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Chaudhary Charan Singh Haryana Agricultural University, Hisar

Chaudhary Charan Singh Haryana Agricultural University popularly known as HAU, is one of Asia's biggest agricultural universities, located at Hisar in the Indian state of Haryana. It is named after India's seventh Prime Minister, Chaudhary Charan Singh. It is a leader in agricultural research in India and contributed significantly to Green Revolution and White Revolution in India in the 1960s and 70s. It has a very large campus and has several research centres throughout the state. It won the Indian Council of Agricultural Research's Award for the Best Institute in 1997. HAU was initially a campus of Punjab Agricultural University, Ludhiana. After the formation of Haryana in 1966, it became an autonomous institution on February 2, 1970 through a Presidential Ordinance, later ratified as Haryana and Punjab Agricultural Universities Act, 1970, passed by the Lok Sabha on March 29, 1970. A. L. Fletcher, the first Vice-Chancellor of the university, was instrumental in its initial growth.

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Subject: Statistics × Author: Deepak Singh × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    Principal component analysis in modelling lactation milk yield in Hariana cows
    (CCSHAU, 2009) Deepak Singh; Saxena, K.K.
    A total of 244 first lactation records of Hariana cows maintained in the Department of Animal Breeding, C.C.S.H.A.U., Hisar over a period of 14 years (1989 to 2003) were analyzed. The first lactation traits used for multiple linear regression and principal component regression model were: first lactation milk yield (FLMY), age at first calving (AFC), first peak yield (FPY), first lactation length (FLL), first dry period (FDP), first service period (FSP) and first calving interval (FCI). The phenotypic correlations among explanatory variables were significant at 5 % level of significance. It gave an indication of multicollinearity. Using stepwise regression technique the maximum value of coefficient of determination was obtained as 0.648. The multicollinearity among different explanatory variables made it very difficult to identify the contribution of each explanatory variable. There were non-significant regression coefficients giving an indication of loosing information on some of important explanatory variables. Therefore principal component regression model was fitted. The phenotypic correlations among the traits were used to derive principal component scores and correlation coefficients of these variables with the original variables (component loadings) were analyzed. The first, second, third and fourth principal components (PC’s) explained 45.24, 25.70, 15.33 and 11.68 of total variation in the data. The first four PC’s explained 97.94 % of variance cumulatively. The correlations of first PC with AFC, FPY and FLL were positive ranging from 0.13 to 0.34, while it’s correlations with FDP, FSP and FCI were very high and negative ranging from -0.91 to -0.93. The correlations of second PC were positive with all the variables (ranging from 0.30 to 0.88) except FDP (-0.27). The third PC was positively correlated with all the variables except FPY (-0.47) while the fourth PC was positively correlated with AFC (0.33), FPY (0.58) and FDP (0.24) and negatively with FLL (-0.44), FSP (-0.03) and FCI (-0.01). The first PC can be interpreted as reproduction and production component, the second and fourth PC as production component and the third PC as maturity component. The number of meaningful PC’s were retained on the basis of Kaiser’s, Scree plot, Proportion of variance accounted for (only those PC’s are retained which account for at least 10 % of variation of data), Cumulative percent of variance accounted for (only those PC’s are retained which account for 85% to 90% of variation of data cumulatively) and Jollife (1972) criterions. When we regressed the retained PC’s on FLMY, PC’s retained on basis of “cumulative percent of variance accounted for” criterion gave best results. When we compare the stepwise regression results with the principal component regression model (when FLMY regressed on first three PC’s) we found that there is no significant change in R2 but in the principal component regression model there is contribution of each and every variable. So principal component regression model increases the accuracy and validity of the model.