments

This study was supported by the Major Innovation Fund, Alberta, Beef Cattle Research Council (10028475), Results Driven Agriculture Research (2020F104R), and Anderson-Chisholm Chair in Animal Care and Welfare. The authors extend their gratitude to W.A. Ranches at the University of Calgary for steer raising and management, Olds College for technical support, as well as to graduate students at the Faculty of Veterinary Medicine, University of Calgary, for their immense help and support throughout this project. Additionally, the authors extend their appreciation to Dr. J. Kastelic for his editorial assistance in refining the manuscript.

Abbreviations

ADG average daily gain

BHV bovine herpesvirus

BRD Bovine Respiratory Disease

BRSV bovine respiratory syncytial virus

BVDV bovine viral diarrhea virus

D0 feedlot arrival day

D40 40 d in the feedlot

DOF days on feed

ELISA enzyme-linked immunosorbent assay

EU experimental unit

HC hair cortisol

HPA hypothalamic-pituitary-adrenal

HPLC high-performance liquid chromatography

IRR incidence rate ratio

OU observational unit

PBS phosphate-buffered saline

PI3 parainfluenza-3

Conflict of Interest Statement

None of the authors has any conflict of interest with publication of the study.
==== Refs
Literature Cited

Accorsi, P. A., E.Carloni, P.Valsecchi, R.Viggiani, M.Gamberoni, C.Tamanini, and E.Seren. 2008. Cortisol determination in hair and faeces from domestic cats and dogs. Gen. Comp. Endocrinol. 155 :398–402. doi:10.1016/j.ygcen.2007.07.002 17727851
Ashley, N. T., P. S.Barboza, B. J.Macbeth, D. M.Janz, M. R. L.Cattet, R. K.Booth, and S. K.Wasser. 2011. Glucocorticosteroid concentrations in feces and hair of captive caribou and reindeer following adrenocorticotropic hormone challenge. Gen. Comp. Endocrinol. 172 :382–391. doi:10.1016/j.ygcen.2011.03.029 21501613
Bikker, J. P., H.van Laar, P.Rump, J.Doorenbos, K.van Meurs, G. M.Griffioen, and J.Dijkstra. 2014. Technical note: evaluation of an ear-attached movement sensor to record cow feeding behavior and activity. J. Dairy Sci. 97 :2974–2979. doi:10.3168/jds.2013-7560 24630647
Braun, U., G.Clavadetscher, M. R.Baumgartner, B.Riond, and T. M.Binz. 2017. Hair cortisol concentration and adrenal gland weight in healthy and ill cows. Schweiz. Arch. Tierheilkd 159 :493–495. doi:10.17236/sat00128 28952959
Buddle, E. A., H. J.Bray, and R. A.Ankeny. 2018. “I feel sorry for them”: Australian meat consumers’ perceptions about sheep and beef cattle transportation. Animals. 8 :171–113. doi:10.3390/ani8100171 30282909
Burnett, T. A., A. M. L.Madureira, B. F.Silper, A.Nadalin, A.Tahmasbi, D. M.Veira, and R. L. A.Cerri. 2014. Short communication: factors affecting hair cortisol concentrations in lactating dairy cows. J. Dairy Sci. 97 :7685–7690. doi:10.3168/jds.2014-8444 25282411
Burnett, T. A., A. M. L.Madureira, B. F.Silper, A.Tahmasbi, A.Nadalin, D. M.Veira, and R. L. A.Cerri. 2015. Relationship of concentrations of cortisol in hair with health, biomarkers in blood, and reproductive status in dairy cows. J. Dairy Sci. 98 :4414–4426. doi:10.3168/jds.2014-8871 25958283
Coetzee, J. F., L. N.Edwards, R. A.Mosher, N. M.Bello, A. M.O’Connor, B.Wang, B.Kukanich, and D. A.Blasi. 2012. Effect of oral meloxicam on health and performance of beef steers relative to bulls castrated on arrival at the feedlot. J. Anim. Sci. 90 :1026–1039. doi:10.2527/jas.2011-4068 21965454
Comin, A., T.Peric, M.Corazzin, M. C.Veronesi, T.Meloni, V.Zufferli, G.Cornacchia, and A.Prandi. 2013. Hair cortisol as a marker of hypothalamic-pituitary-adrenal axis activation in Friesian dairy cows clinically or physiologically compromised. Livest. Sci. 152 :36–41. doi:10.1016/j.livsci.2012.11.021
Conboy, M. H., C. B.Winder, C.Medrano-Galarza, S. J.LeBlanc, D. B.Haley, J. H. C.Costa, M. A.Steele, and D. L.Renaud. 2021. Associations between feeding behaviors collected from an automated milk feeder and disease in group-housed dairy calves in Ontario: a cross-sectional study. J. Dairy Sci. 104 :10183–10193. doi:10.3168/jds.2021-20137 34099289
Creutzinger, K. C., J. M.Stookey, T. W.Marfleet, J. R.Campbell, D. M.Janz, F. J.Marqués, and Y. M.Seddon. 2017. An investigation of hair cortisol as a measure of long-term stress in beef cattle: results from a castration study. Can. J. Anim. Sci. 97 :499–509. doi:10.1139/cjas-2016-0206
González, L. A., A.Ferret, X.Manteca, J. L.Ruíz-De-La-Torre, S.Calsamiglia, M.Devant, and A.Bach. 2008. Performance, behavior, and welfare of Friesian heifers housed in pens with two, four, and eight individuals per concentrate feeding place. J. Anim. Sci. 86 :1446–1458. doi:10.2527/jas.2007-0675 18272856
Grant, R. J., and J. L.Albright. 2001. Effect of animal grouping on feeding behavior and intake of dairy cattle. J. Dairy Sci. 84 :E156–E163. doi:10.3168/jds.s0022-0302(01)70210-x
Hodder, A., E.Pajor, F. V. D.Meer, J.Louden, S.Thompson, and K.Orsel. 2023. Feeding behaviour and activity of beef calves during the first week at the feedlot: impact of calf source and commingling ratios. Appl. Anim. Behav. Sci. 258 :105810. doi:10.1016/j.applanim.2022.105810
Hubbard, A. J., M. J.Foster, and C. L.Daigle. 2021. Impact of social mixing on beef and dairy cattle—a scoping review. Appl. Anim. Behav. Sci. 241 :105389. doi:10.1016/j.applanim.2021.105389
Hulbert, L. E., and S. J.Moisá. 2016. Stress, immunity, and the management of calves. J. Dairy Sci. 99 :3199–3216. doi:10.3168/jds.2015-10198 26805993
Knauer, W. A., S. M.Godden, A.Dietrich, and R. E.James. 2017. The association between daily average feeding behaviors and morbidity in automatically fed group-housed preweaned dairy calves. J. Dairy Sci. 100 :5642–5652. doi:10.3168/jds.2016-12372 28478006
Koren, L., O.Mokady, T.Karaskov, J.Klein, G.Koren, and E.Geffen. 2002. A novel method using hair for determining hormonal levels in wildlife. Anim. Behav. 63 :403–406. doi:10.1006/anbe.2001.1907
Llonch, P., M.Somarriba, C. A.Duthie, S.Troy, R.Roehe, J.Rooke, M. J.Haskell, and S. P.Turner. 2018. Temperament and dominance relate to feeding behaviour and activity in beef cattle: Implications for performance and methane emissions. Animal. 12 :2639–2648. doi:10.1017/S1751731118000617 29606168
Loerch, S. C., and F. L.Fluharty. 1999. Physiological changes and digestive capabilities of newly received feedlot cattle. J. Anim. Sci. 77 :1113–1119. doi:10.2527/1999.7751113x 10340577
Macbeth, B. J., M. R. L.Cattet, G. B.Stenhouse, M. L.Gibeau, and D. M.Janz. 2010. Hair cortisol concentration as a noninvasive measure of long-term stress in free-ranging grizzly bears (Ursus arctos): Considerations with implications for other wildlife. Can. J. Zool. 88 :935–949. doi:10.1139/z10-057
Mijar, S., F.van der Meer, E.Pajor, A.Hodder, J. M.Louden, S.Thompson, and K.Orsel. 2023. Impacts of commingling preconditioned and auction-derived beef calves on bovine respiratory disease related morbidity, mortality, and weight gain. Front. Vet. Sci. 10 :1137078. doi:10.3389/fvets.2023.1137078 37008349
Minton, J. E. 1994. Function of the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system in models of acute stress in domestic farm animals. J. Anim. Sci. 72 :1891–1898. doi:10.2527/1994.7271891x 7928769
Moya, D., K. S.Schwartzkopf-Genswein, and D. M.Veira. 2013. Standardization of a non-invasive methodology to measure cortisol in hair of beef cattle. Livest. Sci. 158 :138–144. doi:10.1016/j.livsci.2013.10.007
Nkrumah, J. D., D. H.Crews, J. A.Basarab, M. A.Price, E. K.Okine, Z.Wang, C.Li, and S. S.Moore. 2007. Genetic and phenotypic relationships of feeding behavior and temperament with performance, feed efficiency, ultrasound, and carcass merit of beef cattle. J. Anim. Sci. 85 :2382–2390. doi:10.2527/jas.2006-657 17591713
Peden, R. S. E., S. P.Turner, L. A.Boyle, and I.Camerlink. 2018. The translation of animal welfare research into practice: the case of mixing aggression between pigs. Appl. Anim. Behav. Sci. 204 :1–9. doi:10.1016/j.applanim.2018.03.003
Russell, E., G.Koren, M.Rieder, and S.Van Uum. 2012. Hair cortisol as a biological marker of chronic stress: current status, future directions and unanswered questions. Psychoneuroendocrinology. 37 :589–601. doi:10.1016/j.psyneuen.2011.09.009 21974976
Salak-Johnson, J. L., and J. J.McGlone. 2007. Making sense of apparently conflicting data: stress and immunity in swine and cattle. J. Anim. Sci. 85 :81–88. doi:10.2527/jas.2006-538
Schubach, K. M., R. F.Cooke, A. P.Brandão, K. D.Lippolis, L. G. T.Silva, R. S.Marques, and D. W.Bohnert. 2017. Impacts of stocking density on development and puberty attainment of replacement beef heifers. Animal. 11 :2260–2267. doi:10.1017/S1751731117001070 28521848
Silva, P. R. B., K. M.Lobeck-Luchterhand, R. L. A.Cerri, D. M.Haines, M. A.Ballou, M. I.Endres, and R. C.Chebel. 2016. Effects of prepartum stocking density on innate and adaptive leukocyte responses and serum and hair cortisol concentrations. Vet. Immunol. Immunopathol. 169 :39–46. doi:10.1016/j.vetimm.2015.11.007 26827837
Stangaferro, M. L., R.Wijma, L. S.Caixeta, M. A.Al-Abri, and J. O.Giordano. 2016. Use of rumination and activity monitoring for the identification of dairy cows with health disorders: part III. Metritis. J. Dairy Sci. 99 :7422–7433. doi:10.3168/jds.2016-11352 27372583
Step, D. L., C. R.Krehbiel, H. A.DePra, J. J.Cranston, R. W.Fulton, J. G.Kirkpatrick, D. R.Gill, M. E.Payton, M. A.Montelongo, and A. W.Confer. 2008. Effects of commingling beef calves from different sources and weaning protocols during a forty-two-day receiving period on performance and bovine respiratory disease. J. Anim. Sci. 86 :3146–3158. doi:10.2527/jas.2008-0883 18567723
Theurer, M. E., D. E.Anderson, B. J.White, M. D.Miesner, D. A.Mosier, J. F.Coetzee, J.Lakritz, and D. E.Amrine. 2013. Effect of Mannheimia haemolytica pneumonia on behavior and physiologic responses of calves during high ambient environmental temperatures. J. Anim. Sci. 91 :3917–3929. doi:10.2527/jas.2012-5823 23658357
Toaff-Rosenstein, R. L., L. J.Gershwin, and C. B.Tucker. 2016. Fever, feeding, and grooming behavior around peak clinical signs in bovine respiratory disease. J. Anim. Sci. 94 :3918–3932. doi:10.2527/jas.2016-0346 27898899
Vesel, U., T.Pavič, J.Ježek, T.Snoj, and J.Starič. 2020. Welfare assessment in dairy cows using hair cortisol as a part of monitoring protocols. J. Dairy Res. 87 :72–78. doi:10.1017/S0022029920000588 33213571
Whalin, L., D. M.Weary, and M. A. G.von Keyserlingk. 2021. Understanding behavioural development of calves in natural settings to inform calf management. Animals. 11 :2446. doi:10.3390/ani11082446 34438903
Wilson, B. K., C. J.Richards, D. L.Step, and C. R.Krehbiel. 2017. Best management practices for newly weaned calves for improved health and well-being. J. Anim. Sci. 95 :2170–2182. doi:10.2527/jas.2016.1006 28727007
Wolfger, B., K. S.Schwartzkopf-Genswein, H. W.Barkema, E. A.Pajor, M.Levy, and K.Orsel. 2015a. Feeding behavior as an early predictor of bovine respiratory disease in North American feedlot systems. J. Anim. Sci. 93 :377–385. doi:10.2527/jas.2013-8030 25568380
Wolfger, B., E.Timsit, E. A.Pajor, N.Cook, H. W.Barkema, and K.Orsel. 2015b. Technical note: Accuracy of an ear tag-attached accelerometer to monitor rumination and feeding behavior in feedlot cattle. J. Anim. Sci. 93 :3164–3168. doi:10.2527/jas.2014-8802 26115302
