Source: WCDS Advances in Dairy Technology
Take Home Messages
- In-vitro produced embryo transfer (IVP-ET) in dairy herds greatly reduces the genetic lag with service sires. The value of this decreased genetic lag may overcome the high cost of IVP-ET.
- The most profitable use of AI and IVP-ET is often a combination of the two. More IVP-ET should be used when the value of surplus calves is high and the cost of IVP-ET is low, among many other factors.
- In the future, use of IVP-ET will increase by more accurately identifying superior donors and recipients, reducing the generation interval, and achieving greater efficiency in embryo production.
Introduction
Artificial insemination (AI) and in-vitro produced (IVP) embryos for embryo transfer (ET) are two reproductive technologies that result in genetic gain by propagating offspring from animals with greater genetic merit. The National Association of Animal Breeders (NAAB, 2019) reported 23,196,413 units of dairy semen sold in the U.S. in 2017. The number of transferable IVP embryos of dairy breeds produced in North America during 2017 was 311,867, of which 95.5% (≈ 298,000) were produced in the U.S. (Viana, 2018). Adding units of semen sold to transferable IVP embryos in the U.S. during 2017 shows that approximately 1.3% of dairy breedings were with IVP embryos. Use of IVP-ET is growing fast in North America; the number of IVP embryos doubled between 2013 and 2017.
Genetic gain has been accelerating since 2010 when genomic testing became widely used to select service sires. The 5-year moving average rate of genetic gain in predicted transmitting ability (PTA) for the economic selection index Lifetime Net Merit (NM$) is now greater than $70 per year for sires born between 2013 and 2017 (CDCB, 2019). This rate of genetic gain was just $28 per year for sires born between 2003 and 2007. Dairy farms that use only AI make genetic gain in their herds because of genetic gain in marketed AI sires. The Council on Dairy Cattle Breeding (CDCB) data also show that the genetic merit of cows is less than that of service sires. The difference is constant as long as the rate of genetic gain in service sires is constant. Genetic merit of cows lags behind the genetic merit of service sires.
Value of the level of genetic merit in a dairy herd should be based on the difference (genetic lag) in genetic merit between the average cow in the herd and the best available sires (the genetic nucleus; Dechow and Rogers, 2018). This genetic lag is an opportunity cost: each cow consists of ‘old’ sire genetics. For example, when only AI is used and no selection occurs within the herd, the average cow in the herd may be 3.5 years old. If we assume an annual increase of $50 per year in PTA for NM$, then service sires 3.5 years ago had a $175 lesser PTA than today’s service sires. The genetic merit of a cow, however, can be thought to consist of 50% her sire + 25% her dam’s sire + 12.5% her granddam’s sire + 6.25% of her great grand dam’s sire, etc. If the generation interval stays the same between generations, then the genetic lag of the average cow in the herd with the genetic nucleus would be $350 PTA of NM$ (200% × 3.5 × $50). This is a doubling of the genetic lag of the first generation. The genetic lag increases with a greater rate of genetic gain in service sires. If the annual increase in PTA of NM$ is $70 per year, then the genetic lag between the average cow and the genetic nucleus is $490 PTA of NM$ (200% × 3.5 × $70). This math is a simplification of reality, but illustrates the important principle of genetic lag.
Selection of superior females in the herd reduces the genetic lag with service sires. For example, use of female sexed semen in younger animals or selection of surplus heifer calves based on genomic test results produces dairy calves that are on average better than the average unselected dairy calf from the herd. The result is a decrease in the genetic lag with the best available service sires. Use of IVP-ET can greatly decrease this genetic lag as will be illustrated later. Use of technologies such as AI, sexed semen, IVP-ET, and selection of surplus animals all contribute to a reduction in genetic lag.
A greater rate of genetic gain means differences in genetic merit resulting from age become greater. In other words, the difference in genetic merit of the best heifers in the herd compared with the genetic merit of the average cow in the herd is becoming greater. As a result, capturing and propagating the best genetics in the herd is becoming more valuable.
From the perspective of a typical herd, the genetic merit of available service sires is a given factor that cannot be controlled. When the rate of genetic gain of service sires is constant over time, and the reproduction and selection program for females in the herd are constant over time, it follows that use of technologies like IVP-ET in a herd do not accelerate the rate of genetic gain (that is, they do not increase the annual change) as is often thought. They do reduce, however, genetic lag with service sires compared with use of AI.
What is the opportunity cost of genetic lag? Using again simple math, a genetic lag of $350 PTA of NM$ is equivalent to a genetic lag of $700 estimated breeding value (EBV) of NM$ (2 × $350 because EBV = 2 × PTA). We use EBV to express the genetic merit of the female herself, whereas PTA is the genetic merit transmitted to her offspring). The $700 is expressed per lifetime, which is 2.8 lactations, or approximately 3 years. Thus, the opportunity cost of this genetic lag of $350 PTA of NM$ is $700 ÷ 3 = $233 per cow per year. Using a program that would reduce the genetic lag by $50 is worth approximately 2 × $50 ÷ 3 = $33 per cow per year. One dollar reduction in genetic lag is worth $0.67 dollar per cow per year (simplified). This math does not include any discounting for time value of money, differences in actual lifespan, and phenotypic response to selection, and assumes that NM$ is the ideal measure of profitability.
The toolbox of technologies such as AI, sexed semen, beef semen, IVP-ET, genetic evaluations, genomic testing, and fertility programs all affect genetic lag. In addition, these technologies have various direct costs and may affect the phenotypic performance of the herd, such as conception risk. For example, the cost to produce a pregnancy with an IVP embryo is much greater than the cost to produce a pregnancy with AI, but the genetic lag using IVP-ET is smaller. The net benefit of using IVP-ET over AI is not immediately clear. Another question is how much IVP-ET use in a herd is optimal, if not to create 100% of pregnancies. The goal of this paper is to provide some insight into these questions.
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