Using Net Merit To Select Dairy Bulls

Prepared by: Dr. Mike Schutz
Assistant Professor and
Extension Dairy Specialist
Department of Animal Sciences
Purdue University

Every 3 months, the Animal Improvement Programs Laboratory (AIPL) of the United States Department of Agriculture releases the newest USDA-DHIA genetic evaluations for Dairy Bulls and Cows. Dairy cattle are evaluated for the traits of milk, fat, and protein yield, length of productive life, and somatic cell score (an indicator of mastitis). Evaluation procedures combine information from all known female relatives of the evaluated animal and from the animal itself in the case of cows. Additionally, numerous type or conformation traits are evaluated routinely. For Holsteins, type traits are evaluated by the Holstein Association, and AIPL calculates genetic evaluations for type for other breeds.

It becomes a real challenge for producers to select the sires that will produce the most profitable daughters when presented with so many traits, all of which have some economic impact and must be considered in selection programs. Fortunately, AIPL also provides a tool that combines this information into a single number that reflects overall economic value…Net Merit. This value is usually abbreviated as "NM" or "$NM" in genetic information or bull listings. Let's look at what is included in the Net Merit genetic index, how it is calculated, and how it may be used.

What's in $NM

Now, AIPL uses predicted values for future milk prices and fat and protein differentials. The predictions may not be entirely accurate, but they are not changed every year either. They change only once every 5 years when the genetic base is updated. For many years AIPL has reported an economic index called MFP$, or "MFP Dollars". Formerly, this economic index combined Predicted Transmitting Abilities for Milk, Fat, and Protein with appropriate economic weights based on average US milk prices and fat and protein differentials from the previous calendar year. Because the heifer calves that result from matings being planned now would not reach mature production until at least 4 years from now (genetic evaluation procedures in the US assume milk, fat, and protein yields are adjusted for age to a mature equivalent basis or what the cow would be expected to produce at maturity). Using values from the past year did not represent expected returns from added productivity very well. Also, economic weights changed every year, making it difficult to assess the amount of progress being made over time.

MFP$ is the basis for calculating $NM. Additional production of milk, fat, or protein comes at a cost that is not reflected in the MFP$ index. The cost is the additional feed required to produce more pounds of milk, fat, or protein. Therefore, MFP$ is reduced by 30% to account for those additional costs. In essence, MFP$ represents a gross value and reduction by 30% represents a net value.

Productive life is measured as the number of months a cow is in production by 7 years of age. For cows less than 7 years old, various DHI information, such as whether she completed a given lactation, can be used to predict how many months she will produce by age 7. This is similar to predicting (projecting) how much a cow will produce in 305 days. Cows over 7 years of age get no additional credit for production. Only the first 10 months of production in any lactation contribute to months of productive life. PTA for productive life are calculated by combining the direct measures with indirect measures (linear type data from Holstein Association USA) for Holsteins. Use of indirect data to predict PTA for productive life is especially helpful when most daughters of a bull are less than 7 years old.

Length of productive life is important for a number of reasons:

1. Lower herd replacement costs, because fewer heifers are needed to replace cows.
2. A longer period to recover the rearing costs for the cow.
3. A higher percentage of the herd will be producing milk at mature levels.

Research indicates that productive life should receive about 40% as much weight as milk, fat, and protein production in selection programs.

Genetic improvement of dairy cattle for resistance to mastitis is difficult, because cases of clinical mastitis are not routinely recorded in the US. It's time consuming for producers to record every case of mastitis, and this ignores subclinical mastitis altogether. Besides, most producers would probably have their own unique recording methods. Fortunately, there is consistency by Dairy Herd Improvement (DHI) in recording somatic cells in milk testing programs. Somatic cell counts (SCC) serve as an indicator of mastitis and have the added bonus of being related to both clinical and subclinical mastitis. Because SCCs are already recorded by DHI, these records can follow the same flow of information that's well established for genetic evaluation of milk, fat, and protein.

Selection to reduce SCCs not only reduces mastitis incidence but also results in higher milk payments in markets with quality premiums for milk with low SCCs. Milk with low SCCs has longer shelf life and yields more cheese. This added value is reflected in the quality premiums paid by some dairy processors. Other economic advantages to reducing SCCs and mastitis include lower treatment costs, lower replacement costs, less discarded milk, and less milk production loss due to subclinical mastitis.

Progress in reducing mastitis through genetics will be slow for several reasons:

1. The relationship between Somatic cell score (SCS) and mastitis is not absolute.
2. SCS is under less genetic control than milk yield
3. Reliability, which is the degree of confidence we have in a PTA, will be lower for SCS than for yield traits because of the lower heritability and fewer records.

Applying enough selection pressure to reduce clinical mastitis dramatically isn't recommended. Research has shown the genetic relationship between milk yield and SCS is antagonistic. Therefore, selection only for lower SCS would result in less milk yield as a correlated response. Somatic cell score should receive about 10 percent as much weight as milk, fat, and protein production.

Remember, milk pays the bills; therefore, it's reasonable that yield should get the largest weight in an optimum selection index. Weights for the Net Merit index are 10:4:-1 for increased yield (10), longer productive life (4), and decreased SCS (-1), respectively.

How $NM Is Calculated

1. Begin with MFP$

Currently, MFP$ is calculated as:

MFP$ = $.031 (PTAMilk) + $.80 (PTA FAT) + $2.00 (PTA Protein),

based on a predicted milk price of $12.30 per hundredweight with 3.5 percent fat and 3.2 percent protein and differentials of 8.0 cents for fat and 20.0 cents for protein.

2. Account for feed costs

Additional feed costs required to produced increased milk, fat, and protein are accounted for by reducing MFP$ by 30%.

MFP$adj = .70 x (MFP$)

3. Include PTA for PL and SCS

NM$ = MFP$adj + $11.30 (PTA PL) - $28.22 (PTA SCS - breed average SCS)

The additional value of each month of productive life has been estimated to be $11.30 per cow per lactation. The value of an increase of one somatic cell score near breed average is -$28.22. This value includes possible milk quality premiums, decreased health costs, and less discarded milk. It does not account for milk lost due to subclinical mastitis, which is already accounted for in PTA Milk. Breed average SCS is in the following table.

Average First Lactation SCS of cows born in 1990, by breed

4. Round Off

Round NM$ to the nearest whole number.

5. Foreign Bulls

Some foreign bulls will not have PTAs for productive life or somatic cell scores. In this case NM$ can be approximated by the following equation, using values from the table.

NM$ = Intercept + Slope (MFP$)

Intercepts and slopes to estimate NM$ from MFP$ when PTA for Productive Life and Somatic Cell Scores are unknown, by breed.

Example:

Consider the Indiana Holstein bull, REIFF-FARM MASCOT JAVLIN (registration number 2195207, stud code 7H4654) who has the following PTAs:

(picture courtesy of Select Sires)

Predicted Transmitting Abilities for
REIFF-FARM MASCOT JAVLIN

1. MFP$ = $.031 (+2403) + $.80 (+81) + $2.00 (+79) = 297.293
2. MFP$adj = .70 x (297.293) = 208.105
3. NM$ = 208.105 + $11.30 (+1.6) - $28.22 (3.09 - 3.20) = 229.28
4. NM$ = 229
5. Assume that JAVELIN has no information for Somatic Cell Score or Productive Life.

Estimate the NM$ as:

NM$ = .82280 + .77183 (297) = 230

How to use Net Merit

Net Merit is a useful tool for genetic selection of dairy cattle, especially in commercial herds for which it was designed. Net Merit incorporates many economically important traits into a single index with appropriate relative weights. As with any genetic evaluation, Predicted Transmitting Ability, or selection index, pay more attention to differences among cows or bulls and how they rank than to exact numbers. Exact numbers will differ from herd to herd because of different environments and management levels, but on the average differences in Net Merit among bulls will be the same across all herds.

Select AI bulls that are in the top 30 percent of the breed for Net Merit and make the heaviest use of those bulls in the top 15 percent. Minimum NM$ for the top 30% (70^th percentile) and the top 15% (85^th percentile) are shown in the following table. Plenty of AI sires ranking in the 85^th percentile are available for $20 or less per unit. After using Net Merit to select the best group of sires that can be afforded, breeders may select individual matings for specific traits from within that group of sires. Avoid initial selection for too many traits. For heifers, select a group of sires that rank best for Net Merit and then make heaviest use of the sires with the smallest percentage of difficult births in heifers.

Minimum Net Merit required to reach 70th and 85th
percentile for Net Merit in the November 1998
USDA-DHIA genetic evaluation for bulls.