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Net merit as a measure of lifetime profit: 2017 revision
P.M. VanRaden |
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$) were updated in April 2017, cow livability (LIV) was included as a new trait, and body weight composite (BWC) replaced body size composite (BSC). Cows that die or are euthanized on the farm generate no beef income and may have more health expenses than cows that are culled. Research by Holstein USA (2016) redefined BSC to predict body weight more accurately using recent weight and linear type data from research herds with measured feed intake. These changes shifted some economic value from productive life (PL) to LIV, and the selection against BWC is larger than that against BSC in the 2014 index. The 2017 revision should lead to healthier and more efficient cows.
Most other traits received slightly less relative emphasis because LIV and BWC were emphasized more. Milk component prices were revised with a slight shift from protein to fat and lower milk price than forecast in 2014 NM$. Other economic values were updated very little. For recent bulls, the 2017 and 2014 NM$ indexes were correlated by 0.989.
This document describes changes made for the 2017 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 2014 NM$ index.
Updated economic values
New economic values for each unit of predicted transmitting ability (PTA) and relative economic values of traits will be implemented with April 2017 evaluations:
| Trait | Units | Standard deviation (SD) |
Value ($/PTA unit) | Relative value (%) | ||||||
| NM$ | CM$ | FM$ | GM$ | NM$ | CM$ | FM$ | GM$ | |||
| Protein | Pounds | 18 | 3.81 | 5.42 | 0 | 3.54 | 18.3 | 22.0 | 0.0 | 16.0 |
| Fat | Pounds | 25 | 3.56 | 3.56 | 3.56 | 3.30 | 23.7 | 20.1 | 24.3 | 20.7 |
| Milk | Pounds | 672 | −0.004 | −0.052 | 0.111 | −0.003 | −0.7 | −7.9 | 20.4 | −0.5 |
| PL | Months | 2.4 | 21 | 21 | 21 | 13 | 13.4 | 11.4 | 13.8 | 7.8 |
| SCS | Log | 0.21 | −117 | −148 | −56 | −104 | −6.5 | −7.0 | −3.2 | −5.5 |
| Udder | Composite | 0.90 | 31 | 31 | 31 | 33 | 7.4 | 6.3 | 7.6 | 7.5 |
| Feet/legs | Composite | 1.03 | 10 | 10 | 10 | 11 | 2.7 | 2.3 | 2.8 | 2.8 |
| Body weight | Composite | 1.10 | −20 | −20 | −20 | −22 | −5.9 | −5.0 | −6.0 | −6.1 |
| DPR | Percent | 2.3 | 11 | 11 | 11 | 31 | 6.7 | 5.7 | 6.9 | 17.9 |
| HCR | Percent | 2.4 | 2.2 | 2.2 | 2.2 | 4.1 | 1.4 | 1.2 | 1.4 | 2.5 |
| CCR | Percent | 2.8 | 2.2 | 2.2 | 2.2 | 6.3 | 1.6 | 1.4 | 1.7 | 4.4 |
| CA$ | Dollars | 18 | 1 | 1 | 1 | 1 | 4.8 | 4.1 | 4.9 | 4.5 |
| LIV | Percent | 2.3 | 12 | 12 | 12 | 8.7 | 7.4 | 6.2 | 7.5 | 5.0 |
The SDs listed above are for true transmitting abilities (TTAs) in a hypothetical unselected population. The SDs of TTAs for NM$, cheese merit (CM$), and fluid merit (FM$) are all estimated to be $193, nearly the same as the $195 for the previous 2014 indexes. The SD for grazing merit (GM$) would be larger because of longer PL in grazing herds except that milk yield differences are often reduced in grazing 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 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 |
| BWC | −1.0 | 85 |
| DPR | +0.3 | 55 |
| HCR | +0.5 | 60 |
| CCR | +1.2 | 50 |
| CA$ | +30 | 90 |
| LIV | +1.8 | 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 $732, CM$ is $750, FM$ is $692, and GM$ is $685. Calculation of NM$ also can be expressed in matrix form:
NM$ = a′u,
where a contains the economic values for the 13 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 13 traits and genetic correlations among those traits. The NM$ REL is the variance of predicted NM$ divided by the variance of true NM$:
REL NM$ = r′Gr/v′Gv,
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 13 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 13 PTA traits:
| PTA trait | PTA trait | ||||||||||||
| Milk | Fat | Protein | PL | SCS | BWC | Udder | Feet/legs | DPR | HCR | CCR | CA$ | LIV | |
| Milk | 0.201 | 0.43 | 0.83 | 0.10 | 0.02 | −0.12 | −0.10 | −0.02 | −0.23 | −0.03 | −0.16 | 0.19 | 0.03 |
| Fat | 0.69 | 0.20 | 0.59 | 0.15 | −0.09 | −0.05 | −0.07 | 0.01 | −0.15 | 0.03 | −0.10 | 0.13 | 0.06 |
| Protein | 0.90 | 0.75 | 0.20 | 0.13 | 0.04 | −0.09 | −0.14 | −0.01 | −0.18 | −0.07 | −0.15 | 0.22 | 0.05 |
| PL | 0.15 | 0.17 | 0.16 | 0.08 | −0.45 | −0.10 | 0.18 | 0.14 | 0.64 | 0.32 | 0.62 | 0.40 | 0.70 |
| SCS | −0.10 | −0.10 | −0.10 | −0.40 | 0.12 | −0.10 | −0.23 | −0.15 | −0.27 | −0.12 | −0.25 | −0.14 | −0.25 |
| BWC | 0.06 | 0.05 | 0.05 | −0.20 | −0.11 | 0.40 | 0.27 | 0.38 | −0.052 | −0.01 | −0.01 | −0.07 | −0.14 |
| 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 | 0.08 |
| 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 | 0.06 |
| 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 |
| 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 | 0.22 |
| 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 | 0.43 |
| 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 | 0.36 |
| LIV | 0.11 | 0.13 | 0.12 | 0.70 | −0.40 | −0.20 | 0.10 | 0.05 | 0.40 | 0.20 | 0.15 | 0.35 | 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 2014 formula are shown for comparison:
| PTA trait | Correlation of PTA with index | Expected genetic progress from NM$ | ||||||
| 2014 NM$ | 2017 NM$ | 2017 CM$ | 2017 FM$ | 2017 GM$ | 2014 NM$ PTA change/year | 2017 NM$ PTA change/year | 2017 NM$ breeding value change/decade | |
| Protein | 0.61 | 0.59 | 0.57 | 0.60 | 0.50 | 3.5 | 3.4 | 68 |
| Fat | 0.63 | 0.65 | 0.64 | 0.62 | 0.54 | 5.1 | 5.3 | 106 |
| Milk | 0.44 | 0.41 | 0.35 | 0.56 | 0.32 | 100 | 93 | 1,866 |
| PL | 0.76 | 0.76 | 0.75 | 0.75 | 0.79 | 0.65 | 0.65 | 13 |
| SCS | −0.37 | −0.34 | −0.36 | −0.27 | −0.36 | −0.02 | −0.02 | −0.42 |
| Udder composite | 0.12 | 0.09 | 0.08 | 0.09 | 0.12 | 0.03 | 0.03 | 0.53 |
| Feet/leg composite | 0.06 | 0.04 | 0.04 | 0.03 | 0.08 | 0.01 | 0.01 | 0.25 |
| BWC | −0.13 | −0.20 | −0.20 | −0.20 | −0.15 | −0.05 | −0.07 | −1.35 |
| DPR | 0.39 | 0.38 | 0.40 | 0.33 | 0.58 | 0.27 | 0.26 | 5.2 |
| HCR | 0.38 | 0.36 | 0.36 | 0.37 | 0.47 | 0.22 | 0.21 | 4.1 |
| CCR | 0.52 | 0.51 | 0.52 | 0.48 | 0.68 | 0.43 | 0.42 | 8.4 |
| CA$ | 0.46 | 0.47 | 0.47 | 0.45 | 0.52 | 3.5 | 3.6 | 72 |
| LIV | 0.40 | 0.48 | 0.48 | 0.47 | 0.47 | 0.31 | 0.37 | 7.4 |
The new indexes are more correlated than 2014 NM$ with LIV, 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 0.37, which is the expected annual trend in SD of NM$. The PTA SDs (not shown) generally are lower than the TTA SDs shown in the first table because of selection and because RELs are less than 1. Genetic trend (change in breeding value) equals twice the expected progress for PTA. Thus, multiplication of annual PTA gain by 20 gives expected genetic progress per decade.
Derivation of economic values
The derivation of economic values is shown below for fertility traits, yield traits, SCS, and PL and LIV. Economic values for most traits in CM$, FM$, and GM$ are the same as in NM$. Primary differences in economic values for grazing versus confinement herds are 2.5 times higher value of fertility to maintain seasonal calving, 15% less production per lactation but 50% more lactations, 25% less death loss, and 25% less mastitis incidence (Gay et al, 2014). Derivation of economic values for CA$ 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 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 $17.50 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 $16.93. Component prices follow, along with marginal feed costs and health costs required for higher yield with the nonyield traits in NM$ 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$ | 16.93 | 2.00 | 2.32 | 0.0297 |
| CM$ | 16.93 | 2.00 | 2.90 | 0.0123 |
| FM$ | 16.93 | 2.00 | 0.99 | 0.0696 |
| Feed cost | 7.68 | 0.65 | 0.90 | 0.0270 |
| Extra health cost | 0.80 | 0.07 | 0.05 | 0.0040 |
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 $0.002 for bulk tank, equipment, and electricity costs to cool and store each pound of milk also is included in the feed cost. Total feed costs were divided into costs for milk, fat, and protein using the approach of Dado et al. (1994), with an additional multiplier to account for increased feed prices and an increased price of corn relative to soybean meal.
The extra health costs associated with yield traits were reduced to 5% from 8% of the milk price because LIV now accounts for more of these health costs. Favorable genetic correlations with LIV, PL, and DPR and unfavorable correlations with yield are used to partially account for health expenses until direct evaluations of health traits become available. Udder composite and SCS account for about half of the costs of mastitis and discarded milk.
Correlations of merit indexes based on recent progeny-tested bulls were 0.995 for NM$ 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$; those that expect high protein premiums should select on CM$; 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 (2.32 − 0.95)2.78 = $3.81.
Prices for milk, fat, and protein are difficult to predict because they vary widely by use of milk and across time. Average prices for milk in Federal order markets are available from USDA's Agricultural Marketing Service. Actual prices since 2006 for class III milk used in cheese making are shown below:
| Year | Milk ($/100 pounds) |
Fat ($/pound) |
Protein ($/pound) |
Volume ($/pound) |
SCC ($/1,000 cells)1 |
| 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 | |||||
| 2017 CM$ | 17.50 | 2.00 | 2.90 | 0.0180 | −0.00090 |
| 2014 CM$ | 18.00 | 1.95 | 3.10 | 0.0131 | −0.00079 |
| 1See SCS section for a fuller explanation of quality premiums. | |||||
Milk prices over the last 4 years averaged $18.39 for class III compared with $18.00 forecast in 2014; the current price as of August 2016 is lower at $16.91. Future contract prices average about $16.30 for 2017, but the USDA World Agricultural Supply and Demand Estimates Report (WASDE) Class III milk price estimate is only $15.50 for 2017. Protein prices over the last 4 years averaged $2.99 and were less than the $3.10 forecast in 2014, whereas butterfat prices averaged $2.02, slightly more than the $1.95 forecast in 2014. Current component prices as of August 2016 are $2.57 for protein and $2.49 for butterfat. Demand for butterfat increased recently when trans fats were banned as an ingredient in food (U.S. Food and Drug Administration, 2015).
Predicted prices used in CM$ are now $2.90 for protein and $2.00 for fat. Fluid milk processors usually pay no premium for extra protein because grocery store milk is not yet labeled or priced by protein content, but a protein premium is included in FM$ 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 $1.30/pound of SNF since 2009, but export markets for powder are now increasing the value of protein by requiring minimum standards for protein. Lactose and SNF yields are not genetically evaluated but are more correlated to milk yield than to protein yield (Welper and Freeman, 1992; Miglior et al, 2007).
The value of protein in NM$ 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$. Prior to 1997, component prices were previous-year average prices. Crude protein prices reported prior to 2000 were converted to true protein prices by multiplying by 1.064.
| Year | Milk | Fat | True protein | Volume |
| 1977 | 12.30 | 1.48 | 1.24 | 0.034 |
| 1978 | 12.23 | 1.51 | 1.18 | 0.034 |
| 1979 | 12.25 | 1.52 | 1.21 | 0.033 |
| 1980 | 12.32 | 1.61 | 1.26 | 0.029 |
| 1981 | 12.35 | 1.63 | 1.28 | 0.028 |
| 1982 | 12.24 | 1.64 | 1.30 | 0.026 |
| 1983 | 12.34 | 1.70 | 1.33 | 0.024 |
| 1984 | 12.32 | 1.75 | 1.33 | 0.022 |
| 1985 | 12.26 | 1.72 | 1.28 | 0.024 |
| 1986 | 12.35 | 1.85 | 1.29 | 0.020 |
| 1987 | 12.28 | 1.74 | 1.23 | 0.025 |
| 1988 | 12.26 | 1.68 | 1.26 | 0.026 |
| 1989 | 12.31 | 1.46 | 1.50 | 0.027 |
| 1990 | 12.33 | 1.13 | 1.39 | 0.042 |
| 1991 | 12.23 | 1.12 | 1.47 | 0.039 |
| 1992 | 12.29 | 0.79 | 1.54 | 0.049 |
| 1993 | 12.33 | 0.70 | 1.66 | 0.049 |
| 1994 | 12.24 | 0.58 | 1.57 | 0.055 |
| 1995 | 12.29 | 0.72 | 1.69 | 0.047 |
| 1996 | 12.27 | 0.89 | 1.65 | 0.042 |
| 1997–99 | 12.30 | 0.80 | 2.12 | 0.031 |
| 2000–03 | 12.68 | 1.15 | 2.55 | 0.010 |
| 2003–06 | 12.70 | 1.30 | 2.30 | 0.013 |
| 2006–09 | 12.70 | 1.50 | 1.95 | 0.016 |
| 2010–14 | 14.36 | 1.63 | 1.94 | 0.029 |
| 2014–17 | 17.43 | 1.95 | 2.48 | 0.032 |
| 2017– | 16.93 | 2.00 | 2.32 | 0.030 |
Milk prices paid to producers increased recently but were stable from 1977 through 2010 when much inflation occurred in labor, feed, and many other input prices. Additional history on economic indexes is provided in the History of NM$ 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 $0.00092 for each 1,000 cell/ml decrease in SCC. This compares with $0.00079 assumed in 2014. Although the premium has increased, the economic value is now slightly smaller than in 2014 because a mathematical error was discovered in the 2014 calculations.
Somatic cell premiums were originally 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). Until 2014, the SCC value per 1,000 cells was 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 $0.00092/0.0072 = $0.128. The actual change in SCC from a 1 unit change in PTA SCS (a doubling of SCC) and the actual SCC differences among bull daughters are now much less than when SCC premiums were introduced. Also, the actual value of PTA SCS is higher for herds with more mastitis and lower for herds with less mastitis because payments are linear with SCC rather than with SCS.
Different premiums for SCS are applied in each index. The full class III premium is applied to SCS in CM$ because manufacturing plants typically provide incentives for improved milk quality. The premium in NM$ uses the assumption that 70% of the milk will be sold in blend markets that are paid the class III premium. 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$, but the actual value of reduced SCS for improving shelf life of fluid milk is substantial. The value of PTA SCS per lactation in NM$ 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 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 and LIV
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. 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 percentage of deaths per lifetime, the economic value is $1,200(0.01) = $12. Replacement heifer prices decreased from an estimate of $1,800 in 2014 to $1,480 in 2017. Replacement costs now are assumed to include a newborn heifer price of $200, a cost of $0.80/pound of growth, and a fixed cost of $400, for a total of $1,480 to raise the heifer to 1,200 pounds. Beef prices for cull cows were high in 2014–15 but decreased in 2016; therefore, the previous estimate of $0.75/pound remains. The interest rate also remains at 5%. The inclusion of LIV with 7% of emphasis and the decrease in replacement costs reduced the emphasis on PL from 19% in 2014 NM$ to 12% in 2017 NM$. Genetic progress for PL will remain about constant but with more progress for LIV.
Lifetime profit
The NM$ 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 2017 NM$ index, which includes the new trait LIV and updated economic values, is correlated by 0.989 with the 2014 NM$ index for recent progeny-tested bulls. An increase in genetic progress worth $2.5 million/year is expected on a national basis, assuming that all of the changes are improvements and that all breeders select on NM$. The 2014 NM$ index, which included new traits HCR and CCR, was correlated by 0.965 with the 2010 NM$ index. The 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. 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) |
|
| Milk | 52 | 27 | −2 | 6 | 5 | 0 | 0 | 0 | −1 | −1 |
| Fat | 48 | 46 | 45 | 25 | 21 | 22 | 23 | 19 | 22 | 24 |
| Protein | … | 27 | 53 | 43 | 36 | 33 | 23 | 16 | 20 | 18 |
| PL | … | … | … | 20 | 14 | 11 | 17 | 22 | 19 | 13 |
| SCS | … | … | … | −6 | −9 | −9 | −9 | −10 | −7 | −7 |
| Udder composite | … | … | … | … | 7 | 7 | 6 | 7 | 8 | 7 |
| Feet/legs composite | … | … | … | … | 4 | 4 | 3 | 4 | 3 | 3 |
| BSC/BWC | … | … | … | … | −4 | −3 | −4 | −6 | −5 | −6 |
| DPR | … | … | … | … | … | 7 | 9 | 11 | 7 | 7 |
| CA$ | … | … | … | … | … | … | 6 | 5 | 5 | 5 |
| HCR | … | … | … | … | … | … | … | … | 1 | 1 |
| CCR | … | … | … | … | … | … | … | … | 2 | 2 |
| LIV | … | … | … | … | … | … | … | … | … | 7 |
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 author thanks John Cole and many university researchers with development of previous NM$ revisions, 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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