Photo credit: Andrew Sandeen
With new programs such as Dairy Revenue Protection and Dairy Margin Coverage to figure out, you may be mulling over new risk management strategies. It might be possible to survive without a formal risk management strategy, but it is still worth investing time to review the business and what will help maintain a successful operation.
Though many risk management conversations revolve around the big financial picture, risk management is also relevant to specific areas of management – such as reproduction. Let’s examine some risks to evaluate related to reproductive management.
Effectiveness of heat detection
There are many heat detection risks. Maybe not enough cows are being detected in heat, or conversely, too many supposed (false positive) heats are being recorded. Too many labor hours might be spent on heat detection alone, or maybe too much cash spent on a nice electronic detection system. The big, overarching risk is that there may not be enough pregnancies established in a timely manner, a costly mistake.
What might help? Invest in training to maximize the number of dairy employees helping with heat detection and ensure they understand the basic signs of estrus and how best to maintain useful, accurate records. Consider investing in an activity monitoring system or some expert AI service help if, and only if, the potential improvement will pay for the system and there is full commitment to using the system by all of those who will be expected to use it.
Timing of insemination
Don’t be too early, but even worse, don’t be too late. Ovulated oocytes don’t remain viable for very long without a sperm cell fertilizing them. The big risk is the same here: a poor plan will result in not enough pregnancies being established in a timely manner.
The optimal time for AI is 12 to 24 hours before ovulation, which correlates to 4 to 16 hours after the onset of standing estrus (Dalton, 2012). There are different approaches to managing the risks associated with timing of insemination, largely depending on the methods used for heat detection and availability of AI technicians. Timed AI is one approach with a lot of proven success.
Something to keep an eye on for your reproduction risk management tool belt is research on the functional distinctions between different categories of cows. Researchers have shown that primiparous (first lactation) cows tend to ovulate sooner than multiparous cows (Stevenson et al., 2014) and may be more likely to conceive with earlier insemination (LeRoy et al., 2018). Blavy et al. (2018) also found that the optimum timing of insemination was approximately 8 hours later for high-producing cows as compared to low-producing cows. There may be a slight benefit to conception rates if primiparous and/or low-producing cows are inseminated within 8 hours of the onset of estrus. However, more research is needed before making radical adjustments.
You have probably heard it before, but it is worth repeating again. Working with mature dairy bulls is risky. Risk #1 is the safety issue for both employees and other cattle. It only takes a brief moment for a bull in a nasty or playful mood to cause life-changing damage. There are other risks too: potential transmission of disease, poor genetic progress in the herd, and infertility. Bulls are susceptible to changes in fertility due to heat stress, diet, or other environmental factors. The use of natural service might continue without problems for a long time, but unchecked, one bout of infertility can bring a reproductive program to a screeching halt if not managed carefully.
The solution? Consider the numerous advantages of AI and avoid the use of natural service whenever it is feasible to incorporate a good AI program.
Pregnancy and rebreeding strategy
In even the most fertile herds, and regardless of the voluntary waiting period before first service, cows sometimes fail to conceive to first service or lose an early pregnancy. The risk is that the days in milk keep ticking. There needs to be an effective plan to minimize the interval between services and establish timely pregnancies.
Pregnancy diagnosis should be performed routinely, and there are several options for accomplishing this including palpation, ultrasound, milk tests, or blood tests. An option that may become increasingly common is to measure progesterone with an in-line milk testing system. A low progesterone value identifies cows that are not pregnant. Progesterone measurements may also help with decisions around the time of AI (Blavy et al., 2018).
Using timed AI to reinseminate in a timely manner, especially for cows not caught in estrus and confirmed open, is a sound strategy. With the Ovsynch protocol, a second dose of prostaglandin F2α (PGF) 24 hours after the first PGF treatment tends to improve conception results (Barletta et al., 2018, Borchardt et al., 2018).
Plan for young stock
Regardless of whether the credit should go to the use of sexed semen, improved reproduction management, dairy genetics, or a combination of multiple factors, it is not uncommon to see a surplus of young dairy animals. Though this may be great for building up assets on a balance sheet, this can sometimes cause problems.
It might be a GOOD idea to: sell surplus heifers at whatever age is most economical (both for the costs of raising and the sale price), cull heifers with a questionable health history (e.g. pneumonia), sell bottom end genomic animals, sell cows with persistent mastitis even if they are producing a high volume of milk, or consider selling heifers or cows pregnant with twins.
It might be a BAD idea to: overcrowd heifer housing, raise more heifers than can be fed or cared for properly, or sell older cows just to make room for young cows.
If there are more heifers than milk cows in a herd that isn’t increasing in size, there are probably more than the operation needs to sustain itself – a good problem to have but a management issue to seriously consider.
Coverage for the more distant future is more of a gamble and may carry some “hard to accept” costs, but there may also be a great payoff. Looking at genetics, the risk is falling behind on the curve of industry change and where you might want to be positioned in the marketplace in the coming years. Nobody knows for sure what demands will become commonplace in the future, but there are clues that can be assessed.
Will researchers such as He et al. (2017) continue to demonstrate that the A2 variant of β-casein in cow milk causes fewer gastrointestinal symptoms in humans than the A1 version of the protein? Will there continue to be a push for more fat and protein content in milk being shipped from the farm? Will there be new threats to the use of routine hormone treatments for managing reproduction, potentially causing fertility and health traits to be more important for the sake of estrus detection? How might we want to account for these issues in our breeding program?
Not all risks are bad. In fact, we want the risk of pregnancy to be high. But a thoughtful approach to all of the risks associated with reproductive management can pay tremendous dividends. There are short-term issues and others that may take a long time to play out. With persistence and patience, a dairy manager can be satisfied with the results of good risk management strategies properly implemented.
- Barletta, R. V., P. D. Carvalho, V. G. Santos, L. F. Melo, C. E. Consentini, A. S. Netto, and P. M. Fricke. 2018. Effect of dose and timing of prostaglandin F2α treatments during a Resynch protocol on luteal regression and fertility to timed artificial insemination in lactating Holstein cows. J. Dairy Sci. 101:1730-1736.
- Blavy, P., N. C. Friggens, K. R. Nielsen, J. M. Christensen, and M. Derks. 2018. Estimating probability of insemination success using milk progesterone measurements. J. Dairy Sci. 101:1648-1660.
- Borchardt, S., A. Pohl, P. D. Carvalho, P. M. Fricke, and W. Heuwieser. 2018. Effect of adding a second prostaglandin F2α injection during the Ovsynch protocol on luteal regression and fertility in lactating dairy cows: A meta-analysis. J. Dairy Sci. 101:8566-8571.
- Dalton, J. C. 2012. Strategies for success in heat detection and artificial insemination.
- He, M., J. Sun, Z. Q. Jiang, and Y. X. Yang. 2017. Effects of cow’s milk beta-casein variants on symptoms of milk tolerance in Chinese adults: A multicentre, randomised controlled study. Nutr. J. 16:72.
- LeRoy, C. N. S., J. S. Walton, and S. J. LeBlanc. 2018. Estrous detection intensity and accuracy and optimal timing of insemination with automated activity monitors for dairy cows. J. Dairy Sci. 101:1638-1647.
- Stevenson, J. S., S. L. Hill, R. L. Nebel, and J. M. DeJarnette. 2014. Ovulation timing and conception risk after automated activity monitoring in lactating dairy cows. J. Dairy Sci. 97:4296-4308.