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. 2019 Nov 16;97(12):4721–4731. doi: 10.1093/jas/skz324

Relationships between temperament, meat quality, and carcass traits in Nellore cattle1

Aline Cristina Sant’anna 1, Tiago Da Silva Valente 2, Ana Fabrícia Braga Magalhães 3, Rafael Espigolan 4, Maria Camila Ceballos 5, Lucia Galvão de Albuquerque 6,7, Mateus José Rodrigues Paranhos da Costa 6,7,✉
PMCID: PMC6915207  PMID: 31616922

Abstract

The aim of this study was to evaluate the relationship between temperament in Nellore bulls with carcass and meat quality traits. In total, 1,400 bulls were studied, and temperament was assessed using two measurements: movement score (MOV) and flight speed test (FS). Both MOV and FS were measured at two time points, with background (MOVb and FSb) temperament measured at yearling age, ~550 d after birth, and the preslaughter (MOVps and FSps) temperament measured at the end of the feedlot period. The change of temperament resulting in an increase or decrease in reactivity was also used to measure meat quality. The traits used to define carcass and meat quality included carcass bruises (BRU), hot carcass weight (HCW, kg), ribeye area (REA, cm2), backfat thickness (BFT, cm), marbling score (MS), meat pH after thawing (pH), presence or absence of dark cutters, color parameters of luminosity (L*), redness (a*) and yellowness (b*), cooking loss (CL, %), and Warner–Bratzler shear force (WBSF, kg). A principal component (PC) analysis was initially applied to the carcass and meat quality traits, followed by logistic regression models and linear mixed models to evaluate the effects of temperament on carcass and meat quality. The risks of carcass bruises and dark cutters did not differ as a function of any temperament trait (P > 0.05). In turn, animals classified as high MOVb (reactive) had lower PC3 values (P = 0.05), CL (P = 0.02), and tended to have lower MS (P = 0.08). In addition, animals classified as high FSb (faster and reactive cattle) produced carcasses with smaller REA (P < 0.01), higher meat pH (P < 0.01), lower color gradients (L*, P = 0.04; b*, P < 0.01), and lower PC1 and PC4 scores (P < 0.01) when compared with the low FSb class. For preslaughter temperament, high MOVps was related to lower color a* (P = 0.04), whereas high FSps was related to lower HCW, MS, and PC2 (P < 0.01) than the calmer ones (low FSps). The reduction in MOV was related to more tender meat, and the reduction in FS to heavier carcass and brighter meat. We conclude that excitable temperament in Nellore cattle may have negative effects in some of the carcass and meat quality attributes assessed, mainly those related to muscle deposition on carcass and color gradients. Measurement of temperament before the cattle entered the feedlot was a better predictor of carcass and meat quality traits, compared with temperament assessment at the end of the feeding period.

Keywords: beef cattle, behavior, flight speed, reactivity, welfare

Introduction

Cattle temperament has been related to susceptibility to stress during a wide range of handling procedures (Haskell et al., 2014). Excitable animals show higher pituitary and adrenal gland activation compared with calmer ones (Curley et al., 2008; Cafe et al., 2011a, Braga et al., 2018). This is a consequence of a high stress responsiveness that leads to a decrease in performance, increased carcass downgrading, and inferior meat quality (Cafe et al., 2011b).

Although there are a number of studies that assess the relationship of cattle temperament to carcass and meat quality traits, the results from these studies are conflicting. Some reported detrimental effects of excitable temperament on most of the carcass and meat quality traits assessed (Voisinet et al., 1997a, 1997b; Behrends et al., 2009; Cafe et al., 2011b, Francisco et al., 2015), whereas others reported similar detrimental effects in only a few of the assessed traits (King et al., 2006; Hall et al., 2011; Della Rosa et al., 2018; Yang et al., 2019). Other studies did not find any relationship among these traits (Fordyce et al., 1985; Turner et al., 2011), suggesting that the current findings in literature regarding temperament and meat quality are divergent.

Additionally, further research is still necessary to determine which temperament indicator provides the most valuable information to assess the effects on carcass and meat quality traits, and the time at which temperament should be measured requires investigation as well. For example, in Brazil, it is unknown whether assessing temperament at yearling period (before animals enter the feedlot) or at the end of the feedlot period (right before loading the animals to slaughter) is more beneficial for assessment. The answers to these questions have proven difficult, since the relationships between these traits are potentially influenced by several underlying factors (Haskell et al., 2014), among them, the way that cattle are handled (Cooke et al., 2009; Ceballos et al., 2018a), which is also not well described in many of the publications assessing this subject.

There are few studies examining the relationship of temperament with carcass and meat quality traits for Nellore cattle (Ribeiro et al., 2012; Francisco et al., 2015; Coutinho et al., 2017), which is the main beef breed raised in Brazil. Nellore meat has been regarded as tough (Aroeira et al., 2016); however, there is a wide variation in meat attributes in this breed, revealing opportunities for improvement (Pflanzer and de Felício, 2009; Pereira et al., 2015). In this way, recent studies have been investigating the role of genetics (Rosa et al., 2018), biochemistry (Silva et al., 2019), nutrition (Luz et al., 2019), and handling (Gómez et al., 2019) on Nellore meat quality traits.

Thus, the aim of this study was to evaluate the relationships of temperament with carcass and meat quality traits in Nellore cattle, considering two temperament traits assessed at two different times, before entering in the feedlot (yearling period), and at the end of the feedlot period (five months later). Moreover, the variation in temperament between the yearling and the preslaughter measures was also considered. We hypothesized that temperament has an effect on carcass and meat quality traits in Nellore cattle, and that assessing temperament at the end of the feedlot period offers a better prediction about its consequences.

Material and Methods

This research was approved by the Committee of Ethical Use of Animals from Faculdade de Ciências Agrárias e Veterinárias, UNESP, Jaboticabal, SP, Brazil (Protocol number 14197/14).

Animals and Raising Conditions

The study was conducted with phenotypic information of 1,400 uncastrated Nellore males from a commercial herd (Fazenda São Marcelo, Grupo JD). All animals were raised on tropical pastures with free access to mineral supplementation and water, and weaned at 210 d age, approximately. After weaning, the animals were shipped to another farm of the same company, where they were kept on tropical pastures and received mineral supplementation until reaching approximately 20 mo of age, when they were housed in an outdoor feedlot for a finishing period of 90 d. Although kept on pasture, the animals were only occasionally handled in the corral (for weighing and vaccination) with scarce contact with humans. During the feedlot period, they were in contact with livestock people daily for feeding management and inspections. Additionally, they were handled in the corral twice, at the point of entry into the feedlot, and exit from the feedlot just before going to the slaughterhouse. At the end of the feedlot period, all animals were slaughtered, at the age of 724.31 ± 41.97 d and 500.82 ± 35.35 kg of live weight, on average.

Temperament Traits

Cattle temperament was assessed by two measurements: movement score (MOV) and flight speed test (FS). MOV and FS assessments were carried out at two time points. The first time point was at the yearling period, around 550 d of age, and was used to define the background measurements (MOVb and FSb). The second time point was at the end of the feedlot period (MOVps and FSps), just before loading animals to slaughter. MOV was assessed as described by Sant’Anna et al. (2013). One trained observer scored each animal reaction immediately after its entry into the squeeze chute. Animals were enclosed using gates located at the front and rear of the chute, without using restraining devices such as head bail or squeeze sides. MOV records were taken 4 s after closing the squeeze chute gates, using the following scoring guide: 1 = no movement; 2 = little movement, during less than half of the observation time; 3 = frequent movement but not vigorous, during half of the observation time or more; 4 = constant and vigorous movement; 5 = constant and vigorous movement, animal has jumped and raised its forelimbs off the ground.

The flight speed test measured the speed at which each animal exited the chute after being weighed (Burrow et al., 1988). This measure was taken using a Duboi flight speed electronic device composed of two pairs of photoelectric cells, a chronometer, and a small processor programmed to record the time taken by each animal to cover a 2-m distance over a concrete floor corridor. Distance was later converted to speed (m/s), determining faster animals as having a more excitable temperament.

Carcass and Meat Quality Traits

The animals were slaughtered in a commercial slaughterhouse located at Tangará da Serra, MT, Brazil, 56 km from the feedlot facilities. The feedlot pen groups were not mixed during loading, transportation, or in the slaughterhouse lairage. The pen group sizes ranged from 100 to 130 animals. Each group was transported from feedlot pens to the handling corral, where they were loaded to small three axles trucks (17 bulls per loading) or double level five axles trucks (34 bulls per loading). Cattle were kept in the slaughterhouse lairage for approximately 12 h prior to slaughter. After slaughter, 706 carcasses were inspected by a trained technician who recorded the occurrence of bruises using the Australian Carcass Bruising Scoring System (ACBSS; Anderson and Horder, 1979). Carcasses were then weighed, cut in half, and chilled to 4 °C for, at least, 24-h postmortem.

Samples of longissimus thoracis muscle, with 2.54-cm- hickness, between the 12th and 13th ribs were obtained on the left half-carcass of each animal. These samples were frozen and transported to the Universidade Estadual Paulista (UNESP), Faculdade de Ciências Agrárias e Veterinárias, Câmpus Jaboticabal, SP, Brazil, where they were stored at −18 °C for later analyses.

Twelve carcass and meat quality traits were evaluated: carcass bruises (BRU) the occurrence or not of any bruise ≥ 2 cm diameter; hot carcass weight (HCW) measured in kg; ribeye area (REA) measured in cm2; backfat thickness (BFT) measured in cm; marbling score (MS); meat pH after thawing (pH); dark cutters, recording the presence or absence of this measure; color parameters of luminosity (L*); redness (a*) and yellowness (b*) gradients; cooking loss (CL) measured as a percent; and Warner–Bratzler shear force (WBSF) measured in kg.

Prior to physicochemical analyses, the meat samples were slowly thawed (for 12-hours) and kept at −2 to 2 °C. The methodologies used for these analyses are described in detail in Magalhães et al. (2018). Briefly, ribeye area was assessed using a ribeye grid (1 × 1 cm) placed on the meat sample surface to cover the entire perimeter, then quadrants were counted to calculate the area in cm2. A digital electronic pachymeter was used to measure BFT in a 45° angle from the geometric center of the sample. The color parameters were assessed using a colorimeter KONICA MINOLTA–CR 400 (Minolta Co. Ltd). The meat pH was measured by using a digital pH meter, when samples reached a temperature between 18 and 19 °C. The color and pH measures were obtained in three sampling points and averages were used. Meat samples were classified as dark cutters when their pH was higher than 6.0. Marbling score was recorded according to the method proposed by USDA (2017), assigning scores from 1 to 10, as follows: 1 = practically devoid; 2 = traces; 3 = slight; 4 = small; 5 = modest; 6 = moderate; 7 = slightly abundant; 8 = moderately abundant; 9 = abundant; and 10 = very abundant.

All meat samples were weighed before and after cooking in an oven until the internal temperature reached 71 °C to calculate CL (expressed as % of the initial weight) and prior to measuring WBSF. The cooking temperature was controlled by using a thermocouple (Equipe, Sorocaba-SP, Brazil), and inserting the sensor into the center of each sample. The samples were then cooled at room temperature and stored in a refrigerator for 12 to 24 h. Eight meat cylinders (measuring 1.27 cm of diameter each) were removed longitudinally to the muscle fibers, as recommended by Wheeler et al. (1995). The meat cylinders were used to measure tenderness, which was done by using a Warner–Bratzler Shear Force machine (GR-Manufacturing, KS), with 25-kg capacity and sectioning speed of 20 cm/min. The tenderness value of each sample was calculated by the arithmetic mean of the cylinders.

Statistical Analyses

Descriptive statistics were performed for all temperament, carcass, and meat quality traits and their distributions were checked. Chi-square test and paired t-test were used to evaluate the evolution of cattle temperament (MOV and FS, respectively) from background to the end of the feedlot period (preslaughter assessment). Additionally, chords diagrams were built using R software, chorddiag package (R Studio, version 1.0.143 2009–2016, RStudio, Inc.) to display how often cattle were consistent or changed the MOV and FS classes from the background to the preslaughter assessment.

The principal component analysis (PCA) was applied to the continuous carcass and meat quality traits (HCW, REA, BFT, MS, pH, color a*, b*, L* CL, WBSF) aiming to combine a set of variables in interpretable indices (the principal components), expressing the variation of all traits in an integrative view, as recommended by Baldassini et al. (2017). Animal’s scores in the first 4 principal components (PC1 to PC4) were considered for further analyses.

Logistic regression models were fitted to test the hypothesis that the risks of carcass bruises (BRU) and dark cutters were affected by cattle temperament. The PROC GENMOD of SAS (version 9.2, SAS Institute Inc., Cary, NC) was used, with logistic link function and binomial distribution for the response variables (nonoccurrence = 0 or occurrence = 1). The models included fixed effects of slaughter year (2014 and 2015) and one temperament trait (MOVb, FSb, MOVps, or FSps). To conduct logistic regression and further analyses, 3 classes of MOVb and MOVps, were defined: low (MOV = 1), intermediate (MOV = 2), and high (MOV from 3 to 5). Similarly, the FSb and FSps classes were defined based on the FSb mean ± 0.5 SD, as follows: low (mean − 0.5 SD), intermediate (mean ± 0.5 SD), and high (mean + 0.5 SD).

To evaluate the effects of temperament (MOVb, FSb, MOVps, and FSps) on carcass and meat quality traits, linear mixed models were fitted using PROC MIXED of SAS. All statistical models included the fixed effects of slaughter year (2014 and 2015), one temperament trait (MOVb, FSb, MOVps, or FSps), and their interactions. Handling groups at yearling (background period) and at the end of the feedlot period were included in the models as random effects. Post hoc Tukey test was used for means comparisons. P-values were assumed as significant when < 0.05, and as a trend when < 0.10.

Considering that the effects of temperament on the performance traits would be more pronounced in animals with more extreme reactions to handling (Francisco et al., 2015), the carcass and meat quality traits of the 10%-least reactive (n = 110) and 10%-most reactive animals (n = 110) according to their ranking of FSb and FSps were compared using t-test (PROC TTEST of SAS). Finally, we considered the change of behavioral responses over time as an additional aspect of cattle temperament, and thus, we compared the carcass and meat quality of animals that reduced or increased MOV and FS over time, either using t-test.

Results

Temperament Traits

Movement score distribution showed a decrease in the frequencies of animals scored as 2, 3, 4, and 5, with a consequent increase in the frequency of score 1 (chi-square = 26.15, P < 0.001) from background to the end of the feedlot period measurements (Figure 1). The same pattern was observed for flight speed, with higher mean of FSb than FSps (1.69 ± 0.71 and 1.49 ± 0.51 m/s, respectively, paired t-test = 9.04, P < 0.001) (Table 1). The FSb of the 10%-least reactive animals ranged from 0.27 to 0.83 m/s and for the 10%-most reactive ranged from 2.62 to 4.61 m/s. For FSps these ranges were 0.33 to 0.95 m/s (10%-least reactive) and from 2.06 to 4.19 m/s (10%-most reactive).

Figure 1.

Figure 1.

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Distributions of the frequencies of movement scores at the background (MOVb, n = 1,344) and at the end of the feedlot period (MOVps, n = 1,162).

Table 1.

Description of the FS and MOV classes (low, intermediate, and high) at background (b) and preslaughter (ps) assessments

Trait Parameter Low (least reactive) Intermediate High (most reactive) Total
MOVb N (%) 312 (23.21%) 473 (35.19%) 559 (41.59%) 1344
Score 1 2 3–5 1–5
MOVps N (%) 547 (47.07%) 352 (30.29%) 263 (22.63%) 1162
Score 1 2 3–5 1–5
FSb, m/s N (%) 480 (36.20%) 449 (33.86%) 397 (29.94%) 1326
Min–Max 0.207–1.311 1.321–2.013 2.025–4.618 0.207–4.618
Average ± SD 0.960 ± 0.267 1.658 ± 0.206 2.535 ± 0.450 1.668 ± 0.712
CV (%) 27.81 12.43 17.75 42.70
FSps, m/s N 458 (39.55%) 533 (46.03%) 167 (14.42%) 1158
Min–Max 0.336–1.308 1.313–2.000 2.031–4.193 0.336–4.193
Average ± SD 1.025 ± 0.207 1.611 ± 0.206 2.366 ± 0.348 1.488 ± 0.507
CV (%) 20.24 12.81 14.73 34.06
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Regarding the individual variation from background to preslaughter assessment, we found that 36.33% (403/1109) of the animals were consistent for MOV, remaining in the same class in both assessments, but most of them changed its MOV class, reducing (46.16%, 512/1109) or increasing (17.49%, 194/1109) reactivity inside the squeeze chute. For FS, a higher proportion of animals were considered consistent (47.03%, 514/1093), whereas 33.94% (371/1093) reduced and 19% (208/1093) increased FS class from background to preslaughter assessment. In the further analyses as “Reduced MOV,” we included only animals that changed from “high” to “low” class (16.68%, 185/1109) and those which changed from “low” and “intermediate” to “high” were regarded as “Increased MOV” (10.91%, 121/1109) (Figure 2a). Similarly, as “Reduced FS,” we included animals which changed from “high” to “low” class (6.77%, 74/1093) and the “Increased FS” changed from “low” and “intermediate” to “high” class (5.76%, 63/1093) (Figure 2b).

Figure 2.

Figure 2.

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Chord diagrams expressing the consistency and changes of cattle in the (a) movement score (MOV, n = 1109) and (b) flight speed (FS, n = 1093) classes from background to the end of feedlot period. Where: H = high, I = intermediate, and L = low classes. Percentages are within parentheses.

Carcass and Meat Quality Traits

Almost one quarter of the carcasses had bruises (23.65%, 167/706), with 17.14% (121/706) of the carcasses showing a single bruise, followed by 2 (3.97%, 28/706) and 3 or more (2.54%, 18/706). Only a small percentage of meat samples (8.92%, 78/874) were defined as dark cutters. The descriptive statistics for the other carcass and meat quality traits are summarized in Table 2.

Table 2.

Descriptive statistics for carcass and meat quality traits

Variables1 N Mean ± SD Min–Max CV%
HCW, kg 1,395 293.55 ± 27.71 214.40–399.80 9.44
REA, cm2 1,119 68.28 ± 7.83 31.00–108.00 11.47
BFT, cm 1,117 5.65 ± 1.93 1.85–12.00 34.12
MS 1,106 2.76 ± 0.30 2.00–3.70 10.82
pH 1,106 5.76 ± 0.17 5.41–6.82 2.88
L* 1,116 36.77 ± 2.64 23.72–44.32 7.19
a* 1,109 10.98 ± 1.79 6.32–16.04 16.35
b* 1,111 11.77 ± 1.40 7.56–16.13 11.94
CL, % 1,101 24.80 ± 3.74 12.79–35.29 15.10
WBSF, kg 1,119 6.05 ± 1.58 1.79–11.35 26.08
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1pH = meat pH after thawing; L* = lightness; a* = redness; b* = yellowness.

The PCA performed including all traits in Table 2 generated 4 principal components (PC) with eigenvalues exceeding 1.0. These PCs explained 66.74% of the total variance among carcass and meat quality traits (Table 3). For PC1 (capturing 29.68% of the variance), color a*, b*, and L* had positive loadings above 0.4, suggesting that samples with greater loadings in this factor had better coloration aspect (more intense lightness, redness, and yellowness), being characterized as “meat color” component (Figure 3a). The PC2 explained 14.87% of the variance and had higher loadings for HCW and REA. These traits are associated with growth and muscle deposition in the carcass, named as “muscle deposition” component. The PC3, the variables with higher loadings were CL and WBSF, being related to reduced meat tenderness and water-holding capacity, explaining 12.15% of the variance, regarded as a “toughness and water loss” component (Figure 3b). Finally, the PC4 retained 10.04% of the remaining variance and had higher positive loadings for BFT and higher negative loadings for WBSF, representing the attributes of “fatness and tenderness.”

Table 3.

Principal component analysis of carcass and meat quality traits

Variables1 PC1 meat color PC2 muscle deposition PC3 toughness and water loss PC4 fatness and tenderness
HCW 0.302 0.532 0.198 0.006
REA 0.114 0.638 0.193 −0.218
BFT 0.089 0.188 0.320 0.714
MS −0.326 0.021 0.243 0.393
pH −0.376 0.345 −0.182 −0.164
L* 0.410 −0.080 −0.229 0.127
a* 0.436 0.056 −0.033 −0.033
b* 0.492 −0.158 −0.166 0.162
CL −0.014 −0.306 0.628 −0.039
WBSF 0.198 −0.161 0.503 −0.465
Eigenvalues 2.968 1.486 1.215 1.004
Variance explained 29.68% 14.87% 12.15% 10.04%
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Loadings in bold represent the higher values (above 0.4) of each PC.

1pH = meat pH after thawing; L* = lightness; a* = redness; b* = yellowness.

Figure 3.

Figure 3.

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Scores of animals in the first four principal components (PC) in which PC1 = meat color; PC2 = muscle deposition; PC3 = toughness and water loss, and PC4 = fatness and tenderness (N = 807); HCW = hot carcass weight; REA = ribeye area; BFT = backfat thickness; L* = lightness; a* = redness; b* = yellowness; CL = cooking loss; WBSF = Warner–Bratzler shear force.

Relationships Between Temperament With Carcass and Meat Quality Traits

According to the logistic models, the risks of carcass bruises and dark cutters did not differ as a function of any temperament trait (P > 0.05 for MOVb, FSb, MOVps, and FSps). On the other hand, temperament assessed at background had significant effects (or trends) on 6 carcass and meat quality traits (MS, CL, REA, L*, b*, and pH), and on PC1 (“meat color”), PC3 (“toughness and water loss”), and PC4 (“fatness and tenderness”), see Table 4. Although temperament at preslaughter handling significantly affected 5 carcass and meat quality traits (a*, b*, HCW, REA, and MS), besides PC2 (“muscle deposition”) (Table 4). Animals classified as excitable inside the squeeze chute at background (high MOVb) had lower MS and PC3 means than the intermediate cattle, and lower CL mean compared with the calmer ones. Likewise, animals classified as high FSb (faster and excitable cattle) produced carcasses with smaller REA, higher meat pH and lower colors L* and b* means, compared with the other classes. Faster FSb also had lower PC1 (“meat color”) and PC4 (“fatness and tenderness”) scores than their calmer contemporaries.

Table 4.

Adjusted means (± SE) of carcass, meat quality traits, and principal components (when P < 0.10) for each temperament class of movement score (MOV) and FS at the background (b) and at the end of feedlot period (ps)

Dependent variables1 Low (least reactive) Intermediate High (most reactive) F P-value
MOVb
MS 2.79 ± 0.02ab 2.81 ± 0.02a 2.77 ± 0.01b 2.50 0.08
CL, % 25.39 ± 0.37a 25.38 ± 0.35a 24.74 ± 0.34b 3.99 0.02
PC3 0.036 ± 0.122ab 0.136 ± 0116a −0.056 ± 0.114b 2.96 0.05
MOVps
a* 10.82 ± 0.17ab 10.92 ± 0.18a 10.60 ± 0.19b 3.22 0.04
b* 11.68 ± 0.16a 11.65 ± 0.17ab 11.45 ± 0.18b 2.41 0.09
FSb
REA, cm2 67.74 ± 0.70ab 68.76 ± 0.71a 66.98 ± 0.70b 4.64 < 0.01
pH 5.74 ± 0.016b 5.78 ± 0.016a 5.77 ± 0.016a 6.71 < 0.01
L* 36.82 ± 0.26a 36.50 ± 0.26ab 36.41 ± 0.26b 3.22 0.04
b* 11.88 ± 0.16a 11.65 ± 0.16b 11.58 ± 0.16b 5.81 < 0.01
PC1 −0.030 ± 0.124a −0.256 ± 0.126ab −0.351 ± 0.126b 6.24 < 0.01
PC4 0.181 ± 0.099a −0.080 ± 0.100b 0.039 ± 0.100 b 5.81 < 0.01
FSps
HCW, kg 290.51 ± 3.19a 286.93 ± 3.14b 283.09 ± 3.63b 4.76 < 0.01
REA, cm2 69.02 ± 0.54a 68.01 ± 0.52b 67.49 ± 0.79b 2.51 0.08
MS 2.79 ± 0.01a 2.82 ± 0.01a 2.74 ± 0.02b 4.90 < 0.01
PC2 0.126 ± 0.137a −0.118 ± 0.134b −0.193 ± 0.164b 4.85 < 0.01
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1pH = meat pH after thawing; L* = lightness; a* = redness; b* = yellowness; PC1 = meat color; PC2 = muscle deposition; PC3 = toughness and water loss; PC4 = fatness and tenderness.

a, bAdjusted means without a common letter differ statistically from each other (Tukey test, P < 0.05).

These results were partially confirmed when the temperament was recorded at the end of feedlot period (during preslaughter handling), in which excitable animals with high MOVps showed lower means of colors a* and b* than intermediate and calm animals, respectively. On the other hand, animals classified as excitable with FSps showed lower HCW, REA, MS, and PC2 (“muscle deposition”) means than the calm ones (low FSps).

The carcass and meat quality traits of the 10%-least reactive and 10%-most reactive animals according to FSb and FSps were also compared. For both measurements, the 10%-most reactive animals had lower HCW, smaller REA, lower L*, a* and b* color gradients, and lower PC1 than the 10%-least reactive ones (Table 5). Besides, the 10%-most reactive animals at background (FSb) had also higher MS and pH means and, at the end of feedlot (FSps), they showed higher CL and lower PC2 than the 10%-least reactive group (Table 5).

Table 5.

Means (± SE) of carcass, meat quality traits, and principal components (when P < 0.10) for the extreme values of flight speed at the background (FSb) and in the end of feedlot period (FSps)

Dependent variables1 10%-least reactive 10%-most reactive t P-value
FSb
HCW, kg 293.77 ± 2.85 280.75 ± 2.33 3.53 < 0.001
REA, cm2 68.80 ± 0.79 66.88 ± 0.79b 1.72 0.09
MS 2.74 ± 0.03 2.85 ± 0.02 −2.81 < 0.01
pH 5.74 ± 0.01 5.79 ± 0.02 −2.17 < 0.05
L* 37.13 ± 0.25 35.92 ± 0.27 3.29 < 0.01
a* 11.29 ± 0.17 10.17 ± 0.16 4.74 <0.001
b* 11.94 ± 0.15 11.23 ± 0.13 3.59 < 0.001
PC1 0.260 ± 0.167 −0.892 ± 0.174 4.79 < 0.001
FSps
HCW, kg 293.80 ± 2.61 278.97 ± 2.60 4.03 < 0.001
REA, cm2 69.90 ± 0.69 66.24 ± 0.79 3.48 < 0.001
L* 36.54 ± 0.22 35.69 ± 0.27 2.43 < 0.05
a* 11.09 ± 0.16 10.48 ± 0.18 2.52 < 0.05
b* 11.65 ± 0.12 11.29 ± 0.14 1.98 < 0.05
CL 24.35 ± 0.36 25.44 ± 0.38 −2.11 < 0.05
PC1 −0.005 ± 0.159 −0.715 ± 0.182 2.94 < 0.01
PC2 0.214 ± 0.111 −0.319 ± 0.133 3.08 < 0.01
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1pH = meat pH after thawing; L* = lightness; a* = redness; b* = yellowness; PC1 = meat color; PC2 = muscle deposition.

Finally, considering that the pattern of behavioral change over time might reflect an additional aspect of cattle temperament, we compared the meat quality of animals that reduced or increased MOV and FS over time. Animals that increased MOV from background to preslaughter period produced tougher meat (higher WBSF) than those that reduced MOV (Table 6). In its turn, individuals that increased FS showed lower HCW and L* color than those that became calmer (reduced FS) throughout the studied period.

Table 6.

Means (± SE) of carcass and meat quality (when P < 0.10) for animals that reduced and increased movement score (MOV) and flight speed (FS) from the background to the end of feedlot period

Dependent variables1 Reduced overtime Increased overtime t P-value
MOV
WBSF, kg 5.91 ± 0.17 6.45 ± 0.12 2.60 < 0.01
FS
HCW, kg 297.63 ± 3.26 286.34 ± 3.22 −2.45 < 0.05
L* 36.19 ± 0.29 35.16 ± 0.45 −1.95 0.06
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1L* = lightness.

Discussion

Nellore cattle with excitable temperament show higher behavioral reactivity and sudden reactions during routine handlings and additionally have more intense physiological responses when facing stressful situations (Ceballos et al., 2018a; Braga et al., 2018). Thus, we hypothesized that cattle temperament has an effect on the risks of carcass and meat defects. Our results partially confirmed this hypothesis since some of the carcass and meat quality traits assessed in the present study were affected by temperament, particularly, when assessed by flight speed test and the background assessment. The differences in carcass and meat quality were more pronounced when comparing extreme temperaments (least reactive vs. most reactive individuals).

A significant reduction was observed in cattle reactivity from the background to the end of feedlot period. It is well documented that feedlot systems can reduce individuals’ reactivity to handling, ameliorating temperament in Zebu cattle (Titto et al., 2010; Cafe et al., 2011b; Braga et al., 2018). After habituating to the handling routines, the situation previously perceived as threatening becomes to be perceived as neutral, leading to a reduction in the observed variation in cattle temperament, as evinced by our results. It explains the lower frequencies of high classes for MOV (41.59% to 22.63%) and FS (29.94% to 14.42%) in the preslaughter compared with the background assessment. Moreover, the maximum range of FS high class also reduced from 4.62 to 4.19 m/s. Despite being handled only twice in the handling corral during the feedlot period, the bulls had intensive and frequent contact with stockpersons in the feedlot pens.

When the variation between the background and preslaughter measures was individually assessed, it was possible to confirm that only a subset of the animals was consistent and remained in the same temperament class. As expected, the habituation to handling was the predominant process, because most of the individuals that changed their reactivity showed reduction of MOV and FS. However, a smaller group of animals displayed an opposite response of increased reactivity, and probably had sensitized over time. Animals do not habituate (and sensitize) when extremely severe treatments are applied (Grandin and Shivley, 2015), which seemingly did not happen during this study, because aversive handlings were not applied. However, it is plausible that even stimuli perceived as neutral, or minor painful procedures (e.g., vaccinations) for humans, may be perceived as scarring or aversive for the animals, which increased their reactivity. Thus, the temporal variations in the animals’ reactions to being handled can be dependent on how bulls perceive all their previous experiences. The measured patterns of temporal change in the behavioral responses to handling can be part of an animal individuality and understood as an additional dimension of its temperament. According to Behrends et al. (2009), the assessment of temperament prior to the animals habituation (or sensitization) to the handling routines might generate a better prediction of inherited animal’s temperament.

There were previous attempts to evaluate the incidence of carcass bruises as a function of cattle temperament (Fordyce et al., 1985, 1988; Burrow and Dillon, 1997; Petherick et al., 2002; Francisco et al., 2015); however, only two of studies concluded there was a significant association between carcass bruises and temperament (Fordyce et al., 1988; Francisco et al., 2015). In the other studies, the occurrences of bruises were independent of cattle temperament (Fordyce et al., 1985; Burrow and Dillon, 1997; Petherick et al., 2002), similarly to the present study. Burrow and Dillon (1997) supposed that temperamental animals could cause contusions not only in themselves, but also in others. This behavior could also affect the calmer animals, masking the relationships between temperament and carcass bruises. Furthermore, Fordyce et al. (1985) proposed that this lack of association could be a consequence of the docile temperament of the animals assessed and the small sample size used in their research. In the present study, similar results were found, even though a larger number of animals were used with a wide range of temperament types. According to our understanding, additional factors should be taken into account when interpreting these results, including the quality of preslaughter handling. The Nellore animals assessed in our study were handled by trained stockpeople who had training on good-practices of cattle handling. It has been proven that animals handled by trained stoockpeople are less stressed, and less prone to have accidents during handling (Ceballos et al., 2018b). The animals in the present study were handled in a well-maintained corral and were only transported for short distances, which may have contributed to the lack of relationship observed between temperament and bruises. In fact, the incidence of bruised carcasses found in the present study was much lower than those of previous reports obtained in Mato Grosso State, Brazil (Moreira et al., 2014; Polizel Neto et al., 2015). Possibly, under these favorable preslaughter handling conditions, the impacts of divergent temperaments on bruises become less pronounced. More studies are needed to better understand how temperament, quality of preslaughter handling, and the cattle behavior in slaughterhouse lairage may interact to increase the incidence of dark cutters and produce low-quality meat.

It was also expected that assessing temperament at the end of the feedlot period (prior to slaughter) would offer a better measure to investigate its influences on carcass and meat quality in Nellore cattle. Of note, most of the significant effects of temperament in carcass and meat quality traits (including those in the principal components) were found in the background assessment, at yearling age. The preslaughter temperament was related to fewer performance traits and became more significant when the extreme values of FSps were compared (10%-least reactive vs. 10%-most reactive individuals). According to Lockwood et al. (2015), after 84 d of testing with successive handlings, the habituation of cattle to handlers and facilities reduced the innate temperament variation, leading to lack of effects of temperament on performance, as was observed in the present study. Regardless of being closer to slaughter in time, the temperament traits measured at the end of the feedlot period may be more influenced by learning and may have potentially masked the innate variation in cattle temperament. Therefore, MOV and FS assessed on-farm while loading the animals to slaughter can be less adequate than the background measures as behavioral indicators of cattle susceptibility to stress during the overall pre-slaughter handling.

Considering the temperament assessed in the background period, MOVb intermediate animals tended to produce meat with higher marbling scores than reactive animals. For European cattle, excitable animals have shown lower predisposition to deposit fat (Reinhardt et al., 2009; Hall et al., 2011). The same trend was reported by Francisco et al. (2015) for Nellore cattle fed with a high-concentrate diet (90% concentrate: 10% forage), where animals with “adequate” temperaments had more marbling and muscle fat than the “excitable” ones. The authors attributed this difference to the greater blood insulin and lower cortisol concentrations found in the “adequate” group, given that insulin is related to fat deposition for cattle fed on high-concentrate diets (Francisco et al., 2015). Nevertheless, the same is not expected for animals fed with high-forage diets; thus, it is less likely that excitable temperaments would affect muscle lipid content under high-forage diets (Della Rosa et al., 2018). In the present study, animals received high-concentrate diets (ranging from 70% concentrate in the adaptation diet to 88% in the final diet) and the relations of both fat attributes (MS and BFT) with temperament were not straightforward. Curiously, the 10%-most reactive animals according to FSb showed more marbling than the 10%-least reactive animals.

The MOVb also had a significant effect on the cooking loss, which was not observed for FSb and FSps. Animals classified as reactive by MOVb had lower CL than the intermediate and calm ones and tended to show lower scores in PC3 (“toughness and water loss”), also indicating less water loss. It is well known that cooking loss is widely influenced by meat pH. An increase of meat water-holding capacity (water retention) is expected under higher or lower than normal pH values, resulting that high pH meat may have less drip loss than normal pH samples (Muchenje et al., 2009).

It is clear that stress prior to slaughter has a major effect on meat pH, given that physical and psychological stressors lead to the depletion in muscle glycogen reserves, impairing the rate of pH decline (Apple et al., 2005). Additionally, there is evidence that more excitable cattle show more intense physiological response to stress, having higher cortisol concentrations than calmer ones (King et al., 2006; Cafe et al., 2011a). Based on this, one could hypothesize that more reactive animals prior to slaughter (high FSps and MOVps) would result in higher ultimate meat pH. However, this was not confirmed by our results, since FSps and MOVps did not affect meat pH, but FSb did.

Cattle classified as less reactive by FSb showed significantly lower meat pH than the more reactive ones, once again suggesting that the background flight speed had higher potential to reveal animals more susceptible to stress during the overall preslaughter handling, influencing meat quality attributes. It is important to note that, despite being significantly different, the average pH of the 3 temperament classes ranged between 5.7 and 5.8, and classified as “adequate” for Nellore meat (Pflanzer and de Felício, 2011). Even when the 10%-calmer animals were compared with the 10%-most reactives by FSb, the difference found in meat pH was in the same adequate range of 5.74 to 5.79. This result is in accordance with the low occurrence of dark cutters (pH > 6.0) in our data set and can be related to the lack of significance observed in temperament and the risk of dark cutters.

The postmortem rate of pH decline and, consequently, the ultimate pH also affect meat color and tenderness (Muchenje et al., 2009). Meat with a higher pH tends to show lower lightness, redness, and yellowness than normal pH meat. In the scientific literature, meat color was not associated with cattle temperament, and this was observed despite the meat pH differing for the divergent temperaments (Petherick et al., 2002; King et al., 2006; del Campo et al., 2010; Hall et al., 2011; Turner et al., 2011, Della Rosa et al., 2018). Specifically, for Nellore cattle, Francisco et al. (2015) reported no significant effect on meat color and pH for temperament assessed by an average of the chute score and flight speed recorded over the feedlot period. This was not the case in the present study, whereby FSb most reactive animals had higher meat pH, lower b* and L* gradients, as well as lower scores in PC1 (the “meat color” component of PCA) than the calmer ones. In turn, temperament assessed at the end of the feedlot period (FSps) did not affect any meat color attribute. However, when comparing the meat of the 10%-least reactive with 10%-most reactive animals, significant differences were found on all meat color attributes for both FSb and FSps. This suggests that the impact of temperament on meat color is not linear and is more marked in highly reactive animals.

Given the known relationships between meat pH and tenderness, we expected that meat from excitable cattle would be tougher (less tender) than those of their calmer cohorts, as previously shown (King et al., 2006; del Campo et al., 2010; Hall et al., 2011; Ribeiro et al., 2012). We only found an impact of temperament change over time in meat tenderness, in animals that had increased MOV from the background to preslaughter period, producing tougher meat than those that had reduced MOV. Although the temperament traits did not affect WBSF when assessed individually, we found that FSb had a significant effect on PC4 (“fatness and tenderness”), an index composed mainly by backfat thickness (BFT) and WBSF. Thus, animals classified as FSb-calm tended to produce carcasses with more subcutaneous fat cover and lower WBSF meat. Potentially, the effect of temperament on tenderness may be dependent on the degree of carcass subcutaneous fat, as BFT and WBSF are intimately associated. More abundant subcutaneous fat thickness reduced the occurrence of cold shortening by preventing that carcasses were chilled rapidly (Pflanzer and de Felício, 2009). An alternative explanation to the effect of temperament on WBSF was given by Coutinho et al. (2017), who showed that excitable temperament in Zebu cattle may affect postmortem enzymatic activity by increasing the activity of calpastatin. This enzyme is responsible for reducing the postmortem degradation of muscle by calpains, and consequently lowering tenderization. The results of the present study allow us to infer that, in Nellore animals, the excitable temperament may result in less tender meat when associated with scarce subcutaneous fat cover in carcasses.

Regarding the temperament assessed at the end of feedlot period, flight speed had significant effects on both HCW and REA, as well as on the PC2 (“muscle deposition” component). The FSps calm animals produced heavier carcasses and received higher scores in the PC2. This result is in accordance with the wider ribeye area found in calm FSb animals. Pronounced differences were observed when comparing the 10%-least reactive bulls with the 10%-most reactive bulls, in which less reactive cattle had heavier carcass weights of 13.02 and 14.83 kg on average, compared with the most reactive ones, for both FSb and FSps, respectively. A similar trend was also found for REA. Moreover, animals that exhibited reduced FS overtime (habituated to handling) produced heavier carcasses of 11.29 kg on average, compared with those that showed increased FS (sensitized to handling). Both carcass weight and ribeye area are interrelated to growth rate (Silva et al., 2017). Calmer animals (lower FS) were usually reported to grow faster, with greater average daily gains compared with the reactive ones (Petherick et al., 2002; Cafe et al., 2011b). It could explain the relationships among flight speed, hot carcass weight, and ribeye area, which corroborate previous findings in Zebu (Burrow and Dillon, 1997; Cafe et al., 2011b; Francisco et al., 2015) and European cattle (Hall et al., 2011; Yang et al., 2019). The REA was the only carcass quality trait assessed in which the effects of FS were consistent at both background and end of feedlot periods, in contrast to MOV, which did not show the same consistencies. These results confirm that the impact of cattle temperament on meat quality and carcass traits is widely variable, namely, due to the indicator used to assess temperament and the time that the test is measured in animals’ life.

We conclude that an excitable temperament in Nellore bulls may have a negative effect on some carcass and meat quality traits, and in turn a negative effect on the beef cattle enterprise. Undoubtedly, this association depends upon how and when temperament is evaluated. In general, flight speed test carried out before the entry of animals in the feedlot pens offered a better prediction about its effect on meat quality. Nellore animals classified as more reactive at yearling age were more prone to produce carcasses with lower muscle deposition, higher values of meat pH, and darker color gradients. The pattern of change in behavioral responses toward human handling can be considered an additional aspect of cattle temperament, as there are individuals that show consistencies in behavior while others do not, and a smaller subset increase their reactivity over time. The reduction of MOV was related to more tender meat, and the reduction of FS led to heavier carcasses and brighter meat. These findings reinforce the hypothesis that temperament can have an effect in the performance traits assessed, mainly those related to muscle deposition on carcass and color gradients. Environmental factors related to the quality of cattle handling, diet and post-slaughter processes may influence and to an extent attenuate the impacts of excitable temperament on meat quality and carcass traits. Thus, further research is required to elucidate how the environmental and handling factors interact with temperament to produce such variable impacts on cattle performance.

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