Genome-Wide Associative Study of Phenotypic Parameters of the 3D Body Model of Aberdeen Angus Cattle with Multiple Depth Cameras

Cattle Body Size Measurement Based on DUOS-PointNet+

The common non-contact, automatic body size measurement methods based on the whole livestock point cloud are complex and prone to errors. Therefore, a cattle body measuring system is proposed. The system includes a new algorithm called dynamic unbalanced octree grouping (DUOS), based on PointNet++, and an efficient method of body size measurement based on segmentation results. This system is suitable for livestock body feature sampling. The network divides the cow into seven parts, including the body and legs. Moreover, the key points of body size are located in the different parts. It combines density measurement, point cloud slicing, contour extraction, point cloud repair, etc. A total of 137 items of cattle data are collected. Compared with some of the other models, the DUOS algorithm improves the accuracy of the segmentation task and mean intersection by 0.53% and 1.21%, respectively. Moreover, compared with the manual measurement results, the relative errors of the experimental measurement results are as follows: withers height, 1.18%; hip height, 1.34%; body length, 2.52%; thoracic circumference, 2.12%; abdominal circumference, 2.26%; and cannon circumference, 2.78%. In summary, the model is proven to have a good segmentation effect on cattle bodies and is suitable for cattle body size measurement.

Analysis of Runs of Homozygosity in Aberdeen Angus Cattle

A large number of cattle breeds have marked phenotypic differences. They are valuable models for studying genome evolution. ROH analysis can facilitate the discovery of genomic regions that may explain phenotypic differences between breeds affecting traits of economic importance. This paper investigates genome-wide ROH of 189 Aberdeen Angus bulls using the Illumina Bovine GGP HD Beadchip150K to structurally and functionally annotate genes located within or in close ROH of the Aberdeen Angus cattle genome. The method of sequential SNP detection was used to determine the ROH. Based on this parameter, two ROH classes were allocated. The total length of all ROH islands was 11,493 Mb. As a result of studying the genomic architecture of the experimental population of Aberdeen Angus bulls, nine ROH islands and 255 SNPs were identified. Thirteen of these overlapped with regions bearing 'selection imprints' previously identified in other breeds of cattle, and five of these regions were identified in other Aberdeen Angus populations. The total length of the ROH islands was 11,493 Mb. The size of individual islands ranged from 0.038 to 1.812 Mb. Structural annotation showed the presence of 87 genes within the identified ROH islets.

On-Barn Forecasting Beef Cattle Production Based on Automated Non-Contact Body Measurement System

The main task of selective breeding is to determine the early productivity of offspring. The sooner the economic value of an animal is determined, the more profitable the result will be, due to the proper estimation of high and low productive calves and distribution of the resources among them, accordingly. To predict productivity, we offer to use a systematic assessment of animals by using the main genetic parameters (correlation coefficients, heritability, and regression) based on data such as the measurement of morphological characteristics of animals, obtained using the automated non-contact body measurement system based on RGB-D image capture. The usefulness of the image capture system lies in significant time reduction that is spent on data collection and improvement in data collection accuracy due to the absence of subjective measurement errors. We used the RGB-D image capture system to measure the live weight of mother cows, as well as the live weight and body size of their calves (height at the withers, height in the sacrum, oblique length of the trunk, chest depth, chest girth, pastern girth). Cows and cattle of black-and-white and Holstein breeds (n = 561) were selected as the object of the study. Correlation analysis revealed the main indices for the forecast of meat productivity-live weight and measurements of animals at birth. Calculation of the selection effect is necessary for planning breeding work, since it can determine the value of economically beneficial traits in subsequent generations, which is very important for increasing the profitability of livestock production. This approach can be used in livestock farms for predicting the meat productivity of black-and-white cattle.