基于Logistic_exp模型的苏尼特羊早期生长发育规律研究
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摘要
为了探索苏尼特羊的生长发育规律,提高非线性模型的拟合度(R~2)和预测准确度,提出一种新的结合气象因子的生长模型Logistic_exp,针对该模型首先采用灰色关联分析法得出影响苏尼特羊月增重的气象因子排序,其次将最大影响因子风速以线性、指数方式引入传统生长模型中,并对所有非线性模型进行拟合。结果表明:新模型的R~2达到0.999,最后使用2012年的体重和气象数据对Logistic_exp模型进行验证,Logistic_exp模型在各月龄的估计值和实际值最吻合,误差最小,具有更高的R~2。
In order to explore the growth and development law of Sunite sheep and improve the fitting degree(R~2) and prediction accuracy of the nonlinear model, a new growth model Logistic_exp combining meteorological factors was proposed. In this model, firstly, grey relational analysis was used to obtain the order of meteorological factors influencing the monthly weight gain of Sunite sheep. Secondly, the maximum impact factor wind speed is introduced into the traditional growth model in a linear and exponential manner, and all nonlinear models are fitted. The results showed that the fitting degree of the new model reached 0.999. Finally, the results of the Logistic_exp model were verified using body weight and meteorological data for nearly one year. It is found that the Logistic_exp model is the most consistent with the actual value of each month, with the smallest error and higher fitting degree(R~2).
In order to explore the growth and development law of Sunite sheep and improve the fitting degree(R~2) and prediction accuracy of the nonlinear model, a new growth model Logistic_exp combining meteorological factors was proposed. In this model, firstly, grey relational analysis was used to obtain the order of meteorological factors influencing the monthly weight gain of Sunite sheep. Secondly, the maximum impact factor wind speed is introduced into the traditional growth model in a linear and exponential manner, and all nonlinear models are fitted. The results showed that the fitting degree of the new model reached 0.999. Finally, the results of the Logistic_exp model were verified using body weight and meteorological data for nearly one year. It is found that the Logistic_exp model is the most consistent with the actual value of each month, with the smallest error and higher fitting degree(R~2).
引文
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[3] 索效军,张年,熊琪,等.麻城黑山羊生长曲线的拟合与分析[J].江苏农业科学,2015, 43(11):291-293.
[4] 赵中利,李欣,张芳毓,等.吉林芦花鸡生长发育规律研究[J].安徽农业科学,2018,46(6):71-72,116.
[5] 马发顺,梁秀丽,宋玉伟,等.豫北小尾寒羊生长模型的拟合与分析[J].中国动物保健, 2013,15(5):12-15.
[6] MOREIRA R P, PEDROSA V B, FALC?O P R, et al. Growth curves for Ile de France female sheep raised in feedlot[J].Semina Cienc Agrar,2016,37(1):303-310.
[7] 张勇,郭武君,李晓梅,等.甘肃高山细毛羊羔羊生长曲线模型选择及生长曲线分析[J]. 河南农业科学,2016,45(9):125-129.
[8] 方亚,黄勇富,王高富.酉州乌羊生长曲线拟合分析研究[J].畜禽业,2011(12):32-34.
[9] 陈玲,吕晓阳,王庆增,等.湖羊生长发育曲线模型预测及趋势分析[J].中国畜牧兽医, 2014,41(12):239-243.
[10] WAHEED A,EYDURAN E,TARIQ M M, et al.Comparison of the non-linear models defining the growth of Thalli sheep under desert conditions[J].Pak J Zool,2016, 48(2):423-426.
[11] HOSSEINZADEH N G, GOLSHANI M. Comparison of non-linear models to describe growth of Iranian Guilan sheep[J].Rev Colomb Cienc Pec,2016,29(3): 199-209.
[12] SIEKLICKI M D F,PEDROSA V B,ROCHA C G, et al. Growth curves of texel male lambs[J].Acta Sci Vet,2016,44(1):6-11.
[13] 李琦,刘双.影响苏尼特放牧羊体质量变化的相关因素分析[J].浙江农业学报,2017, 29(10):1630-1636.
[14] 史激光,王英舜,武魁,等.典型草原放牧绵羊体重动态变化特征分析[J].中国草食动物科学,2009,29(2):20-22.
[15] 刘金祥,李志坚,周道玮,等.高山草原放牧绵羊体重动态变化及其限制因素的灰色关联度分析[J].家畜生态,2000,21(4):1-4.