预测肉牛脂肪覆盖率以做出农场管理决策:脂肪评估和脂肪沉积建模综述

IF 1.3 Q3 AGRICULTURE, DAIRY & ANIMAL SCIENCE Translational Animal Science Pub Date : 2024-04-11 DOI:10.1093/tas/txae058
M. McPhee
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引用次数: 0

摘要

国内外市场对肉牛胴体重量和脂肪覆盖率的要求促使人们开发了预测脂肪覆盖率的肉牛生长模型,以帮助农场管理者做出管理决策。本文的目标有四个方面:(1)简要回顾脂肪组织增生的生物学基础;(2)简要回顾肉牛的活体和胴体评估,以及用于制定定量成分和质量指数的胴体分级系统;(3)回顾脂肪沉积模型:戴维斯生长模型(DGM)、法国国家农业研究所生长模型(IGM)、康奈尔价值发现系统(CVDS)和 BeefSpecs 起草工具(BeefSpecsDT);(4)评估将科学和实用技能转化为研究/决策支持工具的过程,以帮助肉牛业提高盈利能力。使用多种技术对一系列物种和性状进行活体和胴体动物评估的 r2 分别为 0.61 至 0.99 和 0.52 至 0.99。对 DGM 和 IGM 的模型评估是使用现有出版物中的萨勒氏小母牛(n = 24)和安格斯-赫福德小公牛(n = 15)进行的,对 CVDS 和 BeefSpecsDT 的模型评估是使用研究试验中的安格斯小公牛(n = 33)进行的,该试验中的小公牛在商业饲养场进行了 101 天的谷物加工。评估观察到的和预测的脂肪量(FM)是本综述的重点。萨勒氏母牛的脂肪量平均偏差(MB)分别为 7.5 千克和 1.3 千克,预测均方根误差(RMSEP)分别为 31.2 千克和 27.8 千克;安格斯-赫里福德母牛的脂肪量平均偏差(MB)分别为-4.0 千克和-10.5 千克,DGM 和 IGM 的预测均方根误差(RMSEP)分别为 9.14 千克和 21.5 千克。CVDS 和 BeefSpecsDT 的安格斯母牛 FM MB 分别为-5.61 千克和-2.93 千克,RMSEP 分别为 12.3 千克和 13.4 千克。CVDS和BeefSpecsDT的偏差、斜率和偏差分解率分别为21%、12%和68%,以及5%、4%和91%。CVDS 和 BeefSpecsDT 的建模效率分别为 0.38 和 0.27,模型在 20 千克误差范围内的比例分别为 91% 和 88%。本综述中报告的脂肪沉积模型有可能帮助牛肉业在屠宰前对活牛进行农场管理决策,并提高盈利能力。建模人员需要不断评估和改进他们的模型,但要注意以下几点:(1) 尽量减少投入,(2) 选择现成的农场投入。
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Predicting fat cover in beef cattle to make on-farm management decisions: a review of assessing fat and of modeling fat deposition
Demands of domestic and foreign market specifications of carcass weight and fat cover, of beef cattle, have led to the development of cattle growth models that predict fat cover to assist on-farm managers make management decisions. The objectives of this paper are 4-fold: (1) conduct a brief review of the biological basis of adipose tissue accretion, (2) briefly review live and carcass assessments of beef cattle, and carcass grading systems used to develop quantitative compositional and quality indices, (3) review fat deposition models: Davis growth model (DGM), French National Institute for Agricultural Research growth model (IGM), Cornell Value Discovery System (CVDS), and BeefSpecs drafting tool (BeefSpecsDT) and (4) appraise the process of translating science and practical skills into research/decision support tools that assist the Beef industry improve profitability. The r2 for live and carcass animal assessments, using several techniques across a range of species and traits, ranged from 0.61 to 0.99 and from 0.52 to 0.99, respectively. Model evaluations of DGM and IGM were conducted using Salers heifers (n = 24) and Angus-Hereford steers (n = 15) from an existing publication and model evaluations of CVDS and BeefSpecsDT were conducted using Angus steers (n = 33) from a research trial where steers were grain finished for 101 days in a commercial feedlot. Evaluating the observed and predicted fat mass (FM) is the focus of this review. The FM mean bias (MB) for Salers heifers were 7.5 and 1.3kg and the root mean square error of prediction (RMSEP) were 31.2 and 27.8kg and for Angus-Hereford steers the MB were -4.0 and -10.5kg and the RMSEP were 9.14 and 21.5kg for DGM and IGM, respectively. The FM MB for Angus steers were -5.61 and -2.93kg and the RMSEP were 12.3 and 13.4kg for CVDS and BeefSpecsDT, respectively. The decomposition for bias, slope, and deviance were 21, 12, and 68% and 5, 4, and 91% for CVDS and BeefSpecsDT, respectively. The modeling efficiencies were 0.38 and 0.27 and the models were within a 20kg level of tolerance 91 and 88% for CVDS and BeefSpecsDT, respectively. Fat deposition models reported in this review have the potential to assist the beef industry make on-farm management decisions on live cattle before slaughter and improve profitability. Modelers need to continually assess and improve their models but with a caveat of: (1) striving to minimize inputs, and (2) choosing on-farm inputs that are readily available.
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来源期刊
Translational Animal Science
Translational Animal Science Veterinary-Veterinary (all)
CiteScore
2.80
自引率
15.40%
发文量
149
审稿时长
8 weeks
期刊介绍: Translational Animal Science (TAS) is the first open access-open review animal science journal, encompassing a broad scope of research topics in animal science. TAS focuses on translating basic science to innovation, and validation of these innovations by various segments of the allied animal industry. Readers of TAS will typically represent education, industry, and government, including research, teaching, administration, extension, management, quality assurance, product development, and technical services. Those interested in TAS typically include animal breeders, economists, embryologists, engineers, food scientists, geneticists, microbiologists, nutritionists, veterinarians, physiologists, processors, public health professionals, and others with an interest in animal production and applied aspects of animal sciences.
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