UC Davis

Emerging precision livestock technologies can collect real-time data on individual animal feed intake and body weights in feedlot production systems. With this individual animal data, feedlot producers are transitioning towards managing and marketing animals individually to improve efficiency, carcass uniformity, and profitability. Accurate prediction of feedlot cattle growth and body composition is necessary for optimal management and marketing of feedlot cattle. Broadly, this paper will evaluate the application of Davis Growth Model (DGM) for predicting maintenance energy requirements, growth, and economic returns in feedlot systems. One hundred and twenty Angus-cross steers (initial body weight = 348 ± 25 kg) were allocated into two feeding groups: 1) 24 steers fed individually using Insentec, Roughage Intake Control (RIC) feed bunks, and 2) 96 steers fed using conventional concrete bunks (CON). Steers in the CON group were sorted by either body weight (i.e., light and heavy) or expected days on feed (i.e., short and long) determined using the DGM. There were two replicates of each sort treatment, for a total of eight pens with twelve steers in each pen. Four of the eight CON pens were equipped with bunk cameras that captured images at one-minute intervals. All groups were fed a high concentrate finishing ration for a minimum of 84 d before harvest.

Measurements for body weight (BW), hip height, back fat thickness (BF), and ribeye area (REA) were taken at 28 d intervals while on the finishing ration. Feeding behavior was collected on RIC steers using radio frequency identification data from the Insentec system. Conventional steers in pens with cameras were uniquely identified using colored adhesive patches and trained observers reviewed the images and recorded feeding behavior for individual animals. Feeding behavior traits considered were total daily eating duration (ED), bunk visit (BV) frequency, mean BV duration, meal frequency, mean meal duration, and total daily meal duration (MD). Repeated BW and composition measurements were used to create individual and pen-level growth curves for BW, empty body fat (EBF) percent, Yield Grade (YG), and Quality Grade (QG). The DGM was used to evaluate changes in maintenance energy requirements (alpha) and protein synthesis (K2) parameter estimates from the original values. Alpha was correlated with production parameters and feeding behavior measured using linear regression, and feeding behavior was compared between RIC and CON groups using regression analysis. Dry matter intake curves were developed on and individual and pen-basis for RIC and CON groups, respectively. Projections from the growth and DMI curves were used to generate marginal cost and revenue curves to estimate profitability and the optimal harvest day for individual RIC animals and CON pens. Effect of sorting by BW and days on feed (DOF) were evaluated using a t-test and an F-test to compare treatment means and variances, respectively.

Alpha and K2 have increased by 14.4 and 9.6% compared to the previous alpha and K2 estimates, suggesting increases in both apparent maintenance energy requirements and rates of protein synthesis. Steers with decreased maintenance energy requirements tended to be faster growing with increased rates of protein synthesis and greater EBF percent at harvest. Residual feed intake (RFI), DMI, SBW, and BF were able to explain 78% of the variation in alpha. Feeding behavior traits were not influential on alpha. Compared to the RIC cattle, CON steers had a smaller number of longer feeding bouts, increased ED, and decreased DMI. Cattle with low RFI tended to have increased BV frequency (P = 0.08), decreased mean BV duration (P = 0.02), and slower eating rates (P = 0.06). Results from the growth curves indicated BW, YG, and QG all increase with longer DOF, but marketing optimums vary based on grid prices and feed costs. Sorting by DOF improved carcass uniformity, decreased carcass discounts, and improved profitability. With individual animal growth trajectories, feedlot operators can make decisions regarding pen sorting, feed and health management, and marketing. By combining these models with DMI data, incremental cost of gain can be calculated, which can be used to pinpoint exact marketing optimums, and increase profitability by decreasing within pen variation, reducing overfeeding, and improving carcass uniformity.