Regadas Filho J G L, Tedeschi L O, Cannas A, Vieira R A M, Rodrigues M T
Departamento de Zootecnia, Universidade Federal de Viçosa, 36570 Minas Gerais, Brazil.
Department of Animal Science, Texas A&M University, College Station 77843-2471.
J Dairy Sci. 2014 Nov;97(11):7185-96. doi: 10.3168/jds.2014-8632. Epub 2014 Sep 6.
The first objective of this research was to assess the ability of the Small Ruminant Nutrition System (SRNS) mechanistic model to predict metabolizable energy intake (MEI) and milk yield (MY) when using a heterogeneous fiber pool scenario (GnG1), compared with a traditional, homogeneous scenario (G1). The second objective was to evaluate an alternative approach to estimating the dry matter intake (DMI) of goats to be used in the SRNS model. The GnG1 scenario considers an age-dependent fractional transference rate for fiber particles from the first ruminal fiber pool (raft) to an escapable pool (λr), and that this second ruminal fiber pool (i.e., escapable pool) follows an age-independent fractional escape rate for fiber particles (ke). Scenario G1 adopted only a single fractional passage rate (kp). All parameters were estimated individually by using equations published in the literature, except for 2 passage rate equations in the G1 scenario: 1 developed with sheep data (G1-S) and another developed with goat data (G1-G). The alternative approach to estimating DMI was based on an optimization process using a series of dietary constraints. The DMI, MEI, and MY estimated for the GnG1 and G1 scenarios were compared with the results of an independent dataset (n=327) that contained information regarding DMI, MEI, MY, and milk and dietary compositions. The evaluation of the scenarios was performed using the coefficient of determination (R(2)) between the observed and predicted values, mean bias (MB), bias correction factor (Cb), and concordance correlation coefficient. The MEI estimated by the GnG1 scenario yielded precise and accurate values (R(2) = 082; MB = 0.21 Mcal/d; Cb = 0.98) similar to those of the G1-S (R(2) = 0.85; MB = 0.10 Mcal/d; Cb=0.99) and G1-G (R(2) = 0.84; MB = 0.18 Mcal/d; Cb = 0.98) scenarios. The results were also similar for the MY, but a substantial MB was found as follows: GnG1 (R(2) = 0.74; MB = 0.70 kg/d; Cb = 0.79), G1-S (R(2) = 0.71; MB = 0.58 kg/d(1); Cb = 0.85) and G1-G (R(2) = 0.71; MB = 0.65 kg/d; Cb = 0.82). The alternative approach for DMI prediction provided better results with the G1-G scenario (R(2)=0.88; MB = -71.67 g/d; Cb = 0.98). We concluded that the GnG1 scenario is valid within mechanistic models such as the SRNS and that the alternative approach for estimating DMI is reasonable and can be used in diet formulations for goats.
本研究的首要目标是评估小型反刍动物营养系统（SRNS）机理模型在使用异质纤维库情景（GnG1）时预测可代谢能量摄入量（MEI）和产奶量（MY）的能力，并与传统的同质情景（G1）进行比较。第二个目标是评估一种用于估计SRNS模型中山羊干物质摄入量（DMI）的替代方法。GnG1情景考虑了纤维颗粒从第一个瘤胃纤维库（筏）到可逃逸库的年龄依赖性分数转移率（λr），并且第二个瘤胃纤维库（即可逃逸库）遵循纤维颗粒的年龄无关分数逃逸率（ke）。情景G1仅采用单一的分数通过率（kp）。除了G1情景中的2个通过率方程外，所有参数均使用文献中发表的方程单独估计：1个是根据绵羊数据开发的（G1-S），另一个是根据山羊数据开发的（G1-G）。估计DMI的替代方法基于使用一系列饮食限制的优化过程。将GnG1和G1情景估计的DMI、MEI和MY与一个独立数据集（n = 327）的结果进行比较，该数据集包含有关DMI、MEI、MY以及牛奶和饮食组成的信息。使用观测值和预测值之间的决定系数（R²）、平均偏差（MB）、偏差校正因子（Cb）和一致性相关系数对情景进行评估。GnG1情景估计的MEI产生了精确且准确的值（R² = 0.82；MB = 0.21Mcal/d；Cb = 0.98），与G1-S（R² = 0.85；MB = 0.10Mcal/d；Cb = 0.99）和G1-G（R² = 0.84；MB = 0.18Mcal/d；Cb = 0.98）情景相似。MY的结果也相似，但发现了较大的MB，如下所示：GnG1（R² = 0.74；MB = 0.70kg/d；Cb = 0.79）、G1-S（R² = 0.71；MB = 0.58kg/d；Cb = 0.85）和G1-G（R² = 0.71；MB = 0.65kg/d；Cb = 0.82）。DMI预测的替代方法在G1-G情景中提供了更好的结果（R² = 0.88；MB = -71.67g/d；Cb = 0.98）。我们得出结论，GnG1情景在诸如SRNS的机理模型中是有效的，并且估计DMI的替代方法是合理的，可用于山羊的日粮配方。
