Feed Efficiency in the Beef Industry

Feed Efficiency in the Beef Industry provides a thorough and concise overview of feed efficiency in beef cattle.  It frames the great importance of feed efficiency to the industry and details the latest findings of the many scientific disciplines that intersect and aim to improve efficient and sustainable production of nutritious beef. The vast majority of production costs are directly tied to feed. With increased demand for grains to feed a rapidly increasing world population and to supply a new demand for alternative fuels, feed costs continue to increase. In recent years, the negative environmental impacts of inefficient feeding have also been realized; as such feed efficiency is an important factor in both economic viability and environmental sustainability of cattle production.

Feed Efficiency in the Beef Industry covers a broad range of topics ranging from economic evaluation of feed efficiency to the physiological and genetic bases of efficient conversion of feed to high quality beef. Chapters also look at how a fuller understanding of feed efficiency is leading to new selective breeding efforts to develop more efficient cattle.

With wide-ranging coverage from leading international researchers, Feed Efficiency will be a valuable resource for producers who wish to understand the complexities, challenges, and opportunities to reduce their cost of production, for students studying the topic and for researchers and professionals working in the beef industry.

• Language: English
• Publisher: Wiley
• Release date: Jun 18, 2012
• ISBN: 9781118388242

Book preview

Introduction

Rodney A. Hill

Reader Guide to Scope

This book was conceived with the aim of providing a broad readership with information written by expert authors, with comprehensive coverage of the field. An important notion has been to provide information in a progression. The organization of the book is such that the early chapters are pitched with the assumption that the reader who has little scientific training could find interesting and useful information about feed efficiency in the beef industry. It is my hope that producers who have not yet had the opportunity to learn about the latest discoveries around feed efficiency in the beef industry will find the information useful. In progressing through the middle chapters, I anticipate that the reader would need a greater knowledge of some industry-specific information and further training in science. The latter chapters progress such that the reader would need advanced training in science to benefit from the information provided. Thus, my hope is that even lay readers with interest in the topic will find the early chapters informative and understandable while the middle chapters might be a stretch, but still provide greater insights and understanding. I expect those who have more specific industry knowledge, but not necessarily scientific training, will find the middle chapters readily understandable and informative.

In addition, for highly trained industry professionals, scientists, and graduate students, my hope is that the early chapters provide industry context as well as background that set the stage for the latter chapters. In the chapters that delve into the more mechanistic and basic science, I have encouraged the authors to directly point to some of the molecular mechanisms known to underpin variation in beef cattle feed efficiency. This is an emerging field and the biological drivers of this variation are only poorly understood. Thus I have also encouraged the authors to provide basic information that can suggest where the advances in understanding of underlying mechanisms may emerge. This also means that some speculation is included about which of these mechanisms might be important.

Important Distinctions between Feed Efficiency Metrics and Methodologies and Their Strengths and Weaknesses

Across the many topics covered in this book, the authors have dealt with feed efficiency in different contexts. Although there is mention of several different ways that scientists think about and approach the measurement of feed efficiency, there have been two different measures that have predominated across scientific enquiry and emphasized in this book. I provide a discussion of both and my perspectives on the strengths and weaknesses of each are presented below. The two quite different measures of feed efficiency are: (1) The ratio of the amount of feed (or more precisely quantity of nutrients) consumed, to the gain in animal weight over a set period. This can be termed feed conversion ratio (FCR) also termed feed:gain (F:G) and its mathematical inverse gain:feed (G:F). There are scientific and statistical analysis considerations and interpretation contexts that determine which of these may be preferred. (2) The other measure of feed efficiency is termed residual feed intake (also termed net feed intake or even net feed efficiency). These are identical. The term residual comes from the mathematic relationship of the measured feed efficiency of an individual animal to its predicted intake based on the population of animals in which it was evaluated. The feed efficiency of the population can be described by a mathematical relationship termed a regression. Thus, residual is really a mathematical term that refers to the difference between the amount of feed that the animal consumed and what it was predicted to consume based upon its size (weight) and its rate of weight gain.

The great strength of the ratio measurements noted in (1) above is that in a beef cattle management context, they convey important information about the performance of the particular set of animals that are being studied. Information that is immediately useful, describing animal performance (growth) in response to measured feed intake can provide estimates of the costs and benefits associated with that particular study. Traditionally in animal science and animal production, these ratio measurements have been recorded on a pen basis, so that feed intake for the entire pen is measured. Thus, the information has overall value for that specific pen group or may be used for comparison to equivalent replicate pen groups or, for example, to equivalent pens of animals offered a different diet. Unfortunately this approach does not provide any information about the individual variation of animal intake within the pen. As the reader of this book you will also learn, in considering the genetic potential of animals, or to make predictions about the performance of progeny from animals measured using ratio analysis, even when individual intakes are known, there are confounding issues. Thus, the ratio analysis is flawed as a genetic prediction tool.

On the other hand, the approach to efficiency measurement noted in (2) above: residual feed intake (RFI) is very well suited to use in genetic evaluation programs. This measurement exploits the large individual variation in feed intake for a given level of growth rate and animal size. The magnitude of this variation is large in beef cattle. Within a population of animals of the same class (e.g., a group of weaned heifers of the same age and breed and management group) the spread (variation) in feed intake of two animals gaining at the same rate can be 35% or more. Thus, there is huge scope for reducing the level of intake for the same level of performance (growth). The next strength of RFI is that as a genetic selection trait, it is largely independent of most other performance traits and thus can be relatively easily incorporated into a selection index. Thus, it can be possible to select for multiple desirable traits, and by including RFI, it is possible to select for feed efficiency without compromising other desirable traits. (Although not mentioned above, FCR or F:G is also highly correlated to growth rate and mature size. If these ratio traits were used in a selection context, coselection for larger frame size animals would result. This especially has a downside as heifers would also become larger, increasing time to puberty and decreasing the number of larger mature cows that could be run on a given body of feed or rangeland area. Overall this would lead to fewer calves produced for the same feed resource and negatively affect cow-calf production efficiency.) Another trait that is also confounded by its high correlation with gain and mature size is termed residual gain (RG), and it is similarly flawed as a selection tool as outlined for the ratio traits.

The other feature of RFI worth noting here is that it is moderately heritable. This means that there will be a response to selection that will result in improvement in feed efficiency of selectively bred animals. This also suggests that there are many other factors, both environmental (e) and the interaction of genetics with environmental effects (g × e) that contribute to variation in RFI. Thus, for scientists in the disciplines of nutrition or physiology, there is scope to understand the nutritional and physiological drivers of variation in RFI and to work on these to improve RFI for the benefit of the industry.

A caveat for use of RFI: There are two production factors that are known correlates and must be noted here. Feed intake is correlated to RFI. Thus, care in use of selection for lower RFI (desirable, more efficient animals) is needed to ensure that animals are not inadvertently coselected for lower feed intake. The other factor to mention is body composition that accounts for about 5% of the total variation in RFI. Animals that are RFI inefficient tend to be fatter than RFI-efficient animals that tend to be leaner. Scientists are strongly aware of this relationship and many now include a measure of body composition in the model that estimates RFI. This procedure eliminates the contribution of variation in body composition when calculating RFI within a contemporary group. However, awareness of these two correlates is important and ongoing monitoring for both level of feed intake and body composition is essential.

In considering disadvantages, for both ratio measurements and RFI, when feed intake of individual animals is a necessary piece of data, the cost of collecting this information is substantial. The advent of electronic equipment that allows individual intake of group-housed animals to be accurately recorded has been a boon to improving feed efficiency in the beef industry. The capital cost and upkeep costs of the equipment are such that (up until recently) feed intake testing has been limited to the research context. However, the industry drive and awareness of the potential savings that can be realized through selection for improved RFI is rapidly spreading. Recently, forward-thinking producers have established bull-test facilities that include individual feed intake measurement. The combination of research and commercial feed intake testing will accelerate progress. Although there is certainly a substantial upfront cost to testing, the reader of this book will discover that the potential return on investment is also substantial.

The Complexity of Feed Efficiency Concepts, Scientific Interpretation, and Some Consequences

Scientists are trained to build a healthy skepticism and we often vary in interpretation of data. I should point out that for the lay reader, scientists’ understanding of feed efficiency is framed by their specialist training and experience. For example, a nutritionist has a very different perspective from a quantitative geneticist.

Feed efficiency is a complex concept. To gain a complete understanding, specialist knowledge from many different scientific disciplines is needed and exchange of ideas among disciplines shapes our interpretations and perspectives. Scientists who are highly trained in one discipline have to rely on those with expertise in completely different disciplines to contribute as studies are designed, the data collected and interpreted, and new knowledge discovered. The apparent complexity of the concepts becomes greater as scientists with deep knowledge within their discipline probe their understanding at finer and deeper levels of biological detail. This is an established scientific approach to discovery. Scientists interpret the data and evolve their own perspectives from their discipline focus. This can sometimes lead to quite different and even opposing views. I believe that it is this complexity and variation in interpretation that has underpinned some of the scientific debates on aspects of the topic of feed efficiency and is typical of the scientific discovery process. Unfortunately, an unintended consequence is that different messages from different scientists can lead to confusion for industry, producers, and lay people.

As a scientist, I have a strong belief that the scientific method is robust and that additional research will shine clarifying lights as we learn more. This process will allow scientists to gain understanding at deeper levels and to resolve differences in perspective. This will also have a flow-on benefit in removing the unintended consequences referred to above.

Unfortunately, for an industry that is currently being affected by unprecedented increases in the cost of feedstuffs and energy and general volatility in costs, there is an urgent need to identify ways to reduce the cost of producing quality beef. This must be done using strategies that will achieve these cost reductions in a sustainable manner. As one of the experts in this field, having worked with the pros and cons of many different approaches to improving feed efficiency, I see the greatest strategic benefits for the industry in adopting RFI as a preferred metric. It has great advantages and potential for improvement in response to genetic selection and in response to improving the finer aspects of nutrition. As a physiologist, I also see that by improving and refining our knowledge of the physiological drivers of the variation in RFI, we can improve both management and genetic selection strategies.

The Role of New Technologies in Improving Feed Efficiency

There are at least two scientific disciplines that have huge potential to contribute to great advances in understanding and improving feed efficiency in beef cattle. Molecular physiology is the study of body systems at the molecular level. Genomics is the study of molecular interactions at the level of genes. These two disciplines have some overlap. At present there is some controversy about the contribution of the technologies that underpin these disciplines in improving understanding of feed efficiency, and certainly not all scientists agree. My perspective is that the technologies that inform these disciplines are advancing very rapidly and the power of analysis of new genomics and molecular physiology data is impressive. My prediction is that within the next few years, we will have substantial new knowledge and technical capacity that will allow us to link molecular physiology and genomics data to data generated from standardized RFI feed efficiency testing of animals, which will result in greater accuracy of estimates of genetic potential as well as improving our understanding of the genetic and physiological drivers of variation in feed efficiency. In fact, science is moving already in linking these elements together.

To be clear, my perspective is that feed intake/feed efficiency testing animals using a standardized protocol along with tester dedication to testing rigor will be essential and will be the gold standard for improving feed efficiency in the beef industry for many years to come. As noted above, the pace of advances in molecular technologies is impressive. Clearly, if molecular markers or major genes of effect can be identified, they will add to the knowledge base and potentially improve genetic prediction accuracy. The cost of animal testing is a concern for the industry and new technologies also have the potential to reduce the cost of accurate and reliable animal evaluation. My word of caution to the industry is to be sure that judgment of the value of a testing method should not be primarily linked to the cost of the test. Its value should be judged on the quality of the information it provides. This is a strategic perspective, vital for underpinning the long-term sustainability of the industry. A secondary consideration in evaluating a performance metric is its cost-benefit. This can be difficult to judge in an industry in which profit margins are narrow and today in a scenario in which costs of inputs and return on investment are both volatile. Despite the cost of testing animals for feed efficiency, I foresee that well into the future, molecular-level information will need to be regularly calibrated against animal testing to ensure that we continue to make progress in improving feed efficiency and that unintended drift in undesirable production or product quality attributes does not occur.

The Opportunity

Improving feed efficiency in the beef industry is an opportunity that has potential to benefit all sectors of the industry.

Many scientists across multiple disciplines are making discoveries and finding ways forward. There is great industry awareness of the opportunities and progressive thinkers out there are implementing and adopting feed efficiency to improve their businesses. This book brings together many of the aspects of the science that, perhaps for the first time, provide a ready reference and source for producers, students, and scholars. It is my hope that the text will also stimulate discussion in cattle barns, coffee shops, and classrooms that lead to further insights to improve our understanding of the underlying biological drivers of variation in feed efficiency and ultimately bring greater benefits to the beef industry.

1

Input Factors Affecting Profitability: a Changing Paradigm and a Challenging Time

Jason K. Ahola and Rodney A. Hill

Introduction

Since their creation in the 1960s, US beef cattle improvement programs have predominantly focused on improving output-related traits through genetic selection of beef seedstock cattle. Such traits historically included economically relevant weight and carcass traits by much of the seedstock industry and, more recently, fertility traits by a few select breed associations. However, during that time almost no emphasis was placed on cost-related traits, including feed intake, feed efficiency, and/or feed utilization associated with the output traits, based on the absence of genetic predictions for these traits by US beef breed associations (Rumph, 2005). The apparent lack of interest in selecting cattle based on economically relevant cost traits has probably been due to relatively low-priced feed inputs (at least up until late 2006) and high costs associated with individually measuring feed intake in cattle.

Because of inherent physiological differences, beef cattle are less efficient at converting grain to meat protein than other meat animal species (e.g., pork, poultry), thus each pound of beef protein requires a higher proportion of feed energy to produce it (Ritchie, 2001). Dickerson (1978) estimated that of all the dietary energy required to produce beef, only 5% is used for protein deposition in progeny that are slaughtered. Granted, most of the life-cycle energy used by beef cattle is acquired via forages unusable by monogastrics. However, the beef industry's efficiency is unfavorable when compared to 14% and 22% of dietary energy going to protein deposition in slaughter progeny in the pork and poultry production industries, respectively.

As a result, beef producers began to recognize the importance of identifying cattle that are genetically superior at converting feedstuffs to pounds of meat product. However, Ritchie (2001) pointed out that it's unreasonable for beef producers to expect to achieve the feed efficiency levels of competing monogastric species. Significant changes started to occur when feed prices began increasing in late 2006 when the US beef seedstock industry began a genetic evaluation program for feed intake and efficiency (BIF, 2010). It is assumed that this was caused by the fact that feed is the largest variable cost associated with the production of beef. Such genetic evaluation programs included the development of a uniform set of procedures for collecting individual feed intake data on seedstock cattle during a postweaning growth phase for use in the development of genetic predictions for feed intake and efficiency (BIF, 2010). A more comprehensive description of the feed intake guidelines being used by scientists working in genetic improvement of feed efficiency is presented in Chapter 2. However, it remains unclear how quickly and aggressively beef producers will increase emphasis on the importance of selecting for improved feed efficiency. If effective improvement in feed efficiency is to occur through genetic selection strategies, it is necessary for the industry to routinely collect raw feed intake data, to use these data to develop genetic predictions, and to incorporate predictions into selection programs.

Figure 1.1 Number of beef cow/calf operations in the United States from 1986 to 2010 (USDA).

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Influence of Input and Feed Costs on the Beef Production Industry

Profitability within the beef production system requires maximizing outputs (revenues) while minimizing inputs (costs). The profitability equation can be denoted as:

Unnumbered Display Equation

Profitability of cow/calf producers has become a concern within the US beef industry, based on the consistent loss of cow/calf producers from the industry. From the mid-1980s to the early 2000s, nearly a quarter million cow/calf producers left the industry—approximately 9,000 per year (Figure 1.1).

Historically, cow/calf profitability was driven more by the revenue side of the profitability equation than the cost side. This can be seen in the comparison of estimated cow/calf returns with total cattle inventory from 1982 to 2011 (Figure 1.2). Prior to 2006, the average cow/calf producer was consistently unprofitable (light gray bars) during times when the US cattle inventory was near a peak (thin black line), due to a reduction in income resulting from an oversupply of calves and beef in the marketplace and thus lower cattle prices. Conversely, the average cow/calf producer was profitable (dark gray bars) when cattle inventory was relatively low, primarily due to higher calf prices caused by a reduced supply of calves to feedyards. However, beginning in 2006, this strong relationship between cow/calf profitability and total cattle inventory weakened. This can be seen in cow/calf profitability that was concurrent with peak inventory during 2006 and 2007. Since that time, financial losses during 2008 and 2009 have been attributed to elevated input costs.

Figure 1.2 Total cattle inventory and estimated annual cow/calf returns in the United States (USDA, compilation and analysis by LMIC.)

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As the predominant driver of cow/calf profitability moves from primarily supply and demand (and the historical cattle cycle) and more toward input costs, the importance of evaluating beef production as a system becomes vital. Massey (1993) provided a sound synopsis of the importance of the systems concept of beef production in a Beef Improvement Federation Fact Sheet. He stated that the historical emphasis on increasing production (e.g., milk, gain, mature size) by performance-oriented seedstock and commercial cow/calf producers did not result in a parallel increase in profit over time. Those producers failed to consider additional aspects in the decision-making process for their operation—as would have otherwise been done within a systems approach where more than just outputs are included. Massey (1993) further stated that overall efficiency of the enterprise—in other words, net return … should be the most important consideration by a beef cattle operation. A true system should include all components that influence net return, including cost. The general absence of vertical integration within the beef industry, particularly at the cow/calf level, contributes to the beef industry's multifactorial production system. This has generally led cow/calf producers to be less likely to consider a systems approach in their decision-making process.

Figure 1.3 Percent of nonfixed costs that feed-associated costs make up on US cow/calf operations (USDA-ERS, 2011).

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A great opportunity for cow/calf producers to reduce costs is through feed inputs. The USDA Economic Research Service reported that feed-associated costs have represented 56–71% of all nonfixed (operating) expenses on US cow/calf producers from 1982 to 2010 (Figure 1.3). The average percentage of 65.4% during 2006 to 2010, when feed prices were elevated above historical levels, is noticeably higher than the previous average of 62.0% from 1982 to 2005. In addition to comprising a larger percentage of nonfixed costs in recent years, the amount that feed-associated costs made up has been more volatile (both the lowest and highest percentages occurred within the 5-year period from 2005 to 2009).

It has been estimated that 55 to 75% of total costs associated with beef cattle production are feed related (NRC, 2000), suggesting that emphasis on improving feed efficiency in beef cattle is a tremendous opportunity for producers (Lamb et al., 2011). Additionally, more than half of the feed required by the US beef production industry is utilized by the breeding cowherd, compared to their progeny, which are fed out until harvest (Carstens and Tedeschi, 2006; Lamb et al., 2011). Because of the large amount of animal-to-animal variation present in the maintenance energy (ME) requirements among cattle (Johnson et al., 2003), selection for feed efficiency is logical.

Beyond native range and improved grass pastures, harvested feedstuffs serve as the primary feed inputs for most of the US beef industry: hay (grass and alfalfa) for the breeding cowherd and corn for feedyard cattle. Corah (2008) identified major challenges facing the US beef industry and its infrastructure of corn feeding. Historically, the US feedyard industry has evolved in an environment in which both energy and corn have been relatively inexpensive. Since 2006, these conditions appear to have begun to change and the trend may be one that will be a permanent and an ever-increasing challenge that must be faced and addressed by the industry.

According to USDA-NASS data, prices for alfalfa and other hay increased gradually but steadily for a 30-year period until 2006 (Figure 1.4). However, the rate of price increase, and associated volatility, increased dramatically in late 2006.

Much of the increase in hay price has been driven by elevation in corn price. During the same time period, the per-bushel price of corn actually remained flat, although somewhat volatile, until 2006 (Figure 1.5).

Figure 1.4 US average annual prices for alfalfa and other hay. (USDA-NASS Monthly Agricultural Prices, summarized by LMIC.)

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Figure 1.5 US average annual price for corn. (USDA-NASS Monthly Agricultural Prices, summarized by LMIC.)

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As the primary component of feedyard diets, the price of corn has influenced feedyard cost-of-gain based on summarized data by Kansas State University (Figure 1.6).

Figure 1.6 Feedyard cost of gain among 190 Kansas feedyards. (Focus on Feedlots, http://www.asi.ksu.edu/p.aspx?tabid=302.)

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A discussion of historical profitability in the cow/calf and feedyard sectors, as well as main drivers of profitability, will help to clarify the importance of feed efficiency to the beef industry. As discussed earlier, cow/calf profitability during the 1980s, 1990s, and early 2000s generally responded to the cattle cycle and total inventory of cattle in the United States. On the basis of the cow/calf estimates reported in Figure 1.7, after significant losses occurred in the early 1980s, short periods of sustained profitability occurred from 1986 to 1994 and from 1999 to 2007. These periods were interrupted by short periods of losses during the mid-1990s and 2007 to 2008.

Figure 1.7 Estimated average annual returns for US cow/calf producers over cash cost. (Includes pasture rent; LMIC.)

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Figure 1.8 Estimated average annual returns to US feedyard operations based on feeding 725 lb steers in the southern plains (LMIC).

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To determine the key factors that have affected cow/calf profit, Miller et al. (2001) used standardized performance analysis data to evaluate several variables that may affect profitability (measured as return to unpaid labor and management per cow (RLM)). The researchers used data from 225 cow/calf producers in Iowa and Illinois collected from 1996 to 1999. Using a correlation analysis, it was determined that feed cost was the largest factor influencing return to RLM compared to 12 other economic and production traits and in two models explained 52% and 57% of the variation in profit. Further, the authors reported that factors associated with cost explained more variation in profit than traits related to production, reproduction, and marketing.

Similarly, using financial records from North Dakota cow/calf producers, Hughes (1991) also documented that total feed cost explained the most variation in profit. More recent research also verifies that cost differences across cow/calf producers account for more variation in profit than income differences (Dhuyvetter, 2011).

Unlike profitability in the cow/calf sector, average estimated returns to feedyards since the early 1980s has not been cyclic and has been fairly unprofitable (Figure 1.8). For much of the 1980s, estimated returns to the average feedyard were positive. However, for a 20-year period beginning in 1990, feedyards were only profitable during about 1 in 4 years (based on estimated annual returns). Further, with the exception of 2003 (where annual profit exceeded $100/head for the only time in 30 years), average annual profit was about $20/head or less. In contrast, during years when a financial loss for the year occurred, 7 of those years had losses in excess of $50/head. As a result of sustained losses in the feedyard sector for a 6-year period (2004 to 2009), a massive amount of equity was lost by cattle feeders.

Factors that affected the profitability of feedyard cattle were evaluated using 5,286 head of steers and heifers enrolled in the University of Idaho's A-to-Z Retained Ownership Program over an 11-year period (1992 to 2003; Glaze et al., 2004). The authors reported that profit would increase $33.95/head if feed conversion ratio decreased by 1 unit (i.e., from 7 to 6 lbs feed for 1 lb gain), suggesting the importance of feed efficiency on feedyard profitability.

Similarly, using a computer model, Fox et al. (2001) reported that a 10% improvement in rate of gain increased profit by 18% (due to fewer days on feed and less yardage, as well as less feed required for maintenance due to fewer days on feed). Conversely, a 10% improvement in feed efficiency due to more efficient use of metabolizable energy increased profit by 43%. As with the Idaho data, both scenarios reported by Fox et al. (2001) utilized feed (i.e., corn) prices more reflective of historical averages ($2.50/bu), rather than two- and threefold higher prices since 2006. Thus, if we assume that all other factors remain relatively constant, the effect of feed efficiency on feedyard profitability at a time of higher feed cost would be even larger.

When evaluating the opportunity that each beef industry sector has in terms of capitalizing on genetically improving feed efficiency in beef cattle, it would seem that cow/calf operations have the most to gain, primarily due to the significant feed input required to maintain a cowherd year round. However, during the 1990s and early 2000s, cow/calf producers were generally more profitable than feedyards, and thus better equipped to withstand elevated feed costs (primarily hay). In contrast, feedyards have suffered financially in the high-priced corn markets from 2006 to 2011. Thus, it appears that there may in fact be more opportunity among cattle feeders for cattle that are genetically superior for feed efficiency, even though less than half of the feed inputs required to produce a pound of beef are in the form of a high concentrate feedyard ration. Feedyards that have the opportunity to utilize less high-priced corn may be able to overcome major financial losses that have plagued the feeding industry. However, it should be noted that increased demand by the ethanol industry has focused on corn. If (and probably when) cellulosic ethanol becomes a reality, it's likely that low-priced feedstuffs typically fed to cowherds during winter (e.g., cornstalks, straw, etc.) may increase costs to cow/calf producers so that cowherd feed efficiency is a greater opportunity than in the feedyard. Regardless, mature cows will likely continue to consume the majority of their annual caloric needs via pasture grazing. In contrast, feedyard demand for feed efficiency is unlikely to decline, assuming days on feed do not substantially decrease.

Evolving Factors Affecting Feed Cost in the Beef Production Industry (the Changing Marketplace for Feed Grains)

In addition to the consistent trend of increasing prices for feed input, the volatility associated with input prices has dramatically increased since late 2006. Similarly, volatility associated with output prices (i.e., weaned calves, feeder cattle, fed steers, market cows, etc.) has similarly increased. Producers are realizing that they need to focus on low-cost production strategies, as well as market risk mitigation strategies.

A quick review of factors that influence the higher cost of inputs is relevant toward understanding the likelihood that input prices will remain high or even continue to increase. Since the prices of typical feedstuffs utilized by cowherds (hay, corn stalks, pasture, etc.) are mainly driven by feedgrains (primarily corn), it is logical to focus on the factors that are driving the increased price of corn.

The major users of corn include ethanol, livestock, and commercial food production. Additionally, there is strong demand for the exportation of corn out of the United States, primarily for livestock and commercial food uses. Such international demand is largely the result of sustained economic growth among Asian countries. However, US biofuels policy has provided unwavering support to the corn-based ethanol production industry. Such policies include the Energy Independence and Security Act of 2007, as well as the Renewable Fuel Standard. Westcott (2007) indicated that demand for corn by the ethanol industry has caused an increase in both corn and land prices, which will ultimately lead to a reduction in the US production of corn-dependent livestock species including pork, poultry, and beef. Finally, uncertainty associated with agricultural production—typically due to variation in crop yields due to highly variable weather, disease incidence, and so on—is contributing to the rise in corn price.

In addition to governmental support for renewable fuels production, ethanol-based demand for corn has also resulted from an elevated and volatile global oil market. Whether global unrest among some oil-rich countries or production strategies by major oil exporting countries intended to keep oil price high (i.e., OPEC), the general price of energy is elevated. Finally, the fact that the number of available acres of farmland has peaked further suggests that future food production costs will be substantially higher.

Moving forward, the US agriculture industry will be faced with a huge challenge. As United States and global populations increase, while land resources stagnate and demand for animal-derived protein increases among developing countries (i.e., China), it's unclear how production levels can increase to meet supply in a sustainable manner. The world population is projected to exceed 8.3 billion by 2030 (FAO, 2002) and demand for agricultural products is growing by 1.5% per year. Thus, competing demands on corn for human consumption, as feed for livestock, and as a source of energy (for ethanol production) will play out in a complex dance. Further, water availability issues in the semiarid and arid portions of the United States are expected to be nearly insurmountable obstacles.

As a result, there will be a changed landscape of beef production in light of sustained elevation and volatility of feed costs. Historically, the beef industry has focused on output traits (e.g., weight, gain, percent pregnant cows, percent calf crop, carcass traits, end-product quality, and yield), primarily due to the successful reliance on relatively cheap input costs (i.e., feed). Low-cost corn enabled beef producers to generate a product with amazing palatability compared to pork and poultry, but only at a slightly higher price. In the future, assuming feed grain prices remain much higher than historical averages, the beef industry will adopt a major emphasis on reducing input costs associated with the outputs it produces.

Drivers for Increased Focus on Feed Efficiency within the Beef Industry

There is a huge opportunity for the beef industry to genetically select for feed efficiency based on the fact that a considerable amount of animal-to-animal variation exists for feed efficiency. A relatively new measure of feed efficiency is calculated as the difference between the amount of feed consumer by an animal and the amount of feed that it is predicted to consume (for its size (weight) and rate of gain). It is known as residual feed intake (RFI), named for the mathematical relationship between actual feed consumed versus predicted (the difference being the mathematical residual in a regression or average prediction value). The measurement was originally described by Koch et al. (1963) and its advantage is that it appears to be independent of many other performance traits. There is also a large range of variation in RFI—more than 35%. Further, the heritability of RFI is estimated to be low (h² = 0.16) to moderate (h² = 0.43; Herd et al., 2003), indicating that significant genetic progress can be achieved through performance-based breeding programs.

The use of RFI in a breeding program offers a genetic selection method to improve beef cattle feed efficiency without also increasing growth rate and mature size (Johnson et al., 2003). Selection for efficiency using the RFI trait could potentially improve feed efficiency in cattle through reduced feed intake (Herd et al., 2003). Animals that are more RFI-efficient eat less than predicted and have RFI values that are expressed as a negative number (the difference between actual and predicted feed intake). Animals that are RFI-inefficient eat more than predicted and have RFI values that are positive. It has been reported that selection of parents with low RFI values (considered more feed efficient) resulted in progeny that consumed less feed as yearlings but weighed the same at harvest as offspring from high-RFI parents (Richardson et al., 2001). In addition, preliminary evidence suggests that selection for RFI probably does not negatively affect mature cow weight or carcass quality of progeny, but can offer an advantage in selection for reduced cow maintenance requirements (Johnson et al., 2003).

ME requirements are likely contributing to the relatively high cost (and energy requirements) for the production of beef (Ritchie, 2001). Johnson (1984) estimated that approximately 50% of the total energy required to produce beef is for maintenance of the beef cow. This calculation is based upon assumptions that 71% of dietary energy needed by the beef industry goes only to maintenance, and 70% of that ME is required by the cowherd.

Unfortunately, throughout all of the twentieth century, the maintenance requirements of cattle have really not changed, even as a result of some selection pressure placed on maintenance requirements (Johnson et al., 2003). However, due to the general lack of individually measured feed intake and efficiency data, likely due to low and consistent feed prices, the US beef industry did not seek to specifically improve the efficiency of feed utilization among cattle that were growing or at maintenance.

For the first time in 2002, the seedstock industry (via the Red Angus Association of America (RAAA)) developed its first genetic prediction for the ME required by future daughters of a sire—the ME EPD (Evans et al., 2002). This genetic prediction was meant to help bull buyers match their cows’ feed requirements with their environment and reduce winter supplementation of beef cows without negatively affecting body condition score, reproductive performance, growth, or carcass traits. A prototype EPD was created to predict these differences in energy requirements among mature cows and was published on a megacalorie per month (Mcal/mo) basis. This was the first opportunity for producers to select females based on ME requirements, which contribute significantly to differences in feed efficiency. However, the EPD values were based on the only readily available data related to cow energy requirements (mature cow body weight adjusted to a common body condition score, and a small adjustment using milk EPD) and does not include any actual feed intake data from cattle on test (Evans et al., 2002).

A few years later, the American Angus Association (AAA) added a similar genetic prediction—the Cow Energy Value ($EN). In contrast to the Red Angus ME EPD, $EN is expressed in dollars saved per cow per year based on estimates for energy density and price of hay. Also, a negative ME EPD value is considered favorable by RAAA, while a negative $EN is unfavorable by AAA. Even with these initial genetic predictions for daughters’ ME requirements, limited individual feed intake data has been collected or utilized to generate genetic predictions for feed intake and efficiency in feedyard steers and heifers or mature cows. This has been the result of generally low feed input prices coupled with high costs associated with the technology, facilities, and labor necessary to collect individual feed intake data in beef cattle.

It has been reported that improving beef cattle feed efficiency via genetic selection, which can be accomplished readily based on reasons discussed earlier, could greatly overshadow improvements in ADG. For instance, Gibb and McAllister (1999) estimated that the economic benefit of improving feed efficiency by 5% could be approximately fourfold higher than a similar improvement in ADG.

Beyond the cost reduction effects that result from selecting for improved feed efficiency, benefits related to the environmental effect on beef cattle production have been reported. Scientists from Canada (Okine et al., 2001) and Australia (Hegarty et al., 2007) reported reductions in manure production and methane emissions from cattle selected for low net feed efficiency (another term used instead of RFI, but identical to RFI). These reductions included a 9–12% reduction in methane and a 15–17% reduction in the production of manure.

Beyond reductions in by-products, improvements to feed efficiency in beef cattle may have a larger influence on the increasingly important carbon footprint calculation associated with the production of beef. Concern among end-product consumers about beef's sustainability, due to its overall efficiency (pounds of feed in vs. pounds of product out), may be addressed mostly effectively via improvements to the both efficiency of carbon utilization as well as carbon-related outputs as greenhouse gases, per pound of output.

Implications for Improved Efficiency of Feed Utilization in the United States (Based on Number of Beef Cattle in United States—Cow/Calf, Stocker, and Feedyard)

Ultimately, the beef industry's ability to accurately identify and propagate cattle that are efficient in converting feed into body weight could result in significant changes to the beef industry. These are likely to include:

1. Improved profitability among cow/calf, stocker, and feedyard sectors due to reduced input costs without negative consequences to productivity. Operations will be more able to manage within a climate of elevated feedstuff prices, as well as highly volatile markets, if less feed inputs are needed.

2. Expansion of the US cowherd inventory by increasing productivity while using the same feed resources available. Reducing inputs (per animal) will enable cow/calf producers to have more cows and more efficiently manage their overhead.

3. Increased net beef supply for domestic and international consumers through increased beef production using the same available feed resources.

4. Reduced end-product price at retail for consumers due to cost savings resulting from a reduction in input cost. Such a price reduction will enable beef to be more price competitive with competing animal proteins (pork and poultry).

5. Stronger rural agricultural communities and the agricultural economy in general due to enhanced profitability of cow/calf operations.

6. Enhanced environmental sustainability of beef production practices due to greater efficiency (fewer inputs vs. outputs) of production and reductions in the production of manure and greenhouse gases. A beef industry driven by consumer demands for sustainability could result in greater demand for beef.

Improving feed efficiency in beef cattle will have numerous wide-reaching positive effects on the environment, consumers, and agricultural communities, particularly since beef cattle are less efficient at converting grain to meat protein compared to pork and poultry. As a result, high feed costs will be the key driver for the beef industry's focus on feed efficiency (Corah, 2008). Since feed efficiency directly affects the unit cost of production for beef, altering it will improve the US beef industry's competitiveness with other meat producers, profitability, and long-term sustainability (Ritchie, 2001).

References

BIF. 2010. Guidelines for Uniform Beef Improvement Programs, 9th edn, L.V. Cundiff, L.D. Van Vleck, and W.D. Hohenboken (eds.). North Carolina State University, Raleigh, NC.

Carstens, G.E. and L.O. Tedeschi. 2006. Defining feed efficiency in beef cattle. In: Proceedings of the Beef Improvement Federation, Choctaw, Mississippi, pp. 12–21.

Corah, L.R. 2008. ASAS centennial paper: development of a corn-based beef industry. J Anim Sci 86: 3635–3639.

Dhuyvetter, K.C. 2011. Cow-calf economics. In: Presented on February 16, 2011 at the Kansas State University Ag Profitability Conference, Hoisington, KS. Available at: http://www.agmanager.info/Faculty/dhuyvetter/presentations/2011/KCD_Cow-CalfEconomics--AgProfitability(Hoisington).pdf. Accessed July 1, 2011

Dickerson, G.E. 1978. Animal size and efficiency: Basic concepts. Animal Production 27: 367–379.

Evans, J. L. 2001. Genetic prediction of mature weight and mature cow maintenance energy requirements in Red Angus cattle. Ph.D. Dissertation Colorado State University, Fort Collins.

FAO. 2002. World agriculture 2030: main findings. Available at: http://www.fao.org/docrep/005/y7352e/y7352e00.htm. Accessed October 22, 2011.

Fox, D.G., L.O. Tedeschi, and P.J. Guiroy. 2001. A decision support system for individual cattle management. In: Proceedings of the Cornell Nutrition Conference for Feed Manufacturers, Rochester, NY, pp. 64–76

Gibb, D.J. and T.A. McAllister. 1999. The impact of feed intake and feeding behaviour of cattle on feedlot and feedbunk management. In: D. Korver and J. Morrison (eds.) Proceedings of the 20th Western Nutrition Conference on Marketing to the 21st Century, pp. 101–116.

Glaze, J.B. Jr., B. Wilhelm, N.R. Rimbey, K.S. Jensen, G.C. Keetch, W.F. Cook, C.W. Gray, J.N. Hawkins, E.J. Morrison, S.K. Williams, J.A. Church, and P.A. Momont. 2004. A to Z Retained Ownership, Inc.: factors affecting profitability in the inland northwest. Proc West Sec Am Soc Anim Sci 55: 198–201.

Hegarty, R.S., J.P. Goopy, R.M. Herd, and B. McCorkell. 2007. Cattle selected for lower residual feed intake have reduced daily methane production. J Anim Sci 85: 1479–1486.

Herd, R.M., J.A. Archer, and P.F. Arthur. 2003. Reducing the cost of beef production through genetic improvement in residual feed intake: opportunity and challenges to application. J Anim Sci 81(E. Suppl. 1): E9–E17.

Hughes, H. 1991. Financial performance of North Dakota's beef cow enterprises—the critical success factors. Proceedings of the Annual Convention of American Association Bovine Practitioners, p. 100.

Johnson, D.E. 1984. Maintenance requirements for beef cattle: importance and physiological and environmental causes of variation. In: Proceedings of the Beef Cow Efficiency Forum, Fort Collins, Colorado, p. 6.

Johnson, D.E., C.L. Ferrell, and T.G. Jenkins. 2003. The history of energetic efficiency research: where have we been and where are we going? J Anim Sci 81: E27–E38.

Koch, R.M., L.A. Swiger, D. Chambers, and K.E. Gregory. 1963. Efficiency of feed use in beef cattle. J Anim Sci 22: 486–494.

Lamb, G.C., T.E. Black, K.M. Bischoff, and V.R.G. Mercadantel. 2011. Implications of selection for residual feed intake in the cowherd. In: Proceedings of the Florida Beef Cattle Short Course. Available at: http://www.animal.ufl.edu/extension/beef/BCSC/BCSC2011/documents/elamb.pdf. Accessed July 1, 2011

Massey, J. 1993. The systems concept of beef production: BIF fact sheet G2037. University of Missouri Extension. Available at: http://extension.missouri.edu/p/G2037. Accessed July 1, 2011.

Miller, A.J., D.B. Faulkner, R.K. Knipe, D.R. Strohbehn, D.F. Parrett, and L.L. Berger. 2001. Critical control points for profitability in the cow-calf enterprise. Prof Anim Sci 17: 295–302.

NRC. 2000. Nutrient requirements of beef cattle: Update 2000, 8th edn. National Academy Press, Washington, DC.

Okine, E.K., J.A. Basarab, V. Baron, and M.A. Price. 2001. Net feed efficiency in young growing cattle: III. Relationships to methane and manure production. Can J Anim Sci 81: 614.

Richardson, E.C., R.M. Herd, V.H. Oddy, J.M. Thompson, J.A. Archer, and P.F. Arthur. 2001. Body composition and implications for heat production of Angus steer progeny of parents selected for and against residual feed intake. Aust J Exp Agric 41: 1065–1072.

Ritchie, H.D. 2001. Why is efficiency so important to the beef industry? In: Fort Dodge Animal Health Feedlot/Nutritionist Meeting, April 27–28, Carefree, AZ. Available at: https://www.msu.edu/~ritchieh/papers/fortdodgeefficiency.html. Accessed July 1, 2011.

Rumph, J.M. 2005. Interpretation and utilization of expected progeny differences. In: Beef Sire Selection Manual, National Beef Cattle Evaluation Consortium, Ithaca, NY. pp. 43–50

USDA-ERS. 2011. U.S. cow-calf production cash costs and returns, 1982–2010. Available at: http://www.ers.usda.gov/Data/CostsAndReturns/testpick.htm. Accessed July 1, 2011.

Westcott, P.C. 2007. Ethanol expansion in the United States. How will the agricultural sector adjust? USDA ERS Research Report FDS-07D-01. USDA ERS, Washington, DC.

2

Measuring Individual Feed Intake and Utilization in Growing Cattle

D.H. (Denny) Crews, Jr. and Gordon E. Carstens

Introduction

In measuring feed efficiency, it is imperative that the limits and the required accuracy of data measurements be well understood by the scientists who develop the protocols, students who learn about the driving principles, practicalities, and application limits from their scientist mentors, and the producers who benefit from the generation of reliable and informative data. In all of the measures of feed efficiency included in this book, there are two essential pieces of data that must be collected with the greatest possible accuracy and precision: (1) dry matter (feed) intake and (2) live-weight gain. The limitations of measuring live-weight gain are covered in greater detail in Chapter 10.

The primary objective of this chapter is to make minimum recommendations for the collection of daily dry matter (DM) feed intake of individual growing cattle that are group fed. The use of feed intake to compute various alternative measures of feed utilization depends heavily on the integrity of the individual dry matter intake (DMI) records. The definition of alternative feed utilization measurements and their respective utility has been the subject of much debate among scientists as well as producers. Since the 1960s, more than two dozen measures of efficiency have been proposed in the scientific literature (Archer et al., 1999).

Since the mid-1990s, there has been a dramatic increase in the capacity for collection of individual intake data on group-fed cattle, largely due to technological advancements