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Smith CL, Thompson TW, Harr K, Goretska M, Mayer TR, Schwartz TE, Borders SE, Gehring KB, Bass PD, Pfeiffer MM, Mafi GG, Pendell DL, Morgan JB, Griffin DB, Savell JW, Scanga JA, Nair MN, Belk KE. National Beef Quality Audit-2022 Phase 1: face-to-face and digital interviews. Transl Anim Sci 2024; 8:txae034. [PMID: 38562215 PMCID: PMC10983070 DOI: 10.1093/tas/txae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/11/2024] [Indexed: 04/04/2024] Open
Abstract
The National Beef Quality Audit (NBQA) has been conducted regularly since 1991 to assess and benchmark quality in the U.S. beef industry, with the most recent iteration conducted in 2022. The goal of NBQA Phase I is to evaluate what needs to be managed to improve beef quality and demand. Interviews (n = 130) of industry personnel were conducted with the aid of routing software. In total, packers (n = 24), retailers (n = 20), further processors (n = 26), foodservice (n = 18), and allied government agencies and trade organizations (n = 42) were interviewed. Interviews were routed in software based on interviewee involvement in either the fed steer and heifer market cow and bull sectors, or both. Interviews were structured to elicit random responses in the order of determining "must-have" criteria (quality factors that are required to make a purchase), best/worst ranking (of quality factors based on importance), how interviewees defined quality terms, a strength, weakness, opportunities, threats (SWOT) analysis, general beef industry questions, and sustainability goals (the latter four being open-ended). Quality factors were 1) visual characteristics, 2) cattle genetics, 3) food safety, 4) eating satisfaction, 5) animal well-being, 6) weight and size, and 7) lean, fat, and bone. Best/worst analysis revealed that "food safety" was the most (P < 0.05) important factor in beef purchasing decisions for all market sectors and frequently was described as "everything" and "a way of business." Culture surrounding food safety changed compared to previous NBQAs with interviewees no longer considering food safety as a purchasing criterion, but rather as a market expectation. The SWOT analysis indicated that "eating quality of U.S. beef" was the greatest strength, and cited that educating both consumers and producers on beef production would benefit the industry. Irrespective of whether companies' products were fed or market cow/bull beef, respondents said that they believed "environmental concerns" were among the major threats to the industry. Perceived image of the beef industry in the market sectors has improved since NBQA-2016 for both fed cattle and market cow/bull beef.
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Affiliation(s)
- Colton L Smith
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA
| | - Tyler W Thompson
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA
| | - Keayla Harr
- Department of Animal and Food Sciences, Oklahoma State University, Stillwater, OK, USA
| | - Macey Goretska
- Department of Animal and Food Sciences, Oklahoma State University, Stillwater, OK, USA
| | - Thachary R Mayer
- Department of Animal Science, Texas A&M University, College Station, TX, USA
| | - Trent E Schwartz
- Department of Animal Science, Texas A&M University, College Station, TX, USA
| | - Sydni E Borders
- Department of Animal Science, Texas A&M University, College Station, TX, USA
| | - Kerri B Gehring
- Department of Animal Science, Texas A&M University, College Station, TX, USA
| | - Phil D Bass
- Department of Animal Sciences, University of Idaho, Moscow, ID, USA
| | - Morgan M Pfeiffer
- Department of Animal and Food Sciences, Oklahoma State University, Stillwater, OK, USA
| | - Gretchen G Mafi
- Department of Animal and Food Sciences, Oklahoma State University, Stillwater, OK, USA
| | - Dustin L Pendell
- Department of Agricultural Economics, Kansas State University, Manhattan, KS, USA
| | | | - Davey B Griffin
- Department of Animal Science, Texas A&M University, College Station, TX, USA
| | - Jeffrey W Savell
- Department of Animal Science, Texas A&M University, College Station, TX, USA
| | - John A Scanga
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA
| | - Mahesh N Nair
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA
| | - Keith E Belk
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, USA
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Borders SE, Schwartz TE, Mayer TR, Gehring KB, Griffin DB, Kerth CR, Belk KE, Edwards-Callaway L, Scanga JA, Nair MN, Morgan JB, Douglas JB, Pfeiffer MM, Mafi GG, Harr KM, Lawrence TE, Tennant TC, Lucherk LW, O’Quinn TG, Beyer ES, Bass PD, Garcia LG, Bohrer BM, Pempek JA, Garmyn AJ, Maddock RJ, Carr CC, Pringle TD, Scheffler TL, Scheffler JM, Stelzleni AM, Gonzalez JM, Underwood KR, Harsh BN, Waters CM, Savell JW. National Beef Quality Audit-2022: Transportation, mobility, live cattle, and hide assessments to determine producer-related defects that affect animal welfare and the value of market cows and bulls at processing facilities. Transl Anim Sci 2024; 8:txae033. [PMID: 38616995 PMCID: PMC11015891 DOI: 10.1093/tas/txae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/08/2024] [Indexed: 04/16/2024] Open
Abstract
The National Beef Quality Audit (NBQA)-2022 serves as a benchmark of the current market cow and bull sectors of the U.S. beef industry and allows comparison to previous audits as a method of monitoring industry progress. From September 2021 through May 2022, livestock trailers (n = 125), live animals (n = 5,430), and post-slaughter hide-on animals (n = 6,674) were surveyed at 20 commercial beef processing facilities across the U.S. Cattle were transported in a variety of trailer types for an average distance of 490.6 km and a mean transport time of 6.3 h. During transit, cattle averaged 2.3 m2 of trailer space per animal indicating sufficient space was provided according to industry guidelines. Of all trailers surveyed, 55.3% transported cattle from an auction barn to a processing facility. When surveyed, 63.6% of all truck drivers reported to be Beef Quality Assurance certified. The majority (77.0%) of cattle were sound when evaluated for mobility. Mean body condition scores (9-point scale) for beef cows and bulls were 3.8 and 4.4, respectively, whereas mean body condition scores (5-point scale) for dairy cows and bulls were 2.3 and 2.6, respectively. Of the cattle surveyed, 45.1% had no visible live animal defects, and 37.9% had only a single defect. Of defects present in cows, 64.6% were attributed to an udder problem. Full udders were observed in 47.5% of all cows. Nearly all cattle were free of visible abscesses and knots (97.9% and 98.2%, respectively). No horns were observed in 89.4% of all cattle surveyed. Beef cattle were predominantly black-hided (68.9% and 67.4% of cows and bulls, respectively). Holstein was the predominant dairy animal observed and accounted for 85.7% of the cows and 98.0% of the bulls. Only 3.1% of all animals had no form of identification. Findings from the NBQA-2022 show improvements within the industry and identify areas that require continued education and research to improve market cow and bull welfare and beef quality.
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Affiliation(s)
- Sydni E Borders
- Department of Animal Science, Texas A&M AgriLife Research, Texas A&M University, College Station, TX, 77843-2471, USA
| | - Trent E Schwartz
- Department of Animal Science, Texas A&M AgriLife Research, Texas A&M University, College Station, TX, 77843-2471, USA
| | - Thachary R Mayer
- Department of Animal Science, Texas A&M AgriLife Research, Texas A&M University, College Station, TX, 77843-2471, USA
| | - Kerri B Gehring
- Department of Animal Science, Texas A&M AgriLife Research, Texas A&M University, College Station, TX, 77843-2471, USA
| | - Davey B Griffin
- Department of Animal Science, Texas A&M AgriLife Research, Texas A&M University, College Station, TX, 77843-2471, USA
| | - Christopher R Kerth
- Department of Animal Science, Texas A&M AgriLife Research, Texas A&M University, College Station, TX, 77843-2471, USA
| | - Keith E Belk
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, 80523-1171, USA
| | - Lily Edwards-Callaway
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, 80523-1171, USA
| | - John A Scanga
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, 80523-1171, USA
| | - Mahesh N Nair
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, 80523-1171, USA
| | - J Brad Morgan
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, 80523-1171, USA
| | - Jarrett B Douglas
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, 80523-1171, USA
| | - Morgan M Pfeiffer
- Department of Animal Science, Oklahoma State University, Stillwater, OK, 74078, USA
| | - Gretchen G Mafi
- Department of Animal Science, Oklahoma State University, Stillwater, OK, 74078, USA
| | - Keayla M Harr
- Department of Animal Science, Oklahoma State University, Stillwater, OK, 74078, USA
| | - Ty E Lawrence
- Department of Agricultural Sciences, West Texas A&M University, Canyon, TX, 79016, USA
| | - Travis C Tennant
- Department of Agricultural Sciences, West Texas A&M University, Canyon, TX, 79016, USA
| | - Loni W Lucherk
- Department of Agricultural Sciences, West Texas A&M University, Canyon, TX, 79016, USA
| | - Travis G O’Quinn
- Department of Animal Sciences and Industry, Kansas State University, Manhattan, KS, 66506, USA
| | - Erin S Beyer
- Department of Animal Sciences and Industry, Kansas State University, Manhattan, KS, 66506, USA
| | - Phil D Bass
- Department of Animal, Veterinary and Food Sciences, University of Idaho, Moscow, ID, 83844-2330, USA
| | - Lyda G Garcia
- Department of Animal Science, The Ohio State University, Columbus, OH, 43210, USA
| | - Benjamin M Bohrer
- Department of Animal Science, The Ohio State University, Columbus, OH, 43210, USA
| | - Jessica A Pempek
- Department of Animal Science, The Ohio State University, Columbus, OH, 43210, USA
| | - Andrea J Garmyn
- Department of Animal Science, Michigan State University, East Lansing, MI 48824, USA
| | - Robert J Maddock
- Department of Animal Sciences, North Dakota State University, Fargo, ND, 58108-6050, USA
| | - C Chad Carr
- Department of Animal Sciences, University of Florida, Gainesville, FL, 32611-0910, USA
| | - T Dean Pringle
- Department of Animal Sciences, University of Florida, Gainesville, FL, 32611-0910, USA
| | - Tracy L Scheffler
- Department of Animal Sciences, University of Florida, Gainesville, FL, 32611-0910, USA
| | - Jason M Scheffler
- Department of Animal Sciences, University of Florida, Gainesville, FL, 32611-0910, USA
| | | | - John M Gonzalez
- Animal & Dairy Science, University of Georgia, Athens, GA, 30602-6755, USA
| | - Keith R Underwood
- Department of Animal Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Bailey N Harsh
- Department of Animal Sciences, University of Illinois at Urbana - Champaign, Urbana, IL 61801, USA
| | - Crystal M Waters
- College of Agriculture, California State University, Chico, CA, 95929, USA
| | - Jeffrey W Savell
- Department of Animal Science, Texas A&M AgriLife Research, Texas A&M University, College Station, TX, 77843-2471, USA
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Moore CB, Bass PD, Green MD, Chapman PL, O'Connor ME, Yates LD, Scanga JA, Tatum JD, Smith GC, Belk KE. Establishing an appropriate mode of comparison for measuring the performance of marbling score output from video image analysis beef carcass grading systems. J Anim Sci 2010; 88:2464-75. [PMID: 20348376 DOI: 10.2527/jas.2009-2593] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
A beef carcass instrument grading system that improves accuracy and consistency of marbling score (MS) evaluation would have the potential to advance value-based marketing efforts and reduce disparity in quality grading among USDA graders, shifts, and plants. The objectives of this study were to use output data from the Video Image Analysis-Computer Vision System (VIA-CVS, Research Management Systems Inc., Fort Collins, CO) to develop an appropriate method by which performance of video image analysis MS output could be evaluated for accuracy, precision, and repeatability for purposes of seeking official USDA approval for using an instrument in commerce to augment assessment of quality grade, and to use the developed standards to gain approval for VIA-CVS to assist USDA personnel in assigning official beef carcass MS. An initial MS output algorithm was developed (phase I) for the VIA-CVS before 2 separate preliminary instrument evaluation trials (phases II and III) were conducted. During phases II and III, a 3-member panel of USDA expert graders independently assigned MS to 1,068 and 1,242 stationary carcasses, respectively. Mean expert MS was calculated for each carcass. Additionally, a separate 3-member USDA expert panel developed a consensus MS for each carcass in phase III. In phase II, VIA-CVS stationary triple-placement and triple-trigger instrument repeatability values (n = 262 and 260, respectively), measured as the percentage of total variance explained by carcasses, were 99.9 and 99.8%, respectively. In phases II and III, 95% of carcasses were assigned expert MS for which differences between individual expert MS, and for which the consensus MS in phase III only, was < or = 96 MS units. Two differing approaches to simple regression analysis, as well as a separate method-comparability analysis that accommodates error in both dependent and independent variables, were used to assess accuracy and precision of instrument MS predictions vs. mean expert MS. Method-comparability analysis was more appropriate in assessing the bias and precision of instrument MS predictions. Ether-extractable fat percentages (n = 257; phase II) differed among MS (P < 0.05) but were not suitable to predict or validate assigned MS. The performance and reproducibility of expert MS assignment in future evaluations was considered, and an official USDA performance standard was established, to which an instrument must conform to be approved for official on-line MS assessment. The VIA-CVS subsequently was approved to assign MS to carcasses on-line after completion of a 2006 USDA instrument approval trial conducted according to methods developed during completion of this study.
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Affiliation(s)
- C B Moore
- Cargill Meat Solutions, Wichita, KS 67219, USA
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