The Importance of Wagyu Cattle Feed Efficiency
Table of Contents
Feeding cattle, which can make up to three-quarters of the total direct costs (Nielsen et al. 2013), is one of the most important economic factors that affects how profitable a beef cattle business is. Concerns about stricter environmental laws have also made people look more closely at beef farming. Because of this, there is a lot of interest in making feed more efficient as a way to make beef production methods more sustainable in terms of both money and the environment.
There are many different meanings of feed economy for animals, and each one has a different use (Berry and Crowley, 2013). In the past, people often used the feed conversion ratio (i.e., feed:gain) or its mathematical opposite, the feed conversion efficiency (i.e., gain:feed). The chosen measurement these days is residual feed intake (RFI), which is the difference between the amount of feed that was actually eaten and the amount that was predicted to be needed to keep the animal’s body weight (BW) and support growth (Savietto et al. 2014). The RFI index can be calculated without taking into account the level of animal production. This makes it a useful concept for studying the biological processes that cause differences in feed efficiency between animals (Berry and Crowley, 2013).
Measuring and Calculating Residual Feed Intake (RFI)
Measuring feed efficiency in beef cattle requires a standardized process as outlined by experts in the field (Nielsen et al., 2013). This involves recording feed intake over a period of at least 70 days, with a preceding acclimatization phase of at least 21 days, and periodic recording of live weights throughout (Beef Improvement Federation, 2010).
Recent studies have explored shortening the test duration, with reports of successful tests lasting as short as 35 to 42 days, offering potential cost savings and increased testing throughput. Residual feed intake (RFI) serves as a key metric in assessing feed efficiency, calculated as the difference between observed and predicted feed intake (Savietto et al., 2014).
This computation involves using a multiple regression model that includes factors such as maintenance, growth, and activity, with the aim of making RFI independent of traits used to predict feed intake (Berry and Crowley, 2013).
However, variations exist in the degree to which the model explains feed intake variation, with energy-dense diets showing higher explanatory values compared to mainly forage-based diets (Goonewardene et al., 2004). Adjustments for body composition in the RFI model can improve accuracy, although the significance of these adjustments isn’t always clear.
Predicting feed intake in pregnant and lactating cows presents additional challenges, with models used for fast-growing cattle potentially not applying well to these scenarios. Despite these challenges, advancements in measurement techniques and genetic understanding continue to improve our ability to enhance feed efficiency in cattle, benefiting ranchers and the industry as a whole.
Sources of Biological Variation in Phenotypic RFI
There’s significant variation in feed efficiency among cattle, even when they’re offered feed as much as they want. Studies have shown differences in feed intake of up to 15% in young growing cattle and up to 25% in pregnant beef cows between the most and least efficient groups. This variation isn’t just due to genetics but also other biological factors (Kenny et al. 2018).
Maternal Traits and Fertility
While the primary focus of improved RFI is often on growing cattle, its effects on maternal productivity and fertility are crucial for overall herd management.
Colostrum and milk yield play significant roles in beef calf health and growth. Studies on beef cows have shown mixed results regarding the association between RFI status and colostrum quality or milk yield. Some studies report no significant relationship, while others indicate slight variations in milk composition, such as lactose concentration. However, these differences in milk quality don’t seem to impact calf pre-weaning growth significantly. Concerning reproductive performance, there’s limited consensus on the effect of RFI status on pregnancy, calving, and weaning rates in beef females. While some studies report no differences, others suggest lower rates in low-RFI females. The timing of calving also varies, with some low-RFI females calving later in the season, potentially due to delayed puberty onset. However, adjustments for factors like body fatness often mitigate these effects, suggesting a complex interplay between RFI status and reproductive traits.
Bull fertility is another crucial aspect of herd productivity. Studies have yielded mixed results on the relationship between RFI status and semen quality or scrotal circumference. While some suggest a negative association, others find no significant impact. Overall, evidence suggests that selection for improved feed efficiency may not adversely affect bull reproductive performance (Kenny et al. 2018).
Nutrient Partitioning: Protein and Fat Deposition
In Wagyu production, understanding how nutrients are distributed between muscle and fat tissues is crucial for optimizing the overall energy status of the cattle. The residual feed intake (RFI) trait, which measures feed efficiency, is particularly important in this context.
However, there’s considerable variation in research findings regarding how RFI status relates to body composition in Wagyu cattle. Some studies suggest a link between RFI status and measures of fat depth, while others find no significant correlation. Similarly, the effects of metabolic indicators like insulin and IGF-I on RFI status vary across studies. Meta-analyses conducted specifically on Wagyu cattle offered energy-dense diets haven’t conclusively established a direct relationship between RFI status and muscle accretion or fat depth. This inconsistency may stem from factors such as breed differences and variations in the physiological maturity of the cattle studied.
While the direct relationship between RFI status and body composition in Wagyu remains unclear, it’s important to consider the impact of body fatness on critical reproductive events when selecting animals for improved energy efficiency. This underscores the necessity of employing comprehensive selection indices in Wagyu breeding programs to ensure overall productivity and quality are optimized (Kenny et al. 2018).
Maintenance Requirements, Mitochondrial Function, and Stress Physiology
In cattle, a significant portion of dietary energy intake is dedicated to maintaining basic bodily functions, with maintenance needs exceeding 50% in adult cattle and typically over 40% in growing cattle consuming forage diets. This high energy requirement for maintenance, also known as homeorhesis, is supported by various physiological and biochemical processes, which can have implications for feed efficiency.
Mitochondria are tiny structures inside cells that are really important for making energy. They use oxygen to do this, and they’re especially crucial in muscles. Research shows that in cows that are really good at using their food efficiently (called low residual feed intake or RFI cows), their muscle cells have better control over how they use oxygen to make energy. But when scientists looked at how many mitochondria these cows had in their muscles and livers, they didn’t find a clear link to how efficient they were at eating.
However, other studies found that there are differences in how well mitochondria work between cows that are good at using food and those that aren’t. For example, in cows that aren’t good at using food efficiently, the way their mitochondria turn food into energy is not as good. On the other hand, low-RFI cows seem to have mitochondria that work faster and produce more energy. But when researchers looked at the genes related to how mitochondria work in these cows’ muscle and liver cells, they got mixed results.
Some studies found differences in how these genes are turned on or off between efficient and inefficient cows, while others didn’t find anything significant. Overall, these studies show that there’s a complicated relationship between mitochondria and how well cows use their food, and we need more research to understand it better. (Kenny et al. 2018).
Genetics of Residual Feed Intake
Genetic Markers for Feed Efficiency
In both beef and Dairy cows scientists have discovered genes that could influence how cows efficiently utilize their food. These genes can control processes like how they break down fats and proteins, move nutrients around their bodies, and manage certain amino acids for growth. Some genes they’ve identified are involved in key pathways like energy production and metabolisms.
To find these genes, researchers have used advanced methods like genome-wide association studies (GWAS) and RNA sequencing have been instrumental in identifying these genes and genetic variants associated with traits like residual feed intake (RFI), dry matter intake (DMI), and energy utilization. While much of the focus has been on the Holstein breed, other breeds like Jersey cattle have also been studied. Despite these advancements, challenges persist, such as the limited integration of multiple omics technologies in dairy cattle studies. They’ve mainly looked at breeds like Holstein cows, but they’ve also studied others like Jersey cows.
Even though we’ve made progress, there are still challenges, like not using all the available technologies in dairy cow research. Still, this work has given us useful markers and candidate genes that help us understand why some cows are better at using food efficiently than others. Now, with whole-genome sequencing, we have even more potential to pinpoint specific gene variations that affect feed efficiency. This understanding not only helps us improve farming practices but also guides us in breeding cows that need less food to produce milk or meat, which is better for the environment and for farmers.
Genetics of Residual Feed Intake
The genetic aspects of residual feed intake (RFI) in cattle breeding programs are crucial for improving feed efficiency, but there are significant challenges. RFI is the difference between an animal’s actual feed intake and its expected intake based on factors like body weight and growth rate. One major obstacle to incorporating RFI into breeding programs is the cost and complexity of measuring it accurately.
However, advances in genomic selection, where genetic information is used to predict an animal’s breeding value, hold promise for improving selection accuracy and speeding up genetic progress. Studies have found that RFI has a moderate heritability, meaning that a significant portion of the variation in RFI is due to genetics. This suggests that RFI could respond well to genomic selection, but current genomic prediction accuracy in beef cattle is not high enough to rely solely on genetic information without phenotypic measurements. Building a reference population where RFI is measured and animals are genotyped is essential for genomic selection, but such a population does not yet exist for beef cattle.
Research is focused on identifying genetic markers associated with feed efficiency traits, but it’s crucial that these markers are robust across different breeds, developmental stages, and diets. Collaborative projects, like the Canadian Cattle Genome Project, aim to develop genomic tools to enhance beef production sustainability. Future success in breeding for improved feed efficiency will depend on incorporating genetic information into multi-trait genomic selection programs (Kenny et al. 2018).
Understanding the genetic parameters of feed efficiency traits is essential for their inclusion in breeding programs. Heritability (h2) measures the proportion of variation in a trait that is due to genetics, and it’s an indicator of how much genetic progress can be made through selection. Studies have shown that feed efficiency traits, assessed through various indicators like RFI, have moderate heritability, suggesting that genetic selection could effectively improve feed efficiency.
However, the exact heritability estimates can vary depending on the population studied. It’s important to estimate genetic parameters specific to each population to guide breeding decisions accurately. Additionally, selecting for improved feed efficiency could affect other economically important traits because of genetic correlations between them. For example, genetic correlations between feed efficiency indicators and traits like milk production or body condition score have been reported in dairy cattle (Kenny et al. 2018).
Impact of Gut Microbiome on feed efficiency
The microorganisms in the cow’s stomach play a crucial role in the feed efficiency of these animals such as cattle. This complex ecosystem consists of bacteria, ciliate protozoa, fungi, and archaea, all of which work together to digest fibrous feed. Ruminants rely on these microbial communities to break down tough plant materials into products necessary for survival, while the microbes, in turn, depend on the host for a habitat. This symbiotic relationship underscores the importance of the rumen microbiome in the overall metabolic efficiency of the animal. As feed efficiency is closely tied to the effective utilization of metabolic energy, the metabolic efficiency of the rumen microbiota significantly influences feed utilization.
Studies have shown that the rumen microbial community exhibits rapid adaptability, as evidenced by the stabilization of ruminal pH and volatile fatty acid concentration within 24 hours after the exchange of rumen contents between two cows. This adaptability suggests that the assembly of the microbial community could be influenced, at least in part, by the host animal. This variability implies that individual ruminants may harbor distinct rumen microbial profiles, opening up the possibility of genetic selection for desirable microbiome profiles alongside other management practices like diet optimization (Luiz et al. 2020).
Recent research employing omics technology has shed light on the role of the rumen microbiome in feed efficiency. These “-omics” technologies enable the quantification of microbial content, gene expression, and metabolic activities associated with feed efficiency traits. Studies have revealed differential abundances of specific bacterial families across different metabolic efficiencies, suggesting a potential link between microbial composition and feed efficiency (Luiz et al. 2020). suggesting that the microorganisms in cow’s digestive system can greatly improve how they can process the food that we give to them.
Furthermore, estimates of heritability on rumen microbial traits have been conducted, indicating significant genetic components underlying microbial variation. Studies have identified specific bacterial taxa with moderate to high heritability estimates, highlighting the genetic influence on rumen microbiome composition which in turn can be selected for breeding and transfer to future generations for a more efficient animal (Luiz et al. 2020).
Repeatability and Genotype along with Environment Interaction for RFI
To make sure residual feed intake (RFI) is useful for selecting animals in breeding programs, it needs to be consistent across different stages of life and diets. This is important because beef cattle are often fed differently throughout their lives. Studies show that RFI tends to stay fairly consistent over time and across different diets, but there can be some changes in ranking.
For example, cows ranked as low-RFI indoors didn’t necessarily eat less when they were grazing outside. However, there are factors like maturity, diet differences, and changes in the animals’ condition that can affect RFI measurements. Overall, RFI seems to be a moderately repeatable trait in beef cattle, but there’s still some variability depending on the circumstances (Kenny et al. 2018)
Data collection and implementation of genomic evaluations
Data collection and implementation of genomic evaluations for improved feed efficiency involve several steps and considerations. Initially, traditional methods of measuring individual feed intake and related traits like body weight have been limited due to cost and feasibility, hindering genetic selection for feed efficiency. However, the advent of genomic selection has provided a solution by utilizing genome-wide genetic markers to predict the breeding values of selection candidates based on their genotypes.
Genomic selection relies on the accurate estimation of single nucleotide polymorphism (SNP) effects, which are determined using genomic and phenotypic datasets from a training population. The size of this training population directly influences the accuracy of genomic estimated breeding values (GEBVs). Other factors affecting GEBV accuracy include SNP panel density, trait heritability, linkage disequilibrium, and the relationship between the training and validation populations. Although genomic selection for feed efficiency, particularly residual feed intake (RFI), is feasible, accuracies are still lower compared to other traits. Studies suggest that training populations with over 30,000 individuals may be necessary to achieve satisfactory reliability for traits like RFI. However, GEBV accuracies are expected to improve as training populations grow.
Efforts to increase the training population for feed efficiency include utilizing data from nutrition studies, collaborating across different countries or breeds, and initiatives like the global dry matter initiative (gDMI). Collaboration allows for data sharing and facilitates the development of international genetic evaluations for feed efficiency. Automated systems for feed intake recording, such as Calan Broadbent and GrowSafe® Feed Intake System, track individual feed intake and feeding behavior using radio-frequency identification. However, implementing these systems for lactating cows is limited by feeding capacity.
International collaborations like the Efficient Dairy Genome Project (EDGP) aim to develop infrastructure for genetic evaluations for improved feed efficiency and reduced methane emissions in dairy cattle. The inclusion of feed efficiency into breeding programs by national organizations and private companies is gaining traction, with selection indexes incorporating indicators of feed efficiency (Luiz et al. 2020).
Conclusion
In conclusion, while maximizing feed efficiency in Wagyu cattle presents various challenges and complexities, advancements in research offer promising avenues for improvement. Through rigorous measurement techniques, enhanced genetic understanding, and a deeper exploration of factors influencing feed efficiency, ranchers can optimize their operations for both economic success and environmental sustainability. By leveraging genetics and embracing innovative technologies, the beef industry can continue to evolve toward more efficient and resilient practices.
FAQS
Residual feed intake (RFI) measures the difference between an animal’s actual feed intake and its expected intake based on factors like body weight and growth rate. It’s crucial in Wagyu production as it allows ranchers to identify cattle that efficiently convert feed into body weight gain, ultimately optimizing feed efficiency and reducing production costs.
Feed efficiency in Wagyu cows is typically measured using standardized processes that involve recording feed intake over a period of time, along with periodic live weight measurements. Residual feed intake (RFI) serves as a key metric, calculated as the difference between observed and predicted feed intake. Advanced statistical models account for factors like maintenance, growth, and activity to assess feed efficiency accurately.
Biological factors such as nutrient partitioning, maintenance requirements, mitochondrial function, and stress physiology contribute to variations in residual feed intake (RFI) among Wagyu cattle. These factors influence how efficiently cattle utilize feed for growth and maintenance, highlighting the multifaceted nature of feed efficiency in beef production.
Genetics plays a significant role in determining feed efficiency traits like RFI in Wagyu cattle. Studies have identified genetic markers associated with metabolic pathways crucial for feed efficiency, offering insights into the underlying genetic mechanisms. Integrating genetic information into breeding programs enables ranchers to select cattle with improved feed efficiency, enhancing overall productivity and profitability.
The gut microbiome, particularly the rumen microbiome, plays a crucial role in the feed efficiency of Wagyu cattle. Microbial communities in the rumen aid in the digestion of fibrous feed and the production of metabolic energy products. Recent research suggests that variations in microbial composition and function can influence feed efficiency, highlighting the potential for genetic selection of desirable microbiome profiles to enhance overall metabolic efficiency in Wagyu production.
References
Nielsen, MK, MacNeil, MD, Dekkers, JCM, Crews, DH, Rathje, TA, Enns, RM and Weaber, RL 2013. Review: Life-cycle, total-industry genetic improvement of feed efficiency in beef cattle: Blueprint for the Beef Improvement Federation. Professional Animal. Scientist 29, 559–565. Google Scholar
Berry, DP and Crowley, JJ 2013. Cell biology symposium: genetics of feed efficiency in dairy and beef cattle. Journal of Animal Science 91, 1594–1613. Google Scholar
Kenny DA, Fitzsimons C, Waters SM, McGee M. Invited review: Improving feed efficiency of beef cattle – the current state of the art and future challenges. animal. 2018;12(9):1815-1826. doi:10.1017/S1751731118000976
Luiz F.Brito, Hinayah R.Oliveira, KerryHoulahan, Pablo A.S.Fonseca, StephanieLam, Adrien M.Butty, Dave J.Seymour, GiovanaVargas, Tatiane C.S.Chud, Fabyano F.Silva, Christine F.Baes, AngelaCánovas, FilippoMiglior, and Flavio S.Schenkel. 2020. Genetic mechanisms underlying feed utilization and implementation of genomic selection for improved feed efficiency in dairy cattle. Canadian Journal of Animal Science. 100(4): 587-604. https://doi.org/10.1139/cjas-2019-0193
Savietto, D, Berry, DP and Friggens, NC 2014. Towards an improved estimation of the biological components of residual feed intake in growing cattle. Journal of Animal Science 92, 467–476
