The phenotypic relationship between residual intake and gain and other feed efficiency traits in Nellore cattle

This study aimed to compare feed efficiency measures of Nellore beef cattle on different residual intake and gain (RIG) classes. We used data from 610 animals weighing on average 236.33 kg and average of 283 days of age from feedlot performance tests carried out between 2005 and 2012. Animals were grouped based on RIG into three different classes: high RIG (>?mean?+?0.5 standard deviation (SD), most efficient; n?=?193), medium RIG (mean?±?0.5 SD; n?=?235), and low RIG (<?mean – 0.5 SD, least efficient; n?=?182). Residual feed intake (RFI), residual gain (RG), feed conversion ratio (FCR), feed efficiency (FE), relative growth rate (RGR), and Kleiber ratio (KR) of animals in each RIG class were compared by Tukey test at 1% of probability. Phenotypic correlations between variables were evaluated as well. Animals on high RIG class showed lower dry matter intake (P?<?0.01) and higher average daily gain (P?<?0.01) than low RIG animals. Consequently, high RIG animals had lower FCR (P?<?0.01) and higher FE (P?<?0.01) than those animals in low RIG class. The most efficient animals based on RIG were also the most efficient animals based on RG and RFI. RIG was negatively correlated to dry matter intake (P?<?0.01) and FCR (P?<?0.01), and a positive correlation was found between RIG and FE (P?<?0.01). Therefore, RIG appears to be a good parameter to select animals with reduced dry matter intake and high productive performance.

     

Phenotypic and genetic relationships between residual energy intake and growth, feed intake, and carcass traits of young bulls.

Residual energy intake, defined as actual minus predicted energy intake during a production period, was estimated for each of 650 bull calves of 31 Holstein Friesian or Brown Swiss sires. Residual energy intake, measured under ad libitum feeding, had heritabilities similar to those of growth rate and energy conversion ratio with an estimate of approximately .3. Residual energy intake was related to average daily energy intake both phenotypically and genetically such that selection for decreased residual energy intake would lead to a decrease in daily feed intake. Such selection would also tend to increase carcass fatness (i.e., genetically fat animals are the most efficient). Residual energy intake estimated with and without correction for carcass composition were closely correlated. Thus, residual energy intake may be estimated without the knowledge of carcass composition in growing bulls of dual-purpose breeds.     

Relationship between body measurements,metabolic hormones,metabolites and residual feed intake in performancetested pedigree beef bulls

The objective of this study was to examine the relationship between easily measured, potential physiological and physical indicators of feed efficiency including metabolic hormones, metabolites, ultrasonic muscle and fat measures with performance and efficiency traits in performance tested pedigree beef bulls (n = 302; initial bodyweight 493 (SD = 64) kg). Animals were offered a high energy concentrate ad libitum plus 1.5 kg fresh weight grass hay daily and individual feed intake was measured for 70 days. Blood samples were collected by coccygeal venipuncture at the start and end of the performance test period and analysed for plasma concentrations of IGF-I, insulin, leptin and various metabolites. Similarly, ultrasonic muscle and fat depths were measured at the start and end of the test period. Residual feed intake (RFI) was computed for each animal as the residuals from a multiple regression model regressing dry matter intake (DMI) on average daily gain (ADG) and mid-test BW^0.75 (MWT). Overall ADG, DMI, feed:gain (F:G) and RFI were 1.91 (SD = 0.29), 10.10 (SD = 1.31), 5.37 (SD = 0.84) and 0.00 (SD = 0.78), respectively. Residual feed intake was strongly correlated with DMI (r = 0.67) and moderately correlated with F:G (r = 0.40). Moderate positive correlations ranging from 0.3 to 0.6 were estimated between ultrasonic measures of final fat and muscle depth and their respective gains over the test period with DMI, ADG and RFI. There was no evidence of a strong association between any of the plasma analytes measured and RFI at either sampling times. However, end of test insulin was negatively correlated (r = − 0.14) with RFI. Final IGF-I concentration was negatively associated with DMI (r = − 0.14) and F:G (r = − 0.15). End of test circulating leptin concentration was positively correlated with DMI (r = 0.14) and F:G (r = 0.15). Plasma glucose concentration at the end of test was negatively related to DMI (r = − 0.21) and F:G (r = − 0.21). A positive relationship was observed between end of test plasma urea concentration and DMI (r = 0.30). Overall, the correlation coefficient estimates between the potential blood markers and measures of intake, performance and efficiency were weak and generally not different from zero. This suggests that it is unlikely that measurement of these metabolic indicators, per se, will be useful in the early identification of feed efficient animals.     

The relationship between residual feed intake and feed intake behavior in group-housed Duroc barrows

Because feed is the major input in pork production, conversion of feed into lean tissue at minimum costs has been a focus for improvement. Several researchers have proposed using residual feed intake (RFI) rather than feed conversion ratio (FCR) for genetic improvement of feed efficiency. Little is known about the variation in RFI in pigs. As several studies suggest a greater RFI is related to greater animal activity levels, the current study investigated the phenotypic relationship between RFI and feed intake (FI) behavior of 104 group-housed growing Duroc barrows allowed ad libitum access to feed. Feed intake, BW gain, feeding time (TIME), feeding frequency (VISITS), RFI, and FCR were calculated for 5 periods of 14, 23, 28, 21, or 23 d in length (periods 1 through 5, respectively) on animals that were between 73 to 95 d of age at the start of the testing period. Barrows that grew faster consumed more feed (P < 0.001), and barrows that consumed more feed were fatter (P < 0.01). There were no correlations between VISITS and TIME, between VISITS and FI, or between VISITS and RFI. Barrows that spent more time at the feeder, however, consumed more feed (P < 0.05) and had greater RFI in periods 1, 3, and 5 (P < 0.05). As expected, FI and FCR were highly correlated with RFI (P < 0.001). These results suggest that a greater FI rather than greater feed intake activity resulted in greater RFI values.     

Effect of selection for residual feed intake on feeding behavior and daily feed intake patterns in Yorkshire swine

Residual feed intake (RFI) is a measure of feed efficiency defined as the difference between observed and predicted feed intake based on average requirements for growth and maintenance. The objective of this study was to evaluate the effect of selection for decreased RFI on feeding behavior traits and to estimate their relationships with RFI. Three data sets from the 4th and 5th generations of a selection experiment with a line selected for reduced RFI (LRFI) and a randomly selected control line (CTRL) were analyzed. Lines were mixed in pens of 16 and evaluated for feeding behavior traits obtained from a single-space electronic feeder over a growing period of ~3 mo before ~115 kg. The following traits were evaluated as averages over the entire test period and over the first and second half of the test period: number of visits per day and hour; occupation time per day, visit, and hour; feed intake (FI) per day, visit, and hour; and FI rate per visit. Models used included fixed effects of line and feeder, covariates of on-test age and FI per day, and random effects of pen, on-test group, sire, and litter. Repeated measures models were used to analyze feeding patterns during the day. The LRFI pigs had significantly less FI per day than CTRL pigs for all 3 data sets. With adjustment for FI per day, line differences of all traits were in the same direction for all 3 data sets but differed in significance and size. Feed intake per visit and hour and visits per day and hour did not differ between lines, but the trend was for LRFI pigs to have fewer visits, in particular during peak eating times. The LRFI pigs had a greater feeding rate and less occupation time per day, visit, and hour than CTRL pigs, but this was not significant for all data sets. Correlations of RFI with FI per day and visit and visits per day were positive. Average daily gain was positively correlated with FI per day and visit and occupation time per visit but negatively correlated with visits per day. Feed intake per day was positively correlated with backfat. In conclusion, feed efficiency may be affected by FI behavior because selection for decreased RFI has resulted in pigs that spend less time eating and eat faster.     

Evidence of residual feed intake reranking in crossbred replacement heifers

The objective of the study was to examine whether residual feed intake (RFI) reranking exists between 2 consecutive periods in replacement heifers fed the same diet. The study collected feed intake and BW data from 190 crossbred heifers over a 3-yr period (61 in 2007, 68 in 2008, and 61 in 2009) during the winter-spring season. The heifers were fed a roughage-based diet (90% barley silage and 10% rolled barley grain) throughout the feeding trial, which was broken down into 2 feeding periods with ADG of 0.94 and 0.90 kg?d(-1) in the first and second periods, respectively. The RFI was calculated for the entire period using different models, which included ADG, mid-metabolic BW, body composition, and feeding activity. Gain:feed ratio and Kleiber ratio were also calculated. Rank correlations among the RFI calculated from different models were obtained, as well as rank correlations between the 2 feeding periods for the feed efficiency measures. Including body composition and feeding activity only improved the R(2) by 1 to 5%. The rank correlations among the different models were high (90 to 95%) for RFI calculated for the entire feeding period. However, the RFI calculated within the second feeding period had greater rank correlation than the RFI calculated from the entire feeding period. Compared with G:F and Kleiber ratio, RFI gave lesser reranking between periods 1 and 2. About 49% of the heifers maintained their RFI class, whereas 51% of the heifers had a different RFI class in period 2. Furthermore, 41% of the heifers changed their RFI in period 2 by <0.5 SD, whereas the rest of the heifers changed by ≥0.5 SD. These results indicate that reranking exists in heifers despite receiving the same diet in the 2 feeding periods and that the reranking may be more serious in heifers (28%) with extreme RFI performances in each period.     

Bayesian analysis of the effect of selection for residual feed intake on growth and feed intake curves in Yorkshire swine

Gompertz growth functions were fitted to longitudinal measurements of daily feed intake (DFI) and BW of 586 boars and 495 gilts from a selection experiment in Yorkshire pigs for residual feed intake (RFI). The selection experiment consists of a line selected for low residual feed intake (LRFI) for 5 generations and a randomly selected control line (CTRL). The objectives of this study were to use Bayesian methods to estimate genetic parameters of the Gompertz curve parameters for DFI and BW, to evaluate the effect of selection for reduced RFI on the Gompertz parameters and shape of curves for DFI and BW, and to develop methodology for quantifying genetic variation at the level of the original phenotypes for DFI and BW based on the Bayesian analysis of the nonlinear model. Separate analyses were done for boars and gilts and for BW and DFI. A hierarchical model was specified in 2 levels: in the first level, the Gompertz function was modeled for each pig, and at the second level, a 3-trait linear mixed model was fitted to the 3 Gompertz parameters (asymptotic value, inflection point, and decay parameter), with fixed effects of line by generation and random effects of additive genetic and environmental effects. Bayesian methods were used to combine the 2 levels of modeling. A total of 30,000 random samples of the posterior distributions after convergence of Markov chains were used for inference. Posterior means of heritability within the first level of the model for the asymptotic value, inflection point, and decay parameter for DFI were 0.74, 0.66, and 0.82 for boars and 0.79, 0.70, and 0.57 for gilts; corresponding estimates for BW were 0.64, 0.58, and 0.60 for boars and 0.46, 0.35, and 0.33 for gilts. For DFI, LRFI boars had a reduced mature DFI (2.91 vs. 3.20 kg/d) and an earlier inflection point (85 vs. 95 d) compared with CTRL boars. For BW, LRFI boars had a lighter mature BW (279 vs. 317 kg), an earlier inflection point (184 vs. 198 d), and a decreased decay parameter (127 vs. 134 d) compared with CTRL boars. In contrast, LRFI gilts had a later inflection point (225 vs. 200 d) and a greater decay parameter (172 vs. 143 d) than CTRL gilts for BW. The other Gompertz curve parameters for DFI and BW for boars and gilts were considered not different between lines, with posterior probabilities of the line differences being greater than zero ranging from 0.1 to 0.9.     

Heritabilities and genetic correlations for postweaning growth and feed intake of beef bulls and steers

Data from studies conducted at Miles City, MT and Lethbridge, AB were pooled to evaluate genetic and environmental variation in feed intake (MEI), growth rate (ADG), MEI-to-gain ratio (M/G), final weight (FWT), and fat thickness (FAT). A total of 124 sires with an average of 4.25 progeny each were represented in the data. Restricted maximum likelihood methods were used to estimate within and between paternal half-sib estimates of variance and covariance. Heritabilities and genetic, phenotypic, and environmental correlations with inference to populations at 365 d of age were calculated from the estimates. Heritabilities were as follows: ADG, .38 +/- .16; MEI, .45 +/- .17; M/G, .26 +/- .15; FWT .25 +/- .15; and FAT .52 +/- .17. The genetic correlation of MEI with ADG was large (.73 +/- .13) and antagonistic to genetic improvement of M/G through selection for ADG. Efficient genetic improvement in M/G was found to depend on using either MEI or an indicator of composition of gain as selection criteria in addition to ADG. Selection to improve M/G using an index that included FWT and FAT, in addition to MEI and ADG, resulted in greater predicted response in ADG and lesser predicted response in MEI than the index of ADG and MEI alone.     

The effect of residual feed intake classification on forage intake by grazing beef cows

Although feed intake and efficiency differences in growing cattle of low and high residual feed intake (RFI) classification have been established, little is known about the difference in grazed forage intake between beef cows of known RFI classification. Two experiments were conducted using Hereford cows for which RFI had been determined as heifers using the GrowSafe 4000E feed intake system, after which heifers had been divided into thirds as low RFI, mid RFI, and high RFI. During Exp. 1, 2 replicates of low and high RFI cows (n = 7/replicate) in mid- to late-gestation were blocked to 1 of 4 non-endophyte-infected tall fescue paddocks (1.8 to 2.4 ha), which they grazed continuously for 84 d during summer. Using grazing exclosures, weekly rising plate meter readings, and forage harvests every 21 d, average forage DMI was calculated. Low and high RFI groups did not differ (P > 0.05) in BW change or BCS change over the trial (19.5 vs. 22.1 kg of BW gain and 0.11 vs. 0.10 BCS gain), but low RFI cows had a 21% numerically lower DMI than high RFI cows (12.4 vs. 15.6 kg/d; P = 0.23). The average area needed per paddock over the trial was similar for low and high RFI cows (1.71 vs. 1.82 ha; P = 0.35), and the average DM on offer over the trial was less for low RFI than for high RFI cows (4,215 vs. 4,376 kg; P = 0.06). During Exp. 2, 3 replicates of low and high RFI cows with their calves (n = 4 pair/replicate) strip-grazed stockpiled and early spring growth tall fescue paddocks (0.7 to 0.9 ha) for 60 d in late winter and early spring. Because of limiting forage availability and quality at trial initiation, cow-calf pairs were also fed 3.31 kg/pair of pelleted soyhulls daily. Pre- and post-grazed forage samples were harvested for 4 grazing periods, and forage growth was estimated using a growing degree days calculation and on-site weather station data. Performance did not differ (P > 0.05) between low and high RFI cows throughout the experiment (18.4 vs. 26.6 kg of BW gain and -0.04 vs. 0.15 BCS gain). Despite the utilization of forage offered being similar for low and high RFI cow-calf pairs (P > 0.05), low RFI cows and their calves had an 11% numerically lower DMI than high RFI pairs (12.5 vs. 14.1 kg/d; P = 0.12). We concluded that either no intake differences existed between low and high RFI cows or that current methodology and small animal numbers limited our ability to detect differences.     

Evaluation of equations to predict body composition in Nellore bulls

The equations developed by Hankins and Howe (1946, HH), Marcondes et al. (2010, M10), Marcondes et al. (in press, M11) and Valadares Filho et al. (2006, V6) were evaluated to predict the body composition from the 9–10–11th rib cut in Nellore bulls. The evaluated equations estimated the physical and the carcass chemical composition, the empty body chemical composition and the noncarcass chemical composition. Thirty-seven Nellore bulls (14±1 months old initially) with shrunk body weight of 259±24.9 kg were used in this experiment. The bulls were randomly divided into three groups: five bulls to the reference group, four bulls were fed at maintenance level and twenty-eight bulls were fed ad libitum. The bulls fed ad libitum were separated into four groups, one of which was slaughtered every 42 days. The diet was composed of corn silage and concentrate (55:45). After slaughter, the 9–10–11th rib cut was dissected into muscle, fat and bone fractions. The remaining carcass was similarly dissected. The others parameters that were evaluated as partial predictors included the empty body weight, the dressing percentage, the visceral fat percentage, the organ and viscera percentage and the composition of the noncarcass components. The values estimated with prediction equations were compared to the observed values. The equations obtained by M11 predicted correctly the carcass physical composition. However, the muscle and fat tissues were under- and overestimated, respectively, by HH. Some constituents of the noncarcass components can be predicted from equations developed by M10. The equations obtained by M10 predicted correctly the carcass and empty body chemical composition. The carcass water was underestimated by HH. The equations by V6 did not predict the carcass or empty body chemical composition. The carcass physical and chemical composition and empty body chemical composition can be predicted from the composition of 9–10–11th rib cut by equations obtained by Marcondes et al., 2010 and MarcondesPlease complete and update the reference given here (preferably with a DOI if the publication data are not known): Marcondes et al. (in press). For references to articles that are to be included in the same (special) issue, please add the words ‘this issue’ wherever this occurs in the list and, if appropriate, in the text. et al., while the composition of these components cannot be predicted by Hankins and Howe (1946) and Valadares Filho et al. (2006) in Nellore bulls.     

Correlated responses in sow appetite, residual feed intake, body composition, and reproduction after divergent selection for residual feed intake in the growing pig

Residual feed intake (RFI) has been explored as an alternative selection criterion to feed conversion ratio to capture the fraction of feed intake not explained by expected production and maintenance requirements. Selection experiments have found that low RFI in the growing pig is genetically correlated with reduced fatness and feed intake. Selection for feed conversion ratio also reduces sow appetite and fatness, which, together with increased prolificacy, has been seen as a hindrance for sow lifetime performance. The aims of our study were to derive equations for sow RFI during lactation (SRFI) and to evaluate the effect of selection for RFI during growth on sow traits during lactation. Data were obtained on 2 divergent lines selected for 7 generations for low and high RFI during growth in purebred Large Whites. The RFI was measured on candidates for selection (1,065 pigs), and sow performance data were available for 480 sows having from 1 to 3 parities (1,071 parities). Traits measured were sow daily feed intake (SDFI); sow BW and body composition before farrowing and at weaning (28.4 ± 1.7d); number of piglets born total, born alive, and surviving at weaning; and litter weight, average piglet BW, and within-litter SD of piglet BW at birth, 21 d of age (when creep feeding was available), and weaning. Sow RFI was defined as the difference between observed SDFI and SDFI predicted for sow maintenance and production. Daily production requirements were quantified by litter size and daily litter BW gain as well as daily changes in sow body reserves. The SRFI represented 24% of the phenotypic variability of SDFI. Heritability estimates for RFI and SRFI were both 0.14. The genetic correlation between RFI and SRFI was 0.29 ± 0.23. Genetic correlations of RFI with sow traits were low to moderate, consistent with responses to selection; selection for low RFI during growth reduced SDFI and increased number of piglets and litter growth, but also increased mobilization of body reserves. No effect on rebreeding performance was found. Metabolic changes previously observed during growth in response to selection might explain part of the better efficiency of the low-RFI sows, decreasing basal metabolism and favoring rapid allocation of resources to lactation. We propose to consider SRFI as an alternative to SDFI to select for efficient sows with reduced input demands during lactation.     

Linkage between the intestinal microbiota and residual feed intake in broiler chickens

Background: Intestinal microbiota plays a key role in nutrient digestion and utilization with a profound impact on feed efficiency of livestock animals. However, the intestinal microbes that are critically involved in feed efficiency remain elusive.Methods: To identify intestinal bacteria associated with residual feed intake(RFI) in chickens, male Cobb broiler chicks were individually housed from day 14 to day 35. Individual RFI values were calculated for 56 chickens.Luminal contents were collected from the ileum, cecum, and cloaca of each animal on day 35. Bacterial DNA was isolated and subjected to 16 S rRNA gene sequencing. Intestinal microbiota was classified to the feature level using Deblur and QIIME 2. High and low RFI groups were formed by selecting 15 and 17 chickens with the most extreme RFI values for subsequent LEfSe comparison of the difference in the microbiota. Spearman correlation analysis was further performed to identify correlations between the intestinal microbiota composition and RFI.Results: No significant difference in evenness, richness, and overall diversity of the microbiota in the ileum, cecum,or cloaca was observed between high and low RFI chickens. However, LEfSe analysis revealed a number of bacterial features being differentially enriched in either high or low RFI chickens. Spearman correlation analysis further identified many differentially enriched bacterial features to be significantly correlated with RFI(P 0.05). Importantly,not all short-chain fatty acid(SCFA) producers showed a positive association with RFI. While two novel members of Oscillibacter and Butyricicoccus were more abundant in low-RFI, high-efficiency chickens, several other SCFA producers such as Subdoligranulum variabile and two related Peptostreptococcaceae members were negatively associated with feed efficiency. Moreover, a few closely-related Lachnospiraceae family members showed a positive correlation with feed efficiency, while others of the same family displayed an opposite relationship.Conclusions: Our results highlight the complexity of the intestinal microbiota and a need to differentiate the bacteria to the species, subspecies, and even strain levels in order to reveal their true association with feed efficiency. Identification of RFI-associated bacteria provides important leads to manipulate the intestinal microbiota for improving production efficiency, profitability, and sustainability of poultry production.     

Piglet gut health linked to high feed intake

如果断奶仔猪采食量得当,它们几乎不会发生肠道损伤.高采食量有利于仔猪消化和吸收蛋白质、淀粉等养分,从而达到想要的生长水平.断奶仔猪的肠道健康及其生长性能研究表明,高采食量会使病原菌濒临绝境,有利于生产出健康的猪.对于养猪生产者而言,用抗生素和锌使仔猪肠道保持健康水平并非是可选方案,因为这两种办法正受到控制,并逐步淘汰.另外,与大肠杆菌相比,链球菌似乎更容易导致仔猪患病和死亡.     

Phenotypic and genetic relationships among feeding behavior traits, feed intake, and residual feed intake in steers fed grower and finisher diets

Data from a 3-yr feeding trial of crossbred steers (n = 331) were used to examine the relationship between feeding behavior traits and feed efficiency in steers fed grower and finisher diets, successively. There were 2 feeding periods each year whereby the steers were fed a grower diet in the first feeding period (P1) and a finisher diet in the second feeding period (P2). Each feeding period lasted for a minimum of 10 wk, ad libitum. In addition to feed intake, records on 3 measures of feeding behavior [feeding duration (FD), head-down time (HDT), and feeding frequency (FF)] were collected using the GrowSafe feeding system. Residual feed intake (RFI) was calculated by regression, after which the steers were classified as low (<0.5 SD), medium (±0.5 SD), or high (>0.5 SD) from the mean. The steers had greater (P < 0.001) FD, HDT, and FF when the grower diet was fed but greater feeding rate (FR) when the finisher diet was fed. Including the measures of feeding behavior as covariates to the feed intake prediction model containing ADG, metabolic midweight, and ultrasound backfat accounted for more variation in DMI than models that did not contain these additional parameters. The FD and HDT were significantly different (P < 0.05) among the RFI classes regardless of the feeding period, whereas no differences (P > 0.90) were found for FR among the RFI classes. For the growing period and finishing period, respectively, FD had phenotypic correlations with HDT (0.79, 0.83), FF (0.14, 0.55), DMI (0.38, 0.34), and FR (-0.34, -0.21). Heritability estimates in P1 and P2 for FD, HDT, and FF were 0.25 ± 0.16, 0.14 ± 0.11; 0.14 ± 0.15, 0.09 ± 0.10; and 0.56 ± 0.19, 0.59 ± 0.18, respectively. Genetic correlations between P1 and P2 were 0.91 ± 0.26, 0.93 ± 0.37, and 0.94 ± 0.11 for FD, HDT, and FF, respectively. The results suggest that it may be appropriate to include feeding behavior traits as covariates to indicate measure(s) of animal activity in the calculation of RFI. Feeding behavior phenotypes were greater during the grower-fed period than the finisher-fed period. During these feeding periods, efficient steers exhibited fewer FF, shorter FD, and shorter HDT than inefficient steers.     

Energy requirement for maintenance and growth of Nellore bulls and steers fed high-forage diets

Data from three comparative slaughter experiments with individually fed Nellore bulls (n = 31) and steers (n = 66) were utilized to determine their NEm and NEg requirements when fed high-forage diets. The experimental design provided ranges in ME intake, BW, and ADG for the development of regression equations to predict NEm and NEg requirements. The Nellore bulls (Trial 1) were divided into two intake levels (ad libitum and 65% of the ad libitum). The steers (Trials 2 and 3) were allocated to three intake levels (ad libitum and 55 and 70% of the ad libitum). In both trials, there were three slaughter groups within each intake level. The three end points for the bulls were different days on treatment (100, 150, and 190 d and 130, 180, and 200 d, respectively, for older and younger animal subgroups). The steers were slaughtered when animals of the ad libitum treatment reached 400, 440, and 480 kg shrunk BW (SBW) on average for the first, second, and third group, respectively. For all body composition determinations, whole empty body components were weighed, ground, and subsampled for chemical analysis. In each of the trials, initial body composition was determined with equations developed from a baseline slaughter group, using SBW and empty BW (EBW), fat (EBF), and protein (EBP) as variables. The NEm was similar for bulls and steers; NEm averaged 77.2 kcal/ kg0.75 EBW. However, the efficiency of conversion of ME to net energy for maintenance was greater for steers than for bulls (68.8 and 65.6%, respectively), indicating that bulls had a greater ME requirement for maintenance than steers (5.4%; P < 0.05). Our analyses do not support the NRC (2000) conclusion that Nellore, a Bos indicus breed, has a lower net energy requirement for maintenance than Bos taurus breeds. An equation developed with the pooled data to predict retained energy (RE) was similar to the NRC (2000) equation. A second equation was developed to predict RE adjusted for degree of maturity (u): RE = (6.45 - 2.58/u) x EWG x e(0.469) x u), where u = current EBW/final EBW in which final EBW was 365 kg for steers and younger bulls and 456 kg for older bulls at 22% EBF, respectively.     

Accuracy of genomic breeding values for residual feed intake in crossbred beef cattle

The benefit of using genomic breeding values (GEBV) in predicting ADG, DMI, and residual feed intake for an admixed population was investigated. Phenotypic data consisting of individual daily feed intake measurements for 721 beef cattle steers tested over 5 yr was available for analysis. The animals used were an admixed population of spring-born steers, progeny of a cross between 3 sire breeds and a composite dam line. Training and validation data sets were defined by randomly splitting the data into training and testing data sets based on sire family so that there was no overlap of sires in the 2 sets. The random split was replicated to obtain 5 separate data sets. Two methods (BayesB and random regression BLUP) were used to estimate marker effects and to define marker panels and ultimately the GEBV. The accuracy of prediction (the correlation between the phenotypes and GEBV) was compared between SNP panels. Accuracy for all traits was low, ranging from 0.223 to 0.479 for marker panels with 200 SNP, and 0.114 to 0.246 for marker panels with 37,959 SNP, depending on the genomic selection method used. This was less than accuracies observed for polygenic EBV accuracies, which ranged from 0.504 to 0.602. The results obtained from this study demonstrate that the utility of genetic markers for genomic prediction of residual feed intake in beef cattle may be suboptimal. Differences in accuracy were observed between sire breeds when the random regression BLUP method was used, which may imply that the correlations obtained by this method were confounded by the ability of the selected SNP to trace breed differences. This may also suggest that prediction equations derived from such an admixed population may be useful only in populations of similar composition. Given the sample size used in this study, there is a need for increased feed intake testing if substantially greater accuracies are to be achieved.     

A prolonged photoperiod improves feed intake and energy metabolism of weanling pigs

In a 2-wk experiment, the effect of photoperiod on performance and energy metabolism of newly weaned pigs was studied. Forty 4-wk-old crossbred weanling barrows weighing 8.0 kg (SE = 0.13) were assigned to one of eight groups (five pigs per group) based on BW and litter. Groups were allotted to one of two lighting schedules: 8 h light:16 h darkness or 23 h light:1 h darkness. Each group was housed in a climate respiration chamber. Piglets had ad libitum access to feed and water. Energy and nitrogen balances, heat production, ADFI, and ADG were measured weekly. Heat production, energy metabolism, and performance were unaffected (P > 0.10) by photoperiod during wk 1. However, in the 2nd wk ADFI (418 vs 302 g/d) and ADG (381 vs 240 g/d) were higher (P < 0.05 and P = 0.05, respectively) for pigs on the 23:1 h lighting schedule than for those on the 8:16 h schedule. Furthermore, heat production (P < 0.10), total energy retention, and energy retained as protein and as fat were higher (P < 0.05) during wk 2 in pigs on the 23:1 h lighting schedule (8, 125, 41, and 350%, respectively) than in those on the 8:16 h schedule. Moreover, metabolizability of energy tended to be higher (P < 0.10) and energy requirements for maintenance were lower (P < 0.05) during wk 2 for pigs on the 23:1 h schedule compared with those on the 8:16 h schedule (P < 0.10). In conclusion, exposing pigs to a longer period of light after weaning stimulated ADFI and ADG. In addition to the feed intake, the high ADG is due to an improved metabolizability of energy and a reduced energy requirement for maintenance. This study suggests that lighting schedule can be used as a tool to stimulate feed intake after weaning.     

肉猪采食量低的原因及其改进措施

<正>1问题的提出某公司把2008年与2009年7-8月的生产数据进行分析统计(表1)。从中可以看出,2009年7-8月出栏猪苗体重、出栏后耗料量、日增重、     

Breed means for average daily gain, feed conversion and intake of beef bulls during postweaning feedlot performance tests

Comparisons of ADG, feed/gain, daily feed intake and daily feed intake as percentage of body weight may be important to beef cattle producers and researchers in breed selection and computer modeling. Data evaluated were postweaning feedlot performance test records collected from 1967 to 1986 of 3,661 individually fed bulls. Bulls originated from University of Arkansas purebred herds, Fayetteville, and the University of Arkansas Cooperative Bull Tests at Fayetteville, Hope and Monticello. Data were analyzed separately for years 1967 to 1976 (P1) (n = 1,654) and 1977 to 1986 (P2) (n = 2,007). Breeds in P1 were Hereford (HH), Angus (AN), Charolais (CH), Polled Hereford (HP) and Santa Gertrudis (SG). Breeds in P2 were HH, AN, CH, HP, SG, Simmental (SM), Maine-Anjou (MA), Brangus (BN) and Beefmaster (BM). The model fitted included initial age, breed and test number (all P less than .01 except for initial age on ADG in P1). Test number included variation of year, location and season. Breed rankings from highest to lowest for ADG in P1 were CH, SG, HH, HP and AN and in P2 were MA, CH, SM, AN, HP, SG, HH, BN and BM. Breed rankings from highest to lowest for feed/gain in P1 were AN, SG, CH, HP and HH and in P2 were BM, BN, AN, SG, HP, SM, HH, MA and CH. Breeds highest to lowest for daily feed intake (as-fed basis) in P1 were CH, SG, AN, HP and HH and in P2 were SM, MA, CH, AN, BN, HP, SG, HH and BM.(ABSTRACT TRUNCATED AT 250 WORDS)