Estimation of dairy goat body composition: A direct calibration and comparison of eight methods

Three-dimensional imaging to estimate in vivo body and carcass chemical composition of growing beef-on-dairy crossbred bulls

The dynamics of cattle body chemical composition during growth and fattening periods determine animal performance and beef carcass quality. The aim of this study was to estimate the empty body (EB) and carcass chemical composition of growing beef-on-dairy crossbred bulls (Brown Swiss breed as dam with Angus, Limousin or Simmental as sire) using three-dimensional (3D) imaging. The 3D images of the cattle's external body shape were recorded in vivo on 48 bulls along growth trajectory (75-520 kg BW and 34-306 kg hot carcass weight [HCW]; set 1) and on 70 bulls at target market slaughter weight, including 18 animals from set 1 (average 517 ± 10 kg BW and 289 ± 10 kg HCW; set 2). The linear, circumference, curve, surface and volume measurements on the 3D body shape were determined. Those predictive variables were used in partial least square regressions, together with the effect of the sire breed whenever significant (P < 0.05), with leave-one-out cross-validation to estimate water, lipid, protein, mineral and energy mass or proportions in the EB and carcass. Mass and proportions were determined directly from postmortem grinding and chemical analyses (set 1) or indirectly using the 11th rib dissection method (set 2). In set 1, bulls' BW and HCW were estimated via 3D imaging, with root mean square error of prediction (RMSEP) of 12 kg and 6 kg, respectively. The EB and carcass chemical component proportions were estimated with RMSEP from 0.2% for EB minerals (observed mean 3.7 ± 0.2%) to 1.8% for EB lipid (11.6 ± 4.2%), close to the RMSEP found for the carcass. In set 2, the RMSEP for estimation via 3D imaging was 9 kg for BW and 6 kg for HCW. The EB energy and protein proportions were estimated, with RMSEP of 0.5 MJ/kg fresh matter (10.1 ± 0.8 MJ/DM) and 0.2% (18.7 ± 0.7%), respectively. Overall, the estimations of chemical component proportions from 3D imaging were slightly less precise for both sets than the mass estimations. The morphological traits from the 3D images appeared to be precise estimators of BW, HCW as well as EB and carcass chemical component masses and proportions.

Effect of Dietary Organic Acids and Botanicals on Metabolic Status and Milk Parameters in Mid-Late Lactating Goats

The microencapsulated mixture of organic acids and pure botanicals (OA/PB) has never been evaluated in goats. The aim of this study was to extend the analysis to mid-late lactating dairy goats, evaluating the effects of OA/PB supplementation on the metabolic status, milk bacteriological and composition characteristics, and milk yield. Eighty mid-late lactating Saanen goats were randomly assigned to two groups: one group was fed the basal total balanced ration (TMR) (CRT; n = 40) and the other was fed a diet that was TMR supplemented with 10 g/head of OA/PB (TRT; n = 40) for 54 days during the summer period. The temperature-humidity index (THI) was recorded hourly. On days T0, T27, and T54, the milk yield was recorded, and blood and milk samples were collected during the morning milking. A linear mixed model was used, considering the fixed effects: diet, time, and their interaction. The THI data (mean ± SD: 73.5 ± 3.83) show that the goats did not endure heat stress. The blood parameters fell within the normal range, confirming that their metabolic status was not negatively influenced by OA/PB supplementation. OA/PB increased the milk fat content (p = 0.04) and milk coagulation index (p = 0.03), which are effects that are looked on as favorable by the dairy industry in relation to cheese production.

Estimation of carcass chemical composition in beef-on-dairy cattle using dual-energy X-ray absorptiometry (DXA) scans of cold half-carcass or 11th rib cut

The aim of the present study was to estimate the chemical composition (water, lipid, protein, mineral, and energy contents) of carcasses measured postmortem using dual-energy X-ray absorptiometry (DXA) scans of cold half-carcass or 11th rib cut. One hundred and twenty beef-on-dairy (dam: Swiss Brown, sire: Angus, Limousin, or Simmental) bulls (n = 66), heifers (n = 42), and steers (n = 12) were included in the study. The reference carcass composition measured after grinding, homogenization, and chemical analyses was estimated from DXA variables using simple or multiple linear regressions with model training on 70% (n = 84) and validation on 30% (n = 36) of the observations. In the validation step, the estimates of water and protein masses from the half-carcass (R2 = 0.998 and 0.997; root mean square error of prediction [RMSEP], 1.0 and 0.5 kg, respectively) and 11th rib DXA scans (R2 = 0.997 and 0.996; RMSEP, 1.5 and 0.5 kg, respectively) were precise. Lipid mass was estimated precisely from the half-carcass DXA scan (R2 = 0.990; RMSEP = 1.0 kg) with a slightly lower precision from the 11th rib DXA scan (R2 = 0.968; RMSEP = 1.7 kg). Mineral mass was estimated from half-carcass (R² = 0.975 and RMSEP = 0.3 kg) and 11th rib DXA scans (R2 = 0.947 and RMSEP = 0.4 kg). For the energy content, the R2 values ranged from 0.989 (11th rib DXA scan) to 0.996 (half-carcass DXA scan), and the RMSEP ranged from 36 (half-carcass) to 55 MJ (11th rib). The proportions of water, lipids, and energy in the carcasses were also precisely estimated (R2 ≥ 0.882) using either the half-carcass (RMSEP ≤ 1.0%) or 11th rib-cut DXA scans (RMSEP ≤ 1.3%). Precision was lower for the protein and mineral proportions (R2 ≤ 0.794, RMSEP ≤ 0.5%). The cattle category (sex and breed of sire) effect was observed only in some estimative models for proportions from the 11th rib cut. In conclusion, DXA imaging of either a cold half-carcass or 11th rib cut is a precise method for estimating the chemical composition of carcasses from beef-on-dairy cattle.

Estimation of Empty Body and Carcass Chemical Composition of Lactating and Growing Cattle: Comparison of Imaging, Adipose Cellularity, and Rib Dissection Methods

The aim of present study was to compare in vivo and post mortem methods for estimating the empty body (EB) and carcass chemical compositions of Simmental lactating and growing cattle. Indirect methods were calibrated against the direct post mortem reference determination of chemical compositions of EB and carcass, determined after grinding and analyzing the water, lipid, protein, mineral masses, and energy content. The indirect methods applied to 12 lactating cows and 10 of their offspring were ultrasound (US), half-carcass and 11th rib dual-energy X-ray absorptiometry (DXA) scans, subcutaneous and perirenal adipose cell size (ACS), and dissection of the 11th rib. Additionally, three-dimensional (3D) images were captured for 8 cows. Multiple linear regressions with leave-one-out-cross-validations were tested between predictive variables derived from the methods tested, and the EB and carcass chemical compositions. Partial least square regressions were used to estimate body composition with morphological traits measured on 3D images. Body weight (BW) alone estimated the EB and carcass composition masses with a root mean squared error of prediction (RMSEP) for the EB from 1 kg for minerals to 12.4 kg for lipids, and for carcass from 0.9 kg for minerals to 7.8 kg for water. Subcutaneous adipose tissue thickness measured by US was the most accurate in vivo predictor when associated with BW to estimate chemical composition, with the EB lipid mass RMSEP = 11 kg and R² = 0.75; carcass water mass RMSEP = 6 kg and R² = 0.98; and carcass energy content RMSEP = 236 MJ and R² = 0.91. Post mortem, carcass lipid mass was best estimated by half-carcass DXA scan (RMSEP = 2 kg, R² = 0.98), 11th rib DXA scan (RMSEP = 3 kg, R² = 0.96), 11th rib dissection (RMSEP = 4 kg, R² = 0.92), and perirenal ACS (RMSEP = 6 kg, R² = 0.79) in this respective order. The results obtained by 11th rib DXA scan were accurate and close to the half-carcass DXA scan with a reduction in scan time. Morphological traits from 3D images delivered promising estimations of the cow EB and carcass chemical component masses with an error less than 13 kg for the EB lipid mass and than 740 MJ for the EB energy. Future research is required to test the 3D imaging method on a larger number of animals to confirm and quantify its interest in estimating body composition in living animals.

Comparative transcriptome analysis of goat (Capra hircus) adipose tissue reveals physiological regulation of body reserve recovery after the peak of lactation

Adipose tissue is the energy storage organ providing energy to other tissues, including mammary gland, that supports the achievement of successive lactation cycles. Our objective was to investigate the ability of goats to restore body fat reserves by comparing lipogenic enzyme activities and by transcriptomic RNA-Seq data at two different physiological stages, mid- and post-lactation. Key lipogenic enzyme activities were higher in goat omental adipose tissue during mid-lactation (74 days in milk) than during the post-lactation period (300 days postpartum). RNA-Sequencing analysis revealed 19,271 expressed genes in the omental adipose tissue. The comparison between adipose transcriptome analysis from mid- and post-lactation goats highlighted 252 differentially expressed genes (p_adj < 0.05) between these two physiological stages. The differential expression of 11 genes was confirmed by RT-qPCR. Functional genomic analysis revealed that 31% were involved in metabolic processes among which 38% in lipid metabolism. Most of the genes involved in lipid synthesis and those in lipid transport and storage were upregulated in adipose tissue of mid- compared to post-lactation goats. In addition, adipose tissue plasticity was emphasized by genes involved in cellular signaling and tissue integrity. Network analyses also highlighted three key regulators of lipid metabolism (LEP, APOE and HNF4A) and a key target gene (VCAM1). The greatest lipogenic enzyme activities with the upregulation of genes involved in lipid metabolism highlighted a higher recovery of lipid reserves after the lactation peak than 4 months post-lactation. This study contributes to a better understanding of the molecular mechanisms controlling the body lipid reserves management during the successive lactations.

Assessing the Feasibility of Using Kinect 3D Images to Predict Light Lamb Carcasses Composition from Leg Volume

This study aimed to evaluate the accuracy of the leg volume obtained by the Microsoft Kinect sensor to predict the composition of light lamb carcasses. The trial was performed on carcasses of twenty-two male lambs (17.6 ± 1.8 kg, body weight). The carcasses were split into eight cuts, divided into three groups according to their commercial value: high-value, medium value, and low-value group. Linear, area, and volume of leg measurements were obtained to predict carcass and cuts composition. The leg volume was acquired by two different methodologies: 3D image reconstruction using a Microsoft Kinect sensor and Archimedes principle. The correlation between these two leg measurements was significant (r = 0.815, p < 0.01). The models to predict cuts and carcass traits that include leg Kinect 3D sensor volume are very good in predicting the weight of the medium value and leg cuts (R² of 0.763 and 0.829, respectively). Furthermore, the model, which includes the Kinect leg volume, explained 85% of its variation for the carcass muscle. The results of this study confirm the good ability to estimate cuts and carcass traits of light lamb carcasses with leg volume obtained with the Kinect 3D sensor.

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reached the point of slaughter represent a burden for the feedlot. There are currently two main approaches (direct and indirect) to measure the BW in livestock. Direct approaches include partial-weight or full-weight industrial scales placed in designated locations on large farms that measure passively or dynamically the weight of livestock. While these devices are very accurate, their acquisition, intended purpose and operation size, repeated calibration and maintenance costs associated with their placement in high-temperature variability, and corrosive environments are significant and beyond the affordability and sustainability limits of small and medium size farms and even of commercial operators. As a more affordable alternative to direct weighing approaches, indirect approaches have been developed based on observed or inferred relationships between biometric and morphometric measurements of livestock and their BW. Initial indirect approaches involved manual measurements of animals using measuring tapes and tubes and the use of regression equations able to correlate such measurements with BW. While such approaches have good BW prediction accuracies, they are time consuming, require trained and skilled farm laborers, and can be stressful for both animals and handlers especially when repeated daily. With the concomitant advancement of contactless electro-optical sensors (e.g., 2D, 3D, infrared cameras), computer vision (CV) technologies, and artificial intelligence fields such as machine learning (ML) and deep learning (DL), 2D and 3D images have started to be used as biometric and morphometric proxies for BW estimations. This manuscript provides a review of CV-based and ML/DL-based BW prediction methods and discusses their strengths, weaknesses, and industry applicability potential.

Non-Destructive Imaging and Spectroscopic Techniques for Assessment of Carcass and Meat Quality in Sheep and Goats: A Review

In the last decade, there has been a significant development in rapid, non-destructive and non-invasive techniques to evaluate carcass composition and meat quality of meat species. This article aims to review the recent technological advances of non-destructive and non-invasive techniques to provide objective data to evaluate carcass composition and quality traits of sheep and goat meat. We highlight imaging and spectroscopy techniques and practical aspects, such as accuracy, reliability, cost, portability, speed and ease of use. For the imaging techniques, recent improvements in the use of dual-energy X-ray absorptiometry, computed tomography and magnetic resonance imaging to assess sheep and goat carcass and meat quality will be addressed. Optical technologies are gaining importance for monitoring and evaluating the quality and safety of carcasses and meat and, among them, those that deserve more attention are visible and infrared reflectance spectroscopy, hyperspectral imagery and Raman spectroscopy. In this work, advances in research involving these techniques in their application to sheep and goats are presented and discussed. In recent years, there has been substantial investment and research in fast, non-destructive and easy-to-use technology to raise the standards of quality and food safety in all stages of sheep and goat meat production.