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广义加性混合模型分析市场体重猪的总运输损失 1.

Generalized additive mixed model on the analysis of total transport losses of market-weight pigs1.

机构信息

Department of Animal Sciences, University of Wisconsin, Madison, WI.

PIC, Pig Improvement Company, Des Moines, IA.

出版信息

J Anim Sci. 2019 Apr 29;97(5):2025-2034. doi: 10.1093/jas/skz087.

Abstract

Transportation losses of market-weight pigs are an animal welfare concern, and result in direct economic impact for producers and abattoirs. Such losses are related to multiple factors including pig genetics, human handling, management, and weather conditions. Understanding the factors associated with total transport losses (TTL) is important to the swine industry because it can aid decision-making, and help in the development of transportation strategies to minimize the risk of losses. Hence, the objective of this study was to investigate factors associated with TTL on market-weight pigs in typically field conditions for Midwestern United States using a generalized additive mixed model (GAMM). The final quasi-binomial GAMM included the fixed (main and interactions) effects of abattoir of destination, type of driver, average market weight, distance traveled, wind speed, precipitation, and temperature-humidity index (THI), as well as the random effects of truck companies and the combination of site of origin and period of the year. Results indicate significant associations between TTL and the main effect of all explanatory variables (P < 0.05), except for wind speed and precipitation. Interactions of average market weight × abattoir, and wind speed × precipitation were also significant. A complex nonlinear relationship between TTL and model covariates were observed for distance traveled, THI, and interaction terms. This study showed that TTL of market-weight pigs are caused by a complex system involving multiple interacting factors, which can be potentially managed to mitigate the risk of losses. In addition, the GAMM showed to be a simple and flexible approach to model TTL because it can capture nonlinear relationships, handle non-normal data, and can potentially accommodate data structure.

摘要

市场体重猪的运输损失是一个动物福利问题,会给生产者和屠宰场带来直接的经济影响。这些损失与多个因素有关,包括猪的遗传、人员处理、管理和天气条件。了解与总运输损失(TTL)相关的因素对养猪业很重要,因为它可以帮助做出决策,并有助于制定运输策略,最大限度地降低损失风险。因此,本研究的目的是使用广义加性混合模型(GAMM),在美国中西部典型的田间条件下,研究与市场体重猪 TTL 相关的因素。最终的拟二项式 GAMM 包括目的地屠宰场、驾驶员类型、平均市场体重、行驶距离、风速、降水和温湿度指数(THI)的固定(主效应和相互作用)效应,以及卡车公司的随机效应和起源地和年份的组合。结果表明,TTL 与所有解释变量的主效应(P<0.05)之间存在显著关联,除了风速和降水。平均市场体重×屠宰场和风速×降水的相互作用也很显著。TTL 与距离、THI 和相互作用项的模型协变量之间存在复杂的非线性关系。本研究表明,市场体重猪的 TTL 是由一个涉及多个相互作用因素的复杂系统引起的,可以通过管理来降低损失风险。此外,GAMM 被证明是一种简单灵活的 TTL 建模方法,因为它可以捕捉非线性关系,处理非正态数据,并可能适应数据结构。

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