Cooperative Research Centre for Beef Genetic Technologies, Beef Industry Centre of Excellence, Industry and Investment New South Wales, Armidale, New South Wales, Australia.
J Anim Sci. 2010 May;88(5):1848-59. doi: 10.2527/jas.2009-2633. Epub 2010 Feb 12.
A decision support tool for predicting subcutaneous fat depths called BeefSpecs, based on the Davis growth model (DGM), has been developed by the Cooperative Research Centre for Beef Genetic Technologies. Currently, the DGM predicts 12th-rib fat thickness (RFT, mm). To allow predictions of fat thickness at the P8 rump (P8FT, mm) site, the standard carcass fat measurement in the Australian beef industry, a relationship was developed between ultrasound RFT and P8FT in steers and heifers from temperate (Angus, Hereford, Shorthorn, and Murray Grey) and tropical (Brahman, Belmont Red, and Santa Gertrudis) breed types. Model development involved fitting various combinations of sex, breed type (BrT), BW, age, and RFT to produce 6 models. The models were challenged with data from 3 independent data sets: 1) Angus steers from 2.4 generations of divergent selection for and against residual feed intake; 2) 2 tropically adapted genotypes [Brahman and tropically adapted composites (combinations of Belmont Red, Charbray, Santa Gertrudis, Senepol, and Brahman breeds)]; and 3) a study using sires from Charolais, Limousin, Belgian Blue, and Black and Red Wagyu breeds and 3 genetic lines of Angus to create divergence in progeny in terms of genetic potential for intramuscular fat percent and retail beef yield. When challenged with data from Angus cattle, the mean biases (MB, mm) for models A to F were -1.23, -0.56, -0.56, -0.02, 0.14, and 0.04, and the root mean square errors of predictions (mm) were 1.53, 0.97, 0.97, 0.92, 0.93, and 0.91, respectively. When challenged with data from Brahman cattle, MB were 0.04, -0.22, -0.14, 0.05, -0.11, and 0.02 and root mean square errors of predictions were 1.30, 1.29, 1.27, 1.23, 1.37, and 1.29, respectively. Generally, model accuracy indicated by MB tended to be less for model E, which contained age rather than BW as a covariate. Models B and C were generally robust when challenged with data from Angus, Brahman, and Tropical Composite cattle as well as crossbred cattle with temperate sires. Model D, which did not contain age, performed the most consistently and was selected for inclusion in the DGM: P8FT, mm = -3.6 (+/-0.14) + 1.3 (+/-0.13) x sex + 0.11 (+/-0.13) x BrT + 0.014 (+/-4.8E(-4)) x BW + 0.96 (+/-0.01) x RFT - 0.73 (+/-0.08) x sex x BrT - 3.8E(-3) (+/-4.2E(-4)) x sex x BW - 0.09 (+/-0.01) x sex x RFT + 1.3E(-3) (+/-3.7E(-4)) x BrT x BW + 0.24 (+/-0.01) x BrT x RFT (adjusted R(2) = 0.86; SE = 0.013). Model D has been implemented in BeefSpecs to predict P8FT.
一种名为 BeefSpecs 的预测皮下脂肪深度的决策支持工具，它基于戴维斯生长模型（DGM），由牛肉遗传技术合作研究中心开发。目前，DGM 可以预测第 12 肋骨处的脂肪厚度（RFT，毫米）。为了能够预测澳大利亚牛肉行业中标准胴体脂肪测量的 P8 臀部脂肪厚度（P8FT，毫米），建立了一个在温带（安格斯牛、赫里福德牛、短角牛和马格雷格牛）和热带（婆罗门牛、贝尔蒙特红牛和圣赫特鲁迪斯牛）品种的公牛和小母牛中，超声 RFT 和 P8FT 之间的关系。模型开发涉及拟合各种性别、品种类型（BrT）、体重、年龄和 RFT 的组合，以产生 6 个模型。这些模型受到了来自 3 个独立数据集的挑战：1）来自 2.4 代对剩余饲料摄入有利和不利选择的安格斯公牛；2）2 个热带适应基因型[婆罗门牛和热带适应复合品种（贝尔蒙特红牛、查尔布雷牛、圣赫特鲁迪斯牛、塞内波尔牛和婆罗门牛品种的组合）]；3）使用夏洛莱牛、利木赞牛、比利时蓝牛和黑毛和红牛的公牛以及安格斯牛的 3 个遗传系，在肌肉内脂肪百分比和零售牛肉产量方面创造了后代的遗传潜力的差异。当用安格斯牛的数据进行挑战时，模型 A 至 F 的平均偏差（MB，毫米）分别为-1.23、-0.56、-0.56、-0.02、0.14 和 0.04，预测的均方根误差（mm）分别为 1.53、0.97、0.97、0.92、0.93 和 0.91。当用婆罗门牛的数据进行挑战时，MB 分别为 0.04、-0.22、-0.14、0.05、-0.11 和 0.02，预测的均方根误差分别为 1.30、1.29、1.27、1.23、1.37 和 1.29。一般来说，模型 E 的年龄而不是体重作为协变量，其表示的模型准确性的平均偏差（MB）往往较小。当用安格斯牛、婆罗门牛和热带复合牛以及带有温带公牛的杂交牛的数据进行挑战时，模型 B 和 C 通常是稳健的。不包含年龄的模型 D 表现最为一致，并被选入 DGM：P8FT，毫米=-3.6（+/-.14）+1.3（+/-.13）x 性别+0.11（+/-.13）x BrT+0.014（+/-.4.8E（-4））x BW+0.96（+/-.01）x RFT-0.73（+/-.08）x 性别 x BrT-3.8E（-3）（+/-.4.2E（-4））x 性别 x BW-0.09（+/-.01）x 性别 x RFT+1.3E（-3）（+/-.3.7E（-4））x BrT x BW+0.24（+/-.01）x BrT x RFT（调整后的 R（2）=0.86；SE=0.013）。模型 D 已在 BeefSpecs 中实现，用于预测 P8FT。
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