Selection of beef cattle for efficiency of lean growth

The aims of this study were: 1) to evaluate measurements for predicting the carcass lean content of live bulls, 2) to assess the importance of different pre -test environmental effects on bull performance, and 3) to compare biological and economic selection indices for use in a terminal sire beef breed.The data comprised live weight, food intake, ultrasonic and carcass measurements on a total of 235 Hereford bulls, performance tested to 400 days of age on ad libitum feeding.Multiple regression equations using live weight and ultrasonic fat area measurements gave the best prediction of carcass leanness. However, the precision achieved varied depending on the machine, the operator and the group of bulls (R2 values 0.61 to 0.77) .Artificially reared bulls had low pre -test growth rate, which led to compensatory growth, and increased the variation in performance on test. Bulls weaned at 84 days of age were least affected by environmental factors such as dam age and year -season of birth, and performed as well as bulls weaned at 168 days of age.There were high phenotypic correlations between growth rate and lean growth rate (0.96) and between food conversion efficiency and lean food conversion efficiency (0.97) . Formulae were therefore derived for predicting the phenotypic and genetic relationship between a product trait, such as lean growth rate, and one component trait.Selection indices were derived which may be suitable for terminal sire breeds in the UK. The indices were insensitive to moderate changes in economic weights and genetic parameters, and were proposed as being superior to the biological indices (product traits) for improving the efficiency of lean meat production

Sheep Face Classification using Convolutional Neural Network

Monitoring sheep species identification and classification in the farming environment can be a tedious task and can be a significant workload for a starting farmer. In this paper, Convolutional Neural Network is proposed to reduce the workload of sheep farmers. This experiment compares which neural architecture model is more useful to classify sheep species based on its face. The experiment was conducted using the training dataset obtained from Kaggle. The dataset contains 420 of each Marino sheep, Suffolk sheep, White Suffolk sheep, and Poll Dorset sheep, totaling 1680 sheep face images. This experiment was run on Google Colab, using the Resnet50 network architecture model and VGG16 network architecture model. The experiment shows good accuracy results on the dataset achieving 86% using the Resnet50 network architecture model. Better accuracy results were achieved using VGG16 network architecture, with an accuracy value of 94%

Application of Machine Learning Algorithms to Predict Body Condition Score from Liveweight Records of Mature Romney Ewes

Publishe

231102

Sheep management and production enhancement are difficult for farmers due to the lack of dynamic response and poor welfare of the sheep. Poor welfare needs to be mitigated, and each farm must receive an expert-level assessment of critical importance. To mitigate poor welfare, researchers have conducted machine learning-based studies to automate the sheep health behavior monitoring process instead of using manual assessment. However, failure to recognize some sheep health behaviors degrades the performance of the model. In addition, behavior challenges, parameters, and analysis must be considered when conducting a study based on machine learning. In this paper, we discuss the different challenges: what are the parameters of the sheep health behaviors, and how to analyze the sheep health behaviors for automated machine learning systems to be helpful in the long term? The hypothesis is based on a different review of the literature of precision-based animal welfare monitoring systems with the potential to improve management and production.info:eu-repo/semantics/publishedVersio