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Objective Measurement

Objective Measurement


MLA’s Objective Measurement (OM) sub-program support development and industry adoption of a range of technologies that measure or estimate key traits that describe livestock productivity and carcase value at appropriate points in the supply chain, for both live animals and carcases. These technologies improve grading accuracy, transparency and enable new value-based trading models that support the industry’s Red Meat 2030 goal to ‘double the value of Australian red meat sales as the trusted source of the highest quality protein’. 

Additionally, live animal R&D activities aim to improve production efficiency, sustainability and welfare.  Investing in both on- and off-farm technologies aligns with MLA’s role and strategy for impact across the value chain, with a strong connection to eating quality delivering consumer satisfaction unpinning red meat demand plus driving a data culture across the industry for decision making.

As well as technology development, the OM program includes other key enablers such calibration of measurements, development of digital data and measurement standards, developing new meat industry language criteria, supporting data integration into business systems, including supporting feedback and producer/seedstock extension programs, and decision support systems.

Successful delivery will support the red meat industry to transition to new business models, underpinned by key objective measurement technologies and related value-based trading.

Strategic focus

The Objective Measurement program contributes towards the outcomes of the following strategic focus areas outlined in MLA’s Strategic Plan 2025:

Strategic focus areas
  • Decisions informed through data and insights - Developing technologies that allow data capture, sharing and analysis across the value chain supports industry in building a data culture. It provides the foundation for value-based pricing and enables producers to have greater access to data and feedback on the performance of their animals to inform production decisions.
  • Targeted investment to address the industry’s big, complex challenges – Providing specific trait measurement feedback data to producers will help them to improve market specification compliance and confidently supply product that aligns with consumer attributes.
  • Enabling new sources of revenue – Objective measurement and automation, underpinned by data and novel technologies, will enable the industry to extract maximum value from the carcase.
  • Beyond today’s farm gate – This program involves leveraging insights and technologies from outside of the food sector as well as strong collaboration with new and current commercial partners to achieve efficiencies and impact. It enables the industry to leverage external funding from partners and commercialisers through the MLA Donor Company.

Core Activities

The OM programs historical focus on off-farm technology development has shifted strongly towards adoption and supporting commercial implementation.  In parallel there is an increased focus on initiating R&D to develop live animal OM technologies. Key activities include:

Key core activities
  • Measurement of carcase quality attributes to enhance MSA and AUS-MEAT grading, by a range of technologies that support current grading and provide new grading opportunities, including investigating opportunities for hot (pre-chiller) beef preliminary MSA grading.
  • Development of new AUS-MEAT language and standards; and supporting new technologies to achieve accreditation.
  • Development and adoption of objective measurement technologies to measure carcase value, composition and animal health attributes for supply chain feedback, and support development of business systems to deliver value.
  • Initiating the development of live animal objective measurement technologies to improve production efficiency, sustainability, and product forecasting and market allocations.

Benefits to Industry

The OM program develops and supports technologies that contribute to the following value propositions:

  • OM based processing of livestock to optimise sales value
  • Genetic trait selection for OM based lean meat yield (LMY) increase while maintaining or improving eating quality, and maintaining pH
  • OM based increase in feedlot marbling while optimising turn-off times
  • Live animal OM measurement of lean meat yield (LMY) for on-farm management and selling decisions
  • Intramuscular fat (IMF) measurement at line-speed to enable the MSA cuts-based sheepmeat model
  • Hot grading to enhance fabrication efficiency and improve market allocation to optimise value.

The economic impact of the above value propositions were modelled on an ex-ante program level in 2020. The results indicated that the annual cattle and sheep net benefit achievable from the likely adoption of both carcase and live animal based objective measurements is $62.7 million in 2030 and $216.9 million in 2045, with the majority of benefits derived from cattle OM ($50.2 million in 2030 and $174.5 million in 2045). This is estimated to bring $1.3 billion net benefit value to the red meat industry over the period 2025 to 2045.

Program 1

1.1 Live LMY

Non-destructive, objective methods of determining LMY that are cost effective and can operate in on-farm commercial environments are essential to enhance productivity and profitability of meat supply chains. Two on-farm technologies have been developed reaching varying levels of commercialisation. A hand-held microwave system has shown good capacity for measuring fat depth in both beef and lamb and has been tested in commercial environments by commercial partners. Similarly, a 3-D imaging system for live cattle has been installed in a marshalling race of a beef feedlot demonstrating successful prediction of carcase attributes

Read more here.

KPI 1.16 Continued improvement of the MiS operating system and algorithm

Portable ultra-wide band microwave system coupled with an open-ended coaxial probe(OCP) or Antipodal Vivaldi Antenna (AVA) was tested as non-invasive objective measurement to predict beef carcase single site fat depth at commercial abattoirs. Hot carcase P8 was measured using an OCP (n=435) across two slaughter groups and by an AVA (n=241) across 4 groups. Cold carcase rib fat was measured using AVA (n=598) across 5 groups. A machine learning stacking ensemble technique was used to create the prediction equations. Datasets were grouped by prediction trait (P8 or ribfat) and probe/antenna then randomly divided into 5 groups based on tissue depth. The precision was greatest using the OCP to predict P8 site with an RMSEP of 2.47 mm and R2 of 0.70. The VPA precision was greatest in the hot carcase on the P8 site with an RMSEP of 2.86 mm and R2 of 0.58 compared to the cold carcase rib fat RMSEP of 2.60 mm and R2 of 0.55.

Read more here.

KPI 2.6 Validation of a prototype microwave device to measure fat depth at the rib and P8 sites on live cattle and validate against the corresponding ultrasound and abattoir measurements

Two experiments were performed to test the ability of a portable microwave system coupled with Vivaldi Patch Antenna (VPA) to objectively measure live cattle subcutaneous fatness to predict corresponding carcase traits. Experiment One was performed on-farm, where commercial feedlot slaughter cattle (n=517) were microwave scanned at the P8 (fat depth on
the rump) and rib fat site (fat depth over the m. longissimus, between rib 12 & 13). Corresponding ultrasound measurements were taken (n=315) at the same time as microwave scanning. A machine learning stacking ensemble method was used to create the microwave prediction equations. Datasets were grouped by prediction trait (P8 or rib fat) and randomly divided into 5 groups based on tissue depth. Live animal microwave scanning had greater precision than ultrasound at predicting carcase P8 and rib fat depth. At the P8 site the average RMSEP was 2.61 mm, R2 0.61, bias 0.179 and slope 0.07 mm, and at the rib fat site the average RMSEP was 2.16 mm, R2 0.60, bias 0.301 mm and slope 0.10.

Experiment Two was performed in the abattoir, where commercial slaughter cattle were microwave scanned at the rib fat site immediately prior to entering the knocking box. Microwave scanning in the abattoir had poor prediction of corresponding carcase trait with an R2 of 0.02, RMSEP 2.24, bias 0.019 and slope 0.757.

Read more here.

KPI 2.7 Ability of live microwave scanning in sheep to predict whole body fat composition as determined by live DEXA scanning

This experiment evaluated the ability of a portable microwave system (MiS) coupled with Vivaldi Patch antenna used in mature ewes (n=835) to predict whole body fatness as determined by dual energy x-ray absorptiometry (DEXA). MiS scanning was performed at the C-site (45 mm from spine midline over the 12th rib), Point A3 (20 mm cranial to C-site), the GR-site (110 mm from spine midline over the 12th rib), and a combination of 8 points across the right thorax. Precision of prediction was greatest at the 8 combined points with an average R2 of 0.51 and RMSEP of 1.85 mm, however there was negligible difference in prediction when scanning at only the GR-site with an average R2 of 0.49 and RMSEP of 1.84 mm.

Read more here.

KPI 2.7 Report on antenna and probe design fro portable microwave system

Experiment One details the design and computer simulation of three different probe/antenna performance to penetrate fat and muscle. The Vivaldi Patch Antenna (VPA) had the widest radiation pattern, where as the open-ended coaxial probe (OCP) has a directional radiation pattern. The periodic log antenna (PLA) demonstrated a high gain at a low frequency, however at higher frequencies the gain unexpectedly decreased making it unsuitable for deep tissue measurements.

Experiment Two details the commercial comparison of the VPA, OCP and PLA on sheep and beef carcases. Predicting beef carcase P8 fat depth, the PLA demonstrated the greatest precision of prediction with an average RMSEP of 1.29 mm and R2 of 0.83. Predicting lamb carcase C-site and GR fat depth the VPA and PLA had similar prediction performance where the OCP had a lower precision of prediction. In Experiment B the VPA C-site prediction had
an average RMSEP of 0.94 mm and R2 of 0.67 where as the OCP had an average RMSEP of 1.04 mm and R2 or 0.56. Experiment B VPA GR site prediction had an average RMSEP of 2.80 mm and R2 of 0.67 vs the OCP average RMSEP of 3.31 mm and R2 of 0.56. Experiment C VPA C-site prediction was similar to Experiment B, with an average RMSEP of
1.02 mm and R2 of 0.66. Experiment C PLA average RMSEP was 1.00 mm and R2 0.67. However Experiment C VPA GR tissue depth prediction was less than Experiment B with an average RMSEP of 3.57 mm and R2 of 0.55. Experiment C PLA GR site prediction was similar to VPA with an average RMSEP of 3.70 mm and R2 of 0.50.

Read more here.

1.2 Carcase LMY-Direct

Non-destructive, objective methods of determining LMY that are cost effective and canoperate at commercial line speeds are essential to enhance productivity and profitability of red meat supply chains.

Read more here.

KPI 3.11 AUS-MEAT accredited lamb DEXA algorithm performance

An accredited algorithm for the prediction of lamb carcass composition was submitted and accepted by AUSMEAT in September 2022, a report of which can be found as technical report 3.11 – Lamb DXA accreditation submission to AMILSC on behalf of Scott Automation and Robotics and MLA. That technical report shows the results of the algorithm’s predictions, and what bands of weight and composition the algorithm is accredited for.

This technical report details the algorithm that was used, and the method of training and calibration using the Scott Automation and Robotics phantom.

Read more here.

KPI 3.11 DEXA Bone detection algorithm for lamb carcases

The accurate detection of bone in a DXA image is crucial to the overall precision and accuracy of its prediction of overall composition, as the non-bone containing pixels are evaluated independent of the bone-containing pixels. Previous algorithms have relied on thresholding bone containing pixels from non-bone containing pixels at the mean R-value of the whole carcass, with those pixels above the mean being classed as bone
containing. While this was somewhat effective, as it emulated the fundamental equations used to quantify bone content of bone containing pixels, it was unable to detect bone edges, and as such could not use an accurate R-value for soft tissue. This became problematic for carcasses that did not have bone content close to most of the population, such as certain breeds with naturally higher bone content (Merinos), or sheep with a lower amount of soft tissue – usually mutton or diseased sheep. An improved algorithm was proposed and has been accredited as part of the larger DXA
accreditation. This algorithm introduced the evaluation of thickness with the R-value calculation when thresholding for bone content. The implementation of this algorithm sees a very large increase in the precision and accuracy of the prediction of CT bone %, from an R2=0.496 to R2=0.925. There are also modest increases in the precision and accuracy of the predictions of CT fat % and CT lean %. Further improvements on the bone detection algorithm will look to machine learning to detect bone edges and dimensions, a process that is otherwise too slow and difficult at the current chain speed.

Read more here.

KPI 3.11 Initial analysis of lamb DEXA predicting GR tissue depth

Recently, a dual energy X-ray absorptiometry (DEXA) system has been successfully developed to measure lamb lean meat yield (LMY) with high precision and accuracy at abattoir line speed. However, the GR tissue depth (thickness of tissue over the 12th rib, 110 mm from the midline) and fat scores remain the Australian industry standard for estimating fatness in lamb carcases. To satisfy AUS-MEAT measurement requirements, DEXA must be able to predict GR tissue depth in lamb carcases within ±2 mm of the score boundary, with a maximum error of 10%. Lambs (n = 189) with a diverse range in carcase weight and fatness were slaughtered at a commercial abattoir. GR tissue depth and hot carcass weight were measured immediately post-slaughter, before the carcases were then chilled and DEXA scanned the following day. Predicting GR tissue depth using DEXA image components data (pixel number and mean negative log of low energy pixel values) along with mean DEXA values did meet AUS-MEAT requirements for 90% accuracy in fat score prediction. This means the lamb DEXA system could predict single site GR tissue depth to current commercial standards. These findings suggest processing plants could use the objective DEXA system which operates at line speed instead of the slow and subjective palpation or GR tissue measure for fat scoring carcases.

Read more here.

KPI 3.11 Lamb DEXA accreditation submission to AMILSC on behalf of Scott Automation and Robotics

This report has been prepared by ALMTech on behalf of the manufacturer of the lamb DXA device (Scott Automation and Robotics) for the accreditation for predicting CT Fat%, CT Lean% and CT Bone% of sheep carcases. The accreditation trial was conducted at WAMMCO, Katanning, using lamb carcases collected over multiple kill groups from February 2022 through to July 2022 (n=338), with a further group of high weight carcases collected
between August 2022 and December 2022 (n=139).

The experimental and analytical procedure used to assess the repeatability and accuracy performance of the DXA device has been described. The performance of the DXA device was compared against version 1 of the AMILSC approved guidelines for experiments to achieve accreditation of technologies for predicting fat%, lean%, and bone% in sheep carcases (“A carcase composition trait for sheep meat grading technologies” presented to AMILSC on the 17/2/2022). As total carcase composition is loosely associated with hot carcase weight, the accreditation requirements for DXA are tested within three weight categories: light (<22kg); medium (22-28kg); and heavy (>28kg). This creates nine discrete groups within which accreditation tests are applied.

We recommend accreditation of DXA for predicting composition in carcases <22kg with CT Fat % between 10.9% and 30.3%, CT Lean% between 53.2% and 65.0%, and CT Bone % between 14.9% and 25.0%. Additionally, we recommend accreditation of DXA for predicting composition in carcases between 22kg and 28kg with CT Fat % between 14.9% and 35.0%, CT Lean % between 50.9% and 66.2%, and CT Bone % between 13.3% and 18.0%. Finally,we recommend accreditation of DXA for predicting composition in carcases >28kg with CT Fat % between 22.0% and 37.1%, CT Lean % between 49% and 60.6%, and CT Bone % between 11.6% and 17.5%.

Read more here.

KPI 3.11 Standardised methodology for sampling & image analysis, & validation resource data for CT as the calibrating standard for LMY measurement

To support computed tomography (CT) as the reference method for measuring lean meat yield within the Australian beef and lamb industry, it is important to understand the robustness of this measure, and the factors that can influence its estimate of carcase composition. This work was undertaken through a series of experiments that established the immediate repeatability of CT estimates of carcase composition, the impact of changes to
scanning methodology such as carcase sectioning and freeze/thaw protocols, and the effect of machine scanning voltage and CT scan slice width. This work demonstrated several outcomes:
1. CT estimates of carcase composition are almost perfectly repeatable.
2. Carcase sectioning has a small impact on CT estimates of carcase composition, so we propose a standardized carcase sectioning method for lamb to be used when CT is used as the reference standard for calibrating other measurement technologies.
3. CT scan slice width has a small impact on CT estimates of carcase composition, so we propose 5mm slice widths as the standard method for lamb to be used when CT is used as the reference standard for calibrating other measurement technologies.
4. CT scan voltage has a substantial impact on CT estimates of carcase composition, so we propose that a standardized voltage of 120kV is applied for beef when CT is used as the reference standard for calibrating other measurement technologies. Scans captured at other voltages can be corrected to give the 120kV equivalent estimate of carcase composition, but for consistency this should be avoided for calibration purposes.
5. Scanning beef butts in a frozen state decreases tissue Hounsfield unit values, particularly for fat and lean tissues, resulting in substantial variation in their estimated composition within beef butts. Alternatively, scanning these same sections after they have thawed produces values very similar to those scanned fresh, implying that carcases can be frozen and then defrosted prior to CT scanning and still deliver consistent results.
6. When the CT methodology is standardised, it demonstrates substantially better repeatability than determining carcase composition using chemical methods.
7. CT scanning plastic phantoms has demonstrated that the values reported across a range of plastics of different densities only vary slightly between scans and machines. These small variations are likely to be readily accounted for through scanning common calibrating phantoms like the XTE-CT test piece.

Read more here.

KPI 3.11 WAMMCO DEXA calibration

The recent installation of the DEXA scanner at WAMMCO, Katanning, required the calibration of the system against computed tomography (CT), as has been undertaken at all previous DEXA sites around Australia.

100 lambs were selected from the MLA resource flock based at the Katanning research station, with a wide range of phenotypes and genotypes. These 100 lambs were slaughtered and scanned in the new DEXA apparatus as ‘hot’ carcasses, prior to entering a chiller. These 100 carcasses were CT scanned at Murdoch University as the gold standard carcass measurement.

The precision and accuracy measurements for fat, lean and bone were similar to previous DEXA sites after the algorithm adjustment, which was successfully conducted on the plastic phantoms supplied by Murdoch University. This adjusted algorithm produced results that are in line with the expected range of lambs through the scanner, with minimal outliers.

Read more here.

KPI 3.12 Bridging of beef DEXA images to provide LMY feedback

  • did not have an abstract

KPI 3.12 DEXA prediction of lean beef trim

This experiment assessed the ability of a commercial-prototype dual energy x-ray absorptiometer (DEXA) to predict the composition of beef sides scanned at abattoir line-speed and thereby determine the lean trim weights from each beef carcase side. It was hypothesised that DEXA provided a better objective measure of whole carcase composition than the current industry standard of P8 fatness, and therefore, would predict lean trim
weights with higher precision and accuracy than P8 fatness. 250 beef carcases representing a wide range in carcase weight and fatness (measured by P8 fat depth) were selected for DEXA scanning and a comprehensive bone-out into retail cuts of meat and trim. Boners assigned lean trim to one of three categories: 60, 85 or 90% chemical lean (CL), based on the visual fatness of the trim according to standard processor protocols. The weight of this trim was predicted in general linear models using hot side weight (HSW) and P8 fatness or DEXA variables as covariates. To address the anticipated difficulty boners faced in visually differentiating 85 and 90% CL, we also used the above models to predict summed 85 and 90% CL trim. In line with our hypothesis, DEXA variables predicted beef trim weights with better precision than P8 fatness, though the differences were small. The precision of prediction varied between trim categories. In the fatter trim category (65% CL), DEXA variables predicted the weight of trim with less precision, with an R-squared of 0.78 and a root mean square error of prediction (RMSEP) of 106 grams. In contrast, precision was improved when predicting the weight of the combined leaner trim category (85+90% CL),
with an R-squared value of 0.92 and an RMSEP of 60 grams. The DEXA system differentiated a greater range in trim weight than P8 fatness and therefore has potential as a novel technology to predict beef trim weights in a commercial setting.

Read more here.

KPI 3.12 Potential for applying DEXA prediction as feedback at the Teys Rockhampton abattoir

  • no abstract on this one

KPI 3.13 The value of precise cut weight prediction in the optimisation of lamb carcase processing

The Australian beef industry is focused on improving the objective measurement of lambcarcase yield to improve the valuation of lamb carcases. However, the value that technologies such as DEXA, that have improved the precision of cut weight prediction, represent to plants in the processing of carcases needs clarity. This project thereby assesses the value of using DEXA cut weight predictions in a processing scenario in the lamb Optimisation tool compared to using hot carcase weight (HCWT) and GR cut weight predictions. 191 lambs representing a wide range in HCWT and fatness were measured for GR, DEXA scanned and comprehensively boned out to weigh a wide selection of retail cuts and associated lean trim
and fat. A scenario was then developed in the Carcase Optimisation Tool with 2 to 3 cut options available for each section of the carcase, with cut weight thresholds applied limiting the carcases that could be assigned to the different cut options based on their cut weights. The costs of processing the lambs were input and well as the market value of each retail cut available and the associated fat and trim. The cut weights of the lambs were then entered into the optimiser to determine the optimal processing of the lambs. The optimiser scenario was then repeated using HCWT, GR, DEXA and DEXA image component predicted cut weights to determine the most profitable processing of the carcases. The profits and cut allocations output by the optimiser models were then assessed to determine the difference in profit using cut weight predictions compared to the actual cut weights and examining the number of misallocations caused by imprecise cut weight predictions. Using DEXA variables to predict cut weights resulted in optimised profits closer to those achieved using actual cut weights, and les cut option misallocations in the rack, loin and hindquarter sections of lamb carcases.

Read more here.

1.3 Carcase LMY- Indirect

Non-destructive, objective methods of determining LMY that are cost effective and can operate at commercial line speeds are essential to enhance productivity and profitability of red meat supply chains. While X-ray based technologies such as DEXA offer excellent “direct measurement” of carcase composition, these can be prohibitively expensive for smaller abattoirs.

Therefore, simpler and more cost effect technologies that predict carcase
composition and therefore provide “indirect measurement” are also required. A microwave system designed and fabricated at Murdoch University has demonstrated excellent precision and accuracy in predicting single site fat depth in beef and cattle. The device is portable, simple to use and poses no risk to human operators or food safety. Similarly, a 3D imaging system for beef, designed at the University of Technology Sydney, and an imaging system for pork commercial known as PorkScan Plus offers robust prediction of lean meat yield.

Read more here.

KPI 1.19 3D imaging in beef carcase LMY

This report details the steps in refining the 3D imaging scanning rig design to integrate into beef abattoir operations at chain speed. In order to provide early LMY estimation the rig was positioned at chiller entrance, acquiring hot carcass side 3D reconstructions as they leave the slaughter floor. The 3D digital shape of the carcass is analysed through a curvature descriptor that is associated to LMY values using a non-linear regression model. We posit that segmenting and identifying consistently correspondences between 3D carcasses allow for improved information gathered. Using 152 carcasses that have been 3D reconstructed and combining the curvature descriptor with HCW resulted in LMY estimation with RMSE 4.1% and R2 of 0:54, indicating that HCW provides independent observations to curvature, and assist in estimating LMY. The presented approach needs to be evaluated with more data to increase the confidence in estimated LMY and evaluating portability of model acrossdifferent deployments.

Read more here.

KPI 1.20 Capability of integrated hyperspectral and 3D imaging cameras to determine subcutaneous fat depth and cover

Hyperspectral imaging has the ability to provide information that goes beyond texture and colour of a surface, near and short-wave infrared spectrum penetrate into the subsurface of materials. Combining disparate sensing modalities, hyperspectral and 3D imaging cameras, could allow to reason about the thickness of the layer of fat over the surface of a carcass
(known as subcutaneous fat). We present a learning-based method to estimate subcutaneous fat depth by leveraging hyperspectral data, modelling light sources and 3D surface shape information. In order to develop such a system, subcutaneous fat depth ground truth is recovered from computed tomography (CT) data via a novel systematic approach involving
ray casting and non-rigidly aligning a 3D reconstruction captured by the 3D imaging camera. We utilise a collection of mid-loins supplied by a commercial abattoir to validate that subcutaneous fat is correlated to total fat with R2 0.76. Our method estimates subcutaneous fat over the entire surface of mid-loins (patches of 1.5mm x 1.5mm) with R2 0.73 and RMSE 1.56mm when compared to CT obtained ground truth. This paves a promising new direction
to rapidly estimate information of carcass fat deposits that are complimentary to other LMY technologies which generally estimated whole carcass properties.

Read more here.

KPI 2.17 On-farm and abattoir suitability of microwave scanner system design

This report details the development of a low cost,portable hand-held Microwave System as an objective measurement technology for measuring various traits in cattle and sheep, live animal and carcase.

Read more here.

KPI 2.17 Report on the ability of a microwave device to predict rib fat and P8 fat depth in beef carcases

A portable ultra-wide band microwave system (MiS) was tested as a non-invasive objective measurement to predict beef carcase single site fat depth at commercial abattoirs. Experiment One used a laboratory calibration technique and tested the effectiveness of MiS coupled with either a Vivaldi Patch Antenna (VPA) or an open-ended coaxial probe (OCP). The VPA was
used to predict hot carcase P8 (fat depth on the rump) across 4 slaughter groups (n=241). The VPA was also used to predict cold carcase rib fat (at the quartering site, 75% along the rib eye muscle) across 5 slaughter groups (n=598). The OCP measured hot carcase P8 across two slaughter groups (n=435). A machine learning stacking ensemble method was used to
create the prediction equations. Datasets were grouped by prediction trait (P8 or ribfat) and probe/antenna then randomly divided into 5 groups based on tissue depth. Precision was greatest using OCP to predict P8 fat depth with a RMSEP of 2.47 mm and R2 of 0.70. The VPA precision was similar for the two tissue depths assessed, hot carcase P8 had an average
RMSEP of 2.86 mm and R2 of 0.58 compared to cold carcase rib fat RMSEP of 2.60 mm and R2 of 0.55.

Experiment two tested the ability of a commercial MiS (C-MiS) device coupled to a VPA probe and on-site calibration to predict P8, rib fat depth and eye muscle area (EMA). The best prediction of hot carcase P8 (n=1650) was from C-MiS device 2 with an average RMSEP of 2.775 and R2 0.81. Hot carcases (n=598) C-MiS scanned to predict cold rib fat had an average
RMSEP of 3.712 and R2 of 0.81. Hot carcases (n=598) C-MiS scanned to predict EMA had an average RMSEP of 10.267 and R2 of 0.43.

Read more here.

KPI 3.14 Compare objective carcase measurement technologies for pig classification systems

Carcass classification systems within the pork industry are based on lean meat yield (LMY). Thus, accurate tools to measure the carcass characteristics predicting LMY are essential to underpin grading systems given they inform pricing grids, herd genetics, product segregation and support consistent trade language, which is essential for industry sustainability. This review summarised the accuracy of several commercially available technologies used for prediction of pork carcass composition. Both optical probes and ultrasound-based devices demonstrated comparable accuracy in the prediction of backfat depth, loin muscle depth, lean meat content and saleable meat yield. Ultrasound devices may have the advantage in that they are non-invasive and in the case of the AutoFom can also predict primal LMY values and cut weights. Some variation in predictions was observed across technologies that could be attributed to operator effect, device settings, reference methods used, and variables included in prediction equations. This highlighted the need for consistent testing methodology, and reference standards to train devices against. Rather than manual dissection methods, which are time consuming, destructive and subject to variation, computed tomography (CT) provides a non-invasive, rapid and highly accurate reference method to train and test device performance against. Continued research into objective measurement technologies for LMY in the Australian
pig industry is recommended, given most investigations have been conducted internationally to data.

Read more here.

Program 2

2.1 Commercialise two technologies for grading eating quality in beef carcases and two technologies for grading eating quality in lamb carcase

This report summarises the experimental work and results evaluation and discusses commercialisation of technologies for grading eating quality in beef and lamb. Five (5) cut surface grading technologies and two (2) grading technologies are in the commercialisation phase in beef and lamb respectively. Importantly, the work conducted by in this section of ALMTech II has assisted in achieving the key deliverable to commercialisation at least of two technologies for grading eating quality in beef and lamb carcases. These technologies are now accredited for use within industry and available for purchase and integration within individual supply chains.

Read More Here.

2.2 Develop and validate new eating quality (EQ) technologies

This report summarises the key scientific deliverables that was achieved, which was to develop and validate new eating quality (EQ) technologies in beef, lamb and pork. This included cut surface, probe and non-invasive technologies. As a result, several devices have progressed towards commercial prototype phases and installation in-plant to facilitate on-line
calibration and validation work.
- SOMA NIR device
- Optical Coherence tomography probe
- Optical coherence elastography
- Nuclear Magnetic Resonance
- ASD NIR device
- Dual energy X-Ray absorptiometry (DEXA)

In addition, the role of fatty acids and distribution of marbling on eating quality in beef was also investigated.

Read more here.

KPI 3.24 Report on the relationship between fatty acid profile and consumer sensory scores

This study aimed to evaluate the potential effect of fatty acid content on eating quality when carcases were selected in a case-control fashion based on marbling. Three cohorts of 36 carcases were selected from Angus, Wagyu Angus F1 cross, purebred Wagyu, and Wagyu Bos indicus F1 cross, all of which had been long fed (≥200 DOF). The chuck roll, bolar blade, striploin, D-rump, and outside flat were consumer tested using the grill cook method. Sensory scores for CMQ4 were analysed within cohorts against cut and carcase traits for Australian consumers. The results showed that cut, IMF%, rib fat and all the fatty acids except linoleic acid have a significant effect on CMQ4. The inclusion of muscle explains 47% of variation in the model and IMF% explains a further 12% of variation. The inclusion of oleic and palmitic fatty acids explains a further 11% and 12% of variation in the model, whereas palmitoleic, myristic and stearic explain only a further 2%, 4% and 5% of the model and linolenic explains no significant variation in the model. Inclusion of monounsaturated (MUFA) and polyunsaturated (PUFA) had significant effect on CMQ4, whereas, saturated fatty acids (SFA) was not significant when included in the model.

Read more here.

KPI 3.26 The association of DEXA images with lamb eating quality

This project analysed data collected at 2 commercial abattoirs (sites) that have installed dual energy x-ray absorptiometry (DEXA) systems used to drive carcass cutting devices in plant and concurrently predict carcass composition of fat, lean and bone. Carcasses were subsequently used in eating quality experiments, enabling the relationship between DEXA and eating quality to be explored. An algorithm has recently been established which better identify bone pixels within the DEXA images allowing better determination of all carcass bone DEXA R values. The all carcass bone DEXA R values and those from individual bones that were manually isolated from DEXA images (humerus, lumbar vertebra and femur) were used to predict eating quality from cuts across the lamb carcass. Data from both sites was analysed independently due to the inability to calibrate DEXA images between the two sites, however future data acquired will utilise phantoms to allow bone DEXA values to be compared. An increase in all carcass and some individual bone DEXA R Mean terms demonstrated an association with decreasing eating quality (overall liking, tenderness, juiciness and flavour). The best prediction was of the loin grill where a decrease of 10.5 and 9 overall liking scores was observed across the range of all carcass bone DEXA R Mean at site 1 and site 2. The other cut with relatively strong associations with bone DEXA was the shoulder roast, however the prediction of eating quality in other cuts was more tenuous and lacked consistency between sites. This experiment also used whole carcass lean % and loin intramuscular fat % to investigate relationships with eating quality. In this experiment the bone DEXA R Mean terms were generally independent predictors of eating quality to those of loin IMF % and carcass lean %. The biology underpinning the relationship between DEXA and eating quality has not been identified, though is likely associated with an index of maturity. Bone mineral content did not directly relate to eating quality, however there were some relationships of bone minerals (magnesium, calcium and phosphorus) with bone DEXA R and carcass composition. The isolation of bone pixels from the carcass during routine DEXA scanning at abattoirs may be able to provide input into a multi-trait eating quality model in the future, however future research is necessary.

Read more here.

KPI 3.26 Ultrasonic assessment of intramuscular fat percentage in beef and lamb loins at 37ºC

Ultrasound is an extensively studied technology for objective measurements in a range of different applications. The driving motivation behind recent improvements in ultrasonic image reconstruction for soft tissues has come from the medical industry. However, few of these improvements have translated into meat quality assessment. This work investigates quantitative ultrasound techniques to estimate the intramuscular fat content of NZ and
Australian Lamb loins. This report reviews recent literature and discusses the application of novel methods for quantifying intramuscular fat content.

Read more here.

KPI 3.27 Report on calibration and initial validation of devices predicting IMF% and other traits in pork

Intramuscular fat (IMF) is related to eating quality in both beef and sheep, and probably in pork. However, the amount of IMF in pork is much lower than in the red meat species which presents problems in its’ measurement. Near infra-red (NIR) and nuclear magnetic resonance (NMR) technologies have been shown to be able to predict IMF in lamb and in beef. However,
the relationship between IMF and these technologies in pork is unknown. The objectives of this project was to determine the chemical IMF content of two pork muscles (Longissimus thoracis et lumborum (LTL) and Semimembranosus (SM)) and relate these values of measures obtained using NIR (with a SOMA device) and NMR in 60 pork carcasses.

The chemical IMF % of the LTL (1.04±0.051%) and SM (1.38±0.065%) were very low compared to other red meat but within the expected range. The IMF content of the SM was higher (p<0.001) than the LTL. All measures of SOMA LTL IMF were highly correlated (p<0.001) with chemical IMF%. Mean SOMA LTL IMF, geometric mean SOMA LTL IMF and the highest SOMA IMF accounted for 36.4%, 31.9% and 36.9% of the variation in chemical
IMF %. There was a significant correlation (p=0.008) between chemical IMF % and NMR average p2f, although only 9% of the variation was accounted for. Interestingly, measures of SOMA IMF% were correlated with NMR p2f, indicating that they were measuring something similar.

All measures of SOMA SM IMF were correlated (p<0.05) with chemical IMF% but not to as great an extent as the LTL, although the correlations were much lower than for the LTL. Mean SOMA, geometric mean SOMA and highest SOMA IMF accounted for 6.7%, 6.4% and 6.4%, respectively, of the variation in chemical SM IMF %.

When muscles were combined, there were highly significant correlations (p<0.001) between SOMA measures of IMF and chemical measures of IMF, and these relationships were slightly improved by including muscle in the model. For example, the inclusion of muscle in the model relating mean SOMA to IMF described 27.7% of the variation compared to 25.5% in the simple model. Similarly, for the other relationships relating SOMA measures of IMF to chemical IMF in the pooled data set.

While these values are unlikely to be good enough to provide confidence in predicting IMF within the low ranges of IMF encountered in Australian pork LTL and SM, they do provide encouragement that with some finessing of the instrumentation and algorithms, which were specifically developed for lamb, an online tool can be developed.

The major conclusion from this project is that both the SOMA and NMR technology appear to be related to pork IMF, particularly in the LTL. These relationships exist despite the very low levels of observed IMF %. It is recommended that pork carcasses be manipulated nutritionally and genetically to increase the range in IMF to further test both SOMA and NMR over a greater range in IMF %.

Read more here.

Program 3

Summary report on livestock offal inspection and sortation with multi-sensor platforms in abattoirs

This program developed a multi-sensor platform combining multi-energy X-ray (MEXA) withvisible and shortwave infra-red (SWIR) hyperspectral camera data and associated algorithms toautomatically detect and sort cattle and sheep organs with defects in abattoirs.

The program was a collaboration between MLA, Rapiscan Systems and The University of Sydney. A multi-sensor platform, software and algorithms were developed as a proof-of-concept to detect defects in organs. The system developed was able to detect organs with defects with an average accuracy of greater than 90% in specific beef offals. The platform seems promising for the automatic detection of defects in organs.

In the next phase, an industry consultation working group comprising of processor representatives and technical experts, including Australian Meat Inspection Services technical representatives shall be consulted. A small consultative working group is scheduled at the conclusion of the extensive validation work at The University of Sydney, Camden pilot plant facility. A prototype demonstration and update of the validation findings will be presented to the industry consultative group to seek input in the potential applications of the developing technology. Other developing objective measurement technologies currently under concurrent review (including P&P Optical technology through project P.PSH.1350) will also be considered for pre-commercial considerations and recommendations for the next phase. At the conclusion of the USyd pilot plant demonstration, willing processor participants (1 to 3 plants) will be invited to participate in a series of pre commercial demonstrations at a beef processing pilot plant(s). It is proposed that more detailed feedback will be sought on potential applications of the pre-commercial prototype technology. These pre-commercial trials will be staged over period to collate the maximum amount of input from processors and technical industry experts on potential applications on offal disease detection methods using the Rapiscan pre-commercial solution.

Future work should use larger datasets to validate and strengthen the algorithms. The algorithms could also be improved using shape analysis in addition to spectral information and using both the spectral data with the X-ray data by the same algorithms.

Read more here.

Program 4

4.1 Compilation of final reports for data flow to industry information delivery systems

The program worked with supply chain collaborators to map out the issues and future requirements to provide enhanced feedback to commercial producers and industry databases. Programs 4 and 5 worked together with the MLA, ISC, LDL and other groups with supply chain projects to improve both communication and collaboration of activities creating a great collaborative platform to continue this approach into the future. A number of case studies were initiated with variable levels of success due to supply chain interruptions and issues with data collection. The lack of reliable hook tracking is more sheep plants still possess a major imposition to the routine collection of individual animal level data. The program has also invested a significant effort to assist with the flow of data from supply chains to industry databases. This work needs to continue to ensure that accurate data is easily exchanged with minimal impost on the supply chain.

Read more here.


4.2 Compilation of final reports for data flow to industry genetic evaluation systems

The program has completed the genetic validation of 7 new genetic tools for the sheep industry:
1. DEXA was validated as an accurate genetic prediction of CTlean and thus LMY breeding values. This provides the evidence for Sheep Genetics to commence using these data in routine genetic evaluation.
2. MEQ was validated as an accurate genetic prediction of IMF and thus IMF breeding values.This provides the evidence for Sheep Genetics to commence using these data in routine genetic evaluation.
3. A preliminary validation was conduction of SOMA, which indicates that it will also be an accurate genetic prediction of IMF. More SOMA data now needs to be collected on resource flock animals to complete this work.
4. Preliminary validation of genomic flock benchmarking in Terminal sheep was conducted and demonstrated accurate results. This provided sufficient evidence to support ongoing development of this product.
5. Preliminary validation of genomic flock benchmarking in Maternal sheep was also conducted and demonstrated accurate results. This provided sufficient evidence to support ongoing development of this product.
6. Analysis of eye muscle dimension data suggests the need for a new definition of these traits, which is likely to lead to new eye muscle width or area traits for sheep. More eye muscle dimension data now needs to be collected on resource flock animals to complete this work.
7. A new model for improved analysis of the shear force trait was also defined and validated for use by Sheep Genetics.

Program 4 also examined the value of using carcase data from industry ram breeding (seedstock) flocks to build upon an industry sheep reference population in Australia. This work concluded that seedstock data can be used if data collection is accurate and consistent with industry standards and this enables new models to work with industry flocks to provide genetic resource
flock data.

During the life of the project, insufficient data was available from new devices on genetic resource animals in beef. Thus, no significant analysis or development work was able to be conducted. Nevertheless, ALMTech II has also continued to work with BREEDPLAN to evolve the eating quality traits away from marble score toward IMF, which is likely to be captured from the new beef rib-eye cameras. In a similar vein, work has commenced to validate the use of DEXA in beef and begin the development of a LMY breeding value for beef.

Read more here.

KPI 3.30 Analysis of eye muscle dimension data sourced from direct carcase measures and those obtained from CT images

This study investigated the genetic relationship between eye muscle width and depth recorded via ultrasound on live animals and on carcases (measured with callipers and computer tomography) in two populations of Australian and New Zealand sheep. Genetic correlations between ultrasound and carcase muscle dimensions were estimated within populations. Carcase eye muscle dimensions have sufficient genetic variation to be included in sheep breeding programs. Genetic correlations between carcase eye muscle depth (CEMD, CTEMD) and width (CEMW, CTEMW), and between CEMW-CTEMW and ultrasound eye muscle depth (PEMD) in Australian sheep were lower than expected. On the other hand, high genetic correlations were observed between ultrasound depth and width recorded in different ages on New Zealand Merinos. These differences indicate further research about CEMW is required and the implications of current selection practises has on carcase eye muscle dimensions.

Read more here.

KPI 3.30 Genetic analysis of DEXA measured lean meat yield

Since the preliminary research in 2017 the DEXA technology and the algorithms behind the conversion of the DEXA image to measures of lean, bone and fat have been updated (Connaughton & Gardner 2023). Coinciding with these developments in technology there has been an influx of DEXA measurements on genetically informed animals via the MLA funded Resource Flock (van der Werf et al. 2010) and companion industry satellite flocks. The
following study builds upon the preliminary research, with the objective to determine the genetic variation in DEXA measured lean and the suitability of using DEXA lean as part of Sheep Genetics which is the National Genetic Evaluation alongside or in conjunction with current CT derived lean meat yield records. Estimates on the DEXA lean data set produced a heritability of 0.32 ± 0.06. However, this should not be considered the final parameter for
implementation in genetic evaluations as the analysis is only a sire model (not all relationships accounted for) and genetic linkage between contemporary groups needs improvement. The phenotypic correlation between CT and DEXA lean measures was estimated at 0.84 ± 0.01 with a corresponding genetic correlation from the sire model of 0.77 ± 0.07. Although more data is required, these results suggest that the DEXA-derived lean meat yield is likely to be the same genetic trait as the CT measured lean meat yield. These results are promising and plans in regards to the utilisation of DEXA data within Sheep Genetics national evaluation should begin.

Read more here.

KPI 3.30 Genetic analysis of intramuscular fat data collected with MEQ probe and SOMA NIR device

The objective of this report was to investigate the genetic association between intramuscular fat predicted with MEQ probe (IMFMEQ) and SOMA NIR device (IMFSOMA) with chemically analysed intramuscular fat, tenderness, carcase eye muscle dimensions and fat and tissue depth. MEQ and SOMA predicted IMF remain lowly recorded traits with data limited to 1,380 and 1,320 records respectively, from the resource flock and associated overlay projects. Consequently, results within this report should be considered as preliminary but none the less are promising. Genetic analysis showed that IMFMEQ has a moderate heritability (0.42 ±0.1) and a high genetic correlation (0.95 ±0.07) with chemical intramuscular fat. Similarly IMFSOMA was estimated to have a moderate heritability (0.42 ±0.1) and a strong genetic correlation with chemical IMF (0.94±0.03). Because of this high correlation IMFMEQ and IMFSOMA can be used to determine intramuscular fat in lamb, however further work is needed to clarify the genetic association between these traits and other carcase and eating quality traits.

Read more here.

KPI 3.30 Preliminary genetic parameters and viability of microwave predictions of carcase C-site fat depth

  • no abstract

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KPI 3.30 Report on the preliminary analysis of model refinement for the genetic evaluation of shear force in lamb

  • no abstract

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KPI 3.30 Value of data from commercial ram breeders’ flocks as an industry reference population for Australian sheep

This report examines the value of using carcase data from industry ram breeding flocks to build upon an industry sheep reference population in Australia. Data from 1,981 lambs managed in 16 commercial ram breeder flocks were collected between 2017 and 2020 for carcass and meat quality measurements: hot carcase weight, tissue depth at the GR site, eye
muscle depth, fat depth at the C site, intramuscular fat and shear force. Industry data were cross-validated with and without reference data from the MLA Resource flock. Industry data did not bias the estimation of breeding values when used in combination with the reference population. Therefore, industry data can be used to expand an industry reference population if data collection is accurate and consistent with industry standards.

Read more here.

Program 5

Compilation of final reports – Data and Decisions

Program 5 was established to develop and deliver systems to utilise the data generated in Programs 1-3 and stored in Program 4 to improve supply chain efficiency and profitability. The strategy was to develop tools to accurately value carcasses.

This provided a foundation for working in partnership with high impact businesses to develop generic data decision tools and customise them for maximum impact within specific supply chains.

The tools included feedback systems to producers and optimisation systems within processing companies. The implementation of these tools is assisting supply chains to extract additional value from the supply chain.

Read more here.


5.1 Carcase value tools

KPI 2.34 Reverse engineering the Carcase Optimisation Tool to determine lamb value

Data from a previous case study to optimise the profit from a population of lambs processed to a domestic lamb specification was used in a ’reverse engineering’ exercise to establish the relative value of lamb carcase types contributing to the carcase inventory; categorised by weight and fat. An improved understanding of the true value of carcases to processors when they are processed through the boning room to a market specification may make it possible for processors to establish pricing signals to lamb suppliers that better reflect processing outcomes. With the deployment of devices in plants that improve the precision and accuracy of measurement of yield and eating quality, it is ever more important for the industry to extract as much value as possible through processing, and to attract supply of carcases that best meet these outcomes. A population of 3,000 lamb carcases with weights from 13kg to 39kg, and lean meat yield from 49% to 65% was optimally allocated to one of three cutting plans according to a domestic lamb specification. Using outputs from the optimisation task, the purchase price for carcases contributing to each of the cutting plans was determined that achieved a nominal processing profit margin of 20% for each carcase processed. The distribution of carcase labour costs, income generated and recommended purchase price was then plotted across the range of carcase and lean meat yield values for the population contributing to each cutting plan. This study demonstrated that within a cutting plan, the profit extracted varied little with weight and fat (<3%) which supports industry practice of having very flat price grids. However, as heavier and fatter carcasses must be trimmed to meet retail requirements of portion size or visual fat they trigger a change of cutting plans and the impact of this is large (>$1.50/kg or>10%). Current lamb price grids do not adequately reflect this value change by lowering the buy price for these carcase types. By reverse engineering the outputs from the carcase optimisation tool, it is possible can gain a better understanding of the relative contribution of carcase types to profit. This may help inform processors to shape lamb purchases (price grids) to better reflect processing outcomes.

Read more here.

KPI 2.36 Re-analysis of beef carcase pricing data with less variation in yield in carcases analysed

This analysis mimics that completed by Pitchford et al. (2020) however with ‘extreme’ carcasses removed to endeavour to better mimic an average processing day, with the aim that the conclusion should change. Again, six methods were used to calculate carcass price ($/kg). Changes to dataset did not result in corresponding change in results, therefore the previous conclusion remains unchanged; that actual measurement of yield is crucial to determining carcass values and providing market signals to beef producers.

Read more here.

KPI 2.43 Lamb Carcase Optimisation Tool Cut Tree Hierarchy

The Cut Tree Hierarchy for the Lamb Carcase Optimisation Tool has been updated to accommodate new lamb cut types contained in the Lamb Value Calculator (Mk 2), and additional ‘nodes’ have been embedded in the tree to identify divergent cut selection pathways that enable a ‘complete carcase’ to be constructed that allow proper functioning of the tool.

Read more here.

KPI 3.31 A framework to ensure data integrity to support the commercial implementation of dual-energy X-ray (DXA) systems and data use in red meat processing

As dual-energy X-ray (DXA) systems are adopted by the red meat processing sector, the full benefits of objective carcase measurement rely on all stakeholders having confidence and trust in the integrity of information. This can only be achieved through transparency of data recording, management, and reporting. This report provides a framework to support red meat processors to successfully integrate DXA-generated and other objective measurement technologies data into business processes, and to use information derived from DXA data for commercial purposes within and external to their business.

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KPI 3.33 Summary of yearly, monthly, daily and between and within lot variation in MSA carcase data

This project arose from questions raised with previous research into beef carcase pricing regarding the variation of the dataset and whether it was representative of variation experienced by processors on any given day, month or year. There is very little research covering commercial carcase variation, so there is very little documentation to compare against to determine if any given dataset has sufficient variation. The dataset used for this research covers 35 variables over four years, derived from over 1.7
million carcases. A number of different summary statistics were completed on the data to cover the different time periods of interest, as well as a mixed model regression to understand where some of the variation may stem from. The variation determined here could be used going forward to determine if a
smaller dataset has sufficient variation in specific traits to be comparable to the variation a processor experiences. The variation experienced by processors in eating quality is greater than that from specific trials, almost by definition given that genetics trials are aimed to evaluate animals born and raised together.

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KPI 3.33 Value-based pricing system based on carcase yield and eating quality traits

The aim of this study was to calculate the value of diverse carcasses and compare pricing mechanisms on their ability to discriminate variation in meat yield and predicted eating quality. A pervious study in this series concluded that yield was the major driver for carcase price paid per kilogram; even when high premiums for eating quality were applied. However, when communicating the result to industry the response was that this did not reflect their experience. Thus, a dataset that more closely reflected industry reality was obtained in order to understand these divergent views. To do this, data was obtained from the Cooperative Research Centre for Cattle and Beef Quality (CRC, n=9,677), and a subset of carcases having full bone out data, an ossification score, and all other data necessary for determination of MQ4 score and overall MSA Index Score were analysed (n=301). Yield only (YO), and yield and eating quality (YEQ) price and value were determined, and the factors affecting YO and YEQ prices were tested against a series of models, which built in complexity. The results of the analysis demonstrated that with a high eating quality premium, i.e. 50% willingness to pay per MSA grade across all cuts, almost none of the variation in price was accounted for by yield. The pricing calculation used in this instance was therefore likely too great and both overestimated the value of quality, and underestimated the variation in quality. However, it is possible the conclusions are reasonable for commercial processors. More work needs to be done to tailor conclusions for specific supply chains and this work is planned in the year ahead.

Read more here.

5.2 Data to decision tools

KPI 2.39 The tactical model in optimization of boning room management

Development of carcase optimisation tools that can be used by industry to identify opportunities to add further value to carcase inventories is a point of focus for ALMTech. The current effort has resulted in the development of a Lamb Carcase Optimisation Tool that has the capability to quantify opportunities to better allocate ‘the right carcase to the right cut’. However, the complexity of the current tool is providing challenging to incorporate into the day-to-day management of a functioning lamb supply chain. Consequently, an alternate approach has been identified in collaboration with Gundagai Meat Processors that is targeted towards allocating carcases in the inventory to pre-set boning (cut) plans.

This “Tactical” optimisation model has been modelled mathematically, and its initial evaluation is presented in this report. Additional evaluation of the model is planned in 2021, and if successful it will likely result in an actionable tool for the rapid adoption by lamb supply chains.

Read more here.

KPI 3.33 Beef primal cut weight prediction from diverse breed crosses

The focus of the current trial was to quantify saleable meat yield variation between breeds and sires within breeds (Ewers et al. 1999). This has also been used for pricing carcasses (Pitchford et al. 2020). The focus on the current report is to use the same data set to predict primal weight cut. Given the extremely diverse breeds, this provides some guidance as to what traits are likely to be important for beef cut weight prediction as DXA systems are implemented.

The aim of this analysis is to test how much various measures add to prediction of cut weight and whether they can sufficiently describe breed differences to prevent needing to know breed which would aid processors. The hypothesis is that simple measures will not be
sufficient and a measure of shape reflecting muscularity will be important especially for hind quarter cuts.

The results demonstrate that measurement of saleable meat yield adds just small additional accuracy in predicting cut weights. It is assumed this will also be the case for measurement of lean meat yield. Thus, the value proposition for DXA needs to be through additional value that can be captured.

The specific hypothesis that initiated this analysis was that a measurement of bone weight would aid description of cut weight variation from diverse breeds. However, this did not prove
to be the case which suggests that a measure of carcass “shape” should be unnecessary in addition to more standard measures.

Read more here.

5.3 Supply chain management

KPI 2.42 PGS Mentoring Lamb Compliance

The aim of this project was to support the training of a new deliverer for the Profitable Grazing Systems (PGS) supported learning program (SLP) ‘Meat the Market – Lamb Compliance.’Using a tailored mentoring approach which included phone and email mentoring support pre-and post-workshop delivery, along with co-delivery of the workshop, the trainer was able to
become familiar with the package content and delivery style. Since the mentee already had technical expertise, the focus for the mentoring was on the delivery approach rather than support for learning technical content.

The desired outcomes of this mentoring activity were achieved, including mentoring the deliverer mentee, as per an approved mentoring plan, to build their capacity to:
1) Deliver a high quality, effective SLP that there is market demand for.
2) Recruit participants for future delivery of this SLP, through developing a relationship with the value chain.
3) Engage and empower producers to have a value chain approach to improving lamb compliance and profitability.
4) Act as a champion and support good governance of the PGS program.

Mentoring a livestock advisor with existing technical knowledge, networks, and relationships within their own region in the delivery of an actual PGS SLP, through the provision of phone and email support along with co delivery opportunities, was a highly effective way of engaging the livestock adviser within the PGS program.

Read more here.

KPI 3.35 Final Report of The Supply Chain Group – 2020 to 2022

The Supply Chain Group is the conduit for the collaboration, co-investment, and capacity building for Advanced Livestock Measurement Technologies (ALMTech), MLA, AMPC, and participating partners in the red meat industry. The group has a common focus on the research development, extension, and adoption of the science of lean meat yield, eating quality and health attributes, objective carcase measurement, genetics, and producer feedback.

A critical success factor of the Supply Chain Group’s meetings is its face-to-face format. As such, the meetings of the group provide its members with a unique relationship-building and networking forum to discuss ideas, peer review, and deliver the current and develop new/large national collaborative projects to grow the red meat supply and value chains. This format also
affords the group’s early career professionals in the industry, and postgraduate researchers the highly valued opportunity to forge relationships across the supply chain that underpin their growth, development, and future career contributions to the industry. Between 2019 and 2022, 24 members of this cohort benefitted greatly from their participation in the Supply Chain Group.

Although scheduled to meet twice yearly, the group was limited to being able to convene three times over the life of the ALMTech project. A period of recess was imposed between February 2020 and November 2022 (loss of four meetings) due to the impacts of COVID on members’ ability to meet face-to-face. Despite the limited opportunities to meet, members of the group placed a high value on each of the three meetings (average meeting score = 8.5 / 10). Member participation at the Adelaide meeting (November 2022) reached an all-time high for the group.

The current funding support for the group concludes at the completion of the ALMTech project. However, current members strongly support the desire to explore opportunities for the group to continue its functions to support innovation in the supply of livestock, processing and measurement of carcasses, and use of objective carcase data along the red meat value

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KPI 3.36 Adoption through Supply Chain Engagement

As the ALMTech technology programs have facilitated the development and deployment in abattoirs of technologies to measure carcase yield and eating quality for both lamb and beef, concurrently the project developed engagement programs for livestock producers and service providers to support the adoption carcase feedback to inform on-farm livestock management, and to improve compliance to specification. ALMTech has developed and delivered producer-focused adoption events and products aligned to MLA’s producer engagement framework,
• Awareness Raising
-Presentations to producer groups, industry events, and meetings.
• Advisor Capability Building
-Train-the-Trainer and mentoring to support consultants to deliver the MtM-LC PGS in partnership with JBS Australia, GMP, and WAMMCO.
-Delivery within MLA’s Livestock Adviser Essentials program for early-career
livestock advisers.
-SA Livestock Advisers Updates and National Livestock Advisers Updates.
• Long Term Practice Change
-Profitable Grazing Systems (PGS) package; Meat the Market – Lamb
Compliance (MtM-LC) in partnership with JBS Australia and Gundagai Meat
-JBS Southern Farm Assurance Producer Engagement R&D Adoption Project.
• Program Approach to Research, Development, and Adoption
-The Supply Chain Group. 

Read more here.