Shrink (SHK) or BW loss during transport is a physiological process associated with loss of urine and manure (fill SHK) and of fluid and tissue (tissue SHK; Coffey et al., 2001). Several factors inciting this complex process include high ambient temperature, excessive handling, diet, supplementation with ionophores, temperament of the animal and preconditioning before transportation (Pritchard and Mendez, 1990; Grandin, 1997; Coffey et al., 2001).
The stress of transportation can produce several negative consequences such as reduction of BW and feed consumption as well as impairment of the immune system and increased morbidity and mortality (Grandin, 1997; Coffey et al., 2001). We quantified the relationships between the distance traveled with health and performance parameters of feedlot cattle (Cernicchiaro et al., 2011). However, SHK is a different measure of transport effects and in some production systems SHK is a more easily accessible piece of information. In a previous study, Camp et al., (1983) found a positive correlation between transit BW loss and incidence of bovine respiratory disease (BRD), yet, there is little additional evidence to determine how data on characteristics of the journey (e.g., SHK) vary by cohort demographics and how that information can be used to predict health risks and expected performance on feedlot cattle. The objective of this retrospective observational study was to determine potential associations between SHK with cattle health (BRD morbidity and overall mortality risks) and performance (HCW and ADG) after feedlot arrival, and to evaluate if the effects of SHK on these outcomes varied by cohort demographics. Quantifying effects of SHK before arrival may be useful to enhance current systems for classification of cattle as high or low risk for BRD and may allow more accurate prediction of cattle performance and expected mortality.
MATERIALS AND METHODS
A dataset of retrospective, operational data on cohort-level demographic, health and performance variables was compiled from 13 feedlots in the central and southern high plains of the U.S. for this study. The existing database included information collected routinely from feedlots on cohort management characteristics, cumulative BRD incidence and overall mortality, and other health, performance and demographic information. The dataset was refined to include only male or female beef cattle cohorts (no mixed), containing more than 20 cattle at arrival, with a mean arrival BW greater than 227 kg and with available information on cohort pay BW and initial BW upon arrival. These data were subsequently exported and re-formatted in STATA 10 (StataCorp LP, College Station, TX) for analysis.
Cohort-level covariates of interest included mean arrival BW, gender, arrival date and the specific feedlot where cattle arrived. The arrival date was used to create an arrival quarter variable (or season) for cattle that arrived in January through March (winter), April through June (spring), July through September (summer), and October through December (fall). The primary transportation factor of interest was the percent SHK which was calculated using the following formula: [(cohort pay BW – cohort arrival BW) / (cohort pay BW)] * 100. Individual cohorts missing any one of the required variables to calculate SHK were removed from the dataset.
Health outcomes of interest included cumulative (over the entire feeding period) BRD morbidity and overall mortality. A BRD case was defined as an animal diagnosed by feedlot personnel with initial clinical signs of BRD and treated with an antimicrobial. Overall mortality included deaths of any cause that occurred between the time of arrival and final cohort closeout. Performance outcomes of interest included cohort-level ADG calculated as [(total BW gain/number of shipped cattle)/days on feed]) over the entire feeding period and mean HCW (unchilled weight takenshortly after slaughter).
Associations between SHK and cattle demographics with cumulative BRD morbidity and overall mortality risks were analyzed with multivariable mixed-effects negative binomial regression models in STATA 10. The hierarchical structure of the data consisted of cattle cohorts nested within feedlots. To account for clustering at the feedlot level, a random intercept term for feedlot was included in the model. Morbidity attributed to BRD was modeled as the count of initial BRD cases in each cohort with the initial number of cattle per cohort at arrival modeled as the exposure variable. Overall mortality was modeled as the counts of deaths of any cause with the initial number of cattle per cohort as the exposure variable of the model. To evaluate performance outcomes (HCW and ADG), multilevel mixed-effects linear regression models (STATA 10) using maximum likelihood estimation and a random intercept for feedlot were employed.
Independent variables evaluated included SHK (categorized as: ≤ 0, 0 to 2.5, 2.6 to 5.0 and > 5.0%), mean arrival BW in kilograms (categorized as: 227 to 271, 272 to 317, 318 to 362, and > 362 kg), gender (male and female), season (or arrival quarter: winter = January to March, spring = April to June, summer = July to September, and fall = October to December), total number of cattle per cohort (or cohort size, 20 to 100, 101 to 150, 151 to 200 and > 200 cattle) and year of arrival (2000 to 2008, with observations from 2000 to 2002 collapsed in 1 category (i.e., 2002) due to sparse data in these years). Shrink, which was recorded on a continuous scale, was categorized because it did not meet the linearity assumption in the BRD morbidity models. The selection of appropriate categories for SHK and other predictors in the dataset was performed by comparing multiple BRD morbidity models that included only these predictor variables using the Akaike's and Bayesian information criteria (AIC and BIC, respectively). The same categorization structures were used for models of all other outcomes variables to facilitate direct comparisons.
During the multivariable model building process variables significant at the 10% level (P < 0.10) in the univariable screen were included in the main effects model. A manual backward elimination procedure was then conducted until only statistically significant (P < 0.05) main effects remained in the model. A correlation analysis was performed using the Pearson's and the Spearman's rank correlation statistics to identify potential collinearity. If the value of the correlation statistic between 2 independent variables was |0.80| or greater at a 5% significance level (P < 0.05), only 1 of the variables was selected for inclusion in the multivariable model based on biological plausibility or the completeness and quality of the data (Dohoo et al., 2009). The linearity assumption between the log odds of the predicted rate of the outcome (for negative binomial models) or the non-transformed outcome (for linear models) and continuous predictors was assessed using graphical methods (i.e., lowess smoothing of the outcome on the continuous predictor). If the assumption was not met, depending on the shape of these relationships, the predictor variable was categorized unless it was more appropriately transformed (Dohoo et al., 2009). All non-significant variables at the 5% level (P < 0.05) were removed from the multivariable model unless they acted as confounding variables or were part of a significant interaction term (P < 0.05). Variables were considered as confounders if they were defined as a non-intervening variable that resulted in a 20% or greater change in the coefficient of a statistically significant variable when the potential confounder was removed from the model (Dohoo et al., 2009).All possible 2-way interaction terms between the main predictor (SHK) and demographic variables suspected to act as confounding effects (i.e., mean arrival BW, gender and season) were tested for statistical significance at the 5% level using manual forward selection.
Probabilities and incidence rate ratios (IRR) and their respective 95% confidence intervals were estimated for predictors included in the final multivariable models for cumulative BRD morbidity and overall mortality risks. Similarly, coefficients and 95% confidence intervals were computed for the mixed-effects linear regression models constructed for the ADG and HCW outcomes. Diagnostics for final multivariable models included the evaluation of the predicted values of the random effects (i.e., feedlot-level residuals) in the model or BLUP, and Pearson and Deviance residuals for observations at the lowest level (i.e., cohort). Normal quantile plots of BLUP and residual plots of cohort-level residuals were visually examined to assess general model fit and to identify potential outliers and influential observations
Data analyzed in this study represented 16,590 cattle cohorts from 13 U.S. commercial feedlots located in the southern and central plains. Descriptive statistics of SHK, cohort-level demographics and outcomes are presented in Table 1. The median SHK among the study cohorts was 3.0% with a mean (± SEM) of 2.4 ± 0.02% (Table 1). Cumulative BRD morbidity risk ranged from 0 to 100%, with a mean (± SEM) of 10.0 ± 0.09% and a median value of 5.8%. Variables significantly (P < 0.05) associated with BRD morbidity risk in the final multivariable model included SHK, gender, season of arrival, cohort size, mean arrival BW, arrival year, and 2-way interactions between SHK and arrival BW, gender, and season (Table 2). Mean (± SEM) overall mortality risk was 1.3 ± 0.01% (median = 0.9%) and ranged from 0 to 25.6%. The mean and median number of days on feed of cohorts experiencing initial BRD cases was 143 and 150 d (range = 23 to 288 d) and 146 and 153 d (range = 20 to 288 d), respectively, for cohorts experiencing deaths due to any cause. Shrink, mean arrival BW, gender, season of arrival, cohort size, year of arrival and 2-way interactions between SHK and mean arrival BW, SHK and gender and SHK with season of arrival were significantly (P < 0.05) associated with the risk of overall mortality (Table 2).
|Body weight loss during transport in % (shrink)||2.4||0.02||3.0||0 to 5.0||-5.8 to 14.8|
|Mean arrival BW in kg||305.6||0.38||302.8||264.4 to 344.7||226.8 to 408.2|
|Days on feed in days||143.3||0.34||150.0||123.0 to 172.0||20.0 to 288.0|
|Cohort size at arrival (number of cattle)||148.1||0.79||111.0||85.0 to 185.0||20.0 to 1968.0|
|Number of bovine respiratory disease first cases||12.3||0.12||7.0||3.0 to 15.0||0 to 318.0|
|Bovine respiratory disease morbidity in % (number of first cases/cohort size at arrival)||10.0||0.09||5.8||2.6 to 12.6||0 to 100|
|Number of deaths from any cause||1.8||0.02||1.0||0 to 2.0||0 to 47.0|
|Mortality of any cause in % (number of deaths/ cohort size)||1.3||0.01||0.9||0 to 1.9||0 to 25.6|
|Mean HCW, kg||327.2||0.50||338.3||312.2 to 365.7||94.4 to 3083.0|
|ADG, kg||1.2||0.003||1.3||1.1 to 1.4||0 to 3.5|
|BRD morbidity||Mortality||HCW (kg)||ADG (kg)|
|Variable||IRR 1||95% CI 2||IRR||95% CI||Coef 3||95% CI||Coef||95% CI|
|Shrink in %|
|≤ 0||ref 4||ref||ref||ref||ref||ref||ref||ref|
|0 to 2.5||0.99||0.88 to 1.12||0.80||0.68 to 0.94||-11.69||-17.06, -6.32||-0.02||-0.67, 0.03|
|2.6 to 5.0||1.27||0.17 to 1.38||0.77||0.69 to 0.87||-8.86||-12.7, -5.02||-0.004||-0.04, 0.03|
|>5.0||1.29||1.17 to 1.41||0.76||0.67 to 0.86||-14.23||-18.46, -9.99||-0.06||-0.10, -0.02|
|Male||1.18||1.12 to 1.24||1.14||1.06 to 1.22||38.81||36.57, 41.05||0.12||0.10, 0.15|
|Mean arrival BW, kg|
|227 to 271||ref||ref||ref||ref||ref||ref||ref||ref|
|272 to 317||0.84||0.79 to 0.88||0.74||0.69 to 0.80||11.30||8.62, 13.97||0.07||0.04, 0.09|
|318 to 362||0.60||0.56 to 0.64||0.53||0.49 to 0.58||26.16||23.25, 29.08||0.12||0.01, 0.15|
|>362||0.39||0.36 to 0.43||0.34||0.31 to 0.39||31.08||27.37, 34.80||0.13||0.09, 0.16|
|Cohort size (number of cattle)|
|20 to 100||ref||ref||ref||ref||ref||ref||ref||ref|
|101 to 150||0.77||0.75 to 0.80||0.94||0.90 to 0.98||-2.68||-4.03, -1.33||-0.02||-0.03, -0.009|
|151 to 200||0.64||0.61 to 0.66||0.87||0.83 to 0.91||-3.66||-5.35, -1.96||-0.03||-0.04, -0.01|
|>200||0.43||0.42 to 0.45||0.76||0.72 to 0.79||-4.46||-6.05, -2.87||-0.05||-0.07, -0.04|
|Season of arrival|
|Winter (Jan to Mar)||ref||ref||ref||ref||ref||ref||ref||ref|
|Spring (Apr to Jun)||1.22||1.14 to 1.31||1.30||1.20 to 1.42||4.70||1.92, 7.47||0.05||0.03, 0.08|
|Summer (Jul to Sep)||1.45||1.36 to 1.55||1.22||1.13 to 1.33||-7.48||-10.23, -4.74||-0.07||-0.01, -0.05|
|Fall (Oct to Dec)||1.25||1.17 to 1.33||1.07||0.98 to 1.16||-6.85||-9.51, -4.19||-0.11||-0.13, -0.08|
|Year of arrival|
|2003||1.55||1.45 to 1.66||1.22||1.12to1.32||-2.04||-5.07, 0.99||-0.03||-0.06, -0.006|
|2004||1.82||1.72 to 1.93||1.29||1.19 to 1.39||5.08||2.38, 7.77||-001||-0.04, 0.01|
|2005||1.47||1.38 to 1.56||1.22||1.13 to 1.31||10.79||8.12, 13.47||-0.01||-0.04, 0.01|
|2006||1.26||1.19 to 1.34||1.31||1.21 to 1.41||8.78||6.10, 11.47||-0.07||-0.10, -0.05|
|2007||1.45||1.37 to 1.54||1.28||1.19 to 1.38||14.71||12.04, 17.38||-0.02||-0.04, 0.005|
|2008||1.31||1.22 to 1.40||1.15||1.06 to 1.26||15.66||12.77, 18.55||-0.001||-0.03, 0.03|
|≤ 0*Male vs. ≤ 0*Female||1.18||1.12 to 1.24||1.14||1.06 to 1.22||-34.87||-32.51, -37.24||0.12||0.10, 0.15|
|0 to 2.5*Male vs. 0 to 2.5*Female||1.23||1.16 to 1.32||1.12||1.03 to 1.23||28.16||25.38, 30.94||0.12||0.09, 0.15|
|2.6 to 5.0*Male vs. 2.6 to 5.0*Female||1.28||1.23 to 1.34||1.19||1.12 to 1.27||27.70||25.69, 29.72||0.08||0.07, 0.10|
|>5.0*Male vs. >5.0*Female||1.45||1.39 to 1.52||1.40||1.32 to 1.50||30.97||28.61, 33.33||0.08||0.06, 0.10|
|≤0*227 to 271||ref||ref||ref||ref||ref||ref||ref||ref|
|≤0*272 to 317||0.84||0.79 to 0.88||0.74||0.69 to 0.80||11.30||8.62, 13.97||0.07||0.04, 0.09|
|≤0*318 to 362||0.60||0.56 to 0.64||0.53||0.49 to 0.58||26.16||23.25, 29.08||0.12||0.10, 0.15|
|≤0*>362||0.39||0.36 to 0.43||0.34||0.31 to 0.39||31.08||27.37, 34.80||0.13||0.09, 0.16|
|0 to 2.5*227 to 271||ref||ref||ref||ref||ref||ref||ref||ref|
|0 to 2.5*272 to 317||0.86||0.78 to 0.95||0.83||0.73 to 0.95||20.51||15.76, 25.27||0.06||0.01, 0.10|
|0 to 2.5*318 to 362||0.57||0.52 to 0.62||0.53||0.46 to 0.60||33.14||28.76, 37.52||0.11||0.07, 0.15|
|0 to 2.5*>362||0.47||0.43 to 0.53||0.42||0.37 to 0.49||45.47||40.84, 50.10||0.12||0.08, 0.16|
|2.6 to 5.0*227 to 271||ref||ref||ref||ref||ref||ref||ref||ref|
|2.6 to 5.0*272 to 317||0.77||0.73 to 0.81||0.82||0.77 to 0.89||16.45||13.9, 18.97||0.05||0.03, 0.08|
|2.6 to 5.0*318 to 362||0.52||0.49 to 0.55||0.55||0.50 to 0.59||32.14||29.27, 34.99||0.11||0.08, 0.14|
|2.6 to 5.0*>362||0.41||0.38 to 0.44||0.44||0.36 to 0.44||42.99||39.54, 46.45||0.13||0.10, 0.16|
|>5.0*227 to 271||ref||ref||ref||ref||ref||ref||ref||ref|
|>5.0*272 to 317||0.73||0.69 to 0.77||0.76||0.70 to 0.81||16.49||13.74, 19.25||0.05||0.03, 0.08|
|>5.0*318 to 362||0.46||0.43 to 0.49||0.46||0.42 to 0.51||30.48||27.14, 33.82||0.14||0.10, 0.17|
|>5.0*>362||0.36||0.32 to 0.40||0.34||0.29 to 0.40||49.33||44.41, 54.24||0.30||0.25, 0.34|
|≤0*Spring||1.22||1.14 to 1.31||1.30||1.20 to 1.42||4.70||1.92, 7.47||0.05||0.03, 0.08|
|≤0*Summer||1.45||1.36 to 1.55||1.22||1.13 to 1.33||-7.48||-10.23, -4.74||-0.07||-0.10, -0.05|
|≤0*Fall||1.25||1.17 to 1.33||1.07||0.98 to 1.16||-6.85||-9.51, -4.19||-0.11||-0.13, -0.08|
|0 to 2.5*Winter||ref||ref||ref||ref||ref||ref||ref||ref|
|0 to 2.5*Spring||1.08||0.98 to 1.18||1.01||0.89 to 1.15||5.07||1.15, 8.98||0.04||0.01, 0.08|
|0 to 2.5*Summer||1.06||0.97 to 1.15||0.95||0.85 to 1.06||5.99||2.53, 9.45||0.004||-0.03, 0.04|
|0 to 2.5*Fall||1.43||1.31 to 1.55||1.29||1.15 to 1.44||-1.35||-5.00, 2.30||-0.12||-0.15, -0.09|
|2.6 to 5.0*Winter||ref||ref||ref||ref||ref||ref||ref||ref|
|2.6 to 5.0*Spring||0.94||0.89 to 0.99||0.94||0.86 to 1.02||-2.78||-5.34, -0.23||0.02||-0.003, 0.04|
|2.6 to 5.0*Summer||1.05||0.99 to 1.11||1.09||1.01 to 1.18||-4.70||-7.23, -2.17||-0.05||-0.07, -0.03|
|2.6 to 5.0*Fall||1.15||1.09 to 1.22||1.44||1.33 to 1.55||-3.38||-6.02, -0.74||-0.11||-0.13, -0.08|
|>5.0*Spring||0.94||0.87 to 1.00||1.12||1.02 to 1.24||0.68||-2.54, 3.91||0.03||-0.001, 0.06|
|>5.0*Summer||1.10||1.02 to 1.17||1.24||1.12 to 1.36||-2.74||-5.97, 0.48||-0.03||-0.06, 0.004|
|>5.0*Fall||1.13||1.05 to 1.21||1.48||1.34 to 1.63||-2.84||-6.31, 0.63||-0.09||-0.12, -0.05|
Cattle in heavier BW classes (272 to 317, 318 to 362, and > 362 kg) displayed similar levels of BRD morbidity and overall mortality risks across all SHK categories, but lighter (227 to 271 kg) cattle showed greater morbidity risk at greater SHK levels (2.6 to 5.0% and > 5.0%) compared with decreased SHK (Table 2, Figure 1). In addition, male cohorts experienced significantly (P < 0.05) greater BRD morbidity and overall mortality risks than female cohorts across all SHK with increasing risk as SHK increased (Table 2).
Cattle arriving in fall and summer months showed a significantly (P < 0.05) greater BRD morbidity and overall mortality risks across all SHK categories compared with cattle arriving in winter experiencing the same SHK percentage. However, for cattle arriving in spring, the BRD morbidity risk was greater for SHK of < 0% and 0 to 2.5% (Figure 2). The risk of overall mortality was significantly (P < 0.05) greater for extreme SHK categories (< 0% and > 5.0%) compared with cattle arriving in winter months experiencing the same percentage of SHK (Table 2, Figure 3).
Factors significantly (P < 0.05) associated with HCW in the multivariable model included SHK, mean arrival BW, gender, cohort size, season of arrival, arrival year and 2-way interactions between SHK and gender, SHK and arrival BW, and SHK with season of arrival (Table 2). In addition, the final multivariable model for ADG included SHK, mean arrival BW, gender, season of arrival, cohort size, arrival year and 2-way interactions between SHK and mean arrival BW, SHK and gender, and SHK with season of arrival (Table 2). Heavier BW cohorts at arrival (> 272 kg) showed significantly (P < 0.05) greater HCW and ADG values across all SHK percentages compared with lighter BW cattle (227 to 271 kg) experiencing the same percentages of BW loss during transit (Table 2). In addition, males had significantly (P < 0.05) greater HCW values for SHK greater than 0% and significantly greater ADG across all SHK categories compared with female cohorts experiencing the same SHK (Table 2).
For cattle arriving in spring, HCW and ADG values were significantly (P < 0.05) greater in cattle with less than 2.5% SHK during transport compared with cattle arriving during winter months with the same SHK levels. During summer and fall months, however, HCW and ADG values significantly (P < 0.05) decreased in cattle experiencing less than 0% and 2.6 to 5.0% SHK compared with cattle arriving during winter months that experienced the same SHK (Table 2, Figures 4 and 5).
Results from this study indicate that BW loss during transport was associated with health and performance variables on specific cohorts of cattle and the SHK effects were modified by cohort gender, cohort mean arrival BW and the season cattle arrived at the feedlot. The stress of transportation can make feeder cattle more susceptible to experiencing morbidity and mortality due to BRD (Ribble et al., 1995). Implementation of specific health interventions such as metaphylaxis is often based on the predicted disease risk of a cohort at arrival (Nickell and White, 2010); therefore, more accurate health risk predictions at feedlot arrival are important for the feeding industry to reduce economic losses associated to BRD and to improve animal welfare.
Length of the journey has important effects on BW loss during transportation, with the greatest losses occurring during the first miles and hours of transit (Barnes et al., 2004; Coffey et al., 2001). The mean SHK among the study cohorts was 2.4% and these results are in agreement with Harman et al. (1989), who found an overall mean SHK of 2.3% in a population of crossbreed steers over a period of 5 yr. Approximately 28% (4700/16590) of the study cohorts experienced BW gain or 0% SHK during transit (mean = -1.8%, SEM = 0.02%, range -5.8 to 0%). The BW gained by these cohorts could have been due to cattle that “shrunk” before the pay BW and potentially had access to feed and water before initial BW at the feedlot. A distinction of which cohorts received feed or water prior or post recording of pay and arrival BW would be valuable; however, that type of information is not routinely recorded and was not available for us to analyze. The objective of this research was to evaluate potential associations between SHK as measured in feedlot operational data and health outcomes; therefore, we elected to include all ranges of BW lost or gained (SHK) in the final analyses.
The BW loss during transit was positively associated to the incidence of BRD in a previous study (Camp et al., 1983). However, several factors including body condition, diet received before transit and length of transportation could affect the amount of BW lost during transit (Cole et al., 1988). In our study, the effect of SHK during transit on BRD morbidity, overall mortality, HCW and ADG depended on the gender of the cohort, season of arrival and mean arrival BW. However, SHK may be a proxy of other predictors such as transportation conditions, handling of cattle during loading and unloading, food and water deprivation, and environmental conditions (e.g., high ambient temperature), which may be the actual stressors (Coffey et al., 2001; Eicher, 2001; Swanson and Morrow-Tesch, 2001).
Previous studies have shown that steers had increased respiratory morbidity (Alexander et al., 1989; Snowder et al., 2006) and mortality (Cusack et al., 2007) risks than heifers. Our data indicated that male cohorts experienced significantly greater BRD morbidity and overall mortality than female cohorts across all BW losses with increasing risk as the percentage of SHK increased. The stress associated with castration and transportation may explain the increased morbidity and mortality risks observed in males (Pinchak et al., 2004; Snowder et al., 2006). Since our dataset did not allow us to differentiate whether male cohorts were castrated at the feedlot or previously, we were unable to account for health risks associated with castration, which has been indicated as a potential risk factor for increased morbidity. Contrarily, males had significantly greater HCW and ADG values than female cohorts for SHK greater than 0%. Similarly, previous studies (Choat et al., 2006; Zinn et al., 2008) investigating how gender affects animal feedlot production performance and finished carcass characteristics, found that steers had greater HCW and ADG than heifers after being fed the same diet during the same feeding period.
Our results indicated that SHK had a greater impact in lighter BW cattle in terms of their risk of BRD morbidity compared with heavier BW cohorts. Heavy cohorts maintained similar reduced risk of BRD across all levels of SHK. In addition, heavier cattle had significantly greater HCW and ADG at all SHK than lighter BW cohorts that experienced the same BW loss during transport. The increase in BRD morbidity and overall mortality risks in lighter compared with heavier BW cattle may be associated with the detrimental effects of transport stress and potential younger age of lighter cohorts (Eicher, 2001; Swanson and Morrow-Tesch, 2001; Ishizaki et al., 2005). Long-haul cattle experience the greatest SHK due to fecal, urine and tissue loss within the first 5 to 11 h in transport (Cole et al., 1988); however, the pre-transport BW can be regained within 8 to 16 h after transport (Knowles et al., 1999). Perhaps, heavier cattle can recover faster from the negative effects of transport due to their resilience to stress compared with their lighter and likely younger counterparts.
Warmer ambient temperature has been reported to be associated with increased BW loss during transport (Coffey et al., 2001). Similarly, Harman et al., (1989) reported that SHK was greater during summer and fall compared with winter and spring seasons. In our study, SHK had a greater effect in cattle arriving in fall months which also showed greater BRD morbidity risk. The temperature fluctuations likely observed during fall and early spring might affect health and performance parameters of the newly arrived cattle that have experienced BW loss after being transported long distances. The potential for a biological threshold in SHK is supported by previous research that showed that cattle after a 10- or 15-h journey had a greater percent BW loss (6.5% and 7.0%, respectively) compared with a 5-h journey (4.6%; Warriss et al., 1995). Shrink had similar effects in cattle arriving in all 4 seasons; however, summer-placed cattle showed a substantial increase especially in HCW values and ADG for SHK of 0 to 2.5%. This increase in HCW and ADG coincided with a considerable ecline in BRD morbidity and overall mortality risks observed in cattle that shrunk 0 to 2.5% while arriving at the feedlot during summer months.
It is important to recognize the limitations of a retrospective, risk factor analysis when interpreting the results of this study (Dohoo et al., 2009) and to identify that causal inferences cannot be assessed between SHK and the different outcomes. However, detecting which cohort-level characteristics modifies BW loss and how they are associated to health and performance variables would still be of relevance to producers
Several factors are associated with SHK including the stress associated with animal handling, cattle temperament, ambient temperature, length of time of food and water deprivation and type of diet (Coffey et al., 2001). Yet, a better understanding of the factors that affect the magnitude of BW loss from stressed cattle during transport is needed to help cattle buyers and sellers arriving to a fair marketing price. However, information at arrival including the BW loss experienced from ranch or sale barn to feedlot, measure that it is easily available to producers, seasonal fluctuations, distance hauled and demographic characteristics of the cohort may help identifying cattle at greater risk of morbidity, mortality or performance losses; thus, cattle procurement practices, health and economic risk management plans, or protocols for managing the cattle at feedlot arrival could be modified accordingly.