实验方法与实验动物 EXPERIMENTAL METHOD AND ANIMAL

舍饲肉用绵羊干物质采食量预测模型构建及评估

  • 闫俊彤 ,
  • 张吉贤 ,
  • 李金辉 ,
  • 严慧 ,
  • 郭云霞 ,
  • 段春辉 ,
  • 刘月琴 ,
  • 纪守坤 ,
  • 张英杰
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  • 河北农业大学动物科技学院, 保定 071000
闫俊彤(2001-),女,河北唐山人,本科生,研究方向为动物营养与饲料科学。E-mail:yanjuntong2001@126.com

收稿日期: 2021-05-16

  网络出版日期: 2021-12-16

基金资助

河北省重点研发(农业高质量发展关键共性技术攻关专项)项目(19226627D);河北省省属高等学校基本科研业务费研究项目(KY202016);河北省自然科学基金青年基金项目(C2020204181);国家肉羊产业技术体系(CARS-39)

Construction and Evaluation of Dry Matter Intake Prediction Model for Dry-Lot Feeding Mutton Sheep

  • YAN Juntong ,
  • ZHANG Jixian ,
  • LI Jinhui ,
  • YAN Hui ,
  • GUO Yunxia ,
  • DUAN Chunhui ,
  • LIU Yueqin ,
  • JI Shoukun ,
  • ZHANG Yingjie
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  • College of Animal Science and Technology, Hebei Agricultural University, Baoding 071000, China

Received date: 2021-05-16

  Online published: 2021-12-16

摘要

通过检索文献构建舍饲肉用绵羊干物质采食量(DMI)及其影响因素数据集,建立舍饲肉用绵羊DMI预测模型并对模型进行评估,以期完善肉用绵羊DMI模型在我国肉用绵羊品种上的应用。检索中国知网和Web of Science等数据库,根据纳入和排除标准筛选文献,构建舍饲肉用绵羊DMI及其影响因素数据库,采用多元回归方法构建DMI预测模型并进行预测模型评估,运用方差分解方法进行DMI的影响因素分析。结果表明:本试验成功构建包括DMI及其15个潜在影响因素的数据库,其中肉用绵羊代谢体重(MBW)、相对成熟度(RM)、平均日增重(ADG)和饲粮代谢能(ME)及干物质(DM)、酸性洗涤纤维(ADF)、钙(Ca)含量均可显著影响肉用绵羊DMI(P<0.05),且不存在共线性(VIF<5);本研究构建的舍饲肉用绵羊DMI预测模型为DMI=98.48×MBW-379.70×RM+1.55×ADG+6.04×DM+26.38×ME+6.42×ADF+172.45×Ca-1 271.67(R2=0.90,P<0.05),模型预测偏差(MB)为0.11 g/d,预测误差(RMSPE)为7.59%,预测准确性高于CNCPS-S(2004)模型、NRC(2007)模型以及AFRC(1998)模型;方差分解结果显示本模型可解释89%的DMI变异,动物(肉用绵羊)因素可解释其中61%的变异而饲粮因素可解释其中39%的变异,MBW、ADG和Ca含量是影响舍饲肉用绵羊DMI的主要因素,三者共解释了69%的DMI变异。综上所述,本研究解析了影响肉用绵羊DMI的因素,所获得舍饲肉用绵羊DMI预测模型预测准确性较高,可以用于我国舍饲肉用绵羊DMI预测。

本文引用格式

闫俊彤 , 张吉贤 , 李金辉 , 严慧 , 郭云霞 , 段春辉 , 刘月琴 , 纪守坤 , 张英杰 . 舍饲肉用绵羊干物质采食量预测模型构建及评估[J]. 动物营养学报, 2021 , 33(12) : 7033 -7041 . DOI: 10.3969/j.issn.1006-267x.2021.12.043

Abstract

By establishing a dataset of dry matter intake (DMI) and its influencing factors in mutton sheep, this research aimed to construct and evaluate DMI prediction model, in order to improve the application of DMI prediction model in mutton sheep breeds in China. We searched databases in CNKI and Web of Science, and dataset of DMI and its influencing factors were constructed according to inclusion and exclusion criteria, then multiple regression methods were used to construct the DMI prediction model and the model were evaluated by comparing the predicted DMI and observed DMI, finally, variance decomposition methods were applied to analyze the influencing factors of DMI. The results showed as follows:a dataset including DMI and its 15 potential influencing factors was successfully constructed, in which seven indicators[metabolic body weight (MBW), relative maturity (RM), average daily gain (ADG) and dry matter (DM), metabolic energy (ME), acid detergent fiber (ADF) and calcium (Ca) content] of mutton sheep were observed significantly affect DMI of mutton sheep (P<0.05); the DMI prediction model for dry-lot mutton sheep was DMI=98.48×MBW-379.70×RM+1.55×ADG+6.04×DM+26.38×ME+6.42×ADF+172.45×Ca-1271.67 (R2=0.90, P<0.05), mean bias (MB) was 0.11 g/d, and root of mean square prediction error (RMSPE) was 7.59%, which was higher in prediction accuracy than that of CNCPS-S (2004), NRC (2007) and AFRC (1998) models; we also observed the model explained 89% of the variation in DMI of dry-lot mutton sheep, and 61% of which was explained by animal factors and 39% by diet factors, and MBW, ADG and Ca content were the main factors influencing DMI in dry-lot mutton sheep, and these three factors together explained 69% of the variation in DMI. In conclusion, the study investigate the factors affecting DMI of dry-lot mutton sheep, and obtain the DMI prediction model with high prediction accuracy, which can be used for DMI prediction of dry-lot mutton sheep in China.

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