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巴西马匹耐热特性的多变量分析。

Multivariate analysis for characteristics of heat tolerance in horses in Brazil.

作者信息

Castanheira Marlos, Paiva Samuel Rezende, Louvandini Helder, Landim Aline, Fiorvanti Maria Clorinda Soares, Paludo Giane Regina, Dallago Bruno Stefano, McManus Concepta

机构信息

Escola de Veterinária, Universidade Federal de Goiás, Goiânia, GO, Brazil.

出版信息

Trop Anim Health Prod. 2010 Feb;42(2):185-91. doi: 10.1007/s11250-009-9404-x. Epub 2009 Jul 7.

Abstract

The environment in which the horse is reared affects its ability to maintain thermal balance which is in turn related to thermal characteristics and regulatory physiological mechanisms. In this study a multivariate analysis of physiological traits in relation to heat tolerance in horses was carried out in the Federal District, Brazil. The aim was to test the ability of these analyses to separate groups of animals and determine which physiological traits are most important in the adaptation to heat stress. Forty adult horses (4 to 13 years) were used, ten from each of four different genetic groups (English thoroughbred, Brazilian showjumper, crossbred and Breton). The traits examined included heart and breathing rate, rectal temperature as well as blood parameters. The data underwent multivariate statistical analysis including cluster, discriminate and canonical using Statistical Analysis System - SAS (R) procedures CLUSTER, STEPDISC, CANCORR and DISCRIM. The tree diagram showed clear distances between groups studied and canonical analysis was able to separate individuals in groups. The discriminate analysis identified the variables which were most important in separating these groups. The multivariate analysis was able to separate the animals into groups with RR, HR and RT being important in this separation.

摘要

马匹的饲养环境会影响其维持热平衡的能力,而热平衡又与热特性和调节生理机制相关。在本研究中,在巴西联邦区对与马匹耐热性相关的生理特征进行了多变量分析。目的是测试这些分析区分动物群体的能力,并确定哪些生理特征在适应热应激方面最为重要。使用了40匹成年马(4至13岁),来自四个不同遗传群体(英国纯种马、巴西障碍赛用马、杂交马和布列塔尼马)各10匹。所检查的特征包括心率、呼吸频率、直肠温度以及血液参数。数据进行了多变量统计分析,包括使用统计分析系统 - SAS(R)程序CLUSTER、STEPDISC、CANCORR和DISCRIM进行聚类、判别和典型相关分析。树形图显示了所研究群体之间的明显距离,典型相关分析能够区分群体中的个体。判别分析确定了在区分这些群体中最重要的变量。多变量分析能够将动物分为不同群体,其中RR、HR和RT在这种区分中很重要。

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