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Effect of Milk Market Pricing on Profitability of Holstein Sire Selection and Cost of Selection Error1
Effect of Milk Market Pricing on Profitability of Holstein Sire Selection and Cost of Selection Error 1 M. A. TOMASZEWSKI, J. F. T A Y L O R , and A. S. BLOOM Department of Animal Science Texas A&M University College Station 77843 ABSTRACT
INTRODUCTION
Effects of state milk prices on profitability of sire selection and on the cost o f ignoring this information were examined. Net present values of semen were calculated for 393 Holstein sires with available semen after January 1987 USDA sire summary according to a 1:0 milk to type selection policy, 50% conception rate, one generation of descendants in the financial planning horizon, and with a 3% real interest rate. Predicted Differences for dollars and net present values were calculated for sires by region according to appropriate milk price. Regional milk prices were determined as the average of monthly prices for 3.5% milk fat milk for the period December 1985 through November 1986 assuming a price of $3.608/kg of milk fat. Milk price was not adjusted for hauling or federal withholding. Sire rankings for both indexes were only affected for states receiving high milk prices. Net presen~ values were calculated for the top 5% o f sires selected on PD for dollars. When this group was compared against the top 5% of sires selected on net present value, there was an average opportunity cost of $120 or more across five state milk marketing programs. Using state milk pricing information in the calculation of net present value sire calculations increases producer profitability.
To improve profitability a producer must evaluate all variables that could affect the profit rankings of AI sires. The proportion of cows bred by AI has increased from 7% in 1948 to 60% in 1987 (6) with current sales of 12.1 million units of dairy semen to US dairy producers. Programs that demonstrate increased profitability to producers could continue the increase in AI usage. Cunningham (4) demonstrated the cost effectiveness of AI breeding programs for milk production with returns of at least $50/$1 invested attainable. However, rates of return were estimated on an industry basis and did not focus upon the requirements of individual dairy producers in their quest to maximize profits. For purposes of ranking sires, economic indexes combining the relative values of milk and milkfat have been used in sire evaluation. The PD dollars (PD$) was developed to weight properly sire evaluations for milk components to determine the product value of milk (7). The PD$ are published semiannually and are calculated using the average US milk price adjusted for hauling and federal withholding reported by the USDA Agricultural Statistics Board. Net present value (PV$) sire summaries have been developed to rank sires for profit according to each dairy producer's selection policy (5, 9). The PV$ incorporates genetic evaluations of AI sires for milk, milk fat, and type characteristics into a phenotypic index for expected daughter profit; PV$ are currently published using PD$ determined by the national average milk price (3). Milk sold under federal order is priced by the proportion of milk sold for fluid consumption, by distance differential reflecting the cost of hauling milk from MN and WI, and by the value of milk fat for butter production. Milk price varies seasonally due to supply and to individual dairy farmers within states due to deductions for hauling and federal withholding.
Recieved July 9, 1987. Accepted November 30, 1987. 1Technical Article 22860 of the Texas Agricultural Experiment Station, College Station 77843. Project 2491, a contribution to Southern Regional Project $49, Genetic Methods of Improving Dairy Cattle for the South. 1988J Dairy Sci 71:1361--1366
1361
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TOMASZEWSKI ET AL.
Because milk prices are not uniform across the US, selecting sires using PD$ or PV$ may result in incorrect decisions due to variations in milk price by state. The potential economic interaction of AI sire selection with state milk pricing information has not been quantified. Objectives were to estimate differences in profitability of sire selection due to state milk price variation and to estimate the cost of errors in sire selection when national rather than state milk prices are used and when sires are selected by PD$ rather than by PV$. MATERIALS AND METHODS
Data were regional milk prices, determined as the average o f monthly state milk prices for 3.5% milk for the period December 1985 through November 1986 from the Agricultural Statistics Board (1) and PD for milk and milk fat and retail semen prices of the 393 active AI Holstein sires with semen available for purchase after the January 1987 USDA Sire Summary. Semen prices were obtained from the Dairy Records Processing Center at Raleigh, NC. Prices per kilogram of SNF milk were calculated for each state assuming a constant milk fat price of $3.608/kg (7), since a uniform national price is set each month. Prices were not adjusted for hauling or federal withholding because variation exists within states on hauling differentials. Regional PD$ were calculated for all sires according to state or US milk price. The PV$ were calculated for a milk-to-type selection policy (1:0), conception rate to first service (50%), and one generation financial planning horizon using regional PD$ evaluations (5, 9). These parameter estimates reflect a constant point in time. A one generation planning horizon results in the smallest net return from selection on PV$ since only one generation of return is considered. Regional milk prices were assumed to be in equilibrium for the duration of the planning horizon. Among other considerations, this requires constancy across time of proportions of milk sold for fluid consumption by region. Using the 1986 average milk price, PD$ and PV$ were calculated for ND, CA, MN, TX, FL; and US, ND and F L were chosen because of their extreme milk prices and CA, MN, and TX because their milk prices and magnitudes of production, ranking second, fifth, and eighth nationally (2). Journal of Dairy Science Vol. 71, No. 5, 1988
Robustness of sire ranking to milk pricing was examined by computing rank correlations and average absolute rank changes among PD$ and PV$ evaluations for different regional milk prices. In addition, rank correlations between PD$ and PV$ within regions were evaluated to determine the coherence of goals of selecting sires to maximize genetic as opposed to economic progress from AI. Correlations and average absolute rank changes between PD$ and PV$ were determined for all 393 sires and also for a selected subset of 197 sires ranking highest on US PD$. Finally, the opportunity cost o f selecting sires on regional PD$ rather than on PV$ was assessed by comparing the average PV$ of sires selected on PV$ with average PV$ of identically ranked sires selected on PD$. Groups comprised 19 sires, or approximately 5% of all sires, such that the first group contained the highest ranked 19 sires for PD$ or PV$ by region. RESULTS A N D DISCUSSION
The effect o f a constant milk fat price is to place considerably more emphasis on selection for SNF milk in states where milk price is high. Seasonal milk utilization patterns affect milk pricing, and this source of price variation may also be o f consequence to breeding decisions. Table 1 shows the average 1986 price received for 3.5% milk fat milk and for SNF milk for selected states and the US using the average US price of $3.608/kg of milk fat. The milk fat price is constant among states but varies slightly in time. However, this variation does not significantly alter regional prices for SNF milk. Average milk price for 3.5% milk fat milk ranged from $.2416/kg for ND to $.3527/kg for FL. Average 1986 US economic weight for SNF milk was $.1404/kg. Florida received the highest price at $.2264/kg and ND the lowest at $.1153/kg. Selection for SNF milk should receive 96% greater emphasis in FL than ND and 61% more than determined by US average milk price. Economic weight for SNF milk in ND was 18% below the national average. Large differences existed within the US for the price received for SNF milk. Table 2 shows that sire ranks changed an average of 25.9 places between ND and F L for PD$ and 34.1 places for PV$. Average rank change between the US and F L was 18.2 places for PD$ and 24.7 for PV$. However, average
MILK PRICE AND SIRE SELECTION
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TABLE 1. Prices of 3.5% milk fat milk and solids-not-fat milk for months December 1985 through November 1986 and numbers of cows for selected states. 3.5% Milk price ~($/kg)
Solids-not-fat
Region
Min
Max
Avg
CV(%)
Milk1, 2 ($/kg)
Cows (1000)
ND CA MN US TX FL
.2341 .2460 .2475 .2589 .2774 .3392
.2592 .2660
.2416 .2523 .2548 .2669 .2944 .3527
3.6 2.5 3.4 3.0 3.5 3.1
.1153 .1261 .1285 .1406 .1681 .2264
974 890 10,122 314 164
.2717
.2834 .3115 .3718
97
Assuming $3.608/kg milk fat. 2 Corresponding to average 3.5% milk fat milk price.
rank changes b e t w e e n CA and US and MN and US were very small, 4.5 and 3.5, respectively, for PD$ and 5.5 and 4.6, respectively, for PV$. R a n k correlations among PD$ were all greater than .95 and a m o n g PV$ were all greater than .92. When the t o p ranked 197 (50%) of sires for US PD$ were evaluated (Table 3), ranks changed on average 21.3 places b e t w e e n ND and F L for PD$ and 15.9 places for PV$. Average rank change b e t w e e n the US and F L was 15.0 places for PD$ and 12.0 for PV$. Average rank changes b e t w e e n CA and US and MN and US
were smaller than for all sires, 3.9 and 3.1, respectively, for PD$ and 2.6 and 2.1, respectively for PV$. R a n k correlations among PD$ were all greater than .88 and among PV$ were all greater than .93. Average rank changes were, relative to the n u m b e r o f sires, greater among the top 50% o f sires than among all sires. Results in Table 2 and 3 suggest that states with the highest milk prices will not place sufficient selection pressure on SNF milk p r o d u c t i o n to m a x i m i z e profit if selection is based upon PD$ or PV$ defined by the US
TABLE 2. Rank correlations and average sire rank changes between regions for PD dollars (PD$) and present value dollars (PV$) for 393 sires. Region
ND
CA
MN
US
TX
FL
15.1 11.6 10.7 7.3
25.9 22.5 21.6 18.2 11.1
Average rank change ND CA MN US TX FL
3.71 4.52 5.3 9.7 19.1 34.1
1.3 5.5 14.8 29.9
4.6 1.0 4.6 14.0 29.0
8.0 4.5 3.6 9.6 24.7
15.3
Rank correlations ND CA MN US TX FL
.991
.99
.99
.98
.95
.99
.99 .99
.99 .99 .99
.97 .97 .98 .99
.992 .99
.99
.99 .97
.99 .98
.99 .99
.99
.92
.94
.94
.96
.98
1PD$ above diagonal. a PV$ below diagonal. Journal of Dairy Science Vol. 71, No. 5, 1988
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TOMASZEWSKI ET AL.
TABLE 3. Rank correlations and average sire rank changes between regions for PD dollars (PD$) and present value dollar (PV$) for highest 197 US PD$ sires. Region
ND
CA
MN
US
TX
FL
12.9 10.0 9.3 6.4
21.3 18.5 17.8 15.0 8.9
Average rank change ND CA MN US TX FL
3.1 a 1.92 2.3 4.2 8.7 15.9
.7 2.6 7.0 14.3
3.8 .9
6.7 3.9 3.1
2.1 6.6 13.8
4.7 12.0
7.5
Rank correlations NC CA MN US TX FL
.99 ~ .992 .99 .99 .98 .93
.99 .99
.99 .99 .98 .94
.99 .99 .99
.99 .99 .94
.96 .97 .98 .99
.99 .96
.88 .91 .92 .94 .98
.98
PD$ above diagonal. 2 PV$ below diagonal.
average milk price. However, deviations o f milk prices f r o m US average price for the major dairying states of CA and MN resulted in o n l y m i n o r ranking differences f r o m the US ranking for b o t h PD$ and PV$. Consequently, errors in sire selection are small for dairy farmers in CA and MN selecting sires o n PD$ o r PV$ calculated f r o m the US average milk price. R a n k correlations a m o n g PD$ and PV$ and average absolute difference b e t w e e n sire ranks for PD$ and PV$ are in Table 4. Highest average change in sire ranks b e t w e e n PD$ and PV$ was 79.4 positions for ND and lowest was 59.2 for FL. Lowest rank correlation b e t w e e n
PD$ and PV$ was .51 for ND and highest was .69 for FL. A m o n g the top 197 sires for US PD$, the highest average sire rank change b e t w e e n PD$ and PV$ was 55.2 for ND and lowest was 46.5 for FL. Lowest rank correlation b e t w e e n PD$ and PV$ was .21 for ND and highest was .39 for FL. Again, average rank changes b e t w e e n PD$ and PV$ were relatively greater a m o n g the t o p 50% of sires than among all sires. The PV$ evaluations comprise two components, semen cost to produce a r e p l a c e m e n t daughter and expected profit f r o m future p r o d u c t i o n of the replacement. Semen price
TABLE 4. Rank correlations and average rank differences between PD dollars (PD$) and present value dollars (PV$) within regions. Highest 197 sires on US PD$
All 393 sires Region ND CA MN US TX FL
Avg. rank change
Rank correlation
Avg. rank change
Rank correlation
79.4 77.3
.51 .52 .53 .55 .60 .69
55.2 54.4 54.1 53.0 50.5 46.5
.21 .22 .23 .25 .29 .39
77.0 74.7
69.0 59.2
Journal of Dairy Science Vol. 71, No. 5, 1988
MILK PRICE AND SIRE SELECTION and variation a m o n g sires for semen price may vary due to regional marketing structure but are generally constant within the region. The effect o f milk price on profit o f future p r o d u c t i o n is three fold. Altering the milk price changes the relative i m p o r t a n c e o f S N F milk to milk fat in the selection objective. It introduces a scale effect w h e r e b y profitability is greatest with the greatest milk price. Finally, it influences the variation among sires for e x p e c t e d profit f r o m f u t u r e daughter p r o d u c t i o n with greatest variation at highest milk price. The latter two points in c o n j u n c t i o n with constancy o f semen price explains w h y the rank correlation between PD$ and PV$ was lowest for ND. As milk price b e c o m e s increasingly large, selection pressure focuses o n S N F milk, variation among sires for e x p e c t e d profit f r o m future daughter p r o d u c t i o n d o m i n a t e s variation among sires for semen price, and the rank correlation b e t w e e n PD$ and PV$ tends to unity. O p p o r t u n i t y costs o f selecting sires using PD$ rather than PV$ are in Table 5 for regional milk prices. O p p o r t u n i t y cost was the dif-
1365
ference b e t w e e n average PV$ o f sires ranked and selected on regional PV$ and average PV$ o f sires ranked and selected on regional PD$. Sire groups were o f 19 sires each or approximately 5% groups o f the 393 AI sires. When the average PV$ of t h e t o p ranked 5% o f sires selected o n PD$ were compared with the average PV$ o f the top ranked 5% of sires selected o n PV$, producers in all five states lost at least $120 per sire by selecting sires on PD$ instead o f PV$. F o r the average T X herd o f 166 cows with an annual r e p l a c e m e n t rate of 31%, the loss in profit f r o m f u t u r e p r o d u c t i o n o f herd replacement cows is $6227. Average PV$ rank o f the top 19 sires selected o n PD$ was 157 for F L but as low as 195 for ND. Combining average PV$ of sires selected on PD$ and o p p o r t u n i t y cost in Table 5 yields the average PV$ of sire groups ranked and selected on PV$. The t o p 76 (20%) sires averaged $130 in ND whereas the top 76 sires averaged $219 in FL, an increase o f 68.5% in average PV$ compared with a 46.0% milk price difference b e t w e e n the states. A significant dollar loss is
TABLE 5. Opportunity cost associated with selecting sires on regional PD dollars (PD$) instead of regional present value dollars (PV$). Rank of selected sires
PV$ 2
Cost 3
Rank 4
PV$
Cost
Rank
PV$
Cost
Rank
1-19 (10) 2~38(29) 3%57(48) 58-76 (67) 7%95(86)
32 92 52 34 102
125 41 68 75 1
195 120 112 146 112
43 101 66 46 94
124 40 62 70 14
192 119 92 141 143
45 103 68 48 95
124 40 62 70 15
192 116 94 141 145
1-19 (10) 2~38 (29) 39-57(48) 58-76(67) 77-95 (86)
PV$ 57 54 137 54 104
Cost 123 100 2 71 13
Rank 188 133 73 143 140
PV$ 84 78 154 84 121
Cost 121 98 5 60 13
Rank 179 126 73 113 143
PV$ 141 139 193 188 108
Cost 120 85 12 -4 66
ND
CA
US
MN
TX
FL Rank 157 109 70 85 138
] Sires selected on PD$. Average rank listed in parentheses. 2Average PV$ of sires selected on PD$. aOpportunity cost = Average PV$ of identically ranked sires when selected on PV$ - Average PV$ of sires selected on PD$. 4 Average PV$ rank of sires selected on PD$. Journal of Dairy Science Vol. 71, No. 5, 1988
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TOMASZEWSKI ET AL.
experienced by producers when they do not account for actual milk market and AI sire semen prices.
market pricing to identify more accurately the most profitable AI sires. REFERENCES
CONCLUSIONS
Most important is our finding that with the exception of states with high milk prices, little difference was found in sire rankings for PD$ or PV$ as regional milk price varied. However, correlations among sire rankings for PD$ and PV$ within region reveal that sires rank differently for genetic merit for product value than for expected profitability to dairy farmers, mediated primarily by semen price. Because semen pricing by AI organizations is determined by demand, semen production, genetic merit for type, and by genetic merit for production traits, commercial dairy producers have considerable opportunity to maximize profits by selecting sires according to PV$. Considerable attention should be focused by dairy farmers and animal breeders to the problem of simultaneous optimization of genetic and economic progress by alleviating the effect of semen price on AI profitability for dairy farmers with low milk prices. Marketing appropriately priced semen from high genetic merit AI sires at low sperm numbers per unit may be a potential solution (8). Because sire selection can be affected by milk pricing, managers may benefit from using their milk
Journal of Dairy Science Vol. 71, No. 5, 1988
1 Agricultural Statistics Board. 1986. Agricultural prices. January to December. Natl. Acad. Stat. Sci., USDA, Washington, DC. 2 Agricultural Statistics Board. 1986. Milk production. National Agricultural Statistic Service, USDA, Washington, DC. 3 Blake, R. W., C. R. Shumway, and M. A. Tomaszewski. 1987. PV$ sire summary changes with industry. Dairy Herd Management 24: 20. 4 Cunningham, E. P. 1980. Economic appraisal of breeding programmes. Page 291 in Proc. World Congr. on Sheep and Beef Cattle Breeding. R. A. Barton and W. C. Smith, Tech. Ed. Dunmore Press, Palmerston North, N. Z. 5 McMahon, R. T., R. W. Blake, C. R. Shumway, D. J. Leatham, M. A. Tomaszewski, and K. R. Butcher. 1985. Effects of planning horizon and conception rate on profit-maximizing selection of artificial insemination sires. J. Dairy Sci. 68: 2295. 6 National Association of Animal Breeders. 1987. Comparative statistics of semen activity. Natl. Assoc. Anim. Breeders, Columbia, MO. 7 Norman, H. D. 1986. Sire evaluation procedures for yield traits. National Cooperative Dairy Herd Improvement Handbook. H-l, Univ. of Maryland, College Park. 8 Taylor, J. F., K. R. Phillips, and M. A. Tomaszewski. 1987. Economic merit of splitting units of semen and of sexed semen for Australian Holstein Herds. J. Dairy Sci. 70(Suppl. 1):156.(Abstr.) 9 Wilcox, M. L., C. R. Shumway, R. W. Blake, and M. A. Tomaszewski. 1984. Selection of artificial insemination sires to maximize profits. J. Dairy Sci. 67: 2407.
INTRODUCTION
Effects of state milk prices on profitability of sire selection and on the cost o f ignoring this information were examined. Net present values of semen were calculated for 393 Holstein sires with available semen after January 1987 USDA sire summary according to a 1:0 milk to type selection policy, 50% conception rate, one generation of descendants in the financial planning horizon, and with a 3% real interest rate. Predicted Differences for dollars and net present values were calculated for sires by region according to appropriate milk price. Regional milk prices were determined as the average of monthly prices for 3.5% milk fat milk for the period December 1985 through November 1986 assuming a price of $3.608/kg of milk fat. Milk price was not adjusted for hauling or federal withholding. Sire rankings for both indexes were only affected for states receiving high milk prices. Net presen~ values were calculated for the top 5% o f sires selected on PD for dollars. When this group was compared against the top 5% of sires selected on net present value, there was an average opportunity cost of $120 or more across five state milk marketing programs. Using state milk pricing information in the calculation of net present value sire calculations increases producer profitability.
To improve profitability a producer must evaluate all variables that could affect the profit rankings of AI sires. The proportion of cows bred by AI has increased from 7% in 1948 to 60% in 1987 (6) with current sales of 12.1 million units of dairy semen to US dairy producers. Programs that demonstrate increased profitability to producers could continue the increase in AI usage. Cunningham (4) demonstrated the cost effectiveness of AI breeding programs for milk production with returns of at least $50/$1 invested attainable. However, rates of return were estimated on an industry basis and did not focus upon the requirements of individual dairy producers in their quest to maximize profits. For purposes of ranking sires, economic indexes combining the relative values of milk and milkfat have been used in sire evaluation. The PD dollars (PD$) was developed to weight properly sire evaluations for milk components to determine the product value of milk (7). The PD$ are published semiannually and are calculated using the average US milk price adjusted for hauling and federal withholding reported by the USDA Agricultural Statistics Board. Net present value (PV$) sire summaries have been developed to rank sires for profit according to each dairy producer's selection policy (5, 9). The PV$ incorporates genetic evaluations of AI sires for milk, milk fat, and type characteristics into a phenotypic index for expected daughter profit; PV$ are currently published using PD$ determined by the national average milk price (3). Milk sold under federal order is priced by the proportion of milk sold for fluid consumption, by distance differential reflecting the cost of hauling milk from MN and WI, and by the value of milk fat for butter production. Milk price varies seasonally due to supply and to individual dairy farmers within states due to deductions for hauling and federal withholding.
Recieved July 9, 1987. Accepted November 30, 1987. 1Technical Article 22860 of the Texas Agricultural Experiment Station, College Station 77843. Project 2491, a contribution to Southern Regional Project $49, Genetic Methods of Improving Dairy Cattle for the South. 1988J Dairy Sci 71:1361--1366
1361
1362
TOMASZEWSKI ET AL.
Because milk prices are not uniform across the US, selecting sires using PD$ or PV$ may result in incorrect decisions due to variations in milk price by state. The potential economic interaction of AI sire selection with state milk pricing information has not been quantified. Objectives were to estimate differences in profitability of sire selection due to state milk price variation and to estimate the cost of errors in sire selection when national rather than state milk prices are used and when sires are selected by PD$ rather than by PV$. MATERIALS AND METHODS
Data were regional milk prices, determined as the average o f monthly state milk prices for 3.5% milk for the period December 1985 through November 1986 from the Agricultural Statistics Board (1) and PD for milk and milk fat and retail semen prices of the 393 active AI Holstein sires with semen available for purchase after the January 1987 USDA Sire Summary. Semen prices were obtained from the Dairy Records Processing Center at Raleigh, NC. Prices per kilogram of SNF milk were calculated for each state assuming a constant milk fat price of $3.608/kg (7), since a uniform national price is set each month. Prices were not adjusted for hauling or federal withholding because variation exists within states on hauling differentials. Regional PD$ were calculated for all sires according to state or US milk price. The PV$ were calculated for a milk-to-type selection policy (1:0), conception rate to first service (50%), and one generation financial planning horizon using regional PD$ evaluations (5, 9). These parameter estimates reflect a constant point in time. A one generation planning horizon results in the smallest net return from selection on PV$ since only one generation of return is considered. Regional milk prices were assumed to be in equilibrium for the duration of the planning horizon. Among other considerations, this requires constancy across time of proportions of milk sold for fluid consumption by region. Using the 1986 average milk price, PD$ and PV$ were calculated for ND, CA, MN, TX, FL; and US, ND and F L were chosen because of their extreme milk prices and CA, MN, and TX because their milk prices and magnitudes of production, ranking second, fifth, and eighth nationally (2). Journal of Dairy Science Vol. 71, No. 5, 1988
Robustness of sire ranking to milk pricing was examined by computing rank correlations and average absolute rank changes among PD$ and PV$ evaluations for different regional milk prices. In addition, rank correlations between PD$ and PV$ within regions were evaluated to determine the coherence of goals of selecting sires to maximize genetic as opposed to economic progress from AI. Correlations and average absolute rank changes between PD$ and PV$ were determined for all 393 sires and also for a selected subset of 197 sires ranking highest on US PD$. Finally, the opportunity cost o f selecting sires on regional PD$ rather than on PV$ was assessed by comparing the average PV$ of sires selected on PV$ with average PV$ of identically ranked sires selected on PD$. Groups comprised 19 sires, or approximately 5% of all sires, such that the first group contained the highest ranked 19 sires for PD$ or PV$ by region. RESULTS A N D DISCUSSION
The effect o f a constant milk fat price is to place considerably more emphasis on selection for SNF milk in states where milk price is high. Seasonal milk utilization patterns affect milk pricing, and this source of price variation may also be o f consequence to breeding decisions. Table 1 shows the average 1986 price received for 3.5% milk fat milk and for SNF milk for selected states and the US using the average US price of $3.608/kg of milk fat. The milk fat price is constant among states but varies slightly in time. However, this variation does not significantly alter regional prices for SNF milk. Average milk price for 3.5% milk fat milk ranged from $.2416/kg for ND to $.3527/kg for FL. Average 1986 US economic weight for SNF milk was $.1404/kg. Florida received the highest price at $.2264/kg and ND the lowest at $.1153/kg. Selection for SNF milk should receive 96% greater emphasis in FL than ND and 61% more than determined by US average milk price. Economic weight for SNF milk in ND was 18% below the national average. Large differences existed within the US for the price received for SNF milk. Table 2 shows that sire ranks changed an average of 25.9 places between ND and F L for PD$ and 34.1 places for PV$. Average rank change between the US and F L was 18.2 places for PD$ and 24.7 for PV$. However, average
MILK PRICE AND SIRE SELECTION
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TABLE 1. Prices of 3.5% milk fat milk and solids-not-fat milk for months December 1985 through November 1986 and numbers of cows for selected states. 3.5% Milk price ~($/kg)
Solids-not-fat
Region
Min
Max
Avg
CV(%)
Milk1, 2 ($/kg)
Cows (1000)
ND CA MN US TX FL
.2341 .2460 .2475 .2589 .2774 .3392
.2592 .2660
.2416 .2523 .2548 .2669 .2944 .3527
3.6 2.5 3.4 3.0 3.5 3.1
.1153 .1261 .1285 .1406 .1681 .2264
974 890 10,122 314 164
.2717
.2834 .3115 .3718
97
Assuming $3.608/kg milk fat. 2 Corresponding to average 3.5% milk fat milk price.
rank changes b e t w e e n CA and US and MN and US were very small, 4.5 and 3.5, respectively, for PD$ and 5.5 and 4.6, respectively, for PV$. R a n k correlations among PD$ were all greater than .95 and a m o n g PV$ were all greater than .92. When the t o p ranked 197 (50%) of sires for US PD$ were evaluated (Table 3), ranks changed on average 21.3 places b e t w e e n ND and F L for PD$ and 15.9 places for PV$. Average rank change b e t w e e n the US and F L was 15.0 places for PD$ and 12.0 for PV$. Average rank changes b e t w e e n CA and US and MN and US
were smaller than for all sires, 3.9 and 3.1, respectively, for PD$ and 2.6 and 2.1, respectively for PV$. R a n k correlations among PD$ were all greater than .88 and among PV$ were all greater than .93. Average rank changes were, relative to the n u m b e r o f sires, greater among the top 50% o f sires than among all sires. Results in Table 2 and 3 suggest that states with the highest milk prices will not place sufficient selection pressure on SNF milk p r o d u c t i o n to m a x i m i z e profit if selection is based upon PD$ or PV$ defined by the US
TABLE 2. Rank correlations and average sire rank changes between regions for PD dollars (PD$) and present value dollars (PV$) for 393 sires. Region
ND
CA
MN
US
TX
FL
15.1 11.6 10.7 7.3
25.9 22.5 21.6 18.2 11.1
Average rank change ND CA MN US TX FL
3.71 4.52 5.3 9.7 19.1 34.1
1.3 5.5 14.8 29.9
4.6 1.0 4.6 14.0 29.0
8.0 4.5 3.6 9.6 24.7
15.3
Rank correlations ND CA MN US TX FL
.991
.99
.99
.98
.95
.99
.99 .99
.99 .99 .99
.97 .97 .98 .99
.992 .99
.99
.99 .97
.99 .98
.99 .99
.99
.92
.94
.94
.96
.98
1PD$ above diagonal. a PV$ below diagonal. Journal of Dairy Science Vol. 71, No. 5, 1988
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TOMASZEWSKI ET AL.
TABLE 3. Rank correlations and average sire rank changes between regions for PD dollars (PD$) and present value dollar (PV$) for highest 197 US PD$ sires. Region
ND
CA
MN
US
TX
FL
12.9 10.0 9.3 6.4
21.3 18.5 17.8 15.0 8.9
Average rank change ND CA MN US TX FL
3.1 a 1.92 2.3 4.2 8.7 15.9
.7 2.6 7.0 14.3
3.8 .9
6.7 3.9 3.1
2.1 6.6 13.8
4.7 12.0
7.5
Rank correlations NC CA MN US TX FL
.99 ~ .992 .99 .99 .98 .93
.99 .99
.99 .99 .98 .94
.99 .99 .99
.99 .99 .94
.96 .97 .98 .99
.99 .96
.88 .91 .92 .94 .98
.98
PD$ above diagonal. 2 PV$ below diagonal.
average milk price. However, deviations o f milk prices f r o m US average price for the major dairying states of CA and MN resulted in o n l y m i n o r ranking differences f r o m the US ranking for b o t h PD$ and PV$. Consequently, errors in sire selection are small for dairy farmers in CA and MN selecting sires o n PD$ o r PV$ calculated f r o m the US average milk price. R a n k correlations a m o n g PD$ and PV$ and average absolute difference b e t w e e n sire ranks for PD$ and PV$ are in Table 4. Highest average change in sire ranks b e t w e e n PD$ and PV$ was 79.4 positions for ND and lowest was 59.2 for FL. Lowest rank correlation b e t w e e n
PD$ and PV$ was .51 for ND and highest was .69 for FL. A m o n g the top 197 sires for US PD$, the highest average sire rank change b e t w e e n PD$ and PV$ was 55.2 for ND and lowest was 46.5 for FL. Lowest rank correlation b e t w e e n PD$ and PV$ was .21 for ND and highest was .39 for FL. Again, average rank changes b e t w e e n PD$ and PV$ were relatively greater a m o n g the t o p 50% of sires than among all sires. The PV$ evaluations comprise two components, semen cost to produce a r e p l a c e m e n t daughter and expected profit f r o m future p r o d u c t i o n of the replacement. Semen price
TABLE 4. Rank correlations and average rank differences between PD dollars (PD$) and present value dollars (PV$) within regions. Highest 197 sires on US PD$
All 393 sires Region ND CA MN US TX FL
Avg. rank change
Rank correlation
Avg. rank change
Rank correlation
79.4 77.3
.51 .52 .53 .55 .60 .69
55.2 54.4 54.1 53.0 50.5 46.5
.21 .22 .23 .25 .29 .39
77.0 74.7
69.0 59.2
Journal of Dairy Science Vol. 71, No. 5, 1988
MILK PRICE AND SIRE SELECTION and variation a m o n g sires for semen price may vary due to regional marketing structure but are generally constant within the region. The effect o f milk price on profit o f future p r o d u c t i o n is three fold. Altering the milk price changes the relative i m p o r t a n c e o f S N F milk to milk fat in the selection objective. It introduces a scale effect w h e r e b y profitability is greatest with the greatest milk price. Finally, it influences the variation among sires for e x p e c t e d profit f r o m f u t u r e daughter p r o d u c t i o n with greatest variation at highest milk price. The latter two points in c o n j u n c t i o n with constancy o f semen price explains w h y the rank correlation between PD$ and PV$ was lowest for ND. As milk price b e c o m e s increasingly large, selection pressure focuses o n S N F milk, variation among sires for e x p e c t e d profit f r o m future daughter p r o d u c t i o n d o m i n a t e s variation among sires for semen price, and the rank correlation b e t w e e n PD$ and PV$ tends to unity. O p p o r t u n i t y costs o f selecting sires using PD$ rather than PV$ are in Table 5 for regional milk prices. O p p o r t u n i t y cost was the dif-
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ference b e t w e e n average PV$ o f sires ranked and selected on regional PV$ and average PV$ o f sires ranked and selected on regional PD$. Sire groups were o f 19 sires each or approximately 5% groups o f the 393 AI sires. When the average PV$ of t h e t o p ranked 5% o f sires selected o n PD$ were compared with the average PV$ o f the top ranked 5% of sires selected o n PV$, producers in all five states lost at least $120 per sire by selecting sires on PD$ instead o f PV$. F o r the average T X herd o f 166 cows with an annual r e p l a c e m e n t rate of 31%, the loss in profit f r o m f u t u r e p r o d u c t i o n o f herd replacement cows is $6227. Average PV$ rank o f the top 19 sires selected o n PD$ was 157 for F L but as low as 195 for ND. Combining average PV$ of sires selected on PD$ and o p p o r t u n i t y cost in Table 5 yields the average PV$ of sire groups ranked and selected on PV$. The t o p 76 (20%) sires averaged $130 in ND whereas the top 76 sires averaged $219 in FL, an increase o f 68.5% in average PV$ compared with a 46.0% milk price difference b e t w e e n the states. A significant dollar loss is
TABLE 5. Opportunity cost associated with selecting sires on regional PD dollars (PD$) instead of regional present value dollars (PV$). Rank of selected sires
PV$ 2
Cost 3
Rank 4
PV$
Cost
Rank
PV$
Cost
Rank
1-19 (10) 2~38(29) 3%57(48) 58-76 (67) 7%95(86)
32 92 52 34 102
125 41 68 75 1
195 120 112 146 112
43 101 66 46 94
124 40 62 70 14
192 119 92 141 143
45 103 68 48 95
124 40 62 70 15
192 116 94 141 145
1-19 (10) 2~38 (29) 39-57(48) 58-76(67) 77-95 (86)
PV$ 57 54 137 54 104
Cost 123 100 2 71 13
Rank 188 133 73 143 140
PV$ 84 78 154 84 121
Cost 121 98 5 60 13
Rank 179 126 73 113 143
PV$ 141 139 193 188 108
Cost 120 85 12 -4 66
ND
CA
US
MN
TX
FL Rank 157 109 70 85 138
] Sires selected on PD$. Average rank listed in parentheses. 2Average PV$ of sires selected on PD$. aOpportunity cost = Average PV$ of identically ranked sires when selected on PV$ - Average PV$ of sires selected on PD$. 4 Average PV$ rank of sires selected on PD$. Journal of Dairy Science Vol. 71, No. 5, 1988
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TOMASZEWSKI ET AL.
experienced by producers when they do not account for actual milk market and AI sire semen prices.
market pricing to identify more accurately the most profitable AI sires. REFERENCES
CONCLUSIONS
Most important is our finding that with the exception of states with high milk prices, little difference was found in sire rankings for PD$ or PV$ as regional milk price varied. However, correlations among sire rankings for PD$ and PV$ within region reveal that sires rank differently for genetic merit for product value than for expected profitability to dairy farmers, mediated primarily by semen price. Because semen pricing by AI organizations is determined by demand, semen production, genetic merit for type, and by genetic merit for production traits, commercial dairy producers have considerable opportunity to maximize profits by selecting sires according to PV$. Considerable attention should be focused by dairy farmers and animal breeders to the problem of simultaneous optimization of genetic and economic progress by alleviating the effect of semen price on AI profitability for dairy farmers with low milk prices. Marketing appropriately priced semen from high genetic merit AI sires at low sperm numbers per unit may be a potential solution (8). Because sire selection can be affected by milk pricing, managers may benefit from using their milk
Journal of Dairy Science Vol. 71, No. 5, 1988
1 Agricultural Statistics Board. 1986. Agricultural prices. January to December. Natl. Acad. Stat. Sci., USDA, Washington, DC. 2 Agricultural Statistics Board. 1986. Milk production. National Agricultural Statistic Service, USDA, Washington, DC. 3 Blake, R. W., C. R. Shumway, and M. A. Tomaszewski. 1987. PV$ sire summary changes with industry. Dairy Herd Management 24: 20. 4 Cunningham, E. P. 1980. Economic appraisal of breeding programmes. Page 291 in Proc. World Congr. on Sheep and Beef Cattle Breeding. R. A. Barton and W. C. Smith, Tech. Ed. Dunmore Press, Palmerston North, N. Z. 5 McMahon, R. T., R. W. Blake, C. R. Shumway, D. J. Leatham, M. A. Tomaszewski, and K. R. Butcher. 1985. Effects of planning horizon and conception rate on profit-maximizing selection of artificial insemination sires. J. Dairy Sci. 68: 2295. 6 National Association of Animal Breeders. 1987. Comparative statistics of semen activity. Natl. Assoc. Anim. Breeders, Columbia, MO. 7 Norman, H. D. 1986. Sire evaluation procedures for yield traits. National Cooperative Dairy Herd Improvement Handbook. H-l, Univ. of Maryland, College Park. 8 Taylor, J. F., K. R. Phillips, and M. A. Tomaszewski. 1987. Economic merit of splitting units of semen and of sexed semen for Australian Holstein Herds. J. Dairy Sci. 70(Suppl. 1):156.(Abstr.) 9 Wilcox, M. L., C. R. Shumway, R. W. Blake, and M. A. Tomaszewski. 1984. Selection of artificial insemination sires to maximize profits. J. Dairy Sci. 67: 2407.