Identification of Cattle Background, Environmental, and Abattoir Factors Influencing Variability in United States Beef Carcasses Using Grading Camera Ribeye Color Scores

Thomas W. Dobbins; Samantha N. Barker; Nicholas C. Hardcastle; Dale R. Woerner; Bradley J. Johnson; Ranjith Ramanathan; Jerrad F. Legako; Thomas W. Dobbins; Samantha N. Barker; Nicholas C. Hardcastle; Dale R. Woerner; Bradley J. Johnson; Ranjith Ramanathan; Jerrad F. Legako

doi:10.22175/mmb.24994

AbbreviationsNBQA

National Beef Quality Audit

HCW

hot carcass weight

REA

ribeye area

VYG

Vision Yield Grade

YG

USDA Yield Grade

Introduction

The National Beef Quality Audit (NBQA) is the gold standard for the evaluation of the trends within the United States’ fed cattle industry since the initial survey in 1992 (Lorenzen et al., 1993) and the most recent in 2022 (Mayer et al., 2024). The NBQA evaluates the present population of cattle being harvested and identifies factors that lead to discounts or premiums of the carcasses during grading. The NBQA reports on the occurrence of visually dark cutting cattle as a defect, but not on classifications of beef from a color perspective. From an economic standpoint, non-normal beef color and discoloration cost the United States upwards of 3.7 billion dollars annually (Ramanathan et al., 2022), emphasizing the importance of characterizing meat color variation in the fed beef population. Specifically, dark cutting beef has been estimated to cost the US beef industry $288 million per year (Harr et al., 2024), as carcasses identified as dark cutting are discounted $39.17/hundred pounds (USDA-AMS, 2025). Currently, the USDA grading system identifies skeletal lean based on maturity and ranges from A to E, and this lean, in combination with marbling score, dictates overall quality grade (USDA-AMS, 2017). Current USDA standards primarily utilize dentition to identify cattle as younger than 30 months of age (USDA-AMS, 2017), with additional evaluation of skeletal and lean maturity when a carcass over 30 months of age is identified by dentition. Yet, the characterization of lean color for USDA standards is not evaluated on a continuous scale. Since the establishment of instrumental grading cameras and color assessment through the use of three-dimensional RGB color space (Gerrard et al., 1996), there is an opportunity to evaluate color from a lean maturity and quality perspective. Currently, there are 4 grading cameras commonly used in the beef industry that are approved for USDA quality grading (USDA-AMS, 2025). These cameras capture an image of the longissimus dorsi between the 12^th and 13^th rib interface, which is then utilized to evaluate the presence of lean and marbling. With these images and carcass measures, packing plants have systems in place to calculate yield grade, marbling score, ribeye color, and other proprietary measurements. The utilization of these existing grading camera measurements can be used to improve the identification of variation in lean maturity while also providing insight into the variability of ribeye color across the feedlot beef cattle population and feeding industry.

The first objective of this study was to identify the proportion of the fed beef population that displays dark, intermediate, normal, or light beef color and characterize the differences in both yield and quality between the color categories. Secondly, this study aims to identify ante-, peri-, and postmortem factors that may contribute to the variation in ribeye color. Finally, this study aims to identify the frequency of beef that displays non-normal color as identified by carcass and cattle characteristics.

Materials and Methods

Animal care and use approval was not required through Texas Tech University’s Animal Care and Use Committee as the study was observational in nature and data were collected from USDA-inspected abattoirs.

Data procurement

Data were collected from two commercial abattoirs over nine consecutive months from June 2023 to March 2024. For one random week per month, data from cattle harvested across an entire week of production were collected from both plants. Data contained cattle background, preharvest, postharvest, and carcass grade data. All cattle over 30 months of age identified by dentition or identified as mature were removed from the data set, in addition to carcasses with incomplete grade data. This yielded a data set of 457,026 carcasses. Carcasses were placed into one of five breed categories based on breed characterization by the feedlot: Bos indicus, Beef × Dairy, Dairy, English, or Exotic. Cattle identified as Bos indicus were identified as Brahman, Brahman crossbred, or other Bos indicus breed types with identifiable humps. Beef × Dairy cattle were identified as having any influence of Jersey or Holstein in addition to another beef breed. Dairy cattle were identified as purebred dairy breeds without crossbreeding. English cattle were identified as breeds such as Angus and Hereford. Exotic cattle were identified as other beef breeds such as Charolais and Limousin. Transport distance was calculated using Google Maps from the identified feedyard to the abattoir. Temperature maximum and minimum of the day of harvest were obtained for each harvest day using the National Weather Service Reports for the location closest in proximity to each plant. After harvest, carcasses were chilled for approximately 48 h before grading. Carcasses were graded approximately 10 to 15 min after ribbing to expose the ribeye between the 12^th and 13^th rib, wherein trained plant personnel captured images of each ribeye using a Vision Grading Camera. Carcasses were placed into color categories based on proprietary color standards the collaborating company utilizes for carcass segregation from the E+V Vision Grading Camera System, which measures objective lightness and provides a color score, similar to but different from the Commission internationale de l'éclairage (CIE) L* commonly found in literature. Cattle were identified as follows: dark (DARK) color score ≤ 55, intermediate (INT) 55 < color score ≤ 59, normal (NORM) 60 ≤ color score ≤ 79, light (LIGHT) color score ≥ 80. Carcasses also placed into descriptive categories based on hot carcass weight (HCW), marbling score, USDA yield grade, backfat thickness, ribeye area (REA), and USDA quality grade using previously established parameters in the NBQA (Mayer et al., 2024). Finally, carcasses were classified based on the antemortem data from the feedlots, including whether cattle entered the finishing period as calves or yearlings, breed type as previously stated, and season based on month of harvest: summer (June, July, and August), fall (September, October, and November) or winter (December, January, and February).

Statistical analyses

Data for all collections were received in Microsoft Excel spreadsheets. Data were downloaded using the readxl package in R statistical software. Data were cleaned and manipulated using the janitor and tidyverse packages of R, resulting in the removal of any carcasses with incomplete data or outliers. Data were combined into one complete spreadsheet using the writeexl package of R.

The means, standard deviation, minimum, and maximum from all relevant camera data were analyzed using the PROC MEANS procedure of SAS 9.4 (SAS Institute, Cary, NC, USA). For the fixed effect of carcass color category, carcass population statistics were analyzed using a generalized mixed linear model using SAS 9.4 (SAS Institute, Cary, NC, USA). For mean comparisons between color categories, kill date and carcass sex were used as covariates. The commercial abattoir was utilized as a random effect. The Kenward-Rogers adjustment was used to estimate the denominator degrees of freedom. A Bonferroni adjustment was utilized for the separation of means. Pearson correlations between the camera color score and carcass characteristics and antemortem factors were determined using the PROC CORR procedure of SAS 9.4 (SAS Institute, Cary, NC, USA).

Classification of carcasses by HCW, marbling score, USDA yield grade, backfat thickness, REA, USDA quality grade, cattle background, breed, and season were also analyzed as fixed effects. Camera data were analyzed using a generalized mixed linear model in SAS 9.4 (SAS Institute, Cary, NC, USA). The Kenward-Rogers adjustment was used to estimate the denominator degrees of freedom.

The frequency of color category within carcass and cattle characteristics was analyzed using the PROC FREQ procedure of SAS 9.4 (SAS Institute, Cary, NC, USA). A chi-square test was utilized to identify if there was an association between the identified characteristic and carcass color category.

Alpha was set at P ≤ 0.05 for fixed effects across all analyses. Covariates were tested and removed from the model in a stepwise manner where P > 0.10. Pairwise comparisons of significant effects were found using the PDIFF option within the LSMEANS statement.

Results

Carcass summary

Means and standard deviation for carcass characteristics are presented in Table 1. The average HCW for the study was 397.1 kg with a standard deviation of 52.31 kg. The mean marbling score from this study was 505.4 or Modest⁰⁵ with a standard deviation of 117 marbling points. Carcasses had an average calculated vision yield grade (VYG) of 3.2 with a standard deviation of 1.01. The average backfat thickness was 1.55 cm with a standard deviation of 0.60 cm. Average ribeye area (REA) for the study was 93.7 cm² with a standard deviation of 11.50 cm². The average color score recorded by the grading camera was 71.9 with a standard deviation of 5.68.

Table 1.

Means of USDA carcass grade components from grading camera

Parameter	Average	SD	Minimum	Maximum
Color Score	71.9	5.68	43.0	109.0
HCW, kg	397.1	52.31	167.4	637.2
Marbling Score^¹	505.4	117.05	144.0	990.0
Vision Yield Grade	3.2	1.01	0.0	9.4
Backfat Thickness, cm	1.56	0.60	0.1	6.3
REA^², cm²	93.7	11.50	40.0	135.5

SD = standard deviation.
100 = Practically Devoid, 200 = Traces, 300 = Slight, 400 = Small, 500 = Modest, 600 = Moderate, 700 = Slightly Abundant, 800 = Moderately Abundant, 900 = Abundant.
Ribeye area.

Assessment of carcasses by color category

The occurrence and description of carcass characteristics by color category is shown in Table 2. In this study, 89.74% of carcasses were identified as having normal color, 8.72% were identified as light, 1.21% were identified as intermediate, and 0.33% of carcasses were identified as dark. There was a main effect of carcass color category for all carcass characteristics (P < 0.001). Hot carcass weight, marbling score, VYG, and backfat thickness were greatest (P < 0.05) in LIGHT carcasses, while NORM carcasses had greater (P < 0.05) values compared with INT and DARK carcasses. Ribeye area was reduced (P < 0.05) in LIGHT carcasses compared with all other color categories.

Table 2.

Least-squares means of carcass grade components from grading cameras and antemortem factors from carcass color categories^¹

	Color Category^¹
Parameter	Dark	Intermediate	Normal	Light	SEM^²	P Value^³
n	1491	5528	410138	39869
n, %	0.33	1.21	89.74	8.72
Color Score	53.5^{^d}	58.0^{^c}	71.1^{^b}	82.4^{^a}	0.12	< 0.001
HCW, kg	364.0^{^c}	361.1^{^c}	396.8^{^b}	405.4^{^a}	11.89	< 0.001
Marbling Score^⁴	459.5^{^c}	458.4^{^c}	501.3^{^b}	556.5^{^a}	32.46	< 0.001
Vision Yield Grade	2.8^{^c}	2.8^{^c}	3.2^{^b}	3.4^{^a}	0.35	< 0.001
Backfat Thickness, cm	1.25^{^c}	1.25^{^c}	1. 55^{^b}	1.74^{^a}	0.10	< 0.001
REA^⁵, cm²	94.0^{^a}	93.8^{^a}	93.7^{^a}	92.8^{^b}	3.14	< 0.001

Carcasses identified based upon camera color values of the ribeye. Dark: color score ≤ 55. Intermediate: 55 < color score ≤ 60. Normal: 60 < color score < 80. Light: color score ≥ 80.
Largest standard error of least-squares means.
Observed significance level.
100 = Practically Devoid, 200 = Traces, 300 = Slight, 400 = Small, 500 = Modest, 600 = Moderate, 700 = Slightly Abundant, 800 = Moderately Abundant, 900 = Abundant.
Ribeye area.
Least-squares means in the same row without a common superscript differ (P < 0.05).

Correlations of ante- and postmortem factors

Carcass characteristics and antemortem factors were correlated with camera color score (P < 0.001; Table 3). However, only marbling score (r² = 0.21) had a weak positive correlation, whereas all other relationships may be negligible.

Table 3.

Correlations of carcass color and components collected by grading camera and antemortem factors

Ante- and Postmortem Factors	Color Score	P Value
Live Weight	0.05	< 0.001
Hot Carcass Weight	0.17	< 0.001
Vision Yield Grade	0.14	< 0.001
Ribeye Area	−0.01	< 0.001
Fat Thickness	0.17	< 0.001
Marbling Score	0.21	< 0.001
Transport Distance	−0.04	< 0.001
Carcass Chill Time	0.05	< 0.001
Lot Head Count	> 0.01	< 0.001
High Temperature	0.07	< 0.001
Low Temperature	0.06	< 0.001

Assessment of carcasses grouped by carcass attributes

There was a main effect of carcass weight category for all carcass attributes (P < 0.001; Table 4). The greatest proportion of carcasses had a HCW between 362.9 and 408.1 kg (33.39%), and the least number of carcasses had a HCW less than 272.2 kg (0.75%). As carcass weight category increased, the camera color score also increased (P < 0.05), indicating a lighter, more pale color with heavier carcasses. Additionally, as carcass weight category progressed, marbling score, VYG, backfat thickness, and REA increased (P < 0.05).

Table 4.

Least squares means of carcass grade components from grading cameras within carcass weight groups

	Carcass Weight Groups, kg
Parameter	<272.2	272.4-317.4	317.5-362.8	362.9-408.1	408.2-453.5	453.6-498.9	>499	SEM^¹	P Value^²
n	3411	24514	90294	152622	120739	53134	12312
n, %	0.75	5.36	19.76	33.39	26.42	11.63	2.69
Color Score	68.7^{^g}	69.7^{^f}	70.9^{^e}	71.8^{^d}	72.5^{^c}	73.0^{^b}	73.6^{^a}	0.49	< 0.001
HCW, kg	256.4^{^g}	302.4^{^f}	344.8^{^e}	386.2^{^d}	428.0^{^c}	470.3^{^b}	516.8^{^a}	0.93	< 0.001
Marbling Score^³	408.5^{^g}	442.7^{^f}	472.3^{^e}	502.9^{^d}	526.5^{^c}	548.4^{^b}	572.4^{^a}	24.48	< 0.001
Vision Yield Grade	2.3^{^g}	2.6^{^f}	2.8^{^e}	3.1^{^d}	3.4^{^c}	3.8^{^b}	4.2^{^a}	0.26	< 0.001
Backfat Thickness, cm	0.75^{^g}	1.01^{^f}	1.26^{^e}	1.51^{^d}	1.75^{^c}	1.99^{^b}	2.26^{^a}	0.03	< 0.001
REA^⁴, cm²	75.7^{^g}	83.0^{^f}	88.7^{^e}	93.2^{^d}	97.1^{^c}	100.5^{^b}	103.7^{^a}	4.18	< 0.001

Largest standard error of least-squares means.
Observed significance level.
100 = Practically Devoid, 200 = Traces, 300 = Slight, 400 = Small, 500 = Modest, 600 = Moderate, 700 = Slightly Abundant, 800 = Moderately Abundant, 900 = Abundant.
Ribeye area.
Least-squares means in the same row without a common superscript differ (P < 0.05).

There was a main effect of marbling category for carcass attributes (P < 0.001; Table 5). The Small marbling category comprised the greatest proportion of carcasses (36.62%), while the Practically Devoid and Traces category made up the smallest proportion of the surveyed carcasses (0.16%). Color score increased (P < 0.05) from the Slight to Abundant marbling category in a step-wise progression, whereas the Practically Devoid and Traces category had similar (P > 0.05) color scores to carcasses with Slight and Small marbling scores. Despite the numeric increase in color score across marbling categories, all marbling classifications fell into the range of NORM color classification, 60 ≤ color score ≤ 79, for this study. Hot carcass weights increased (P < 0.05) from the Practically Devoid and Traces category to the Moderately Abundant category, while there was no difference (P > 0.05) in HCW between Moderately Abundant and Abundant marbled carcasses. Vision yield grade and backfat thickness increased (P < 0.05) as marbling scores progressed from Practically Devoid and Traces to Abundant. Ribeye area was greatest (P < 0.05) in the Small marbling category and least (P < 0.05) in the Practically Devoid and Traces marbling category. Slight, Modest, and Moderate marbled carcasses had increased (P < 0.05) REA compared with Slightly Abundant, Moderately Abundant, and Abundant marbled carcasses. Slight marbled carcasses had increased (P < 0.05) REA compared with Modest and Moderate marbled carcasses, while Modest marbled carcasses had greater (P < 0.05) REA compared with Moderate marbled carcasses.

Table 5.

Least-squares means of carcass grade components from grading cameras within marbling score groups^¹

	Marbling Category^¹
Parameter	Practically Devoid and Traces	Slight	Small	Modest	Moderate	Slightly Abundant	Moderately Abundant	Abundant	SEM^²	P Value^³
n	754	89275	167358	105033	61769	24224	7458	1155
n, %	0.16	19.53	36.62	22.98	13.52	5.30	1.63	0.25
Color Score	70.6^{^gf}	70.3^{^g}	71.1^{^f}	72.3^{^e}	73.5^{^d}	74.9^{^c}	76.4^{^b}	78.8^{^a}	0.67	< 0.001
HCW, kg	305.8^{^g}	372.9^{^f}	394.6^{^e}	403.5^{^d}	408.3^{^c}	412.0^{^b}	416.0^{^a}	419.9^{^a}	8.75	< 0.001
Marbling Score^¹	282.9^{^h}	373.8^{^g}	448.8^{^f}	544.9^{^e}	642.3^{^d}	745.4^{^c}	833.8^{^b}	934.5^{^a}	2.75	< 0.001
Vision Yield Grade	2.4^{^h}	2.8^{^g}	3.1^{^f}	3.4^{^e}	3.6^{^d}	3.7^{^c}	3.8^{^b}	3.9^{^a}	0.27	< 0.001
Backfat Thickness, cm	0.78^{^h}	1.23^{^g}	1.50^{^f}	1.69^{^e}	1.80^{^d}	1.87^{^c}	1.94^{^b}	2.01^{^a}	0.04	< 0.001
REA^⁴, cm²	82.3^{^f}	94.1^{^b}	94.7^{^a}	93.5^{^c}	92.0^{^d}	91.1^{^e}	90.7^{^e}	90.2^{^e}	2.89	< 0.001

100 = Practically Devoid, 200 = Traces, 300 = Slight, 400 = Small, 500 = Modest, 600 = Moderate, 700 = Slightly Abundant, 800 = Moderately Abundant, 900 = Abundant.
Largest standard error of least-squares means.
Observed significance level.
Ribeye area.
Least-squares means in the same row without a common superscript differ (P < 0.05).

Attributes of carcasses categorized by USDA yield grade (YG) are presented in Table 6. In the population of surveyed carcasses, 4881 were not assigned yield grades and thus excluded from analyses. For all carcass attributes, there was a main effect of USDA yield grade (P < 0.001). The largest number of carcasses were YG 3 (35.57%), whereas the least number of carcasses were YG 5 (4.54%). Color score, HCW, marbling score, and backfat thickness increased (P < 0.05) as yield grade increased from 1 to 5. There was an inverse relationship for REA compared with the other carcass attributes, wherein as yield grade increased from 1 to 5, REA decreased (P < 0.05).

Table 6.

Least-squares means of carcass grade components from USDA Yield Grades

	USDA Yield Grade
Parameter	1	2	3	4	5	SEM^¹	P Value^²
n	49226	144306	160830	77272	20511
n, %	10.89	31.92	35.57	17.09	4.54
Color Score	70.2^{^e}	71.2^{^d}	72.3^{^c}	73.0^{^b}	73.4^{^a}	0.59	< 0.001
HCW, kg	368.5^{^e}	384.1^{^d}	400.4^{^c}	415.6^{^b}	433.0^{^a}	6.43	< 0.001
Marbling Score^³	448.4^{^e}	482.1^{^d}	517.5^{^c}	547.1^{^b}	570.0^{^a}	20.38	< 0.001
Vision Yield Grade	1.6^{^e}	2.6^{^d}	3.5^{^c}	4.4^{^b}	5.5^{^a}	0.03	< 0.001
Backfat Thickness, cm	0.94^{^e}	1.27^{^d}	1.65^{^c}	2.07^{^b}	2.62^{^a}	0.04	< 0.001
REA^⁴, cm²	102.0^{^a}	96.2^{^b}	92.1^{^c}	88.6^{^d}	85.4^{^e}	1.71	< 0.001

Largest standard error of least-squares means.
Observed significance level.
100 = Practically Devoid, 200 = Traces, 300 = Slight, 400 = Small, 500 = Modest, 600 = Moderate, 700 = Slightly Abundant, 800 = Moderately Abundant, 900 = Abundant.
Ribeye area.
Least-squares means in the same row without a common superscript differ (P < 0.05).

There was a main effect of backfat thickness for all carcass attributes (P < 0.001; Table 7). The greatest number of carcasses had between 1.52 and 1.77 cm of backfat (19.35%), and the least number of carcasses had greater than 3.05 cm of backfat (1.34%). The camera color score was least (P < 0.05) in carcasses with less than 0.51 cm of backfat and between 0.51 and 0.75 cm of backfat. Camera color score was greatest (P < 0.05) for carcasses with greater than 3.05 cm of backfat. Hot carcass weight, marbling score, and VYG increased (P < 0.05) with each backfat category from less than 0.51 cm to greater than 3.05 cm of backfat thickness. Ribeye area was greatest (P < 0.05) in carcasses with 0.51 to 0.75 cm and 0.76 to 1.01 cm of backfat and least (P < 0.05) in carcasses with greater than 3.05 cm of backfat. For each increase in backfat thickness category from 1.02 to 1.26 cm to greater than 3.05 cm, there was a decrease (P < 0.05) in REA. Carcasses with backfat thickness less than 0.51 cm had similar (P > 0.05) REA to carcasses with backfat thickness between 1.02 and 1.26 cm.

Table 7.

Least-squares means of carcass grade components from grading cameras within backfat thickness groups

	Backfat Thickness, cm
Parameter	<0.51	0.51-0.75	0.76-1.01	1.02-1.26	1.27-1.51	1.52-1.77	1.78-2.02	2.03-2.28	2.29-2.53	2.54-2.78	2.79-3.04	≥3.05	SEM^¹	P Value^²
n	13254	22254	62559	57629	63159	88456	47126	49549	20070	18008	8820	6142
n, %	2.90	4.87	13.69	12.61	13.82	19.35	10.31	10.84	4.39	3.94	1.93	1.34
Color Score	69.9^{^k}	70.0^{^k}	70.3^{^j}	70.9^ⁱ	71.6^{^h}	72.2^{^g}	72.6^{^f}	73.0^{^e}	73.5^{^d}	73.7^{^c}	74.1^{^b}	74.4^{^a}	0.47	< 0.001
HCW, kg	348.7^{^l}	357.8^{^k}	368.3^{^j}	381.0^ⁱ	391.7^{^h}	400.8^{^g}	409.1^{^f}	416.0^{^e}	422.2^{^d}	428.6^{^c}	434.5^{^b}	442.3^{^a}	8.41	< 0.001
Marbling Score^³	427.1^{^l}	437.7^{^k}	454.1^{^j}	476.3^ⁱ	498.0^{^h}	516.6^{^g}	532.3^{^f}	545.5^{^e}	555.5^{^d}	563.6^{^c}	571.8^{^b}	580.2^{^a}	25.12	< 0.001
Vision Yield Grade	2.0^{^l}	2.2^{^k}	2.4^{^j}	2.7^ⁱ	3.0^{^h}	3.3^{^g}	3.6^{^f}	3.8^{^e}	4.1^{^d}	4.4^{^c}	4.7^{^b}	5.2^{^a}	0.24	< 0.001
Backfat Thickness, cm	0.43^{^l}	0.67^{^k}	0.86^{^j}	1.12^ⁱ	1.37^{^h}	1.62^{^g}	1.88^{^f}	2.12^{^e}	2.38^{^d}	2.63^{^c}	2.93^{^b}	3.44^{^a}	> 0.01	< 0.001
REA^⁴, cm²	95.1^{^b}	95.6^{^a}	95.6^{^a}	95.1^{^b}	94.4^{^c}	93.6^{^d}	92.9^{^e}	92.1^{^f}	91.2^{^g}	90.6^{^h}	89.6^ⁱ	88.0^{^j}	2.88	< 0.001

Largest standard error of least-squares means.
Observed significance level.
100 = Practically Devoid, 200 = Traces, 300 = Slight, 400 = Small, 500 = Modest, 600 = Moderate, 700 = Slightly Abundant, 800 = Moderately Abundant, 900 = Abundant.
Ribeye area.
Least-squares means in the same row without a common superscript differ (P < 0.05).

Stratification of carcasses by REA as a main effect for carcass attributes is presented in Table 8 (P < 0.001). The greatest number of carcasses had a REA between 89.8 and 109.0 cm² (54.15%), and the least number of carcasses had a REA less than or equal to 70.3 cm² (1.62%). Color score was greatest (P < 0.05) in carcasses with REA 70.4 and 89.7 cm² and least (P < 0.05) in carcasses with a REA greater than or equal to 109.1 cm². Carcasses with REA less than or equal to 70.3 cm² had greater (P < 0.05) color scores compared with carcasses with REA between 89.8 and 109.0 cm². Hot carcass weight increased (P < 0.05) as REA category increased. Marbling score was greatest (P < 0.05) in carcasses with a REA between 70.4 and 89.7 cm², least (P < 0.05) in carcasses with a REA less than 70.3 cm², and carcasses with a REA less than 70.3 cm² had greater (P < 0.05) marbling scores compared with carcasses which had REA greater than or equal to 109.1 cm². Vision yield grade and backfat thickness decreased (P < 0.05) as REA category increased.

Table 8.

Least-squares means of carcass grade components from grading cameras within REA^¹ groups

	Ribeye Area, cm²
Parameter	≤70.3	70.4–89.7	89.8–109.0	≥109.1	SEM^²	P Value³
n	7412	157461	247499	44654
n, %	1.62	34.45	54.15	9.77
Color Score	72.0^{^b}	72.1^{^a}	71.7^{^c}	71.6^{^d}	0.34	< 0.001
HCW, kg	345.0^{^d}	376.6^{^c}	403.2^{^b}	428.0^{^a}	15.90	< 0.001
Marbling Score^⁴	508.9^{^b}	515.5^{^a}	503.3^{^c}	481.3^{^d}	32.46	< 0.001
Vision Yield Grade	4.1^{^a}	3.6^{^b}	3.1^{^c}	2.5^{^d}	0.26	< 0.001
Backfat Thickness, cm	1.72^{^a}	1.64^{^b}	1.53^{^c}	1.38^{^d}	0.07	< 0.001
REA^¹, cm²	66.5^{^d}	82.8^{^c}	97.6^{^b}	114.4^{^a}	0.65	< 0.001

Ribeye area.
Largest standard error of least-squares means.
Observed significance level.
100 = Practically Devoid, 200 = Traces, 300 = Slight, 400 = Small, 500 = Modest, 600 = Moderate, 700 = Slightly Abundant, 800 = Moderately Abundant, 900 = Abundant.
Least-squares means in the same row without a common superscript differ (P < 0.05).

There was a main effect of quality grade for all carcass attributes (P < 0.001; Table 9). The greatest proportion of carcasses were graded Choice (72.40%), and the least number of carcasses were in the Other category (1.19%), which includes Standard, Commercial, Utility, and carcasses that received no grade. Carcass color score and HCW were greatest (P < 0.05) in Prime carcasses and decreased (P < 0.05) for each subsequent quality grade from Choice to Select to Other. Marbling score, VYG, and backfat thickness were greatest (P < 0.05) in Prime carcasses and least (P < 0.05) in Select carcasses. Marbling score, VYG, and backfat thickness were greater (P < 0.05) in Choice carcasses compared with Other carcasses. Ribeye area was greatest (P < 0.05) in Select carcasses, least (P < 0.05) in Prime carcasses, and Choice carcasses had greater (P < 0.05) REA compared with Other carcasses.

Table 9.

Least-squares means of carcass grade components from USDA Quality Grades

	USDA Quality Grade
Parameter	Prime	Choice	Select	Other^¹	SEM^²	P Value^³
n	33214	330882	87496	5434
n, %	7.27	72.40	19.14	1.19
Color Score	75.1^{^a}	70.1^{^b}	70.4^{^c}	62.3^{^d}	0.07	< 0.001
HCW, kg	413.4^{^a}	401.5^{^b}	376.1^{^c}	363.9^{^d}	0.69	< 0.001
Marbling Score^⁴	766.3^{^a}	513.2^{^b}	379.2^{^d}	462.1^{^c}	1.02	< 0.001
Vision Yield Grade	3.9^{^a}	3.3^{^b}	2.6^{^d}	2.7^{^c}	0.01	< 0.001
Backfat Thickness, cm	1.92^{^a}	1.62^{^b}	1.21^{^d}	1.2^{^c}	0.01	< 0.001
REA^⁵, cm²	89.0^{^d}	93.5^{^b}	96.0^{^a}	92.9^{^c}	0.15	< 0.001

Other includes Standard, Commercial, Utility, and carcasses that received no grade.
Largest standard error of least-squares means.
Observed significance level.
100 = Practically Devoid, 200 = Traces, 300 = Slight, 400 = Small, 500 = Modest, 600 = Moderate, 700 = Slightly Abundant, 800 = Moderately Abundant, 900 = Abundant.
Ribeye area.
Least-squares means in the same row without a common superscript differ (P < 0.05).

Assessment of carcasses by cattle background and breed type

The main effect of timing when cattle entered the feedlot is presented in Table 10 (P < 0.001). Within this population, 48764 of the cattle had unknown time at entrance to the feedlot and were excluded from these analyses. In this data set, most cattle entered the feedlot as yearlings (61.02%) and the remainder entered as calves (28.31%). Cattle that entered the feedlot as yearlings had greater (P < 0.05) color scores compared with calves. Yearling cattle also had greater (P < 0.05) HCW, VYG, backfat thickness, and REA compared with cattle that entered the feedlot as calves. Cattle that entered the feedlot as calves had greater (P < 0.05) marbling scores compared with cattle that entered as yearlings.

Table 10.

Least-squares means of carcass grade components from cattle background

	Cattle Background
Parameter	Calf	Yearling	SEM^¹	P Value^²
n	129374	278888
n, %	28.31	61.02
Color Score	71.4^{^b}	72.2^{^a}	0.31	< 0.001
HCW, kg	394.5^{^b}	401.8^{^a}	7.90	< 0.001
Marbling Score^³	522.9^{^a}	502.1^{^b}	26.32	< 0.001
Vision Yield Grade	3.2^{^b}	3.3^{^a}	0.30	< 0.001
Backfat Thickness, cm	1.51^{^b}	1.63^{^a}	0.06	< 0.001
REA^⁴, cm²	92.9^{^b}	94.5^{^a}	3.34	< 0.001

Largest standard error of least-squares means.
Observed significance level.
100 = Practically Devoid, 200 = Traces, 300 = Slight, 400 = Small, 500 = Modest, 600 = Moderate, 700 = Slightly Abundant, 800 = Moderately Abundant, 900 = Abundant.
Ribeye area.
Least-squares means in the same row without a common superscript differ (P < 0.05).

The main effect of breed type on carcass attributes is presented in Table 11 (P < 0.001). The greatest number of carcasses were from English cattle breeds (72.64%), and the least number of carcasses were from straight-bred Dairy cattle (0.20%). English type cattle had the greatest (P < 0.05) carcass color score, while Dairy cattle had the lowest or darkest (P < 0.05) color score. Exotic and Bos indicus cattle breeds had greater (P < 0.05) color scores compared with Beef × Dairy cattle. English type cattle had the greatest (P < 0.05) HCW; Bos indicus cattle had the lightest (P < 0.05) HCW. Beef × Dairy cattle had greater (P < 0.05) HCW compared with Dairy and Exotic breeds, while Dairy cattle had greater (P < 0.05) HCW compared with Exotic breeds. Marbling score was greatest (P < 0.05) in Dairy breeds, and least (P < 0.05) in Bos indicus cattle. Marbling scores of Beef × Dairy cattle were greater (P < 0.05) than English and Exotic cattle, while English breeds had greater (P < 0.05) marbling scores compared with Exotic breeds. Vision yield grade was greatest (P < 0.05) in English and Dairy breeds and least (P < 0.05) in Bos indicus breeds. Beef × Dairy cattle had greater (P < 0.05) VYG compared with Exotic breeds. Backfat thickness was greatest (P < 0.05) in English cattle and least (P < 0.05) in Dairy cattle. Beef × Dairy cattle had greater (P < 0.05) backfat thickness compared with Exotic and Bos indicus cattle, while Exotic cattle had more (P < 0.05) backfat compared with Bos indicus breeds. Ribeye area was greatest (P < 0.05) in English cattle and least (P < 0.05) in Dairy cattle. Beef × Dairy cattle had greater (P < 0.05) REA compared with Exotic and Bos indicus cattle, while Exotic cattle had larger (P < 0.05) REA compared with Bos indicus breeds.

Table 11.

Least-squares means of carcass grade components from cattle breed types

	Breed Type
Parameter	Beef × Dairy	Dairy	English	Exotic	Bos indicus	SEM^¹	P Value^²
n	55479	894	331969	59549	9135
n, %	12.14	0.20	72.64	13.03	2.00
Color Score	70.7^{^c}	64.9^{^d}	72.2^{^a}	71.0^{^b}	71.2^{^b}	0.06	< 0.001
HCW, kg	394.0^{^b}	372.2^{^c}	401.8^{^a}	368.0^{^d}	357.9^{^e}	7.89	< 0.001
Marbling Score^³	530.8^{^b}	589.7^{^a}	506.9^{^c}	477.9^{^d}	469.2^{^e}	29.17	< 0.001
Vision Yield Grade	3.1^{^b}	3.2^{^a}	3.3^{^a}	2.9^{^c}	2.8^{^d}	0.30	< 0.001
Backfat Thickness, cm	1.42^{^b}	1.05^{^e}	1.64^{^a}	1.30^{^c}	1.21^{^d}	0.05	< 0.001
REA^⁴, cm²	93.2^{^b}	82.2^{^e}	94.2^{^a}	91.6^{^c}	90.7^{^d}	3.47	< 0.001

Largest standard error of least-squares means.
Observed significance level.
100 = Practically Devoid, 200 = Traces, 300 = Slight, 400 = Small, 500 = Modest, 600 = Moderate, 700 = Slightly Abundant, 800 = Moderately Abundant, 900 = Abundant.
Ribeye area.
Least-squares means in the same row without a common superscript differ (P < 0.05).

Seasonality

There was a main effect of season of harvest on carcass attributes (P < 0.001; Table 12). The greatest proportion of carcasses were harvested in the winter (34.62%), and the least number of carcasses were harvested in the summer (32.42%). Camera color score was greater (P < 0.05) in the winter compared with the summer and fall. Hot carcass weight, marbling score, VYG, and backfat thickness increased (P < 0.05) as the seasons progressed from warm weather, summer, to cool weather, fall, and to cold weather, winter. Ribeye area was greatest (P < 0.05) in the fall and least (P < 0.05) in the winter.

Table 12.

Least-squares means of carcass grade components from grading cameras within season of harvest

	Season
Parameter	Summer	Fall	Winter	SEM^¹	P Value^²
n	148177	150636	158213
n, %	32.42	32.96	34.62
HCW, kg	379.9^{^c}	399.6^{^b}	410.63^{^a}	0.13	< 0.001
Color Score	71.8^{^b}	71.8^{^b}	72.1^{^a}	0.01	< 0.001
Marbling Score^³	480.5^{^c}	502.7^{^b}	531.2^{^a}	0.30	< 0.001
Vision Yield Grade	2.7^{^c}	3.2^{^b}	3.7^{^a}	0.003	< 0.001
Backfat Thickness, cm	1.19^{^c}	1.57^{^b}	1.89^{^a}	0.001	< 0.001
REA^⁴, cm²	94.2^{^b}	94.4^{^a}	92.5^{^c}	0.03	< 0.001

Largest standard error of least-squares means.
Observed significance level.
100 = Practically Devoid, 200 = Traces, 300 = Slight, 400 = Small, 500 = Modest, 600 = Moderate, 700 = Slightly Abundant, 800 = Moderately Abundant, 900 = Abundant.
Ribeye area.
Least-squares means in the same row without a common superscript differ (P < 0.05).

Frequency of color categories within carcass attributes

The frequency of each color category within antemortem and postmortem attributes is presented in Table 13. The occurrence of DARK and INT carcasses decreased as HCW classification increased. The frequency of NORM carcasses increased from carcasses weighing less than 272.2 kg to carcasses weighing between 317.5 and 362.8 kg, then decreased with each subsequent weight category. Light carcass frequency decreased from carcasses weighing less than 272.2 kg to carcasses weighing 272.4 and 317.4 kg, then increased as HCW increased for the remaining HCW classifications. The frequency of DARK carcasses decreased from the Practically Devoid and Traces marbling classification to the Slightly Abundant classification. Abundant marbled carcasses have a slightly higher occurrence of DARK color compared with Slightly and Moderately abundant marbled carcasses. The frequency of INT carcasses decreased as marbling score increased. The frequency of NORM carcasses increased from Practically Devoid and Traces marbled carcasses to Small marbled carcasses, then decreased as marbling score increased through Abundant marbled carcasses. The frequency of LIGHT carcasses decreased from Practically Devoid and Traces carcasses to Slight carcasses, then increased through Abundant marbled carcasses. The occurrence of DARK and INT carcasses decreased as yield grade increased. The occurrence of NORM carcasses increased from yield grade 1 to 2, then decreased with each yield grade from 2 to 5. The frequency of LIGHT carcasses increased from yield grade 1 to 5. The frequency of DARK and INT carcasses decreased from carcasses with less than 0.51 cm of backfat to carcasses that have between 2.29 and 2.53 cm of backfat. The frequency of DARK and INT carcasses decreased from 2.29 to 2.53 cm of backfat to 2.54 and 2.78 cm of backfat, before decreasing for the remaining backfat classifications. The frequency of NORM carcasses increased from carcasses that have less than 0.51 cm of backfat to 1.02 and 1.26 cm of backfat, then decreased for the remaining backfat thickness classifications. The frequency of LIGHT carcasses decreased for carcasses that have less than 0.51 cm of backfat thickness to 0.76 and 1.01 cm of backfat, then increased for each subsequent backfat classification. Carcasses with a REA less than or equal to 70.3 cm² had the greatest frequency of DARK and INT carcasses, and carcasses with REA between 70.4 and 89.7 cm² had the lowest frequency of DARK and INT carcasses. The frequency of NORM carcasses decreased as REA category increased. The occurrence of LIGHT carcasses was least in carcasses with REA less than or equal to 70.3 cm² and greatest in carcasses with REA greater than or equal to 109.1 cm².

Table 13.

Distribution of color categories within selected carcass characteristics

	Color Category^¹
Parameter	Dark	Intermediate	Normal	Light	P Value^²
Hot Carcass Weight Classification, kg					< 0.001
<272.2	1.31%	6.12%	86.42%	6.14%
272.4-317.4	0.92%	3.44%	89.85%	5.79%
317.5-362.8	0.52%	1.97%	90.27%	7.24%
362.9-408.1	0.31%	1.15%	89.96%	8.59%
408.2-453.5	0.19%	0.63%	89.63%	9.55%
453.6-498.9	0.08%	0.32%	89.12%	10.48%
>499	0.07%	0.16%	87.62%	12.14%
Marbling Classification					< 0.001
Practically Devoid and Traces	1.46%	3.18%	83.95%	11.41%
Slight	0.52%	2.08%	90.54%	6.86%
Small	0.35%	1.29%	91.46%	6.90%
Modest	0.27%	0.88%	90.20%	8.65%
Moderate	0.19%	0.66%	87.91%	11.23%
Slightly Abundant	0.08%	0.52%	83.09%	16.31%
Moderately Abundant	0.08%	0.25%	77.00%	22.66%
Abundant	0.09%	0.09%	60.52%	39.31%
USDA Yield Grade					< 0.001
1	0.62%	2.49%	89.69%	7.20%
2	0.46%	1.59%	90.35%	7.60%
3	0.23%	0.87%	89.83%	9.08%
4	0.16%	0.55%	88.85%	10.44%
5	0.09%	0.48%	88.22%	11.21%
Backfat Classification, cm					< 0.001
<0.51	0.75%	2.84%	89.37%	7.04%
0.51–0.75	0.62%	2.53%	90.49%	6.36%
0.76–1.01	0.58%	2.26%	90.83%	6.34%
1.02–1.26	0.48%	1.63%	91.10%	6.80%
1.27–1.51	0.36%	1.17%	90.68%	7.79%
1.52–1.77	0.22%	0.91%	90.13%	8.74%
1.78–2.02	0.19%	0.74%	89.21%	9.87%
2.03–2.28	0.15%	0.62%	88.53%	10.69%
2.29–2.53	0.10%	0.39%	87.60%	11.91%
2.54–2.78	0.11%	0.42%	86.51%	12.96%
2.79–3.04	0.09%	0.23%	85.59%	14.09%
≥3.05	0.07%	0.21%	84.01%	15.71%
REA^³ Classification, cm²					< 0.001
≤70.3	0.47%	1.59%	89.84%	8.09%
70.4–89.7	0.30%	1.13%	89.81%	8.76%
89.8–109.0	0.34%	1.25%	89.79%	8.63%
≥109.1	0.34%	1.22%	89.22%	9.21%
Cattle Background					< 0.001
Calf	0.42%	1.40%	91.53%	6.65%
Yearling	0.29%	1.11%	89.25%	9.35%
Cattle Breed
Beef × Dairy	0.61%	1.93%	91.32%	6.13%
Dairy	2.01%	7.27%	88.81%	1.90%
English	0.27%	1.01%	89.60%	9.12%
Exotic	0.35%	1.46%	89.77%	8.41%
Bos indicus	0.46%	1.71%	85.07%	12.76%
Cattle Sex					< 0.001
Female	0.41%	1.61%	90.64%	7.34%
Male	0.25%	0.85%	88.94%	9.95%
Season					< 0.001
Summer	0.34%	1.11%	89.51%	9.03%
Fall	0.42%	1.40%	87.72%	10.46%
Winter	0.22%	1.12%	91.88%	6.78%

Frequency of color category within a characteristic trait. Data presented as percentage of color relative to total of characteristic
Observed significance level for chi-square test.
Ribeye area.

There was a greater frequency of DARK and INT carcasses from cattle that entered the feedlot as calves. There was a greater proportion of LIGHT carcasses from cattle that entered the feedlot as yearlings. Overall, a greater proportion of cattle that entered the feedlot as yearlings had non-normal color. Dairy cattle had the greatest frequency of DARK and INT carcasses, whereas English cattle had the lowest frequency of DARK carcasses. Bos indicus cattle had the greatest occurrence of LIGHT carcasses, and Dairy cattle had the lowest occurrence of LIGHT carcasses. Bos indicus cattle had the greatest occurrence of non-normal carcass color, while Beef × Dairy cattle had the greatest proportion of NORM carcass color. Female cattle had more DARK and INT carcasses, while male cattle had a greater proportion of LIGHT carcasses. Male cattle more frequently produced carcasses with non-normal color. The greatest frequency of all non-normal carcass color categories occurred in the Fall. Conversely, the greatest frequency of NORM carcass color occurred in the Winter.

Discussion

The observed cattle population in the present study is similar to the most recent NBQA in 2022 (Mayer et al., 2024). This includes the recent trends of increased HCW, backfat thickness, and REA (Moore et al., 2012; Boykin et al., 2017a; Mayer et al., 2024). While the present study aligns with recently published trends of the fed beef industry, it is not a comprehensive evaluation of the U.S. beef industry, as the data were only collected from 2 commercial facilities. Where this study does provide novelty and insight is the utilization of data already being collected by grading cameras to further classify carcasses into color categories and identify factors contributing to color differences.

The use of high-concentrate diets during the finishing period is established to improve growth performance and fat development (Crouse et al., 1989; Faucitano et al., 2008; Klopatek et al., 2022). Yet as demonstrated in this dataset, the age at which cattle enter these intensive feeding systems will also impact carcass performance characteristics. Schoonmaker et al. (2002) reported increased REA at harvest in cattle entering the feedlot as yearlings compared to at weaning. However, the differences in REA can be attributed to the age of the cattle at harvest, but cattle who entered the feedlot at earlier ages had increased REA at the same timepoint (Schoonmaker et al., 2002; Grote et al., 2025). López-Campos et al. (2013) identified that yearling-fed steers had greater HCW compared with their calf-fed counterparts but had no differences in the frequency of yield grade or quality grade. Similar findings to the present study were also reported wherein cattle that entered the feedlot at weaning had greater marbling scores compared to those that entered the feedlot as yearlings (Schoonmaker et al., 2002). Earlier entrance into the feedlot allows cattle to reach physiological maturity and potential of skeletal muscle mass earlier, which allows for an increase in adipose accretion for the remainder of the feedlot period (Pethick et al., 2004). Conversely, Reinhardt et al. (2009) reported that as bodyweight increases at entry to the feedlot stage, there is an associated increase in final BW, HCW, and marbling score. It has been illustrated how cattle management strategies can influence carcass quality and yield, yet there is still limited information on how these production strategies influence ribeye color. Girard et al. (2012) identified that calf-fed cattle at slaughter had greater L* values in the semitendinosus and tended to be greater in the gluteus medius compared with yearling-fed cattle. However, the yearling-fed cattle in the aforementioned study were 6 to 8 months older in age at the time of slaughter compared to the calf-fed cattle, which may be driving these differences in L* values. As cattle age, there is an increase in myoglobin content, which can lead to the darkening of lean (Girard et al., 2012; Cho et al., 2015).

Environmental factors have long been focused on as an influence on carcass quality and the occurrence of dark cutting beef. These theories revolve around the idea that as seasons change, there are shifts in feed availability and diet composition as well as changes to the environmental temperature and other antemortem stressors (Ferguson and Warner, 2008; Loredo-Osti et al., 2019). In regard to carcass quality, as temperatures decline and the environment becomes harsher, cattle harvested during the winter months have greater marbling scores (Gray et al., 2012). Conversely, as temperatures increase in the summer months, both marbling scores and HCW decrease (Gray et al., 2012; Boykin et al., 2017b). In addition to changes in carcass quality, increasing temperatures and heat stress are associated with an increase in the occurrence of dark cutting beef (Kadim et al., 2004). Using extensive temperature data tied to feedlots, Steel et al. (2022) identified that a 10% increase in ambient temperature in the days leading up to feedlot exit increased the likelihood of dark cutting beef by 1%. Without the same extensive environmental metrics tied to cattle performance during the feedlot period and shipping, the environmental effects in the present study are not particularly insightful. Yet, the frequency of carcasses with non-normal beef color is greatest from cattle harvested in the fall, which would have been in their final stages of finishing at an intensive feedlot system during peak ambient temperatures in the Summer months of the United States beef industry.

Historically, the NBQA has identified cattle as Native, Dairy, or Bos indicus. In the 2022-NBQA, Mayer et al. (2024) reported that 87.7% of cattle were identified as Native, 11.3% Dairy influenced, and 0.9% as Bos indicus. The present study had slightly different identifiers for cattle breed but highlights the increasing occurrence of the Beef × Dairy population. This increase was previously acknowledged by an increase in beef semen sales, but a reduction in dairy semen sales with a somewhat constant dairy cow population (Foraker et al., 2022a). One of the initial concerns with these crossbred cattle is the production of less desirable meat color associated with dairy breeds. Both Pfuhl et al. (2007) and Page et al. (2001) reported reduced colorimetric measures or darker colored steaks in carcasses from Dairy cattle compared with English/Native cattle. Similar results are reported in the present study wherein dairy cattle breeds had a reduction in color score compared with all other cattle types; however, these effects in the Beef × Dairy carcasses are diluted with a 9.0% increase in color score compared to straightbred Dairy and a 2.1% reduction in color score compared with English-influenced cattle breeds. Similar effects have been identified during a simulated retail display period wherein there were no differences in visual or instrumental measures of color between Beef × Dairy, and conventional beef cattle (Foraker et al., 2022b). Pimentel-Concepción et al. (2024) reported that Holstein × Beef cattle had greater L*, a*, and b* values compared with Holstein carcasses, indicating that the inclusion of beef genetics with dairy genetics improves meat color. Furthermore, in the present study, the Beef × Dairy cattle had a lower occurrence of DARK and INT carcasses compared with the purebred Dairy cattle and had the greatest occurrence of NORM colored carcasses. This illustrates that while Dairy cattle are more predisposed to having darker colored, less desirable carcasses, the inclusion of beef genetics largely mitigates this issue.

Correlation analyses in the present study identified HCW, fat thickness, and marbling score as the carcass characteristics with the greatest influence on carcass color score. While there are correlations between each of these traits, it is important to note that they are weak in nature but still provide some insight into how carcass measurements influence color score. In the first NBQA, 408.2 kg was determined as the upper threshold for HCW, wherein 20.5% of carcasses were heavier than that threshold (Lorenzen et al., 1993). When utilizing that same weight threshold in the present study, 40.74% of the carcasses exceeded that weight. Along with the increase in HCW, fat thickness has increased on average by 4.0% since the first NBQA (Lorenzen et al., 1993). Furthermore, there has been a 19.1% increase in marbling score since 1991, and this study reports a 1.5% increase since 2022 (Lorenzen et al., 1993; Mayer et al., 2024). As mentioned previously, this illustrates how the US beef system has continued to finish cattle at heavier endpoints with increased adipose deposition in the pursuit of improved quality grades (Gray et al., 2012; Boykin et al., 2017a; Mayer et al., 2024). Yet, as cattle increase in size and fatness, there is an increase in the percentage of carcasses with a light or more pale color. Agbeniga and Webb (2018) identified that Longissimus lumborum steaks from lightweight cattle had reduced L* values at 2 d postmortem compared with heavyweight cattle. Furthermore, Lancaster et al. (2020) identified that top round steaks from oversized beef carcasses had greater L* values, indicating a paler appearance in comparison with steaks from average-sized beef carcasses. Within the same study, there was a reduction in temperature decline in the deep portion of the top round in oversized carcasses compared with average carcasses (Lancaster et al., 2020). Similar results of delayed temperature decline and increased pH decline have been identified in the Semimembranosus of heavyweight carcasses compared with lightweight carcasses (Djimsa et al., 2022). This relationship between color and fat thickness was clearly illustrated by Aalhus et al. (2001), as carcasses with increased fat depth had increased L* values at the grade site 24 h postmortem. Additionally, their study revealed that carcasses with more backfat had slower temperature declines and a resulting faster pH decline (Aalhus et al., 2001). Carcasses with a pH of 6 and elevated temperature (40°C) postmortem more frequently develop light or pale colored lean, while carcasses with low temperature (15°C) and a pH of 6 are more likely to yield dark colored carcasses (Hughes et al., 2014). Previous literature has identified that differences in chill time can influence marbling score, color, and overall assessed quality (Calkins et al., 1980). However, in the present study, there were no differences in chill time between color categories, with all carcasses being chilled approximately 48 h before grading (data not shown). Thus, the non-normal carcasses being identified as LIGHT in the present study may be a byproduct of chilling parameters that need to be modified for heavier, fatter carcasses and not solely differences in marbling score. Conversely, carcasses identified as DARK are associated with lighter HCW and less overall adiposity. This is observed in the present study as the prevalence of DARK and INT carcasses decreases as yield grade increases. Previous findings have found similar results, as lighter carcasses are more prone to become dark cutting, and that carcasses with HCW greater than 318 kg are less likely to become dark cutting (Murray, 1989; Mahmood et al., 2016).

Conclusions

This study identified that in the commercial facilities observed, 10.26% of the fed beef carcasses have non-normal color: dark, intermediate, or light. Within the nonnormal color variation, carcasses that have darker muscle, such as the DARK and INT in the current study, are leaner, lighter, and have reduced marbling scores compared with normal colored beef. Conversely, carcasses with more pale lean are fatter, heavier, and have increased marbling scores and yield grades. The frequency of color categories within carcass characteristics associated with carcass weight and adiposity indicates that as cattle are finished to heavier endpoints to increase marbling deposition to maximize the return on quality, there is subsequent development of light/pale colored lean. One area for future investigation is how to optimize carcass chilling parameters to reduce the occurrence of light/pale colored lean in heavy carcasses but not promote the occurrence of dark color in lighter weight, more lean carcasses. Moreover, future studies should aim to survey a larger portion of the beef cattle industry with paired data from feedlots to better characterize processes throughout an animal’s life that can lead to deviation from normal ribeye color.

Acknowledgments

Research coordinated by the National Cattlemen’s Beef Association, a contractor to the Beef Checkoff.

Conflict of Interests

The authors declare no conflict of interest.

Author Contributions

Thomas W. Dobbins: Conceptualization, Methodology, Investigation, Data Curation, Formal Analysis, Visualization, Writing – Original Draft, Writing – Reviewing and Editing

Samantha N. Barker: Conceptualization, Investigation, Funding Acquisition, Writing – Review and Editing.

Nick C. Hardcastle: Conceptualization, Methodology, Investigation, Data Curation, Funding Acquisition, Resources, Supervision, Writing – Reviewing and Editing,

Dale R. Woerner: Conceptualization, Funding Acquisition, Supervision, Writing – Review and Editing

Bradley J. Johnnson: Supervision, Writing – Review and Editing

Ranjith Ramanathan: Methodology, Supervision, Writing – Review and Editing

Jerrad F. Legako: Conceptualization, Methodology, Investigation, Funding Acquisition, Data Curation, Visualization, Project Administration, Resources, Supervision, Writing – Review and Editing

Literature Cited

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