Sources of variation in fibre diameter attributes of Australian alpacas and implications for fleece evaluation and animal selection

Sources of variation in fibre diameter attributes of Australian alpacas and implications for fleece evaluation and animal selection were investigated using data collected in the years 1994–97, from 6 properties in southern Australia. Data were analysed using REML (multiple regression analysis) to determine the effect on mean fibre diameter (MFD) and coefficient of variation of MFD (CV(FD)) of age, origin (property), sex (entire male, female), breed (Huacaya, Suri), liveweight, fibre colour, individual, and interactions of these effects. The mean (n = 100) age (range) was 4.2 years (0.1–11.9), liveweight 72.0 kg (12.0–134 kg), MFD 29.1 μm (17.7–46.6 μm), CV(FD) 24.33% (15.0–36.7%).

A number of variables affected MFD and CV(FD). MFD increased to 7.5 years of age, and correlations between MFD at 1.5 and 2 years of age with the MFD at older ages were much higher than correlations at younger ages. Fibre diameter 'blowout' (increase with age) was positively correlated with the actual MFD at ages 2 years and older. There were important effects of farm, and these effects differed with year and shearing age. Suris were coarser than Huacayas with the effect reducing with increased liveweight; there was no effect of sex. Fleeces of light shade were 1 μm finer than dark fleeces. CV(FD) declined rapidly between birth and 2 years of age, reaching a minimum at about 4 years of age and then increasing; however, CV(FD) measurements on young animals were very poor predictors of CV(FD) at older ages, and the response of CV(FD) to age differed with farm and year. Suris had a higher CV(FD) than Huacayas on most properties, and MFD, liveweight, and sex did not affect CV(FD). Fleeces of dark shade had higher CV(FD) than fleeces of light shade in 2 of the years. It is concluded that there are large opportunities to improve the MFD and CV(FD) of alpaca fibre through selection and breeding. The potential benefit is greatest from reducing the MFD and CV(FD) of fibre from older alpacas, through reducing the between-animal variation in MFD and CV(FD). Sampling alpacas at ages <2 years is likely to substantially decrease selection efficiency for lifetime fibre diameter attributes.

Feature extraction for animal fiber identification

Fiber identification has been a very important task in many industries such as wool growing, textile processing, archaeology, histochernical engineering, and zoology. Over the years, animal fibers have been identified using physical and chemical approaches. Recently, objective identification of animal fibers has been developed based on the cuticular information of fibers. Effective and accurate extraction of representative features is essential to animal fiber identification and classification. In the current work, two different strategies are developed for this purpose. In the first method, explicit features are extracted using image processing. However, only implicit features are used in the second method with an unsupervised artificial neural network. It is found that the use of explicit features increases the accuracy of fiber identification but requires more effort on processing images and solid knowledge of what features are representative ones.

Identification and classification of animal fibres using artificial neural networks

It has been an important and challenging task to classify and evaluate the contents in wool blends. Quantitative characterisation of animal fibre scale patterns has attracted considerable attention, since it is the major evidence for identification and subsequent classification purpose. Although techniques such as imaging processing and linear demarcation functions have been used to identify unknown fibre type with some success, a more comprehensive approach is required to perform this task. In this paper, a new approach is presented, which employs non-linear demarcation functions by using an artificial neural network (ANN). Based on scale pattern features extracted by using image processing techniques the artificial neural network (ANN) model is to classify mohair and merino fibres. It is observed that the techniques developed in this work are very effective and have the potential to be applied to other animal fibres.

(Pan-)animal magics : ecofeminist ethics and aesthetics in the Web

The 1990s has often been dubbed 'The Decade of the Environment'. Pollution, deforestation and desertification, ozone destruction, endangerment of species of animals and wildlife, vanishing wildernesses, and energy conservation are some of the visible environmental concerns which have gained international recognition. A less visible but potentially just as important environmental concern has been raised by ecological feminists. This is a concern about the connections historical, empirical, theoretical, symbolic, and experiential between the domination of women and the domination of non-human nature.

Intelligent animal fibre identification and classification

To develop an objective and repeatable method of identification and classification of animal fibres, two different integrated systems were developed to mimic the human brain's ability to undertake feature extraction and discrimination of animal fibres. Both integrated systems are basically composed of an image processing system and an artificial neural network system.

Australian legal regime on animal based bio-medical research

This thesis, by using evaluative criteria based on overseas law, scientific evidence, philosophy and ethics, concluded that the Australian legal regime regarding animal based bio-medical research for human benefit only partly met one of that regime's major objectives of adequately protecting research animals during the entire research process.

Companion animal visitation and its effectiveness with stroke patients

The study employed a randomised controlled trial design to determine whether a companion animal intervention had any effect on wellbeing and quality of life in patients who were admitted to hospital after suffering a stroke. The study found significant benefits for their wellbeing and improvements in quality of life.