Discover the future of Holstein breeding in our latest genetic assessment roundup. From production powerhouses to health champions, these elite young sires are set to revolutionize dairy herds. Uncover the top performers and learn how to strategically integrate them into your breeding program for maximum genetic gain.
The January 2025 genetic evaluations have unveiled an exceptional lineup of young sires that promises to reshape the genetic landscape of the Holstein breed.
Production Powerhouses
The new generation of sires demonstrates remarkable production potential, with GENOSOURCE BENCHMARK-ET leading at 1777 lbs of milk. His exceptional combination of 93 kg of fat and 78 lbs of protein and an excellent udder composite of 1.74 establishes a new benchmark for well-rounded breeding.
COOKIECUTTER 92469-ET follows with impressive credentials. It offers 1553 lbs of quality milk with solid components, including 87 lbs of fat and 78 lbs of protein. His moderate calving ease makes him particularly valuable for heifer programs, and he maintains strong fitness traits that commercial producers demand.
Health and Fitness Focus
A notable trend in this assessment is the emphasis on health traits. SSI-SIEMERS 50729 showcases this with an excellent Somatic Cell Score of 2.64, while PEAK 40391-ET provides a comprehensive health package with a Somatic Cell Score of 2.87 and ideal body size traits.
Component and Type Excellence
The GENOSOURCE prefix continues to dominate with ORDAIN-ET and ERADICATE-ET, offering exceptional component yields while maintaining health traits. Their well-developed dairy structure and medium-sized frames embody the contemporary Holstein type that commercial producers look for.
Strategic Mating Recommendations
Producers should prioritize complementary breeding matches to drive long-term genetic progress and enhance herd quality. Deep-bodied cows needing udder improvement will benefit from GENOSOURCE BENCHMARK-ET, while those seeking to improve herd life should consider COOKIECUTTER 92469-ET for their breeding program.
Sire Highlights
GENOSOURCE BENCHMARK-ET stands as the current #1 GTPI sire at 3463, showcasing exceptional production potential with a remarkable milk production of 1777 lbs. His outstanding components include 93 lbs of fat and 78 lbs of protein. His Udder Composite (UDC) of 1.74 places him among the breed’s elite for udder improvement. With a favorable SCS of 2.93 and DCE of 4.2, he offers a balanced package for commercial and registered herds focusing on production and type. Use GENOSOURCE BENCHMARK-ET on high-producing cows needing udder improvement.
COOKIECUTTER 92469-ET delivers a compelling package with a GTPI of 3417. His production credentials are impressive, with 1553 lbs of milk, complemented by solid component yields of 87 lbs fat and 78 lbs protein. His moderate DCE of 3.4 makes him particularly attractive for heifer breeding programs. The strong Productive Life (PL) rating of 6.9 suggests improved longevity in his daughters. Consider COOKIECUTTER 92469-ET for breeding heifers and improving herd life.
PEAK 40391-ET emerges as a balanced sire with a GTPI of 3396. His production profile shows 1599 lbs milk, with strong components of 92 lbs fat and 78 lbs protein. The favorable SCS of 2.87 indicates strong resistance to mastitis, while his Body Weight Composite (BWC) of 0.96 suggests ideal-sized daughters. His profile makes him an excellent choice for herds seeking improvement in production and health traits. Select PEAK 40391-ET for balanced improvement across production and health traits.
SSI-SIEMERS 50729 rounds out the elite lineup with a GTPI of 3388. While showing moderate milk production at 1344 lbs, he maintains solid component levels with 85 lbs fat and 78 lbs protein. His standout feature is the excellent SCS of 2.64, among the best in this group for mastitis resistance. This site would work well in programs prioritizing health traits while maintaining production levels. Select SSI-SIEMERS 50729 to enhance udder health and maximize production efficiency.
GENOSOURCE ORDAIN-ET (GTPI 3430) shows impressive production potential, with 1449 lbs milk, 93 lbs fat, and 78 lbs protein. His health traits are favorable, with a 2.47 SCS and PL of 0.8. His traits indicate a more refined dairy build, with a -0.73 FLC. His moderate frame and substantial production numbers make him an excellent choice for commercial herds seeking improved components. Use GENOSOURCE ORDAIN-ET when seeking high component yields with refined dairy character.
SSI-SIEMERS 50424 (GTPI 3403)demonstrates solid production credentials with 1340 lbs of milk, 85 lbs of fat, and 78 lbs of protein, showcasing a balanced profile for efficient dairy operations. His health traits are well-balanced, with an SCS of 2.64 and PL of 0.8. His moderate stature and good dairy form make him particularly suitable for operations focusing on efficiency and health. Choose SSI-SIEMERS 50424 to enhance overall herd health by leveraging its strong production traits while bolstering health characteristics.
GENOSOURCE ERADICATE-ET (GTPI 3396) features a balanced production profile with 1449 lbs milk, 93 lbs fat, and 77 lbs protein. Shows strong health traits with an SCS of 2.42. His fitness traits suggest daughters have good productive life potential. The combination of high components and favorable health traits makes him an attractive option for herds seeking to improve production and fitness traits. Select GENOSOURCE ERADICATE-ET for balanced improvement in both production and fitness traits.
The Bottom Line
These young sires offer unprecedented combinations of traits that address modern dairy producers’ needs. Whether prioritizing production, health, or type, this evaluation provides solutions for every breeding program. Contact your breeding specialist today to develop a targeted strategy using these elite sires. Make informed decisions to shape your herd’s future success with these elite sires.
Summary:
The January 2025 evaluations have introduced a new group of top Holstein sires that may change the breed’s future. GENOSOURCE BENCHMARK-ET stands out with a high GTPI of 3463 and great milk production. Other promising sires like COOKIECUTTER 92469-ET provide strong milk components. Health traits are emphasized with SSI-SIEMERS 50729 having a low Somatic Cell Score and PEAK 40391-ET offering a full health package. GENOSOURCE ORDAIN-ET and ERADICATE-ET deliver excellent component yields and maintain good health. For the best results, farmers should focus on breeding strategies that match their herd’s genetic needs with these elite sires.
Bullvine Daily is your essential e-zine for staying ahead in the dairy industry. With over 30,000 subscribers, we bring you the week’s top news, helping you manage tasks efficiently. Stay informed about milk production, tech adoption, and more, so you can concentrate on your dairy operations.
Discover the December 2024 global genetic evaluations. Who’s leading the dairy revolution? Meet the top sires and their industry impact.
Picture a world where dairy farming is no longer bound by age-old traditions but is propelled forward by genetic insights. Genetic evaluations are not just reshaping the dairy industry but also connecting us on a global scale. The December 2024 global evaluations are a testament to this, showing how genetics refine farming practices and shape the future of agriculture. These evaluations, from the USA to Switzerland, enhance productivity and refine breeding, allowing dairy farmers worldwide to make informed, innovative decisions.
Genosource Captain: The Unrivaled Titan of GTPI
In the intricate world of genetic evaluations, Genosource Captain stands as a towering figure, proudly continuing his reign in the USA with an unwavering grip on the GTPI crown. Now boasting a GTPI of +3336, this formidable leader deftly adds over 1,800 daughters to his impressive American index, further solidifying his status. It’s a testament to his unrivaled genetic prowess, offering a staggering +2,542 kg milk alongside improvements in fat and protein percentages. His unswerving performance ensures his position at the apex isn’t quickly challenged.
However, the Captain’s dominance is not unchallenged. The industry is a dynamic, competitive arena, and new players are always emerging. SDG Cap Garza, a formidable newcomer, makes a striking debut at the second position with a GTPI of +3256, trailing the leader by a mere 80 points. Meanwhile, Plain-Knoll Renegad Trooper is a strong contender, securing the third spot with a commendable GTPI of +3196 and enlisting 712 daughters from 243 herds into his genetic index. This constant evolution keeps the industry engaging and exciting.
The PTAT rankings unveil another sphere of excellence amidst these powerhouses. Here, Redcarpet Story Arc prominently carves his name, reigning supreme with an impressive PTAT of +4.56. His ascent is closely followed by another rising star, Jimtown Nelson, and the renowned SHG Lego, each illustrating their genetic finesse with notable PTAT scores.
Canada’s Dynamic Shift: Sheepster’s Dynasty and Overhaul’s Ascendancy in Dairy Genetics
The latest update in Canadian genetic evaluations presents an intriguing snapshot of innovation and leadership within the dairy industry. Ocd Trooper Sheepster emerges as a pivotal influencer, with no less than 13 of his progeny among the Top 100 gLPI genomic sires. This significant presence underscores Sheepster’s remarkable genetic prowess and lasting impact on future generations. His contribution towards refining genetic traits continues to shape the industry landscape, ensuring competitive progress.
Taking the forefront, Stantons Overhaul P has cemented his standing as the top gLPI genomic sire, achieving a compelling +4001 gLPI, setting a benchmark for others to strive towards.
The Daughter Proven Conformation rankings unveil a fiercely contested arena, where Hyden Limited P and Black Silver Crushabull Stan share the coveted pinnacle, each with an impressive +16 Conformation rating. This level of excellence highlights their extraordinary conformation characteristics, making them desirable breeding choices for improving herd quality. Trailing closely are Vogue A2P2 PP, Blondin Energy, Duckett Crush Tatoo, and Blondin Legend, all exhibiting solid performances with a +15 Conformation rating. These rankings reflect individual achievements and encapsulate the rigorous competition that propels continuous improvement in dairy genetics.
Denovo Harmony’s Rise: A Testament to the UK’s Evolution in Dairy Breeding
In the transformative landscape of the UK dairy sector, the emergence of Denovo Harmony’s unparalleled leadership in young genomic bulls stands as a testament to the industry’s innovative breeding strategies. This remarkable achievement is not just a reflection of genetic prowess, but also an embodiment of strategic foresight in breeding choices that prioritize productivity and lineage improvement. It’s inspiring to see how the industry is constantly pushing the boundaries of what’s possible.
Delving deeper into the Type Merit rankings, Aot Hampshire’s dominance with a +3.10 TM underscores a pivotal shift toward enhancing physical traits essential for longevity and productivity. In a sector where every genetic advantage is crucial, Hampshire’s success accentuates the growing recognition of traits that might have appeared ancillary but now take center stage in breeding decisions.
This focus shift — from mere production metrics to a more holistic view incorporating type assessments — suggests that the UK dairy industry is evolving into a phase where the balance between yield and physical robustness is key. The insights drawn from such merit-based evaluations herald a forward-thinking approach, signaling that today’s strategic choices in breeding, which prioritize physical traits essential for longevity and productivity, are tomorrow’s productivity milestones.
Ecbert’s Dominance and Cookiecutter Hadley’s Challenge: A Tale of Two Titans in Dairy Genetics
The latest Italian genetic evaluations reaffirm Ecbert’s standing as the premier genomic sire, with an impressive gPFT score of +5146. This powerhouse, a Gladius son, remains unchallenged at the top, showcasing the strength and continuity of Italian genetic prowess. However, Ecbert shines on the local stage and in international competitions.
Amidst this global contest, Cookiecutter Hadley emerges as a formidable competitor from the illustrious Cookiecutter MOM Halo VG-88-USA DOM lineage. Leading the pack with a remarkable +5404 gPFT, Hadley demonstrates the robustness of foreign breeding programs and challenges Italian supremacy. As the industry looks for the sires to shape the next generation, these rankings reflect current achievements and set a high bar for future contenders.
Recalibrating the German Genetic Battleground: The Ascendancy of AltaMuller and Pennywise
The German genetic landscape is undergoing noticeable changes, predominantly in the B&W RZG Interbull Genomic rankings. Centrally, AltaMuller and Pennywise have surged to the forefront, each boastfully achieving a robust +161 RZG. In contrast, Real Syn, a once preeminent force, finds itself in the third position, having dropped to +160 RZG—a testament to the ever-evolving arena of dairy genetics.
AltaMuller’s and Pennywise’s ascent signals a shift toward a new era of genetic excellence driven by precision breeding and advanced genomic insights. Their consistent performance underscores the effectiveness of genomic selection strategies, which breeders increasingly embrace to enhance milk production, fertility, and overall herd health.
Meanwhile, Real Syn’s decline raises questions about the lasting influence of genetic supremacy amid rapidly advancing genetic evaluation techniques. Although slight, this drop could signal more profound, underlying shifts in genetic dominance, possibly prompting a reevaluation of existing breeding protocols.
In this climate, the dairy industry must brace for further genetic recalibrations. As bulls like AltaMuller and Pennywise reshape the RZG Interbull Genomic rankings, Real Syn’s slip is a stark reminder of the fluidity inherent in the genetic evaluation landscape. These developments underline the dynamic nature of genomic advancements, encouraging breeders to remain vigilant and adaptable in their quest for genetic superiority.
Swiss Genetic Surge: Beautyman’s Benchmark and the Daughter-Proven Rivalry
The Swiss genetic evaluations reveal an electrifying surge in rankings, dominated by the influential TGD-Holstein Beautyman. With an outstanding +1651 ISET, Beautyman not only eclipses competitors but sets a new benchmark, accelerating the evolution of the Swiss breeding landscape. His prowess signals a shifting paradigm in optimizing genetic potential.
Meanwhile, the domestic daughter-proven index chauffeurs a robust rivalry, with Vogue Letsgo carving a significant niche as an Applicable son with an admirable +1495 ISET. It’s a heated contest, further punctuated by the rising wildcard, Wilder Hotspot P, whose notable +1411 ISET places him within striking distance. This intense race in the daughter-proven segment uncovers a dynamic interplay of genetics and strategy poised to reshape future breeding paradigms in Switzerland.
The Dutch Cadence: Celebrated Titans and Newcomers Shape the Genetic Conquest
In the Dutch rankings, familiar faces once again make their presence felt. Genosource Captain stands unrivaled as the leading Black and White daughter-proven sire, boasting an exceptional +329 gNVI rating, bolstered by 38 daughters in his Dutch index. Following closely is Gigaball, seizing the second position with a +316 gNVI and supported by an impressive 203 daughters. Not far behind, Kax Gladius completes the top three with a solid +313 gNVI.
Bento emerges as the leader in the realm of genomic sires, claiming the #1 spot with a formidable +439 gNVI, reflecting a rise of 16 points. His performance is marked by significant figures such as +2322kgM and +1038 Lifetime. A new addition to the rankings, Soranjo, son of Soysauce, makes an impactful debut by securing the second position at +385 gNVI. Bringing up the third spot is Rockwell, son of Rover, with a commendable +380 gNVI.
The December 2024 genetic evaluations highlight a dynamic and competitive international landscape where leading sires from the USA, Canada, UK, Italy, Germany, and Switzerland showcase impressive advancements in dairy genetics. Genosource Captain sustains supremacy in the USA, while Canada’s genetic scene is characterized by Sheepster’s significant impact and Overhaul’s leadership. The UK sees Denovo Harmony’s rise, reflecting refined breeding efforts. At the same time, Italy was enthralled with Ecbert’s unmatched genomic prowess alongside international luminaries like Cookiecutter Hadley. Germany’s genetic stage is recalibrated with AltaMuller and Pennywise’s strategic ascents. Switzerland’s innovations are marked by Beautyman’s peak performance and fierce domestic competition.
These developments indicate national strengths and collectively underscore a global revolution in dairy farming. They prompt industry professionals to ponder the broader implications of these evaluations—how can they drive forward innovative solutions and sustainable practices in dairy farming? As these genetic achievements evolve, they promise new possibilities for livestock management, productivity, and profitability across the global stage. Dairy farmers and industry stakeholders are encouraged to embrace these changes, anticipating an exciting future of continuous improvement and breakthrough advancements in dairy genetics.
Key Takeaways:
Genosource Captain maintains his dominant position in the USA with a leading GTPI and significant contributions from additional daughters.
Canada’s genetic landscape sees Ocd Trooper Sheepster and Stantons Overhaul P making a significant mark in gLPI rankings, with a notable focus on genomic prowess.
The UK’s Denovo Harmony leads the pack, reflecting the region’s evolution in breeding priorities and highlighting competitive genomic bulls.
In Italy, Ecbert remains the foremost genomic sire, with close competition from Cookiecutter Hadley in the international genetic race.
Germany experiences a recalibration in its genetic rankings, with AltaMuller and Pennywise rising to the forefront of B&W Interbull Genomic standings.
Switzerland marks significant achievements with TGD-Holstein Beautyman atop the ISET rankings, indicating a competitive genetic atmosphere.
Summary:
The December 2024 genetic evaluations have ignited conversations worldwide, highlighting standout performances and intriguing shifts across the dairy farming community. Genosource Captain remains the dominant force in the USA, with SDG Cap Garza in close pursuit. Canada’s celebration centers on Ocd Trooper Sheepster’s significant influence and Stantons Overhaul P’s remarkable rise. In the UK, Denovo Harmony signals a new era of evolution in dairy breeding, while Italy observes Ecbert’s continued supremacy as Cookiecutter Hadley mounts a formidable challenge. Germany experiences recalibrations, with AltaMuller and Pennywise emerging as leaders, whereas Switzerland sees TGD-Holstein Beautyman setting a new benchmark. These dynamic developments underscore the role of competitive rankings, scientific innovations, and international collaborations in transforming the genetics powering dairy production globally.
Join the Revolution!
Bullvine Daily is your essential e-zine for staying ahead in the dairy industry. With over 30,000 subscribers, we bring you the week’s top news, helping you manage tasks efficiently. Stay informed about milk production, tech adoption, and more, so you can concentrate on your dairy operations.
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.