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Statistical models for estimating lamb birth weight using body measurements
Italian Journal of Animal Science ( IF 2.5 ) Pub Date : 2021-07-23 , DOI: 10.1080/1828051x.2021.1937720
Anežka Málková 1 , Martin Ptáček 1 , Alfonso Chay-Canul 2 , Luděk Stádník 1
Affiliation  

Abstract

The objective of this study was to estimate lamb birth weight based on body dimensions. We monitored 101 lambs (61 Charollais lambs, 27 Kent lambs, and 13 their crossbreds) at a selected commercial flock. Birth weight, chest circumference (CC), head circumference (HC), and shin circumference (SC) were measured immediately after birth using a tape measure. Correlation analysis indicated the promising use of CC (r = 0.795; p < .001) or HC (r = 0.679; p < .001) for estimating body weight. Statistical models with one body measurement indicated that a model with CC as a covariate had the highest coefficient of determination and the lowest Akaike’s information criterion, corrected Akaike’s information criterion, and Bayesian information criterion. The defined criteria generally identified that models with SC, HC, and CC and models with HC and CC as covariates were the best. Residual analyses verified our results, but more extensive analyses of other breeds under different breeding conditions should be conducted to confirm and generalise our results.

  • Highlights
  • Statistical models have been proposed to predict the birth weight of lambs.

  • Tape measure can be successfully used for birth weight estimation.

  • Model with chest, head, shin circumferences as covariates were suggested.



中文翻译:

使用身体测量值估算羔羊出生体重的统计模型

摘要

本研究的目的是根据体型估计羔羊的出生体重。我们在选定的商业羊群中监测了 101 只羊羔(61 只夏洛莱羊羔、27 只肯特羊羔和 13 只杂交羊)。出生后立即使用卷尺测量出生体重、胸围 (CC)、头围 (HC) 和胫围 (SC)。相关分析表明 CC ( r  = 0.795; p  < .001) 或 HC ( r  = 0.679; p < .001) 用于估计体重。单次身体测量的统计模型表明,以CC为协变量的模型具有最高的决定系数和最低的Akaike信息准则、校正Akaike信息准则和贝叶斯信息准则。定义的标准通常确定以 SC、HC 和 CC 为协变量的模型以及以 HC 和 CC 作为协变量的模型是最好的。残差分析证实了我们的结果,但应在不同育种条件​​下对其他品种进行更广泛的分析,以确认和概括我们的结果。

  • 强调
  • 已经提出了统计模型来预测羔羊的出生体重。

  • 卷尺可以成功地用于估计出生体重。

  • 建议使用胸围、头围、胫围作为协变量的模型。

更新日期:2021-07-23
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