Predicting fat cover in beef cattle to make on-farm management decisions: a review of assessing fat and of modeling fat deposition.

Demands of domestic and foreign market specifications of carcass weight and fat cover, of beef cattle, have led to the development of cattle growth models that predict fat cover to assist on-farm managers make management decisions. The objectives of this paper are 4-fold: 1) conduct a brief review of the biological basis of adipose tissue accretion, 2) briefly review live and carcass assessments of beef cattle, and carcass grading systems used to develop quantitative compositional and quality indices, 3) review fat deposition models: Davis growth model (DGM), French National Institute for Agricultural Research growth model (IGM), Cornell Value Discovery System (CVDS), and BeefSpecs drafting tool (BeefSpecsDT), and 4) appraise the process of translating science and practical skills into research/decision support tools that assist the Beef industry improve profitability. The r2 for live and carcass animal assessments, using several techniques across a range of species and traits, ranged from 0.61 to 0.99 and from 0.52 to 0.99, respectively. Model evaluations of DGM and IGM were conducted using Salers heifers (n = 24) and Angus-Hereford steers (n = 15) from an existing publication and model evaluations of CVDS and BeefSpecsDT were conducted using Angus steers (n = 33) from a research trial where steers were grain finished for 101 d in a commercial feedlot. Evaluating the observed and predicted fat mass (FM) is the focus of this review. The FM mean bias for Salers heifers were 7.5 and 1.3 kg and the root mean square error of prediction (RMSEP) were 31.2 and 27.8 kg and for Angus-Hereford steers the mean bias were -4.0 and -10.5 kg and the RMSEP were 9.14 and 21.5 kg for DGM and IGM, respectively. The FM mean bias for Angus steers were -5.61 and -2.93 kg and the RMSEP were 12.3 and 13.4 kg for CVDS and BeefSpecsDT, respectively. The decomposition for bias, slope, and deviance were 21%, 12%, and 68% and 5%, 4%, and 91% for CVDS and BeefSpecsDT, respectively. The modeling efficiencies were 0.38 and 0.27 and the models were within a 20 kg level of tolerance 91% and 88% for CVDS and BeefSpecsDT, respectively. Fat deposition models reported in this review have the potential to assist the beef industry make on-farm management decisions on live cattle before slaughter and improve profitability. Modelers need to continually assess and improve their models but with a caveat of 1) striving to minimize inputs, and 2) choosing on-farm inputs that are readily available.

Prediction of ossification from live and carcass traits in young beef cattle: model development and evaluation.

Physiological maturity, measured as carcass ossification [10 unit increments (100, 110, 120, …)], is used by the United States Department of Agriculture and the Meat Standards Australia carcass grading systems to reflect age-associated differences in beef tenderness and determine producer payments. In most commercial cattle herds, the exact age of animals is unknown; thus, prediction of ossification in association with phenotypic prediction systems has the capacity to assist producer decision making to improve carcass and eating quality. This study developed and evaluated prediction equations that use either live animal or carcass traits to predict ossification for use in phenotypic prediction systems to predict meat quality. The average ossification in the model development dataset was 138 with a SD of 21 and a range between 100 and 200. Model development involved regressing various combinations of live animal traits: age at recording, sex, live weight (BW), average daily gain, ultrasound scanned eye muscle area, 12/13th rib and subcutaneous P8 rump fat thickness; or carcass traits: age at slaughter, sex, hot standard carcass weight (HSCW), carcass eye muscle area, marble score, rib, and P8 rump fat (CP8) thickness, against ossification. The models were challenged with data from 3 independent datasets: 1) Angus steers produced by divergent selection for visual muscle score; 2) temperate (Angus, Hereford, Shorthorn and Murray Grey) steers and heifers; and 3) tropically adapted (Brahman and Santa Gertrudis) steers and heifers. Five models with adjusted R2adj above 0.55 were evaluated. When challenged with dataset 1, the absolute mean bias (MB) and root mean square error of prediction (RMSEP) ranged from 0.1 to 4.2, and 9.8 to 10.7, which are within the bounds of the 10 point increment on the ossification scale. When subsequently challenged with dataset 2, MB and RMSEP ranged from 2.8 to 13.4, and 19.6 to 23.7, respectively; and with dataset 3, MB and RMSEP ranged from 14.4 to 17.5, and 23.3 to 31.9, respectively. Generally, when compared in relation to the ossification scale, all evaluated models had similar accuracy. For predicting meat quality, the model containing live animal traits considered most useful was [85.35 + 0.16 × BW + 10.94 × sex - 0.09 × sex × BW (adjusted R2 = 0.59; SE = 13.51)] and the most useful model containing carcass traits was [107.15 + 11.53 × sex + 1.10 × CP8 + 0.16 × HSCW - 0.15 × sex × HSCW (adjusted R2 = 0.60; SE = 13.39)].

Prediction of stage of pregnancy in prolific sheep using ultrasound measurement of fetal bones.

The use of ultrasound to estimate stage of pregnancy was assessed in 32 ewes of a prolific genotype carrying 7 singleton fetuses and 9 twin, 10 triplet and 6 quadruplet litters that were scanned on six occasions from 60 to 120 days of gestation. At least one ultrasound measurement per ewe of fetal metacarpal bone length (MCL), biparietal diameter (BPD), or of both bones was made on over 90% of attempts (n = 152). Measurement of MCL was made on 78% of attempts (n = 371), of BPD on 73% of attempts, and of both bones on 62% of attempts. The equation developed from BPD (mean absolute error (MAE) = 3.2 days) was similar to that developed from measurement of MCL (MAE = 3.3 days) in its capacity to predict stage of pregnancy. Accuracy of prediction was improved using equations that included mean values within litters for BPD (MAE = 2.5 days) and MCL (MAE = 2.6 days). Further improvement in predictive capacity was achieved using multiple regression equations developed from measurement of both bones (individual fetuses: MAE = 2.6 days; equations including mean values within litters: MAE = 2.2 days). The results demonstrate that ultrasound can be used to estimate stage of pregnancy in prolific ewes, and that the use of mean values for bone measurements from different fetuses within litters and/or measurement of bones with different growth allometry can increase the reliability of estimates. The utility of the procedure depends on the number of fetuses measured per ewe, the number of bones measured per fetus and, hence, the time required to measure bones and the degree of accuracy required.