Schinckel A P, de Lange C F
Department of Animal Sciences, Purdue University, West Lafayette, IN 47907-1151, USA.
J Anim Sci. 1996 Aug;74(8):2021-36. doi: 10.2527/1996.7482021x.
Swine growth models have the potential to evaluate alternative management decisions and optimize production systems. However, the lack of economical, yet accurate methods to obtain the growth parameters required to characterize pig genotypes, and which are required by growth models, limits their widespread implementation. The four primary parameters required are 1) daily whole-body protein accretion potential, 2) partitioning of energy, intake over maintenance between protein and lipid accretion, 3) maintenance requirements for energy, and 4) daily feed intake. Estimation of daily protein accretion rates requires that serial estimates of composition and growth be fitted to flexible nonlinear functions. Serial dissection and chemical analysis are too expensive to be routinely conducted on an adequate number of pigs for precise daily protein accretion rates at different live weights. Three alternate methods include 1) serial slaughter and double sampling; 2) use of serial live measurements to estimate composition, i.e., serial ultrasonic measurements; and 3) use of generalized functions that estimate daily protein accretion as a function of mean daily fat-free lean gain over a specified weight interval. The energy partitioning between lipid and protein accretion can be expressed as two interchangeable measurements, either as the slope of protein accretion or the change in the lipid: protein gain ratio as a function of energy intake at each live weight. Both methods require serial estimates of composition and scale feeding of pigs to specified energy intake levels. Maintenance requirements for energy are better expressed as a function of protein mass than body weight. However, differences in body protein mass do not fully explain difference in maintenance requirements between various pig genotypes. Daily feed intakes at each live weight can be estimated by accurately collecting feed intake data at least three live weight ranges and fitting the data to nonlinear functions. An alternative method to estimate daily feed intake is to develop daily lipid and protein accretion curves. On the basis of their energetic costs of lipid and protein deposition and assumed maintenance requirements, daily energy intakes can be estimated. Genetic selection changes the underlying growth parameters. The selection criteria and testing environment direct the relative genetic change for each growth parameter. The different sexes may also be affected differently by selection. For this reason, each closed uniformly selected population must be evaluated for each parameter for each sex.
猪生长模型有潜力评估不同的管理决策并优化生产系统。然而，缺乏经济且准确的方法来获取表征猪基因型所需的生长参数，而这些参数又是生长模型所必需的，这限制了它们的广泛应用。所需的四个主要参数为：1）每日全身蛋白质沉积潜力；2）能量分配，即摄入能量在维持需求与蛋白质和脂肪沉积之间的分配；3）能量维持需求；4）每日采食量。每日蛋白质沉积率的估计要求对组成和生长的系列估计值拟合到灵活的非线性函数。连续解剖和化学分析成本过高，无法对足够数量的猪进行常规操作以获得不同体重下精确的每日蛋白质沉积率。三种替代方法包括：1）连续屠宰和双重采样；2）利用连续活体测量来估计组成，即连续超声测量；3）使用广义函数，将每日蛋白质沉积估计为特定体重区间内平均每日无脂瘦肉增重的函数。脂肪和蛋白质沉积之间的能量分配可以表示为两个可互换的测量值，要么是蛋白质沉积的斜率，要么是每个体重下脂肪：蛋白质增重比随能量摄入的变化。两种方法都需要对猪的组成进行系列估计，并将猪按比例饲养到特定的能量摄入水平。能量维持需求更好地表示为蛋白质质量而非体重的函数。然而，身体蛋白质质量的差异并不能完全解释不同猪基因型之间维持需求的差异。每个体重下的每日采食量可以通过在至少三个体重范围内准确收集采食量数据并将数据拟合到非线性函数来估计。估计每日采食量的另一种方法是绘制每日脂肪和蛋白质沉积曲线。根据脂肪和蛋白质沉积的能量成本以及假定的维持需求，可以估计每日能量摄入量。遗传选择会改变潜在的生长参数。选择标准和测试环境决定了每个生长参数的相对遗传变化。不同性别可能也会受到选择的不同影响。因此，必须对每个封闭的均匀选择群体的每个性别进行每个参数的评估。
