Dairy Reproduction Benchmarks

J.W. Smith¹
W.D. Gilson¹
L.O. Ely¹
W.M. Graves¹

• Methods
• Measures of Reproductive Efficiency
• Dry Period
• Herd Reproduction Program
• Recommendations
• Summary

An abundance of information and numerous resources are available through the Dairy Records Management Systems (DRMS), Raleigh, North Carolina, for use in herd management analysis. The DHI-202 Herd Summary Report is a valuable source of information. Herd summary data can uncover many herd management strengths and weaknesses.

The purpose of this bulletin is to provide reproduction benchmarks for Holstein herds processed by DRMS. Some examples of using and applying benchmark values are provided. This bulletin, however, should be viewed primarily as a comprehensive resource for reproduction benchmark values. These benchmarks will be useful to dairy producers, dairy managers, consultants, veterinarians and agribusiness representatives as a first step in the analysis of herd management practices.

Methods

Herd Summary information was obtained from the DRMS, Raleigh, North Carolina, for Holstein herds last tested in November or December, 2000. Research has shown that management variables may differ by region of the country, herd size and milk production level. Consequently benchmark values are presented for Northeast, Midsouth, Midwest and South regions (Figure 1). Within regions, values are further subdivided by either herd size or rolling herd average milk production. Values in all tables and graphs were limited to herds with a minimum of 25 cows. Minimum rolling herd milk production for herds included in percentile tables was 12,000 pounds. Note that all analyses and calculations are based on herd average information and not on individual cow data.

Figure 1. Map of United States Showing Four Regions

Herds using natural service sires seldom report the number of services. The result is a distortion of many DHI reproductive parameters such as services per pregnancy. Values in this bulletin are listed for herds using predominately artificial insemination (AI) as well as for all herds regardless of sire usage. AI herds are defined as those with 70 percent or more AI services with a minimum services per pregnancy for both pregnant and all cows equal to 1.5. Herds in the all category were not restricted by any reproductive parameter.

Statistics calculated for certain benchmark values were n, mean, standard deviation and percentile ranks. Following are definitions of these terms:

N - The number of observations (herds) included in a specific analysis.

Mean - The average calculated as the sum of all observations divided by N.

Standard Deviation (SD) - A measure of the variability of the observations. The larger the SD, the more variation among the observations.

Percentile Rank - Percentiles are defined as a value such that X% falls short of the value and Y% exceeds the value. For example, if a bench-mark value is at the 75^th percentile, then 75 percent of the herds analyzed had values below and 25 percent had values above the benchmark. A herd value at the 75^th percentile rank is in the top 25 percent of all herds analyzed.

Measures of Reproductive Efficiency

Average days open is an overall indicator of reproductive efficiency status. Projected average days open as listed in this bulletin includes cows with breeding dates and cows without breeding dates in milk longer than the Voluntary Waiting Period (VWP). The VWP is the desired minimum number of days from calving until first service and is selected by the producer. Calving interval is the period of time between calvings and is measured in months. The projected minimum calving interval equals Average Days Open + 280 days/30.4, where 280 days is the average length of gestation and 30.4 days is the average length of a month.

The effect of calving interval on net operating income (income - expenses) to owner's labor and management for a 200-cow dairy is shown in Table 1. The University of Georgia cash flow dairy budget program was used to generate these comparisons.

The herd size was held constant at 200 cows. The number of cows milking and dry is a function of the calving interval. Other assumptions included a 60-day dry period and no replacement rearing expenses. Rations were balanced for each target level of production causing increased feed cost for each higher level of target milk production. The dry cow ration remained constant for each level of production. All other costs were held constant. For each target level of milk production, budgets were run for a calving interval of 13.0, 14.0 or 15.0 months. With increasing calving interval, the number of lactating cows declines as well as net operating income for each level of target milk production. Although feed costs are lower at longer calving intervals since more cows are eating less expensive dry cow ration instead of the milk cow ration, the loss of milk income is greater. As level of target milk production increases, net operating income tends to increase as well.

The potential loss in net operating income for changing calving interval is reported in Table 2. The cost of a longer calving interval is greater with higher levels of production. The cost per cow per added day open is reported in Table 3. This cost changes with level of production and is different for the added days open associated with different calving intervals. This estimate varies between $2.52 to $4.74 per cow per added day open at the different levels of production when changing from a 13- to 14-month or from a 14- to 15-month calving interval.

Many factors affect the actual cost of days open on a particular farm. These factors include milk price, cow numbers, production level, AI and veterinary expenses, bST use, potential replacement losses and replacement rearing expenses. With higher production levels, there is potentially greater income loss as days open and calving interval increase. The use of bST has complicated the evaluation of optimal calving intervals since cows may continue to produce at profitable levels for a much longer period. But if these conditions can be met, higher potential returns will be available with the shorter calving interval. Moreover, many herds are not using bST or are using bST on only a few cows. The high cost of added days open should provide incentive to examine the reproductive efficiency of your herd. Many management tools are now available that can help shorten calving intervals.

Days to first service (DFS), conception rate (CR) and heat detection rate (HDR) all combine to influence days open and calving interval. Average days to first service is affected by the selected voluntary waiting period, heat detection efficiency and herd reproductive health.

Generally, dairy producers set a VWP of 45 to 60 days, which enables the minimum days to first service. The days to first service further sets the minimum calving interval for a cow and the herd. The average time between calvings will be longer than the minimum calving interval because not all cows conceive on first service and some cows experience early embryonic death. The conception rate, heat detection efficiency and postpartum breeding policy strongly influence the calving interval.

Accurate heat detection is especially important for herds breeding by AI; however, it is also important for natural service herds. It provides the basis upon which most reproduction management decisions are made. Detection of the first estrous period following calving provides a reference point upon which to expect subsequent estrous periods. It is also beneficial in determining whether a cow is recovering normally from calving.

Heat detection is a major factor affecting days open and calving interval. The percent of heats observed is an indicator of the overall success of a heat detection program. Not only must estrous be observed, but the observation and breeding must be done on a timely basis for conception to occur.

Measures of conception rate include services per pregnancy and percent successful services. Services per pregnancy are available for pregnant cows as well as all cows. The percent successful services is equal to the number of successful services divided by the number of total services. For herds on routine pregnancy diagnosis, reported pregnancies are used to calculate the percent successful. When actual pregnancy data are not reported, the 65-day non-return rate is used.

Several measures of reproductive efficiency for AI herds by region are presented in Tables 4-7. The data are further divided into five or six groupings based on herd size to better reflect management differences. These tables provide a convenient means of comparing a herd's reproductive efficiency to similar herds in the same region.

Tables 8-11 present a limited selection of measures of reproductive efficiency for all herds sorted by region and herd size. Because data generated by natural service herds are limited, only the information regarding days open and days dry are valid. Evaluation is difficult in these situations. These data, however, can identify those herds that have significant reproductive problems.

Example 1

Jim is a dairy producer in Iowa. His herd consists of 175 cows. The days to first service is 105 and days open is 185. The percentage of heats observed is 45 percent. He is currently averaging 2.3 services per conception with his pregnant cows and a 38 percent successful rate. He would like to compare his herd with similar herds in his region.

Jim uses Table 6 for the Midwest region and looks at the specific table for his size herd. He finds that his 105 days to first service rank him in the bottom 25 percent of similar herds. His 185 days open places him in the bottom 10 percent. He compares the percentage of heats observed and finds that he is in the top 50 percent of comparable herds. Jim further observes that his services per conception ranks him in the top 50 percent and his percent successful rate is slightly below the 50^th percentile. These data lead Jim to conclude that his semen handling and insemination techniques are acceptable; however, heat detection and care of the postpartum cow are of primary concern.

Dry Period

Both long and short dry periods affect profitability. A high percentage of cows in the 40-70 day dry period length is recommended. A short dry period does not provide enough rest and time for mammary gland involution and regeneration. Long dry periods extend the time period when no milk income is available and increase the possibility of over-conditioned cows. Over-conditioned cows are more prone to health and reproductive problems. Long and short dry periods may be a consequence of extended or long calving intervals, inadequate cow identification, poor record-keeping or poor pregnancy diagnosis. Days dry for AI herds are summarized in Tables 4-7. Percentile rankings are given for Average Days Dry as well as the percentage of cows dry between 40 and 70 days.

Example 2

John has a herd of 45 cows in Connecticut with an average days dry of 65 and with 75 percent of the dry periods between 40 and 70 days. His herd ranks in the top 50 percentile for percent dry 40-70 days and a little below the 50^th percentile for average days dry of all herds in the region. (Table 4) This indicates that he is doing above average in percent dry 40-70 days and average in average days dry.

Herd Reproduction Program

A comparison of herd values with the values in Tables 4-12 provides the opportunity to evaluate reproductive performance based upon herd size, region and milk production level. Producers can fine tune their evaluation to pinpoint specific areas that may need improvement. Following are two examples showing how this information may be used in an evaluation of herd reproductive performance.

Example 3

George has a herd of 165 cows in Kentucky. He breeds a few of his best cows by AI but breeds the majority to his three herd bulls. His days open is 150 days, days dry is 70 and percentage of cows with dry periods between 40 and 70 days is 55 percent. Because George breeds primarily by natural service, he is not able to make many in-depth comparisons. He selects Table 9 and finds that his days open place him in the top 50 percent of similar herds. The average days dry for his herd places him between the 25^th and 50^th percentiles. The percent of dry periods between 40 and 70 days for his herd places him between the 10^th and 25^th percentiles. These are areas where he can make improvement, but determining the underlying causes will be difficult because of limited information

Example 4

Bill has a herd in Georgia with a herd average of 19,585 pounds. He checks Table 12 for the South region and finds that similar herds with a 19,000 pound rolling herd average have an average days to first service of 94 and average days open of 173. He further notices that the average services per pregnancy are 2.5 and 4.5 for pregnant cows and all cows, respectively. Days dry average 69 days and the percentage of cows dry 40 to 70 days average 66 percent for similar herds in his region. Any values for his herd that fall outside these ranges indicate strengths or weaknesses in his management.

Recommendations

After an evaluation is complete, establish goals to improve performance. Goals for improvement of parameters such as days open must also consider the inter-relationship with other reproductive parameters. For example, a goal of 115 days open and a 60-day dry period requires that, on average, conception must be at 115 days. If 2.0 services per pregnancy are maintained, then days to first service must average 73 days with a VWP of 60 days. Heat detection must also be excellent.

Summary

Benchmarks provide a convenient method of comparing the performance of your herd with the performance of similar herds. The information provided in this bulletin allows producers or advisors to evaluate a herd's performance with similar herds and determine potential strengths and weaknesses in management. Specific recommendations cannot be made based on this information. Producers are encouraged to consult experts who can help identify specific problems and recommend solutions.

¹Animal and Dairy Science Department

Status and Revision History
Published on May 01, 2002
In Review on Feb 03, 2009
Published on Feb 03, 2009
Published with Minor Revisions on May 27, 2009
Published with Full Review on May 21, 2012
Published with Full Review on Mar 28, 2017