Alpharma beef cattle nutrition symposium: Predictability of feedlot cattle growth performance

Predicting performance is vital to management and marketing decisions in commercial feedlots. Agreement between performance predicted from NE equations or empirical regression relationships and actual performance is generally very good, suggesting that factors affecting performance by finishing cattle are fairly well documented. The challenge for feedlot managers is to predict performance with limited information at the start of the feeding period. Data on sex and initial shrunk BW (ISBW) are typically available when cattle start on feed. Relationships between ISBW, sex, and performance were evaluated using 3,363 pen records collected over 4 yr from 3 commercial feedlots in the Texas Panhandle. Mixed-model regression was used to account for random effects of feedlot × season × year and fixed effects of ISBW (range = 227 to 451 kg), sex (steer or heifer), and ISBW × sex (P < 0.10 for all variables evaluated). Previously developed equations indicated that with intercept and slope adjustments for sex, ISBW accounted for 76 and 84% of the variation in DMI and final shrunk BW (FSBW), respectively. Similarly, newly developed regression equations that included ISBW, sex, and ISBW × sex accounted for 46 and 81% of the variation in ADG and HCW, respectively. Initial BW was negatively related to G:F (R² = 0.22). Including early DMI data (DMI from d 8 to 28) increased R² and decreased prediction error for DMI, indicating that updating predictions with interim intake data might prove beneficial. An independent data set (781 lots of steers and heifers) collected during 1 yr from 2 Texas Panhandle feedlots was used to validate equations developed with the larger database. Dry matter intake predicted from ISBW and sex accounted for 69% of the variation in observed DMI (SE of prediction = 0.47; mean bias = 0.42 kg). Predicting DMI with ISBW, sex, and DMI from d 8 to 28 of the feeding period increased r² to 0.76 and slightly decreased the SE of prediction (0.42 kg), but the equation had a strong linear bias (-0.174; P < 0.001). The r² values for regression of observed on predicted ADG, G:F, FSBW, and HCW were 0.37, 0.08, 0.74, and 0.73, respectively, with positive mean bias (underprediction for all equations). Average daily gain calculated with NE equations from predicted DMI (ISBW and sex equation) and predicted FSBW had a similar r² (0.38) but less mean bias (-0.08 kg) than ADG predicted directly from ISBW and sex. Adjustments to equations for animal type, health, and management effects would likely improve predictions. Nonetheless, results suggest that predicting performance from initial BW with adjustments for steers vs. heifers should have considerable utility in practical settings.