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基于Logistic和Gompertz模型拟合大白猪体重生长曲线的研究

Body Weight Growth Curve of Large White Pigs Based on Logistic and Gompertz Models

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【作者】 颜赛娜陈斌

【Author】 YAN Saina;CHEN Bin;College of Animal Science and Technology, Hunan Agricultural University;

【通讯作者】 陈斌;

【机构】 湖南农业大学动物科技学院

【摘要】 用Logistic和Gompertz模型拟合大白猪的体重生长曲线,旨在找出较符合大白猪实际生长情况的模型。选定大白公、母猪各5头,测定其0,21,35,70,120,150,180 d时的体重,用Logistic和Gompertz模型分别对大白公、母猪进行生长曲线的拟合。结果显示:Logistic模型对大白公、母猪的拟合度(R2)分别为0.999 25,0.999 34,生长拐点分别为(33.51 d,66.95 kg),(34.62 d,65.45 kg);Gompertz模型对大白公、母猪的拟合度分别为0.999 96,0.999 97,生长拐点分别为(137.36 d,74.64 kg),(138.34 d,73.58 kg)。Logistic模型中大白公、母猪的生长拐点均不符合实际情况,且Gompertz模型对大白公、母猪体重生长曲线的拟合度均高于Logistic模型。说明Gompertz模型较适合大白公、母猪体重生长曲线的拟合。

【Abstract】 In this study, the Logistic and Gompertz models were used to fit the body weight growth curve of Large White pigs, so as to find out the model which was more consistent with the actual growth of Large White pigs. In this experiment, 5 males and 5 females of Large White pigs were selected to measure their body weight at 0, 21, 35, 70, 120, 150 and 180 days after birth. Then, the Logistic and Gompertz models were used to fit the growth curves of the male and female pigs. The results showed that the fitting degree(R2) of the Logistic model was 0.999 25 and 0.999 34 for male and female Large White pigs, and the growth inflection points were(33.51 d, 66.95 kg) and(34.62 d, 65.45 kg), respectively. The R2 of the Gompertz model was 0.999 96 and 0.999 97 for male and female Large White pigs, and the growth inflection was(137.36 d, 74.64 kg) and(138.34 d, 73.58 kg), respectively. The growth inflection points of both males and females in the Logistic model were not consistent with the actual situation, while the R2 of the Gompertz model to the growth curve of the body weight of both males and females was higher than that of the Logistic model. The results showed that the Gompertz model could better fit the weight growth curve of the male and female Large White pigs.

【基金】 湖南省自然科学基金项目(2018JJ3219,2018JJ2176,2020JJ4348);湖南省重点研发计划项目(2020NK2024)
  • 【文献出处】 经济动物学报 ,Journal of Economic Animal , 编辑部邮箱 ,2023年01期
  • 【分类号】S828.2
  • 【下载频次】938
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