Variance components for residual feed intake in dairy cows

Feed intake, methane and feed efficiency have important genetic correlations with growth, carcase weights and mature size that need to be considered when breeding for production whilst reducing feed requirements and methane production in the Australian sheep industry. Live weight, growth, fat and muscle have significant antagonistic relationships with feed intake, which may make simultaneous selection for efficiency traits and, growth and meat quality slower. For example, selecting animals that grow faster is known to reduce meat-eating quality. Therefore, we estimated the genetic and phenotypic correlations between feed intake, residual feed intake, methane, carbon dioxide, oxygen, live weight, growth, fat and muscle depth traits. Fat and muscle depth were corrected for live weight. Traits were recorded on Merino sheep (n = 2 717) in Western Australia between 2010 and 2016. Sheep were measured at postweaning (range 753–2 717 records across traits), hogget (∼18 months old; range 602–1 046) and adult ages (>2 years old; range 269–443). Live weight and growth rate had significant moderate to high positive genetic correlations with feed intake, residual feed intake, methane and carbon dioxide at postweaning, hogget and adult ages. Fat and muscle depth measured at the start and finish of the feed intake measurement period generally had negative genetic correlations with residual feed intake, feed intake and methane. These genetic correlations with feed intake and residual feed intake were more negative with fat and muscle measured at the start of the measurement period than at the end. Furthermore, in young sheep, selecting for lower feed intake and residual feed intake will mean a lower change in fat between the start and finish of the intake period. Fat and muscle had significant correlations with feed efficiency and greenhouse gas traits and should therefore be considered when estimating residual feed intake, particularly in young animals.

Genetic parameters for Brahman (BRAH) and Tropical Composite (TCOMP) cattle were estimated for steer production traits recorded at weaning (WEAN), 80 days post-weaning (POSTW), feedlot entry (ENTRY) and after ~120 days feedlot finishing (EXIT). The TCOMP was 50% Bos indicus, African Sanga or other tropically adapted Bos taurus, and 50% non-tropically adapted Bos taurus. Data involved 2216 steers, comprising 1007 BRAH by 53 sires and 1209 TCOMP by 50 sires. Individual daily feed intake (DFI) and residual feed intake (RFI) were assessed on 680 BRAH and 783 TCOMP steers over an ~70-day feedlot test. Other traits were liveweight (LWT), average daily gain (ADG), ultrasonically scanned rump (SP8) fat depth, rib (SRIB) fat depth, M. longissimus area (SEMA) and intra-muscular fat % (SIMF), body condition score (CS), hip height (HH), flight time (FT) and serum insulin-like growth factor-I concentration (IGF-I). BRAH were significantly (P < 0.05) lighter at ENTRY and EXIT, and had lower DFI (10.8 v. 13.2 kg/day) and RFI (–0.30 v. 0.17 kg/day), greater SP8 (5.8 v. 5.1 mm) but similar SRIB at ENTRY, lower SRIB (8.2 v. 8.9 mm) but similar SP8 at EXIT, and greater HH than TCOMP. Heritabilities for DFI, RFI, LWT, ADG, scanned body composition, HH and IGF-I measures, across measurement times, were generally in the 20 to 60% range for both genotypes. Genetic variance for RFI was 0.19 (kg/day)2 in BRAH and 0.41 (kg/day)2 in TCOMP, suggesting a clear potential to genetically change RFI in both genotypes. Trait variances and genetic correlations often differed between the genotypes, supporting the use of genotype-specific parameters in genetic evaluation. The genotype differences may be associated with evolutionary changes that have occurred in B. indicus as a part of their adaptation to tropical environments. Measures with potential to be used as genetic indicators of DFI were LWT measures in BRAH and TCOMP, ADG at ENTRY in TCOMP, and SP8 and SIMF at ENTRY in BRAH. Measures with potential to be genetic indicators of RFI were HH and ADG at ENTRY in BRAH, and IGF-I in both genotypes. Taller and faster-growing BRAH steers at ENTRY had genetically lower RFI. IGF-I was negatively genetically correlated with RFI whether IGF-I was measured at POSTW, ENTRY or EXIT. SRIB fatness at EXIT was strongly positively genetically correlated with RFI in TCOMP but only lowly correlated in BRAH. Fatness at ENTRY was lowly and negatively genetically correlated with RFI. The results emphasise the need for a population-specific understanding of trait relationships and of trait differences between measurement times if genetic indicator traits are to be utilised in genetic evaluation of RFI.

Identification of biological traits associated with differences in residual energy intake among lactating Holstein cows

Hot topic: Definition and implementation of a breeding value for feed efficiency in dairy cows

Improving feed efficiency in cattle is important because it increases profitability by reducing costs, and it also shrinks the environmental footprint of cattle production by decreasing manure and greenhouse gas emissions. Residual feed intake (RFI) is 1 measurement of feed efficiency and is the difference between actual and predicted feed intake. Residual feed intake is a complex trait with moderate heritability, but the genes and biological processes associated with its variation still need to be found. We explored the variation in expression of genes using RNA sequencing to find genes whose expression was associated with RFI and then investigated the pathways that are enriched for these genes. In this study, we used samples from growing Angus bulls (muscle and liver tissues) and lactating Holstein cows (liver tissue and white blood cells) divergently selected for low and high RFI. Within each breed-tissue combination, the correlation between the expression of genes and RFI phenotypes, as well as GEBV, was calculated to determine the genes whose expression was correlated with RFI. There were 16,039 genes expressed in more than 25% of samples in 1 or more tissues. The expression of 6,143 genes was significantly associated with RFI phenotypes, and expression of 2,343 genes was significantly associated with GEBV for RFI ( < 0.05) in at least 1 tissue. The genes whose expression was correlated with RFI phenotype (or GEBV) within each breed-tissue combination were enriched for 158 (78) biological processes (Fisher Exact Statistics for gene-enrichment analysis, EASE score < 0.1) and associated with 13 (13) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways ( < 0.05 and fold enrichment > 2). These biological processes were related to regulation of transcription, translation, energy generation, cell cycling, apoptosis, and proteolysis. However, the direction of the correlation between RFI and gene expression in some cases reversed between tissues. For instance, low levels of proteolysis in muscle were associated with high efficiency in growing bulls, but high levels of proteolysis in white blood cells were associated with efficiency of milk production in lactating cows.

The objective of this study was to determine if beef heifers divergently ranked on phenotypic residual feed intake (RFI) differed in their physiological stress response to an exogenous bovine corticotropin-releasing hormone (bCRH) challenge. Yearling Limousin × Friesian heifers ( = 86) were ranked by RFI. The 15 highest (mean 0.66 kg DM/d; high RFI) and 15 lowest (mean -0.72 kg DM/d; low RFI) ranking animals were used for this study. During the study period, heifers (mean age 485 ± 13 d; mean BW 408 ± 31.4 kg) were housed in a slatted-floor facility. To facilitate intensive blood collection, heifers were fitted aseptically with indwelling jugular catheters. All heifers received dexamethasone (DEX; 20 µg/kg BW i.m.) 12 h before the bCRH challenge (d 0). Heparinized blood samples were collected at -60 and 0 min before administration of DEX, and 12 h after DEX administration. Following DEX administration, cortisol and dehydroepiandrosterone (DHEA) concentrations similarly decreased ( ≥ 0.22) between high and low RFI groups. The response of the HPA axis to a standardized dose of bCRH (0.3 μg/kg BW) was examined. On d 0, serial blood samples were collected at -20, 0, 20, 40, 60, 80, 100, 120, 150, 180, 210, 240, 270, 330, and 390 min relative to the time of bCRH administration (0 min) and were analyzed for plasma cortisol and DHEA concentrations. Blood hematology variables were also determined at -20, 0, 20, 80, 150, 270, 330, and 390 min relative to bCRH administration. Neither an RFI × sampling time interaction nor a direct effect of RFI were detected ( ≥ 0.36) for plasma cortisol, DHEA concentrations, or cortisol:DHEA ratio. An effect of sample time was observed for cortisol ( < 0.001), DHEA ( = 0.04), and cortisol:DHEA ( = 0.02), with cortisol concentration peaking at 60 min post-CRH administration. The maximum concentration and rate of change in cortisol and DHEA concentrations following bCRH administration were not different ( ≥ 0.20) between the high and low RFI phenotypes. Similarly, an effect of RFI was not evident ( ≥ 0.16) for any of the hematology variables examined including neutrophil, lymphocyte, and leukocytes counts; white and red blood cell numbers; and hemoglobin concentrations. Our results suggest that responsiveness of the HPA to stress, at least at the level of the anterior pituitary gland, is not a significant contributor to variation in RFI among growing beef heifers.

This meta-analysis aims to investigate the effects of residual feed intake (RFI) phenotype on performance, nutrient utilization and meat quality traits in Zebu (Bos indicus) cattle. Twenty-three peer-reviewed publications with 37 treatment means were included in the dataset. Weighted mean difference analysis compared animals categorized into low RFI (more efficient) versus medium or high RFI (less efficient) groups. Data heterogeneity via meta-regression and subgroup analysis, considering variables such as animal age, sex class, experimental duration, RFI group, dietary concentrate, and estimated metabolizable energy intake were also explored. The predominant genetic group of cattle in the dataset was Nellore (89.18%), followed by Brahman (10.81%). More efficient animals (low RFI phenotype) exhibited less dry matter intake (DMI; P < 0.010) than medium or high RFI animals (-0.95kg vs. -0.42kg/d). Cattle dietary crude protein and fiber digestibility were consistent across RFI groups (P > 0.05), while dietary ether extract digestibility tended to decrease (P = 0.050) in low RFI animals (-13.20g/kg DM). Low RFI animals tended to increased (P = 0.065) ribeye area (REA) compared to the high/medium RFI groups, while carcass backfat thickness (BFT) decreased (P = 0.042) compared to high/medium RFI groups. Moreover, there was an increase (P < 0.001) of 0.22kg in Warner-Bratzler shear force (WBSF) and a reduction (P < 0.001) in the myofibrillar fragmentation index (MFI) in low RFI animals. Meat color parameters (lightness [L*] and yellowness [b*]) and visual marbling scores were consistent (P > 0.05) across RFI groups. In conclusion, Zebu cattle classified as efficient (low RFI) exhibited reduced DMI, which improves their feed efficiency. However, BFT and meat quality parameters such as tenderness (WBSF and MFI) and redness [a*] were compromised by low RFI phenotype, highlighting the challenge of enhancing feed efficiency and meat quality traits in Zebu cattle.

This study examined the relationship of feed efficiency and performance with feeding behavior, blood metabolic variables, and various body composition measurements in growing beef heifers. Individual DMI and growth were measured in yearling Limousin x Holstein-Friesian heifers [n = 86; initial BW = 191.8 (SD = 37) kg] fed a TMR diet comprising 70:30 concentrate:corn silage on a DM basis (ME of 2.65 Mcal/kg of DM; DM of 580 g/kg) for 82 d. Meal duration (min/d) and meal frequency (events/d) were calculated for each animal on a daily basis using an Insentec computerized feeding system. Physical measurements as well as ultrasonic fat and muscle depths were recorded on 3 equally spaced occasions during the experimental period. Blood samples were collected by jugular venipuncture on 4 equally spaced occasions and analyzed for plasma concentrations of IGF-I, insulin, leptin, and various metabolites. Phenotypic residual feed intake (RFI) was calculated for all animals as the residuals from a multiple regression model regressing DMI on ADG and midtest BW(0.75). Overall, ADG, DMI, feed conversion ratio (FCR), and RFI were 1.51 (SD = 0.13), 6.74 (SD = 0.99), 4.48 (SD = 0.65), and 0.00 (SD = 0.48) kg/d, respectively. Residual feed intake was positively correlated with DMI (r = 0.47) and FCR (r = 0.46), but not with ADG or midtest BW. Positive correlations (ranging from r = 0.27 to r = 0.63) were estimated between ultrasonic measures of final lumbar fat and lumbar fat accretion over the test period and DMI, FCR, and RFI. The inclusion of gain in lumbar fat to the base RFI model increased R(2) (0.77 vs. 0.80) value for the degree of variation in DMI not explained by midtest BW and ADG alone. The Pearson rank correlation between RFI and carcass-adjusted RFI (RFI(c)) was high (r = 0.93). From the plasma analytes measured, NEFA (r = -0.21; P < 0.05) and beta-hydroxybutyrate (r = 0.37; P < 0.05) concentrations were correlated with RFI. Plasma leptin (r = 0.48), glucose:insulin (r = -0.23), NEFA (r = -0.32), and beta-hydroxybutyrate (r = 0.25) were associated with FCR. However, systemic IGF-I and insulin were unrelated (P > 0.05) to any measure of feed efficiency. The feeding behavior traits of eating rate, daily feeding events, and nonfeeding events were positively correlated (P < 0.05) with RFI and RFI(c). This multifactorial study provides new information on some of the biological processes responsible for variation in feed efficiency in beef cattle.

Data were collected in the course of a divergent selection experiment for residual feed intake (RFI) of Large White growing pigs. This data set was used to estimate (i) heritability for RFI and genetic correlations of RFI with growth and carcass traits within the three sexes (male, castrate and female) and (ii) genetic correlations between sexes for these traits. Individual feed intake of animals raised in collective pens was measured by single-place electronic feeders on 1121 males (candidates for selection), 508 females and 535 castrates (sibs of candidates). Variance components were estimated using the REML methodology applied to a multitrait animal model. Estimates of heritability for RFI were 0.16 ± 0.04, 0.16 ± 0.08 and 0.23 ± 0.10 for males, females and castrates, respectively. Estimates of genetic correlations between sexes for homologous traits were not significantly different from 1 (0.88 to 0.99 for RFI, 0.79 to 0.99 for growth traits and 0.65 to 0.99 for carcass composition traits). The relatively low genetic correlations between castrates and males or females for backfat thickness (0.65 and 0.69, respectively) suggest the presence of genotype by sex interactions for this trait.

Residual feed intake (RFI) is commonly used to measure feed efficiency but individual intake recording systems are needed. Feeding behavior may be used as an indicator trait for feed efficiency using less expensive precision livestock farming technologies. Our goal was to estimate genetic parameters for feeding behavior and the genetic correlations with feed efficiency in Holstein cows. Data consisted of 75,877 daily feeding behavior records of 1,328 mid-lactation Holstein cows in 31 experiments conducted from 2009 to 2020 with an automated intake recording system. Feeding behavior traits included number of feeder visits per day, number of meals per day, duration of each feeder visit, duration of each meal, total duration of feeder visits, intake per visit, intake per meal [kg of dry matter (DM)], feeding rate per visit, and feeding rate per meal (kg of DM per min). The meal criterion was estimated as 26.4 min, which means that any pair of feeder visits separated by less than 26.4 min were considered part of the same meal. The statistical model included lactation and days in milk as fixed effects, and experiment-treatment, animal, and permanent environment as random effects. Genetic parameters for feeding behavior traits were estimated using daily records and weekly averages. Estimates of heritability for daily feeding behavior traits ranged from 0.09 ± 0.02 (number of meals; mean ± standard error) to 0.23 ± 0.03 (feeding rate per meal), with repeatability estimates ranging from 0.23 ± 0.01 (number of meals) to 0.52 ± 0.02 (number of feeder visits). Estimates of heritability for weekly averages of feeding behavior traits ranged from 0.19 ± 0.04 (number of meals) to 0.32 ± 0.04 (feeding rate per visit), with repeatability estimates ranging from 0.46 ± 0.02 (duration of each meal) to 0.62 ± 0.02 (feeding rate per visit and per meal). Most of the feeding behavior measures were strongly genetically correlated, showing that with more visits or meals per day, cows spend less time in each feeder visit or meal with lower intake per visit or meal. Weekly averages for feeding behavior traits were analyzed jointly with RFI and its components. Number of meals was genetically correlated with milk energy (0.48), metabolic body weight (-0.27), and RFI (0.19). Duration of each feeder visit and meal were genetically correlated with milk energy (0.43 and 0.44, respectively). Total duration of feeder visits per day was genetically correlated with DM intake (0.29), milk energy (0.62), metabolic body weight (-0.37), and RFI (0.20). Intake per visit and meal were genetically correlated with DM intake (0.63 and 0.87), milk energy (0.47 and 0.69), metabolic body weight (0.47 and 0.68), and RFI (0.31 and 0.65). Feeding rate was genetically correlated with DM intake (0.69), metabolic body weight (0.67), RFI (0.47), and milk energy (0.21). We conclude that measures of feeding behavior could be useful indicators of dairy cow feed efficiency, and individual cows that eat at a slower rate may be more feed efficient.

Prediction of residual feed intake for first-lactation dairy cows using orthogonal polynomial random regression

In view of the concern to maximize the productivity of beef cattle, with lower production cost, the residual feed intake - CAR is the main measure to be used for the selection of animals efficient in the use of food. The important thing to note is that the selection of these animals is necessary, but there can be no antagonism between the CAR and other traits of interest in production, such as adequate fat deposition and carcass yield. In this sense, this study aimed to verify the genetic association between the traits related to feed efficiency and the carcass traits of Nellore cattle. The information of 7,808 males and females Nellore, pure of origin (PO), were consider, evaluated in 142 tests of feeding efficiency carried out in the period from 2011 to 2020, in 15 farms participating in the program Nellore Brazil of the National Association of Breeders and Researchers - NABR. The traits of residual feed intake - RFI, dry matter intake - DMI, rib eye area - REA, carcass finish - CF and marbling - MAR were evaluate. Feed efficiency and carcass traits were also evaluate only in Nellore females. The estimated of the components of variances and covariance, as well as, of heritabilities and genetic correlations were obtain through bicharacteristic analyzes under an animal model, considering the methodology of Maximum Derivative Free Restricted Likelihood. It was check that the heritability characteristics were moderate for the characteristics of RFI (0.22) and DMI (0.29), which proves the existence of genetic variability in the evaluated Nellore population. It was observe that as a genetic correlation were of low magnitude, with values close to zero, for all traits,except between RFI and REA (-0.11). In general, these results infer that the selection for RFI and DMI does not cause genetic damage to the carcass traits in Nellore cattle. However, when evaluating the genetic associations of RFI and DMI with carcass traits, considering the population of females, there were an increase in the estimate of genetic correlation with RFI and DMI, although it is still a favorable genetic association of low magnitude (- 0.30). There was also an increase in the genetic association of REA with the RFI(-0.21), which indicates that there are some favorable response from the selection of more efficient animals with the improvement of the carcass yield, however, it must be emphasized, that such changes are not very significant. It can be conclude that the direct selection for residual feed intake (RFI) and dry matter intake (DMI) will not influence the deposition of subcutaneous fat (CF), marbling (MAR), and rib eye area (REA) of Nellore cattle. However, the selection for the RFI may be generate some favorable response in the deposition of intramuscular fat (MAR) and in rib eye area (REA) of Nellore females.

Feed efficiency traits are important for reducing feeding costs while potentially decreasing the environmental impact of broiler production. This study aimed to compare the predictive ability of different genome-wide prediction models for residual feed intake (RFI) calculated with linear and non-linear regression techniques. We also evaluated the heritability of such RFI as well as their associations with body weight. Genotypes and phenotypes used in this study were recorded from 32,747 purebred broiler chickens of both sexes. The individual feed intake was monitored daily in floor-raised birds using a radio-frequency antenna system. RFI was computed as the residual term of total feed intake (FI) adjusted for the average metabolic weight (AMW), body weight gain (BWG), and sex. The following regression methods were used to compute RFI: multiple linear regression (MLR), generalized additive model using splines (GAM), support vector regression using a radial basis kernel (SVR), and two-hidden layer neural network (NN). The different RFI assessments were fitted jointly with body weight measured at the end of the feeding trial (FBW) using a two-trait mixed model. The effects of contemporary groups were fitted as fixed for both traits, whereas sex was also considered for FBW. RFI (computed with different regression techniques) was predicted using different genomic prediction models (BayesA, BayesB, BayesCπ, GBLUP, RKHS, and SVR). Genomic prediction accuracy (ACC) was assessed using a random forward validation scheme, in which data from previous generations were used for training the models while later generations were used to test predictions. The effect of the RFI computation method on ACC was tested using an unpaired Wilcoxon test. Sample variance observed for RFI ranged between 57,207.04 and 59,254.76, depending on the regression method used to compute it. Overall, the RFI sample variance was smaller for non-linear methods (NN, SVR, and GAM) compared with the linear model (MLR). The regression methods did not impact the genetic parameter estimates for RFI, which presented a heritability value around 0.32 ± 0.015 and genetic correlation with FBW ranging between 0.22 ± 0.04 and 0.25 ± 0.04. Prediction accuracy across genomic regression methods was slightly higher (P-value = 0.038) when RFI was computed with SVR (0.336 ± 0.023) compared with the MLR (0.329 ± 0.025), although this small difference would have little practical implications. No differences in terms of ACC were observed among the other regression methods used to compute RFI. Our preliminary results indicate that the regression technique used to compute RFI does not have major implications for this trait. Therefore, the MLR method is the preferred choice as it is simpler to implement, and it provides easy-to-interpret parameters.

Identification and characterization of hypothalamic circular RNAs associated with bovine residual feed intake

Feed represents a substantial proportion of the variable costs of pig production. Feed efficiency is traditionally expressed as the feed conversion ratio (FCR) and more recently as residual feed intake (RFI). Although feed efficiency can be generally improved indirectly by selection for increased growth rate and decreased adipose tissue, a higher genetic response could be achieved through direct selection of feed intake traits. The aim of this study was to provide a pilot analysis of feed intake data of 281 Czech Large White boars. Data were recorded individually using the Feed Intake Recording Equipment in field performance testing from 2018 to 2020. The analysed feed intake traits were average daily feed intake (ADFI), FCR and RFI. RFI was calculated as the deviation of observed ADFI and average population ADFI predicted on the basis of the model, with mid-test metabolic weight and average daily gain as regressors. The heritability estimates were 0.35 and 0.34 for ADFI and FCR, respectively, and the estimate was slightly higher (0.43) for RFI. The genetic standard deviations ranged from 100 to 110 g of feed per day and 103 g of feed per kg of weight gain. The amounts of explained variability by environmental effects of jointly tested animals were from 0.20 to 0.46. The sufficient amount of genetic variability and moderate heritability estimates give the possibility for selection of feed intake traits, although a larger number of animals will be essential to estimate more precise breeding values.