Stepwise Method and Factor Scoring in Multiple Regression Analysis of Cashmere Production in Liaoning Cashmere Goats

被引:6
作者
Meng, Yang [1 ]
Zhang, Boqi [2 ]
Qin, Zhiyun [1 ]
Chen, Yang [1 ]
Shan, Xuesong [1 ]
Sun, Limin [1 ]
Jiang, Huaizhi [1 ]
机构
[1] Jilin Agr Univ, Coll Anim Sci & Technol, Changchun 130118, Peoples R China
[2] Jilin Univ, Coll Anim Sci, Changchun 130062, Peoples R China
关键词
regression analysis; stepwise analysis; factor analysis; cashmere yield; Liaoning cashmere goats; body weight measurements; FACTOR-ANALYSIS SCORES; BODY-WEIGHT; CARCASS WEIGHT; PREDICTION; TRAITS; ASSOCIATION; PROLACTIN; GROWTH;
D O I
10.3390/ani12151886
中图分类号
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号
0905 ;
摘要
Simple Summary Liaoning cashmere goat is a world-famous cashmere goat breed known for its stable genetic performance and high cashmere yield. This study investigated the relationship between certain body measurements, body weight, and cashmere yield in Liaoning cashmere goats using stepwise and factor score analyses in a multiple regression analysis. These results provide a standardized regression equation and reliable information on multiple trait considerations that should be taken to improve cashmere yield. To clarify the relationship and influence among the body circumference, body weight, and cashmere yield of Liaoning cashmere goat, aiming to provide the basis for the breed selection and germplasm identification of Liaoning cashmere goat. Liaoning cashmere goat is a well-known local cashmere goat breed in China and even in the world. It is famous for producing cashmere with superior quality and high yield. Cashmere yield, body measurements, and body weight are the primary indicators of cashmere goat breeding, but the correlation between them is not yet clear. Therefore, this study investigated the relationship between certain body measurements, body weight, and cashmere yield in Liaoning cashmere goats using stepwise and factor score analyses in a multiple regression analysis. For this purpose, the body measurements (body slanting length (BSL), body height (BH), chest circumference (CC), pipe circumference (PC), chest depth (CD), chest width (CW), hip breadth (HB), body weight (BW) and cashmere yield (CY)) of 200 (2-year-old) Liaoning cashmere goats were collected. Stepwise analysis of the results showed that body weight had the greatest direct effect on cashmere yield, followed by hip breadth, while chest circumference mainly affected cashmere yield indirectly. The results of factor score analysis showed that the independent variable can be represented by two factors, which explained 49.596% and 12.095% of the total variance, respectively. The factor scores used in the regression analysis explained 75.8% of the total variance in Liaoning cashmere yield. The above studies show that the growth traits of Liaoning cashmere goats are closely related to the cashmere yield. Growth traits should be considered important factors in breed selection, germplasm identification, and rearing.
引用
收藏
页数:10
相关论文
共 29 条
[1]  
[Anonymous], 2007, Nutrient requirements of small ruminants
[2]   Molecular Characterization of Prolactin cDNA and Its Expression Pattern in Skin Tissue of Liaoning Cashmere Goat [J].
Bai, W. L. ;
Yin, R. H. ;
Jiang, W. Q. ;
Luo, G. B. ;
Yin, R. L. ;
Li, C. ;
Zhao, Z. H. .
BIOCHEMICAL GENETICS, 2012, 50 (9-10) :694-701
[3]  
Cankaya S., 2009, Anadolu Tarim Bilimleri Dergisi, V24, P98
[4]   Analysis of Variables Affecting Carcass Weight of White Turkeys by Regression Analysis Based on Factor Analysis Scores and Ridge Regression [J].
Celik, S. ;
Sengul, T. ;
Sogut, B. ;
Inci, H. ;
Sengul, A. Y. ;
Kayaokay, A. ;
Ayasan, T. .
BRAZILIAN JOURNAL OF POULTRY SCIENCE, 2018, 20 (02) :273-279
[5]   Microsatellite analysis revealed genetic diversity and population structure among Chinese cashmere goats [J].
Di, R. ;
Vahidi, S. M. Farhad ;
Ma, Y. H. ;
He, X. H. ;
Zhao, Q. J. ;
Han, J. L. ;
Guan, W. J. ;
Chu, M. X. ;
Sun, W. ;
Pu, Y. P. .
ANIMAL GENETICS, 2011, 42 (04) :428-431
[6]  
Draper N.R., 1981, J AM STAT ASSOC, V76, P1012
[7]  
Eyduran E, 2009, BULG J AGRIC SCI, V15, P374
[8]  
Eyduran E, 2010, INT J AGRIC BIOL, V12, P611
[9]  
Pirrientel EDG, 2007, GENET MOL BIOL, V30, P536, DOI 10.1590/S1415-47572007000400006
[10]  
Gujarati D. N., 2003, BASIC ECONOMETRICS