Calculation of the economic values for some udder health traits to estimate the profitability of the selection indices for dairy cows in Egypt

H G El-Awady

Animal Production Department, Faculty of Agriculture, Kafrelsheikh University, PC: 33516, Kafrelsheikh, Egypt
hassanelawady@yahoo.com

Abstract

Three different groups of economic values were calculated for some udder health traits to estimate their profitability and compare several selection indices in a herd of Friesian cows in Egypt. Data were analyzed using MTDFREML program with a model including month and year of calving as fixed effects and animal and error as random effects. Covariance components for 305day milk yield (MY), somatic cell counts (SCC), clinical mastitis (CM) and udder health status (UDHS) along with their groups of relative economic values were used for constructing the selection indices. The groups of relative economic values were: (1) actual relative economic values, (2) actual economic values of the genetic standard deviation and (3) the relative weight as one phenotypic standard deviation of the trait.

Heritability estimates were 0.35, 0.24, 0.18 and 0.16 for MY, SCC, CM and UDHS, respectively. Genetic correlations between milk yield and udder health traits were negative, ranging from -0.75 to -0.39, and between udder health traits were positive ranging from 0.84 to 0.94. Corresponding phenotypic correlations between udder health traits were between 0.73 and 0.88, but were negative ranging from -0.66 to -0.46 between milk yield and udder health traits.

Twelve selection indices were constructed using different groups of the calculated economic values. Selection for MY, SCC, CM and UDHS improved the efficiency of response of the aggregate genotype by 8 to 9% over selection for milk yield alone. This was due to incorporating either one or more of the udder health traits to MY in the indices. The selection indices constructed by each of the three groups of the economic values were recommended for use if the selection is to be practiced at the end of the first lactation.

Rank correlation coefficients among selection indices of 1806 Friesian cows constructed by using the three different groups of relative economic values ranged from 0.95 to 1.00. Therefore, it was recommended to use the third group of relative economic values because of its simplicity and ease of calculation. The present results suggest that the selection for lower SCC and UDHS would help to reduce or eliminate the undesirable correlated responses of clinical mastitis associated with selection for increasing milk yield. Additionally, the economic impact of SCC, CM and UDHS are of sufficient magnitude to warrant their inclusion in a selection criterion for improving the revenues rearing lactating cows.

Key words: Aggregate genotype, clinical mastitis, genetic correlation, heritability, phenotypic correlation, somatic cell count, udder health status

Introduction

Selection index is a worthy method of choice to be used for determining the appropriate weights for traits included in a selection criteria. It allows appropriate consideration of economic values and genetic and phenotypic parameters for the traits of concern (Rogers 1993).

Selection for improving udder health and milk traits are of primary importance in the dairy industry (Interbull 1999). Udder health index including somatic cell counts (SCC), clinical mastitis (CM) and udder health status (UDHS) is expected to give high selection response when compared with indirect selection based on SCC only (De Jong and Lansbergen 1996). Such indices realize an increase in milk yield traits and simultaneously monitor udder health traits beeping them at optimum levels. Low to high negative genetic correlations between milk production, and udder health traits (-0.30 to -0.80) indicate that improving milk production may lead to better udder health traits. Therefore, it is worthy to study the effect of including some udder health traits in the profit index of dairy cows

Though naturally present in the milk samples of healthy cows, the elevated SCC present a clear indication of the udder infection with mastitis because somatic cells form a natural defense mechanism of the udder against mastitis. Selection for improving resistance of clinical mastitis, either through direct or indirect selection on related traits is absolutely desirable (Shook 1989).

The objectives of this study were to calculate the economic values for MY, SCC, CM and UDHS using different methods and use them to estimate the genetic merit of Friesian cows in a herd in Egypt

Materials and methods 

Data and management

Data on udder health measured as incidence of CM, SCC and UDHS and on 305 day milk yield (MY) were taken from the Sakha milk recording unit of the Animal Production Research Institute, Ministry of Agriculture, Egypt. A total of 1806 first lactation records of Friesian cows sired by 39 bulls during the period from 2000 to 2008 were used in the analysis.

Abnormal records of cows affected by disease, disorders or abortion and those of length shorter than 150 days were excluded. Heifers were artificially inseminated (AI) using frozen semen when reaching 18 months of age or about 350 kg of body weight. Pregnancy was detected by rectal palpation 60 days after last service. Cows were loosely housed in open sheds, machine milked twice a day until two months before parturition and kept under controlled system of feeding and management. Milk yield was recorded daily to the nearest 0.1 kg

A case of CM was that the veterinary treated case either with or without teat injury at any time between calving and the end of lactation or until culling (1= normal cow and 2= mastitis cow). SCC was the arithmetic mean of the monthly somatic cell counts from calving to the end of lactation expressed in 1000's cells/ml. UDHS was classified into 11 categories: 0 = normal, 1 = bloody milk, 2 = coagulated milk, 3 = clinical mastitis, 4 = inguinal and mammillitis, 5 = surgical cut in udder or teats, 6 = traumatic inflammation and abrasions, 7 = chronic clinical mastitis, 8 = coagulated milk and teat paralysis, 9 = teat claw and 10 = purulent in udder. Milk production (kg) was based on completed 305-d lactation

Statistical analysis

Data were first analyzed using least-squares analysis of variance (Harvey 1990) in order to determine the fixed effects to be included in the model. The statistical model included month (1 to 12) and year (2000 to 2008) of calving. The effects being significant for all traits were included in the analytical model. Covariance components were estimated for univariate and bivariate analysis for all traits with derivative-free restricted maximum likelihood (REML) procedures using the MTDFREML program of (Boldman et al 1995). The basic multiple model was:
 

Y = Xb + Za + e

Where:

Y = a vector of observations,
b = a vector of fixed effects (level of month and year of calving),
a = a vector of the direct genetic effects, and
e = the vector of residual effects. X and Z are incidence matrices relating records to fixed and direct genetic effects, respectively.

The variance-covariance structure for the model was as follows:

E(y) =Xb and

d, is the number of dams
N is the number of records,
A is the number relationship matrix among animals,
σ²a_1, σ²a_n is the additive direct genetic variance
σ a_i a_j is the direct genetic covariance items between any pair of the traits studies,
σ² e₁, σ² e_n    is the residual variance and
I_a, I_n are identity matrices of appropriate order, the number of dam and number of animals with records respectively.

Heritability (h²) was estimated from the equation:

h²_a = σ²_a / (σ²_a + σ²_e)

Where:

σ²_a = additive genetic variance; and
σ²_e = the variance of the random residual effect associated with each observation

Calculation of economic values

The basic index including the four traits of interest was calculated using the matrix technique described by (Cunningham et al 1970). Prior to computing the complete index, three reduced indices were computed using combinations of the traits under investigation.

The relative economic values for all studied traits were derived as follows:

Actual relative economic values (REV 1)

According to October 2008 prices, the economic weight for each trait was approximated based on the final actual net profit according to the following steps: (1) The net profit/kg of milk: the difference between cost of producing one kg of milk and its selling price in Egyptian pound (LE) (LE = 2.25 – LE 1.65 = LE 0.60), (2) losses in net profit due to elevated SCC: An average 305 day milk yield of 3530 kg realizes a net profit of LE 6.94/cow/day, but losses of LE 1087 due to the elevated SCC reduces the net profit/cow/day to LE 3.38, (3) cost of treatment and losses in milk production due to clinical mastitis infection: these costs were  estimated at  LE 492.8 and  LE 847.2, respectively, which result in losses of LE 2.55 /infected cow/day and reduces the corresponding net profit to LE 4.39 and (4) losses due to other udder health problems: the losses in milk production in this case reach 1697.9 kg of milk and the average costs of treatment and veterinary services are LE 655.10. Therefore, the losses/ infected cow/day was calculated as [(2118 – (1697.9 * 0.6) + 655.1]/305 = LE 1.46 which reduces the profit /cow/day to LE 5.48.

Actual economic value of the genetic standard deviation (REV2)

According to Rogers (1993) the economic values of the traits were calculated by multiplying the genetic standard deviation of a given trait by the treatment costs of one unit of that trait (Table 1).

Relative weights (REV 3)

These were calculated as 1/ σ_p, where σ_p is the phenotypic standard deviation of each trait (Sharma and Basu 1986); (Falconer and Mackay 1997) and (Cameron 1997).

The economic value of MY were set to unity and the (REV'S) of other traits were calculated relatively (Table 2).

The index value was calculated as:

Where:   

bi = partial regression coefficient and,

Pi= phenotypic value of traits

Regression coefficients (b) of all selection indices were estimated as:

Pb = Ga or b = P^-1Ga

Values in vector b and in matrix P were used to calculate index variance σ²I = b¢ P b.

Variance of the total aggregate genotypic σ²H was a¢Ga. Accuracy of the index (R_IH) defined as the correlation between variance of aggregate genotypic value and variance of the index value was σI / σH = σ IH / ( σ I * σ H ), since σ IH = σ²I

The expected genetic gain (DG) for a given traits was i R_IH σI, where i is the selection intensity, which was set to 1.00 for the purpose of comparisons, or was calculated according to Tabler and Touchberry (1959) DG = σI*i*B_YI where i is the selection intensity assuming that the selection differential equals one unit of standard deviation and B_YI is the regression of each trait in the index on the index value. B_YI = b¢ci / b¢Pb where ci is the i th column of G matrix

To compare indices and determine traits which combine best into an index, relative efficiency (RE) was calculated for each index based on R_IH relative to the complete index (I₁). Estimates of genetic and phenotypic variances and covariances of traits were used for constructing various selection indices using Henderson's modifications of Hazel's method (1943)

Results and discussion 

The estimates of genetic and phenotypic variances and covariances components of different traits studied are in table (3). Genetic and phenotypic covariances between MY and all udder health traits were negative, while among udder health traits were positive

Genetic parameters

Heritability estimates of various traits are in table (4). UDHS exhibited the lowest heritability estimate (0.16), and SCC had the highest (0.24). Heritability estimate of CM (0.18) was lower than that for SCC.

The present h² estimate of SCC lies well in the range of 0.15 to 0.26, reported by several investigators (Mrode et al 1998; Rupp and Boichard 1999; Carlén et al 2004; Koivula et al 2005 and El-Arian and El-Awady 2008). Heritability estimate of MY was moderate (0.35) and was in consonance with those of Carlén et al (2004); Koivula et al (2005) and El-Arian and El-Awady (2008) which ranged from 0.30 to 0.35

The strong positive genetic correlations among udder health traits (0.84 to 0.94) are in close agreement with those reported by Pösö and Mäntysaari (1996); Koivula et al (2005), Samore and Groen (2006) and El-Arian and El-Awady (2008). In addition, genetic correlation estimates close to unity found between udder health traits were by Lund et al (1994). The negative genetic correlations between milk yield and udder health traits ranged between -0.75 and -0.39, and the corresponding estimates of phenotypic correlations were from -0.66 to -0.46. Unlike several studies, the present negative genetic correlations between milk yield and udder health traits indicated that improving milk yield traits was associated with reduction in SCC, CM and better udder health status. However, unfavorable genetic association between incidence of clinical mastitis and milk production elevates with advancement of parity were reported by Uribe et al (1995); Nielsen et al (1997); Rupp and Boichard (1999); Heringstad et al (2000); Hansen et al (2002); Carlén et al (2004) and Koivula et al (2005)

Young et al (1960); Seykora and McDaniel (1986) and Rogers et al (1991) reported genetic correlations of 0.60 or more between SCC and CM. The impact of chance and inconsistent and incomplete recording of clinical mastitis may explain the high fluctuation in the value of the genetic correlations among the udder health traits (Weller et al 1992).

Selection indices

Ranking selection indices based on accuracy (R_IH), partial regression coefficients (b's), relative efficiency (RE) and the expected genetic change in different traits (∆G)/ generation are in Tables 5, 6, and 7.

The addition of the udder health traits lead to 8 to 9% improve in the efficiency of response in the aggregate genotype over selection for milk yield alone. UDHS and CM or both had the highest effect on the accuracy of indices under three groups of the economic values used with highest accuracy of the selection index under REV2 (Table 6). The selection index based on MY only (I₅) earned the best response in milk yield under REV 1 to 3.

When the constructed twelve indices were compared, I_1A, I_1G and I_1S which  incorporated  MY, CM and UDHS were the best (RE = 106, 107 and 106, respectively), followed by I_2A, I_2G and I_2S (RE = 105, 107 and 104, respectively) which, combine MY and UDHS, then followed by the indices I_3A, I_3G, and I_3S (RE = 104, 106 and 102, respectively) which combine MY and SCC. The original indices I_4A, I_4G and I_4S which included all four traits ranked the fourth (RE = 100%), therefore, it is recommended to apply selection at the end of first lactation based on this index.

Negligible increase in relative efficiency values occurred when SCC or CM were dropped from the original indices, but dropping MY resulted in a considerable decline in relative efficiency values down to 91.8, 95.3 and 89.7, respectively, which caused their rank to fell down to I_7A, I_7G and I_9S, respectively. Severe, decline in RE values occurred when CM and/or UDHS were dropped from the complete index and therefore, the rank decreased to I_6A, I_8A and I_9A (RE = 97.2, 89.3 and 89.3), respectively; I_6G, I_9G and I_10G (RE = 96.09, 87.19 and 87.19), respectively and I_6S, I_7S and I_8S (RE = 97.4, 90.3 and 89.7), respectively. The highest decrease in RE were 75.9, 75.5 and 78.0  for REV1, REV2 and REV3, respectively and occurred when MY and SCC were dropped from the complete indices I_12A, I_12G and I_12S, respectively, which illustrates the importance of including SCC in any selection index to improve the total merit of dairy cows in that herd.

The maximum ∆G/kg/generation in MY was realized when I₅ was applied using either REV1, REV2 or REV3. However, from the economic point of view, the maximum return can be achieved when applying the original index I₄ which includes all traits

Previous work by Rogers and McDaniel (1989) and Strandberg and Shook (1989) gave no indication of how information on somatic cell score might be incorporated in an index that included udder health traits

Udder health traits in this study were positively correlated, therefore, the determination of their optimal weights require a simultaneous consideration of them in an index and more emphasis should be placed on CM and/ or UDHS when data on SCC are not available and vice versa

The rank correlation coefficients among complete indices when using groups REV1, REV2 and REV3 ranged between 0.95 and unity (Table 8), which indicates quite high similarity of the cows index values under the three different groups of economic value.

Conclusions 

• The study has shown that the economic values of SCC, CM and UDHS are of sufficient magnitude to warrant their inclusion in the selection criteria. In addition, selection to reduce clinical mastitis is justified.

  
  

• Somatic cell counts are likely the most important component of the udder health traits in the index and CM and UDHS are of second importance.

  
  

• Including the udder health traits in a profit index will increase milk production and reduce clinical mastitis

Acknowledgements 

Thanks go to the staff of Animal Production Research Institute, Ministry of Agriculture, Egypt for making the data available for analysis. Acknowledges Prof. Dale Van Vleck Department of Animal Science, University of Nebraska, Lincoln, USA for providing the MTDFREML software and its documents.

References 

Boldman K G, Van Vleck L D and Kachman S D 1995 A manual for use of MTDFREML of animal model, USDA-ARS Clay Center, NE, USA

Cameron N D 1997 Selection indices and predicted of genetic merit in animal breeding Roslin Institute, Edinburgh, U. K., CAB International, P: 91 http://www.cabi.org/bk_bookdisplay.asp?SubjectArea=&Subject=&PID=1360

Carlén E, Strandberg E and Roth A 2004 Genetic parameters for clinical mastitis, somatic cell score and production in the first three lactations of Swedish Holstein cows. Journal of Dairy Science 87:3062-3070 http://jds.fass.org/cgi/reprint/87/9/3062.pdf

Cunningham E P, Moen R A and Gjedrem T 1970 Restriction of selection indexes. Biometrics 26:67

De Jong G and Lansbergen L 1996 Udder health index: selection for clinical mastitis resistance. Proceedings of an International Workshop on Genetic Improvement of Functional Traits in Cattle Gembloux, France, Interbull bulletin 12:42

El-Arian M N and El-Awady H G 2008 Assessment of the Genetic relationships between udder health and milk production traits in relation to selection for improving resistance to clinical mastitis in Friesian cows in Egypt. Journal of Agriculture Science, Mansoura University 33:181

Falconer D S and Mackay 1997 Introduction to quantitative genetic 3^rd edition, Longman Group (PE) LTD printed in Hong Kong

Hansen M, Lund M S, Sørensen M K and Christensen L G 2002 Genetic parameters of dairy character, protein yield, clinical mastitis, and other diseases in the Danish Holstein cattle. Journal of Dairy Science 85:445-452 http://jds.fass.org/cgi/reprint/85/2/445.pdf

Harvey W R 1990 User's Guide for LSMLMW, Mixed Model Least Squares and Maximum Likelihood Computer Program PC-2 version. Ohio State, University, Columbus (Mimeograph) USA

Hazel L N 1943 The genetic basis constructing selection indices. Genetics 28: 476

Heringstad B, Klemestsdal G and Ruane J 2000 Selection clinical mastitis resistance in dairy cattle: A review with focus on the situation in the Nordic countries. Livestock Production. Science 64:95-106

Interbull 1999 Proceedings of International Workshop on Genetic Improvement of Functional Traits in Cattle – Breeding Goals and Selection Schemes, Wageningen, The Netherlands Interbull bulletin 23:221

Koivula M, Mäntysaari E A, Negussie E and Serenius T 2005 Genetic and phenotypic relationships among milk yield and somatic cell count before and after clinical mastitis. Journal of Dairy Science. 88:827-833 http://jds.fass.org/cgi/reprint/88/2/827.pdf

Lund T, Miglior F, Dekkers J C M and Burnside E B 1994 Genetic relationship between clinical mastitis, somatic cell count, and udder conformation in Danish Holstein. Livestock Production. Science 39:243-251

Mrode A R, Swanson G J T and Winter M S 1998 Genetic parameters and evaluations for somatic cell counts and its relationship with production and type traits in some dairy breeds in the United Kingdom. Animal Science 66:569-576

Nielsen U S, Pedersen G A, Pedersen J and Jensen J 1997 Genetic correlations among health traits in different lactations. Proceedings of the international workshop on genetic improvement of functional traits in cattle; health. Uppsala, Sweden, June 1997 Interbull Bulletin 15:68

Pösö J and Mäntysaari E 1996 Relationships between clinical mastitis, somatic cell Score, and production in the first three lactations of Finnish Ayrshire. Journal of Dairy Science. 79:1284-1291 http://jds.fass.org/cgi/reprint/79/7/1284.pdf

Rogers G W 1993 Index selection by using milk yield, somatic cell score, udder depth, teat placement and foot angle. Journal of Dairy Science 76:664-670 http://jds.fass.org/cgi/reprint/76/2/664.pdf

Rogers G W, Hargrove G L, Lawlor T G and Ebersole G L 1991 Correlations among linear type traits and somatic cell counts. Journal of Dairy Science 74:1087-1091 http://jds.fass.org/cgi/reprint/74/3/1087.pdf

Rogers G W and McDaniel B T 1989 The useful- ness of selection for yield and functional type traits. Journal of Dairy Science 72:187-193 http://jds.fass.org/cgi/reprint/72/1/187.pdf

Rupp R and Boichard D 1999 Genetic parameters for clinical mastitis, somatic cell score, production udder type traits, and milking ease in first-lactation Holsteins. Journal of Dairy Science 82:2198-2204 http://jds.fass.org/cgi/reprint/82/10/2198.pdf

Samore A B and Groen A F 2006 Proposal of an udder health genetic index for the Italian Holstein Friesian based on first lactation data. Italian Journal of Animal Science 5:359-370

http://www.aspajournal.it/abstact/04_06/samor.pdf

Seykora A J and McDaniel B T 1986 Genetic statistics and relationships of teat and udder traits, somatic cell counts, and milk production. Journal of Dairy Science 69:2395-2407 http://jds.fass.org/cgi/reprint/69/9/2395.pdf

Sharma A and Basu S B 1986 Incorporation of profit variables for the maximization of genetic gain. Indian Journal Dairy Science 39:35

Shook G E 1989 Selection for disease resistance. Journal of Dairy Science 72:1349-1362 http://jds.fass.org/cgi/reprint/72/5/1349.pdf

Strandberg E and Shook G E 1989 Genetic and economic responses to breeding programs that consider mastitis. Journal of Dairy Science 72: 2136-2142 http://jds.fass.org/cgi/reprint/72/8/2136.pdf

Tabler K A and Touchberry R W 1959 Selection indices for milk and fat yield of Holstein Friesian dairy cattle.  Journal of Dairy Science 42: 123-126 http://jds.fass.org/cgi/reprint/42/1/123.pdf

Uribe H A, Kennedy B W, Martin S W and Kelton D F 1995 Genetic parameters for common health disorders of Holstein cows. Journal of Dairy Science 78: 421-430 http://jds.fass.org/cgi/reprint/78/2/421.pdf 

Weller J I, Saran A and Zeliger V 1992 Genetic and environmental relationships among somatic cell count, bacterial infection. and clinical mastitis. Journal of Dairy Science 75:2532-2540 http://jds.fass.org/cgi/reprint/75/9/2532.pdf

Young C W, Legates J E and Leece J G 1960 Genetic and phenotypic relationships between clinical mastitis, laboratory criteria, and udder height. Journal of Dairy Science 43(1):54-62 http://jds.fass.org/cgi/reprint/43/1/54.pdf

Received 25 April 2009; Accepted 23 June 2009; Published 1 September 2009

Go to top