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腾冲雪鸡与白羽肉鸡杂交后代生长曲线的拟合分析

Comparative Analysis and Modeling of Growth Curves in Hybrids of Tengchong Snow Chicken and White-Feathered Broilers

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【作者】 李鑫璐刘锦楠刘仙花李浩杰王有学葛长荣王坤

【Author】 LI Xinlu;LIU Jinnan;LIU Xianhua;LI Haojie;WANG Youxue;GE Changrong;WANG Kun;College of Animal Science and Technology,Yunnan Agricultural University;College of Animal Science and Technology and College of Veterinary Medicine,Huazhong Agricultural University;Dongchuan District Animal Health Supervision Institute of Kunming City;

【通讯作者】 王坤;

【机构】 云南农业大学动物科学技术学院华中农业大学动物科学技术学院动物医学院昆明市东川区动物卫生监督所

【摘要】 研究旨在分析腾冲雪鸡与白羽肉鸡杂交所得F2代鸡(简称F2代鸡)及腾冲雪鸡与F2代鸡杂交后代(简称F2×雪鸡)的生长特性。选取F2代鸡和F2×雪鸡各300只(公母各半),在相同饲养管理条件下饲养至17周龄,每周记录体重变化,并应用Logistic、Gompertz和Von Bertalanffy 3种非线性生长模型对2个杂交群体的体重数据进行拟合分析。结果表明:所有模型拟合优度(R2)均>0.97,其中Von Bertalanffy模型效果最优:F2代鸡公鸡和母鸡的R2分别为0.995 61和0.995 59,F2×雪鸡公鸡和母鸡的R2分别为0.989 96和0.995 40。表明Von Bertalanffy模型对这2个杂交群体的生长曲线描述具有优势。

【Abstract】 This study aimed to analyze the growth characteristics of F2 generation chickens(abbreviated as F2 generation) obtained from the crossbreeding of Tengchong Snow Chicken and white-feathered broilers, as well as hybrid offspring from crossbreeding Tengchong Snow Chicken with F2 generation chickens(abbreviated as F2×Snow Chicken). A total of 300 F2 generation chickens and 300 F2×Snow Chicken offspring(half males and half females) were raised under identical management conditions until 17 weeks of age. Body weight was recorded weekly and analyzed using three nonlinear growth models: Logistic, Gompertz, and Von Bertalanffy. The results showed that all models exhibited high goodness-of-fit(R2 >0.97). Notably, the Von Bertalanffy model demonstrated the best fit, with R2 values of 0.995 61 for F2 generation roosters, 0.995 59 for hens, 0.989 96 for F2×Snow Chicken roosters, and 0.995 40 for hens. These findings indicate that the Von Bertalanffy model provides significant advantages in describing the growth curves of these two hybrid populations.

【基金】 云南省科技厅基础研究专项-青年项目[202401AU070079];云南省科技厅农业联合专项[202401BD070001-036];云南省动物营养与饲料重点实验室开放基金项目[2023YNPKLANF003]
  • 【分类号】S831
  • 【下载频次】12
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