Regression models from portable NIR spectra for predicting the carcass traits and meat quality of beef cattle

The aims of this study were to predict carcass and meat traits, as well as the chemical composition of the 9th to 11th rib sections of beef cattle from portable NIR spectra. The 9th to 11th rib section was obtained from 60 Nellore bulls and cull cows. NIR spectra were acquired at: P1 -center of Longissimus muscle; and P2 -subcutaneous fat cap. The models accurately estimated (P ≥ 0.083) all carcass and meat quality traits, except those for predicting red (a*) and yellow (b*) intensity from P1, and 12th-rib fat from P2. However, precision was highly variable among the models; those for the prediction of carcass pHu, 12th rib fat, toughness from P1, and those for 12th rib fat, a* and b* from P2 presented high precision (R2 ≥ 0.65 or CCC ≥ 0.63), whereas all other models evaluated presented moderate to low precision (R2 ≤ 0.39). Models built from P1 and P2 accurately estimated (P ≥ 0.066) the chemical composition of the meat plus fat, bones and, meat plus fat plus bones, except those for predicting the ether extract (EE) and crude protein (CP) of bones and the EE of Meat plus bones fraction from P2. However, precision was highly variable among the models (-0.08 ≤ R2 ≤ 0.86) of the 9th and 11th rib section. Those models for the prediction of dry matter (DM) and EE of the bones from P1; of EE from P1; and of EE, mineral matter (MM), CP from P2 of meat plus fat plus bones presented high precision (R2 ≥ 0.76 or CCC ≥ 0.62), whereas all other models evaluated presented moderate to low precision (R2 ≤ 0.45). Thus, models built from portable NIR spectra acquired at different points of the 9th to 11th rib section were recommended for predicting carcass and muscle quality traits as well as for predicting the chemical composition of this section of beef cattle. However, it is noteworthy, that the small sample size was one of the limitations of this study.

Effect of inclusion levels of low-fat dried distillers grains in finishing diets on protein and energy intake and retention and estimation of protein and energy requirements of young Nellore bulls fed with high concentrate diets

The objective was to evaluate the effect of including low-fat dried distillers grains (DDG) in finishing diets on protein and energy intake and retention and to estimate the protein and energy requirement of young Nellore bulls. Thirty-five animals were used: baseline (n = 4), maintenance (n = 4), and ad libitum intake (n = 27). Ad libitum animals were divided into four groups: diets with the inclusion of DDG at the levels of 0, 150, 300, and 450 g/kg (dry matter basis). At the end of the experiment, all animals were slaughtered. There was a linear reduction with increasing DDG levels in the total digestible nutrients intake (p = 0.008), metabolizable energy (ME) intake (p < 0.010), in total retained energy (p = 0.065), and in heat production (p < 0.001). Metabolizable protein (MP) intake increased linearly (p < 0.010) but retained protein did not differ (p = 0.499). Daily net energy and ME requirement for maintenance were 75.9 and 122 kcal/kg^0.75 EBW, respectively. Daily MP for maintenance was 3.6 g/kg^0.75 shrunk body weight. DDG inclusion in finishing diets reduces energy intake and deposition, and we recommend the equations of this study to estimate the requirements of young Nellore bulls.

Nutrient requirements and evaluation of equations to predict chemical body composition of dairy crossbred steers

Objective

Objectives were to estimate energy and protein requirements of dairy crossbred steers, as well as to evaluate equations previously described in the literature (HH46 and CS16) to predict the carcass and empty body chemical composition of crossbred dairy cattle.

Methods

Thirty-three Holstein×Zebu steers, aged 19±1 months old, with an initial shrunk body weight (BW) of 324±7.7 kg, were randomly divided into three groups: reference group (n = 5), maintenance level (1.17% BW; n = 4), and the remaining 24 steers were randomly allocated to 1 of 4 treatments. Treatments were: intake restricted to 85% of ad libitum feed intake for either 0, 28, 42, or 84 d of an 84-d finishing period.

Results

The net energy and the metabolizable protein requirements for maintenance were 0.083 Mcal/EBW^0.75/d and 4.40 g/EBW^0.75, respectively. The net energy (NE_G) and protein (NP_G) requirements for growth can be estimated with the following equations: NE_G (Mcal/kg EBG) = 0.2973_(±0.1212) ×EBW^{0.4336(±0.1002)} and NP_G (g/d) = 183.6_(±22.5333)×EBG−2.0693_(±4.7254)×RE, where EBW, empty BW; EBG, empty body gain; and RE, retained energy. Crude protein (CP) and ether extract (EE) chemical contents in carcass, and all the chemical components in the empty body were precisely and accurately estimated by CS16 equations. However, water content in carcass was better predicted by HH46 equation.

Conclusion

The equations proposed in this study can be used for estimating the energy and protein requirements of crossbred dairy steers. The CS16 equations were the best estimator for CP and EE chemical contents in carcass, and all chemical components in the empty body of crossbred dairy steers, whereas water in carcass was better estimated using the HH46 equations.

Predicting the chemical composition of the body and the carcass of hair sheep using body parts and carcass measurements

Determination of the chemical composition in the body and carcass of ruminants is important for both nutritional requirement studies and the meat industry. This study aimed to develop equations to predict the body and carcass chemical composition of hair sheep using the chemical composition of body parts, carcass measurements and shrunk BW as predictors. A database containing 107 individual records for castrated male hair sheep ranging from 24 to 43 kg BW was gathered from two body composition studies. The empty body, carcass and body parts were analyzed for water, ash, fat and protein contents (%). The body parts used to estimate body and carcass composition were fore leg, hind leg and 9-11th rib section. The carcass measurements used were leg length, thoracic circumference, hind circumference, hind width, thoracic width, thoracic depth and chest width. Each model performance was evaluated using a leave-one-out cross-validation. Multiple regression analysis considering the study as a random effect revealed that body parts in association with carcass measurements were significant for predicting the chemical composition in the body of castrate male sheep. However, the use of the chemical composition of hind leg produced the best models for predicting the ash and fat contents in the empty body, whereas the water and protein contents in the empty body were better predicted when using the chemical compositions of 9-11th rib section and fore leg, respectively. Multiple regression analysis also revealed that most body parts were suitable for predicting the carcass composition, except for 9-11th rib section whose chemical composition did not produce significant prediction equations for ash and protein carcass contents. The use of the chemical composition of hind leg in association with carcass measurements produced the best models for predicting the water and fat contents in the carcass, while the ash and protein contents in the carcass were better predicted when using the chemical composition of fore leg. In conclusion, precision, accuracy and goodness-of-fit of the equations drove the selection of the chemical composition of hind leg and carcass measurements in a multivariate approach, as the most suitable predictors of the chemical composition of the body and carcass of hair sheep. However, the chemical composition of fore leg may be used as well. The developed equations could improve the accuracy of the empty body and carcass composition estimations in sheep, optimizing the estimation of nutrient requirements, as well as the carcass quality evaluation for this species.

Post-weaning growth rate effects on body composition of Nellore bulls

Context Previously feed-restricted cattle may exhibit compensatory growth during the finishing phase. However, the efficiency in converting feed into carcass should be evaluated since cattle undergoing compensatory growth usually have high non-carcass weight gain. Aims The objective of this study was to evaluate the effect of growth rate throughout the post-weaning growing phase on subsequent feed efficiency, carcass gain, and gain composition. Methods Thirty-nine weaned young Nellore bulls averaging 230.4 ± 5.62 kg of bodyweight and 8.5 ± 0.25 months of age were used. Initially, five bulls were slaughtered as a reference initial group. The remaining bulls were randomly assigned to one of three nutritional plans to achieve Low (0 kg/day), Medium (0.6 kg/day) or High (1.2 kg/day) average daily gain (ADG) throughout the post-weaning growing phase, followed by high growth rate during the finishing phase. One-half of the bulls from each treatment were slaughtered at the end of the post-weaning growing phase, and the other one-half after the finishing phase. During both phases the feed intake, apparent digestibility, performance, and body composition were evaluated. Key results Throughout the post-weaning growing phase, High bulls had greater ADG and more efficiently converted feed into carcass, compared with other nutritional plans (P &lt; 0.01). Throughout the finishing phase, Low bulls had greater ADG, carcass gain, and feed efficiency than High and Medium bulls (P &lt; 0.01). Previous feed restriction did not affect (P &gt; 0.05) apparent digestibility. During the finishing phase, previously restricted bulls fully compensated for the lost visceral organ weight, whereas the losses of bodyweight and carcass weight were only partially compensated. Throughout finishing, Low bulls had the greatest feed efficiency and profitability among nutritional plans. However, considering the overall experiment, Hight bulls converted feed into carcass more efficiently than Low bulls (P = 0.02), but did not differ from Medium (P &gt; 0.05). Conclusions Although previously restricted bulls had greater performance and efficiency throughout finishing, the improvement was not enough to reach the same carcass weight at the same age of the unrestricted bulls. Implications Despite the greater profitability of previously restricted bulls throughout finishing, unrestricted bulls were more profitable considering both growing and finishing phases.

Filho SC, Pucetti P, C. Pacheco MV, Godoi LA, Zanetti D, Alhadas HM, Paulino MF, Caton JS. 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).

In vivo ultrasound and biometric measurements predict the empty body chemical composition in Nellore cattle

Evaluation of the body chemical composition of beef cattle can only be measured postmortem and those data cannot be used in real production scenarios to adjust nutritional plans. The objective of this study was to develop multiple linear regression equations from in vivo measurements, such as ultrasound parameters [backfat thickness (uBFT, mm), rump fat thickness (uRF, mm), and ribeye area (uLMA, cm2)], shrunk body weight (SBW, kg), age (AG, d), hip height (HH, m), as well as from postmortem measurements (composition of the 9th to 11th rib section) to predict the empty body and carcass chemical composition for Nellore cattle. Thirty-three young bulls were used (339 ± 36.15 kg and 448 ± 17.78 d for initial weight and age, respectively). Empty body chemical composition (protein, fat, water, and ash in kg) was obtained by combining noncarcass and carcass components. Data were analyzed using the PROC REG procedure of SAS software. Mallows' Cp values were close to the ideal value of number of independent variables in the prediction equations plus one. Equations to predict chemical components of both empty body and carcass using in vivo measurements presented higher R2 values than those determined by postmortem measurements. Chemical composition of the empty body using in vivo measurements was predicted with R2 > 0.73. Equations to predict chemical composition of the carcass from in vivo measurements showed R2 lower (R2< 0.68) than observed for empty body, except for the water (R2 = 0.84). The independent variables SBW, uRF, and AG were sufficient to predict the fat, water, energy components of the empty body, whereas for estimation of protein content the uRF, HH, and SBW were satisfactory. For the calculation of the ash, the SBW variable in the equation was sufficient. Chemical compounds from components of the empty body of Nellore cattle can be calculated by the following equations: protein (kg) = 47.92 + 0.18 × SBW - 1.46 × uRF - 30.72 × HH (R2 = 0.94, RMSPE = 1.79); fat (kg) = 11.33 + 0.16 × SBW + 2.09 × uRF - 0.06 × AG (R2 = 0.74, RMSPE = 4.18); water (kg) = - 34.00 + 0.55 × SBW + 0.10 × AG - 2.34 × uRF (R2 = 0.96, RMSPE = 5.47). In conclusion, the coefficients of determination (for determining the chemical composition of the empty body) of the equations derived from in vivo measures were higher than those of the equations obtained from rib section measurements taken postmortem, and better than coefficients of determination of the equations to predict the chemical composition of the carcass.

Development of equations, based on milk intake, to predict starter feed intake of preweaned dairy calves

There is a lack of studies that provide models or equations capable of predicting starter feed intake (SFI) for milk-fed dairy calves. Therefore, a multi-study analysis was conducted to identify variables that influence SFI, and to develop equations to predict SFI in milk-fed dairy calves up to 64 days of age. The database was composed of individual data of 176 calves from eight experiments, totaling 6426 daily observations of intake. The information collected from the studies were: birth BW (kg), SFI (kg/day), fluid milk or milk replacer intake (MI; l/day), sex (male or female), breed (Holstein or Holstein×Gyr crossbred) and age (days). Correlations between SFI and the quantitative variables MI, birth BW, metabolic birth BW, fat intake, CP intake, metabolizable energy intake, and age were calculated. Subsequently, data were graphed, and based on a visual appraisal of the pattern of the data, an exponential function was chosen. Data were evaluated using a meta-analysis approach to estimate fixed and random effects of the experiments using nonlinear mixed coefficient statistical models. A negative correlation between SFI and MI was observed (r=-0.39), but age was positively correlated with SFI (r=0.66). No effect of liquid feed source (milk or milk replacer) was observed in developing the equation. Two equations, significantly different for all parameters, were fit to predict SFI for calves that consume less than 5 (SFI5) l/day of milk or milk replacer: ${\rm SFI}_{{\,\lt\,5}} {\equals}0.1839_{{\,\pm\,0.0581}} {\times}{\rm MI}{\times}{\rm exp}^{{\left( {\left( {0.0333_{{\,\pm\,0.0021 }} {\minus}0.0040_{{\,\pm\,0.0011}} {\times}{\rm MI}} \right){\times}\left( {{\rm A}{\minus}{\rm }\left( {0.8302_{{\,\pm\,0.5092}} {\plus}6.0332_{{\,\pm\,0.3583}} {\times}{\rm MI}} \right)} \right)} \right)}} {\minus}\left( {0.12{\times}{\rm MI}} \right)$ ; ${\rm SFI}_{{\,\gt\,5}} {\equals}0.1225_{{\,\pm\,0.0005 }} {\times}{\rm MI}{\times}{\rm exp}^{{\left( {\left( {0.0217_{{\,\pm\,0.0006 }} {\minus}0.0015_{{\,\pm\,0.0001}} {\times}{\rm MI}} \right){\times}\left( {{\rm A}{\minus}\left( {3.5382_{{\,\pm\,1.3140 }} {\plus}1.9508_{{\,\pm\,0.1710}} {\times}{\rm MI}} \right)} \right)} \right)}} {\minus}\left( {0.12{\times}{\rm MI}} \right)$ where MI is the milk or milk replacer intake (l/day) and A the age (days). Cross-validation and bootstrap analyses demonstrated that these equations had high accuracy and moderate precision. In conclusion, the use of milk or milk replacer as liquid feed did not affect SFI, or development of SFI over time, which increased exponentially with calf age. Because SFI of calves receiving more than 5 l/day of milk/milk replacer had a different pattern over time than those receiving &lt;5 l/day, separate prediction equations are recommended.

Can the body composition of crossbred dairy cattle be predicted by equations for beef cattle?

Objective

The aim of the study was to evaluate the efficiency of the Hankins and Howe (HH46), Valadares Filho (V06), and Marcondes (M12) equations for predicting the physical and chemical composition of dairy crossbred bulls carcasses, as well as the chemical composition of their empty bodies.

Methods

This study was conducted using 30 dairy crossbred bulls. One group of five animals was slaughtered at the beginning of the experiment, and the remaining were slaughtered 112 days later. Animals were distributed in a completely randomized design into treatments consisting different levels of concentrate (0%, 17%, 34%, 51%, and 68%). The physical and chemical compositions of the cattle were obtained from the right half of the carcass and using samples taken between the 9th and 11th ribs of the left half of the carcass. The estimated and experimentally determined values were compared using the correlation and concordance coefficient, as well as the mean square error of prediction (MSEP) and its components.

Results

The HH46 equations were better at estimating the amount of muscle plus fat in the carcass. The amount of bone in the carcasses could not be well estimated by the HH46 and M12 models. The M12, HH46, and V06 equations were worst at estimating the amounts of protein, ether extract, and water in the carcass, respectively. In the empty body, the amounts of protein and water were well estimated by the HH46 equations. Protein, ether extract, and water were accurately estimated by the V06 equations, and ether extract by the M12 equations.

Conclusion

The physical and chemical composition of dairy crossbred bull carcasses, as well as the chemical composition of their empty bodies, can be predicted using the equations tested here. The amount of bone in these carcasses could not be accurately predicted.

Evaluation of predictive equations developed to assess body composition of F1 Nellore × Angus bulls and steers

We evaluated and compared empirical equations used for assessing beef cattle body composition, developed in 2010 (M10), 2012 (M12), 2006 (V06) and 1946 (HH46). Forty-eight F1 Nellore × Angus bulls and steers, aged 12.5 ± 0.51 months old, with initial shrunk bodyweight of 233 ± 23.5 kg and 238 ± 24.6 kg, respectively, were used in this experiment. The trial was a randomised factorial arrangement of treatments (two genders and five slaughter weights). The animals were randomly assigned to five slaughter-weight-based groups: baseline, maintenance, and 380, 440 and 500 kg. The diet comprised maize silage and concentrate (60 : 40). After slaughter, the 9th–11th rib section cut was dissected into muscle, fat and bone. The remaining carcass was similarly dissected. Other variables evaluated as partial predictors of body composition included empty bodyweight, dressing percentage, visceral fat percentage, and organ and viscera percentage. The values estimated with predictive equations were compared with observed values. For the physically separable carcass composition, only the M12 equation estimated precisely and accurately the amount of muscle (r2 = 0.98, root-mean-square error (RMSE) = 5.64 kg, concordance correlation coefficient (CCC) = 0.96) and fat (r2 = 0.94, RMSE = 4.91 kg, CCC = 0.96) tissue present in the carcass. The V06 and M10 equations estimated precisely and accurately the amount of carcass chemical components; HH46 could explain only the amount of crude protein (r2 = 0.84, RMSE = 4.71 kg, CCC = 0.90) content in the carcass. The equations used to predict empty body chemical composition failed to estimate correctly the amount of chemical contents present in the empty bodyweight. However, V06 can be used to estimate the crude protein (r2 = 0.91, RMSE = 5.97 kg, CCC = 0.93) content in the empty bodyweight. Furthermore, M10 could be used to estimate ether extract (r2 = 0.94, RMSE = 8.13 kg, CCC = 0.84) content, although this had to be analysed by gender, because such variables (i.e. ether extract) presented a pronounced effect, especially for steers, on total chemical fat.