Scoping Study for Genetic Evaluation of Australian Dairy Goats

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2 Scoping Study for Genetic Evaluation of Australian Dairy Goats by R.G. Banks and S. Walkom March 2016 RIRDC Publication No 15/109 RIRDC Project No PRJ

3 2016 Rural Industries Research and Development Corporation. All rights reserved. ISBN ISSN Scoping Study for Genetic Evaluation of Australian Dairy Goats Publication No. 15/109 Project No. PRJ 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 regions. You must not rely on any information contained in this publication without taking specialist advice relevant to your particular circumstances. While reasonable care has been taken in preparing this publication to ensure that information is true and correct, the Commonwealth of Australia gives no assurance as to the accuracy of any information in this publication. The Commonwealth of Australia, the Rural Industries Research and Development Corporation (RIRDC), the authors or contributors expressly disclaim, to the maximum extent permitted by law, all responsibility and liability to any person, arising directly or indirectly from any act or omission, or for any consequences of any such act or omission, made in reliance on the contents of this publication, whether or not caused by any negligence on the part of the Commonwealth of Australia, RIRDC, the authors or contributors. The Commonwealth of Australia does not necessarily endorse the views in this publication. This publication is copyright. Apart from any use as permitted under the Copyright Act 1968, all other rights are reserved. However, wide dissemination is encouraged. Requests and inquiries concerning reproduction and rights should be addressed to RIRDC Communications on phone Researcher Contact Details Name: R. G. Banks Address: Animal Genetics and Breeding Unit University of New England Armidale NSW 2351 Phone: rbanks@une.edu.au 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 2, 15 National Circuit BARTON ACT 2600 PO Box 4776 KINGSTON ACT 2604 Phone: Fax: rirdc@rirdc.gov.au. Web: Electronically published by RIRDC in March 2016 Print-on-demand by Union Offset Printing, Canberra at or phone ii

4 Foreword All agricultural industries must address the challenge of prices rising faster for inputs or costs, than for outputs or products (this is commonly referred to as the cost-price squeeze). Continuous productivity improvement is needed to meet this challenge. A key strategy contributing to achieving continuous productivity improvement in all modern, viable industries is genetic improvement. Genetic improvement consists of identifying individuals within the population with superior genes, and using those individuals preferentially as parents of the each successive generation. Over time, this means that each generation is genetically better than the previous ones, and if the improvement is targeted at traits that contribute to either income or cost, the improvements can offset the cost-price squeeze. Identifying the individuals with the best genes is achieved in essentially all livestock industries by the use of advanced statistical methods to analyse pedigree and performance data. Identifying the individuals with the best genes is referred to as genetic evaluation. This project explored the potential to apply such methods to breeding programs in the Australian Dairy Goat industry. If possible, application of these methods would assist dairy goat breeders to make genetic improvement, almost certainly at a faster rate than is being achieved currently. The benefits of that faster genetic progress would then get spread through the wider population through genetically superior bucks. This project is a scoping project, on a modest scale. The results show that there is real potential for genetic improvement of production and health traits in Australian dairy goats, and that there is some scope for applying the advanced statistical techniques currently used in other industries. The results highlight an exciting opportunity for Australian Dairy Goat breeders to breed more productive and healthier populations in a way that will underpin profitability for goat producers. This project was funded by the Rural Industries R&D Corporation, New, Emerging and Maturing Animal Industries RD&E Program. This report is an addition to RIRDC s diverse range of over 2000 research publications and it forms part of our New, Developing and Maturing Animal Industries RD&E program, which aims to enhance industry success through targeted industry-specific RD&E. Most of RIRDC s publications are available for viewing, free downloading or purchasing online at Purchases can also be made by phoning Christine Quick Acting Managing Director Rural Industries Research and Development Corporation iii

5 Acknowledgments Mr Sandy Cameron of Meredith Dairy provided data for the project, and shared details of dairy goat production and enterprise economics which have been invaluable to the project. iv

6 Contents Foreword... iii Acknowledgments... iv Contents... v Executive Summary... vi Introduction... 1 Objectives... 2 Methodology... 3 Trials... 4 Results... 9 Implications Recommendations Appendix 1: Summary of Literature Estimates of Parameters References Tables Table different scenarios for how the replacement bucks and does were selected were modelled Table 2. Responses to selection in a 1,250-doe herd for either of 2 breeding objectives, and under 14 scenarios for records available and mating ratios Table 3. Costs and Returns under a range of recording and selection scenarios v

7 Executive Summary What the report is about This project explores the feasibility of applying modern techniques for statistical analysis (known as BLUP methods) to the genetic evaluation of dairy goats. Who is the report targeted at? The report is most relevant to Australian Dairy Goat breeders, and to the dairy goat industry and R&D managers. Where are the relevant industries located in Australia? There are a small number of dairy goat breeders located across Australia, mainly in the SE corner (the higher rainfall zone). Dairy goat producers are located through these areas. Note that in this report, breeders are distinguished from producers. Breeders are individuals that identify, select and potentially sell bucks and does to other individuals for use in breeding replacement commercial animals, or keep them to breed commercial animals themselves. Producers are individuals or enterprises who manage dairy goats for production, but do not select or sell bucks or does. Producers may also be breeders. Aims/objectives The aims/objectives of this work were to: a) conduct an evaluation of the potential for BLUP genetic analysis of dairy goat data collected in Australian herds b) subject to the availability of suitable data (the core requirements being consistent performance records within herds, and pedigree information), completed BLUP genetic analyses for the herds with appropriate data, delivering Estimated Breeding Values for production and any disease traits recorded, for bucks and does, along with estimates of genetic progress being achieved in those herds for the recorded traits. c) make recommendations regarding improvements to current performance and pedigree recording that would facilitate genetic evaluation and hence genetic improvement. d) assess the scope for and value of using DNA pedigree to assist in genetic evaluation. e) building on the information obtained and analysed under a) to d) above, an economic evaluation of the potential return on investment in dairy goat genetics R&D and in increased performance recording, to assist industry and individual enterprises in decision-making. Together, these deliverables would allow industry and individuals to make informed decisions about how best to achieve profitable genetic improvement in Australian dairy goats, and subject to the data available, accurate identification of genetically superior animals to underpin an immediate lift in genetic merit. vi

8 Methods used Project work has comprised 4 lines of activity: - Literature review on genetic parameters (heritabilities of different traits, genetic correlations between traits) for dairy goats - Consultation with breeders, in particular Mr Sandy Cameron, of Meredith Dairy, to obtain performance data for potential analysis - Modelling responses to selection under various recording and selection strategies, including use of DNA information - Modelling to estimate benefit cost of implementing selection schemes Outcomes from these are reported against the project aims. Results/key findings a) Literature Review A comprehensive literature review was completed, and the findings indicate that unless Australian dairy goats are genetically completely different from those managed in other countries, there is ample scope for genetic improvement of important traits in the Australian population. b) Consultation with breeders to obtain performance data for potential analysis We were able to obtain datasets which allowed initial non-genetic analysis, from Sandy Cameron. Genetic analysis of the data was not possible because no pedigree records have been kept to date. We are now working with Sandy Cameron to collect pedigree records on bucks and does, which will enable full genetic analysis. c) Modelling responses to selection under various recording strategies Extensive modelling of a range of recording and selection options for a typical dairy, based on the Meredith operation, was completed. The key findings include: - Solely recording milk production, but with no pedigree or composition data, provides a basis for modest genetic progress in milk yield, but likely decline in the proportion of fat and protein - Recording pedigree enables substantially more genetic progress to be achieved 5-7 times more progress in milk yield than when pedigree is not available - Recording composition and health traits enables balanced selection milk production can be increased with no decline in composition or health traits - DNA information would be useful as a means of obtaining pedigree, but is not essential d) Modelling to estimate benefit to cost return of recording and selection A comprehensive benefit-cost model was developed, with assumptions based around typical enterprise parameters. The model results show that performance recording and selection can be highly profitable, but only if pedigree data is collected and used in genetic analysis. If the methods scoped in this project are adopted, continuous and valuable improvements in productivity, product quality and animal health can be achieved in industry. These improvements could offset the cost-price squeeze and provide industry with a basis for confidence to expand. vii

9 Implications for relevant stakeholders: The principal implication for industry is that genetic improvement in dairy goats is quite feasible, and will generate significant benefits for dairy goat producers. Achieving valuable genetic improvement will however require some changes in current practice, principally in ensuring that records of milking performance (and other traits affecting income and cost) are systematically collected, along with pedigree, and that the resulting data is appropriately analysed. The most obvious challenge to be overcome in achieving this is to make such recording simple and cost-effective, and standard practice, and to achieve this across enough dairy goat breeders to allow real scope for selection. Some breeders will see relatively immediate benefit from this, if they are also dairy producers their goats will improve over time and generate more income in the dairy operation. Breeders typically with smaller herds who are more focussed on sale of bucks or semen to others, will depend on feedback from their clients, and so will be dependent on the herd recording practices of those clients. This is likely to require sustained effort across industry, particularly in extension and communication, and this may require sustained support from government and/or bodies such as RIRDC. Sustained genetic improvement in the dairy cow industry has required a long period of heavy public support for R&D, extension, and initially, milk recording, and even now is underpinned by strong whole-of-industry coordination. The dairy goat industry does not have, and is unlikely to ever receive, the same proportionate level of support. This means that success will likely depend on some form of support for coordinated effort across breeders and producers, with a clear vision and strategy for at least a year program. Recommendations The primary recommendation is that breeders should systematically collect records of pedigree and performance, and that this data should be analysed using modern genetic analysis methods, to produce Estimated Breeding Values (EBVs) for the recorded traits. These EBVs will make selection of replacement bucks and does significantly more effective, and allow valuable genetic progress to be achieved. The second recommendation is that industry should plan a sustained program of extension around recording, and seek ways to make recording as standardised as possible. A common system for animal identification makes pedigree analysis across herds much easier, and agreed trait definitions makes data analysis much easier. The third recommendation is that if possible, pedigrees from different herds should be collected together into a single database, so that genetic analysis across herds can be conducted, allowing selection of the best bucks for artificial insemination from the entire Australian population. The final recommendation is that industry, likely through the Goat Industry Council of Australia, should develop a vision and strategy for long-term genetic improvement, and seek to maintain the R&D and extension funding to support it. It is quite likely that some individuals will move to implement modern genetic improvement systems of their own accord, but the trickle-down flow of superior genetics from such a fragmented approach will be slow, and in the absence of industry-wide genetic improvement, goat dairying will remain a relatively small-scale industry. Sustained, wellfocussed genetic improvement can help in making goat dairying much more viable, and that is a worthwhile goal for the industry. viii

10 Introduction It is important to begin by spelling out what genetic evaluation and genetic improvement mean, and why they are important. Genetic evaluation: this means collecting records of performance and (usually) pedigree, and analysing that data to identify the animals with the best genetic make-up for the traits of interest. Genetic improvement: this means selecting the animals with the best genetic makeup, or estimated to have the best genetic makeup, as parents of the next generation. This means that each successive generation is genetically better than the previous one, and over time, performance levels for all recorded traits can be improved. Importance: all livestock enterprises have to earn income from products (such as milk, or offspring) to offset fixed and variable costs. For most livestock industries, prices received do not increase in real terms (the price received adjusted for inflation), but both fixed and variable costs do, resulting in what is usually known as the cost-price squeeze. The effect of this is that profit from an enterprise declines in real terms over time. The only way to offset this decline is to make constant productivity improvement(s). These may come from increasing scale, better feeding and/or health management, or genetic improvement. Genetic improvement is attractive because its effects are cumulative each year s improvement builds on the previous, and so provided that improvement is sufficiently rapid, the cost-price squeeze can be offset. This is now the case in industries such as pigs and poultry, and the Australian lamb industry. Livestock industries that are not making genetic improvement are quite literally inevitably going backwards. The basic requirements for genetic evaluation and improvement are: a) Genetic parameters for the traits affecting income and cost in the dairy enterprise b) Performance records for the traits c) Genetic analysis of the performance records, to produce Estimated Breeding Values (EBVs), allowing d) Selection of the genetically best animals as parents of the next generation The Australian dairy goat industry has previously investigated in genetic improvement, and a very comprehensive set of recommendations was developed (Lindsay and Skerritt, 2003). It is not clear that any real progress has been made in implementing these recommendations, despite reported support and enthusiasm. One possible gap is the absence of any clear examples of local success good data analysed to produce EBVs, and used to make observable genetic progress. This project sought to address that gap by identifying suitable datasets for analysis, and producing useful EBVs. In addition, the project aimed to examine the feasibility (and usefulness) of using DNA methods for pedigree to assist in genetic evaluation. This assessment formed part of an overall evaluation of the benefit-cost of implementing recording and selection in dairy goats, including collection of pedigree and a range of selection strategies. 1

11 Objectives The project objectives were to deliver: a) an evaluation of the potential for BLUP genetic analysis of dairy goat data collected in Australian herds b) subject to the availability of suitable data (the core requirements being consistent performance records within herds, and pedigree information), completed BLUP genetic analyses for the herds with appropriate data, delivering Estimated Breeding Values for production and any disease traits recorded, for bucks and does, along with estimates of genetic progress being achieved in those herds for the recorded traits. c) recommendations regarding improvements to current performance and pedigree recording that would facilitate genetic evaluation and hence genetic improvement. d) assessment of the scope for and value of using DNA pedigree to assist in genetic evaluation. e) building on the information obtained and analysed under a) to d) above, an economic evaluation of the potential return on investment in dairy goat genetics R&D and in increased performance recording, to assist industry and individual enterprises in decision-making. Together, these deliverables will allow industry and individuals to make informed decisions about how best to achieve profitable genetic improvement in Australian dairy goats, and subject to the data available, accurate identification of genetically superior animals to underpin an immediate lift in genetic merit. 2

12 Methodology The methods used in the 4 main phases of the project are: a) Evaluation of the potential for BLUP analysis This was tackled through literature review to assess likely levels of genetic variation for the key traits. There is essentially no literature on genetic parameters for Australian dairy goats, so this review focusses on overseas research, principally from Europe. BLUP analysis itself is possible provided that there is performance and pedigree data, meaning that the the main determinant of potential for such analysis is the availability of suitable data. b) Subject to availability of suitable data, conduct BLUP analysis of Australian dairy goats BLUP analysis involves using software written by AGBU to analyse appropriate data sets. c) Assess the scope for using DNA to assist genetic evaluation This was conducted within the wider benefit-cost evaluation flagged under e). d) Evaluate the benefit-cost of implementing a genetic improvement program for dairy goats For this, a model of a dairy goat enterprise was built where replacement bucks and does could be selected from within the population, and a range of recording and selection strategies applied. For each combination, costs derived from consultation with Mr Sandy Cameron or from genotyping laboratories were used. The model enabled prediction of genetic change over time, and calculation of returns from changes in milk production and composition, and of costs for each recording and selection strategy. The returns and costs formed the basis for calculating return on investment over time. The model used allows variation of the key parameters, such as herd size, recording and genotyping costs, and prices for milk. 3

13 Trials a) An evaluation of the potential for use of BLUP evaluation methods for genetic evaluation in Australian dairy goats The basic requirements for genetic evaluation are: - Genetic parameters for the trait(s) being recorded Ideally, these will be calculated from the data itself. The data must have pedigree and some record of performance. Unfortunately, we have been unable to identify a suitable dataset in Australia. Literature review results can be summarised as, firstly for heritability: Trait Heritability range 17-37% Fat concentration (%) 16-62% Protein concentration (%) 38-67% Lactation somatic cell count 20-24% The correlations amongst these traits, from literature estimates, are: Milk Yield Fat % Protein % Somatic cell count Milk yield Fat % Protein % Somatic cell count NB: phenotypic correlations are above the diagonal (in italics), and genetic correlations are below the diagonal (in normal font). These values tells us the higher milk yield is strongly genetically associated with lower fat % and protein %, and moderately genetically associated with somatic cell count. Fat % and protein % are strongly genetically associated. These estimates of heritabilities and genetic correlations could be used initially in genetic analysis of Australian data, but local estimates should be generated as soon as data on approximately 5,000 animals with records has been collected. A more comprehensive literature summary is included as Appendix 1. 4

14 - Performance records for those traits Data provided by Sandy Cameron on 2,841 does, averaged litres for an adjusted 290- day lactation. Genetic parameters could not at this stage be estimated from this data set, because no pedigree was available. Once genetic parameters and performance data are available, genetic analysis is possible. BLUP which stands for Best Linear Unbiassed Prediction is the method of choice for analysis of performance records for genetic selection in essentially all farmed species. Prior to BLUP, selection was usually based on either: - Selection on individual records, possibly adjusted for factors such as age of the animal - Selection on an index combining records from an animal and its relatives such as its half-sibs. BLUP methods have superseded both these approaches primarily because they allow use of information from all relatives, including those born and/or recorded in different farms and years. This makes the estimation of animals genetic merit significantly more accurate across a population of many herds, but even within a single herd, the ability to identify the best animals across years enables more intense selection of the best animals from all those available and hence significantly faster genetic improvement. Provided that breeders have accurate records of lactation performance (which can include volume as well as % protein and fat), and pedigree data, then BLUP genetic evaluation can be conducted. The result or output from such analysis is a set of Estimated Breeding Values (or EBVs) for the animals with records and pedigree. EBVs describe animals genetic merit for the recorded traits, in units of production so that EBVs for milk production would be in litres. An animal with an EBV of +1 l for milk volume has genes that are worth an extra 1 litre of milk per lactation. Such an animal would pass on half this superiority to its progeny. A BLUP analysis will also include taking account of, or adjusting for, non-genetic factors, which for dairy goat recording can include: - Year of the record, and month in which the lactation started - Age of the animal at the start of the lactation for which records have been collected - Its own birth status, and the number of kids born for the lactation being recorded - Management group in which the animal is run (the group of animals it runs with for example, if a herd contains two mobs which are kept in separate paddocks, these should be identified) - Length of lactation in days - Where any health treatments are applied to animals within a management group, this should also be recorded These features of BLUP genetic analysis mean that a breeder can identify the best animals for breeding from everything that is on hand, including older animals. This feature alone usually generates significant increases in the rate of genetic progress in a breeding program. Calculations of potential responses to selection, using the literature estimates of genetic parameters, suggests that approximately 5-8 times as much genetic progress would be possible through use of BLUP methods for evaluation and selection, compared with simply selecting the does with the best 5

15 lactation records, but taking no account of pedigree and hence the performance of their relatives. This estimate is consistent with experience in other livestock species. AGBU has available software to generate BLUP EBVs for dairy goat data, tested on simulated data. This software can handle essentially any size of data set (we use similar software for analysis of datasets of several million animals in other species). Provision of an ongoing service based on use of this software is discussed under item e) below. b) Subject to availability of suitable data, conduct an analysis of data to generate EBVs for recorded traits using BLUP methods We have worked with Sandy Cameron, Meredith Dairy, to understand the data collected there. Due to the current mating program approach, to this point individual pedigree data is not available, and hence to this point, no genetic analysis to produce EBVs is possible. We have calculated summary statistics from the performance, and the results obtained suggest that it is likely that the genetic make-up of dairy goat production in Australian goats is broadly similar to that in the more intensively studied populations in France and the UK. This means that while the levels of production may differ, the degree to which traits are inherited and the strength of genetic relationships between traits, seem likely to be similar. This is not particularly surprising, but does provide additional reason to believe that effective selection to improve dairy production in Australian dairy goats should be quite achievable. We are working with Sandy Cameron to start building pedigree information on the current herd, and to gradually fill in the pedigree back through influential sires. This is being done by use of microsatellite DNA mapping (an approach to using DNA information to identify parentage. This work is fully funded by Sandy Cameron. We anticipate that within 6 months, there will be sufficient data on animals with pedigree and performance records to conduct a full BLUP analysis. The approach is completely applicable to any breeder/herd where pedigree and performance data is available, and we aim to work with RIRDC and the goat industry to identify any such herds. We have also consulted with the Goat Industry Council of Australia, and in particular with Chris Lamin. We have provided information to Chris and to the Holstein Association, with whom a group of dairy goat breeders are exploring establishing a pedigree database for dairy goats. We anticipate discussions with this group within the next 3-6 months on providing a genetic analysis option for them and industry. c) Make any appropriate recommendations concerning improvements to current recording practices that will assist in genetic evaluation and improvement The principal recommendations are simply to keep pedigree (at least sire) of goats entering lactation, and to record milk volume and test %. Where breeders mate does to more than one buck (ie there are several bucks in with a mob of does) and so individual pedigree is not normally possible, we can outline approaches using DNA methods. These are becoming more cost effective as DNA technologies come down in price. AGBU has the expertise to use various forms of DNA information to construct pedigree relationships in multi-buck mating programs, allowing the data to be used for genetic (BLUP) analysis. The key information to be collected should include: - ID (if multiple herds provide data, this should include a numeric identifier for herd) - Sire 6

16 - Dam - Date of birth of the animal - Date of kidding for the current lactation - Number of kids born at the start of the current lactation - Volume of milk produced in this lactation (in kg) - Days of lactation to produce this volume - Fat % if a test has been conducted - Protein % if a test has been conducted Some breeders are keeping records of a number of conformation traits. These are potentially able to be analysed, but on their own do not provide any direct estimate of genetic merit for milking ability. If there is sufficient interest and data, AGBU can conduct analysis of conformation data to produce EBVs. If goat breeders do not establish a separate database for milk records, AGBU could certainly provide a database, potentially with on-line data entry. Pricing this service would need to be negotiated. d) Assess the potential value of DNA technology for use in genetic evaluation and improvement As noted under c) above, DNA technology can be very beneficial for use in dairy goat genetic evaluation and improvement, in facilitating pedigree determination. Two basic approaches are currently available: - Micro-satellites: cost c. $40 per animal tested. These allow confirmation of parent-offspring pairs in situations where a number of sires are the potential parents. This approach involves testing, or reading, a small number of locations in the genome, and looking for congruent matches between pairs of animals in their makeup at those locations. - SNP Chip: this method involves reading much larger numbers of locations across the genome, currently typically around 50,000 locations, and determining the degree of similarity in genetic makeup at those 50,000 locations between animals. This increased number of DNA reads produces marginally more accurate pedigree determination at the level of parentoffspring, but most importantly, provides an accurate estimate of the degree of relationship between all animals, not just those with direct lines of relationship. The 50k Goat SNP Chip is available from the French Research Organisation INRA for approximately $200 per animal. We would like to explore the possibly of getting key ancestor animals in the Meredith and potentially other herds genotyped using this chip, as this will facilitate use of less dense (less DNA locations read) and hence cheaper approaches in the near future. We have explored with local and overseas colleagues the option of accessing the Dairy Goat Research SNP chip. This has been created by an international research consortium, of which Australia is not currently a member There is no doubt that DNA tools can be very useful for Australian dairy goat breeding. We would like to explore trialling this approach with industry and potentially RIRDC. 7

17 e) Estimate ongoing costs associated with providing genetic evaluation service for the industry There are 2 main sources of cost associated with genetic evaluation for the industry: - The on-farm cost of recording, including obtaining pedigree. This will depend on several factors including herd size, milk recording equipment or methods in use, and design of the mating program (if does are mated in single buck mobs, then pedigree is straight-forward; if not, then DNA methods will be needed to make use of the data) - The cost of analysing the data. This is a minor element the data being generated by Meredith dairy can be analysed to determine the genetic structure of the traits and then generate EBVs within 2-3 days per year, or perhaps $2,500 pa. This would be a reasonable estimate of per herd cost of data handling and analysis f) Estimate benefit:cost of investment into genetic evaluation and improvement for the industry A simple but comprehensive benefit-cost analysis (BCA) comparing a range of breeding program options and corresponding levels of investment has been completed. Factors modelled include: - Herd size: the analysis was for a 1,250 doe herd, but this can be varied - Recording costs per animal - Cost of obtaining pedigree per animal - Genotyping cost per animal Within this example, the key results are: - Investment in genetic improvement of dairy goats can be very profitable: simply using lactation records but no pedigree can produce an income increase of approximately $100k in total over 10 years. This option essentially adds no cost to current operations assuming the operation is already collecting comprehensive lactation records (presumably for management purposes). If however, collecting milk production records does come at a cost, then this approach to selection is not profitable, because it cannot generate sufficient increase in milk production to offset the cost of recording. - Higher levels of investment can generate higher returns: investment in pedigree increases returns 5-7 times, and increases 10 year profit by $ k, depending on the extent of genotyping. This BCA model can be customised to any herd size, milk price recording and genotyping price scenario, but the broad principles can be expected to be consistent. Genetic improvement can be very profitable, but does require good record-keeping and some form of pedigree. 8

18 Results a) an evaluation of the potential for BLUP genetic analysis of dairy goat data collected in Australian herds; and b) subject to the availability of suitable data (the core requirements being consistent performance records within herds, and pedigree information), completed BLUP genetic analyses for the herds with appropriate data, delivering Estimated Breeding Values for production and any disease traits recorded, for bucks and does, along with estimates of genetic progress being achieved in those herds for the recorded traits. There is no reason why BLUP analysis cannot be applied to genetic analysis of dairy goat data. The issue is that at present, little or no suitable data meaning pedigree and performance data - is available. Steps are being taken to address this with Sandy Cameron, and full BLUP analysis of data from Meredith dairy will be completed in early Initial discussions with goat breeders around establishment of a performance database will be held in November 2015, and subject to data being lodged in a database, BLUP genetic analysis can proceed in c) recommendations regarding improvements to current performance and pedigree recording that would facilitate genetic evaluation and hence genetic improvement. Simple recommendations have been made (see p. 6-7) of this report. d) assessment of the scope for and value of using DNA pedigree to assist in genetic evaluation. Results show that using pedigree information in genetic analysis will enable significantly faster genetic progress, whatever combination of traits is selected for (see next section). While DNA pedigree may be more expensive than simply recording buck and doe manually, it is costeffective to use this technique (see next point), and may offer substantial benefits through simplicity and convenience, especially in larger herds where multi-sire joining is practised. Options for use of DNA are rapidly evolving, meaning that utility and price are both improving all the time. Industry should monitor the DNA tools available, and seek to work with the meat goat industry to explore custom goat DNA chips. e) building on the information obtained and analysed under a) to d) above, an economic evaluation of the potential return on investment in dairy goat genetics R&D and in increased performance recording, to assist industry and individual enterprises in decision-making. Potential responses to selection: The analysis modelled a herd with the following parameters: - 1,252 does mated each year, producing 1,628 kids a year - Does average 2.39 lactations each - First lactation at 12mths - Bucks mated at 12mths and used once 9

19 2 different breeding objectives (the breeding objective is what the flock is breeding for), were modelled: - Maximise 290-d yields - Maximise yields but maintain fat, protein and LSCC at current levels Table different scenarios for how the replacement bucks and does were selected were modelled. Traits Sire mating Number of records # Records available measured ratio own dam sire full sibs half sibs progeny 1 own (1 record) Yield 1: own (record each lactation) Yield 1: own (>1 rec) + dam pedigree Yield 1: own (>1 rec) + sire pedigree Yield 1: own (>1 rec) + sire pedigree Yield 1: full pedigree Yield 1: full pedigree Yield 1: own (1 record) Yield + others 1: own (record each lactation) Yield + others 1: own (>1 rec) + dam pedigree Yield + others 1: own (>1 rec) + sire pedigree Yield + others 1: own (>1 rec) + sire pedigree Yield + others 1: full pedigree Yield + others 1: full pedigree Yield + others 1: Notes: - 1 record means 1 lactation record - Yield is milk yield - The sire mating ratio means the number of does per sire (so 1:50 means that each buck is mated to 50 does each year) - Others is fat % and protein %, and somatic cell count In this part of the analysis, how the pedigree is collected is not an issue it is simply assumed to be available in the scenarios where it is included. For each scenario, response to selection over 10 years was calculated, using mid-range genetic parameters. Predicted responses to selection are shown over page. 10

20 Table 2. Responses to selection in a 1,250-doe herd for either of 2 breeding objectives, and under 14 scenarios for records available and mating ratios Maximising Yield Maximising Yield + Maintain Quality YLD FAT PRO LSCC YLD FAT PRO LSCC Only measure milk yield Measure milk yield, Fat %, Protein % and LSCC The key points of these results are: - Selection based on recording only milk yield, simply using does own records, and selecting solely for increased milk yield (scenario 1), is predicted to increase lactation yield per doe by l over 10 years. This will be accompanied by reductions in fat% and protein%, and no change in somatic cell count. - Conversely, if the only trait being recorded is milk yield, then selecting to maintain quality is exactly the reverse if a breeder wishes to maintain quality but only records milk yield, all they can do is select against increased milk yield. - Focussing on the top left-hand quadrant of the table, the seven scenarios reflect increasing amounts of pedigree information being recorded and analysed to find and select the best animals. This means that selection will become more accurate, and more response will be possible. - The first 3 scenarios only use dam pedigree this is of limited value as dams only get a small number of lactation records. Scenarios 4-7 all include identification of sire pedigree, and as sires have more progeny than dams, this significantly increases the accuracy of selection of replacement does and bucks, and accordingly, much more genetic improvement is possible. This is clearly shown by the predictions of l genetic improvement in yield over 10 years for scenarios 4-7, compared with the l improvement possible without sire pedigree recording. - Adding pedigree information but only recording milk yield makes no difference in ability to maintain quality in fact the enhanced selection for milk yield simply means larger changes in the quality traits, in the wrong direction if selection is for milk yield. This can be seen by comparing the left and right blocks in the top half of the table of responses. - The bottom half of the table shows the predicted responses to selection when recording includes milk yield, fat%, protein% and somatic cell count. Now there are 3 significant patterns in the results: 11

21 - Recording and using more pedigree information increases the responses possible (for the same reasons as outlined above) - Recording the quality traits provides extra information to help select the best animals for yield (compare scenario 1, selection for maximising yield, with scenario 8, selection for maximising yield). This is because there are genetic correlations between traits for example, some of the genes for fat% also affect milk yield, so that if we record fat%, we get some extra information about milk yield. This effect is not large in most cases. - Having records on the quality traits enables the genetic change in those traits to be managed compare the bottom right-hand blocks with the bottom left-hand block in the table. By selecting to maintain quality while increasing yield, the changes in the quality traits, which were negative when no records on the quality traits were recorded are now all zero. At the same time, there is predicted to be a slight reduction in the genetic improvement in milk yield. The effect of selection based on BLUP is illustrated by the comparisons of scenarios 1-3 v 4-7, and 8-10 v 11-14: in all comparable cases, the response achieved via selection that uses pedigree recording is over 7 times higher. In economic terms there is also advantage to using BLUP (ie pedigree information used in the genetic analysis) because it enables a better balance of selection across the traits. Summing all this up the key messages are: - Modest improvement in milk yield is possible if does lactation records are used, and no other information. The improvement is approximately 75 l over 10 year, or approximately 1.5% per year - If quality traits (fat%, protein% and somatic cell count) are important, they should be recorded and their EBVs used in selecting replacements - Keeping pedigree information, particularly sire, is very useful if it is available, lactation yield could be increased by up to 600 l (essentially doubled) over 10 years. - Using pedigree information will also enable much better balance of response between milk production and the quality traits. Evaluation of the benefit-cost of recording and selection programs: The second phase of this modelling study included consideration of costs for recording and for the various levels of pedigree collection: - Herd recording and milk testing is assumed to cost $33k per year for a 1,250 doe herd. This assumes that all traits are recorded each lactation on all does - Cost to collect sire pedigree is $2.00 / breeding doe - Cost to collect dam pedigree is $8.00 / breeding doe - Cost to collect full pedigree information is $10.00 / breeding doe - Cost of genotyping is $25.00 per individual These cost estimates were developed based on consultation with Sandy Cameron, and with genotyping labs in Australia and overseas. The returns from selection are calculated using the appropriate scenario with selection to maximise milk yield and maintain quality scenarios 9, 10, 11 and 13 in Table 2. 12

22 Table 3. Costs and Returns under a range of recording and selection scenarios Selection Scenario Breeding Program Does milked per year 10yr yield gains per doe 10 year yield gains per herd (A) Cost of pedigree collection over 10 years (B) Trait recording cost over 10 years (C) 10yr Profit (A B C) 9 Record traits 10 Record traits + dam 11 Record traits + sire 13 Record traits + full pedigree 11 Record traits + genotype ewes + sires 13 Record traits + genotype everything 1,252 $75.39 $94,394 $330,000 -$235,066 1,252 $87.31 $109,315 $100,160 $330,000 -$320,845 1,252 $ $665,322 $25,040 $330,000 $310,282 1,252 $ $738,509 $125,000 $330,000 $283,509 1,252 $ $665,322 $57,467 $330,000 $277,855 1,252 $ $738,509 $276,692 $330,000 $131,817 It is important to treat this BCA as indicative: - the exact results for a particular herd will depend on the cost of basic trait recording and pedigree collection that apply in that enterprise - The costs of genotyping will likely decline significantly over the next 5 years Recognising these points, there are nevertheless several key messages in these results: - Simply recording milk production for selection purposes alone is unlikely to be profitable. It is not possible to generate enough improvement in milk production to justify the cost of recording milk production. (There may be management benefits that justify this expense). - Collecting some form of pedigree information in particular sire identity and using it in conjunction with performance records to calculate BLUP EBVs for use in selecting replacements is likely to be profitable. Whether manually collecting full pedigree, and/or using genotyping, will generate superior economic returns compared to simply identifying sire, will depend on the costs associated with the individual enterprise, and the cost of genotyping. - Higher levels of investment can generate higher returns: investment in pedigree increases returns 5-7 times, and increases 10 year profit by $ k, depending on the extent of genotyping. 13

23 This BCA model can be customised to any herd size, milk price recording and genotyping price scenario, but the broad principles can be expected to be consistent. Genetic improvement can be very profitable, but does require good record-keeping and some form of pedigree. 14

24 Implications RIRDC Project Report 08/207 (Stubbs and Abud, 2009) estimates the Australian Dairy Goat population at approximately 12,000 does. In principle, the entire Australian population could be achieving the rate of genetic improvement estimated here, and assuming that the parameters for prices and costs used in the benefit-cost modelling are representative, could achieve similar economic returns. This would mean an increase in gross margin for industry of between $ m over 10 years. Achieving this would require ideally common recording protocols, such as those outlined here, a database to store the records from different producers for analysis, a genetic analysis as outlined here, and effective selection using EBVs. In principle this should be feasible, but it will likely require some coordination and communication across industry. 15

25 Recommendations If appropriate, provide recommendations on the activities or other steps that may be taken to further develop, disseminate or to exploit commercially the results of the project The primary recommendation is that dairy goat producers implement 3 steps: - Systematically record production of all milking does, ideally including composition tests and somatic cell counts. - Collect pedigree on bucks and does, potentially using DNA tools. - Get the performance and pedigree records analysed to produce EBVs for milk production and other recorded traits. - Use EBVs in selecting replacement bucks and does. Secondary recommendations following on from the primary ones are: - Industry should coordinate recording, ideally to have a common pedigree and performance database system and ID format. - The aim should be to produce EBVs that compare animals across herds (especially bucks). This type of analysis is standard in dairy and beef cattle, and sheep, and enables breeders to accurately select the best animals from the entire population. - Tools are available to help in designing breeding programs, so that inbreeding is minimised while maximising genetic progress. Use of such tools will be important given the relatively small size of the dairy goat population. Finally, given that achieving the potential outcomes modelled here will require sustained effort and some coordination amongst breeders, RIRDC and the appropriate goat industry organisations should consider how best to support industry implementation of the recommendations. There are certainly motivated breeders, but some coordination or extension effort, along with a combined approach to a database, is likely to be very beneficial. 16

26 17 Appendix 1: Summary of Literature Estimates of Parameters Trait Breed Records Mean sd. Phenotypic Variation Heritability Reference Production traits Volume of milk Total volume to day 305 (adjusted) Multiple (US) 33,725 1, kg kg 58,847 ± ± 0.02 Castaneda-Bustos et al Total volume for 210 day Murciano-Granadina (ES) 10, kg 126 kg 10, Analla et al.1996 lactation (adjusted) Total volume to day 305 (adjusted) Multiple (MEX) 4,007 1,095 kg 292 kg 40, ± 0.04 Torres-Vázquez et al Total volume to day 250 Alpine & Saanen (FRA) 67,882 (a) 668 kg (a) 158 kg (a) 0.30 ± (a) 0.34 ± Rupp et al (adjusted) 49,709 (s) 698 kg (s) 178 kg (s) (s) Daily milk yield (lactation 1) Alpine, Saanen & 180, kg 0.23 to 0.45 Mucha et al.2014 Toggenburg cross (UK) Daily milk yield (lactation 2) Alpine, Saanen & 109, kg 0.14 to 0.34 Mucha et al.2014 Toggenburg cross (UK) Daily milk yield (lactation 3) Alpine, Saanen & 63, kg 0.15 to 0.25 Mucha et al.2014 Toggenburg cross (UK) Daily milk yield (lactation 4) Alpine, Saanen & 36, kg 0.10 to 0.28 Mucha et al.2014 Toggenburg cross (UK) Total volume to day 250 (adjusted) Alpine & Saanen (FRA) 33,341 (a) 20,700 (s) 648 (a) 676 (s) 167 (a) 182 (s) 0.34 ± (a) 0.32 ± (s) Bélichon et al Total fat content and concentration Total volume to day 305 (adjusted) Multiple (US) 33, kg kg ± ± 0.02 Castaneda-Bustos et al Total volume to day 305 (adjusted) Multiple (MEX) 2, kg 8.95 kg ± 0.05 Torres-Vázquez et al Total volume to day 250 Alpine & Saanen (FRA) 67,882 (a) 24.4 kg (a) 6.3 kg (a) 0.32 ± (a) 0.35 ± Rupp et al (adjusted) 49,709 (s) 23.7 kg (s) 6.8 kg (s) (s) Fat percentage adjust over lactation (day 15 to 305) Multiple (US) 33, % 0.87% 0.26 ± ± 0.02 Castaneda-Bustos et al Fat percentage adjust over lactation (210 day lactation) Murciano-Granadina (ES) 10, % 0.85% Analla et al.1996

27 18 Trait Breed Records Mean sd. Phenotypic Variation Heritability Reference Fat percentage adjust over lactation (305 day lactation) Multiple (MEX) 2, % 0.35% ± 0.06 Torres-Vázquez et al Fat content (g/kg, across 250 Alpine & Saanen (FRA) 67,882 (a) 36.5 g/kg (a) 4.8 g/kg (a) 0.62 ± (a) 0.61 ± Rupp et al day lactation) 49,709 (s) 33.9 g/kg (s) 4.5 g/kg (s) (s) Fat yield volume to day 250 (kg, adjusted) Alpine & Saanen (FRA) 33,341 (a) 20,700 (s) 22.7 (a) 21.8 (s) 6.3 (a) 6.5 (s) 0.37 ± (a) 0.40 ± (s) Bélichon et al Fat content (g/kg) Alpine & Saanen (FRA) 33,341 (a) 20,700 (s) 35.1 (a) 32.2 (s) 4.6 (a) 4.2 (s) 0.58 ± (a) 0.60 ± (s) Bélichon et al Total protein content and concentration Total volume to day 305 (adjusted) Multiple (US) 33, kg 7.59 kg ± ± 0.02 Castaneda-Bustos et al Total volume to day 305 (adjusted) Multiple (MEX) 2, kg 7.10 kg ± 0.04 Torres-Vázquez et al Total volume to day 250 Alpine & Saanen (FRA) 67,882 (a) 20.9 kg (a) 4.9 kg (a) 0.31 ± (a) 0.34 ± Rupp et al (adjusted) 49,709 (s) 21.0 kg (s) 5.3 kg (s) (s) Protein percentage adjust over lactation (day 15 to 305) Multiple (US) 33, % 0.47% 0.08 ± ± 0.02 Castaneda-Bustos et al Protein percentage adjust over Murciano-Granadina (ES) 10, % 0.39% Analla et al.1996 lactation (210 day lactation) Protein percentage adjust over lactation (305 day lactation) Multiple (MEX) 2, % 0.17% ± 0.07 Torres-Vázquez et al Protein content (g/kg, across Alpine & Saanen (FRA) 67,882 (a) 31.4 g/kg (a) 2.5 g/kg (a) 0.67 ± (a) 0.60 ± Rupp et al day lactation) 49,709 (s) 30.1 g/kg (s) 2.1 g/kg (s) (s) Protein yield volume to day 250 (kg, adjusted) Alpine & Saanen (FRA) 33,341 (a) 20,700 (s) 648 (a) 676 (s) 167 (a) 182 (s) 0.36 ± (a) 0.34 ± (s) Bélichon et al Protein content (g/kg) Alpine & Saanen (FRA) 33,341 (a) 20,700 (s) 648 (a) 676 (s) 167 (a) 182 (s) 0.58 ± (a) 0.50 ± (s) Bélichon et al Total solids Total volume of fat and protein to day 305 (adjusted) Multiple (US) 33, kg kg ± ± 0.02 Castaneda-Bustos et al Characteristic traits Somatic cell count Mean somatic cell score across lactation (250-day) Alpine & Saanen (FRA) 67,882 (a) 49,709 (s) 5.09 (a) 5.32 (s) 1.36 (a) 1.19 (s) 0.20 ± 0.01 (a) 0.24 ± 0.01 (s) Rupp et al. 2011

28 19 Trait Breed Records Mean sd. log transformed somatic cell count Ease of milking (Udder confirmation) Fore udder Alpine & Saanen (FRA) 67,882 (a) 3.33 (a) (Extent of forward extention 49,709 (s) 3.49 (s) of udder, 1 to 9 score) Rear udder (level of marked medial ligament, 1 to 9 score) Udder floor position (1 to 9 score) Alpine & Saanen (FRA) Alpine & Saanen (FRA) 67,882 (a) 49,709 (s) 67,882 (a) 49,709 (s) Udder profile (1 to 9 score) Alpine & Saanen (FRA) 67,882 (a) 49,709 (s) Rear udder attachment (1 to 9 score) Alpine & Saanen (FRA) 67,882 (a) 49,709 (s) Teat length (cm) Alpine & Saanen (FRA) 67,882 (a) 49,709 (s) Teat width cm) Alpine & Saanen (FRA) 67,882 (a) 49,709 (s) Teat form (conical to cylindrical shape, 1 to 9 score) Teat placement (1 to 9 score) Alpine & Saanen (FRA) Alpine & Saanen (FRA) 67,882 (a) 49,709 (s) 67,882 (a) 49,709 (s) Teat angle (1 to 9 score) Alpine & Saanen (FRA) 67,882 (a) 49,709 (s) 5.57 (a) 5.19 (s) 6.34 (a) 6.17 (s) 5.81 (a) 6.21 (s) 4.96 (a) 5.35 (s) 5.52 (a) 6.20 (s) 2.96 (a) 3.16 (s) 4.90 (a) 4.92 (s) 3.61 (a) 4.13 (s) 5.02 (a) 5.10 (s) 1.10 (a) 1.20 (s) 1.23 (a) 1.16 (s) 1.06 (a) 1.14 (s) 1.33 (a) 1.28 (s) 1.36 (a) 1.49 (s) 1.49 (a) 1.73 (s) 0.93 (a) 1.07 (s) 1.30 (a) 1.40 (s) 0.99 (a) 0.92 (s) 0.87 (a) 0.80 (s) Phenotypic Variation Heritability Reference 0.30 ± (a) 0.25 ± (s) 0.29 ± (a) 0.24 ± (s) 0.34 ± (a) 0.37 ± (s) 0.40 ± (a) 0.28 ± (s) 0.23 ± (a) 0.29 ± (s) 0.50 ± (a) 0.46 ± (s) 0.41 ± (a) 0.45 ± (s) 0.27 ± (a) 0.26 ± (s) 0.38 ± (a) 0.30 ± (s) 0.22 ± (a) 0.20 ± (s) Rupp et al Manfredi et al Rupp et al Manfredi et al Rupp et al Manfredi et al Rupp et al Manfredi et al Rupp et al Manfredi et al Rupp et al Manfredi et al Rupp et al Manfredi et al Rupp et al Manfredi et al Rupp et al Manfredi et al Rupp et al Manfredi et al. 2001

29 20 Trait Breed Records Mean sd. Teat orientation(1 to 9 Alpine & Saanen (FRA) 67,882 (a) 3.67 (a) 0.89 (a) score) 49,709 (s) 4.07 (s) 0.84 (s) Reproduction Litter size Phenotypic Variation Heritability Reference 0.35 ± (a) 0.32 ± Rupp et al (s) Manfredi et al Multiple breeds (POL) Multiple breeds (NOR) 8,479 (p) 64,903 (n) 1.51 (p) 1.23 (n) 0.56 (p) 0.43 (n) 0.14 ± (p) 0.18 ± (n) Bagnicka et al.2007 Number of kids born (alive) Multiple breeds (POL) 8,479 (p) 1.48 (p) 0.60 (p) 0.11 ± (p) Bagnicka et al.2007 Kid losses (to 3mths) Multiple breeds (POL) 8,479 (p) 0.11 (p) 0.36 (p) 0.04 ± (p) Bagnicka et al.2007 Age at first kidding (days) Multiple breeds (POL) 7,730 (p) (p) 115.3(p) 0.13 ± 0.038(p) Bagnicka et al.2007 Time between first and second kidding (days) Multiple breeds (POL) Multiple breeds (NOR) 7,730 (p) 45,730 (n) (p) (n) 66.1 (p) 86.8 (n) ± (p) 0.03 ± (n) Bagnicka et al.2007 Age at first kidding (months) Multiple (MEX) ± 0.09 Torres-Vázquez et al. 2009

30 References Lindsay, D. and Skerritt, J. (2003) Improved Breeding in Milking Goats and Dairy Sheep (Guidelines for the Development of National Breeding Plans). RIRDC Publication No. 02/150 Stubbs, A.K. and Abud, G.L. (2009) Farming and Marketing Goat and Sheep Milk Products. RIRDC Publication No. 08/207 21

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