- IGS Feeder Profit Calculator
- Non-breed specific, independent
- Free to all IMI Global customers
CASTLE ROCK, Colo., July 16, 2018 (GLOBE NEWSWIRE) -- Where Food Comes From, Inc. (WFCF) (OTCQB:WFCF), the most trusted resource for independent, third-party verification of food production practices in North America, today announced that its IMI Global division has established an exclusive partnership with International Genetic Solutions (IGS) to offer the IGS Feeder Profit Calculator within its suite of verification services for beef producers.
The IGS Feeder Profit Calculator utilizes the largest and most comprehensive set of management and genetic data available in the beef industry to calculate the Relative Value of feeder calves in a one-of-a-kind, breed agnostic, independent manner. The evaluation and resulting value are based on both management and genetic criteria. The Relative Value, as indicated on a formal certificate, enables producers to benchmark their work to continuously improve management and genetic decisions in their herds. For buyers of the cattle, it provides insight into projected feedlot and carcass trait performance and overall profit potential.
“The IGS Feeder Profit Calculator is the perfect addition to our suite of value-added services for our beef producers,” said Leann Saunders, President of IMI Global, a division of Where Food Comes From. “We have been searching for this kind of solution for years, and feel that the IGS tool is far and away the most inclusive and sophisticated calculator available in the industry today. By enabling beef producers to see the value their management and genetic decisions are providing to their operation, it enables them to have a benchmark from which they can make confident, knowledgeable choices about how to continuously improve their operations. As my dad has always said, ‘If you buy unknown genetics you never know if they are going to finish like an elephant or an ant.’ Knowledge matters and the IGS Calculator provides producers with one more tool in their toolbox to make transparent, informed management decisions.”
The IGS Feeder Profit Calculator will be offered to all of IMI Global’s customers at no added cost to their existing verification programs.
“Deciding to establish a partnership with a company like IMI Global was an easy decision for us,” said Chip Kemp, Director of IGS Commercial and Industry Operations. “Even in today’s data-driven world, this level of genetic awareness in the commercial cattle sector is woefully inadequate. Price discovery as we know it today most often does not take into account the actual performance potential of a producer’s cattle. The IGS Feeder Profit Calculator is unique in that it offers a level of genetic awareness of feeder calves that have not been previously possible in the beef business. This, combined with the progressive, market-driven programs IMI Global provides, will enable their producers to market calves with the ultimate value-added package.”
To learn more, visit http://www.internationalgeneticsolutions.com/index.php/feeder-profit-calc.
About International Genetic Solutions
International Genetic Solutions (IGS) delivers objectively described, user-friendly and science-based genetic predictions to enhance the profitability of beef cattle producers. To learn more, visit http://www.internationalgeneticsolutions.com.
About Where Food Comes From, Inc.
Where Food Comes From, Inc. is America’s trusted resource for third-party verification of food production practices. The Company supports more than 15,000 farmers, ranchers, vineyards, wineries, processors, retailers, distributors, trade associations, consumer brands and restaurants with a wide variety of value-added services through its IMI Global, International Certification Services, Validus Verification Services, SureHarvest, A Bee Organic and Sterling Solutions units. In addition, the Company’s Where Food Comes From® retail and restaurant labeling program utilizes the verification of product attributes to connect consumers to the sources of the food they purchase through product labeling and web-based information sharing and education. Visit www.wherefoodcomesfrom.com for additional information.
This news release contains "forward-looking statements" within the meaning of the U.S. Private Securities Litigation Reform Act of 1995, based on current expectations, estimates, and projections that are subject to risk. Forward-looking statements are inherently uncertain, and actual events could differ materially from the Company’s predictions. Important factors that could cause actual events to vary from predictions include those discussed in our SEC filings. Specifically, statements in this news release about industry leadership and demand for, and impact and efficacy of, the Company’s products and services on the marketplace, are forward-looking statements that are subject to a variety of factors, including availability of capital, personnel, and other resources; competition; governmental regulation of the agricultural industry; the market for beef and other commodities; and other factors. Readers should not place undue reliance on these forward-looking statements. The Company assumes no obligation to update its forward-looking statements to reflect new information or developments. For a more extensive discussion of the Company’s business, please refer to the Company’s SEC filings at www.sec.gov.
Chief Executive Officer
Pfeiffer High Investor Relations, Inc.
Or go to: http://www.agnews890.com/episode/10-31-16-farm-talk-segment-5
Dr. Bob Hough, WLJ correspondent
As EPDs and other breeding tools get more complicated, some ranchers have returned to the “tradition” of just looking at animals to determine their genetic worth. DON’T. EPDs work.
Due to objective genetic predictions such as EPDs (expected progeny differences) and indexes, the cattle industry has made tremendous progress in production and efficiency. However, as the models that produce the predictions become more sophisticated and producers understand less of the mathematics behind them, some people are turning off from the technology.
This is a problem because, although calculation of modern genetic predictions has become complicated, the precision and reliability of the EPDs have likewise improved.
An EPD is defined as the difference in expected performance of future progeny of an individual, compared with expected performance at some base point for the population. EPDs are estimated from phenotypic and genomic merit of an individual and all its relatives. They are generally reported in units of measurement for the trait (e.g., lb., cm., etc.). EPDs are best used for comparing the relative genetic transmission differences to progeny between individuals.
What it boils down to is EPDs let a producer sort out genetic differences between animals, eliminating the “noise” of the environment. Some producers think they can do this better with their eyes or just a simple set of scales. This has been soundly proven wrong. The most glaring example of this occurred in Red Angus.
The breed was founded based on performance principles in 1954 with performance reporting as a requirement for registration from the very beginning. Although all Red Angus breeders had weights and measures from the beginning, the breed made no genetic progress for over 20 years. That all changed when it began converting this data into information in the form of EPDs. Since the breed started calculating EPDs, the genetic trend for traits measured has improved linearly.
Red Angus also studied the phenotypes for various traits and how they compared to the genetic predictions of the population. An example is weaning weight EPDs, which have been increasing linearly. This lines up perfectly with the breed’s adjusted weaning weights, which have improved at the same rate as the EPDs. EPDs have also allowed the breed to beat genetic antagonisms like increasing weaning weights without increasing birth weight.
Indexes are an even more powerful tool for genetic improvement. Certified Angus Beef studied when cows were flushed to either low or high $B ($Beef terminal index) bulls and all progeny were fed out and harvested. The progeny out of the high $B bulls were significantly better for all input traits into the index including weight per day of age, age at harvest, carcass weight, quality grade, and yield grade. The progeny of the high $B sires had $48.65 lower feedlot production costs and produced carcasses with $166.82 more value for a total financial benefit of $215.47.
The prediction models have also been proven to be unbiased. Cornell University did a retrospective study of the American Simmental Association’s cattle by going back and adding two years of data at a time. They then observed the differences in how cattle’s genetic predictions changed as they went from pedigree estimates through being proven sires. Animals changed up and down as the possible change chart indicated they would, as more information was added to the genetic predictions. They equally moved either up or down demonstrating no bias in the model producing the genetic predictions. If the model was biased, the predictions would tend to move in only one direction.
The basic input into genetic predictions is contemporary group deviations, and the models assume there is no environment by genotype interaction. Cornell also studied this in the Simmental population, and the assumption was validated as true.
That the models have been improving over time only makes the genetic predictions and indexes even that much more valuable.
Genetic predictions using field data were first introduced to the industry with the 1971 Simmental Sire Summary, but those early models were fraught with problems. The early models were based on sires and all dams were assumed to have equal genetic merit, which of course is not correct.
Early models also didn’t account for mating bias. The most common case of mating bias occurs when high-priced artificial insemination sires are only mated to producers’ top cows, so accounting for this bias is important. Over time, these and many more problems have been eliminated. However, with these improvements, the models have become ever more complicated and more of a challenge for the layperson to understand how they work.
This brings us to today’s modern genomic models, which are light years better than the old models, but the complicated statistics that go into the genetic predictions are admittedly hard to understand. The goal of the genetic predictions has always been to sort out what is genetic—thus will be transmitted to progeny—from what is due to environment. Marker-assisted selection is the ultimate way to determine genetic value because, by definition, genomics are not influenced by environment.
Adding genomics to traditional information that goes into genetic predictions—like contemporary group deviations, heritability, and trait correlations—all adds up to predictions that are more precise and reliable. They do a much better job of establishing genetic relationship between animals, as well as identifying markers associated with causative genes, all to improve the accuracy of genetic predictions.
The whole goal of animal breeding is to improve cattle genetically. This means different things to different people—some are looking to optimize genetics to their environments while others are looking to maximize the genetic potential for traits.
Whatever a producer’s goal, EPDs, and indexes are the best way to achieve it. Today’s prediction models do an unprecedented job of removing all the noise from EPDs and indexes, allowing producers to make the most informed genetic selection decisions possible.
It has been demonstrated time and again that visual evaluation and simple weights and measures are inferior substitutes for modern genetic prediction. Those who ignore objective genetic predictions do so at the long-term peril of their business’ ability to compete.
Performance pioneer Don Vaniman summed it up nicely in 1978 when he wrote, “Is it those who feel cattle that look good must perform, or those who accept that animals that perform look good?” — Dr. Bob Hough, WLJ correspondent
Dr. Bob Hough is the retired executive vice president of the Red Angus Association of America and a freelance writer.
International Genetic Solutions (IGS) is an unprecedented collaboration between progressive breed associations fervently committed to enhancing commercial profitability. The collaboration has yielded the world’s largest genetic evaluation of beef cattle with over 17 million animals and 120,000+ genotypes.
In keeping with our commitment to the cattle industry, IGS is pleased to announce the IGS Multi-breed Genetic Evaluation powered by BOLT. The new genetic evaluation provides more predictive EPDs, better use of genomics, more accurate accuracy reported with EPDs, all with weekly evaluations. The announcement ushers in a new era in genetic evaluation — an era made possible by a genetic evaluation system dubbed BOLT (Biometric Open Language Tools, owned by Theta Solutions, LLC).
The concept for BOLT started in 2014 as a research endeavor between the American Simmental Association and Drs. Bruce Golden and Dorian Garrick. BOLT is, quite simply, the most revolutionary and powerful genetic evaluation system in existence. Its power allows IGS to leverage genetic evaluation methodology that was once thought to be untenable on large databases — methodology that significantly improves genetic prediction.
In December 2016, IGS published a multi-breed stayability, the industry’s first EPD using BOLT and the first single-step methodology applied to a large beef cattle database. Since that time, the IGS genetic evaluation team has worked toward fully implementing BOLT with an automated system that enables weekly evaluations for an entire suite of EPDs. As of May 5th, 2018, ASA is the first of the IGS partners to publish a full suite of EPDs generated by the IGS Multi-breed Genetic Evaluation powered by BOLT. Each IGS partner has complete autonomy to determine the release date that best fits their organization. As such, the release of EPDs by the other IGS partners is likely to be staggered over the next several weeks. As always, we look forward to your questions and comments about what you see.
Here are the notable changes in the evaluation:
Movement of EPDs and reranking. EPDs and indexes will change. These changes will be more dramatic for younger, lower accuracy cattle. The IGS team has tested the changes and proven the new EPDs result in superior predictions of genetic merit.
Shrinking of EPD range. You will notice a reduction in the range of EPDs for most traits. The IGS evaluation team tested the statistical veracity of the reduction and it has proven to be in line with expectations based on the genetic variation in the population.
Improved use of genomics. With the switch to the BOLT software, IGS will use single-step genomic evaluation on all EPDs. Single-step uses DNA markers, pedigree information, and phenotypic data simultaneously in the prediction of EPDs. Previously, molecular breeding values (MBVs) were calculated from the genomic information and those MBVs were blended in a separate procedure into the EPD predictions. The single-step method squeezes more information from the DNA markers than the previous approach allowed. Additionally, with single-step, the genomic information will not only enhance each EPD for the genotyped animals but also will be used in the EPD estimates of relatives.
The table below shows how many progeny records it takes for an animal without genomics to have the same BIF accuracy as the young animal with genomics (but no progeny). In other words, EPD on a genotyped 1-month-old calf will be as accurate as an animal with birth weights on 21 calves, weaning weights on 22 calves, etc. The carcass traits represent actual carcass records, not ultrasound records. You may notice the maternal calving ease gets the least boost from genomics. This is due in part to such few females being genotyped.
It is important to note, continued collection of phenotypic records remains a vital part of genetic predictions. DNA testing will never replace the need to record and submit phenotypes.
It is well established that DNA markers vary greatly in their effect on traits — ranging from large to virtually no impact. To leverage this biological fact in a statistically advantageous manner, the BOLT single-step method only uses markers that have a meaningful impact on the traits of interest, while ignoring those that have little to no effect. Research has shown that by using this approach, BOLT reduces statistical “noise” and thereby increases the accuracy of the EPD prediction compared to other single-step methods.
More accurate accuracy. In the previous IGS evaluation platform and all others in existence other than BOLT, the calculation of the accuracy associated with each EPD is achieved through “approximation” methods. It has long been known these methods are a less than optimal approach to the calculation of accuracy — tending to overestimate accuracy. By employing unique computing strategies that leverage both software and hardware efficiencies, BOLT performs what was previously unthinkable — utilizing a sampling methodology to calculate what is essentially true accuracy. Unlike approximated accuracies, BOLT-derived accuracies will result in predicted movements associated with possible change holding true over time. This is not the case with the previous IGS software or any other system currently in existence.
While the IGS evaluation team and partners are excited to release this new chapter in genetic evaluation, the new genetic evaluation system will only realize its true potential if the selection is made using its EPD and index values. Hands down, there is no better (more accurate) way to select for quantitative traits than an EPD. Economic indexes predict net profit by weighing the EPD for economically relevant traits coupled with economic estimates. To compete with other protein sources, it is imperative that the beef industry adopts the best science and technology to make better breeding selection decisions.
Please note, each IGS breed association has the latitude to publish the BOLT generated EPDs when the timing is right for their association.
For more information about the IGS Multi-breed Genetic Evaluation powered by BOLT, go to www.internationalgeneticsolutions.com.
The new genetic evaluation, Multi-breed Genetic Evaluation powered by BOLT, offers groundbreaking advances in the prediction of EPDs for the IGS group. Here are some frequently asked questions and answers to help you better understand Multi-breed Single-step.
1. What are the key features of the Multi-breed Genetic Evaluation powered by BOLT?
• Faster and more automated system allowing for frequent genetic evaluations.
• Improved use of genomic data.
• Improved methodology for predictions of all traits.
• More accurate accuracy.
• More flexibility to add additional traits or change methods for future improvements.
2. How is ASA’s single-step approach different from blending for genomic evaluation?
The blending approach uses separate steps to calculate genomically enhanced EPDs. This approach requires two steps. The first step is to estimate the effects of DNA markers through a process called “training” or “calibration”. These effects are then used to calculate molecular breeding values (MBVs) on genotyped animals. The MBVs are then combined with traditionally calculated EPDs to enhance the accuracy of the traditionally calculated EPDs. The blending process is only performed on genotyped animals.
Befitting its name, the single-step approach calculates genomically enhanced EPDs in one step — using DNA, pedigree information, and phenotypes simultaneously. As a result, the DNA information not only improves the accuracy of prediction on genotyped animals, but also on the relatives and contemporaries of the genotyped animals. In a sense, all animals are genomically enhanced under the single-step approach.
There are also issues inherent in the blending process that are solved with single-step. Similar to the fact that only reporting phenotypes on a selected group of animals in your herd can lead to less informative (and more biased) EPDs with traditional evaluation, problems can exist with blending as it only involves genotyped animals — and genotyped animals tend to be highly selected. However, because single-step includes information from non-genotyped as well as genotyped animals, the issues are corrected.
3. How is the Multi-breed Genetic Evaluation powered by BOLT different than other single-step models used in other genetic evaluations?
It is well established that DNA markers vary greatly in their effect on traits — ranging from a large to no impact. To leverage this biological fact in a statistically advantageous manner, the BOLT single-step method only utilizes markers that have a meaningful impact on the traits of interest, while ignoring those that have little to no effect. By using this approach, BOLT reduces the statistical “noise” and thereby increases the accuracy of prediction. By circumventing the “noise,” BOLT-generated EPDs tend to be more accurate than EPDs generated by organizations that are relegated to using all markers in their single-step evaluation.
4. How many DNA markers are being used?
The Multi-breed Genetic Evaluation powered by BOLT uses a subset of weighted markers based on a research study performed by Drs. Mahdi Saatchi and Dorian Garrick, while they were scientists at Iowa State University. Drs. Saatchi and Garrick first used the 50,000 markers to determine a subset of weighted markers that are highly associated with economically relevant traits in beef cattle with consistent effects across breeds. Because the IGS evaluation is for multiple breeds, it is important to remove markers with inconsistent effects or no effects in different breeds.
The Saatchi and Garrick research also found that utilizing genotypes on animals of multiple breeds consistently increased the accuracy of prediction within a particular breed when compared to limiting DNA utilization to only animals of a particular breed.
5. Why are some traits influenced by markers and others are not?
The genetic architectures of various traits are different. Some are controlled by few genes with large effects and some are controlled by many small effects genes. In the current DNA profilers, there are some markers with high correlations with corresponding genes for some traits and low correlations with others. That’s why we see the different DNA added values for different traits. It is hard to change the genetic architecture of a trait. But, new DNA profilers or future technologies may help to improve the value of DNA information for such traits. Furthermore, some maternal traits, like Maternal Calving Ease and Milk, are difficult to predict with genomics because there are so few females genotyped. Increasing the number of cows and heifers genotyped will improve the ability to use genomics to predict maternal traits.
6. Will genomic testing replace the need to submit phenotype records?
No, reporting actual records is critical. The value of genomic predictions increases as the amount of phenotypic information increases. Furthermore, at this point, animals cannot achieve high accuracy with genomic data alone. High accuracy EPDs are only achievable by collecting many phenotypic records on offspring.
7. How do we know predictions via BOLT are better than the previous system (Cornell software)?
The IGS evaluation team has conducted a series of validations to compare the BOLT system to the Cornell system. BOLT-derived EPDs had higher correlations to birth, weaning and yearling weights (0.34, 0.29, and 0.26, respectively) than the Cornell derived EPDs (0.27, 0.19, and 0.20, respectively). Furthermore, there was a larger difference in average progeny performance (birth, weaning, and yearling) of the top 1% compared to the bottom 1% animals in the BOLT derived EPDs compared to the Cornell calculated EPDs. Both validations suggest the BOLT EPDs align better with the actual phenotypes than the Cornell EPDs.
8. Why do some animals have substantial changes in their indexes?
Though the correlations between the previous (Cornell derived) EPDs/indexes and the BOLT derived EPDs/indexes are relatively strong, there will be some animals that happen to move in a consistently favorable or unfavorable direction in a number of EPDs. Because indexes are comprised of several EPDs, even though movement in individual EPDs may be considered small, movement in the same direction across EPDs may yield sizable movements in the index value. This is particularly true for animals that have consistent movement in traits that are drivers of a particular index. Though in a large population like ours we would expect to see several animals with substantial index movement, these animals will be the exception to the rule.
9. How does BOLT improve our calculation of accuracy?
“True” accuracy can be thought of as the gold standard of accuracy. It is statistically unbiased, and therefore the ultimate measure of accuracy. True accuracy is the accuracy resulting from direct calculation. Unfortunately, even with the massively powerful computing capacity now in existence, the direct calculation of accuracy is not possible on datasets the size of ours. Because we cannot calculate accuracy directly, other approaches to accuracy calculation have been developed.
In our Cornell evaluation platform and all others in existence other than BOLT, the calculation of the accuracy associated with each EPD is achieved through “approximation” methods. It has long been known these methods are a very crude approach to the calculation of accuracy — tending to overestimate accuracy.
Another approach to the calculation of accuracy is via “sampling” methodology. Sampling is shown to be a more accurate predictor of accuracy. In fact, the results of this method were reported to be virtually identical to true accuracy. Unfortunately, due to its computationally intense nature, sampling has long been thought an infeasible approach to the calculation of accuracy on large databases.
BOLT, however, has changed the landscape in this area. By employing unique computing strategies that leverage both software and hardware efficiencies, BOLT performs what was previously unthinkable — utilizing a sampling methodology to calculate what is essentially true accuracy.
Because BOLT can calculate true accuracy, we can put more confidence in our accuracy metrics. Put another way, unlike with approximation, we can count on the predicted movements associated with possible change holding true over time. This was not the case with our Cornell system nor any other system in existence.
10. Why do the carcass EPDs generally have an increase in accuracy with BOLT while this is not a case for other traits?
You will notice that while the Multi-breed Genetic Evaluation powered by BOLT will generally produce lower accuracies than the Cornell system for growth and calving ease traits, the opposite is true for carcass traits.
One reason behind the differing accuracy outcomes is several years ago ASA staff developed a way to temper inflated accuracies in the Cornell carcass evaluation. Unfortunately, this was not possible for growth traits.
Another reason is that the Cornell system only used the carcass and its corresponding ultrasound trait (e.g., marbling score and IMF) to predict carcass EPDs, while records on several additional correlated traits are leveraged with the BOLT system.
A new feature of the BOLT evaluation is a new approach to the calculation of Carcass Weight EPDs. Due to limitations, our previous Carcass Weight EPDs did not incorporate actual carcass weights. They were predicted through an index of birth, weaning, and yearling weights. Besides using prior growth records (weaning, post weaning), the new approach also includes actual carcass weights. This feature will undoubtedly lead to a more accurate prediction of carcass weight.
11. What can I do to improve the predictions on my herd?
Whole Herd Reporting — If you haven’t already, you should consider enrolling your entire herd with a breed association total herd reporting program as it offers the most complete picture of the genetics involved in your herd.
Proper contemporary groups — It is important for the genetic evaluation that you group, to the best of your ability, animals that were treated uniformly. Proper reporting of contemporary groups ensures better predictions for all.
Take data collection and reporting seriously — Phenotypes are the fuel that drives the genetic evaluation. Take pride in collecting accurate data. If possible, try to collect additional phenotypes like mature cow weight, cow body condition score, feed intake, and carcass data.
Use genomics — DNA testing adds more information to what we know about an animal. The more genotypes we collect, the better we can predict DNA-tested animals in the future. Also, the more relatives genotyped, the better we can predict their relatives in future generations. Therefore, to ensure your bloodlines are well represented in the predictions, genotype your animals.
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Seedstock and commercial producers share their firsthand experience with ASA’s new and innovative feeder calf value prediction. |
By Emme Troendle and Lilly Platts |
What is it? Historically, the primary limitation of valuing feeder calves has been accurately gauging the profit potential of the largest genetic group within the industry — the crossbred calf. International Genetic Solutions (IGS), a collaborative effort of numerous breed associations, has developed a tool to assist in determining feeder calf value, called the Feeder Profit CalculatorTM (FPC).
“The FPC offers an objective way of describing the genetic merit on a set of calves,” comments John Irvine of Irvine Ranch, owner and manager of a 225-head operation of registered Simmental located outside of Manhattan, Kansas. “To date, there has not been a more accurate way to quantify calf value. Many producers try to do the right things in terms of management such as weaning and using sound vaccination practices, to prepare calves for the challenges they will face in the feedlot. The FPC offers a common language to bridge communication between those selling and purchasing feeder cattle.”
The FPC incorporates genetic knowledge of mainstream sires, regardless of breed, preconditioning and vaccination information, and weaning management and responsible health programs to evaluate the value on a set of calves. “As this tool gains traction and becomes commonplace for cattle buyers to use, producers will be better rewarded for their efforts, in respect to their investments in better genetics as well as improved efforts in preconditioning calves to offer a better product for the next link in the industry chain,” says Mike Forman, owner and operator of Trinity Farms, a 700-head ranch of registered SimAngusTM cows in Ellensburg, WA.
The finished product is a certificate that highlights the genetic and management predictions on calves along with certain carcass and growth traits. All producer-provided information is highlighted on the official certificate, and an additional page is included, indicating all the genetic information provided. “Our hope is that with a certificate in hand, our customers who are already making investments in quality genetics, making further commitments to provide better health and management of their calves, will be rewarded,” Forman continues.
As seedstock producers, Irvine and Forman share their insight on the FPC:
Q: Why is having this information valuable to those supplying feeder calves?
Forman: FPC is a well-designed third-party validation that helps to provide structured reasoning to establish value relative to the average feeder calf price dependent upon the producer inputs in regard to genetic selection, health and management. It helps to reinforce what our position has been for years – producers have three things to sell – genetics, health and the management the cattle are under.
Irvine: The FPC provides a great metric to gauge value for producers that have routinely invested in quality genetics as well as practice good management. Additionally, by scoring their calves, FPC provides the cow-calf producer a benchmark to make measurable progress moving forward, whether it be on the management or genetics side.
Q: What would you say to someone hesitant about using the Feeder Profit Calculator?
by Chip Kemp
In the March Issue of SimTalk, we introduced the revolutionary IGS Feeder Profit Calculator™ and its role in providing true awareness of feeder calf profit potential. In this article, we are going to walk through the simple and straightforward process of getting an IGS Feeder Profit Calculator certificate generated on a specific set of calves.
The first step is to get to the IGS website. You can either use the IGS link at the top of Simmental.org or you can go directly to InternationalGeneticSolutions.com. The IGS Feeder Profit Calculator link can be found in the upper right-hand corner.
The second step will take you to the input form. Complete the form and submit that information for certification. You will provide contact and location information, weaning and herd health specifics, marketing weights and timelines, and of course registration numbers on sires.
It is possible that staff will reach out seeking additional information, but roughly three business days following your submission you will receive an email providing you a digital copy of your IGS Feeder Profit Calculator certificate.
Now it is time to interpret the information on your certificate. On the left side of the certificate will be all information provided by the producer. This gives confidence and knowledge to a potential buyer, knowing you are hanging your credibility on the details you provided. The buyer is able to quickly gauge your management and health practices that built value into this set of calves.
The lower right-hand-side of the certificate focuses in on five categories that are crucial to feedlot and carcass success. The star metrics reflect the ranking of your calf genetics versus the IGS database.
The upper right portion of the certificate is the true foundation and core of the IGS Feeder Profit Calculator. Using the largest genetic database in the industry and some of the elite minds in the business we have leveraged known genetics, herd health, current economic conditions, and basic accounting principles to provide the most robust indicator of feedlot profit potential to date. It breaks it down to a language we all understand — dollars and cents. Frankly, feedlot buyers want to know if a set of calves has a reasonable chance to turn a profit.
Three measures are highlighted on the certificate:
Relative Genetic Value: Predicted difference in value due to genetics between the calves being evaluated and the average Angus calves of the same sex, starting weight and management conditions.
Relative Management Value: Predicted difference in value due to management between the calves being evaluated and those same calves under the assumption of an industry average of 60% of calves being vaccinated against BRD and 60% of calves being weaned for 30 days or more.
Total Relative Value: A combination of Relative Genetic Value and Relative Management Value.
When evaluating each of the relative value categories it is important to be aware that the average in each category is zero. A $0.00/cwt means these calves reflect the breakeven potential of the average calf. There is no artificial adjustment to the base just for marketing advantage or to provide a feel-good effect. You expect the truth and the facts. So do your customers and your buyers.
On the example certificate provided, we predict a breakeven price (at the time they are sold as feeder calves) based on their predicted feedlot performance of an additional $9.84/cwt. In laymen’s terms, that means the buyer at your local auction market or through your online platform could afford to pay an extra $9.84/cwt over the average animal on that day and still come out breaking even. To be clear, the buyer isn’t looking at these calves to break even. Like you, the buyer has an eye on profit. But in this example, the buyer has true awareness, through IGS, that leads him to believe these calves are a safer bet. So is he looking to pay an additional $9.84/cwt? No. Is he willing to give $2, $3 or $4 more on a safe bet rather than risking everything? We think he is.
Additionally, a second page highlighting all registration numbers and known genetics accompanies each certificate.
It is really that simple. And you won’t pay a thing. Roughly 20 minutes of work will provide you with the most credible and trusted information available on the potential feedlot performance of your calves. Trust is the Gold Standard.
Your success is wrapped up in the value of each year’s calf crop. You’ve invested years, significant dollars, and countless hours of sweat to get the calves to this point. Why leave calf knowledge to chance? You can either Know or Guess. Choose Know.
DNA profiles provide additional information about the genetic merit of a DNA tested animal and increase the accuracy of EPDs, which are called Genomic Enhanced EPD or GE-EPDs. In the IGS Single-step process, the DNA marker genotypes are directly incorporated into the genetic evaluation along with the phenotypes (performance data) and the pedigree. As a result, the DNA information has an impact not only on the genotyped individual but also on all the relatives of that genotyped individual. This allows for the DNA information to improve the accuracy of non-genotyped relatives. To measure the impact of DNA information on accuracies of GE-EPDs in the IGS Singlestep genetic evaluations, we compared the average BIF accuracies of GE-EPDs of DNA tested young animals (born in 2016 with no progeny) to the average BIF accuracies of nongenotyped sires born in 2010-2014. Only sires with non-genotyped calves were used for this comparison. We found that the average BIF accuracy of GE-EPD for a DNA tested young animal is equivalent to the average BIF accuracy of a non-genotyped sire with 21, 22 and 24 calves with observed phenotypes for birth, weaning and yearling weights, respectively (Figure 1, where a horizontal line cross a curve for a specific trait (e.g. red line and blue curve cross each other at the data point correspond to y(accuracy)=0.46) and x(progeny)=21 for birth weight)). The progeny equivalent (PE) for direct calving ease was 15 and it was only 3 for total maternal calving ease due to limited genotypes on cows. The PE for milk and stayability were 18 and 25, respectively (Figure 1).
The Multi-breed Evaluation powered by BOLT is a breakthrough in GE-EPD accuracy improvement. Enabling technologies such as BOLT software will allow for even faster genetic progress with more accurate EPDs earlier in an animal’s life. We (IGS) are dedicated to using the best available technology to deliver more accurate GE-EPDs to our members so they have the best tools available for their selection decisions.
By Mahdi Saatchi, Rohan L. Fernando, Lauren Hyde, Jackie Atkins, Steve McGuire, Wade Shafer, Matt L. Spangler, and Bruce Golden, IGS Genetic Evaluation Team, and Consultants.
The ASA and International Genetic Solution (IGS) partners invested in a new and improved genetic evaluation software called BOLT to replace the Cornell EPD evaluation system. Among other benefits, this enables the use of Single-step methods for incorporating genomic information into the National Cattle Evaluation instead of the blending approach. In the Single-step process, the DNA marker genotypes are directly incorporated into the genetic evaluation along with the phenotypes (performance data) and the pedigree. As a result, the genomic data has an impact not only on the genotyped individual but also on all the relatives of that genotyped individual. This allows for the genomic information to improve the accuracy of non-genotyped relatives.
The IGS Multi-breed Single-step powered by BOLT squeezes more information from the DNA markers by allowing for certain DNA markers to have a larger influence on predicting the genetic merit of an animal than other DNA markers while some DNA markers to have no effects on trait(s) of interest (for progeny equivalents of select traits, see page 46). This model is closer to what we expect based on biology where some parts of an animal’s genome (or genes) play more important roles than other parts of its genome (or genes). This is unique to the IGS Single-step method compared to other organizations where the DNA marker information is used to adjust relationships among the individuals.
Many ASA members and IGS partners wonder if the BOLT EPDs are more accurate than the Cornell derived EPDs in the real world? To answer this question, we performed a validation study where we ran a data set (pedigree, performance, genomics) through both genetic evaluation software (BOLT and Cornell) to compare the accuracies of the EPDs produced. To enable a fair comparison, we removed the performance records of animals born in 2015 and later from the evaluation in both systems to be used as progeny performance records for validation purposes. Table 1 shows the correlations between EPDs and progeny performance of non-genotyped sires evaluated in both systems that have progeny born in 2015 or later with recorded birth, weaning, and yearling weights. As shown, the BOLT EPDs are more accurate than Cornell EPDs as the correlations are higher for BOLT EPDs with sires’ progeny performances.
Table 1- The correlations between BOLT vs. Cornell EPDs with progeny performance of non-genotyped sires for birth, weaning and yearling weights.
|Trait 1||N of Sires||BOLT||Cornell|
To have a better sense of improvement in accuracies, we ranked sires based on either BOLT or Cornell EPDs for birth, weaning and yearling weights. Then, we compared the progeny performance of the top 1% vs bottom 1% ranked sires for each trait in each evaluation system. The results are shown in Table 2.
Table 2 – The average progeny performance of non-genotyped sires ranked based on either BOLT or Cornell EPDs
|Cornell||BOLT vs Cornell|
|Trait||N of sires||Top 1%||
|Difference||Top 1%||Bottom 1%||Difference||Top 1%|
As you can see, the BOLT EPDs ranked sires more accurately than EPDs from the Cornell software, where progenies of top 1% ranked sires based on the BOLT EPDs are +3.1, +16.8 and +40.2 lb heavier at birth, weaning and yearling. These results are exciting and show that our investment in new technology will lead to more accurate EPDs.