Nutrition, management and other environmental influences on the quality and production of mohair and cashmere with particular reference to mediterranean and annual temperate climatic zones : a review

Goat fibre production is affected by genetic and environmental influences. Environmental influences which are the subject of this review include bio–geophysical factors (photoperiod, climate–herbage system and soil–plant trace nutrient composition), nutrition factors and management factors. Nutrition and management influences discussed include rate of stocking, supplementary feeding of energy and protein, liveweight change, parturition and management during shearing. While experimental data suggest affects of seasonal photoperiod on the growth of mohair and cashmere are large, these results may have confounded changes in temperature with photoperiod. The nutritional variation within and among years is the most important climatic factor influencing mohair and cashmere production and quality. Mohair quality and growth is affected significantly by rate of stocking and during periods of liveweight loss by supplementary feeding of either energy or protein. Strategic use of supplements, methods for rapid introduction of cereal grains, influence of dietary roughage on intake and the economics of supplementary feeding are discussed. Cashmere production of young, low producing goats does not appear to be affected by energy supplementation, but large responses to energy supplementation have been measured in more productive cashmere goat strains. The designs of these cashmere nutrition experiments are reviewed. Evidence for the hypothesis that energy-deprived cashmere goats divert nutrients preferentially to cashmere growth is reviewed. The influence and potential use of liveweight manipulation in affecting mohair and cashmere production and quality are described. Estimates of the energy requirements for the maintenance of fibre goats and the effect of pregnancy and lactation on mohair and cashmere growth are summarised. The effects and importance of management and hygiene during fibre harvesting (shearing) in producing quality fibre is emphasised. The review concludes that it is important to assess the results of scientific experiments for the total environmental content within which they were conducted. The review supports the view that scientific experiments should use control treatments appropriate to the environment under study as well as having controls relevant for other environments. In mediterranean and annual temperate environments, appropriate controls are liveweight loss and liveweight maintenance treatments. Mohair producers must graze goats at moderate rates of stocking to maximise animal welfare, but in so doing, they will produce heavier goats and coarser mohair. In mediterranean and annual temperate environments, seasonal changes in liveweight are large and influence both quality and production of mohair and cashmere. Mohair and cashmere producers can manipulate liveweight by supplementary feeding energy during dry seasons to minimise liveweight loss, but the economics of such feeding needs to be carefully examined. Strategic benefits can be obtained by enhancing the growth of young does prior to mating and for higher producing cashmere goats.

The value of visual fleece assessment in addition to objective measurements in identifying Angora goats of greater clean mohair production

Visual assessment of the fleece of Merino sheep is an accepted method to aid genetic improvement but there is little evidence to support the use of visual assessment for improving mohair production. This paper examines the extent that visual traits, including staple length, character (staple crimp), staple definition, tippiness, style and staple entanglement, are related to clean fleece weight in animals of similar live weight and mean fibre diameter (MFD) from the same flock. Measurements were made over 9 shearing periods on a population of castrated Angora males (wethers) goats representing the current range and diversity of genetic origins in Australia, including South African, Texan and interbred admixtures of these and Australian sources (these different genetic origins are defined as Breed in this work). Data on genetic origin, sire, dam, lifetime characteristics (date of birth, dam age, birth weight, birth parity (single or twin), weaning weight), live weight, fleece growth and visual fleece attributes were recorded. A restricted maximum likelihood (REML) model was developed to relate clean fleece weight with age, MFD, average fleece-free live weight, lifetime characteristics and visual fleece attributes. There were separate linear responses of clean fleece weight to MFD and staple length for each age group, a quadratic response to the square root of average fleece-free live weight, an effect of sire breed and linear responses to dam age, staple definition score and character. Depending on age at shearing, the increase in clean fleece weight was between about 50 and 80. g for each increase of 1. μm in MFD. At similar MFD, clean fleece weight was generally greater at summer shearings compared with winter shearings. There was a strong increase in clean fleece weight with average fleece-free live weight up to around 50. kg but little response in clean fleece weight for animals larger than 50. kg. There was some evidence of a smaller increase in clean fleece weight as the age of dam increased. There was an effect of Breed in the model but this effect disappeared when a random sire effect was included in the model. There was a positive response to staple length at some age groups but the response did not differ from zero in other age groups. This response varied from negligible to about 70. g per 1. cm increase in staple length. Clean fleece weight increased about 40. g per unit increase in staple definition score and increased about 30. g for every 4 units increase in the number of staple crimps. There was no evidence that clean fleece weight was affected by staple style, staple tip score or staple entanglement score or lifetime factors such as birth weight, date of birth, birth parity, or weaning weight. The results show that using a combination of measuring MFD and visually assessing the fleece for staple length, staple definition and crimps can help identify the most profitable Angora goats. In this process, the objective measurement of MFD appears essential. Visual assessment will provide some extra benefit in identifying these animals above that provided by measuring MFD alone. Animal size should be considered by mohair producers when identifying more productive mohair producing animals. © 2014 Elsevier B.V.

Lifetime and fleece quality traits associated with the occurrence of entangled mohair staples

Staple entanglement in mohair fleeces occurs when adhesions form between longer and faster growing fibres and shorter and slower growing fibres. This results in accentuated crimp of the longer fibres and an "apparently" reduced staple length. The appearance in the fleece of Angora goats of staple entanglements can lead to the downgrading of the mohair to poorer style and shorter length grades, resulting in up to 60% price reductions. This study examined how staple entanglement score (SES) is related to lifetime factors of Angora goats, and how this relationship can be explained by variations in animal size and fleece attributes. SES was scored using a five-point scale: 5, long free fibres easily separated as no adhesions; 4, some adhesions between fibres; 3, some effort to separate fibres as many adhesions; 2, many adhesions, staple fibres entangled, shortening of staple; 1, very entangled and shortened staple. Measurements were made over 9 shearing periods on a population of Angora castrated males (wethers) goats representing the current range and diversity of genetic origins in Australia, including South African, Texan and interbred admixtures of these and Australian sources. Data on genetic origin, sire, dam, date of birth, dam age, birth weight, birth parity, weaning weight, live weight, fleece growth and fleece attributes were recorded. Two restricted maximum likelihood (REML) models were developed to relate SES with age, animal lifetime factors, fleece quality attributes and live weight. One model allowed fleece quality and live weight traits in the model and the other excluded these traits. Staple entanglement was almost eliminated in mohair harvested from goats shorn every 3. months but was common in mohair from goats shorn twice or once per year. SES was less in goats of Texan genetic background, and was generally less in winter grown mohair. SES was higher for mohair with low fibre curvature (FC, 10°/mm) and a high clean washing yield (CWY, 90%) compared with mohair with low FC and lower CWY (80%), and compared with all mohair with high FC (18°/mm). The response of SES to shearing regime, genetic background, shearing season, age of goat and a response to dam age were almost identical whether or not an adjustment was made for CWY and FC. There was a moderate amount of variability due to sires and individuals. We can conclude that a large part of these effects observed, namely breed, dam age, sire, and a component of the FC and CWY effects, are genetic. Mohair producers can manage the genetic effects by careful selection of sires, especially avoiding those with low CWY or high FC, and avoiding sires with higher levels of staple entanglement or that have produced progeny with higher levels of staple entanglement. Also, unidentified environmental effects are affecting staple entanglement, although a lack of a live weight change effect on entanglement indicates that this effect might not be due to nutrition. © 2013 Elsevier B.V.

Contribution of objective and subjective attributes to the variation in the whiteness and brightness of commercial mohair sale lots

Colour properties are measured prior to the sale of merino wool as they are of commercial importance when greasy wool is sold and when wool is dyed. With the paucity of knowledge of the colour properties of commercial mohair, this study aimed to identify and quantify the factors affecting the brightness (Y) and yellowness (Y-Z) values of commercial lots of Australian mohair. The research database comprised 520 sale lots (>500,000 kg mohair), which had tristimulus tests, and was sold during the period 2001–2009. Mohair was subjectively classed and sale lots objectively tested using international standard methods for mean fibre diameter (MFD, μm), fibre diameter coefficient of variation (%), International Wool Testing Organization (IWTO) clean wool base (IWTO yield, %w/w), vegetable matter (VM, %w/w) and the tristimulus values X, Y and Z (T units). The tristimulus values of Australian mohair were affected by the objective measurements of MFD, VM%, the subjective classing of stain, cotting, kemp and length and by the year and selling season. Variation in Y was more easily predicted with 90.5% of variance explained by the best model compared with variation in Y-Z, where the best model explained 51.6% of the total variance. Visually assessed properties of the mohair were very important in separating mohair of different Y properties, accounting for almost 80% of the total variance, but were far less important in accounting for the variance in Y-Z, accounting for about 9–10% of the total variance. The most important effects on the Y of mohair were associated with subjectively determined fault categories determined before the sale of mohair. In particular, stain fault explained about two-thirds of the variance in brightness of mohair sale lots. Stained mohair had much lower brightness than mohair free of stain but stain fault explained very little of the variation in yellowness of mohair sale lots. The extent of the differences in tristimulus values between seasons and years were not large for Y but were more important for yellowness (Y-Z), and these effects are likely to be of commercial importance. Generally, brightness decreased and yellowness increased as MFD increased up to about 30 μm. Both cotting and kemp fault were associated with reduced brightness and increased yellowness. The effects of VM% on tristimulus values were small. IWTO yield was associated with changes in tristimulus values, but in the best model, IWTO yield was not a significant determinant. This study indicates that commercial Australian fleece (nonfaulted) mohair was essentially white. Faulted mohair on the other hand exhibited poorer colour characteristics. The mohair subjectively identified as stained prior to sale comprised all the mohair which would be regarded as not white, and this investigation indicates that the effect of staining is on the brightness of mohair rather than the Y-Z measurement. Unlike the situation with merino wool, there was little relationship between the naturally occurring contaminants, as measured by the IWTO washing yield, and either Y or Y-Z.

Wool statistics, including mohair and other animal fibers.

Cover-title.

Mohair histogenesis, maturation, and shedding in the Angora goat /

Issued Nov. 1974.

Wool and mohair.

Latest issue consulted: 1998, Mar. 25, 1999.

Benchmarking mohair production in Australia

This report provides factual evidence on the financial and production performance of real mohair farms for the first time. This process engaged mohair farmers in detailed record keeping and measurements of their animals. The project involved farm business economists in comparative financial studies of mohair and wool enterprises. Exhaustive statistical analyses have been used to quantify the changes in key production attributes of mohair enterprises. This work is important as it will guide mohair producers and the industry to focus on key performance indicators of profit and production. The outcomes provide objective evidence that can be used to attract future investment into mohair enterprises.

Intelligent animal fiber classification with artificial neural networks

Artificial neural networks (ANN) are increasingly used to solve many problems related to pattern recognition and object classification. In this paper, we report on a study using artificial neural networks to classify two kinds of animal fibers: merino and mohair. We have developed two different models, one extracting nine scale parameters with image processing, and the other using an unsupervised artificial neural network to extract features automatically, which are determined in accordance with the complexity of the scale structure and the accuracy of the model. Although the first model can achieve higher accuracy, it requires more effort for image processing and more prior knowledge, since the accuracy of the ANN largely depends on the parameters selected. The second model is more robust than the first, since only raw images are used. Because only ordinary optical images taken with a microscope are employed, we can use the approach for many textile applications without expensive equipment such as scanning electron microscopy.

Tensile behaviour of non-uniform fibres and fibrous composites

This work investigates the tensile behaviour of non-uniform fibres and fibrous composites. Wool fibres are used as an example of non-uniform fibres because they're physical, morphological and geometrical properties vary greatly not only between fibres but also within a fibre. The focus of this work is on the effect of both between-fibre and within-fibre diameter variations on fibre tensile behaviour. In addition, fit to the Weibull distribution by the non-brittle and non-uniform visco-elastic wool fibres is examined, and the Weibull model is developed further for non-uniform fibres with diameter variation along the fibre length. A novel model fibre composite is introduced to facilitate the investigation into the tensile behaviour of fibre-reinforced composites. This work first confirms that for processed wool, its coefficient of variation in break force can be predicted from that of minimum fibre diameters, and the prediction is better for longer fibres. This implies that even for processed wool, fibre breakage is closely associated with the occurrence of thin sections along a fibre, and damage to fibres during processing is not the main cause of fibre breakage. The effect of along-fibre diameter variation on fibre tensile behaviour of scoured wool and mohair is examined next. Only wet wool samples were examined in the past. The extensions of individual segments of single non-uniform fibres are measured at different strain levels. An important finding is the maximum extension (%) (Normally at the thinnest section) equals the average fibre extension (%) plus the diameter variation (CV %) among the fibre segments. This relationship has not been reported before. During a tensile test, it is only the average fibre extension that is measured. The third part of this work is on the applicability of Weibull distribution to the strength of non-uniform visco-elastic wool fibres. Little work has been done for wool fibres in this area, even though the Weibull model has been widely applied to many brittle fibres. An improved Weibull model incorporating within-fibre diameter variations has been developed for non-uniform fibres. This model predicts the gauge length effect more accurately than the conventional Weibull model. In studies of fibre-reinforced composites, ideal composite specimens are usually prepared and used in the experiments. Sample preparation has been a tedious process. A novel fibre reinforced composite is developed and used in this work to investigate the tensile behaviour of fibre-reinforced composites. The results obtained from the novel composite specimen are consistent with that obtained from the normal specimens.

Frequency of shearing increases growth of fibre and changes objective and subjective attributes of Angora goat fleeces

The impact of genotype and of frequency and timing of shearing, on mohair attributes and production of modern Angora goats was studied. Goats in the southern hemisphere grazed pastures between February 2004 and 2006. There were seven shearing treatments by three genetic strains with four or eight replicates of individual goats. Treatments were: three different 6-month shearing intervals and two of 12-month shearing intervals with different months of shearing, a 7-month winter shearing interval and a 3-month shearing interval. Genetic strain was based on sire line: 1·0 South African; 1·0 Texan; and Mixed 0·5 South African and 0·5 Texan. Annual greasy mohair production was 5·08 kg, and average clean fleece production was 4·37 kg. The Angora goats produced an annual clean fleece equivalent to 0·122 of their mean fleece-free live weight which was equal to 0·34 g/kg/day. Measurements were analysed over the period of spring 2004 shearing to spring 2005 shearing, excluding the June–December shearing treatment. Increased frequency of shearing increased fleece growth and affected 13 objective and subjective attributes of mohair that were evaluated including clean washing yield, fibre diameter and fibre diameter variation, incidence of medullated fibres, staple length, fibre curvature, crimp frequency, style, staple definition, staple fibre entanglement and staple tip shape. The direction of these effects were generally favourable and for most attributes the magnitude of the response was linear and commercially important. Each additional shearing resulted in an additional 149 g of clean mohair representing 0·034 of the annual clean mohair production. This increase was associated with a 0·6 cm increase in staple length and 0·32 μm increase in mean fibre diameter. In conclusion, Angora goats shorn less frequently grew less mohair that was more likely to be entangled in spring. Managers of Angora goats should take note of these findings.

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.