Genetic Evaluations for Productive Life, Somatic Cell Score and Net Merit Dollars

Source: Holstein Association USA

Genetic evaluations for Productive Life, Somatic Cell Score and Net Merit Dollars of bulls have been available in the U.S. since January 1994. This information is reported for bulls in Sections 1 and 2. Similar genetic evaluations for cows were first published in July 1995.

PRODUCTIVE LIFE
(Source: Kent Weigel and Tom Lawlor; Holstein Association USA and Paul Van Raden and George Wiggans; USDA-AIPL)

During the past 10 years, researchers in the U.S. and other countries have become increasingly interested in measuring the genetic ability of a dairy cow to resist culling. This research has focused on direct analysis of culling information, and indirect analysis using traits correlated with the cow’s ability to resist culling.

Actual culling data are most informative. However, sufficient data may not be available until late in the cow’s life, and projections calculated at an early age account for only a small portion of the total variation in productive life. In addition, culling data is “measured” subjectively by the dairy producer and, as such, it is susceptible to biases due to personal preferences.

Length of productive life is highly correlated with production and with linear type trait scores, particularly for the udder traits. Production and type data are available earlier in life and are measured objectively. Considerable variation in productive life, however, exists beyond that which is due to the contributions of production and type.
PTAs for Productive Life were computed by combining genetic evaluations for productive life based on direct and correlated traits, respectively. The method used to combine direct PL evaluations with indirect evaluations from correlated production and type traits approximates a multiple-trait genetic evaluation for productive life.

The two components of PTA PL were calculated as:

a) Direct trait: USDA-AIPL computed the direct measurement of productive life using DHIA culling data. Productive life measures the total number of months-in-milk for a cow, in a way, that mimics the lactation curves. For example, the months surrounding the peak of production, during early lactation, receive more credit than months at the end; a third lactation receives more weight than a first lactation, etc.

b) Correlated traits: The contribution of linear type to indirect prediction of productive life was calculated by the Holstein Association USA, while the indirect prediction of productive life from production traits (milk and fat) was calculated by USDA-AIPL. The indirect predictions from linear type traits and production traits were joined to produce PTAs for productive life based on indirect measurements.

For young AI bulls whose progeny do not yet have direct culling information available, USDA-AIPL provided parent averages (PA) for direct PL. These PAs were combined with the indirect productive life PTAs obtained from linear type and production data.

Incorporating information from correlated traits increases the PTA PL reliabilities most for bulls that have a majority of first lactation daughters. As reliability of direct PL increases, the contributions of linear type and production decrease. Consequently, evaluations for AI bulls with many second-crop progeny essentially contain only direct PL information.

SOMATIC CELL SCORE
(Source: M. M. Schutz and R. L. Powell; USDA-AIPL)

The process used by USDA-AIPL to calculate PTA for Somatic Cell Score (SCS) is similar to that for calculating the PTAs for yield traits. Lactation average SCS for the first five lactations is edited and adjusted for calving age and season. An animal’s evaluation is calculated relative to cows born in 1995 and then is added to the average for first lactation SCS (standardized for age, calving month, and lactation length) of cows born in 1995. The average for Holsteins is approximately 3.1.

Using PTA SCS in an index is recommended so that appropriate selection might be given to improving mastitis resistance. Genetic selection to reduce SCS does not replace superior management and preventative care as the most effective means of controlling mastitis. Using PTA SCS in an index and placing 5% as much weight on SCS as on yield will help slow the increase in mastitis without sacrificing much in terms of increased yield.

Reliabilities for PTA SCS are lower than for the yield traits because of a lower heritability (10% vs. 25%) and generally fewer daughter records with SCS data. Progeny-test bulls may attain 60% reliability for PTA SCS. A high level of reliability will only be reached when second-crop daughters are added to a bull’s proof.

NET MERIT
Net Merit measures the additional net profit that an offspring of an animal will provide over its lifetime. Income and expenses for a typical dairy operation have been estimated, so that a measure of overall net profit can be calculated.

Three different values (Net, Fluid and Cheese) of lifetime profitability are presented. The primary difference between the formulas is the emphasis that is placed on the components. Producers should select the index that is closest to the milk payment in your area. Net merit is based upon the future anticipated average milk price for all of the U.S. Fluid Merit would be for producers who do not receive any payment for protein. In the Fluid Merit formula a negative value is placed on protein because additional feed is required to produce additional protein. Without a direct payment for the additional protein, this results in a negative value. Cheese Merit may be appropriate for farmers selling their milk directly to a cheese plant. More information about the new Net Merit may be found at the USDA website: aipl.arsusda.gov.