Empty Body Gain Research Articles

Growth and body composition of dairy calves fed only milk replacer at 3 intakes

Determination of energy requirements for growth depends on measuring the composition of body weight (BW) gain. Previous studies have shown that the composition of gain can be altered in young dairy calves by composition of the milk replacer diet. Here, our objective was to determine body composition and the composition of empty body gain in young calves fed increasing amounts of a milk replacer containing adequate CP. Male Holstein calves underwent an adjustment period of 14 d after birth in which they were fed whole waste milk at 10% of BW. Calves were then stratified by BW and randomly assigned to either an initial harvest group (n = 11) or to groups fed 1 of 3 milk replacer amounts and harvested after 35 d of growth. All treatments consumed the same milk replacer containing 24.8% CP (dry matter [DM] basis; from all milk proteins) and 18.9% fat, reconstituted to 12.5% solids. Treatments were milk replacer fed at 1.25% of BW (DM basis; n = 6), 1.75% of BW (n = 6), or 2.25% of BW (n = 8), adjusted weekly as calves grew. Calves fed at 1.25% or 1.75% of BW were fed twice daily and those fed 2.25% of BW were fed 3 times daily. No starter was offered. Post harvest, the bodies of calves were separated into 4 fractions: carcass; total viscera minus digesta; head, hide, feet, and tail; and blood. The sum of those 4 fractions was empty BW, which increased linearly as amount of milk replacer increased. Final heart girth and body length, but not withers height, increased linearly as intake increased. Gain:feed increased linearly with increasing milk replacer. Feeding more milk replacer increased the amounts of lean tissue and fat in the body. The percentages of water and protein in the final body decreased linearly, whereas fat percentage and energy content increased linearly as intake increased. As gain increased, the percentage of protein in gain decreased and the percentage of fat increased, resulting in an increase of energy content of EBW gain. Efficiency of energy use (retained energy:gross energy intake) increased linearly but retained energy:metabolizable energy available for growth was not different among treatments. Efficiency of protein use increased quadratically as feeding rate increased; there was no further increase at 2.25% of BW. Plasma insulin-like growth factor 1, insulin, and glucose increased linearly, whereas urea-N decreased linearly, as milk replacer intake increased. Our data document changes in body composition that affect estimates of retained energy in the bodies of calves harvested at a common age. These data are important for calculations of energy requirements for young calves.

Protein requirements for pregnant dairy cows

We aimed to estimate the protein requirements of pregnant Holstein × Gyr cows. Sixty-one Holstein × Gyr (HG) cows were used, with an average initial BW of 480 ± 10.1 kg and 5 ± 0.5 yrs of age. Cows were divided into 3 groups: pregnant (n = 43), non-pregnant (n = 12), and baseline (n = 6). Baseline animals were slaughtered before starting the experiment to estimate the initial body composition of the remaining animals. Pregnant and non-pregnant cows received 2 diets: maintenance and ad libitum. Pregnant cows were slaughtered at 139, 199, 241, and 268 gestation days (GD). First, we used data only from non-pregnant cows to determine the requirements for maintenance and growth in adult cows. The requirements of metabolizable protein for maintenance (MPm; g/EBW0.75/d) were estimated using a linear regression between the metabolizable protein intake (MPI, g/d) and average daily gain (g/d), and the MPm was defined as the intercept divided by the average BW0.75. Net protein requirements for gain (NPg; g/d) were estimated by the first derivative of the allometric equation between final crude protein (CP) in the body (kg) and the final EBW (kg). The efficiency of use of metabolizable protein for gain (k) was calculated from the regression between the retained protein (g/EBW0.75/d) and the MPI (g/EBW0.75/d), and k was the slope of this regression. The MPI was estimated by combining microbial protein synthesis (purine derivatives) with the digestible rumen undegradable protein [(total protein intake minus RDP) × intestinal digestibility]. Second, an exponential model was used to fit the protein accumulation in the gestational components in the function of GD. The first derivative of that model was considered the net requirement for gestation (NPgest). The efficiency of protein utilization for gestation (kgest) was calculated by the iterative method using the equation: Δ = MPI - (MPm + NPg/kg + NPgest/kgest). The iteration was performed aiming at a zero deviation between observed MPI and MP estimated by the requirements determined herein. We obtained a value of 3.88 g/EBW0.75/d for MPm. The estimation of NPg can be calculated according to the following equation: NPg = 0.716 × (EBWopen-0.308) × EBGcorrected, where EBWopen is the empty BW (kg) for non-pregnant animals and EBGcorrected is the empty body gain (kg/d) corrected for the gestational component. The k was determined as 0.347. The net protein requirements for gestation (NPgest) were determined as NPgest (g/d) = 0.0008722 × exp(0.01784 ×GD) × (calfweight/35). The efficiency of using metabolizable protein for gestation (kgest) was 0.625. It is important to highlight that different methods of MP estimates should not be mixed and that the proposed method requires the estimate of microbial protein (estimate it via urinary estimates) and might be a limitation for practical application. In conclusion, new studies should be conducted to validate our results and the methodology adopted to determine protein requirements for pregnancy in dairy cows. Due to the pattern of protein accumulation in the gestational components, we suggest that exponential model to describe protein requirements for pregnancy for dairy cows.

98 Utilizing gas Flux from an Automated Head Chamber System to Estimate Dietary Energy Values in Cattle Fed a Finishing Diet

Abstract Automated head chamber systems (AHCS; GreenFeed, C-Lock Inc., Rapid City, SD) provide estimates of daily flux of methane (CH4), carbon dioxide (CO2), and oxygen (O2) from free roaming cattle. This provides the opportunity to conduct energetic research from cattle in their production environment. One opportunity is to evaluate agreement between performance-estimated (P) net energy [Mcal×(kg DMI)-1] for maintenance (NEm) and gain (NEg; pNEm and pNEg, respectively) and gas-estimated (G) NEm and NEg (gNEm and gNEg, respectively). To assess this, yearling steers (n = 54; initial BW = 484.1 ± 26.0 kg) were assigned to one of two pens containing an AHCS and feed bunks designed to measure individual feed intake (Calan Gate; American Calan, Northwood, NH). Steers were transitioned to one of three finishing diets and were then fed for 80 days. Paired day initial and final unshrunk BW were measured on day 0 and 1, and on day 79 and 80, respectively. Following the feeding period, steers were transported to a commercial abattoir where carcass data were collected. Average daily gas flux was utilized to calculate heat production (HP), ignoring the energy losses from urinary nitrogen. Empty body weight (EBW) and empty body gain (EBG) were calculated and used to estimate retained energy (RE). The estimates of RE and HP were used to calculate ME intake and DMI was used to quantify dietary ME content. Through previously established cubic models, gNEm and gNEg were calculated. Ultimately, gNEm and gNEg were estimated from gas flux, DMI, ADG, and initial and final BW. A previously established quadratic model was used to estimate pNEm and pNEg using calculated energy requirements and available carcass parameters. Statistical analyses to compare the P and G energy values were conducted utilizing R (v.4.1.0) to assess precision (Pearson Correlation; r), method agreement (Lin’s Concordance Coefficient Correlation; CCC), Root Mean Square Error of Prediction (RMSEP), and Relative Prediction Error (RPE; RMSEP as a % of average performance estimated values). On average, P provided greater estimates of NEm [0.074 Mcal×(kg DMI)-1] and NEg [0.070 Mcal×(kg DMI)-1] relative to G. The G and P provided values which were highly correlated and had excellent agreement (r = 0.92, P < 0.01; CCC ≥ 0.87) for both NEm and NEg. Additionally, predictive capability was high for NEm (RMSEP = 0.12; RPE = 5.3%) and NEg (RMSEP = 0.11; RPE = 6.8%) between P and G values. The excellent agreement between P and G derived NEm and NEg values suggest that researchers can utilize AHCS to conduct energetic studies with cattle in their production environment. Moreover, the G methodology provides an alternative method for retrospective assessment of animal energetic efficiency.

Level of feeding and stage of maturity affects diet digestibility and protein and fat deposition in cross-bred lambs.

Metabolizable energy intake is the key determining factor for the expression of an animal's genetic potential for growth, and current predictive growth models are not capable of accounting for all the nutritional variation that is commonly observed. The current study was designed to investigate energy transactions as lambs grow using CT scanning to assess body compositional changes at two levels of intake and two stages of maturity, and compare results to predictive equations. A pelleted diet was provided to cross-bred lambs (n=108) at approximately 2.5 and 3.5% of liveweight (LW) in dry matter when the lambs were approximately four (31.8 ± 0.3kg LW) and eight (40.5 ± 0.3kg LW) months of age. A digestibility trial was run sequentially using 10 lambs of the same genetic and nutritional history fed at the same feeding levels to determine the digestibility of the diet. In the first feeding period, metabolizable energy intake was 15.3 ± 0.03 and 9.5 ± 0.03 MJ ME/d for high and low feeding levels respectively, resulting in higher rates of empty body gain for high feeding level lambs (197.7 ± 7.8 v. 72.8 ± 8.2g/d; P<0.001). In the second feeding period, metabolizable energy intake was 15.2 ± 0.01 and 12.0 ± 0.01 MJ ME/d for high and low feeding levels respectively, resulting in higher rates of empty body gain for high feeding level lambs (176.3 ± 5.4 v. 73.9 ± 5.3; P<0.001). Lambs at later stages of maturity retained proportionately more energy as fat for every unit of retained energy compared to younger lambs (95.4 ± 0.40 v. 90.0 ± 0.42%; P<0.001). Lambs fed the lower feeding level in period two also retained proportionately more energy as fat for every unit of retained energy than lambs at the higher feeding level (97.1 ± 0.36 v. 94.0 ± 0.37%; P<0.001) which is hypothesised to be because of the rapid response of visceral lean tissue to changes in nutrition. There were no significant interactions between treatments in the first and second feeding periods, indicating an absence of a compensatory gain response to a nutritional restriction in the first feeding period. This experiment highlights the significance of a changing feed supply and the subsequent effects on body composition and the partitioning of energy to lean and fat tissue deposition. For improvements in the accuracy of predictive ruminant growth models it is necessary to gain a greater understanding of the different tissue responses over time to changes in nutrition.

Energy requirements of Holstein, Gyr, and Holstein × Gyr crossbred heifers using the respirometry technique

We aimed to determine the energy requirements for maintenance and gain of 18 prepubertal dairy heifers of three breed compositions (BC; Holstein, Gyr, and Holstein × Gyr). Diets were formulated for gains of 0, 400, and 800 g/day, corresponding to 1.0×, 1.5×, and 2.0× maintenance, respectively. Each dairy BC had six animals with an initial body weight (iBW) of 219.8 ± 32 kg, 215.8 ± 33 kg, and 228.3 ± 33 kg for Holstein, Gyr, and Holstein × Gyr, respectively. The experiment was designed as a completely randomized design in a factorial scheme 3 × 3 [three BC and three feeding levels (FL)]. Digestibility and metabolism assays were performed to determine energy losses through feces and urine. Heat production was determined using the continuous measurement of oxygen consumption, carbon dioxide production, and methane emissions in respiration chambers. Energy requirements for maintenance (NEm) were calculated based on the relationship between heat production (HP) and metabolizable energy intake (MEI). The efficiency of use of metabolizable energy for maintenance (km) was obtained from the ratio between NEm and metabolizable energy requirements for maintenance. The net energy requirements for growth (NEg) were estimated from the model RE = β0 × EBW0.75 × EBGβ1, where RE is the retained energy (Mcal/day), EBW is empty body weight (kg0.75), and EBG is the empty body gain (kg/day). The efficiency of use of metabolizable energy for gain (kg) was estimated as the slope of the regression between RE and MEI for gain. Gyr heifers presented NEm 15% lower (98 kcal/kg of BW0.75) than HG crossbred animals. Holstein and crossbred heifers had similar NEm, 102 and 112 kcal/kg of BW0.75, respectively. The km was 0.71, 0.74, and 0.75 for HG, Holstein, and Gyr, respectively. Net energy requirement for gain (NEg) did not differ across BC, and a single equation was fit for all BC: RE = 0.069 × BW0.75 × BGW0.852. A single kg of 0.65 was observed for all three BC. Breed composition affected the energy requirements for maintenance and the energy partition, and those differences should be considered when estimating requirements for Gyr, Holstein × Gyr crossbred, and Holstein heifers.

217 Assessment of Ultrasound Carcass Composition Equations to Predict Empty Body Gain

Abstract The objective of this study was to assess the accuracy of equations used to estimate ultrasound carcass composition for prediction of empty body gain (EBG). The dataset consisted of 8 trials with Angus and Brangus bulls or Brangus heifers (N = 897) fed moderate energy diets for 70 d. Initial and final ultrasound carcass measurements were obtained by trained technicians. Initial and final empty body fat percentage (EBF) was computed using 5 equations: 1) carcass trait equation from Guiroy et al. (2001; 10.2527/2001.7981983x), 2) carcass trait equation 1 from Tedeschi et al. (2004; 10.1016/S0308-521X(03)00070-2), 3) carcass trait equation 6 from Baker et al. (2006; 10.2527/jas.2006-006), 4) ultrasound trait equation 8 from Baker et al. (2006; 10.2527/jas.2006-006), and 5) pEBF = 27.1902 + 47.6268 × uBF – 0.5687 × uLMA – 0.1178 × uBF × pHCW + 0.0014 × uLMA × pHCW; R2 = 0.64, RMSE = 2.46% developed from the training dataset of Baker et al. (2006). Empty body gain was predicted using NASEM (2016) equations. Eq. 5 had slightly improved R2 (0.64 vs. 0.62) and RMSE (2.46 vs. 2.49%) compared with Eq. 4 in the training dataset. When comparing observed and predicted EBG, Eq. 4 had intercept and linear coefficients nearest to 0 and 1, respectively, the greatest CCC (0.799), and mean bias closest to 0 (0.025 kg/d) compared with the other equations. ADG, ultrasound traits, and sex were significant (P &amp;lt; 0.05) variables accounting for variation in differences between observed and predicted EBG, suggesting that these equations are somewhat lacking in their ability to capture the relationships between ultrasound carcass composition traits and retained energy. ADG was less important and gain in LMA was more important in explaining differences between observed and predicted EBG for Eq. 5 than the other equations. These results indicate that use of live animal ultrasound measurements can predict retained energy with reasonably accuracy.

PSIV-7 Evaluating Relationships Among Empty Body, Protein, and Fat Gain and Retained Energy for Pre- and Postweaning Calves

Abstract Discrepancies in the predictability of the nutritional equation used to predict empty body gain in preweaning and postweaning calves could be due to differences in proportion of protein and fat in the gain. The objective of this study was to understand the differences in the relationships of empty body weight gain (EBG) with protein (EBP) and fat (EBF) gain, and retained energy (RE) in preweaning and postweaning calves. A literature search was performed to create a dataset comprised of EBG and composition of gain in calves. The relationships of EBG with EBP, EBF, and RE was compared in preweaning and postweaning calves using R statistical software. Pearson correlation coefficients of EBG with EBP, EBF, and RE were 0.971, 0.839, and 0.934 and 0.805, 0.807, and 0.889 for preweaning and postweaning calves, respectively. A nonlinear regression model was used to evaluate the relationship between EBG and RE including the effects of age (defined as 0 for preweaning and 1 for postweaning calves) and type (defined as 0 for dairy and 1 for beef calves), [EBG = (a + b * age + c * type) * RE^ (d + e * age + f * type)]. The power coefficients were not different (P &amp;gt; 0.05) between preweaning and postweaning calves (0.379 and 0.446 ± 0.041, respectively), but the exponents were different (P ≤ 0.05; 0.921 and 0.558 ± 0.079, respectively). In conclusion, the relationship between empty body weight gain and composition of gain differs between preweaning and postweaning calves, suggesting that a more robust equation or group of equations should be used in nutrition models.

204 Effect of Breed Type on Maintenance Energy Requirements and Partial Efficiency of Metabolizable Energy use in Growing/Finishing Cattle

Abstract Consensus is lacking on whether genetic differences exist in the partial efficiency of metabolizable energy use. Genetic differences in net energy required for maintenance have been reported, but independence of body composition was not addressed. The objective was to determine contribution of genetic differences to net energy for maintenance and partial efficiency of metabolizable energy use in growing/finishing cattle independent of body composition. A literature search was performed to compile data (31 studies, 214 treatment means) on metabolizable energy intake (MEI) and composition of empty body gain in growing steers. Dietary roughage concentration and breed were recorded. Data analyses were performed using R statistical package for linear models. For analysis, breeds were categorized as British (Angus, Hereford, English crosses), Continental (Charolais, Simmental, Limousin), Crossbred (British x Continental breeds), Dairy (Holstein), Double-muscled (Belgian Blue, Piedmontese), and Heat-tolerant (Boran, Brahman, Tuli). The base regression model of log10 of heat production on MEI had R2 of 0.8830 and intercept of 1.912 (81.6 kcal/kg.75). Inclusion of breed type significantly (P &amp;lt; 0.0001) affected the intercept and slope of the model. Dietary roughage concentration, empty body fat percentage, and energy retained as protein or fat, but not empty body protein percentage, explained a significant amount of variation (P &amp;lt; 0.01) resulting in R2 of 0.9851. In this final model, Heat-tolerant breeds had 12% lesser (P &amp;lt; 0.05) net energy for maintenance than British with Continental, Crossbred, and Double-muscle being intermediate, and Dairy had 27% greater (P &amp;lt; 0.05) net energy for maintenance than other breed types. Also, breed type by MEI interaction was significant (P &amp;lt; 0.0001) with Dairy having lesser (P &amp;lt; 0.05) slope than other breeds, and Heat-tolerant having greater slope than British. In conclusion, breed type affected net energy for maintenance and partial efficiency of metabolizable energy use after accounting for body composition and dietary roughage concentration suggesting that genetic variation exists.

Determination of energy and protein requirements of preweaned dairy calves: A multistudy approach

The first studies concerning nutrient requirements for preweaned dairy calves were from the 1920s and 1930s; however, few studies were published in the following decades. We aimed to determine energy and protein requirements of preweaning Holstein and Holstein × Gyr dairy calves in a multistudy meta-regression. We used a database composed of individual measurements of 166 preweaned male calves (138 submitted to treatments and 28 used as the reference group) from 4 studies that used the methodology of comparative slaughter. Animals with less than 15/16 of Holstein genetic composition were considered crossbred Holstein × Gyr, whereas other animals were considered Holstein. Net energy requirements for maintenance (NEM) were determined by the regression between heat production and metabolizable energy intake (MEI). The metabolizable energy requirements for maintenance were calculated by the iterative method, and the efficiency of use of metabolizable energy for maintenance was obtained by NEM divided by the metabolizable energy requirements for maintenance. Net energy requirements for gain (NEG) were estimated using a regression of the retained energy (RE) as a function of empty body weight (EBW) and empty body gain (EBG). The efficiency of use of metabolizable energy for gain was estimated by the regression of RE as a function of MEI, but with partitioning the MEI into MEI from liquid feed and MEI from starter feed. Additionally, the effect of a liquid feed (milk or milk replacer) was tested on the slope of the regression. The metabolizable protein requirements for maintenance (MPM) were estimated using the intercept of the regression between the metabolizable protein intake (MPI) and average daily gain. The MPM was determined as the ratio between the intercept and the metabolic body weight. Net protein requirements for gain (NPG) were estimated by the regression between retained protein, EBG, and RE. The efficiency of use of metabolizable protein for gain was estimated by the regression of the retained protein as a function of MPI, but with partitioning the MPI into MPI from liquid feed and MPI from starter feed. Additionally, the effect of a liquid feed (milk or milk replacer) was tested on the regression slope. Breed did not influence any of the nutrient requirements' estimates. The NEM was estimated as 70.2 kcal/metabolic body weight per day. The efficiency of use of metabolizable energy for maintenance observed was 66%. The NEG was estimated by the equation NEG = 0.0901 × EBW0.75 × EBG0.9539. The efficiency of use of metabolizable energy for gain was estimated as 57.6, 49.3, and 41.2% for milk, milk replacer, and starter feed, respectively. The MPM was estimated as 4.22 g/EBW0.75 per day, and the NPG was determined by the equation: NPG = 30.06 × EBG + 70.98 × RE. The efficiency of use of metabolizable protein for gain was estimated as 71.9, 59.2, and 44.4% for milk, milk replacer, and starter feed, respectively. We concluded that no differences were observed in energy and protein requirements between Holstein and Holstein × Gyr crossbred cows. The efficiencies of use of metabolizable energy and protein are greater for milk when compared with milk replacer and starter feed. Therefore, we propose that the equations generated herein should be used to estimate energy and protein requirements of preweaned Holstein and Holstein × Gyr crossbred dairy calves raised under tropical conditions.

PSIV-2 Evaluation of Relationship Between Feed Efficiency Traits and Energy Metabolism Using Comparative Slaughter Studies in Growing and Finishing Cattle

Abstract There is uncertainty whether feed efficiency traits are related to energetic efficiency. The objective of this study was to utilize comparative slaughter data to evaluate the relationships of feed efficiency traits with maintenance energy requirements (MEm) and efficiency of metabolizable energy (ME) use for maintenance (km) and gain (kg). Published data were compiled (31 studies, 214 treatment means) on metabolizable energy intake (MEI) and composition of empty body gain in growing cattle. Data analyses were performed using R statistical software considering each treatment mean as an independent experimental unit. Assuming fasting heat production (FHP) varies only due to empty body protein (EBP) composition, it was computed as 295 kcal/kg EBP.75. MEm, km, and kg were computed from the nonlinear relationship between heat production and MEI. Residual intake (lower is more efficient) was computed as the residual from linear regression of MEI on EBW and EBW gain (RMEI) or MEI on EBP, retained energy as protein and retained energy as fat (RMEIc). Residual gain (higher is more efficient) was computed as the residual from linear regression of EBW gain on EBW and MEI (REBG) or retained energy on EBP and MEI (RRE). MEI was positively correlated with RMEI (0.46) and RMEIc (0.44), and EBW gain was correlated with REBG (0.58) and RRE (0.39). FHP was correlated with RMEIc (-0.25). MEm was weakly correlated with RMEI (0.19), RMEIc (0.22), and REBG (-0.26), but strongly correlated with RRE (-0.51). km was moderately correlated with RMEI (-0.35), but strongly correlated with REBG (0.49), RMEIc (-0.59), and RRE (0.79). kg was strongly correlated with RMEI (-0.69), REBG (0.47), RMEIc (-0.89), and RRE (0.70). Correlations among feed efficiency traits were strong (&amp;gt;±0.48). In conclusion, feed efficiency traits using retained energy as the dependent variable had stronger correlations with maintenance energy requirements than those using feed intake as the dependent variable.

Protein and energy partitioning for growth by rabbits fed soybean cheese waste diet and lablab hay

Thirty crossbred rabbits were randomly allocated to five treatment combinations of soybean waste meal and lablab forage hay in a completely randomized design. The treatments comprised graded levels of soybean cheese waste meal diet (SCW) and lablab hay (LH) on a gram: gram basis: 1) 100g SCW: 60g LH (100:60S), 2) 75g SCW: 85g LH (75:85S), 3) 50g SCW: 110g LH (50:110S), 4) 25g SCW: 135g LH (25:135S), 5) 100g rabbit diet: 60g LH (100:60R,control). This gives a total feed supply of 160g/rabbit/day. Metabolic weight gain, empty body gain and composition of gain were significantly (p&lt;0.001) higher for rabbits on 100:60S than 100:60R (control) and decreases with decrease in level of soybean cheese waste diet. Digestible crude protein (DCP) intake was higher (p&lt;0.05) for the control (100:60R) than the SCW levels. DCP maintenance (DCPm) was significantly (p&lt;0.05) lower for rabbits on 50:110S than 100:60S but similar for all other treatments. DCP content of gain, total DCP required for protein synthesis decreased significantly (p&lt;0.001) as level of SCW decreased and LH increased and was higher for 100:60S than the control and other levels. Efficiency of DCP utilization for growth decreased as SCW level decreased and ranged between 0.41-0.85. Digestible energy (DE) intake of rabbits was significantly higher (p&lt;0.001) on 25:135S and lower for 100:60S. More DE was retained as protein, fat and for growth by rabbits on 100:60S than the control and other SCW levels. DE. DE requirement for protein, fat, growth and total energy required for growth also decreased with decrease in level of SCW. The control and 75:85S rabbits significantly had higher DE requirement than 25:135S. Efficiency of DE utilization for growth was similar for the control, 100:60S and 75:85S than 25:135S. Efficiency of DE utilization ranged between 0.21-0.24 and was lowest on 25:135S.

Development of equations to predict carcass weight, empty body gain, and retained energy of Zebu beef cattle

The accurate supply of energy is essential to optimize livestock productivity and profitability. Furthermore, replacing empty BW gain (EBG) with carcass gain (CG) might be a suitable alternative to estimate the retained energy (RE) of beef cattle. Thus, this multi-analysis study was conducted aiming to estimate and validate new equations to predict carcass weight (CW), EBG, and RE of Zebu, beef crossbred, and dairy crossbred. A database composed by 1112 animals encompassing bulls, steers, heifers of different genetic groups (Zebu, beef crossbred, and dairy crossbred), and two types of slaughter plants (commercial and experimental) was used for generating the new CW equation. For the development of the EBG and RE equations, a database of 636 observations composed of bulls, steers, and heifers of different genetic groups (Zebu, beef crossbred, and dairy crossbred) was assembled. The validation of new equations was performed using independent databases composed by 137 observations (80 for CW and 57 for EBG and RE). The new approaches for EBG and RE validation also included data from our research group studies (Inside) and independent data from literature publications (Outside). Furthermore, the new RE equation was compared to the current model devised by the nutritional requirements, diet formulation, and performance prediction of Zebu and crossbred cattle (BR-CORTE, 2016). Validation analyses were performed by using the Model Evaluation System (MES; 3.1.13, College Station, US). The CW was accurately estimated by the new equation when using both commercial and experimental data. Also, the equations developed in this study accurately estimated EBG and RE using both inside and outside data. In conclusion, equations proposed in this study accurately and precisely estimated CW, EBG, and RE of Zebu beef cattle that composed validation data set. Therefore, we suggest the following equations to estimate CW, EBG, and RE of Zebu cattle: CW, kg = −11.0±1.56 + P + ((0.609±0.005 + G + B) × SBW); EBG (kg) = 0.044±0.017 + 1.47±0.026 × CG; RE (MJ/d) = 4.184 × (0.082±0.002 × EQEBW0.75 × CG0.777±0.039), where P = slaughter plant effect, if commercial = −10.98, if experimental =0; G = gender effect, if steer = 0, if bull = 0.008169 and if heifer = −0.00612; B = genotype effect, if Zebu = 0, if dairy crossbreds = −0.03301 and if beef crossbreds = −0.01595; SBW = shrunk BW; CG = carcass gain; EQEBW = equivalent empty BW.

223 Evaluation of dietary characteristics and empty body chemical components affecting the relationship between heat production and metabolizable energy intake in growing beef cattle

Abstract Metabolizable energy required for maintenance varies with diet and empty body chemical composition. The objective was to quantify the relationships of dietary characteristics and empty body chemical composition with heat production. A literature search was performed to compile data (31 studies, 214 treatment means) on metabolizable energy intake (MEI) and composition of empty body gain in growing steers and heifers. Data analysis were performed using R statistical package for mixed models with study as random variable. Nonlinear regression of energy gain (EG) on MEI indicated the relationship was not curvilinear in this data set, likely due to lack of negative values of EG. Further analyses were conducted using a linear model. Dietary characteristics of roughage level (0–100% of diet DM) and CP (10–25% diet DM), metabolizable energy concentration (1.3–3.3 Mcal/kg DM), and roughage type were evaluated in the model. Roughage sources were categorized into no roughage, silage, hay, pellets, silage + pellets, and hay + pellets. Of the empty body chemical components, proportion of fat in the empty body (EBFp) and in the gain (EBFgp) had a significant (P &amp;lt; 0.001) interaction with MEI on HP. Of the dietary characteristics, roughage level and type had a significant (P &amp;lt; 0.001) interaction with MEI on HP; however, when both were included in the model, roughage type was not significant (P &amp;gt; 0.10). The final model was 47.01 ± 12.54 + 0.630 ± 0.05*MEI – 132.3 ± 64.7*EBFp + 0.0007 ± 0.0001*MEI*Roughage level + 0.753 ± 0.24*MEI*EBFp – 0.268 ± 0.032*MEI*EBFgp with an R2 of 0.919 and an AIC of 1614 compared with 0.867 and 1695 for the simple linear regression model of HP on MEI. In conclusion, greater empty body fat decreased the intercept, and greater empty body fat proportion and levels of roughage in the diet increased the slope between HP and MEI, whereas greater percentage of fat in the empty body gain decreased the slope between HP and MEI.

222 Partitioning of retained energy between protein and fat gain, and heat of product formation and support metabolism in growing beef cattle

Abstract Multiple linear regression inaccurately computes the efficiency of energy use for protein and fat gain. The objective was to quantify efficiency of metabolizable energy use for protein and fat gain along with heats of product formation and support metabolism. A literature search was performed to compile data (31 studies, 214 treatment means) on metabolizable energy intake (MEI) and composition of empty body gain in growing steers and heifers. Data analyses were performed using R statistical package for mixed models with study as random variable. Linear regression of MEI on energy gain (EG; P &amp;lt; 0.001; R2 = 0.627) resulted in an estimate of metabolizable energy for maintenance (MEm) of 156 kcal/kg.75 and efficiency of ME use for gain of 0.518. Linear regression of MEI on EG as protein and fat (P &amp;lt; 0.001; R2 = 0.623) resulted in an estimate of MEm of 149 kcal/kg.75, and efficiency of protein (kp) and fat (kf) gain of 0.274 and 0.585, respectively, resulting in an overall efficiency of EG of 0.520. Nonlinear regression model (EG = kg*(MEI-MEm)) resulted in an estimate of MEm of 103 kcal/kg.75 and efficiency of EG of 0.342. The heat of product formation was assumed to be 0.48 (1 – 0.52) and the heat of support metabolism (HiEv) 0.18 (0.52 – 0.34). Multivariate regression was used to fit simultaneous models for EG as protein (EGp = (kp+HiEvp)*k*MEA) and fat (EGf = (kf+(0.18-HiEvp))*(1-k)*MEA). Estimates (P &amp;lt; 0.001) of kp and kf were 0.12 ± 0.01 and 0.63 ± 0.02, and HiEvp and proportion of ME available for protein gain (k) were 0.11 ± 0.01 and 0.75 ± 0.01, respectively. The heat of product formation and support metabolism for protein were 0.77 and 0.11, and fat were 0.30 and 0.07, respectively. In conclusion, efficiency of ME use for protein was lesser than for fat gain, and heat of support metabolism was greater for protein than fat gain.

Feeding behavior, water intake, and energy and protein requirements of young Nellore bulls with different residual feed intakes.

This study aimed to determine feeding behavior, water intake (WI), and energy requirements of high- and low-residual feed intake (RFI) Nellore bulls. Data were collected from 42 weaned Nellore bulls (initial body weight [BW] 260 ± 8.1 kg; age 7 ± 1.0 mo) housed in a feedlot in group pens that contained electronic feeders, waterers, and a scale connected to the waterers. The individual dry matter intake (DMI), WI, and BW were recorded daily. The indexes of average daily gain (ADG), feed efficiency (gain to feed ratio), and RFI were calculated based on the data collected. The number of feeder and waterer visits and the time spent feeding or drinking water per animal per day were recorded as feeding behavior measures. Energy requirements for maintenance and gain were calculated according to the BR-CORTE system. Low-RFI bulls had lower DMI (P < 0.01) than high-RFI bulls, and no differences (P > 0.05) were observed between the two groups regarding WI, performance, and feeding behavior measurements. The net energy requirements for maintenance, metabolizable energy for maintenance, and efficiency of metabolizable energy utilization were 63.4, 98.6 kcal/metabolic empty body weight (EBW)0.75 daily, and 64.3%, respectively, for low-RFI bulls, and 78.1, 123.9 kcal/EBW0.75 daily, and 63.0%, respectively, for high-RFI bulls. The equations obtained for net energy for gain (NEg) were: NEg (Mcal/EBW0.75) daily = 0.0528 × EBW0.75 × EBG0.5459 for low-RFI and 0.054 × EBW0.75 × EBG0.8618 for high-RFI bulls, where EBG is the empty body gain. We did not observe any difference (P > 0.05) regarding the composition of gain in terms of protein or fat deposition between the two groups. Both groups also presented similar (P > 0.05) carcass and non-carcass traits. Therefore, our study shows that low-RFI Nellore bulls eat less, grow at a similar rate, and have lower maintenance energy requirements than high-RFI bulls. We also suggest that the lower feed intake did not compromise the carcass traits of more efficient animals, which would reduce production costs and increase the competitiveness of the Brazilian beef sector on the world market.

Energy and protein requirements of Holstein × Gyr crossbred heifers

Nutrient requirements in cattle are dependent on physiological stage, breed and environmental conditions. In Holstein × Gyr crossbred dairy heifers, the lack of data remains a limiting factor for estimating energy and protein requirements. Thus, we aimed to estimate the energy and protein requirements of Holstein × Gyr crossbred heifers raised under tropical conditions. Twenty-two crossbred (½ Holstein × ½ Gyr) heifers with an average initial BW of 102.2 ± 3.4 kg and 3 to 4 months of age were used. To estimate requirements, the comparative slaughter technique was used: four animals were assigned to the reference group, slaughtered at the beginning of the experiment to estimate the initial empty BW (EBW) and composition of the animals that remained in the experiment. The remaining animals were randomized into three treatments based on targeted rates of BW gain: high (1.0 kg/day), low (0.5 kg/day) and close to maintenance (0.1 kg/day). At the end of the experiment, all animals were slaughtered to determine EBW, empty body gain (EBG) and body energy and protein contents. The linear regression parameters were estimated using PROC MIXED of SAS (version 9.4). Estimates of the parameters of non-linear regressions were adjusted through PROC NLIN of SAS using the Gauss–Newton method for parameter fit. The net requirements of energy for maintenance (NEm) and metabolizable energy for maintenance (MEm) were 0.303 and 0.469 MJ/EBW0.75 per day, respectively. The efficiency of use of MEm was 64.5%. The estimated equation to predict the net energy requirement for gain (NEg) was: NEg (MJ/day) = 0.299 × EBW0.75 × EBG0.601. The efficiency of use of ME for gain (kg) was 30.7%. The requirement of metabolizable protein for maintenance was 3.52 g/EBW0.75 per day. The equation to predict net protein requirement for gain (NPg) was: NPg (g/day) = 243.65 × EBW−0.091 × EBG. The efficiency of use of metabolizable protein for gain (k) was 50.8%. We observed noteworthy differences when comparing to ME and protein requirements of Holstein × Gyr crossbred heifers with other systems. In addition, we also observed differences in estimates for NEm, NEg, NPg, kg and k. Therefore, we propose that the equations generated in the present study should be used to estimate energy and protein requirements for Holstein × Gyr crossbred dairy heifers raised in tropical conditions in the post-weaning phase up to 185 kg of BW.

Development of an equation to predict net protein requirements for the growth of Zebu beef cattle

The accurate estimation of protein requirements for beef cattle is a key factor in increasing livestock profitability and decreasing the environmental impacts of excessive N excretion due to mismatching between assumed requirements and diet formulation. A meta-analysis was conducted to evaluate and validate a new equation to predict the net protein requirements for growth (NPg) of Zebu beef cattle. For the development of the new approach, a database of 552 observations comprised of bulls, steers, and heifers of different genetic groups (Zebu, beef crossbreed, and dairy crossbreed) was assembled. The new approach was evaluated and compared to current models devised by the international nutrient requirements system committees (Agricultural Research Council, 1980; Beef Cattle Nutrient Requirements Model, 2016; BR-CORTE, 2016) to predict NPg. The model evaluation was performed through the model evaluation system (version 3.1.16) using an independent data set (n = 177 observations). An equation was considered the best estimator of NPg if the following conditions were met: (1) the intercept and slope of the regression between ordinary residues and/or predicted NPg values must have been equal to zero and one, respectively; and (2) the greatest concordance correlation coefficient (CCC) and determination coefficient (R), and lowest mean squared error of prediction (MSEP) were attained. Based on the regression models of the observed v. predicted NPg of Zebu beef cattle, both the new approach and that of the ARC (1980) correctly estimated NPg, since the intercept and slope were not different (P > 0.05) from zero and one, respectively. Additionally, the new approach’s determination coefficient was the greatest and the closest to one. The fact that the new model achieved a higher CCC and lower MSEP than the existing models indicated its superior reproducibility and accuracy. The equations proposed by BR-CORTE (2016) and the BCNRM (2016) did not correctly estimate NPg in that the intercept and slope were different (P < 0.01) from zero and one, respectively. Thus, the equations proposed by the new approach and the ARC (1980) accurately and precisely estimated NPg and are recommended for Zebu cattle. Furthermore, the inclusion of equivalent empty BW (EQEBW) in the new approach improves the estimation of NPg. We suggest the use of the following equation to calculate NPg for Zebu beef cattle: NPg = 176.01 × EBG – 0.381 × EQEBW0.75 × EBG1.035 (R = 0.80 and CCC = 0.75); where NPg = net protein requirements for growth, EBG = empty body gain, and EQEBW = equivalent empty BW.

Energy and protein requirements of crossbred Holstein × Gyr calves fed milk with milk replacer containing increasing dry-matter concentrations

Context There is a lack of studies concerning the nutrient requirements of dairy calves, mainly, evaluating different genetic groups. Aims The objective was to quantify energy and protein requirements of dairy calves up to 60 days, testing the influence of genetic composition (Holstein or crossbred Holstein × Gyr) on these requirements. Methods The study involved 42 bull calves (3 days of age), including animals with less than 15/16 Holstein composition (considered crossbred) and animals with more than 15/16 Holstein pedigree (considered purebred). Six calves were slaughtered at the start of the experiment to estimate the initial body composition of the animals. Of the remaining animals, four formed the maintenance group (fed 3 L/day of raw milk), and the other 32 were distributed into four treatments, which consisted of 6 L/day of raw milk, with increasing DM contents of 13.5%, 16.1%, 18.2% and 20.4% respectively. The DM contents were corrected for adding milk replacer to the raw milk. All animals had free access to starter feed and water. Digestibility trials were conducted at 28 and 56 days of life, with total faeces collection being performed for 5 days and urine collection for a period of 24 h. At 60 days of life, the animals were slaughtered to determine their body composition. Key results Net energy requirements for maintenance and metabolisable energy requirements for maintenance were 57.6 and 86.8 kcal/(empty bodyweight, EBW)0.75.day respectively. The efficiency of utilisation of metabolisable energy for maintenance was 66%. Net energy requirements for gain (NEg, Mcal/day) can be estimated by the following equation: , where EBG is empty body gain (kg/day) and EBW is in kilograms. The efficiency of utilisation of metabolisable energy for gain was 27%. The metabolisable-protein requirement for maintenance was 3.22 g/EBW0.75.day. Net protein requirement for gain (NPg, g/day) can be estimated by the following equation:, where RE is retained energy (Mcal/day). The efficiency of utilisation of metabolisable protein for gain was 59.1%. Conclusions Genetic group does not affect energy or protein requirements of pre-weaned calves. The estimates presented here can be used to calculate nutrient requirements of pre-weaned calves aged up to 60 days. Implications Inclusion of milk replacer in the liquid feed had a negative impact on diet quality.

Oscillating and static dietary crude protein supply: II. Energy and protein requirements of young Nellore bulls.

:The objective of this study was to evaluate whole body chemical composition and energy and protein nutrient requirements for maintenance and gain of Nellore bulls. Fifty young bulls, with an average age of 7 ± 1 mo and initial body weight (BW) of 260.0 ± 8.1 kg, were used in this experiment. Four bulls were used as baseline reference animals and were slaughtered at the beginning of the experiment. Four bulls were fed at maintenance (12 g dry matter [DM]/kg of BW), whereas 42 bulls were divided into six groups (n = 7/group) and were randomly assigned to the following dietary treatments 105 (low [LO]), 125 (medium [MD]), or 145 (high [HI]) g crude protein (CP)/kg DM, LO to HI (LH), LO to MD (LM), or MD to HI (MH) oscillating CP at a 48-h interval for 140 d. At the end of the experiment, bulls were slaughtered and samples of the whole body were collected. All samples were lyophilized, ground, and composed as percentage of component of empty body weight (EBW) from each bull. A power model was used to estimate carcass, non-carcass components, and gastrointestinal content of the shrunk body weight (SBW), and CP and water present in the empty body, whereas an exponential model was used to estimate adipose tissue and ether extract (EE) present in the EBW. Nonlinear regression equations were developed to predict heat production from metabolizable energy (ME) intake and retained energy (RE). The net energy requirements for maintenance and ME for maintenance were 77 and 122.75 kcal/EBW0.75/d, respectively. The efficiency of ME utilization for maintenance was 62.7%. The equation obtained for net energy for gain (NEg) was: NEg (Mcal/EBW0.75/d) = 0.0535 × EBW0.75 × EBG0.7131, where EBG is the empty body gain, and the efficiency was 24.25%. Net protein requirement for growth (NPg) was: NPg (g/d) = 227.372 × EBG – 19.479 × RE. There was a linear increase for carcass, CP, and water present in the EBW as the animal grew. The EE deposition exponentially increased as EBW increased.

California net energy system for Bos taurus indicus.

The California net energy system (CNES) was the reference for the development of most energy requirement systems worldwide, such as Nutrient Requirements of Beef Cattle (NASEM, Nutrient requirements of beef cattle, 8th Revised ed, 2016) and Brazilian Nutrient Requirements of Zebu and Crossbred Cattle (Valadares Filho, S. C., L. F. C. Silva, M. P. Gionbelli, P. P. Rotta, M. I. Marcondes, M. L. Chizzotti, and L. F. Prados, BR-CORTE: nutrient requirements of zebu and crossbred cattle, 3rd ed, 2016). This review aimed to compare methods used by NASEM and BR-CORTE to estimate the energy requirements for beef cattle. The net energy requirements for maintenance (NEm) of BR-CORTE is based on empty body weight (EBW), whereas NASEM uses shrunk body weight (SBW), but the Bos taurus indicus presents 10% to 8% lower NEm than Bos taurus taurus. We have compared animals with different EBW and SBW but with same equivalent empty body weight/standard reference weight ratio (0.75), as both systems have suggested different mature weights. Both systems predicted similar net energy requirements for gain (NEg) for animals with 1.8 kg of daily gain. However, estimated empty body gain was lower for NASEM estimations when the same metabolizable energy for gain is available. For pregnancy and lactation of beef cows, the NEm and net energy requirements for pregnancy (NEp) of a Zebu cow estimated by BR-CORTE were lower than the values estimated by NASEM. Furthermore, the magnitude of differences between these systems regarding NEp increased as pregnancy days increase. The NASEM and BR-CORTE systems have presented similar values for energy requirement for lactation (0.72 and 0.75 Mcal/kg milk, respectively).