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Selecting High Performing Angoras

A report for the Rural Industries Research and Development Corporation by M.B. Ferguson and B.A. McGregor September 2005 RIRDC Publication No 05/141 RIRDC Project No DAV-191A

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© 2005 Rural Industries Research and Development Corporation. All rights reserved. ISBN 1 74151 204 2 ISSN 1440-6845 Selecting High Performing Angoras Publication No. 05/141 Project No. DAV-191A The information contained in this publication is intended for general use to assist public knowledge and discussion and to help improve the development of sustainable industries. The information should not be relied upon for the purpose of a particular matter. Specialist and/or appropriate legal advice should be obtained before any action or decision is taken on the basis of any material in this document. The Commonwealth of Australia, Rural Industries Research and Development Corporation, the authors or contributors do not assume liability of any kind whatsoever resulting from any person's use or reliance upon the content of this document. This publication is copyright. However, RIRDC encourages wide dissemination of its research, providing the Corporation is clearly acknowledged. For any other enquiries concerning reproduction, contact the Publications Manager on phone 02 6272 3186. Researcher Contact Details Mark Ferguson Formerly: Primary Industries Research Victoria (PIRVic) Department of Primary Industries, Hamilton, Victoria, 3300.

Author for correspondence: Dr. Bruce McGregor Primary Industries Research Victoria (PIRVic) Department of Primary Industries, Attwood, Victoria, 3049. Phone: 03 9217 4200 Fax: 03 9217 4299 Email: [email protected]

In submitting this report, the researcher has agreed to RIRDC publishing this material in its edited form. RIRDC Contact Details Rural Industries Research and Development Corporation Level 1, AMA House 42 Macquarie Street BARTON ACT 2600 PO Box 4776 KINGSTON ACT 2604 Phone: 02 6272 4819 Fax: 02 6272 5877 Email: [email protected]. Website: http://www.rirdc.gov.au Published in September 2005 Printed on environmentally friendly paper by Canprint

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Foreword The Australian Mohair Industry has identified genetic improvement as one of its priorities. In order for genetic improvement to occur across the national flock there is a requirement for an accurate means by which superior bucks can be identified. This project aimed to develop a model for progeny testing mohair goats that could be used by industry to accurately assess bucks. As part of this process eleven bucks were comprehensively evaluated through detailed assessment of the performance of their progeny. This report describes the method used to evaluate the bucks and provides a model that can be followed by industry. It also provides extensive information on the bucks that were evaluated. The results presented in this report demonstrate the large variation that exists between bucks currently used by industry and highlights the potential for genetic gain if superior bucks are accurately identified and then extensively used by industry. This project was funded from industry revenue, which was matched by funds provided by the Australian Government, and by the Department of Primary Industries, Victoria. This report, an addition to RIRDC’s diverse range of over 1200 research publications, forms part of our Rare Natural Fibres R&D program, which aims to facilitate the development of new and established industries based on rare natural fibres. Most of our publications are available for viewing, downloading or purchasing online through our website: • downloads at www.rirdc.gov.au/fullreports/index.html • purchases at www.rirdc.gov.au/eshop Peter O’Brien Managing Director Rural Industries Research and Development Corporation

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Acknowledgments Robert and June Liddy and Rowena and Glen Doyle provided their property and animals for use in this project. The project would not have been possible without the excellent assistance and support provided by them. They are sincerely thanked for making the project possible under exceptionally trying conditions. This project would not be possible without the assistance from the following: Paul Hamilton Craig Clancy Tim Ferguson Darren Gordon Australian Mohair Marketing Organisation Val Ross Trevor Linke Micaela Murray Mick Doak Andrew Gossip Kon Konstantinov Rod Turnor Dr Doug Stapleton Pam Goble Ken Slatter National Mohair Pools John Bennett Tom Harmsworth Sally Reynoldson Daniel Brown Brian Smith LAMBPLAN The breeders who entered sires are listed elsewhere in this report and are thanked for making this project possible.

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Abbreviations

Abbreviation Description 1st First shearing at 6 months of age 2nd Second shearing at 12 months of age 3rd Third shearing at 18 months of age

MFD Mean fibre diameter SD Standard deviation of fibre diameter CV Co-efficient of variation of fibre diameter

Curv Fibre curvature MEDN Percentage of medullated fibres by number MSSL Mid side Staple Length GFW Greasy fleece weight CFW Clean fleece weight BWT Birth weight MWT Marking weight WWT Weaning weight

% Import Percentage of imported blood Tex Imported from Texas, USA SA Imported from South Africa

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Contents

Foreword .......................................................................................................................................................... iii Acknowledgments............................................................................................................................................ iv Abbreviations.................................................................................................................................................... v Executive Summary........................................................................................................................................ vii 1. Introduction .................................................................................................................................................. 1 1.1 Background................................................................................................................................................... 1 1.2 Genetic gain.................................................................................................................................................. 1 2. Objectives ...................................................................................................................................................... 3 3. Method of Sire Evaluation ........................................................................................................................... 4 3.1 Committee of management........................................................................................................................... 4 3.2 Trial location and flock................................................................................................................................. 4 3.3 Sire selection ................................................................................................................................................ 4 3.4 Artificial insemination .................................................................................................................................. 5 3.5 Kidding ......................................................................................................................................................... 6 3.6 Progeny management ................................................................................................................................... 7 3.7 Data collection.............................................................................................................................................. 7 3.8 Data analysis............................................................................................................................................... 10 4. Results.......................................................................................................................................................... 11 4.1 Seasonal conditions .................................................................................................................................... 11 4.2 Conception and kidding.............................................................................................................................. 11 4.3 Birth weight and growth to weaning .......................................................................................................... 13 4.4 Fleece production and quality..................................................................................................................... 15 4.5 Subjective evaluation.................................................................................................................................. 21 4.6 Fleece classing and valuation ..................................................................................................................... 24 5. Angora Goat Genetics ................................................................................................................................ 26 5.1 Introduction to genetics .............................................................................................................................. 26 5.2 Selective breeding....................................................................................................................................... 26 5.3 Genetic and phenotypic parameters for important traits............................................................................. 27 5.4 Estimated breeding values .......................................................................................................................... 31 6. Model for Progeny Testing Angora Sires ................................................................................................. 35 6.1 Site selection............................................................................................................................................... 35 6.2 Doe selection .............................................................................................................................................. 35 6.3 Sire selection .............................................................................................................................................. 37 6.4 Joining ........................................................................................................................................................ 37 6.5 Doe management ........................................................................................................................................ 38 6.6 Kidding ....................................................................................................................................................... 38 6.7 Progeny management ................................................................................................................................. 39 6.8 Data collection............................................................................................................................................ 39 7. Discussion of Results .................................................................................................................................. 42 7.1 The model ................................................................................................................................................... 42 7.2 Sire differences ........................................................................................................................................... 42 7.3 Financial implications................................................................................................................................. 42 7.4 Impact of seasonal conditions..................................................................................................................... 42 8. Implications for Industry........................................................................................................................... 44 9. Recommendations....................................................................................................................................... 45 10. Appendices ................................................................................................................................................ 46 Appendix 1. Sire nomination form. .................................................................................................................. 46 References ....................................................................................................................................................... 50

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Executive Summary In livestock enterprises, increasing costs of production and decreasing value of product in real terms mean that producers must find ways to either make the production system more efficient or to increase the value of the product through improvements in quality. Genetic gain in economically important traits can facilitate both improvements in efficiency of production and product quality. In the Angora goat, genetically reducing fibre diameter and medullation and increasing staple length, fleece weight and reproductive rate results in both gains in efficiency of production and product value. In order to make genetic gain in the mohair industry there is a requirement to determine superior Angora sires within the population and increase their use. Establishing which sires are superior requires an understanding of their performance in relation to others. The most accurate way to test a sire’s genetic worth is to generate progeny from the animal and compare them with progeny from other animals. This is known as central test sire evaluation. Other livestock industries such as the Merino sheep industry have made extensive use of central test sire evaluation in recent times to identify the individual sires within industry that have a potential to make significant genetic improvement to the national flock. However, prior to this project central sire evaluation had never been completed in fibre goats in Australia. A central progeny test site was established in 2002 to determine the genetic variation that existed between eleven prominent sires in use in the mohair industry and to demonstrate the usefulness of modern genetic techniques in identifying elite individuals. The project was designed to evaluate the progeny of the selected sires at their first three shearings. Through the use of genetic linkage the project was able to use information from an additional four sires under evaluation at a separate site. The difference between the highest mohair value and lowest mohair value from each sire’s progeny groups was $5.70, $7.80 and $5.10 per progeny at the first, second and third shearings respectively, highlighting the large difference in profitability that exists between sires in use in the Australian mohair industry. When market and fleece test data were used together to estimate fleece value the difference between sires progeny was around $9.00 per progeny at the third shearing. Widespread industry use of the most profitable sires identified in this project will result in an increase in profitability of Australian mohair production enterprises. The evaluation of further sires, with links to the current data would substantially enhance the value of this information, and result in more efficient identification and use of superior sires in the industry.

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1. Introduction 1.1 Background The Angora goat has been farmed in Australia for over 100 years and the mohair industry is currently valued at $3 - $3.5 million annually with the vast majority of mohair being exported. The Angora goat is a highly prolific fibre producer, and is thought to be the most efficient producer of fibre of all small stock breeds (Herselman et al. 1998). The Angora is primarily run for its mohair fleece, while meat, a bi-product also contributes to their profitability. Mean fibre diameter (MFD) is the most important mohair property for processing and it is closely associated with greasy mohair price (Hunter, 1993). The majority of non-seasonal sources of variation in Australian mohair value are explained by MFD, while length, fibre style and level of medullation and vegetable matter contamination also significantly contribute (McGregor and Butler 2004). In addition to improving mohair value Angora selection programs also aim to increase body size because of its relationship with reproductive performance in does and increased carcass value of larger goats (Yalcin 1982). In livestock enterprises, increasing costs of production and decreasing value of product in real terms mean that producers must find ways to either make the production system more efficient or to increase the value of the product through improvements in quality. There is also market pressure to ensure that production systems are environmentally sustainable and socially acceptable (Coffey 2004). Genetic gain in economically important traits can facilitate both improvements in efficiency of production and product quality without negatively impacting on the environment. In the Angora goat genetically reducing fibre diameter and medullation and increasing staple length, fleece weight and reproductive rate results in both gains in efficiency of production and product value. The modern Australian Angora consists of genetics from an “Australian” population, which had been isolated from other stock since the 1920s (Stapleton 1978) and from relatively recent importations of genetics from North America (Texas) and South Africa from 1985 onwards. The imported stock is genetically superior to the original Australian lines in terms of freedom from medullation and increased staple length and fleece weight, but inferior in terms of MFD (Maw 1993, Murray-Prior and Murray Prior 1997; Stapleton 1997). Despite the dramatic improvements in the Australian Angora since the importations there is still potential to increase the productivity of the Angora goats in Australia through increasing or maintaining fleece weight while reducing the mean fibre diameter and medullation, increasing staple length, and improving reproductive traits (Stapleton, 1997). 1.2 Genetic gain To make genetic gain in the mohair industry there is a requirement to determine superior Angora sires within the population and increase their use. Establishing which sires are superior requires an understanding of their performance in relation to others. The most accurate way to test a sire’s genetic worth is to generate progeny from the animal and compare them with progeny from other animals. This is known as central test sire evaluation (Anon. 2004). Other livestock industries such as the Merino sheep industry have made extensive use of central test sire evaluation in recent times to identify the individual sires within industry that have a potential to make significant genetic improvement to the national flock. However, prior to this project central sire evaluation had never been completed in fibre goats in Australia and there was very little objective information on which sire selection can be based, and it was impossible to identify the genetically superior animals that exist within the national flock. A performance recording system known as MOPLAN was established in 1992 (Lollback and Stapleton, 1995). However, this system was not adopted by industry.

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The majority of animal selection that occurs in the Angora goat population in Australia at present is by subjective assessment, based on visual and tactile appraisal. Some producers are increasingly guided by objective measurement. The reliance on subjective assessment often results in selection for perceived trueness to existing types. The accuracy of subjective selection varies with the experience of the person. Unfortunately basing any selection on subjective appraisal alone leaves the breeder open to many variables and environmental influences. The rate of genetic gain is unknown or marginal at best. The industry has a requirement to identify the best genetics in Australia and use advanced breeding technologies to widely disperse these quality genes. A central progeny test site was established in 2002 to determine the genetic variation that existed between eleven prominent sires in use in the mohair industry and to demonstrate the usefulness of modern genetic techniques in identifying elite individuals. The project was designed to evaluate the progeny of the selected sires at their first three shearings. Through the use of genetic linkage the project was able to use information from an additional four sires under evaluation at a separate site as part of RIRDC project UNE-69A.

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2. Objectives This project was designed to develop a model for applying central test sire evaluation to Angora goats. This model would be provided to industry as a recommended way to run sire evaluations in the future. As part of this process the aim was also to comprehensively evaluate a number of Angora sires, including a demonstration of their economic merit and performance characteristics and to make this information publicly available. The database of estimated breeding values for a range of performance traits on each tested sire would also be provided to industry to be built on in the future. The project was also designed to educate industry participants on the potential impact on profitability of tested sires of superior genetic merit and the range that exists between sires within the national herd. Industry participants would also be educated on the conduct and operation of sire evaluation through progeny testing and the usefulness of objective measurement and modern genetic evaluation in Angora goat breeding programs.

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3. Method of Sire Evaluation 3.1 Committee of management An advisory committee was set up to assist with management decisions particularly sire selection. This committee had representatives from the Victorian Farmers Federation, Department of Primary Industries, Mohair Australia Limited (MAL; nominated by division members in each state) and the property owners. At its first meeting Dr Bruce McGregor was nominated and elected as chairperson. The other members on the committee were Tom Harmsworth (VFF), Trevor Linke (MAL, South Australia), Rod Turnor (MAL, Western Australia), Brian Smith (MAL, Tasmania), Ken Slatter (MAL, Victoria), Val Ross (MAL, New South Wales), June Liddy (property owner) and Mark Ferguson (Project Manager). 3.2 Trial location and flock The project site was established 15 km east of Horsham (36º43'00"S, 142º14'30"E), in the northwest of Victoria, Australia, on “Sylvania Park”, the property of June and Robert Liddy and family. The property had available a small amount of irrigation to be used to grow out weaned kids over summer. A total of 511 does were used in the trial, the majority of these does had kidded prior to the trial, there were a small number of maidens required to be used. The doe flock had a mixture of Australian, Texan and South African genetics and had a large amount of pedigree information, which was utilised in the analysis. The doe flock is shown in Figure 1. The owners of the property were the sole owners of mohair produced from this trial and sold the mohair at their discretion.

Figure 1. Trial does being fed grain just prior to kidding at “Sylvania Park”. 3.3 Sire selection Entries were invited from all members of the Australian mohair industry through national advertising. The objectives of the trial and the process to be followed were clearly described as part of the nomination form (Appendix 1). It was also clearly stated that the ownership of the progeny would solely rest with the property owners. Interested parties were asked to nominate sires and to provide pedigree and performance information (including fleece weights and fibre diameters) on each sire. The aim was to both select sires from a range of genetic backgrounds, that is, both South African and Texan type goats and their crosses and to select sires perceived to be of high genetic merit. A total of 16 Angora sires were nominated for inclusion in the Mohair Sire Evaluation project, with 11 of these being used. Several sires were withdrawn due to difficulties in obtaining semen. The advisory committee selected the sires that it deemed to be the most appropriate to be assessed. The sires used and some pedigree information is shown in Table 1. The selected group of 11 sires

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included South African, Texan and interbred sires and was representative of the genetics available in Australia. The third fleece performance information supplied by the breeders is shown in Table 2. Table 1. Details of bucks used in the trial Code Name Owner Sire Dam % Import *

1 Yandiah Texan The General Bev Helyar and John Gillett Yandiah Great

Vintage Yandiah Texan Pixie 100% Tex

2 Bernelea Blizzard DA & JH Payne Bernelea Bagheera Terraweena 1274

50%Tex 50% SA

3 Yullungah Park 0-45 Yullungah Park P/L Yullungah Park 98-13

Yullungah Park 97.37 100% SA

4 Brenelle 983 Brenelle Grazing – Mick Doak Boodjerakine 3425 AGF 1844 50% Tex

50% SA

5 Wilton Park Clarion Val and Brian Ross Pember & Son 1943

Texsynd TI 23083 100% Tex

6 Vermont African Lancelot Pam Goble Terraweena 63 Terraweena 1465

100% Sth African

7 Willean Maddison EW & LJ Scott Terraweena 1176 Willean Miss Mavis

37.5% Tex 50% SA

8 Wilton Park Cassanova Val and Brian Ross Texsynd TI 2178 Wilton Park 345 100% Tex

9 Currajong Timbillica Gay and Ron Harris Capricorn Texas Maurice

Amavale Ambrilla 75% Tex

10 Yarran Park Rambooka Keith Cowen Bel Aire Zambezi Yarran Park Sapphire

50% Tex 50% SA

11 Capricorn 655 Tim and Liz Hamblin Yandiah Texan Jameson

Capricorn Texas Rosita 100% Tex

* Percentage of Imported blood from either North America – Texas (Tex) or South Africa (SA) Table 2. Third shearing fleece weight (3rd Fleece weight) and mean fibre diameter (3rd MFD) of entered bucks supplied by their owners.

Code Name 3rd Fleece Weight (kg)

3rd MFD (µm)

1 Yandiah Texan The General 3.6 27.1 2 Bernelea Blizzard 4.1 32.0 3 Yullungah Park 0-45 5.6 27.8 4 Brenelle 983 5.7 23.6 5 Wilton Park Clarion 3.5 26.5 6 Vermont African Lancelot 3.3 25.9 7 Willean Maddison 3.6 31.7 8 Wilton Park Cassanova 4.9 28.6 9 Currajong Timbillica 5.5 29.6

10 Yarran Park Rambooka 7.2 33.0 11 Capricorn 655 2.8 27.6

3.4 Artificial insemination The owners of the bucks that had been selected were asked to supply (at their cost) sufficient semen to be used in the artificial insemination program (AI) conducted as part of the project. The project does were split into four groups based on age and genotype and does from each group were then randomly allocated to the sires by continuously alternating the semen in use. The project does were managed to enable insemination over four days between the 12th and 20th of April, 2002. Paul Hamilton, Semtech Animal Breeding Service, inseminated 511 does, with at least 45 does being inseminated to each buck. The majority of artificial insemination was carried out using the trans-cervical technique. The laparoscopic AI method was used on a small number of maiden does. The does were supplementary fed leading up to and following AI with pasture hay, oats and faba beans. The does were blood tested for trace element deficiencies and Faecal Worm Egg Counts were regularly monitored.

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3.5 Kidding Kidding was carried out in a semi-intensive system to try and maximise kid survival. Does kidded in the paddocks under surveillance from Maremma guard dogs (Figure 2). Once kidded, does and their kids were bought in to small pens for at least 24 hours (Figure 3). They were then moved into small grazing groups (approximately 20 does and their kids) for at least a further 24 hours (Figure 8) prior to returning to grazing pastures in a larger herd. The average kidding date was 11th of September 2002 and a total of 345 progeny were born with a large number of multiple births. Kids were weighed and tagged at birth and their dam, sex, birth type and birth coat score recorded.

Figure 2. Does kidded in the paddock under the watchful eye of Maremma guard dogs.

Figure 3. Pens set up to individually house does and their kids.

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3.6 Progeny management All progeny were tagged at birth with a unique number (Figure 7). At six weeks of age (marking) all males were castrated and all females were tattooed with a Commercial prefix (“C”) to ensure they could not be sold as stud animals. All animals were tattooed with their identification number at marking as a back up means of identification. All animals were vaccinated at marking and again at weaning at 16 weeks of age on the 30th of January 2003. Faecal worm egg counts were regularly monitored and based on high egg counts, progeny were drenched at weaning and at 11 months old. All progeny were wigged and crutched at weaning and again at 10 and 16 months of age. All animals were hoof trimmed at 10 months of age. All animals were evaluated until 18 months of age (third shearing). Kids were run with their mothers as a single group until weaning and then as a single group of kids for the whole of the evaluation period. 3.7 Data collection 3.7.1 Subjective data Within 48 hours of birth Rowena Doyle and Mark Ferguson assessed the birth coat of all kids. Prior to each shearing, progeny were assessed for subjective traits (Figure 4). The subjective assessment of animals was completed without the classer knowing any information about the individual animal (other than sex). The descriptions and range of subjective assessments completed are displayed in Table 3. The first shearing occurred on the 1st and 2nd of March 2003 when the animals were approximately 6 months old. At this shearing Mr Trevor Linke, Topaz Angora Stud, Willunga, South Australia completed subjective assessments and Dr Doug Stapleton, National Mohair Pool, completed mohair classing. The second shearing occurred on 30th and 31st of August 2003 and subjective assessments were carried out by Mr Andrew Gossip, Crookwell, NSW. Craig Clancy of the Australian Mohair Marketing Organisation completed mohair classing at the second shearing. The third shearing was completed on the 21st and 22nd of February 2004, subjective assessments were completed by Ms Sally Reynoldson, Woodstock and Mrs June Liddy, Horsham, Victoria and mohair classing completed by Mrs Rowena Doyle. All fibre from all shearings was re-classed and sold through the Australian Mohair Marketing Organisation on 18th June 2004 in the B2004 sale season. The results of this classing were provided to project staff and were used to calculate fleece values for each animal.

Figure 4. Subjective animal assessments being completed at the second shearing by Andrew Gossip.

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Table 3. Subjective traits assessed on all progeny.

Trait Scoring Explanation Birth coat 1 to 5

1=straight 5= very curly

The birth coat of the animal was assessed within 48 hours of birth and was given a score between 1 and 5 with 1 indicating a kid with straight hair and 5 indicating a kid with a very curly birth coat.

Style & Character 1 to 5 1=none 5=very good

Style (the degree to which the fibre twists or spirals) and character (the degree to which the fibre crimps or waves) were assessed as one trait taking into consideration an appropriate balance. A score of one was given where the fibre exhibited very little or no style and character, a score of five was given where the fibre displayed an excellent balance of style and character

Lustre 1 to 5 1=dull 5=very lustrous

Lustre, the degree to which the fibre reflects light, is one of mohair’s best-known qualities. A score of one was given if the fibre was dull; a score of 5 was given to fibre that was bright and highly lustrous.

Evenness 1 to 5 1= uneven 5= very even

The evenness of the fleece is explained as the similarity of staple structure, staple length and fibre diameter, from the front of the animal to the back. A score of one was given if there were large differences in fibre across the fleece; a score of five was given if the fibre was very similar across the whole fleece

Cover 1 to 5 1=minimum cover 5=well covered

Cover was defined as the amount of mohair on the head, legs and tails of the animals. Some breeders breed toward more cover and some toward less. A score of 1 was given to an animal that had little mohair on its head; legs and tail, a score of 5 was given if an animal had excessive mohair on its head, legs and tail.

Softness 1 to 5 1=harsh 5=soft

Softness was an assessment of the type of fibre present on the animals face particular on the ears and around the eyes and the softness of the fleece itself. A score of one was given if chalky white fibres were dominant on the face and the fleece was harsh to handle; a score of five was given if the fibre was fine, soft and silky and the fleece was soft.

Head/horns 1 to 5 1= poor structure 5=excellent

The head and horns were assessed together with horn shape, muzzle width and head cover taken into account. A score of one was given if the animal displayed weak or poorly shaped horns and a narrow muzzle; a score of five was given to an animal with well-shaped horns and good muzzle.

Black Pigment 1 to 5 1=excessive pigmentation 5=no pigment

The nose, eye and ears were assessed for the presence of black pigmented skin and the presence of black hair growing from the pigmented areas. A score of one was given to an animal that had excessive pigmentation on the stated areas; a five was given to an animal that was totally free of pigment on these areas. Any animal that had a black fibre growing from the pigmented areas was automatically given a one for this trait.

Size 1 to 5 1=small 5=large frame

The animals were assessed for their general size including length, barrel and frame. The sex of the animal was taken into account when making the assessment. A one was given to an animal that was small and narrow, a five was given to an animal that was considered to be long with a good barrel and well grown.

Conformation (Assessed at third shearing only)

1 to 5 1=serious faults 5= free from faults

The conformation of the animals was assessed. A one was given to an animal with an obvious and serious structural fault that would limit the ability of the animal to survive and thrive eg serious hockiness. A five was given to an animal that was free from structural faults.

Feet 1 to 5 1= poor hoof shape 5=excellent hoof shape

Hoof shape is an important characteristic of interest to commercial growers, animals with poorly shaped hooves are more likely to develop feet problems and require trimming more often. A one was given to an animal that had poor hoof shape. A score of five was given to an animal that had excellent hoof shape that would seldom require trimming.

Overall 1=cull 2=flock 3=stud

The assessor was also asked to place animals into one of three pseudo groups: to be culled; to go into a commercial flock; or to go into a stud flock.

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3.7.2 Objective data Body weight of all trial animals was recorded at birth, marking and weaning and other key times throughout the evaluation of the progeny. Animals were shorn at six months of age, 12 months of age and again at 18 months of age. Prior to each shearing, a mid side sample was removed from all animals. A randomly selected staple from the mid side staple was manually measured for length (SL). The mid side samples were measured for mean fibre diameter (MFD), standard deviation of fibre diameter (SD), coefficient of variation of fibre diameter (CVD), fibre curvature (Curv) and percentage of medullated fibres (MED). The testing was carried out utilising an Optical Fibre Diameter Analyser 100 (OFDA100) using a mohair calibration developed from mohair top supplied by CSIR, Port Elizabeth, South Africa, and following standards IWTO-47-98 and IWTO-57-98. The samples were then tested for clean washing yield (YLD). At shearing animals were shorn in random order and greasy fleece weight (GFW) was recorded, and this was later multiplied by YLD to determine clean fleece weight (CFW). An industry classer classed each fleece into its component parts and each component was weighed (Figure 5), the weight of each component was then multiplied by its value to determine an overall fleece value for each animal.

(a) (b) Figure 5. a) Craig Clancy assisted by Doug Stapleton classing fleeces at the second shearing; and b) Dr Bruce McGregor weighing component parts of fleeces following classing.

(a) (b) Figure 6. Tim Ferguson shearing at the second, a) and third, b), shearings.

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Ultrasonic scanning is commonly used in sheep to measure fat and eye muscle depth at the C site on live animals for genetic evaluation purposes (Gilmour et al. 1994). A LAMBPLAN accredited scanner was employed to scan the progeny at 15 months to determine the variation between progeny and between sires, which had not previously been completed on Angora goats. 3.8 Data analysis A REML variance components analysis was completed, the fixed model included birth type, dam age, sex and sire. There was no random model specified. Dam age was included in the analysis to account for both maternal environment and genetic differences. All traits were analysed separately and all interactions were included in the initial analysis. For all traits there were no interactions (P>0.05). The analysis was undertaken using GenStat 5.42 (GenStat Committee, 2000). The genetic analysis was completed by the Animal Breeding and Genetics Unit, University of New England utilising the OVIS software (as used by KIDPLAN and LAMBPLAN).

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4. Results 4.1 Seasonal conditions The site experienced a severe drought in 2002, the year in which the project does were joined and kidded. The median rainfall at Longerenong, the closest Bureau of Meteorology weather station, over the last 105 years is 414 mm. Total rainfall for 2002 was 234 mm. As a result there was very little pasture growth and there was no water for irrigation during 2002/03 and 2003/04. Does and kids were supplementary fed with pasture hay and grain (oats and faba beans) for the majority of the project. 4.2 Conception and kidding Conception to the AI program was at the lowest end of expectations but in line with experience under drought conditions. It is likely that the conception rates were a result of the poor seasonal conditions and requirement for large levels of supplementary feeding for maintenance. The results show that few does lighter than 30 kg at joining kidded. Given that does lost weight during the time of conception, greater quantities of feed should be provided in order to maximise potential conception rates. As is expected in an AI program there was a higher than normal incidence of multiple births. The majority of the kids were born over a 10-day period. Table 4 shows kidding percentages, kid survival, and the number of kids that were alive at the end of kidding for each sire. Table 4. Kidding percentages, kid survival and kids at marking.

Sire Sire name Does kidded / doe mated

(%)

Kids born / doe mated

(%)

Kids born / doe kidded

(%)

Kids survival

(%)

No. alive kids

1 Yandiah Texan The General 41 65 132 83 25 2 Bernelea Blizzard 41 67 137 84 26 3 Yullungah Park 0-45 30 39 123 94 16 4 Brenelle 983 35 72 175 85 28 5 Wilton Park Clarion 48 70 141 97 31 6 Vermont African Lancelot 55 106 158 82 41 7 Willean Maddison 34 60 150 86 24 8 Wilton Park Cassanova 41 76 142 77 27 9 Currajong Timbillica 44 71 145 91 29

10 Yarran Park Rambooka 37 61 139 83 25 11 Capricorn 655 40 57 126 89 24

Average 41 68 167 86 27

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Figure 7. Kids were tagged and weighed at birth.

Figure 8. Does and kids were managed in small crèche groups following individual penning.

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4.3 Birth weight and growth to weaning Due to the large number of multiple births in the data set and the unfavourable seasonal conditions the mean birth weight and growth to weaning of the progeny is lower than is generally reported (Günes et al. 2002; Snyman 2002) and were similar to those reported by Parry et al. (1993) and McGregor (1995). Despite the poor conditions good growth to marking and weaning meant the weaning weights were reasonable (Table 5) and were at the lower end of the range of previously reported results (Parry et al. 1993; McGregor 1995; Günes et al. 2002; Snyman 2002). For all weights male kids were heavier than female kids and singles were heavier than twins as would be expected (Günes et al. 2002). Table 5. Predicted means of birth, marking and weaning weight for each sire, predicted mean (se) of the group, statistical significance of sire differences and predicted means for singles, twins, females and wethers.

Sire Sire name Birth Weight (kg)

Marking Weight (kg)

Weaning Weight (kg)

1 Yandiah Texan The General 2.31 10.8 13.6 2 Bernelea Blizzard 2.41 11.1 13.8 3 Yullungah Park 0-45 2.40 10.9 12.6 4 Brenelle 983 2.31 10.9 14.2 5 Wilton Park Clarion 2.49 10.6 13.8 6 Vermont African Lancelot 2.35 11.0 13.5 7 Willean Maddison 2.31 10.8 13.9 8 Wilton Park Cassanova 2.22 12.2 15.6 9 Currajong Timbillica 2.29 10.4 12.9

10 Yarran Park Rambooka 2.22 9.9 12.9 11 Capricorn 655 2.40 10.1 13.5

Mean 2.34 10.8 13.7 se 0.08 0.69 0.98 Singles 3.01 14.3 16.4 Twins 2.50 10.6 12.4 Females 2.20 9.9 12.7 Wethers 2.47 11.7 14.6 Significance of sire NS NS NS

Figure 9. Progeny group at weaning.

14

Progeny growth was negligible between weaning and 10 months of age, however due to the improved seasonal conditions and availability of green pasture, progeny live weight increased considerably between 10 and 15 months of age (Figure 10; Table 6). Following this period of rapid growth the fat and eye muscle depth at the C site was measured using ultrasonic scanning. There were small yet significant differences (P<0.01) between sires (Table 6). Figure 10. Live weight change for single and twin born progeny. Table 6. Predicted means of live weight at 10 months (LW10), 14 months (LW14), 15 months (LW15), hogget C site fat (HCF), hogget eye muscle depth (HEMD) at 15 months plus predicted mean (se) of the group, statistical significance of sire differences and predicted mean for singles, twins, females and wethers.

Sire Sire name LW10 (kg)

LW14 (kg)

LW15 (kg)

HCF (mm)

HEMD (mm)

1 Yandiah Texan The General 14.6 21.4 24.6 2.5 14.6 2 Bernelea Blizzard 15.1 22.4 24.5 2.7 14.9 3 Yullungah Park 0-45 14.7 22.7 25.2 2.7 15.2 4 Brenelle 983 15.4 22.2 26.0 2.8 15.6 5 Wilton Park Clarion 14.7 22.2 25.7 2.6 15.4 6 Vermont African Lancelot 14.4 20.2 22.2 2.4 14.4 7 Willean Maddison 15.7 24.1 27.5 2.8 16.5 8 Wilton Park Cassanova 16.5 23.2 27.2 2.7 15.7 9 Currajong Timbillica 14.0 20.7 22.8 2.5 14.1

10 Yarran Park Rambooka 14.7 22.6 25.1 2.7 14.7 11 Capricorn 655 14.6 22.0 25.5 2.7 15.2

Mean 14.9 22.1 25.1 2.7 15.1 se 0.77 1.08 1.27 0.11 0.53 Singles 16.7 23.8 26.7 2.6 15.9 Twins 14.8 22.2 24.7 2.6 15.5 Females 14.0 20.9 23.7 2.7 14.8 Males 15.9 23.4 26.5 2.6 15.5 Significance of sire NS P<0.05 P<0.001 P<0.01 P<0.01

0

5

10

15

20

25

30

0 100 200 300 400 500

Days from Birth

Live

wei

ght (

kg)

SinglesTwins

15

4.4 Fleece production and quality 4.4.1 Six months of age The fleece weights from the first shearing were lower than is generally reported (McGregor 1995; Günes et al. 2002) and correspondingly the mean fibre diameter was lower than normal (Snyman 2002). The very fine and relatively light fleeces were a result of the poor seasonal experience. Due to both the seasonal conditions and the maternal effects on progeny fleece traits there were small yet significant differences between sires for many of the fleece traits (Table 7; Table 8). Table 7. Predicted means of first shearing results for mean fibre diameter (MFD1), standard deviation of fibre diameter (SD1), coefficient of variation of fibre diameter (CV1), fibre curvature (Curv1), percentage medullation by number (MEDN1) for each sire, and the predicted mean (se) of the group, statistical significance of sire differences, predicted mean for singles, twins, females and wethers.

Sire Sire name MFD1 (µm)

SD1 (µm)

CV1 (%)

Curv1 (°/mm)

MEDN1 (%)



Mean 19.9 6.1 30.8 19.3 1.3 se 0.53 0.21 0.87 0.83 0.16 Singles 21.1 6.5 31.0 18.4 1.4 Twins 19.6 6.1 31.0 20.2 1.3 Females 19.9 6.2 31.2 18.9 1.3 Wethers 20.0 6.1 30.4 19.6 1.2 Significance of sire P<0.001 P<0.001 P<0.01 P<0.001 P<0.01

16

Table 8. Predicted means of first shearing results for greasy fleece weight (GFW1), clean fleece weight (CFW1), mid-side staple length (MSSL1), washing yield (YIELD1) for each sire and the predicted mean (se) of the group, statistical significance of sire differences, predicted mean for singles, twins, females and wethers.

Sire Sire name GFW1 (kg)

CFW1 (kg)

MSSL1 (cm)

YIELD1 (%)

1 Yandiah Texan The General 0.83 0.68 14.1 81.9 2 Bernelea Blizzard 0.84 0.66 14.6 78.8 3 Yullungah Park 0-45 0.70 0.60 13.8 84.7 4 Brenelle 983 0.81 0.68 14.4 84.3 5 Wilton Park Clarion 0.81 0.67 14.2 82.5 6 Vermont African Lancelot 0.79 0.67 13.8 84.9 7 Willean Maddison 0.83 0.67 13.6 81.0 8 Wilton Park Cassanova 0.91 0.74 14.4 81.6 9 Currajong Timbillica 0.88 0.73 14.6 83.1 10 Yarran Park Rambooka 0.79 0.64 14.6 82.5 11 Capricorn 655 0.80 0.65 13.8 82.2

Mean 0.82 0.67 14.2 82.5 se 0.06 0.05 0.35 1.37 Singles 1.01 0.80 14.6 79.7 Twins 0.75 0.61 14.1 81.1 Females 0.76 0.63 14.2 82.7 Wethers 0.87 0.72 14.2 82.3 Significance of sire NS NS P<0.01 P<0.001

Figure 11. Kids in the catching pens at the first shearing. There is a positive phenotypic and genetic correlation between mean fibre diameter and fleece weight in Angoras (Snyman and Olivier 1996; Allain and Roguet 2003). This phenotypic correlation was also evident (r=0.63) in the progeny data at the first shearing of the trial goats (Figure 12).

17

Figure 12. Relationship between mean fibre diameter (MFD) and clean fleece weight (CFW) at the first shearing for individual animals (r=0.63). 4.4.2 Twelve months of age Despite extensive supplementary feeding, the poor seasonal conditions contributed to low second shearing fleece weights and mean fibre diameters for all progeny groups. The MFD was considerably lower than previous reports (McGregor 1995; Snyman and Olivier 1999) for animals of the same age. A difference between some sire groups was evident for some measured fleece parameters (Table 9; Table 10). There was again a strong correlation (r=0.51) between mean fibre diameter and clean fleece weight at the second shearing (Figure 14). Table 9. Predicted means of second shearing results for mean fibre diameter (MFD2), standard deviation of fibre diameter (SD2), coefficient of variation of fibre diameter (CV2), fibre curvature (Curv2), percentage medullation by number (MEDN2) for each sire and the predicted mean (se) of the group, statistical significance of sire differences, predicted mean for singles, twins, females and wethers.


SD2 (µm)

CV2 (%)

Curv2 (°/mm)

MEDN2 (%)



Mean 20.9 5.5 26.2 18.1 0.5 se 0.56 0.25 1.04 0.96 0.09 Singles 21.2 5.2 24.6 18.2 0.6 Twins 21.2 5.5 26.0 18.9 0.6 Females 20.9 5.5 26.2 17.9 0.5 Wethers 21.0 5.5 26.3 18.3 0.5 Significance of sire P<0.001 NS P<0.05 P<0.001 P<0.05

0

0.2

0.4

0.6

0.8

1

1.2

1.4

14 16 18 20 22 24 26 28

First Shearing MFD (micron)

Firs

t She

arin

g C

FW(k

g)

18

Table 10. Predicted means of second shearing results for greasy fleece weight (GFW2), clean fleece weight (CFW2), mid-side staple length (MSSL2), washing yield (YIELD2) for each sire and the predicted mean (se) of the group, statistical significance of sire differences, predicted mean for singles, twins, females and wethers.


CFW2 (kg)

MSSL2 (cm)

YIELD2 (%)

1 Yandiah Texan The General 1.22 0.96 10.7 79.3 2 Bernelea Blizzard 1.21 1.00 11.4 83.1 3 Yullungah Park 0-45 1.14 0.91 10.6 80.2 4 Brenelle 983 1.13 0.94 11.7 83.5 5 Wilton Park Clarion 1.15 0.93 11.0 81.6 6 Vermont African Lancelot 1.14 0.96 11.9 83.8 7 Willean Maddison 1.30 1.07 11.0 82.1 8 Wilton Park Cassanova 1.23 0.97 11.0 79.1 9 Currajong Timbillica 1.14 0.94 11.3 82.8

10 Yarran Park Rambooka 1.20 0.99 11.9 82.6 11 Capricorn 655 1.22 0.96 10.5 79.2

Mean 1.19 0.97 11.2 81.6 se 0.06 0.05 0.55 1.38 Singles 1.20 0.98 11.6 81.5 Twins 1.14 0.92 11.6 81.7 Females 1.14 0.93 11.4 81.9 Wethers 1.24 1.00 11.0 81.2 Significance of sire NS NS NS P<0.001

Figure 13. Progeny just prior to second shearing.

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Figure 14. Relationship between mean fibre diameter (MFD) and clean fleece weight (CFW) at the second shearing (r=0.51). 4.4.3 Eighteen months of age With the improvement in seasonal conditions and associated increase in nutrition the progeny groups had an opportunity to grow and to express their genetic potential. The third shearing greasy fleece weights were similar to those reported by Snyman and Olivier (1999) yet the progeny had considerably lower mean fibre diameters than those reported by these authors. The differences in measured traits between progeny groups were largest at the third shearing (Table 11; Table 12). There was again a relationship (r=0.46) between GFW and MFD at the third shearing (Figure 15). Table 11. Predicted means of third shearing results for mean fibre diameter (MFD3), standard deviation of fibre diameter (SD3), coefficient of variation of fibre diameter (CV3), fibre curvature (Curv3), percentage medullation by number (MEDN3) for each sire and the predicted mean (se) of the group, statistical significance of sire differences, predicted mean for singles, twins, females and wethers.


SD3 (µm)

CV3 (%)

Curv3 (°/mm)

MEDN3 (%)



Mean 28.7 7.3 25.4 13.9 1.0 se 0.78 0.32 1.14 0.54 0.08 Singles 28.7 7.1 25.1 14.4 1.1 Twins 28.8 7.5 26.2 14.3 1.1 Females 29.0 7.3 25.2 13.6 0.9 Wethers 28.4 7.3 25.7 14.2 1.0 Significance of sire P<0.001 NS P<0.001 P<0.001 NS

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

14 16 18 20 22 24 26 28 30

Second Shearing MFD (micron)

Seco

nd S

hear

ing

CFW

(kg)

20

Table 12. Predicted means of third shearing results for greasy fleece weight (GFW3), clean fleece weight (CFW3), mid-side staple length (MSSL3), washing yield (YIELD3) for each sire and the predicted mean (se) of the group, statistical significance of sire differences, predicted mean for singles, twins, females and wethers.


CFW3 (kg)

MSSL3 (cm)

YIELD3 (%)

1 Yandiah Texan The General 2.35 1.92 11.3 83.0 2 Bernelea Blizzard 2.17 1.80 11.9 84.0 3 Yullungah Park 0-45 2.20 1.85 11.6 85.5 4 Brenelle 983 2.32 1.98 13.0 86.5 5 Wilton Park Clarion 2.16 1.82 11.8 85.4 6 Vermont African Lancelot 2.11 1.84 12.5 86.6 7 Willean Maddison 2.38 1.94 11.7 82.7 8 Wilton Park Cassanova 2.18 1.81 12.3 83.4 9 Currajong Timbillica 2.09 1.74 11.9 84.7

10 Yarran Park Rambooka 2.31 1.95 12.3 85.3 11 Capricorn 655 2.28 1.85 11.2 82.3 Mean 2.23 1.86 12.0 84.5 se 0.11 0.09 0.46 1.22 Singles 2.27 1.88 11.4 83.7 Twins 2.13 1.78 11.7 83.7 Females 2.16 1.81 11.8 84.8 Wethers 2.30 1.92 12.1 84.2 Significance of sire P<0.05 NS P<0.05 P<0.001

Figure 15. Relationship between mean fibre diameter (MFD) and clean fleece weight (CFW) at the third shearing (r=0.46).

0

0.5

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1.5

2

2.5

3

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14 16 18 20 22 24 26 28 30 32 34 36 38 40

Third Shearing MFD (micron)

Third

She

arin

g C

FW (k

g)

21

Figure 16. Rowena Doyle classing fibre at the third shearing. 4.5 Subjective evaluation Prior to shearing all animals were assessed for a number of traits. The traits selected to be assessed and a brief explanation is in the methods section in Table 6. The scoring of these traits obviously depends on the preferences of the assessor. The average of each sire group for a range of traits at each shearing are presented in Tables 13 to 18. Table 13. Predicted means (se) and statistical significance of sire differences of subjective assessments of style and character (S&C1), fleece evenness (Even1), Lustre (Lust1), head softness (Soft1) and point coverage (Cover1) when animals were six months of age.

Sire Sire name S&C1 Even1 Lust1 Soft1 Cover1 1 Yandiah Texan The General 2.5 2.3 2.7 2.8 3.4 2 Bernelea Blizzard 3.2 3.2 3.5 2.9 3.4 3 Yullungah Park 0-45 2.8 2.8 3.2 3.4 3.1 4 Brenelle 983 2.5 3.0 3.0 2.9 2.6 5 Wilton Park Clarion 2.3 2.3 3.0 2.7 2.6 6 Vermont African Lancelot 3.1 3.1 3.6 3.1 3.0 7 Willean Maddison 2.9 2.9 3.4 3.1 2.8 8 Wilton Park Cassanova 3.0 3.0 3.5 2.5 3.1 9 Currajong Timbillica 3.0 3.0 3.5 2.6 3.3


Mean 2.9 2.9 3.3 2.9 3.0 se 0.14 0.13 0.10 0.09 0.13 Significance of sire NS P<0.01 P<0.001 P<0.001 NS

22

Table 14. Predicted means (se) and statistical significance of sire differences of subjective assessments of birth coat (Coat) and at six months of age; skin pigmentation (Pgmt1), head and horns (HH1), size (Size1) and overall class (Oall1).

Sire Sire name Coat Pgmt1 HH1 Size1 Oall1 1 Yandiah Texan The General 4.0 3.8 2.9 3.0 1.6 2 Bernelea Blizzard 3.7 4.5 3.4 3.3 2.4 3 Yullungah Park 0-45 3.7 3.4 3.7 3.3 2.0 4 Brenelle 983 3.6 3.4 2.8 3.2 1.8 5 Wilton Park Clarion 3.5 3.6 3.2 3.0 1.5 6 Vermont African Lancelot 3.4 3.8 3.1 3.3 2.2 7 Willean Maddison 4.0 3.7 3.5 3.3 2.0 8 Wilton Park Cassanova 3.3 4.5 3.4 3.7 2.2 9 Currajong Timbillica 3.9 3.3 3.2 3.1 2.2


Mean 3.6 3.7 3.2 3.3 2.0 se 0.15 0.16 0.15 0.17 0.12 Significance of sire P<0.001 P<0.001 NS NS P<0.05

Table 15. Predicted means (se) and statistical significance of sire differences of subjective assessments of style and character (S&C2), fleece evenness (Even2), Lustre (Lust2), head softness (Soft2) and point coverage (Cover2) at twelve months of age.



Mean 2.7 3.2 3.3 3.1 3.2 se 0.12 0.13 0.12 0.14 0.15 Significance of sire P<0.01 P<0.05 P<0.01 NS P<0.001

Table 16. Predicted means (se) and statistical significance of sire differences of subjective assessments of skin pigmentation (Pgmt2), head and horns (HH2), size (Size2), feet shape (Feet2) and overall class (Oall2) at twelve months of age.

Sire Sire name Pgmt2 HH2 Size2 Feet2 Oall2 1 Yandiah Texan The General 4.2 2.6 2.9 3.4 2.0 2 Bernelea Blizzard 4.4 3.8 3.7 3.8 2.2 3 Yullungah Park 0-45 3.7 3.4 3.0 3.5 1.7 4 Brenelle 983 3.9 3.6 3.4 4.0 1.6 5 Wilton Park Clarion 3.9 3.7 3.3 4.1 1.9 6 Vermont African Lancelot 3.9 3.8 3.4 3.4 2.0 7 Willean Maddison 4.0 3.7 3.5 4.0 2.3 8 Wilton Park Cassanova 3.9 3.5 3.5 3.7 2.0 9 Currajong Timbillica 3.8 3.4 2.8 4.2 1.9


Mean 4.0 3.5 3.3 3.8 1.9 se 0.08 0.13 0.14 0.14 0.06 Significance of sire P<0.05 P<0.001 NS P<0.05 P<0.05

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Table 17. Predicted means (se) and statistical significance of sire differences of subjective assessments of style and character (S&C3), fleece evenness (Even3), Lustre (Lust3), head softness (Soft3) and point coverage (Cover3) at eighteen months of age.



Mean 2.6 2.9 2.8 3.0 3.2 se 0.15 0.15 0.14 0.12 0.13 Significance of sire 0.011 NS NS 0.043 0.072

Table 18. Predicted means (se) and statistical significance of sire differences of subjective assessment scores of skin pigmentation (Pgmt3), head and horns (HH3), size (Size3), feet shape (Feet3), conformation (Conf3) and overall class (Oall3) at eighteen months of age.

Sire Sire name Pgmt3 HH3 Size3 Feet3 Conf3 Oall3 1 Yandiah Texan The General 3.8 2.6 3.2 3.2 3.4 2.0 2 Bernelea Blizzard 3.9 3.3 3.8 3.9 3.6 2.0 3 Yullungah Park 0-45 3.3 2.8 3.2 3.5 3.5 1.9 4 Brenelle 983 2.9 3.3 4.1 3.8 4.1 1.7 5 Wilton Park Clarion 3.4 3.4 3.5 3.6 3.4 2.0 6 Vermont African Lancelot 3.3 3.1 3.4 3.3 3.3 1.9 7 Willean Maddison 3.3 3.4 3.9 3.6 4.0 2.0 8 Wilton Park Cassanova 3.6 3.1 3.8 3.6 3.8 2.0 9 Currajong Timbillica 3.2 2.7 2.9 3.5 3.2 1.9

10 Yarran Park Rambooka 3.3 3.5 3.4 3.7 3.7 1.9 11 Capricorn 655 3.3 3.0 3.5 3.6 3.6 2.1

Mean 3.4 3.1 3.5 3.6 3.6 1.9 se 0.16 0.13 0.12 0.13 0.14 0.07 Significance of sire NS P<0.01 P<0.001 NS P<0.05 NS

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4.6 Fleece classing and valuation The value of mohair produced by the progeny at each of the shearings is a useful combination of a variety of traits. The values for each sire and differences between sire will vary as mohair prices vary. Classing each fleece into its component parts and then multiplying the weight of each component by the value of that line determined the value of each individual fleece, the predicted means for each sire group were then calculated (Table 19). Table 19. Predicted means of fleece value at the first (Value1), second (Value2) and third (Value3) shearings for each sire and the predicted mean (se) of the group, statistical significance of sire differences, predicted mean for singles, twins, females and wethers.

Sire Sire name Value1 ($)

Value2 ($)

Value3 ($)

1 Yandiah Texan The General 18.21 27.16 19.80 2 Bernelea Blizzard 18.64 26.09 17.83 3 Yullungah Park 0-45 15.93 22.14 19.59 4 Brenelle 983 18.46 22.75 18.95 5 Wilton Park Clarion 17.87 25.00 18.21 6 Vermont African Lancelot 18.47 25.54 18.87 7 Willean Maddison 18.15 29.92 20.89 8 Wilton Park Cassanova 21.62 26.43 18.31 9 Currajong Timbillica 19.07 25.45 15.67

10 Yarran Park Rambooka 18.31 27.39 19.20 11 Capricorn 655 18.85 25.54 19.96

Mean 18.51 25.76 18.84 se 1.83 1.96 1.26 Singles 23.65 27.73 21.92 Twins 17.31 25.16 18.67 Females 17.52 24.58 17.70 Wethers 19.50 26.95 19.98 Significance of sire NS P<0.05 P<0.001

The fibre diameter of mohair largely determines its value and its textile application and performance and is therefore a very important characteristic (Hunter 1993). In Australia mohair classing is generally based on style attributes, which when combined into lines, roughly meet a set of measured criteria including mean fibre diameter. In this system of classing subtle reductions in fibre diameter such as those that could be expected between sire progeny groups are not rewarded with improved value, because on a visual and sensory basis they cannot be differentiated (McGregor and Butler 2004). An analysis of variations in value of Australian mohair sold between 1998 and 2001 by McGregor and Butler (2004) characterised significant price premiums for mean fibre diameter and provided a description of the various discounts and premiums that apply in the Australian market place. Applying the various discounts and premiums determined by McGregor and Butler (2004) to the measured results from each sire group is an alternative way of determining the value of the mohair from each sire group and therefore the sire’s contribution in terms of mohair value. The every goat tested system that operates in South Africa (van der Vyver pers. comm.) based on the every sheep tested system developed by the Australian Wool Testing Authority (Stadler and Gillies 1994) uses measure mid side fibre diameters to determine which line the fibre will be classed into. Determining the value of an animals fleece based on the measured attributes and market premiums and discounts and the animals fleece weight is thought to reward or penalise changes in fleece quality in a way similar to the market place. It is proposed that for animal selection purposes the theoretical fleece value is a more useful indicator of animal superiority than valuation of fleeces by visual classing. The mohair at the first two shearings was very fine and generally outside the normal ranges of fibre sold in Australia (McGregor and Butler 2004) and therefore it is difficult to assign a value on changes in

25

fibre diameter at these already low levels. The third fleece however provided an opportunity to compare the value of the fleece as classed and sold versus a theoretical value based on market premiums and discounts. The rankings of sires for fleece value at the shearing changed considerably depending on the method used to calculate it (Figure 17).

Figure 17. Mohair value at third shearing for each sire group using either visual classing or test data and market premiums and discounts based on McGregor and Butler (2004).

Visual classing ($) 14 15 16 17 18 19 20 21 22 23 24

Test

dat

a an

d m

arke

t val

ue ($

)

10

12

14

16

18

20

22

24

26

28

30

1

2

3

4

5

6 78

9

10

11

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5. Angora Goat Genetics 5.1 Introduction to genetics Genes as we know them are segments of DNA which code for particular functions or traits. There are thousands of genes on each of the thirty pairs of chromosomes that are present in each cell of the Angora goat. These genes combine with the environment to provide a particular phenotype. 5.1.1 Genotype versus phenotype The genetic make-up of an animal is commonly known as its genotype. The phenotype of an animal is what we can see and measure. It is a product of both the genotype of the animal and the environment in which it has been raised. In general, Angora breeders make assumptions about the genotype of the animal based on the seen or measured characteristics of the phenotype of the animal. The more accurately breeders can estimate the genotype of the animal (rather than the phenotype) the faster the rate of genetic gain that can be expected. 5.1.2 Selection for important traits Some traits are controlled by a single gene, such as the presence of horns in goats. In this situation the presence of the horned allele (one of a series of possible alternative forms of a given gene, a single allele for each chromosome location is inherited separately from each parent) which is dominant the goat will have horns, if only polled alleles are present the goat will not display horns. Selection for these traits is generally simple and gains can be quickly made. It can be difficult to select out all of the recessive allele carriers in the population if selecting for the dominant allele and some rare recessive genotypes can be born, black coat colour in Angoras is a good example of this. The majority of traits that Angora breeders are interested in are controlled by a number of genes that combine, together with the environment, to provide a particular phenotypic performance, these traits are known as quantitative traits. When selection is carried out for any quantitative trait, the aim is to select for breeding those animals from the population that have the highest breeding values for that trait with the aim of achieving the highest possible average performance in the offspring of the selected animals. Knowledge of the actual breeding value of an animal for a particular trait would allow maximum accuracy of selection and therefore maximum genetic gain for that trait. In practice however, the true breeding value of an animal for any trait is never known. Instead we need to make assumptions about the breeding value of an animal for a particular trait based on assessments or measurements of the phenotype of either the animal or of its relatives to generate what is known as an Estimated Breeding Value (EBV) for a particular trait. The more information that is generated about an animal the closer the EBV gets to the true breeding value, that is, the accuracy of the EBV increases. 5.2 Selective breeding 5.2.1 The breeding objective Prior to embarking on a breeding program, Angora breeders must first decide on an appropriate breeding objective. A breeding objective should consider: the herd structure, production system and marketing strategy that are being employed; the potential sources of income and expense in the production system; the biological traits that are likely to influence the income or expenses of the production system; and the economic value of each biological trait (Ponzoni and Gifford 1990). Developing a breeding objective involves drawing a list of traits that could be improved, irrespective of the whether it is possible to measure them or not.

27

The sources for income in Angora farming are made up of the product of fleece value and fleece quantity and the value and number of surplus stock. Mohair value is determined by a range of traits and can be increased by reducing mean fibre diameter, improving fibre style, increasing length and reducing the incidence of medullated fibre or kemp (McGregor and Butler 2004). Increasing the size and body weight is important in Angora breeding because of its relationship with reproductive rate in females and its importance in kid survival and growth (Yalcin 1982). Increasing growth rate and size is also is an important part of Angora breeding because of its influence on improving the value of surplus stock. Increasing the reproductive performance of Angoras is desired because it both increases the selection pressure and therefore genetic gain and it increases the number of surplus animals. The main operating expenses in Angora farming are feed, husbandry, harvesting and marketing, these are generally fixed costs. Selection for evenness across the fleece of animals may reduce harvesting and marketing costs because classing fibre at shearing will be less labour intensive and fibre will able to be marketed in fewer lines. Improving the use of feed (improving feed use efficiency) has the potential to reduce the cost of feed. However there is no available information on the genetic variation that exists within the Angora population for this trait. The economic value of each of the biological traits that can be influenced will depend on the production and marketing systems employed and will be specific to each enterprise. Angora breeders need to understand the main factors influencing their profit and determine the relative weight that should be placed on each trait in their selection programs. 5.2.2 Selection criteria Decisions also need to be made regarding the selection criteria that are going to be employed. Selection criteria are the traits that are actually going to be measured on the animals being assessed and/or their relatives. These may be the same as the traits in the breeding objective, or they may be different. There is no limit to what can go into the selection criteria as long as they are measurable and that there are reasonable estimates of the relevant genetic and phenotypic parameters available. For example, the breeding objective may contain the desire to increase reproductive rate, considering this is difficult and slow to assess, scrotal circumference in males may be in the selection criteria with the aim of improving reproductive rate. 5.2.3 Relative economic value of included traits Once the breeding objective and selection criteria have been developed for a breeding program, the relative weight or importance of each trait will receive in the selection process needs to be determined. This is commonly achieved by developing indexes that take into account the heritability of traits in question and the correlations between them. 5.3 Genetic and phenotypic parameters for important traits Any selection program in Angora breeding relies on an understanding and knowledge of the heritability and repeatability of economically important traits. The heritability of a trait is the proportion of the phenotypic variation (visual or measurable difference) that is explained by genetic differences. The higher the heritability of a trait the more rapid the genetic progress is expected from selection for that trait. Repeatability is the degree to which an early measurement is correlated with later measurements from the same trait. It is important because traits that are highly repeatable allow early selection of superior individuals, which enables early use of these animals in breeding. The genotypic and phenotypic correlations that exist between traits also needs to be considered and will determine the expected change to a trait resultant from selection pressure on a related trait. Previous research has been completed estimating the genetic and phenotypic parameters for Angora populations in South Africa (Snyman and Olivier 1999), Texas (Shelton and Basset 1970), Turkey (Yalcin et al. 1979), Australia (Gifford et al. 1991), Argentina (Taddeo et al. 1998), France (Allain

28

and Roguet 2003; Allain and Roguet 2004) and New Zealand (Nicoll et al. 1989). A summary of the estimates of heritability (Table 20) and estimates of repeatability (Table 21) are provided. Table 20. Heritability estimates for production traits including: greasy fleece weight (GFW), clean fleece weight (CFW), washing yield (Yield), mean fibre diameter (MFD), standard deviation of fibre diameter (SD), staple length (SL), percentage of kemp fibres (Kemp%), percentage of medullated fibres (MEDN), birth weight (BWT), weaning weight (WWT), live weight (LW), subjectively assessed kemp (Kemp), softness, face cover, pigmentation (Pigm.), neck cover, style, character, evenness, bellies and points (B/P) from a range of authors and an average of the estimates.

Trait Author Average 1 2 3 4 5 6 7 8 9

GFW 0.22 0.22

0.36 0.1

9 0.13

0.40

0.26

0.42 0.28

CFW 0.12

0.20 0.4

1 0.24

Yield 0.43

0.48 0.0

2 0.31

MFD 0.30 0.29

0.51

0.50

0.32

0.19

0.11

0.33

0.08 0.29

SD 0.21 0.21

SL 0.18

0.12

0.79 0.1

3 0.28

Kemp% 0.02

0.35 0.19

MEDN 0.16

0.26 0.1

0 0.39 0.23

BWT 0.21 0.21

WWT 0.10 0.1

7 0.14

LW 0.35 0.47

0.24 0.2

4 0.50 0.2

2 0.34

Kemp 0.01 0.32 0.1

3 0.37 0.4

3 0.42 0.28

Softness 0.07 0.33 0.20

Face cover 0.33 0.66 0.1

4 0.38

Pigm. 0.43 0.49 0.46

Neck cover 0.13 0.33 0.23

Style 0.13 0.23 0.18

Character 0.14 0.34 0.24

Evenness 0.26 0.16 0.21

B/P 0.30 0.30 0.30

1. Snyman and Olivier (1999) – South African stud Angoras 2. Snyman and Olivier (1999) – South African commercial Angoras 3. Nicoll et al. (1989) 4. Allain and Roguet (2004) 5. Allain and Roguet (2003)

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6. Yalcin et al. (1979) 7. Shelton and Bassett (1970) 8. Taddeo et al. (1998) 9. Gifford et al. (1991) With few exceptions the economically important traits have medium to high heritabilities (greater than 0.2) and gains can be expected for selection for these traits. There is variability between authors, which is due both to different populations, and different levels of error within the calculations. Repeatabilities are generally low to medium for the important traits. However, most authors recorded a medium to high repeatability for MFD which means early selection for reduced fibre diameter should be possible. Table 21. Repeatability estimates for production traits including: greasy fleece weight (GFW), clean fleece weight (CFW), washing yield (Yield), mean fibre diameter (MFD), staple length (SL), percentage of medullated fibres (MEDN), birth weight (BWT), weaning weight (WWT), live weight (LW), subjectively assessed kemp (Kemp), softness, face cover, pigmentation (Pigm.), neck cover, style, character, evenness, bellies and points (B/P) from a range of authors and an average of the estimates.

Trait Author Average 1 2 3 4 5

GFW 0.41 0.27 0.40 0.56 0.45 0.42 CFW 0.49 0.44 0.47 Yield 0.64 0.08 0.36 MFD 0.68 0.35 0.72 0.62 0.30 0.59 SL 0.35 0.03 0.19 MEDN 0.62 0.39 0.51 BWT 0.27 0.27 WWT 0.33 0.33 LW 0.53 0.63 0.62 0.18 0.49 Kemp 0.29 0.25 0.20 0.25 Softness 0.32 0.31 0.32 Face cover 0.37 0.60 0.19 0.39 Pigm. 0.62 0.62 0.62 Neck cover 0.26 0.39 0.33 Style 0.24 0.17 0.21 Character 0.35 0.39 0.37 Evenness 0.23 0.13 0.18 B/P 0.22 0.12 0.17

1. Snyman and Olivier (1999) – South African stud Angoras 2. Snyman and Olivier (1999) – South African commercial Angoras 3. Yalcin et al. (1979) 4. Taddeo et al. (1998) 5. Gifford et al. (1991)

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Table 22. Genetic and phenotypic correlations between important production traits. Phenotypic correlations are below the diagonal, genetic correlations are above the diagonal. LW GFW MFD Yield SL Medullation Kemp

LW

0.67 a

-0.24b

-0.26e 0.17 f

0.56a

0.19b 0.00 e 0.14 f

-0.29 e 0.33 f

-0.32 e 0.24 f 0.35b

GFW

0.57a 0.54b

0.39c

0.10e 0.19f

0.55a

0.98b

0.75 e -0.28 f 0.51g 0.35h

0.02 e -0.33 f

-0.33 e 0.39 f 0.25 h

-0.72g 0.57 h

MFD

0.55a

0.37b

0.23c

0.13 e 0.26 f

0.57a

0.55b 0.37c 0.22 e 0.14 f 0.64 h

-0.02 e 0.27 f

-0.68 e 0.01f

0.30d

-0.18g 0.44d -0.09 h

Yield -0.07c 0.10 e 0.07 f

0.11c -0.18 e -0.18 f

0.30c 0.13 e 0.14 f

0.90 e 0.68 f

SL 0.21c 0.04 e 0.05 f

0.40c 0.18 e 0.34 f 0.11 h

0.28c 0.11 e 0.20 f 0.07 h

0.18c 0.44 e 0.22 f

0.12 h

Medullation 0.10b -0.04c 0.14c 0.39c

0.23d 0.13c 0.01c 0.62d

Kemp 0.11b 0.18c

-0.18c -0.14 h

0.00c 0.41d -0.23 h

0.04c -0.04c 0.14 h

0.05c 0.33d

* Phenotypic correlations are below the diagonal, genetic correlations are above the diagonal. aSnyman and Olivier (1996) bNicoll et al. (1989) cGifford et al. (1991) dAllain and Roguet (2004) eShelton and Bassett (1970) fYalcin et al. (1979) gTaddeo et al. (1998) hAllain and Roguet (2003) There is a positive and strong phenotypic and genetic correlation between MFD and GFW and selection for lower fibre diameter will result in a decrease in greasy fleece weight unless selection pressure is also placed on fleece weight. Likewise the positive correlation between live weight and mean fibre diameter needs to be considered when selecting animals.

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5.4 Estimated breeding values 5.4.1 Introduction to estimated breeding values An estimated breeding value (EBV) is an estimate of the genetic worth, or merit, of an animal for a particular trait. It is a picture of an animal’s genes for that trait. EBVs are expressed as a deviation from zero. If a sire producers progeny that have a higher than average value for any particular trait – eg higher than average fleece weight, the EBV will be above zero. Conversely, if a sire produces progeny that have a lower than average value for any particular trait – eg lower than average fibre diameter the EBV will be below zero ie a negative value. EBV’s are calculated by multiplying the estimated superiority of an animal for a particular trait by the heritability of that trait. EBV (Fleece weight) = Heritability (Fleece weight) x Estimated superiority (Fleece weight) EBVs are a more accurate indicator of a sire’s relative genetic merit than simple sire averages as they take into account:

1. the heritability of the trait, i.e., how much of the superiority is actually due to the sire’s genes and can be passed on to its progeny

2. the number of progeny a sire has in the analysis 3. the effects of birth type, rear type, age of dam, management etc 4. the measurements of other traits. Where two traits are affected by the same genes (i.e., the

traits are genetically correlated) the progeny records for both traits give us additional information to make the EBVs for both traits more accurate.

5.4.2 Estimated breeding values of entered sires EBVs for important production traits were calculated for the data collected in this project. In addition to the eleven sires entered, four sires used in the RIRDC project “Breeding for helminth resistance in fibre goats” conducted near Armidale, NSW were used in the analysis of EBV’s. This was facilitated by the use of two link sires at both locations (Vermont African Lancelot and Capricorn 655).

Example The average of a management group’s fleece weight is 3kg at the third shearing. At the third shearing animal A has a fleece weight of 4.2kg.

Animal A has an estimated superiority of: 4.2 kg – 3.0 kg = 1.2 kg. The heritability of fleece weight in Angoras is estimated to be 0.28, see Table 20. That is 0.28 or 28% of what is measured is actually due to genetics and can be passed on to progeny. The EBV for fleece weight for animal A is therefore: 1.2 kg x 0.28 = +0.34 kg.

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Table 23. Number of progeny (n) analysed for each sire, estimated breeding value (EBV) of each sire for birth weight (BWT), weaning weight (WWT), post-weaning weight (PWT), yearling weight (YWT), hogget weight (HWT), fat depth at the C-site at hogget age (HCF) and eye muscle depth at the C site at Hogget age (HEMD).

Sire Sire name n BWT WWT PWT YWT HWT HCF HEMD kg kg kg kg kg mm mm

1 Yandiah Texan The General 30 0.0 0.3 0.2 0.0 0.0 -1.0 -0.6 2 Bernelea Blizzard 31 0.1 0.1 0.2 0.4 -0.9 0.2 0.0 3 Yullungah Park 0-45 17 0.1 0.0 0.4 0.6 0.7 -0.6 -0.2 4 Brenelle 983 33 -0.1 -0.2 0.6 -0.1 1.0 0.1 0.3 5 Wilton Park Clarion 32 0.2 0.4 -0.7 0.4 0.8 -1.1 0.0 6 Vermont African Lancelot 84 0.0 -0.6 -0.8 -2.4 -3.4 0.2 0.2 7 Willean Maddison 28 0.0 0.2 0.5 2.7 3.0 -1.3 0.7 8 Wilton Park Cassanova 35 -0.2 1.4 1.4 1.7 2.4 -1.0 -0.4 9 Currajong Timbillica 32 0.0 -0.6 -1.0 -1.6 -2.9 1.5 -0.4

10 Yarran Park Rambooka 30 -0.1 -1.4 -0.8 -0.2 -0.6 0.5 -0.5 11 Capricorn 655 52 0.1 0.3 -0.2 -0.2 0.3 -0.4 -0.1 12 Ancor Sheehan 21 -0.2 1.6 1.2 1.0 1.1 -0.3 -0.1 13 Yarran Park 908 24 0.1 0.0 0.3 0.3 -0.2 -0.3 -0.1 14 Phezulu Jay Jay 23 0.0 -1.3 -1.5 -1.1 -1.4 -0.3 -0.1 15 Saatech Park Denver 35 0.0 1.5 2.0 2.2 2.7 -0.3 -0.2 Table 24. Estimated Breeding Value of each sire for second greasy fleece weight (GFW2), third greasy fleece weight (GFW3), second clean fleece weight (CFW2), third clean fleece weight (CFW3), second mean fibre diameter (MFD2), third mean fibre diameter (MFD3), second co-efficient of variation of fibre diameter (CV2), third co-efficient of variation of fibre diameter (CV3), second mid-side staple length (MSSL2) and third mid-side staple length (MSSL3).

Sire Sire name GFW2 GFW3 CFW2 CFW3 MFD2 MFD3 CV2 CV3 MSSL2

MSSL3

kg kg kg kg µm µm % % cm cm 1 Yandiah Texan The General 0.1 0.2 0.1 0.2 0.2 -1.0 1.4 1.8 0.0 -1.1 2 Bernelea Blizzard 0.0 -0.1 0.1 -0.1 1.4 1.5 0.3 -0.1 -0.1 -0.6 3 Yullungah Park 0-45 0.0 0.0 0.0 0.0 -0.2 -0.1 -0.6 -1.5 -0.9 -0.6 4 Brenelle 983 -0.1 0.1 0.0 0.2 1.5 3.2 -1.3 -2.8 0.6 1.1 5 Wilton Park Clarion -0.1 0.0 -0.1 0.0 -1.7 -1.3 0.7 1.8 -0.6 -0.3 6 Vermont African Lancelot 0.0 0.0 0.0 0.0 -0.6 -0.2 -2.7 -2.5 -0.5 0.6 7 Willean Maddison 0.1 0.3 0.1 0.2 0.5 0.5 -0.8 -0.2 -0.7 -0.8 8 Wilton Park Cassanova 0.0 0.0 0.0 0.0 1.1 0.7 -1.7 -1.1 -0.5 0.4 9 Currajong Timbillica -0.1 -0.2 0.0 -0.2 -1.1 -1.6 1.4 1.9 -0.3 -0.2 10 Yarran Park Rambooka 0.0 0.1 0.1 0.1 -0.5 0.2 0.1 -0.3 0.9 0.5 11 Capricorn 655 0.0 0.1 0.0 0.0 -0.9 -1.8 1.1 1.6 0.2 -0.6 12 Ancor Sheehan -0.2 -0.2 -0.2 -0.2 -0.2 -0.3 -0.7 -1.1 -0.2 0.0 13 Yarran Park 908 0.1 0.0 0.1 0.0 -1.2 -2.1 1.0 0.9 -0.3 -0.7 14 Phezulu Jay Jay -0.2 -0.3 -0.1 -0.1 1.2 1.5 -1.5 -1.2 -0.6 -0.5 15 Saatech Park Denver 0.2 0.2 0.2 0.1 3.3 3.9 -1.9 -2.7 1.8 1.7

A useful way of viewing EBVs is graphing them against each other to select animals that are superior in two traits. Angora breeders generally aim to breed animals that produce more fibre that is finer. Figure 18 plots the EBVs for each sire for third mean fibre diameter and third clean fleece weight against each other. Animals in the top left corner of the graph are finer than average and produce heavier fleece weight than average. The sire numbers on the graph correspond to the various sires, the sire codes for each sire are shown in Tables 23 and 24.

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10

12

1

13 11

7

8

154

214

6

9

35

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0

Third mean fibre diameter (µm)

Thi

rd c

lean

flee

ce w

eigh

t (kg

Figure 18. Third mean fibre diameter EBV and third clean fleece weight EBV for all sires. Processors generally pay higher prices for fibre that is both fine and long, it is therefore important for breeders to produce animals that combine these attributes. Figure 19 displays the EBVs for third staple length and third mean fibre diameter for each sire.

10

12

1

1311

7

8

15

4

214

6

9

3

5

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0


Thi

rd s

tapl

e le

ngth

(cm

Figure 19. Third mean fibre diameter EBV and third staple length EBV for all sires.

34

Body size is an important trait in Angora goat breeding, due to the relationship between live weight and reproductive performance of does (Yalcin 1982) and the fact that faster growing, larger animals have a higher carcass value. Animal growth and live weight are considered important among mohair producers. Heavier animals obviously have a higher value as meat animals but perhaps more importantly there are positive correlations between live weight and survival and reproduction traits. The breeding objective of mohair producers often includes selection toward large animals that also grow fine mohair. Figure 20 shows the EBV for hogget live weight and the EBV for mean fibre diameter at the third shearing for all sires.

5 3

96

142

4

158

7

11

13

1

12

10

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0


Hog

get l

ive

wei

ght (

kg

Figure 20. Third mean fibre diameter EBV and hogget live weight EBV for all sires. 5.4.3 Using indexes based on EBVs Estimated breeding values are most easily understood when combined into a single EBV, these combined EBVs are commonly known as indexes. Indexes combine the traits in a way that allows the most desirable animals (depending on which index is used) to have the highest value. There is currently no suitable index available for Angora goats in use in Australia, however Kidplan will be able to define custom indexes if the Angora data in their databases is increased through use by Angora breeders.

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6. Model for Progeny Testing Angora Sires 6.1 Site selection As far as is practical, a site should be selected that is accessible to many mohair producers who may be interested to visit the site and view the progeny and should therefore be an area central to mohair producers. The site owners must be willing to run the progeny in a single group or small number of groups until their fourth shearing. The site owners must be willing to run both wether and female progeny until they are two years old. It is imperative that the site has the necessary facilities to handle the number of goats required to run the trial in accordance with the code of practice for the welfare of goats (Anon. 2001). 6.2 Doe selection 6.2.1 Number of does The number of does required depends on whether artificial insemination or natural mating is going to be used, the number of sires to be evaluated, and whether intensive kidding management is going to be used. The accuracy of the assessment of the genetic merit of an individual sire by progeny testing is a function of the heritability of the trait and the number of the sire’s progeny assessed for that trait. This is important in planning the required number of progeny from each sire and hence the number of does to be joined to each sire. The heritability of most production traits in mohair goats are between 0.2 and 0.4. MOPLAN uses heritability estimates of 0.25 for first fleece weight and 0.35 for second fleece weight and 0.2 for fibre diameter. Based on these values it is estimated that a minimum of 20 progeny per sire is required to get an acceptable level of accuracy, and the aim should therefore to get 25 progeny weaned. Artificial insemination conception rates are variable and difficult to predict. Conception rates of 60-70% are common with 40% conception being a minimum in most cases. The minimum number of weaned kids from each sire should be 25, the estimated number of does required is shown in Table 25. Calculations assume a 50% conception to AI (conservative estimate), 95% conception to natural mating (assumes 3 cycles), a twinning rate of 40% in natural mating and 80% in AI mating and 90% survival if intensively kidded and 60% survival if extensively kidded from AI mating and 70% survival if extensively kidded from natural mating. Table 25. Estimated number of does required to be joined depending on the system of kidding to be used, the number of sires and the method of joining (see text for assumptions).

Intensive Kidding

Extensive Kidding

Number of Sires Natural mating

AI Natural mating

AI

4 84 124 108 188 5 105 155 135 235 6 126 186 162 282 7 147 217 189 329 8 168 248 216 376 9 189 279 243 423

10 210 310 270 470 12 231 341 297 517 13 252 372 324 564 14 273 403 351 611 15 294 434 378 658

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6.2.2 Allocating does to sires Does should be selected to be as uniform group as possible, that is, they should be similar age and similar genetic background. Preference should be given to does that have a known pedigree. This pedigree can be used in the analysis and will allow for more accurate determination of the genetic worth of the tested sires.

Figure 21. Drafting does into groups of similar types. The allocation of does to each sire is an important foundation of the trial and should be balanced so that each sire gets an even representation of doe ages and genetic background. This should be completed by sorting does into similar groups based on genetics and doe age and then either randomly allocating each group either by randomly drafting the group into as many sub-groups as there are sires or by randomly allocating by tag number. It is important that each group of does to be mated to each sire has a similar composition as the original whole group (Figure 22). Figure 22. Schematic diagram of method of forming each sire group.

Original mob of mixed age and genotype

Separate mob into its component parts (age groups, genotype etc)

Sire groups with even representation from

each sub-group

37

6.2.3 Identification of does Care should be taken to ensure all does selected for use in the evaluation have unique identification (ear tags). If possible duplicate identification is ideal so that lost tags don’t result in lost data and progeny that are un-identifiable to a sire. A tattoo is a very effective way of ensuring all does can be identified. A discussion on ear tagging goats is provided elsewhere (McGregor 2001a). 6.3 Sire selection The sire selection process will be governed by the objectives of the evaluation site. The normal process is to call for nominations from industry and then select the required number of sires from the nominated list. It should also be remembered that for the data to be able to collated with information from this project and other sire evaluation sites, at least two sires that were used in this project must be used in other sire evaluations to provide genetic linkage. Semen from these “link” sires will need to be purchased from the owners of the bucks. Breeders who enter sires are responsible for collecting semen and providing it to the sire evaluation site at no cost. 6.4 Joining Natural mating or artificial insemination (AI) can be used depending on the resources available. If natural mating is to be used, several small secure paddocks will be required to carry out single sire matings. A five-week joining period should be used. If natural mating is used, careful management of the animal health status of entrant bucks will be of critical importance. The use of artificial insemination allows the use of bucks from a wide area. A qualified AI technician should be employed to carry out the work. The method of AI (trans-cervical or laproscopic) will depend on the preference of the employed technician. If back-up sires are to be used by natural mating for those animals that do not conceive to the AI, sufficient linkage between the AI program and natural mating program will be required. That is, at least 2 of the bucks used in the AI program will also need to be used in the natural mating back-up program to allow accurate analysis of the resultant data. The management of large AI programs requires careful planning and considerable labour to ensure a high possibility of success, and consideration should be given to the ability to meet these requirements.

Figure 23. Paul Hamilton performing trans-cervical artificial insemination with frozen semen.

38

6.5 Doe management Does should be managed following best-practice management regimes, the condition of the does should be maintained around condition score 3 if possible to allow for the genetic potential of the growing foetus to be met, see McGregor (2001b; 2001c). After mating has been completed all does should be run as a single flock. If it is unavoidable that does need to be run in more than one group, equal representation from all of the sire groups should be present in each sub-group. Does should be pregnancy scanned to determine the pregnancy status. The scanning operator should be asked to differentiate between single and multiple foetuses and determine the age of the foetus if both AI and natural mating have been used. The does that are identified as dry or not kidding to the tested sires can be removed from the herd to make management easier if desired. 6.6 Kidding Kidding is one of the most important components of the progeny test, and careful recording at this time will enhance the use of information collected later. At kidding accurate identification and recording of birth type, ie single, twin or triplet is very important as are recording of dam and sire information. It is preferable for all does to be run together over kidding because differences in doe nutrition in pregnancy can alter the performance of the kids for their lifetime (McGregor 1995; McGregor 1998). If does do need to be separated, the time apart should be minimised as far as possible. 6.6.1 Kidding system Kidding can either be carried out in an extensive or an intensive manner. It will depend on the facilities available and the preferences of the property owner as to the system to be employed. Considerable thought should go into the most appropriate process and the appropriate preparations made to ensure all is organised prior to the peak of kidding, especially if an AI program has been used. Operators must have experience of kidding. All required numbered tags must be on hand prior to kidding. Kid survival is an important trait in Angora breeding. If information on kid survival from each sire is required, the kidding system has to reflect normal commercial practice. Intervention in terms of intensive kidding systems increases kid survival but will remove the variation between sires in kid survival. There is a trade-off between having enough kids alive to make a valid assessment of performance traits and also gathering information on kid survival, which needs to be considered prior to mating. 6.6.2 Recording and identification at kidding Documenting the required records is best completed soon after the doe has kidded. The kids should be weighed and tagged and their sex and birth type recorded. Kids that die at kidding should also be weighed and the sex recorded. If does are being moved into individual pens, recording of the details of the kids can be carried out at the same time. If does are remaining in the paddock, a team of two should, either by foot or by a vehicle that is familiar to the does, approach the doe and new born kid/s slowly. The doe tag number should be noted and the kid caught, tagged and weighed and the appropriate information recorded. The kid/s should then be left at the birth site, and most does will return once the operators have left. Some does may need to be persuaded to return back to their kids! The tags used for identifying kids should have a unique sequence and care should be taken to ensure that they would be easy to read for the next 18 months. Tag read errors are likely to be a major source of errors if small tags of dark colour are used.

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6.7 Progeny management Best practice management should be followed for the progeny (Anon. 2001) and as far as possible they should be run as one herd for the duration of the evaluation. If this is not possible, care should be taken when deciding on sub-herds to ensure equal representation of each sire group in each of the sub-herds and tag numbers should be recorded of animals in each sub-herd for later use in the analysis of generated data. Faecal egg counts need to monitored in times when worm infestations are likely. All deaths should be recorded with a short explanation of the most likely cause of death. 6.7.1 Marking Depending on the requirements of the site owner and project committee male kids can either be left entire or castrated. Whatever the decision all animals must be treated the same. That is, either all entire or all castrated, leaving some entire and some as wethers will reduce the usefulness of generated data. If the decision is made to castrate males, this should be completed between 4 and 8 weeks of age. All kids should be weighed and vaccinated at this point. This is also an opportunity to tattoo a permanent number into the ear of all progeny to ensure identity is maintained if an ear tag is lost or damaged. 6.7.2 Weaning All animals should receive their second vaccination at weaning and should be weighed, depending on the area. A drench may also be required at this time. Weaners should be placed on good feed if available. If it is likely that they will need to be supplementary fed over summer, some of the supplement to be used should be offered to the kids while still on their mothers in the weeks leading up to weaning to allow them to become accustomed to the supplement. Depending on the circumstances crutching and wigging may also be carried out at or around this time. 6.7.3 Post weaning It is preferable to run kids together however, wethers (castrated males) and females can be run separately following weaning. If males have been left entire, they will obviously need to be managed separately. Animals should be hoof trimmed as required and crutched between shearings. 6.8 Data collection 6.8.1 Live weight Birth weight is most simply measured by placing the newborn kid in a bucket and using a set of accurate clock-face scales connected to the handle of the bucket to weigh the animal, care should be taken to ensure the scales are tared to account for bucket weight. Accurate animal scales should be used to take all other live weight measurements. Before weighing and at regular intervals throughout, a check weight should be used to ensure the scales are weighing accurately. Animals will lose weight as they empty out when taken off feed. Care should be taken to ensure that all animals are weighed in as short as time as possible to reduce the differences in gut fill. A similar process should be followed at each weighing to ensure the live weights are repeatable. That is, if animals are weighed straight out of the paddock at the first weighing, they should be weighed straight out of the paddock at each subsequent weighing. Live weight is required to be taken at or around each shearing. This weight can either be taken pre-shearing and then the fleece weight subtracted from the weight following shearing or at least a week off shears. Weighing immediately following shearing may provide inaccurate data because of the differences in time the animals have been off feed and water. The minimum requirements for live weight recording are shown in Table 26.

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6.8.2 Subjective assessment Subjective assessment is used to score the progeny on traits that are not measurable. This provides useful information for breeders who have not seen the progeny. Subjective assessment should be completed close to shearing so that the classer can view the animals in full fleece. An experienced animal classer should be used to score the traits that have been selected as important by the management committee, if possible the same classer should be used at all shearings so that there is some consistency. The traits that are assessed should be selected from those studied in this project or there may others that the management committee think are important. 6.8.3 Fleece measurements A fibre sample should be collected prior to each shearing from the mid side of each animal (Figure 24). A sample of at least 20 grams should be removed at skin level using either a shearing handpiece or Oster animal clippers. Three staples should be randomly selected from the sample and measured for staple length to the nearest 5mm using a ruler. The sample should then be submitted for measurement at a testing laboratory that uses an OFDA100 and is equipped to measure mohair including medullation. The laboratory should be asked to provide: mean fibre diameter, standard deviation of fibre diameter, coefficient of variation of fibre diameter, medullation percentage and washing yield. At shearing, animals should be shorn in random order, the entire fleece of each animal should be weighed by scales accurate to at least 0.05 kg.

Figure 24. Mid side sampling site on the last long rib. 6.8.4 Fleece value Following measuring greasy fleece weight the fleece should be classed to industry standards. Each component part of the fleece should then be weighed and the line it is assigned to recorded with the weight. Based on average sale data, a value should then be given to each line. Adding together the values of the component parts will provide an overall fleece value for each individual animal. The classer used needs to have considerable experience and should preferably be the same person at each of the shearings of the progeny to provide some continuity of classing standards. The process of maintaining the identity of the fleece from the shearing board to the classing bins requires vigilance, especially if shearing and classing are occurring at slightly different rates as is often the case. The simplest method to achieve this is to write the animals tag number on a card as it is being shorn, this card then stays with the fleece throughout the weighing and classing process, fleece weights and classing results can be written on the card and then cards are collated at the end of the classing process.

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6.8.5 Measurement calendar The data collected during a central sire progeny test will vary depending on the preferences of the management committee, however there is a minimum data set that should be collected in all circumstances so that data can be combined Table 26. Minimum requirements for measuring progeny. Measurement Event Age Birth weight Kidding <12 hours Marking live weight Marking 4 to 8 weeks Weaning live weight Weaning 12-16 weeks 6 month live weight First Shearing 6 months 1st Subjective assessment First Shearing 6 months 1st Mean fibre diameter First Shearing 6 months 1st Standard deviation of fibre diameter First Shearing 6 months 1st Medullation percentage First Shearing 6 months 1st Staple length First Shearing 6 months 1st Washing yield First Shearing 6 months 1st Greasy fleece weight First Shearing 6 months 1st Fleece value First Shearing 6 months 12 month live weight Second Shearing 12 months 2nd Subjective assessment Second Shearing 12 months 2nd Mean fibre diameter Second Shearing 12 months 2nd Standard deviation of fibre diameter Second Shearing 12 months 2nd Medullation percentage Second Shearing 12 months 2nd Staple length Second Shearing 12 months 2nd Washing yield Second Shearing 12 months 2nd Greasy fleece weight Second Shearing 12 months 2nd Fleece value Second Shearing 12 months 18 month live weight Second Shearing 18 months 3rd Subjective assessment Third Shearing 18 months 3rd Mean fibre diameter Third Shearing 18 months 3rd Standard deviation of fibre diameter Third Shearing 18 months 3rd Medullation percentage Third Shearing 18 months 3rd Staple length Third Shearing 18 months 3rd Washing yield Third Shearing 18 months 3rd Greasy fleece weight Third Shearing 18 months 3rd Fleece value Third Shearing 18 months

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7. Discussion of Results 7.1 The model This project has provided a model for progeny testing sires in the mohair industry. The model can be applied at any location to provide a valid comparison between sires. The project has also provided a database of tested sires that can be built on by industry by either further sire evaluation or use of the links generated in this project to form the basis of an on-farm performance recording database. This project generated considerable interest among industry participants through site field days and presentations at a range of industry events. 7.2 Sire differences The results of this evaluation show that considerable variation exists for the economically important traits in the Angora sires in use in the Australian mohair industry. Weaning weight is an indicator of early growth and vigour, there was a difference of 2.8 kg between the estimated breeding value of evaluated sires for this trait. The differences were larger for the EBV of yearling weight (5.1 kg range) and hogget weight (6.4 kg range), which would equate to large differences in carcass value of progeny. The main focus of Angora breeding is on improving fibre production characteristics. There was a large difference in the EBV of sires for the important fleece traits. A difference of 0.4 kg and 0.6 kg between sires was shown for the second greasy fleece weight and third greasy fleece weight respectively. Fibre diameter is the most important fibre quality trait and there was a 5 µm and 6 µm difference of EBV of sires evaluated for this trait. A difference of 2.7 cm and 2.8 cm of EBV of sires for staple length also contributed to the large differentiation seen between entered sires. Importantly there were some sires that were superior in terms of EBV for more than one trait and selection and use of these sires will result in genetic gain across the national herd. As would be expected with such large differences in the important production traits there were considerable differences between fleece value of progeny. 7.3 Financial implications The maximum differences between sires were $5.69, $7.17 and $5.22 for the first, second and third shearings respectively, based on industry classing and auction value. These improvements, when multiplied by the large number of progeny that sires normally produce, result in large differences in profitability of mohair production. The third fleece provided an opportunity for the fibre to be valued utilising equations based on premiums and discounts that occur on Australian mohair sold at auction. When valued using this method, there was around a $9.00 difference between sires at the third shearing. 7.4 Impact of seasonal conditions Although the poor seasonal conditions hampered the project, a successful evaluation of eleven industry sires was completed. There is interest in the impact that the drought seasons had on the actual production levels achieved by the goats. Fortunately there has been research conducted with Australian Angora goats on the impact of severe nutrition on fleece development and quality (McGregor 1995; McGregor 1998). The likely impacts can be summarised into three areas: pregnancy and the first six months; the second and third fleeces; and assessing genetic impacts.

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7.4.1 Pregnancy and the first six months Poor nutrition during pregnancy and lactation is likely to have reduced the live weight of kids up to the age of 9 months (McGregor 1995). Poor nutrition is likely to have also affected skin follicle development by reducing the number and density of secondary skin follicles and reducing the fleece weight at first shearing (McGregor 1995). The effects of the reduced skin follicles are unlikely to be seen at the first shearing as lactation and rearing effects have the greatest effect until weaning and the period from weaning to shearing is a transition phase for weaned goats (McGregor 1995). 7.4.2 The second and third fleeces At this time two opposite effects come into play. The first is that the goats were smaller than would normally be expected and so the correlation between live weight and mean fibre diameter indicates that, on average, the mean fibre diameter would be less than normal. This is certainly the case for the second fleece. As the goats grew, the impact of poor nutrition during pregnancy and lactation on their skin follicle development would begin to show in fleece production. These impacts include increased mean fibre diameter and a higher incidence of medullated fibres (McGregor 1995). Given the severity of the drought during the winter of 2003, most of these impacts would be more clearly seen in the third fleece. 7.4.3 Assessing genetic impacts The nutritional impacts on the phenotype or appearance of the goats have no influence on the genotype of the goat. For example, let us say that as a consequence of the drought, all the progeny in this project produced fleeces that were 2 µm coarser than under normal conditions. Such a result would have no influence on the genetic performance of these goats. The goats look and measure coarser than normal but they will still produce normal kids under normal conditions. This is one reason why breeders who only assess goats on what they look like are often mistaken, as they frequently do not know the entire history of all their goats. Just because the goats in this project were subject to difficult conditions and may have grown poorer fleeces than under normal conditions does not mean that they will breed progeny with poorer fleeces. There is one situation where the relative ranking of the sires could be affected by conditions. This occurs when the phenotype or appearance of the progeny is affected by genotype x environment (G x E) interactions. In other words there may be some bucks whose progeny do relatively badly under harsh conditions and perhaps the progeny do relatively much better under excellent conditions. If this happens in Angora goats then it follows that:

1. Angora sires need to be assessed in a range of environments to quantify the extent and direction of G x E interactions;

2. The poor ranking of a sire in the present project does not mean it will perform poorly in all environments; and

3. If you buy bucks then you should assess or seek assessments of the bucks in the environment in which they will be used.

We have been unable to locate any reliable data about the extent of G x E interactions in Angora goats.

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8. Implications for Industry Widespread industry use of the most profitable sires identified in this project will result in an increase in profitability of Australian mohair enterprises. The improved returns for producers using superior sires can be estimated by utilising a simple model. The following table demonstrates the net present value of the extra income generated from using a superior sire rather than an existing sire. There is a range of net present values based on a range of average mohair prices (clean) and a range of possible reductions in mean fibre diameter, based on a 10% fibre diameter premium. A diameter premium is defined as the percentage increase in price received per kilogram if the mean fibre diameter is reduced by 1 µm. Table 27 demonstrates different profitability scenarios of genetic superiority of a new buck over one that is currently in use. The figures demonstrate the large increase in profitability that can be realised by relatively small changes in production characteristics bought about by using superior sires. Assumptions:

• Sire mates 80 does each year for 5 years • Weaning rate of 110% and survival rate of 95% across all age groups • Average clean fleece weight across flock of 3 kg per annum • A 10% micron premium • Discount rate of 10% to determine net present value

Table 27. Estimated net present value of extra profit across a range of average clean prices received resulting from using a sire that produces progeny with the same fleece weight but are 1, 2, 3 or 4 µm finer than an existing sire. The highlighted area indicates the most likely scenarios.

Average price received for mohair ($/kg clean) Mean fibre diameter reduction

$ 6.00 $ 8.00 $ 10.00 $ 12.00 $ 14.00 $ 16.00

1 µm $1,340 $1,786 $2,233 $2,680 $3,126 $3,573 2 µm $2,680 $3,573 $4,466 $5,359 $6,252 $7,145 3 µm $4,019 $5,359 $6,699 $8,039 $9,378 $10,718 4 µm $5,359 $7,145 $8,932 $10,718 $12,505 $14,291

The evaluation of further sires, with links to the current data would substantially enhance the value of this information, and result in more efficient identification and use of superior sires in the industry. Australian mohair producers are now aware of the large genetic differences that exist between Angora sires in use in industry and the impact these differences have on the profitability of production of mohair.

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9. Recommendations The information generated by this project should from a foundation for further genetic evaluation in the mohair industry. While further central sire evaluations sites would generate some very useful information, much wider evaluation and more rapid genetic gain will be realised through the encouragement of on-farm evaluation and central recording and analysis. The industry needs to decide on a genetic evaluation provider and encourage interested breeders to collate the information and forward to the provider for analysis. The data from this project would be particularly useful in providing a genetic link between properties because of the wide genetic pool that was evaluated.

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10. Appendices Appendix 1. Sire nomination form. Mohair Sire Evaluation – Sire Selection Protocol An advisory committee guides the mohair sire evaluation project. This committee is made up of representatives from the Victorian Farmers Federation, Department of Primary Industries, Mohair Australia Ltd (representatives from each state) and the property owners. The advisory committee is responsible for sire selection. This document outlines the process to be followed for the selection of sires into the mohair sire evaluation. Objectives of the Sire Evaluation The relevant stated objectives are:

• To comprehensively evaluate Angora sires, including a demonstration of their economic merit and performance characteristics.

• To demonstrate the potential impact on profitability of tested sires of superior genetic merit. Principles:

• The overall group of sires selected will be of the highest genetic and economic merit possible based on performance characteristics.

• The selected group will have an adequate mix of bucks from South African and Texan bloodlines (possible inclusion of Australian genetics) and will be from a wide geographical distribution.

• There will be no age limit set on bucks gaining entry, however preference will be given to younger bucks rather than old sires that have already been frequently used in industry. Those entering old bucks will need to collect sufficient semen to cover point 7.

• Bucks must be entered by the breeder of the animal and bucks must be currently registered or able to be registered by the start of the trial with Mohair Australia Ltd.

• If too many sires are entered, the advisory committee will be responsible for selection of those bucks to be used, based on the above stated principles.

• It is noted that it is difficult to compare data, such as fleece weight and fibre diameter, obtained from animals in different environments and this data will be treated as indicative and not absolute.

• Entrants will be required to pay for semen collection and transport costs and will be required to guarantee in writing, that semen (200 doses) will be available for sale (by public auction) at the conclusion of the trial.

• Bucks selected for use in the trial will be required to test negative for Johnes disease and to undergo a general health assessment by a veterinarian at the owners cost

Information required with any entry:

• Pedigree • Date of Birth • Fleece weights (2nd and 3rd shearings), most recent fleece weight and average of the drop if

available • Mean fibre diameter (2nd and 3rd shearings) plus most recent fibre diameter and historical

diameters and average of the drop if available • Body weight, current • Progeny performance • List of show ring success • Other relevant information

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Selection process This document, along with information supplied by entrants, will be supplied to all mohair central progeny test committee members. Following discussion, the committee will be asked to nominate the sires that they think should be used. A ballot type system will be used in the first instance, to identify preferred entrants. The bucks most frequently nominated will be those that are used in the trial. If numbers exceed the limit, breeders who have nominated more than one buck may be asked to withdraw one of their bucks.

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Mohair Sire Evaluation - Nomination Form Entrants are advised to read the Sire Selection Protocol document attached. Please complete all details and attach any relevant supporting material. Breeder’s details

Name:

Stud Prefix:

Address:

Ph. Fax: Email:

Prefix and name:

Sire details

Tag: Tattoo: Reg. No:

DOB: % Texan % Aust. % SA

Pedigree

Sire

Dam

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Performance information

Second shearing details

Fleece weight Kg Av. of drop Kg

Fibre diameter micron Av. of drop

Third shearing details

Fleece weight Kg

Fibre diameter micron

Av. of drop Kg

Av. of drop

Progeny performance (if applicable):

Show ring success (if applicable):

Other relevant information:

Current details

Fleece weight Kg Fibre diameter micron CV %

Bodyweight Kg

CV %

CV %

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References Allain, D. and Roguet, J.M. (2004). Genetic variability of OFDA medullation content in the fleece of French Angora goats. Book of Abstracts of the 8th International Conference on Goats, South Africa, 4-9 July. Allain, D. and Roguet, J.M. (2003). Genetic and non-genetic factors influencing mohair production traits within the national selection scheme of Angora goats in France. Livestock Production Science, 82, 129-37. Anon. (2001). Code of Accepted Farming Practice for the Welfare of Goats (Victoria). (DPI: Melbourne). Anon. (2004). http://mss.anprod.csiro.au/ Coffey, S. (2004). Horizons in livestock sciences: the impact of the new biology. Australian Journal Experimental Agriculture 44(11), iii. Ferguson, M.B. and Kearney, G. (2004). A comparison of the OFDA2000 with conventional mid-side testing of mohair. South African Journal of Animal Science 34 (5): 4-6. Ferguson, M.B. and McGregor, B.A. (2004). An evaluation of Angora sires through progeny testing- A progress report. South African Journal of Animal Science 34 (5): 7-9. GenStat Committee (2000). GenStat for Windows. Release 4.2. Fifth Edition. VSN International Ltd., Oxford. Gifford, D.R., Ponzoni, R.W., Lampe, R.J. and Burr, J. (1991). Phenotypic and genetic parameters of fleece traits and live weight in South Australian Angora goats. Small Ruminant Research, 4, 293-302. Gilmour, A.R., Luff, A.F., Fogarty, N.M. and Banks, R. (1994). Genetic Parameters for Ultrasound Fat Depth and Eye Muscle Measurements in Live Poll Dorset Sheep. Australian Journal of Agricultural Research. 45, 1281-91.

Günes, H., Horst, P., Evrim, M. and Valle-Zárate, A. (2002). Studies on improvement of the productivity of Turkish Angora goats by crossing with South African Angora goats. Small Ruminant Research, 45, 115-22. Herselman, M.J., Olivier, J.J. and Snyman, M.A. (1998). Studies on small ruminant breeds with inherent differences in fibre production and ewe productivity 1. Relationship between ewe productivity and wool production potential. South African Journal of Animal Science 28, 1-8. Hunter, L. (1993). Mohair: a review of its properties, processing and applications. (CSIR: Port Elizabeth). IWTO-47-98 (1998). The Woolmark Company, Ilkley, West Yorkshire. IWTO-57-98 (1998). The Woolmark Company, Ilkley, West Yorkshire. Lollback, M.W. and Stapleton, D.L. (1995). MOPLAN-A national performance recording scheme for Angora goats. Proceedings Association for the Advancement of Animal Breeding and Genetics 11, 678-681. Maw, T. (1993) A role for new genetics. In "Mohair beyond 2000: Mohair quality the gateway to value adding." Conference Proceedings Series No. 58: 23-29. Ed. H.M. Day and B.A. McGregor. (Department of Agriculture, Melbourne). McGregor, B.A. (1995). Improving kid survival and mohair quality by improved nutritional management of reproducing does. Final Report RIRDC Project DAV 42A. pp 94. (Agriculture Victoria: Attwood). McGregor, B.A. (1998). 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. Small Ruminant Research 28, 199-215.

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McGregor, B.A. (2001a). Ear tagging goats. Australian Goat Notes p.75. Ed. A.J. Simmonds. (Australian Cashmere Growers Association: Kellyville, NSW). McGregor, B.A. (2001b). Managers’s guide for breeding fibre goats Part 1: How to optimise reproductive performance. Australian Goat Notes pp.51-54. ed. A.J. Simmonds. (Australian Cashmere Growers Association: Kellyville, NSW). McGregor, B.A. (2001c). Managers’s guide for breeding fibre goats Part 2: Pregnant and lactating does. Australian Goat Notes pp.55-59. Ed. A.J. Simmonds. (Australian Cashmere Growers Association: Kellyville, NSW). McGregor, B.A. and Butler, K.L. (2004). Contribution of objective and subjective attributes to the variation in commercial value of Australian mohair: Implications for mohair production, genetic improvement and mohair marketing. Australian Journal of Agricultural Research 55, 1283-98. Murray-Prior, G. and Murray-Prior, G. (1997). Imported Genetics: What is the contribution to the Australian Mohair Industry? Mohair Australia, 6(1). Technical Issues. Nicoll, G.B., Bigham, M.L. and Alderton, M.J. (1989). Estimates of environmental effects and genetic parameters for live weights and fleece traits of Angora goats. Proceedings of the New Zealand Society of Animal Production 49, 183-189. Parry, A.L., Death, A.F., Dick, H.B., Miller, C.M. and Craven, A.J. (1993). Skin follicle development in South African (SA) and SA x New Zealand Angora goats. Proceedings of the New Zealand Society of Animal Production 53, 327-30. Ponzoni, R.W. and Gifford, D.R. (1990). Developing breeding objectives for Australian Cashmere Goats. Journal of Animal Breeding and Genetics 107, 351-70. Shelton, M. and Bassett, J.W. (1970). Estimate of certain genetic parameters relating to Angora goats. Texas Agricultural Station Research Reports (PR-2750), 38-41.

Snyman, M.A. and Olivier, J.J. (1996). Genetic parameters for body weight, fleece weight and fibre diameter in South African Angora goats. Livestock Production Science 47, 1-6. Snyman, M.A. and Olivier, J.J. (1999). Repeatability and heritability of objective and subjective fleece traits and body weight in South African Angora goats. Small Ruminant Research 34, 103-9. Snyman, M.A. (2002). Evaluation of a genetically fine mohair producing herd. Small Ruminant Research 43, 105-13. Stadler, W. and Gillies, R.I. (1994). A case study for the use of mid-side samples to predict the mean fibre diameter of classed lines. Wool Technology and Sheep Breeding 42, 319-26. Stapleton, D.L. (1978). The Australian Angora goat and Industry. PhD Thesis, Armidale, N.S.W. Stapleton, D.L. (1997). Breeding Angora goats for mohair production. Proceedings Association for the Advancement of Animal Breeding and Genetics 12, 377-381. Taddeo, H.R., Allain, D., Mueller, J., Rochambeau, H. and Manfredi, E. (1998). Genetic parameter estimates of production traits of Angora goats in Argentina. Small Ruminant Research 28, 217-23. Yalcin, B.C., Ariturk, E., Imeryuz, F., Sincer, N. and Muftu oglu, S. (1979). Genetic and environmental aspects of Angora goat production. 2. Phenotypic and genetic parameters for the important production traits. Instanbul Univ. Vet. Fak. Derg. 5, 19-34. Yalcin, B.C. (1982). Angora goat breeding. Proc. Third International Conference on Goats, January 10-15, Tucson, AZ, USA, pp. 269-278.

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