Understanding Feed Efficiency in the Feedlot

Introduction

Near record high feed, fuel, and fertilizer prices, and high inflation rates all have put increasing economic burden on beef producers. The increased cost of feeding cattle means that increasing feed efficiency is critical to feedlot production. Historically, feed costs represent about 70% of the total cost of production for feedlot operators and therefore, feed efficiency has 4 x greater economic return when compared to the same improvement in feedlot growth (Gibb and McAllister, 1999). In addition to economic benefits, improved efficiency of production also has some positive environmental benefits, including lowered greenhouse gas emissions, and reduced water demand and manure output (Terry et al., 2020). This paper will investigate concepts in feed efficiency as it pertains to feedlot productions systems, as well as discuss some practical approaches which can be used to improve feed efficiency on commercial operations.

What is feed efficiency?

One of the challenges with feed efficiency it that it can refer to a broad scope of definitions, where efficiency can be used to describe impacts on everything from whole-farm systems to individual animal differences. More generally, feed efficiency relates to the ratio between production system inputs (usually feed) to system outputs (usually growth or gain), but refinements can be made to this definition for systems approaches, individual animal efficiencies, or to unit of saleable product. For feedlot operations often feed conversion ratios (either feed to gain (F:G) or the inverse, gain to feed (G:F)) or residual feed intake (RFI) are routinely used to describe feed efficiency for these production systems. Feed conversion ratios are simple to calculate, dependency on body weight and free choice intake favours animals with high mature bodyweights, feed intake, animal growth rate, and mature size, as these factors are all highly correlated (Terry et al., 2020). Therefore, feed conversion ratios may not actually identify animals that are truly metabolically more efficient because of these co-dependencies. As a result, residual feed intake (RFI) was developed to help normalize for growth rates and body weight and therefore may better reflect true metabolic efficiency in growing feedlot animals. In general, RFI is calculated by determining the “residuals” between an individual animal’s observed feed intake, subtracted from their predicted feed intake (Koch et al., 1963). Predicted intake is most often determined by linear regression using data from a contemporary group of animals, and normalizing for growth (ADG), midpoint BW (or midpointBW0.75) and may also contain other relevant factors like estimations of body composition (ultrasound backfat measurements) in feedlot animals. Therefore, animals with negative RFI score represent the efficient phenotype, and consumes less feed than is predicted when BW and rate of gain are held constant. However, since RFI depends on measurement of free-choice intake, it can be difficult to measure in commercial operations largely due to requirement of specialized feeding equipment needed to obtain these individual intake measurements. However, RFI has been commonly used in genetics and research applications for feedlot production.

Genetic improvement in feed efficiency is an important strategy in meeting beef industry feed efficiency improvement goals. In particular, RFI in growing feedlot cattle is considered a moderately heritable trait, with h² reported between 0.14 and 0.43 % (Berry and Crowley, 2013). Genetics approaches and efficiency traits are nicely summarized in a review by Terry et al., (2020). While different beef breed types (British, Continental, dairy) may reflect differences in growth, lean gain, and mature body size –traits which are often correlated with efficiency traits, research into benefits of heterosis in complex traits like efficiency, longevity, and resiliency are continuing in hopes of better understanding how crossbreeding impacts these traits. More advanced genomic techniques like identification of single nucleotide polymorphisms (SNPs) and genome-wide association studies (GWAS) are also helping to identify new genetic traits associated with efficiency measures. With identification of new genetic markers, molecular breeding values for feedlot efficiency traits can more easily and accurately be developed.

Beyond the genetic composition of the animal itself, the composition of the rumen microbiome can also account for as much as 20% of the variation in performance and efficiency between animals (Paz et al., 2018). In particular, a more diverse rumen microbiome has been associated with improved efficiency traits in cattle (Guan et al., 2008, Lam et al., 2018). This may lead to new areas of research where strategies to manipulate the rumen environment may help improve efficiency in the feedlot and further understanding on how the host interacts with the microbial community.

Although RFI has been a useful tool for genetic improvement of cattle, this measure of efficiency is also more likely to reflect metabolic differences in efficiency and energy partitioning between individual animals. Richardson and Herd (2004) identified that traits like body composition, feeding patterns, digestibility, heat increment of feeding, metabolic factors and stress, which all contribute to animal-to-animal variation in feed efficiency (Figure 1). However, many of these metabolic factors remain poorly defined for feedlot animals. A closer look at these biological factors influencing inter-animal variation in feed efficiency and are well reviewed in Cantalapiedra-Hijar et al., (2018) and Kenny et al., (2018). However these reviews demonstrate how complex and multi-factorial traits influencing feed efficiency can be from a metabolic point of view.

Figure 1: Factors contributing to individual animal differences in residual feed intake. (adapted from Richardson and Herd, (2004)

Energy partitioning is another component of understanding efficiency in feedlot cattle. With growing cattle and increasing time on feed, cattle begin to reach their mature size and begin to shift their composition of weight gain. As the growth curve begins to plateau as the animal matures, protein accretion begins to decrease and fat accretion increases. As the energy density of adipose tissue is greater than that of lean tissue, greater energy intake is needed to maintain the same level of gain as an animal within the lean gain phase of their growth curve. Steer growth and composition of gain is reviewed in detail by Owens et al., (1995) and Pethick et al., (2004). Although the decrease in feed efficiency with increasing time on feed is largely attributed to these changes in composition of gain, other metabolic factors may also be contributing to feed inefficiencies. In a serial slaughter experiment by our group (Kim et al., 2017), steers were slaughtered in 42 day increments until d 162, and performance traits along with tissues were collected to look at potential metabolic changes influencing efficiency. Results suggest that insulin sensitivity was impaired with increasing time on feed, as linear decreases in circulating insulin, and glucose to insulin ratio were observed. In addition, increased hepatic protein expression of insulin receptor, in line with blood results. Hepatic citrate synthase activity also decreased linearly with increasing time on feed. As citrate synthase is a common biomarker for mitochondrial efficiency (Trounce et al., 1996), this data suggests that mitochondria efficiency may also be reduced with increasing time on feed. This suggests that metabolic changes may also be responsible for reductions in feed efficiency with increasing time on feed.

Although research into increasing the understanding of mechanisms behind feed efficiency in feedlot cattle continues, there are numerous more practical strategies which are easily adaptable to feedlot operations which can help to improve overall feed efficiency.

Feed management

Although research often focuses on more technical aspects of metabolism and genetics in understanding feed efficiency, other simple feed management techniques also can greatly help reduce feed waste and spoilage. These include proper feed storage and harvest techniques to reduce spoilage and feed shrink, proper calibration of TMR mixers to optimize feed mixing, and a consistent bunk management strategy once feed is delivered to animals (Van Schaik and Wood, 2020; Figure 2). In addition, routine feed analysis can help to ensure rations are in line with diet formulations so that feed formulations are as accurate as possible.

Figure 2: Summary of on farm feed management strategies which can improve overall on-farm feed efficiency. Adapted from Van Schaik and Wood, 2020

Improvements to Feeds and Feeding

Independent of animal factors influencing feed efficiency, improvements in feed, feed formulation, feed digestibility, and better-defined nutritional requirements can also help improve efficiencies in the feedlot (Terry et al., 2020).

Firstly, increased understanding of the intricacies of animal nutritional requirements and the growth prediction models from nutrients ingested will enable nutritionists and producers to more accurately align animal requirements and feed supplied. For example, unpublished work from our group (Williams et al., 2022) has investigated how better defining fibre in feedlot rations can help to increase feedlot performance prediction. Using a meta-analysis approach, a dataset of 58 highgrain (up to 10% forage) feedlot treatment means from 22 experiments were used to fit growth prediction models using diet parameters, ionophore, tylosin inclusion etc. In particular, the model looked at replacing dietary NDF in the model with undigestible NDF estimates (from feed library averages) and found that uNDF estimates improved steer growth model fit (lower RMSPE and increased CCC) and were also able to effectively predict HCW, feed efficiency, dressing percentage. This suggests that uNDF is a parameter which may be more effective at predicting feedlot performance, and nutritionist should consider including uNDF in feed analysis to generate more accurate performance models.

For high-grain diets, improvements in feed digestibility, either through plant breeding or feed processing technologies also can help improve feed efficiency. In feedlots, where high-grain diets are fed, feed processing index and particle size often has strong associations with rumen fermentability and pH, and gut health. Therefore, feed processing should aim for an optimization rather than maximization approach. Feed should be processed enough to increase digestibility, but not so processed that gut health becomes a risk factor for decreasing animal performance. Feed processing may include everything from simple grinding and/or tempering, to more complex feed processing methods like steam-flaking or other treatments which can increase digestibility and improve feed utilization by the animal (Terry et al., 2020). With high-grain diets, monitoring fecal starch may help producers optimize feed processing to ensure maximal starch digestion and efficiency. Although traditionally this analysis is conducted using wet chemistry approaches, new applications of near infrared (NIR) spectroscopy have been shown to accurately estimate fecal starch (Jancewicz et al., 2016). As NIR technology decreases in cost, it may be possible for feedlots to use this technology on farm, to make near real-time adjustments to grain processing, and regularly monitor fecal starch to ideally keep fecal starch below 5%.

Conclusion

In summary, although the economic and environmental need for improved feed efficiency is great within the cattle industry, the complex and multifaceted aspects of feed efficiency are challenging. There are still many unknown mechanisms controlling feed efficiency in cattle production, however researchers are beginning to have a better understanding of some of these biological mechanisms and genetic and feed management technologies continue to be developed. Despite these unknowns, there are many opportunities for improved efficiency on commercial farms through relatively simple management changes.

Presented at the 2022 Animal Nutrition Conference of Canada. For information on the next edition, click here.