Lifetime Net Merit 2018
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AIP RESEARCH REPORT
NM$7 (5-18)

Updated July 5, 2018

Net merit as a measure of lifetime profit: 2018 revision

P.M. VanRaden,1 J.B. Cole,1 and K.L. Parker Gaddis2
1Animal 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
2Council on Dairy Cattle Breeding, Bowie, MD 20716
240-334-4164, ext. 323 (voice) ~ kristen.gaddis@uscdcb.com ~ https://www.uscdcb.com/
 

Overview  |  Updated economic values  |  Net merit calculation  |  Trait parameters  |  Expected genetic progress  |  Derivation of economic values  |  Health traits  |  Fertility traits  |  Yield traits  |  Somatic cell score  |  Productive life/livability  |  Type traits  |  Calving ability  |  Lifetime profit  |  History of net merit  |  Acknowledgments  |  References

Overview 

The lifetime net merit (NM$) index ranks dairy animals based on their combined genetic merit for economically important traits. Indexes are updated periodically to include new traits and to reflect prices expected in the next few years. The August 2018 update of NM$ includes genetic evaluations for 6 new health traits recorded by producers: clinical mastitis (MAST), ketosis (KETO), retained placenta (REPL), metritis (METR), displaced abomasum (DA), and milk fever (MFEV; hypocalcemia). Cows with genes that keep them healthy are more profitable than cows with health conditions that require extra farm labor, veterinary treatment, and medicine.

Economic values of the 6 new traits were obtained as averages of 2 recent research studies plus additional yield losses not fully accounted for in published genetic evaluations for yield traits. Liang et al. (2017) estimated direct treatment, labor, and discarded milk costs for health disorders from veterinary and producer survey responses, and Donnelly (2017) obtained health treatment costs from 8 cooperating herds in Minnesota. Some yield losses associated with health conditions are not fully accounted for when 305-day lactation records include adjusted test days that are coded as sick or abnormal. Total costs for the 6 traits are added to NM$ in the form of a health trait subindex (HTH$) that is not published separately. This is similar to the calving trait subindex (CA$) that combines 4 traits and is not published or to conformation traits, which are grouped into an udder composite, feet and leg composite, and body weight composite (BWC).

Relative emphasis on most other traits was slightly less because of the addition of HTH$. However, yield trait emphasis increased slightly and somatic cell score (SCS) emphasis decreased greatly because correlated health costs previously assigned indirectly to yield and SCS are now assigned directly to HTH$. Other economic values were updated very little. The 6 health traits are currently evaluated only for Holsteins. The 2018 and 2017 NM$ (VanRaden, 2017) indexes were correlated by 0.994 for recent Holstein bulls.

Updated economic values 

New economic values for each unit of predicted transmitting ability (PTA) and relative economic values of traits will be implemented in August 2018 for NM$, cheese merit (CM$), fluid merit (FM$), and grazing merit (GM$). The traits are now displayed in the historical order that they were included in NM$:

Trait Units Standard
deviation
(SD)
Value ($/PTA unit) Relative value (%)
NM$ CM$ FM$ GM$ NM$ CM$ FM$ GM$
Milk Pounds 672 −0.004 −0.052 0.102 −0.004 −0.7 −7.9 18.4 −0.7
Fat Pounds 25 4.03 4.03 4.03 3.67 26.8 22.8 27.1 22.9
Protein Pounds 18 3.53 5.14 0.00 3.21 16.9 20.9 0.0 14.4
Productive life (PL) Months 2.4 19 19 19 11 12.1 10.3 12.2 6.6
SCS Log 0.21 −72 −93 −41 −66 −4.0 −4.4 −2.3 −3.5
BWC Composite 1.10 −18 −18 −18 −21 −5.3 −4.5 −5.3 −5.8
Udder Composite 0.90 31 31 31 33 7.4 6.3 7.5 7.4
Feet/legs Composite 1.03 10 10 10 11 2.7 2.3 2.8 2.8
Daughter pregnancy rate (DPR) Percent 2.3 11 11 11 31 6.7 5.7 6.8 17.8
CA$ Dollars 18.00 1 1 1 1 4.8 4.1 4.8 4.5
Heifer conception rate (HCR) Percent 2.4 2.2 2.2 2.2 4.0 1.4 1.2 1.4 2.4
Cow conception rate (CCR) Percent 2.8 2.2 2.2 2.2 6.2 1.6 1.4 1.7 4.3
Livability (LIV) Percent 2.3 12 12 12 8.6 7.3 6.2 7.4 4.9
HTH$ Dollars 8.50 1 1 1 1 2.3 1.9 2.3 2.1

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 $197, slightly greater than the $193 for previous 2017 indexes (VanRaden, 2017). The SD for GM$ would be larger because of longer PL in grazing herds except that milk yield differences are often reduced in such herds. Economic values in GM$ are 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. Multitrait methods currently are used within the trait groups of conformation, fertility, and PL with LIV. 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 and HTH$ are computed only for Holsteins. The Brown Swiss CA$ includes only sire calving ease and daughter calving ease. For Brown Swiss, relative values of the other traits each increase in all the indexes by a factor of approximately 1.02 because the emphasis on HTH$ is excluded. For the remaining breeds, relative values of the other traits each increase by a factor of approximately 1.07 for NM$, FM$, and GM$ and by a factor of approximately 1.06 for CM$ because CA$ and HTH$ are excluded.

NM$ calculation 

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

Trait Example PTA Example REL (%)
Milk +2,000 90
Fat +80 90
Protein +70 90
PL +2.5 60
SCS 2.95 (−3.00) 75
BWC −1.0 85
Udder +1.5 80
Feet/legs +0.5 75
DPR +0.3 55
CA$ +30 90
HCR +0.5 60
CCR +1.2 50
LIV +1.8 50
HTH$ +15 50

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 $756, CM$ is $773, FM$ is $722, and GM$ is $756. Calculation of NM$ also can be expressed in matrix form:

NM$ = au,

where a contains the economic values for the 14 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 14 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 among the 14 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 14 PTA traits:

PTA trait PTA trait
Milk Fat Protein PL SCS BWC Udder Feet/legs DPR CA$ HCR CCR LIV HTH$
Milk 0.201 0.43 0.83 0.10 0.02 −0.12 −0.10 −0.02 −0.23 0.19 −0.03 −0.16 0.03 0.03
Fat 0.69 0.20 0.59 0.15 −0.09 −0.05 −0.07 0.01 −0.15 0.13 0.03 −0.10 0.06 0.08
Protein 0.90 0.75 0.20 0.13 0.04 −0.09 −0.14 −0.01 −0.18 0.22 −0.07 −0.15 0.05 0.04
PL 0.15 0.17 0.16 0.08 −0.45 −0.10 0.18 0.14 0.64 0.40 0.32 0.62 0.70 0.56
SCS −0.10 −0.10 −0.10 −0.40 0.12 −0.10 −0.23 −0.15 −0.27 −0.14 −0.12 −0.25 −0.25 −0.44
BWC 0.06 0.05 0.05 −0.20 −0.11 0.40 0.27 0.38 −0.052 −0.07 −0.01 −0.01 −0.14 −0.26
Udder −0.02 −0.05 −0.06 0.15 −0.30 0.45 0.27 0.45 0.09 0.10 0.03 0.04 0.08 −0.01
Feet/legs −0.14 −0.11 −0.18 0.08 −0.02 0.35 0.40 0.15 0.03 −0.01 −0.01 −0.04 0.06 0.02
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 0.43 0.42
CA$ 0.02 0.02 0.02 0.20 −0.03 −0.10 0.00 −0.02 0.09 0.07 0.16 0.34 0.36 0.33
HCR −0.05 −0.05 −0.05 0.10 −0.04 −0.02 −0.05 −0.05 0.10 0.16 0.01 0.54 0.22 0.18
CCR −0.10 −0.10 −0.10 0.40 −0.20 −0.10 0.03 −0.04 0.70 0.20 0.45 0.02 0.43 0.36
LIV 0.11 0.13 0.12 0.70 −0.40 −0.20 0.10 0.05 0.40 0.35 0.20 0.15 0.01 0.55
HTH$ 0.02 0.04 0.02 0.28 −0.22 −0.13 0.00 0.01 0.21 0.17 0.09 0.18 0.28 0.01
1Holstein heritabilities in orange on diagonal; heritabilities for other breeds are the same except for BWC (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 2007 through 2011. 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 2017 formula (VanRaden, 2017) are shown for comparison:

PTA trait Correlation of PTA with index Expected genetic progress from NM$
2017 NM$ 2018 NM$ 2018 CM$ 2018 FM$ 2018 GM$ 2017 NM$ PTA change/year 2018 NM$ PTA change/year 2018 NM$ breeding value change/decade
Milk 0.46 0.46 0.40 0.59 0.36 104 104 2,083
Fat 0.68 0.72 0.72 0.70 0.64 5.5 5.9 117
Protein 0.63 0.64 0.63 0.64 0.54 3.7 3.8 76
PL 0.73 0.69 0.68 0.68 0.73 0.54 0.51 10
SCS −0.35 −0.30 −0.31 −0.26 −0.36 −0.02 −0.02 −0.38
BWC −0.20 −0.20 −0.19 −0.22 −0.15 −0.08 −0.08 −1.58
Udder composite 0.18 0.20 0.20 0.22 0.12 0.05 0.05 1.09
Feet/leg composite 0.07 0.09 0.09 0.08 0.08 0.02 0.03 0.59
DPR 0.26 0.23 0.24 0.19 0.58 0.18 0.16 3.2
CA$ 0.51 0.49 0.49 0.48 0.52 3.5 3.4 68
HCR 0.36 0.35 0.34 0.35 0.47 0.21 0.20 4.1
CCR 0.51 0.51 0.52 0.48 0.68 0.42 0.42 8.5
LIV 0.48 0.48 0.48 0.47 0.47 0.38 0.38 7.6
HTH$ 0.47 0.46 0.47 0.43 0.50 0.9 0.9 17.7

The expected PTA progress was obtained as the correlation of PTA with NM$ multiplied by the SD of PTA multiplied by 0.37, which is the expected annual trend in SD of NM$. The PTA SDs (not shown) generally are lower than the TTA SDs shown in the first table because of selection and because RELs are less than 1. Genetic trend (change in breeding value) equals twice the expected progress for PTA. Thus, multiplication of annual PTA gain by 20 gives expected genetic progress per decade. The 2018 NM$ was expected to be, but is not, more correlated than the 2017 NM$ with HTH$, because the updated economic values for yield and SCS removed more correlated emphasis than the direct emphasis on HTH$ added.

Derivation of economic values 

Derivation of economic values is shown below for health traits, fertility traits, yield traits, SCS, PL and LIV, and type traits. Economic values for most traits in CM$, FM$, and GM$ are the same as in NM$. 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 MAST incidence (Gay et al, 2014).

Health traits 

Economic values, relative values, and standard deviations of TTAs for the 6 health traits and LIV are compared in the following table:

Trait
(cases/lactation, %)
TTA SD Value ($/case)
(direct cost + yield adjustment)
Relative value (%)
HTH$ NM$
MFEV 0.4 34 (38 − 4) 2.3 0.05
DA 0.7 197 (178 + 19) 23.3 0.54
KETO 1.0 28 (28 + 0) 4.7 0.11
MAST 2.6 75 (72 + 3) 32.9 0.77
METR 1.4 112 (105 + 7) 26.5 0.62
RETP 0.9 68 (64 + 4) 10.3 0.24
HTH$ $8.50 100 2.3
LIV/lactation 0.8 1,200 7.3

The economic values include direct costs per case plus additional yield losses not accounted for by yield PTAs because those are adjusted for abnormal test days.

Healthy and unhealthy cows were compared with and without the test-day milk, fat, and protein adjustments of Wiggans et al. (2003). Most health traits had only 2-pound differences for fat and 1-pound differences for protein between adjusted and unadjusted lactation yields. The value per lactation was $1.23 for fat and $1.32 for protein, resulting in only about $4 more value to add to direct health costs/case to account for unadjusted yield minus published adjusted yield. Only DA had bigger differences of 6 pounds for fat and 8.5 pounds for protein, but those differences added only $19 to the $178 value of direct costs assumed for DA. Because DA has acute effects requiring surgery, cows with DA may be more likely be coded as sick or detected as abnormal on test day. Thus, adjustments to published evaluations for yield contribute little to total direct health costs. Relative values for each trait again are obtained by multiplying economic value by TTA SD and then dividing each individual value by the sum of the absolute values.

To calculate HTH$, the PTAs for each trait are converted from percentages by multiplying by 0.01. Then they are multiplied by the corresponding economic value ($/case) and converted to lifetime values by multiplying by 2.8, which is the assumed number of lactations in a lifetime. The HTH$ index is the sum of the lifetime values for all traits.

Calculation of HTH$ can be demonstrated using the following example Holstein:

Trait Example PTA Example lifetime value ($)
MFEV −0.2 −0.19
DA +0.5 2.76
KETO +1.2 0.94
MAST +2.0 4.20
METR +2.1 6.59
RETP −0.1 −0.19

The lifetime value for MFEV is calculated as the animal's PTA of −0.2 times 0.01 times the economic value of $34/case times 2.8 lactations = −$0.19. After the lifetime values for all traits are summed, this example Holstein's HTH$ is $14.10.

Fertility traits 

Measures of fertility in merit indexes 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, 2012), 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. 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.

Yield traits 

A base price of $17.00 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 $16.43. Component prices follow, along with marginal feed costs required for higher yield with the nonyield traits in NM$ 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$ 16.43 2.10 2.17 0.0257
CM$ 16.43 2.10 2.75 0.0083
FM$ 16.43 2.10 0.90 0.0638
Feed cost 7.68 0.65 0.90 0.0271

Feed costs are assumed to average about half of the milk price. 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 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.

Previously, extra health costs that were correlated with yield traits were deducted from the economic values for milk, fat, and protein, but because health costs are now assigned directly to individual traits, those indirect costs are no longer deducted from yield to avoid double-counting the health expenses. This caused the relative economic values for milk, fat, and protein to increase slightly (about 1–2%).

Correlations of merit indexes based on recent progeny-tested bulls were 0.995 for NM$ with CM$, 0.968 for NM$ with FM$, and 0.939 for FM$ 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 low future protein premiums should select on FM$, and those that expect high protein premiums should select on CM$; breeders targeting the U.S. average price 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 (2.17 − 0.90)2.78 = $3.53.

Prices for milk, fat, and protein are difficult to predict because they vary 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
($/1,000 cells)1
2017 16.17 2.61 1.87 0.0143 −0.00082
2016 15.80 2.31 2.10 0.0142 −0.00082
2015 15.80 2.30 2.24 0.0103 −0.00083
2014 22.34 2.38 3.39 0.0384 −0.00110
2013 17.99 1.67 3.30 0.0225 −0.00090
2012 17.44 1.72 3.04 0.0230 −0.00085
2011 18.37 2.15 2.97 0.0194 −0.00091
2010 14.41 1.85 2.31 0.0101 −0.00076
2009 10.29 1.20 1.99 0.0012 −0.00062
2008 17.44 1.57 3.89 0.0028 −0.00094
2007 18.04 1.47 3.51 0.0024 −0.00084
2006 11.89 1.33 2.09 0.0097 −0.00063
Forecast
2018 CM$ 17.00 2.10 2.75 0.0140 −0.00090
2017 CM$ 17.50 2.00 2.90 0.0180 −0.00090
1See SCS section for a fuller explanation of quality premiums.

Milk prices over the last 4 years averaged $17.53 for class III and was forecast at $17.50 in 2017 (VanRaden, 2017); however, the current price as of April 2018 is lower at $14.22. Future contract prices for 2018 and the USDA World Agricultural Supply and Demand Estimates Report (WASDE) Class III milk price estimate for 2018 are about $16.00. Protein prices over the last 4 years averaged $2.40 and are less than the $2.90 forecast in 2017. Butterfat prices also averaged $2.40, and are more than the $2.00 forecast in 2017. Current component prices as of April 2018 are $1.81 for protein and $2.43 for butterfat. Demand for butterfat has increased after trans fats were banned as an ingredient in food (U.S. Food and Drug Administration, 2015).

Predicted prices used in CM$ are now $2.75 for protein and $2.10 for fat. Fluid milk processors usually pay no premium for extra protein because grocery store milk is not yet labeled or priced by protein content, but a protein premium is included in FM$ 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 have paid 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$ represents an average across milk markets of price formulas paid to producers. Before 2014, 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$) index was first introduced (Norman et al., 1979) and is still used to charge processors in Federal Orders. However, 7 of the 10 Federal Orders ignore the actual usage of milk when paying producers and instead pay component prices to producers as if all milk is used for cheese. Use of the average prices received by producers instead of average prices charged to processors makes the NM$ price much closer to CM$ than in the past.

The following historical table shows the component prices used since 1977 to calculate NM$ and MFP$:

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
2010–13 14.36 1.63 1.94 0.029
2014–16 17.43 1.95 2.48 0.032
2017 16.93 2.00 2.32 0.030
2018 16.43 2.10 2.17 0.031

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. Milk prices paid to producers increased during the last decade 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 

Inclusion of MAST reduces the value assigned to SCS in the NM$ formula. Previously the per lactation value of PTA SCS included $24 for direct premiums and $20 for indirect MAST costs such as labor, drugs, discarded milk, and milk shipments lost because of antibiotic residue. The 2017 NM$ formula gave −6.5% emphasis to PTA SCS, but that emphasis will reduce to −4.0% with MAST now included directly. The MAST costs and correlations with yield are both lower for 2018 NM$ than had been assumed for 2017 NM$.

Lower PTA SCS gives 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 $0.00092 for each 1,000 cell/ml decrease in SCC.

Somatic cell premiums were originally converted from SCC scale to SCS scale with an assumed 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). Until 2014, the SCC value per 1,000 cells was converted to the SCS value/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 $0.00092/0.0072 = $0.128. 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 MAST and lower for herds with less MAST because payments are linear with SCC rather than with SCS.

Different premiums for SCS are applied in each index. The full class III premium is applied to SCS in CM$ because manufacturing plants typically provide incentives for improved milk quality. The premium in NM$ uses the assumption that 80% of the milk will be sold in blend markets that are paid the class III premium. Because some producers in fluid markets receive premiums for improved milk quality, 50% of the premium was assigned to SCS in FM$. The actual value of reduced SCS in fluid milk is substantial because of improved shelf life and taste (Ma et al., 2000).

PL and LIV  

Cow LIV was included as a new trait in April 2017. Cows that die or are euthanized on the farm generate no beef income and may have more health expenses than cows that are culled. The value of PL was reduced because beef income is now directly tied to cow LIV rather than indirectly to PL and because lower replacement heifer prices reduced the value of later lactations. Thus, this change shifted some economic value from PL to LIV.

Cows that die are assumed to generate $1,200 less income than those sold for beef, calculated as 1,500 pounds times $0.75/pound plus $75/death for on-farm labor and cow disposal charges. Because PTA LIV is expressed as the percentage of deaths per lifetime, the economic value is $1,200(0.01) = $12. Replacement costs now are assumed to include a newborn heifer price of $200, a cost of $0.75/pound of growth, and a fixed cost of $400, for a total of $1,425 to raise the heifer to 1,200 pounds. The interest rate also remains at 5%. The inclusion of LIV with 7% of emphasis and a decrease in replacement costs reduced the emphasis on PL from 19% in 2014 NM$ to 13% in 2017 NM$. Genetic progress for PL remained about constant but with more progress for LIV, resulting in healthier cows.

Type traits 

Linear type traits provide additional information about incomes and expenses. Instead of directly using PTAs for all type traits, composites are used in NM$. For Holsteins, udder composite, feet/legs composite, and BWC are calculated by Holstein Association USA (Holstein Association USA, 2017). For other breeds, published PTAs for linear traits are converted to standardized transmitting abilities (STAs) by dividing by TTA SD and then are combined into composites that are not published. Estimated genetic SDs follow:

Trait SD
Ayrshire Brown
Swiss
Guernsey Holstein Jersey Milking
Shorthorn
Stature 1.8 1.0 1.8 1.0 1.3 1.6
Strength 0.8 0.7 0.9 1.0 0.8 0.9
Body depth 1.0
Dairy form 0.9 0.7 1.4 1.0 1.1 1.0
Rump angle 0.8 1.0 1.3 1.0 1.0 0.9
Thurl width 1.0 0.6 1.2 1.0 0.7 0.8
Rear legs (side view) 0.6 0.7 0.6 1.0 0.7 0.4
Rear legs (rear view) 1.0 1.0
Foot angle 0.7 0.6 0.5 1.0 0.7 0.6
Feet & legs score 1.0
Fore udder 0.7 1.0 1.3 1.0 1.1 1.0
Rear udder height 0.9 0.9 1.2 1.0 1.2 0.9
Rear udder width 0.8 0.7 1.2 1.0 1.1 0.7
Udder cleft 0.7 0.9 0.8 1.0 0.8 0.6
Udder depth 0.9 1.0 1.5 1.0 1.5 1.1
Teat placement 0.8 0.8 1.0 1.0 1.1 1.1
Teat length 1.2 1.1 1.1 1.0 0.9 1.3
Rear teat placement 1.0

Relative values of udder and feet/legs traits for Jerseys, Guernseys, and Brown Swiss are obtained from the official Functional Trait Indexes or Functional Udder Indexes of those 3 breed associations. Jersey values are applied to Ayrshires and Milking Shorthorns. Breed association Functional Trait Index formulas were obtained from correlations with PL, but partial regressions are difficult to estimate in small populations with many traits.

Udder composite. The formula for Holstein udder composite was updated by Holstein USA in August 2017 (Holstein USA, 2017) and applied in merit indexes in December 2017. The Holstein udder composite now adjusts for the correlated influence of stature, and intermediate optima are assigned for both teat length and rear teat placement. Current relative weights used for merit index calculations are:

Trait Relative value (%)
Holstein Brown
Swiss
Guernsey Jersey and
other breeds
Stature −20
Fore udder 16 21 15 7
Rear udder height 23 6 15 33
Rear udder width 19 1 5 19
Udder cleft 8 2 15 1
Udder depth 20 35 33 31
Teat placement 4 11 15 4
Rear teat placement (nonlinear) 5
Teat length 5 −24 −2 4
Udder composite 1 100 100 100
1Holstein values are weights (expressed as percentages) from composite formula calculated by Holstein Association USA (2017) and, therefore, do not sum to 100.

Feet/legs composite. The formula for Holstein feet/legs composite was updated by Holstein USA in August 2017 (Holstein USA, 2017) and applied in merit indexes in December 2017. The Holstein feet/legs composite now adjusts for the correlated influence of stature. Because rear legs (rear view) and feet & legs score are not available for breeds other than Holstein, STA for foot angle and rear legs (side view) are included in the feet/legs composite for other breeds.Current relative weights used for merit index calculations are:

Trait Relative value (%)
Holstein Brown
Swiss
Guernsey Jersey and
other breeds
Stature −17
Rear legs (side view) −32 −16 −30
Rear legs (rear view) 18 36
Foot angle 8 68 48 70
Feet & legs score 58
Feet/legs composite 1 100 100 100
1Holstein values are weights (expressed as percentages) from composite formula calculated by Holstein Association USA (2017) and, therefore, do not sum to 100.

BWC. Body size composite (BSC) was replaced by BWC in April 2017. Research by Holstein USA (2016) redefined BSC to predict body weight more accurately using recent weight and linear type data from research herds with measured feed intake. The selection against BWC in 2017 (VanRaden, 2017) was larger than that against BSC in the 2014 index, leading to more efficient cows. In December 2017, a new BWC was introduced for Jerseys based on research by the American Jersey Cattle Association and the University of Wisconsin (American Jersey Cattle Association, 2017). The Jersey BWC was also used for Brown Swiss because neither breed scores body depth. Holstein BWC is used for breeds other than Jersey and Brown Swiss. Current relative weights used for merit index calculations are:

Trait Relative value (%)
Holstein and other breeds Jersey and
Brown Swiss
Stature 23 28
Strength 72 28
Body depth 8
Dairy form −47 −35
Rump width 17 9
BWC 1 100
1Holstein values are weights (expressed as percentages) from composite formula calculated by Holstein Association USA (2017) and, therefore, do not sum to 100.

Large cows and bulls were favored by dairy cattle breeders for many years, but several research studies concluded that cow size should have negative value in an index because milk income already was accounted for but feed costs were not. Within each breed, the larger cows tend to eat more feed and are less efficient (Dickinson et al., 1969). Feed costs are the largest cost of producing milk and currently are assumed to average 51% of the value of extra milk production.

Body size expenses include the increased cost of feed per lactation that is eaten by heavier cows for body maintenance [$0.20/pound of cow weight based on findings by the National Research Council (2001)] plus increased housing costs ($0.03/pound of cow weight) plus the extra cost of growing replacements minus the extra beef income from heavier cull cows and minus the income from heavier calf weights. The direct selection emphasis in NM$ is now 5.3% against large body weight.

Calving ability 

Calves that die or are born with difficulty reduce dairy farm profit. In the 2003 revision of NM$ (VanRaden and Seykora, 2003), calf death losses were indirect expenses correlated with calving ease, whereas in the 2006 revision (VanRaden and Multi-State Project S-1008, 2006), evaluations for stillbirth (Cole et al., 2006) allowed calf loss to be separated from remaining expenses. Because calving ease and stillbirth effects from the service sire and the dam differ, CA$ includes 4 traits: service sire calving ease (SCE), daughter calving ease (DCE), service sire stillbirths (SSB), and daughter stillbirths (DSB). Many other countries use the terms direct and maternal or paternal and maternal instead of service sire and daughter. Comparisons of evaluations can be confusing because of terminology, direction of scales, and evaluation of pure maternal effects by several countries with an animal model instead of a sire-MGS model. The CA$ index is not published.

Economic values for stillbirths of Holsteins were derived as follows. Value of 2-day-old calves was assumed to be $150 for bulls and $450 for heifers as compared with $100 for bulls and $150 for heifers for 2003 NM$ (VanRaden and Seykora, 2003). Stillbirth evaluations are the percentage of calves that die as a difference from a base of 8%. Lifetime value of a 1% decrease in DSB is 2.8 lactations multiplied by average calf value: 2.8($150 + $450)/2(100) = $8.40. For SSB, this value must be halved because SSB measures the full effect of the service sire, whereas DSB measures only half of the dam's effect. Other breeds had insufficient data to begin stillbirth evaluations.

The value of DCE includes $70 per difficult birth (score 4 or 5) for farm labor and veterinary charges as well as a 1.5% increased probability of cow death multiplied by $1,800. Those expenses are multiplied by 2 because scores 2 and 3 contribute additional smaller effects that occur more frequently. Difficulty in later parities is 0.3 as great, which results in a lifetime incidence of 1 + 0.3(1.8) = 1.5. Total value of DCE is [$70 + 0.015($1,800)]2(1.5)/100 = $2.91. Calving ease costs are based primarily on research by Dematawewa and Berger (1997).

The value of SCE also includes losses in the bull's mates of $100 for yield and $75 for fertility and longevity. Difficult births reduce 305-day milk yield by 700 pounds and delay the bull's mates from becoming pregnant again by 20 days on average. Such losses are not charged to DCE because the bull's daughter evaluations for yield, fertility, and longevity already account for them. The value of SCE must be halved, as with SSB. This step was done incorrectly in 2003 (DCE value was doubled instead of halving the SCE value). Total value of SCE is [$50 + 0.015($1,800) + $100 + $75]2(1.5)/2(100) = $3.78. Values were then rounded to $4 for SCE, $3 for DCE, $4 for SSB, and $8 for DSB. The units of CA$ are the lifetime dollar value that the calving traits contribute to NM$. Calculation requires subtracting trait means, multiplying by economic values, and reversing direction to obtain net benefit instead of net cost:

CA$ = −4(SCE − 8) − 3(DCE − 8) − 4(SSB − 8) − 8(DSB − 8).

The CA$ index had a genetic correlation of 0.85 with the combined SCE and DCE values in 2003 NM$ and 0.77 with DCE in TPI. Thus, stillbirth evaluations can provide additional value beyond that of calving ease. A preliminary study (Berger et al., 1998) reported less benefit because only service sire effects were examined. For Brown Swiss, economic values are −6 for SCE and −8 for DCE because separate stillbirth evaluations are not available and calving ease values include the correlated response in stillbirth. The TTA SDs are 1.7 for SCE, 1.4 for DCE, 1.0 for SSB, and 1.7 for DSB with corresponding relative emphasis of 25, 15, 15, and 45% in CA$. The CA$ SD is $18, and the relative emphasis on calving traits in NM$ is 4.8%.

Mating programs should assign bulls with low and high PTAs for service sire effects to heifers and to cows, respectively. The economic value used in NM$ is a weighted average of losses for cows and heifers. Thus, when ranking sires for heifer use, another $4 should be subtracted from NM$ for each percentage of SCE, and $2 for each percentage of SCE should be added back to NM$ when ranking service sires for cows. These minor adjustments for the differing economic values in heifer vs. cow matings can be handled with computerized mating programs.

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 2018 NM$ index, which includes 6 new health traits and updated economic values, is correlated by 0.994 with the 2017 NM$ index (VanRaden, 2017). An increase in genetic progress worth $1.4 million/year is expected on a national basis, assuming that all of the changes are improvements and that all breeders select on NM$. The 2017 index included the new trait LIV and was correlated by 0.989 with the 2014 NM$ index (VanRaden and Cole, 2014)for recent progeny-tested bulls. The 2014 NM$ index , which included new traits HCR and CCR, was correlated by 0.965 with the 2010 NM$ index (Cole et al., 2009). The 2010 NM$ index was correlated by 0.99 with the 2006 NM$ formula (VanRaden and Multi-State Project S-1008, 2006); 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 (VanRaden and Seykora, 2003) 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(VanRaden and Seykora, 2003), cow fertility and calving ease were incorporated into NM$. In the 2000 NM$ revision (VanRaden, 2000), 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. Before 2000, breed association indexes had included type traits but not health traits, and NM$ had included health traits but not type traits. 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)
NM$
(2017)
NM$
(2018)
Milk 52 27 −2 6 5 0 0 0 −1 −1 −1
Fat 48 46 45 25 21 22 23 19 22 24 27
Protein 27 53 43 36 33 23 16 20 18 17
PL 20 14 11 17 22 19 13 12
SCS −6 −9 −9 −9 −10 −7 −7 −4
BSC/BWC −4 −3 −4 −6 −5 −6 −5
Udder composite 7 7 6 7 8 7 7
Feet/legs composite 4 4 3 4 3 3 3
DPR 7 9 11 7 7 7
CA$ 6 5 5 5 5
HCR 1 1 1
CCR 2 2 2
LIV 7 7
HTH$ 2

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 

Thanks to the collaboration between CDCB and AGIL, AGIL provides much of the research to update the 4 genetic indexes including NM$. CDCB applies that research to the routine evaluation after the research phase is concluded and results have been tested, reviewed and accepted by the industry.

The authors thank many university researchers with development of previous NM$ revisions, industry experts for helpful discussion of income and expense formulas, Lillian Bacheller for computing the lactation yields adjusted and unadjusted for abnormal test days, and Suzanne Hubbard and Ezequiel Nicolazzi for review and revision of this research report.

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