Life Sci. Int. J., Vol: 3, Issue-2,April 2009, Page: 1126-1129http://www.isphyderabad.com.pk

Muzzle Dermatoglyphics of Holstein and its Applicability in Predicting the Major Milk Contents

N.N. Al-Anbari1 and F.R. Al-Samarai2

1Department of Animal Res./ College of Agriculture / Univ. of Baghdad, Iraq2Department of Veterinary Public Health /College of Veterinary Medicine/ Univ.of Baghdad, Baghdad, IraqE-mail: nasr_noori@yahoo.com firas_rashad@yahoo.com

ABSTRACTA study included 70 Holstein cows was conducted at the Nasr Dairy Cattle Station, United Company for Animal Resources Ltd., Al-Soueira (50 km south of Baghdad) in 2007. Seventy primiparous cows were chosen randomly from the herd. The photograph of the muzzle of all cows was used to identify the different shapes of dermatoglyphics on the surface of muzzle (simple arch, fork, enclosure, islands and short ridges) and to measure the muzzle width. The aim of this research was to analyze the relationship between the dermatoglyphics and major milk contents (fat, protein and lactose). Regression coefficient of milk contents on muzzle dermatoglyphics was estimated by using SAS program to get prediction equations. Results revealed that most dermatoglyphics could be good predictors of milk contents, particularly enclosures which had highest (R²) compared with others.

Key words: Holstein, dermatoglyphics, prediction equation, milk contents.

INTRODUCTIONMuzzle patterns of cattles have uneven features on their skin surface. They are different from each other like the fingerprints of human beings. For this reason they can be used to identify cattles (Niu and Sasaki 1993; Neary 2001; Minagawa et al., 2007). Population-genetic researches of cattle breeds established high heritability of some quantitative and qualitative characters of muzzle dermatoglyphics of cattles (Baranov et al., 1993; Graml et al., 1993; Parna et al., 1996). A few researches were conducted to investigate the relationships between muzzle dermatoglyphics of cattles and milk contents. The existance of such relationships could lead to improve the performance of cows by using prediction equations as a tool of selection. Arising from this assumption, this work was submitted to analyze the relationships between the muzzle dermatoglyphics and major milk contents in Holstein cows to get prediction equations because the prediction equation displayed an utmost importance for selecting cows in early time.

MATERIALS AND METHODSThe research was conducted at the Nasr Dairy Cattle Station, United Company for Animal Resources Ltd., Al-Soueira (50 km south of Baghdad) in 2007. Seventy primiparous cows were chosen randomly from the herd. Muzzle of all primiparous cows was photographed. All photographs were analyzed to identify the different shapes on the surface of muzzle such as, simple arches (SA), forks (F), enclosures (E),

islands (I) and short ridges (SR) and to measure the muzzle width.Milk contents of all cows were adjusted for the effect of stage of lactation. Protein and fat in milk were estimated by used spectophotometric determination type PYE-UNICAM (Naki and Le 1970), whereas fat in milk was estimated according to method described by Tribold and Aurand (1963).The materials required in this procedure were as follows:A Sony Digital video camera model DCR-TRV740E (Sony Corporation, Tokyo, Japan) with 8 mm card memory 700X, optical 15 X, and photograph digital 87. A computer with the following programs:A-Microsoft Windows XP Professional (5.1, Build 2600) Intel (R) Pentium (R) 4 CPU 2.00 GHz 256 MB RAM. This program can convert the storage of images in the camera to computers directly.B-Photograph impression 3.0 program used to let i photographs more adjacent and focus.C-Adobe Photoshop 7.0 program was used to analyze the dermatoglyphics of muzzle.

Procedures of image and measurement dermatoglyphicsThe animals were restrained by using a cattle master immobilizer crush. The muzzle was then washed and dried with a cotton towel after photographs by the camera. All photographs were converted to computer and then analyzed by Adobe Photoshop program. When the photograph was not clear we used photograph impression program to increase the image clearness.

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Statistical analysis The simple regression coefficient of the major milk contents ((fat, protein and lactose) on dermatolyphics was estimated by using SAS (2001) program to get prediction equations of all milk contents.Ŷ = β0 + β1YWhere Ŷ the prediction of a certain trait, β0 = intercept, β1Y = coefficient of regression of milk contents on dermatolyphics.

RESULTS AND DISCUSSIONRegressions are defined as the amount of change in one variable that can be expected for a given amount of change in another variable. Regressions are really just rates of change, and the units of regression reflect that fact.Regressions are used to help predict a value based on some other pieces of information.

1. The regressions of milk contents on simple arches

Table 1 revealed that the regressions of fat and protein yield on simple arches (Figure 1) were significant (P < 0.01) with coefficients of 0.214 and 0.131 kg/arche respectively. These results indicate that the two traits can be used as trait indicator to select the cows for increasing fat and protein yield due to their high coefficient of determination (R²) values (0.79 , 0.86). On the other hand, the regression coefficient of lactose on simple arches was non significant.

2. The regressions of milk contents on number of forks

The coefficient of regressions of fat (- 0.239) and lactose (- 0.502) on number of forks (Figure 2) were negative and significant (P < 0.01), and the R² were 0.54 and 0.92 respectively (Table 1).

3. The regressions of milk contents on number of enclosures

Results revealed that the coefficients of regression of all traits (fat, protein and lactose) on number of enclosures (Figure 3) were positive and significant (P < 0.01) (Table 2) with R² 0.92, 0.78 and 0.93 respectively. The significant coefficients and the high R² of all traits reflect the possibility of using these traits in selection of cows to improve these traits.

4. The regressions of milk contents on number of islands

Regression coefficients of fat (0.449) and lactose (0.528) on number of islands (Figure 4) were positive and significant (P < 0.05) with R² 0.87 and 0.62 respectively, whereas, the regression of protein on number of islands was negative and non significant (Table 2).

5. The regressions of milk contents on number of short ridges

Table 3 indicated that all regressions were positive and significant (P < 0.01), with high estimates of R². Hence the number of short ridges (Figure 5) could be used to improve the milk contents of cattle.

6. The regressions of milk contents on number of muzzle width

Results of this research indicated that the regression equation of protein on muzzle width was positive and significant whereas the two traits were not (Table 3). However, all previous traits mentioned in this research defined as discreet traits and the muzzle width (Figure 6) considered as continuous trait. The last trait represent unusual case because it could measure easily compared with other traits. Thus the use of this trait in animal breeding is more practical. Moreover, these results were supported by Singh and Patel, (2002).

CONCLUSIONSIt could be concluded, from this study, that most regression coefficients between muzzle dermatoglyphics and milk contents were positive, hence, they could be used as good predictors of milk contents, particularly number of enclosures which had highest R² compared with others. That was not the only reason for their superiority, there was an additional reason, represented in the high estimates of heritability of some muzzle dermatoglyphics of cattle which were reported by several researches. Due to high heritability estimates, selection of cows for muzzle dermatoglyphics could lead to a good response in these traits. In view of this study, it is imperative to conduct other researches to estimate genetic parameters of muzzle dermatoglyphics and to determine exist of correlated response between these traits and productive traits.

Table1: Coefficient of regressions of milk contents on simple arches and forks of Holstein muzzle

** (P < 0.01)NS= Non significant? Denotes as ŷ

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Table2: Coefficient of regressions of milk contents on enclosures and islands of Holstein muzzle

* (P < 0.05)NS= Non significant? Denotes as ŷ

Table3: Coefficient of regressions of milk contents on short ridges and muzzle width of Holstein

* (P < 0.05)NS = Non significantSr = short ridge? Denotes as ŷ

Figure 1: Simple arch in the muzzle of Holstein

Figure 2: Fork in the muzzle of Holstein

Figure 3: Enclosure in the muzzle of Holstein

Figure 4: Island in the muzzle of Holstein

Figure 5: Short ridge in the muzzle of Holstein

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Figure 6: Muzzle width in Holstein

REFERENCESBaranov, A. S., R., Graml, F., Pirchner, and D.O., Schmid, 1993. Breed differences and intra-breed genetic variability of dermatoglyphic pattern of cattle. Jour. Anim. Breed. Genet., vol. 110: 385-392.

Graml, R., A.S., Baranov, J., Buchberger, and F., Pirchner, 1993. Milk protein genotypes and dermatoglyphics of cattle muzzle. Arch. Anim. Breed., 36 (2): 139-144.

Nakai, S and Le, A.C. 1970. Spectrophotometric determination of protein and fat in milk simultaneously. J. Dairy Sci. 53: 276-278.

Neary, M. 2001. Methods of livestock identification. http://www.ces.purdue.ed.u/extmedia.

Niu, Y. and Sasaki, Y., 1993: Recognition of muzzle pattern using local features in cattle. Bioimages 1:81-88. 

Minagawa, H., Fujimura, T., Ichiyanagi, M and Tanaka, K. 2007. Identification of beef cattle by analyzing images of their muzzle patterns lifted on paper. 596-600. zoushoku.narc.affrc.go.jp/

Parna, E., A.S., Baranov, and A.V., Yablokov, 1996. Association studies on production and muzzle dermatoglyphic in Estonian cattle of different genetic origin. XXVth International Conference on Animal Genetics. ISAG International Society for Animal Genetics. Section E: Association between markers and traits. 21-25 July 1996. Tours - France. 166.

SAS, 2001. SAS / STAT Users Guide for Personal Computers. Release 6.12. SAS Institute Inc., Cary, NC., USA.

Singh, N. P. and A.M., Patel, 2002. Muzzle measurements and characteristics of surti and characteristics of Surti and Jaffarabadi buffalo and its applicability in predicting the age and production performance. Gujarat Agricultural University, Anand, Gujarat 388001, India. Buffalo Bulletin (March 2002) Vol. 21, No.1. Indian Journal of Animal Sciences (2001). 71(11): 1062-1065. 2-5611369.

Tribold, H.O. and Aurand L.W. 1963. Food Composition and Analysis. De Van, nostramd, Co. Inc. NY.

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