Indian J. Anim. Res., 50 (6) 2016 : 851-855
AGRICULTURAL RESEARCH COMMUNICATION CENTRE
Print ISSN:0367-6722 / Online ISSN:0976-0555
www.arccjournals.com/www.ijaronline.in
Efficiency of sire evaluation methods by using phase and stayablity traits to improve milk yield of Murrah buffaloes Kamaldeep*, A.S.Yadav, S.S. Dhaka, Ankit Magotra and Anika Malik1 Department of Animal Genetics and Breeding, College of Veterinary Science, LUVAS, Hisar-125 004, India. Received: 19-05-2015 Accepted: 17-09-2015
DOI: 10.18805/ijar.5707
ABSTRACT The present investigation was conducted on records of 345 Murrah buffaloes maintained at Buffalo Research Centre (BRC), Hisar distributed over 20 years (1993-2012) to compare progeny of 61 sires. Three sire evaluation procedures [ordinary least squares (OLS), regressed least squares (RLS), and best linear unbiased prediction (BLUP)] based on estimated breeding value of phase traits such as ascending phase milk yield (APY), peak phase milk yield (PPY),descending phase milk yield (DPY)and stayablity trait such as stayablity life (STAYAB) in Murrah buffalo. The results indicated that sire number 26 had the highest merit computed by OLS, RLS and sire number 2 had the highest merit computed by BLUP method for APY. Product–moment correlations were comparatively lower than those of rank correlations barring a few exceptions. When comparison was made on the basis of coefficient of skewness, BLUP was found superior for APY, PPY, and DPY. When comparison was made on the basis of coefficient of kurtosis, OLS was better for APY and DPY whereas RLS was found superior for PPY and STAYAB. When coefficient of Determination, was considered OLS was found to be more accurate followed by RLS method for all the traits, whereas RLS method was most appropriate when coefficient of variation was considered. Key words: Breeding values, Correlations, Murrah, Phase traits, Stayablity traits. INTRODUCTION The world buffalo population is estimated at 185.29 million, spreading across 42 countries, of which 179.75 million (97%) are in Asia (FAO, 2008). India has a population of 108.7 million buffalo (DAHD, 2012). Genetic improvement for growth in Murrah buffaloes is of great importance as it contribute 52.6% of the total milk production (BAHS, 2012). Buffalo produces 62.35 million tonnes milk out of total 121.85 million tonnes in India and Haryana contributes 5.24 million tonnes buffalo milk to it (DAHDGOI -2011). Moreover buffalo provides up to 30 per cent of the draft power for agricultural operations . Murrah is one of the renowned breed of buffaloes in world by virtue of its milk producing capacity combined with tremendous potential for further genetic improvement.. The aim of animal breeder is to select the genetically superior bull to bring out genetic improvement in the productive as well as reproductive performance of the herd. Therefore, suitable selection criterion which gives best discrimination among sires should be formulated to evaluate sires on the basis of performance of their daughters considering both phase as well as stayablity life traits. To improve the efficiency and accuracy of sire evaluation programmes many sire indices has been developed by using the procedures of Least -Squares (OLS), Regressed Least -Squares (RLS) and Best Linear Unbiased Prediction
(BLUP).The literature is dotted with conflicting reports Pundir et al. (2004); Dhaka et al. (2004); Banik and Gandhi, (2006); Raja, (2010) on comparative evaluation of various sire evaluation techniques in Murrah buffaloes. Therefore, an effort has been made to estimate breeding values for different phase and stayablity life traits by different methods in Murrah buffalo and comparison of these methods to find out the most suitable method for sire evaluation. MATERIALS AND METHODS The data was collected from history cum pedigree sheets maintained at Buffalo Research Centre, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, India, over a period of 20 years from 1993 to 2012 and progeny of 61 bulls were considered. Hisar town is situated in semi-arid region and climatic condition is sub-tropical in nature. Geographically, Hisar is situated at 29° 10' N latitude, 75° 40' E longitude and 215.2 meters altitude. The phase traits and stayablity trait recorded were : ascending phase milk yield (APY); Peak phase milk yield (PPY); descending phase milk yield (DPY) and Stayablity life (STAYAB). Bulls with minimum three progenies were considered for the present study. Estimation of breeding value: Breeding value of sires for different phase traits and stayablity trait (APY, PPY, DPY
*Corresponding author’s e-mail:
[email protected] 1 Department of Veterinary and Animal Husbandry Extension Education, College of Veterinary Science, LUVAS, Hisar-125 004, India.
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and STAYAB) were computed separately by using different sire evaluation procedures : Ordinary Least Squares (OLS), Regressed Least Squares (RLS): The regressed least-squares have been discussed by Harvey (1990). Best Linear Unbiased Predictor (BLUP): Henderson (1973). The genetic and non genetic factor affecting the phase traits and stayablity traits were analyzed using the following mixed model for nonorthogonal data (Harvey, 1990)
Where Y ijkl
= is the lth record of individual of the ith sire calved in jth period and kth season µ = is the overall population mean si = is the random effect of ith sire hj = is the fixed effect of jth period of calving Ck = is the fixed effect of kth season of calving b1 and b2 = are linear and quadratic partial regression coefficients of age at first calving. Xijkl = is the age at first calving. X = is the mean of age at first calving. e ijkl = is the error associated with each observation with mean zero and assumed to be normally and independently distributed and variance 2 e Comparative evaluation of different methods: Spearman rank correlation among ranks and simple product moment correlation coefficient among estimates of sire merit (Steel and Torrie, 1981) were used to judge the relative efficiency of different methods. The following criteria were used to judge accuracy of different sire evaluation procedures: the coefficient of skewness, the coefficient of kurtosis, standard error of an estimate, coefficient of variation and coefficient of determination. The coefficient of skewness and coefficient of variation close to zero and coefficient of kurtosis close to 3 is considered as estimate perfect indicating that the population follows a normal distribution, whereas low standard error of the estimate and high value for coefficient of determination are an indicator of the better accuracy of method. RESULTS AND DISCUSSION The breeding value of sires were computed using the Ordinary Least Square (OLS), Regressed Least Square (RLS) and Best Linear Unbiased Prediction (BLUP) procedures for different phase and stayablity traits. The above mentioned procedures were compared to assess accuracy utilizing standard error of the estimates, the coefficient of skewness and kurtosis, coefficient of variation and coefficient of determination The estimated breeding values by OLS, RLS and BLUP ranged from 215.11 to 392.45, 258.73 to 311.89 and 230.58 to 322.39 for APY; 74.99 to 166.07, 105.73 to 130.81 and 91.87 to 136.84 for PPY; 1112.04 to 2811.02, 1463.65 to 1918.12 and 1204.56 to 2043.22 for DPY and 423.07 to
2094, 964.5 to 1358.51 and 897.76 to 1715.79 for STAYAB. (Table 1 and 2 ). The result for APY revealed that sire number 26 had the highest merit computed by OLS (392.45), RLS (311.89) and same sire ranked second when breeding value calculated by BLUP (318.14) method (Table 2). Six sires out of top ten, shared their ranks by being in top ten irrespective of methods employed for computation of breeding value of sires (Table 2). Sire number 33 was found to be of lowest in merit by OLS (215.11), BLUP (230.58) and same sire ranked second lowest by RLS (261.84) (Table 1).Seven sires out of bottom ten, shared their ranks by being in bottom ten when breeding value was calculated by either of three methods included in the study (Table 1). The result for PPY revealed that sire number 26 had the highest merit computed by OLS (166.07), RLS (130.81) and BLUP (136.84) methods (Table 2). Eight sires out of top ten, shared their ranks by being in top ten irrespective of methods employed for computation of breeding value of sires (Table 2). Sire number 33 was found to be of lowest in merit by OLS (74.99), RLS (105.73) and BLUP (91.87) method (Table 1). Four sires out of bottom ten, shared their ranks by being in bottom ten when breeding value was calculated by either of three methods included in the present investigation(Table 1) The result for DPY revealed that sire number 60 had the highest merit computed by OLS (2811.02), RLS (1918.12) and BLUP (2043.22) methods (Table 2). Seven sires out of top ten, shared their ranks by being in top ten irrespective of methods employed for computation of breeding value of sires (Table 2). Sire number 57 was found to be of lowest in merit by OLS (1112.04), BLUP (1204.56) and same sire ranked fourth lowest by RLS (1508.86) method (Table 1). Four sires out of bottom ten, shared their ranks by being in bottom ten when breeding value was calculated by either of three methods included in the study (Table 1). The result for STAYAB revealed that sire number 28 had the highest merit computed by OLS (2095), RLS (1358.51) and same sire ranked second when breeding value calculated by BLUP (1565.01) method (Table 2). Seven sires out of top ten, shared their ranks by being in top ten irrespective of methods employed for computation of breeding value of sires (Table 2). Sire number 6 was found to be of lowest in merit by OLS (423.07), third lowest by RLS (1091.11) and same sire ranked fifth lowest by BLUP (987.98) method (Table 1). Five sires out of bottom ten, shared their ranks by being in bottom ten when breeding value was calculated by either of three methods included in the study (Table 1) Rank and product – moment correlations: The rank and product- moment correlations among merit of sires for
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Volume 50 Issue 6 (2016) Table 1: Ranking of bottom ten sires for various phase and stayablity traits using different procedures Rank
Methods
61
OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP
60
59
58
57
56
55
54
53
52
APY
PPY
DPY
STAYAB
215.11(33) 258.73(5) 230.58(33) 215.92(37) 261.84(33) 241.02(57) 226.36(53) 262.07(39) 251.14(11) 227.32(39) 262.79(39) 254.37(7) 227.49(5) 264.15(7) 254.81(37) 236.50(570 265.02(53) 254.96(1) 240.81(7) 266.11(15) 257.28(53) 245(17) 266.22(38) 258.17(5) 245.35(23) 266.83(46) 258.41(39) 246.56(58) 267.88(57) 260.53(46)
74.99(33) 105.73(33) 91.87(33) 77.35(37) 106.38(37) 103.03(57) 89.37(3) 109.52(39) 104.3(37) 92.91(39) 109.59(3) 104.73(1) 97.57(57) 110.03(29) 105.29(29) 99.89(17) 110.83(46) 106.02(52) 100.18(31) 110.88(43) 106.81(34) 100.77(36) 111.14(5) 107.12(17) 101.40(29) 111.15(31) 107.44(46) 101.47(34) 111.77(17) 108.87(5)
1112.04(57) 1463.65(35) 1204.56(57) 1190.32(9) 1483.53(15) 1326.19(1) 1194.02(35) 1503.01(46) 1383.71(52) 1207.36(28) 1508.86(57) 1412.76(15) 1214.18(6) 1529.01(50) 1416.19(13) 1223.04(37) 1531.82(28) 1466.69(53) 1234.06(53) 1535(17) 1467.17(46) 1245.21(15) 1535.60(37) 1467.79(9) 1250.17(20) 1536.38(23) 1471.30(23) 1272.96(13) 1538.25(53) 1482.40(35)
423.07(6) 964.5(46) 897.76(46) 481.41(46) 1043.22(50) 939.81(43) 532.31(48) 1091.11(6) 944.91(39) 630.26(50) 1106.34(48) 974.33(26) 729.19(49) 1115.16(17) 987.98(6) 797.14(4) 1125.85(43) 1004.01(38) 803.39(17) 1127.90(4) 1017.88(50) 832.29(26) 1133.80(49) 1021.59(55) 835.38(53) 1134.15(26) 1032.76(61) 895.52(61) 1134.70(53) 1035.79(48)
production efficiency traits by various sire evaluation methods presented in the (Table 3 and 4). The rank correlations calculated through different methods were very high and it ranged from 0.83 to 0.99 for OLS X BLUP and OLS X RLS for STAYAB and APY and PPY respectively (Table 3). All the product moment correlations between estimated sire merit calculated by different methods were also very high and ranged from 0.83 to 0.99 for OLS X BLUP, RLS X BLUP and OLS X RLS for APY, DPY, STAYAB and APY respectively (Table 4). Similar findings also reported by various researchers: Dalal et al. (1999), Gaur et al. (2001), Dubey et al. (2006), Dhaka et al. (2004), Banik and Gandhi (2006), Bajetha (2006), Mukherjee et al.(2007), Kumar et al. (2008) and Moges et al. (2009). Rank and product moment correlations among sire merit calculated by various sire evaluation procedures for different production efficiency traits revealed that product moment correlations were comparatively higher than those of rank correlations barring few exceptions. Comparison of different sire evaluation methods: The accuracy of three methods used in the study for the estimation of breeding values of sires was judged through the coefficient of skewness, the coefficient of kurtosis,
standard error of an estimate, coefficient of variation and coefficient of determination. The content of Table 5 revealed that on the basis of the standard error of the estimates RLS method was found to be more accurate followed by BLUP method for all the phase and stayablity traits under study. When comparison was made on the basis of coefficient of skewness BLUP method was found superior for estimation of breeding values for all the traits (Table 5). When comparison was made on the basis of coefficient of kurtosis OLS procedure showed nearly perfect normal distribution for APY and DPY, while RLS showed normal distribution for PPY and STAYAB (Table 5). When coefficient of determination was considered OLS was found to be more accurate followed by RLS method for all the traits whereas, RLS method was observed most appropriate when coefficient of Variation was considered (Table 5). Kumar and Gandhi (2010) and Pandey et al. (2013) also supported the results pertinent to coefficient of determination. Harvey (1990) also pointed that BLUP was more accurate (1-7%) than RLS when the usual assumptions were met and even when moderate amount of heterogenous error variance exit.
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Table 2: Ranking of top ten sires for various phase and stayablity traits using different procedures Rank 1
2
3
4
5
6
7
8
9
10
Method
APY
OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP OLS RLS BLUP
PPY
392.45(26) 311.8926) 322.39(2) 374.16(24) 306.73(24) 318.14(26) 366.68(2) 304.62(20) 308.71(24) 362.83(44) 303.53(44) 306.86(51) 353.49(60) 303.44(60) 303.45(44) 351.26(61) 300.90(60) 299.42(16) 350.68(59) 296.40(61) 298.89(60) 322.51(55) 294.15(5) 298.89(60) 321.01(25) 289.57(16) 297.98(41) 312.51(4) 289.51(25) 295.52(4)
DPY
166.07(26) 130.81(26) 136.84(26) 154.04(24) 128.91(11) 133.51(2) 151.15(2) 127.49(24) 130.72(24) 146.23(59) 126.7(2) 130.48(16) 145.14(61) 126.69(59) 128.06(41) 144.77(55) 126.43(16) 127.83(59) 142.27(16) 126.22(55) 127.46(11) 141.02(11) 123.85(18) 127.46(11) 138.59(25) 123.56(61) 126.38(18) 136.38(14) 122.63(14) 125.38(14)
STAYAB
2811.02(60) 1918.12(60) 2043.22(60) 2599.66(26) 1867.21(26) 1974.54(2) 2544.14(61) 1809.59(61) 1939.83(26) 2290.64(24) 1795.93(59) 1918.11(16) 2268.78(2) 1792.77(24) 1910.70(51) 2202.20(59) 1789.42(11) 1905.84(18) 2104.00(14) 1589.07(2) 1885.48(14) 2074.89(16) 1776.71(16) 1868.58(61) 2051.45(18) 1769.15(18) 1800.44(47) 2026.93(34) 1768.80(51) 1787.47(24)
2094(28) 1358.51(22) 1715.79(2) 1954.78(2) 1347.77(1) 1565.01(28) 1768.64(33) 1333.59(51) 1519.9(54) 1717.28(54) 1327.01(51) 1481.31(47) 1595.78(31) 1306.71(5) 1441.86(33) 1590.59(5) 1300.52(33) 1421.67(1) 1589.54(51) 1296.40(31) 1350.69(7) 1580.33(36) 1291.93(10) 1345.26(31) 1576.72(1) 1291.39(54) 1331.76(51) 1570.94(41) 1280.38(36) 1326.15(41)
Table 3: Spearman Rank correlations among estimated sire merits for different phase and stayablity traits calculated by various sire evaluation methods.
Table 4: Product – moment correlations among estimated sire merits for different phase and stayablity traits calculated by various sire evaluation methods.
Variable
RLS X BLUP
Variable
0.84 0.84 0.84 0.85
APY PPY DPY STAYAB
OLS X RLS
APY PPY DPY STAYAB
OLS X BLUP
0.99 0.99 0.97 0.97
0.84 0.84 0.84 0.83
OLS X RLS
OLS X BLUP
0.99 0.98 0.97 0.95
0.83 0.88 0.83 0.83
RLS X BLUP 0.83 0.87 0.84 0.83
Table 5: Moments of different phase and stayablity traits. Variables
APY PPY DPY STAYAB
Coefficient of Skewness OLS 1.69 1.62 1.64 -0.26
RLS BLUP 1.69 0.45 1.50 0.60 1.54 0.52 -0.70 0.75
Coefficient of Kurtosis OLS 3.42 3.41 3.01 0.74
RLS BLUP 3.90 2.04 3.24 1.28 2.70 1.03 2.19 0.56
Coefficient of determination OLS 0.431 0.400 0.392 0.333
RLS 0.268 0.255 0.211 0.215
BLUP 0.105 0.110 0.031 0.098
Coefficient of variation OLS 22.55 19.93 22.83 25.26
RLS 8.40 4.00 5.60 7.14
Standard error
BLUP OLS RLS BLUP 9.13 0.12 0.04 0.05 8.01 0.02 0.01 0.01 9.13 0.04 0.01 0.01 10.69 176.53 49.84 75.31
Volume 50 Issue 6 (2016) CONCLUSION When sire ranking is taken as criterion then either of the two methods (OLS and RLS) select exactly the same bull and consequently will result in same genetic gain. Choice among methods also to a greater extent depends upon computational difficulty and relative accuracy. RLS computations are more tedious than BLUP because of the size of the matrix that must be inverted to get to the inverse elements needed for computation of RLS estimates. Contrarily, OLS and BLUP are easy to compute since the least - squares and mixed model equations are well suited to the iterative solution and consequently inversion is not
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required. Moreover, RLS is not a suitable method for evaluating sires as compared to mixed model equation method (Henderson, 1978). On a theoretical basis, the BLUP is the best and has minimum prediction error variance provided that true variance of random effects is known. Therefore, it is suggested to use BLUP procedure in a situation where correct ratio of residual variance to sire variance is known. Trade – offs between what is computationally ideal and what is practically feasible, are a fact of life in animal breeding and hence, use of OLS is suggested in situations where correct ratio of residual variance to sire variance is not known.
REFERENCES BAHS. (2012). Basic Animal Husbandry Statistics, Department of Animal Husbandry, Dairying and Fisheries, Ministry of Agriculture. Government of India. Krishi Bhawan, New Delhi. Bajetha, G. (2006). Selection of sires by using different sires evaluation methods in crossbred cattle. Ph.D. Thesis, G.B. Pant University of Agriculture & Technology, Pantnagar, U. S. Nagar, Uttarakhand Banik, S. and Gandhi, R.S. (2006). Animal model versus conventional methods of sire evaluation in Sahiwal cattle. AsianAust. J. Anim. Sci.19: 1225-1228. DAHD-GOI-(2011) Department of Animal Husbandry, Dairying and Fisheries. Ministry of Agriculture. Government of India. Krishi Bhawan, New Delhi. DAHD-GOI (2012). Department of Animal Husbandry, Dairying and Fisheries. Ministry of Agriculture. Government of India. Krishi Bhawan, New Delhi. Dalal, D.S., Rathi, S.S. and Raheja, K.L.(1999). Relationship between sires estimated breeding value for first lactation and lifetime performance traits in Hariana cattle. Indian J. Anim.Sci. 64: 592-595. Dhaka, S.S., Chaudhary, S.R., Raheja, K.L., Yadav, A.S. and Pander, B.L. (2004).Accuracy of different methods of sire evaluation for production efficiency traits in Sahiwal cattle. Indian J. Anim. Sci. 74: 296-298. Dubey, P.P., Singh, C.V. and Prasad, R.B. (2006). Relationship between sire’s estimated breeding values for first lactation and life time traits and ranking of sires in Sahiwal and its cross. Indian J. Anim. Sci. 76: 824-828. FAO. (2008). Food and Agriculture Organization. Production Year Book. 2008, Rome, Italy. Gaur, G.K., Tripathi, V.N., Mukherjee, S. and Choudhary, V.K. (2001). Efficiency of sire evaluation procedures in Frieswal cattle. Indian J. Vet. Res., 10: 1-6. Harvey, W.R. (1990). User’s guide for LSMLMW, mixed model least-squares and maximum likelihood computer program, Ohio State Univ., Columbus, Mimeo. Henderson, C.R. (1973). Sire evaluation and genetic trends. Proc. Anim. Breed and Genetics Symposium in honour of Dr. J. L. Lush, pp. 10-14. American Soc. Anim. Sci. Assoc. Champaign, Illinois. Henderson, C.R. (1978). Undesirable properties of regressed least squere prediction of breeding values. J. Dairy. Sci. 61: 114-120. Kumar, A. and Gandhi, R.S. (2010). Comparison of sire evaluation methods for first lactation milk yield and lifetime traits in Sahiwal cattle. Indian J. Anim. Sci., 80: 1198-1202. Kumar, A., Gandhi, R.S., Singh, A. and Aynalemhaile. (2008). Comparison of animal model with other conventional methods of sire evaluation for milk production in Karan Fries cattle. Indian Journal of Animal Sciences 78: 1393–96. Moges, T. G., Singh, C.V., Barwal, R.S, Kumar, D. and Singh, C.B. (2009). Evaluation of sires using different multitrait sire evaluation methods in crossbred cattle . Indian Journal of Dairy Sciences 62: 1–4. Mukherjee,S., Joshi,B.K and Gaur,G.K.(2007).Sire evaluationmethods in Frieswal cattle. Indian Journal of Animal Sciences. 77: 123–26. Pandey, H.O., Tomar, A.K.S. and Dutt, T. (2013). Comparison of sire evaluation methods in Vrindamani cattle. Indian J. Anim. Sci. 83: 419-422. Pundir, R.K., Malik, R.P.S. and Prakash, B.(2004). Comparison of different sire evaluation methods in Sahiwal cattle. Indian J. Anim. Sci. 74: 229-231. Raja, T.V. (2010). Part lactation records for Sahiwal sire evaluation. Ph.D. Thesis, NDRI, (Deemed University), Karnal, India. Steel, R.G.D. and Torrie, J.H. (1981). Principles and Procedures of Statistics. Ed.2.
