Schinckel A P, Mahan D C, Wiseman T G, Einstein M E
Department of Animal Sciences, Purdue University, 915 West State Street, West Lafayette, IN 47907-2054, USA.
J Anim Sci. 2008 Feb;86(2):460-71. doi: 10.2527/jas.2007-0625. Epub 2007 Nov 27.
Two genetic lines of barrows and gilts with different lean growth rates were used to determine the BW and chemical composition growth from 23 to 125 kg of BW. The experiment was a 2 x 2 x 5 factorial arrangement of treatments in a completely randomized design conducted in 2 replicates. Six pigs from each sex and genetic line were killed at approximately 25-kg intervals from 23 kg to 125 kg of BW. At slaughter, tissues were collected and weighed. All components were ground and frozen until analyzed for water, protein, lipid, and ash. Serial BW data were fitted to alternative functions of day of age. Based on Akaike's information criteria values, the random effects model, BW(i, t) = (1 + c(i))(b(0) + b(1)t + b(2)t(2)), was the best mixed model equation. The chemical component mass data were fitted to alternative functions of BW. The allometric function, chemical component mass = aBW(b), provided the best fit to the data. Daily deposition rates of each chemical component were predicted by using the derivatives of the 2 functions. The overall ADG of the 2 genetic lines were not different. Barrows had 0.052 kg/d greater (P = 0.03) ADG than gilts. Allometric growth coefficients for all 4 chemical components were different (P < 0.01) for each genetic line. Allometric coefficients and predicted relative growth (g/kg of BW gain) for protein and moisture mass were greater (P < 0.01) for the high lean-gain pigs than the low lean-gain pigs. Allometric coefficients for lipid mass were smaller (P = 0.001) for the high lean-gain pigs than the low lean-gain pigs overall. Allometric coefficients and predicted relative growth rates for lipid mass were greater (P < 0.01) and for moisture and protein mass were lesser (P < 0.002) than the gilts. Compared with low lean-gain pigs, high lean-gain pigs had (1) 32.8% lesser predicted daily rates of lipid deposition (200 vs. 305 +/- 80 g/d), with the difference increasing from 23 to 37% from 25 to 125 kg of BW; (2) 12.3% greater daily rates of protein deposition (118.7 vs. 106.0 +/- 3.3 g/d); and (3) 18.8% greater predicted daily moisture accretion rates (423 vs. 356 +/- 9 g/d). Overall, barrows had 21.3% greater lipid deposition (279 vs. 230 +/- 78.2 g/d) than gilts. In this study, barrows and gilts had similar predicted daily moisture, protein, and ash accretion rates.
选用两个瘦肉生长速度不同的公猪和母猪品系，测定体重从23千克增长到125千克期间的体重及化学成分变化。本试验采用2×2×5析因设计，完全随机安排处理，共进行2次重复。从每个性别和品系中选取6头猪，体重从23千克到125千克，每隔约25千克屠宰一批。屠宰时，采集组织并称重。所有成分均研磨后冷冻，直至分析水分、蛋白质、脂质和灰分。连续的体重数据拟合到日龄的替代函数。根据赤池信息准则值，随机效应模型BW(i, t) = (1 + c(i))(b(0) + b(1)t + b(2)t(2))是最佳混合模型方程。化学成分质量数据拟合到体重的替代函数。异速生长函数化学成分质量 = aBW(b)对数据拟合最佳。通过这两个函数的导数预测各化学成分的日沉积率。两个遗传品系的总体平均日增重无差异。公猪的平均日增重比母猪高0.052千克/天（P = 0.03）。每个遗传品系的4种化学成分的异速生长系数均不同（P < 0.01）。高瘦肉生长速度猪的蛋白质和水分质量的异速生长系数及预测相对生长率（每千克体重增加量中的克数）高于低瘦肉生长速度猪（P < 0.01）。总体而言，高瘦肉生长速度猪的脂质质量异速生长系数低于低瘦肉生长速度猪（P = 0.001）。与母猪相比，公猪的脂质质量异速生长系数和预测相对生长率更高（P < 0.01），而水分和蛋白质质量的则更低（P < 0.002）。与低瘦肉生长速度猪相比，高瘦肉生长速度猪：（1）预测的每日脂质沉积率低32.8%（200对305±80克/天），从25千克到125千克体重时差异从23%增加到37%；（2）每日蛋白质沉积率高12.3%（118.7对106.0±3.3克/天）；（3）预测的每日水分增加率高18.8%（423对356±9克/天）。总体而言，公猪的脂质沉积比母猪高21.3%（279对230±78.2克/天）。在本研究中，公猪和母猪的预测每日水分、蛋白质和灰分增加率相似。
