Giles L R, Eamens G J, Arthur P F, Barchia I M, James K J, Taylor R D
New South Wales Department of Primary Industries, Elizabeth Macarthur Agricultural Institute, Camden, New South Wales 2570, Australia.
J Anim Sci. 2009 May;87(5):1648-58. doi: 10.2527/jas.2008-1437. Epub 2008 Dec 19.
Data from 54 hybrid (mainly Large White x Landrace) pigs (18 boars, 18 gilts, and 18 barrows) were used to quantify and mathematically describe the differential growth and development of body components of live pigs. The pigs were 32.4 +/- 3.2 kg of BW and 70 +/- 1 d of age (mean +/- SD) at the beginning of the study, were individually penned and fed ad libitum, and were weighed weekly. Computed tomography (CT) imaging was used to determine the weights of lean, fat, bone, and skin tissue in the live pig at 30, 60, 90, 120, and 150 kg of BW. For each target BW, the sum of all the weights of the body components, as assessed by CT, was referred to as CT BW. Linear and nonlinear models were developed to evaluate the patterns of growth and development of each body component relative to CT BW. The correlation between the actual BW and CT BW was close to unity (r = 0.99), indicating that CT scanning could accurately predict the BW of pigs. Across sex and castrate status, percentage of fat (fat weight/CT BW) in the pig was least (11.2%) at the 30-kg target BW and continued to increase to 22.6% by the 150-kg target BW. Percentage of lean, however, was greatest (67.2%) at the 30-kg target BW and continued to decrease to 53.4% by the 150-kg target BW. The sex or castrate status x target BW interaction was significant (P < 0.05) for all the body components, indicating that the developmental patterns were different among sex or castrate status. Barrows were fatter relative to gilts, which in turn were fatter than boars. For lean, the observed pattern for sex or castrate status differences was opposite that for fat. To predict responses to management strategies on growth and development in pigs, accurate mathematical models are required, and the results of this study indicate that the nonlinear (e.g., augmented allometric and generalized nonlinear) functions provided better descriptions of the growth and development of most body components of the live pig than did the simpler (e.g., linear and allometric) models.
选取54头杂交猪（主要为大白猪×长白猪）（18头公猪、18头后备母猪和18头阉猪）的数据，用于量化和数学描述生猪身体各部位的差异生长和发育情况。研究开始时，这些猪体重为32.4±3.2千克，年龄为70±1天（平均值±标准差），单栏饲养，自由采食，并每周称重。利用计算机断层扫描（CT）成像技术测定体重分别为30、60、90、120和150千克时生猪体内瘦肉、脂肪、骨骼和皮肤组织的重量。对于每个目标体重，通过CT评估的身体各部位重量总和称为CT体重。建立线性和非线性模型来评估每个身体部位相对于CT体重的生长和发育模式。实际体重与CT体重之间的相关性接近1（r = 0.99），表明CT扫描能够准确预测猪的体重。在不同性别和去势状态下，猪的脂肪百分比（脂肪重量/CT体重）在30千克目标体重时最低（11.2%），到150千克目标体重时持续增加至22.6%。然而，瘦肉百分比在30千克目标体重时最高（67.2%），到150千克目标体重时持续下降至53.4%。所有身体部位的性别或去势状态×目标体重交互作用均显著（P < 0.05），表明不同性别或去势状态下的发育模式不同。阉猪比后备母猪更肥，而后备母猪又比公猪更肥。对于瘦肉，观察到的性别或去势状态差异模式与脂肪相反。为了预测猪生长发育对管理策略的反应，需要精确的数学模型，本研究结果表明，非线性（如增强异速生长和广义非线性）函数比简单（如线性和异速生长）模型能更好地描述生猪大多数身体部位的生长和发育情况。
