Suppr超能文献

活畜预测牛肉胴体成分和大理石花纹评分:模型的建立与评估。

Live animal predictions of carcass components and marble score in beef cattle: model development and evaluation.

机构信息

NSW Department of Primary Industries, Livestock Industries Centre, University of New England, Trevenna Road, Armidale, New South Wales, 2351, Australia.

Animal Genetics and Breeding Unit, NSW Department of Primary Industries, University of New England, Armidale, New South Wales, 2351, Australia.

出版信息

Animal. 2020 Aug;14(S2):s396-s405. doi: 10.1017/S1751731120000324. Epub 2020 Mar 16.

Abstract

Until recently, beef carcass payment grids were predominantly based on weight and fatness categories with some adjustment for age, defined as number of adult teeth, to determine the price received by Australian beef producers for slaughter cattle. With the introduction of the Meat Standards Australia (MSA) grading system, the beef industry has moved towards payments that account for intramuscular fat (IMF) content (marble score (MarbSc)) and MSA grades. The possibility of a payment system based on lean meat yield (LMY, %) has also been raised. The BeefSpecs suite of tools has been developed to assist producers to meet current market specifications, specifically P8-rump fat and hot standard carcass weight (HCW). A series of equations have now been developed to partition empty body fat and fat-free weight into carcass fat-free mass (FFM) and fat mass (FM) and then into flesh FFM (FleshFFM) and flesh FM (FleshFM) to predict carcass components from live cattle assessments. These components then predict denuded lean (kg) and finally LMY (%) that contribute to emerging market specifications. The equations, along with the MarbSc equation, are described and then evaluated using two independent datasets. The decomposition of evaluation datasets demonstrates that error in prediction of HCW (kg), bone weight (BoneWt, kg), FleshFFM (kg), FleshFM (kg), MarbSc and chemical IMF percentage (ChemIMF%) is shown to be largely random error (%) in evaluation dataset 1, though error for ChemIMF% was primarily slope bias (%) in evaluation dataset 1, and BoneWt had substantial mean bias (%) in evaluation dataset 2. High modelling efficiencies of 0.97 and 0.95 for predicting HCW for evaluation datasets 1 and 2, respectively, suggest a high level of accuracy and precision in the prediction of HCW. The new outputs of the model are then described as to their role in estimating MSA index scores. The modelling system to partition chemical components of the empty body into carcass components is not dependent on the base modelling system used to derive empty body FFM and FM. This can be considered a general process that could be used with any appropriate model of body composition.

摘要

直到最近,牛肉胴体的计价网格主要基于重量和脂肪分类,再根据年龄(以成年牙齿数来定义)进行一些调整,以确定澳大利亚牛肉生产者屠宰牛的价格。随着澳大利亚肉类标准(MSA)分级系统的引入,牛肉行业已开始根据肌肉内脂肪(IMF)含量(大理石花纹评分(MarbSc))和 MSA 等级进行支付。基于瘦肉产量(LMY,%)的支付系统的可能性也已经提出。BeefSpecs 工具套件旨在帮助生产者满足当前市场规格,特别是 P8-臀部脂肪和热标准胴体重量(HCW)。现在已经开发了一系列方程,将空体脂肪和无脂体重分割成胴体无脂体重(FFM)和脂肪体重(FM),然后再分割成肉质 FFM(FleshFFM)和肉质 FM(FleshFM),以便从活牛评估中预测胴体成分。这些成分然后预测赤裸瘦肉(kg),最后是 LMY(%),这有助于新兴市场规格。这些方程与 MarbSc 方程一起进行了描述,然后使用两个独立的数据集进行了评估。评估数据集的分解表明,HCW(kg)、骨重(BoneWt,kg)、FleshFFM(kg)、FleshFM(kg)、MarbSc 和化学 IMF 百分比(ChemIMF%)预测的误差在评估数据集 1 中主要是随机误差(%),尽管评估数据集 1 中的 ChemIMF%误差主要是斜率偏差(%),而评估数据集 2 中的 BoneWt 则有很大的平均偏差(%)。用于预测评估数据集 1 和 2 的 HCW 的高建模效率分别为 0.97 和 0.95,这表明 HCW 的预测具有很高的准确性和精密度。然后,描述模型的新输出,以了解它们在估计 MSA 指数得分方面的作用。将空体化学成分分割成胴体成分的模型系统不依赖于用于推导空体 FFM 和 FM 的基础模型系统。这可以被认为是一种通用过程,可以与任何适当的身体成分模型一起使用。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验