PART 2 : MEASUREMENT AND YIELD OF PRODUCT IN THE BEEF INDUSTRY
By Blake A . Foraker , Ph . D ., Assistant Professor in Meat Science , Texas Tech University
This is the second in a series of three articles exploring the Yield Grade ( YG ) formulation and , specifically , its deficiencies in determining actual product measurement . A third article will explain how the evolution of cattle type and management may necessitate an update of the current tool to better characterize today ’ s cattle . This article series , on behalf of the Red Meat Yield Round Table , aims to raise awareness of current carcass yield assessments and make advancements more accessible to producers who select higher-value cattle and receive accurate recognition for true carcass yield . Understanding Product Yield
In 1960 , a group of scientists at Texas A & M University fabricated 162 beef carcasses to predict the percentage of closely trimmed retail cuts on a bone-in and boneless basis from the round , loin , rib and chuck primals . They evaluated a variety of factors to develop such a prediction , including body length , round plumpness index , round circumference , and ratio of major to minor retail cuts . Ultimately , the most predictive model included four factors : a single measurement of external fat thickness , surface area of the ribeye , hot carcass weight , and percentage of kidney , pelvic and heart ( KPH ) fat . This research served as the basis for the USDA YG system introduced in June 1965 .
External fat is the predominant fat depot in carcasses from fed beef cattle and is highly related to the percentage of retail cuts . In beef carcasses , fat thickness measurement is obtained between the 12th and 13th ribs at a distance from the vertebral column that is three-quarters the length of the ribeye . Not all external fat negatively influences retail cut yield , as some external fat is left on many products through the supply chain ( e . g ., New York strip steak ). Today ’ s beef industry produces subprimals and retail cuts to a much more closely trimmed , specified external fat thickness ( e . g ., 1 / 8 inch ) than it did 50-plus years ago ( e . g ., 1 / 4 to 1 / 2 inch ). Hence , the current YG formulation may not accurately account for the considerable amount of external fat being removed in the present-day industry .
Ribeye area was included in the YG equation because , in tandem with hot carcass weight , it accounts for the proportion of muscle and bone in a carcass . The original YG system was based on the linear relationship between the four predicting factors and the percentage of retail cuts . As carcass weight increases , YG does not change if the ribeye area also increases at a directly proportional and linear rate . However , research of historical averages of the ribeye area and hot carcass weight has very clearly demonstrated that the biological relationship between these two factors is , in fact , very non-linear . Hot carcass weight has increased by a nearly constant 5 lbs every year for each of the past 50-plus years . Thus , the heavy carcass weights of modern-day cattle far exceed the proficiency of the equation to accurately model the biological relationship between ribeye area and hot carcass weight .
The original YG research speculated that KPH fat may be related to non-external fat depots , predominantly in the form of seam fat , or fat between muscles . At the time , KPH fat was expressed as a percentage of the carcass weight from an estimated visual appraisal , not an actual weight , which likely introduced error into the prediction . In today ’ s fed cattle population , the average percentage of actual ( not estimated ) KPH fat is 3 % to 3.5 % but can commonly exceed 6 % in certain cattle types . Some commercial processors remove KPH fat at the time of harvest ( after obtaining hot carcass weight ) and before grading to facilitate chilling and fabrication . Moreover , today ’ s fabrication styles include the separation of a greater number of whole muscle cuts and less corresponding seam fat than when YG was developed . Thus , the importance of seam fat to carcass yield must not be overlooked in modern cattle .
The development and implementation of camera grading systems in the U . S . have immensely improved the ability to obtain accurate measurements of fat thickness and ribeye area at the 12th rib . Measurement of KPH fat is much less standardized and more difficult given current industry practices . Many processors calculate and pay for cattle based on company yield grades determined by either a standardized or camera-predicted KPH fat percentage that is often much less than the industry average . Nevertheless , it is not the measurement of individual factors that contribute to the inaccuracy of YG ; the inaccuracy exists because the relationship between the factors is no longer predictive of the actual carcass yield in modern cattle .
Research efforts to better assess carcass yield are ongoing . One of the research challenges lies in defining the outcome — carcass yield . Packers generally define yield as the weight of finished subprimals ( i . e ., boxed beef ) and trimmings . Contrarily , retailers define yield as the weight of product packaged for retail display after sectioning subprimals , removing portions of fat and ( or ) bone , and accounting for purge lost during transport , aging and cutting . Some research has defined carcass yield as the composition of muscle , fat and bone in their pure form , which is generally very precise but not entirely reflective of how beef is merchandised in the supply chain . The way cuts are produced , or the cutout style , also influences the definition of yield , and the yields associated with differing fabrication schemes are highly company-specific and proprietary . Consumer demand greatly influences fat thickness specifications , bone-in versus boneless cutout style , and the popularity of lesser known but innovative cuts . The collection of yield data is also exorbitantly laborious and , consequently , expensive because the weight of every cut must be captured . It is also critical that error from imprecise knife work be avoided , as it is difficult to accurately predict inconsistent outcomes . Even so , yield data is most industry-relevant when it is collected at the rapid line-speed of commercial production .
The original YG was very soundly developed to reflect yield differences among cattle at the time . Even today , YG directionally separates high- and low-yielding groups of animals , especially because it accounts for external fat thickness . The real challenge exists when this information is utilized on an individual animal basis to make genetic progress or management decisions . In a modernday era of precision technology and big data management , the ability to develop not only a more accurate but also a substantially more dynamic and adaptive carcass yield assessment system is more possible than it has ever been . Such a system is likely to persist well into the future as cattle populations continue to adjust to suit industry needs .
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