National Beef Quality Audit – 2022: Instrument grading assessments of carcass characteristics of fed steers and heifers

ABSTRACT

The instrument grading assessment portion of the National Beef Quality Audit (NBQA)—2022 allowed for the evaluation of beef carcass traits over a 12-mo period. One week of instrument grading data was collected each month from 6 major beef processing companies from July 2021 to June 2022 (n = 4,418,768 carcasses). The sample pool was composed of 58.0% steer carcasses and 42.0% heifer carcasses, and the breed type distribution was 98.0% native, 1.6% dairy, and 0.3% Bos indicus. Means for USDA Yield Grade (YG) and YG factors were YG, 3.26, adjusted fat thickness, 1.55 cm, HCW, 400.6 kg, ribeye area, 91.6 cm², and KPH, 2.1%. Frequency distribution of USDA YG was YG 1 = 7.87%, YG 2 = 31.70%, YG 3 = 40.03%, YG 4 = 17.07%, and YG 5 = 3.35%. Mean marbling score was Modest¹⁶, and the distribution of USDA quality grades was Prime = 8.19%, Choice = 74.84%, Select = 15.68%, and other = 1.31%. Frequency of carcasses grading Prime on Monday (10.89%), a 3.27%-point increase compared to the Prime average for the other days of the week (7.72%), demonstrates the potential advantage of additional postmortem chilling over the weekend from Friday and/or Saturday slaughter days. Comparisons of breed type and sex class revealed differences (P < 0.05) in marbling with dairy heifers (Modest⁵⁵) > native heifers (Modest³¹) > dairy steers (Modest¹²) > native steers (Modest⁰³), and ribeye area with native steers (93.3 cm²) > native heifers (90.9 cm²) > dairy steers (81.8 cm²) > dairy heifers (80.3 cm²). Month-to-month frequency distributions in beef carcass traits revealed numerical differences in marbling, USDA quality grade distribution, HCW, and adjusted fat thicknesses. Percentage distribution of dark cutting carcasses revealed numerically higher incidences during the summer and fall seasons compared to winter and spring. Findings from the instrument grading assessment of the NBQA-2022 provide the beef industry with the most comprehensive and current beef carcass quality and yield information available.

This aspect of the National Beef Quality Audit—2022 focused on the beef quality and yield information from the fed steer and heifer population gathered through instrument grading. Current USDA Quality and Yield Grade averages and distributions as well as comparisons to previous audits over the past decade show trends that have evolved in important value-determining carcass traits.

INTRODUCTION

The National Beef Quality Audit (NBQA) is a recurring study conducted approximately every 5 years since 1991. The goal of this research is to provide a baseline of the current state of the beef industry in the United States and to aid in development of best methods to improve beef production strategies. The instrument grading data section, collected twice to date (Gray et al., 2012; Boykin et al., 2017b), focuses on the carcass grading characteristics of fed steers and heifers.

In 1980, the USDA’s Agricultural Research Service began developing instruments to evaluate beef yield and quality grades (Woerner and Belk, 2008). USDA’s Agricultural Marketing Service collaborated with NASA and the Jet Propulsion Laboratory to develop technology to be utilized in beef grading, and subsequent research supported the use and validated the accuracy of using these technologies for in-line beef grading (Cross et al., 1983; Steiner et al., 2003; Moore et al., 2010). Instrument grading technologies were approved for use in 2007 as an attempt to reduce human subjectivity and improve accuracy in beef carcass grading. Now, these technologies are consistently employed in large beef processing facilities around the country. Beef carcass grading information in the first four NBQAs were conducted solely by the research team in the plants; however, with the approval of instrument grading usage in 2009, the NBQA-2011 (Gray et al., 2012) was the first audit to include these data, followed by NBQA-2016 (Boykin et al., 2017b), and has continued in the NBQA-2022. Relative to in-plant collection strategies, the instrument grading data collection strategy provides a substantially greater number of total sampled carcasses. The year-long observation window also allowed for analysis of seasonality trends in carcass traits that are used in the determination of beef yield and quality grades.

MATERIALS AND METHODS

Institutional Animal Care and Use Committee approval was not required because the study only used camera-based instrument carcass grading information obtained from beef processors.

General Overview

Instrument grading data were requested from 6 major beef processing companies. Data included 1 wk’s worth of carcass grade data per month from July 2021 to June 2022, which resulted in data from 4,418,768 carcasses across 17 different facilities. Each plant included in data collection was asked to provide data from the same days each month. Specifically, data were collected from the first full week of the month in which Monday through Saturday all fell within that calendar month. The recorded day of the week reflected the date the carcasses were graded. If the grading date was not provided, data were submitted based on harvest dates that corresponded to carcasses expected to be graded during the specified week.

The period for collecting the instrument grading data coincided with the in-plant phase of the NBQA-2022 (Mayer et al., 2024), which was delayed to allow some of the facilities to return to full operations following workforce and livestock supply issues caused by the COVID-19 pandemic (Ciotti et al., 2020; Padhan and Prabheesh, 2021). Mayer et al. (2024) collected quality- and yield-indicating characteristics of beef carcasses from fed steers and heifers (n = 9,746) representing 10% of 1-d production from 35 beef processing facilities.

Companies collected data during the grading process, and each month, these were downloaded into Microsoft Excel spreadsheets and transferred to Texas A&M University for further analyses. Because of the periodic collection of data, observations were made in the daily and/or monthly trends that occurred during one year of beef processing. Instrument grading data collected in the study included, but were not limited to: slaughter date; grade date; breed classification (native [cattle not exhibiting dairy characteristics, usually of British/Continental breed type], dairy [predominantly Holstein influenced]) or Bos indicus [as noted by in-plant personnel most likely based on dorsal thoracis hump height]; sex (steer/heifer); marbling scores; maturity scores; defects (hardbone, dark cutter, and blood splash); certified beef programs; adjusted fat thickness, 12^th rib (AFT); ribeye area, 12^th rib; hot carcass weight (HCW); kidney, pelvic, and heart fat (KPH) percentage; calculated USDA Yield Grade (YG); and USDA Quality Grade (QG).

Statistical Analyses

For statistical analyses, Excel and JMP Software (JMP Pro, Version 16. SAS Institute Inc. Cary, NC, 1989-2021) were used to analyze all data. Fit Y by X functions were used for analysis of variance, and a student’s t-test was used to conduct least-squares means comparisons. Distributions, frequencies, means, standard deviations, minimums, and maximums were calculated in JMP using the distribution function.

RESULTS AND DISCUSSION

Overall Findings

Presented in Table 1 are the instrument grading means, standard deviations, and minimum and maximum values for the USDA beef carcass grading traits. Comparisons to the in-plant carcass traits (Mayer et al., 2024) will be made in subsequent sections.

Table 1.

National Beef Quality Audit—2022: Instrument grading means, SD, and minimum and maximum values for USDA beef carcass grading traits

Trait	n	Mean	SD	Minimum	Maximum
USDA Quality Grade component
Marbling Score1	4,401,790	516	117.8	100	1099
USDA Yield Grade and components
USDA Yield Grade	4,319,132	3.26	0.92	-1.7	10.1
Adjusted fat thickness, cm	4,404,769	1.55	0.546	0.003	7.6
HCW, kg	4,414,611	400.6	49.69	122.5	668.1
Ribeye area, cm2	4,404,897	91.6	11.21	27.1	223.87
KPH, %	4,335,340	2.1	0.35	0.0001	8.112

¹100 = Practically devoid⁰⁰, 200 = Traces⁰⁰, 300 = Slight⁰⁰, 400 = Small⁰⁰, 500 = Modest⁰⁰, 600 = Moderate⁰⁰, 700 = Slightly Abundant⁰⁰, 800 = Moderately Abundant⁰⁰, 900 = Abundant⁰⁰, and 1000 = Very Abundant⁰⁰ (U.S. Department of Agriculture, 2017).

Steer carcasses represented 58.0% of the data set, and heifer carcasses comprised 42.0% of the data set (data not reported in tabular form). In evaluating the U.S. Department of Agriculture (2025) Cattle on Feed information for four quarters during this time period, 62% were steers and 38% were heifers. In comparison to the previous NBQA instrument grading studies, Gray et al. (2012) reported 59.2% steer and 40.8% heifer carcasses, whereas Boykin et al. (2017b) reported 65.9% steer carcasses and 34.1% heifer carcasses. Classifications of breed types were Native, 98%; dairy, 1.6%; and Bos indicus, 0.3%. The percentage of dairy carcasses observed within the instrument grading data is substantially lower than Mayer et al. (2024) noted for the in-plant assessments (11.3%), which was a more comprehensive survey of processing plants across the United States. With the increasing prevalence of beef on dairy cattle (Foraker et al., 2022), the future numbers of carcasses from dairy cattle may be impacted.

Comparisons of USDA QG and YG traits across the native and dairy steers and heifers are shown in Table 2 (not all carcass observations provided breed type and sex-class designations, thus the substantially lower numbers of carcasses reported here). For every trait measured, there were statistical differences between each breed type/sex class category. For marbling scores, dairy heifers > native heifers > dairy steers > native steers (P < 0.05). For USDA YG, native steers > native heifers > dairy heifers > dairy steers (P < 0.05). For HCW, native steers > dairy steers > dairy heifers > native heifers (P < 0.05). In general, native steers and native heifers had greater adjusted fat thicknesses and larger ribeye areas than did the dairy steers and dairy heifers.

Table 2.

National Beef Quality Audit—2022: Least squares means (SEM) for USDA Quality and Yield Grade traits stratified according to apparent breed type and sex class

Trait	Native steers (n = 1,450,507)	Native heifers (n = 1,038,514)	Dairy steers (n = 34,419)	Dairy heifers (n = 6,782)
USDA Quality Grade component
Marbling Score1	503d	531b	512c	555a
	(0.10)	(0.11)	(0.62)	(1.40)
USDA Yield Grade and components
USDA Yield Grade	3.4a	3.3b	3.0d	3.1c
	(0.00)	(0.00)	(0.01)	(0.01)
Adjusted fat thickness, cm	1.56b	1.66a	0.90d	0.99c
	(0.00)	(0.00)	(0.00)	(0.01)
HCW, kg	420.6a	379.3d	386.1b	382.3c
	(0.04)	(0.04)	(0.24)	(0.55)
Ribeye area, cm2	93.3a	90.9b	81.8c	80.3d
	(0.01)	(0.01)	(0.06)	(0.14)

^a,b,c,dMeans within a row lacking a common superscript letter differ (P < 0.05).

²300 = Slight⁰⁰, 400 = Small⁰⁰, 500 = Modest⁰⁰, 600 = Moderate⁰⁰, 700 = Slightly Abundant⁰⁰, 800 = Moderately Abundant⁰⁰, and 900 = Abundant⁰⁰ (U.S. Department of Agriculture, 2017).

Table 3 presents the percentage distribution of carcasses stratified across each USDA QG and YG. The summed percentages for each USDA QG were for Prime = 8.19%, Choice = 74.84%, and Select = 15.68%. For the NBQA-2011 (Gray et al., 2012), Prime = 2.7%, Choice = 61.5%, and Select = 31.5%, and for the NBQA-2016 (Boykin et al., 2017b), Prime = 4.2%, Choice = 71.4%, and Select = 21.7%. For USDA YG, the percentages were YG 1 = 7.87%, YG 2 = 31.70%, YG 3 = 40.03%, YG 4 = 17.07%, and YG 5 = 3.35%. For the NBQA-2011 (Gray et al., 2012), YG 1 = 15.7%, YG 2 = 41.0%, YG 3 = 33.8%, YG 4 = 8.5%, and YG 5 = 0.9%, and for NBQA-2016, YG 1 = 9.5%, YG 2 = 34.6%, YG 3 = 38.8%, YG 4 = 14.6%, and YG 5 = 2.5%. Overall, for NBQA-2022, 78.48% of the carcasses fell into the Prime, Choice, and Select, and Yield Grades 1, 2, and 3 categories, indicating that a significant majority met the primary marketplace targets. However, for the NBQA-2022, the increasing numerical percentages of Prime and Choice carcasses are coinciding with the increasing numerical percentages of YG 4 and YG 5 carcasses compared to previous audits (Gray et al., 2012; Boykin et al., 2017b).

Table 3.

National Beef Quality Audit—2022: Instrument grading percentage distribution¹ of carcasses stratified by USDA Quality and Yield Grades

	USDA Quality Grade, %
USDA Yield Grade	Prime	Choice	Select	Other2	Total
1	0.06 (n = 2,525)	4.12 (n = 177,541)	3.41 (n = 146,923)	0.28 (n = 11,960)	7.87
2	1.11 (n = 47, 835)	23.07 (n = 995,097)	7.06 (n = 304,280)	0.46 (n = 20,006)	31.70
3	3.62 (n = 156,094)	31.89 (n = 1,375,108)	4.14 (n = 178,548)	0.38 (n = 16,449)	40.03
4	2.67 (n = 115, 098)	13.31 (n = 573,896)	0.94 (n = 40,365)	0.15 (n = 6,482)	17.07
5	0.73 (n = 31,388)	2.45 (n = 105,601)	0.13 (n = 5,741)	0.04 (n = 1,639)	3.35
Total	8.19	74.84	15.68	1.31

¹Carcasses with missing values for USDA Quality or Yield grades are not included.

²Other includes Standard, Commercial, Utility, dark cutter, blood splash, hard bone, and calloused ribeye.

The NBQA-2016 (Boykin et al., 2017b) was the first NBQA to analyze the effect of the day of the week on carcass grading, specifically its impact on the percentage of Prime and Choice carcasses. Due to the benefits of longer postmortem chilling times, the findings of Calkins et al. (1980) have been cited as a reason to avoid over- or under-sampling carcasses chilled over the weekend for the in-plant portion of the NBQA. Boykin et al. (2017b) reported that 6.43% of carcasses graded on Monday were Prime, compared to the overall Prime percentage of 4.2%. Carcasses graded on Monday likely come from cattle slaughtered on Friday or Saturday, allowing for much longer postmortem chilling times (48 h+) than those slaughtered during the week, where carcasses may be chilled for 24 hours or fewer before grading. In the current study (Table 4), carcasses graded on Monday had the highest numerical frequency of Prime (10.89%), which is a 3.27%-point increase compared to the frequency of Prime grading carcasses the other days of the week (7.72%). Conversely, Select carcasses graded on Monday had the lowest numerical frequency (12.36%), a 3.67%-point decrease compared to the Select average for the other days of the week (16.03%). These findings suggest shifts from Select to Choice and from Choice to Prime occur with additional chilling, indicating that postmortem chilling time has financial implications due to differential market values between quality grades.

Table 4.

National Beef Quality Audit—2022: Frequency of USDA Quality Grade by day of week graded (n = 3,353,588)

	USDA Quality Grade, %
Day of the week graded	Prime	Choice	Select	Other1
Monday	10.89	75.60	12.36	1.15
	(n = 60,581)	(n = 420,438)	(n = 68,718)	(n = 6,417)
Tuesday	8.63	74.73	15.32	1.32
	(n = 52,657)	(n = 455,851)	(n = 93,424)	(n = 8,056)
Wednesday	7.69	75.06	15.83	1.41
	(n = 46,798)	(n = 456,703)	(n = 96,311)	(n = 8,604)
Thursday	7.40	74.59	16.45	1.56
	(n = 46,315)	(n = 467,028)	(n = 102,999)	(n = 9,760)
Friday	7.51	74.26	16.63	1.60
	(n = 47,420)	(n = 468,599)	(n = 104,909)	(n = 10,085)
Saturday	7.37	75.30	15.90	1.44
	(n = 23,728)	(n = 242,387)	(n = 51,170)	(n = 4,630)

¹Other includes Standard, Commercial, Utility, dark cutter, blood splash, hard bone, and calloused ribeye.

Month-to-Month Variability

Frequency distributions, by months, for various beef carcass traits are reported in Figures 1 through 7. For the frequency distributions of beef carcass quality traits of grade (Figure 1) and marbling (Figure 2), Feb-22, Mar-22, and Apr-22 were the months that had the numerically highest percentages of Prime and Choice, numerically lowest percentages of Select, and had among the highest marbling scores. Jul-21, Aug-21, Sep-21, Oct-21, and Jun-22 were among the lowest numerical percentages of Prime and highest percentages of Select (Figure 1), which corresponded to months with the numerically lowest marbling scores (Figure 2). It should not be surprising that average marbling scores somewhat predict USDA QG distributions, and these two figures demonstrate how even small changes in marbling can have a profound effect on USDA beef quality grade distribution.

Figure 1.

National Beef Quality Audit—2022: Percentage frequency of beef carcasses grading Prime, Choice or Select by month.

Figure 7.

National Beef Quality Audit—2022: Percentage frequency of dark cutters by month.

Figure 2.

National Beef Quality Audit—2022: Mean marbling scores (400 = Small⁰⁰, 500 = Modest⁰⁰, and 600 = Moderate⁰⁰; U.S. Department of Agriculture, 2017) by month.

We detected seasonal trends for the USDA beef yield grading factors including fat thickness (Figure 3), carcass weights (Figure 4), and ribeye area (Figure 5). Seasonal trends in these carcass traits also were reported in the NBQA-2011 (Gray et al., 2012) and NBQA-2016 (Boykin et al., 2017b). The months of Sep-21, Oct-21, Nov-21, Dec-21, and Jan-22 were numerically the greatest for fat thickness (Figure 3). The heaviest numerical carcass weights were observed in Oct-21, Nov-21, Dec-21, Jan-22, and Feb-22 (Figure 4). The greatest numerical ribeye areas numerically were found in Sep-21, Oct-21, Nov-21, Dec-21, and Jan-22 (Figure 6). It is probably not surprising that there were common months beginning in the fall and continuing through the winter months where these carcass traits were the fattest, heaviest, and had the greatest ribeye areas.

Figure 3.

National Beef Quality Audit—2022: Mean adjusted fat thickness, cm, by month.

Figure 4.

National Beef Quality Audit—2022: Mean HCW, kg, by month.

Figure 5.

National Beef Quality Audit—2022: Mean REA, cm², by month.

Figure 6.

National Beef Quality Audit—2022: Percentage frequency of beef carcasses that were Yield Grade 4 or 5 by month.

Figure 6 displays the frequency distributions, by month, of YG 4 and YG 5 carcasses. Four months stand out with numerically lower percentages of YG 4 and YG 5: Jul-21, Aug 21, May-22, and Jun-22. As would be expected, these were months where the fat thicknesses (Figure 3) were numerically among the lowest and carcass weights (Figure 4) were numerically among the lightest. Seasonality patterns exist in factors that determine USDA beef quality and yield grades, and Figures 1 through 6 clearly demonstrate these.

The percentage frequency of dark cutting carcasses, by month, is reported in Figure 7. Boykin et al. (2017b) reported clear trends for dark cutting carcasses that began with the influence of heat stress for the summer months of July and August followed by the impact of the weather changes brought about by the arrival of fall where September and October where the highest months, numerically, with a gradual decline in dark cutting carcasses in November and December. The present data (Figure 7) do not present the exact same trends: Jul-21 was the highest numerical frequency of dark cutting carcasses followed by Sep-21 and Oct-21. What both the current study and that of Boykin et al. (2017b) agree on with respect to dark cutting carcasses is that, for the most part, the first 6 mo of the year, January through June, representing winter and spring, have the lowest numerical frequencies of dark cutters. From a weather-related stress standpoint, it may be that even through weather changes do occur during the winter and spring seasons, cattle may be better prepared to adapt to them compared to the effects of heat in the summer and the first changes from heat to cold in the fall. Kreikemeier et al. (1998) found that the highest incidences of dark cutting beef in their study were in August, September, and October. In a comprehensive study of dark cutting beef in Australia, Steel et al. (2022) found that factors in addition to weather played a role in the incidence of dark cutting.

Comparison to In-Plant Data

Comparisons between the instrument grading portion of NBQA-2022, which included 4,418,768 total carcasses, and the in-plant portion (Mayer et al., 2024), which included 9,746 carcasses, are reported in Table 5. USDA YG was the same between the two sections and HCW was also very consistent (401.9 kg in-plant vs. 400.6 kg instrument grading). Mean ribeye area was 0.6 cm² greater for instrument grading (91.6 cm²) when compared to in-plant (91.0 cm²). The small differences between the two studies were a lower fat thickness reported for the in-plant assessment (-0.06 cm) and slightly lower numerical marbling scores reported for the in-plant versus instrument grading assessment (Small⁹⁸ vs. Modest¹⁶, respectively). Comparisons in frequency of USDA QG are illustrated in Figure 8. Primary differences between in-plant and instrument grading can be observed in the percentages of Choice (69.2% and 74.7%, respectively) and other carcasses (6.8% and 1.4%, respectively). Even so, the percentages of Select between the two assessments are within 0.6% and Prime is within 0.7%. Comparisons in the frequency distribution of USDA YG are shown in Figure 9. Of the five USDA YG classifications, none was off by more than 3.3% between both data sets, and three of them were within 0.2 percentage points of the other.

Table 5.

National Beef Quality Audit—2022: Means for USDA beef carcass grade traits between in-plant and instrument data

Trait	In-plant survey1 (n = 9,746)	Instrument data (n = 4,418,768)
USDA Quality Grade component
Marbling Score2	498	516
USDA Yield Grade and components
USDA Yield Grade	3.3	3.3
Adjusted fat thickness, cm	1.49	1.55
HCW, kg	401.9	400.6
Ribeye area, cm2	91.0	91.6
KPH, %	2.5	2.1

¹Mayer et al. (2024).

²400 = Small⁰⁰, 500 = Modest⁰⁰, 600 = Moderate⁰⁰, 700 = Slightly Abundant⁰⁰, 800 = Moderately Abundant⁰⁰, and 900 = Abundant⁰⁰ (U.S. Department of Agriculture, 2017).

Figure 8.

National Beef Quality Audit—2022: Percentage frequency comparison between in plant and instrument grading of beef carcasses grading Prime, Choice, Select or other.

Figure 9.

National Beef Quality Audit—2022: Percentage frequency comparison between in plant and instrument grading of beef carcasses grading Yield Grade 1, Yield Grade 2, Yield Grade 2, Yield Grade 4 or Yield Grade 5.

During NBQA-2016, Boykin et al. (2017b) noted similar findings in how close data appeared between the two data collection methodologies. Because there are no noticeable inconsistencies between in-plant and instrument grading, it allows for further validation of the outcomes of previous NBQAs that occurred before implementation of instrument grading in 2011.

Comparison to Previous Instrument Grading

Table 6 reports the instrument grading information from the past three NBQAs: Gray et al. (2012), Boykin et al. (2017b), and the present study. In every trait measured, gradual increases indicate that beef carcasses are becoming heavier, fatter, with greater ribeye areas containing more marbling. In-plant beef carcass assessments from the past three NBQAs (Moore et al., 2012; Boykin et al., 2017a; Mayer et al., 2024) support these findings, and it is apparent that genetic, management, and economic factors are driving these trends in the U.S. cattle population.

Table 6.

National Beef Quality Audit—2022: Means for USDA carcass grade traits using instrument data across the past three National Beef Quality Audits

	NBQA-20111 (n = 2,427,074)	NBQA-20162 (n = 4,544,635)	NBQA-2022 (n = 4,418,768)
USDA Quality Grade component
Marbling Score3	449	475	516
USDA Yield Grade and components
USDA Yield Grade	2.86	3.10	3.26
Adjusted fat thickness, cm	1.20	1.37	1.55
HCW, kg	371.28	393.6	400.65
Ribeye area, cm2	88.45	88.9	91.6

¹NBQA-2011 (Gray et al., 2012).

²NBQA-2016 (Boykin et al., 2017b).

³300 = Slight ⁰⁰, 400 = Small⁰⁰, 500 = Modest⁰⁰, 600 = Moderate^00, 700 = Slightly Abundant⁰⁰, 800 = Moderately Abundant⁰⁰, and 900 = Abundant⁰⁰ (U.S. Department of Agriculture, 2017).

Other Considerations

During NBQA-2016, Boykin et al. (2017b) reported a shift in acceptable HCW for USDA certified beef programs from what was noted during NBQA-2011 (Gray et al., 2012). The range of 272.2 to 477.3 kg is still the accepted weight range for most certified programs during the timespan of NBQA-2022 and encompasses all G programs that appeared during carcass sampling. In the present study, 93.4% of carcasses fell within that accepted carcass weight range; NBQA-2016 (Boykin et al., 2017b) reported 95.0% of carcasses within that same interval. Additional HCW frequencies for current industry weight classification are provided. For native steer carcasses, less than or equal to 385.4 kg = 21.7%, 385.5 to 453.5 kg = 54.8%, 453.6 to 498.9 kg = 19.4%, and greater than 498.9 kg = 4.1%. For native heifer carcasses, less than or equal to 385.4 kg = 56.8%, 385.5 to 453.5 kg = 38.8%, 453.6 to 498.9 kg = 3.9%, and greater than 498.9 kg = 0.5%.

Much like the increases in mean HCW, it comes as no surprise that more carcasses are exceeding maximum ribeye area specifications. The acceptable range of ribeye area for certified beef programs falls between 64.5 cm² and 103.2 cm². 84.2% of surveyed carcasses were within the optimal ribeye area range, a numerical decrease from the reported 86.7% during NBQA-2016 (Boykin et al., 2017b).

CONCLUSIONS

Instrument grading data from large commercial beef processors allowed for valuable observations over the course of one 12-mo period during NBQA-2022. Marbling scores in NBQA-2022 are the highest observed when compared to all previous audits, with great increases in the percentage of USDA Prime and Choice carcasses. Additionally, mean values for USDA YG, HCW, ribeye area, and fat thickness all demonstrated increases, indicating growth in overall size, muscling, and fat deposition in fed cattle. The data also exhibited a large decrease in the percentage of purebred dairy carcasses when compared to NBQA-2016 possibly due to the increase in beef on dairy cattle being currently marketed. In comparison to previous instrument grading evaluations in the NBQA, the observations and changes in average carcass characteristics in the current study demonstrate a similar trend. The results of this study provide an updated overview of the carcass characteristics associated with beef quality and yield grading within the U.S. fed beef industry.

Author Contributions

Thachary Mayer (Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review & editing), Sydni Borders (Investigation, Writing - review & editing), Trent Schwartz (Investigation, Writing - review & editing), Kerri Gehring (Conceptualization, Investigation, Methodology, Writing - original draft, Writing - review & editing), Davey Griffin (Investigation, Writing - review & editing), Christopher Kerth (Formal analysis, Investigation, Methodology, Writing - review & editing), Keith Belk (Conceptualization, Methodology, Writing - review & editing), John Scanga (Investigation, Writing - review & editing), Mahesh Nair (Investigation, Writing - review & editing), Morgan Pfeiffer (Conceptualization, Methodology, Writing - review & editing), Gretchen Mafi (Conceptualization, Methodology, Writing - review & editing), Keayla Harr (Investigation, Writing - review & editing), Ty Lawrence (Conceptualization, Writing - review & editing), Travis Tennant (Investigation, Writing - review & editing), Loni Lucherk (Investigation, Writing - review & editing), Travis O'Quinn (Investigation, Writing - review & editing), Erin Beyer (Investigation, Writing - review & editing), Phillip Bass (Investigation, Writing - review & editing), Lyda Garcia (Investigation, Writing - review & editing), Benjamin Bohrer (Investigation, Writing - review & editing), Jessica Pempek (Investigation, Writing - review & editing), Andrea Garmyn (Investigation, Writing - review & editing), Robert Maddock (Investigation, Writing - review & editing), Chad Carr (Investigation, Writing - review & editing), Dean Pringle (Investigation, Writing - review & editing), Tracy Scheffler (Investigation, Writing - review & editing), Jason Scheffler (Investigation, Writing - review & editing), Alexander Stelzleni (Investigation, Writing - review & editing), John Gonzalez (Investigation, Writing - review & editing), Keith Underwood (Investigation, Writing - review & editing), Bailey Harsh (Investigation, Writing - review & editing), Crystal Waters (Investigation, Writing - review & editing), and Jeffrey Savell (Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Visualization, Writing - original draft, Writing - review & editing)

Conflict of Interest statement

There are no known conflicts of interest by any of the authors.