Lifetime Net Merit 2014
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AIP RESEARCH REPORT
NM$5 (10-14)

Net merit as a measure of lifetime profit: 2014 revision

P.M. VanRaden and J.B. Cole
Animal Improvement Program, Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350
301-504-8334 (voice) ~ 301-504-8092 (fax) ~ paul.vanraden@ars.usda.gov ~ https://aipl.arsusda.gov
 
Updated economic values  |  Net merit calculation  |  Trait parameters  |  Expected genetic progress  |  Derivation of economic values  |  Fertility traits  |  Yield traits  |  Somatic cell score  |  Productive life  |  Lifetime profit  |  History of net merit  |  Acknowledgments  |  References
 

Economic values in net merit (NM$) were updated in 2014, and 2 more fertility traits were included. A fourth index called grazing merit (GM$) was introduced to rank animals for grazing herds along with the cheese merit (CM$) and fluid merit (FM$) indexes already calculated for herds in differing milk markets. These indexes previously included daughter pregnancy rate (DPR) and now also include heifer conception rate (HCR) and cow conception rate (CCR). Benefits of fertility not already included in productive life (PL) are earlier age at first calving; decreased units of semen needed per pregnancy; decreased labor and supplies for heat detection, synchronization, inseminations, and pregnancy checks; additional calves produced; and higher yields because more ideal lactation lengths are achieved. Recent decreases in the cost of replacements and increases in the beef price for cull cows have decreased the economic values of both PL and fertility traits as compared with the 2010 index.

The rapid decline of average somatic cell count (SCC) from 320,000 cells/ml in 2002 to 199,000 in 2013 has reduced the actual change in SCC caused by a 1-unit change in somatic cell score (SCS) and the actual genetic variation in SCC, with smaller differences among daughter averages of bulls. The value of SCS thus has been reduced because milk quality premiums are paid as a linear function of SCC rather than SCS, and the standard deviation of SCC has decreased greatly. Yield traits will receive more relative emphasis if PL, SCS, and fertility get less emphasis, and slight revisions were made to component prices; however, the milk price is higher than forecast in 2010 NM$. Economic values for calving ability (CA$, an index that includes service-sire calving ease, daughter calving ease, service-sire stillbirth, and daughter stillbirth), udder composite, feet/legs composite, and body size were updated only slightly. For recent bulls, the 2014 and 2010 NM$ indexes were correlated by 0.965.

Economic values for grazing herds were examined by Gay et al. (2014), and the GM$ index is based primarily on their derivations. Pasture-based dairy producers in the United States face costs, revenue streams, and management challenges that may differ from those associated with conventional dairy production systems. The value of fertility in GM$ is higher than in NM$ because seasonal calving is used in many grazing herds. However, emphasis on productive life is decreased in GM$ because grazing cattle are estimated to remain in the herd considerably longer, which diminishes the marginal value of productive life. The milk price and several of the other costs in NM$ were assumed to be the same in GM$. For recent bulls, the GM$ index was correlated by 0.984 with NM$, 0.982 with CM$, and 0.938 with FM$.

This document describes changes made for the 2014 revision of NM$. Further details regarding the calculation of NM$ and component traits are provided in the historical reports for previous revisions such as the 2010 NM$ index. Members of Project SCC084 (Genetic Selection and Mating Strategies To Improve the Well-Being and Efficiency of Dairy Cattle) provided updated incomes and expenses used to estimate lifetime profit.

Updated economic values 

New economic values for each unit of predicted transmitting ability (PTA) and relative economic values of traits will be implemented with December 2014 evaluations:

Trait Units Standard
deviation
(SD)
Value ($/PTA unit) Relative value (%)
NM$ CM$ FM$ GM$ NM$ CM$ FM$ GM$
Protein Pounds 18 4.14 5.86 0 3.92 20 24 0 18
Fat Pounds 25 3.22 3.22 3.22 3.05 22 19 23 20
Milk Pounds 672 −0.006 −0.058 0.118 −0.006 −1 −9 23 −1
PL Months 2.4 29 29 29 19 19 16 20 10
SCS Log 0.21 −122 −152 −56 −111 −7 −7 −3 −6
Udder Composite 0.90 31 31 31 34 8 6 8 8
Feet/legs Composite 1.03 10 10 10 11 3 2 3 3
Body size Composite 1.03 −16 −16 −16 −17 −5 −4 −5 −4
DPR Percent 2.3 11 11 11 32 7 6 7 19
HCR Percent 2.4 2.3 2.3 2.3 4.2 2 1 2 3
CCR Percent 2.8 2.2 2.2 2.2 6.5 1 1 2 5
CA$ Dollars 18 1 1 1 1 5 4 5 5

The SDs listed above are for true transmitting abilities (TTAs) in a hypothetical unselected population. The SDs of TTAs for NM$, CM$, and FM$ are all estimated to be $194, somewhat smaller than the $210 in 2010 indexes. The SD for GM$ would have been larger because of longer PL in grazing herds, but milk yield differences are often reduced in grazing herds. Economic values in GM$ were rescaled to make the SD equal to the other indexes. An economic value is the added profit caused when a given trait changes by 1 unit and all other traits in the index remain constant. For example, an economic value for protein is determined by holding pounds of milk and fat constant and examining the increase in price when milk contains an extra pound of protein. The genetic merit for each trait of economic value ideally should be predicted from both direct and indirect measures, but multitrait methods currently are used only for conformation traits, fertility traits, and PL. The economic value of a trait may change when other correlated traits are added to the index. Selection of animals to be parents of the next generation is most accurate when all traits of economic value are included in the index.

Relative values for each trait expressed as a percentage of total selection emphasis are obtained by multiplying the economic value by the SD for TTA and then dividing each individual value by the sum of the absolute values. Currently, stillbirth evaluations are computed only for Holsteins. The Brown Swiss CA$ includes only sire calving ease and daughter calving ease. For the remaining breeds, relative values of the other traits in NM$ and FM$ each increase by a factor of 1.05 because the 5% emphasis on CA$ is excluded. A corresponding increase of 1.03 applies to the relative weights in CM$ for the other breeds.

NM$ calculation 

Calculation of NM$ and reliability (REL) of NM$ can be demonstrated using the following example Holstein:

Trait PTA REL (%)
Protein +70 90
Fat +80 90
Milk +2,000 90
PL +2.5 60
SCS 2.95 (− 3.00) 75
Udder +1.5 80
Feet/legs +0.5 75
Body size −1.0 85
DPR +0.3 55
HCR +0.5 60
CCR +1.2 50
CA$ +30 90

The PTAs for each trait are multiplied by the corresponding economic value and then summed. An average of 3 must be subtracted from PTA for SCS for all breeds. After subtraction, the NM$ for this example animal is $719, CM$ is $736, FM$ is $673, and GM$ is $682. Calculation of NM$ also can be expressed in matrix form:

NM$ = au,

where a contains the economic values for the 12 PTA traits and u contains the trait evaluation. The average of 3.00 for SCS is removed from the corresponding element of u. Calculations are the same for males and females with one exception: CA$. Cow PTAs for CA$ are not available because a sire-maternal grandsire (MGS) model (instead of an animal model) is used for evaluation of CA$ traits. Therefore, a pedigree index (0.5 sire PTA + 0.25 MGS PTA + 0.125 maternal great-grandsire PTA, etc.) is substituted for PTA for all generations of the maternal line, with breed average replacing any unknown ancestors.

The REL of NM$ is computed using matrix algebra from REL of the 12 traits and genetic correlations among those traits. The NM$ REL is the variance of predicted NM$ divided by the variance of true NM$:

REL NM$ = rGr/vGv,

where r contains the relative economic values multiplied by the square root of REL for each PTA trait, G contains the genetic correlations between the 12 PTA traits, and v contains the relative economic values for the traits.

Trait parameters 

Genetic correlations among all traits and composites were estimated from correlations among PTAs of Holstein bulls with high REL because restricted maximum-likelihood estimates were not available between all traits. Genetic correlations are above the diagonal, phenotypic correlations are below the diagonal, and heritabilities are on the diagonal for each of the 12 PTA traits:

PTA trait PTA trait
Milk Fat Protein PL SCS Body size Udder Feet/legs DPR HCR CCR CA$
Milk 0.201 0.43 0.83 0.10 0.02 −0.10 −0.10 −0.02 −0.23 −0.03 −0.16 0.19
Fat 0.69 0.20 0.59 0.15 −0.09 −0.07 −0.07 0.01 −0.15 0.03 −0.10 0.13
Protein 0.90 0.75 0.20 0.13 0.04 −0.17 −0.14 −0.01 −0.18 −0.07 −0.15 0.22
PL 0.15 0.17 0.16 0.08 −0.45 −0.27 0.18 0.14 0.64 0.32 0.62 0.40
SCS −0.10 −0.10 −0.10 −0.40 0.12 −0.07 −0.23 −0.15 −0.27 −0.12 −0.25 −0.14
Body size 0.06 0.05 0.05 −0.20 −0.11 0.40 0.45 0.38 −0.12 −0.02 −0.15 −0.16
Udder −0.02 −0.05 −0.06 0.15 −0.30 0.45 0.27 0.45 0.09 0.03 0.04 0.10
Feet/legs −0.14 −0.11 −0.18 0.08 −0.02 0.35 0.40 0.15 0.03 −0.01 −0.04 −0.01
DPR −0.10 −0.10 −0.10 0.20 −0.05 0.00 0.00 0.00 0.04 0.41 0.87 0.35
HCR −0.05 −0.05 −0.05 0.10 −0.04 −0.02 −0.05 −0.05 0.10 0.01 0.54 0.16
CCR −0.10 −0.10 −0.10 0.40 −0.20 −0.10 0.03 −0.04 0.70 0.45 0.02 0.34
CA$ 0.02 0.02 0.02 0.20 −0.03 −0.10 0.00 −0.02 0.09 0.16 0.20 0.07
1Holstein heritabilities in orange on diagonal; heritabilities for other breeds are the same except for size (0.35), udder (0.20), and Jersey and Brown Swiss yield traits (0.23).
 

Expected genetic progress 

Correlations of PTAs for each trait with NM$, FM$, CM$, and GM$ were obtained from progeny-tested Holstein bulls born from 2002 through 2006. Bulls were required to have an REL of at least 80% for milk yield and an evaluation for each trait in the index. Correlations with NM$ based on the 2010 formula are shown for comparison:

PTA trait Correlation of PTA with index Expected genetic progress from NM$
2010 NM$ 2014 NM$ 2014 CM$ 2014 FM$ 2014 GM$ PTA change/year Breeding value change/decade
Protein 0.49 0.62 0.60 0.64 0.56 4.7 94
Fat 0.59 0.70 0.69 0.69 0.65 7.2 144
Milk 0.34 0.46 0.38 0.62 0.39 134 2,679
PL 0.80 0.68 0.68 0.64 0.70 0.64 13
SCS −0.54 −0.44 −0.46 −0.36 −0.43 −0.04 −0.75
Udder 0.12 0.09 0.09 0.08 0.11 0.04 0.75
Feet/legs 0.13 0.11 0.11 0.09 0.11 0.05 1.04
Body size −0.23 −0.20 −0.20 −0.20 −0.19 −0.09 −1.80
DPR 0.50 0.35 0.37 0.29 0.49 0.22 4.4
HCR 0.18 0.15 0.14 0.15 0.23 0.10 2.0
CCR 0.48 0.34 0.35 0.31 0.48 0.34 6.7
CA$ 0.30 0.37 0.36 0.36 0.41 2.8 57

The new indexes are more correlated than 2010 NM$ with yield traits and less correlated with PL, SCS, and fertility, as expected from the updated economic values. Expected PTA progress was obtained as the correlation of PTA with NM$ multiplied by the SD of PTA multiplied by 0.45, which is the expected annual trend in SD of NM$. The PTA SDs (not shown) generally are lower than the TTA SDs shown in the first table because of selection and because RELs are less than 1. Genetic trend (change in breeding value) equals twice the expected progress for PTA. Thus, multiplication of annual PTA gain by 20 gives expected genetic progress per decade.

Derivation of economic values 

Primary differences in economic values for grazing versus confinement herds are 2.5 times higher value of fertility to maintain seasonal calving, 15% less production per lactation but 50% more lactations, 25% less death loss, and 25% less mastitis incidence (Gay et al, 2014). Economic values for other traits in GM$ were mainly the same as in NM$.

The derivation of economic values is shown below for fertility traits, yield traits, SCS, and PL. Derivation of economic values for CA$ and for udder, feet/legs, and body size composites are described in historical reports for previous net merit revisions (VanRaden and Multi-State Project S-1008, 2006; Cole et al., 2009).

Fertility traits 

Measures of fertility in merit indexes now include HCR and CCR along with DPR. Separating the benefits from CCR and DPR is not simple because the 2 traits overlap. Both are major components of PL, but the benefits from more lactations are already included in the PL economic value. Economic values were obtained with the following assumptions.

Numbers of services were assumed to average 1.8 for heifers and 2.9/lactation for cows, which is equivalent to conception rates of 56% and 34%, respectively. Semen price ($15/unit), insemination labor costs ($5/unit), and heat detection labor and supplies ($5 for heifers and $7 for cows) were assumed to be proportional to the number of services. Synchronization costs are higher than simple heat detection and range from $13 to $25 per insemination (Stevenson, 2010), but synchronization can improve conception rates and reduce calving intervals. Pregnancy checks ($10/exam) were assumed to increase by 0.4 times the number of services.

For heifers, each 1% increase in HCR should decrease age at first calving by 1.8(30/100) = 0.54 days, assuming that failed services increase age at first calving by 30 instead of 21 days because of incomplete heat detection and abortion loss. A cost of $2.10/day was assumed for calving after the optimum age (Wilson, 2006). Losses from culling heifers for poor fertility should be included in HCR because PL does not include those losses. If heifers are culled after 5 unsuccessful services, (1 – 0.56)5 = 1.6% of heifers would be culled, with 0.2% more for each 1% lower HCR. Alternatively, natural service might be used for problem breeders, but with potentially higher cost than for artificial insemination. When infertile heifers are culled at about 1,000 pounds live weight, economic loss equals the raising cost of $1,200 minus the beef value of $900. Total value of HCR including age at first calving, insemination costs, heat detection, pregnancy checks, and reproductive culling was $2.10(0.54) + [$15 + $5 + $5 + $10(0.4)]1.8/100 + $300(0.002) = $2.26.

For cows, reduced profit from lactations longer or shorter than optimum was estimated to be $0.75/day open. Poor cow fertility is correlated with other unmeasured health expenses, and $0.20/day open was added to account for these. The economic loss for 1 day open is then converted to DPR by multiplying by −4. Numbers of calves born increase with both DPR and PL. At a constant PTA PL, 1% higher DPR results in about 1% more calves per lifetime with an average value of $150, which then results in an extra $1.50/PTA unit of DPR. Per lactation costs for CCR and days open are converted to lifetime values by multiplying by 2.5, which assumes that cows have 2.8 lactations but that no inseminations are attempted for 30% of the cows during their final lactation because a decision to cull was made previously for other reasons (2.5 = 2.8 − 0.3). Total value of CCR was 2.5[($15 + $5 + $7 + $10(0.4)]2.9/100 = $2.25. Total value of DPR was 2.5(4)($0.75 + $0.20) + $1.50 = $11.

Relative emphasis in NM$ for traits HCR, CCR, and DPR are 1.5, 1.7, and 7.0%, respectively, with TTA standard deviations of 2.4, 2.8, and 2.3. The combined emphasis of 10% for the 3 fertility traits is slightly less than the 11% for DPR in the 2010 NM$.

Yield traits 

A base price of $18.00 was assumed for milk containing 3.5% fat, 3% true protein, and 350,000 somatic cells/ml before deducting hauling charges, which were assumed to be $0.57 based on actual costs (about $0.0057/100 pounds/loaded mile in 2009). The milk price after hauling charges was equal to $17.43. Component prices follow, along with marginal feed costs and health costs required for higher yield with the nonyield traits in NM$ held constant; values in the volume column are computed as (milk value) − 3.5(fat value) − 3(protein value) divided by 100:

Index Milk
($/100 pounds)
Fat
($/pound)
Protein
($/pound)
Volume
($/pound)
NM$ and GM$ 17.43 1.95 2.48 0.0317
CM$ 17.43 1.95 3.10 0.0131
FM$ 17.43 1.95 0.99 0.0764
Feed cost 7.15 0.65 0.90 0.0270
Extra health cost 1.44 0.14 0.09 0.0068

Feed costs are assumed to average 50% of the milk price and have increased faster than milk prices. The new USDA Margin Protection Program calculates feed cost as 1.0728(corn price/bushel) + 0.00735(soybean meal price/ton) + .0137(alfalfa hay price/ton). Using projected prices of $4.00, $350, and $200 for corn, soybean meal, and alfalfa hay, respectively, feed costs = $9.60/100 pounds milk, slightly more than 50% of the forecast milk price. By participating in the program, producers can insure that their margin between milk and feed price does not become too narrow.

The feed cost for milk volume accounts for the $0.20 required to produce a pound of lactose in each 20 pounds of milk. A cost of $0.002 for bulk tank, equipment, and electricity costs to cool and store each pound of milk also is included in the feed cost. Total feed costs were divided into costs for milk, fat, and protein using the approach of Dado et al. (1994), with an additional multiplier to account for increased feed prices and an increased price of corn relative to soybean meal.

Extra health costs equal 8% of the milk price based on a literature review conducted by A.J. Seykora (2006, personal communication). The other traits in NM$ such as PL and DPR account for replacement costs and some (but not all) health costs. Udder composite and SCS account for about half of the mastitis and discarded milk costs. The residual antagonistic genetic correlations between milk and health traits should be used to account for health expenses until direct evaluations of health traits become available. Examples of research studies that estimated costs of health traits and correlations with production are Dunklee et al. (1994), Jones et al. (1994), Simianer et al. (1991), Uribe et al. (1995), Van Dorp et al. (1998), and Zwald et al. (2004). The studies indicate that higher milk yield is more correlated than fat or protein yield to increased health costs and also to poorer heat tolerance (Bohmanova et al., 2005).

Correlations of merit indexes based on recent progeny-tested bulls were 0.994 for NM$ with CM$, 0.961 for NM$ with FM$, and 0.925 for FM$ with CM$. A small protein premium equal to feed cost plus health cost is included to make FM$ more acceptable as a breeding goal and results in no direct selection for or against protein in the FM$ index. Producers that expect future premiums less than $1.74/pound of protein should select on FM$; those that expect premiums greater than $2.79/pound of protein should select on CM$. Most U.S. producers are likely to expect protein premiums between $1.74 and $2.79 and should select on NM$.

The value of milk, fat, and protein is converted from a lactation basis to a net lifetime basis by subtracting feed and health costs and then multiplying by the average number of record equivalents in a lifetime. For Holsteins, the average number of record equivalents is 2.78, and the lifetime value of PTA protein in NM$ is (2.48− 0.99)2.78 = $4.14. Yield traits together account for 43% of total selection emphasis in NM$.

Prices for milk, fat, and protein are difficult to predict because they vary widely by use of milk and across time. Average prices for milk in Federal order markets are available from USDA's Agricultural Marketing Service. Actual prices since 2006 for class III milk used in cheese making are shown below:

Year Milk
($/100 pounds)
Fat
($/pound)
Protein
($/pound)
Volume
($/pound)
SCC
($/1,000 cells)1
2013 17.99 1.67 3.30 0.0225 −0.00090
2012 17.44 1.72 3.04 0.0230 −0.00085
2011 18.37 2.15 2.97 0.0194 −0.00091
2010 14.41 1.85 2.31 0.0101 −0.00076
2009 10.29 1.20 1.99 0.0012 −0.00062
2008 17.44 1.57 3.89 0.0028 −0.00094
2007 18.04 1.47 3.51 0.0024 −0.00084
2006 11.89 1.33 2.09 0.0097 −0.00063
Forecast
2014 CM$ 18.00 1.95 3.10 0.0131 −0.00079
2010 CM$ 14.93 1.63 3.35 0.0012 −0.00076
1See the section on SCS for a fuller explanation of quality premiums.

Milk prices over the last 4 years averaged $17.05 for class III compared with $14.93 forecast in 2010; the current price as of August 2014 is much higher at $22.25. Future contract prices average about $18.00 for 2015, and the USDA World Agricultural Supply and Demand Estimates Report (WASDE) Class III milk price estimate is $17.50 for 2015. Protein prices over the last 4 years averaged $2.91 and were less than the $3.35 forecast in 2010, whereas butterfat prices averaged $1.85 and were more than the $1.63 forecast in 2010. Current component prices as of August 2014 are $3.15 for protein and $2.52 for butterfat. The price of other solids increased even more compared with forecast prices. For example, recent and future contract prices for dried whey are about $0.60/pound compared with $0.25 forecast in 2010. The net effect of these changes are to increase the value of milk volume relative to components.

Predicted prices used in CM$ are now $3.10 for protein and $1.95 for fat. Fluid milk processors usually pay no premium for extra protein because grocery store milk is not yet labeled or priced by protein content, but a protein premium is included in FM$ to prevent the actual value of protein from becoming negative after feed costs are subtracted. Selection on FM$ is appropriate mainly in southeastern states. California processors usually pay premiums based on solids-not-fat (SNF) content instead of protein, and fluid milk in California is fortified to a minimum SNF standard. Protein is not more valuable than lactose or mineral in products such as ice cream or yogurt. Powder processing plants paid premiums averaging $1.30/pound of SNF since 2009, but export markets for powder are now increasing the value of protein by requiring minimum standards for protein. Lactose and SNF yields are not genetically evaluated but are more correlated to milk yield than to protein yield (Welper and Freeman, 1992; Miglior et al, 2007).

The value of protein in NM$ now represents an average across milk markets of price formulas paid to producers. Previously NM$ was a weighted average of prices paid by processors for the 4 usage classes: 1) fluid milk, 2) soft/frozen products, 3) hard cheese, and 4) butter/powdered milk. That approach was used since the milk-fat-protein dollars (MFP$) index was first introduced (Norman et al., 1979) and is still used to charge processors in Federal Orders. However, 7 of the 10 Federal Orders ignore the actual usage of milk when paying producers and instead pay component prices to producers as if all milk is used for cheese. This policy gives producers more incentive to select for high solids, low volume milk and less incentive to select for the high volume, lower solids milk that is marketed by fluid processors. Herds and breeds with high protein percentages such as Jersey receive higher prices and herds and breeds such as Holstein with low protein percentages receive lower prices than the actual market value of the components to processors. By using the average prices received by producers instead of average prices charged to processors, the NM$ price is now much closer to CM$ than in the past.

The following historical table shows the component prices used since 1977 to calculate NM$ and MFP$. Prior to 1997, component prices were previous-year average prices. Crude protein prices reported prior to 2000 were converted to true protein prices by multiplying by 1.064.

Year Milk Fat True protein Volume
1977 12.30 1.48 1.24 0.034
1978 12.23 1.51 1.18 0.034
1979 12.25 1.52 1.21 0.033
1980 12.32 1.61 1.26 0.029
1981 12.35 1.63 1.28 0.028
1982 12.24 1.64 1.30 0.026
1983 12.34 1.70 1.33 0.024
1984 12.32 1.75 1.33 0.022
1985 12.26 1.72 1.28 0.024
1986 12.35 1.85 1.29 0.020
1987 12.28 1.74 1.23 0.025
1988 12.26 1.68 1.26 0.026
1989 12.31 1.46 1.50 0.027
1990 12.33 1.13 1.39 0.042
1991 12.23 1.12 1.47 0.039
1992 12.29 0.79 1.54 0.049
1993 12.33 0.70 1.66 0.049
1994 12.24 0.58 1.57 0.055
1995 12.29 0.72 1.69 0.047
1996 12.27 0.89 1.65 0.042
1997–99 12.30 0.80 2.12 0.031
2000–03 12.68 1.15 2.55 0.010
2003–06 12.70 1.30 2.30 0.013
2006–09 12.70 1.50 1.95 0.016
2010–14 14.36 1.63 1.94 0.029
2014– 17.43 1.95 2.48 0.032

Milk prices paid to producers increased recently but were stable from 1977 through 2010 when much inflation occurred in labor, feed, and many other input prices. Additional history on economic indexes is provided in the History of NM$ section below.

SCS 

Selection for lower SCS reduces the labor, discarded milk, antibiotic, and other health costs associated with clinical mastitis. Lower PTA SCS also leads to higher milk prices in markets where quality premiums are paid. For the last 4 years, premiums and penalties in Federal orders for class III milk averaged a price increase of $0.00086 for each 1,000 cell/ml decrease in SCC. This compares with $0.00076 assumed in 2010. Somatic cell premiums were previously converted from SCC scale to SCS scale assuming an average of 350,000, but the Dairy Herd Information average of 320,000 in 2002 fell rapidly to 199,000 by 2013 (Norman and Walton, 2014). The SCC value per 1,000 cells was previously converted to the SCS value per double by dividing by 0.0041, which was the difference between log base 2 of 351,000 and log base 2 of 350,000, but now is converted by dividing by 0.0072 which is the difference between log base 2 of 201,000 and log base 2 of 200,000. The value of SCC/100 pounds of milk is now converted to the value of SCS as $0.00086/0.0072 = $0.125.

The actual change in SCC from a 1 unit change in PTA SCS (a doubling of SCC) and the actual SCC differences among bull daughters are now much less than when SCC premiums were introduced. Also, the actual value of PTA SCS is higher for herds with more mastitis and lower for herds with less mastitis because payments are linear with SCC rather than with SCS. The premium per 1,000 cells increased only slightly since 2010 and contributes much of the SCS economic value, but the smaller phenotypic mean and SD for SCC will decrease the relative emphasis on SCS to 8% from 10% in 2010.

Different premiums for SCS are applied in each index. The full class III premium of $0.13 is applied to SCS in CM$ because manufacturing plants typically provide incentives for improved milk quality. A premium of $0.09 is used for NM$ on the assumption that 70% of the milk will be sold in blend markets that are paid the class III premium [$0.13(0.70) = $0.09]. Some producers in fluid markets receive a small premium for improved milk quality, but estimates of those payments were difficult to find. No premium was assigned to SCS in FM$, but the actual value of reduced SCS for improving shelf life of fluid milk is substantial.

The value of PTA SCS per lactation in NM$ was set at −$44, which includes a lost premium of $24 ($0.09 for 26,654 pounds milk) plus $20 for labor, drugs, discarded milk, and milk shipments lost because of antibiotic residue. The value of SCS is greater for CM$ (−$54) and less for FM$ (−$20). The large economic losses caused by reduced milk yield are not included in the SCS value because those already are accounted for in PTA milk.

PL 

The value of an additional lactation was adjusted to account for lower replacement heifer and higher beef prices that reduced the cost of culling and for a lower interest rate that increased the value of later lactations. Replacement heifer prices decreased from an estimate of $1,910 in 2010 to near $1,500 for the past 3 years but have increased to about $1,800 currently. Replacement costs now are assumed to include a newborn heifer price of $200, a cost of $1.00/pound of growth, and a fixed cost of $400, for a total of $1,700 to raise the heifer to 1,200 pounds. Beef prices for cull cows have increased from a previous estimate of $0.54/pound to about $0.80 currently. The previous interest rate of 7.5% was reduced to 5%. The combination of these factors reduced the emphasis on PL from 22% in 2010 NM$ to 19% in 2014 NM$.

Lifetime profit  

The NM$ index is defined as expected lifetime profit as compared with the breed base cows born in 2010. Incomes and expenses that repeat for each lactation are multiplied by the cow's expected number of lactations. This multiplication makes the economic function a nonlinear function of the original traits. For official NM$, a linear approximation of this nonlinear function is used as recommended by Goddard (1983). The linear function is much simpler to use and was correlated with the nonlinear function by 0.999.

Index selection based on computer calculation is efficient, and computer mating programs that account for inbreeding using complete pedigrees also should be used. Selection and mating programs both can have large, nearly additive effects on future profit. Gains from mating programs do not accumulate across generations, whereas gains from selection do. Cows and bulls within each breed are ranked with the same NM$ even though the timing of gene expression differs by sex.

The NM$ measures additional lifetime profit that is expected to be transmitted to an average daughter but does not include additional profit that will be expressed in granddaughters and more remote descendants. Gene flow methods and discounting of future profits could provide a more complete summary of the total profit from all descendants. Animal welfare may be a goal of society but is not assigned a monetary value in NM$. Healthier cows can make dairying a more enjoyable occupation, and traits associated with cow health may deserve more emphasis as labor costs increase. Production of organic milk with fewer treatment options could require cows with more natural ability to resist disease and remain functional.

The profit function approach used in deriving NM$ lets breeders select for many traits by combining the incomes and expenses for each trait into an accurate measure of overall profit. Averages and SDs of the various traits in the profit function may differ by breed, but official NM$ is calculated by using Holstein values instead of having a slightly different NM$ formula for each breed. Producers should use the lifetime merit index (NM$, CM$, FM$, or GM$) that corresponds to the market pricing that they expect a few years in the future when buying breeding stock and 5 years in the future when buying semen.

History of NM$ 

The 2014 NM$ index, which includes the new traits HCR and CCR and updated economic values, is correlated by 0.965 with the 2010 NM$ index for recent progeny-tested bulls. An increase in genetic progress worth $8 million/year is expected on a national basis, assuming that all of the changes are improvements and that all breeders select on NM$. The January 2010 NM$ index was correlated by 0.99 with the 2006 NM$ formula; the 2010 changes were mostly caused by an increase in the price of feed, decrease in the value of heifer calves, and higher cost of raising replacements, but no new traits. The 2006 NM$ index was correlated by 0.975 with the 2003 NM$ formula for recent progeny-tested bulls; about half the changes were caused by the PTA PL revision and the rest from addition of stillbirth and updates of trait economic values.

In the 2003 NM$ revision, cow fertility and calving ease were incorporated into NM$. In the 2000 NM$ revision, type traits were included along with yield and health traits using a lifetime profit function based on research of scientists in the S-284 Health Traits Research Group. In 1994, PL and SCS were combined with yield traits into NM$ using economic values that were obtained as averages of independent literature estimates (VanRaden and Wiggans, 1995). In the 1980s as part of Project NC-2 of the North Central Regional Association of Agricultural Research Experiment Station Directors, researchers developed a profit function to compare genetic lines in their experimental herds:

lifetime profit = milk value + salvage value + value of calves
− rearing cost − feed energy − feed protein − health cost − breeding cost.

Relative net income also was developed to measure profit from field data with adjustment for opportunity cost to more fairly compare short- and long-term investments (Cassell et al., 1993). The main difference between NM$ and the profit function approaches is that a PTA is calculated for each evaluated trait and then combined instead of combining each cow's phenotypic data directly. The PTA approach is more accurate because heritabilities of traits differ, genetic correlations are not the same as phenotypic correlations, and all phenotypes are not available at the same time.

In 1984 and 1977, economic index formulas based on cheese yield price (CY$) and protein price (MFP$), respectively, were introduced. In 1971, USDA introduced its first genetic-economic index called Predicted Difference Dollars (PD$), which combined only milk and fat yield. The 3 different milk pricing formulas (Norman, 1986) continued to be published until 1999 when they were replaced by the more complete merit indexes CM$, NM$, and FM$, respectively (see the Yield Traits section for a history of milk price formulas).

A history of the main changes in USDA genetic-economic indexes for dairy cattle and the percentage of relative emphasis on traits included in the indexes follows:

Traits included USDA genetic-economic index (and year introduced)
PD$
(1971)
MFP$
(1976)
CY$
(1984)
NM$
(1994)
NM$
(2000)
NM$
(2003)
NM$
(2006)
NM$
(2010)
NM$
(2014)
Milk 52 27 −2 6 5 0 0 0 −1
Fat 48 46 45 25 21 22 23 19 22
Protein 27 53 43 36 33 23 16 20
PL 20 14 11 17 22 19
SCS −6 −9 −9 −9 −10 −7
Udder composite 7 7 6 7 8
Feet/legs composite 4 4 3 4 3
Body size composite −4 −3 −4 −6 −5
DPR 7 9 11 7
CCR 2
HCR 1
CA$ 6 5 5

Emphasis on yield traits has declined as other fitness traits were introduced. As protein yield became more important, milk volume became less important because of the high correlation of those 2 traits. A more complete history and comparisons with selection indexes used by other countries are available (Shook, 2006; VanRaden, 2002; VanRaden, 2004).

Acknowledgments 

The authors thank Erick Metzger for helpful discussion of milk price formulas and Suzanne Hubbard for review and revision of this research report.

References 

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