Once again, Genosource Captain has demonstrated his supremacy, boosting his GTPI level to an impressive +3331 GTPI, and maintaining his position as the breed’s #1 daughter-proven GTPI bull. Right behind him, albeit with a gap of 110 points, is RMD-Dotterer SSI Gameday, coming in at +3221 GTPI. Completing the top three, we have Plain-Knoll Renegad Trooper, holding steady at +3201 GTPI.
Siemers Renegad Parfect also performed well, jumping to the 8th spot. He added 1,514 daughters to his index, bringing his total to +3121 GTPI, an increase of +77 GTPI. Coupled with a PTAT of +2.40, Parfect is now the #1 PTAT sire in this ranking within the top 100 International GTPI bulls.
On the genomic sire lists, Ocd Thorson Ripcord emerged as the leading GTPI sire over 12 months with a robust +3416 GTPI, paired with +1509 NM$. Following close behind, Progenesis Watchman holds the second spot at +3408 GTPI, and S-S-I Sheepster Mican rounds out the top three at +3401 GTPI.
As you dive into the new genetic evaluations, it’s essential to understand how the implementation of 305-AA has influenced PTAs.
For Holsteins, there’s good news! An increase in Predicted Transmitting Ability (PTA) for Milk, Fat, and Protein results in a slight upward trend, adding about +10 to +15 NM$, depending on the bull group (genomic or proven).
Jerseys, however, have experienced a notable decline. Their PTAs for Milk, Fat, and Protein have dropped significantly (around -100, -6, and -6 pounds, respectively). This decrease translates into a reduction in NM$, averaging between -70 and -50 NM$.
Brown Swiss, Guernsey, and Ayrshire bulls, on the other hand, have remained relatively stable, with only minor fluctuations around zero.
The introduction of 305-AA (Average Age) stands out as the most significant change in the August 2024 evaluations. This new standardization for yield records has moved from the 305-ME mature equivalent to a 36-month average age. Age, parity, and season adjustment factors have been updated. Season adjustments are now based on five U.S. climate regions rather than the previous three, providing a more accurate reflection of environmental differences. Importantly, these new factors are breed-specific, meaning each breed has experienced different impacts from this change.
In a sensational turn of events, S-S-I Zoar Cassiopeia has soared to the top of the Canadian Genomics LPI index with an impressive +4050 gLPI. Hot on his heels, we find Claynook Zeus boasting a solid +4016 gLPI. Completing this elite tier is Kenyon-Hill Ltchwrth Oli, recording a notable +4000 gLPI.
In the Daughter Proven Conformation ranking, we’ve got a tie at the summit: both Hyden Limited P and Black Silver Crushabull Stan clinch the top spot with an outstanding +16 Conformation. Close behind, Blondin Legend and Golden-Oaks Master share the second spot, each with a commendable +15 Conformation. Wilt Enzo, one of Canada’s premier daughters, has proven Conformation sires, maintaining a strong +13 Conformation.
Leading the rankings is Aot Hampshire with an impressive +3.14 TM. Hot on its heels is Clwch Rhapsody at +3.02 TM, and rounding out the top three is Stantons Right Stuff PP with +3.00 TM. In the gPLI Genomic bulls category, Denovo 20723 Columbia stands tall at +938 gPLI. T-Spruce Harmony claims the second spot with +934 gPLI, followed closely by Denovo 20771 Segment in third.
Real Syn, a Rover son, is leading the B&W RZG Interbull Genomic ranking for the third time, with an impressive +166 RZG. Right behind, we have the Arizona brothers—Alaska at +163 RZG and Argentum at +161 RZG. Over in the R&W Interbull Genomic ranking, Simply Red takes the top spot at +159 RZG. He is followed closely by Malaga Red, a Mask Red son, with +158 RZG. Party P, Skill Red, and Redwood are sharing the third spot, all at +157 RZG.
The much-anticipated Swiss numbers have just been released, sparking excitement. Leading the Swiss index, we find a Blakely son, Swissgen Enrico, sharing the top spot with TGD-Holstein Beautyman at an impressive +1651 ISET. Monteverdi’s son, OCD Milan, is completing the podium at +1642 ISET.
Turning our attention to the Interbull daughter-proven index, S-S-I Hodedoe Montley retains the lead for the third consecutive time with a score of +1573 ISET. Close on his heels is Sandy-Valley Profile in second place with +1570 ISET, and rounding out the top three is Wilra S-S-I Rivet Genuine at +1556 ISET. These figures are not just numbers; they represent the pinnacle of dairy genetics today.
The latest indexes from Italy have just been released, and it’s time to celebrate! Gladius’ son, Ecbert, has solidified his reputation by increasing his gift to an impressive +5155, up by 32 points. Following closely in the second spot is Isolabella Baltimore, a Royalflush son who achieved a gPFT of +5149. The top three are WEH Alcione, boasting a gPFT of +5138.
On the daughter-proven ranking front, Crisalis takes the lead with a gPFT of +4719. Not far behind is Yoox, who topped the April ’24 index run with a gPFT of +4701, now holding the second position. Completing the top three is Isolabella Inseme Distefano, with a gift of +4637. Tirsvad Hotspot Geyser P, a Hotspot P son, claims the fourth spot at +4623 gPFT. Finally, Wilder Holocron sits comfortably at fifth with a gPFT of +4613.
The July genomic run generated 17 new Holstein females over 3229 GTPI and 1299 Net Merit. Three new ones are over 3.00 on udder composite and plus for fertility index.
Explore the evolution of selection indices in dairy farming, their impact on genetic diversity, and how they cater to unique farming needs. Are there too many indices? Find out here.
If you’ve ever wondered how selection indices evolved over time, then you’re in for a treat. Once regarded as just another ticker tape in the realms of dairy farming, selection indices have morphed into a more nuanced system underpinned by advances in data analytics. It’s a constellation of traits, each bearing its own weight, culminating in a nuanced system we see today. But before we delve deeper, let’s start at the beginning.
“The selection index journey began with the USDA’s Predicted Difference Dollars index which was based only on milk and fat production. The shift over time has been influenced by emerging knowledge on the biology of the cow, innovation in data collection and the ever-evolving dairy economics.”
Now, we see indices that bear the weight of multiple physical and economic traits. Intriguingly, with every leap in scientific understanding and data analytics, the focus expanded from just production to fitness and conformation traits as well. Ready to know how it evolved and transformed over the years? Let’s dive in!
What Traits are in the Index?
So, what really goes into these selection indices? Let’s lay it all out. You’ll notice that over time, the level of emphasis on each trait has seen dynamic shifts with each index revision. What’s interesting is, there’s been a notable quickening in the rate of new traits making their way into the index. This can be attributed to shifts in dairy economics, a deeper grasp of bovine biology, and the enhanced ease of data gathering and transmission.
Travel back in time to 1971 when the USDA released the Predicted Difference Dollars index. This was the first of its kind and primarily revolved around milk and fat production (Norman and Dickinson, 1971). While other traits were considered economically significant back then, milk and fat were the stars of the show due to the ample phenotypic data available.
Fast forward to 1976, and things begin to spice up. Protein yield was incorporated into the Predicted Difference Dollars index, birthing the Milk-Fat-Protein Dollars index (Norman et al., 1979). Later on, in 1984, an exciting new index was introduced focusing on cheese yield (Norman, 1986).
It all fundamentally changed in 1994 when productive life and Somatic Cell Score (SCS) found a place alongside yield traits, marking the first rendition of the Lifetime Net Merit index (VanRaden and Wiggans, 1995). Here’s where it gets ultra-interesting. The amalgamation of fitness, conformation, and production traits made NM$ stand out from its contemporaries.
Meanwhile, Scandinavian countries had already begun recorded health and fertility data in the 1960s, computing genetic evaluations for these traits in the 1970s (Philipsson and Lindhé, 2003). They discovered that selection objectives encompassing traits with low heritabilities could lead to significant improvements in cow health and fertility. Leitch (1994) examined 19 modern selection indices and found that merely two – Danish S-Index and US NM$ included mastitis resistance, and only one incorporated fertility (Danish S-Index) and productive life (US NM$).
In a revealing review based on an independent survey, Philipsson et al., 1994 identified several other countries’ indices (Finland, Norway, Slovenia, and Sweden) that included fitness traits as well. This trend took off, and a decade later, each of the 17 indices reviewed encompassed at least one fitness trait (Miglior et al., 2005).
Today, indices are being progressively packed with more fitness traits (Cole and VanRaden, 2018), to the point that it’s considered unusual if an index fails to include such traits. With this shift in focus and the continued development of these indices, one thing is clear – the understanding and evaluation of overall dairy cow merit is moving towards a more holistic paradigm.
There is No Universal Standard
While it may be tempting to define a single, universal total merit index, the reality is that this is not attainable. The reason being is that every farmer operates in a unique economic and environmental context from their neighboring farms. This concept was first proposed by Gjedrem in 1972, who theorized that every farm should actually be using its own customized selection index that is tailored to its specific financial situation and business objectives.
In practice, farms with overlapping operating and financial characteristics can potentially use the same index with minimal efficiency loss. However, there are challenges in assigning direct economic values to some traits, most notably conformation traits. Breeders’ goals vary considerably which directly impacts their breeding programs. For instance, a commercial dairy that primarily earns its income from the sale of milk solids will have differing income streams and expenses compared to a seedstock breeder who also sells embryos and premium germplasm. Hence, using different indices may be beneficial.
Specifically, the Lifetime Net Merit (LNM) was explicitly developed for use by commercial dairy farmers (VanRaden, 2004), whereas the Holstein Association USA’s Total Performance Index is aimed at registered cattle breeders who often sell both genetics and milk. The need for more than one index stems from the fact that farmers sell their products to varying markets (VanRaden, 2000), and they have personal preferences (Martin-Collado et al., 2015), as well as different strategies for maximizing profits (Berry et al., 2019).
Recognizing these variations, the CDCB currently publishes four separate indices (Lifetime Net Merit, Fluid Merit, Cheese Merit, and Grazing Merit) to offer farmers options that best suit their needs. This approach of providing multiple indices to farmers isn’t unique to the United States. For instance, when the Australian Dairy Herd Improvement Scheme (now DataGene) revised the Australian Profit Ranking index in 2016, it was replaced with three new indices – Balanced Performance Index, Health Weighted Index, and Type Weighted Index (Byrne et al., 2016). These indices offer their farmers the ability to focus on trait groups that align with their priorities and are sound from a technical standpoint.
Are There Too Many Indices Already?
The recent years have witnessed the emergence of numerous new selection indices that are being aggressively marketed to commercial dairy farmers. This is different from the norm observed with the Net Merit Dollars (NM$) and indices released by noteworthy organizations such as the Purebred Dairy Cattle Association (PDCA). Many of these novel indices are being promoted by breeding companies as a strategy to differentiate their products.
Table 2 provides a list of a few selection indices currently available to American dairy farmers. However, this list is not exhaustive, as some organizations prefer to keep their indices confidential. These indices have been developed by different agencies such as the United States Department of Agriculture (USDA), PDCA – particularly by the American Jersey Cattle Association, and commercial establishments like Zoetis.
Despite the variations, there’s a remarkable similarity among most indices, with a bidirectional focus on productivity (a key source of income for most farms) and fitness traits (often directly linked to costs). However, making direct comparisons is a challenge due to availability restrictions, as some indices are only accessible for bulls promoted by the index publisher.
Differences between indices are typically attributed to the inclusion of unique sets of traits, or the differential priority given to these traits in the index. Some companies even opt for proprietary evaluations to differentiate their offerings from their competitors. Correlations among these indices are generally very strong, resulting in minimal reranking of bulls when switching from one index to another. Nevertheless, many farmers may struggle to clearly describe the differences between each index, thereby creating room for confusion. Furthermore, there are concerns that marketers might exaggerate the significance of differences between the indices. Table 2: Some selection indices currently offered to US dairy farmers
Note: The correspondence between the indices is often quite remarkable, revealed by the work of T. J. Lawlor Jr., from the Holstein Association USA (personal communication), and this minimal reranking of bulls when transitioning from one index to another.
1 Due to rounding, columns will sometimes sum to a value slightly smaller or larger than 100. BS PPR = Brown Swiss Progressive Performance Ranking (Brown Swiss Association, 2017); AY CPI = Cow Performance Index (U.S. Ayrshire Breeders’ Association, 2020); GU PTI = Performance and Type Index (American Guernsey Association, 2020); JE JPI = Jersey Performance Index (Tauchen, 2020); HO ICC$ = Ideal Commercial Cows for Holsteins (Genex, 2020a, Genex, 2020b); JE ICC$ = Ideal Commercial Cows for Jerseys (Genex, 2020a,b); HO TPI = Total Performance Index (Holstein Association USA, 2020); USDA NM$ = Net Merit Dollars (VanRaden et al., 2018).2 PL = productive life; UC = udder composite (varies by breed and index); FLC = feet and legs composite; BWC = body weight composite; DPR = daughter pregnancy rate; SCE = sire (direct) calving ease; DCE = daughter (maternal) calving ease; CA$ = calving ability dollars; HCR = heifer conception rate; CCR = cow conception rate; LIV = cow livability; HLTH = health traits (varies by breed and index); MO = mobility (Brown Swiss); TYPE = type (conformation) composite (varies by breed); UDEP = udder depth; STR = strength; STAT = stature; DENS = milk density; FEED = feed intake/feed cost (varies by breed and index); SSB = sire (direct) stillbirth; DSB = daughter (maternal) stillbirth; POLL = polled status; HAPL = haplotypes affecting fertility; LOCO = locomotion; HOOF = hoof health; MAST = clinical mastitis; SPD = milking speed; TEMP = milking temperament; CALF = calf survivability; EFC = early first calving (age at first calving).
Are Selection Indices Responsible for Reducing Diversity in Some Breeds?
At first glance, you’d be forgiven for suggesting that the continued decrease in genetic diversity, particularly in US Holsteins (e.g., Maltecca et al., 2020) could be attributed to breeders doggedly pursuing high-index animals. However, the reality is not so straightforward. The escalation in inbreeding rates are, in fact, more likely sparked by improvements in selection intensity, largely thanks to advances in genomic technology (García-Ruiz et al., 2016).
The rapid cycling of generations, paired with significant gains achieved in each, has led seedstock producers to place heavy focus on the lines that have consistently produced successful bull families. With limited resources available for the identification of elite animals, the threat of losing market share to rivals is a far more potent concern now than in the days of traditional progeny testing programs. This is because of the rapidly accruing genetic gains. As such, the expected decline in the rate of inbreeding under genomic selection, as anticipated by Daetwyler et al., 2007, has not come to pass. Simply put, no major AI company is prepared to risk sourcing largely from outcross families.
Should there be a market for outcross bulls, the collected phenotypes would primarily come from daughters of popular families, leading to a drop in prediction accuracies for the outcross animals. However, looking at the long-term picture, the benefits of diversifying the genetic base could well justify a bit of short-term inaccuracy. You could compare this situation to the balancing act in optimal contribution theory, where alterations in inbreeding are offset by rates of genetic improvement (Clark et al., 2013).
It’s also feasible that the surging number of indices could encourage the emerging development of more distinct Holstein strains. This would increase inbreeding within individual strains but would enhance diversity overall when the strains are crossed. Strategies like this echo those proposed for nucleus herd programs (e.g., Meuwissen, 1998), common features in the swine and poultry sectors. In fact, certain breeding companies offer mating schemes predicated on assigning young sires to genetic lines within the breed (e.g., Select Sires Inc, 2020). The specifics of how bulls are assigned to lines, however, remain undisclosed.
The Bottom Line
In the final analysis, we’ve seen how selection indices have evolved over time, progressively expanding to encompass a wider array of traits reflecting economic, health, and fitness factors. This holistically reflects the varying needs and goals of individual farmers, set in their unique farm environments and economic situations. While there’s been an exponential increase in selection indices, each serves a distinct purpose and is aimed at providing the best possible outcomes for different farming models. Even though multiple indices could induce a level of complexity and confusion among farmers, their fundamental similarity lies in striking a balance between productivity and fitness traits.
One common criticism regarding indices, increased inbreeding resulting in reduced genetic diversity, is not solely tied to the selection indices. Advanced or genomic technologies have accelerated this trend more than the indices themselves. It is paramount that the value of broadening the genetic base is considered, possibly at the expense of some short-term gains. The potential for diversity may also lie in the development of various strains within breeds due to the multiple indices available.
The selection index is a potent tool that empowers farmers to make informed decisions that align with their individual operational context, ultimately working towards the shared goal of maximizing productivity and profitability. Considering the nuances of the indices and striving towards an understanding that serves their unique needs can make a crucial difference to their farming success. As developments and research continue within this field, the hope is for the creation and application of even more comprehensive and farmer-oriented indices in the future.
Summary: Selection indices have evolved over time, starting with the USDA’s Predicted Difference Dollars index in 1971. They have expanded to include physical and economic traits, fitness, and conformation traits. The Lifetime Net Merit index was introduced in 1994, which combined fitness, conformation, and production traits. Scandinavian countries discovered that selection objectives encompassing traits with low heritabilities could lead to significant improvements in cow health and fertility. In 1994, several other countries’ indices (Finland, Norway, Slovenia, and Sweden) also included fitness traits, leading to the development of the Lifetime Net Merit index. Today, indices are increasingly packed with more fitness traits, making it unusual if an index fails to include such traits. This shift in focus and continued development of these indices make the understanding and evaluation of overall dairy cow merit moving towards a more holistic paradigm. There is no universal total merit index, as every farmer operates in a unique economic and environmental context. The concept of using a customized selection index tailored to its specific financial situation and business objectives was first proposed by Gjedrem in 1972. The CDCB currently publishes four separate indices (Lifetime Net Merit, Fluid Merit, Cheese Merit, and Grazing Merit) to offer farmers options that best suit their needs. The emergence of numerous new selection indices has led to an aggressive marketing strategy for commercial dairy farmers, different from the norm observed with Net Merit Dollars (NM$) and indices released by organizations like the Purebred Dairy Cattle Association (PDCA).
Discover how genetic selection in dairy cattle can revolutionize farming and combat climate change by significantly reducing methane emissions. Will you join the change?
It’s undeniable; the dairy industry is under immense pressure to reduce its environmental impact. One of most the significant culprits? Methane emissions. This potent greenhouse gas is drawing increasing attention as we grapple with the realities of climate change. Amidst growing calls for sustainable development, innovative strategies are stepping into the spotlight. One such strategy is genetic selection in dairy cattle, an unconventional yet promising approach. In this article, we will explore how this technique can help curtail methane outputs from dairy cattle and introduce more sustainable farming practices.
Climate change, sparked by an upsurge in greenhouse gases (GHGs) in our atmosphere, has become a paramount global concern. Why has one specific GHG – methane (CH4) – garnered attention more than others? And how can genetic strategies in our cattle help mitigate these emissions? Stick around, as we delve into these pressing questions and more.
Understanding Methane Emissions in Dairy Farming
Imagine if you could reduce the amount of methane released by cows simply by choosing the right genetics. Here’s how it works: Dairy cows, like all ruminants, naturally produce methane as they digest food. This methane production is a byproduct of enteric fermentation, a fascinating biological process that involves the fermentation of plant material by a rich community of microbes inside the animal’s stomach. Now, methane, as you may know, is a mighty force in terms of its greenhouse gas potency. It’s over 25 times more potent than carbon dioxide! That’s a significant blow our environment takes every time a cow belches, which it does quite frequently.
The dairy sector worldwide is, unsurprisingly, under close scrutiny to reduce its methane contributions for the betterment of our environment. The good news is that solutions are being sought diligently in the realm of science and technology. One of these innovative strategies is genetic selection in cattle, which showcases promising possibilities. Hang in there, and we’ll dive into how exactly genetic selection can curb methane emissions from our lovely dairy cows, paving the way for more environmentally friendly dairy farming practices.
Intriguingly, methane production varies among individual cows. An average Holstein cow, one of the popular dairy breeds, can release almost 500 grams of methane daily, which is roughly 397 lbs annually. But get ready for an interesting twist in our methane saga: some cows produce 30% more than the average, while others release 30% less than the average. You’re probably confused. Here’s what it means: two cows in the same herd could be releasing vastly different amounts of methane – we’re talking differences of around 238 lbs annually! But here’s the silver lining – such genetic variations among cows make genetic selection a potent tool to reduce methane emissions. After all, if there’s a heritable attribute that influences how much methane a cow releases, it makes perfect sense to choose the cows with the most favorable genetics for breeding purposes, doesn’t it?
The Role of Genetic Selection
As you explore options to curtail the issue of methane emissions, you’ll find that genetic selection plays a pivotal role. This process zeroes in on those cattle that organically emit less methane, providing an environmentally-friendly solution to the issue at hand. It works by picking out individuals based on certain characteristics or genetic identifiers that are connected to reduced methane production. Intriguingly, studies demonstrate a noticeable difference in methane output between cows, implying that genetic components significantly affect this trait. Hence, an investment in genetic selection is an investment in a healthier, more sustainable future for our dairy farming industry.
Identifying Low-Methane Emitters
How do scientists go about identifying cattle that produce less methane? It’s no simple task. They resort to multiple methodologies, such as examining the microbial composition in the gut or measuring the gas directly from the air cows exhale. These intricate analysis methods aimed at identifying lower methane emitters are the first step towards making a real difference in methane emissions.
Breeding Programs
After identifying the low-methane emitters, what’s next in the playbook? Breeding them preferentially. This innovative breeding strategy steers the genetic makeup of future generations towards lower methane production, all without compromising dairy productivity. Doesn’t that make for a compelling approach?
Technological Advancements
Coming to the rescue in this challenging process, today’s advanced technological developments, like genomic sequencing and cutting-edge statistical models, are crucial. They assist in identifying the genetic markers linked to low methane emission. This level of precision allows the dairy industry to implement more effective and efficient selection procedures, revolutionizing their approach to methane emissions.
Using Genetics to Reduce Methane Emissions
Picture this: A cleaner, more environmentally-friendly world of dairy farming than exists today. It may sound like a far-off dream, but trust us – it’s closer to reality than you might think! A robust, lasting solution to reduce methane emissions revolves around genetically selecting cows that emit less methane (CH4). It’s crucial to mention, though, while this method has been proven effective, the high costs associated with methane measurements can make it seem daunting—resulting in few cows with substantial CH4 data. That’s where our heroes enter the picture—a group of tenacious researchers at the University of Guelph and Lactanet, working hand-in-hand with Semex, have broken down this barrier by discovering alternative ways to accurately predict the methane emissions of our bovine friends. Thanks to their ground-breaking work, we’ve unearthed a treasure trove of opportunities for efficiently managing and cutting back on greenhouse gas emissions in dairy farming. This game-changing method became possible, in part, thanks to research conducted at the University of Guelph, which determined that milk’s mid-infrared spectrometer data could serve as a reliable predictor of methane emissions. The research made innovative use of machine learning technology, a subtype of artificial intelligence (AI). Mid-infrared spectrometer data is a common resource for milk testing organizations, providing information about milk’s fat and protein percentage, along with other test results from daily milk samples. Surprisingly, this valuable data is often discarded after testing, but at Lactanet, they’ve been saving every snippet since 2018—just in case it might later prove useful for research! The endeavor to collate methane emission data from research herds was driven by two large-scale international projects and encompassed two Canadian research herds totaling 700 cows. These herds were equipped with GreenFeed machines, considered the “gold standard” for measuring methane emissions because they suction in every breath exhaled by the cows. An alternative and more economical method is using a sniffer, a device that calculates gas density and can be fitted into a milking robot. Now, with at least 30 commercial farms across Canada using sniffers, an even broader dataset is being accumulated to validate the original process. Not to be left out, data from other cattle breeds is also being gathered to extend methane efficiency proofing in the near future.
Collected Data
Figure 1. GreenFeed system used to measure gas fluxes including methane from individual animals.
You’ll be fascinated to learn that under the frameworks of the Efficient Dairy Genome Project (EDGP) and the Resilient Dairy Genome Project (RDGP), which can be accessed at http://www.resilientdairy.ca/, teams of diligent researchers are amassing a wealth of data regarding CH4 production. This data promises to serve as a valuable reference population for the calculation of genomic evaluations. In order to collect this data, the primary approach has largely centered around the greenfeed system, which cleverly gauges gas fluxes—including that of CH4—from single animals each time they utilize the feed trough component of the machine (figure 1). Despite its ingenuity, this process presents challenges in the form of great labor intensity, high costs, and limited feasibility for application on commercial dairy farms, which has thus far resulted in a relatively small sample of animals with measured CH4 emission phenotypes. Rising to the challenge, researchers from the University of Guelph have introduced a cutting-edge alternative, fueled by artificial intelligence and machine learning methodologies, designed to deliver large-scale predictions of CH4 emissions, as the ongoing collection of emission data marches forward.
Predicted Data
Researchers have discovered fascinating correlations between the composition of cow’s milk – especially fatty acids – and the animal’s methane (CH4) emissions, which are largely driven by enteric fermentation. Because of this relationship, we can leverage the milk composition data to accurately forecast a cow’s methane emissions. An innovative method employed in this process is mid-infrared (MIR) spectroscopy, which discerns a milk sample’s chemical makeup by observing how light is absorbed by the milk. Already successfully used to pinpoint specific milk constituents like fat and protein percentages, or beta-hydroxybutyrate (BHB), the technology holds immense potential for CH4 emission prediction. Each MIR examination of a milk sample generates over a thousand data points, all of which are collected and stored in the expansive Lactanet database, thanks to our milk recording services and laboratory milk sample analysis. Lactanet has used these spectral data, in combination with previously gathered methane data from research herds across Canada, to develop a sophisticated methane prediction system via machine learning. Utilizing only the first lactation data spanning from 120 to 185 days in milk, it is found that the algorithm’s predicted methane emissions demonstrate an impressive 85% genetic correlation with collected methane data, boasting a relatively high heritability of 23%. This illustrates how cutting-edge science and technology are working hand in hand to help us effectively manage our carbon footprint in dairy farming.
Methane Efficiency Evaluations
You’re probably wondering how it’s even possible to measure methane emissions on an individual cow-by-cow basis. Believe it or not, it’s not only feasible but also cost-effective, thanks to the use of milk spectral data. Lactanet has developed a method that can accurately predict CH4 emissions for a large number of cows without breaking the bank. This breakthrough has opened the door to genetic evaluations for CH4 emissions, a critical step in reducing their overall impact. Supporting Dairy Farmers of Canada’s long-held goal of attaining net-zero GHG emissions from farm-level dairy production by 2050, Lactanet, working with the University of Guelph and Semex, has launch the first-ever national genetic evaluation to decrease CH4 emissions from dairy cattle.
This game-changing initiative will take effect from April 2023, when the single-step genomic evaluation of predicted CH4 will yield Relative Breeding Values (RBV) for methane efficiency, specifically in the Holstein breed. Dairy producers, take note! This means you have the opportunity to select traits that decrease CH4 emissions, without any negative repercussions on production traits. And with the substantial reference population at our disposal, the average reliability of methane efficiency for genotyped young bulls and heifers is expected to exceed 70%.
FIGURE 1. DISTRIBUTION OF METHANE EFFICIENCY RELATIVE BREEDING VALUES (RBV) FOR OFFICIAL SIRES
In plain English, the measure of methane efficiency (ME) in Canada is expressed similarly to other traits: an average of 100 with a standard deviation of 5. Scores usually fall between 85 to 115, with cattle scoring above 100 demonstrating greater methane efficiency i.e., they produce less methane than their counterparts with scores below 100. To put this into perspective, a bull that scores one standard deviation above the mean (say, 105) should father daughters that will emit 3kg or 6.6lb less methane annually – a minor reduction that over time and generations can accumulate significantly. If a breeder consistently selects bulls with a 105 ME rating, by 2050 their herd could have 20-30% lower methane emissions than today. Methane efficiency computation utilizes a single-step evaluation model, which conveniently incorporates all pedigree, performance, and genotype data into one calculation. The aim remains steadfast—to reduce methane emissions without disturbing milk, fat, and protein yields. To that end, methane efficiency is represented in such a way that it is genetically unconnected to these yields. The reliability of this trait for young genotyped bulls and heifers remains over a reassuring 70%.
FIGURE 2. HOLSTEIN PROOF CORRELATIONS BETWEEN METHANE EFFICIENCY AND OTHER TRAITS (SHADED AREA REPRESENTS CORRELATIONS WITHIN ± 15%)
It’s worth noting that methane efficiency does not bear any significant negative correlations with other essential characteristics, such as lpi or pro$. In context, correlations oscillating between ±0.15 are usually not deemed significant. On the upside, evidence suggests some crucial, albeit minor, positive associations with metabolic disease resistance, daughter fertility, and with broader health and fertility indicators. Now, consider this: methane emissions account for an energy loss of approximately 4-7% of total intake. Therefore, energy preserved, which could have otherwise been wasted on methane emissions, seems to be funneled towards boosting health outcomes. The meticulous crafting of this trait to ensure its independence from other production characteristics offers an explanation for its minuscule correlations with yield traits. Likely, this arrangement likewise influences its negative correlation of -.14% with feed efficiency. Therefore, the genetic selection for methane efficiency appears to bring along added health benefits while leaving other crucial production traits untouched.
Allow me to paint a picture for you with some top performers, providing insight into potential superior sires spearheading methane efficiency. Topping the chart with an awe-inspiring score of 118 is the bull S-S-I Renegade Improbable, a product of the prolific collaboration between S-S-I PR Renegade-ET and S-S-I Took 7261 8495-ET at Select Sires. This table of honour comprises not only methane efficiency but also feed efficiency, placing a spotlight on the intertwined relationship between these traits. An outlier that shatters the norm while excelling in both metrics is Drumdale Allday P, boasting a methane efficiency score of 115 and a feed efficiency score of 106. Tracing his lineage reveals a rich genetic heritage marked by Cherry-Lily Zip Luster-P, View-Home Powerball-P, and tracing back to Boldi V S G Epic Allie. Talk about genetic royalty, right? Moving forward, the key to ensuring continuous breed-wide improvement for this trait lies in its inclusion in the Total Index. It’s exciting news that Canada has initiated a modernization process for the LPI (Lifetime Profit Index), transitioning this evaluation model from a 3-sub-index to a 6-sub-index system as of April 2025. The Sustainability Index, a dynamic new sub-index, is anticipated to embrace both feed efficiency and body maintenance. And you guessed it – methane efficiency will proudly occupy a spot on that inclusive sustainability roster. Genetic selection coupled with comprehensive performance assessment, as you can see, has the capacity to transform the dairy industry’s impact on the environment dramatically.
Imagine our planet enveloped in a layer of greenhouse gases much like a protective blanket; these gases stop the sun’s heat from bouncing away, which maintains Earth’s average temperature at around 14⁰C (57⁰F). Absent this natural greenhouse effect, Earth’s temperature could plummet to -18⁰C (-0.4⁰F). The density of this gas layer has remained surprisingly consistent over millennia, largely because the primary greenhouse gas—carbon dioxide—takes an astounding 1,000 years to break down. Our other significant greenhouse gases include methane and nitrous oxide; methane, although it breaks down within just a dozen years, is 27 times more effective at trapping heat than carbon dioxide, while nitrous-oxide, despite a lengthy 120-year breakdown time, is an incredible 265 times more potent.
The relative constancy of our greenhouse gas layer, however, began to change with the onset of the industrial era. That’s when we started burning vast quantities of fossil fuels and pumping massive amounts of carbon dioxide into the atmosphere. Compounding the problem, the human population ballooned from 2 billion in 1924 to 8 billion by 2024, while forest coverage tumbled from two-thirds to just one-third. Between 1970 and 2004, our total greenhouse gas emissions shot up by 70%, driving atmospheric carbon dioxide density from 410 parts per million (ppm) in 1970 to 425 ppm today.
Against this backdrop, cutting methane emissions offers an attractive, short-term opportunity for decreasing overall greenhouse gas density. Since any reduction in methane levels will manifest in a comparable decrease in total atmospheric greenhouse gases within 12 years, and considering that methane contributes to 19% of the total greenhouse gas effect (with half of that coming from ruminants), it’s clear that we need to focus on this area. Indeed, since 1984, atmospheric methane has surged from 1,650 parts per billion to 1,900 parts per billion.
Moving forward, we should also tackle nitrous-oxide emissions, largely linked to excessive nitrogen fertilizer use. The production of ammonia—the foundation of nitrogen fertilizers—consumes significant quantities of natural gas and results in three tons of carbon dioxide being released for every ton of ammonia we produce. Combined, nitrous-oxide and the carbon dioxide produced during ammonia production account for 7.5% of the total greenhouse gas effect. Key to reducing our reliance on nitrogen fertilizers will be the expanded use of legumes, the improvement and increased use of inoculants to facilitate nitrogen fixation by grass species, the inclusion of mixed forage crops and perennials, and a pivot toward cover crops and minimum-tillage methods.
The benefits of genetic selection for low methane emissions extend beyond environmental impacts:
Improved Efficiency: Cattle that produce less methane often digest food more efficiently, translating into better feed conversion ratios and potentially higher milk yields.
Economic Advantages: Lower methane emissions can also mean reduced costs associated with feed, as more energy from feed is used for growth and production rather than lost as methane.
Health and Welfare Improvements: Genetic advancements can lead to healthier cattle with better overall well-being, which is increasingly important to consumers.
The Bottom Line
In essence, the deployment of genetic selection marks a revolutionary pivot in the way the dairy sector counters its ecological hurdles. This innovative strategy of curbing methane emissions via purposeful breeding methods empowers dairy farmers to join hands in the global combat against climate change, while simultaneously beefing up the sustainability and efficacy of their individual businesses. The evolution of this domain holds immense potential in orchestrating the destiny of dairy farming, aligning it seamlessly with worldwide sustainability objectives.
Summary: The dairy industry is working to reduce its environmental impact, particularly in the area of methane emissions, which are over 25 times more potent than carbon dioxide. To mitigate these emissions, innovative strategies are being sought in science and technology, such as genetic selection in dairy cattle. Genetic selection helps reduce methane emissions by choosing cows with the most favorable genetics for breeding purposes. Advanced technological developments, such as genomic sequencing and statistical models, are crucial in identifying genetic markers linked to low methane emission. This level of precision allows the dairy industry to implement more effective and efficient selection procedures, revolutionizing their approach to methane emissions. Researchers at the University of Guelph, working with Semex and Lactanet, have discovered alternative ways to accurately predict methane emissions in dairy farming using machine learning technology. They discovered fascinating correlations between cow’s milk composition and methane emissions, driven by enteric fermentation. Mid-infrared spectroscopy is employed in this process, generating over a thousand data points for each MIR examination of a milk sample.
The most significant change for the April 2, 2024, triannual evaluations is an adjustment in the trait model for six CDCB health evaluations – Resistance to Milk Fever (MFEV), Displaced Abomasum (DA), Ketosis (KETO), Mastitis (MAST), Metritis (METR) and Retained Placenta (RETP).
Since these traits debuted six years ago, the number of health records in the National Cooperator Database has tripled or quadrupled – depending on the trait. Detail here. With this data surge, the trait model has been adjusted with new variance component estimates and adjusted weights, effective with April 2024 evaluations. This evolution follows the typical progression of newer traits.
These CDCB evaluations for disease resistance were first launched for Holstein in April 2018, Jersey in April 2020, and Brown Swiss in August 2022. Variance components were originally estimated in 2018, when Holstein records available ranged from 1.2 to 2.2 million per trait. Current volume ranges from 4.3 to 7.7 million for the three breeds, with Mastitis having the most records in CDCB’s database.
In a test run comparing the previous and updated model, correlations of genomic estimated breeding values (GEBV) for five of the traits were ≥0.96 for Holstein, ≥0.90 for Jersey and ≥0.92 for Brown Swiss. For Displaced Abomasum, lower correlations were observed (≥0.95 HO, ≥0.82 JE and ≥0.81 BS) due to the largest change in heritability.
With the model adjustment, variation in Predicted Transmitting Ability (PTA) for some individual animals, particularly Jersey and Brown Swiss, was expected. The impact on Net Merit is very small, given the weighting of these traits in the index.
Read detail on health records, model effects, new variance component estimates, adjusted weights and correlations between old and new model in the triannual change documentby CDCB and USDA AGIL.
The number of genotypes of crossbred animals is increasing in US dairy farms.
Including crossbred data in genomic evaluations is possible.
This study analyzed purebred and crossbred data together.
Single-step genomic predictions for crossbred cows were more accurate than predictions based on SNP effects and breed proportions.
The number of crossbred genotypes in the dairy cattle sector has increased, necessitating the inclusion of crossbred animals in genomic evaluations. This study aimed to investigate the feasibility of including crossbred genotypes in multibreed, single-step genomic BLUP (ssGBLUP) evaluations. The Council of Dairy Cattle Breeding provided over 47 million lactation records registered between 2000 and 2021 in purebred Holstein and Jersey and their crosses. A total of 27 million animals were included in the analysis, of which 1.4 million were genotyped. Milk, fat, and protein yields were analyzed in a 3-trait repeatability model using BLUP or ssGBLUP. The two models were validated using prediction bias and accuracy computed for genotyped cows with no records in the truncated dataset and at least one lactation in the complete dataset.
The genomic predictions of crossbred genotyped cows were slightly more accurate than purebred cows. Multistep evaluations are still the official route to obtaining genomic predictions for dairy cattle in the United States, which comprises a multibreed best linear unbiased predictor (BLUP) followed by a single-breed estimation of single nucleotide polymorphism (SNP) effects. After estimating single-breed SNP effects, direct genomic values (DGV) are computed for genotyped animals as a sum of SNP effects weighted by the genotype content. Genomic PTA are then calculated as a linear combination of DGV and parent average (PA).
However, routine genomic evaluations for dairy cattle do not consider crossbreds and are typically made separately by breed. There are several studies about genetic and genomic predictions for crossbred cattle, such as breed composition (BC) or proportion. In the United States, the number of available genotypes of crossbred cattle quickly increased to 150,000 in 2021. New concepts were proposed in the genomic era: genomic BC (Hulsegge et al., 2013) and breed base representation (BBR; VanRaden and Cooper, 2015). Both methods partition the genotype of a crossbred animal according to the proportion of the genome originating from each breed, and the genomic predictions of the purebreds are usually proportionally combined to evaluate the crossbred animals.
Computing SNP effects based on crossbred reference populations in multistep methods could help increase reliabilities, but this option becomes less straightforward when the breed proportion varies within the population and there are no clear boundaries between classes to create proper training sets. A different approach to obtaining genomic predictions for crossbred animals is to include their genotypes in the single-step GBLUP (ssGBLUP) method, which relies on the use of the inverse of a modified relationship matrix (H), combining the numerator relationship matrix (A) and the genomic relationship matrix (G).
Cesarani et al., 2022, conducted a multibreed ssGBLUP evaluation for Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey cattle. The authors found that reliabilities from the multibreed model were similar to those from single-breed models, which was surprising due to the unbalanced number of genotyped animals within each breed. However, proper modeling of genetic differences among breeds helped to avoid loss of predictive power when using only purebred animals.
As the number of genotyped crossbred animals in US dairy cattle is rapidly increasing, it would make sense to consider them in the evaluation together with their purebred ancestors. Some studies reported increased reliabilities of this approach in dairy cattle using less than 10k genotyped individuals in ssGBLUP and less than 50k in GBLUP and BayesR. This study aims to expand on the research findings of Cesarani et al., 2022, and include genotypes for crossbreds in a large-scale, joint Holstein-Jersey ssGBLUP evaluation in the United States.
Data used in the official multibreed genomic evaluations for US dairy cattle breeds were provided by the Council on Dairy Cattle Breeding. The analyses considered 305-d milk (MY), fat (FY), and protein (PY) yields for the first 5 lactations recorded from January 1, 2000, to August 2021. All data were preadjusted to have the genetic variance equal across time, breed, and herd and to have the same heritability of 0.20.
Animals were genotyped with 48 different arrays ranging from less than 3k to more than 600k SNPs. Genotypes were imputed, within each breed, to a common set of 79,294 selected SNPs using Findhap v3. Crossbreds were imputed separately, and genotypes for the purebred parents of all breeds were included to improve imputation.
Two evaluation methods were considered: (1) traditional BLUP and (2) ssGBLUP with unknown parent groups (UPG) for A and A22. A total of 16 UPG were considered and defined based on breed (HO or JE), sex, and year of birth. The algorithm for proven and young (APY) was used for ssGBLUP with 45,000 randomly selected animals as the core.
The data were analyzed with a 3-trait repeatability animal model that included herd management, age-parity, inbreeding coefficient, and heterosis as fixed effects; UPG as fixed effect; and herd-sire, animal, and permanent environment as random effects. Heterosis was calculated from the full pedigrees going back as many generations as recorded. For ssGBLUP, all the genotyped animals were used simultaneously in the construction of G, which was blended with 5% of A22 to avoid singularity and include a residual polygenic effect.
The study aimed to validate the predictive ability of a genomic model for crossbred cattle using BLUP and single-step genomic BLUP (ssGBLUP). Three sets were created: purebred Holstein (n = 688,985), purebred Jersey (n = 119,743), and CROSS animals (n = 3,235). The CROSS group only had cows because most of the crossbred animals are genotyped to accelerate commercial herd management. Two datasets were considered: complete (with phenotypes recorded from January 2000 to August 2021) and reduced (up to August 2017). Genotyped cows with phenotypes in the complete but not in the reduced dataset were included in the validation set.
Average predictive abilities across traits estimated with BLUP were 0.33, 0.30, and 0.26 for HO, JE, and CROSS groups, respectively. As expected, genomic information improved the predictability for all traits and groups. The breeding values estimated in the present paper for purebred HO and JE cows were compared with those estimated in Cesarani et al., 2022 to investigate the impact of including crossbred animals in the analysis. A total of 17.6 million and 1.7 million HO and JE animals were shared between the two analyses, and correlations between BV estimated in the two studies ranged from 0.98 (MY for JE) to 1.00. The correlation for young bulls was also larger than 0.99.
In terms of regression coefficients of YADJ on EBV from BLUP, the inclusion of crossbred phenotypes led to poorer results compared with Cesarani et al., 2022. However, values calculated for the two purebreds using ssGBLUP were almost the same with or without the crossbred data, suggesting greater stability of the genomic model.
The average predictive ability and stability computed using BLUP for crossbred animals were lower than for the two purebreds, but the predictive ability computed for MY in the CROSS group was larger than the values for HO (0.30) and JE (0.33). Under ssGBLUP, average values for predictive ability and stability were slightly higher in CROSS than in HO (0.55 and 0.95) and JE (0.50 and 0.93) cows. Predictive abilities consider adjusted phenotypes, which remove fixed effects from the phenotypes. In the present study, using genomic information within the ssGBLUP model could have partially overcome the absence of breed as a fixed effect. Assuming that accuracies are inflated for crossbreds due to incomplete accounting for BC, the inflation can be reduced by better accounting for this effect (Misztal et al., 2022).
The higher accuracies for crossbreds in MY could be explained by the larger phenotypic difference between HO and JE, reflecting a greater genetic difference between the two originating breeds. These breed differences, which can be easily predicted from the genotyped animals, can contribute to larger reliabilities in the crossbred population in a scenario where the genomic predictions of crossbred animals are weighted according to each breed’s DNA proportion (VanRaden et al., 2020).
Higher accuracy reported for crossbred animals is not uncommon in dairy cattle (Winkelman et al., 2015; Khansefid et al., 2020), and other species (Hidalgo et al., 2016). In their study, predictions for crosses were consistently more accurate than for Jersey, except for longevity. Crossbred dairy cattle had higher accuracy when their data were considered in the reference population (Khansefid et al., 2020).
In the present study, the benefits from directly including the genomic information in a single step exceeded any initial disadvantage in pedigree modeling. The average improvement with genomics varied according to the BBR of the crossbred cows: the largest increase was observed for cows with BBR between 75% and 89%. The average improvement using genomics reported by VanRaden et al., 2020, is much lower than the improvements found in the present study.
For dairy cattle, inflation values of 1 ± 0.15 are still acceptable (Tsuruta et al., 2011). According to the Interbull validation, the b1 values estimated with ssGBLUP were all within 1 ± 0.1. The average value was 1.02 ± 0.06, ranging from 0.90 to 1.09, whereas for BLUP, the EBVs were more inflated (0.81 ± 0.09) and with a more extensive range (0.72–0.91). In ssGBLUP, all validation groups showed nonbiased average predictions. The number of genotyped animals considered in the present study was very similar to VanRaden et al., 2020, but larger than other studies.
The genomic era has revolutionized the process of assigning the proportion of a crossbred individual’s genotype to the originating breeds. However, identifying a specific breed origin for each SNP can be challenging. In this study, genotypes of purebred and crossbred were considered together, and G accounted for the relationship among them. Genomic predictions of less numerous breeds and crossbred animals from ssGBLUP could be worsened if there is an imbalanced number of genotypes among breeds.
In the present study, crossbred animals represented less than 1% of the genotyped animals, and most (about 80%) were considered validation animals. However, including crossbred and purebred data in a ssGBLUP model could enhance the prediction of crossbred animals through the H matrix. The impact of including a fixed number of purebred and crossbred animals in the core for APY deserves further investigation.
The genomic setup took about 10 hours, while the EBV computation took around 4 hours. The solving process for ssGBLUP took 3 more hours, resulting in a genomic process carried out in less than one day for these three traits. Further computational improvements could be achieved by indirectly predicting young genotyped animals or using solutions from previous runs.
Crossbred data can be included in multibreed US dairy cattle single-step evaluations without reducing accuracy or increasing inflation of genomic EBV for purebred animals. This evaluation system allows similar gains in accuracy for purebreds and crossbreds, simplifying genetic evaluation pipelines and increasing computing efficiency while delivering predictions for managing commercial crossbred herds.
Genosource Captain stays at the top of the International GTPI daughterproven ranking, with +3287 GTPI (+34 for GTPI). Gameday comes in second with +3163 GTPI (+125 for GTPI), with Westcoast Lambeau rounding out the stage at +3147. Ripcord is the top GTPI sire over 12 months with NAAB-code, with +3390 GTPI and +1507 NM$. Darth Vader comes in second at +3342 GTPI, with +1482 NM$ and +2458kgM, while Genosource Bonjour rounds out the stage with +3314 GTPI. SHG Lego remains the world’s leading PTAT sire, with +4.69 PTAT, he is a Siemers Fitters Choice kid from the #1 PTAT cow (>2 years).The #1 PTAT Red Carrier bull is SHG Lazer *RC with +4.24 PTAT. The genomic Holstein and Jersey lists have seen strong new leaders, with OCD Thorson Darth Vader-ET claiming the top spot on the Holstein Net Merit (NM$), Cheese Merit (CM$), and Total Performance Index (TPI) lists. Darth Vader’s numbers are over 100 points better than December’s leader on those traits and a GTPI of 3342. In the Jersey breed, JX Peak AltaFarva {6}-ET has risen to the top of both the CM$ and JPI lists. A.I. organizations reported 6,697 bulls active to the National Association of Animal Breeders (NAAB) for this proof round, with 4,566 genomic bulls, giving young sires a 68% market share. Holstein sires totaled 5,502, and 889 Jersey bulls were included, making up 95% of all bulls reported and more than 96% of all genomic bulls reported, both consistent with the December evaluations.
Beyond HI-Power now leads the Canadian LPI index with +4002 gLPI. He is followed by Kenyon-Hill Ltchwrth Oli, who has +3958 gLPI. The stage concluded with T-Spruce Ethan, the #1 gLPI sire of the December ’23 run, with +3956 gLPI. In the top LPI Domestic daughter proven list, Genosource Captain has the highest gLPI at +3761. The Genomic sire Progenesis Aneesh is now the #1 TYPE bull, with a nog with less than +18 Conformation. Hyden Limited P is the #1 daughter confirmed TYPE bull with a +17 conformation.
Ecbert (s. Gladius) is the new leader in the Italian gPFT genomic (domestic) list, with +5123 gPFT. Alanzo’s son Al.Co.Bia Essence comes in second at +5048 gPFT, while Al.Co.Bia Soproni, a Zingler x Mojo, rounds out the top three with +5002 gPFT. Yoox leads the Italian daughter proven ranking with +4545 gPFT, followed by Aristocrat son Wilder Holocron at +4524 gPFT and Isolabella Inseme Distefano at +4501 gPFT.
Diamond Genetics bred the top three with PLI Genomics bulls for the April 24 run. DG Peace leads this list with +908 PLI (+22). He is the Captain son of Paessens Jezebel VG-86-NL, a 2-year-old cow from the Meier-Madows EL Jezebel EX-92-USA herd. He is followed by another Captain son, DG Space of the Ladys-Manor Ruby D cow family, who has +873 PLI (+13). DG Dillon, bred by Diamond Genetics and sold to Cogent, rounds out the top three with +868 PLI. Genosource Captain remains at the top of the PLI Daughter Proven list, with +874 PLI, followed by Westcoast River at +778 PLI and FB Kenobi Targaryen at +710 PLI.
The Scandinavian nations’ indices (Denmark, Sweden, and Finland) are now accessible online. There have been no changes to the top three with NTM genomics bulls this run. Mecanico remains the top NTM genomic sire, with +46 NTM, followed by VH Karat *RC at +43 NTM and Dixon at +42 NTM. VH Deco *RC, VH Fillman, Yoda, and Youngster tied for the top place in the NTM daughter-proven ranking with +28 NTM each.
We begin today with the first indices arriving from Switzerland. Blakely’s son Swissgen Enrico is the new leader on the Swiss chart, with +1667 ISET. He is followed by the #1 ISET sire of the December ’23 run, TGD-Holstein Beautyman (+1647 ISET), and Swissgen Empire (s. Blakely) (+1633 ISET). S-S-I Hodedoe Montley remains at the top of the Interbull daughter-proven index, with +1572 ISET. He is followed by Wilra SSI Rivet Genuine at +1552 ISET, while Larcrest Commitment comes in third with +1532 ISET.
All top ranks may be seen by clicking on this link.
Due to a base adjustment, the breeding values for all bulls having a gNVI breeding value have decreased by roughly 20 NVI points this run. The publishing criteria for the conformation breeding values of imported bulls have also been updated. Delta Boyan (s. Warren P *RC) is the #1 NVI B&W Genomic sire this run, with +391 gNVI, followed by Tigerwoods De La Vigne at +386 gNVI and Sitron at +379 gNVI rounding out the top three. Furthermore, we discover in this top 20 DG Dr. No @ AI-Total at +328 gNVI and +1950kgM. Delta Cream P Red is this run’s #1 NVI R&W Genomic sire, with +375 gNVI. At the fourth slot, we discover NH Skyliner-Red (s. Sputnik *RC) at +358 gNVI, +3739 kgM, and +532 INET.
There have been no changes to the top three B&W RZG Interbull Genomic rankings. The B&W RZG Interbull Genomic rating is topped by a Rover son, Real Syn, who has +166 RZG (-5 for RZG)! He is followed by Vivify at +161 RZG, who completes the stage with Rome at +160 RZG! Skill Red leads the R&W Interbull Genomic ranking with +161 RZG. CR7 P, Redford, and Handout P finished second with +158 RZG, while Koepon Redbull, Pringle-Red, and Kretos-Red finished third with +157 RZG. Genosource Captain remains the top B&W Interbull Dtr proven sire, with +153 RZG, followed by Ginetta at +150 RZG and Madboy, AltaZarek, Pursuit, and Commitment in a tie for third place at +148 RZG. Zoom Red and Freestyle-Red are the #1 R&W Interbull Dtr proven sires, with +148 RZG.
Genosource Captain stays at the top of the International GTPI daughterproven ranking, with +3287 GTPI (+34 for GTPI). Gameday comes in second with +3163 GTPI (+125 for GTPI), with Westcoast Lambeau rounding out the stage at +3147. Ripcord is the top GTPI sire over 12 months with NAAB-code, with +3390 GTPI and +1507 NM$. Darth Vader comes in second at +3342 GTPI, with +1482 NM$ and +2458kgM, while Genosource Bonjour rounds out the stage with +3314 GTPI. SHG Lego remains the world’s leading PTAT sire, with +4.69 PTAT, he is a Siemers Fitters Choice kid from the #1 PTAT cow (>2 years).The #1 PTAT Red Carrier bull is SHG Lazer *RC with +4.24 PTAT.
Beyond HI-Power now leads the Canadian LPI index with +4002 gLPI. He is followed by Kenyon-Hill Ltchwrth Oli, who has +3958 gLPI. The stage concluded with T-Spruce Ethan, the #1 gLPI sire of the December ’23 run, with +3956 gLPI. In the top LPI Domestic daughter proven list, Genosource Captain has the highest gLPI at +3761. The Genomic sire Progenesis Aneesh is now the #1 TYPE bull, with a nog with less than +18 Conformation. Hyden Limited P is the #1 daughter confirmed TYPE bull with a +17 conformation.
There have been no changes to the top three B&W RZG Interbull Genomic rankings. The B&W RZG Interbull Genomic rating is topped by a Rover son, Real Syn, who has +166 RZG (-5 for RZG)! He is followed by Vivify at +161 RZG, who completes the stage with Rome at +160 RZG! Skill Red leads the R&W Interbull Genomic ranking with +161 RZG. CR7 P, Redford, and Handout P finished second with +158 RZG, while Koepon Redbull, Pringle-Red, and Kretos-Red finished third with +157 RZG. Genosource Captain remains the top B&W Interbull Dtr proven sire, with +153 RZG, followed by Ginetta at +150 RZG and Madboy, AltaZarek, Pursuit, and Commitment in a tie for third place at +148 RZG. Zoom Red and Freestyle-Red are the #1 R&W Interbull Dtr proven sires, with +148 RZG.
Due to a base adjustment, the breeding values for all bulls having a gNVI breeding value have decreased by roughly 20 NVI points this run. The publishing criteria for the conformation breeding values of imported bulls have also been updated. Delta Boyan (s. Warren P *RC) is the #1 NVI B&W Genomic sire this run, with +391 gNVI, followed by Tigerwoods De La Vigne at +386 gNVI and Sitron at +379 gNVI rounding out the top three. Furthermore, we discover in this top 20 DG Dr. No @ AI-Total at +328 gNVI and +1950kgM. Delta Cream P Red is this run’s #1 NVI R&W Genomic sire, with +375 gNVI. At the fourth slot, we discover NH Skyliner-Red (s. Sputnik *RC) at +358 gNVI, +3739 kgM, and +532 INET.
Ecbert (s. Gladius) is the new leader in the Italian gPFT genomic (domestic) list, with +5123 gPFT. Alanzo’s son Al.Co.Bia Essence comes in second at +5048 gPFT, while Al.Co.Bia Soproni, a Zingler x Mojo, rounds out the top three with +5002 gPFT. Yoox leads the Italian daughter proven ranking with +4545 gPFT, followed by Aristocrat son Wilder Holocron at +4524 gPFT and Isolabella Inseme Distefano at +4501 gPFT.
Diamond Genetics bred the top three with PLI Genomics bulls for the April 24 run. DG Peace leads this list with +908 PLI (+22). He is the Captain son of Paessens Jezebel VG-86-NL, a 2-year-old cow from the Meier-Madows EL Jezebel EX-92-USA herd. He is followed by another Captain son, DG Space of the Ladys-Manor Ruby D cow family, who has +873 PLI (+13). DG Dillon, bred by Diamond Genetics and sold to Cogent, rounds out the top three with +868 PLI. Genosource Captain remains at the top of the PLI Daughter Proven list, with +874 PLI, followed by Westcoast River at +778 PLI and FB Kenobi Targaryen at +710 PLI.
The Scandinavian nations’ indices (Denmark, Sweden, and Finland) are now accessible online. There have been no changes to the top three with NTM genomics bulls this run. Mecanico remains the top NTM genomic sire, with +46 NTM, followed by VH Karat *RC at +43 NTM and Dixon at +42 NTM. VH Deco *RC, VH Fillman, Yoda, and Youngster tied for the top place in the NTM daughter-proven ranking with +28 NTM each.
We begin today with the first indices arriving from Switzerland. Blakely’s son Swissgen Enrico is the new leader on the Swiss chart, with +1667 ISET. He is followed by the #1 ISET sire of the December ’23 run, TGD-Holstein Beautyman (+1647 ISET), and Swissgen Empire (s. Blakely) (+1633 ISET). S-S-I Hodedoe Montley remains at the top of the Interbull daughter-proven index, with +1572 ISET. He is followed by Wilra SSI Rivet Genuine at +1552 ISET, while Larcrest Commitment comes in third with +1532 ISET.
All top ranks may be seen by clicking on this link.
Each year, the genetic base used to express genetic evaluations in Canada is updated in conjunction with the first official release.
The definition of each genetic base used is therefore as follows:
Breed(s)
Traits
Genetic Base Definition Used
All
Production
Cows born during a 3-year period centred seven years ago (2016, 2017 or 2018) that have test day records in the Canadian Test Day Model genetic evaluation analysis. The same base group is also used for Pro$.
Holstein
Conformation
Proven bulls born in the most recent complete 10-year period (2009 to 2018).
Coloured
Conformation
Proven bulls born in the most recent complete 15-year period (2004 to 2018). For Canadienne and Milking Shorthorn breeds, the base period starts with proven bulls born in 1984 and for the Guernsey breed it starts with proven bulls born in 1994.
The table below indicates the amount of base change realized in 2024 compared to 2023 for each trait and breed. For LPI, the following base adjustments reflect the change to the new scale with half the variance compared to previous years.
Base Changes for 2024 Versus 2023
AY
BS
CN
GU
HO
JE
MS
LPI1
0
0
0
0
0
0
0
Milk (kg)
90
66
48
55
105
74
24
Fat (kg)
4.3
2.9
0.6
1.1
5.6
3.5
1.8
Protein (kg)
3.8
3.1
0.7
1.8
4.7
2.9
1.0
Conformation3
0.42
0.47
0.00
0.10
0.74
0.13
0.18
Mammary System3
0.37
0.43
0.00
0.07
0.89
0.15
0.16
Feet & Legs3
0.34
0.25
0.00
0.00
0.34
0.01
0.15
Dairy Strength3
0.38
0.49
0.00
0.10
0.22
0.11
0.06
Rump3
0.24
0.22
0.00
0.08
-0.03
-0.01
0.08
Herd Life2
0.05
0.25
-0.02
0.12
0.52
0.06
0.11
Somatic Cell Score2
0.02
0.25
0.09
0.39
0.61
0.23
0.21
Daughter Fertility2
-0.18
-0.04
0.04
0.03
0.60
0.05
-0.03
1 – Base change for LPI is set to zero since it is already reflected by the change in the “Constant” included in the LPI formula. 2 – Traits expressed on scale of Relative Breeding Values (RBV). 3 – The base change for Conformation traits are based on genetic evaluations calculated using daughter classifications and not composite indexes as introduced in April 2021
With the April 2024 official genetic evaluation release just around the corner, let’s take some time to highlight the exciting new services and updates that are coming. In addition to the usual annual updates,Lactanetis finetuning the trait adjustments in the type composite indexes, publishing carrier probability values for two new haplotypes, expanding the functionality of the Inbreeding Calculator to better manage mating decisions,and offeringgenetic evaluation uploading for DairyComp users.
Pro$ Updates
Every year the Pro$ formula for the Holstein, Ayrshire, and Jersey breeds is updated with the latest economic values. As costs have continued to rise during the past year, with relatively small changes to the milk pricing levels contributing to revenues, the profit calculated for cows up to six years of age or disposal have decreased compared to the 2023 Pro$ calculation. The scale of Pro$ is maintained such that each Pro$ point is equal to an extra dollar of profit for each daughter. A decline of overall profitability therefore causes a decrease in Pro$ for top animals in the population. Together with the April 2024 base change update, the top 100 proven sires for Pro$ last December will see their Pro$ decrease an average of 645, 293, and 479 dollars for the Holstein, Ayrshire, and Jersey breeds, respectively. The Pro$ values of these top animals are lower but Pro$ continues to allow for the selection that maximizes genetic response for daughter profitability.
Traits Adjustments in the Type Composite Indexes
For the April 2024 genetic evaluation release, modifications will be made to the adjustments to Stature and Teat Length in the Mammary System composite, as well as to Stature and Rear Legs Side View in the Feet & Legs composite. The magnitude of change for the various trait adjustments for each breed are very minor.The one notable change is the adjustment to Rear Legs Side View (RLSV) in Holstein.The original Feet & Leg composite index in this breed required additional weighting on RLSV to achieve neutrality and away from selecting toward straight legs, however, today, the correlation is no longer present, and the adjustment is nearly eliminated.Therefore, Holsteins with more extreme RLSV proofs will see greater change in their Feet & Legs proofs.
Annual Updates
At the time of the April release each year, there are several updates that are automatically conducted. These include the annual updates to the genetic base used for each trait in the seven breeds and updatedparameters used in the LPI formula, in addition to the Pro$ update discussed above. Also, in April in more recent years, Lactanet has been updating the sire proof interpretation table for linear type traits for all breeds. These tables were created in December 2020 to aid in the understanding of sire proofs and their relationship with the expected average daughter linear scores.The April 2024 Interpretation tables can be found here.
Inclusion of New Haplotypes
Two new haplotypes will be added to the Lactanet website this genetic evaluation release, including Early Onset Muscle Weakness Syndrome in Holsteins (HMW) and the Brown Swiss Fertility Haplotype BH14. BH14 is a lethal haplotype that causes early pregnancy loss and was first reported by Switzerland in 2022. The CDCB began reporting BH14 haplotype results in April 2023 and Lactanet is now using the CDCB results to calculate carrier probability values for non-genotyped animals as well. For this reason, BH14 Carrier Probability values will be displayed on the Lactanet website in advance of April 2024.
Early Onset Muscle Weakness Syndrome was first discovered in Holsteins in 2022, which is characterized by calves that are unable to stand within the first six weeks of life and presents itself with varying degrees of severity. It is now recognized as a genetic condition by Holstein Canada and other national associations. Based on DNA from affected calves, a gene test was developed, which is now used by AI companies to identify bulls that are carriers or free of the undesired gene. Lactanet has received over 14,000 gene test results for Muscle Weakness, and will continue to do so on an ongoing basis, which will be displayed on the website in the following format: MWF for tested non-carrier (i.e.: Free), MWC for tested carrier (heterozygous), and MWS for tested true carrier (homozygous). The same MW condition codes will be displayed on the Holstein Canada website and included in outgoing data files from Lactanet.
Like BH14, Lactanet has calculated carrier probability values for the Haplotype for Muscle Weakness (HMW), based on haplotype results first released by CDCB in December 2023, along with known gene test results and pedigree data. There is some complexity to the carrier probabilities as “Probable Carriers” and homozygous animals are sometimes able to survive. Carrier Probability values will be displayed on the Lactanet website using asterisks similar to the Haplotype Associated with Cholesterol Deficiency (HCD) where a double asterisk (**) signals the animal is expected to be affected (i.e.: homozygous) and a single asterisk (*) indicates the animal has a possibility of being affected. In advance of the April 2024 genetic evaluation release, Muscle Weakness gene test results and carrier probability values for HMW will be available on the Lactanet website.
Tool to Manage Undesirable Conditions and Haplotypes
Over time, strong genetic selection in the dairy industry has led to a higher genetic relationship between top bulls and females. This close relationship has resulted in higher inbreeding levels and the spread of undesirable genetic abnormalities. To help manage known undesirable genetic conditions and haplotypes and make better breeding decisions, Lactanet is modifying the current Inbreeding Calculator to identify potential matings that have a risk of producing a pregnancy or calf affected by these undesirable genes. The Inbreeding Calculator is a popular tool on the Lactanet and CDN websites used for over 20 years to view pedigree inbreeding levels and Parent Averages for each potential mating under consideration. A new column titled “GC”, meaning “Genetic Conditions”, is being added to the Inbreeding Calculator display to highlight genetic conditions with a carrier probability of 25% or higher for the selected mating animal. A mating risk calculation will be done across all undesirable genes to reflect the probability the resulting pregnancy or calf will be affected by at least one of the undesirable conditions or haplotypes. In the list of potential mates for the given animal, a warning sign or stop sign will be shown in the “GC” column to reflect the mating risk:
If the mating risk is below 1% then the “GA” column will be left blank for that specific combination of the animal and potential mate.
⚠Producers should proceed with caution with the mating, as the probability of producing an affected pregnancy or calf is at least 1% but less than 6.25%.
⯃It is not recommended to proceed with the mating as the probability of producing an affected pregnancy or calf is 6.25% or higher.
In addition to these changes, a pop-up window with carrier probability values for the main genetic conditions and haplotypes known in the breed will also be added to each animal’s Genetic Evaluation Summary, Inbreeding Calculator and Pedigree pages on the Lactanet website. This pop-up box stemmed from a Lactanet Resolution and can be found by hovering over the animal’s name. The upgraded Inbreeding Calculator and pop-up box will help producers easily view genetic conditions and haplotypes to avoid problematic matings. The pop-up box will be available before the April 2024 genetic evaluation release and the Inbreeding Calculator changes will be launched shortly after in April. Keep an eye out for additional information!
Genetic Evaluations in DairyComp
Effective April 2024, Canadian producers will have easy access to their genetic and genomic results right in DairyComp! A feature will be added to DairyComp that will allow users to import the data file containing 30+ genetic values from the iLOOP. The first files will be available for the official genetic evaluation release on April 2, 2024. Each DairyComp user will have the ability to select from the 30+ genetic fields including LPI, Pro$, Production, Type, and Functional traits. It is not a complete list of genetic evaluations, but this can be expanded, and we welcome feedback. In addition, the genomic status for every herdbook registered female in the herd will be imported allowing users to easily see if the data is a genomic evaluation (i.e.: GEBV, GPA).
The genetic evaluations uploaded to DairyComp are the most accurate for herd management and genetic selection decisions since the evaluations are based on unsupervised milk recording as well as unofficial monthly updates as new performance data gets added. The same evaluations are also used in Compass, for creating the DHI Genetic Herd Inventory reports, and shared with AI companies offering a mating program in Canada. As such, the specific values may be different compared to those displayed on the Lactanet and industry partner websites, which are updated only in April, August and December each year. After initial setup, the genetic and genomic data will be automatically uploaded to DairyComp once a month for all registered animals in the DHI herd inventory. As heifers get genotyped, their initial parent average (PA) values will automatically be updated to their genomic parent average (GPA). Contact DairyComp customer services support today to get set up!
Summary
As genetics continues to evolve, Lactanetremainsdedicated to providing updates and improvements to our genetic tools and services. The genetic evaluation release in April 2024 will include key annual updates, revisions to the typecomposite traits adjustments, Muscle Weakness and BH14 haplotypes, as well as new services to the Inbreeding Calculator and DairyComp!
Lactanet is collaborating with Angus Genetics Incorporated (AGI) to share genotyping of Angus bulls from Canada, the U.S., and Australia to assist in beef-on-dairy breeding decisions. This move comes as dairy sector stakeholders call for better information on crossbred calves, which are a significant potential income stream on Canadian dairy farms. The “perfect trifecta” of conditions led to calls for better beef-on-dairy information, with genomic testing of dairy animals allowing for accurate rankings within a herd, regardless of age, of the best dams from which to build bloodlines.
The movement to breed the rest of the animals to beef sires has increased the chance of getting a heifer calf from those top ranking animals to 95%. Nearly 40% of Holstein breeders and 30% of Jersey and Ayrshire breeders across Canada use some form of this strategy, with producers now looking at other traits such as calving ease when selecting beef bulls for lower-ranking dams. To aid beef-on-dairy decisions, Lactanet can collect on-farm data it already performs on participating Dairy Herd Improvement and DairyComp herds.
Lactanet also has access to data on calf move-ins and move-outs through its leadership of the DairyTrace national traceability program, but generally knows little on the beef sire side. Genotyping the crossbred calves could grow and strengthen the database, but it is unlikely any dairy producer will pay to genotype animals that will leave the farm within a few weeks.
A key difference between the beef-on-dairy strategy and a genetic program to build dairy strengths over the long term is that the beef strategy typically doesn’t aim to build long-lasting bloodlines. Lactanet will not invest in a beef-on-dairy genotyping initiative, as it would require a huge investment of money, people, and time with little chance of a return on investment. Instead, a collaboration has begun with Missouri-based Angus Genetics Incorporated (AGI) for access to genomic data from Angus bulls in the U.S., Australia, and Canada. The organization will soon include a “Beef on Dairy Query” on its website, alongside other “Query” options for searching bull or cow information.
Breed breed associations could eventually share their information about carcass weights or carcass quality if they’re collecting such data. For the most reliable information, more research is necessary in North America into beef-on-dairy breeding.
Genetics is important in dairy production since it determines cow productivity and health. Farmers often use breeding programs to select features like milk production, fertility, and disease resistance. However, while evaluating the link between genetics and desired outcomes, it is critical to realize that correlation does not necessarily indicate cause. Correlation establishes a statistical relationship between two variables, while causation suggests that one variable directly influences the other[1]. This difference is critical in genetic research because it prevents data misinterpretation and ensures that breeding and management techniques are founded on an accurate knowledge of genetic factors.
Structural Equation Modeling (SEM) in Dairy Cattle Genetics
Structural equation modelling (SEM) is a statistical approach for representing causal links between phenotypic features and estimating their size. SEM investigates the functional relationships between variables in a phenotypic network, enabling researchers to use one characteristic as a predictor of another. This strategy has been used to separate the effects of single-nucleotide polymorphisms (SNPs) on characteristics into direct and indirect components, as well as to find genomic areas with pleiotropic effects that explain observed genetic correlations. For example, SEM has been used to study the genetic architecture of udder health in dairy cattle, a feature with important economic and animal welfare consequences owing to illnesses such as mastitis.[2]
Genetic Correlations and Causal Effects
However, demonstrating a link between genetics and a characteristic does not always imply that one causes the other. Other factors might be at play, influencing both variables separately. Environmental variables affecting dairy cow production and health include feed, housing conditions, and management approaches.
Furthermore, correlations might be spurious, which means they occur by chance rather than reflecting a real link. Without strong experimental data, it is difficult to tell if a correlation indicates a causal link.
Consider a hypothetical situation in which researchers discover a substantial relationship between a certain gene variation and milk output in dairy animals. While this association may imply a genetic effect on milk production, further research is required to determine causality.
Research has shown intricate genetic and phenotypic links between many fitness components in cattle. For example, genetic connections and causal impacts of fighting abilities have been shown to influence fitness parameters such as milk production, somatic cells, and fertility. However, correlations may not necessarily indicate causality. For example, SEM demonstrated a negative link between fighting skill and lifespan, but MTM revealed a favorable correlation. This disparity shows that dominant cows are retained longer for economic reasons, rather than because fighting ability leads to longer life.
Experimental investigations, such as genetic modification or controlled breeding experiments, may give stronger proof of causation. Researchers can determine if certain genes directly impact desired outcomes in dairy cattle by systematically manipulating genetic components and analyzing the subsequent changes in attributes.
Longitudinal studies that follow cattle performance throughout numerous generations may also assist in understanding the intricate interaction between genetics and environmental influences. These investigations enable researchers to analyze how genetic features are transmitted and expressed under various situations, shedding light on their practical relevance for dairy producers.
Methane Emission and Genetic Correlations
A meta-analysis of the genetic contribution to methane emission in dairy cows found heritability estimates for multiple methane emission variables and negative genetic associations with adjusted milk output for fat, protein, and energy. This suggests that, although there is a genetic foundation for methane emission, it does not always result in changes in milk output or composition. [3]
Social Dominance and Fitness Traits
Social dominance in cattle has been linked to genetic features including muscle mass, fertility, and udder health. However, these relationships, whether direct or indirect, do not demonstrate causality but rather point to probable evolutionary trade-offs.
Implications for Dairy Cattle Breeding
The difference between correlation and causality has a substantial impact on breeding methods. Efficient sire selection, for example, has an influence on genetics in a dairy business; nevertheless, to minimize unexpected repercussions, features should be selected based on causal linkages rather than correlations.
Genetics of Survival in Dairy Cows
Understanding the genetics of survival is particularly important, since early death and culling result in large losses. Genetic relationships between survival and other variables such as longevity and fertility are positive, but low heritability estimates for survival suggest that environmental factors may play a greater role.
The Bullvine Bottom Line
In dairy cow genetics, SEM and other statistical approaches have shown genetic relationships between several phenotypes. However, correlation does not necessarily indicate causality. This information is critical for making educated choices about breeding and management approaches that improve dairy herd health and production. Although correlations between dairy cow genetics and variables such as milk production might help discover possible relationships, they should be taken with care. Correlations cannot clearly demonstrate causality in the absence of robust experimental validation. Dairy producers and academics must acknowledge the limits of correlation and emphasize rigorous scientific methodologies to find the actual causes of cow production and health.
[1] Genetic correlations and causal effects of fighting ability on fitness traits in cattle reveal antagonistic trade-offs https://www.frontiersin.org/articles/10.3389/fevo.2022.972093/full
[2] Structural equation modeling for investigating multi-trait genetic architecture of udder health in dairy cattle https://www.nature.com/articles/s41598-020-64575-3
[3] Structural equation modeling for investigating multi-trait genetic architecture of udder health in dairy cattle https://www.nature.com/articles/s41598-020-64575-3
The purpose of this research was to determine whether or not utilizing a cow’s parents’ genotypes for imputing single nucleotide polymorphisms (SNPs) affected the calculation of genomic inbreeding coefficients. The imputation genotypes of 68,127 Italian Holstein dairy cows were examined using a variety of genotyping methods. Genomic inbreeding coefficients were calculated using four PLINK v1.9 estimators, two genomic relationship matrix (grm)-based estimators, and one run of homozygosity (ROH; FROH) estimator. When at least one of the parents was genotyped, the findings revealed consistently high genomic inbreeding coefficients. However, skewed genomic inbreeding coefficients were seen in cows genotyped with MD SNP panels whose SNPs were poorly represented in the chosen imputation SNP data set and did not have their parents genotyped, in contrast to what was predicted based on actual genotype data. For cows genotyped with MD, the estimators Fhat1, Fhat2, and Fgrm gave greater genomic inbreeding coefficients, even when both parents and the maternal grandsire were genotyped. Overall, FROH was the strongest estimator, followed by F and Fhat3.
Word genome single nucleotide polymorphisms (SNPs) are frequently employed in cattle breeding programs throughout the globe to estimate genomic breeding values, as well as genome-wide association studies, population genetics, and determining realized homozygosity and inbreeding. Breeding businesses may cut genotyping costs by genotyping a small number of core animals with HD SNP panels and a large number of LD/HD animals, then projecting them to HD genotypes or a set of predetermined SNPs.
Imputation success in dairy cattle breeding is determined by three major factors: the relationship between core animals genotyped in HD and those to be imputed from LD/MD to HD, the distribution along the genome and the number of SNPs in the LD/MD panels, and the linkage disequilibrium between SNPs in the LD/MD and HD. Although there are strategies for achieving high imputation accuracy, variability in genomic estimations based on imputed SNP data is to be anticipated.
This work expands on recent findings on whole genome imputation SNP-based genomic inbreeding coefficients (FSNP) in dairy cattle. Extreme genomic inbreeding coefficients might be the outcome of imputation, particularly in cows genotyped with MD SNP panels with just a handful of their SNPs included in the final imputation SNP data. The goal of this research was to see whether significant genomic inbreeding coefficients in cows genotyped with MD SNP panels that had few of their SNPs included in the final imputation SNP data might be attributed to not having their parents genotyped during the imputation process.
Whole genome SNP data is frequently imputed in dairy cow breeding, which may significantly decrease genotyping costs. However, there may be cows with incorrect imputation owing to the lack of genotyped parents. The findings revealed that whole genome SNP inbreeding coefficients might be skewed for cows who did not have parents or maternal grandparents genotyped and were also genotyped with an SNP panel with low representation of its SNPs on the chosen imputation SNP data.
Advances in dairy genetic research have created an ever-increasing amount of information for dairy farmers to take into consideration for sire selection. Dairy sire proofs contain a mix of numbers, acronyms, and other terminology. This reference guide covers common sire proof information and what it means.
What Are Your Goals for Your Herd?
A good place to start thinking about sire selection is identifying a few main goals you want to improve in your herd. Not sure where to start? Consider the following information.
Production: How are you paid for your milk? Some regions of the country value volume over total solids. In the Upper Midwest, our markets tend to place a greater value on components that add to cheese yield, such as fat and protein.
Fitness: Are there common health or longevity issues you would like to address? Productive life, somatic cell score, and daughter pregnancy rate are a few examples of health and fitness traits that can be incorporated into selection.
Type: Are there common structural issues in your herd limiting production or longevity? Udder conformation, foot and leg conformation, and body size are examples.
Other considerations: Calving ease emphasis will be dependent upon whether mature cows or heifers are being mated. Are you looking for sires available in sexed semen or conventional? What is the average price per unit of semen that fits your budget?
Dairy Sire Proof Traits, Abbreviations, and Definitions
Selection indexes: A combination of production, fitness, fertility, and type into one sire ranking number.
Production traits: Pounds of milk, fat, and protein, residual feed intake and feed saved; these traits measure the productivity and efficiency of your herd.
Health, fitness, & fertility traits: These measure the health and longevity of the animal.
Productive Life (PL): Measurement of longevity, including yield information. Higher numbers indicate staying in the herd longer (a related trait is Livability).
Daughter Pregnancy Rate (DPR): The percentage of non-pregnant cows that become pregnant during each 21-day period. A bull with a DPR of 1 indicates that his daughters have 1% higher pregnancy rate than a bull with a DPR of 0 (related traits are cow conception rate and heifer conception rate).
Somatic Cell Score (SCS): An indicator trait for mastitis resistance based on the direct measure of somatic cells in milk samples.
Sire Calving Ease (SCE): The percentage of difficult births in first-lactation heifers.
Additional Health & Fitness Traits: Early lactation health traits are also being developed and released for sire proofs and may vary depending on the entity evaluating these traits. The Council on Dairy Cattle Breeding (CDCB) wellness traits include mastitis, metritis, retained placenta, displaced abomasum, ketosis, and milk fever. Be aware that reliability may be low when evaluating these traits.
Linear Type Traits
Stature
(-) Short ⟺ (+) Tall
Fore Udder Attachment
(-) Weak ⟺ (+) Strong
Strength
(-) Frail ⟺ (+) Strong
Rear Udder Height
(-) Low ⟺ (+) High
Body Depth
(-) Shallow ⟺ (+) Deep
Rear Udder Width
(-) Narrow ⟺ (+) Wide
Dairy Form
(-) Tight ⟺ (+) Open
Udder Cleft
(-) Weak ⟺ (+) Strong
Rump Angle
(-) High ⟺ (+) Sloped
Udder Depth
(-) Deep ⟺ (+) Shallow
Rear Legs – Side
(-) Posty ⟺ (+) Sickled
Front Teat Placement
(-) Wide ⟺ (+) Close
Rear Legs – Rear
(-) Hock-in ⟺ (+) Straight
Rear Teat Placement
(-) Wide ⟺ (+) Close
Foot Angle
(-) Low ⟺ (+) Steep
Teat Length
(-) Short ⟺ (+) Long
0 points = Breed average for that base year; 1 point = one standard deviation above or below average; 2 points = two standard deviations above or below average.
Type and Conformation Composites:
Predicted Transmitting Ability Type (PTAT): Overall type score.
Breed Udder Indexes: Combined look at udder traits, such as udder depth and attachments. Weighting of traits is breed-dependent.
Feet and Leg Composite: Index of foot angle, rear legs – side view & rear view, feet & leg score, with the weighting of traits varying by breed.
Additional composites for Dairy Capacity and Body Capacity may also be published, depending upon breed.
Genetic Codes and Haplotypes:
You may see letter codes associated with pedigree information or genetic proofs. Oftentimes, these codes refer to an animal’s status as a carrier or tested free of a genetic recessive. For example, animals tested for Complex Vertebral Malformation, an undesirable recessive condition, are designated as CVM for carriers and TV if tested negative.
Genetics codes are also published for polled, horned, and hair coat color traits. “RC” designates a carrier of the red hair coat gene, whereas “TR” would designate a non-carrier. The polled trait can be designated as PO for observed polled, PP for tested homozygous polled, PC for tested heterozygous polled, or TP for carriers of both horned genes.
Haplotypes represent a DNA sequence. Genomic testing has uncovered certain haplotypes that are lethal or highly detrimental when combined. Mating programs may take haplotype information (carrier vs non-carrier) into consideration when making a recommendation. New haplotypes are being researched and released specific to individual breeds.
The CDCB maintains a list of haplotypes and recessive genes being tested for, specific to breed at: https://uscdcb.com/haplotypes/
The purpose of this research was to identify dairy farmers’ data and technological requirements in order to enhance herd health and guide innovation development. Eighteen focus groups were performed with 80 dairy producers from Belgium, Ireland, the Netherlands, Norway, Sweden, and the United Kingdom. Data analysis using Template Analysis identified six themes that reflect core needs: autonomy, comfort, competence, community and relatedness, purpose, and security. Farmers liked technology that promoted convenience, knowledge, and self-sufficiency. Data sharing, accessibility, and program usability were all obstacles that hampered technology adoption. Farmers also had difficulties in workforce recruitment and management, necessitating stress-reduction techniques. Controlling barn environmental factors like as air quality, cleanliness, and stocking density was of special interest. The results imply that developers should include farmers in the design process to create a great user experience and boost accessibility.
The dairy sector is gradually being urged to embrace technology that will enhance its economic, environmental, and social sustainability. To achieve this, efficiency and milk production costs must be improved, which may be accomplished by using a variety of technologies. Such technologies include automated milking systems (AMS), automatic feeders, activity sensors, and oestrus detection devices. However, a significant minority of farmers still do not use these technology, notably data-capture systems and those unrelated to milking techniques.
One explanation for the low adoption of certain technologies is that agricultural innovations are often designed from the top down, with minimal input from end users during the early phases of development. This may lead to unequal adoption of innovations by farmers, since designers prefer to concentrate on the advantages that technology can provide for farms. However, technology may also have negative consequences, such as the relocation of agricultural workers and the marginalization of some farms.
A Responsible Innovation method has been proposed for the development of agricultural technology, particularly those used in the dairy sector. This method recognizes that innovators must respond to the social and ethical problems of research and innovation via an interactive process including stakeholders. Anticipating possible effects, responding to social requirements, including key stakeholders throughout the development process, and reflecting on motives and assumptions are all important aspects of Responsible Innovation.
Living Labs provide an approach for generating ideas while meeting Responsible Innovation objectives. Living Labs are user-centered innovation environments based on daily experience and research that enable user input in open and distributed innovation processes including all relevant partners in real-world scenarios. They are divided into three stages: idea, prototype, and innovation, each having three phases: exploration, design, and evaluation.
The idea stage of Living Labs is critical for innovation creation because it enables users to maximize their effect by focusing the design. Three theoretical streams impact the Living Lab method of gathering knowledge about user needs: soft systems thinking, needfinding, and appreciative inquiry. Qualitative techniques are used to investigate user experiences, motivations, and future aspirations.
Researchers may utilize the typology of basic requirements to determine the needs of users, which consists of 13 fundamental needs and 52 sub-needs. The requirements typology served as a coding framework for assessing qualitative data. Overall, users’ needs may be formed by collecting data on their experiences, motivations, and objectives utilizing Living Labs’ theoretical underpinnings, and then applying the data to a basic need typology.
Many studies have examined the variables that influence technology adoption on dairy farms, but few have focused on farmers’ experiences with technology. These studies often concentrate on the implications of technology for human-animal connection and labor practices, rather than addressing dairy farmers’ technological demands. One research focused on smartphone applications, although it only examined the early phases of tool development.
Previous research on dairy farmers’ experiences with technology has solely examined technologies for adult dairy cows, which may have distinct demands in terms of youngstock management. This project employed a Living Lab technique to get a wide understanding of farmers’ demands for agricultural technology and data, with the goal of developing technological ideas that meet their needs.
Data from focus groups in six countries were studied to better understand dairy producers’ farm technology requirements for mature cows and youngstock. The studies revealed that farmers have demands for autonomy, comfort, competence, community and relatedness, purpose, and security. The study underlines the need of doing user experience research throughout technology development to promote intuitive usage and favorable emotional experiences.
Technologies might help meet needs in areas including workload, labor efficiency, and communication. Farmers also want tools that gave guidance, such as goal planning and recognizing areas that need attention. The Living Lab concept promotes Responsible Innovation by including farmers from the start of the innovation process and enabling researchers to respond to farmers’ requirements.
The Council on Dairy Cattle Breeding (CDCB) and USDA Animal Genomics Improvement Laboratory (AGIL) announce enhancements in the U.S. dairy genetic evaluations on April 2, 2024.
New Variance Component Estimates and Variance Adjusted Weights for Health Traits
By Taylor Marie McWhorter, Kristen Parker Gaddis, Ezequiel Nicolazzi, and Paul VanRaden
Since the 2018 debut of CDCB evaluations for disease resistance, the number of health records in the National Cooperator Database has tripled or quadrupled – depending on the trait. With this data surge, the trait model has been adjusted with new variance component estimates and adjusted weights, effective with the April 2024 evaluations. This follows a typical progression and evolution of newer traits.
CDCB genetic evaluations are provided for six direct health traits for the resistance to Milk Fever (MFEV), Displaced Abomasum (DA), Ketosis (KETO), Mastitis (MAST), Metritis (METR) and Retained Placenta (RETP). The health evaluations were first incorporated for Holstein in April 2018, Jersey in April 2020, and Brown Swiss in August 2022. Variance components were originally estimated in 2018, when Holstein records available for each trait ranged from 1.2 to 2.2 million. The available records by trait are summarized in the first table.
Milk Fever
Displaced Abomasum
Ketosis
Mastitis
Metritis
Retained Placenta
# Records, Millions (HO) 2018 Estimate
1.2 M
1.9 M
1.4 M
2.4 M
2.0 M
2.2 M
# Records in Database, Millions (HO, JE, BS) Dec 2023
5.8 M
5.8 M
4.3 M
7.7 M
6.3 M
7.6 M
Incidence Rates Dec 2023
1.06%
1.63%
5.84%
11.72%
7.16%
3.39%
A single-trait linear animal repeatability model with random effects (additive, permanent environment, herd-by-year, and herd-by-sire) is used to estimate genomic Predicted Transmitting Ability (PTA) for animals. New variance components were estimated for all six health traits. The new estimates affect overall heritabilities (h2; all lactations together), h2-by-lactation, variance ratios for other random effects, and repeatability.
Milk Fever
Displaced Abomasum
Ketosis
Mastitis
Metritis
Retained Placenta
h22018
0.6%
1.1%
1.2%
3.1%
1.4%
1.0%
h2 2023
0.6%
3.1%
1.7%
3.2%
1.6%
1.3%
In addition, weights applied to health traits were updated from 0 or 1 to be a value estimated from the variance components. These variance-adjusted weights are used to standardize genetic variance across differing parities that have differing heritabilities. The new weights are a function of repeatability, h2, and h2-by-lactation. The previous weights of 0 or 1 are now 0 or 0.25-1.46 depending on lactation and trait.
In evaluations, both the existing variance adjusted phenotypes and the new variance adjusted weights are employed to account for the heterogeneous variance.
To investigate the impact of the upgrades made to the health evaluations, a test using data from the December 2023 run was completed. Correlations between the official December 2023 evaluation and the test evaluation with the updates were examined for old and young animals by breed and for genomic estimated breeding values (GEBV) and genomic reliability (GREL). GEBV (GREL) correlations for MFEV, KETO, MAST, METR, and RETP were ≥0.96 (≥0.98) for Holstein, ≥0.90 (≥0.95) for Jersey and ≥0.92 (≥0.99) for Brown Swiss. The DA GEBV (GREL) correlations were ≥0.95 (≥0.93) For Holstein, ≥0.82 (≥0.84) for Jersey and ≥0.81 (≥0.96) for Brown Swiss. The lower correlations observed for DA are due to the largest change in h2, which also impacts the variance-adjusted weights.
Given the new variance components and correlations between official and test run, some variation is expected in PTA for individual animals. However, the variance adjustments effectively capture the categorical trait of incidence in a linear model. The impact on Net Merit is expected to be very small, given the weighting of these traits in the index.
Foreign Unknown Parent Groups
By Paul VanRaden and Andres Legarra
Unknown Parent Groups (UPG) are used in the genetic evaluation to provide an average Predicted Transmitting Ability (PTA) value to all animals with missing pedigree information. Starting in the April 2024 evaluation, all foreign UPG effects will be merged with domestic UPGs.
Accurately estimating UPG effects requires that foreign unknown parents have descendants with domestic phenotypes. That has been the case with Canadian dams and international sires, which describe the first foreign-genotyped animals. As genotyping has expanded across the world, a high proportion of recently genotyped foreign unknown parents do not have descendants with domestic phenotypes. Thus, the automated system defining UPG has recently been detecting a much larger need for UPGs, alongside a drastic reduction in the available information to estimate them. Furthermore, a recent study showed the behavior of these foreign UPG could be introducing bias in the evaluation. For this reason, starting in the April 2024 evaluation, all foreign UPG effects will be merged with domestic UPGs. Foreign genetic trends were assumed equal to domestic trends, which is a reasonable assumption for Europe and Canada. Lack of information to estimate actual trends in South America or Asia, where most of the unknown parents are, makes this a need to reduce overall bias.
Before implementation, AGIL and CDCB tested the impacts and correlations. For example, fertility traits of Daughter Pregnancy Rate (DPR), Cow Conception Rate (CCR), Heifer Conception Rate (HCR), and Early First Calving (EFC) were compared with and without separate foreign UPGs in the pedigree model before adding genomic information. For Holstein bulls born in the last 10 years with >50% reliability, PTA correlations before and after merging the groups were >99.98% for all four traits. Across all years, PTA correlations were >99.97%, except EFC with correlation of 99.86% due to a 10% faster estimated genetic trend. For the EFC trait, the largest changes were for European bulls of breeds Simmental or Montbeliard, and for U.S. bulls with Canadian dam IDs but no further pedigree.
Generally, this change will have little effect on animal evaluations across the population, although there will be significant effect on specific animals that used the removed UPG.
Breed Base Representation (BBR) Update Delayed to August
By Ezequiel L. Nicolazzi
The Breed Base Representation (BBR) reference population update is typically updated on an annual basis, in the April evaluations. This year’s BBR update will be delayed for four months, as we are working to introduce a new SNP list. To avoid two consecutive changes to BBR reference animals, the new SNP set and update to the BBR reference population are scheduled to be implemented together in August 2024.
Interbull Validation on Fertility, Somatic Cell Score and Mastitis
By Rodrigo Mota and Taylor Marie McWhorter
Since 1995, the U.S. evaluation system has participated to the Multiple Across-Country Evaluation (MACE) for bulls, managed by Interbull. This multi-country evaluation is beneficial because it allows receipt of evaluations, on U.S. scale, for bulls with daughters in other countries. This enhances the comparison of performance of most of the bulls in the world, while improving the accuracy of parent averages of their U.S. progeny (if any). In order to exchange evaluations, countries are required to achieve Interbull validation that their evaluations are free from bias. This validation happens every two years, or when a trait has model changes, as is the case for Mastitis.*
CDCB fertility traits, Somatic Cell Score and Mastitis were due for a check-up in 2024, and all traits were successfully validated for all breeds.
*The model changes for Mastitis are described in the first section, “New variance component estimates and variance adjusted weights for health traits.”
Artificial insemination businesses provide top genetics for dairy cow genetic improvement projects, with the most marketable bulls having the most genetic value. This extensive within-family selection results in higher degrees of connection, which leads to increased inbreeding. Reducing or even restricting inbreeding often results in the selection of lower-index bulls. As improved reproductive methods become more widely used and long-term technologies like as in vitro breeding evolve, selection intensities on bull and cow dams may skyrocket, worsening the situation even further.
The dairy genetics community must now decide who is prepared to decrease the pace of genetic gain in order to effectively regulate inbreeding. The literature has several instances of the negative impacts of inbreeding, however it is not true that all inbreeding is equal, and a distinct boundary exists between safe and risky. There is likely to be a tipping point beyond which we do not want to go, but its position is uncertain. Lush (1945) proposed that generations of high-intensity selection may necessarily result in large percentages of homozygosity.
To analyze the present situation, we may compare changes in phenotypic performance during inbreeding to rates of genetic gain. In all but one situation, annual genetic benefits outweigh inbreeding depression: the daughter pregnancy rate, which decreases by 0.02 PTA units each year. This is not to say that there is nothing to be worried about; it just means that we are not yet losing ground. As the phrase goes, “When you’re in a hole, stop digging,” but we need to act first.
The desire for elite genetics contributes to the continual loss of genetic variety in dairy cow herds, making it harder to preserve heterozygosity. Elite bulls are no longer enough; they must also have good breeding values for milk, components, fertility, and other attributes. Most marketed bulls cannot hit all of these criteria, but it is often assumed that they will.
Some measures may be used to reduce inbreeding rates in dairy cow herds. Refine PTA Adjustments: Genetic assessments in the United States are altered to account for the potential implications of future inbreeding. Predicted transmitting abilities (PTA) for bulls closely related to the population are reduced to account for inbreeding depression, while PTA for bulls less related to the population than average are increased to account for advantageous heterosis. However, the problem with this strategy is that the top AI bulls causing genetic change in the population are often severe outliers, and post-hoc modifications lack theoretical support.
Use the Right Metric: Genomic inbreeding assesses both identity-in-state and identity-by-descent, while pedigree inbreeding solely considers the latter. One technique to lower apparent inbreeding rates is to use pedigree inbreeding instead of genomic inbreeding. A more tempting strategy is to shift away from crude measurements of inbreeding and toward more accurate measures of diversity, such as runs of homozygosity (ROH) or direct measures of identity-by-descent (IBD). Timing is important, and older inbreeding is less concerning than current inbreeding since older haplotypes have been cleared of possible recessive genes.
Trim Pedigrees: Pedigree information is simple and inexpensive to record, but it has numerous limitations, including incompleteness, greater than expected genuine inbreeding, and underestimation of real ties among people. Trimming pedigrees to just a specific number of generations is a simple technique to minimize inbreeding. A similar alternative is to use a changing base year for inbreeding rather than a set reference year (the US uses 1960 as its base population).
Trimming pedigrees does not directly address rates of inbreeding in the population, either pedigree or genomic, but it is proposed because it may drive selection decisions away from families with the highest rates of recent inbreeding, which are the lineages most likely to contain genetic load that has not yet been purged.
The area of genetic selection has various obstacles, including the issue of bulls with the highest PTA being in high demand, independent culling levels, and implementing optimum contribution selection. To remedy this, it is proposed that instead of publicizing PTA for specific qualities, bulls be randomly assigned to the cow population. Bulls might also be given red, yellow, or green badges for each characteristic to signify their PTA, although this may not be desirable.
Homomorphic encryption may be used to hide the PTA provided to software from end users, although this is not a usual practice, and implementation is difficult. Hiding PTA would be a significant departure from existing methods, and most consumers or salesmen may not find this approach desirable.
Additional measures of genetic variety might be added to selection indices as a trait, exerting direct selection pressure on heterozygosity. This implies that choosing high-index bulls would entail some selection for increased heterozygosity, rather than trusting that inbreeding is taken into account during the mating process. However, this is a less efficient method of achieving optimum contribution selection, and it has been largely neglected in the United States since its inception.
Varona et al. (2019) recommended that an artificial purging system be built around a selection index that includes both breeding values and inbreeding burdens. This is a more attractive approach than include an ad hoc measure of variety in the index, and there is a strong theoretical underpinning to support its usage.
The implementation of optimum contribution selection in certain places, such as the United States, is not especially contentious owing to its complexity and the impression that the advantages do not outweigh the increased complexity. Many genetics end users are unwilling to accept the trade-off between genetic trend and heterozygosity preservation, and in a competitive market, they may just buy their sperm and embryos from another provider.
Finally, genetic protection procedures that limit germplasm interchange are allowing breeding projects run by several AI corporations to develop into distinct subpopulations within breeds. While it is unclear if these groupings are distinct enough to result in widespread outbreeding, current study indicates that bigger variances between families within a breed may exist than previously thought.
Genetics corporations may concentrate on selling embryos that represent perfect terminal dairy cows, necessitating a large increase in the scope of dairy embryo transfer. This would result in a system that is more like modern swine production than ancient dairy cow rearing. Some genetics businesses may begin selling embryos into smaller-scale specialist markets, such as those seeking high-genetic-merit polled animals.
Gene editing technologies have advanced, leading in increased efficiency and the capacity to stack numerous alterations. This might be a technique to sustain rates of genetic gain without experiencing negative repercussions or relying on natural purging. However, this strategy has several obstacles, including a lack of understanding about editing targets, an inability to edit dozens or hundreds of targets at the same time, and an ever-changing regulatory environment.
To effectively address the challenges posed by inbreeding, a collaboration between AI companies and the scientific community must persuade farmers that there is a real problem to be solved, that our proposed solutions will work, and that they are not being asked to jeopardize their livelihoods by participating in this process. The size of the issue is important, since many farmers are worried about the long-term impacts of inbreeding but do not necessarily agree that vigorous action must be done now. There is also common belief that the individuals who created the issue cannot be trusted to repair it, and that things function differently in the actual world than they do on paper.
Inbreeding is expected to attract greater attention in the future because of its impact on social license rather than production economics. Most laypeople are unaware of the fundamental distinction between inbreeding with stringent performance selection, as is used in animal breeding programs, and inbreeding with no performance selection. Increased genetic load also impairs an animal’s capacity to adapt to changes, which is concerning as the environment shifts.
Dairy cow breeding is a loosely connected method that allows for efficient management across the production chain. Effective population control will be challenging and will need adjustments on the side of AI businesses and farmers.
The U.S. dairy herd is 1% larger today but produces 19.2% more milk and 32.2% more butterfat.
The industry’s Net Merit index, a proxy for cattle genetic progress on sustainability, shows that a cow produced by combining the best-known genes would be three times more sustainable based on improved genetics than the top Holstein bull on the market today.
The introduction of genomics in 2008 has significantly improved milk, butterfat, and protein production, cow health, and cow longevity.
The average genetic gain from 2000 to 2004 was $13.50 per year for marketed Holstein bulls.
By 2010, genetic improvement leapt forward, more than doubling from $36.90 per year from 2004 to 2009 to the $83.33 annual genetic gain from 2010 to 2022.
This six-fold improvement equates to a $329 million annual net gain when extrapolating this $70 annual gain per female across the annual U.S. dairy heifer calf crop.
This development greatly enhances sustainability by generating more nutrition with less inputs.
The aggregate gain since 2010 would be $4.28 billion when calculating the full genomic impact.
This means the U.S. dairy industry needs fewer dairy cows each passing year to supply the same amount of milk to serve the domestic market or has transformed new growth in milk, butterfat, and protein into dairy products to deliver nutrition to international customers.
ABS Global and Genus recently built a solar project at one of the world’s largest AI facilities, a bull barn in Wisconsin, housing 480 elite AI bulls.
The complex includes a lab for processing bull semen, which accounts for nearly half of all sales in the U.S.
This sexed semen product allows dairy farmers to use beef semen on dairy cows, reducing the number of replacements and generating more immediate revenue from dairy-beef cross calves.
Research indicates that beef-dairy crossed calves are approaching the same efficiency as beef cattle and are 30% more efficient at converting feed to beef than Holstein steers.
The combined amount of carbon saved by the solar panels’ production of electricity for the facility over 10 years is less than the carbon savings from just one month of genetic progress from future offspring of the facility’s 480 bulls.
The genetic gain on offspring is measured by more production, better health, and longer cow life via the Net Merit Index.
The impact for dairy processors is significant, with butterfat and protein production from Holstein cows improving annually by 0.46% and 1.23% annually in the genomic era.
Genomic testing is making significant inroads as more females get tested each year, leading to less methane production, a reduced carbon footprint, and less feed for each additional unit of milk.
By August 2023, CDCB received its 8 millionth genomic test, with 93% of genomic tests run on female dairy animals.
The genetic super cow is not in sight, with the dairy community only 24% of the way to breeding the perfect Holstein cow.
Genomic science can identify new traits that will help reduce methane production and carbon footprint, with the “feed saved” trait being the tip of the iceberg.
In the past 15 years, dairy farmers have been using genomic science to select cattle traits that improve productivity and support sustainability. The U.S. dairy herd today is 1% larger than in 2008 but produces 19.2% more pounds of milk and 32.2% more pounds of butterfat. The dairy industry still has room to grow more efficient through genomics. The industry’s Net Merit Index (NM$) shows that a cow produced by combining the best-known genes would be three times more sustainable based on improved genetics than the top Holstein bull on the market today.
Genomics has been the most important reason for improvements in milk, butterfat, and protein production, cow health, and cow longevity. The average genetic gain from 2000 to 2004 was $13.50 per year for marketed Holstein bulls. By 2010, genomic testing became commercially viable in the United States, genetic improvement leapt forward, more than doubling from $36.90 per year from 2004 to 2009 to $83.33 annual genetic gain from 2010 to 2022. This profound development greatly enhances sustainability, as new Holstein bulls entering active A.I. service father offspring that generate $83.33 in combined gains that enhance sustainability-related metrics.
Genetics has become a significant factor in the economic development of dairy farming, with research showing that beef-dairy crossed calves are approaching the same efficiency as beef cattle and are 30% more efficient at converting feed to beef than Holstein steers. This improvement in feed conversion and fewer days on feed prior to harvest yields a net carbon footprint reduction and reduced methane emissions. ABS Global found that the combined amount of carbon saved by the solar panels’ production of electricity for the facility over 10 years is less than the carbon savings from just one month of genetic progress from future offspring of the facility’s 480 bulls.
The impact of genomics on dairy processors has been significant, with butterfat and protein production improving annually by 0.46% and 1.75% annually in the genomic era. Commercial dairy farms have started to test more heifer calves at birth to determine those animals that would make the best replacements by making the best use of resources. Since 2020, it has taken less than 12 months to accumulate an additional 1 million genomic tests, with estimates indicating that 20% to 25% of the U.S. dairy heifer calf population is being tested annually.
However, genetic improvement is not yet reaching its ceiling, with only 24% of the dairy community being close to breeding the perfect Holstein cow. Genomic science can identify new traits that will help reduce methane production and carbon footprint, with the “feed saved” trait being the tip of the iceberg.
Hurtgenlea Richard CHARL’s son: Genosource Captain leads the International GTPI index once again! Captain gained 1068 daughters to his US index for the December 2023 run, bringing him GTPI to +3253 (+69). He has +3253 GTPI, +1317 NM$, and +2474kgM! Farnear Upside (s. Charl) is in second place with +3112 GTPI! Ladys-Manor Outcome is ranked third with a GTPI of +3078! Beyond Overdo Hardin with +3280 GTPI is the new chart leader in the GTPI ranking with all bulls >12 months with NAAB-code. He is followed by Ocd Fugleman Radical at +3254 GTPI and Genosource Captain at +3253 GTPI, rounding out the Top 3.
T-Spruce Ethan is the top gLPI Genomic Sire this run, with +3923 gLPI. Progenesis Punch and Progenesis Peyto tied for second place with +3923 gLPI. Progenesis Aneesh and Black Silver M5085 lead the Conformation Genomic standings with +18 Conformation! Lindt, Leggett, Luxury, Hothouse, Bakul, Black Silver 35080, and Blondin Crossover are next at +17 Conformation. This run’s #1 Daughter Proven Conformation sire is Black Silver Crushabull Stan, who stands at +16 Conformation. Sidekick, Crown Royal, and Golden-Eye come after him.
Again this round, the top two stay the same. The top NTM genomic sire in this December 2023 evidence is Mecanico, with +47 NTM, and VH Karat *RC, with +44 NTM. Dixon makes his debut as the #3 NTM at +43 NTM.
This December 2023 run at +814 gPLI in conjunction with +1.50 TM (Type), Genosource Captain is once again the #1 gPLI daughter confirmed sire. He is followed by Kenobi’s son Targaryen at +808 gPLI, while Westcoast River is third at +789 gPLI. Peak AltaOrvar leads the gPLI genomics sire index with +925 gPLI. He is followed by Denovo Leeds, the previous #1 gPLI sire, with +901 gPLI.
The new Italian indices have just arrived! Zingler son Go-Farm Abacuc leads the genomic PFT Domestic ranking with +5075 gPFT. Ecbert, a Gladius x AltaDateline son, comes in second at +5061 gPFT, with Isolabella Baltimora rounding out the top three at +5034 gPFT. Yoox leads the daughter-proven domestic gPFT index with +4455 gPFT. He is followed by Guarantee son Go-Farm Bernini at +4382 gPFT, and Bernini’s brother Go-Farm Bulova at +4321 gPFT.
It’s PROOF DAY! We begin today with the first indices from Switzerland! TGD-Holstein Beautyman at +1683 ISET is the new leader on the Swiss chart. He is followed by Monteverdi’s son OCD Milan at +1659 ISET and Swissgen Lewitan at +1641 ISET, completing the stage. S-S-I Hodedoe Montley leads the confirmed Interbull daughter list with +1592 ISET. Larcrest Commitment comes in second at +1572 ISET, while Wilra SSI Rivet Genuine comes in third at +1566 ISET.
Rover son Real Syn with +171 RZG once again tops the B&W RZG Interbull Genomic standings. He is followed by Vivify at +165 RZG and Rome at +164 RZG, completing the stage. Redford (s. Ranger-Red), Borax-Red (s. Freestyle-Red), CR 7 P (s. Cartoon P), Skyliner (s. Sputnik *RC), and Handout P (s. Hugo PP *RC) share top place in the R&W Interbull Genomic ranking at +162 RZG. Ginetta is a new leader in the B&W RZG Interbull daughter-proven rating, with +157 (+3) RZG. He is followed by AltaZarek at +153 RZG and a tie for third place at +152 RZG for Matchup, Woody, Tampa, Commitment, AltaZazzle, and Kudos.
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Highlights
Hurtgenlea Richard CHARL’s son: Genosource Captain leads the International GTPI index once again! Captain gained 1068 daughters to his US index for the December 2023 run, bringing him GTPI to +3253 (+69). He has +3253 GTPI, +1317 NM$, and +2474kgM! Farnear Upside (s. Charl) is in second place with +3112 GTPI! Ladys-Manor Outcome is ranked third with a GTPI of +3078! Beyond Overdo Hardin with +3280 GTPI is the new chart leader in the GTPI ranking with all bulls >12 months with NAAB-code. He is followed by Ocd Fugleman Radical at +3254 GTPI and Genosource Captain at +3253 GTPI, rounding out the Top 3. 
T-Spruce Ethan is the top gLPI Genomic Sire this run, with +3923 gLPI. Progenesis Punch and Progenesis Peyto tied for second place with +3923 gLPI. Progenesis Aneesh and Black Silver M5085 lead the Conformation Genomic standings with +18 Conformation! Lindt, Leggett, Luxury, Hothouse, Bakul, Black Silver 35080, and Blondin Crossover are next at +17 Conformation. This run’s #1 Daughter Proven Conformation sire is Black Silver Crushabull Stan, who stands at +16 Conformation. Sidekick, Crown Royal, and Golden-Eye come after him.
This December 2023 run at +814 gPLI in conjunction with +1.50 TM (Type), Genosource Captain is once again the #1 gPLI daughter confirmed sire. He is followed by Kenobi’s son Targaryen at +808 gPLI, while Westcoast River is third at +789 gPLI. Peak AltaOrvar leads the gPLI genomics sire index with +925 gPLI. He is followed by Denovo Leeds, the previous #1 gPLI sire, with +901 gPLI. 
The new Italian indices have just arrived! Zingler son Go-Farm Abacuc leads the genomic PFT Domestic ranking with +5075 gPFT. Ecbert, a Gladius x AltaDateline son, comes in second at +5061 gPFT, with Isolabella Baltimora rounding out the top three at +5034 gPFT. Yoox leads the daughter-proven domestic gPFT index with +4455 gPFT. He is followed by Guarantee son Go-Farm Bernini at +4382 gPFT, and Bernini’s brother Go-Farm Bulova at +4321 gPFT.
It’s PROOF DAY! We begin today with the first indices from Switzerland! TGD-Holstein Beautyman at +1683 ISET is the new leader on the Swiss chart. He is followed by Monteverdi’s son OCD Milan at +1659 ISET and Swissgen Lewitan at +1641 ISET, completing the stage. S-S-I Hodedoe Montley leads the confirmed Interbull daughter list with +1592 ISET. Larcrest Commitment comes in second at +1572 ISET, while Wilra SSI Rivet Genuine comes in third at +1566 ISET. 
Rover son Real Syn with +171 RZG once again tops the B&W RZG Interbull Genomic standings. He is followed by Vivify at +165 RZG and Rome at +164 RZG, completing the stage. Redford (s. Ranger-Red), Borax-Red (s. Freestyle-Red), CR 7 P (s. Cartoon P), Skyliner (s. Sputnik *RC), and Handout P (s. Hugo PP *RC) share top place in the R&W Interbull Genomic ranking at +162 RZG. Ginetta is a new leader in the B&W RZG Interbull daughter-proven rating, with +157 (+3) RZG. He is followed by AltaZarek at +153 RZG and a tie for third place at +152 RZG for Matchup, Woody, Tampa, Commitment, AltaZazzle, and Kudos.
