Improving the measurement of live weight and body condition score in sheep : a thesis presented in fulfilment of the requirements for the degree of Doctor of Philosophy in Animal Science, Massey University, Turitea, Palmerston North, New Zealand

Liveweight (LW) and body condition score (BCS) are important performance indicators in sheep management, providing a basis for decision making. Therefore, accurate measurement of these traits is imperative. The overall aims of this thesis were to 1) Determine the factors affecting the rate of LW loss of fasting sheep, 2) derive equations to predict LW and LW change over a short time period (1 to 8 hours), 3) evaluate the factors affecting the relationship between ewe LW and BCS, and 4) derive equations for predicting ewe BCS. In the LW studies, lambs were offered three herbage availability levels (Low, Medium and High) in autumn or winter. Similarly, mixed-aged ewes at different physiological states were offered two herbage levels (Low or High). These studies were conducted in two stages: A) calibration stage and B) validation stage. Equations to predict without delay LW were developed at the calibration stage and validated on data collected from independent ewes from different farms. The rate of ewe LW loss was influenced by herbage type and availability, and season. Further, in pregnant ewes, liveweight loss was influenced by the stage of pregnancy, but not pregnancy-rank. Applying correction equations improved the prediction accuracy of without delay LW estimates up to 55% and 69% in ewe lambs and mixed aged ewes compared with using the delayed weights, respectively. For the BCS studies, LW and BCS data of ewes were collected at regular times of the annual production cycle until they were six years of age. Using a ewe’s LW and BCS records to predict their current BCS using a linear model gave moderately accurate estimates. A different dataset, which included foetal- and fleece weight-adjusted LW and height at withers was then used. It was found that equations combining LW, LW change and previous BCS explained more variability in current BCS and were more accurate than LW-alone based models but the addition of adjusted LW and height at withers gave no further benefit to the BCS prediction models. Applying machine learning classification algorithms such as extreme gradient boosted trees and Random forest on a 3-point BCS scale achieved very good BCS prediction accuracies (> 85%). These combined findings provide useful prediction equations that could be incorporated into weighing systems, which along with EID would improve sheep production by aiding management decision making

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%

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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

Genetic relationship between ewe longevity and structural foot and leg traits in sheep

2022 Summer.Includes bibliographical references.To view the abstract, please see the full text of the document

The Practice of Sheep Veterinary Medicine

This book is intended to be a reference text for veterinarians who provide clinical services to sheep producers. It is directed first and foremost at Australian sheep-raising systems, but the approaches described herein will have wide application in all countries where sheep are raised under extensive grazing conditions. Most of the important conditions of sheep in Australia are relatively straightforward to diagnose, but the establishment of effective and economically sound control strategies is often the most difficult part of health management, particularly for those who are less familiar with sheep production systems. With six initial chapters focusing on providing readers with a basic understanding of the business and science underpinning sheep production, this book focuses its remaining chapters on reproduction and disease conditions, ordered largely on a systems basis. The book provides details about the way disease processes develop and manifest in sheep flocks, with numerous references for those who wish to read further. Australian sheep production is a profitable and fulfilling agricultural pursuit for a large number of farm owners, and this book is intended to assist those who work in the industry to add to the profitability and efficiency of sheep production systems, the quality of sheep products and the welfare of the sheep in those systems

Sensing solutions for improving the performance, health and wellbeing of small ruminants

Diversity of production systems and specific socio-economic barriers are key reasons explaining why the implementation of new technologies in small ruminants, despite being needed and beneficial for farmers, is harder than in other livestock species. There are, however, helpful peculiarities where small ruminants are concerned: the compulsory use of electronic identification created a unique scenario in Europe in which all small ruminant breeding stock became searchable by appropriate sensing solutions, and the largest small ruminant population in the world is located in Asia, close to the areas producing new technologies. Notwithstanding, only a few research initiatives and literature reviews have addressed the development of new technologies in small ruminants. This Research Reflection focuses on small ruminants (with emphasis on dairy goats and sheep) and reviews in a non-exhaustive way the basic concepts, the currently available sensor solutions and the structure and elements needed for the implementation of sensor-based husbandry decision support. Finally, some examples of results obtained using several sensor solutions adapted from large animals or newly developed for small ruminants are discussed. Significant room for improvement is recognized and a large number of multiple-sensor solutions are expected to be developed in the relatively near future

Advances in Fibre Production Science in South American Camelids and other Fibre Animals

Animal fibres from South American camelids and other fibre or wool bearing species provide important products for use by the human population. The contemporary context includes the competition with petrocarbon-based artificial fibres and concern about excessive persistence of these in the natural environment. Animal fibres present highly valuable characteristics for sustainable production and processing as they are both natural and renewable. On the other hand, their use is recognised to depend on availability of appropriate quality and quantity, the production of which is underpinned by a range of sciences and processes which support development to meet market requirements. This collection of papers combines international experience from South and North America, China and Europe. The focus lies on domestic South American camelids (alpacas, llamas) and also includes research on sheep and goats. It considers latest advances in sustainable development under climate change, breeding and genetics, reproduction and pathology, nutrition, meat and fibre production and fibre metrology. Publication of this book is supported by the Animal Fibre Working Group of the European Federation of Animal Science (EAAP). ‘Advances in Fibre Production Science in South American Camelids and other Fibre Animals’ addresses issues of importance to scientists and animal breeders, textile processors and manufacturers, specialised governmental policy makers and students studying veterinary, animal and applied biological sciences