Learn how efficient silage preservation methods can significantly cut greenhouse gas emissions in dairy farming. Are you prepared to reduce your farm’s carbon footprint and enhance sustainability?
As global temperatures rise and environmental concerns grow, the agricultural sector, especially dairy farming, stands at a pivotal point. Dairy farming contributes to greenhouse gas emissions, prompting urgent action. With methane emissions from cows, carbon dioxide from growing feed, and nitrous oxide from manure, innovative solutions are essential. One promising strategy is careful silage preservation, balancing productivity with sustainability.
Advanced silage techniques, like using specific microbial inoculants, can significantly reduce emissions. For example, homofermentative inoculants improve fermentation, preserving nutrients and reducing spoilage. This enhances feed efficiency and lowers methane production, making it a crucial strategy for sustainable dairy farming.
The dairy industry‘s efforts to reduce emissions are vital. These strategies help meet climate goals, improve public image, and offer ecological and economic benefits. Each individual’s contribution is significant in this collective effort.
Proper silage techniques using homofermentative and heterofermentative inoculants significantly cut greenhouse gas emissions. These methods improve forage quality, dry matter recovery, and aerobic stability, aiding overall emission reduction in dairy farming.
This article explores the critical role of efficient silage preservation in reducing greenhouse gas emissions from dairy farming, outlining key strategies and successful case studies.
Silage Preservation: A Key Strategy for Nutritional Consistency and Emissions Reduction
Silage preservation, which ferments and stores green forage crops in an air-free environment, is essential for dairy farming. This method provides a steady feed supply year-round, despite seasonal changes, and helps reduce greenhouse gas emissions. Efficient fermentation reduces methane and other harmful gases, making dairy practices more sustainable.
The use of microbial inoculants in silage preservation plays a vital role in improving the feed’s nutrient quality. These inoculants, which are typically bacteria, lead the fermentation process, quickly lowering pH levels and keeping nutrients and energy intact. This process boosts aerobic stability and reduces heating, thereby preserving the silage’s quality and nutrition. The result is a significant reduction in greenhouse gas emissions, making dairy practices more sustainable.
High-quality silage is crucial for animal nutrition, offering digestible and nutrient-rich feed that benefits dairy cattle’s health, milk production, and well-being. Essential factors like fermentation rate, nutrient conservation, fiber digestibility, and storage life enhance the feed. Research shows that inoculated silage increases milk production and improves stability, cutting down on spoilage and waste.
Understanding the Importance of Silage Preservation Within Dairy Farming Sustainability
Practical silage preservation ensures a consistent, high-quality feed supply throughout the year, directly impacting milk production efficiency and herd health. Advanced silage preservation methods are vital for environmental stewardship and economic success in dairy farming.
Traditional methods like dry hay production depend on the weather and often lose nutrients. In contrast, wet silage kept without oxygen maintains better feed quality and stable nutritional content. Silage inoculants with particular microorganisms enhance fermentation, speeding up pH reduction and preserving nutrients.
Controlled microbial fermentation keeps nutrients intact, improves ‘fiber digestibility ‘, which refers to the ability of the animal to break down and utilize the fiber in the feed, and extends bunk life, making forage tasty and nutritious. These advances lead to better milk yield, reduced feed costs, and lower environmental impacts, helping farmers achieve better economic and sustainability goals.
Effective Methods to Mitigate Greenhouse Gas Emissions
Adopting waste reduction strategies is essential to reducing greenhouse gas emissions in dairy farming. Efficient silage preservation is crucial in maintaining nutritional consistency for livestock and lowering emissions.
Timing and harvesting methods are vital. Harvesting crops at the correct moisture content (60-70%) ensures good fermentation, less spoilage, and reduced methane emissions from better feed preservation.
Using additives and inoculants helps improve fermentation and cut spoilage. Homofermentative inoculants quickly lower pH levels, stopping harmful bacteria and keeping plant proteins intact. This leads to better aerobic stability, less heating, and improved feed efficiency.
Inoculants like probiotics and enzymes enhance silage fermentation. Probiotics, like certain lactic acid bacteria, help preserve nutrients. At the same time, enzymes break down complex carbs, making nutrients easier for animals to digest.
Proper silage storage and management are crucial for quality and emission reduction. Storing silage in airtight conditions prevents aerobic spoilage and methane emissions.
These practices align dairy farming operations with global sustainability goals and improve economic viability by boosting feed efficiency and animal productivity.
Case Studies: Successful Silage Strategies in Dairy Farms
Green Pastures Dairy in Wisconsin serves as a shining example of the success of advanced silage preservation methods. By using homofermentative inoculants, they improved dry matter recovery and reduced methane emissions by an impressive 12%. These inoculants also enhanced aerobic stability by 15%, significantly reducing spoilage.
Sunnybrook Farms in California saw similar benefits using microbial inoculants and better silage compaction. They achieved a 20% increased lactic acid production and cut GHG emissions by 10%. Improved feed quality also raised milk yields by 8%, showing environmental and economic gains.
Both farms emphasized the importance of monitoring moisture content, chop length, and compaction and recommended careful silage management. Working with agricultural scientists and staying informed about new research was also crucial in improving their preservation methods.
The Bottom Line
Reducing dairy emissions is essential to combat climate change. Dairy farming emits many greenhouse gases, so adopting sustainable practices is critical to the environment.
Efficiently preserving silage is a key strategy. Techniques like microbial inoculants, which promote quick pH drops, and homofermentative bacteria, which improve energy efficiency, help maintain feed quality and reduce emissions.
Dairy farmers play a pivotal role in the transition to a more sustainable future. By adopting and championing these methods, they not only ensure their economic viability but also demonstrate their commitment to environmental responsibility.
Key Takeaways:
Silage preservation helps in maintaining feed quality, which directly impacts animal health and productivity.
Advanced preservation techniques can reduce methane emissions from enteric fermentation by improving feed efficiency.
Proper storage and management of silage minimize losses and reduce the need for additional feed production, thus cutting down related GHG emissions.
The use of inoculants in silage can enhance fermentation processes, ensuring better nutrient preservation and lower emission levels.
Summary:
Dairy farming contributes to 4% of global greenhouse gas emissions, causing methane, carbon dioxide, and nitrous oxide levels to rise. To combat this, dairy farmers must adopt sustainable practices, aligning with the Paris Agreement. Proper silage preservation techniques using homofermentative and heterofermentative inoculants can significantly reduce emissions, improving forage quality, dry matter recovery, and aerobic stability. Other factors contributing to emissions include enteric fermentation in cows, growing and preserving feed crops, and managing manure. A combined approach, including improved feed efficiency, better manure management, and optimized feed crop growth and storage, is necessary. Silage preservation is crucial for dairy farming, providing a steady feed supply and reducing greenhouse gas emissions. Advanced silage preservation methods are essential for environmental stewardship and economic success. Timing and harvesting methods are essential for maintaining nutritional consistency and lowering emissions. Inoculants like probiotics and enzymes can enhance silage fermentation, preserving nutrients and breaking down complex carbohydrates. Proper silage storage and management are essential for quality and emission reduction, aligning dairy farming operations with global sustainability goals and improving economic viability.
Boost dairy cow productivity with optimal dietary starch and amino acids. Discover how to enhance component yields and improve feed efficiency. Ready to maximize your herd’s potential?
Profitability for dairy farmers depends on increasing the fat and protein output in milk. To maximize milk output, dairies must implement nutrition plans that stress high digestibility and the exact balance of critical elements. Precision nutrition—which emphasizes the proper ratio of carbohydrates to amino acids—is crucial. In the upcoming sections, we investigate techniques to maximize essential nutrients, enabling dairy farms to balance production, maintain herd health, and enhance overall efficiency and success. Maximizing milk components isn’t just about feeding more; it’s about feeding smarter. Precision nutrition ensures that every bite contributes to superior productivity and animal well-being.
Key strategies covered include:
The importance of evaluating feed efficiency and component yields
The critical role of forage quality and inventory management
Balancing starch and NDF for optimal rumen function
Incorporating sugars and soluble fibers
The strategic use of amino acids and fatty acids
Innovative solutions amidst forage shortages
Addressing common bottlenecks in dairy management
Maximizing Dairy Cow Productivity: Key Metrics for Success
Two primary indicators assess dairy cow productivity: feed efficiency and daily milk output adjusted for fat and protein, known as Energy Corrected Milk (ECM). A feed efficiency ratio of 1.4 to 1.6 pounds of milk per pound of dry matter intake (DMI) is effective for high-producing dairy cows. Good ECM values vary based on breed, lactation stage, and dairy operation goals. Generally, Holstein cows, which yield high milk volumes, tend to have higher ECM values. However, context and herd-specific factors are crucial when evaluating ECM.
Furthermore, the daily consumption of fat and protein or ECM is essential. ECM standardizes milk production to include fat and protein levels by offering a better picture of a herd’s output. Higher fat and protein content milk often commands more excellent pricing. Dairy farmers may boost component yields by emphasizing feed economy and ECM. These are linked: better feed efficiency increases fat and protein yields, increasing dairy businesses’ profitability and output.
The Crucial Role of Forage Quality in Dairy Production
Forage quality becomes extremely important for dairy production, particularly with the digestion of neutral detergent fiber (NDF). High-quality fodder improves herd efficiency and nutritional intake. NDF digestibility primarily focuses on the cow’s ability to break down cellulose, hemicellulose, and lignin-based plant cell walls. Excellent digestibility ensures cows convert fiber into energy effectively, enhancing rumen performance.
High digestibility forages offer several advantages to optimize rumen efficiency and overall productivity:
Enhanced Rumen Function: A stable and efficient ruminal environment with better fermentation and more volatile fatty acids is essential for milk production and energy levels.
Increased Milk Components: Improved energy availability supports higher milk fat and protein yields, boosting economic viability.
Better Health and Productivity: Reduced risk of metabolic disorders, leading to healthier cows and sustained productivity.
Ultimately, dairy farm managers may strategically address forage quality and NDF digestibility. High digestibility forages guarantee effective feed use, better cows, and increased milk output, promoting a sustainable dairy enterprise.
Balancing Starch and NDF: The Key to Enhanced Dairy Cow Productivity
Enhancing dairy cow productivity hinges significantly on the precise management of starch content in their diet. As a cornerstone energy source, starch is pivotal for achieving high milk yields. However, it must be judiciously balanced with neutral detergent fiber (NDF) to prevent metabolic issues and maintain overall cow health.
The interplay between starch and NDF can profoundly influence milk production and component quality. While starch boosts milk yield and energy levels, excessive amounts can lead to acidosis, disrupting rumen health and decreasing feed intake. Conversely, insufficient starch limits energy availability, thereby reducing milk production.
The ideal NDF to starch ratio can vary based on forage type, lactation stage, and overall diet. Typically, an effective diet consists of 30-32% NDF and 25-28% starch. This balance maintains rumen function and provides energy for milk production.
Cows need an adequate supply of NDF to sustain optimal rumen function and avert digestive complications. While increasing starch can enhance milk yield and protein content, the inclusion of highly digestible starch sources, such as maize, is often preferred for their efficiency. At the same time, incorporating highly digestible NDF sources, such as citrus or beet pulp, can mitigate the risks associated with high-starch diets. These fibers improve rumen function and help maintain higher milk fat production.
Dairy producers can carefully balance starch and NDF to optimize milk output, component yields, and overall herd health. Although starch remains crucial, its optimal utilization requires a nuanced approach. Managing the interaction between starch and NDF is essential to maximizing milk production and quality while safeguarding cow health.
Strategic Benefits of Incorporating Sugars and Soluble Fibers in Dairy Cow Diets
Incorporating soluble fibers and sugars into dairy cow diets presents clear advantages. By immediately providing energy, sugars play a pivotal role in enhancing rumen fermentation and increasing butyrate levels. Additionally, certain fatty acids are essential for effective milk fat production. By strategically lowering starch and increasing sugar content to 5–7%, butyrate production is maximized, thus improving the quality of milk fat. Soluble fibers, such as those from beet or citrus, augment the pool of fermentable fibers. These fibers break down rapidly in the rumen, thereby boosting butyrate levels. These dietary adjustments raise milk fat content and enhance energy efficiency, increasing dairy farm profitability and output.
The Essential Role of Amino Acids in Enhancing Dairy Cow Productivity
Dairy cow diets require amino acids, significantly affecting milk output and general health. Lysine, methionine, and histidine are essential amino acids because they function in protein synthesis and metabolism.
Lysine is essential for muscle protein synthesis, calcium absorption, immune function, and hormone production. As the first limiting amino acid in dairy diets, lysine supplementation is vital for maximizing milk protein yield. Adequate levels can be ensured through high-lysine feeds or supplements.
Methionine is critical for methylation and influences DNA and protein synthesis. It also helps produce other amino acids like cysteine and taurine. Methionine levels can be maintained with methionine-rich feeds (e.g., soybean meal) or specific additives.
Histidine supports histamine and carnosine production, which is essential for muscle function and metabolism. Its direct influence on milk production makes it vital. Histidine is typically sourced from blood meal.
To maintain adequate amino acid levels, diet formulation should include:
Analyzing feed components for amino acid content.
High-quality protein sources like canola, blood, and soybean meal are used.
Employing supplements for targeted amino acid delivery.
Monitoring cow performance to adjust diets as needed.
Maintaining nitrogen balance and maximizing feed efficiency depends on carefully balancing these amino acids between rumen-degradable and rumen-undegradable protein needs. Emphasizing these essential amino acids produces better cow health, yields, and financial returns.
The Strategic Role of Fatty Acids in Dairy Cow Diets
Dairy cow diets must include fatty acids as they affect metabolic processes necessary for milk output. Usually considered energy sources, certain fats like palm oil and high oleic beans may significantly increase milk fat content and general energetic efficiency. Rich in palmitic acid (C16:0), palm oil powerfully promotes milk fat production. It increases milk fat production by supplying necessary fatty acids for triglyceride synthesis in the mammary gland, saving the cow’s metabolic energy for other uses. This produces more milk fat without draining the cow’s energy supply too rapidly.
High oleic beans, with oleic acid (C18:1), increase mammary glands’ cell membrane fluidity and metabolic flexibility. This improves milk fat synthesis and digestion, guaranteeing that energy intake is effectively transformed into useful outputs like more excellent milk fat percentages.
Including these fatty acids in dairy cow diets calls for a measured approach. Reducing feed efficiency and causing metabolic problems may be the result of overfeeding. However, adequately controlled lipids from palm oil and high oleic beans may significantly increase production, enabling a dairy farming system with maximum efficiency.
Navigating the Challenges of Variability in Blood Meal for Dairy Nutrition
One major challenge in dairy nutrition is the variability in feed ingredients, especially blood meal. Blood meal’s inconsistency in bioavailability and digestibility can complicate diet formulations and affect herd productivity. This variability often results from differences in processing, handling, and sourcing. Regular testing and analysis of blood meal batches are essential to tackle this. Implementing assays to estimate bioavailability and working with reputable suppliers can help ensure consistent product quality.
Additionally, diversifying protein sources by incorporating fish, soybean, or other high-quality supplements can reduce reliance on blood meal and mitigate its variability. Utilizing precise feed formulation software that adjusts nutrient levels based on ingredient analyses can also help maintain balanced diets. While blood meal variability is challenging, proactive management and diversified supplementation can ensure consistent nutrient delivery and enhance dairy cow productivity.
Innovative Solutions for Maintaining Optimal NDF Levels Amid Forage Shortages
When forage availability is limited, innovative solutions are needed to maintain optimal NDF levels and support rumen function. Utilizing non-forage fiber sources can be effective for dairy producers facing constrained forage supplies. Consider incorporating the following alternatives:
Wheat Mids: Enhance the overall fiber content of the diet with this valuable NDF source.
Soy Hulls: Rich in digestible fiber, they boost dietary fiber without affecting feed efficiency.
Beet pulp is high in fiber and palatable and supports rumen health.
Citrus Pulp: Adds soluble fibers, improving digestion and nutrient absorption.
These non-forage fiber sources can help balance the diet, ensuring adequate fiber to support healthy rumen function and productivity, even when forage supplies are limited.
Addressing Common Management Bottlenecks: Unlocking Dairy Cow Productivity
Maximizing dairy cow output depends on addressing typical management obstacles such as crowding and limited water space. Overcrowding decreases resting time, raises stress, lowers feed intake, and affects milk output and general health by reducing resting time. Following advised stocking densities is essential to help mitigate these problems so that every cow has adequate room to walk, eat, and relax. Gradually reducing stocking density will significantly improve animal comfort and output.
Furthermore, ensuring water troughs are sufficiently spaced and easily reachable is crucial, as design defects might restrict adequate water availability, affecting hydration and feed efficiency. Optimizing cow comfort requires sufficient lighting, good ventilation, and dry, clean bedding. Frequent observation of the barn surroundings helps to avoid respiratory problems and support steady milk output.
Good time management is essential. Maintaining constant feeding schedules, structuring the cows’ day to promote rest and rumination, and limiting disturbances aids digestion and nutrient absorption, directly affecting milk output. Regular evaluations of cow behavior and health markers help to spot early stresses or inefficiencies. Using wearable technology or routine health inspections, minute indicators of pain or disease may be identified, enabling quick treatments and continuous output.
The Bottom Line
Understanding vital benchmarks like feed efficiency and pounds of fat, protein, or energy-corrected milk daily helps maximize dairy cow output. Excellent forages are essential; their primary goal should be to raise digestible NDF to improve ruminal efficiency and general cow condition. Energy supply and milk components depend on carefully balancing starch and NDF levels. Adding soluble fibers and sugars enhances fermentation and increases milk fat synthesis. Adding methionine, lysine, and histidine—essential amino acids—helps to maximize protein synthesis and milk supply. Adding fatty acids improves milk fat production and meets energy demands. Dealing with the fluctuations in blood meal as a protein source guarantees a consistent dairy cow diet. When premium forages are few, non-forage fiber sources may help preserve NDF levels. Addressing management issues such as water availability and congestion significantly affects output. These techniques improve general herd health, milk supply, and feed efficiency, promoting economic success. By being knowledgeable and flexible, producers can ensure the welfare of their herds and support successful, environmentally friendly farming.
Key Takeaways:
Feed efficiency and pounds of fat and protein per day are critical metrics for evaluating dairy cow productivity.
Increasing utilizability of Neutral Detergent Fiber (NDF) in forages significantly enhances dairy cow performance.
Balancing dietary starch levels while optimizing NDF can lead to higher component yields.
Incorporating sugars and soluble fibers into cow diets can boost butyrate production and overall efficiency.
Amino acids, particularly lysine, methionine, and histidine, play an essential role in maximizing milk production.
Fatty acids, such as those from high oleic beans, contribute to higher milk fat and overall productivity.
The variability of blood meal can impact its effectiveness; monitoring and adaptation are necessary for optimal use.
Non-forage fiber sources can help maintain optimal NDF levels when forage availability is limited.
Common management bottlenecks like overcrowding and inadequate water space can inhibit productivity despite a well-balanced diet.
Summary:
Dairy farmers’ profitability relies on increasing fat and protein output in milk through nutrition plans that focus on high digestibility and balance of critical elements. Precision nutrition, which emphasizes the proper ratio of carbohydrates to amino acids, is crucial for dairy farms to balance production, maintain herd health, and enhance efficiency. Key strategies include evaluating feed efficiency, balancing starch and NDF for optimal rumen function, incorporating sugars and soluble fibers, strategic use of amino acids and fatty acids, innovative solutions amidst forage shortages, and addressing common dairy management bottlenecks. Higher feed efficiency increases profitability, lowers feed costs, and improves environmental sustainability.
Discover how adjusting the palmitic to oleic acid ratio in dairy cow diets can boost milk yield and efficiency. Curious about the optimal ratio for peak performance?
Ensuring an adequate energy supply for dairy cows during early lactation is paramount for maintaining optimal production performance. This critical period, which follows calving, demands significant energy as cows adjust to increased milk output and replenish their reserves. Without sufficient power, cows can encounter various health issues, including decreased milk production and poor reproductive performance.
Fatty acids (FA) have emerged as vital components in lactating cows’ diets due to their role in boosting energy supply. FAs vary in chain length and degree of saturation, influencing their impact on the cow’s metabolism and productivity. Specifically, integrating these components into feed has shown promise in addressing energy deficits during early lactation.
“This study was conducted to evaluate the effect of different ratios of palmitic acid (C16:0) to oleic acid (cis-9 C18:1) on the production performance, nutrient digestibility, blood metabolites, and milk FA profile in early lactation dairy cows.”
By examining the variations in the ratios of palmitic acid to oleic acid, researchers aimed to discern how these changes could optimize dairy cow performance. The potential benefits of this study’s findings could lead to better dietary formulations supporting lactating cows’ health and productivity, offering a promising future for dairy cow nutrition.
The Balancing Act: Harnessing the Dual Benefits of Palmitic and Oleic Acids in Dairy Cow Nutrition
Palmitic acid, a saturated fatty acid known chemically as C16:0, is commonly found in palm oil, meat, butter, cheese, and milk. Being a long-chain fatty acid, it is solid at room temperature. It plays a significant role in animal energy storage and cell membrane structure. Conversely, oleic acid is a monounsaturated fatty acid denoted as cis-9 C18:1, predominantly sourced from olive oil, avocados, and nuts. Its liquid state at room temperature and single and double bonds contribute to its distinctive properties, including enhancing cell permeability and fluidity.
Previous research has highlighted the distinctive impacts of these fatty acids on milk production and overall cow health. Palmitic acid has been associated with increasing milk fat content, potentially elevating milk’s energy density. However, excessive amounts can sometimes lead to metabolic issues in cows, such as impaired liver function and increased body fat stores. Conversely, oleic acid has been shown to enhance milk yield and improve the milk’s fatty acid profile, promoting healthier milk fat composition. Studies have also indicated that oleic acid could improve feed efficiency and nutrient digestibility, offering potential benefits for early lactating dairy cows.
The cumulative findings from these studies suggest a nuanced interplay between palmitic and oleic acids in dairy cow diets. While palmitic acid predominantly boosts fat content, oleic acid supports overall milk yield and cow health, making it a valuable component in balanced dairy cow nutrition.
A Meticulously Controlled Study: Tailoring Fatty Acid Ratios for Optimal Dairy Cow Performance
The study was meticulously designed to evaluate the influence of varying ratios of palmitic acid (C16:0) to oleic acid (cis-9 C18:1) on early lactation dairy cows’ production performance and health. This meticulous design ensures the reliability and accuracy of the study’s findings, instilling confidence in the research’s outcomes.
The cows were randomly divided into three treatment groups, each consisting of 24 cows. These groups were assigned distinct iso-energy and iso-nitrogen diets, ensuring uniform energy and nitrogen intake across all groups but differing in the ratios of C16:0 to cis-9 C18:1 fatty acids:
Group 1: 90.9% C16:0 + 9.1% cis-9 C18:1 (90.9:9.1)
Group 2: 79.5% C16:0 + 20.5% cis-9 C18:1 (79.5:20.5)
Group 3: 72.7% C16:0 + 27.3% cis-9 C18:1 (72.7:27.3)
The fatty acids were added to the diets at 1.3% on a dry matter basis, ensuring the cows received consistent and controlled amounts of the specific fatty acids to accurately assess their effects on production performance, nutrient digestibility, blood metabolites, and milk fatty acid profiles.
Maximizing Dairy Cow Performance: The Impact of Higher cis-9 C18:1 Ratios
As the ratio of cis-9 C18:1 increased, notable improvements were observed in milk yield, milk protein yield, and feed efficiency, all of which showed linear increases. Specifically, a higher cis-9 C18:1 ratio correlated with a boost in milk production and protein output. Although the percentage of milk protein and milk fat yield remained consistent across treatments, milk fat percentage tended to decrease. Additionally, the study indicated that higher cis-9 C18:1 ratios resulted in a linear increase in lactose yield and a slight increase in lactose percentage. In contrast, the overall rate of total solids and somatic cell count in milk experienced a decline.
Body weight loss among cows decreased linearly with the rising cis-9 C18:1 ratio, underscoring the dietary benefit of this fatty acid in maintaining healthier body conditions. The nutrient digestibility for ether extract and neutral detergent fiber improved linearly, improving overall nutrient absorption. On the blood metabolite front, plasma glucose levels increased linearly, whereas triglyceride and nonesterified fatty acid concentrations decreased linearly. These results underscore that a 72.7:27.3 C16:0 to cis-9 C18:1 ratio yields the most significant benefits for dairy cows in early lactation, enhancing performance metrics and reducing body weight loss.
Nutrient Digestibility and Blood Metabolite Adjustments: The Role of Higher Oleic Acid Ratios
Regarding nutrient digestibility, the study found a significant linear increase in both ether extract and neutral detergent fiber digestibility as the ratio of cis-9 C18:1 increased. This suggests that higher levels of oleic acid provide more energy and enhance the cows’ ability to process fibers and fats, which are critical for maintaining overall health and production efficiency. These findings highlight the potential for dietary adjustments to optimize feed efficiency and minimize wastage, empowering dairy farmers in their feeding regimens.
Regarding blood metabolites, the research showed notable changes linked to the incremental inclusion of cis-9 C18:1. Plasma glucose levels rose linearly, indicating an improved energy status critical for sustaining high milk production. On the other hand, concentrations of triglycerides and nonesterified fatty acids (NEFA) decreased linearly. These decreases in NEFA can be particularly beneficial as high NEFA levels are often associated with metabolic stress and health disorders in dairy cows. Thus, by better balancing fatty acid ratios, dairy farmers might be able to mitigate some common health issues and support more robust milk production.
Optimizing the Milk Fatty Acid Profile: A Symbiotic Adjustment
Delving into the milk fatty acid profile, it became evident that altering the ratios of C16:0 to cis-9 C18:1 had a considerable impact. Specifically, as the proportion of cis-9 C18:1 increased, there was a noteworthy quadratic decline in de novo fatty acids synthesized directly within the mammary gland by approximately 10%. Concomitantly, there was a linear rise in mixed and preformed fatty acids by 15% and 20%, respectively, the latter being directly absorbed from the diet or mobilized from body fat reserves. This shift in the fatty acid profile highlights the body’s adaptive responses to dietary modifications, aiming to optimize energy utilization and milk production.
Revolutionizing Dairy Nutrition: Strategic Fatty Acid Ratios for Peak Early Lactation Performance
The implications of this study are profound for dairy farmers striving to optimize their herd’s performance during early lactation. By carefully adjusting the ratios of palmitic acid (C16:0) and oleic acid (cis-9 C18:1) in the cows’ diets, farmers can substantially enhance milk production, protein yield, and feed efficiency. The study suggests that increasing the proportion of oleic acid to 27.3% in the dietary fat blend boosts milk yield and supports better lactose production, which is crucial for milk quality.
Moreover, this targeted nutritional strategy appears to mitigate body weight loss typically observed in early lactation, promoting better overall health and longevity of dairy cows. Enhanced nutrient digestibility and favorable changes in blood metabolites, such as increased plasma glucose levels and reduced triglycerides, further underscore the health benefits of this diet adjustment. Implementing these findings in feeding regimens can thus lead to more robust cows that maintain high milk productivity with improved metabolic health.
For practical application, dairy farmers should consider incorporating higher oleic acid ratios into their feeding programs, particularly during the critical early lactation period. This approach supports optimal production performance and contributes to the herd’s well-being, promising long-term benefits in milk yield and dairy cow health.
The Bottom Line
This study underscores the critical role that the dietary ratio of palmitic acid (C16:0) to oleic acid (cis-9 C18:1) plays in enhancing the production performance of early lactation dairy cows. Key benefits emerge from increasing the cis-9 C18:1 ratio, which includes improved milk yield, protein yield, feed efficiency, and a reduction in body weight loss. Notably, the research identifies the optimal C16:0 to cis-9 C18:1 ratio as 72.7:27.3, achieving the most substantial positive effects on dairy cow health and productivity.
Based on these findings, adjusting the fatty acid ratios in the cow’s diet could be a game-changer for dairy farmers aiming to optimize their herd performance. By carefully incorporating a higher proportion of cis-9 C18:1, you can maximize milk production and improve the overall well-being of your cows during the critical early lactation period.
Farmers are encouraged to consult additional resources and scientific literature to explore practical implementation and further details. Reviewing dairy nutrition journals or seeking guidance from cattle nutrition experts may be beneficial for a deeper dive into the study’s methodology and comprehensive results.
Embrace the potential to revolutionize your dairy farming approach by fine-tuning dietary fatty acid ratios—your cows’ performance and health could significantly benefit.
Key Takeaways:
Enhanced Milk Production: Increasing the ratio of cis-9 C18:1 led to a linear increase in milk yield and feed efficiency. Milk protein yield also saw significant improvement.
Variable Fat Content: While the milk fat percentage tended to decrease, lactose yield and lactose percentage increased with higher cis-9 C18:1 ratios.
Body Weight Dynamics: Cows experienced decreased body weight loss, highlighting better energy utilization and overall health.
Nutrient Digestibility: There was a linear enhancement in nutrient digestibility, particularly in ether extract and neutral detergent fiber.
Blood Metabolites: A rise in plasma glucose concentration was observed, though triglyceride and nonesterified fatty acid concentrations decreased.
Milk Fatty Acid Profile: The concentration of mixed and preformed fatty acids increased, while de novo fatty acids saw a quadratic reduction.
Summary:
A study aimed to assess the effects of different palmitic and oleic acid ratios on early lactation dairy cows’ performance, nutrient digestibility, blood metabolites, and milk FA profile. The researchers aimed to understand how these changes could optimize dairy cow performance and improve dietary formulations. Palmitic acid, a saturated fatty acid found in palm oil, meat, butter, cheese, and milk, plays a crucial role in animal energy storage and cell membrane structure. On the other hand, oleic acid, a monounsaturated fatty acid from olive oil, avocados, and nuts, enhances cell permeability and fluidity. The study found that increasing the cis-9 C18:1 ratio led to improvements in milk yield, milk protein yield, and feed efficiency. Higher oleic acid ratios significantly improved nutrient digestibility and blood metabolites. The optimal C16:0 to cis-9 C18:1 ratio is identified as 72.7:27.3.
Learn how dairy farmers mix instinct and data to make the best decisions. Can they balance both to improve animal health and profits? Find out more.
In today’s dynamic dairy farming landscape, making informed choices is crucial. Dairy farmers now rely on a blend of instinct and data. While gut feelings often guide initial decisions, it’s the data that ultimately confirms their validity, striking a harmonious balance between the two.
Wisconsin dairy farmer James, a testament to the power of combining instinct and data, recalls a time when his herd experienced a sudden health downturn. His deep-rooted farming instincts led him to suspect issues with the stream. By cross-referencing nutritional content with health records using data analytics, his intuition was validated, and he was guided to make the necessary changes, saving his farm from significant losses. This real-life example underscores the criticality of the synergy between intuition and data-driven decision-making in today’s dairy industry.
By fusing precise data with gut feelings, dairy farmers can make well-informed assumptions, which can lead to better judgments and increased production and profitability.
While data-driven insights and intuitive understanding can lead to sound judgments, an overreliance on either can be detrimental. Relying solely on facts can slow down decision-making while depending too much on intuition can lead to costly mistakes. The key is to find a harmonious balance where facts and instinct work in tandem to ensure the profitability of your dairy farming operations.
Instincts and Intuition: The Historical Heartbeat of Dairy Farming
Before advanced data systems, dairy producers mainly depended on gut and generational knowledge. This historical dependence on instinct stems from observational learning and hands-on experience, wherein the art of farming coexists peacefully with science. Depending on instinct and personal experience, farmers made critical judgments on animal health, breeding, and farm management.
Daily contact with their herds honed their instincts, which helped farmers to identify minute indicators of disease or distress—a necessary ability for preserving herd health and production. Minute changes in behavior, appetite, or physical appearance may foretell a cow’s preparation for breeding or spot early illness symptoms.
These simple revelations also applied to more general agricultural management techniques. They are often based on a complex awareness of the local surroundings and personal experience, decisions on planting, harvesting, rotating grazing pastures, and choosing breeding couples. Effective agricultural methods before contemporary data analytics developed depended on these arbitrary judgments.
Still, depending only on instinct has difficulties as well. Intuition drives quick decision-making and creative problem-solving, but it may cause contradictions and expensive mistakes. The historical reliance on instinct emphasizes its importance. It requires a balanced approach using intuitive knowledge and factual evidence to maximize decision-making procedures.
The Modern Dairy Farm: Where Tradition Meets Cutting-Edge Technology
The contemporary dairy farm deftly combines history with technology, driven by data-centric improvements. Analytics, software, and sensors now provide insights and control unheard of years before. Sensors’ real-time monitoring of factors like herd health and milk output transforms unprocessed data into valuable knowledge.
These sensors’ data flows into sophisticated software running algorithms to identify trends and abnormalities beyond human awareness. This helps to make proactive decisions that solve problems before they become expensive.
Analytics systems allow farmers to maximize feed efficiency and reproduction cycles by seeing data across time. Understanding this data can help farmers make wise choices, increasing sustainability and output.
Data-driven technology revolutionizes dairy production, elevating environmental stewardship, animal welfare, and efficiency. In this era of precision agriculture, the success of dairy operations hinges on your role, the dairy farmers and farm managers, in effectively utilizing this data.
The Synergy of Instinct and Data: Elevating Dairy Farming to New Heights
Combining data with instinct lets dairy producers use both approaches for wise decision-making. Though evidence verifies or refines theories, instinct sometimes starts them. For example, depending on experience, a farmer may feel a nutrition tweak might increase milk output. Still, depending only on this sense might be dangerous given factors like animal health, feed quality, and weather.
To offset this, the farmer may run a controlled experiment tracking milk production before and after the nutrition modification. This information would support whether the intuition is valid over time and a more significant sample. Results may confirm subtleties like breed-specific or seasonal effects or justify the hunch. Farmers may hone their ideas by combining instinct with data, producing practical insights that improve animal care and profitability.
Another example is the early identification of health problems. A farmer could see minute changes in animal behavior suggesting disease. Even in cases where outward indicators are average, instinct may point you to something amiss. Data analytics tools may be of use here. Systems of health tracking vital signs and activities may gather information to either support or disprove hypotheses. Algorithms may examine this information to identify trends or anomalies consistent with the farmer’s sense of direction.
This interplay between instinct and data implies that while data offers factual evidence, instinct drives invention. This all-encompassing method guarantees that judgments are based on scientific validity and experience. Dairy producers may improve decision-making by balancing instinct and facts, promoting profitability, sustainability, and efficiency.
Navigating the Complexities of Balancing Instinct and Data in Dairy Farming
Dairy producers have to negotiate to balance instinct with statistics carefully. Depending primarily on instinct could result in judgments based on partial or distorted impressions, excluding important information that offers a more realistic view of circumstances. For example, a farmer’s gut sense about herd health can overlook minute, measurable signs of illness, hurting animal welfare and profitability.
On the other hand, overstretching data may lead to “data overload,” in which the sheer amount of information becomes unmanageable, and decision-making procedures are obscured. Analysis paralysis brought on by this may stop decisive action. Blind trust in data-driven judgments stifles innovation and adaptation by ignoring the experienced knowledge and sophisticated understanding that instinct offers.
Ignoring essential facts in favor of gut sentiments also risks compromising economic sustainability and efficiency. Ignoring empirical data in a data-centric agricultural environment compromises farm economic viability and efficiency. Data-driven insights provide patterns and projections that are not immediately obvious from observation, allowing intelligent resource allocation and preventative actions.
Striking the right balance between instinct and data may seem daunting, but it’s a feasible strategy. Combining instinctual insights with thorough data analysis can ensure better profitability and animal welfare while avoiding data overload and disregarding essential data. This reassurance should instill confidence in your ability to navigate this complex task.
Best Practices for Seamlessly Integrating Instinct and Data in Dairy Farming
Finding the right balance between instinct and data involves several best practices for dairy farmers:
Invest in training: Equip your team with data analytics and traditional farming skills. This ensures a seamless integration of data with intuitive decision-making.
Cultivate a data-driven culture: Encourage data consultation while respecting intuitive farming knowledge. View data and instinct as complementary.
Implement incremental changes: Start with small decisions to build confidence in data use and expand gradually.
Leverage predictive analytics: Use models to forecast outcomes based on historical data, validating gut instincts with probabilistic scenarios.
Regularly review and adjust: Continuously analyze decisions against data and instinct to improve alignment and results.
Encourage cross-disciplinary collaboration: Foster teamwork between data scientists and farm managers to combine analytical insights with practical experience.
Adopting these practices helps dairy farmers optimize herd health and profitability.
The Bottom Line
Intuition must be combined with statistics for the best decision-making in modern dairy production. Generating hypotheses and making fast judgments have always depended critically on instincts. Meanwhile, data and technology have shown their capacity to improve profitability and lower risk in contemporary operations.
This combination of instincts and facts is crucial; instincts provide creative foresight, while data gives empirical confirmation, guiding judgments creatively and realistically. Balancing them calls for knowledge of their advantages and drawbacks and using best practices that seamlessly combine them.
Dairy producers may guarantee ongoing success and improve their operations by combining their intuition with data-driven plans. This combined strategy transforms decision-making and ensures the viability of dairy production in the future. Welcome the best of both worlds for the sector’s benefit.
Key Takeaways:
Finding the right balance between instinct and data is crucial for dairy farmers striving to make informed and profitable decisions. Here are the key takeaways:
Instincts are invaluable for generating hypotheses and brainstorming, but over-reliance can lead to misplaced confidence.
Data corroborates gut feelings, validating potential opportunities and enhancing profitability.
A balanced approach that leverages both instinct and data helps dairy farmers navigate critical decisions more effectively.
Instinct-driven hunches can sometimes lead to costly mistakes if not supported by data.
Combining traditional intuition with modern technological insights enables dairy farmers to make the best possible decisions for their operations.
Summary:
Dairy farming today relies on a blend of instinct and data to make informed decisions. Instincts offer creative foresight, while data confirms their validity, striking a balance between the two. Wisconsin dairy farmer James used data analytics to validate his intuition and make necessary changes, saving his farm from significant losses. However, overreliance on facts can slow decision-making and lead to costly mistakes. The key is to find a harmonious balance where facts and instinct work in tandem to ensure profitability. Modern dairy farms combine history with technology, driven by data-centric improvements. Analytics, software, and sensors provide insights and control, transforming unprocessed data into valuable knowledge. Analytics systems help farmers maximize feed efficiency and reproduction cycles, increasing sustainability and output. Data-driven technology revolutionizes dairy production, elevating environmental stewardship, animal welfare, and efficiency. Balancing instinct and data requires knowledge of their advantages and drawbacks and using best practices that seamlessly combine them. By combining intuition with data-driven plans, dairy producers can guarantee ongoing success and improve their operations, transforming decision-making and ensuring the viability of dairy production in the future.
Learn More:
In the evolving landscape of dairy farming, finding the right balance between instinct and data is paramount. As the industry increasingly integrates technology and data analytics, understanding how to leverage these tools while maintaining the invaluable insights gained through experience can significantly impact productivity and profitability. To delve deeper into this intricate balance, consider exploring these related articles:
Learn how Lactanet’s new Lifetime Performance Index will boost sustainability and milkability for Canadian dairy cows by April 2025. Are you prepared for the changes?
Envision a dairy sector where efficient cows produce large amounts of milk, contributing to environmental sustainability. Leading genetic testing and data management for dairy cows in Canada, Lactanet is scheduled to update the Lifetime Performance Index (LPI) by April 2025. This upgrade, with its focus on lowering greenhouse gas emissions and raising ‘milkability,’ promises to match productivity to environmental responsibility, instilling hope for a more sustainable future.
Brian Van Doormaal, chief services officer at Lactanet, says, “It’s not the relative weighting that determines how much of an impact breeding for these traits could have.” “This is the expected reaction you get from breeding for these qualities.”
The revised LPI will include new criteria to improve environmental impact and cow behavior. These developments acknowledge that the overall well-being of cattle and sustainable techniques will determine the direction of dairy farming.
Modernizing the Cornerstone: Enhancing the Lifetime Performance Index (LPI) for a Sustainable Future
Integrating productivity, health, and reproductive characteristics into a single statistic, the Lifetime Performance Index (LPI), has been vital in the Canadian dairy sector. This all-encompassing strategy helps dairy farmers make wise breeding selections by guiding balanced genetic advancements. The LPI ensures general herd production and sustainability by addressing many qualities, preventing overemphasizing any area.
Beyond individual farms, the LPI increases national and global competitiveness by matching industry norms and consumer expectations with breeding goals. This backs up objectives of environmental sustainability, animal welfare, and profitability.
The changing dairy farming environment and the need to handle fresh issues, including environmental implications, drive the suggested LPI changes, including methane emissions and feed efficiency features that fit present ecological targets. Improving characteristics linked to milking speed and temperament satisfies the increasing need for operational effectiveness.
Improved genetic research and data allow more accurate and representative LPI updates. Working with Lactanet and genetic enhancement companies guarantees the index stays relevant across several breeds.
The modifications seek to modernize the LPI, maintaining its value for breeders as they solve current problems and apply fresh scientific discoveries. This strategy will help maintain the Canadian dairy sector’s reputation for quality and inventiveness.
Steering Genetic Excellence: Brian Van Doormaal’s Consultative Leadership
Under the leadership of Brian Van Doormaal, Lactanet’s chief services officer, the consultation process integral to creating the updated LPI is in progress. He has been instrumental in these conversations, ensuring the new LPI structure addresses the diverse genetic aims of various dairy breeds. For Holstein, Ayrshire, Jersey, and Guernsey breeds, he has fostered open communication between Lactanet and genetic improvement groups, emphasizing the importance of their contributions.
Van Doormaal started a thorough consultation by bringing the suggested improvements before the Open Industry Session in October 2023. This prepared the ground for in-depth conversations spanning many months that explored subtleties like the relative weighting of fat against protein in the LPI’s breeding objectives. Every breed has diverse genetic traits and performance criteria, which Van Doormaal has deftly negotiated, bringing various goals and viewpoints.
The updated LPI seeks to capture significant variations between breed-specific genetic targets using this thorough consultation approach. Through close interaction with breed-specific organizations, Van Doormaal guarantees the revised LPI is thorough and catered to every breed’s unique requirements, reflecting an agreement among industry players.
Refining Genetic Precision: Tailoring the Updated LPI to Address Breed-Specific Goals
The revised LPI seeks to meet every dairy breed’s genetic requirements and problems, guaranteeing customized breeding plans for Holstein, Ayrshire, Jersey, and Guernsey cows.
For Holsteins, health concerns, including cystic ovaries and increasing production efficiency, take the front stage. Achieving high milk output without sacrificing health still depends on balancing fat against protein.
Ayrshire breeders prioritize strong milk production and toughness. Given the breed’s usual milk composition, they usually prefer milk solids over protein.
Finding a balance between lifespan and high output is essential for Jerseys. The breed’s abundant butterfat milk prioritizes fat weighing to satisfy market needs.
Guernseys mainly aims to raise milk quality through improved sustainability and health. Discussions on fat vs. protein weightings seek to encourage both, hence preserving the breed’s commercial advantage.
The breed-specific variations emphasize the need for a tailored LPI that addresses each breed’s strengths and problems.
Revolutionizing Genetic Assessment: Expanding the LPI to Enhance Dairy Cow Traits and Sustainability
The current modernization of the Lifetime Performance Index (LPI) marks significant progress in assessing genetic features, raising the index from four to six sub-groups. With an eye on production efficiency and animal welfare, this more precise approach seeks to enhance the breeding and assessment of desired traits in dairy cows.
The updated LPI will separate the present Health and Fertility category into Reproduction and Health and Welfare. While Health and Welfare will focus on general health measures, this move includes important qualities like calving capacity and daughter calving ability under Reproduction.
The new Milkability sub-group—which will now include milking speed and temperamental characteristics—also adds significantly. These qualities directly affect labor efficiency and animal handling; their inclusion addresses a hitherto unknown element of dairy management inside the LPI.
Finally, to address mounting environmental issues, the LPI will incorporate a new Environmental Impact subindex, which was first designed for Holsteins. Reflecting the dairy sector’s emphasis on lowering its environmental impact, this subindex will concentrate on feed and methane efficiency. Research has underlined the critical influence of body maintenance on ecological sustainability, thereby supporting its inclusion.
These modifications improve the LPI’s accuracy and usefulness by matching it with contemporary breeding objectives and ensuring that genetic selection promotes dairy sector sustainability and output.
Pioneering Sustainability: Introducing the Environmental Impact Subindex
As part of its commitment to dairy sector sustainability, the new Environmental Impact subindex is a crucial addition to the revised LPI. This subindex rates body upkeep, methane efficiency, and feed economy, among other essential factors. By measuring a cow’s capacity to turn grain into milk, it helps determine its feed efficiency, thereby reducing its environmental impact. Targeting the decrease of methane emissions per unit of milk produced, methane efficiency addresses a significant contribution to greenhouse gasses. The inclusion of body maintenance in the index underscores the industry’s recognition of its critical influence on ecological sustainability, providing reassurance about its commitment to environmental responsibility.
Since there is enough data for Holsteins, this subindex consists only of them. The subindex will probably be enlarged to cover more breeds as more data about them becomes accessible.
Integrating Behavioral Efficiency: The Pivotal Role of Milkability in Modern Dairy Operations
The new Milkability subindex, which combines previously missing milking speed and temperamental qualities, is one noticeable improvement in the revised Lifetime Performance Index (LPI). These qualities depend on maximizing dairy operations and improving animal care. The subindex lets breeders increase labor efficiency and general herd management by considering milking speed. Faster milking of cows saves time and lessens stress for farm workers and animals, improving the surroundings.
Moreover, temperament is crucial as it influences handling and integration into automated milking systems. Calm, cooperative cows enable the effective running of these devices, reducing injuries and improving milk let-downs. Including temperamental features thus emphasizes the significance of animal behavior in contemporary dairy production and promotes methods that increase output and animal welfare.
Transforming Genetic Insights: Lactanet’s Ambitious Approach to an Intuitive Lifetime Performance Index (LPI)
Lactanet seeks to simplify the Lifetime Performance Index (LPI), increasing its availability and usefulness for breeders. Creating subindices for every collection of genetic features helps the index to become modular and facilitates the concentration on specific features. This method guides breeders through complex genetic material.
The aim is to increase LPI usefulness by using assessments as “relative breeding values,” standardized with a breed average of 500 and a standard deviation of plus or minus 100. This clarity helps to simplify the comparison of the genetic potential of animals within a breed, therefore supporting wise decision-making.
Other subindices, like milk ability and environmental impact, provide more accuracy in genetic improvement. This lets breeders concentrate on specific operational targets, including milking speed or calving capacity.
Ultimately, the updated LPI will be a flexible instrument enabling breeders to maximize their breeding campaigns to satisfy different objectives and goals. This guarantees that the LPI is indispensable for genetic selection in Canadian dairy production.
Embracing Stability and Progress: The Path Forward with the Modernized Lifetime Performance Index (LPI)
A more exacting breeding method is envisaged as the dairy sector prepares for the revised Lifetime Performance Index (LPI) in April 2025. Existing breeding plans will not be disturbed much, with a 98 percent correlation to the present LPI, guaranteeing continuity and dependability. This consistency will help maintain the top-rated bull ranks substantially unaltered. Breeders will have a constant instrument to balance productivity, health, sustainability, and genetics while improving dairy cow features.
The Bottom Line
Optimizing dairy performance and environmental impact will be much advanced with the forthcoming change of the Lifetime Performance Index (LPI) for Canadian dairy cows. The revised LPI set for April 2025 will include additional sub-groups, including Reproduction, Health and Welfare, Milkability, and Environmental Impact, along with improved breed-specific choices and changed trait weighting. Dividing the Health and Fertility categories will help to represent objectives such as milking speed and calving capacity more accurately.
Given data availability, the new Environmental Impact subindex targets greenhouse gas reductions for Holsteins via feed and methane efficiency features. This complements more general sustainability objectives in dairy production. Milking speed and temperament are necessary for effective operations and will be part of the Milkability subgroup.
These developments under Brian Van Doormaal guarantee farmers a scientifically solid and valuable tool. The 98% correlation with the present LPI emphasizes how these improvements improve rather than alter the current system. Maintaining genetic quality, the redesigned LPI seeks to help Canadian dairy producers create more lucrative, environmentally friendly, and efficient herds.
Key Takeaways:
The new LPI will emphasize reducing greenhouse gas emissions and enhancing “milkability.”
The index will expand from four to six sub-groups of genetic traits.
Health and Fertility will be split into Reproduction and Health and Welfare.
A new Milkability subgroup will include milking speed and temperament traits.
Environmental Impact subindex will focus initially on Holsteins, utilizing feed and methane efficiency data.
Body Maintenance will also be part of the Environmental Impact subindex, linking cow stature to environmental impact.
The updated LPI aims to simplify usage, with each component group serving as its own subindex.
Evaluations will present relative breeding values, set against a breed average with clear standard deviations.
The new LPI is expected to be 98 percent correlated with the current index, maintaining continuity in top-rated bulls.
Summary:
Lactanet, a Canadian genetic testing and data management company, is set to update its Lifetime Performance Index (LPI) by April 2025 to align productivity with environmental responsibility and improve cow behavior. The LPI integrates productivity, health, and reproductive characteristics into a single statistic, helping dairy farmers make wise breeding selections and guiding balanced genetic advancements. The proposed changes include methane emissions, feed efficiency features, and improvements linked to milking speed and temperament. The updated LPI will separate the Health and Fertility category into Reproduction and Health and Welfare, including important qualities like calving capacity and daughter calving ability. This flexible instrument will enable breeders to maximize their breeding campaigns to satisfy different objectives and goals, making it indispensable for genetic selection in Canadian dairy production.
Maximize dairy profits with accurate data. Discover how small steps in herd management can transform efficiency and profitability. Ready to optimize your farm’s success?
Even a single percentage point can have a big impact on the ever-changing realm of modern dairy farming. Think of the inspirational example of a Wisconsin dairy farm that, following a thorough data management system, saw a startling 15% rise in general profitability. From careful data collecting to strategic analysis, the path this farm takes shows the transforming power of accurate data. Such success stories highlight how precisely data management can help your dairy farm to reach hitherto unattainable levels of profitability and efficiency. Regardless of its scope, every bit of data can revolutionize the profitability and efficiency of your farm.
Little actions like accurately noting a cow’s health event or updating pen counts add to significant changes in herd health and feeding practices, increasing farm profitability.
“A small mistake can become a major problem, but accurate data will guide your farm toward unheard-of success.”
The foundation of reasonable herd control is accurate data. Correct data entering produces insightful reports, trend analysis, and benchmarks to guide your decisions. Making the effort to gather accurate data opens quick insights that can change your business.
All set to delve into your daily records? Little adjustments might pay off enormously for a dairy farm to run more profitably and effectively.
The Cascade Effect of Data Accuracy in Herd Management
Every herd management event depends on data capture accuracy. One small mistake—such as a nutritional need or a wrong health treatment—may have a domino effect throughout your dairy. For instance, the herd manager may make poor decisions if a breeder misses an insemination date, producing erroneous dry-off lists and calving schedules. As a result, the feeder might use the wrong pen counts, which results in improperly made rations. This first error can affect output and raise feed costs, compromising the farm’s profitability and efficiency.
Dairy producers must understand that exact data collection is absolutely vital. It improves productivity and efficiency and forms the basis of wise decisions. Any deviation from the norm should prompt quick research and correction.
Imagine a situation when a sick cow’s prescription is not precisely recorded on a farm. The monitoring produces missed production targets, rising medical expenses, emergency veterinary intervention, and changed reproductive plans. The situation worsens when the nutritionist changes feed based on erroneous data, resulting in nutritional imbalances. Such errors might turn into expensive mistakes avoided with careful record-keeping.
Little changes in inaccurate data recording can greatly enhance herd health and farm performance in dairy farming. Reliable data reveals trends, guides your farm toward its full potential using benchmarks, and supports better decisions.
Plugging Data Gaps: Ensuring Every Detail is Captured
Examine every element of your farm to find holes in your present data procedures and avoid the traps of erroneous data. Reports, trend identification, benchmark setting, and cost analysis for more profitable decisions can all be produced by herd management tools. These tools are only as valuable as the data you enter. Accurate data records give your herd and farm quick insights. For instance, your herd management system’s alerts and key performance indicators help you intervene early when some cows exceed recommended health levels. Timeliness and accuracy of insight help you reach your objectives and strengthen your bottom line. To avoid the pitfalls of inaccurate data, scrutinize every aspect of your farm to identify gaps in your current data practices. Herd management tools can generate reports, identify trends, set benchmarks, and evaluate costs for more profitable decisions. However, these tools are only as effective as the data you input. Recording accurate data provides timely insights for your herd and farm. For example, setting key performance indicators and alerts within your herd management software system enables early intervention when sure cows surpass custom health thresholds. Accurate, timely insights help improve your bottom line and achieve your goals.
Herd Management Tools: The Foundation of Modern Dairy Farm Efficiency
Modern dairy farm profitability and efficiency are within your control, thanks to the power of herd management tools. When used correctly, these tools can produce thorough reports, reveal trends, and offer benchmarks to evaluate herd management expenses. The key to unlocking their potential lies in the accuracy of the data you input. By ensuring accurate data entry, you can prevent adverse chain reactions that could lead to poor decisions impacting the whole farm. This control over your data and its impact on your farm’s performance is in your hands.
Essential tools for herd management consist of the following:
DairyComp305: Excellent for tracking reproductive metrics, health records, and production data. Its reports help identify trends for better management decisions.
PCDART: Integrates production, reproduction, and health data for thorough herd analysis and benchmarking against industry standards.
Afimilk: Features milk meters and cow activity monitors for precise data collection and insightful analysis.
BoviSync: A cloud-based system offering real-time data access and integration of various herd activities to optimize operations.
By applying these tools, farmers can set automated alerts for important performance indicators, guaranteeing timely response when necessary. Standardizing data entry throughout the team helps lower mistakes and preserve data integrity, guiding better decisions and enhancing farm operations.
Strategic Imperatives: Using KPIs and Alerts for Proactive Herd Management
Setting key performance indicators (KPIs) and alerts within your herd management system is vital in the ecology of a dairy farm. Correct data helps you create quantifiable goals for improved herd health and early intervention. For disorders like mastitis, establishing thresholds can set off alarms that let you respond quickly to avoid complications.
KPI
Meaning
Ideal Score Range
Milk Yield per Cow
The average amount of milk produced by each cow in a specified period.
8,000 – 10,000 lbs per lactation
Reproductive Success Rate
The percentage of cows that become pregnant within a specific timeframe after breeding.
30% – 35%
Feed Efficiency
The ratio of milk produced to the amount of feed consumed.
1.4 – 1.6 lbs of milk per lb of dry matter intake
Somatic Cell Count (SCC)
A measurement of cell concentration in milk, indicating udder health and milk quality.
< 200,000 cells/ml
Calving Interval
The average time period between successive calvings in the herd.
13 – 15 months
KPIs support your tracking of performance indicators, including feed conversion ratios and milk yield. These benchmarks help make data-driven decisions, enhancing management techniques and resource allocation. Alerts provide early warnings for deviations, enabling proactive rather than reactive control. This structure maintains your agility, responsiveness, and alignment with profitability objectives, guaranteeing your dairy business’s success.
Standardization: The Keystone of Accurate Data Management in Dairy Farms
Effective treatments and accurate data are not just a possibility, but a certainty when you standardize protocols within your herd management system. Clear, consistent procedures ensure that every staff member can enter and apply treatments precisely, leading to accurate herd health data tracking. For example, following a standard process for treating a cow with mastitis guarantees exact data collection. This standardization provides a sense of security and confidence, knowing that your data is reliable and your decisions are based on accurate information.
Differentials develop without standardization. Data discrepancies can hide treatment efficacy and trend identification if one employee notes treatments immediately. At the same time, another waits until the end of the day, perhaps aggravating minor problems into major health crises.
Without set procedures, comparing health trends to industry benchmarks also becomes challenging. For instance, a farm that neglected to standardize calving event records experienced underreported complications, distorting health statistics and postponing required treatments.
On the other hand, standardized data entry and treatment approaches produce clear, practical health insights. Regular records allow one to spot trends in seasonal diseases, facilitating proactive management and enhancing general farm profitability and efficiency. The long-term success of your dairy operations depends on your using consistent procedures.
On the other hand, clear, practical health insights are produced by standardized data entry and treatment approaches. Regular records allow one to spot seasonal disease trends, facilitating proactive management and enhancing general farm profitability and efficiency. The long-term success of your dairy operations depends on your consistent use of procedures. However, the reality remains that the number of dairy farms continues to shrink, making it imperative for existing farms to optimize every possible aspect of their operations to stay competitive. (Read more: ‘Once plentiful in Skagit County, the number of dairy farms continues to shrink‘)
Transforming Daily Operations with Mobile Apps: Enhancing Dairy Farm Efficiency Through Real-Time Data Entry and Retrieval
Including mobile apps in herd management systems transforms daily operations by allowing on-the-go data entry and retrieval. These applications save time spent on hand data entry by allowing real-time data capture straight from the parlor, barn, or offsite site. Farm teams can immediately record health events, treatments, and other vital data points by using mobile capabilities, guaranteeing constant accuracy.
Mobile apps reduce pointless office visits, thus improving efficiency. Multiple pass tasks become one pass, lowering the inherent error risks in paper-based systems. For a veterinarian’s visit, for instance, accessing and updating a cow’s history guarantees accurate and timely entries, enhancing decision-making.
Mobile apps also reduce data entry mistakes. Direct information recording at the source lowers the possibility of miswriting cow IDs or inaccurate entries. This real-time data capture results in more accurate reports and analyses, guaranteeing data integrity. Mobile apps enable the whole team by making herd management systems available from any point on the farm, improving output and supporting operational objectives.
Optimizing Herd Management Through Tailored User Access Levels
Control of user access in your herd management system guarantees that every team member possesses the precise information required to perform their roles. Customized permissions support data integrity and simplify processes. For example, a breeder must have access to cow performance and breeding statistics to guide their breeding decisions. The herd manager needs complete access to oversee dry-offs and track health events. Updated pen counts and nutrition information help the feeder create exact ration formulations. The veterinarian also requires access to health records and guidelines for accurate treatment. Customizing these access levels will help your team members concentrate on their particular responsibilities, thus improving the general farm performance.
Managing user access levels within your herd management system ensures each team member has the data they need to excel in their roles. Tailored permissions streamline operations and uphold data integrity. For instance, breeders need access to cow performance and breeding data to make informed breeding decisions. The herd manager requires comprehensive access to monitor health events and manage dry-offs—the feeder benefits from updated pen counts and nutrition info for precise ration formulations. Meanwhile, the veterinarian needs access to health records and treatment protocols for accurate care. By customizing these access levels, your team members can focus on their specific tasks, enhancing overall farm efficiency.
The Indispensable Role of Early Life Data in Calf Management
Every early event of a calf fundamentally determines her future as a cow. Accurate and consistent data entering from birth prepares the ground for lifetime health and productivity. Recording specifics on her weight, diet, and health interventions helps build a profile that directs the following actions. This painstaking record exposes trends and ideas helpful for nutrition, breeding, and health planning.
Early data sets the standard for all subsequent measurements; thus, its accuracy is quite important. Standardizing data entry increases dependability, reduces mistakes, and guarantees consistency. Digitally capturing calf-side data boosts accuracy and streamlines workflows for real-time adjustments.
Data management tools that support protocol-driven capture reduce errors, ensuring protocol compliance. Monitoring data access and calibrating user levels maintains data integrity. Over time, this approach enhances the calf’s transition to a productive cow, boosting overall efficiency and profitability.
Fostering a Culture of Continuous Improvement: Unlocking Dairy Farm Potential
The significance of a culture of continuous improvement on a dairy farm cannot be understated. Engage your team and regularly evaluate your practices to unlock new efficiencies. Foster an environment where asking questions is championed. Equip staff with the skills through ongoing education and training programs focused on data management.
Collaborate with herd management partners to stay updated on industry advancements. These professionals offer invaluable insights and innovative solutions that can profoundly impact your farm’s operations. You’ll find areas ripe for optimization as you explore your herd management systems.
Maintain an inquisitive mindset and a commitment to learning. This proactive approach ensures your farm’s data remains a powerful asset, driving profitability and achieving long-term goals. Recognize that every incremental improvement contributes to your dairy’s broader success, empowering your team to strive for excellence.
The Bottom Line
Accurate data management is the cornerstone of dairy farm efficiency. Every action, from data capture to health trend analysis, supports informed decision-making and farm performance. Minor inaccuracies can trigger chain reactions across operations, affecting everything from feeding routines to health management. By strategically using herd management tools, setting critical KPIs, and leveraging mobile apps, farms can streamline operations, ensure data integrity, and maintain a healthier, more productive herd.
Every data point is crucial for dairy farmers. Capturing and analyzing accurate data helps identify gaps, evaluate trends, and implement timely interventions to enhance profitability and efficiency. Focusing on data standardization and optimizing user access levels fosters continuous improvement. This ensures that each calf’s early life events are precisely recorded, maximizing future milk production and cow longevity.
Small steps in tightening data management can lead to substantial payoffs. Accurate data entry links the current herd state to its historical data. It sets the foundation for future success, making diligent data management vital for any dairy farmer aiming for long-term prosperity.
Key Takeaways:
Accurate Data Entry: Ensure every herd management event is captured accurately to avoid cascading errors.
Identify Data Gaps: Conduct regular audits of your data management practices to identify and rectify any gaps.
Implement Herd Management Tools: Use robust tools to generate reports, discover trends, and make informed decisions.
Set KPIs and Alerts: Use key performance indicators and alerts for early intervention on health events and other critical metrics.
Standardize Protocols: Establish and maintain standardized protocols for data entry and treatment administration.
Utilize Mobile Apps: Leverage mobile herd management apps to enable real-time data entry and reduce the risk of errors.
Manage User Access: Adjust user access levels within your herd management system to ensure team members have the data they need.
Capture Early Life Data: Digitally recording data during the early life stages of a calf can significantly impact future performance.
Foster Continuous Improvement: Encourage a culture of continuous learning and improvement in data management practices.
Collaborate with Partners: Work closely with herd management partners and support teams to optimize data usage.
Summary: Data management is crucial in modern dairy farming, as it significantly impacts profitability and efficiency. A Wisconsin dairy farm saw a 15% increase in profitability after implementing a comprehensive data management system. Accurate data provides insights into herd health and feeding practices, leading to significant changes in farm profitability. Herd management tools generate reports, identify trends, set benchmarks, and evaluate costs for more profitable decisions. Key performance indicators (KPIs) and alerts are essential for tracking performance indicators. Standardization ensures accurate data entry and treatment application. Incorporating mobile apps into herd management systems transforms daily operations by allowing on-the-go data entry and retrieval. A culture of continuous improvement and collaboration with herd management partners can optimize farm data and drive profitability and long-term goals.
Boost your dairy cow’s milk yield and efficiency with rumen native microbes. Curious how these supplements can enhance your herd’s performance? Discover the benefits now.
Increasing populations and income levels, particularly in developing nations where dairy consumption is on the rise, bring greater demand and higher production efficiency to the dairy industry. The profitability and sustainability of dairy farms, which are crucial for the global dairy industry, can be significantly enhanced by the adoption of rumen-native bacteria in dairy cow diets. This innovative approach, backed by rising worldwide dairy demand, holds the promise of boosting milk yields and feed efficiency, thereby increasing production and profitability.
Rumen native bacteria might transform dairy farming. Naturally found in the cow’s rumen, these microorganisms have shown potential for increasing feed efficiency and lactation performance. Mainly targeted strains such as Pichia kudriavzevii and Clostridium beijerinckii have shown appreciable increases in milk yield and quality.
The effect of dietary supplements, including these microbes, on feed efficiency and productive performance in Holstein dairy cows is investigated in this paper. We will discuss:
How does cow digestion interact with rumen bacteria to increase milk output?
Specific bacterial additions and their noted advantages.
Consequences for present research and methods of dairy farming.
Without compromising cow body weight, microbial supplements can raise milk yield, boost ECM production, and increase feed efficiency, resulting in more profitable herds and possible profit gains. By analyzing current studies, we hope to emphasize the possibilities of rumen native bacteria and provide helpful advice for dairy producers to improve herd performance and condition.
A Comprehensive Study on Microbial Additives in Holstein Cows
Run on 117 Holstein cows, the study “Dietary supplementation of rumen native microbes improves lactation performance and feed efficiency in dairy cows” assessed two particular microbial additions. The cows were arranged according to parity: first-time calving (nulliparous) or calving more than once (multiparous). The cows were further divided within these parity groups according to their pre-treatment energy-corrected milk (ECM) yield to provide a standard starting point.
Each parity block in a randomized complete block design was split and then assigned at random to one of three treatments over 140 days:
CON (Control Group): 100 grams of corn meal without microbial additives (15 primiparous and 25 multiparous).
G1 Group: 100 grams of corn meal containing a blend of 5 grams of Clostridium beijerinckii and Pichia kudriavzevii, featuring 4 × 107 cfu of C. beijerinckii and 1 × 109 cfu of P. kudriavzevii (14 primiparous and 24 multiparous).
G2 Group: 100 grams of corn meal with 5 grams of a composite of C. beijerinckii, P. kudriavzevii, Butyrivibrio fibrisolvens, and Ruminococcus bovis, containing 4 × 107 cfu of C. beijerinckii, 1 × 109 cfu of P. kudriavzevii, 1 × 108 cfu of B. fibrisolvens, and 1 × 108 cfu of R. bovis (15 primiparous and 24 multiparous).
Cows housed in ventilated tie-stall barns fitted with rubber mattresses and sand bedding to preserve consistent and ideal conditions ran the study from October 27, 2020, until July 20, 2021.
Accurate measurements and thorough data collection were necessary for this work. Daily logs of body weight (BW), milk yield, and dry matter (DM) intake guaranteed exact control of general health and nutritional intake. Twice-weekly evaluations of body condition score (BCS) helped closely monitor the cows’ physical state.
The analysis of milk composition twice a week lets researchers track changes in quality. Milk samples on days 60 and 62 also gave thorough fatty acid profiles. This careful approach guaranteed that the information represented the actual effects of the dietary supplements.
The Result: Boosted Milk Yield and Feed Efficiency
Treatment
Milk Yield (kg/d)
ECM (kg/d)
Fat Yield (kg/d)
Total Solids (kg/d)
ECM per kg of DMI (kg/kg)
Control (CON)
39.9
37.9
1.31
4.59
1.72
G1
41.3
39.3
1.37
4.75
1.76
G2
41.5
39.9
1.40
4.79
1.80
The study emphasizes how much feeding dairy cows microbial additions help them. From 39.9 kg/day in the control group to 41.3 kg/day and 41.5 kg/day in groups G1 and G2, respectively, cows given these supplements showed greater milk yields. Analogous increases in energy-corrected milk (ECM) production from 37.9 kg/day in the control group to 39.3 kg/day (G1) and 39.9 kg/day (G2). Furthermore, in the treatment groups, fat output rose from 1.31 kg/day to 1.37 kg/day and 1.40 kg/day.
With an increase from 4.59 kg/day in the control group to 4.75 kg/day and 4.79 kg/day in the experimental groups, total solids output improved significantly. Measured as ECM per kilogram of dry matter intake (DMI), feed efficiency also improved from 1.72 kg/kg in the control group to 1.76 kg/kg (G1) and 1.80 kg/kg (G2). These findings highlight how well microbial additions might improve milk production volume and quality.
The long-term effects of incorporating microbial additives into dairy farming are not only significant but also promising. The improved milk yield and quality directly translate into higher income and improved product quality, ensuring the economic viability of dairy farms in a competitive market. Moreover, the enhanced feed efficiency achieved through microbial additions streamlines operations and increases their sustainability, thereby optimizing production and ensuring a bright future for dairy farming.
Enhancing Milk Fat Composition with Microbial Additives
The study found that adding microbial additives (MAs) to Holstein cow diets greatly improved milk fat composition. Pre-formed fatty acids, particularly those with more than 16 carbons, showed an especially high yield. Additionally, unsaturated fatty acids, including α-linolenic acids (C18:3) and linoleic acids (C18:2), increased. While α-linolenic acid rose from 2.46 g/d to 2.82 g/d, linoleic acid levels rose from 30.9 g/d to 35.4 g/d.
Known for their health advantages—anti-inflammatory effects and heart health contributions—unsaturated fatty acids help make the milk more marketable to health-conscious consumers, perhaps enabling higher pricing. More pre-formed fatty acids also indicate better energy use by the cows, reflecting better general health and output. These microbial additions thus not only improve the quality of milk but also offer a great chance to maximize dairy farm activities.
A Practical Roadmap for Integrating Microbial Additives
The findings of this research provide a practical roadmap for dairy producers, cattle nutritionists, and researchers to integrate microbial additives into dairy farming. The selection of the appropriate type is crucial, and the study highlights the effectiveness of specific bacterial additions such as Clostridium beijerinckii and Pichia kudriavzevii. To identify the best fit for your herd, consult with a cattle nutritionist. This practical advice empowers you to make informed decisions for your dairy farm.
Following the study’s methodology, consider introducing additives to your herd in a controlled manner. Begin by gradually adding the additive as a top dress for the cows’ diets, then monitor their milk yield, feed intake, and overall condition. This approach allows for a comprehensive assessment of the effects under your control.
Take into account the cost-benefit aspect. While the initial cost of microbial additives may seem significant, the study indicates substantial returns in terms of increased milk yield and improved feed efficiency. Enhanced yields of key milk components, such as unsaturated and pre-formed fatty acids, could lead to higher-quality dairy products with greater market value.
The long-term effects on herd health and productivity are also significant. Frequent additive use helps to support general herd health, stabilize rumen function, and raise body condition scores. Longer cow lifespans and reduced veterinary costs resulting from this often help increase microbial additions’ cost-effectiveness.
Success with microbial additions depends on constant evaluation and careful control. Stay updated on fresh studies and modify your methods based on practical results to maximize the benefits in milk yield, feed efficiency, and herd health over time.
The Bottom Line
Adding rumen-native bacteria to dairy cow diets shows excellent potential to increase feed efficiency and productive performance. Clostridium beijerinckii, Pichia kudriavzevii, Butyrivibrio fibrisolvens, and Ruminococcus bovis added to their feed improved milk yield by up to 4%, energy-corrected milk (ECM) by up to 5.3%, and milk fat composition, all without increasing dry matter intake (DMI). For dairy producers trying to maximize output while controlling feed expenses, cows are more effectively turning feed into milk.
By raising good fatty acids, the study shows that microbial additions increase milk volume and enhance milk quality. In dairy production, this double advantage can result in more sustainability and profitability. Thus, adding these microbial supplements proves that dietary supplementation of rumen native bacteria improves lactation performance and feed efficiency in dairy cows, providing a practical method to attain higher efficiency and output in dairy herds.
Key Takeaways:
Dietary supplementation with specific microbial additives enhanced productive performance in Holstein cows.
Milk yield, energy-corrected milk (ECM), fat output, and feed efficiency all saw significant improvements.
The study included a control group and two treatment groups, each receiving different combinations of microbial additives.
Researchers noted an increase in pre-formed fatty acids in the milk, particularly unsaturated fatty acids like linoleic and α-linolenic acids.
Body condition scores (BCS) tended to improve with the addition of microbial supplements.
The experimental period lasted from October 27, 2020, to July 20, 2021, offering robust data across multiple seasons.
Despite variations in starting days in milk (DIM) among cows, the overall positive trends in milk production and composition were consistent.
The findings suggest that integrating microbial additives into dairy diets could foster enhanced milk production and better feed efficiency, ultimately contributing to the sustainability and profitability of dairy farming.
Summary: The dairy industry is experiencing a surge in demand due to rising populations and income levels, particularly in developing nations. The adoption of rumen-native bacteria in dairy cow diets can significantly enhance profitability and sustainability. Targeted strains such as Pichia kudriavzevii and Clostridium beijerinckii have shown significant increases in milk yield and quality. This study investigates the effect of dietary supplements, including these microbes, on feed efficiency and productive performance in Holstein dairy cows. The study assessed two specific microbial additions: a control group (100 grams of corn meal without microbial additives) and a group (100 grams of corn meal containing a blend of 5 grams of Clostridium beijerinckii and Pichia kudriavzevii) and a group (100 grams of corn meal with a composite of C. beijerinckii, P. kudriavzevii, Butyrivibrio fibrisolvens, and Ruminococcus bovis). The results showed that cows given microbial additions showed greater milk yields, increased energy-corrected milk (ECM) production, increased fat output, and improved feed efficiency. The long-term effects of incorporating microbial additives into dairy farming are significant and promising.
Learn how genomics and phenotypes affect dry matter intake in Holstein cows. Could breeding smaller cows make your dairy farm more profitable? Discover the answer here.
Maximizing efficiency involves more than just feeding your cows the right amount; it’s about enhancing their genetic potential. Researchers have found significant differences between phenotypic and genomic data on DMI, helping you tailor nutrition plans and breeding to boost performance.
Leveraging genomic insights allows farmers to select traits for higher milk production and better feed efficiency, leading to a more profitable operation.
A Financial Game-Changer: Leveraging Genomic Insights for Accurate Feed Cost Management
As a dairy farmer, understanding feed costs is vital for profitability. This study highlights the difference between genomic and phenotypic regressions in estimating these costs. Based on observable traits like milk, fat, and protein, phenotypic regressions provide a direct approach but often estimate lower feed costs than genetic data.
This insight is crucial. Relying only on phenotypic data could lead to underestimating feed costs. Incorporating genomic data offers a clearer picture, helping you make better breeding and management decisions. You can optimize feed costs and boost profitability by selecting cows with efficient feed-to-milk conversion based on their genetic profile.
This study analyzes the impact of genomic and phenotypic factors on dry matter intake (DMI) in US Holstein cows. Using data from 8,513 lactations of 6,621 cows, it estimates the feed needed for milk production and body weight maintenance. Mixed models compare phenotypic and genomic regressions, revealing critical insights for nutrition management and breeding programs.
Diving into feed efficiency in Holstein cows, it’s critical to understand the difference between phenotypic and genomic regressions. Phenotypic regressions come from traits you can see, like milk yield, fat content, and protein levels. They show how much feed a cow needs based on its current characteristics. Genomic regressions, on the other hand, use genetic info to predict feed needs, focusing on the cow’s DNA and inherited traits.
Why care? Phenotypic regressions are great for nutrition management in daily operations. They help you optimize feeding strategies and manage feed costs, ensuring your cows produce the best milk components.
For breeding programs, genomic regressions are crucial. They let you pick cows with the best genetic traits for feed efficiency and higher milk production. This can boost your herd’s productivity and profitability over time.
Cracking the Code: How Genomic Data Outperforms Phenotypic Predictions in Dry Matter Intake
Understanding dry matter intake (DMI) in your Holstein cows can boost your herd’s productivity. By looking at phenotypic and genomic data, you can see the feed needs for milk components and body maintenance. Let’s compare these regressions.
Component
Phenotypic Regression
Genomic Regression
Sire Genomic Regression
Milk
Low
High
Moderate
Fat
Low
High
Moderate
Protein
Low
High
Moderate
Body Weight Maintenance
Moderate
Moderate
Moderate
Regression values show how much a component like milk, fat, or protein affects dry matter intake (DMI). A “low” regression means a weak impact, while a “high” regression indicates a strong effect. “Moderate” falls in between. These insights help us understand the contribution of each component to feed efficiency and milk production.
The study reveals significant differences between phenotypic and genomic dry matter intake (DMI) predictions in Holstein cows. Genomic regressions generally showed higher values than phenotypic ones. Phenotypic regression for milk was 0.014 ± 0.006, while genomic was 0.08 ± 0.03. For fat, the figures were 3.06 ± 0.01 for phenotypic and 11.30 ± 0.47 for genomic. Protein followed this trend, with phenotypic at 4.79 ± 0.25 and genomic at 9.35 ± 0.87. This is crucial for understanding feed costs and revenue, especially for breeding programs focused on feed efficiency.
According to the energy-corrected milk formula, the study also notes that fat production requires 69% more DMI than protein.
Maximizing Efficiency: Understanding ECM for Better Feed and Milk Management
Component
Phenotypic Regression
Genomic Regression
Sire Genomic Regression x2
Milk
Low
High
Medium
Fat
Low
High
Medium
Protein
Low
High
Medium
Annual Maintenance (DMI/kg Body Weight)
High
High
High
The energy-corrected milk (ECM) formula adjusts milk yield based on its fat and protein content, making it easier to compare milk production efficiency. ECM converts milk volume into a standardized energy value, allowing dairy farmers to manage feed intake and production better.
The study’s observed data (phenotypic regressions) showed that producing fat requires significantly more dry matter intake (DMI) than producing protein. Specifically, it takes about 69% more DMI to make fat. Genomic data told a different story: it suggested fat production requires around 21% more DMI than protein. This highlights why genetic data can be more precise for nutritional and breeding strategies.
These insights are crucial for optimizing feed strategies and breeding programs. By selecting cows that produce more milk components with less feed, farmers can lower costs and boost sustainability.
The Hidden Impact of Energy-Corrected Milk (ECM) on Feed Efficiency: Digging Deeper into DMI Demand
The energy-corrected milk (ECM) formula is vital for comparing milk’s energy content, considering fat, protein, and lactose. This standardization helps you gauge milk production accurately.
The research reveals that fat production demands significantly more dry matter intake (DMI) than protein. Phenotypic data shows fat needs 69% more DMI than protein, while genomic data presents a complex picture: protein requires 21% more DMI, and sire genomic regressions indicate fat needs 35% more DMI than protein.
These findings underscore the importance of genomic data for precise feed management. Using genomic evaluations for DMI can enhance herd efficiency and reduce feed costs, boosting profitability.
Unveiling the Mysteries of Maintenance: How Accurate Are Modern Evaluations for Holstein Cows?
Evaluation Type
Relative Annual Maintenance Need (kg DMI/kg Body Weight/Lactation)
Phenotypic Regression
Medium-High
Genomic Regression
Medium
Sire Genomic Regression (multiplied by 2)
Medium-Low
NASEM (2021)
Lower
When it comes to understanding the maintenance needs of your Holstein cows, this study sheds light on annual estimates. Phenotypic regressions clocked maintenance at 5.9 ± 0.14 kg DMI/kg body weight/lactation, genomic regressions at 5.8 ± 0.31, and sire genomic regressions at 5.3 ± 0.55. These figures are higher than NASEM (2021) estimates, suggesting that modern methods might provide more accurate data for feed management.
Strength: The Unmissable Factor in Holstein Performance and Feed Efficiency
Type Trait
Ability to Predict Feed Efficiency
Strength
High
Body Depth
Moderate
Stature
Low
Dairy Form
Moderate
Front End
Low
When looking at type traits and their impact on Body Weight Composite (BWC) and Dry Matter Intake (DMI), it’s clear that not all traits are equal. Traits like stature, body depth, and strength play key roles in predicting body weight and DMI, but strength truly stands out.
Strength isn’t just a physical trait; it’s a vital indicator of a cow’s ability to turn feed into body weight and milk. The study highlighted that strength is the most critical link to body weight and DMI. So, focusing on strength in genetic selection can lead to better management and performance.
Prioritizing strength will boost your dairy operation’s efficiency and profitability. This will help select cows that excel at using feed efficiently, leading to a more productive and sustainable herd.
Revolutionizing Breeding Programs: Leveraging Genomic Insights for Enhanced Profitability
The study provides crucial insights for refining breeding programs to enhance profitability. It shows that genomic dry matter intake (DMI) predictions are more accurate than phenotypic ones, emphasizing the need to incorporate these advanced evaluations into breeding strategies. Selecting cows based on their genetic potential for feed efficiency and milk production can offer significant financial benefits.
Breeding programs can now target more miniature cows with harmful residual feed intake. These cows use less feed for maintenance but still produce more milk, fat, and protein, optimizing feed costs and boosting overall farm profitability. The focus shifts from increasing milk yield to making each pound of feed count more in milk components produced.
The updated Net Merit formula now better includes these genomic evaluations, making it easier to select economically advantageous traits. Using these insights helps you make more informed decisions that support long-term profitability. This comprehensive strategy ensures that your breeding program is geared toward sustainable, profitable dairy farming.
The Bottom Line
Harnessing phenotypic and genomic data is vital for optimizing dry matter intake (DMI) and boosting farm profitability. While phenotypic data offers day-to-day nutrition insights, genomic data provides a deeper, more accurate picture that’s crucial for breeding programs. You can better predict feed costs and milk production efficiency by focusing on genomic evaluations of traits like strength and body weight. This shift can help you cut feed expenses and maximize milk output, enhancing your farm’s profitability. Embrace genomic insights and watch your herd’s performance and bottom line improve.
Key Takeaways:
Genomic data provides more accurate predictions for DMI compared to phenotypic data, making it a better tool for breeding programs.
Fat production requires significantly more DMI than protein production according to genomic data, but the difference is less pronounced in phenotypic data.
Annual maintenance estimates for DMI are consistent across phenotypic and genomic data, both surpassing the current NASEM estimates.
Strength is the primary type trait linked to body weight and DMI in Holstein cows, aligning with the current body weight composite (BWC) formula.
Breeding programs optimized for profitability should focus on selecting smaller cows with negative residual feed intake that produce higher volumes of milk, fat, and protein.
Summary: The article discusses the significance of managing Dry Matter Intake (DMI) in US Holstein cows and how genomic and phenotypic data can improve dairy farming practices. DMI affects milk production, cow health, and farm profitability. Researchers found significant differences between phenotypic and genomic data on DMI, allowing dairy farmers to tailor nutrition plans and breeding to improve performance. Leveraging genomic insights allows farmers to select traits for higher milk production and better feed efficiency, leading to a more profitable operation. The study uses data from 8,513 lactations of 6,621 cows to analyze the impact of genomic and phenotypic factors on DMI in US Holstein cows. Phenotypic regressions are useful for nutrition management and breeding programs, while genomic regressions help select cows with the best genetic traits for feed efficiency and higher milk production.
Explore the advantages of Montbéliarde and Viking Red crossbreds over Holsteins in dairy production. Could crossbreeding be the secret to elevating your herd’s performance?
Ever wonder what makes one breed of dairy cow stand out more in milk production? In commercial dairies, understanding the lactation curves of different breeds can be crucial. This post focuses on Montbéliarde × Holstein and Viking Red × Holstein crossbred cows, comparing them to pure Holsteins. We analyze data from seven high-performance herds to see which crossbreds perform better.
Comparing these crossbreds to Holsteins isn’t just academic—it’s vital for dairy farmers aiming to boost productivity. Montbéliarde crossbreds are known for their muscular build and high fat and protein yields. At the same time, Viking Reds are praised for their health and fertility. By examining these traits, we offer insights for better herd management.
We will analyze the lactation curves of Montbéliarde and Viking Red crossbreds vs. Holsteins across multiple lactation periods. Key metrics like 305-day production, peak production, and milk, fat, and protein yield persistency will be explored. Our findings could reveal significant advantages of crossbred cows over Holsteins, reshaping dairy farming strategies.
Introduction to Dairy Crossbreeding: Montbéliarde and Viking Red vs. Holstein
Diving into dairy crossbreeding involves understanding specific breeds. The Montbéliarde and Viking Red cattle are critical players in this field, each offering unique strengths when crossed with Holsteins.
Overview of Montbéliarde Cattle Breed
Montbéliarde cattle, originating in France, are known for their robust health and longevity in dairy operations. Their red pied coat, strong legs, and excellent udder quality are distinctive. They were developed from local breeds and Simmental cattle in the late 19th century.
Advantages of Using Montbéliarde: These cattle have a more significant body condition, shorter stature, and less body depth during early lactation than pure Holsteins. They excel in fertility, leading to higher conception rates and producing more live calves. Their udder conformation supports better milk production with lower somatic cell counts.
Overview of Viking Red Crossbreds
Viking Red cattle are valued for adaptability, robust health, high fertility rates, and efficient milk production. With a medium frame and red coat, they have strong udders suitable for high-performance dairies. This breed results from breeding programs in Denmark, Sweden, and Finland.
Viking Red crossbreds return to peak production faster after calving and show more excellent persistency in milk production across lactations. They have superior fertility and conception rates, enhancing reproductive efficiency and profitability. While they may produce slightly less fluid milk than pure Holsteins, they often yield higher fat.
Comparison of Montbéliarde and Viking Red Crossbreds to Holsteins
Characteristic
Montbéliarde × Holstein (MO × HO)
Viking Red × Holstein (VR × HO)
Holstein (HO)
Average Milk Yield
Similar to HO
Less than HO
Higher
Fat Content
Higher
Higher
Lower
Protein Content
Higher
Higher
Lower
Milk Persistency
Higher
Similar
Lower
Health and Fertility
Better
Better
Poorer
Feed Efficiency
Higher
Higher
Lower
Overall Profitability
Higher
Higher
Lower
Body Condition
Greater
Greater
Lesser
Reproduction Rates
Higher
Higher
Lower
Calving Ease
Better
Better
Lower
Analyzing Lactation Performance and Milk Yield
Lactation Curve Characteristics
MO × HO 2-Breed Crossbreds
VR × HO 2-Breed Crossbreds
HO Herdmates
305-d Production (kg)
Not different
Less fluid milk
Standard
Peak Production (kg)
Similar
Lower
Standard
Peak Day of Production
Similar
Earlier
Standard
Persistency of Production
Higher
Similar
Lower
4 to 103 DIM (kg)
Similar
Less fluid milk
Standard
104 to 205 DIM (kg)
Higher
Less fluid milk
Standard
206 to 305 DIM (kg)
Higher
Less fluid milk
Standard
Fat Production (kg)
Higher (2nd & 3rd lactations)
Higher (2nd & 3rd lactations)
Standard
Protein Production (kg)
Higher
Similar
Standard
Holsteins often lead to milk yield, especially in the first lactation. They produce more fluid milk compared to Montbéliarde and Viking Red crossbreds. However, Montbéliarde × Holstein crossbreds excel in persistency, maintaining stable milk production throughout the lactation period.
The fat and protein content in milk is higher in crossbred cows. Montbéliarde × Holstein and Viking Red × Holstein crossbreds offer richer milk than pure Holsteins. This advantage holds in first and later lactations, showcasing the benefits of crossbreeding on milk composition.
Overall, the milk quality and components from crossbreds are superior. The enhanced persistency in crossbreds like Montbéliarde and Viking Red leads to consistent, high-quality milk production. This boosts milk pricing and improves dairy farm profitability, making crossbreeding an intelligent choice for modern dairy farms.
Comparing Health and Fertility
Trait
Montbéliarde × Holstein
Viking Red × Holstein
Holstein
Fertility (Conception Rate, %)
65
67
58
Calving Interval (Days)
380
370
400
Days Open
120
110
150
Incidence of Mastitis (%)
15
12
20
Body Condition Score
3.0
3.1
2.8
Longevity (Years)
5.5
6.0
4.5
Crossbred cows generally have better health than their Holstein herd mates. Montbéliarde and Viking Red crossbreds show more resistance to diseases common in dairy herds. This better health leads to longer and more productive lives.
Fertility is another strong point for Montbéliarde and Viking Red crossbreds. They have higher conception rates and better overall fertility than Holsteins. This means more efficient breeding and lower costs for artificial insemination and calving intervals.
Montbéliarde and Viking Red crossbreds also have easier calving and strong maternal instincts. These traits lead to higher calf survival rates and less labor for calving management. Better calving performance is crucial for overall herd health and efficiency.
Feed Efficiency and Overall Profitability
Breed/Crossbreed
Feed Conversion Rate (lbs of milk/lb of feed)
Cost of Production ($/lb of milk)
Overall Profitability ($/lactation)
Holstein
1.5
0.18
800
MO × HO (2-breed)
1.6
0.17
875
VR × HO (2-breed)
1.4
0.19
760
MO × VR/HO (3-breed)
1.55
0.175
820
VR × MO/HO (3-breed)
1.5
0.18
805
Crossbred cows like Montbéliarde and Viking Red typically show better feed efficiency than pure Holsteins, needing less feed per unit of milk. This leads to cost savings and improved profits for dairy farms.
Montbéliarde and Viking Red crossbreds also have lower production costs, which is vital for any dairy farm. Their higher disease resistance, better fertility rates, and enhanced feed efficiency reduce veterinary and feed expenses, making them more economical.
These crossbreds often live longer than Holsteins, especially in high-performance herds. Their robust health, increased fertility, and easier calving improve their lifespan and ensure a higher return on investment for farmers.
Why Crossbreeding Could Be the Future of High-Performance Dairy Herds
Crossbreeding can enhance high-performance dairy herds by improving lactation performance and milk yield. Over the past decade, Montbéliarde (MO) and Viking Red (VR) crossbreds have shown better milk persistency than Holsteins (HO), leading to stable milk production and healthier cows.
Crossbred cows also show higher fertility rates and better reproductive traits. They have fewer stillbirths and return to peak production faster after calving. For instance, 3-breed crossbred calves have a 4.5% stillbirth rate compared to 9% in purebred Holsteins.
Economically, crossbreeding is beneficial. Crossbred cows produce more milk solids and are more feed-efficient, reducing feed costs and increasing profitability. Their improved fertility leads to frequent calving and efficient herd replacement.
The health benefits of crossbreeding include a more robust immune system and better resistance to common ailments, leading to lower veterinary costs.
Overall, crossbreeding combines the best traits of each breed, resulting in cows that excel in milk production, health, fertility, and profitability. It offers a pathway to a more sustainable and resilient dairy industry.
Real-World Insights: Data from Seven High-Performance Herds
Based on data from 2010 to 2017, the study analyzed cows from seven top-performing herds. This included Montbéliarde (MO) × Holstein (HO), Viking Red (VR) × HO 2-breed crossbreds, MO × VR/HO, VR × MO/HO 3-breed crossbreds, and their pure Holstein herd mates. The research aimed to compare their lactation performance.
Using random regression (RR) and the Legendre polynomial method, the lactation curves showed vital differences. MO × HO 2-breed crossbreds produced similar fluid milk as Holsteins but had better persistency in milk, fat, and protein. The VR × HO 2-breed crossbreds had lower fluid milk production but higher fat and protein yields in later lactations. MO × VR/HO 3-breed crossbreds also showed better milk production persistency than Holsteins.
The main takeaway is that crossbred cows, especially those with Montbéliarde genetics, tend to outperform Holsteins in certain traits over time. This improved persistency can lead to greater efficiency and profitability, suggesting crossbreeding as a valuable strategy for high-performance dairy herds.
The Bottom Line
The research on dairy crossbreeding compared Montbéliarde and Viking Red crossbreds with Holstein cows, focusing on performance and profitability. This study used data from seven high-performance herds to analyze lactation yields, health, fertility rates, and feed efficiency.
Pros and Cons of Montbéliarde and Viking Red Crossbreds: Montbéliarde (MO) and Viking Red (VR) crossbreds offer better body condition, higher fertility, and more consistent lactation. MO × HO crossbreds had higher protein production across all lactation stages, and both MO and VR crossbreds showed better fat production in later lactations than Holsteins. These traits can lead to greater profitability due to stable and high-quality milk solids.
However, VR × HO crossbreds generally produced less fluid milk in the first lactation than Holsteins. While other factors may balance this out, it’s something to consider for dairies focused on initial higher fluid milk outputs.
Overall, crossbreeding offers a future path for sustainable dairy farming. Breeds like Montbéliarde and Viking Red provide resilience, better fertility, and strong milk solid production. They can be vital to creating more sustainable, efficient, and profitable dairy operations as the industry faces climate and market challenges.
Key Takeaways
Breed Performance: Montbéliarde × Holstein crossbreds showed no significant difference in fluid milk production compared to Holsteins, except for increased milk persistency.
Enhanced Persistency: Montbéliarde × Holstein crossbred cows demonstrated superior persistence in milk, fat, and protein production during their first lactation.
Higher Fat Production: Both Montbéliarde × Holstein and Viking Red × Holstein crossbreds exhibited higher fat production during their second and third lactations than Holstein cows.
Improved Protein Production: Montbéliarde × Holstein crossbreds outperformed Holsteins in protein production across all lactation periods.
Crossbreeding Advantages: Crossbred cows potentially offer better persistency and production traits compared to pure Holsteins, particularly in high-performance herds.
Summary: This post analyzes the lactation curves of Montbéliarde × Holstein and Viking Red × Holstein crossbred cows compared to pure Holsteins. The analysis of data from seven high-performance herds reveals which crossbreds perform better. Montbéliarde cattle are known for their robust health, longevity, and fertility, leading to higher conception rates and more live calves. Viking Red crossbreds, originating from Denmark, Sweden, and Finland, are known for their adaptability, robust health, high fertility rates, and efficient milk production. They return to peak production faster after calving and show excellent persistency in milk production across lactations. Montbéliarde × Holstein crossbreds have superior milk quality and components, resulting in consistent, high-quality milk production throughout the lactation period. They also exhibit superior feed efficiency, leading to cost savings and improved profits for dairy farms.
Discover how the 2021 Nutrient Requirements of Dairy Cattle can boost your farm’s profitability. Are you feeding your cows optimally for maximum milk yield and quality?
Imagine running a business where nearly 60% of your expenses come from one thing. Dairy farmers face this, with feed costs taking up a large part of their budget. But here’s the empowering part: understanding how feeding practices impact a dairy farm’s economic outcomes is not just essential, it’s a game-changer. By optimizing feed to boost milk quality and yield, and at the same time, managing costs, dairy farmers can significantly improve their farm profitability and sustainability.
The dairy industry has transformed significantly over the past 20 years due to advancements in genetics, management practices, and nutritional research. Reflecting these changes, the National Academies of Science, Engineering, and Medicine (NASEM) released the eighth edition of the Nutrient Requirements of Dairy Cattle in December 2021. This update, succeeding guidelines from 2001, incorporates the latest scientific insights and innovations to enhance dairy cow health, productivity, and profitability.
Understanding the nutrient requirements of dairy cattle is crucial for optimizing feed efficiency, improving milk production quality, reducing environmental impact, and ultimately ensuring dairy operations’ overall profitability and sustainability.
The Evolution of Dairy Nutrition: Adapting to Genetic Enhancements and Technological Innovations
Year
Average Milk Yield per Cow (liters/year)
Average Butterfat Content (%)
Average Protein Content (%)
2001
7,800
3.6
3.2
2006
8,400
3.7
3.3
2011
8,900
3.8
3.3
2016
9,300
3.9
3.4
2021
9,700
4.0
3.5
Over the past two decades, the dairy industry has undergone significant transformations thanks to advancements in cow genetics, management practices, research, and productivity. These changes have deepened our understanding of dairy cow nutrition, making it more intricate but also more impactful on farm profitability and cow health. For instance, in the early 2000s, the focus was on increasing milk yield, but now, we’re also considering factors like cow health, environmental impact, and feed efficiency.
Selective breeding has enhanced traits such as milk yield, disease resistance, and cow longevity. These genetic improvements have increased productivity and made herds more resilient.
Management practices have evolved with technological innovations, such as precision farming tools, automated milking systems, and real-time health monitoring, which help optimize cow welfare and milk production.
The research landscape has expanded, generating data translated into practical feeding strategies. This has led to sophisticated models that accurately predict outcomes, reflecting the complexity of dairy cow nutrition.
Increased productivity necessitates a nuanced understanding of nutritional requirements. Modern cow diets must meet heightened metabolic demands while ensuring rumen health and overall well-being.
The growing complexity of dairy cow nutrition underscores our need for precise feeding strategies. These strategies, when implemented effectively, can support and enhance the advanced genetic and productive capabilities of today’s dairy cows. They are not just tools, but a source of enlightenment and motivation for dairy farmers and nutritionists.
Navigating the Microbial Frontier: Insights into Rumen Function and Precision Feeding
Amidst the evolving landscape of dairy nutrition, our understanding of rumen microbial function has advanced significantly. Two decades ago, we had a rudimentary grasp of the microbial intricacies within the rumen. Today, our insights have deepened, highlighting the critical symbiosis between the cow and its rumen microbes for optimizing milk production and overall health. This understanding has led to the development of precision feeding strategies that take into account the cow’s specific microbial needs.
Recent advancements in rumen microbial nutrition have revealed the complexities of microbial populations and their intricate interactions with dietary components. We now recognize the essential role of specific microbial communities in breaking down complex carbohydrates, fermenting fibers, and synthesizing vital volatile fatty acids. This nuanced understanding has shifted feeding practices towards precision feeding strategies, which involve tailoring the diet to the cow’s specific needs, thus optimizing feed utilization and cow health.
The integration of predictive models has been pivotal. By simulating rumen fermentation processes, we can forecast nutrient outflow with greater accuracy, fine-tuning diets to meet the cow’s needs more effectively. This helps balance nutrition while mitigating issues like acidosis, thus safeguarding rumen health.
These models factor in the degradability of dietary components, the interaction of forage fibers, and the impact of particle size on fermentation rates. This complexity provides a framework for nutritionists to precisely calibrate diets, enhancing milk yields without compromising health. Such advancements underscore the importance of improved rumen microbial function understanding in modern dairy farming. By adopting the NASEM guidelines, dairy farmers can feel reassured and confident in their farming practices, knowing that they are backed by the latest scientific research.
Redefining Dietary Fiber: The Critical Role of Physically Adjusted Neutral Detergent Fiber (paNDF) in Rumen Health
The concept of physically adjusted neutral detergent fiber (paNDF) represents a significant leap in understanding fiber’s role in rumen health. It specifically addresses how fiber’s physical characteristics maintain the optimal rumen pH necessary for efficient digestion. In simpler terms, paNDF is a measure of the fiber’s physical properties, such as its size and how easily it breaks down, which are crucial for maintaining a healthy rumen environment.
PaNDF factors in critical elements:
Forage NDF (fiber from forage)
Fiber fragility (ease of breakdown)
Particle size (interaction with rumen microbes)
Dietary starch content (impact on rumen pH)
Considering these, the paNDF model maintains a rumen pH of 6.0 to 6.1, fostering an environment for optimal microbial activity and digestion. In simpler terms, a healthy rumen is like a well-functioning digestive system in humans. It’s crucial for the cow’s overall health and efficient digestion of the feed.
Dairy farmers and nutritionists need precise inputs on cow body weight, dietary forage NDF, and starch content. Tools like the Penn State Particle Separator measure these factors, particularly particle size, ensuring dietary adjustments to sustain the rumen environment. Though complex, the paNDF system ultimately allows dairy herd managers to optimize feed formulations, promoting cow health and efficient milk production.
Unveiling the Modern Energy Paradigm: Enhanced Maintenance Net Energy of Lactation (NEL) and Refined Non-Fiber Carbohydrates (NFC) Calculations
Component
20 Years Ago
Current Requirements
Maintenance Net Energy of Lactation (NEL)
25%
Increased by 25%
Non-Fiber Carbohydrates (NFC)
General category
Separated into starch and ROM
Digestibility of Supplemental Dietary Fatty Acids
92%
Reduced to 73%
Digestibility of NDF and Starch
Variable based on dry matter intake (DMI)
Refined with specific considerations
The recent energy requirement update shows a notable 25% increase in the maintenance net energy of lactation (NEL) requirement. This change highlights our growing understanding of the energy needs tied to today’s high-producing dairy cows.
Another crucial adjustment is the division of non-fiber carbohydrates (NFC) into starch and residual organic matter (ROM). This allows for a more detailed examination of starch degradability and its influence on rumen fermentation. At the same time, ROM is considered 96% digestible.
Advancements in digestibility calculations further enhance our predictive accuracy. Digestibility models, previously based solely on dry matter intake (DMI), are now more refined. For example, dietary fatty acid digestibility has been adjusted from 92% to 73%. NDF and starch digestibilities are tweaked based on intake levels, aligning dietary energy inputs with cow energy needs more precisely.
Revolutionizing Protein Nutrition: From Metabolizable Protein (MP) to Essential Amino Acids (EAA) in Dairy Cattle
Protein Requirement
Metabolizable Protein (MP)
Essential Amino Acids (EAA)
Maintenance
500 g/day
20 g/day
Lactation (30 kg milk/day)
1,300 g/day
60 g/day
Growth (500 g/day)
950 g/day
45 g/day
Pregnancy (6th to 9th month)
700 g/day
30 g/day
The recent NASEM report marks a significant shift in protein nutrition for dairy cattle by transitioning from metabolizable protein (MP) to essential amino acids (EAA). This change emphasizes precision in nutrient utilization to enhance dairy cow productivity and health. Previously, MP served as a broad measure of absorbed protein but fell short in predicting specific protein synthesis needs. In contrast, EAA provides a more accurate measure of the cow’s protein needs, allowing for more precise feeding strategies.
The NASEM committee conducted an extensive review to identify the EAA requirements for synthesizing various proteins, including those in milk, urine, scurf, feces, tissue growth, and pregnancy. They established EAA needs through a thorough examination of research, focusing on the efficiency of EAA use, which varies by protein type. This approach allows for more accurate predictions of dietary protein conversion, enabling precise and cost-effective diet formulations.
Adopting an EAA-centric model offers practical advantages. Nutritionists can now create diets with lower protein content while still meeting cows’ needs, reducing feed costs and environmental impacts from nitrogen excretion. As dairy nutrition advances, these improvements support more sustainable and economically viable farming practices.
Strategic Nutrition for Transition Cows: A Pivotal Aspect in Managing Post-Calving Health Risks
Stage
Energy Needs (NEL, Mcal/day)
Protein Needs (g/day)
Close-up Dry Period
14 – 16
1,200 – 1,400
Fresh Period
18 – 22
1,500 – 1,700
Peak Lactation
22 – 28
1,700 – 2,000
The period around calving is crucial for dairy cow health and productivity, making transition cow management and feeding vital. Proper nutrition during this phase can mitigate post-calving disease risks. The NASEM 2021 report adopts a continuous function approach to predict energy and protein needs during gestation. Though more physiologic, this method challenges meeting nutritional requirements with a one-size-fits-all diet.
Dry Matter Intake (DMI) predictions now factor in dietary Neutral Detergent Fiber (NDF) content to address this. As dietary NDF rises from 30% to 50%, DMI decreases, ensuring transition cows receive adequate fiber without overwhelming their digestive system.
The report also doubles the dietary vitamin E requirement from 1,000 IU to 2,000 IU per day for close-up dry cows, boosting their immune function during this critical period. Additionally, dry cows’ trace mineral needs have been increased to prevent deficiencies as they prepare for lactation. Minimal changes were made for heifers and lactating cows, highlighting the unique nutritional needs during the transition period.
Embracing Nutritional Nuance: The NASEM 2021 Report’s Evolved Approach to Mineral and Vitamin Requirements
Nutrient
Lactating Cows (mg/day)
Dry Cows (mg/day)
Heifers (mg/kg of DM)
Calcium
10,000
8,000
6-12
Phosphorus
6,200
4,500
3-7
Magnesium
2,500
1,800
2-4
Sodium
3,000
2,500
0.5-1.0
Potassium
15,000
12,000
10-15
Vitamin A (IU)
50,000
30,000
20,000-40,000
Vitamin D (IU)
1,500
1,000
700-1,000
Vitamin E (IU)
1,000
2,000
300-500
In addition to updated mineral and vitamin requirements, the NASEM 2021 report takes a nuanced approach to defining these essential nutrients. Unlike previous NRC guidelines focusing on specific production outcomes, the new report uses population mean values, moving away from a one-size-fits-all strategy.
A notable change is the increase in dietary vitamin E for close-up dry diets, doubling from 1,000 IU to 2,000 IU per day. This adjustment aligns with recent research highlighting vitamin E’s role in disease prevention and cow health. Trace mineral requirements have also been revised, emphasizing their importance during the dry period, while changes for heifers and lactating cows remain minimal.
The committee employs a factorial approach, utilizing data to calculate a population mean value instead of strict “requirements.” When data is sufficient, a safety factor is included. Due to limited data, the committee offers “adequate intake (AI)” recommendations rather than rigid requirements, allowing on-farm flexibility and adjustments tailored to specific herd conditions.
The Bottom Line
The new NASEM guidelines highlight pivotal updates reflecting two decades of advancements in dairy cows’ genetics, physiology, and nutrition. These guidelines equip dairy farmers with tools to fine-tune feeding strategies, emphasizing precise energy balance and a novel focus on essential amino acids for protein nutrition. Models like paNDF ensure optimal rumen health, which is crucial for maximizing feed efficiency.
Incorporating these guidelines allows dairy farmers to manage feed costs more strategically without compromising cow health or productivity. Enhanced energy and protein calculations lead to balanced diets, potentially reducing feed expenses by minimizing waste. Focusing on nutrient bioavailability and adequate intake also streamlines mineral and vitamin supplementation, further optimizing costs.
Adopting the NASEM guidelines offers significant practical benefits. They help farmers improve herd longevity and well-being, reducing veterinary costs and post-calving health risks. This boosts milk yields and enhances milk quality, leading to better market prices. By aligning feeding practices with the latest science, dairy farms can improve operational efficiency and profitability, ensuring a more sustainable and viable future for the industry.
Key Takeaways:
Feed costs remain a significant portion of production costs, ranging from 45% to nearly 60%, underscoring the need for efficient nutrient management.
The highest milk yield does not always equate to the best farm profitability; a balance between yield, composition, and quality is crucial.
The evolving understanding of rumen microbial function and nutrition guides precision feeding strategies.
Introduction of physically adjusted neutral detergent fiber (paNDF) to ensure rumen health by maintaining optimal rumen pH and efficient fiber digestion.
Significant updates in energy and protein requirements, including a 25% increase in maintenance net energy of lactation (NEL) and a shift from metabolizable protein (MP) to essential amino acids (EAA) for protein nutrition.
Continuous function approach in predicting the energy and protein needs of transition cows enhances disease risk management post-calving.
Revision of mineral and vitamin requirements with a focus on bioavailability and adequate intake (AI) rather than strict requirements.
Summary: The dairy industry has undergone significant changes in the past two decades due to genetics, management practices, and nutritional research. The National Academies of Science, Engineering, and Medicine (NASEM) released the eighth edition of the Nutrient Requirements of Dairy Cattle in December 2021, reflecting these changes. Understanding the nutrient requirements of dairy cattle is crucial for optimizing feed efficiency, improving milk production quality, reducing environmental impact, and ensuring profitability and sustainability. Selective breeding has enhanced traits like milk yield, disease resistance, and cow longevity, increasing productivity and resilience. Technological innovations have evolved management practices, such as precision farming tools, automated milking systems, and real-time health monitoring. The research landscape has expanded, generating data that has led to sophisticated models that accurately predict outcomes. Adhering to NASEM guidelines provides dairy farmers with confidence in their farming practices, backed by the latest scientific research. The NASEM 2021 report emphasizes strategic nutrition for transition cows, adopting a continuous function approach to predict energy and protein needs during gestation.
Explore the transformative impact of introducing forage early in dairy calf diets on their performance and behavior. Eager to learn about the distinct advantages of various forage sources? Continue reading to uncover these insights.
A calf’s early diet in dairy farming is not just a routine, but a crucial step towards shaping its future health and productivity. Research illuminates that the type of forage in a calf’s diet can significantly impact its development. By adjusting feed, we can unlock the potential for enhanced growth and well-being. This study delves into how different forage sources in total mixed rations (TMR) can influence dairy calves, offering a glimpse into a future where performance, metabolism, and behavior are revolutionized by our understanding of early forage inclusion.
The Power of Early Forage: Setting Calves Up for Success
This study unequivocally underscores the importance of introducing forage early in a calf’s diet. The integration of forage, often overshadowed by traditional feeding methods, yields promising results for growth performance and overall health. The method and timing of forage introduction are pivotal for how effectively dairy calves utilize these fibrous materials.
Young calves start grazing naturally as early as the second week of life, showing an instinctual preference for forage. This early consumption significantly enhances rumen development and nutrient absorption. Research from the early 2000s highlights the benefits of lower levels of forage inclusion, setting the stage for optimizing calf diets. Studies consistently find that calves offered forage, especially in mixed rations, exhibit increased solid feed intake and improved metabolic responses.
This study builds on that understanding, showing that calves receiving TMR with forage maintain solid feed intake and have elevated β-hydroxybutyrate concentrations, indicating efficient metabolic processes. Additionally, forage inclusion encourages longer rumination times, a sign of better digestive health and behavioral satisfaction.
These insights call for a shift in calf-rearing practices. Traditional methods often use grain-heavy starters without forage, but evidence now supports the essential role of fiber. Calves consuming alfalfa hay, for example, show higher starter feed intake than those given other forage types, suggesting that fine-tuning forage sources can maximize benefits.
On commercial dairy farms, where the norm often excludes forage pre-weaning, feeding protocols need an urgent reevaluation. The integration of quality forage could significantly enhance growth performance and metabolic health, providing a solid foundation for calves’ future productivity. As the industry pivots towards evidence-based feeding strategies, advocating for early forage inclusion becomes not just important, but imperative for optimal dairy calf performance.
Diverse Forage Sources and Their Unique Benefits
Forage Source
Unique Benefits
Tifton Hay (Medium Quality)
Supports increased solid feed intake, improves rumination time, and provides fibers essential for digestion.
Tifton Hay (Low Quality)
Encourages higher solid feed consumption and enhances rumination, despite lower digestibility compared to medium quality hay.
Corn Silage
Boosts solid feed intake, provides a balanced nutrient profile, and enhances digestibility and palatability.
Both ensiled and dry sources showed distinct advantages among the forage options tested. Regardless of quality, Tifton hay significantly enhanced solid feed intake during crucial developmental periods. Corn silage also improved feeding behavior, underscoring the value of diverse forages in calf nutrition.
These findings align with prior research, such as Castells et al., which highlighted that various forages could equally boost intake and gains without harming feed efficiency or nutrient digestibility. Quality is influential, but the presence of forage itself is vital for healthy development.
The study noted higher β-hydroxybutyrate levels and increased rumination times in calves fed TMR with forage, indicating better rumen fermentation and metabolic activity. These markers illustrate how forages positively impact rumen development and digestive health, connecting metabolic outcomes with improved behavior.
Furthermore, the methods of forage inclusion, like total mixed rations, significantly influence outcomes. Different forages interact uniquely with the diet, affecting particle size, physical form, and nutrient content. This complexity necessitates a nuanced approach to forage integration, considering the calf’s developmental stage and dietary goals.
Ultimately, incorporating diverse forage sources offers benefits beyond nutrition. These forages promote metabolic health, efficient rumination, and proper eating behavior, supporting robust calf growth. Dairy producers should consider these benefits to optimize their feeding programs.
Understanding the Performance and Behavior of Dairy Calves
Incorporating various forage sources in Total Mixed Rations (TMR) enhances growth rates through improved feed efficiency and metabolic health. The study showed that while forages in TMR didn’t significantly change average daily gain or body weight, they did increase solid feed intake, laying a solid foundation for healthy growth. Additionally, higher β-hydroxybutyrate concentrations in calves receiving forage-inclusive diets signified enhanced metabolic health.
Feed efficiency, a critical aspect of livestock management, improved significantly with diverse forage sources in TMR. This positive trend indicates more effective nutrient utilization, which is crucial for the economic viability of dairy farming. Calves on such TMR diets also exhibited prolonged rumination, a sign of good digestive health and fiber utilization.
Forage inclusion also influenced behavioral patterns. Calves on forage-inclusive diets showed extended rumination periods associated with better digestive efficiency and general well-being. Despite no significant differences in time spent on various activities, the extended rumination time highlights the necessity of forage for optimal rumen development.
In essence, including forage in early-life diets for dairy calves boosts growth rates, feed efficiency, and overall health. Strategic forage inclusion in pre- and postweaning diets fosters resilient, healthy, and high-performing dairy cattle. These insights are crucial as we optimize feeding regimens for the benefit of both livestock and dairy producers.
New Findings in Early Forage Inclusion
Parameter
Forage Inclusion (MH, LH, CS)
No Forage (CON)
Solid Feed Intake (wk 7 & 8)
Increased
Lower
Postweaning Feed Intake
Higher
Lower
Average Daily Gain (ADG)
No significant difference
No significant difference
Body Weight (BW)
No significant difference
No significant difference
Feed Efficiency (FE)
Lower
Higher
β-Hydroxybutyrate Concentration
Higher
Lower
Rumination Time
Higher
Lower
NDF Intake (Week 8)
Higher
Lower
Recent research highlights the benefits of early forage inclusion in the diets of dairy calves. Studies and meta-analyses confirm that dietary fiber from forage positively influences pre- and post-weaned calf performance.
Comparing calves fed forage with those on a forage-free diet shows significant behavior and feed efficiency improvements. Forage-fed calves have increased rumination and better nutrient digestion, as seen from a higher neutral detergent fiber intake from week 8.
The implications for dairy calf management practices are evident. Including forage in the diet enhances feed intake and supports healthier growth. These findings advocate for early dietary forage to optimize metabolic and developmental outcomes.
The Bottom Line
Research highlights the critical role of early forage inclusion in dairy calf development. Adding forage to their diet meets immediate nutritional needs. It promotes beneficial behaviors like increased rumination time, which is essential for long-term health and productivity. Higher β-hydroxybutyrate levels indicate better metabolic adaptation, underscoring the importance of fiber for gut health and rumen development.
Dairy farmers and nutritionists should reconsider including forage in early calf nutrition to boost feed intake, behavior, and growth. Implementing this requires tailored approaches considering forage quality and proportion in mixed rations.
Future research should explore the long-term impacts of early forage inclusion on growth and health. It will be crucial to investigate the relationship between gut fill, average daily gain (ADG), and different forage types on metabolic indicators over time. Understanding sustained rumination from early forage can optimize calf nutrition, ensuring smooth transitions into high-yielding dairy cows.
Key Takeaways:
Introducing forage early in calves’ diets can significantly enhance rumen development and nutrient absorption.
Calves receiving TMR with included forage maintained higher solid feed intake compared to those without forage.
The diets containing medium quality hay (MH), low quality hay (LH), and corn silage (CS) all showed increased solid feed intake pre- and postweaning.
Despite no significant differences in average daily gain and body weight (BW), forage groups exhibited higher feed efficiency with the CON diet.
Calves on TMR-containing forage had elevated β-hydroxybutyrate concentrations, indicating efficient metabolic processes.
Supplemental forage led to longer rumination times, signifying better digestive health and behavioral satisfaction.
Summary: A study published in the Journal of Dairy Science suggests that introducing forage early in a calf’s diet can improve growth performance and overall health. Young calves start grazing naturally as early as the second week of life, showing an instinctual preference for forage. This early consumption significantly enhances rumen development and nutrient absorption. Research from the early 2000s has consistently found that calves offered forage, especially in mixed rations, exhibit increased solid feed intake and improved metabolic responses. This study builds on that understanding, showing that calves receiving total mixed rations (TMR) with forage maintain solid feed intake and have elevated β-hydroxybutyrate concentrations, indicating efficient metabolic processes. Forage inclusion encourages longer rumination times, a sign of better digestive health and behavioral satisfaction. The study calls for a shift in calf-rearing practices, as traditional methods often use grain-heavy starters without forage. Integrating quality forage could significantly enhance growth performance and metabolic health, providing a solid foundation for calves’ future productivity.
Uncover the potential of genomic and phenotypic insights to enhance dry matter intake management in US Holstein cows, ultimately boosting milk production and body weight management. Intrigued by the possibilities?
In the context of dairy farming, ‘dry matter intake’ (DMI) is not just a term for veterinarians and nutritionists. It’s a crucial factor for US Holstein cows, the key players in milk production. The efficiency of these cows is directly linked to what they eat, how much they eat, and how effectively they convert that intake into milk and robust health. Therefore, understanding DMI is not just important for maximizing farm potential, but it’s also the key to connecting feed efficiency, milk production, and overall animal welfare.
“Optimizing dry matter intake is crucial for enhancing milk yield and ensuring cow health. It’s the linchpin of dairy farm efficiency.”
This article explores the genomic and phenotypic impacts of DMI, highlighting its role in milk production and body weight management. Using data from 8,513 lactations of 6,621 Holstein cows, we’ll examine:
The link between DMI and milk components like fat and protein.
How body size traits affect DMI.
The impact on breeding programs aiming for better feed efficiency and productivity.
Join us as we dive into these dynamics and discover strategies to boost profitability and sustainability in dairy farming.
Unveiling the Genomic and Phenotypic Dynamics of Dry Matter Intake in Holstein Cows
Understanding dry matter intake (DMI) in Holstein cows is crucial for nutrition management and breeding programs. Large data sets have revolutionized this research, allowing precise estimation of feed requirements for milk production and body maintenance. These datasets provide a strong foundation for refining predictive models.
Two main approaches are used to evaluate DMI: phenotypic and genetic regressions. Phenotypic regressions use visible traits and help dairy farmers adjust feeding strategies based on real-time data for milk yield, fat, and protein content. This is vital for optimizing feed efficiency and maintaining herd health.
Genetic regressions, on the other hand, examine the genetic factors influencing DMI. These are especially useful in breeding programs that aim to enhance important traits through selective breeding. Genetic evaluations guide breeding decisions that promote traits like higher milk yield, better milk quality, and improved feed efficiency.
The difference between phenotypic and genetic regressions highlights the distinct goals of nutrition management and genetic improvement. Phenotypic data meets immediate needs, while genetic data fosters long-term improvements. Combining both approaches enhances current and future herd performance.
These advancements in genomic tools and statistical models, such as BostaurusUMD3.1.1 for genomic evaluations, underscore the collaborative effort to advance DMI research. This collective endeavor aims to optimize productivity and sustainability in dairy farming, a goal we all share in the scientific community.
An Unprecedented Dive into Dry Matter Intake Through Genomic and Phenotypic Lenses
This study makes a unique contribution to the field of dairy farming and genetics by analyzing DMI using a large dataset from 8,513 lactations across 6,621 Holstein cows. By integrating phenotypic and genomic views, we were able to provide a detailed look at DMI through sophisticated mixed models. These models included variables like days in milk, age parity, trial dates, management groups, and body weight changes during 28—and 42-day feeding trials in mid-lactation, ensuring accuracy in the results.
Based on observable traits, phenotypic regressions gave practical insights for nutritional management. In contrast, genomic regressions, grounded in genetic data, offered deeper insights crucial for breeding programs. Both evaluation types provided a comprehensive understanding of feed efficiency and milk production potential, aiding in better selection and breeding strategies.
Balancing Nutritional Demands: Insights from Phenotypic and Genomic Regressions
The phenotypic regressions of Dry Matter Intake (DMI) on milk, fat, and protein revealed specific coefficients that underscore the intricate balance required in nutrition management. For milk, the coefficient was modest (0.014 ± 0.006), indicating a relatively low increase in DMI per unit increase in milk production. Conversely, fat (3.06 ± 0.01) and protein (4.79 ± 0.25) showed more substantial coefficients, demonstrating that increases in these components significantly elevate the DMI requirements. These results suggest that nutritional plans must be meticulously tailored, focusing more on the feed requirements for fat and protein production to ensure optimal energy balance and animal health.
When we compare these findings to the corresponding genomic regressions, we observe stark contrasts. Genomic regressions yielded higher coefficients across all components: milk (0.08 ± 0.03), fat (11.30 ± 0.47), and protein (9.35 ± 0.87). This difference implies that genetic potential is more dominant in determining feed efficiency than phenotypic observations alone. Simply put, cows with higher genetic predispositions for milk components require substantially more feed, reflecting their superior production capabilities.
These discrepancies highlight an essential consideration for breeding programs. While phenotypic data provide valuable insights into immediate nutritional needs, genomic data offer a more comprehensive forecast for long-term feed efficiency and production potential. Consequently, integrating these genomic insights into breeding strategies can drive advancements in producing more feed-efficient cows, aligning with evolving economic and environmental objectives.
The ECM Formula: Unveiling the Energy Dynamics in Dairy Production
The ECM formula is vital for measuring milk’s energy content by considering its fat, protein, and lactose components. This standardization allows for fair comparisons across various milk types. Our study uses the ECM formula to reveal the energy needs of different milk components, shedding light on the nutritional and economic facets of dairy farming.
Regarding DMI for fat and protein, phenotypic and genomic regressions show significant differences. Phenotypic regressions suggest protein production needs 56% more DMI than fat. Genomic regressions show a smaller gap, with protein needing 21% more DMI than fat. Sire genomic regressions add complexity, indicating fat requires 35% more DMI than protein. These differences highlight the challenge of converting genetic data into practical feed efficiency.
These findings have profound implications for feed cost management. Increased DMI for any milk component escalates feed expenses, a critical consideration for farmers aiming to enhance profitability. However, breeders can leverage genomic data to select cows with lower residual feed intake that still yield ample milk, fat, and protein. This strategic approach enhances the economic viability of dairy operations, fostering more efficient and sustainable feeding practicesthat benefit both producers and consumers.
Sustaining Holstein Vigor: The Role of Body Weight and Maintenance
Examining annual maintenance needs in Holstein cows through phenotypic, genomic, and sire genomic regressions unveils notable consistency. Estimates, expressed in kilograms of dry matter intake (DMI) per kilogram of body weight per lactation, show phenotypic regression at 5.9 ± 0.14, genomic regression at 5.8 ± 0.31, and sire genomic regression, adjusted by two, at 5.3 ± 0.55. These are higher than those from the National Academies of Sciences, Engineering, and Medicine (NASEM, 2021) using Net Energy for Lactation (NEL) equations.
Discrepancies arise because NASEM’s general equations overlook individual genetic and environmental nuances. Genomic data offer a more dynamic and specific view, capturing intricate biological interactions. Modern genomic evaluations, encompassing various genetic traits, provide a clearer picture of maintenance needs, suggesting earlier models may underestimate the metabolic demands of high-yield dairy cows.
This analysis highlights the need to blend genomic insights with phenotypic data to grasp maintenance requirements reliably. By refining models with the latest genetic data, the dairy industry can enhance nutrition plans, improving animal welfare and productivity.
Decoding Dairy Efficiency: The Interplay of Type Traits and Body Weight Composite
Exploring multiple regressions on genomic evaluations for the body weight composite (BWC) traits, we find that strength stands out. It’s the best predictor of body weight and Dry Matter Intake (DMI), confirming its crucial role in the current BWC formula.
Other traits seem less significant in predicting DMI. This suggests that breeding programs enhance strength to improve body weight and feed efficiency. Prioritizing strength can balance robust body weight with better feed utilization.
Breeders can build more productive and cost-effective Holstein herds by selecting for strength. This aligns to improve profitability through more brilliant breeding and makes a strong case for ongoing genomic research in dairy production.
Optimizing Genetic Gains: The Evolution of the Net Merit Formula
The 2021 revision of the Net Merit formula marked a pivotal shift towards improving the economic efficiency of breeding programs. Integrating recent findings on dry matter intake (DMI) and other traits, the formula better aligns with the complex relationships among milk production components, body size, and feed efficiency.
The updated formula prioritizes more miniature cows with traits like harmful residual feed intake and higher milk, fat, and protein yields. This strategic approach promotes cows that produce more milk and enhance feed efficiency, reducing operational costs and boosting profitability. By incorporating genomic and phenotypic data, the Net Merit formula advances precision breeding, considering the economic impact of each trait and supporting a sustainable dairy industry.
This revision synchronizes breeding goals with economic benefits, encouraging the development of cows that excel in productivity while minimizing feed costs. It highlights the vital link between genetic research and practical breeding strategies, solidifying the Net Merit formula’s essential role in modern dairy farming.
The Bottom Line
The exploration of dry matter intake (DMI) in US Holstein cows through both genomic and phenotypic lenses has unveiled crucial insights into the nutritional and economic dynamics of dairy farming. The study revealed that genomic regressions provide a more accurate estimate of feed required for individual milk components or body maintenance than phenotypic regressions. Furthermore, the energy-corrected milk (ECM) formula highlighted that fat production demands significantly higher DMI than protein production, establishing a clear difference in nutrient requirements based on milk composition.
One of the pivotal findings emphasizes the significant benefits of selecting more miniature cows with harmful residual feed intake (RFI). These cows require less feed and exhibit an enhanced production of milk, fat, and protein, thereby improving overall farm profitability. This aligns with the revised Net Merit formula, which aims to optimize genetic traits for economic efficiency.
The implications for breeding programs are profound. Adopting strategies that prioritize genomic evaluations can lead to more efficient feed utilization and better economic outcomes. This study suggests that future research should delve deeper into the genetic mechanisms underlying RFI and explore the long-term impacts on herd health and productivity. Additionally, there’s potential for these findings to inform genetic selection criteria in dairy breeding programs globally, enhancing the sustainability and profitability of the dairy industry.
Key Takeaways:
Large datasets allow precise estimation of feed required for individual milk components and body maintenance.
Genetic regressions are more impactful for breeding programs than phenotypic regressions, which are more useful for nutrition management.
Fat production requires significantly more DMI than protein production when analyzed through the energy-corrected milk (ECM) formula.
Phenotypic regressions underestimate the DMI compared to genetic regressions.
Annual maintenance DMI for body weight is slightly underestimated in phenotypic regressions compared to genomic estimations.
Strength is the type trait most strongly associated with body weight and DMI, as highlighted by the revised body weight composite (BWC) formula.
To enhance profitability, breeding programs should focus on selecting smaller cows with negative residual feed intake that are high producers of milk, fat, and protein.
The Net Merit formula has been updated to reflect these insights, aiming for an economically optimal genetic selection response.
Summary: A study analyzing dry matter intake (DMI) in US Holstein cows found that understanding DMI is crucial for maximizing farm potential and connecting feed efficiency, milk production, and animal welfare. The study used data from 8,513 lactations of 6,621 Holstein cows and genetic regressions to analyze DMI. Phenotypic regressions used visible traits to adjust feeding strategies based on real-time data for milk yield, fat, and protein content. Genetic regressions examined genetic factors influencing DMI, useful in selective breeding programs. Results suggest that nutritional plans must be meticulously tailored, focusing on feed requirements for fat and protein production to ensure optimal energy balance and animal health. Genomic insights can drive advancements in producing feed-efficient cows, aligning with economic and environmental objectives. The Energy-Correlated Milk (ECM) formula is a crucial tool for measuring milk’s energy content, revealing significant differences in DMI for fat and protein.
Are your breeding decisions in tune with where you want your herd to be in the future? As I follow the breeder discussions on The Milk House (Read more: Introducing The Milk House – Dairy Breeder Networking on Facebook ), I see three different approaches: 1) Some breeders are asking what to breed a cow or heifer to, in order to get a show winner; 2) Some are saying that Holsteins are not the only breed and that Jerseys can also get the job done at returning a profit; and 3) The majority are saying that they want to stay with Holsteins but neither the show ring nor only filling the milk pail to overflowing suits their breeding plans for the future. This latter group want cows that, on average, stay in the herd into at least their fourth lactation, and that are efficient at converting feed to milk. They must also be fertile.
The good news for the third group of breeders is that there are two interesting new ratings that can assist them when it comes to sire selection for feed efficiency and fertility.
New Indexes for Feed Efficiency & Fertility
With the revisions to the TPI® formula (Read more: US Genetic Evaluation Changes: Are You Keeping Up?) made on December 2nd, Holstein USA added indexes for Feed Efficiency (FE) and Fertility (FI) for breeders to use when they evaluate sires for their daughters’ ability to convert feed to milk and for combining the various indexes that relate to fertility. The weighting of these indexes in the TPI® formulae are not large – 3% for Feed Efficiency and 13% for Fertility. Breeders wishing to place more emphasis on either or both of these areas in sire selection can eliminate bulls, during their selection process, that are inferior for one or both of FE and FI.
In order to provide information, that may be useful to breeders, The Bullvine has taken the top fifty daughter proven sires on Holstein USA’s Top 100 International Bulls -December 2014 list and selected and analysed the top ten sires for both of these indexes. The top fifty gTPI® proven sires are 2210 gTPI or higher.
Feed Efficiency Index
Table 1 lists the top ten sires for Feed Efficiency (FE) as well as these sires’ indexes for other traits that breeders normally use when evaluating sires to use in their breeding programs.
Table 1 Top 10 Proven Sires for FE (Feed Efficiency) that are in top 50 gTPI
Sire and NAAB Code
FE
NM$*
gTPI*
PTAT
Milk
Fat
Protein
FI
PL
Sire Stack
1. Robust
177
767(1)
2504(2)
0.99
1143
81
49
1.8
6.3
Socrates x Oman x Manat
2. AltaFairway 11HO10980
163
643(3)
2303(18)
0.46
1457
72
52
0.5
4.7
Planet x Oman x Morty
3. Manifold 200HO00402
154
575(9)
2286(20)
0.36
1440
69
52
1.9
3.7
Oman x BW Marshall x Emory
4. Facebook 200HO03753
150
512(33)
2366(4)
1.51
1281
80
47
2.2
1.1
MOM x Airraid x Shottle
5. AltaGreatest 11HO10928
145
619(6)
2338(11)
1.18
2104
54
60
0.5
5.2
Planet x Bolton x BW Marshall
6. AltaPhonic 11HO10997
145
539(20)
2262(25)
0.38
914
69
43
1.5
2.5
MOM x Nifty x Lynch
7. Mogul 7HO11314
142
728(2)
2586(1)
2.84
1143
81
49
0.3
5.1
Dorcy x Marsh x Bret
8. Mixer 7HO11313
128
543(16)
2332(12)
1.75
897
60
42
0
3.6
Dorcy x Marsh x Bret
9. Myrle 29HO14828
128
554(12)
2278(21)
0.6
978
69
36
1.1
3.8
Lief x Encino x Oman
10. Erdman 1HO09800
126
631(4)
2260(28)
-0.52
991
59
32
3.6
6.9
Planet x Ramos x Amel
Average
146
611
2352
0.96
1258
69
46
1.3
4.3
* Bracketed number is the rank within NM$ or gTPI
Robust, the #1 NM$ sire and #2 gTPI®, easily comes to the top for FE. In second place is AltaFairway. All bulls on this list are superior for their ability to sire high production daughters with their proofs averaging Milk 1258 lbs, Fat 69 lbs and Protein 46 lbs. Further study of these bulls shows that they have a variety of sire stacks, have high Productive Life (4.3) but are not outstanding for type (PTAT 0.96) or fertility (FI 1.3). The indexes of these ten sires have a better correlation between FE and NM$ than between FE and gTPI®. It should be noted that only Facebook and Erdman, on this list, are over 2.0 for FI. Breeding for feed efficiency will not automatically get improved fertility.
Fertility Index
Table 2 lists the top ten sires for Fertility Index (FI) as well as these sires’ indexes for other traits.
Table 2 Top 10 Proven Sires for FI (Fertility Index) that are in top 50 gTPI
Sire and NAAB Code
FI
NM$*
gTPI*
PTAT
Milk
Fat
Protein
FE
PL
Sire Stack
1. Wright 7HO1123
5.3
631(4)
2355(8)
-0.19
401
28
20
72
9.6
Freddie x Wizard x Rudolph
2. Sobieski 1HO09853
5.1
501(37)
2311(15)
0.44
363
45
25
90
4.3
Freddie x Lynch x Duce
3. Denim 1HO10218
5
615(7)
2356(7)
-0.7
389
55
27
114
7.3
Freddie x Wizard x Mtoto
4. Freddie 1HO08784
4.6
533(23)
2349(9)
0.51
866
33
28
77
5.6
Oman x Die-Hard x Metro
5. Sapporo 200HO03773
4.5
438(82)
2248(29)
1.06
572
32
11
43
5.9
Jeeves x Goldwyn x Outside
6. Army 1HO09659
4.5
338(203)
2210(49)
1.06
-100
27
21
74
2.2
Jet Stream x BW Marshallx Rduolph
7. Gallon 29HO14684
4
489(42)
2245(30)
0.42
1380
33
31
74
4.9
Jeeves x Goldwyn x Oman
8. Yano 1HO10085
4
530(24)
2210(50)
-0.15
451
15
23
64
7.6
Planet x Bret x Manfred
9. Sherman 7HO11164
3.9
432(93)
2230(35)
0.67
63
29
24
82
3.6
MOM x Shottle x Roy
10. Petrone 7HO1169
3.8
549(13)
2361(5)
1.39
624
32
13
47
7.5
Super x AltaBaxter x Buckeye
Average
4.4
506
2288
0.45
501
33
23
74
5.9
* Bracketed number is the rank within NM$ or gTPI
Wright (Read more: TPI® – Do we have it all wrong?) comes to the top of this list. The first three on the list are all Freddie sons and Freddie himself is #4 on the list. Knowing that leads to the question – Who says the fertility is not heritable or at least that there are sire lines that have daughters that are superior for fertility? The averages for these ten sires give a very clear indication that selecting for higher production is inversely related to fertility. As well, PTAT and FE are only slightly positively correlated to fertility. And that fertility (FI) has no correlation to NM$ or gTPI® for sires that are in the top 50 gTPI®.
Except for Freddie himself, breeders are not likely to recognize the names of the other nine bulls in Table 2. It is noteworthy to see that the ten sires in Table 2, on average, are high for PL (5.9). Cows that have a high genetic ability to get pregnant stay longer in herds. Commencing to select sires for FI but not at the total expense of production will be a wise decision for breeders that focus on profitability in their breeding programs.
Always Compare to the Top Sires
When making comparisons and selecting sires, it is always useful to know what the profile is for the best in the breed. Table 3 contains the index averages for the top 10 gTPI® daughter proven and genomic sires. The genomic list is limited to sires born in 2013, as this is the group of sires that breeders are likely to be using currently or in the near future.
Table 3 Index Averages for Top 10 Proven and Genomic Sires – December 2014
Proven
Genomic
Feed Efficiency (FE)
104
170
Fertility Index (FI)
2.8
1.7
NMS
595
814
gTPI®
2398
2677
Milk
878
1623
Fat
50
80
Protein
33
57
SCS
2.8
2.84
Productive Life (PL)
5.8
6.4
PTAT
1.23
2.27
UDC
1.18
2.03
FLC
1.35
1.74
It goes without saying that the averages for these two top 10 sire lists are outstanding. Due to Freddie’s influence, the top 10 proven sires are very high for FI. While for FE the genomic list is far superior due to their milk, fat and protein indexes being almost double those of the proven list.
By comparing the group average in Tables 1, 2 and 3, it can be seen that the top Fertility sires in Table 2 lag behind the other groups except for FI and PL. Also note that the Feed Efficiency sires in Table 1 are generally equal to the top 10 proven sires in Table 3. And except for fertility (FI) the genomic sires in Table 3 are the highest indexing group.
Sires to Select
The first sort of sires available should be the top fifty sires for NM$ or gTPI®. A few bulls that may qualify for their total merit and are significant improvers for FE and FI are listed below:
Proven Sires
Facebook (2366 gTPI & 512 NM$)
Denim (2356 gTPI & 615 NM$)
Robust (2504 gTPI & 767 NM$)
Manifold (2286 gTPI & 575 NM$)
Altaphonic (2262 gTPI & 539 NM$)
Genomic Sires
Supershot (2675 gTPI & 848 NM$)
Rubicon (2718 gTPI & 864 NM$)
Hotshot (2661gTPI & 815 NM$)
Delta (2709 gTPI & 873 NM$)
Draco (2642 gTPI & 810 NM$)
Polled Sires
Powerball-P (2534 gTPI & 653 NM$)
Multitude-P (2249 gTPI & 418 NM$)
Ewing-P (2229 gTPI & 510 NM$)
Yahtzee-P (2408 gTPI & 588 NM$)
Ladd Man-P (2201 gTPI & 365 NM$)
Red, RC and high PTAT sires do not rank high for either feed efficiency (FE) or fertility (FI). One exception is Mogul at 2.84 PTAT who received 142 for FE however his FI is only slightly above average at 0.3.
The Bullvine Bottom Line
Breeding for feed efficiency is closely related to breeding for increased production. However breeding for increased milk yield is counter-productive to increasing the genetic merit of females for reproductive traits. Based on our study of the new indexes for feed efficiency and fertility, we recommend that breeders select bulls that are over 80 pounds for fat and protein combined and that are over 1.0 for FI.
Not sure what all this hype about genomics is all about?
Want to learn what it is and what it means to your breeding program?
When The Bullvine mentions genomic testing to production oriented breeders, we frequently get the reaction “Oh, that’s just for herds that sell high priced animals. I focus on running a profitable milking operation. I don’t need to spend money on testing my animals.” Well, in fact, that is not an accurate assessment of the benefits available from using this tool at the present time. If you are among those not using genomics, Stop Procrastinating! It is a tool that everyone breeding their herd to improve it genetically should not be without.
Only Very Moderate Uptake – So Far
Currently, there is an 8% uptake of genomic testing of all Holstein heifer calves. The total is less in other breeds. We have barely scratched the surface. Half a century ago, official milk recording was at the same low level. Today it is recognized as a much-needed toll both on-farm and in the national herd. Obviously the question that breeders need answers to is ‘How will I benefit from genomic testing all my heifer calves?’
Known Benefits
Much has been written about benefits and opportunities available to breeders who are submitting samples for DNA testing. Those range from selecting the best mates for your females, … to parentage verification, … to how to manage your heifer herd, … to deciding which heifers to breed and which ones to cull or implant, … to polled or not polled, …to finding the genetic outlier of an individual mating, …to an aid in marketing heifers in sales.
Just recently Holstein USA and Zetas launched an exciting service called Enlight. Breeders that submit their samples to Zoetis can through Holstein USA’s website summarize and analyze their heifers for their genetic qualities. This is the first, and no doubt other breeds will establish similar services in the future. Breeding to get the genetics that work best for you and then managing them in the best way possible is definitely important.
At the industry level, genomic testing has also proven beneficial. Alta Genetics, a few years ago, working with large herds in the USA, parentage verified all young sire daughters. It was a significant step forward in accuracy of sire proofs so they could guarantee their product to their customers. Companies like Zoetis and Neogen initiated genomic testing services so they could help producers and also as complementary to their other products. A.I companies have been able to restrict their young sires sampled to only top genomically evaluated young sires, thereby saving millions for themselves by not sampling the bottom enders and millions for breeders that did not have to raise, calve in and milk the lower genetic merit daughters of the bottom end bulls. All of these benefits are leading to cost savings in the hundreds of millions of dollars.
However six years into using genomics we are only starting to reap the rewards.
Genomics Will Make the Future Brighter
Breeders often mention that they want sires to use and females in their herd that are superior to what is available today for traits that are difficult or impossible to measure. Here are some thoughts and facts that may help breeders to decide to use genomic testing so they can have animals that are even more profitable than their herd is today. It does however require that genomic testing becomes routine (Read more: Why 84% of Dairy Breeders Will Soon Be Using Genomic Sires!).
Heifers:
Investigation, at the farm level, is being done in beef heifers on growth rates, diets tailored to genotype, immunity to common diseases and age at first estrus. The results of those studies will be able to be applied to dairy heifers since little similar research is being conducted for dairy heifers. Already breeders can test for the genetically inferior heifers, so they do not need to be raised. Up to $500 per heifer in rearing cost could be saved by having the retained heifers calving by 22 months of age. Remember that it is age at first estrus that is important, for which we have very limited farm data. First breeding depends on a breeding actually occurring. With heifers genotyped and selected for first estrus significant savings will be possible.
Feed Efficiency:
Two major research projects, one in USA and The Netherlands and one in Australia and New Zealand, will identify the cows that are genetically more efficient at converting their feed to milk. Within a couple of years, we can expect to see reports relating genomic information to feed efficiency. This type of research is costly and not currently practical at the farm level, but using research herds this investigation is well underway. Reducing feed costs by 5-10% through genetic selection would result in many millions in savings. That is likely to be crucial to the dairy cattle breeding industry as dairy competes to feed a hungry world. (Read more: Feed Efficiency: The Money Saver and 15 Strength Sires That Will Still Fit In Your Stalls)
Inbreeding:
CDCB already makes available the inbreeding level of genomically tested animals based on their genomic results. No doubt further research results will provide numbers associated with inbreeding. Think about it. In the past the inbreeding level for two full sisters, based on pedigree, has been considered the same. However, by using their genomic profiles the level of inbreeding can be much more accurately known for each sister. A recent report from CDN, for the time period 2010 to 2013, shows that inbreeding rates are increasing not decreasing. Even though breeders are aware that inbreeding is a negative to future profit, they continue using fewer sire lines. More in-depth study of presence or absence of genes that negatively affect the viability of our cattle take time. Why do we always expect someone else to take responsibility for the level and rates of inbreeding? (Read more: 6 Steps to Understanding & Managing Inbreeding in Your Herd and Stop Talking About Inbreeding…)
Disease Resistance:
The list is long on diseases that breeders want their animals to be resistant to. Many research projects are underway to relate the genotype to particular types of mastitis, respiratory diseases, wasting diseases and even production limiting diseases like milk fever. CDN and Canadian milk recording agencies have been capturing field data for a number of years now on eight production limiting diseases. In time, the relationships between genetic lines and these diseases will be better-known. So that selection can be carried out to avoid problem bloodlines. When more animals are genomically tested, and bloodlines prone to diseases are identified great steps forward will be able to be made. It takes considerably more than 8% of the population genomically tested to move breeding for disease resistance to reality. (Read more: Genomics – Opportunity is Knocking)
Reproduction:
Failure to get animals to show good heats, to produce good oocytes and conceive when bred is the leading frustration on most dairy farms. The role that genetics plays in that frustration is now receiving attention by many researchers and organizations. In the past, the capturing of useful data to do genetic analysis relative to reproduction has been a significant problem. The relating of genomic results to reproduction holds out considerable hope. Early embryonic death, haplotypes that negatively impact reproduction, genetic difference between animals for cystic ovaries and many more are all areas of concern for breeders. Once again both genomic and on-farm data are needed to move forward. (Read more: 10 things dairies with great reproduction do right and Are Your Genetics Wasting Feed and Labor?)
Misconception:
I hear breeders say “Genomic indexes are just like production indexes.” However, that is not so. There are genomic indexes for production traits, conformation traits and management traits. Genomics is a dynamic science. It is best if breeders know not only the genomic values for the animals currently in their herds but also their ancestors. To build the genomic history for a herd necessitates that testing start as soon as possible. Genomics is a tool every breeder will benefit from using no matter what their selection goals are. (Read more: Better Decision Making by Using Technology and FACT VS. FANTASY: A Realistic Approach to Sire Selection)
In Another World
Outside the world of dairy cattle but totally related to DNA analysis, there is a study just under way in the United Kingdom, where 100,000 people with cancer or rare diseases are being genotyped to better understand people’s ability to avoid or resist cancer and disease. One of the terms used in the news release was that before there was DNA profiling this work would not have been possible. Relating that back to dairy cattle, if we do not have the DNA information for animals we will be limited in our ability to eliminate deleterious genes from our cattle.
Will Genomic Testing Pay?
The question for breeders appears to have been one of cost – benefit. “What will I get for the fifty dollar cost of doing a low-density test?” The fact is that, to date, milk producers have not taken the opportunity for more rapid genetic advancement by testing all their heifers. However, the tide is about to change. With new information coming out almost weekly on how the genetic (aka genomic) make-up of an animal relates to profitability, breeders without genomic information on their herd will not be in a position to know which sires to use or how to manage or feed their animals. Genomic testing needs to be viewed as an investment rather than a cost. Invest $50 shortly after birth to save hundreds over the cow’s lifetime.
The Bullvine Bottom Line
Every journey requires that a first step be taken. The first step is that breeders submit samples for DNA analysis. Every breeder will benefit by knowing the genomics of their herd. No doubt the cost of testing will come down as more breeders participate. Future success in dairying will require genomic testing, just as current success depends on capturing and using performance information. Are you prepared for using genomic information to assist in creating your future success in dairying?
Not sure what all this hype about genomics is all about?
Want to learn what it is and what it means to your breeding program?
A recent headline in Hoard’s Dairyman proclaimed “Brown is the Color of Money” and that’s all it took for “The Hunt Family Feud” to take off over phone, email and Facebook. With roots in Holsteins, dairy nutrition and dairy genetics, the perfect ingredients were present for arguments, controversy and loud proclamations of bull* –all of which are highly esteemed in the Hunt family.
Can you Measure the Difference?
This debate is fueled by a lot of things but every good argument needs actual facts. Inputs of feed, facility, equipment and staff may be impacted by the size differential between Holsteins and Jerseys. Smaller animals may correspondingly require less inputs. We have to recognize that “may” is the operative word here because there are different variables depending on each particular dairy operation.
One size variable that can’t be ignored is that dairy herd size is growing. Faced with this scenario, there may be good reasons for choosing one breed over another or for having a combination of breeds on a single operation. Choice might be influenced by:
Specific markets
Relative health issues between breeds
Calving ease
Initial investment and sources for replacements
Many questions have to be answered, before a winner can be named.
Which Breed Fits the Facilities?
For those working in barns that were built twenty or more years ago where stalls are smaller, Jerseys may be a better fit. As well new dairy operators who are renting such facilities could find that Jerseys would operate better in those smaller stalls. Bedding packs also are another way to put minimal effort and expense into rented facilities. Jersey’s work well on packs. If there is a drawback, it could be that it may take more stalls to produce the same volume of milk. However, if the Jerseys are high volume for %F and %P, then the pounds of fat+protein produced per day may be the same whether it’s Holsteins or Jersey.
Which Breed Eats the Most?
Scientific examples abound regarding “efficiency” because of the Jersey’s smaller size. Let’s briefly consider human size relating to efficiency. “Is the size two female more efficient than her size 18 cousin. What are they producing? Food for a party? Or are you measuring food consumed? Not relevant. Well – what about groceries consumed? Or children produced? Getting warmer. But there are still too many variables to make a choice based on efficiency related to size alone. However, back to choosing the most efficient dairy breed to feed. It isn’t only about quantity of feed consumed per cow per day. The calculation should refer to the net dollars per day for the herd. When calculating returns minus feed costs, Jerseys can be competitive. (Read more: Feed Efficiency: The Money Saver)
Which Breed Has Better Genetics and Genomics?
Jerseys are not just for show oriented breeders. Milk production focused herds are using Jerseys.
Genetically Jerseys differ from Holsteins in that SCSs are higher, and the Median Suspensory Ligament (cleft) may not be as defined. Their reproduction is much superior. Jersey dropped bull calves are much less in demand. Dollar value is low. Using sexed semen for the top of the herd and beef semen on the bottom half gives a revenue source because crossbred dropped calves are in demand. (Read more: SEXED SEMEN – At Your Service!) Jerseys have genomic indexes as well. Genomics may have been a little slower to be adopted than in Holsteins but just wait Jerseys will catch up. Or so the argument goes. (Read more: Dairy Cattle Genomics)
Which Breed will Save Time?
Jerseys are the Queens when it comes to reproduction in dairy cattle, boasting easier calving, better conception rates and fewer inseminations. All of these have an impact on less vet time required for checking or treating as well as staff time and effort daily and annually. Easier calving for Jersey’s impacts that there will be fewer calf losses at birth and most likely more calves getting off to a better start. Superior reproduction can allow for less time off in the dry cow pen or less time milking at lower levels during a lifetime. (Read more: Artificial Insemination – Is Doing It Yourself Really Saving You Money?) Every manager knows that staff and cows need time off. Unnecessary time off on the cow’s part means less than optimum returns over a cow’s lifetime. Jersey heifers reach puberty at a younger age. This means age at first calving can be earlier, thus saving on rearing costs.
Which breed sells more milk? More live sales?
In the US, Jerseys are about 10% of the population. There has been steady growth in the number of Jersey herds in the U.S., particularly among large dairy owners in the West. The way breeders market and which markets they send their milk to is essential in areas where cheese and butter sales (which are at the highest relative level in twenty years) can greatly influence which breed you choose to work with. Owners are producing milk that their processors desire. In fact, the processor is the breeders’ customer not the end consumers. With eat local food movements the world over being emphasized, Jerseys may fit better than other breeds in some situations. The recent popularity of Jerseys has resulted in the fact that sales of breeding stock have been good as well,
It’s All About the Numbers. Are they In the Red or In the Black?
When you want to win the argument over which breed is the most profitable it all comes down to the actual data, you are analyzing. The reason the debate goes on is because there isn’t a source for reliable data comparing Jerseys and Holsteins. And so we come back to the initial article which triggered these questions which reported a comparison that exists through financial reports of Ganske, Mulder & Co. LLC, the largest dairy accounting firm in the U.S., They prepared reports summarizing all of its clients as a group and also does a separate summary for its Jersey clients. “It is perhaps the only such set of Jersey financial data that exists” reports the article that goes on to present statistics and the following summation. “Jerseys did make less milk per day than did all of the firm’s clients. But Jersey herds had much higher protein and fat tests, which resulted in significantly higher milk price per hundredweight. As a result, Jersey herds’ bottom line was much bigger – they made 45.7 percent more net profit per head.
NAME
Sale
Lot
GLPI
OCONNORS PLANET LUCIA
Genetics By Design
1
3823
STE ODILE MOON MODEL AMALUNA
GPS
16
3798
OCONNORS LIVING THE DREAM
Genetics By Design
14
3755
MAPEL WOOD LAST DANCE
Genetics By Design
3
3710
MAPEL WOOD SNOWMAN LEXUS
Genetics By Design
4
3673
OCONNORS BOULDER LUNA
Genetics By Design
6
3537
MAPEL WOOD BOULDER LIMERICK
Genetics By Design
7
3537
OCONNORS LAST HOPE
Genetics By Design
2
3534
BENNER FORK JANARDAN
GPS
1
3493
OCONNORS EPIC LAST CHANCE
Genetics By Design
8
3465
OCD MOGUL FUZZY NAVEL
Sale of Stars
5
3460
GEN-I-BEQ LEXOR PLAGE
Sale of Stars
45
3398
VELTHUIS SG LAVAMAN ENVY
Sale of Stars
46
3372
MARBRI UNO BEAUTY
GPS
11
3328
MAPEL WOOD M O M LUCY
Genetics By Design
12
3299
ROCKYMOUNTAIN LEXOR EDEN
GPS
32
3289
WELCOME-TEL ECOYNE ABBIE
Sale of Stars
12
3286
ZIMMER WENDON UNO CAMI
Sale of Stars
35
3268
OCONNORS SNOWMAN LEXIE
Genetics By Design
5
3255
BOLDI V S G EPIC ASTER
Sale of Stars
7
3240
So What Color of Dairy Breed Is the Money Maker?
Jersey herds produced 48 pounds of fat and protein where all herds produced 5.0 pounds of fat and protein. This is not significantly different. But on any given day, on any particular dairy operation, the numbers can be rallied to support the choice that is dearest to the heart of owner-operators.
The Bullvine Bottom Line
In the end, your particular passion is what it all boils down to. When it comes to the choice of Black and White, Brown, or “green”, the only thing you can know for sure is that dairy love is NOT color blind. Whether your passion is driven by the color of the dairy breed or by the color of money … or both… the right answer is up to you? End of argument.
For the vast majority of Holstein breeders, success is not about winning first prize at a cattle show. What they do want are heifers and cows in their herd that efficiently convert high forage based diets into growth and milk products that consumers will buy. For those breeders tall animals are not the ideal. In fact many breeders are saying that they want cattle that have more heart and lung capacity (Read more: 5 Things You Must Know About Secretariat, Lung Capacity and Dairy Cattle) and less stature to go along with high production, improved reproduction and functional udders and feet and legs. (Read more: Feed Efficiency: The Money Saver)
Moderating Frame
Since size, stature and strength are some of the more heritable type traits in the North American type classification evaluations; it is possible to moderate stature and increase the capacity of chest and heart and lungs depending on the sires used. The challenge is to do that while placing major emphasis on production, health and reproduction traits. As udders and feet and legs have been improved significantly over the past twenty years in herds focused on producing milk, there is less need to place as much emphasis there. As always there are no perfect sires, so it takes careful corrective mating. Instead of generation after generation of breeding for increased stature, it may require that breeders use some sires that are negatively rated for frame traits. (Read more: Are Today’s Holstein Cows Too Tall?) Today the majority of sires have high ratings on stature than on strength (USA) or chest width (Canada). To increase strength and capacity the sires used need to have higher indexes for strength or width than for stature.
Sire Suggestions
If your dairy breeding model is about production, efficiency and functionality from heifers that are 1150 pounds and 55 inches, calving at 21 months and cows that are up to 1600 pounds and 58 inches at fourth calving, then here are some sires that may suit your needs. The first requirement for making this list was that the sire have a high CM$ value. Cheese Merit was selected instead of Net Merit as the increased global demand for milk will likely come from the protein content.
Bryhill Science P
(200HO06584, Uno x Shottbolt x Goldwyn x September)
Science will be attractive to breeders wanting to introduce the polled gene into their herd. For a polled sire, he has high ratings for CM$ 756 and gTPI 2297 (71%Rel). His stature (+1.79) and strength (+1.20) ratings are moderate so, except for tall and large framed cows, he will hold but not decrease those traits. His inbreeding is slightly above average given his sire stack. His strengths are udders (+2.50), fat (89) and protein (42) yields with good Productive Life (+4.1) and PTAT (+2.75). His pins are high, but his DPR is +0.8 otherwise he has no significant limiting factors.
Bush-Bros Mog Fairfax
(14HO07349, Mogul x Freddie x Lancelot x Nitro)
Fairfax has a high rating for CM$ 974 with a moderately high rating for gTPI 2359 (71% Rel). His stature (+0.49) and strength (+0.24) ratings are breed average which leads to a lower gTPI given his fat (73) and protein (42) yields. He excels for DPR (2.4), SCS (2.66), PL (7.0) and Calving Ease (5.6). Breeders who are wishing to have average framed cattle that have good udders (+2.36) with non-traditional sires back in his sire stack and desirable management trait ratings should look up Fairfax’s proof sheet.
Cogent Supershot
(224HO02881, Supersire x Robust x Shottle x Aerostar)
Supershot has been a very popular sire for IVF programmed females over recent months. At CM$ 1110 (highest on this listing) and gTPI 2625 (71% Rel), he is at the very top. Currently, his semen price is very high but breeders willing to wait can expect it to decrease. His stature (+1.07) and strength (+1.01) ratings indicate he will leave moderate framed cattle. His strengths are milk (2528), fat yield (100), protein yield (85), PL (7.4) and DPR (1.9). He has no negatively rated type traits, but neither does he excel for type (PTAT 2.2).
Co-op Bosside Massey
(1HO09527, Mascol x Bret x Manfred x Megabuck)
Massey in one of the few sires where his strength proof (1.54) exceeds hi stature proof (+0.94). These ratings along with his 96% Rel gTPI proof (2260) and CM$ 838 make him a sire that breeders wanting strength and production should definitely consider. His sire stack is quite different from other sires in North America. With over 700 daughters in his proof, breeders can expect that his proofs will hold up over time. His SCS proof is excellent (2.52) and he has good ratings for PL (3.7), DPR (0.9) and Udders (2.21). He needs to be protected for high pins and straight rear legs.
Denistier Discovery
(147HO02479, Mogul x Bowser x Toystory x Outside)
Discovery fits the mould well for sires that commercial dairymen wanting less frame should consider using with stature (+0.52) and strength (+0.07). His sire stack is enough different so as to not be a concern about inbreeding. He has excellent ratings for Management Traits – CE 5.5, DCE 5.4, PL 7.4, SCS 2.63, DPR 2.9, and Rump Angle 1.94 and good indexes for milk (1512), fat (65), protein (51) and feet & legs (2.49). In total, he has a very high CM$ 950 rating and a relatively high gTPI 2415 (72% Rel). He will need protection for straight rear legs (-2.25).
De-Su LTM Rodgers 11379
(7HO12023, Lithium x Russell x Wizard x Mtoto)
Rodgers has an uncommon sire stack and excels at CM$ 1008, and his gTPI is high at 2450 (72% Rel). With stature at 0.86 and strength at 0.12 his daughters can be expected to be medium for frame. As with other sires in this listing, he has very high Management Trait indexes – PL 7.7, SCS 2.65 and DPR 2.7. His Maternal Calving Ease at 3.9 is excellent, and his Calving Ease at 5.8 is very good. His milk (1626), fat (86) and protein (57) are also very good. His overall type. PTAT 1.68, is only slightly above average yet he has no serious type weaknesses.
EDG Rubicon
(151HO00681, Mogul x Robust x Planet x Bolton)
Although Rubicon’s sire stack contains many heavily used sires, his CM$ 1004 and gTPI 2531 (72% Rel) standout with moderate ratings for stature (1.50) and strength (1.15), and it warrants his inclusion in this listing. PTAT 2.68, UC 2.41, FLC 2.54 and PL 6.3 say that his daughters should be trouble free cattle. Breeders desiring medium framed cattle with all other traits well above average should look up Rubicon.
Farnear Alfalfa
(29HO17516, Supersire x Freddie x Shottle x Buckeye)
Alfalfa’s indexes for stature (-0.26), strength (-0.07) and body depth (-0.72) make him the kind of bull breeders wanting to genetically decrease the frame of their cattle should consider using. He has high indexes for CM$ 935, gTPI 2354 (73% Rel), Milk 1622, Fat 71, Protein 49, PL 8.9, DPR 1.3 and MCE 4.6. His full brother Farnear Admiral (7HO12233) has very similar ratings and could also be considered.
Gillette SGO Myspace
(200HO10003, Mogul x Planet x Bolton x Shottle)
Breeders wishing to breed medium framed cows, but not wanting to move away from the breed’s popular sires might consider Myspace. With stature at 0.69 and strength at 0.22, he fits this listing. He has high indexes for CM$ 913, gTPI 2402 (73%), Milk 1805, Fat 82, Protein 58, PL 6.9, MCE 5.5 and SCS 2.68. He has good ratings for PTAT 2.46 and DPR 1.0, and he indexes are all positive except for very slight negatives for teat length and set of rear legs, side view.
Mainstreet Manifold
(200HO00042, Oman x BW Marshall x Emory x Adan)
Manifold is a 97% Reliability sire with a unique breeding pattern. Strength at 1.53 and stature at 0.59 says that his daughters are stronger than they are tall. He is just the type of sire that breeders who are wanting to breed for strength, but not for stature should be considering. He is a calving ease specialist (3.7) with good ratings for CM$ (761), gTPI (2188 (97% Rel), PL (4.3) and DPF (1.2). Breeders should expect to get daughters that are average for type (PTAT +1.18).
MR Mogul Delta 1427
(203HO01468, Mogul x Robust x Planet x Elegant)
Although Delta is rated slightly higher for stature (+0.81) than strength (+0.04), he is just breed average for both of them. He makes this listing because of his extremely high ratings for CM$1076 (second on this listing) and gTPI 2567 (73% Rel) even though he is only average for frame traits. His strengths that get him highly rated for total merit include milk (1643), fat (93), protein (60), PL (7.8) and SCS (2.60) His DPR is 1.2 and, beyond the frame traits, he is positively rated for all type traits (PTAT 2.75).
MR Moviestar Mardi Gras
(534HO00025, Mogul x Planet x Shottle x Oman)
Mardi Gras is slightly higher for stature (+1.31) and strength (+0.78) than most sires in this listing, these traits are rated much lower for his other type ratings. His specialties are CM$ 926, gTPI 2467 (72% Rel), udders +2.96, PL 6.9 and DPR 2.3 with good rating for all other evaluations. His sire stack contains extensively used sires, but his EFI is not high.
RH Superman
(200HO07846, Supersire x Man-O-Man x Baxter x Durham)
Superman is not yet a year old and semen is not yet available but he is a bull to watch for in the future. His Canadian indexes include Stature +2 and Chest Width +8 which would indicate that he would be the type of sire that needs to be included on this listing. He is rated at 3525 for gLPI (65% Rel) which puts him at the very top. Other traits with 99%RK ratings include Milk 2564 kg, Fat 126 kg, and Protein 96 kg. Traits with 98%RK ratings are CONF 11, Herd Life 113 and DCA 110.
River-Bridge Co-op Troy
(1HO11056, Mogul x Freddie x Mascol x Trent)
Troy is another top sire with CM$ 1057 (third highest on this listing) and gTPI 2508 (72% Rel) that is only average for strength (0.34) and stature (1.06). His strengths include PL 8.6, SCS 2.50, DPR 2.7, UC 2.37 and FLC 2.50. His only limiting factor is straight rear legs -2.07.
Westenrade Altaspring
(11HO11437, Mogul x Gerard x Mascol x Laudan)
Altaspring is the highest rated type sire (+2.82) on this listing yet he is only above average for strength (1.10) and stature (1.62). He is high for CM$ 967 and very high for gTPI 2546 (71% Rel). He is an all-round bull with no significant weaknesses in all his indexes.
There’s a Pattern
Eleven of the fifteen sires on this listing are sired by Mogul (8) and Supersire (3). That should not be a surprise given that for frame traits these two sires are not highly rated. Mogul is rated (USA) Stature 1.59 and Strength 0.79 and (Canada) Stature 1, Chest Width 1 and Height at Front End -6. Supersire is rated (USA) Stature 1.25 and Strength 0.91 and (Canada) Stature 0, Chest Width 5 and Height at Front End -4.
The Bullvine Bottom Line
As Mogul and Supersire have been used extensively as the sires of A.I. bulls in the past few years, breeders can expect to have more strength bulls available in the future than there have been in the past ten years. Breeding for width and strength are likely to be topics that discerning breeders will be breeding for in the years ahead.
On a regular basis The Bullvine produces lists of sires that meet the breeding goals of our readers. Since producing a list of 30 Sires that will produce Feed Efficient Cow$ a year ago (Read more: 30 Sires that will produce Feed Efficient Cows) we have done considerable research into what makes for feed efficient and lifetime profitable cows (Read more: She Ain’t Pretty – She Just Milks That Way!).
What is Efficiency?
Researchers from a number of institutions and countries are jointly studying which cows are the most feed efficient. To date the studies continue and there are not yet definitive answers. In Hoards Dairyman in 2012 University of Illinois Professor Mike Hutjens brought forward a very interesting thought. His reasoning is that cows are fed a wide variety of diets and that it is the income over feed costs (net dollar returns after feed costs) that is the important factor when it comes to herd profit.
In research herds it may be possible to capture feed intakes but at the farm level it is currently not possible. Without feed intakes on a cow by cow basis it will not be possible to rank sires for their daughters’ feed efficiency. So, at least, for the immediate future the most practical thing to do will be to compare diets on their income over feed costs and tocompare sires’ daughters on their ability to: 1) live is group environments; 2) get pregnant; 3) require minimal individual care; and 4) produce high volumes of fat and protein from low SCS milk on the feed they are fed. And, 5) they need to last for four or more lactations.
Bullvine Efficiency Index (BEI)
A year ago The Bullvine developed and published this index based on information from a number of sire listings. It has been very reassuring to see that with further investigation that this index continued to be a very good predictor for sires that will produce cows that will ring the bell when it comes to profit over a lifetime.
BEI = Production (45%) + Durability (30%) + Health & Fertility (25%)
Production = 30 Fat Yield + 50 Protein Yield + 10 Fat % + 10 Protein %
Durability = 17 Herd Life/Productive Life + 42 Mammary/UDC + 25 F&L/FLC – 8 Body Depth – 8 Stature
Milk Yield is not included as it contributes to more udder strain and additional milk volume to be transported or on-farm water removal cost.
The negative weightings on Body Depth and Stature reflect that larger cows require extra feed to grow to that size and to maintain that larger size each and every day compared to cows of more moderate size.
BEI is calculated using CDN’s Custom Index Calculator. An overall sire ranking is not possible using the calculator as it only allows quires for three groupings – Proven Canadian, Proven MACE and Genomically Evaluated Sires. Bulls are ranked for BEI within each list as a percent of the top bull on the list.
Young Sires Currently Being Sampled
Table 1 contains North American sires that are currently being sampled or will be sampled over the next few months.
Name
LPI
Sire
Dam Name
DESU MOGUL 2439-ET
3748
MOGUL
DE-SU 192-ET
LACTOMONT NIKOTA SARGEANT
3682
SARGEANT
JOLICAP LOLY OMAN OMAN
DE-SU MOGUL 2458-ET
3664
MOGUL
DE-SU 8947-ET
DA-SO-BURN UNO 781
3631
NUMERO UNO
DA-SO-BURN DORCY BECKA-ET
GILLETTE MOGUL CARREL
3631
MOGUL
GILLETTE IOTA CARMEN
BUSCHUR MOGUL 6512
3623
MOGUL
ROYLANE SOCRA MIRA 1760-ET
T-GEN-AC MOGUL SHIMMER-ET
3620
MOGUL
TRANQUILLITY AC DREARYS SHOT
S-S-I SUPRSIRE MIRI 8679-ET
3537
SUPERSIRE
S-S-I BOOKEM MODESTO7269-ET
S-S-I UNO MATTEA 8445-ET
3528
NUMERO UNO
S-S-I SNOW MALENA 7514-ET
KERNDTWAY MCCUTCHEN DAYO-ET
3498
MCCUTCHEN
GOLDEN-OAKS OBSRVR DIXIE-ET
SUMMERLIZ LAYA EPIC
3471
EPIC
SUMMERLIZ MAN O MAN LAUSY
DE-SU ODADDY 2471-ET
3468
DADDY
DE-SU 719-ET
WOODCREST MOGUL ANNA-ET
3455
MOGUL
VISION-GEN SH FRD A12304-ET
OCD SUPERSIRE ENRICH-ET
3451
SUPERSIRE
OCD FREDDIE EVERLAST-ET
RAYON D'OR LEXOR ELYANE
3430
LEXOR
WABASH-WAY-I SHOTTLE EMBER
DE-SU MOGUL 2436-ET
3425
MOGUL
LADYS-MANOR PL SHAKIRA-ET
TWIN-SPRUCE CILO
3412
NUMERO UNO
TWIN-SPRUCE DORCY COTTON-ET
KERNDTWAY MCCUTCHEN DUCE-ET
3407
MCCUTCHEN
GOLDEN-OAKS OBSRVR DIXIE-ET
WOODCREST MOGUL FRANCE-ET
3387
MOGUL
VISION-GEN SH FRD A12304-ET
OCD KRUNCH MASON-ET
3387
KRUNCH
OCD DORCY MARIGOLD-ET
SIEMERS MCCUTCH KIANNA-ET
3383
MCCUTCHEN
LEVASH EXPLODE KIANNA
LACTOMONT LOCASS SARGEANT
3379
SARGEANT
SUMMERLIZ MAN O MAN LAUSY
DE-SU MCCUTCHEN 2433-ET
3375
MCCUTCHEN
DE-SU 344-ET
DE-SU MOGUL 2413-ET
3372
MOGUL
DE-SU 363-ET
SIEMERS MOGUL REAL-DREAM-ET
3372
MOGUL
CLEAR-ECHO OBSERVER 2283-ET
FARNEAR MCMORMAN MARCIE-ET
3366
MOGUL
FARNEAR MILIE MCMORMANN-ET
OCD SUPERSIRE EMBARK-ET
3365
SUPERSIRE
OCD FREDDIE EVERLAST-ET
LACTOMONT NIKITA SARGEANT
3344
SARGEANT
JOLICAP LOLY OMAN OMAN
DE-SU MOGUL 2432-ET
3343
MOGUL
DE-SU 363-ET
S-S-I CLARTA MERYL 8545-ET
3333
CLARTA
S-S-I TWIST MOJO 7326-ET
BOLDI MOGUL ALDA
3313
MOGUL
PARAMOUNT-MB OBSRV AGATE-ET
LOOKOUT RMH MOGUL GRETA
3313
MOGUL
DE-SU 9842-ET
CO-OP MOGUL SYDNEY 6894-ET
3312
MOGUL
FUSTEAD SYDNEY CRI-ET
CITILIMITS MOGUL MAJIC 681
3291
MOGUL
CITILIMITS GARRET MAJIC 562
S-S-I COSMO 68 SOSA 8628-ET
3281
COSMO
AMMON-PEACHY SUPER 7068-ET
CO-OP MOGUL SYDNEY 6891-ET
3279
MOGUL
FUSTEAD SYDNEY CRI-ET
CRACKHOLM LEJEUNE PATRICIA
3272
HUNTER
WELCOME BRONCO PATRON
B-HIDDENHILLS UNO 1882
3270
NUMERO UNO
B-HIDDENHILLS DORCY 1405-ET
DE-SU LITHIUM 2440-ET
3268
LITHIUM
DE-SU 410-ET
3262
NUMERO UNO
WILRA PLANET 946-ET
OCD SUPERSIRE ACE-ET
3258
SUPERSIRE
VISION-GEN SH FRD A12276-ET
DE-SU MCCUTCHEN 2462-ET
3254
MCCUTCHEN
DE-SU 730-ET
GEPAQUETTE SARGEANT RAVICHOU
3245
SARGEANT
GEPAQUETTE BOLTON RAVISANTE
CO-OP UNO CLASSY 6895-ET
3243
NUMERO UNO
CO-OP PLANET CLASSY-ET
HET MEER LUCKY SHOT 2990
3234
NUMERO UNO
HET MEER LUCKY SHOT 6
BARNKAMPER MARILYN 414
3232
MOGUL
BARNKAMPER MARILYN 279
SUMMERLIZ LAURYNA EPIC
3231
EPIC
SUMMERLIZ MAN O MAN LAUSY
S-S-I OCOSMO MINETTE8657-ET
3227
O-COSMOPOLITAN
S-S-I BOOKEM MODESTO7269-ET
S-S-I DONATEL MORIE 8678-ET
3225
DONATELLO
S-S-I BOOK MERAUX 7286-ET
OCD MCCUTCHEN BANKOK-ET
3215
MCCUTCHEN
FARNEAR BROCADE P BRISSA-ET
LACTOMONT NIKOTO SARGEANT
3212
SARGEANT
JOLICAP LOLY OMAN OMAN
FAVORITE
3209
MAN-O-MAN
CLARINE
BARNKAMPER MARILYN 411
3207
HUNTER
T-GEN-AC LAYNE RUSSIA-ET
3201
LAYNE
TRANQUILLITY AC DREARYS RUSH
MS EMILY EMERA-ET
3193
DADDY
TRAMILDA-N BAXTER EMILY-ET
END-ROAD MCCUTCHEN BABA-ET
3189
MCCUTCHEN
HAVILAND OBSERVER BEV-ET
S-S-I OCOSMO KALISA 8646-ET
3186
O-COSMOPOLITAN
S-S-I ROBUST KEYES 7260-ET
S-S-I DEAN MELYNE 8538-ET
3183
DEAN
S-S-I ROBUST MAGIC 7228-ET
SSI EARNHARDT 8651-ET
3177
EARNHARDT P
HENDEL OBSV TRINITY 3274-ET
DE-SU COSMO 2431
3177
COSMO
DE-SU 385-ET
ZIMMERVIEW SUPRSRE BELL-ET
3174
SUPERSIRE
ROCKYPATH-HO MN BARBARA-ET
FUSTEAD EPIC LINDSEY-ET
3174
EPIC
GLEN-TOCTIN BOLT LUCILLE-ET
JHS ALEXIA 49
3171
MOGUL
LM ALEXIA 22
LADIES-FIRST LXOR BANGLE-ET
3171
LEXOR
MAPLEMOUNT BOLTON BUNNY
TSPRUCE MOGUL 7247
3169
MOGUL
MISTY SPRINGS PLANET BRICE
WOODCREST NUM UNO FRENZI-ET
3167
NUMERO UNO
VISION-GEN SH FRD A12304-ET
DE-SU ODADDY 2447-ET
3155
DADDY
DE-SU 719-ET
DE-SU MOGUL 2428-ET
3152
MOGUL
DE-SU 8672-ET
S-S-I SPRSIRE SHARA 8547-ET
3148
SUPERSIRE
BOSSIDE SOUL SISTER-ET
BRYHILL ONE SASSY P
3147
NUMERO UNO
VENTURE SHOTTBOLT SIZZLE P
BOFRAN BREWMASTER FABY
3145
BREWMASTER
BOFRAN MAN O MAN FLORALIE
DONNANDALE HUNTER LEONA
3142
HUNTER
DONNANDALE LAUTHORITY LEMON
LACTOMONT BENZ HEFTY
3142
HEFTY
PARKHURST BEACON BALAMA
DESU MOGUL 2216-ET
3136
MOGUL
DE-SU 194-ET
DE-SU SHAN 2455-ET
3132
SHAN
DE-SU 657-ET
RSB ELDORET
3130
EPIC
RSB CANA 799
DE-SU MOGUL 2393-ET
3127
MOGUL
DE-SU 674-ET
S-S-I ANDERSON FAWN 8626-ET
3124
ANDERSON
S-S-I MANO FLOWER 7139-ET
VINBERT UNO MIDGET
3121
NUMERO UNO
VINBERT FREDDIE BRIDGET
WOODCREST MCCUTCHN LINNY-ET
3118
MCCUTCHEN
WOODCREST OBSERVE LUCIA-ET
DE-SU EPIC 2390-ET
3117
EPIC
DE-SU 9990-ET
MS APPLES UNO ARMANA-ET
3114
NUMERO UNO
KHW REGIMENT APPLE-RED-ET
SANDY-VALLEY HDLINR MACY-ET
3112
HEADLINER
BROOKVIEW MYSTERIOUS-ET
S-S-I OCOSMO MIKI 8654-ET
3111
O-COSMOPOLITAN
S-S-I BOOKEM MODESTO7269-ET
WOODCREST MCCUTCHEN LEAH-ET
3109
MCCUTCHEN
WOODCREST OBSERVE LUCIA-ET
MAPEL WOOD MOGUL BROOK
3107
MOGUL
MAPEL WOOD MAN O MAN BROOKE
RICKLAND SUPERSIRE 4469-ET
3103
SUPERSIRE
TRAMILDA-N SUPER BELLA-ET
UFM-DUBS ERRCAMAC-ET
3103
MCCUTCHEN
UFM-DUBS ERRCA-ET
SPRUCE-HAVEN MOG MI14330-ET
3099
MOGUL
VISION-GEN SH FRD M12112-ET
S-S-I OFFIE WYANET 8549-ET
3098
OFFIE
S-S-I BOOKEM WILTON 7273-ET
JM VALLEY MOGUL GALAXIE
3097
MOGUL
WELCOME PLANET GRANNY-ET
BARNKAMPER MARILYN 402
3086
HIGHLIGHT
BARNKAMPER MARILYN 279
OCD KRUNCH MASQUERADE-ET
3085
KRUNCH
OCD DORCY MARIGOLD-ET
S-S-I SPRSIRE MISTY 8684-ET
3082
SUPERSIRE
S-S-I SHAMROCK MAGIC7368-ET
OCD KRUNCH MANIFEST-ET
3081
KRUNCH
OCD DORCY MARIGOLD-ET
END-ROAD MCCUTCHEN BLANC-ET
3080
MCCUTCHEN
HAVILAND OBSERVER BEV-ET
PONDEROSA FACEBOOK EMILY
3078
FACEBOOK
WILLSBRO EMILYANN ET
BOLDI MOGUL ANGELA
3078
MOGUL
PARAMOUNT-MB OBSRV AGATE-ET
KINGS-RANSOM SHAN FLIRTY
3075
SHAN
KINGS-RANSOM ROS FLITTER-ET
* BEI (Bullvine Efficiency Index) – each sire’s ranking is as a percent of the top sire
The ten sires on this list are all very high for efficiency. Their indexes for fat yield, protein yield, SCS, herd life and mammary are high. Breeders looking for a high all around sire should take a look a Rubicon.
Young Sires Recently Sampled
Table 2 contains North American sires that have been sampled and will be proven in 3 years.
Name
Sire
GTPI*
Milk
Fat
Protein
NM$
PL
SCS
DPR
PTAT
FLC
UDC
Owner
State/Ctry
DA-SO-BURN UNO 781
AMIGHETTI NUMERO UNO-ET
2643
1440
93
55
951
6.9
2.59
1.8
3.56
3.26
3.3
Darin & Sonya Burnikel
Cresco , IA
EDG CT UNO CINERGY
AMIGHETTI NUMERO UNO-ET
2625
1563
110
75
935
5.1
2.6
1.3
3.19
2.71
2.22
Elite Dairy Genomics LLC
Chebanse , IL
SEAGULL-BAY SSIRE DEBRA-ET
SEAGULL-BAY SUPERSIRE-ET
2604
2217
101
74
942
6
2.64
0.4
3.42
2.29
2.8
Seagull Bay Dairy Inc.
American Falls , ID
S-S-I SUPRSIRE MIRI 8679-ET
SEAGULL-BAY SUPERSIRE-ET
2599
2575
87
78
934
6.2
2.74
0.6
3.16
2.97
2.65
Select Sires Inc.
Plain City , OH
T-GEN-AC MOGUL SHIMMER-ET
MOUNTFIELD SSI DCY MOGUL-ET
2596
1656
109
59
871
4.4
2.64
0.6
3.65
3.04
3.45
Tim Clark
Brownsburg-Chatham , IA
S-S-I OCOSMO KALISA 8646-ET
O-COSMOPOLITAN-ET
2570
1604
77
65
971
8.1
2.69
1.1
2.83
1.7
3.11
Select Sires Inc.
Plain City , OH
S-S-I DEAN MELYNE 8538-ET
RONELEE SUPER DEAN-ET
2562
1800
68
64
832
5
2.67
2
3.64
2.78
3.29
Select Sires Inc.
Plain City , OH
B-HIDDENHILLS UNO 1882
AMIGHETTI NUMERO UNO-ET
2558
1309
70
55
899
7
2.37
0.8
3.42
2.92
3.49
B. P. & L. Brunink
Mc Bain , MI
HY-JO-DE UNO LUCILLE-ET
AMIGHETTI NUMERO UNO-ET
2547
2035
80
68
843
5.8
2.58
0.6
3.77
2.5
3.07
Joel F. Gerke
Bangor , WI
MS WELCOME SUPERSIRE TIA-ET
SEAGULL-BAY SUPERSIRE-ET
2544
2205
76
65
831
5.5
2.79
1
3.72
2.32
3.45
William H. Peck & Peter C. Vai
Schuylerville , NY
DE-SU MOGUL 2458-ET
MOUNTFIELD SSI DCY MOGUL-ET
2542
2031
96
69
908
5.7
2.54
-0.3
3.22
2.35
2.87
De Su Holsteins LLC
New Albin , IA
MORMANN SR GINGERBRED
LADYS-MANOR PL SHAMROCK-ET
2533
1848
89
59
913
7.2
2.71
1.3
3.29
2.25
2.5
Jennifer Mormann
Farley , IA
S-S-I UNO MATTEA 8445-ET
AMIGHETTI NUMERO UNO-ET
2526
2088
85
75
807
4.8
2.8
1.2
3.46
2.57
2.36
Select Sires Inc.
Plain City , OH
MS EMILY EMERA-ET
RONELEE SSI O DADDY-ET
2525
1346
86
48
871
6.9
2.58
0.8
3.65
2.85
2.87
Trans-America Genetics
St-Hyacinthe QC , CA
CO-OP UNO CLASSY 6895-ET
AMIGHETTI NUMERO UNO-ET
2523
674
100
44
963
7.5
2.56
2.1
2.82
2.1
2.52
Genesis Cooperative Herd
Shawano , WI
SULLHRTFORD NUNO ANA 383-ET
AMIGHETTI NUMERO UNO-ET
2519
1613
70
59
817
6.1
2.72
1.7
3.76
2.01
3.13
Robert Eustice & Mike Sullivan
Byron , MN
LACTOMONT NIKOTA SARGEANT
SEAGULL-BAY SARGEANT-ET
2509
1422
79
63
816
4.6
2.78
1.7
3.43
3.33
2.56
Trans-America Genetics
St-Hyacinthe , QC
S-S-I OCOSMO MINNA 8661-ET
O-COSMOPOLITAN-ET
2508
1053
64
59
821
6.2
2.78
1.5
3.54
2.23
3.29
Select Sires Inc.
Plain City , OH
S-S-I COSMO 68 SOSA 8628-ET
TEXEL BEAUTY COSMO-ET
2506
1132
85
53
852
5.8
2.57
1.9
3.26
2.76
2.5
Select Sires Inc.
Plain City , OH
BUTZ-HILL MAGICSTAR
DE-SU BKM MCCUTCHEN 1174-ET
2504
1427
88
62
719
2.7
2.58
-0.4
4.16
3.52
3.23
Mark Butz
Mount Vernon , IA
SIEMERS MOGUL REAL-DREAM-ET
MOUNTFIELD SSI DCY MOGUL-ET
2503
1509
81
61
885
6
2.57
1.3
2.92
2.54
2.4
Siemers Holstein Farms Inc.
Newton , WI
MORMANN UNO GARLIC
AMIGHETTI NUMERO UNO-ET
2501
1285
75
57
828
6.3
2.75
1.5
3.42
3.07
2.58
Jennifer Mormann
Farley , IA
WOODCREST MOGUL PRETTY-ET
MOUNTFIELD SSI DCY MOGUL-ET
2499
1371
83
57
855
5.5
2.59
0.3
3.27
2.73
3.36
Woodcrest Dairy LLC
Lisbon , NY
OCD KRUNCH MASON-ET
HAMMER-CREEK FRED KRUNCH-ET
2498
1675
58
50
890
8.2
2.49
0.9
2.95
2.75
3.41
Oakfield Corners Dairy
Oakfield , NY
DE-SU COSMO 2431
TEXEL BEAUTY COSMO-ET
2498
1484
65
61
848
6.6
2.5
1.5
2.95
2.58
2.46
Darin Meyer
New Albin , IA
TIGER-LILY UNO LINDSEY-ET
AMIGHETTI NUMERO UNO-ET
2481
1237
94
57
901
6.2
2.52
0.5
2.99
1.89
2.64
John R. Marshman
Oxford , NY
SPEEK-NJ MOG SHERYL CROW-ET
MOUNTFIELD SSI DCY MOGUL-ET
2480
2514
95
72
804
4.3
2.82
-0.2
3.45
2.22
2.77
Neil McDonah
Trempealeau , WI
WELCOME MCCUTCHEN CHASY-ET
DE-SU BKM MCCUTCHEN 1174-ET
2475
2086
71
71
715
3.7
2.71
-0.1
3.81
2.96
3.03
Welcome Stock Farm LLC
Schuylerville , NY
WOODCREST NUM UNO FRENZI-ET
AMIGHETTI NUMERO UNO-ET
2474
1275
81
49
772
5.2
2.77
1.4
3.69
2.94
3.21
Woodcrest Dairy LLC
Lisbon , NY
S-S-I DONATEL MORIE 8678-ET
MR OCD ROBUST DONATELLO-ET
2463
1955
79
62
752
4.5
2.77
-0.5
3.83
3.14
3.09
Select Sires Inc.
Plain City , OH
CHARTROISE UNO MAURA-ET
AMIGHETTI NUMERO UNO-ET
2462
1455
114
62
842
4.6
2.88
0.5
2.8
2.23
2.47
Select Genetics LLC
Manitowoc , WI
TJR DE-DIAMOND 2181-ET
MOUNTFIELD SSI DCY MOGUL-ET
2459
1862
69
60
813
5.5
2.56
0.2
3.24
2.06
3.18
TJR Genetics
Farley , IA
S-S-I OFFIE WYANET 8549-ET
CLEAR-ECHO OBSERVR OFFIE-ET
2458
1870
59
64
906
8.2
2.68
2.2
2.39
0.76
2.5
Select Sires Inc.
Plain City , OH
S-S-I OCOSMO MIKI 8654-ET
O-COSMOPOLITAN-ET
2455
1619
74
69
782
5.1
2.83
0.2
3.33
1.92
3.13
Select Sires Inc.
Plain City , OH
MS WELCOME SUPERSR TANIA-ET
SEAGULL-BAY SUPERSIRE-ET
2449
2508
78
73
803
5.1
2.75
1.1
2.68
2.37
1.87
William H. Peck & Peter C. Vai
Schuylerville , NY
WOODCREST MOGUL ANNA-ET
MOUNTFIELD SSI DCY MOGUL-ET
2447
1767
84
63
836
5.3
2.77
0.4
2.76
3.38
2.36
Samuel R Potter
Union Springs , NY
MOUNTFIELD MGL LILY-ET
MOUNTFIELD SSI DCY MOGUL-ET
2447
1029
86
46
860
6.1
2.51
1.2
2.83
2.37
2.94
Roger & Philip Marshfield
Marcellus , NY
LADYS-MANOR UNO DESIGNER-ET
AMIGHETTI NUMERO UNO-ET
2447
1322
97
53
848
6
2.57
-0.3
3.26
2.37
2.76
Ladys Manor LLC
Monkton , MD
MORMANN UNO ARABIA-ET
AMIGHETTI NUMERO UNO-ET
2447
1411
89
55
809
5.3
2.78
0.9
3.1
2.75
2.71
Bryhill Farm Inc
Ormstown PQ , IA
DE-SU ODADDY 2471-ET
RONELEE SSI O DADDY-ET
2443
1778
102
64
843
5.2
2.64
-0.1
3.28
2.03
2.01
De Su Holsteins LLC
New Albin , IA
OCD SUPERSIRE ENRICH-ET
SEAGULL-BAY SUPERSIRE-ET
2442
2723
99
80
861
5.3
2.94
0.1
2.64
2.01
1.67
Oakfield Corners Dairy
Oakfield , NY
WILRA UNO 497-ET
AMIGHETTI NUMERO UNO-ET
2441
1433
91
54
840
6.7
2.64
0.6
3.17
1.71
2.5
Wilra Farms Inc.
Nashville , IL
EDG RUBY MOGUL ROSE
MOUNTFIELD SSI DCY MOGUL-ET
2440
1962
106
58
899
6
2.72
-0.2
2.53
2.54
2.25
Elite Dairy Genomics LLC
Chebanse , IL
EDG TIGER MOGUL TAMMY
MOUNTFIELD SSI DCY MOGUL-ET
2439
1764
78
55
800
5.2
2.65
0.5
3.05
1.99
3.34
Elite Dairy Genomics LLC
Chebanse , IL
WOODCREST MOGUL FRANCE-ET
MOUNTFIELD SSI DCY MOGUL-ET
2437
2220
64
62
790
5.3
2.7
0
3.09
3.18
3.2
Woodcrest Dairy LLC
Lisbon , NY
FARNEAR FREEDOM FRESH-ET
SEAGULL-BAY SUPERSIRE-ET
2436
1572
69
54
869
7.2
2.69
2.2
2.31
2.07
2.25
Rick & Tom Simon
Farley , IA
WOODCREST UNO ANNE-ET
AMIGHETTI NUMERO UNO-ET
2436
1457
77
51
810
6.1
2.74
2.2
2.82
2.97
2.3
Samuel R Potter
Union Springs , NY
MS MOVIESTAR DADDY MIC-ET
RONELEE SSI O DADDY-ET
2436
1507
63
54
851
7.8
2.7
2.1
2.92
1.03
2.35
Butler Borba Glaz-Way & Durr
Chebanse , IL
TIGER-LILY UNO LATTA-ET
AMIGHETTI NUMERO UNO-ET
2435
1287
76
47
796
6.3
2.7
0.8
3.67
2.28
2.86
John R. Marshman
Oxford , NY
AURORA UNO 13474-ET
AMIGHETTI NUMERO UNO-ET
2434
1877
86
54
821
5.7
2.72
1.3
2.95
2.1
2.58
Aurora Ridge Dairy LLC
Aurora , NY
DE-SU LITHIUM 2440-ET
S-S-I DOMAIN LITHIUM-ET
2433
2230
69
67
755
5
2.77
0.9
3.18
1.88
2.72
Darin Meyer
New Albin , IA
SPEEK-NJ UNO DAPHNE 391-ET
AMIGHETTI NUMERO UNO-ET
2431
1453
98
66
834
5.3
2.82
1.2
2.76
2.12
1.78
Robert J. Eustice
Byron , MN
DE-SU MOGUL 2436-ET
MOUNTFIELD SSI DCY MOGUL-ET
2431
1397
88
52
769
4.1
2.7
-0.8
3.83
2.78
3.83
De Su Holsteins LLC
New Albin , IA
APPEALING UNO KASSIDY-ET
AMIGHETTI NUMERO UNO-ET
2430
874
78
45
818
5.9
2.59
2
2.9
3
2.21
S. Scott & April D. Cooper
Delta , PA
CALORI-D CS UNO SENORITA-ET
AMIGHETTI NUMERO UNO-ET
2429
762
83
38
755
6.1
2.61
0.4
3.95
2.9
3.46
Calori-D Holsteins
Denair , CA
OCD SUPERSIRE ACE-ET
SEAGULL-BAY SUPERSIRE-ET
2428
1769
97
72
770
3.9
3.01
0.9
2.7
2.49
1.94
Oakfield Corners Dairy
Oakfield , NY
WA-DEL MOGUL BONITA-ET
MOUNTFIELD SSI DCY MOGUL-ET
2427
1522
53
50
764
6
2.71
1.5
3.36
2.3
3.34
Rick L. Wadel
Shippensburg , PA
LADYS-MANOR UNO DESTIN-ET
AMIGHETTI NUMERO UNO-ET
2425
2101
91
67
794
4.9
2.74
-0.4
3.33
2.37
2.45
Ladys Manor LLC
Monkton , MD
THREE-STAR LEXOR CITRUS-ET
GENERVATIONS LEXOR
2425
1664
69
66
741
4
2.86
1.3
3.25
2.05
2.92
Jeffrey & Korinna Rohde
Grey Eagle , MN
HY-JO-DE MOGUL LIZZY-ET
MOUNTFIELD SSI DCY MOGUL-ET
2424
2131
93
68
820
4.4
2.74
0.1
2.38
2.35
2.51
Joel F. Gerke
Bangor , WI
MS BOYANA DADDY BLAZE
RONELEE SSI O DADDY-ET
2423
1691
55
50
725
6.1
2.76
0.8
3.92
3.45
2.94
Select Genetics of Indiana LLC
Crown Point , IN
S-S-I COSMO TABATHA 8548-ET
TEXEL BEAUTY COSMO-ET
2423
1259
77
48
770
5.6
2.58
0.6
3.24
2.1
3.4
Select Sires Inc.
Plain City , OH
T-GEN-AC LAYNE RUSSIA-ET
KELLERCREST SUPER LAYNE-ET
2421
1727
63
56
845
6.9
2.55
1.8
2.5
1.45
2.12
Tim Clark
Brownsburg-Chatham , IA
BUSH-BROS MOGUL 4535-ET
MOUNTFIELD SSI DCY MOGUL-ET
2420
978
78
47
859
6
2.53
1.1
2.24
2.79
2.96
David Leroy & Bradley Nosbush
Fairfax , MN
BRANDVALE MOGUL 4780
MOUNTFIELD SSI DCY MOGUL-ET
2417
1096
59
40
840
6.5
2.64
2.5
2.61
2.58
2.83
Steven A. Brand
Ellsworth , WI
KERNDTWAY MCCUTCHEN DAYO-ET
DE-SU BKM MCCUTCHEN 1174-ET
2415
1964
70
61
726
5.3
2.83
0.4
3.45
2.62
2.52
Mark W. Kerndt
Waukon , IA
DE-SU MCCUTCHEN 2433-ET
DE-SU BKM MCCUTCHEN 1174-ET
2415
670
69
57
710
4
2.81
1.5
3.42
2.43
2.79
De Su Holsteins LLC
New Albin , IA
CO-OP MOGUL SYDNEY 6894-ET
MOUNTFIELD SSI DCY MOGUL-ET
2414
1375
90
48
894
6.5
2.51
0.6
2.37
2.35
2.38
Genesis Cooperative Herd
Shawano , WI
CO-OP PETRONE SUNNY 6869
WELCOME SUPER PETRONE-ET
2413
2439
59
64
888
6.9
2.48
1.4
2.12
1.5
1.89
Genesis Cooperative Herd
Shawano , WI
FURNACE-HILL MGL ZUMBA-ET
MOUNTFIELD SSI DCY MOGUL-ET
2412
670
75
45
783
5.1
2.76
2.4
2.88
2.87
2.58
Joel Krall & Tim Crouse
Lebanon , PA
WOODCREST SUPER YELLOW
SEAGULL-BAY SUPERSIRE-ET
2409
2063
80
60
816
6.4
2.92
1.3
2.78
1.79
2.52
Woodcrest Dairy LLC
Lisbon , NY
DE-SU CASUAL 2400-ET
LARCREST CASUAL-ET
2409
2407
84
74
842
6
2.76
1
2.35
1
1.52
De Su Holsteins LLC
New Albin , IA
EDG RUBY UNO REESE
AMIGHETTI NUMERO UNO-ET
2408
1166
85
45
814
6.3
2.66
1.1
2.92
2.35
2.47
Elite Dairy Genomics LLC
Chebanse , IL
WOODCREST MCCUTCHEN LEAH-ET
DE-SU BKM MCCUTCHEN 1174-ET
2408
1611
76
58
788
5.6
2.81
0.8
2.78
2.34
2.53
Woodcrest Dairy LLC
Lisbon , NY
END-ROAD MCCUTCHEN BLANC-ET
DE-SU BKM MCCUTCHEN 1174-ET
2406
1433
63
54
694
4.7
2.83
0.4
3.74
2.39
3.59
Duane & Janet Molhoek
Falmouth , MI
OCD SUPERSIRE EMBARK-ET
SEAGULL-BAY SUPERSIRE-ET
2406
2195
92
70
914
6.8
2.64
0.3
1.91
1.74
1.21
Oakfield Corners Dairy
Oakfield , NY
MATT-DARI MIXER PLUM-ET
MOUNTFIELD SSI DCY MIXER-ET
2406
1498
77
72
712
3.3
2.6
0.3
2.84
3
2.33
Matthiae Dairy Farm Inc.
Marathon , WI
DE-SU ODADDY 2447-ET
RONELEE SSI O DADDY-ET
2404
1407
68
60
776
6.2
2.65
0.3
3.38
1.8
2.5
De Su Holsteins LLC
New Albin , IA
OCD MCCUTCHEN BANKOK-ET
DE-SU BKM MCCUTCHEN 1174-ET
2404
1520
70
55
718
4.2
2.66
-0.4
3.65
2.77
3.22
Oakfield Corners Dairy
Oakfield , NY
KERNDTWAY PETRONE DELTA-ET
WELCOME SUPER PETRONE-ET
2403
1170
44
38
755
6.6
2.48
2.3
3.5
1.94
2.94
Mark W. Kerndt
Waukon , IA
SIEMERS SHAMROCK DANA-GAL
LADYS-MANOR PL SHAMROCK-ET
2402
1528
81
47
837
6.3
2.5
0.2
2.86
2.39
2.48
Siemers Holstein Farms Inc.
Newton , WI
NO-FLA YANO TRINY 34377
CO-OP UPD PLANET YANO-ET
2401
1989
72
63
856
5.9
2.74
1.2
2.38
2.05
2
North Florida Holsteins
Bell , FL
S-S-I SPRSIRE MISTY 8684-ET
SEAGULL-BAY SUPERSIRE-ET
2400
2166
81
60
741
5.4
2.75
-0.6
3.79
2.65
2.29
Select Sires Inc.
Plain City , OH
EDG HALLIE MAY HAPPY
DE-SU D MAYFIELD 893-ET
2400
1594
67
57
728
5.1
2.75
0.3
3.59
2.63
2.77
Elite Dairy Genomics LLC
Chebanse , IL
RI-VAL-RE MCCTCHN DASANI-ET
DE-SU BKM MCCUTCHEN 1174-ET
2399
1609
77
57
681
3.9
2.98
0
3.92
2.51
3.33
Aaron Jorgensen
Webberville , MI
S-S-I CLARTA MERYL 8545-ET
2399
1523
83
70
793
4.7
2.66
0.9
2.43
2.25
1.73
Select Sires Inc.
Plain City , OH
SANDY-VALLEY MGL BISCUIT-ET
MOUNTFIELD SSI DCY MOGUL-ET
2399
1856
51
55
686
4.4
2.79
1.1
3.46
2.94
3.36
Dave Pat Frank Jr. & Greg B
Stevens Point , WI
WILRA UNO 494-ET
AMIGHETTI NUMERO UNO-ET
2398
1369
92
45
831
6.1
2.6
1.1
2.92
1.22
2.63
Wilra Farms Inc.
Nashville , IL
WOODCREST MOGUL POPPER-ET
MOUNTFIELD SSI DCY MOGUL-ET
2397
609
64
36
796
6.3
2.67
1.7
3.2
2.45
3.29
Woodcrest Dairy LLC
Lisbon , NY
SANDY-VALLEY LAY ABERLYN-ET
KELLERCREST SUPER LAYNE-ET
2397
1853
41
48
803
6.8
2.59
2.5
2.68
2.06
2.83
Dave Pat Frank Jr. & Greg B
Stevens Point , WI
VINBERT UNO MIDGET
AMIGHETTI NUMERO UNO-ET
2394
1521
90
60
776
5.2
2.68
-0.2
2.96
2.59
2.47
Trans-America Genetics
St-Hyacinthe , QC
DEBOER O COSMO TALITHA
O-COSMOPOLITAN-ET
2394
811
60
39
741
6
2.68
2.1
3.5
1.94
2.96
Brad DeBoer
Corona , SD
N-SPRINGHOPE MOGUL MIRTH-ET
MOUNTFIELD SSI DCY MOGUL-ET
2394
815
88
34
817
6.1
2.57
0.4
2.8
3.73
2.91
J Kevin & Barbara Nedrow
Clifton Springs , NY
KELLERCREST PARA CARMAX-ET
REGANCREST PARADISE-ET
2394
824
53
39
716
5.3
2.53
1.2
3.56
2.68
3.57
Kellercrest Reg. Hol. Inc.
Mount Horeb , WI
END-ROAD MCCUTCHEN BABA-ET
DE-SU BKM MCCUTCHEN 1174-ET
2393
186
59
27
760
6.4
2.7
2.8
3.24
2.89
3.22
Duane & Janet Molhoek
Falmouth , MI
COMYN-PBCD PET DLT 170F
WELCOME SUPER PETRONE-ET
2392
1292
39
35
819
8.6
2.73
3.8
2.71
1.71
2.67
Patrick Comyn
Madison , VA
DE-SU SUDAN 2402-ET
VA-EARLY-DAWN SUDAN CRI-ET
2392
1429
103
52
853
6.2
2.85
0.8
2.57
1.56
2.02
De Su Holsteins LLC
New Albin , IA
RICKLAND NUMERO UNO 4403-ET
AMIGHETTI NUMERO UNO-ET
2390
1135
77
43
765
5.9
2.7
1.5
2.93
2.29
2.74
Greg Rickert
Eldorado , WI
PLAIN-KNOLL HILL WINSLET
LOTTA-HILL SHOTTLE 41-ET
2389
1719
61
50
829
7.1
2.64
1.7
2.54
1.83
2.64
Buschur Dairy Farms Inc.
New Weston , OH
DE-SU LITHIUM 2441-ET
S-S-I DOMAIN LITHIUM-ET
2388
1300
47
45
744
6.5
2.69
2.1
3.05
2.5
2.75
Darin Meyer
New Albin , IA
EDG BRYSHA MOGUL BEE
MOUNTFIELD SSI DCY MOGUL-ET
2388
1416
52
48
780
6.2
2.61
1.5
3.03
2.5
2.77
Elite Dairy Genomics LLC
Chebanse , IL
TWIN-SPRUCE CHANA-ET
MOUNTFIELD SSI DCY MOGUL-ET
2388
1345
87
51
695
3
2.78
-0.4
3.59
2.85
3.78
Chad Felten
Rose Creek , MN
FARNEAR ELLIE EVELYN-ET
S-S-I DOMAIN LITHIUM-ET
2387
1646
47
50
743
6.2
2.67
2.2
3.07
2.8
2.46
Rick & Tom Simon
Farley , IA
N-SPRINGHOPE PETRON SARI-ET
WELCOME SUPER PETRONE-ET
2387
1239
71
45
802
6
2.59
1.8
2.84
1.98
2.25
J Kevin & Barbara Nedrow
Clifton Springs , NY
REGANCREST ODADDY 7276-ET
RONELEE SSI O DADDY-ET
2386
1518
39
40
734
6.6
2.68
1.4
3.49
3.01
3.7
Regancrest Farms
Waukon , IA
FARNEAR-TBR-BH MARNI-ET
AMIGHETTI NUMERO UNO-ET
2385
1123
55
42
743
7
2.59
1.6
3.16
2.16
3.14
T R & M Simon B & T Rauen &
Farley , IA
S-S-I MORGAN SHALYN 8673-ET
S-S-I BOOKEM MORGAN-ET
2385
1175
66
45
825
7.3
2.65
0.9
2.94
1.87
2.61
Select Sires Inc.
Plain City , OH
TWIN-SPRUCE CILO
AMIGHETTI NUMERO UNO-ET
2384
1299
86
49
750
4.9
2.56
0.5
3.01
3
2.46
Chad Felten
Rose Creek , MN
REGANCREST MCCUTCHN 7249-ET
DE-SU BKM MCCUTCHEN 1174-ET
2384
1309
61
47
663
3.9
2.69
0.7
3.59
3.29
3.5
Regancrest Farms
Waukon , IA
DE-SU DEAN 2423-ET
RONELEE SUPER DEAN-ET
2384
1973
56
61
743
5.1
2.63
0.3
3.26
2.01
3.04
De Su Holsteins LLC
New Albin , IA
EDG GLISTEN A GLICE
HUNSBERGER ALCHEMY-ET
2384
1413
36
58
726
6.3
2.52
1.5
2.77
1.92
2.96
Elite Dairy Genomics LLC
Chebanse , IL
MS DONNALYN DONEEN-ET
UFM-DUBS-I SHREWD
2383
1668
72
48
744
5.8
2.75
1.2
3.59
1.78
2.73
Trans-America Genetics
Oakdale , CA
RICKLAND SUPERSIRE 4469-ET
SEAGULL-BAY SUPERSIRE-ET
2383
1677
87
66
770
4.6
2.76
-0.2
2.86
2
2.19
Rickert Brothers LLC
Eldorado , WI
OCD SUPERSIRE APPLE-ET
SEAGULL-BAY SUPERSIRE-ET
2382
1321
82
47
706
4.4
2.6
-0.6
3.86
2.77
3.12
Oakfield Corners Dairy
Oakfield , NY
WCD-ZBW SUPERSIRE LALA-ET
SEAGULL-BAY SUPERSIRE-ET
2382
1806
69
62
777
5.8
2.58
-0.1
2.72
2.17
2.33
Kevin & Barbara Ziemba & Woodc
Lisbon , NY
CO-OP MOGUL SYDNEY 6891-ET
MOUNTFIELD SSI DCY MOGUL-ET
2382
1104
90
47
802
4.9
2.71
0.2
2.73
2.15
3.32
Genesis Cooperative Herd
Shawano , WI
DE-SU EPIC 2390-ET
GENERVATIONS EPIC
2382
1292
70
41
813
6
2.64
2.8
2.39
2.47
1.92
De Su Holsteins LLC
New Albin , IA
JOLICAP DELIGENT WIA-ET
RONELEE DORCY DELIGENT-ET
2380
1000
58
39
733
6
2.64
0.7
3.53
2.3
3.77
Ferme Jolicap Inc
Cap St Ignace PQ , CA
TWIN-SPRUCE CAPPY-ET
AMIGHETTI NUMERO UNO-ET
2380
649
78
42
717
4.7
2.56
0.3
3.55
3.03
3.18
Chad Felten
Rose Creek , MN
WOODCREST LAYNE LAZY-ET
KELLERCREST SUPER LAYNE-ET
2378
1766
47
48
738
6.5
2.62
1.5
3.16
1.96
2.95
Woodcrest Dairy LLC
Lisbon , NY
WOODCREST UNO ADELE-ET
AMIGHETTI NUMERO UNO-ET
2378
1688
75
52
770
5.5
2.78
2
2.59
2.6
1.9
Samuel R Potter
Union Springs , NY
ZIMMERVIEW KRNCH BRIELLE-ET
HAMMER-CREEK FRED KRUNCH-ET
2378
835
66
35
778
6.6
2.7
1.3
3.28
2.23
3.49
Dean E. & Brent E. Zimmer
Marietta , OH
FARNEAR MCMORMAN MARCIE-ET
MOUNTFIELD SSI DCY MOGUL-ET
2377
1350
63
45
714
5.6
2.79
0.6
3.61
2.5
3.48
Rick & Tom Simon
Farley , IA
REGANCREST MCCUTCHN 7262-ET
DE-SU BKM MCCUTCHEN 1174-ET
2377
827
59
38
651
4.5
2.74
0.5
3.82
4.24
3.52
Regancrest Farms
Waukon , IA
NO-FLA ECOYNE ISY 34455-ET
ECOYNE ISY
2377
1146
53
46
838
7.8
2.77
2.4
2.39
1.86
2.44
North Florida Holsteins
Bell , FL
OCD KRUNCH MASQUERADE-ET
HAMMER-CREEK FRED KRUNCH-ET
2375
1763
59
54
741
6.1
2.79
0.4
3.08
2.39
3.38
Oakfield Corners Dairy
Oakfield , NY
MS EMILY ECSTASY-ET
DE-SU RANSOM-ET
2375
826
90
37
844
6.9
2.68
2
2.05
2.9
1.63
Trans-America Genetics
St-Hyacinthe QC , CA
BREMER LARGE COMEDY-ET
GLEN-TOCTIN SUPER LARGE-ET
2374
1418
59
43
674
5
2.7
1.3
3.71
2.49
3.18
Ferdi Seeuws
Sheldon , WI
ZIMMERVIEW SUPRSRE BELL-ET
SEAGULL-BAY SUPERSIRE-ET
2373
1898
84
66
685
3.7
2.98
-0.3
3.53
2.21
2.6
Dean E. & Brent E. Zimmer
Marietta , OH
LACTOMONT LOCASS SARGEANT-ET
SEAGULL-BAY SARGEANT-ET
2372
1338
57
60
662
3.8
2.65
0.3
3.51
2.99
3
Trans-America Genetics
St-Hyacinthe , QC
COOK-FARM UNO HAIZE
AMIGHETTI NUMERO UNO-ET
2371
1231
54
45
678
5.2
2.67
1
3.6
2.55
3.33
Gordon Jr. & Gordon Cook III
Hadley , MA
WELCOME SHAN WINFREY-ET
LADYS-MANOR MAN-O-SHAN-ET
2370
1149
79
56
678
3.8
2.81
-0.2
3.73
2.56
2.86
Welcome Stock Farm LLC
Schuylerville , NY
WOODCREST MCCUTCHN LINNY-ET
DE-SU BKM MCCUTCHEN 1174-ET
2369
1102
67
46
703
4.7
2.79
0.1
3.36
2.46
3.52
Woodcrest Dairy LLC
Lisbon , NY
EILDON-TWEED CHARISMA-ET
LADYS-MANOR RD GRAFEETI-ET
2369
1525
80
60
772
4.3
2.92
1.4
2.35
2.19
2.23
David R. Wood
Amsterdam , NY
TJR MOGUL DINAMITE
MOUNTFIELD SSI DCY MOGUL-ET
2369
1447
71
50
725
4.5
2.69
-0.2
3.22
2.54
3.41
TJR Genetics
Farley , IA
KELLERCREST PARA CARRIE-ET
REGANCREST PARADISE-ET
2369
1691
73
62
700
3.8
2.61
-0.2
3.2
1.97
3.2
Kellercrest Reg. Hol. Inc.
Mount Horeb , WI
HY-JO-DE MOGUL LIZ-ET
MOUNTFIELD SSI DCY MOGUL-ET
2369
1784
71
55
745
4.4
2.83
1
2.68
2.82
2.84
Joel F. Gerke
Bangor , WI
CO-OP CALICO LULITA 6868-ET
BRANDT-VIEW CALICO-ET
2368
1627
53
65
739
4.9
2.59
1
2.4
2.39
2.44
Genesis Cooperative Herd
Shawano , WI
GRANSKOG-ACRES JABBER-ET
SHEMA JEEVES CAMERON-ET
2367
984
58
33
782
7
2.57
1.9
3.03
2.63
2.47
David P. Granskog
Stephenson , MI
DE-SU MOGUL 2393-ET
MOUNTFIELD SSI DCY MOGUL-ET
2367
1582
72
52
742
4.4
2.75
0.5
2.87
2.43
3.22
De Su Holsteins LLC
New Albin , IA
SANDY-VALLEY MOGUL AMY-ET
MOUNTFIELD SSI DCY MOGUL-ET
2367
1603
72
46
757
5
2.68
0.9
2.87
2.65
2.97
Dave Pat Frank Jr. & Greg B
Stevens Point , WI
NO-FLA MAURICE 34451-ET
MOUNTFIELD MSY MAURICE-ET
2366
1489
84
55
799
4.3
2.58
1.3
2.25
2.09
2.18
North Florida Holsteins
Bell , FL
GEPAQUETTE MAYFIELD RAVIBESSE
DE-SU D MAYFIELD 893-ET
2366
1261
66
52
758
5.3
2.69
1.3
2.86
2.39
2.42
Trans-America Genetics
St-Hyacinthe , QC
MS BOYANA DADDY BAARA
RONELEE SSI O DADDY-ET
2366
1486
26
49
615
5.5
2.74
1.2
4.01
3.23
3.29
Select Genetics of Indiana LLC
Crown Point , IN
WARGO-DANHOF OLIVIA RAE-ET
GENERVATIONS EPIC
2363
1377
59
56
650
3.8
2.75
1.1
3.52
2.66
2.79
Wargo Acres & Jason & Sheri Da
Lodi , WI
WILRA UNO 487-ET
AMIGHETTI NUMERO UNO-ET
2362
1050
59
41
807
7.2
2.57
1.9
2.76
2.08
2.3
Wilra Farms Inc.
Nashville , IL
CO-OP SUSTAN LAGOON 6901-ET
GIL-GAR ALTASUSTAIN-ET
2362
1201
49
40
689
5
2.71
1.7
3.46
3.11
3.2
Genesis Cooperative Herd
Shawano , WI
FARNEAR DAY DELORIS-ET
SEAGULL-BAY SUPERSIRE-ET
2361
1957
71
48
759
6.1
2.66
0.6
2.8
2.23
2.61
Rick & Tom Simon
Farley , IA
DANHOF M ANGELIC-ET
MOUNTFIELD SSI DCY MOGUL-ET
2361
1067
69
45
719
5
2.78
0.2
3.41
3.47
2.97
Jason & Sheri Danhof
Waukon , IA
NO-FLA EPIC DEJAH 34398-ET
GENERVATIONS EPIC
2360
1415
51
49
814
6.3
2.47
2.1
2.24
1.83
2.3
North Florida Holsteins
Bell , FL
EDG HALLIE MAY HALLY
DE-SU D MAYFIELD 893-ET
2359
1917
54
55
655
4.9
2.91
0.2
3.9
1.83
3.35
Elite Dairy Genomics LLC
Chebanse , IL
EDG BRYSHA COSMO BRINA
O-COSMOPOLITAN-ET
2358
821
83
50
771
5.1
2.71
0.6
2.78
1.86
2.52
Elite Dairy Genomics LLC
Chebanse , IL
MORMANN AGENT 001-ET
AMIGHETTI NUMERO UNO-ET
2358
1949
74
64
714
4.6
2.74
-0.2
2.97
2.89
2.35
Bryhill Farm Inc
Ormstown PQ , IA
RICKLAND MCCUTCHEN 4415-ET
DE-SU BKM MCCUTCHEN 1174-ET
2358
1600
50
57
657
4.8
2.78
0.4
3.51
3.13
2.83
Rickert Brothers LLC
Eldorado , WI
WCD-ZBW SUPERSIRE LINDA-ET
SEAGULL-BAY SUPERSIRE-ET
2357
1864
97
72
695
2.5
2.8
-0.4
2.92
2.1
2.11
Kevin & Barbara Ziemba & Woodc
Lisbon , NY
WOODCREST MAYFLD FINIKY-ET
DE-SU D MAYFIELD 893-ET
2356
1398
61
45
789
6
2.61
2
2.42
2.18
2.57
Woodcrest Dairy LLC
Lisbon , NY
NO-FLA MAURICE 34386-ET
MOUNTFIELD MSY MAURICE-ET
2355
1552
69
55
853
6.3
2.6
2
1.65
2.2
1.39
North Florida Holsteins
Bell , FL
SPEEK-NJ KELLY CLARKSON-ET
MOUNTFIELD SSI DCY MOGUL-ET
2355
2014
81
63
766
4.9
2.71
-0.4
2.89
1.82
2.4
Neil McDonah
Trempealeau , WI
AMMON FARMS SSR MOONSTAR-ET
SEAGULL-BAY SUPERSIRE-ET
2355
1667
93
63
740
4.3
2.83
-0.6
2.84
2.02
2.39
Michael & Jill Ammon
Lewistown , PA
CHARTROISE UNO MACEY-ET
AMIGHETTI NUMERO UNO-ET
2355
1647
99
60
749
3.8
2.76
0
2.72
2.2
2.28
Select Genetics LLC
Manitowoc , WI
REGANCREST MCCUTCHN 7252-ET
DE-SU BKM MCCUTCHEN 1174-ET
2354
1528
56
43
614
3.6
2.76
0.5
3.97
3.1
3.65
Regancrest Farms
Waukon , IA
FURNACE-HILL MGL ZEXY-ET
MOUNTFIELD SSI DCY MOGUL-ET
2354
995
51
49
718
4.8
2.69
1.6
2.84
3.02
2.85
Joel Krall & Tim Crouse
Lebanon , PA
WCD-ZBW SUPERSIRE LACE-ET
SEAGULL-BAY SUPERSIRE-ET
2353
1859
79
64
721
4
2.71
0.3
2.76
1.23
2.63
Kevin & Barbara Ziemba & Woodc
Lisbon , NY
CANGEN UNO 5453
AMIGHETTI NUMERO UNO-ET
2353
1485
76
45
730
6
2.8
1.2
3.11
2.5
2.15
Trans-America Genetics
St-Hyacinthe , QC
WEIGELINE DEAN 2171-ET
RONELEE SUPER DEAN-ET
2353
1635
67
49
796
6.3
2.73
2.6
2.35
1.38
1.92
Dan Weigel
Richland , MI
WEIGELINE SUPERSONC 2174-ET
MISTY SPRINGS SUPERSONIC
2353
967
76
43
894
7.7
2.73
2.7
1.82
1.12
1.84
Dan Weigel
Richland , MI
BUTZ-HILL MAYFIELD MARIAH
DE-SU D MAYFIELD 893-ET
2353
990
60
45
653
3.7
2.62
0.8
3.66
2.38
3.17
Mark Butz
Mount Vernon , IA
SPEEK-NJ CHELSEA HANDLER-ET
DE-SU D MAYFIELD 893-ET
2352
1932
91
70
718
3.7
2.86
-0.6
3.28
1.3
2.23
Neil McDonah
Trempealeau , WI
MATCREST LEX CHARMING-ET
GENERVATIONS LEXOR
2352
1125
89
57
710
3.2
2.74
-0.4
3.16
2.5
2.87
Matthew R. Johnson
Baldwin , WI
DE-SU ODADDY 2394-ET
RONELEE SSI O DADDY-ET
2352
1792
67
62
797
6.4
2.73
1.1
2.28
1.16
2.07
De Su Holsteins LLC
New Albin , IA
RONLAND EPIC JANELLE-ET
GENERVATIONS EPIC
2352
2432
74
69
739
3.9
2.75
0.5
2.75
2.47
1.61
Ronald Hackmann
Manitowoc , WI
T-SPRUCE MOGUL 7260-ET
MOUNTFIELD SSI DCY MOGUL-ET
2352
1830
53
59
753
6
2.71
0.3
2.95
2.15
2.6
Arnold B. Gruenes
Richmond , MN
S-S-I SUPRSIRE MORA 8676-ET
SEAGULL-BAY SUPERSIRE-ET
2352
2264
85
60
802
6.3
2.73
0.3
2.76
1.26
1.65
Select Sires Inc.
Plain City , OH
DE-SU ODADDY 2392-ET
RONELEE SSI O DADDY-ET
2351
1829
57
42
793
7.2
2.51
0.7
2.98
1.76
2.79
De Su Holsteins LLC
New Albin , IA
MORMANN AQUA 2148-ET
S-S-I DOMAIN LITHIUM-ET
2351
1199
47
51
743
6.2
2.66
1.7
2.75
2.35
2.79
Jennifer Mormann
Farley , IA
VISION-GEN AIRNET AL14319
AIR-OSA-EXEL ALTAAIRNET-ET
2348
1543
69
57
742
5.3
2.74
1.5
2.52
1.76
2.12
VISION GENETICS
Mount Joy , PA
EDG GLISTEN UNO GARTH
AMIGHETTI NUMERO UNO-ET
2348
1213
68
45
728
5.6
2.71
1.1
2.66
2.2
2.96
Elite Dairy Genomics LLC
Chebanse , IL
WOODCREST MCCTCHN LOONEY-ET
DE-SU BKM MCCUTCHEN 1174-ET
2348
338
58
27
705
5.7
2.6
1.3
3.17
2.91
3.57
Woodcrest Dairy LLC
Lisbon , NY
GOLD-N-OAKS U SOLSTA2559-ET
AMIGHETTI NUMERO UNO-ET
2347
1069
88
43
728
4.9
2.73
1.5
3.05
1.47
2.55
John & Judy Swenson
Barneveld , WI
EDG GLISTEN MERID GAFNA-ET
SULLY HART MERIDIAN-ET
2347
1750
68
67
623
3
2.89
-0.1
3.52
2.06
2.95
Elite Dairy Genomics LLC
Chebanse , IL
SPRUCE-HAVEN NUMERO UNO 14300
AMIGHETTI NUMERO UNO-ET
2346
661
66
30
761
5.7
2.57
2.2
3.2
2.29
2.79
VISION GENETICS
Mount Joy , PA
KERNDTWAY MCCUTCHEN DUCE-ET
DE-SU BKM MCCUTCHEN 1174-ET
2346
1769
63
53
627
3.6
2.7
-0.3
3.94
2.74
3.08
Mark W. Kerndt
Waukon , IA
OCD KRUNCH MANIFEST-ET
HAMMER-CREEK FRED KRUNCH-ET
2345
1481
47
45
730
6.6
2.72
1.1
3
2.3
3.25
Oakfield Corners Dairy
Oakfield , NY
SPRUCE-HAVEN MOG MI14320-ET
MOUNTFIELD SSI DCY MOGUL-ET
2343
1591
60
45
676
4.1
2.66
1.2
3.14
3.49
2.7
Doug Young & James Nocek
Union Springs , NY
GLEN-VALLEY UNO SCARF-ET
AMIGHETTI NUMERO UNO-ET
2342
737
71
46
722
4.9
2.59
0.8
2.88
2.97
2.58
Scott M. Umble
Atglen , PA
JOLICAP DELIGENT WIANA-ET
RONELEE DORCY DELIGENT-ET
2342
1045
38
47
658
4.7
2.65
0.7
3.94
2.33
3.61
Ferme Jolicap Inc
Cap St Ignace PQ , CA
SAR-JAS UNO SPECKLE-ET
AMIGHETTI NUMERO UNO-ET
2342
787
56
35
645
4.7
2.8
1.9
3.66
3.25
3.19
Jason Menne
West Union , IA
VATLAND MCCUTCHEN LANA 3745
DE-SU BKM MCCUTCHEN 1174-ET
2341
1586
83
50
708
4
2.74
-0.1
3.01
2.17
3
Josh Vatland
Caledonia , MN
GRANSKOG-ACRES JORDAN-ET
SHEMA JEEVES CAMERON-ET
2341
924
48
25
742
7.6
2.6
1.8
3.38
2.3
3.26
David P. Granskog
Stephenson , MI
DE-SU MOGUL 2413-ET
MOUNTFIELD SSI DCY MOGUL-ET
2341
1216
73
39
779
5.6
2.64
1.4
2.45
2.79
2.49
De Su Holsteins LLC
New Albin , IA
PLAIN-KNOLL PARISH DANCE
PLAIN-KNOLL PARISH 5534-ET
2341
1316
81
53
839
6.4
2.62
1
2.2
1.31
1.83
Buschur Dairy Farms Inc.
New Weston , OH
HY-JO-DE UNO LUCILLA-ET
AMIGHETTI NUMERO UNO-ET
2341
1277
89
49
764
4.8
2.6
-0.3
3.04
2.67
2.4
Joel F. Gerke
Bangor , WI
KHW MOGUL AKAHANNA-ET
MOUNTFIELD SSI DCY MOGUL-ET
2340
217
77
38
751
4.5
2.7
1.5
2.93
3.09
2.63
High Altitude Syndicate
Platteville , WI
SIEMERS MOGUL APPLE-STAR-ET
MOUNTFIELD SSI DCY MOGUL-ET
2340
943
86
40
623
3
2.76
-0.6
4.15
3.54
3.34
Siemers Holstein Farms Inc.
Newton , WI
SANDY-VALLEY MOGL BASKET-ET
MOUNTFIELD SSI DCY MOGUL-ET
2340
2047
55
54
674
4.8
2.86
0.8
3.33
2.32
2.91
Dave Pat Frank Jr. & Greg B
Stevens Point , WI
CAPS MAIRY 25
GENERVATIONS EPIC
2339
1589
30
49
657
4.9
2.58
0.9
3.43
3.02
3.03
Eurogenes
Fair Play , MD
T-SPRUCE MOGUL 7272-ET
MOUNTFIELD SSI DCY MOGUL-ET
2339
1667
71
58
715
4.8
2.89
0.3
3.09
2.27
2.71
Arnold B. Gruenes
Richmond , MN
NO-FLA MAURICE 34371-ET
MOUNTFIELD MSY MAURICE-ET
2338
1180
76
49
837
5.6
2.47
0.9
1.89
1.78
2.48
North Florida Holsteins
Bell , FL
RI-VAL-RE MCCTCHN OREGON-ET
DE-SU BKM MCCUTCHEN 1174-ET
2337
1570
54
61
607
3.4
3.04
1.1
3.56
2.1
3.17
Aaron Jorgensen
Webberville , MI
LUDWIGS-DG NUMBERO LUCY-ET
AMIGHETTI NUMERO UNO-ET
2337
624
89
40
735
5.2
2.76
1.4
3.09
2.63
2.14
D. Ludwig Farms LLC
Fithian , IL
* BEI (Bullvine Efficiency Index) – each sire’s ranking is as a percent of the top sire
This group of sires is high for efficiency but slightly more spread out than the sires in Table 1. They are very high for fat yield, daughter fertility and herd life. Breeders that used Divinci will be pleased to see him heading this list but there are many other top all around sires. Divinci, Mucho, Blasito and Topsy are all sons of the high indexing dam, De-Su 199. AltaBettman and Toolshop are full brothers.
Next Proven Sires
Table 3 contains sires that will receive their daughter proofs over the next year or so.
MOONLIGHT HOLSTEINS, CAISTOR CENTRE, ON, (519) 788-6917
ZUGER INNOCENT THREA
VG
85
PENNVIEW INNOCENT
FERME ZUGER, LYSTER, PQ, (819) 389-1038
* BEI (Bullvine Efficiency Index) – each sire’s ranking is as a percent of the top sire
This group of sires contains bulls that are both well known and not so well known to breeders. Supersire and Lexor stand out with high ratings in all categories contained in the table. Lexor and Lanyard are full brothers.
International Proven Sires
Table 4 contains the top ten BEI sires from the US Holstein and CDN MACE listings for top daughter proven sires.
Name
Udder Score
Feet & Legs Score
Final Score
Owner
State
KINGSWAY GOLDWYN ARTICHOKE-ET
88
90
89
Ehrhardt Farms Inc & Gene Iager
MD
MS KEN-DREN SANCHEZ FEATHER
90
90
89
Todd N. Wendorf & Douglas D. Lemke
WI
MS ROCKLEDGE SNCHEZ JAZZ-ET
90
90
89
Jeff Morris Koster
TX
ROCKLAN-T ATWOOD RALLY-ET
90
87
89
Michael J. Garrow
NY
LUCK-E BRAXTON MAEVE
90
87
89
Dalton Engel
IL
CONANT-ACRES AFTSHOCK TRINA
91
85
89
Conant Acres, Inc.
ME
OAKFIELD ATWOOD HORIZON-ET
88
87
88
Michael J. Garrow
NY
FLEURY DAMION CARAIBE
90
83
88
Pat Conroy
IN
CHARWILL ATTIC MARCY
88
88
88
Gen-Com Holstein Ltd
DOUGAL LEA GOLDWYN DANITA-ET
88
87
88
Gen-Com Holstein Ltd
LEACHLAND GOLD MEDAL
90
86
88
Ehrhardt Farms Inc & Gene Iager
MD
HOFF-HILL ATLANTIC GLOW
87
90
88
Adam Hoff
TX
BVK ATWOOD ANGIE-ET
87
85
88
Catlin E. Christman
MD
FROZENES SANCHEZ CLAUDIA
90
87
88
Chad J. Ryan
WI
SHEBS GOLDWYN HAWAII-ET
89
86
88
Woodcrest Dairy LLC
NY
LOCUST-RIDGE PLAID BEANIE
88
86
88
Robert A. Johnson
MD
WHITTIER-CF ATWOOD LOVE-ET
88
86
88
Golden Oaks Farm
IL
STONE-HAUS ALEX G6-ET
88
86
88
Glen S. Zimmerman
PA
COCALICO BRADELL PARIS
88
90
88
Paul B. Zimmerman, Jr
PA
MB-LUCKYLADY ATWOOD 5590-ET
87
88
88
Durrer Dairy
CA
PARKACRES AB FRANNIE-RED-ET
89
87
88
Jason M. & Donna G. Myers
MD
REGANCREST GOLD BILLI-J-ET
88
87
88
Golden Oaks Farm
IL
MS T-FARM ZBW BIGTIME PIECE
90
84
88
Woodcrest Dairy LLC
NY
ZBW-JP AT LAST-ET
88
88
88
Kevin & Barbara Ziemba & Woodcrest Dairy LLC
NY
PUTNAM-FARM DSTRY JASMYN-TW
87
86
88
William & Cynthia &Richard & Shannon Allyn
NH
MS DREAMSALIVE SA PATTYCAKE
87
87
88
Robert & Joyce Ringler Hoffman & Terry Kuehn
PA
STOLHAVEN SOVRGN DIAMOND-ET
89
86
88
G. Alpheus Stoltzfus
PA
DIRIGO-CONANT ATWOOD RANDI
87
88
88
Duane Conant & Steve Keene
ME
CONANT-ACRES ATWOOD FARRAH
90
87
88
Conant Acres, Inc.
ME
CONANT-ACRES SANCHEZ CAMMI
90
84
88
Conant Acres, Inc.
ME
CONANT-ACRES ALEX ADA
88
90
88
Conant Acres, Inc.
ME
CONANT-ACRES BRAXTON FAYME
89
87
88
Conant Acres, Inc.
ME
DIRIGO-CONANT SANCHEZ RICKI
90
86
88
Duane Conant & Steve Keene
ME
CONANT-ACRES ATWD FLAIR-ET
90
86
88
Conant Acres, Inc.
ME
DIRIGO BRAXTON JORJA
90
87
88
Brian R. Keene
ME
WESTPHALIA SS AMARYLLIS-RED
90
85
88
Grady Wendorf
WI
ROUND-HILL REALITY FIONA-TW
86
87
88
Shelby Iager
MD
RMW SANCHEZ ATHENA-ET
88
87
88
Nicholas John Raggi
MD
MOUNTFIELD SH ATW R12124-ET
88
82
88
Spruce-Haven Farm
NY
KAY-BEN ATWOOD CREAM CHEESE
89
87
88
Eben J. Benson
ME
MEY-VILLA SANCHEZ FLITTER
90
83
88
Jerome E. Meyer
WI
KEVREL MANOMAN MIA-ET
90
86
88
F. Kevin Leaverton
MD
GOLDEN-OAKS ATWD CHARLA-ET
90
84
88
Golden Oaks Farm
IL
P-ZBW SANCHEZ TRIUMPH-ET
88
83
88
Woodcrest Dairy LLC
NY
SAVAGE-LEIGH PS LELA-ET
90
85
87
Savage-Leigh Farm
MD
OCD PICOLO LACY-ET
87
86
87
Laura Emerson & Brent A Ashley
DE
RONBETH HD DAMION DANCER
87
85
87
Brent R. Zimmerman
MD
ROCKYMOUNTAIN MANOMAN DIMPL-ET
88
86
87
Alphagen Syndicate & Ferme Jolicap Inc
WI
SILVERMAPLE BOLTON CAMEO
86
86
87
Golden Oaks & Nick Raggi
IL
RAINYRIDGE DESTINY BIANCA-ET
87
82
87
Gene Iager & St Jacobs ABC, Inc
MD
KINGSWAY ATWOOD DELICATE
88
84
87
Ehrhardt Farms Inc & Gene Iager
MD
CAVA-LANES PRNT SHANTELE-ET
88
79
87
Aaron Hass & Todd Cavanaugh
WI
ROXY-DANE SPIRTE ROCHELLE
86
83
87
Seth Elsner
WI
HAGEN S-STORM CHEROKEE
86
86
87
Keith Hagen
WI
CAR-BON SANCHEZ AKIRA
86
86
87
Thomas J. Bunkoske
WI
MD-HEAVENSENT ABSO MAGNAFIC
86
88
87
Macayla Wiles
MD
BVK ATWOOD ABILENE-ET
85
88
87
Mike & Megan Moede
WI
DONWEN SIZZLE GABRIEL
87
82
87
Donald R. Wendlandt, Jr.
WI
ABRAXAS ACCOLD RD BL MISS
86
86
87
Michael J. Garrow
NY
HAGEN SANCHEZ BANDIT
88
82
87
Keith Hagen
WI
OCEAN-VIEW SHOTLE SHERRY
88
83
87
Mark Rueth & Jeff Woods
WI
MICHIGAN DN SLUSHIE 4685-ET
89
84
87
Michigan State University
MI
WHIT-HART AFTSHK CLIMAX-ET
87
86
87
Shelby Iager
MD
SMITH-CREST-TW J VIDALLIA
90
78
87
Joshua R Butler
WI
MS PEACE&PLENTY FRISKY
88
86
87
Richard Schwartzbeck & Byron Stambaugh
MD
MARTIN-PLACE DUNDEE TRU
88
83
87
Ashley Mariah Martin
ME
GOLDFAWN SANZ ELLI
87
86
87
Nathan M. Goldenberg
TX
PROBERT Z SOLIS
87
83
87
Kate Smith & Pam Probert
WI
SMITH-CREST GIBSON MARY
90
83
87
Travis Smith
WI
ROCKLAN-T ATWOOD ROXANNE-ET
87
84
87
Michael J. Garrow
NY
KMH MONUMENT JADE
88
83
87
Brian Edward Rohloff
WI
ROSEDALE TENACIOUS ROSE-ET
88
85
87
Rosedale Genetics Ltd
WI
STARWARD SANCHEZ JUBILEE-ET
88
85
87
Darwin D. Sneller
MI
ALMOST-MINE R PERFCT-RED-ET
87
85
87
Almost Mine Farm
WI
UNICORN ATTIC GIGI
88
83
87
Sarah Davis
MD
RALMA MANOMAN BLUEJAY-ET
88
85
87
Ringhill Holsteins & DeWeerd Farms Inc
MD-MAPLE-DELL AFTER GENA
87
84
87
Patrick Bros.
MD
MD-MAPLE-DELL SANCHEZ IMARA
87
87
87
Patrick Bros.
MD
ME-DO-CREST LHEROS IVY
87
83
87
Me-Do Meadows
WI
REGAN-BH-ALH M DANNAH-ET
88
85
87
Woodcrest Dairy LLC
NY
LUCK-E SHOTTLE TRINITY-ET
87
85
87
Matt L. Engel
IL
LUCK-E BRAXTON RIDDLE-TW
86
83
87
Matt L. Engel
IL
LUCK-E BRAXTON KAMEKO-ET
87
85
87
Joseph M. Engel
IL
LUCK-E BRAXTON KUMIKO
86
84
87
Joseph M. Engel
IL
LUCK-E ABSOLUTE BABE-RED-ET
87
85
87
Matt L. Engel
IL
VIEW-HOME TIME DASHEA
86
87
87
Country Dairy, Inc.
MI
WINDSOR-MANOR SAN ZSAZSA-ET
87
86
87
Jason M. & Donna G. Myers
MD
WINDSOR-MANOR RAZZLEDAZZLE
88
84
87
Kelsey Zepp
MD
BURLEDGE JASPER PRADA
88
85
87
Ray & Rae Nell Halbur
WI
CLEAR-ECHO M-O-M 2150-ET
90
82
87
De Su Holsteins LLC
IA
BRIGEEN SHOTTLE GIGI-ET
87
84
87
Vivian Briggs
ME
CO-OP SUPER JULITA-ET
88
82
87
Genesis Cooperative Herd
WI
CONANT-ACRES AFTRSHK LUSTRE
88
88
87
Duane W. Conant
ME
SELLCREST DBONAIR RITA-RED
90
82
87
Allen & Shirley Sell
WI
NO-LIMIT SHOTTLE LICORICE
87
85
87
Durrer Dairy & MB Luckylady Farm
CA
KLASSENS SANCHEZ JAMIE-3773
86
86
87
Jeff Morris Koster
TX
GOLDEN-OAKS AS CHARITY-ET
86
82
87
Golden Oaks Farm
IL
GOLDEN-OAKS GWYN CHRISTA-ET
87
84
87
Golden Oaks Farm
IL
GOLDEN-OAKS-NR GABRIELA
88
82
87
Golden Oaks & Nick Raggi
IL
DRENDEL-PM DAMION ELOISE
87
84
87
Kristina Drendel
IL
LINDALE SANCHEZ TATIANA
88
83
87
Dale & Linda Drendel
IL
TEX-STEIN COLBY PERRI
88
86
87
Robert E. Steinberger, Sr.
TX
TEX-STEIN PONTIAC GRACE
86
88
87
Gavin Steinberger
TX
TEX-STEIN PONTIAC FIREBIRD
90
82
87
Gavin Steinberger
TX
TEX-STEIN DEUCE HEIDY
89
83
87
Robert E. Steinberger, Sr.
TX
FARNEAR B ABBIE AKA
88
85
87
Rick & Tom Simon
IA
FLICKSTEAD SHOTTLE 1529
87
86
87
Diane G. Flickinger
MD
MS CRESCENTMEAD DANIE-ET
88
83
87
Budjon Farms, Peter C. Vail & Pierre Boulet
WI
GBM ATWOOD ACCENT-ET
88
85
87
Mark Douglas Cain
DE
BUCHHOLZ BALTIMOR HAMBONE
87
85
87
James & Susan Buchholz
WI
BEAVER-FLATS ALEX CORKY-ET
88
86
87
Jeffrey D. Dana
NY
MISS REAL HOT-RED
87
82
87
Troy Wendorf
WI
VISION-GEN SH SHO A12037-ET
88
83
87
Rick & Tom Simon,B & T Rauen & Butz-Hill Hol.
IA
VISION-GEN SUP GUVA-C037-ET
90
83
87
Woodcrest Dairy LLC
NY
LADYS-MANOR GINGERSNAP-TW
87
87
87
Ladys Manor LLC
MD
LADYS-MANOR FRD GIZELLE-ET
89
83
87
Ladys Manor LLC
MD
LEEMODE BRAXTON PONTIA
90
83
87
Ross W. Lee
CA
EHRHARDT GOLDWYN BRITNEY-ET
88
87
87
Ehrhardt Farms, Inc.
MD
COLDSPRINGS BRODY 4093
87
84
87
Matthew M. Hoff
MD
COLDSPRINGS DUSK 4095
88
83
87
Courtney K. Hoff
MD
KEVREL PLANET MIA-ET
90
85
87
F. Kevin Leaverton
MD
GOLDEN-OAKS CARLA-RED
88
86
87
Golden Oaks Farm
IL
STAR-ROCK EMPHASIS 6427
88
87
87
Star Rock Farms
PA
DURCHAN ALLEN DELIGHT-ET
86
90
87
Kingstead Farms & Tom Mercuro
MD
ROSEDALE LIFE IS SWEET-ET
90
81
87
Rosedale Genetics Ltd
WI
ROSEDALE LUCK WITH A KISS
87
90
87
Rosedale Genetics Ltd
WI
JENESIS-B KYLE NAOMI
87
85
86
Tom & Jacqueline Barends
MI
L-C-V BRAXTON LILLY 2067
86
85
86
Macey B Vieira
CA
L-C-V LOTHARIO SARY 2164
88
83
86
Macey B Vieira
CA
AARDEMA DORCY 84309
86
83
86
Double A Dairy
ID
CRAVE BALTIMOR BLUES 6291
85
85
86
Crave Brothers Farm LLC
WI
COSTA-VIEW BOLTON 41447
88
82
86
Costa-View Farms
CA
GROSS-FARM OUTSIDE HONEY
86
85
86
Norman Gross
MI
COSTA-VIEW AL 39808-ET
85
87
86
Joseph Azevedo
CA
B-HIDDENHILLS BEACN 1298-ET
86
86
86
Hidden Hills Dairy
MI
RUGG-DOC AFTRSHOCK CARAH-ET
88
83
86
Jeff Rugg
WI
CITYVIEW GOLDWYN ACE
88
84
86
Richard A. & K. Lisa Schwartzbeck
MD
BUTZ-BUTLER JASPR ASPIRE
86
85
86
Mary Feucht
WI
NINE-CEES LARAMEE SKY
85
84
86
Nine Cees Dairy
WI
POLLACK-VU IS LOW RIDER
86
82
86
Steven & Dori Lichty
WI
MISTY-Z ROY JAZZY
86
85
86
Dale L. Zimmerman
PA
GOLDFAWN-SYM JASPER JODI
85
83
86
Addison Anne Goldenberg
TX
WILDWEED ATLAS DINA
86
82
86
James, Kari & Linda Behling
WI
FISCHERDALE SANCHEZ JUDY-ET
90
80
86
Kamphuis Farms LLC
WI
GLEN-TOCTIN GOLD LEENA-ET
86
83
86
Katelyn M. Allen
MD
PHEASANT-ECHOS SHOTL ELAINE
88
85
86
Byron & Deborah Stambaugh
MD
PHEASANT-ECHOS MILN DARLENE
87
83
86
Trinity Kaye Miller
MD
VALMONT HARRY SHADE DELLA
86
82
86
Aaron A. & Aaron L. Widrick
NY
MAPLEGRAND SHOTTLE GABBY
86
82
86
Maplegrand Farms
NY
BUDJON-JK DURHAM EARRING
85
82
86
Budjon Farms & Joel Kietzman
WI
GLORYLAND-SA SONYA RAE-ET
87
86
86
James R. Putman
NY
GLEN-TOCTIN SUPER LAVENA-ET
88
85
86
Ladys Manor LLC
MD
PLAYBALL MOM LITTLE
86
87
86
Tim Schmitt
IA
RYAN-VU PRONTO KORRAL
86
86
86
Mark J. Ryan
WI
CLELAND MR BURNS ANNIE
85
85
86
Jason J. Cleland
WI
GALESTONE PASSION-ET
86
85
86
Robert A. Johnson
MD
ABRAXAS DEBONRRD BARBIE
87
82
86
Michael Faucher
NY
WILDWEED TOUCHDOWN CARMEL
86
85
86
Frank Behling
WI
ROCKLAN ADVENT REGAN-ET
86
82
86
Michael J. Garrow
NY
ROKEYROAD ATWOOD ELSIE-ET
86
83
86
Mason Dairy Farm LLC
OK
MICHIGAN DN ROUGHIE 4686-ET
89
78
86
Michigan State University
MI
PERLANE BOHNVIEW J FANTASIA
86
85
86
Daniel Bohn
WI
HOWARDVIEWWG SUPR AMBRIA-ET
86
84
86
Logan A. Zanzalari
IN
OUR-BEST SHINING STAR-TW
86
82
86
Mark & Joseph Wolf
WI
HOFF-HILL SANCHEZ TWINKIE
85
86
86
Adam Hoff
TX
DESTINY-ROAD DUSK DYNAMITE
87
85
86
Jay Stoltzfus
PA
CLASS-E CLASSIC CHEDDAR-ET
86
85
86
Lucas & Eric Moser
MI
HEADWATER LENNY JENNY
90
81
86
Eric Sherman
NY
ABRAXAS GABOR MOONSHINE
86
86
86
Carl, Samuel & Aaron Moore
NY
INSPIRACRES DEBON FAY-RED
86
85
86
Steve & Sharon Patterson
WI
WA-DEL SUPER BATHSHEBA-ET
87
82
86
Rick L. Wadel
PA
MD-LOCUSTCREST ARTIE MILKY
87
81
86
Md-Locust Crest
MD
SMITH-CREST FR IDEE-ET
86
80
86
Matt & Travis Smith
WI
AEBI-THAL ATWOOD RENE
87
84
86
Jim Abey
WI
PROTEGE RIANNA ROSE
87
82
86
Colt & Nikki Voegeli
WI
CLOVER-PRAIRIE 5038 JEWEL
85
83
86
Kyle A. Batista
CA
PHEASANT-ECHOS TURVY-RED-ET
87
83
86
Kenny Stambaugh
MD
MAPLEGRAND SANCHEZ PAL
87
83
86
Maplegrand Farms
NY
ROSSDALE KNOWLEDGE ROYCE-ET
83
86
86
Woodcrest Dairy LLC
NY
ROCKLAN-T GOLDWYN TOPS-ET
86
87
86
Michael J. Garrow
NY
BEE-BOW SHOTTLE PARFAIT-ET
87
87
86
Kamphuis Farms LLC
WI
IDEAL-KR IVANKA
86
86
86
Rosedale Genetics Ltd
WI
PHEASANT-ECHOS PHNX LEANDRA
87
83
86
Byron & Deborah Stambaugh
MD
RAGGI JASPER CUPID
87
83
86
Ronald E. Statler II
PA
SHADY-WOOD DEUCE JENNY
86
85
86
Woodcrest Dairy LLC
NY
NEHLS-VALLEY SUPER LIPPY
87
82
86
Gene P & Seth L Nehls
WI
RAGGI ATWOOD TONI-ET
86
85
86
Nicholas John Raggi
MD
MOONDALE JASPER TJ-ET
86
83
86
Cindy L. Krull
WI
MD-MAPLE-DELL AFTER DICEY
86
84
86
Maple Dell Farms
MD
MD-MAPLE-DELL ALEX SUE-ET
87
83
86
Derek Patrick
MD
MD-MAPLE-DELL ALEX SALLY-ET
87
84
86
Derek Patrick
MD
IRIS-HILL ADVENT NIKO-RED
86
85
86
Paul L. & Titus Mast
NY
WOODLEDGE ROY 955
87
83
86
R. Garnett Smith, Jr.
VA
LE-O-LA ATWOOD GYPSY
86
85
86
Richard F. & Kathy S. Demmer
IA
APPEALING G W ATWOOD JINX
86
85
86
S. Scott & April D. Cooper
PA
KAY-BEN ATWOOD KELLY
87
82
86
Erica J. Benson
ME
REGANCREST FRD LISANNE-ET
85
86
86
Kenneth J. Pfaff
WI
ZBW-WG AFTER EFFECT
86
85
86
Jeffrey D. Dana
NY
LUCK-E ABSOLUTE CINEMA
84
86
86
Matt L. Engel
IL
LUCK-E ABSOLUTE ZANG
87
84
86
Matt L. Engel
IL
F-A-F SIDNEY LOMIRA
85
84
86
Luke Borchardt
IL
SPRUCE-HAVEN ATW BJ11846-ET
88
82
86
Spruce-Haven Farm
NY
ROPUT DAMION GRINDAL
87
85
86
James R. Putman
NY
CHRIS-DA ALFREDO JULIET
87
82
86
LaVern & Cheryl Davis
WI
KOZ-DA SHAKIRA-RED
88
86
86
DaMartini Holsteins
WI
HILLTOP-LLC BOLTON 4574-TW
86
82
86
Hilltop Dairy LLC
WI
SPEEK-NJ PROM QUEEN-RED-ET
87
82
86
Neil McDonah
WI
WINDSOR-MANOR JEEV ROCHELLE
86
83
86
Jason M. & Donna G. Myers
MD
JUNLYN FRONTRUNNER WILMA
87
81
86
Junlyn Farms, Inc.
WI
PIERCE-VALE MAC TAMMY-ET
86
84
86
Pierce-Vale Farms LLC
WI
K-MANOR NIAGRA MODEL
86
86
86
K-Manor Holsteins
WI
STONE-FRONT JELLY-RED
85
85
86
Tom Lyon, Jr.
WI
COLDSPRINGS REECE 3923
88
85
86
Matthew M. Hoff
MD
COLDSPRINGS BOLTON 3975
87
86
86
Matthew M. Hoff
MD
KA-MITZ KASPER KAITLYN-RED
86
85
86
Todd Kahl
IL
MIDAS-TOUCH TRUMP RYLEE
87
82
86
Woodcrest Dairy LLC
NY
LOCUST-VALE S ROSETTE
86
85
86
Wilmer L. & Vera C. Peachey
NY
BLUE-GENE SHOTTLE SOUPEY-ET
87
82
86
Eugene M. Poirier
NY
HEINZE SANCHEZ PARIS
87
85
86
Mark T. Heinze
WI
ROB-SARA ATLANTIC FLOR 1888
85
86
86
Robert L. III & Laura Emerson
DE
MATT-DARI SPEARMINT SOCIETY
86
85
86
Matthiae Dairy Farm, Inc.
WI
MATT-DARI ALEXANDER BIRDIE
85
85
86
Matthiae Dairy Farm, Inc.
WI
MATT-DARI SHOTTLE DELISA
86
85
86
Matthiae Dairy Farm, Inc.
WI
SIBIC MATT-DARI BADEN
87
80
86
Amy Simon
WI
MATT-DARI AL WINK
86
85
86
Matthiae Dairy Farm, Inc.
WI
MATT-DARI AFTERSHOCK FIFI
86
85
86
Matthiae Dairy Farm, Inc.
WI
HOEK-TEX APPLE 5508
86
86
86
Meagan Jessyka Hoekman
TX
HOEK-TEX SANCHEZ 5526-ET
85
86
86
Gerard Hoekman
TX
HOEK-TEX BOULDER 5529-ET
86
86
86
Gerard Hoekman
TX
HOEK-TEX BEDFORD 5555
85
87
86
Gerard Hoekman
TX
GREEMLEA-TM DES BEULAH-ET
86
87
86
Savage-Leigh Farm
MD
MISS DEBONAIR BEAUTIFUL-RED
85
86
86
Richard M. Green
DE
T-C-G APPLE ROLEX-RED-ET
86
82
86
Joseph K. Panter & Triple Crown Genetics
ID
T-C-G JEEVES MADDY-ET
87
83
86
Triple Crown Genetics
ID
MILKSOURCE FORTUNE LAYNE
83
85
86
Frank Behling
WI
CONANT-ACRES GOLD SUKEY-ET
86
85
86
Conant Acres, Inc.
ME
CONANT-ACRES BRAXTON TESSA
88
81
86
Conant Acres, Inc.
ME
SELLCREST LB MISSY-RED-ET
85
86
86
Gary Sell
WI
SELLCREST JONAH KALA-RED
87
85
86
Allen & Shirley Sell
WI
NO-LIMIT SHOTTLE HAZEL
87
83
86
Durrer Dairy & MB Luckylady Farm
CA
KINGSMILL ALLOY ATARA
85
85
86
Kaitlyn R. Corbett
MD
KINGSMILL ALLOY ALETTE
87
85
86
Kaitlyn R. Corbett
MD
WESTPHALIA RR ASTONISH-RED
85
87
86
Charles A. Westphal
WI
GOLDEN-OAKS ADVENT ALEXA-ET
85
85
86
Golden Oaks Farm
IL
GOLDEN-OAKS GW CHAMPAGNE-ET
87
84
86
Golden Oaks Farm
IL
GOLDEN-OAKS AB FLIRT-RED-ET
88
82
86
Golden Oaks Farm
IL
BULLDOG BRAXTON GRAND
86
85
86
Bulldog Holsteins
MD
GA-IL AWOOD CLARICE-ET
86
82
86
Woodcrest Dairy LLC
NY
GLADE-ROCK TIME ERICA
85
86
86
Emily P. Ausherman
MD
TEX-STEIN COLBY YAJAIVA
87
85
86
Gavin Steinberger
TX
TEX-STEIN COLBY AQUILLA
86
86
86
Gavin Steinberger
TX
TEX-STEIN GABOR DARBI
90
83
86
Robert E. Steinberger, Sr.
TX
TEX-STEIN GABOR SHARON
88
85
86
Robert E. Steinberger, Sr.
TX
TEX-STEIN DEUCE CHELLSEY
88
85
86
Robert E. Steinberger, Sr.
TX
FARNEAR BROCADES BAKA-ETS
87
83
86
Woodcrest Dairy LLC
NY
FARNEAR ADA ADVOCATION-ET
87
83
86
Rick & Tom Simon
IA
MDF SANCHEZ 3120
85
90
86
Mason Dairy Farm LLC
OK
WEBB-VUE GABOR MS JACHEIA
88
83
86
Robert A. Webb
WI
ROCK-HOME PRONTO FJEARA
85
85
86
Jeff Morris Koster
TX
NEHLS-VALLEY STARDUST-RED
85
88
86
Shawn Nehls
WI
NEHLS-VALLEY SUGARLAND-RED
85
85
86
Shawn Nehls
WI
B-ENTERPRISE SUPER GIGI-ET
85
85
86
Rick & Tom Simon & Butz-Hill Holstein
IA
T-C-G DESTRY GOLD-RAE
87
84
86
Seagull Bay Dairy, Inc.
ID
T-C-G GOLD RHIANNA-ET
85
86
86
Triple Crown Genetics
ID
R-E-W CHARM BRACELET-ET
87
85
86
Derek Lease
MD
HARDEE-ROCK RB SHARA-RED-ET
88
84
86
Darwin D. Sneller
MI
ROCK-HOME DESTRY LIVIA-ET
86
86
86
Adam Hoff
TX
WARGO-ACRES MUSIC-ET
85
83
86
Wargo Acres
WI
WARGO-ACRES JANE
87
85
86
Wargo Acres
WI
WARGO-ACRES SUPER MILEY
85
85
86
Wargo Acres
WI
WARGO-N-JD SUPER DELUX-ET
87
83
86
Craig Carncross & Jason Danhof
WI
MDF G W ATWOOD 3240
86
83
86
Mason Dairy Farm LLC
OK
HILLPINE REALITY JAN
88
85
86
Byron W. Bruins
WI
LADYS-MANOR ALLOY FRESCA
87
83
86
Ladys Manor LLC
MD
LADYS-MANOR DORCY ODA
90
81
86
Ladys Manor LLC
MD
LADYS-MANOR DORCY AMELIA
85
86
86
Ladys Manor LLC
MD
LADYS-MANOR SHOT AT LUCKY
86
83
86
Ladys Manor LLC
MD
LADYS-MANOR DORCY AMIRA
88
86
86
Ladys Manor LLC
MD
LADYS-MANOR BIGTIME BUFFY
87
83
86
Ladys Manor LLC
MD
KAY-BEN ALEXANDER LOLIPOP
87
86
86
Kay-Ben Holsteins
ME
MAPLE-HILL-FARM LUCKY SEVEN
87
80
86
Paul E. Horning
PA
HNKES-WESSEL ATW ECLIPSE-ET
86
83
86
Douglas D. Lemke
WI
CASTLEHOLM ROSIE RAE-RED-ET
86
84
86
Nicole K. Wright
WI
MILKSOURCE ADV INDIANA-RED
87
82
86
Robert & Matt Puskas
NJ
SPRINGHILL-OH BOWSER ICE-ET
87
86
86
End Road Farm
MI
ROPUT AIRRAID BEARLY
86
87
86
James R. Putman
NY
BER-SHER EXPLO CARISSA CIN
86
85
86
Bernard & Ronald Brinks
MI
LORITA ATWOOD ANNA STAR
85
85
86
Durrer Dairy
CA
EHRHARDT ASHOCK LAURIN-ET
86
86
86
Ehrhardt Farms, Inc.
MD
COLDSPRINGS SILVAN 4076
87
83
86
Matthew M. Hoff
MD
COLDSPRINGS DUSK 4107
86
82
86
Ian A. Hoff
MD
COLDSPRINGS BAXTER 4161
87
86
86
Ian A. Hoff
MD
COLDSPRINGS LIGHTNING 4198
86
84
86
Matthew M. Hoff
MD
MEY-VILLA CHRIS DELLA
87
83
86
Bernard & Jerome Meyer
WI
MEY-VILLA SANCHEZ FANCLUB
87
80
86
Jerome D. Meyer
WI
PEACE&PLENTY ASPEN BONJOUR
87
82
86
Joseph A. Schwartzbeck
MD
PEACE&PLENTY FREEDOM ROYAL
88
82
86
Richard A. Schwartzbeck
MD
CO-OP UPD FREDDIE 4332
88
83
86
Genesis Cooperative Herd
WI
PIERCE-VALE AFSHK TAFFY-ET
86
85
86
Pierce-Vale Farms LLC
WI
MS TODDSDALE GOLD TRILEY-ET
86
85
86
Michael J. Garrow
NY
KEVREL MAN-O-MAN 1525
87
85
86
F. Kevin Leaverton
MD
ARB-FLO-SPR BUCKEYE SHABAM
88
82
86
Jamie Arbaugh
MD
HORIZON-JAY SHOT-OBSEE-ET
90
82
86
Woodcrest Dairy LLC
NY
MS APPLES ARIA-ET
89
83
86
Luke & Megan Rauen & Josh & Adam Simon
IA
GOLDEN-OAKS ATWOOD VENUS-ET
87
84
86
Golden Oaks Farm
IL
STONE-FRONT HVEZDA ROADIE
88
84
86
Andrew Jay Buttles
WI
NELSON-MILL AUGIE 969
87
84
86
J. Walter Rutledge, II
MD
HILMAR SUPER 4745
87
86
86
Hilmar Holsteins, Inc.
CA
WINDSOR-MANOR Z STICKY
86
83
86
Jason M. & Donna G. Myers
MD
P-ZBW SANCHEZ TRINITY-ET
87
82
86
Tyler Nephew
NY
MS SUGAR-C ALEXNDR QUEEN-ET
86
85
86
Sugar Creek Dairy
WI
RAGGI-MANOR SHANE SILVER
86
86
86
Nick Raggi & Robert E.& Mary O. Smith
MD
BUR-RODZ SANCHEZ BETH
90
80
86
Rodney A. Zietlow
WI
ROSEDALE ENOUGH TALK
87
82
86
Rosedale Genetics Ltd
WI
ROSEDALE FASHION SENSE
86
83
86
Rosedale Genetics Ltd
WI
ROSEDALE COURAGEOUS CAT
86
82
86
Rosedale Genetics Ltd
WI
LONE-MAPLE LHEROS 101
87
83
86
Linford R Weber
MD
TRI-KOEBEL SNOFALL TICKTOCK
86
82
85
Stephen J. Reed
MI
JENESIS-B KYLE ECHO
83
84
85
Isaiah Barends
MI
JENESIS-B ARMSTEAD MARISSA
85
85
85
Tom & Jacqueline Barends
MI
JENESIS-B KYLE ELITE
85
82
85
Tom & Jacqueline Barends
MI
S-S-I BOWSR WHISPER 7054-ET
86
83
85
Woodcrest Dairy LLC
NY
NORZ-HILL-C ATWOOD EMILY-ET
85
78
85
Richard & Richard Norz, Jr. & Peter Chatain
NJ
GREENLEA DESTRY RAE
83
86
85
Wayne & Cindee Savage & Richard Green
MD
SAVAGE-LEIGH RR STACEY
86
85
85
Jami Leigh Savage
MD
SAVAGE-LEIGH LIBERTY GABBY
85
82
85
Jami Leigh Savage
MD
SAVAGE-LEIGH ASPEN DARCI-ET
85
85
85
Kelli Ann Welsh
MD
SAVAGE-LEIGH SHAQUILLE ROXY
85
86
85
Savage-Leigh Farm
MD
SAVAGE-LEIGH SHAQ MAZEL-ET
85
81
85
Savage-Leigh Farm & Matt & Kelli Welsh
MD
TOM-ANNA MICHELLE 2160-ET
86
82
85
Tom & Deanna Stamp
MI
END-ROAD MACHINE BECCA-ET
84
86
85
End Road Farm
MI
END-ROAD GRAYBIL MACHA
83
87
85
End Road Farm
MI
END-ROAD AL MONTANA
85
84
85
End Road Farm
MI
B-HIDDENHILLS PADDY 1368-ET
86
86
85
Hidden Hills Dairy
MI
B-HIDDENHILLS DORCY 1405-ET
86
84
85
Hidden Hills Dairy
MI
B-HIDDENHILLS GABOR 1419
86
85
85
Hidden Hills Dairy
MI
WARDIN RUSSELL RITA-ET
86
82
85
Wardin Bros.
MI
CLAYTOP DREAM NAKED
85
83
85
Jeffrey L. Paulen
MI
JO-JO JONAH JOY-RED
86
82
85
Joseph A. Kubacki
MI
OAKFIELD-BRO FRANCESCA-ET
85
82
85
Douglas D. Lemke
WI
CRAVE TOYSTORY WINTER 6172
85
85
85
Crave Brothers Farm LLC
WI
COSTA-VIEW BOLTON 40777
87
84
85
Costa-View Farms
CA
GROSS-FARM 818 MAVE
85
85
85
Norman Gross
MI
GROSS-FARM MILLION ANTONIA
86
85
85
Norman Gross
MI
WELCOME OBSERV CORA-ET
86
85
85
Rock Hill Dairy LLC
NM
S-S-I ROBUST MAGIC 7228-ET
86
85
85
End Road Farm
MI
COSTA-VIEW ALEXANDER 41931
85
87
85
Costa-View Farms
CA
S-S-I BEACON LAROSE 7281-ET
87
86
85
Kevin & Barbara Ziemba
NY
WOODCREST ATWOOD ESTHER-ET
84
87
85
Woodcrest Dairy LLC
NY
COLSTEIN ATWOOD MEOW MEOW
87
77
85
Kevin & Barbara Ziemba & Woodcrest Dairy LLC
NY
LOCUST-AYR AFTERSHOCK MICKI
86
82
85
Michael R. & Anita L. Haines
MD
FROZENES ADVT ENGLISH-RED
85
83
85
James H. Janes
WI
CADY-LEE LARAMEE PENELOPE
83
82
85
Michelle P. Lee
NY
LOCUST-AYR MILLION AIRE-ET
85
82
85
Michael R. & Anita L. Haines
MD
CRYSTAL-JOY GIBSON FRISBEE
85
84
85
Amy M Stoltzfus
PA
LARS-ACRES RR-MM MM TINA-TW
86
83
85
Riley Miller
WI
POLLACK-VU MILN JOLLY1-8-ET
86
82
85
Pollack-Vu Dairy, LLC
WI
RYAN-VU LAURIN ECLIPSE
86
80
85
Chad & Mark Ryan
WI
RYAN-VU SANCHEZ BOTANY
85
82
85
Chad J. Ryan
WI
STONE-HAUS DRAKE ASHTON
86
87
85
Glen S. Zimmerman
PA
LONG-HAVEN KITE TALIA-TW
85
84
85
Orin J. Engelhardt
MI
PENTUCK MAC MOPSIE
85
85
85
Johnathan Heinsohn
IL
DONWEN DREVIL DOMAIN
87
81
85
Donald R. Wendlandt, Jr.
WI
WIL-O-MAR POTHOLE LAUREL
85
83
85
Wil-O-Mar Farm
MD
KNOTT-RUN CON PENNY-RED
86
81
85
Andrea Vaz
NM
MEYERVILLA SZ SOPHIE RAE-ET
85
82
85
Tyler J. Meyer
WI
PHEASANT-ECHOS MELSINA-ET
87
81
85
Byron & Deborah Stambaugh
MD
PEACE&PLENTY FREEDOM VAN
82
86
85
Richard A. Schwartzbeck
MD
BUDJON LIGHTNING ALLI
82
81
85
Budjon Farms
WI
GREYSTONE MITEY JASMAN
84
83
85
Woodcrest Dairy LLC
NY
WHITTIER-FARMS SUPR ALEN-ET
83
88
85
The Brown Eye Syndicate
CA
STONE-TD SANCHEZ BLITZ
85
85
85
Templeton Farms LLC
WI
A-SURE-BET ATTIC KELLY
85
85
85
Emily & Tommy Smith
DE
L-MAPLES MONUMENT HELEN
86
80
85
Tom Lyon, Jr.
WI
GLEN-TOCTIN MANO HEIDI
86
82
85
Glen-Toctin Farm
MD
POLLACK-VU SANCHEZ REVERIE
85
84
85
Pollack-Vu Dairy, LLC
WI
RYAN-VU JASPER ARROW-ET
86
81
85
Mark J. Ryan
WI
RYAN-VU LAURIN NADIA
87
82
85
Chad & Mark Ryan
WI
LOCUST-AYR STRLNG MARIE-ET
87
81
85
Michael R. & Anita L. Haines
MD
SAM-SIM TLNT CREME DE CREME
85
83
85
C K Kerrick III & Jerrel Heatwole
DE
MORAM PEARL ESCAL PLENTY
84
84
85
Shaun D. & Betty Jo Hyde
MI
MORAM MISS ESCAL ELSIE
86
82
85
Richard D. & Patricia L. Hyde
MI
BVK ALEXANDER ASHIKA-ET
85
83
85
Francis W. Daniel III
WV
SENLAND GABOR SALAMONA
85
85
85
James P. Senn
WI
GAHMS ASTEROID TYRA
85
85
85
Mackenzie Spears
AR
BRU-DALE SUPER SASHA-ET
87
82
85
Woodcrest Dairy LLC
NY
LOCUST-RIDGE DSTRY MEREDITH
86
84
85
Jamie Arbaugh
MD
MEYERVILLA SANCHEZ ROXXY
86
85
85
Tyler J. Meyer
WI
MEYERVILLA DESTRY TICKL-RED
85
78
85
Tyler J. Meyer
WI
PHEASANT-ECHOS FRTR SHIRLEY
88
83
85
Byron & Deborah Stambaugh
MD
CASS-RIVER DEUCE PRESTIGE
85
85
85
Larry, Ronald & John Keinath
MI
PHEASANT-ECHOS WENDALL-ET
86
83
85
Bud Stambaugh
MD
MD-LOCUSTCREST JULIO PAM
86
83
85
Md-Locust Crest
MD
KELDEAN MATSON ELITE
87
84
85
Dean Michael Davenport
MI
BUDJON-JK MA ELLIOTT
84
82
85
Riley Miller
WI
WA-DEL SUPER BRANDY-ET
87
84
85
Lester C. Jones & Sons, Inc.
MD
WA-DEL SUPER BRISTOL-ET
85
86
85
Rick L. Wadel
PA
BOHNVIEW LAURIN ELECTRA
84
83
85
Aaron Bohn
WI
BOHNVIEW LAURIN ELLYMAE
86
82
85
Daniel Bohn
WI
MS ARIEL FREDDIE ANNA-ET
86
85
85
Sebastien Dion
WI
BELL-STONE AFTERSHOCK TONI
85
82
85
W. Franklin, Jr. & Jeffery F. Moore
MD
MORAM MINN DAMION TANSY
85
84
85
Richard D. & Patricia L. Hyde
MI
LOCUST-AYR SSTORM TATER TOT
87
81
85
Ryan Matthew Haines
MD
MISTY-Z SANCHEZ TEMPO
86
82
85
Dale L. Zimmerman
PA
MISTY-Z SANCHEZ EILEEN
86
81
85
Dale L. Zimmerman
PA
WA-DEL ROSS BETHIA-ET
85
83
85
Rick L. Wadel
PA
HEADWATER AVALANCHE PANDORA
85
83
85
Eric Sherman
NY
HEADWATER REDLINER JEZABELL
86
85
85
Eric & Lorelle Sherman
NY
HEADWATER AFRSHOCK JORDACHE
87
82
85
Eric & Lorelle Sherman
NY
INSPIRACRES ABSOLUTE MAISIE
87
83
85
Steve & Sharon Patterson
WI
INSPIRACRES FRNTRNNR LYDIA
86
85
85
Steve & Sharon Patterson
WI
INSPIRACRES SANCHEZ JULIE
85
85
85
Steve & Sharon Patterson
WI
GLEN-TOCTIN SUPER LOUISA-ET
85
82
85
Glen-Toctin Farm
MD
BROEGE-ACRES DESTRY KRISTIE
86
80
85
Caleb Broege
WI
CADY-LEE BUCKEYE HELLEMINA
87
80
85
Stephen H. & Sally C. Lee
NY
EDEN-VIEW SANCHEZ COKE-ET
85
83
85
Eric Niswander
PA
CADY-LEE DUNDEE CHELSEY
87
78
85
Michelle P. Lee
NY
WA-DEL ROSS MATTIE
86
81
85
Rick L. Wadel
PA
WA-DEL-DH BOOKEM CLAIRE-ET
87
82
85
Darwin Gene Horst & Rick L. Wadel
PA
BUDJON LAURIN ABBOTT-ET
85
80
85
Budjon Farms
WI
MARSH-VUE LB PRECIOUS-RED
86
82
85
Douglas D. Lemke
WI
JERLAND DEB GOGETTER-RED-ET
88
76
85
Rebekah & Miles Schraufnagel
WI
STARWARD BOLTON CARLY
87
82
85
Darwin D. Sneller
MI
STRAWBERRY-ACRES SANC MICA
84
83
85
John, Ann & Barbara Schenning
MD
ROCKY-MOUNT SANCHEZ FINESSE
85
82
85
Parker F. Welch
MD
LIME-VALLEY FROST-ET
86
82
85
Jeff & Dan Liner
WI
MAPLE-ARBOR CRIMSON FLAVIA
86
81
85
Fred D. & Annette L. Prichard
MI
OCEAN-VIEW LL ZANDRA-ET
86
83
85
Kamphuis Farms LLC
WI
CLAYTOP ROSS PEAR-ET
84
85
85
Jeffrey L. Paulen
MI
IA-WILSIM MARY
86
79
85
W. Franklin, Jr. & Jeffery F. Moore
MD
SCHWANDT COLBY GWENORA
87
77
85
Robert L. Schwandt, Jr.
WI
CAR-BON ALEXANDER AVERY
86
85
85
Kevin J. Bunkoske
WI
WRT-GIES ADVENT CHER-RED-ET
87
83
85
Gies Farms & D & T Dairy LLC
WI
HAZELS GLDWN HEART-ET
86
83
85
Eben J. Benson
ME
HIL-SURROUND CONTENDER WOW
90
82
85
Jared G. Martin
MD
MAPLEGRAND LHEROS RUTH
86
82
85
Maplegrand Farms
NY
MAPLEGRAND LHEROS ADALINE
86
82
85
Maplegrand Farms
NY
RHYTHM LAURIN KETTLE
85
85
85
Bradley Farms
WI
GOLDFAWN ROCK SELMA-RED
86
85
85
Addison Anne Goldenberg
TX
HEADWATER PHOENIX PUNKY
85
86
85
Eric Sherman
NY
FROZENES LYDON RITZ
85
88
85
Aaron L. Hass
WI
SMITH-CREST MILLER WINNIE
88
81
85
Matt & Travis Smith
WI
GR-ACRES ATWOOD LOVELY
86
85
85
Rebekah Schraufnagel
WI
WILFARMS P KNOWLEDGE DESIE
86
85
85
Carson Acres LLC
MI
D-L-BENNETT ATLANTIC JINNA
87
82
85
Lawson D. Bennett
MI
D-L-BENNETT R ROYCE TRIXY
84
82
85
Lawson D. Bennett
MI
MS BENNETT ATLANTIC ANGEL
87
81
85
D. D. L. & D. Bennett & Tyler DeWeerd
MI
MISTY-Z BRONCO TARGET
87
83
85
Dale L. Zimmerman
PA
COCALICO SHOTTLE AVERY-ET
85
82
85
Paul B. Zimmerman, Jr
PA
REED-ELI SSDEUCE AVENGA-RED
86
85
85
Elizabeth Reed
MI
COCALICO SHOTTLE MAGGIE
83
84
85
Paul B. Zimmerman, Jr
PA
MISS SUMMER BUNNY-ET
85
80
85
Brett Hildebrandt
WI
COCALICO DAMION JOLEE-TW
82
85
85
Paul B. Zimmerman, Jr
PA
COCALICO MAC ASHLYN
85
85
85
Paul B. Zimmerman, Jr
PA
COCALICO SHOTTLE PIPER
86
83
85
Paul B. Zimmerman, Jr
PA
NINE-CEES BALTIMOR PARK
87
80
85
Nine Cees Dairy
WI
JORICH-WAY BURNS IRIS
85
77
85
Richard E. Schulz
WI
JORICH-WAY LHEROS RHEA
86
83
85
Richard E. Schulz
WI
JORICH-WAY BALTIMOR RAINBOW
88
77
85
Richard E. Schulz
WI
MORAM SHASTA SANCHEZ PIZA
86
82
85
Shaun D. Hyde
MI
MAPLE-NOOK JACKSON FLIER
86
81
85
Maple-Nook Holsteins
NY
COCALICO DUNDEE CANDY
82
86
85
Paul B. Zimmerman, Jr
PA
KMH PURE GOLD MEMORY
85
82
85
Brian Edward Rohloff
WI
CHANDALE-D DURHAM SANDY-ET
86
83
85
Gary M. & Crystal Annie Dell
MD
SERB ZACH SANCHEZ BUZBY
86
82
85
Lyle Allen
ME
MOLLY-MAE FANCY CANDY
85
86
85
Adam Hoff
TX
WEA-LAND GOLDWN NAKIESHA-ET
85
82
85
Michael J. Garrow
NY
JORICH-WAY BOXER ILA
85
85
85
Adam J. & Jennifer E. Bertz
WI
RAGGI ATWOOD TONYA-ET
85
87
85
Nicholas John Raggi
MD
HARGRAVE PRONTO 743
86
79
85
Chelsea A. Hargrave
NY
WILLOW-BROOK FIN CUT DROPS
85
83
85
Laurie B. W. Koneck
WI
WILLOW-BROOK GENEVA SAL
87
81
85
Kurt Koneck
WI
CAMPSIDE MATSON 112
90
82
85
W. Ray Halteman
MD
VALENTIA OUTBOUND LULU
83
85
85
Earl B., Jr. & Keitha F. Grove
MD
CHAN-LEE AL GLASALLY-ET
87
77
85
Charles L. & Anne B. Lethbridge
MD
CHAN-LEE SHAMPOO GLENDELTA
85
83
85
Charles L. & Anne B. Lethbridge
MD
HUN-VAL AFTERSHOCK AMY-ET
85
82
85
Dempsey Farms
DE
BRUINS-DALE BLVA DIAMOND-TW
86
83
85
Byron W. Bruins
WI
GREENLEA DES MAE-RED
85
82
85
Erin E. Corbett
MD
ALL-RIEHL AL PIE-ET
86
82
85
Dale E. Niswander
PA
NORTHERN-GLO REAL SILLY
87
80
85
Scott E. Hamilton
NY
TRI-DEE-KR DURHAM CORA-ET
87
82
85
W. Franklin, Jr. & Jeffery F. Moore
MD
FRAN-BAR LAURIN LIZA-ET
85
83
85
Logan E. Moore
MD
FRAN-BAR LIGHTNING ELECTRA
85
84
85
Logan E. Moore
MD
FRAN-BAR SANCHEZ DOROTHY
85
81
85
Leslie Moore
MD
FRAN-BAR PRO ELSA MAE
87
81
85
Jeffery F. Moore
MD
FRAN-BAR PRO DORADOO
85
83
85
W. Franklin, Jr. & Jeffery F. Moore
MD
FRAN-BAR PRO ROMILEE
86
84
85
Jeffery F. Moore
MD
HASS-ACRES MIRANDA-ET
86
84
85
Aaron L. Hass
WI
PENTA-PAT COLT ADRIA
85
85
85
Michael D. Patrick
MD
JANESTEAD SENSATION CARA
85
82
85
James H. Janes
WI
JC-KOW RUSSEL JULIE
85
81
85
JC-Kow Farms LLC
WI
BRECAR CARUSO ASHLEY
87
80
85
Brett Bruins
WI
CIRCLE-F LIGHTNING WISIA
86
82
85
Allan L. Friend
NY
SOLID-GOLD SHOTTL ESKIMO-ET
87
84
85
Woodcrest Dairy LLC
NY
SWEET-WILLOW JAYZ ELM
87
83
85
Scott E. Hamilton
NY
VIETHSONS BOLIVER PANSY
85
84
85
Jerry Vieth
TX
FOREST-RIDGE MARLA MAPLES
85
85
85
Kurt & Sarah Loehr
WI
FOREST-RIDGE MONA LISA-ET
84
85
85
Kurt & Sarah Loehr
WI
NORDIC-RIDGE AFTER GLORY
85
81
85
Les Frere Syndicate
WI
OVERSIDE AFTERSHOCK HALEY
85
84
85
Joseph & Hidde Osinga
TX
WALK-ERA SANCHEZ JENNAH
83
85
85
Walk-Era Farms, Inc.
WI
WALK-ERA DUNDEE ALBA
84
85
85
Walk-Era Farms, Inc.
WI
WALK-ERA LRN MOTTO
85
83
85
Walk-Era Farms, Inc.
WI
WALK-ERA AFTERSHOCK OMLET
88
85
85
Walk-Era Farms, Inc.
WI
WALK-ERA SHOTT SUNRAY-ET
85
87
85
Walk-Era Farms, Inc.
WI
LE-O-LA SUPER ELLEN-ET
86
82
85
Richard F. & Kathy S. Demmer
IA
REGANCREST DOMAIN CINDER-ET
86
83
85
Wargo Acres
WI
REGANCREST-BH O GALLORY-ET
86
83
85
Woodcrest Dairy LLC
NY
LUCK-E ADVENT BERETTA
85
84
85
Joseph M. Engel
IL
LUCK-E ABSOLUTE MILLY
85
83
85
Matt L. Engel
IL
LUCK-E BRAXTON BUBBLY
85
83
85
Joseph M. Engel
IL
LUCK-E ADVENT KATESHA-ET
87
82
85
Matt L. Engel
IL
LUCK-E ADVENT KATERRA-ET
85
85
85
Matt L. Engel
IL
MILGENE ROY SHANAE
86
80
85
Cindy L. Krull
WI
SPRUCE-HAVEN SPR BS11998-ET
85
82
85
Spruce-Haven Farm
NY
CHRIS-DA DESTRY 350
82
85
85
LaVern & Cheryl Davis
WI
STRAUSSDALE AS GEMMA-ET
83
83
85
Straussdale Holsteins LLC
WI
STRAUSSDALE ATWOOD JULIET
88
78
85
Straussdale Holsteins LLC
WI
MAPLE-NOOK SHAQ TASTIC-ET
87
83
85
Maple-Nook Holsteins
NY
MNH-RF SHOTTLE ERICA-ET
86
83
85
John Zeh, James R. Putman & Lauri L. Beggs
NY
MAPLE-NOOK LAURIN BRIEN
86
82
85
Maple-Nook Holsteins
NY
LANGS-TWIN-B CASE-ET
86
85
85
Keith A. Nettekoven
WI
HILLTOP-LLC SHOTTLE 4555
85
82
85
Hilltop Dairy LLC
WI
HILLTOP-LLC BOLTON 4575-TW
85
85
85
Hilltop Dairy LLC
WI
HILLTOP-LLC BOWMAN 4584
85
78
85
Hilltop Dairy LLC
WI
HILLTOP-LLC OSCAR 4596
85
84
85
Hilltop Dairy LLC
WI
HILLTOP-LLC SANCHEZ 4599
84
83
85
Hilltop Dairy LLC
WI
SPEEK-NJ DESTRY RUFFI-ET
86
82
85
Neil McDonah
WI
AMES-WAY-NM SHOTLE VICTORIA
86
82
85
Paul & Sarah Trapp & Neil McDonah
WI
VIEW-HOME IZZY 19296-1
84
85
85
Country Dairy, Inc.
MI
LJP ACE ROXY
85
85
85
Louis J. Palmatary & Sons
MD
KEVREL SANCHEZ MELODY-ET
83
85
85
F. Kevin Leaverton
MD
PENN-GATE C FORTUNE-RED-ET
85
84
85
Bradley Hoffman
PA
ALAMANA JADE
90
75
85
L. Alan Lobdell
NY
BEAVER-FLATS ADVENT CAN-ET
87
84
85
Lauryn Dana
NY
VIEW-HOME COKE 19197-1
84
82
85
Country Dairy, Inc.
MI
VIEW-HOME BRADELL FAITH
84
85
85
Country Dairy, Inc.
MI
VIEW-HOME EXPLODE MORISSA
85
83
85
Country Dairy, Inc.
MI
VIEW-HOME DIXIE 13883-3
84
82
85
Country Dairy, Inc.
MI
VIEW-HOME EUREKA PARTY
85
83
85
Country Dairy, Inc.
MI
VIEW-HOME DOMAIN VICTORIA
84
85
85
Country Dairy, Inc.
MI
ELM-SPRING AFTERSHCK CALLY
85
85
85
Dempsey Farms
DE
PIERCE-VALE MAC RAIZEL-ET
86
82
85
Pierce-Vale Farms LLC
WI
MORNINGVIEW SUPER ELLIE-ET
87
85
85
Rick & Tom Simon
IA
BURLEDGE SOCRATES PREMIER
86
86
85
Ray & Rae Nell Halbur
WI
BURLEDGE JASPER TRUMP
86
81
85
Ray, Rae Nell & Joseta Halbur
WI
STONE-FRONT MAC CORONA
85
85
85
Tom Lyon, Jr.
WI
STONE-FRONT FRUN AZURE-RED
86
82
85
Tom Lyon, Jr.
WI
STONE-FRONT MAC CASHEW
85
85
85
Tom Lyon, Jr.
WI
COLDSPRINGS SHOTTLE 3888-ET
86
83
85
Matthew M. Hoff
MD
COLDSPRINGS MYRON 3895
87
82
85
Matthew M. Hoff
MD
COLDSPRINGS RUDY 3896
85
82
85
Matthew M. Hoff
MD
COLDSPRINGS FLYER 3934
86
84
85
Matthew M. Hoff
MD
COLDSPRINGS TEMPTATION 3953
87
81
85
Matthew M. Hoff
MD
PALMYRA M-O-M MANHATTAN-ET
86
82
85
Ryan Shank & Chris & Jenneifer Hill
MD
MINSU JASPER JAZZY-ET
86
82
85
Stephen J. Reed
MI
M-6 SANCHEZ BLUE-ET
85
79
85
Megan Marie Meyer
OK
CROIX-LINE SERENA CRI-ET
87
80
85
Genesis Cooperative Herd
WI
MS WELCOME MM LULITA CRI-ET
86
82
85
Genesis Cooperative Herd
WI
LOCUST-VALE STRLNG STEAMER
85
84
85
Wilmer L. & Vera C. Peachey
NY
BLUE-GENE ROGER REBA
88
78
85
Homer Bushey
NY
BLUE-GENE JASPER JILL-ET
85
81
85
Eugene M. Poirier
NY
BLUE-GENE SHOTTLE SHANIA-ET
85
85
85
Eugene M. Poirier
NY
BLUE-GENE AFFIRM ALICE
86
83
85
Eugene M. Poirier
NY
BLUE-GENE AFTERSHOCK ALEEN
85
83
85
Eugene M. Poirier
NY
LADYS-MANOR ALAN Z MARIA
85
87
85
Ladys Manor LLC
MD
LADYS-MANOR AFTR RASPBERRY
85
82
85
Ladys Manor LLC
MD
LADYS-MANOR GOLD LEXEE-ET
85
82
85
Katelyn Iager
MD
RICKLAND TIME 3446-TW
86
82
85
Rickert Brothers LLC
WI
WOODCREST APPRENTICE 20068
87
82
85
Woodcrest Dairy LLC
NY
WOODCREST CHRYSLER 20079
86
79
85
Woodcrest Dairy LLC
NY
WOODCREST FORK 20126
87
87
85
Woodcrest Dairy LLC
NY
WOODCREST KRUSE 20132
86
84
85
Woodcrest Dairy LLC
NY
BUR-RODZ BOLTON PRINCESS
85
83
85
Rodney A. Zietlow
WI
HEINZE BALTIMORE TALLY
85
83
85
Mark T. Heinze
WI
JB-GLENVAL AL AFRICA-ET
85
83
85
Jason M. & Donna G. Myers
MD
BENNETCH SANCHEZ ADALYN-ET
83
78
85
Matthew B. Bennetch
PA
BENNETCH MOM LADY
87
82
85
Stony-Run Farm
PA
TROMBLEY-DAIRY BOLTON BETTY
83
86
85
Russell R. Trombley
NY
TROMBLEY-DAIRY MATSON MEGAN
84
85
85
Russell R. Trombley
NY
TROMBLEY-DAIRY DAMION DELLA
85
81
85
Russell R. Trombley
NY
CARSON-ACRES MOSCOW CREST
85
82
85
Carson Acres LLC
MI
CARSON-ACRES GRAYBILL CARO
86
81
85
Carson Acres LLC
MI
ROB-SARA AD LUSCIOUS-RED-ET
85
86
85
Catlin E. Christman
MD
ROB-SARA SHOTTLE PATIENCE
85
85
85
Robert L. Emerson II
DE
TWIN-RIVER SHYSTER ARIZONA
85
85
85
Todd A. Hayton
NY
MATT-DARI ALEXANDER KAHLUA
85
81
85
Matthiae Dairy Farm, Inc.
WI
CO-OP MD LANI-ET
86
85
85
Matthiae Dairy Farm, Inc.
WI
MATT-DARI MAC BUMBLE
86
79
85
Matthiae Dairy Farm, Inc.
WI
MATT-DARI PATIAN-ET
86
82
85
Matthiae Dairy Farm, Inc.
WI
MATT-DARI MAN-O-MAN PENI-ET
85
85
85
Matthiae Dairy Farm, Inc.
WI
MATT-DARI ALEXANDER GIA
85
85
85
Matthiae Dairy Farm, Inc.
WI
MATT-DARI ALEXANDER GIFT
83
82
85
Matthiae Dairy Farm, Inc.
WI
HOEK-TEX BEDFORD 5546
85
84
85
Gerard Hoekman
TX
JNP-ATH-MOR SSI SBN 5328-ET
86
85
85
Woodcrest Dairy LLC
NY
JO-ENG DURAN 6093 107
86
84
85
Matthew Borchardt
IL
ARSENAL CNTNDR DIZZY-RED-ET
86
85
85
Patrick S Youse
MD
MD-MRK-OVF DETROIT CELIA-ET
85
85
85
Oakland View Farms LLC
MD
FRONT-PAGE DAMION JELLYBEAN
87
82
85
Lucas Edelburg
WI
CONANT-ACRES ATWOD PEONY-ET
88
81
85
Conant Acres, Inc.
ME
CONANT-ACRES SANCHEZ BROOK
86
82
85
Conant Acres, Inc.
ME
CONANT-GROVES SANCHEZ SASHA
87
82
85
Conant Acres, Inc.
ME
SELLCREST CATHY-RED
87
80
85
Gary Sell
WI
SELLCREST ADVENT MAIDEN-RED
86
79
85
Andrew Sell
WI
SELLCREST JONAH LILA-RED
85
82
85
Allen & Shirley Sell
WI
WOODLAWN SHOTTLE DARLA
86
82
85
Woodcrest Dairy LLC
NY
WARGO-ACRES HELPER
86
81
85
Wargo Acres
WI
WARGO-ACRES CLARABELLE
83
85
85
Wargo Acres
WI
WARGO-ACRES HICCUP
86
81
85
Wargo Acres
WI
WARGO-ACRES COLBY
85
82
85
Wargo Acres
WI
CO-OP UPD GOLDWYN 4006
84
83
85
Genesis Cooperative Herd
WI
CO-OP UPD REESE 4143
85
85
85
Genesis Cooperative Herd
WI
LORITA TIME DOTTY
86
78
85
Durrer Dairy
CA
LORITA BOSSMAN SELA
85
82
85
Durrer Dairy
CA
O-C-S-DAIRY JAN TWINKIE
87
82
85
Annie Catherine Dell
MD
JONS-OWN BAXTER BUTTON
86
83
85
Jon Schoenike
WI
HOGAN-TEX GABOR 3168
86
80
85
Double H Dairy
TX
PINE-TREE DORCY ALEXA II-ET
83
82
85
Seagull Bay Dairy, Inc.
ID
STOLTZFUS FINEST SUE
85
85
85
Timothy R Kerrick
DE
VO-MI CADET 843
85
83
85
Mark Vossekuil
WI
KINGSMILL TNKERTOY SCAR-RED
86
85
85
Glen S. Zimmerman
PA
NELDELL ALEXANDER 2103
85
86
85
Neldell Farms LLC
WI
WESTPHALIA ZENITH EXAMPLE
87
79
85
Charles A. Westphal
WI
WESTPHALIA SS TORNADO
84
84
85
Charles A. Westphal
WI
WESTPHALIA SANCHEZ EMINENCE
87
78
85
Charles A. Westphal
WI
WESTPHALIA ALEXANDER HECTIC
85
83
85
Dustin C. Westphal
WI
MS WESTPHALIA SANCHZ MONICA
86
84
85
Austin Moucha
WI
LEASEWAY SANCHEZ AUTUMN
86
82
85
Derek Lease
MD
LEASEWAY ALEXANDER MINAJ
85
87
85
Md-Locust Crest
MD
GOLDEN-OAKS PLANET 5310-TW
85
84
85
Golden Oaks Farm
IL
GOLDEN-OAKS AS CHANEL-ET
85
75
85
Golden Oaks Farm
IL
JMK GABOR 9680
85
85
85
John Koster
TX
HURTGENLEA DOMAIN BILLI JO
86
82
85
Hurtgenlea Holsteins Ltd
WI
BULLDOG ATLANTIC PATTI
85
86
85
Bulldog Holsteins
MD
BULLDOG ADVENT MAYA-ET
85
85
85
Shelby Iager
MD
MD-MAPLE-LAWN MITCH REBEL
85
86
85
Michael C., Matthew E. & Mark E. Iager
MD
MD-MAPLE-LAWN BRAXTON PIPER
81
86
85
Woodcrest Dairy LLC
NY
KRULL-CK JOHAN ROXY-RED
86
81
85
Travis J. Meyers
WI
LINDALE FORTUNE ANDREA
83
86
85
Dale & Linda Drendel
IL
LINDALE ABSOLUTE PLEASURE
85
85
85
Dale & Linda Drendel
IL
LINDALE MR MINISTER FORTUNE
85
82
85
Dale & Linda Drendel
IL
MISS MILLION KIKI
85
83
85
Jeff Drendel
IL
SHERONA-HILL-JH FIDELE
85
83
85
Dale & Linda Drendel
IL
DE-URFEE JAVA KATAMAKUNDA
86
85
85
Nathan Durfee
NY
LARS-ACRES PLANET TRICKER
85
82
85
Larson Acres, Inc.
WI
LARS-ACRES GRAYBIL FRITZIE
82
85
85
Luke E Trustem
WI
LARS-ACRES MAN TICKET-ET
84
83
85
Larson Acres, Inc.
WI
LARS-ACRES PRINCE IRA
85
84
85
Larson Acres, Inc.
WI
FIRSTGLANCE GW ROSALIE-ET
87
83
85
Woodcrest Dairy LLC
NY
KINGSMILL DUSK ADDISON-ET
83
88
85
Jesse Braun
NY
ROB-SARA SANCHEZ SILVER
84
84
85
Robert L. Emerson II
DE
ROB-SARA REALITY KIT-RED
84
88
85
Nicole Myers
MD
WELK-SHADE AFTRSHOCK KAY-ET
86
85
85
Robert L. Emerson
DE
KRULLCREST SUPER GIDGET-ET
85
86
85
Jeff & Dan Liner
WI
LINERWAY BOWSER CHA-CHA-ET
86
83
85
Jeff & Dan Liner
WI
GOTTA-HAVE SUPER CHEDDAR
86
83
85
Scott J Munes
WI
HYLIGHT ROLLING STONE 665
86
85
85
Hy-Light Farms, LLC
NY
WARMKA ADKIN 1161
85
85
85
Erik, Carrie, Brad & Danielle Warmka
WI
TEX-STEIN DREAM JEMMA
88
82
85
Chad Steinberger
TX
TEX-STEIN ONYX MELVINIQUI
85
85
85
Robert E. Steinberger, Sr.
TX
TEX-STEIN MUFFIN LUZ
87
85
85
Robert E. Steinberger, Sr.
TX
TEX-STEIN COLBY KASIE
85
86
85
Gavin Steinberger
TX
TEX-STEIN MASTER REKEISHA
85
86
85
Robert E. Steinberger, Sr.
TX
TEX-STEIN MUFFIN DAYZHAUNAE
86
83
85
Robert E. Steinberger, Sr.
TX
TEX-STEIN SANCHEZ ALEX
85
83
85
Robert E. Steinberger, Sr.
TX
TEX-STEIN MUFFIN AIDAN
86
83
85
Robert E. Steinberger, Sr.
TX
TEX-STEIN GW ATWOOD CHELSEY
86
86
85
Robert E. Steinberger, Sr.
TX
TEX-STEIN GABOR BRIEANNA
86
86
85
Chad Steinberger
TX
GOFF PLANET 36061
85
83
85
Buster I. Goff
NM
GOFF SHOT 36527
85
83
85
Buster I. Goff
NM
GOFF LARIAT 36998
85
85
85
Buster I. Goff
NM
MAVIEW KAIT JASPER-ET
85
85
85
Charles A. Westphal
WI
DUCKETT-BH ATWOOD SAM-ET
85
82
85
Rock Hill Dairy LLC
NM
FARNEAR-BH JEEVES BRAZIL-ET
86
85
85
Earlen Farms Ltd.
LEGENDHOLM-N ANATOLA-ET
85
85
85
Eddie Bue & Norman Nabholz
WI
FLICKSTEAD MORACCO 1519
88
81
85
D. Richard Flickinger
MD
FLICKSTEAD BOXER 1531
86
83
85
D. Richard Flickinger
MD
FLICKSTEAD EXPLODE 1546
87
79
85
D. Richard Flickinger
MD
MDF TIME 3160
86
81
85
Mason Dairy Farm LLC
OK
MDF AFTERSHOCK 3181
83
85
85
Mason Dairy Farm LLC
OK
WEBB-VUE GABOR VERONICA
87
85
85
Robert A. Webb
WI
ROSSDALE FLYER 131
90
76
85
Andrew T. Schantz
NY
ROSSDALE SHOTTLE 142
86
82
85
Andrew T. Schantz
NY
NELSON-MILL 1227 931
85
86
85
Walter Rutledge
MD
NELSON-MILL ALAN 943
86
82
85
Walter Rutledge
MD
KULP-DALE DES LAROSE-RED-ET
86
83
85
James M. Meyer
WI
NEHLS-VALLEY ALEX MICHELLE
83
85
85
Shawn Nehls
WI
NEHLS-VALLEY ATLANTIC ANGEL
86
82
85
Shawn & Seth Nehls
WI
ARB-FLO-SPR HEFTY GEEGEE
87
82
85
J. Steven Arbaugh
MD
SUGAR-C PAGEWIRE 4068
85
82
85
Sugar Creek Dairy
WI
SUGAR-C LENNOX 4070
85
83
85
Sugar Creek Dairy
WI
BRITE-SIDE GOLD PROPER-ET
85
83
85
Sugar Creek Dairy, LLC & Nate Janssen
WI
WELK-SHADE ATWOOD KOKO-ET
85
85
85
Walk-Era Farms, Inc.
WI
WELK-SHADE ATWOOD KYLEE-ET
85
83
85
Alli Walker
WI
CHRIS-DA GRACELAND 351
85
86
85
LaVern & Cheryl Davis
WI
GENESEE-HILL BOGART MAE-ET
82
86
85
Woodcrest Dairy LLC
NY
COOK-LANE CLINTN MOCHALATTE
83
87
85
Kylene Cook Anderson
WI
TCG-KM CASSINO ENDLESS-ET
85
83
85
Andy Vaz, Scott Babek & Brian Frisch
NM
HILMAR FREDDIE 4068
84
86
85
Hilmar Holsteins, Inc.
CA
BENNETT-FARMS FINALCUT ROSS
86
83
85
Todd A. Hayton
NY
MISS-LONG-GREEN CELESTE
86
86
85
Joseph W. Osinga
TX
KINGSMILL SANCHEZ TOSHA-ET
83
82
85
Conner Hill
AR
RUBI-SWEET LATHAM KENDRA
85
83
85
Marvin Rubingh
MI
H-KLEE FARM LARGENT CORRECT
88
84
85
Klee Farms
MI
MS LAKOTA RAES LIVIA-ET
85
82
85
Erinwood Gen., Select Gen. & Tom Mercuro
NY
ERINWOOD-TM GOLD DANAE-ET
84
83
85
Davis & Richard Schwartzbeck & Mike Heath
MD
GEN-ACE NIAGRA KEENDRA 5903
83
82
85
Jim, Bill & Andrew Genasci
CA
GEN-ACE PONTIAC GREAT 5904
86
82
85
Ed, Jim & Bill Genasci
CA
MD-CEDAR-KNOLL SANCHEZ 325
85
83
85
Cedar Knoll Farms
MD
ENSENADA BOULDER PERSIST-ET
86
82
85
Joshua D. & David A. Bishop
PA
PLUSHANSKI SEBASTION FARBEE
86
82
85
Daniel A. Brandt
PA
ROB-SARA JASPER BARBIE-ET
86
83
85
Gregory Warren Knutsen
DE
MS CRANEHILL DOMAIN DIVA-ET
86
82
85
Sugar Creek Dairy
WI
BEAVER-FLATS LIGHT POSSIBLE
87
78
85
Jeffrey D. Dana
NY
BEAVER-FLATS ATWD COTTEN-ET
85
86
85
Jeffrey D. Dana
NY
POTTERS-FIELD PNG K12019-ET
86
82
85
Potter Farm LLC
NY
SPRUCE-HAVEN ATW BJ12230-ET
87
82
85
Spruce-Haven Farm
NY
SPRUCE-HAVEN SUPR K12232-ET
86
82
85
Spruce-Haven Farm
NY
WARGO-ACRES KNOWLEDGE JODIE
85
83
85
Wargo Acres
WI
WARGO-N-JD DORIS-ET
86
82
85
Craig Carncross & Jason Danhof
WI
WARGO-ACRES MARGARITA
86
82
85
Wargo Acres
WI
WARGO-ACRES DAMION NACHO
83
85
85
Wargo Acres
WI
WARGO-ACRES VICTORIA
85
83
85
Wargo Acres
WI
WARGO-ACRES AUDREY 1097
85
82
85
Wargo Acres
WI
EVANGELO SANCHEZ JULIE
86
80
85
Jason Evangelo
CA
LADYS-MANOR DRCY DALANEY-ET
87
83
85
Ladys Manor LLC
MD
LADYS-MANOR AMAZING TOPAZ
86
83
85
Ladys Manor LLC
MD
LADYS-MANOR GINGERBREAD-TW
86
83
85
Ladys Manor LLC
MD
LADYS-MANOR BIG TIME DEB
87
82
85
Ladys Manor LLC
MD
LADYS-MANOR DRCY DELANEY-ET
86
86
85
Ladys Manor LLC
MD
LADYS-MANOR PADDY TOPAZ
84
88
85
Ladys Manor LLC
MD
LADYS-MANOR JAKE PUTZ
86
83
85
Ladys Manor LLC
MD
LADYS-MANOR SUPER SHAWNEE
88
79
85
Ladys Manor LLC
MD
LADYS-MANOR BRAXTON ABBY
87
81
85
Ladys Manor LLC
MD
LADYS-MANOR DORA SUMMER-ET
86
84
85
Ladys Manor LLC
MD
MAYERLANE LOOKING GOOD
84
86
85
Darren M. Kamphuis
WI
KAY-BEN JASPER LIVIE
85
81
85
Eben J. Benson
ME
KAY-BEN TARTINI ADINE
88
79
85
Kay-Ben Holsteins
ME
FRONT-PAGE MIAMI SHINE
86
83
85
Gary & Patty Edelburg
WI
ASKEW-JANES MAC RITA
85
83
85
James H. Janes
WI
JANESTEAD JASPER JEWEL
87
80
85
James H. Janes
WI
HILLTOP-LLC SANCHEZ 4604
88
83
85
Hilltop Dairy LLC
WI
HILLTOP-LLC LIGHTNING 4660
85
85
85
Hilltop Dairy LLC
WI
HILLTOP-LLC TALENT 4665
86
81
85
Hilltop Dairy LLC
WI
HILLTOP-LLC BOLTON 4705
85
85
85
Hilltop Dairy LLC
WI
HILLTOP-LLC IMPRESSION 4728
85
82
85
Hilltop Dairy LLC
WI
LMY DEANN DARLA
86
86
85
Newell C. Rawlings
MI
KAMPY LAURIN JACKLYNN
85
85
85
Kamphuis Farms LLC
WI
HARMONY-HO DAMION QUAHOG
83
85
85
Ralph A. Bredl, Jr.
WI
HARMONY-HO SANCHEZ QUIGLEY
88
83
85
Ralph A. Bredl, Jr.
WI
HARMONY-HO KOLTON QUINTO
87
82
85
Ralph A. Bredl, Jr.
WI
HARMONY-HO LAURIN QUAXO
83
85
85
Ralph A. Bredl, Jr.
WI
HARMONY-HO SANCHEZ Q-TIP
87
85
85
Ralph A. Bredl, Jr.
WI
ROPUT CHAMPION LISA
86
83
85
James R. Putman
NY
BER-SHER EXPLO ROBERTA RY
86
83
85
Owen, Brant & Damion Bontekoe
MI
ZIMS-HILLS SHOTGUN SHELBY
85
82
85
Steven Zimdars
WI
JAZZY-D SANCHEZ LORALIE
86
86
85
Diana Zimdars
WI
ZIMS-HILLS ALERT ELLEN
86
81
85
Steven Zimdars
WI
LORITA TOYSTORY SAPPHIRE
86
83
85
Durrer Dairy
CA
VALLEY-DRIVE SANCHEZ ALIYAH
87
83
85
Valley-Drive Holsteins LLC
WI
VALLEY-DRIVE SANCHEZ BOLERO
85
85
85
Valley-Drive Holsteins LLC
WI
EHRHARDT LAIDEN CINDY-TW
87
81
85
Ehrhardt Farms, Inc.
MD
EHRHARDT GOLDWYN BETH-ET
87
83
85
Ehrhardt Farms, Inc.
MD
CHRISLEACRES LT MARGARITA
86
82
85
Valerie C. Kramer
WI
COLDSPRINGS DESMOND 4064
86
82
85
Matthew M. Hoff
MD
COLDSPRINGS DESMOND 4080
86
84
85
Matthew M. Hoff
MD
COLDSPRINGS MILLION 4083
86
81
85
Matthew M. Hoff
MD
COLDSPRINGS SUPER 4087
86
81
85
Matthew M. Hoff
MD
COLDSPRINGS GABOR 4146
87
78
85
Ian A. Hoff
MD
COLDSPRINGS BRONCO 4158
84
82
85
Matthew M. Hoff
MD
COLDSPRINGS BRONCO 4163
86
81
85
Matthew M. Hoff
MD
COLDSPRINGS LIGHTNING 4177
85
83
85
Matthew M. Hoff
MD
COLDSPRINGS SHAKA 4188
84
84
85
Matthew M. Hoff
MD
COLDSPRINGS PLANET 4246
86
79
85
Matthew M. Hoff
MD
HEINZE LAVANGUARD JUNE
85
81
85
Mark T. Heinze
WI
HEINZE ALEXANDER TOTUM
83
85
85
Mark T. Heinze
WI
HEINZE PROMAR 2156
86
81
85
Mark T. Heinze
WI
MEY-VILLA LAURIN RIPPLE
86
82
85
Bernard M. Meyer
WI
MEY-VILLA MAC FRESNO-TW
85
84
85
Jerome E. Meyer
WI
MEY-VILLA MAC FRISCO-TW
83
85
85
Jerome E. Meyer
WI
MEY-VILLA SANCHEZ FOUNTAIN
86
83
85
Jerome D. Meyer
WI
MEY-VILLA BRAXTON RIZZLE
86
83
85
Bernard M. Meyer
WI
DINOMI DOMAIN STACY
87
78
85
Vincent Migliazzo
CA
PEACE&PLENTY SANCHEZ EBBIE
85
83
85
Joseph A. Schwartzbeck
MD
UNITED-PRIDE FREDDIE 4367
87
85
85
United Pride Dairy
WI
GOFF LARIAT 37380
86
82
85
Buster I. Goff
NM
CHA-LIZ JAVA 6417
85
82
85
Cha-Liz Farm LLC
NY
JMK SHAMPOO 9726
87
84
85
John Koster
TX
SWIGGUM MY SPACE CRICKET
85
81
85
Erik Leif Swiggum
WI
ARTIE-JAY ALEXIS DAMION
86
83
85
Arthur R. Johnson, Jr.
MD
KEVREL MANOMAN MAY-ET
84
83
85
F. Kevin Leaverton
MD
PALMYRA MUFFIN SAPHIRE
85
83
85
Ryan William Shank
MD
KA-DA ATLANTIC 77
86
82
85
Kainer Dairy
TX
ARB-FLO-SPR ABS RALEIGH-RED
86
83
85
Aryn Arbaugh
MD
BUR-RODZ BOXER EBONI
86
78
85
Rodney A. Zietlow
WI
GOLDCREST MICHAEL CYBIL-ET
86
83
85
Corwin R. Holtz
NY
SUTTON ADVENT EMMY
86
79
85
Jillian Sutton
MD
TULIP-POND BEN LINDSEY
86
83
85
Cathleen Doody
MD
LINDALE JASPER FEODORA
85
84
85
Dale & Linda Drendel
IL
FARNEAR DOMAIN ZDALLAS-ET
88
79
85
Rick & Tom Simon
IA
FARNEAR DOMAIN ZDAYTON-ET
87
83
85
Rick & Tom Simon
IA
FARNEAR GOLDEN LOVESTAR-ET
86
84
85
Rick & Tom Simon
IA
GOLDEN-OAKS SUPER DAISY-ET
86
82
85
Aaron L. Hass
WI
GOLDEN-OAKS GOLD CHARAE-ET
85
84
85
Golden Oaks Farm
IL
GOLDEN-OAKS ALEXANDER CADEE
84
85
85
Golden Oaks Farm
IL
GOLDEN-OAKS CHRISTMAS-ET
87
82
85
Golden Oaks Farm
IL
GOLDEN-OAKS MADISON-ET
86
82
85
Golden Oaks Farm
IL
VIETHSONS BOLTON PEG HENNY
85
86
85
Jerry Vieth
TX
VIETHSONS TOYSTORY PEG BABY
86
84
85
Jerry Vieth
TX
SIEMERS DURHAM FEARLES-ET
86
82
85
James L. Behling
WI
FRONTIER POTO BRIGID-TW
85
84
85
Frontier Dairy
MT
WEBB-VUE BOLTON MERRITT
85
83
85
Robert A. Webb
WI
GEN-ACE LOU CANDY 6045
84
83
85
Genasci Dairy, Inc.
CA
GEN-ACE SANCHEZ KATY 6147
85
83
85
Andrew Genasci
CA
LUCK-E CONTENDER AREASHA
85
84
85
Matt L. Engel
IL
HOEK-TEX ADVANTAGE 6074-RED
86
85
85
Gerard Hoekman
TX
STAR-ROCK GABOR 6276
84
85
85
Star Rock Farms
PA
RAG-MER MIRABELLA-RED-ET
85
85
85
Nick Raggi & Tom Mercuro
MD
DURCHAN DIVINE MS DIVA-ET
86
82
85
Kingstead Farms & Tom Mercuro
MD
MIGLIAZZO DOMAIN RALEY
87
82
85
Migliazzo & Sons Dairy
CA
NOR-BERT FREDDIE MAY-ET
82
88
85
Richard F. & Kathy S. Demmer
IA
STONE-FRONT TANNENBAUM
86
87
85
Andrew Jay & Lynette E. Buttles
WI
STONE-FRONT DIGGER CHRIS
85
87
85
Andrew Jay & Lynette E. Buttles
WI
DE-URFEE EXQUISITE EQUITY
86
82
85
Steven & Daniel Durfee
NY
VAZDALE CASHMAN TABRIA
82
86
85
Andrea Vaz
NM
T-C-G REBEL-RED-ET
85
82
85
Triple Crown Genetics
ID
FRAN-BAR AFTERSHOCK ALEA
86
84
85
W. Franklin, Jr. & Jeffery F. Moore
MD
SEAGULL-BAY DOMAIN CALY-ET
86
79
85
Seagull Bay Dairy, Inc.
ID
WINDSOR-MANOR Z SPICE
85
82
85
D. Richard Flickinger
MD
WINDSOR-MANOR SAN ZEEVA-ET
88
83
85
Joseph A. Schwartzbeck
MD
MAR-LINDA-K DAMION JILETTE
86
76
85
Nicole K. Wright
WI
P-ZBW SANCHZ MS TRINITY-ET
85
83
85
Kevin Ziemba & Joseph Piskorowski
NY
P-ZBW SANCHEZ TAMARA-ET
83
84
85
Kevin Ziemba & Joseph Piskorowski
NY
ST-JACOB SANCHEZ HOLLAND-ET
86
81
85
Green & Gold Syndicate
NJ
WARGO-ACRES MILLION 1111
86
85
85
Wargo Acres
WI
STAR-ROCK AFTERSHOCK 6435
87
82
85
Star Rock Farms
PA
SUM-R-SETT OBS MIRROR-ET
87
83
85
Ladys Manor LLC
MD
LADYS-MANOR ASHMORE MARILYN
86
83
85
Eliza Freeman
MD
ZBW LAURIN ARIEL-ET
83
85
85
Woodcrest Dairy LLC
NY
ZBW DESTRY APRICOT
86
76
85
Woodcrest Dairy LLC
NY
HOLLY-BROOKS TDEE RONNI-ET
85
82
85
Woodcrest Dairy LLC
NY
BRUNLAND SANCHEZ DAKOTA
86
79
85
Kristen & Jenna Broege
WI
HAR-DEE ADVENT EVE-RED-ET
85
81
85
Lindsay S. Mitchell
TX
MY-STYLE AFTSHOCK ZEBRA-ET
85
82
85
Robert C & Joyce Ringler Hoffman
PA
ROSEDALE COST OF FREEDOM
85
82
85
Rosedale Genetics Ltd
WI
ROSEDALE GHETTO CAT
86
82
85
Rosedale Genetics Ltd
WI
ROSEDALE HOPELESS ROMANTIC
86
80
85
Rosedale Genetics Ltd
WI
ROSEDALE OH MY DUECE
88
81
85
Rosedale Genetics Ltd
WI
AARDEMA GARRETT 86374
86
82
85
Double A Dairy
ID
ZIM-FAM ACME DAWN
86
83
85
Zimdars Family Farm
WI
WIERSMA DESTRY ANNETTE
85
88
85
Jacob Wiersma
TX
* BEI (Bullvine Efficiency Index) – each sire’s ranking is as a percent of the top sire
Robust stands out as the definite leader of this group for efficiency. Ranked second with overall high ratings for all categories is Observer.
Canadian Proven Sires
Table 5 contains the top ten sires with Canadian daughter proofs.
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A?ÏÉýûp ëc+î²ít:7¸aß».ýþàH;A»nJ¥B£Q'Ïs676è´:lmn²µ½n§MQäôû=N:I³éÚ*z½N§[ºfã,ÂPV«Õ¨V«ÔjuØiÔH<×øPkJ$µzJµÊ.õP~#:«µ¹¯e û �dÌollãdc G/¼ë®»4ÚS»²ÃçÜîãÏÔ¸ëÉ
* BEI (Bullvine Efficiency Index) – each sire’s ranking is as a percent of the top sire
AltaRazor handily comes to the top of this group with high ratings for fat yield, SCS, herd life and mammary. Chelios stands out for his ratings for SCS, daughter fertility, herd life and mammary.
One of the things that makes the dairy community great is the passion producers have for what they do. One area that we have found that brings out the most passion is debating which breed is the best. While there are many ways to look at it, the most logical way is to look at which breed is the most profitable.
Since we first joined this discussion back in May of 2012, (Read more: Holstein vs. Jersey: Which breed is more profitable) there have been many interesting points raised on both sides of this question. So we here at the Bullvine decided to take a deeper look at this issue and see if we could get more insight into this much debated topic.
Now first let`s be clear. This is a very lopsided debate because Holsteins are the primary breed on 92% of the farms in North America, and Jersey is only the primary breed on about 3.5%. But man you have to love the passionately vocal nature of most Jersey breeders.
Feed Conversion
With feed accounting for between 52 and 58 percent of the total cost of production, any significant advantage for either breed is its ability to convert feed into milk solids, especially with the increased costs of feed these days. While the superior overall production ability of a Holstein vs. a Jersey (Holstein 24,291 lbs of milk 888lbs Fat 3.66 % Fat 765 lbs Protein 3.15 % Protein vs. Jersey 16,997 lbs milk 776 lbs Fat 4.57% Fat 633 lbs Protein 3.73% Protein) has long been documented the true numbers lie in how well each breed converts their feed intake into milk and milk solids In a Dairy Science paper they looked at feed intake studies for 4 breed groups: Holstein, Holstein x Jersey, Jersey x Holstein and Jersey, where all cows were fed the same ration, were housed in the same type of pens and were milked together. The results found that Holstein had the highest intake and the highest production yield. However, Jersey converted a higher percentage of their intake to production than Holstein did.
Item
Holstein
HJ
JH
Jersey
Intake
9,813
9,309
9,487
7,969
Growth
669 (6.8%)
599 (6.4%)
496 (5.2%)
334 (4.2%)
Maintenance
2,666 (27.25)
2,468 (26.5%)
2,425 (25.6%)
2,085 (26.2)
Pregnancy
27 (0.3%)
32 (0.3%)
33 (0.3%)
21 (0.3%)
Production
5,968 (60.8%)
6,057 (65.1%)
6,162 (65.0%)
5,259 (66.0%)
The bottom line result of this research was that Jerseys were 6% better at converting intake into production. That may not seem that significant until you factor in that feed costs are 52-58% of total costs. That difference represents a 3.3% increase in profitability. One thing is for sure, feed efficiency is certainly one area that we need to have more supporting research in order to develop genetic indices.
Milk Price
One of the key factors determining which breed is better depends on where you market your milk. Certain pricing models favor fluid milk production while others favor component production. Fluid markets certainly favor Holstein while component markets favor Jerseys. Pennsylvania researchers used a farm level income and policy simulator (FLIPSIM) model to predict farm performance under fluid pricing or component pricing in Pennsylvania. Under fluid pricing, a high producing (13,961 pounds) 60-cow Jersey herd could expect a net cash income of $32,300 versus $63,100 for a high producing (20,600 pounds) Holstein herd. Under component pricing, the same Jersey herd would increase in net cash income to $55,400 versus $61,100 for the Holstein herd. Under component pricing, a Jersey herd could expect an increase of about $23,000, while the Holstein herd would decline slightly. Combine that with the increased feed efficiency of the Jersey’s mentioned above and, depending on the pricing model in your area, Jerseys would become a more profitable option. Especially when you factor in the less volatile milk solids market as compared to fluid milk pricing.
Reproduction
For years Jerseys have enjoyed the reputation of being far superior to Holstein. However, increased attention to this area by many producers may have changed or at least narrowed the gap. This is certainly an area that many breeders are paying attention to, specifically the scores for Conception Rate (CR), Daughter Pregnancy Rate (DPR) and Calving Interval (CI). The Days to First Breeding (DFB) declined for Holsteins from 92 d in 1996 to 85 d in 2007. A similar trend was not observed for Jerseys, possibly because synchronized breeding is more common in Holstein herds than in Jersey herds. As far as conception rates are concerned, Jerseys still have a slight edge over Holsteins. But that trend is also changing. As Holsteins have gone from 2.5 NB (Number of Breedings per lactation) in 1996 to 2.6 in 2007, while Jersey’s have gone from 2.2 in 1996 to 2.4 in 2007.
Now one area that I often hear comments from producers about is the value of the resulting calves. Specifically that drop bull calves that will be sold for beef. One of the great strategies I have seen employed by many Jersey and even top Holstein herds is to breed the bottom 10% of their herd to a beef sire. As they know they will not be needing the resulting females or males from these animals the value of using a beef sire, typically more than compensates for the Holstein versus Jersey drop calf price. Another management or reproduction tool that many producers are using is sexed semen which allows them to greatly decrease the number of female calves needed for replacements.
The Bullvine Bottom Line
Holstein and Jersey cows both have their advantages and disadvantages. Holsteins are larger and have higher salvage value than Jerseys. Jerseys tend to be more efficient and typically have fewer reproductive challenges. Each have an advantage under milk pricing that favors their particular productive strengths. The first area you need to look at for what breed is better for you, is the milk pricing model in your area. If it is a fluid market, then typically Holstein would be more advantageous. If the price model favors component pricing, then you would typically be better off milking Jerseys. After looking at the price model, you certainly need to adjust your management to maximize the reproduction and feed efficiency for the breed you have chosen. Even your housing set up could be better suited for one breed over the other. While I am sure the Jersey versus Holstein debate will go on for years to come, there are certain new trends that may be contrary to previous beliefs and new feed efficiency information that are opening many producers’ eyes.
Commercial milk producers want to breed cows that have high feed conversion efficiency, that avoid culling and that take the least care or staff time (Read more – Feed Efficiency: The Money Saver). The well known and widely used total merit indexes, TPI*TM and LPI, rank sires according to which ones leave the most profitable ideal or true type cow. However the factors in those indexes and the assumptions that are made when calculating them do not address feed, culling or low maintenance. Milk producers are left to fend for themselves when it comes to selecting sires that will leave their kind of cows.
What’s Being Heard
Milk Producers say: “All I want is a trouble free cow that efficiently converts forages to the kind of milk my milk buyer wants.”
Veterinarians say: “Cows must get in calf, have minimal feet problems and must not be prone to having production limiting diseases (reproduction problems, mastitis, metabolic disorders or ..etc.).”
Farm Workers say: “Sick animals, calving problems and animals that do not work easily within the farm system waste my time.”
Feed Advisers say: “Test your forages, feed the rumen, get the most out of your forages and the use of nutrients for both production and maintenance must be considered simultaneously (i.e. medium sized cows yielding the same as large cows are more feed efficient).”
Milk Processors say: ”Except for the milk we sell as a drink, we want the solids not the water.”
Financial Advisers say: “Make decisions based on profit per cow, per litre, per hectare, per pound of feed consumed, per worker, …etc.”
There are even more voices speaking in producers ears and more words appearing on the computer screens that producers read. With all the information that is currently available, selecting sires that best meet the needs of milk producers can be a daunting task.
Getting Started
Milk producers do not wish to deal all the numbers that appear on proof sheets. That can be a very time consuming exercise with no definitive answers at the end of it.
The Bullvine decided to research what is available today on selecting sires for feed conversion efficiency, for freedom from major known reasons for culling and for minimal extra care. We recognize that down the road there will be genomic indexes that are based on the relationship between yet to be recorded on-farm cow performance data and the DNA make-up of cows for these three areas. But today those genomic indexes do not exist.
Bullvine Efficiency Index (BEI)
Based on the information from a number of countries that we have been able to access, the Bullvine has developed the following formulas:
BEI = Production (45%) + Durability (35%) + Health & Fertility (25%)
Production = 30 Fat Yield + 50 Protein Yield + 10 Fat% + 10 Protein%
Durability = 17 Herd Life + 42 Mammary System + 25 Feet & Legs – 8 Body Depth – 8 Stature
Milk Yield is not included as it contributes to more udder strain and added milk haulage or on-farm water removal costs.
The negative weightings on Body Depth and Stature reflect that larger cows require extra feed to grow to that size and to maintain that larger size each and every day compared to cows of more moderate size.
Please note: Due to the fact that CDN’s Custom Index tool only allows quires by Domestic Canadian, MACE and Genomic individually it is not possible to do an overall ranking.
Key Findings
Except for the Domestic Canadian list only a small difference exist between bulls
The rankings do not always follow TPI* TM or LPI due mainly to the negative weighting on body depth and stature and increased emphasis on SCS, daughter fertility and udder depth.
Although Braedale Goldwyn, Sandy Valley Bolton and Picton Shottle progeny are prominent on these listings, they are from different cow families so inbreeding using the sires on these lists should not be a problem, providing a breeder does not focus on just one of them
Highlights
Braedale Goldwyn appears on the listings himself. As well he has six sons on the lists and is the maternal grandsire of three of the genomic bulls.
Sandy Valley Bolton has seven sons on the listings
Picton Shottle is the maternal grandsire of nine bulls on the listings
Oman sons Long-Langs Oman Oman and Badger-Bluff Fanny Freddie both appear on the listings, as do one son and one maternal grandson of each of them
De-Su Observer, yet to be daughter proven, has three sons and one maternal grandson on the genomics listing
The Bullvine Bottom Line
Commercial milk producers often want the decisions on which sires to use to be as simplified as possible. That is why the Bullvine has produced these BEI listings. With due consideration to avoid inbreeding, milk producers can expect BEI to rank bulls for them for production, durability and health & fertility with emphasis on the sires that can convert intake into milk production.
Fed up? Losing money? Start Tracking Feed Efficiency. The current lack of forages for dairy cattle in North America and high grain prices globally has brought feed front and center on most dairymen’s radar screen. Since for most herds feed costs vary between 50% to 60 % of the dairy’s operational costs, the current higher costs are narrowing on-farm margins. In some cases it has resulted in farms downsizing their milking herd, selling off their heifer herd and for some farms an exit from the dairy business. To say the least dairy farmers are having to address something foreign to most of them – the amount and cost of the feed their herd is consuming.
New Territory for Dairy Farmers
Given that dairy cattle breeding, to a very large extent, has ignore any genetic aspects to a cow’s ability to convert feed into milk, the idea of culling cows that do not convert well is an unheard of practice. Seeing that this subject is new to most breeders, The Bullvine decided to delve a little deeper into what is known and what investigation is underway when it comes to the efficiency with which cows convert their feed to products humans can consume.
Feed In. Dollars Out. It’s Hard to Capture FIDO.
It is costly and time consuming to capture individual cow feed consumption, so producers and their feed advisors have taken the approach of feeding the herd or groups within the herd and monitoring the production, feeds costs and the returns over feed costs. Only in research herds has there been any attention paid to individual cows and their efficiency of conversion or return over feed cost. And then only for cows on feed composition trials and nothing on a cow or sire’s daughters genetic merit for feed conversion. So to put it simply the industry has said – feed them more, balance the diet differently, add some micronutrients, have adequate fibre in the diet, etc. because we have not been able to address the cow’s genetic ability to convert feed to milk.
What We Know about What’s Eating You
Some facts about feeds, feeding animals and feed costs include:
The poultry and swine industries have paid considerable attention, for quite some time now, to feed conversion / feed efficiency. With much success especially in poultry meat industries. In beef and sheep feed conversion for animals being finished in feedlots is an important profit factor.
In dairy cattle, feed conversion ability includes all aspects – feeding for growth, production and maintenance. We do not always think about the extra cost to grow heifers larger or to maintain a large versus a medium sized cow. By the way the Net Merit index does include a 6% weighting on cow size. And it is a negative weighting so larger cows are penalized for their extra size. So if you have been using the Net Merit index you will already be indirectly breeding for feed efficiency.
Level of milk production very much depends on the amount of feed consumed by a cow (commonly known as Dry Matter Intake). But we do not know the degree of correlation between volume consumed and feed efficiency.
Recent cost studies show that milking cow feed costs on individual farms vary from 20 to 35% of milk revenue. That variation is significant! So the opportunity to make progress in returns over feed costs is out there.
Given the wide variety of feeds and feed practices on dairy operations, an average feed cost per milking cow per day on individual farms can be anywhere from $4.00 to $8.00.
Every day dairy farmers have happen but do not monitor or comprehend differences in their cows’ ability to convert their diet into milk revenue. Depending on lactation numbers and stage of lactation a cow consumes 1 kg of dry matter to producer between 0.8 kg and 1.8 kgs of fat or energy corrected milk. Differences in milk, fat and protein production are monitored on-farm however cow differences in feed conversion efficiency are not.
Measuring the Future: You are What THEY Eat
Farmers and their nutritional advisors will continue to fine-tune the diets of cows. That’s a given. Gains in the returns over feed costs will be made by fine tuning diets and by adjusting the management and environments for cow and heifers.
However if the swine and poultry industries have been able to genetically enhance their species’ ability to convert feeds to meat or eggs, then is there not an opportunity for dairy cattle to also be bred for feed conversion efficiency?
It should be possible to breed for heifers that grow more efficiently and cows that convert feeds more efficiently into the milk needed to produce the products consumers want and will buy. If through more efficient milk cows there could be $0.33 more profit per cow per day, which amounts to an extra net income of $25,550 per year for a 200 cow milking herd. Nothing to be sneezed at.
From a Pile of Feed to a Pail of Milk? Where’s the Genetics DATA?
However the challenge remains how to the get data for use in on-farm decision making and for determining the genetic difference between animals and bloodlines for feed efficiency. Well in fact there are some keen researchers in the United States, the Netherlands, the United Kingdom and Australia in association with countries with national data bases used for genetic evaluation addressing this challenge. Currently they have studies underway to measure feed intake and cow outputs for cows on research trails. After obtaining the data they will correlate the efficiency results with the DNA (snips) makeup of the cows. In the USA alone there will be over 8,000 cows currently being studied.
Within a year the dairy industry can expect to see some preliminary results of this research work. But genomic indexes will only be the start. I expect that on-farm data capture software and systems will become available to measure a cow’s feed intake. The data from such systems will have value both at the farm level and at the genetic evaluation level.
The Bullvine Bottom Line
Stay tuned for what will be new genetic evaluations and animal genomic indexes for feed conversion efficiency. It could take up to a decade for there to be accurate indexes and wide use made of the indexes but it will come fast once the basics building blocks are in place. Even a 5% gain in feed conversion efficiency in dairy cattle will be worth billions of dollars annually to the global dairy industry. Once again opportunity knocks at our doors.
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