彭县黄鸡产蛋率和累计产蛋数曲线拟合分析

发布时间：2018-07-11 16:49

本文选题：彭县黄鸡 + 数学模型　；参考：《四川农业大学学报》2017年03期

【摘要】：【目的】探讨彭县黄鸡产蛋率和产蛋数变化规律,为提高其产蛋性能提供理论依据。【方法】统计彭县黄鸡选育群体21~43周龄的每周产蛋率和累计产蛋数,采用伍德、分室和杨宁模型对产蛋率曲线进行拟合分析,以及采用Logistic、Gompertz和Von Bertalanffy模型对累计产蛋数曲线进行拟合分析,并与实际生产值进行比较。【结果】杨宁模型的拟合度最高(R~2=0.968),分室模型的次之(R~2=0.908),伍德模型的效果最差(R~2=0.787),杨宁模型的预测值与实际观察值最接近;Von Bertalanffy模型对累积产蛋数曲线的拟合度最高(R~2=0.998),预测的拐点产蛋周龄为13.386周,拐点产蛋数为48.534枚,预计累积产蛋数85.13枚,比实际低1.31枚。【结论】杨宁模型和Von Bertalanffy模型分别对彭县黄鸡的产蛋率和累计产蛋数拟合效果最佳,可用于彭县黄鸡产蛋规律的预测和分析,可为彭县黄鸡的选育和饲养管理提供参考。
[Abstract]:[objective] to study the rule of egg laying rate and egg number variation in Pengxian yellow hens, and to provide a theoretical basis for improving their egg laying performance. [methods] the weekly egg laying rate and accumulative egg number of 21 ~ 43 weeks old selected populations of Pengxian yellow hens were analyzed, and Wood was used. The egg yield curve was fitted by compartments and Yang Ning models, and the cumulative egg number curve was analyzed by logistic Gompertz and Von Bertalanffy models. Compared with actual production value, Yang Ning model had the best fit (RG2N 0.968), division model had the second place (RG2O0.908), Wood model had the worst effect (RP20.7087), and the predicted value of Yang Ning model was closest to the observed value of Von Bertalanffy model for cumulative production. The curve of egg number had the highest fitting (R _ (2) 0. 998), and the predicted age of egg laying was 13.386 weeks at the inflection point. The number of eggs laid at inflexion point was 48.534, and the cumulative number of eggs was 85.13, which was 1.31 lower than the actual number. [conclusion] Yang Ning model and Von Bertalanffy model were the best fitting results for egg production rate and accumulative egg number of Pengxian yellow hens, respectively. It can be used to predict and analyze the laying rules of yellow chickens in Pengxian County and provide reference for breeding and raising management of yellow chickens in Pengxian County.
【作者单位】：贺州市水产畜牧兽医局;四川农业大学动物科技学院;
【基金】：成都市农业技术成果应用示范项目(2015-NY01-00036-NC)
【分类号】：S831

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