J. W. Oltjen

Performance, residual feed intake, digestibility, carcass traits, and profitability of Angus-Hereford steers housed in individual or group pens.

Even though the concept of residual feed intake (RFI) is well accepted, several questions remain regarding other traits that may be associated with selection for decreased RFI. These include DM digestibility, carcass composition, profitability, and performance. The objective of this study was to investigate the difference in those traits between low- and high-RFI cattle. Sixty Angus x Hereford crossbred steers (296 kg of initial BW) were fed a corn-based finishing ration (1.68 Mcal of NE(m)/kg, 13% CP on a DM basis) during 2 periods of 60 d each. For both phases, the regression equation fitted without the intercept (not statistically significant) was DMI (kg/d) = 0.0701 x BW(0.75) + 2.714 x ADG, r(2) = 0.42. The 15 greatest and least RFI steers were classed as high and low RFI groups. There were no differences between low and high RFI groups for days on feed (162 vs. 168 d), slaughter weight (503 vs. 511 kg), HCW (317 vs. 315 kg), LM area (76.5 vs. 77.1 cm(2)), backfat (1.23 vs. 1.27 cm), KPH (3.1 vs. 3.7%), quality grade (average Choice for both groups), or carcass fat (32.4 vs. 33.1%). Visceral organ masses and abdominal fat were similar for low and high RFI groups (32.25 vs. 31.24 kg and 37.48 vs. 36.95 kg, respectively). These results do not support the existence of major differences in composition and organ mass between low and high RFI steers at slaughter. The RFI grouping had a significant effect on DMI, G:F, and RFI values. Stepwise regression showed that G:F alone or DMI and ADG together explained 98.5% of the variance in cost of BW gain, whereas RFI alone explained only 18%. We conclude that RFI is less useful than G:F as an indicator of feedlot efficiency and profitability.

Growth-promoting technologies decrease the carbon footprint, ammonia emissions, and costs of California beef production systems

Increased animal performance is suggested as one of the most effective mitigation strategies to decrease greenhouse gas (GHG) and ammonia (NH(3)) emissions from livestock production per unit of product produced. Little information exists, however, on the effects of increased animal productivity on the net decrease in emission from beef production systems. A partial life cycle assessment (LCA) was conducted using the Integrated Farm System Model (IFSM) to estimate GHG and NH(3) emissions from representative beef production systems in California that use various management technologies to enhance animal performance. The IFSM is a farm process model that simulates crop growth, feed production, animal performance, and manure production and handling through time to predict the performance, economics, and environmental impacts of production systems. The simulated beef production systems compared were 1) Angus-natural, with no use of growth-enhancing technologies, 2) Angus-implant, with ionophore and growth-promoting implant (e.g., estrogen/trenbolone acetate-based) application, 3) Angus-ß2-adrenergic agonists (BAA; e.g., zilpaterol), with ionophore, growth-promoting implant, and BAA application, 4) Holstein-implant, with growth implant and ionophore application, and 5) Holstein-BAA, with ionophore, growth implant, and BAA use. During the feedlot phase, use of BAA decreased NH(3) emission by 4 to 9 g/kg HCW, resulting in a 7% decrease in NH(3) loss from the full production system. Combined use of ionophore, growth implant, and BAA treatments decreased NH(3) emission from the full production system by 14 g/kg HCW, or 13%. The C footprint of beef was decreased by 2.2 kg carbon dioxide equivalent (CO(2)e)/kg HCW using all the growth-promoting technologies, and the Holstein beef footprint was decreased by 0.5 kg CO(2)e/kg HCW using BAA. Over the full production systems, these decreases were relatively small at 9% and 5% for Angus and Holstein beef, respectively. The growth-promoting technologies we evaluated are a cost-effective way to mitigate GHG and NH(3) emissions, but naturally managed cattle can bring a similar net return to Angus cattle treated with growth-promoting technologies when sold at an 8% greater premium price.

Estimating feed efficiency: Evaluation of mathematical models to predict individual intakes of steers fed in group pens

To evaluate feed efficiency using residual feed intake (RFI), it is necessary to measure and record daily feed intake for each animal. This can be accomplished by housing them in individual pens or by using sophisticated electronic feeders in group pens. All the available options are very expensive and very laborious; therefore, several researchers have developed methods to predict individual DMI of cattle fed in group pens. Three intake models were tested with a data set of 60 Angus × Hereford steers fed a corn-based finishing diet in both group and individual pens. After the first 60 d (period 1) of the study, animals were switched from group to individual pens, and then vice versa for another 60 d (period 2); thus, the entire feeding trial was 120 d long. No difference was observed in DMI between periods for steers fed individually (period 1 = 10.9 kg/d and period 2 = 11.2 kg/d, P = 0.44), but a difference was observed in group pens (period 1 = 12.7 kg/d and period 2 = 10.9 kg/d, P < 0.01). In addition, no difference (P ≥ 0.15) was observed in carcass characteristics, such as HCW, dressing percentage, quality grade, LM area, KPH percentage, yield grade, or backfat between RFI groups (low, medium, and high). Average daily gain and G:F were not different between RFI groups within each period (P ≥ 0.06), but there were period differences (P < 0.001). Models 1 and 2 were based on growth, carcass composition, and nutrient requirements, whereas model 3 was based on the heterogeneity of pen intakes when cattle were rotated through the pens on a daily basis. Models 1 and 2 were forced through the mean observed DMI, so the mean bias was zero, but they were not precise, with a slope bias greater than 50%. Model 3 showed low accuracy (mean bias = 20%), but it was precise, with a slope bias of 21%. Because RFI is the error of the DMI equation, any inaccuracy when estimating intake will lead to a bias in the prediction of RFI. In conclusion, these models could be used to predict mean DMI, but they were not adequate for estimating RFI.

Predicting fat, lean and the weights of primal cuts for growing pigs of different genotypes and sexes using computed tomography.

The aim of the present study was to find single equations to predict the amounts of fat, lean, and the weights of the primal cuts (ham, loin, belly, and shoulder) as well as ham composition of pigs from 30 to 120 kg BW of different genotypes (GEN; Exp. 1) and sexual conditions (SEX; Exp. 2). Two types of regression equations, taking into account different work situations, were developed: 1) research applications, using computed tomography (CT) parameters, and 2) potential on-farm applications, which could be obtained using easily accessible equipment. Two data sets were used: Exp. 1 included 90 gilts from 3 different GEN: 30 Duroc × (Landrace × Large White), 30 Pietrain × (Landrace × Large White), and 30 Landrace × Large White, and Exp. 2 included 92 Pietrain × (Landrace × Duroc) pigs of different SEX: 24 each of females, entire males, castrated males, and 20 immunocastrated males. Pigs were fully CT scanned in vivo at 30, 70, 100, and 120 kg BW. A subsample of pigs of each GEN ( = 5) or SEX ( = 4) were slaughtered at 30, 70, and 100 kg BW, and all remaining pigs were slaughtered after weighing and scanning at 120 kg BW. For all the slaughtered pigs, the 4 main cuts were fully (GEN) or partially dissected (SEX). CT images were analyzed and used to predict the lean and fat contents as well as the weights of the primal cuts and the composition of the ham. Total amounts of fat and lean for both populations were predicted with high levels of accuracy ( = 0.994 and 0.993, respectively) and proportions of random error for GEN and SEX effects (0.998 and 0.946 for the fat and 0.997 and 0.836 for the lean predictions, respectively). Moreover, the composition of ham (fat, lean, and bone) was very well predicted with high proportions (> 80%) of random error for GEN and SEX effect using CT and potential on-farm predictors.

A methodological approach to estimate the lactation curve and net energy and protein requirements of beef cows using nonlinear mixed-effects modeling1

The objective of this study was to evaluate methods to predict the secretion of milk and net energy and protein requirements of beef cows (Bos indicus and B. taurus) after approximately 1 mo postpartum using nonlinear mixed-effect modeling (NLME). Twenty Caracu × Nellore (CN) and 10 Nellore (NL) cows were inseminated to Red Angus bulls, and 10 Angus × Nellore (AN) were bred to Canchim bulls. Cows were evaluated from just after calving (25 ± 11 d) to weaning (220 d). Milk yield was estimated by weighing calves before and after suckling (WSW) and by machine milking (MM) methods at 25, 52, 80, 109, 136, 164, 193, and 220 ± 11 d of lactation. Brody and simple linear equations were consecutively fitted to the data and compared using information criteria. For the Brody equation, a NLME model was used to estimate all lactation profiles incorporating different sources of variation (calf sex and breed of cow, cow as a nested random effect, and within-cow auto-correlation). The CV for the MM method (29%) was less than WSW (45%). Consequently, the WSW method was responsible for reducing the variance about 1.5 times among individuals, which minimized the ability to detect differences among cows. As a result, only milk yield MM data were used in the NLME models. The Brody equation provided the best fit to this dataset, and inclusion of a continuous autoregressive process improved fit (P < 0.01). Milk, energy and protein yield at the beginning of lactation were affected by cow genotype and calf sex (P < 0.001). The exponential decay of the lactation curves was affected only by genotype (P < 0.001). Angus × Nellore cows produced 15 and 48% more milk than CN and NL during the trial, respectively (P < 0.05). Caracu × Nellore cows produced 29% more milk than NL (P < 0.05). The net energy and net protein requirements for milk yield followed a similar ranking. Male calves stimulated their dams to produce 11.7, 11.4, and 11.9% more milk, energy and protein, respectively (P < 0.05). The MM method is better than the WSW technique to detect genetic or environmental differences in milk yield among beef cows. The data obtained by the MM method and analyzed by NLME models allows the inclusion of fixed effects, random effects and an auto-regressive process in lactation equations to describe lactation curves and net energy and protein requirements. The NLME is a powerful tool to describe differences in the secretion of milk due to heterosis and cell mammary external stimulus in beef cows.