GrassGroTM simulation of pasture, animal performance and greenhouse emissions on low and high sheep productivity grazing systems: 1-year validation and 25-year analysis.

Globally, there is a focus on reducing the absolute methane (CH4) and nitrous oxide emissions, and the emissions intensity (EI, kg CO2e/kg animal product) of livestock production. Increasing the productivity of mixed pasture systems has the potential to increase food (e.g., lamb) and textile fibre (e.g., wool) production while reducing the EI of those products from grazing livestock. The objective of this study was to quantify the differences in greenhouse gas (GHG) emissions and EI between sheep on Low (i.e., low sustainable stocking rate) and High (i.e., high sustainable stocking rate) productivity grazing systems (PGSs). Therefore, a replicated breeding-ewe trial on 18 paddocks was established across 2 - years. Three flocks on Low (3 × 16 ewes/flock) and High PGSs (3 × 32 ewes/flock) rotated across three land-classes and three paddocks per PGS. In year 1, the observed on-farm pasture quantity, quality, and botanical composition, together with lamb BW (kg), and daily CH4 production (DMP, g CH4/head per day) using Open Path Fourier Transformed Infrared (OP-FTIR) spectrometers data were measured. Subsequently, two simulations using GrassGroTM were conducted: (1) a 1-year GrassGroTM simulation that used the observed on-farm data to adjust parameters: date of mating, paddock fertility, and weight of mature ewes to validate GrassGroTM predictions to achieve accuracy and precision targets; and (2) a 25-year (1986-2011) simulation to analyse the effects of Low and High PGSs on sheep production and GHG emissions across a variable climate. The 1-year validation predictions fitted well with the observed on-farm data for: pasture biomass (kg/ha), DM digestibility (%), botanical composition (kg/ha), lamb (kg) product, and DMP (g CH4/head per day). The subsequent predicted results from the 25-year GrassGroTM simulation showed minimal effect of PGS on the mean DM intake (kg DM/day) or DMP for Low and High PGSs, but this was thought to be due to the biomass in both PGSs exceeding 1 500 kg DM/ha. The EI, over the 25-year simulation, on the High PGS was 16.5% lower than the Low PGS. Additional calculations of DMP were conducted using a recent global equation, giving estimates of DMP that closely matched the observed on-farm OP-FTIR DMP measurements, but these were lower than the GrassGroTM predictions and improved the accuracy and precision. It is concluded that in some pasture situations, managing pastures and stock numbers to intensify grazing systems can allow increased livestock production, without increasing daily CH4 emissions/head while substantially decreasing the EI of the animal products generated.

Live animal predictions of carcass components and marble score in beef cattle: model development and evaluation.

Until recently, beef carcass payment grids were predominantly based on weight and fatness categories with some adjustment for age, defined as number of adult teeth, to determine the price received by Australian beef producers for slaughter cattle. With the introduction of the Meat Standards Australia (MSA) grading system, the beef industry has moved towards payments that account for intramuscular fat (IMF) content (marble score (MarbSc)) and MSA grades. The possibility of a payment system based on lean meat yield (LMY, %) has also been raised. The BeefSpecs suite of tools has been developed to assist producers to meet current market specifications, specifically P8-rump fat and hot standard carcass weight (HCW). A series of equations have now been developed to partition empty body fat and fat-free weight into carcass fat-free mass (FFM) and fat mass (FM) and then into flesh FFM (FleshFFM) and flesh FM (FleshFM) to predict carcass components from live cattle assessments. These components then predict denuded lean (kg) and finally LMY (%) that contribute to emerging market specifications. The equations, along with the MarbSc equation, are described and then evaluated using two independent datasets. The decomposition of evaluation datasets demonstrates that error in prediction of HCW (kg), bone weight (BoneWt, kg), FleshFFM (kg), FleshFM (kg), MarbSc and chemical IMF percentage (ChemIMF%) is shown to be largely random error (%) in evaluation dataset 1, though error for ChemIMF% was primarily slope bias (%) in evaluation dataset 1, and BoneWt had substantial mean bias (%) in evaluation dataset 2. High modelling efficiencies of 0.97 and 0.95 for predicting HCW for evaluation datasets 1 and 2, respectively, suggest a high level of accuracy and precision in the prediction of HCW. The new outputs of the model are then described as to their role in estimating MSA index scores. The modelling system to partition chemical components of the empty body into carcass components is not dependent on the base modelling system used to derive empty body FFM and FM. This can be considered a general process that could be used with any appropriate model of body composition.

Evaluation of remote monitoring units for estimating body weight and supplement intake of grazing cattle.

Automated weighing systems to monitor BW and supplement intake (SI) of individual grazing cattle are being developed to better understand the seasonal nutrition and performance of grazing livestock. This study established (1) the accuracy and repeatability of a commercial walk-over weighing (WoW) system for estimating BW and (2) the accuracy of an automatic supplement weighing (ASW) unit for estimating SI based on measuring time spent at the unit. The WoW and ASW units monitored BW and SI of 112 cattle consisting of 55 cows and 57 calves grazed on a 32.5 ha paddock for 41 days, with an average of 258 BW records collected per day. Static BWs were recorded at each mustering event (n = 7) and were compared to repeated measurements collected by the WoW on the day of each mustering event. Body weight was overestimated by the WoW, with the predicted BW of calves and cows averaging 10 and 21 kg heavier, respectively, than actual, and root MS prediction errors (RMSPE) of 5.1% and 5.5% of the static BW, respectively. For both calves and cows, 38% of the MS prediction errors (MSPE) was mean bias (MB) error and 9% of MSPE was slope bias error. The concordance correlation coefficient (CCC; 0.90 v. 0.80) and modelling efficiency (MEF; 0.78 v. 0.62) of WoW BW for calves were higher than for cows, indicating that the predicted values were deviating from a 1 : 1 relationship and in particular as weight increases. A rolling average across five or more consecutive BW measures improved the accuracy of the WoW BW estimates. Regarding estimates of SI, the aggregated time the herd spent at the ASW unit was strongly associated with total SI (R2 = 0.92; P < 0.001). Further, positive linear relationships (P < 0.001) existed between cumulative weighted time spent at the ASW unit (min) and concentration of fenbendazole (FBZ) used as an intake marker and its derivatives (oxfendazole and oxfendazole sulfone) in the plasma of individual cows, with R2 of 0.54, 0.73 and 0.75, respectively. Although the WoW overestimated static BW, the low bias in the slope indicated that a linear regression model could be developed to adjust the WoW BW to reduce the MB and improve the estimate of WoW BW. The significant positive relationship between time spent at the ASW unit and individual blood FBZ concentration identified the suitability of the ASW unit for estimating SI by grazing cattle.

Live animal assessments of rump fat and muscle score in Angus cows and steers using 3-dimensional imaging.

The objective of this study was to develop a proof of concept for using off-the-shelf Red Green Blue-Depth (RGB-D) Microsoft Kinect cameras to objectively assess P8 rump fat (P8 fat; mm) and muscle score (MS) traits in Angus cows and steers. Data from low and high muscled cattle (156 cows and 79 steers) were collected at multiple locations and time points. The following steps were required for the 3-dimensional (3D) image data and subsequent machine learning techniques to learn the traits: 1) reduce the high dimensionality of the point cloud data by extracting features from the input signals to produce a compact and representative feature vector, 2) perform global optimization of the signatures using machine learning algorithms and a parallel genetic algorithm, and 3) train a sensor model using regression-supervised learning techniques on the ultrasound P8 fat and the classified learning techniques for the assessed MS for each animal in the data set. The correlation of estimating hip height (cm) between visually measured and assessed 3D data from RGB-D cameras on cows and steers was 0.75 and 0.90, respectively. The supervised machine learning and global optimization approach correctly classified MS (mean [SD]) 80 (4.7) and 83% [6.6%] for cows and steers, respectively. Kappa tests of MS were 0.74 and 0.79 in cows and steers, respectively, indicating substantial agreement between visual assessment and the learning approaches of RGB-D camera images. A stratified 10-fold cross-validation for P8 fat did not find any differences in the mean bias ( = 0.62 and = 0.42 for cows and steers, respectively). The root mean square error of P8 fat was 1.54 and 1.00 mm for cows and steers, respectively. Additional data is required to strengthen the capacity of machine learning to estimate measured P8 fat and assessed MS. Data sets for and continental cattle are also required to broaden the use of 3D cameras to assess cattle. The results demonstrate the importance of capturing curvature as a form of representing body shape. A data-driven model from shape to trait has established a proof of concept using optimized machine learning techniques to assess P8 fat and MS in Angus cows and steers.

Postweaning substitution of grazed forage with a high-energy concentrate has variable long-term effects on subcutaneous fat and marbling in Bos taurus genotypes.

The objective of this study was to quantify the effects and interactions of stage of growth and genotype on commercial carcass traits and intramuscular fat (IMF) content in 5 muscles of steers ( = 165) and to test the hypothesis that substituting pasture with a high-energy concentrate during the immediate postweaning period increases IMF. Cattle of 3 genotypes (Angus, Hereford, and Wagyu × Angus; = 55/genotype) were selected at weaning from commercial herds, targeting genotypic differences in marbling and subcutaneous fatness. Following weaning, steers were fed for 168 d within 2 different improved, temperate pasture-based nutritional systems: a forage-only system (FS) and forage with high-energy supplemented system (SS), with 2 replicates per system. The supplement was fed at a level of 1% of average BW adjusted every 2 wk to provide an estimated 50% of energy requirements for 168 d from weaning. Pasture on offer in both systems was managed to match the BW of the FS and SS steers during the postweaning treatment period to avoid confounding due to differences in growth rate during this period. Steers were then regrouped into 2 replicates and backgrounded on improved, temperate pasture for 158 d and then grain fed within 1 group for 105 d (short fed) or 259 d (long fed). Groups were slaughtered at commencement (d 0) and end of postweaning nutritional treatments (d 168), end of backgrounding (d 326), and after short (d 431) or long feedlotting (d 585). Serial slaughter stage had an effect on all traits assessed ( < 0.01). The FS steers had more rib fat ( < 0.01) and higher Meat Standards Australia marbling score ( < 0.05) and a tendency ( < 0.10) to have greater eye muscle area than the SS steers throughout the study. Genotypic differences were evident ( < 0.05) for all traits assessed except HCW, dressing percentage, rib fat depth, ossification score, ultimate pH, and IMF in the semitendinosus muscle. The results for marbling and IMF do not support the use of a high-energy feed as a substitute for an equivalent amount of energy from pasture during the immediate postweaning period to enhance development of marbling.

Modeling the relationship between scanned rump and 12th-rib fat in young temperate and tropical bovines: model development and evaluation.

A decision support tool for predicting subcutaneous fat depths called BeefSpecs, based on the Davis growth model (DGM), has been developed by the Cooperative Research Centre for Beef Genetic Technologies. Currently, the DGM predicts 12th-rib fat thickness (RFT, mm). To allow predictions of fat thickness at the P8 rump (P8FT, mm) site, the standard carcass fat measurement in the Australian beef industry, a relationship was developed between ultrasound RFT and P8FT in steers and heifers from temperate (Angus, Hereford, Shorthorn, and Murray Grey) and tropical (Brahman, Belmont Red, and Santa Gertrudis) breed types. Model development involved fitting various combinations of sex, breed type (BrT), BW, age, and RFT to produce 6 models. The models were challenged with data from 3 independent data sets: 1) Angus steers from 2.4 generations of divergent selection for and against residual feed intake; 2) 2 tropically adapted genotypes [Brahman and tropically adapted composites (combinations of Belmont Red, Charbray, Santa Gertrudis, Senepol, and Brahman breeds)]; and 3) a study using sires from Charolais, Limousin, Belgian Blue, and Black and Red Wagyu breeds and 3 genetic lines of Angus to create divergence in progeny in terms of genetic potential for intramuscular fat percent and retail beef yield. When challenged with data from Angus cattle, the mean biases (MB, mm) for models A to F were -1.23, -0.56, -0.56, -0.02, 0.14, and 0.04, and the root mean square errors of predictions (mm) were 1.53, 0.97, 0.97, 0.92, 0.93, and 0.91, respectively. When challenged with data from Brahman cattle, MB were 0.04, -0.22, -0.14, 0.05, -0.11, and 0.02 and root mean square errors of predictions were 1.30, 1.29, 1.27, 1.23, 1.37, and 1.29, respectively. Generally, model accuracy indicated by MB tended to be less for model E, which contained age rather than BW as a covariate. Models B and C were generally robust when challenged with data from Angus, Brahman, and Tropical Composite cattle as well as crossbred cattle with temperate sires. Model D, which did not contain age, performed the most consistently and was selected for inclusion in the DGM: P8FT, mm = -3.6 (+/-0.14) + 1.3 (+/-0.13) x sex + 0.11 (+/-0.13) x BrT + 0.014 (+/-4.8E(-4)) x BW + 0.96 (+/-0.01) x RFT - 0.73 (+/-0.08) x sex x BrT - 3.8E(-3) (+/-4.2E(-4)) x sex x BW - 0.09 (+/-0.01) x sex x RFT + 1.3E(-3) (+/-3.7E(-4)) x BrT x BW + 0.24 (+/-0.01) x BrT x RFT (adjusted R(2) = 0.86; SE = 0.013). Model D has been implemented in BeefSpecs to predict P8FT.

Development and evaluation of empirical equations to interconvert between twelfth-rib fat and kidney, pelvic, and heart fat respective fat weights and to predict initial conditions of fat deposition models for beef cattle.

The Davis growth model (DGM) simulates growth and body composition of beef cattle and predicts development of 4 fat depots. Model development and evaluation require quantitative data on fat weights, but sometimes it is necessary to use carcass data that are more commonly reported. Regression equations were developed based on published data to interconvert between carcass characteristics and kilograms of fat in various depots and to predict the initial conditions for the DGM. Equations include those evaluating the relationship between the following: subcutaneous fat (SUB, kg) and 12th-rib fat thickness (mm); visceral fat (VIS, kg) and KPH (kg); DNA (g) in intermuscular, intramuscular, subcutaneous, and visceral fat depots and empty body weight; and contributions of fat (kg) in intramuscular (INTRA), SUB, and VIS fat depots and total body fat (kg). The intermuscular fat (INTER, kg) contribution was found by difference. The linear regression equations were as follows: SUB vs. 12th-rib fat thickness (n = 75; P < 0.01) with R(2) = 0.88 and SE = 10.00; VIS vs. KPH (kg; n = 78; P < 0.01) with R(2) = 0.95 and SE = 2.82; the DNA (g) equations for INTER, INTRA, SUB, and VIS fat depots vs. empty body weight (n = 6, 5, 6, and 6; P = 0.08, P < 0.01, P < 0.01, and P = 0.05) with R(2) = 0.57, 0.93, 0.93, and 0.66, and SE = 0.03, 0.003, 0.02, and 0.03, respectively; and initial contribution of INTRA, SUB, and VIS fat depots vs. total body fat (n = 23; P < 0.01) for each depot, with R(2) = 0.97, 0.99, and 0.97, and SE = 0.61, 0.93, and 1.41, respectively. All empirical equations except for DNA were challenged with independent data sets (n = 12 and 10 for SUB and VIS equations and n = 9 for the initial INTER, INTRA, SUB, and VIS fat depots). The mean biases were -2.21 (P = 0.12) and 2.11 (P < 0.01) kg for the SUB and VIS equations, respectively, and 0.05 (P = 0.97), -0.37 (P = 0.27), 1.82 (P = 0.08), and -1.50 (P = 0.06) kg for the initial contributions of INTER, INTRA, SUB, and VIS fat depots, respectively. The random components of the mean square error of prediction were 73 and 26% for the SUB and VIS equations, respectively, and similarly were 99, 85, 62, and 61% for the initial contributions of INTER, INTRA, SUB, and VIS fat depots, respectively. Both the SUB and VIS equations predicted accurately within the bounds of experimental error. The equations to predict initial fat contribution (kg) were considered adequate for initializing the fat depot differential equations for the DGM and other beef cattle simulation models.

Meta-analysis of factors affecting carcass characteristics of feedlot steers.

A meta-analysis was conducted to assess the effects of biological type (early-moderate or late maturity) and implant status (estrogenic, combination, or nonimplanted; repeats included) on HCW (kg); LM area (cm2); 12th-rib fat thickness (fat thickness, cm); KPH (%), and intramuscular fat (%) at harvest, to provide inputs to an ongoing program for modeling beef cattle growth and carcass quality. Forty-three publications from 1982 to 2004 with consistent intramuscular fat data were evaluated. Two studies were undertaken: 1) with fat thickness as a covariate and 2) with BW as a covariate. The intercept-slope covariance estimate was not statistically different from 0 for LM area (P = 0.11), KPH (P = 0.19), and intramuscular fat (P = 0.74) in study 1, and for LM area (P = 0.44), fat thickness (P = 0.11), KPH (P = 0.19), and intramuscular fat (P = 0.74) in study 2; therefore, a reduced model without a covariance component was fitted for these carcass characteristics. A covariance component was fitted for HCW (P = 0.01, study 1 and P = 0.05, study 2) and for intramuscular fat (P = 0.05, study 2). In study 1, the results for maturity indicated differences between early-moderate and late maturity for HCW (P < 0.01) and LM area (P < 0.01) but no differences for KPH (P = 0.26) and intramuscular fat (P = 0.50); for implant status, an estrogenic or combination implant increased HCW by 2.9% (P = 0.27) or 4.8% (P < 0.01), increased LM area by 3.2% (P = 0.23) or 6.3% (P < 0.01), decreased intramuscular fat by 8.1% (P < 0.01) or 5.4% (P < 0.01), respectively, and decreased KPH by 7.6% (P = 0.34) for estrogenic implants but increased KPH by 1.1% (P = 0.36) for combination implants, compared with nonimplanted steers. In study 2, the results at 600 kg of BW for implant status (implant or nonimplant) indicated no differences for HCW (P = 0.63) and LM area (P = 0.73), but there were differences for fat thickness (P < 0.01), KPH (P < 0.01), and intramuscular fat (P < 0.01); the results for maturity (early-moderate or late maturity) indicated no differences for HCW (P = 0.94), but there were differences for LM area (P < 0.01), fat thickness (P < 0.01), KPH (P < 0.01), and intramuscular fat (P < 0.01). The difference between early-moderate and late maturity (studies 1 and 2) confirmed that frame size accounts for a substantial portion of the variation in carcass composition. Studies 1 and 2 also indicate that implant status had significant effects on carcass quality.

Prediction of the chemical composition of lamb carcasses from multi-frequency impedance data.

Multi-frequency bioimpedance analysis (MFBIA) was used to determine the impedance, reactance and resistance of 103 lamb carcasses (17.1-34.2 kg) immediately after slaughter and evisceration. Carcasses were halved, frozen and one half subsequently homogenized and analysed for water, crude protein and fat content. Three measures of carcass length were obtained. Diagonal length between the electrodes (right side biceps femoris to left side of neck) explained a greater proportion of the variance in water mass than did estimates of spinal length and was selected for use in the index L2/Z to predict the mass of chemical components in the carcass. Use of impedance (Z) measured at the characteristic frequency (Zc) instead of 50 kHz (Z50) did not improve the power of the model to predict the mass of water, protein or fat in the carcass. While L2/Z50 explained a significant proportion of variation in the masses of body water (r(2) 0.64), protein (r(2) 0.34) and fat (r(2) 0.35), its inclusion in multi-variate indices offered small or no increases in predictive capacity when hot carcass weight (HCW) and a measure of rib fat-depth (GR) were present in the model. Optimized equations were able to account for 65-90% of the variance observed in the weight of chemical components in the carcass. It is concluded that single frequency impedance data do not provide better prediction of carcass composition than can be obtained from measures of HCW and GR. Indices of intracellular water mass derived from impedance at zero frequency and the characteristic frequency explained a similar proportion of the variance in carcass protein mass as did the index L2/Z50.