On determination of residual feed intake and associations of infrared thermography with efficiency and ultrasound traits in beef bulls

Department of Animal & Poultry Science, University of Guelph, Guelph, Ontario, Canada, N1G 2W1
Livestock Science (Impact Factor: 1.17). 10/2009; 125(125):22-30. DOI: 10.1016/j.livsci.2009.02.022


To determine the relationship of infrared thermography (IR) and ultrasound measures (US) with the variation in feed efficiency and daily dry matter intake (DMI), alternative models for calculating residual feed intake (RFI) were tested using DMI, average daily gain (ADG), mid-trial body weight (BW), body surface temperature measured using IR, and mid-trial US (ribeye area, backfat thickness and marbling score) from 154 crossbred bulls. The original model (Koch's model) for RFI (RFIkoch), based on a regression of DMI on BW and ADG had the lowest coefficient of determination (R2; 0.58) and was included as the base model in all the other alternate extended models tested. The residual or error term from the model represents the calculated RFI. RFI calculated including ribeye area (RFIrbea) had the highest R2 (0.62) of all US at mid-trial. RFI including all US traits (RFIusdt) had the same R2 (0.63) but higher, less desirable, Bayesian information criterion (BIC; 456.2 vs. 334.9) than the model where only feet temperature was added to Koch's model. A model combining mid-trial US and feet IR (RFIusir) had the greatest R2 (0.67) and BIC (327.7). RFIkoch was correlated with eye, cheek and feet temperature (0.24 to 0.43) but not with ribs, rear area or scrotum temperature. Bulls were also sorted into RFIkoch groups (high, medium, low). Eye, cheek and feet had lower temperature in low-RFI bulls compared to high-RFI bulls (less efficient). The feed to gain ratio (F:G) and DMI was higher for high-RFI than for low-RFI bulls. Further analysis demonstrated that 28% of the RFIkoch variation was explained by US (4%) and IR (24%), where feet and cheek were the major IR traits, representing 74% of the IR contribution to the explained variation in RFIkoch. Our results provide evidence that: (1) Koch's model might be improved by adding US traits; (2) US and IR traits are useful to explain the DMI variation; (3) IR of different body locations have distinct relationships with RFI (RFIkock and RFIusdt), and with other efficiency traits and US; and (4) heat production (IR) explains a greater proportion of the variation in RFI than body composition (US). This study demonstrates the potential for improving RFI determination and also the application of IR in the assessment of feed efficiency. Further studies to develop standard procedures for performing infrared imaging might increase the predictability of this technology for assessing feed efficiency, especially residual feed intake, in cattle.

Download full-text


Available from: Flavio Schramm Schenkel,
  • Source
    • "Arthur et al (2003) observed a 5% improvement in DMI prediction after back fat at the end of the test was included in the model. The model that included REA, marbling, and back fat, improved DMI prediction by 9% compared with Koch model (Montanholi et al 2009). Likewise, Arthur et al (2003) and Basarab et al (2003) observed a 6% increase using similar models in young beef cattle. "
    [Show abstract] [Hide abstract]
    ABSTRACT: A high percentage of buffaloes in Colombia are managed under dual-purpose systems, therefore a study was carried out to evaluate feed efficiency traits in growing buffaloes. Two performance tests were conducted, each lasting 112 days, with the first 28 days serving as adaptation to the facilities and diet, and the remaining 84 days used for data collection. Growth, dry matter intake (DMI) and ultrasound were measured in 61 buffaloes, whereas nutrient digestibility (ND) was measured in 33 randomly selected buffaloes. Buffaloes were intact males and had an average initial weight and age of 262±53 kg y 461±50 d, respectively. Residual feed intake (RFI) was computed as the difference between actual and expected feed intake. Buffaloes were assigned to three RFI groups: high (> 0.12 kg DM/d), low (< -0.12 kg DM/d), and medium RFI (between ± 0.12 kg DM/d). A fixed effect model was used to analyze differences between groups for growth traits, intake, ND, efficiency and ultrasound measurements. Stepwise regression analyses were used to evaluate the contribution of ultrasound measurements and ND in explaining the variations in DMI. No differences were observed for initial and final BW, ADG, relative growth rate (RGR), Kleiber ratio (KR) or ultrasound measurements among RFI groups. RFI was correlated with DMI, DM and CP digestibility (r = 0.53, -0.58 and -0.59, respectively). RFI correlations with initial and final BW, ADG, RGR, and KR were not different from zero. In contrast, high correlations were observed between ADG and RGR, KR and FCR (r = 0.91, 0.96 and -0.93, respectively). The variation in DMI was explained by MBW (58%), DMD (15%), digestibility of NDF (4%) and ADG (4%). Our results indicate that: digestibility traits were useful in predicting DMI whereas ultrasound measurements were not; RFI can be used to identify the most efficient buffaloes. This article can be downloaded from the LRRD journal page: http://www.lrrd.org/lrrd26/7/boli26131.html
    Livestock Research for Rural Development 07/2014; 26(7):131.
  • Source
    • "Several models were tested to calculate RFI, similar to the calculations explained by Montanholi et al. (2009). The most appropriate model for explaining variation in feed intake had an R 2 of 0.43 and the lowest Bayesian information criteria and was composed as follows: DMI = "
    [Show abstract] [Hide abstract]
    ABSTRACT: The characterization of blood metabolite concentrations over the circadian period and across physiological stages is important for understanding the biological basis of feed efficiency, and may culminate in indirect methods for assessing feed efficiency. Hematological analyses for albumin, urea, creatine kinase, glutamate dehydrogenase, aspartate aminotransferase, carbon dioxide, and acetate were carried out in growing and gestating heifers. These measures were carried out in a sample of 36 Bos taurus crossed beef heifers held under the same husbandry conditions. Hourly blood samples were collected over a 24-h period on three separate sampling occasions, corresponding approximately to the yearling (and open), early-gestation and late-gestation stages. This design was used to determine variation throughout the day, effects due to physiological status and any associations with feed efficiency, as measured by residual feed intake. Blood analyte levels varied with time of day, with the most variation occurring between 0800 and 1600 h. There were also considerable differences in analyte levels across the three physiological stages; for example, creatine kinase was higher (P<0.05) in open heifers, followed by early- and late-gestation heifers. Feed efficiency was also associated with analyte abundance. In more feed-efficient open heifers, there were higher activities of creatine kinase (P<0.05) and aspartate aminotransferase (P<0.05), and lower concentrations of carbon dioxide (P<0.05). Furthermore, in late gestation, more efficient heifers had lower urea concentrations (P<0.05) and lower creatine kinase levels (P<0.05). Over the whole experimental period, carbon dioxide concentrations were numerically lower in more feed efficient heifers (P=0.079). Differences were also observed across physiological stages. For instance, open heifers had increased levels (P<0.05) of creatine kinase, aspartate aminotransferase, carbon dioxide than early and late pregnancy heifers. In essence, this study revealed relevant information about the metabolic profile in the context of feed efficiency and physiological stages. Further optimization of our approach, along with the evaluation of complementary analytes, will aid in the development of robust, indirect assessments of feed efficiency.
    animal 06/2014; DOI:10.1017/S1751731114001463 · 1.84 Impact Factor
  • Source
    • "Comparing results from Table  4 with similarly grouped RFI animal studies in beef cattle shows a marked difference. In a study by Montanholi et al. [17], the most efficient beef bulls ranked based on RFI (low) had significantly lower DFI than the medium and high groups. This was also the case for Irish performance tested beef bulls, for which again the most efficient bulls ranked based on RFI had significantly lower DFI than the medium and high groups [9]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Since feed represents 70% of the total cost in poultry production systems, an animal's ability to convert feed is an important trait. In this study, residual feed intake (RFI) and residual body weight gain (RG), and their linear combination into residual feed intake and body weight gain (RIG) were studied to estimate their genetic parameters and analyze the potential differences in feed intake between the top ranked birds based on the criteria for each trait. Phenotypic and genetic analyses were completed on 8340 growing tom turkeys that were measured for feed intake and body weight gain over a four-week period from 16 to 20 weeks of age. The heritabilities of RG and RIG were 0.19 +/- 0.03 and 0.23 +/- 0.03, respectively. Residual body weight gain had moderate genetic correlations with feed intake (0.41) and body weight gain (0.43). All three linear combinations to form the RIG traits had genetic correlations ranging from 0.62 to 0.52 with feed intake, and slightly weaker, 0.22 to 0.34, with body weight gain. Sorted into three equal groups (low, medium, high) based on RG, the most efficient group (high) gained 0.62 and 1.70 kg more (P < 0.001) body weight than that of the medium and low groups, yet the feed intake for the high group was less (P < 0.05) than that of the medium group (19.52 vs. 19.75 kg). When separated into similar partitions, the high RIG group (most efficient) had both the lowest (P < 0.001) feed intake (18.86 vs. 19.57 and 20.41 kg) and the highest (P < 0.001) body weight gain (7.41 vs. 7.03 and 6.43 kg) relative to the medium and low groups, respectively. The difference in feed intake between the top ranked birds based on different residual feed efficiency traits may be small when looking at the average individual, however, when extrapolated to the production level, the lower feed intake values could lead to significant savings in feed costs over time.
    Genetics Selection Evolution 07/2013; 45(1):26. DOI:10.1186/1297-9686-45-26 · 3.82 Impact Factor
Show more