Archive for Milk recording

Crampy Dairy Cows – An Lactanet Project Update

Find out how Canadian dairy farmers can lower Crampy in cows. Get the latest data, genetic insights, and future strategies to boost herd health.

Summary: Crampy, also known as Bovine Spastic Syndrome, increasingly concerns Canadian dairy farmers due to its progressive neuromuscular symptoms. Lactanet’s data collection initiative aimed to provide a clearer picture of its prevalence and explore genomic evaluations for mitigation. Their analysis, involving 2,807 Crampy cases from 801 herds, revealed that genetic selection could significantly reduce its occurrence. With the heritability of Crampy estimated at 6.8%, prioritizing top-rated sires can lower the risk. Gabriella Condello’s M.Sc. thesis highlighted that Crampy primarily affects cattle between two and seven years old, with a higher incidence in younger age groups. The study emphasizes the need for ongoing data collection to refine genetic evaluations and develop effective control strategies.

  • Crampy affects Canadian dairy cows as a neuromuscular disorder, primarily in the hind limbs.
  • Lactanet’s data collection received 2,807 Crampy cases from 801 herds, aiding research.
  • Genomic evaluations suggest genetic selection can reduce Crampy prevalence.
  • Heritability of Crampy is estimated at 6.8%, indicating a genetic component.
  • Crampy affects cows mainly between two and seven years of age, with severe cases often seen in younger cattle.
  • Ongoing data collection and genotyping are crucial to improving genetic evaluations and mitigation strategies.
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Canadian dairy producers are growing concerned about crampy cows, often known as Bovine Spastic Syndrome. Imagine spending years nurturing a healthy herd only to have your cows suffer devastating neuromuscular disorders out of the blue. Wouldn’t it be frustrating to watch your carefully controlled herd’s health deteriorating? You’re not alone in feeling this way. Crampy doesn’t just afflict cows. It affects milk production, raises veterinary expenses, and may result in significant losses. Are you willing to let these obstacles eat your profitability and peace of mind? Let’s examine why this problem is growing more widespread and what you can do about it. The answers may surprise you and, more importantly, provide a path ahead.

Unpacking Crampy: What Dairy Farmers Need to Know 

So, what precisely is Crampy/Bovine Spastic Syndrome? It is a degenerative neuromuscular illness that mainly affects cattle between two and seven years old. The signs are pretty obvious: spastic spasms in the muscles of one or both hindlimbs, which spread to the back and finally the whole body. You may see your cattle shivering, straining against the neck rail as they rise, or exhibiting indications of lameness even though they can still walk with total weight.

Is it now being diagnosed as Crampy? This is when things become challenging. The course of symptoms might vary greatly, making it difficult to determine the underlying reason. This cannot be diagnosed quickly or early, complicating management and therapy options.

To complicate matters further, there’s Paresis, a similar disorder to Crampy. However, Paresis usually appears in younger animals and affects just one hindlimb. You’ll notice a “pegged leg” look rather than the trembling associated with Crampy.

Understanding these distinctions allows us to understand the broad picture when both illnesses impact herds with overlapping age groups. Crampy often affects older cattle, while Paresis affects younger ones. Both illnesses provide diagnostic hurdles and need individualized treatment options.

Lactanet’s Blitz: Farmers Rally to Combat Crampy with Data 

Lactanet’s data-collecting blitz was critical in combating Crampy. This program aimed to collect thorough information on the occurrence of Crampy and Paresis in Canadian dairy herds. The blitz ran from September 2021 to April 2022, providing a limited window for gathering critical information.

During this time, dairy producers nationwide reacted enthusiastically, reporting data on 2,807 Crampy instances and 219 Paresis cases from 801 dairy herds. This excellent engagement demonstrated the dairy community’s dedication to tackling this neuromuscular condition.

The efforts of dairy producers were significant. Their willingness to offer thorough information aided the first estimate of Crampy’s prevalence and paved the way for future genetic screening methods. These activities are critical in furthering our knowledge of Crampy and finding measures to limit its effect, eventually benefiting the health and production of dairy herds throughout the country.

Digging Deep: How Detailed Data Matching and Genetic Research Could Be the Game-Changer for Crampy Control

To determine the true incidence of Crampy in the Canadian dairy sector, Lactanet methodically linked acquired data from dairy herds to herdbook-registered herd mates. This means they checked each affected cow’s information against the official records of their farm colleagues. This was critical for accurately presenting the herd’s overall health state and ensuring that the study was valid.

This extensive data was then given to the University of Guelph for further analysis. Gabriella Condello’s M.Sc. thesis focused on estimating the occurrence of cramps on Canadian dairy farms and investigating their genetics.

First, the researchers reviewed the cases to see how common Crampy was across different herds. With this baseline established, the next step was to investigate the genetic data. The idea was to see whether specific genes rendered cows more prone to Crampy. The thesis attempted to examine the possibility of gene selection as a feasible strategy for reducing Crampy’s occurrence in herds.

Age Matters: Unveiling the Alarming Spike in Severe Crampy Cases Among Younger Cattle

According to current data collecting, Crampy affects cattle of varied ages, with a maximum age of 12 years. However, most instances occur in the lower age groups, particularly between the ages of two and seven. Many cases have been detected among these cattle, with younger animals showing a specific surge in severity. Specifically, 566 severe Crampy instances were observed at younger ages, emphasizing the need for early detection and management techniques in afflicted herds.

Genetic Selection: Your Key to Combating Crampy in Dairy Herds

Extensive data analysis revealed that Crampy’s genetic component has the potential to minimize its occurrence. We reduced the overlap between Crampy and Paresis instances by concentrating on cows aged three or older with neuromuscular disease indications. This filtering yielded 1,952 Holstein cows, giving a solid dataset for further analysis.

Crampy’s average within-herd prevalence rate was determined to be 4.7%. This value changes amongst herds, indicating the role of genetics and environmental influences. Crampy has a heritability of 6.8%, highlighting the role of genetic selection in alleviating the ailment.

An essential part of this research was determining the association between sire estimated breeding values (EBVs) and the occurrence of Crampy in their daughters. Daughters of low-rated sires were shown to be 3.2 times more likely to acquire Crampy than sons of high-rated fathers. This association indicates that choosing against sires with greater Crampy frequencies may dramatically lower its prevalence, demonstrating the importance of genetic assessment and selection in long-term genetic improvement.

Why Prioritizing Genetics Could Be Your Best Move Against Crampy 

The research presents numerous essential insights for the dairy business. First, Crampy’s average within-herd incidence rate is estimated at 4.7%, implying genetic and environmental factors. Crampy’s heritability was determined to be 6.8%, showing a high potential for genetic selection. Furthermore, daughters of low-rated sires are 3.2 times more likely to develop Crampy, emphasizing the need to focus on top-ranked sires to minimize prevalence rates.

These data indicate that targeting low-rated sires might benefit genetic improvement. Furthermore, the research discovered large genomic areas related to Crampy, demonstrating that numerous genes regulate it. This opens the path for genetic selection as a powerful tool to combat Crampy.

However, more data collecting is required before a nationwide genetic assessment system can be created. Implement a nationwide plan to monitor Crampy symptoms in nursing cows throughout time. Both afflicted and unaffected cows should be genotyped to improve the accuracy of future genomic assessment systems. To fully utilize the promise of genetic and genomic technologies in the fight against Crampy, the dairy sector must engage in a cost-effective, ongoing data-gathering effort.

The Bottom Line

As the dairy sector deals with Crampy, a planned, continuing nationwide data-gathering approach centered on lactating cows during milk recording is critical. Genotyping afflicted and unaffected cows will improve genomic assessments and the precision of genetic selection. The Canadian dairy sector must develop a cost-effective method for identifying Crampy cows over time, assuring sustainability and efficacy, resulting in healthier herds and more resilient dairy operations.

Learn more: 

Do dairy breeders need to classify, milk record and register their dairy cattle?

It has been six years since genomic, genetic evaluations were introduced in North America.  Since that time, every part of the dairy improvement industry has changed. The business of artificial insemination has changed from selling predominantly proven sires and having to reward breeders for using young sires to young sire semen which now costs more than proven sire semen and accounts for more than half the semen sales.  On the one hand, there’s a growing misconception that genomics will replace traditional data recording systems, such as those offered by DHI and breed associations.  However, the reality is that, with genomics, accurate and complete performance data is required, in order to maintain the accuracy of genetic evaluations and allow a wider list of traits to be evaluated.

The question becomes where will that genetic information come from, if everyone stops classifying, registering and milk recording?

Accuracy comes from validating data with proven sires

Current genomic evaluations are more accurate than previous traditional evaluations primarily as a result of the large reference population of genotyped progeny proven sires. Without such a significant reference population, genomic evaluations would only offer small gains in accuracy compared to the significant move from 33% to 66% accuracy that a 50K genomic tested young sire currently receives.

The collection of performance data leads to a steady supply of new progeny proven bulls. Without these bulls continually expanding  the reference population, young bulls selected for A.I. would get further away  (and therefore less genetically related) from the proven sires in the reference population. Over time, this would negatively affect the accuracy of genomic evaluations, and we would actually start to see reliability figures decline.

Genomics have allowed us to make even faster genetic progress, however we still need field data for production, health, and conformation, in order to keep and even increase the reliability of the current genetic evaluation system.

Without genomics, test day records or a classification, a cow would maintain her Parent Average (PA) for all production and type traits for her entire life. She would thereby miss out on the opportunity to further enhance the accuracy of her genetic evaluations. Milk recording and classification data are added to the cow’s contribution from PA to produce an Estimated Breeding Value (EBV) that is more accurate than without it. For example, consider a first lactation cow that was genotyped as a heifer. Upon classification, the reliability of this animal’s Conformation index will increase from 68% to 75%. Once a lactation is completed, the reliability of her production index will increase from 73% to 78%. Despite the jump in reliability achieved by genotyping, the incorporation of performance data boosts the reliability, making the cow’s evaluation indexes even more accurate by approximately 10%. (Read more: Three Reasons Why Performance Data Will Always Be Important for Genetic Improvement)

Where does that data come from?

One of the more pertinent questions I hear being asked more frequently is, do we need to use official milk recording and type classification systems in order to validate this data?

With the introduction of on-farm computer systems, many breeders are not finding it necessary to use official DHIA milk recording systems.  That means instead of doing bi-weekly or monthly or sporadic tests for production, components and Somatic Cell Score, breeders who use Robots, for example, get this information with every milking.  This is a far more accurate way to measure production values. Instead of using algorithms to merely predict the in-between production data, these systems are working with the actual numbers.  In fact, these systems are such a complete herd management tool, `that they have metrics and information on many areas the current systems cannot even begin to predict. (Read more: The Future of Dairy Cattle Breeding Is in the Data, and Forget Genomics– Epigenomonics & Nutrigenomics are the future)

In speaking with many of the principal suppliers  in the robotic milking marketplace, they  often comment on that  the dairy breeding industry not only could have more accurate information, but  could also add indexes for more directly applicable evaluations such as feed efficiency.  While many organizations are trying to present algorithms to predict this measure, we could actually have performance data, which would significantly accelerate the accuracy and the rate of genetic gain in this core profitability area.

I have often heard the opposing argument from supporters of the current system. They cite that, since these numbers are not validated or conducted by a non-biased third party, how accurate can they be?  I find this argument doesn’t have any weight at all.   I have seen many hot house herds which have been able to “skew” the current numbers when they needed to. The argument that a third party verifies things means nothing.   With the fact that most new systems are computer based, there is actually the potential to implement a much more secure system for data integrity than the current process allows.  So really, the case for mandatory use of DHIA records is actually allowing far greater inaccuracy of the system, than if we accepted more modern computerized methods.

What about type classification?

The argument for the need for type classification is slightly different.  Since there is no computerized system to score a cow or to measure a cow’s conformation, there is no second data set that could be used instead of classification. Or is there?

Type classification was created in order to predict a cow’s longevity.  Isn’t that exactly what herd life and productive life measure?  Moreover, instead of being based on a prediction, they are  rooted in 100% accurate longevity data.  (Read more: Is Type Classification Still Important?,  She Ain’t Pretty – She Just Milks That Way! and Does Classifying Excellent Mean Profitable? Now? In The Future?)

Hence, the argument for the need to validate conformation data through classification is missing the boat.  Instead of trying to hold on to a system rooted in the past, we should embrace the more relevant data and information available. We should change the systems to evaluate genetic progress and merit based on actual information and should not continue to rely on a subjective system which tries to make predictions. The actual information is available.

What About Registration?

On-farm systems are such an accurate and efficient way to record breeding, calving and parenting information that the arm’s length breed association registration is duplication. Genomic testing provides 100% verification of parentage. (Read more: What is the Role of a Dairy Cattle Breed Association?)

The Bullvine Bottom Line

And so we see that the arguments supporting the need to continue type classification, milk recording and registry are becoming redundant.  Instead of trying to keep a system that validates old school genetic evaluation systems that are based on trying to use algorithms to predict genetic merit, we should be embracing the wealth of new and more accurate information that is available. We should be creating a new system that is based on measurable profitability and herd improvement statistics. The only reason that is left for keeping these three expensive programs is because we feel a need to validate an old animal model.  Instead, we should be creating a new animal model. One that accurately reflects the way modern dairy farmers operate.


The Dairy Breeders No BS Guide to Genomics

 

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Data Systems in the Future – Are We Ready?

Three times in the past two weeks serious dedicated dairy cattle breeders have asked the Bullvine questions that we too have been wondering about.

QUESTION 1: Why do we accept breeders collecting DNA samples but not owner recorded milk weights?

QUESTION 2: Why can’t milk weights from robotic systems be considered for publication purposes?

QUESTION 3: Why don’t milk recording programs take all relevant details about a cow when the milk yield data is captured?

We decided to turn those queries into a think piece so that even more breeder input can be brought into the discussion.

The Reality Is

The current data included in national data bases is based on what was the norm a couple of decades back. As well it is based on the previously accepted fact that only human eyes could determine if a recording was accurate or unbiased.

Times have changed. Today robots milk cows without human oversight. Technology is coming out every year on ways to capture more details that can help in breeding, feeding and managing dairy animals.

It is true that individual owners own their animal’s data. They paid for its capture, but only through having all the data for dairy cows in one or inter-linked data systems will breeders be able to advance their animals as quickly as possible.  No one breeder is an island onto themselves so the approach must be to use and make available all the animal data.

The reality is that it is time to put energy and resources into addressing the needs and possibilities when it comes to the data captured, stored and reported.

Capturing Cow Data

In both robotic and large herds owners do not milk the cows. The computers or cow milkers have no bias towards any one cow. Also systems are being used in some tie stall barns where the RFID tag identifies the cow and the system electronically captures the yield. In these systems the data is captured for each and every milking.

QUESTION 4: Why is that data not available for others to see?

QUESTION 5: What can be more accurate than recording every milking?

Surely we are not prepared to argue that eight to ten single milking observations in a lactation by a third party person is more accurate than every milking captured by the milking system.

Canada found twenty years ago that owner recorded milk weights and collected milk samples were accurate enough for sire proving purposes. Data that is 95% accurate is much superior to no data at all.

In the foreseeable future there will be parlour systems that can instantaneously provide readings for butterfat %, protein %, SCS, milk temperature and hormone levels and we expect in time readings for fat composition, protein composition and a host of other readings. Wow won’t that be useful information to use to breed, feed and manage?

Question 6: Will this further information be moved off the farm into the national data system?

Just last week it was reported at the Progressive Dairy Operators Conference that RFID ear tags may have use for measuring temperature and ear movement to monitor heats in tie stall barns. That is interesting.

Data Starts Early

Calves are to be identified at birth with RFID tags.

Question 7: Why is it not possible to use technology that now exists to collect a piece of the ear tissue for DNA analysis?

That way every animal would have a DNA profile at birth. With the very interesting things we are learning on DNA profiles and heifer management, we have just scratched the surface of this crystal ball.

Calves are now being fed by computers from day three or four of age. There will potentially be a very useful data set there that can be of great benefit when determining genetic merit, feeding programs and management practices.

Let’s Dream the Possible Dream

But it does not end there! Many other details and data sets exist that are not part of the national data base but that can be useful for animal traceability, food safety (mastitis and other drug treatment), foot care, reproduction, production limiting diseases (i.e. Johnes), pedometers, rumen boluses (i.e. temperature),… and the list goes on.

Question 8: Are plans being made to link all dairy cattle data bases?

But Is It Official?

In the past, if a piece of information could not be authenticated then it could not be published.  In the future, every farm using genetics to advance their animals will, out of necessity, need to capture and use more data than they have ever had to in the past. Official and unofficial applied when breeders were or were not prepared to trust the method of data capture.

In today’s world there are many systems of marketing and commerce that are monitored as necessary but without a third party observing every event. Breeders are routinely putting on Facebook events about their cows, including their milk yields, an animal’s profit per day, flushing history and ability to come into heat when milking 120 pounds per day. The world of dairy cow information is changing and changing quickly.

QUESTION 9:  What does the current “official” actually mean in the bigger future scheme of things?

The Bullvine Bottom Line

THE ALMOST FINAL ANSWER: Future data standards will need to address that more information will be needed and that data must be universally available. Breeder input is needed now to guide the development of future standards for data captured, stored and reported.

 

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