A total of 543 pigs (PIC 1050 × 327: PIC Hendersonville, TN) were used in 2 consecutive
experiments with initial BW of 105 and 125 lb in Experiments 1 and 2, respectively.
The objective was to validate the regression equations predicting growth rate
and feed efficiency of growing-finishing pigs based on dietary NE content by comparing
actual and predicted performance. Thus, the 5 treatments included diets with:
(1) 30% dried distillers grains with solubles (DDGS), 20% wheat middlings, and 4 to
5% soybean hulls (low-energy); (2) 20% wheat middlings and 4 to 5% soybean hulls
(low-energy); (3) a corn-soybean meal diet (medium-energy); (4) diet 2 supplemented
with 3.7% choice white grease (CWG) to equalize NE level to diet 3 (medium-energy);
and (5) a corn-soybean meal diet with 3.7% CWG (high-energy). In Experiments 1 and
2, increasing dietary NE increased (linear, P < 0.01) final weight, ADG, and improved
feed efficiency but decreased (P < 0.11) ADFI. Only small differences were observed
between the predicted and observed values of ADG and feed efficiency, except for the
low-energy diet containing the highest fiber content (30% DDGS, wheat middlings
and soy hulls; diet 1). Carcass weight and carcass yield increased (linear, P = 0.01)
with increasing dietary NE. Also, backfat depth increased (linear, P = 0.01), loin depth
decreased (quadratic, P = 0.05), and lean percentage decreased (linear, P = 0.01) with
increasing dietary NE (linear, P = 0.01). Jowl iodine value (IV) also decreased with
increasing dietary NE. No differences (P > 0.26) in net energy caloric efficiency (NEE)
on a live weight basis were observed with increasing dietary NE. Nevertheless, feeding
30% DDGS (diet 1) resulted in a poorer (P = 0.05) NEE on a carcass basis compared
with feeding the other diets. In conclusion, the prediction equations provided a good
estimate of growth rate and feed efficiency of growing-finishing pigs fed different levels
of dietary NE except for the pigs fed low-energy diet containing highest fiber content
(diet 1). These predictions of growth performance can be used to model the economic
value of different dietary energy strategies.
