| 3种不同方法对肉牛胴体性状预测能力的比较研究 |
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| 引用本文： | 张立敏,张猛,周正奎,刘喜冬,陈翠,陈晓杰,李姣,袁峥嵘,高雪,高会江,许尚忠,李俊雅.3种不同方法对肉牛胴体性状预测能力的比较研究[J].畜牧兽医学报,2012(3):368-375. |
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| 作者姓名： | 张立敏 张猛 周正奎 刘喜冬 陈翠 陈晓杰 李姣 袁峥嵘 高雪 高会江 许尚忠 李俊雅 |
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| 作者单位： | 中国农业科学院北京畜牧兽医研究所肉牛研究中心农业部畜禽遗传资源与利用重点开放实验室;西北农林科技大学动物科技学院;东北农业大学动物科技学院 |
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| 基金项目： | 农业部专项(Nycytx-38);国家自然基金(30871774);转基因生物新品种培育重大专项(2009ZX08007-005B);“十一五”转基因重大专项(2008ZX08007-2);“十二五”科技支撑计划课题(2011BAD28B04);中国农业科学院基本科研业务费专项资金课题(2010jC-2) |
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| 摘 要： | 本研究为了寻求一种对肉牛胴体性状预测准确性较高的方法,运用DPS数据处理系统和SAS软件比较偏最小二乘回归、GM(1,N)灰色系统和BP神经网络3种常用的预测模型对肉牛胴体性状的预测能力。选择肉牛7个宰前生长性状(体高、体长、胸围、腹围、管围、宰前活体质量、平均日增体质量),对2个重要的胴体性状(胴体质量和净肉质量)进行预测。结果表明:偏最小二乘回归方法在肉牛胴体性状预测方面准确性最高;GM(1,N)灰色系统和BP神经网络预测准确度偏低。本研究还将3种预测结果相结合,取其均值,大大提高了预测的准确性。这一研究将为肉牛生产实践提供一定的科学参考。
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| 关 键 词： | 偏最小二乘回归 GM(1 N)灰色系统 BP神经网络 预测 胴体性状 |
Comparison of Three Methods to Predict Carcass Traits in Bovine |
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| ZHANG Li-min,ZHANG Meng,ZHOU Zheng-kui,LIU Xi-dong,CHEN Cui,CHEN Xiao-jie,LI Jiao,YUAN Zheng-rong,GAO Xue,GAO Hui-jiang,XU Shang-zhong,LI Jun-ya.Comparison of Three Methods to Predict Carcass Traits in Bovine[J].Acta Veterinaria et Zootechnica Sinica,2012(3):368-375. |
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| Authors: | ZHANG Li-min ZHANG Meng ZHOU Zheng-kui LIU Xi-dong CHEN Cui CHEN Xiao-jie LI Jiao YUAN Zheng-rong GAO Xue GAO Hui-jiang XU Shang-zhong LI Jun-ya |
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| Institution: | 1 (1.Key Laboratory of Farm Animal Genetic Resources and Utilization of Ministry of Agriculture,Beef Cattle Research Center,Institute of Animal Science,Chinese Academy of Agricultural Sciences,Beijing 100193,China;2.College of Animal Science and Technology,Northwest A&F University,Yangling 712100,China;3.College of Animal Science and Technology,Northeast Agricultural University,Harbin 150030,China) |
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| Abstract: | To search for a method to predict accurately carcass traits in bovine,in this study,DPS and SAS software were used to compare the methods of partial least squares regression,GM(1,N) gray system and BP neural network,in order to observe their accuracy in predicting carcass traits in bovine.Seven preslaughter growth traits including body height,body length,chest circumference,abdominal circumference,cannon bone circumference,live weight and average daily gain were used to predict the carcass weight and meat weight.The results showed that the partial least squares regression gave the highest accuracy,while the average relative errors of GM(1,N) gray system and BP neural network were lower.In this study,the three predicted results were combined and their mean value were calculated as the predictive values,which would greatly improve the accuracy of prediction.The results would provide some scientific references to beef production. |
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| Keywords: | PLSR GM(1 N) gray system BP neural network prediction carcass traits |
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