New Zealand Dairy. Herd Improvement Database Review

Transcription

1 New Zealand Dairy Herd Improvement Database Review 2009

2 Commissioned by DairyNZ

3 NEW ZEALAND DAIRY HERD IMPROVEMENT DATABASE REVIEW ANDERSON COMMITTEE REPORT MAY

4 2

5 CONTENTS Page Chairman s Introduction... 4 Recommendations A. Herd testing... 6 B. Structural principles... 7 C. Transition and role of LIC D. High level maps of data flows Background to the Review Review Committee Responses to Questions in Terms of Reference Review Committee A. Personnel 17 B. Meetings.. 17 C. Central issues Submissions and Interviews A. Commissioned International Experts B. Volunteered Submissions C. Interviews 20 APPENDICES A Terms of Reference B Submission received from Dr Wickham C Submission received from Professor Garrick D Submission received from Dr Weigel E List of parties making written submissions F List of interviewed parties 3

6 CHAIRMAN S INTRODUCTION This particular review is the latest in a series that has been undertaken periodically ever since the herd improvement movement began in New Zealand last century. The fact that such reviews have been regularly conducted in one case having the status of a Royal Commission is indicative of an industry that has long realised the value of objective animal performance recording to underpin critical decision making, for both animal selection, in particular, and farm management purposes, more generally. This realisation is arguably the fundamental reason for the New Zealand dairy industry being amongst the most efficient and competitive anywhere in the world. The various reviews on herd recording and related matters over the decades have all been motivated by the need to enhance on-farm profitability and to optimally-position the wider sector for the future, especially in terms of harnessing new technological developments. This review is intended to build upon that tradition. The Review Committee was also mindful of the fact that what might be termed the non-genetic fields in the database to inform decisions pertaining to animal health, greenhouse gas emissions, biosecurity, compliance, general farm management and to facilitate traceability, will grow in importance. Accordingly, in inviting submissions from a wide range of interested parties, all were asked to specifically address the following question: Given your knowledge of the features of New Zealand dairy farming industry, and in the light of impending technological developments and the changing business landscape, what is the preferred design for a national data recording and information management system to ensure optimal genetic gain in the national herd primarily, and also to provide scope for advancing on-farm decision making practice, over the next 25 years? In the conduct of its deliberations, the Review Committee was mindful that, the acknowledged success of the nation s dairy herd recording and related activities notwithstanding, it is an arena that hasn t been without controversy and occasional legal dispute. Accordingly, the Review Committee took special care to follow a framework for its decision making that, as far as possible, was underpinned by sound scientific knowledge and experience. It decided to solicit (and publish) technical submissions from three internationally-renowned experts, together with obtaining input from a wide range of stake-holding individuals and organisations. It also met face-to-face with a substantial number of the latter group. A pivotal recommendation arising from this Review concerns the future custodial and ongoing developmental responsibilities for the New Zealand Dairy Core Database. The Review Committee proposes that these responsibilities, hitherto discharged by the Livestock Improvement Corporation (LIC), be re-assigned to an industry-good agency having no interest in utilising the Core Database for competing commercial services. In making this recommendation, the Committee wishes to record its appreciation to the Directors and Senior Management of LIC on two counts: First, for the leadership in developing and managing the Core Database over the years, and; secondly, for the insightful and constructive advice to the Committee in helping it to formulate its proposals for change. The willingness expressed by the Directors and Senior Management of LIC to assist with the (extensive) transitional agenda that will be necessary to drive this particular recommendation to a successful conclusion stands to be enormously helpful to those who will be involved. 4

7 Significantly, the vision of the Review Committee extends beyond just a recommended re-assignment of the custodial / trusteeship and the on-going developmental responsibilities for the New Zealand Dairy Core Database. Indeed, it envisages the creation of a one-stop New Zealand Dairy Industry data warehouse within which the data pertaining to herd improvement considerations will be a subset. As identified above, the industry now needs a national repository of accurately recorded data encompassing many non-genetic fields. Harmonisation with NAIT will also be essential. Obviously, this vision needs to be tested in detail through the design and transition phases going forward. Logic would dictate that responsibility for such a warehouse should also be vested in an industry good agency, rather than one particular commercial enterprise whose core business doesn t naturally extend to embracing these additional non-genetic contexts. It is also the view of the Review Committee that since some of these additional data recording activities derive from priorities deemed to be in the national interest beyond just the dairy industry, a case for (part) funding by the Government exists. In addition, the Review Committee envisages expanded opportunities to generate revenue from the appropriate sale of data and information (under a cost-recovery, rather than a profit centre, motive). The Committee is strongly of the view that a commitment industry-wide to a National Breeding Objective (NBO) remains essential. It respects the fact that individual breeding companies will always wish to differentiate their services to clients through developing and promoting alternate breeding objectives. Nevertheless, it is of the view that the Breeding Worth (BW) pertaining to the National Breeding Objective must be determined for all animals and should be added as an additional field in a re-defined National Dairy Database. The availability of that particular BW is important not only for the purposes of informed animal selection, but also, it is increasingly important for the valuation of animals that are being traded throughout the industry. The correlation between the NBO BW and any breeding indices derived from the breeding objectives set by those companies seeking to secure a sustainable tenure within the industry will be high. Achieving these aspirations will demand first-class industry governance arrangements. Finally, it remains for me to express genuine appreciation to the fellow members of the Review Committee, Harry Bayliss and Graeme Milne, for their substantial insight and effort. Thanks are also due to Bill Montgomerie (NZAEL), Bruce Thorrold (DairyNZ), and Russell Knutson and Gillian Mangin (MAF) for their excellent support of the workings of the Committee. Robert D. Anderson, ONZM May,

8 RECOMMENDATIONS The Review Committee s focus has been on identifying actions that will advance the future benefits to be derived from a new National Dairy Database in New Zealand. A. Herd testing and other animal records A.1 The New Zealand Dairy Core Database as it exists today should be retained, it should be refreshed with new data on an ongoing basis, and it should be maintained, owned, developed and controlled as an industry good facility. A.2 Herd testing should continue to be regulated by statute over the next five years, requiring that all herd testers must be certified and that they must continue to submit a defined minimum data set that captures the essential features of the dairy production performance of animals in the national herd. However, the Review Committee recommends that the dairy industry should aim for self-regulation of herd testing within the next five years. This recommendation derives from the recognition that there are long lead times associated with amending regulations that are governed by statute, a situation that is not well suited to circumstances where technology is evolving rapidly. A.3 The current herd testing standard for certified herd testers (New Zealand Standard Dairy Herd Testing [NZS 8100:2007]) is not suitable for the circumstances where farm dairies are fitted with automatic recording devices for milk yields and/or milk components. The Standard needs to be up-dated in view of the increasing farmer use of these devices. Key issues with automated systems include the assurance of data integrity and the establishment of cost-effective database interfaces. A.4 Certified herd testers should continue to be required to validate farm, animal, event and production data prior to submission to the New Zealand Dairy Core Database. A.5 The dairy industry has operated an effective scheme for recording non-production traits (described as Traits Other than Production and commonly known as TOP Traits ) since These records include liveweight measurements obtained outside the certified herd testing system and are an integral part of the national genetic evaluation system (the Animal Evaluation System). Accordingly, the future data maintenance and access arrangements must safeguard the on-going availability of these records for the Animal Evaluation System. The governance of the data collection, maintenance and access arrangements in this particular context should be vested in the users of the TOP Scheme, which includes participating breeding companies and breed societies. Future scope A.6 Moving forward, and subject to authorisation by an industry governing entity such as the DairyNZ subsidiary New Zealand Animal Evaluation Limited, new database fields should be defined and added, or deleted where appropriate, as new animal traits of relevance for genetic evaluation purposes emerge. A.7 As a specific example, the national database requirements for genetic improvement purposes should be reviewed in five years to identify industry good opportunities associated with advances in bovine genomics technology. 6

9 A.8 As appropriate, new database fields should also be defined and added to support farm management decisions, or industry supply chain requirements, or to conform with government and/or industry requirements for animal recording for disease prevention or quality assurance purposes. B. Structural and operational principles for a future national database The Review Committee has noted and agrees with the expert submission of Professor Garrick that database design and operations to date have facilitated annual rates of genetic gain that are in line with international best practice. However, stakeholder needs have changed over the past decade and data recording technology is evolving rapidly. Accordingly the optimal structure for a national database will be different over the next 25 years. B.1 The existing national herd improvement database should be re-structured to be operated as an industry good facility (a new National Dairy Database) that is owned, managed and further developed by an industry good organisation established for the purpose through the agreement of the key industry stakeholders, including DairyNZ. The precise design and operation of this facility should be the subject of a DairyNZ study referred to in Recommendation C1. While it is possible that such an arrangement will increase overall costs compared with current arrangements, benefits will also arise from transparent independence of such a new National Dairy Database from any individual service provider and these benefits should be recognised in the DairyNZ analysis. Funding B.2 The new National Dairy Database industry good organisation should arrange on-going funding for the re-structured new National Dairy Database. A mixture of industry and user-pays funding is envisaged. There is also a case for government co-funding in order that the new National Dairy Database can effectively support the biosecurity, animal health and trade access policies of the government. Mandate B.3 The mandate of the new National Dairy Database industry good organisation should preclude that organisation from competing in the commercial markets within which its stakeholders operate. This provision is necessary both from the definition of industry good services, and also to avoid disincentives for immediate service providers to invest in developing new services or to commit to data sharing. B.4 A key function of the new National Dairy Database should be the provision of estimated breeding values and indices of genetic merit for all dairy animals in New Zealand as generated by the National Breeding Objective and genetic evaluation system sanctioned by DairyNZ. Submissions strongly supported industry good provision of a single national method for estimating breeding values and provision of a National Breeding Objective. B.5 The Review Committee supports the current policy for the National Breeding Objective. Economic valuation of milk might in the future include attributes not currently used in payment systems, providing opportunities beyond a single kind of commodity milk. A single National Breeding Objective will not be able to represent all these value propositions. DairyNZ needs to be able to support breeding objectives for 7

10 every distinctive and significant circumstance (while acknowledging that more than one National Breeding Objective for identical production, management and economic circumstances is not warranted). B.6 The new National Dairy Database industry good organisation should continue the current policy that parties sourcing breeding value information from the national genetic evaluation system for the purposes of publication, including provision of herd reports, are required to publish the industry s benchmark index of total merit (the industry good service provision currently represented by the BW index). Access B.7 National Dairy Database services should be extended to include industry good information, defined by the new National Dairy Database industry good organisation, for providers of advisory services about the management of dairy cattle in New Zealand. B.8 As a basic principle, New Zealand commercial service providers should have access to the new National Dairy Database on behalf of their clients, subject to meeting the costs for the services provided by the new National Dairy Database. B.9 The new National Dairy Database industry good organisation should review the access protocols for the new National Dairy Database, with a view to achieving more widespread use of such a facility. B.10 New Zealand research organisations should have ready access to the new National Dairy Database subject to meeting the costs for the services provided. The conditions set by the new National Dairy Database industry good organisation for access in this category should be minimally restrictive. However, criteria for granting access should retain the conditions in the existing regulations that access is only granted if the governors of access protocols are satisfied that to do so is likely to be beneficial to the New Zealand dairy industry or are satisfied that to do so would not be harmful to the New Zealand dairy industry. C. Transition and role of LIC Transition to a new National Dairy Database framework that is operated as an industry good facility and is owned, managed and further developed by a new National Dairy Database industry good organisation, will involve a large number of issues. The LIC custodianship of the New Zealand Dairy Core Database, and its experience with its own database over the past 25 years, mean that it has a deep understanding of the issues associated with transition. In moving forward it is in the interests of the wider industry that this extensive experience and understanding is harnessed effectively. LIC has provided the Review Committee with valuable insights into the transition issues. The Review Committee makes the following recommendations about ways to move forward to a new database framework. C.1 The Board of DairyNZ should undertake a design and transition study (including costs and benefits) of establishing and maintaining a new National Dairy Database with the features described in recommendations A1 through to B10. 8

11 C.2 Informed by the results of that study, a small establishment team (headed by a person outside the employment of any key commercial stakeholders) should be appointed to design the new National Dairy Database framework. Widespread consultation with industry stakeholders will be essential. C.3 The establishment team should operate in accordance with terms of reference established by DairyNZ and agreed by key stakeholders. C.4 The establishment team (in consultation with DairyNZ and key stakeholders) should define the industry good purposes of the new National Dairy Database, identifying the sources and types of data needed for the identified purposes. C.5 The establishment team should ensure that the design features of the new National Dairy Database harmonise with the emerging design of NAIT (National Animal Identification and Tracing). C.6 The establishment team should draw upon a range of expertise. Central to this will be expertise from LIC on the current operations of the core database system, and the national breeding value and breeding index estimation procedures. C.7 The establishment team s conceptual design of the new National Dairy Database (including costings, expected revenues, timelines for implementation, and implications for legislation) should be subjected to assessment by dairy farmers, key stakeholders and the Ministry of Agriculture and Forestry before implementation. C.8 Dairy farmers (through their various representative agencies), other key stakeholders and the Ministry of Agriculture and Forestry should agree on the structure of the industry good entity responsible for the new National Dairy Database. 9

12 D. High level maps of data to information flows Figure 1 depicts the current transformation of raw data to genetic information showing multiple data sources, with some of the data needed for genetic evaluation subject to Ministry of Agriculture and Forestry regulation, while other data is subject to industry selfregulation. The current arrangement is characterised by a lack of clarity on ownership and control of some of the input data sources, and by a lack of clarity around access arrangements to the derived genetic information. Figure 2 depicts the proposed transformation of raw data to genetic information showing all necessary input data and the genetic information outputs of the system: accessible from the new National Dairy Database, and under the control of the whole industry. 10

13 Figure 3 depicts the proposed transformation of raw data to farm management information showing delivery of derived information to stakeholders for incorporation into their service provision to customers. A current example of this form of data sharing and information delivery is provided by the InCalf project. 11

14 BACKGROUND TO THE REVIEW PREAMBLE History New Zealand has a long history of combined actions by dairy farmers to advance the effectiveness of herd improvement (Herd Testing and Related Services, Macdonald Committee Report, 1992, Appendix F). A common thread in this history has been the efforts to achieve data collection and sharing about the characteristics of cows in the national herd. Restructuring 2001 At the time of the dairy industry restructuring in 2001, Government recognised the importance of maintaining a comprehensive database of dairy herd information. The Dairy Industry (Herd Testing and New Zealand Dairy Core Database) Regulations 2001 followed. The Herd Testing Regulations provided the foundation for the collection of a specified set of data characterising the performance of the national bovine dairy herd. NZS 8100:2002 Dairy herd testing, was subsequently developed to define the inputs required to meet the objectives of the Herd Testing Regulations. This has subsequently been superseded by NZS 8100:2007 Dairy herd testing published by Standards New Zealand. Certified herd testers are required to populate the Core Database fields. LIC has been a certified herd tester for many years. CRV AmBreed became a certified herd tester in Purpose of Core Database fields The forty-six data fields in NZS 8100:2007 Dairy herd testing are known as core data and are held in the New Zealand Dairy Core Database, which, under the Herd Testing Regulations, is managed by Livestock Improvement Corporation Limited. The purpose of the data held in the core database is to facilitate herd performance recording and enable research and animal evaluation of the genetic productive potential of the national herd and its members, for the benefit of all New Zealand dairy farmers (Paul Reynolds, Assistant Director-General (Policy), MAF, 23 May 2007, Preface to NZS 8100:2007 Dairy herd testing). The Animal Evaluation System The national genetic evaluation system for dairy cattle is known as the Animal Evaluation System (Animal Evaluation Technical Advisory Committee Report, 1996). The Animal Evaluation System makes use of core data and other non-core animal records, such as liveweight data. The utilised records include information gathered by certified herd testers under the Herd Testing Regulations, information gathered as part of traits other than production services and information provided by dairy breed associations. These performance records both core and non-core data are used to derive outputs of the Animal Evaluation System. These outputs are estimates of genetic merit (breeding values) for over twenty traits of New Zealand dairy animals, and of productive merit (production values) for a smaller number of traits. Additional outputs are indices for profit ranking of animals. The Breeding Worth index (BW) ranks animals on their expected ability to breed replacements that are efficient converters of feed into profit. The Production Worth index (PW) ranks females on their expected lifetime ability to convert feed into profit. 12

15 New technologies Data sharing is not the norm in the commercial world, nor is it ubiquitous in science. Since 1996, new technologies have emerged for selecting animals for breeding and for performance recording. Both categories of new technology come with in-built incentives to restrict data sharing. Selection of animals for breeding is increasingly being influenced by use of DNA data by breeding companies. These data are not readily available for sharing, due to commercial requirements for breeding companies to prohibit or severely limit data sharing to protect proprietary interests. New solutions to data sharing in this area need to be devised. Automated and in-line devices for recording performance of cows are becoming increasingly available. Developers of these emerging technologies do not have normal commercial incentives to share data. The same observation about the need for new solutions to overcome barriers to data sharing apply in this area as well. Advances in automatic data capture also offer opportunities to improve the quantity and quality of data available for animal improvement. Both areas are relevant for the purpose of a core database to facilitate herd performance recording and enable research and animal evaluation of the genetic productive potential of the national herd and its members, for the benefit of all New Zealand dairy farmers. Access and pricing With the advent of competitive herd testing and herd recording since 2001, issues have arisen about access to data, and pricing of data extracts. (i) Core data: Under the Herd Testing Regulations, the New Zealand Dairy Core Database Access Panel adjudicates upon applications for access to core data. The Panel does not set LIC s charges for access, which are subject to a gazetted methodology (available at There is an annual audit of LIC compliance with the Regulations. (ii) Non-core data: Access and pricing for outputs of the Animal Evaluation System (which are non-core data) are decided by LIC. Concern over pricing has been advanced as a key reason for CRV AmBreed developing an alternative breeding value estimation method outside the Animal Evaluation System. Establishment of the review At its meeting on 12 th August 2008, the DairyNZ Board resolved to establish a Herd Improvement Database Review Committee to inquire into animal data requirements for the Animal Evaluation System, and related matter. Terms of Reference DairyNZ established Terms of Reference for the Review Committee (Appendix A). 13

16 REVIEW COMMITTEE RESPONSES TO QUESTIONS RAISED IN TERMS OF REFERENCE (a) Is the current system effective for obtaining animal performance records for the Animal Evaluation System, including cost efficiency and relevance of records? The Review Committee agrees that the current system continues to be effective and has facilitated annual rates of genetic gain that are in line with international best practice. However, stakeholder needs have changed over the past decade and data recording technology is evolving rapidly. Accordingly, the optimal structure and operating arrangements for a national database will be different over the next 25 years. (b) What are the future opportunities for gathering performance records using automatic recording devices that are not currently linked to any national animal recording database? Local and overseas evidence is that there will be many new opportunities arising from automatic generation of raw data. Dr Weigel summarised many of the opportunities as follows: As herds grow larger, labour costs, consumer demands, and animal welfare concerns will become increasingly important. Farmers must strive to achieve optimal production and reproduction efficiency while minimizing disease incidence and severity and environmental impact, and while producing a product of desired quality. Ingvartsen (2008) discussed this issue and emphasized the need for automated precision management of dairy herds. Farmers will have numerous tools and a wealth of information at their disposal. In a review of the impact of new on-farm technologies on dairy cattle breeding, Miglior et al. (2008) noted the advantages of automated data collection, which include improved accuracy, reduced cost, and availability of new traits, as well as the disadvantages, which include recording errors and the need to compensate farmers for their investments in these technologies. Earlier, Wade (2006) noted that both short and long-term implications of the use of automated on-farm data recording systems should be considered. In the short term, automated systems will provide a wealth of data for traits that are already considered in the breeding goal, and this will increase the accuracy of selection. In the long-term, management practices will change due to increased automation, and researchers must strive to identify the types of animals that are best suited for these new management systems, such that new traits may be needed in the breeding goal. (c) Is the current Herd Testing Standard for certified herd testers appropriate in light of recording options currently available or about to become available? The current Herd Testing Standard needs to be revised, taking into consideration both recording options that are currently available and recording options that can be anticipated in the future. 14

17 (d) Should the scope of core data be enlarged to include all inputs into and outputs of the Animal Evaluation System, extending to both the PW and BW indexes? What impacts would such an enlargement have on obligations and benefits of providing input data to the Core Database and the Animal Evaluation System? The scope of data in the new National Dairy Database should include all inputs and outputs for Breeding Value estimation in the Animal Evaluation System, and associated breeding indices. Submissions made to the Review Committee did not indicate that Production Value and Production Worth outputs of the Animal Evaluation System are in strong demand to be provided as industry good services. Consideration of industry good provision of the PW index via the National Dairy Database can be undertaken as a design and transition issue for the establishment team, recognising prior investments by LIC in developing the index. (e) Is LIC s ownership, operation and control of the LIC Database (of which the New Zealand Dairy Core Database is a subset) appropriate in terms of: (i) Meeting the DairyNZ aims to secure and enhance the profitability, sustainability and competitiveness of New Zealand dairy farming; Establishment of a separate and independently managed industry good National Dairy Database stands to advance the wider industry needs more effectively than retention of the New Zealand Dairy Core Database as a subset of the LICs private database activities. DairyNZ should undertake a study to analyse the costs and benefits associated with a National Dairy Database that is re-structured to be operated as an industry good facility by a national database industry good organisation established for the purpose through the agreement of the key industry stakeholders. (ii) The breadth and completeness of data collected; Breadth and completeness of data capture has been well achieved, but emerging signs indicate that this will be less readily achieved in the new era of automatic recording devices and under the expanded scope that private software developers wish to pursue. (iii) Accessibility of data for industry users; A number of submissions to the Review Committee commented that the absence of public domain listings of top cows for consideration as bull dams, together with perceived time delays and debatable pricing arrangements, pointed to sub-optimal access to industry good data under the current database arrangements. (iv) Adaptability to the changing needs of the dairy industry; The Review Committee did not observe any material examples of rigidity in the face of changing needs. It does note, however, that the current Herd Testing Standard makes the capture of data from automatic recording devices difficult. (v) Accuracy and integrity of the data recorded; The Review Committee and all organisations making submissions recognise that maintaining high levels of accuracy in livestock recording is a never-ending challenge. The evidence provided to the Review Committee did not indicate that there are material problems affecting accuracy and integrity of data recorded on the New Zealand Dairy Core Database other than problems that are already being addressed. 15

18 (vi) The cost to industry users; The Review Committee has no reason to form any view other than that the LIC operation of the New Zealand Dairy Core Database has been cost efficient for the industry. (f) Other matters that should be considered in reviewing or inquiring into (a) to (e) above including the role of industry good/dairynz investment and ownership. The Review Committee considered the practicality of a regulated approach to national database capture of raw genomic data. It does not consider that such an approach is appropriate in the immediate future due to the rapidly changing technology for recording single nucleotide polymorphisms. 16

19 REVIEW COMMITTEE A. Personnel DairyNZ appointed the following people to the Review Committee: Professor R D Anderson (Chairman) Mr H G Bayliss Mr G R Milne B. Meetings The Review Committee met on 19 December 2008, 26 February 2009, March 2009, 7 April 2009 and 20 May Gillian Mangin and Russell Knutson (Ministry of Agriculture and Forestry), Bruce Thorrold (DairyNZ), and Bill Montgomerie (New Zealand Animal Evaluation Limited) attended these meetings as observers apart from Gillian Mangin and Russell Knutson being unable to attend the meeting on 20 May. The Chairman attended New Zealand Animal Evaluation Ltd's User Group Meeting (Meeting 17) on 5 February 2009, where he outlined the approach of the Review Committee to its task. The meeting also featured a presentation by Dr Jenny Jago, Senior Scientist, DairyNZ, on The future for automatic recording technologies on New Zealand dairy farms. The Review Committee also made an initial report at an open meeting engaging with the submitting parties which was held on 7 May C. Central issues The Review Committee identified central issues for its attention. Genetic improvement The Review Committee agreed that the National Herd Improvement Database had been, and remained, a crucially important instrument in the pursuit of optimal genetic improvement in the national dairy herd. However, the business landscape and available technologies for herd improvement service providers are now substantially different compared to the time at which the Database and related infrastructure were designed and implemented over 20 years ago. Changes in data recording methods and purposes Data recording opportunities and requirements for general farm management purposes have changed markedly over the past 20 years. The Review Committee identified that obtaining and managing data to enhance on-farm decision making, especially in the case of current larger-scale enterprises, now gives rise to an important additional dimension that the Review Committee needed to consider. At its meeting on 19 December 2008, the Review Committee agreed that optimising genetic gain in the national herd, using NZ pasture based farming systems as the reference environment, remained an overarching primary imperative. It also identified that there were likely to be additional data recording opportunities to leverage the value of a National Database. 17

20 Expert submissions The Review Committee undertook to obtain expert submissions from internationally recognised authorities based offshore. Understanding of the term database The scope of the Review Committee s task required that it adopt an agreed understanding of the term database. The Review Committee adopted the working definition provided by Dr Wickham s expert submission, namely; an integrated system for: gathering and validating data, storing data, analysing data, compiling information, and distributing information. The Review Committee also adopted Dr Wickham s description of a national database as a database in which a defined set of data pertaining to one country is held and shared between multiple users. The Review Committee confined its deliberations to data of relevance to farm production in the New Zealand dairy industry. Contacts for consultation The Review Committee established a list of interested parties in New Zealand to approach for submissions, and contacted: 16 organisations involved with milking systems; 7 organisations associated with milk processing; 29 institutions with discovered links to the dairy industry supply chain; 9 dairy farm advisory organisations; 15 dairy cattle breeding companies; 7 dairy breed societies and the NZ Dairy Breeds Federation; 333 individual contacts through the NZAEL User Group. 18

21 SUBMISSIONS AND INTERVIEWS A. Commissioned International Experts The Review Committee invited input from three internationally-recognised authorities with expertise in the fields of (i) animal breeding and genetics and (ii) on-farm decision support systems. The authorities invited to make these expert submissions were: Dr Brian Wickham Chief Executive Irish Cattle Breeding Federation Society Ltd (ICBF) Ireland Professor Dorian Garrick Lush Chair in Animal Breeding & Genetics Department of Animal Science Iowa State University USA Dr Kent Weigel Associate Professor & Extension Genetics Specialist Department of Dairy Science University of Wisconsin USA As part of the request for the submissions these experts were given the Review Committee s terms of Reference, and also asked to address the following topic: Given your knowledge of the features of New Zealand dairy farming industry, and in the light of impending technological developments and the changing business landscape, what is the preferred design for a national data recording and information management system to ensure optimal genetic gain in the national herd primarily, and also to provide scope for advancing on-farm decision making practice, over the next 25 years? The Review Committee also requested examples of best practice, or pitfalls to be avoided. The expert submissions are appended: Dr Wickham submission (Appendix B) Professor Garrick submission (Appendix C) Dr Weigel submission (Appendix D) As well as his formal submission that is reproduced in the Appendices, Dr Weigel also provided the Review Committee with detailed commentary through exchanges. 19

22 B. Submissions from New Zealand based organisations and individuals Of the 83 local organisations and 333 individuals contacted with an invitation for submissions, the Review Committee received written submissions from 18 organisations and 4 individuals (Appendix E). The Review Committee received these submissions in confidence. C. Interviews The Review Committee interviewed representatives from 10 organisations (Appendix F). 20

23 APPENDIX A DAIRYNZ TERMS OF REFERENCE FOR HERD IMPROVEMENT DATABASE REVIEW Having regard to the legislative provisions governing the dairy industry and the overall aims of DairyNZ to secure and enhance the profitability, sustainability and competitiveness of New Zealand dairy farming, the Herd Improvement Database Review Committee shall inquire into and answer the following questions: a. Is the current system effective for obtaining animal performance records for the Animal Evaluation System, including cost efficiency and relevance of records? b. What are the future opportunities for gathering performance records using automatic recording devices that are not currently linked to any national animal recording database? c. Is the current Herd Testing Standard for certified herd testers appropriate in light of recording options currently available or about to become available? d. Should the scope of core data be enlarged to include all inputs into and outputs of the Animal Evaluation System, extending to both the PW and BW indexes? What impacts would such an enlargement have on obligations and benefits of providing input data to the Core Database and the Animal Evaluation System? e. Is LIC s ownership, operation and control of the LIC Database (of which the New Zealand Dairy Core Database is a subset) appropriate in terms of: (i) meeting the DairyNZ aims to secure and enhance the profitability, sustainability and competitiveness of New Zealand dairy farming; (ii) the breadth and completeness of data collected; (iii) accessibility of data for industry users; (iv) adaptability to the changing needs of the dairy industry; (v) accuracy and integrity of the data recorded; (vi) the cost to industry users? f. Other matters that should be considered in reviewing or inquiring into (a) to (e) above including the role of industry good/dairynz investment and ownership. Conduct of review DairyNZ will advise the Committee on consultation procedures with New Zealand dairy farmers, herd recording agencies, farm advisory services, breeding companies and the Ministry of Agriculture and Forestry. This procedure will include a presentation to the stakeholders consulted before finalising the Committee s report. Completion of Review Having reviewed and inquired into the above matters, the Committee shall make whatever recommendations it considers appropriate and shall report them in writing to the DairyNZ Board. 21

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25 APPENDIX B Submission by Brian Wickham. SUBMISSION TO: NEW ZEALAND NATIONAL DAIRY HERD IMPROVEMENT DATABASE REVIEW BY BRIAN W. WICKHAM (PHD) (14 TH JANUARY 2009) Chief Executive Irish Cattle Breeding Federation Society Ltd Highfield House Shinagh Bandon Co. Cork Ireland [email protected] Web:

26 APPENDIX B Submission by Brian Wickham. TABLE OF CONTENTS 1 TERMS OF REFERENCE BACKGROUND PERSONAL TERMINOLOGY PURPOSE OF A NATIONAL DATABASE STAKEHOLDERS DESIGN CONSIDERATIONS STRUCTURE INTELLECTUAL CAPABILITY COMPUTING INFRASTRUCTURE FUNDING ACCESS SUMMARY RECOMMENDATIONS Terms of Reference This paper has been prepared in response to a request for a submission to address a question posed by the New Zealand National Dairy Herd Improvement Database Review Committee. That is; Given your knowledge of the features of New Zealand dairy farming industry, and in the light of impending technological developments and the changing business landscape, what is the preferred design for a national data recording and information management system to ensure optimal genetic gain in the national herd primarily, and also to provide scope for advancing on-farm decision making practice, over the next 25 years. 2 Background This section contains a brief summary of my background and thus the basis on which my opinions have been formed. More detail can be provided if required. 2.1 Personal The basis of this, my initial submission, is my experience and knowledge of the NZ scene and more recently in leading a redevelopment of the information systems used in Ireland. Here is a brief summary of the key points. NZ Experience. 20 years with LIC and predecessors through to December Led the development of LIC database and genetic evaluation systems. Focus was on genetic improvement and the provision of information services to dairy farmers. Ireland Experience Cattle. Last ten years (1998 to 2008) established the Irish Cattle Breeding Federation Society Ltd (ICBF) and led total redevelopment of cattle (dairy & beef) breeding information infrastructure in Ireland. Established: 24

27 Submission by Brian Wickham. APPENDIX B Integrated national database for cattle breeding shared by some forty organisations, including Herd Books (20), AI (8), Herd Testing (8), Research, Advisory, Milk Processing, Meat Processing, and Animal Health. This database is currently being used by over 90% of all cattle farms in Ireland. Breeding objectives and an across breed genetic evaluation system for all Irish cattle (dairy & beef). This system has maximising farm profit as its overall objective. It is linked with genetic evaluation systems servicing other international populations through Interbull and a number of bilateral arrangements. Theoretical optimal breeding scheme designs for dairy and beef. Implemented operational breeding schemes (G N IR LAND ), targeted at achieving optimal rates of gain for dairy and beef. The G N IR LAND program comprises a bull procurement and progeny testing service for a number of competing AI Companies, some of which have international affiliations. Ireland Experience Sheep. Over the last few years, I have become increasingly involved with Sheep Breeding in Ireland. In 2008 this culminated in a Department of Agriculture Food & Fisheries (DAFF) funded initiative to apply the experience from ICBF (for cattle) to Sheep. The contributions of the Irish Strategy Review Group led by Peter Amer (AbacusBio, NZ) are of particular relevance to the New Zealand review. A copy of the review is available in the publications section of our Sheep Ireland website ( Some of what I have to say below builds on the concept of supply chain information support contained therein. Interbull. While at LIC (and its predecessors) I was closely involved (for 13 years as chairman of the Steering Committee) with establishing this organisation that now plays a key role in facilitating the international exchange of genetic evaluation information for dairy cattle. Currently, I am leading a three-year effort to extend Interbull services to beef breeds and traits. I also have a close interest in extending Interbull services to facilitate the international use of genomic information in dairy and beef cattle breeding decisions. 2.2 Terminology Database for simplicity, I will use the word database to mean an integrated system for: Gathering and validating data, Storing data, Analysing data, Compiling information, and Distributing information. National database a national database is a database in which a defined set of data pertaining to one country is held and shared between multiple users. For the purpose of this submission the national database is restricted to the data of relevance to farm production in the NZ dairy industry. 25

28 Submission by Brian Wickham. APPENDIX B 3 Purpose of a National Database The purpose of a national database for the NZ dairy industry needs careful consideration and agreement of the key stakeholders. My assessment of the potential purposes for a national database includes the following: Information Services. These include: breeding information (from genetic evaluations), milk production (from herd testing), milk quality (from herd testing and bulk milk supply), meat production (from slaughter), reproduction (from calving, mating, pregnancy testing), and herd health (from disease testing). These information services may be provided by a number of different organisations and the database must be able to support information services to farmers from a number of different organisations. Supply Chain & Industry Profitability. The dairy industry is, in effect, a system for supplying milk products to customers around the world. The farm production sector is a key component of this supply chain. To maximise the overall profitability of the industry access is required to extensive and detailed information on a wide range of factors affecting production costs, milk quality, milk supply, quality assurance (including traceability for milk and meat) and future supply. A national database is a very effective means of meeting a large part, but not all, of this information need. Genetic Gain. The need for a national database to facilitate optimal rates of genetic gain is well established and I see little need to repeat the case here. On Farm Decision Support. Farm owners have a constant and on-going need for information to support strategic (long term), tactical (short term) and operational (day-today and minute-to-minute) decisions. Operational decisions tend to depend on locally (on-farm) accumulating data while tactical and strategic decisions require access to information derived from other farms, farm production research (using data from other farms and research farms), markets (national and international), and information about competing land (labour & capital) uses. Ready access by individual farmers to data from their own farms, from other farms nationally and internationally, is best facilitated through a well-organised highly responsive national database. The extent to which a national database can support operational decisions is partly determined by the speed and reliability of electronic networks connecting the farm to the national database. Knowledge Discovery. NZ dairy farming is rapidly evolving and is under constant pressure from international competition, a range of diseases and other threats, including environmental issues. The ability of the industry to thrive and prosper is heavily dependent on its ability to discover and exploit new knowledge. A national database is a very powerful tool for use in knowledge discovery. Quality Assurance. As the dairy supply chain becomes more sophisticated it becomes ever more important that all aspects of production, from conception to processing and distribution are traceable and any breakdowns can be readily contained. A national database is essential for world-class quality assurance for the NZ dairy industry. 26

29 Submission by Brian Wickham. APPENDIX B Animal Health. There are a wide range of conditions that can impact on animal health with consequences for production costs, product quality and human health. The identification, containment, control and eradication of animal health threats is greatly facilitated by the availability of a national database. To varying extents these reasons for a national database are complementary and synergistic. They extend beyond the terms of reference which are restricted to genetic improvement and on-farm decision support. In my view, you should consider these other benefits of a national database. While it is possible to provide many of the benefits of a national database with a number of purpose specific databases there is an inevitable increase in; duplication of effort (data collection, data storage, data processing), errors (different versions of the truth), and a degrading of functionality. Thus the alternative to a well run national database is more expensive, more error prone and less able to deliver benefits. 4 Stakeholders The creation and maintenance of an effective national dairy industry database requires strong and continuing support from a number of key stakeholder groups. These include: Farmers. Since much of the data held in a national database is farm and animal based, and a large part of the information output is directed to individual farmers, it is imperative that farmers are happy to have the data collected by them or on their behalf by service providers (AI, Herd Testing, etc), stored in the database and available for a wide range of potential uses. With the time periods involved it is important to recognise that farmers refers to those currently farming as well as those that will be farming in the future. Processors/Marketers of farm production milk & meat. Milk processors and to a lesser extent meat processors are an intermediary step in the supply chain for the NZ dairy industry. They collect data on product quality and quantity and provide signals in a number of forms including prices and quality grades. Effective management of this supply chain relies on feedback from the market to the processors and producer. The vice versa feedback from farmer to processor to market is also potentially valuable. For example, farm of origin labelling, and an organic production label. A national database fully supporting this link in both directions is only possible with processor and producer agreement. Farm Inputs Service providers. The providers of a wide range of services to farmers are key stakeholders in a national database. The key issues that must be addressed are data access, data ownership, service quality and funding. One of the greatest dangers occurs where one of the service providers exercises control over data access to the point that other service providers decide to operate outside the national database. There is 27

30 Submission by Brian Wickham. APPENDIX B some evidence that this is happening in NZ. Currently the main service providers are: AI. Competing AI companies provide semen, and artificial insemination services. Farmers may use the services of one or more of these companies and change from year to year. AI companies depend on databases and genetic evaluation systems (nationally & internationally) to identify and source bulls from which semen can be marketed. Their insemination services collect mating data of considerable value to the national database. Farm information. Competing providers of information services use data to assemble information that farmers can use in strategic, tactical and operational decisions. Herd Testing is the traditional source of information but information services have evolved substantially over the last twenty years with the development of computer technology. Recent developments in instrumentation and genomics are giving rise to many opportunities for improved information services. These developments should be encouraged and facilitated by an effective shared national database. The key point is that the cost of collecting various items of data is declining rapidly and may, over relatively short periods of time, replace traditional data collection methods. Farm equipment. As information technology has become less expensive it is being increasingly incorporated into various items of farm equipment. Examples include; milking machines that contain in-line measurement of milk characteristics, electronic identification, and automatic drafting. The developers of farm equipment need ready access to standard protocols for accessing data and information (from the national database) and for providing data to the national database. During the R&D phase of a new product the developer could benefit substantially (i.e. develop a better product) from access to the national database. Similarly during the marketing of a product there could be substantial commercial benefit arising from access (within the limits of data protection) to the national database. Farm Advisors. Many farmers rely on advisors, consultants and veterinarians (collectively referred to as advisors) when making strategic, tactical, or operational decisions. These advisors may be able to provide better advice if they have ready access to relevant data and information from the national database. Research. The research stakeholders are those organisations that are focused on establishing new knowledge that can be used by the NZ dairy industry to achieve its goals. Research stakeholders need ready access to potentially any data in the national database, subject to data protection, and be free to manipulate the data (using best scientific practices) in order to examine research questions. Where researchers operate dedicated facilities, or subject animals in commercial herds to a range of novel treatments 28

31 APPENDIX B Submission by Brian Wickham. and measurement, it is important that they can access all known existing data and associate it with any extra data gathered in the course of their research. Traditionally national databases have not been well linked to research users. Education. Education s main interest in the national database is as a tool for helping individuals to learn. The key consideration is data protection and the protection of data integrity. 5 Design Considerations The key to establishing a national database to service the NZ dairy industry for the next twenty years is the design of the ownership and decision making structure. Get this right and an enormously powerful and effective facility is possible, get it wrong and no amount of effort or funding will result in a national database. The key considerations, in no particular sequence, in the design of the ownership and decision making structure are outlined here. Each of these is equally important. All need to be addressed fully. 5.1 Structure There must be a structure which owns the national database. Some of the key attributes of this structure include: It is highly motivated to achieve its mission of ensuring the delivery on the purpose for which the national database has been established. It has control of the resources required to establish, operate and develop the national database. It takes a long term view. It has the support and confidence of all key stakeholders. It operates to the highest ethical and professional standards particularly with respect to data protection, conflicts of interest, and service quality. It operates very efficiently. 5.2 Intellectual Capability A national database, especially one taking a long term view, charged with holding data for the production sector of a major supply chain is a major intellectual challenge. The challenge comes from: the range of data, the rapid rate with which the data processing technology is evolving, the rapid rate with which data collecting technology is evolving, and the rapid rate with which the biological technologies underpinning the NZ dairy industry are evolving. In this environment the maintenance and ongoing development of a national database requires a team of five to ten people with motivation, expertise, knowledge and industry awareness of the highest order. 29

32 APPENDIX B Submission by Brian Wickham. 5.3 Computing Infrastructure The computing infrastructure required to facilitate a national database is: evolving rapidly, rapidly becoming less expensive, rapidly becoming easier to establish; and is rapidly becoming more easily deployed. The big cost with a national database is the need for intellectual input (people) in extracting useful information from data and in ensuring continuous improvement in the services it provides. 5.4 Funding A national database is a strategic asset and it needs to be funded accordingly. A model in which there is a mixture of tax payer, industry and user pays funding can work effectively. Too much emphasis on any one of these three increases the risk of the underfunded component being neglected. 5.5 Access Potentially the most difficult design issue is that associated with data access. Certainly this has plagued the database developments in NZ and many other countries. We have developed a model in Ireland which is proving effective. The key elements are: The national database is owned independently of service providers. The herd owner controls access to the data for the current herd. That is, the herd owner designates the service providers who can have access to the herd data. All data is available for research subject to minimal conditions associated with data protection and intellectual property. The cost of extraction tends to be small and is covered by the research organisation. Genetic evaluations are an integral part of the national database. Breeding scheme design is an integral part of the national database. Service providers have access to data for their clients. Service providers are organised into groupings for which largely similar services are available from the national database. Examples include; milk recording, herd books, AI and Advisors. Service providers are charged for services provided to them by the national database. The national database provides a range of services to AI companies underpinning optimal rates of genetic gain. The database provides an information service to herd owners to support on-farm decision making. The database does not charge herd owners for data collection. While this model may not fit readily into NZ it does highlight what is possible and achievable in a relatively short time period. 30

33 APPENDIX B Submission by Brian Wickham. 6 Summary The benefits of a national database to the NZ dairy industry are many and extensive. Organisational structure is the key design issue in achieving an efficient and effective national database. A data access model has been developed in Ireland which has facilitated the establishment of a national database for dairy and beef cattle and also sheep. 7 Recommendations To ensure the long term viability and profitability of dairy production in NZ a national database be established. That the national database be owned, managed and further developed by an organisation established for the purpose by the key stakeholders. That the national database encompass all data of direct relevance to dairy (and beef?) production as part of the milk product supply chain in New Zealand. That the purpose of the national database includes: -Information Services -Supply Chain & Industry Profitability -Genetic Gain -On Farm Decision Support -Knowledge Discovery - Quality Assurance -Animal Health That a data access model be established to meet the information needs of the key stakeholders while ensuring the long term viability of the national database. That funding of the national database is shared between national, industry and service user beneficiaries. Reference:\\Icbf-server1a\data\Shared\Company\NZCS\Wickham Submission Jan 2009 Final.doc Dated: 14 th January 2009 Irish Cattle Breeding Federation Society Ltd 31

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35 APPENDIX C SUBMISSION TO NEW ZEALAND NATIONAL DAIRY HERD IMPROVEMENT DATABASE REVIEW Comment on Review of Herd Testing in New Zealand BY Professor Dorian Garrick Lush Chair in Animal Breeding & Genetics Department of Animal Science Iowa State University USA 33

36 Comment on Review of Herdtesting in New Zealand APPENDIX C NEW ZEALAND HERD IMPROVEMENT DATABASE REVIEW COMMITTEE Given your knowledge of the features of New Zealand dairy farming industry, and in the light of impending technological developments and the changing business landscape, what is the preferred design for a national data recording and information management system to ensure optimal genetic gain in the national herd primarily, and also to provide scope for advancing on-farm decision making practice, over the next 25 years. Introduction Given my expertise and interests, my comments will be more focused on ensuring optimal genetic gain, than in addressing the scope to advance on-farm decision-making practice. In so doing, it is worthwhile to recall some of the basic principles of genetic improvement, in order to contrast the critical issues that are likely to be relevant during the next 25 years, in relation to those that were paramount at the time of the MacDonald Committee report. Background Annual rates of genetic improvement in dairy cattle populations are dictated by four pathways of selection: bulls to breed bulls (BB), bulls to breed cows (BC), cows to breed bulls (CB); and cows to breed cows (CC). In each pathway, there are three particular characteristics that are relevant: the intensity of selection, denoted by i-bar (ijk) and a function of the proportion selected; the accuracy of predicted merit (rjk) expressed as a correlation, the square root of reliability; and the generation interval or average age of parents when offspring are born (Ljk), with the jk subscripts identifying the relevant pathway. These factors, appropriately parameterized for the four pathways, jointly determine the theoretical annual rate of change in genetic standard deviation units, shown in equation [1]. Consideration of the economic factors associated with evaluating the merit of candidates, securing parents, maintaining populations of candidates during the evaluation procedure, and disseminating improved genetic material, in conjunction with the value proposition of genetic improvement, leads to the identification of an optimal breeding strategy. That optimal strategy is seldom, if ever, associated with the highest potential annual rate of gain. A practical breeding programme frequently represents a departure from the optimal strategy due to aspects of market failure, long time intervals for return on investment and inadequate procedures for capturing some of the benefits from historical improvements, or leverage promised future returns, in order to fund the immediate investment required for ongoing improvement. 34

37 Comment on Review of Herdtesting in New Zealand APPENDIX C Some ballpark values of the relevant parameters for New Zealand circumstances in recent years are given in Table 1. Table 1 Statistics relating to the four pathways of selection in a conceptual model of the New Zealand dairy improvement scheme Using these numbers in equation 1 gives the annual rate of gain (delta-g): Multiplying this response by the genetic standard deviation for breeding worth (BW) will give an indication of the theoretical annual rate of genetic improvement in terms more familiar to farmers (i.e. NZ dollars per 4.5 t DM). More details on the phenotypic sources of information, along with the genetic and economic parameters used in the construction of BW are required in order to determine the response in particular traits that contribute to BW. The annual response of about one-quarter genetic standard deviations per year represents current best practice from an international viewpoint. It has long been recognized that minimum values for the generation intervals in the denominator are dictated by biological factors associated with age at puberty and gestation length that enable individual performance measures to be assessed on cows, and progeny information to be selected on bulls, prior to informed selection decisions. The accuracy or correlation used in the numerator depends upon the availability of individual and progeny information, and these values are markedly reduced if generation intervals are shortened by selecting candidates before they have their own herdtests in the case of cows or before their daughters have their first herdtests in the case of bulls. The intensities of selection are critical determinants of gain that could be most easily manipulated. The Working Group appointed by the MacDonald committee studied equation [1] in a New Zealand context, and reported their findings as Appendix E in that report. 35

38 Comment on Review of Herdtesting in New Zealand APPENDIX C During the period from the 1960 s to 1980 s, a major opportunity arose for increasing the annual rate of genetic gain by increasing the intensity of selection or selection differential on the sire pathways, most notably the bulls to breed cows (BC), due to the development in New Zealand of fresh semen technology and associated levels of dilution of each ejaculate. Accordingly, from the same number of progeny-tested candidate bulls, a smaller number of individuals were required to be selected in order to meet the national requirements for semen doses for AI than would be the case using frozen semen or natural mating bulls. By the time of the MacDonald committee report, the opportunities for further extending the use of individual bulls was limited by the orders of magnitude improvements that had already been achieved, and by the fact that further reduction in the size of the bull team would lead to increased rates of inbreeding in purebred offspring within the national herd. Improving the selection intensity in cow mothers, the CC pathway, was limited by the seasonal nature of dairying in New Zealand, and the inability to control the sex ratio in favour of female AI-bred replacements. The pathway that simultaneously offered the most opportunity for enhancing and the largest threat to reducing future genetic gains was the selection of bull mothers. Historically, bull mothers throughout the world had been required to be registered animals, greatly limiting the resource value of national herds that comprise many animals unregistered with Breed Associations. New Zealand was a leader in relaxing the requirement to allow an unregistered cow to be used in the CB pathway provided her pedigree represented three generations of AI bulls (sire, maternal grandsire and dam s maternal grandsire). The Working Group coined the term active cows to describe these animals that could actively contribute to enhanced genetic gain. Further, the group demonstrated that these animals numbered around 85,000 but were predicted to increase rapidly over the next few years. However, these active cows were distributed throughout the national herd, with many herds containing no more than 10 such cows. In order to evaluate the performance of these cows, they must be herdtested, along with their herdmates. Hence, properly managed national herdtesting linked to routine data collection, storage and analysis was key to maintaining and enhancing genetic improvement at levels that compete with best international practice. Clearly, these three characteristics of each pathway (i, r and L), are influenced by biological, technological, economic, and political factors. It is continuous changes in these factors that necessitate a dynamic approach in order to maintain an optimal national improvement strategy. The last six decades may be characterized, in a dairy improvement framework, by at least four major developments. These are: the development of progeny testing and associated bull evaluation procedures; the dilution of bull ejaculates allowing intense selection of a few elite bulls; and the national identification, herdtesting and evaluation systems that allow timely identification and selection of active cows from throughout the national herd. A fourth major development has been the establishment of a sound breeding objective, ensuring that the genetic change that results from selection is focused on economic improvement. This latter development differs from the former three in that it increases the genetic standard deviation in [1], or dollar value of genetic change by focusing selection on economic traits, rather than by improving the three factors in the four pathways that dictate the annual rate of genetic change. 36

39 Comment on Review of Herdtesting in New Zealand APPENDIX C Current opportunities Animal breeders have long been identifying and characterizing the value of further modifying these pathways in terms of likely impact on the potential rate of annual gain and costeffectiveness of that improvement. It is therefore well known that three classes of technologies could have major impact on national improvement strategies. These are, in no particular order: new approaches to collect more accurate or diverse phenotypic information, including traits such as fertility, disease resistance, feed intake; reproductive strategies to control sex ratio and overcome limitations associated with puberty; analytical methods that can reduce the age at which the accuracy of evaluation can be advanced beyond the levels attainable using knowledge of the parent average. Recent developments in at least two of these classes of technologies are pertinent to the discussion in this document. Genomic technology, in particular, has the potential to completely refocus the parameterization of equation [1], with emphasis shifting from increasing selection differentials to markedly reduce the generation interval, albeit at the cost of reduced selection accuracy. Collectively these changes to the numerator and denominator of [1] might provide for some slight enhancement of annual rates of genetic gain, but more importantly offers considerable reduction in the time period between investing in the evaluation of selection candidates and the realization of improved performance in offspring comprising the national herd. Costs may be reduced slightly, depending upon the extent to which public-funded research is leveraged. Further, genomic technology might aid in extending the breeding objective to include new traits that are known to be economically-relevant, but have not been practical to measure in the past. Current genetic evaluation strategies are based on exploiting records of individual performance and that of relatives, in comparison to herdmates. The value of relatives is dictated by their relationship, more formally through their genetic covariance. Genetic covariation is quantified from ancestral relationships using expected rather than realized values. For example in non-inbred individuals, the relationship between parent and offspring or between full sibs is one-half, and the relationship between half sibs is one-quarter, of the additive genetic variance. These relationships lead to the result, exploited in the so-called animal model, that there are only three sources of information for evaluating the genetic merit of an individual. These sources are: the parent average genetic merit; the individual s own performance relative to herdmates; and the average genetic merit of offspring, adjusted for the merit of the mate. The consequences of this approach are that the parent average merit of an offspring can be predicted before conception, with no opportunity for improving that estimate, other than through improved knowledge of the parents, until the offspring has its own record or produces progeny that are of an age to be recorded. Full sibs will all have identical estimates of merit, until extra information is available, and it is impossible to identify young individuals that are better than their best parent, yet these are precisely the animals that contribute to selection advance. Current improvement programmes invest time and money in waiting for performance information to become available. The DNA sequences in the germ lines of selection candidates are not changing during this time, in the absence of germline mutation and epigenetic modification, as genetic composition is set at conception. This waiting period prolongs the generation interval, directly reducing the potential annual rate of genetic progress. Annual response to selection could in theory be at least doubled, if age at selection could be reduced to puberty in both sexes. Further advance in the rate of gains could be achieved if the accuracy of female selection could be increased beyond the levels possible from individual herd tests. 37

40 Comment on Review of Herdtesting in New Zealand APPENDIX C Marker-assisted Selection It has long been known that offspring inherit half their genome from each parent in the form of chromosomes, and that tracing the inheritance of chromosome fragments between parents and offspring in various generations would provide information to characterize the realized rather than average genetic covariance between relatives. This would allow the merit of genotyped individuals to be immediately predicted from knowledge of any other genotyped animals with predicted genetic merit. The first attempts to use this approach used blood groups as markers and were limited by their inability to characterize chromosome fragments other than in a few regions. Later approaches used up to a few hundred DNA microsatellite markers at a cost of about $10 per marker, to find chromosome regions with large effects known as quantitative trait loci or QTL. Many QTL were discovered in this manner, for a wide variety of traits, but few accounted for sufficient variation to enable cost-effective modification of breeding strategies. Genomic Prediction From January 2008 new marker technology became widely available as a result of two developments, both leveraged from human genomics research, which changed the landscape for cattle and other livestock industries. The first development was the availability of a large portfolio of DNA polymorphisms spread throughout the genome, including single nucleotide polymorphisms (SNP), which were identified through the sequencing (and targeted resequencing) of the bovine genome. The second development was a miniaturized bead technology that enabled the characterization of some 60,000 loci to be determined in a single chemical reaction for under US$300. Prior to this development, research had been published indicating various approaches that could be used to exploit these genotypes in the evaluation of animals. One approach used the genotypes directly to obtain genetic covariances between all animals that could then be used to replace the usual covariance matrix obtained from pedigree information. The other seemingly disparate approach, involved the estimation of the substitution effect (or BV) associated with every chromosome fragment. The merit of new genotyped individuals could then be predicted by summing the value of each fragment the animal was known to have inherited. The two methods give identical evaluations and can be shown to be algebraically equivalent. Either method is now broadly referred to as genomic selection, but would more appropriately be described as genomic prediction. It facilitates, in theory, the identification and selection of elite animals that are better than their parents, prior to puberty or the collection of any direct information on the candidate or its offspring. The extent to which the available portfolio of SNP genotypes can adequately be used to reliably infer the covariances between animals depends upon a number of factors. The SNP loci that are present on the Illumina50k SNP chip are, with two or three exceptions, simply biallelic markers well distributed across the genome and are not expected to represent any causal polymorphisms for any dairy-related traits. In order for them to have predictive ability across families, these markers need to be in high linkage disequilibrium (LD) with causal polymorphisms. The extent to which the markers achieve this high LD with QTL is not known, although their mutual LD can be quantified. It will be better for predictions among purebreds, where LD extends over a longer genomic region than is the case in crossbred animals. The accuracy of predictions will erode as the LD is broken down, and the rate at which this will happen is not known. However, markers that are in high LD and are in close physical proximity with QTL will have persisted since the early origins of domestication and will be expected to maintain such association for some time into the future. 38

41 Comment on Review of Herdtesting in New Zealand APPENDIX C Markers that are currently in high LD but are not physically close, are more likely to erode in a shorter timeframe, and have less predictive ability in other breeds or crossbreds. Such markers may be commonly found in purebred populations where LD persists over longer genomic intervals than is the case in crossbreds. The reliable estimation of the effects of each chromosome fragment identified by SNP alleles would be greatest if the performance of many animals could be assessed in an experiment where every chromosome fragment was cross-classified with every other chromosome fragment in a balanced or orthogonal manner. The estimation of SNP effects, or equivalently the estimation of variation contributed by each genomic region, is known as genomic training and is required to construct the covariance between relatives. In practice, the joint combination of observed fragments is not controlled by the experimenter but determined by the SNP haplotypes that happen to have been carried by the sires, maternal grandsires and other influential relatives represented in the study. The estimation procedure may be able to obtain reliable estimates of large chromosome fragments that have been repeatedly observed, but the smaller contiguous fragments that form the large units and have seldom been observed in a cross-classified manner may have large mutual prediction error covariances with the result that the estimation of new animals that exhibit meiotic recombinations will be inaccurately estimated. In other words, animals with similar genotypes to those in the training analysis will be more reliably estimated than animals with new combinations of SNP alleles. There are also genetic reasons for expecting erosion, over time, of the predictive ability of genomic approaches to evaluation of genetic merit. First, QTL effects may exhibit dominance, in which case the average effect of each allele will vary according to its gene frequency and this may vary over time, and among breeds and crosses. Second, QTL effects may exhibit epistasis, such that the effect of an allele is dependent on the genotype at one or more other loci. In different genetic backgrounds, the average effect of each epistatic locus will vary. Third, genotype-environment interactions may exist, whereby genes that contribute to superior performance in one environment may have little or no impact on performance in some other environment. It is currently understood that mild genotype-environment interactions commonly exist, and that they result from circumstances that modify the extent of stress on animals. Common stressors include nutritional limitations, climatic extremes and exposure to diseases. For all these reasons, continued phenotyping of populations will be required until such a time as it can be demonstrated that robust genomic predictions can be achieved over multiple generations without recourse to performance recording. Structural changes The promise of genomic prediction was greatest for traits that were difficult to improve by conventional means, particularly traits that were sex-limited, lowly heritable, required challenges such as exposure to disease, or were measured late in life or after slaughter. However, these traits are also the most difficult to obtain estimates of genomic effects as less informative phenotypic data is available than for routinely measured traits such as lactation yield. If genomic prediction realizes its promise, it will allow separation of performance recording and selection, two activities that have previously had to involve the same candidates. 39

42 Comment on Review of Herdtesting in New Zealand APPENDIX C Nucleus animals (AI bulls, bulls undergoing progeny test and active cows) presently comprise the most valuable animals from the viewpoint of selection and have also been the most important animals on which to obtain phenotypic measures of performance relative to their contemporaries. That is, what we might describe as the selection nucleus and the information nucleus involved identical animals. If estimates of genomic effects can be reliably obtained in a subset of the national herd, it should be possible, to separate these two activities. For example, the training process of estimating genomic effects could be undertaken in a few intensely recorded herds under commercial management and environmental conditions and the estimated effects could then be applied to animals in the selection nucleus regardless of their location. It may not be necessary to record any parentage on animals in either the selection or information nuclei. Genomic Prediction Today Genomic prediction analyses that have been undertaken to date in various cattle populations have provided variable results as assessed by validation of animals that were not in the training set. In some cases, the regression of genomic prediction on performance of animals has been characterized by coefficients less than the desired value of 1. The correlation between genomic prediction and information on new animals has varied but seldom exceed 0.7. A correlation below 0.8 would account for less than two-thirds of the genetic variation. In contrast, simulation studies had suggested that correlations between should be easily attainable provided QTL are additive and most of the variation is due to 100 or fewer QTL. Analysis of field data would suggest that most traits are much closer to an infinitesimal model than had been assumed in the simulations. A practical target of accounting for 50% additive genetic variation is probably a realistic goal at the present time. It is not known whether, more animals, or more genotypes per animal, or both, will be required to improve on that goal. Genetic evaluation can be considered to involve two components the prediction of parent average merit, and the prediction of the mendelian sampling. Mendelian sampling is a term that refers to the deviation between true merit and parent average merit. Realized correlations of 0.7 between genomic predictions and progeny test predictions imply that 50% genetic variation can be accounted from using SNP genotypes. It might seem reasonable to assume that this 50% is equally partitioned among the parent average and the mendelian sampling component. However, it appears that is easier to identify genetic markers that account for current parent average than it is to find markers that are predictive of future mendelian sampling. Furthermore, it is the mendelian sampling component that needs to be reliably identified in order to effectively exploit genomic prediction, as parent average merit is already evaluated with a reasonable level of accuracy. Until the relative contribution of genomic prediction to each of these two components can be more adequately quantified, the current status of the technology is ill defined. Investment The history of research into dairy cattle improvement was characterized by considerable government investment. In contrast, research into the use of genomic prediction to enhance annual gain has seen considerable investment by AI companies, most notably in The Netherlands and in New Zealand. Furthermore, the pace of research has increased rapidly, and expectations for the timeframe between research investment and realized gains from the technology have shrunk drastically. This provides less opportunity for validation and reflection on results than had been the case in the past. 40

43 Comment on Review of Herdtesting in New Zealand APPENDIX C Further, AI companies have historically enjoyed considerable barriers to entry of new players. It is estimated that it costs some $25,000-$50,000 to progeny test each bull, and with about ten percent of tested bulls graduating for subsequent use, the cost of each proven sire is between one-quarter and one-half million dollars. In addition, the time delay between purchasing young candidate bulls and seeing them through the progeny test process requires investment for at least six years before any significant returns can be obtained. The possibilities for genomic prediction have removed many of these barriers. A new company, especially a foreign player, could enter the marketplace and rapidly offer semen from bulls evaluated on the basis of genomic data. This has increased the risk for existing AI companies, although in New Zealand but not elsewhere in the world there are limitations to the use of sons sired by bulls marketed by existing companies. Genomic selection had promised higher rates of genetic gain and lower costs of bull testing, offering considerable advantages to AI companies. However, in practice, current uncertainty as to the reliability of the predictions has resulted in many companies maintaining their normal practice, in addition to investing in the genotyping of bulls and using a small portfolio of bulls with high genomic predictions that would not otherwise have been used. Collectively these activities, in addition to any privately funded research undertaken in tool development, has led to an increase rather than a reduction in the annual investment by AI companies in genetic improvement. The promise of genomic selection has also attracted new investors in the animal genomics area. Two competing animal health companies, Merial and Pfizer, have invested multi millions of dollars in bovine genomic training, in both dairy and beef cattle. Pfizer has publicly announced that it has or will soon genotype some 30,000 cattle with high-density marker panels. The business models that these companies will use to provide return on their investments is not yet entirely clear, but will include the marketing of marker panels that have been constructed from the most informative markers identified from 50k panels. These companies can undertake genomic training by purchasing semen from marketed bulls and analyzing this in conjunction with published information on the merit of the bulls. This can and is being done on an international basis. The cost associated with genotyping animals for genomic training is substantial. Pfizer s claim of 30,000 animals represents a genotyping investment of almost US$10 million. It therefore makes sense that such animals would have been phenotyped for a diverse portfolio of traits. Feed intake, animal health, and human healthfulness of the resulting meat or milk products are clearly important targets in these endeavours. It is not unreasonable that the cost of collecting these non-standard phenotypes will exceed the cost of genotyping. It is this international landscape, with new players and new technologies that the New Zealand dairy industry will be forced to compete with in the near to medium future. The first official USDA genomic evaluations of dairy cattle were released in January It will likely be 2-3 years before substantial numbers of animals will have flowed through the system to provide independent phenotypic validation of the accuracy of genomic selection. The success of the predictions at that time will have a major impact on the rate of adoption of this technology on an international basis. Over that intervening time period, it is likely that denser marker panels (i.e. >50k) will be developed and applied to cattle data, and that various parties currently competing will collaborate and share data in order to improve the accuracy of their training exercises. 41

44 Comment on Review of Herdtesting in New Zealand APPENDIX C The ability to reliably predict the merit of individual animals without phenotypes opens the door to one-off phenotyping exercises for the purposes of genomic training. Such exercises are taking place using much more refined phenotypes than has been the case in the past. Rather than an interest in milk protein, or milkfat, it is likely that particular protein, fat or other components will be targeted. Whereas in the past the national herdtesting samples were particularly valuable because they enabled increased intensity of selection, the future might dictate the need for more accurate measures of composition on a smaller subset of the national herd. This may lead to the development of concentrated herds of animals for detailed composition measures. These animals may also be of principal interest as selection candidates, as their phenotypic measurement will improve the accuracy of predicting their merit for that fraction of the additive genetic variance (currently 50%) that cannot be accounted for using SNP information. In that case, the dairy industry structure would move to a nucleus herd model, rather than the current situation characterized by a distributed nucleus. A critical question in the New Zealand context is what, if any organization should invest in such phenotyping or nucleus herd strategies. Should it be from individual activities or partnerships among stakeholders such as milk companies like Fonterra, AI companies like LIC, industry good agencies like DairyNZ, foreign companies like Pfizer, or government investment through Universities, CRIs or FoRST? National testing Assuming that genomic selection does prove to be an effective technology for animal improvement, the question arises as to what, if any, phenotyping needs remain for routine farm management purposes. What will be the value proposition of measuring milk yield if not required for national genetic improvement? There is little evidence that current herdtesting practices are cost-effective for individual farmers if the only benefit from herdtesting is the culling of existing cows in their own herd. The introduction of genomic prediction is unlikely to change this circumstance. Dairy farmers might be interested in herdtesting for several reasons. First, to determine yield per cow in order to objectively cull poor performers. Second, to quantify somatic cell count of individual animals to determine individuals with the highest levels of shedding in order to treat or cull these individuals. Third, to take advantage of technologies for determining reproductive and/or disease status from milk samples. Finally, to validate herd inventories for the purposes of national identification, disease control and quality assurance. 42

45 Comment on Review of Herdtesting in New Zealand APPENDIX C Conclusion This document has failed to specify the preferred new design for national herdtesting over the next 25 years. In the meantime, there is nothing to suggest the current design is inappropriate it is in fact enabling annual rates of genetic gain that are in line with international best practice. However, this document has identified some technological factors that will change the optimal design in future from that which existed in recent years. The uncertainty associated with the technology as it stands today has been highlighted, as have some of the other technological and stakeholder issues that will dictate the future optimal design. It is expected that some of these factors will be clarified over the next 2-3 years, facilitating more concrete future comment on the design needs for New Zealand. In the meantime, the following recommendations are appropriate to ensure genetic gain remains at international best practice. The national database that exists today should continue to be maintained, owned and developed from an industry good perspective. The national database should be collectively funded by relevant parties including industry and users. The existing fields should be extended to encompass new traits of relevance. Regulations relating to the manner in which data is collected on farm, checked for errors and entered in the database need to be retained. The database should be accessible by relevant parties according to an agreed procedure. This includes, but is not limited to, access to provide genetic evaluation of economically-relevant traits and the measures of aggregate merit or overall worth that are necessary for genetic improvement. The use of the database should be as broad as possible to include supporting farm management decisions, national individual identification, and animal location and movement information for quality assurance and disease-related activities. The use of the database for genetic improvement purposes should be reviewed within the next 5-10 years to facilitate structural changes that might be required to take maximum advantage of developments in bovine genomics. 43

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47 COLLECTION OF DATA FOR GENETIC SELECTION AND HERD MANAGEMENT: A NORTH AMERICAN PERSPECTIVE Dr. Kent A. Weigel Department of Dairy Science University of Wisconsin - Madison USA APPENDIX D BACKGROUND Today s consumers desire safe, inexpensive, and nutritious food products from farms with healthy, well-treated animals. In North America, genetic improvement programs for dairy cattle have gradually shifted from selection for gross income to selection for net profit. As we look into the future of genetic selection and herd management programs, we might consider the cow shown below as ideal: This cow looks nothing like the true-type models created (in portrait or statue form) by various dairy cattle breed societies, nor does she remotely resemble any of the trophy-winning bovines one might see at World Dairy Expo or other international dairy shows. However, this Wisconsin cow called Granny carried out all of the key tasks listed in the above figure flawlessly, and in doing so she accumulated an extraordinary 4,541 days in milk and produced an unbelievable lifetime total of 429,132 pounds of milk. This cow not only represents an ideal standard in terms of the lifetime net profit she generated for her owners, she also represents the conflict that exists between the anonymous cow preferred by most commercial dairymen and the superstar cow preferred by most pedigree breeders. In the terminology of DairyComp 305, the predominant herd management software program in North America, Granny is a 4-event cow ; That is, when the herdsman looks her up in the computer he sees only four management events associated with her lactation record: FRESH, BRED, PREG, and DRY. She remains anonymous to her owner year after year, as Granny did, precisely because she requires no special treatment and never appears on any of the daily action lists associated with various management or veterinary interventions. 45

48 APPENDIX D As herds grow larger, labor costs, consumer demands, and animal welfare concerns will become increasingly important. Farmers must strive to achieve optimal production and reproduction efficiency while minimizing disease incidence and severity and environmental impact, and while producing a product of desired quality. Ingvartsen (2008) discussed this issue and emphasized the need for automated precision management of dairy herds. Farmers will have numerous tools and a wealth of information at their disposal. In a review of the impact of new on-farm technologies on dairy cattle breeding, Miglior et al. (2008) noted the advantages of automated data collection, which include improved accuracy, reduced cost, and availability of new traits, as well as the disadvantages, which include recording errors and the need to compensate farmers for their investments in these technologies. Earlier, Wade (2006) noted that both short and long-term implications of the use of automated on-farm data recording systems should be considered. In the short term, automated systems will provide a wealth of data for traits that are already considered in the breeding goal, and this will increase the accuracy of selection. In the long-term, management practices will change due to increased automation, and researchers must strive to identify the types of animals that are best suited for these new management systems, such that new traits may be needed in the breeding goal. In the following pages, I will provide a brief history, summarize the current situation, and describe the future outlook for genetic improvement and herd management programs for dairy cattle in North America, considering the six fundamental categories noted previously in the photo of Granny. PRODUCTION EFFICIENCY AND PRODUCT QUALITY Selection programs for milk, fat, and protein production are well established, and approximately 60% of the phenotypic change in these traits, which has been about two-fold over the past four decades, can be attributed to genetic improvement. Significant improvements have been made in statistical methodology for evaluating production traits, including developments such as the animal model evaluation system and the test-day model. On the other hand, improvements in data collection have been negligible. The availability of 5or 7-day averages for milk weight in herds with automated recording systems has been beneficial, but most herds that lack automated recording systems have moved toward AM- PM testing schedules (i.e., AM testing one month, PM testing the next) for the recording of milk weights and analysis of milk components. In the US, differences among herds in the frequency of measurement of milk weights and component percentages are managed via the use of data collection ratings, which provide a quantitative measure of the accuracy of data from various testing plans, based on the frequency and timing of milk recording and sample collection. These data collection ratings are used for weighting records in genetic evaluations, as well as for determining incentive payments in progeny test herds. However, despite the aforementioned developments in statistical methods for data analysis, new measures of production efficiency and product quality have been lacking. Advances in genetic selection programs for production efficiency and product quality will come in several areas, including selection for improved nutritional properties of milk and other dairy products, measurement of traits related to labor efficiency, adjustment for differences in trait expression during the life cycle, accommodation of genotype by environment interactions, and investigation of the possibilities for improving feed conversion efficiency. 46

49 APPENDIX D Selection for improved nutritional properties of milk and other dairy products, through modification of fatty acid and protein profiles, has been a hot topic in recent years. Breed differences in the concentrations of specific fatty acids have been documented (e.g., Soyeurt et al., 2006). Bobe et al. (2008) investigated the presence of genetic variation in milk fatty acid profiles among Holstein cows using gas chromatography and concluded that sufficient variation exists to allow selective breeding for improved nutritional and textural properties of milk fat. Although gas chromatography is the gold standard, it is too expensive and timeconsuming for routine measurement of milk composition in genetic selection programs. The Belgians have led the way in terms of research regarding the potential for prediction of milk composition using mid-infrared spectrometry data. Soyeurt et al. (2007) estimated genetic parameters of butter hardness using mid-infrared spectrometry data, with the objective of assessing the potential to create softer, more spreadable, and less adhesive butter through genetic selection. The estimated heritability of the ratio of saturated to unsaturated fatty acids was 0.22 on a lactation basis and 0.11 on an individual sample basis. Stoop et al. (2008) reported preliminary results from the Dutch Milk Genomics Initiative, in which nearly 2000 primiparous Holstein cows from 400 farms contributed milk samples for the analysis of fatty acid content. Heritability estimates ranged from 0.42 to 0.67 for C4:0, C6:0 to C12:0, C14:0, and C16:0 fatty acids. Estimates for saturated and unsaturated C18 fatty acids were approximately 0.25, with the exception of conjugated linoleic acid (C18:2 cis-9, trans-11), which was The authors concluded that fat composition could be changed readily by genetic selection, with the objective of improving the human health and manufacturing properties of milk. Some studies have also considered the size distribution of milk fat globules (Martini et al., 2006), as this could be associated with milk digestibility, but automated recording of this trait could be challenging. Selection for concentrations of specific caseins and whey proteins has also been the subject of many studies. Schopen et al. (2008) reported moderate to high within-herd heritability estimates for relative proportions of the major milk proteins, indicating an opportunity to ----casein through genetic selection. Cassandro et al. (2008) studied rennet coagulation time and curd firmness using milk samples from Italian Holsteins and obtained heritability estimates of 0.25 and 0.15, respectively. They suggested that selection for high casein content, coupled with selection for low somatic cell count, would lead to improved milk coagulation properties without sacrificing milk yield or milk quality. Rossoni et al. (2008) described a genetic analysis of casein content, as a predictor of cheese yield, in Italian Brown Swiss cattle. Milk composition was evaluated by Fourier transformed infrared spectroscopy, and the authors indicated that future plans include a change from selection for protein yield to selection for casein yield in the national breeding program. In addition, Cecchinato et al. (2008) studied differences in milk coagulation properties among samples from different Brown Swiss cows. Rennet coagulation time and curd firmness of individual samples were associated with mid-infrared spectrometry data to generate calibration equations, such that these equations could be used for widespread, indirect measurement of milk coagulation properties on commercial dairy farms. Lastly, Soyeurt et al. (2008) noted that accurate calibration equations can be developed for predicting the calcium and phosphorous content of milk samples from inexpensive, easy to record mid-infrared spectrometry data. 47

50 APPENDIX D Electronic measurement of milking duration (i.e., milking speed) is straightforward in most on-farm parlor software programs. This trait may have implications with regard to udder health, but more importantly it can be used as an indicator of labor efficiency, because the number of turns of the parlor per hour (i.e. throughput rate) is an important consideration on many commercial dairy farms. Zwald et al. (2005) was among the first to report a genetic analysis of milking speed in dairy sires using electronically recording milking duration data of their daughters, and this study clearly indicated the potential for changing milking duration through genetic selection. Behavior traits have largely been ignored in dairy cattle breeding, with the exception of various survey-based measures of temperament. However, survey data are inherently unreliable, and the usefulness of survey-based assessments of temperament, milking speed, and related variables will continue to decline as average herd size increases. As a side note, recent studies (Muir and Bijma, 2006; Bijma and Muir, 2006) have not only considered genetic variation in the behavior of individual animals, but also behavioral interactions among animals (also known as competition effects ) and have suggested that these interactions may have an impact on genetic evaluations for quantitative traits. Lastly, information from robotic milking systems may provide new measures of behavior that were previously considered as unimportant, such as the number of entrances, number of rejections, and number of complete or incomplete milkings per day. Another aspect of labor efficiency is milking frequency. In North America, some large herds milk early lactation cows more frequently (typically twice as often, by milking certain pens at the beginning and end of each milking), resulting in 4X or 6X milking frequency during part of the lactation. At the other extreme, farmers in some countries utilize 1X milking, at least during a portion of the lactation. Guinard-Flament et al. (2008) attempted to correlate plasma lactose level with the ability of dairy cows to tolerate 1X milking and, although this effort was unsuccessful, other biological or genetic markers might be used for this purpose in the future. An interesting consideration, although it has not yet been explored in dairy cattle, is selection for reduced residual variance, after all known genetic and environmental effects have been removed (Mulder et al., 2008). In simpler terms, this refers to selection for phenotypic uniformity, which could be a valuable trait in large commercial herds in which animals must be managed as a group. Genetic differences between breeds in maturity rate are well known, and within-breed genetic differences have been reported in several studies (e.g., Suzuki et al., 2006). This information could be used to select sires with daughters that mature earlier, which would tend to increase discounted net profits, or to select sires with daughters that mature later, which would tend to decrease replacement costs. Many studies (e.g., Strabel, 2006) have considered the merits of adding genetic evaluations for lactation persistency to the breeding goal, with the objective of decreasing feed costs and metabolic stress near the peak of the lactation. Although most measures of persistency are simple by-products of test-day genetic evaluations, one must consider the economic implications of different definitions of this trait. Specifically, we should attempt to increase lactation persistency without inadvertently favoring animals that peak at a low level of production due to genetic inferiority or underlying health problems. 48

51 APPENDIX D Sensitivity of an individual to the environment or management system (or its ability to adapt) can be assessed quantitatively using reaction norms (e.g., Pegolo et al., 2006). Strandberg (2006) described methodology for assessing genotype by environment interactions using reaction norms and noted that defining an objective, continuous environmental scale is a key challenge when implementing such models. Several recent studies have addressed the topic of genetic variation among dairy cattle in susceptibility to heat stress. Recently, Sanchez et al. (2008) proposed a genetic evaluation model that contained an animal-specific threshold at which heat stress, as measured by temperature-humidity index at the nearest weather station, began to impair phenotypic performance for traits such as milk yield or female fertility. Earlier, Misztal et al. (2006) noted that daily temperature and humidity data from weather stations more effectively captured genetic variation between animals in susceptibility to heat stress than monthly (i.e., test-day) weather data collected on individual farms. Genetic selection for improved feed efficiency, or more specifically reduced residual feed intake (e.g., Arthur and Herd, 2006), has been hampered by our inability to measure dry matter intake on large populations of animals. However, genomic selection for feed efficiency may be possible with the advent of reliable equipment (e.g., systems by Insentec or Growsafe) for measuring the intake and refusal of each cow at each meal in experimental, nucleus, or contract herds, because estimated single nucleotide polymorphism effects from these herds can be used to predict genomic breeding values for other animals in the general population. Assessing forage intake in grazing ruminants is more challenging, although this may be possible using internal and external markers for estimating fecal output and dry matter digestibility, or using a fecal index based on near infrared reflectance spectroscopy, noting that the latter can provide more accurate data if properly calibrated and monitored (Coleman, 2006). Overall, it is likely that effective selection programs for improving the nutritional properties of dairy products will be developed using mid-infrared spectrometry or related technologies to assess the proportions of specific fatty acids, caseins, whey proteins, and minerals in milk samples from commercial farms. Furthermore, development of genetic evaluations for milking speed using electronically recorded milking times from on-farm parlor software is straightforward. Development of selection tools for changing lactation persistency or maturity rate are equally straightforward, although the economic value of these traits is questionable. Additional data will be needed to precisely define environmental conditions if we choose to capitalize on genotype by environment interactions through the development of environment-specific breeding values (or more likely genetic or genomic breeding values that are a continuous function of some environmental gradient). Much of the initial work involving genetic susceptibility to heat stress has already been completed, and in this case sufficient environmental data can be obtained easily from nearby weather stations. Consideration of other environmental or management factors will require development of accurate, objective methods for assessing the conditions affecting a given animal. Lastly, it is unlikely that conventional selection can be used to improve feed efficiency, because widespread measurement of dry matter intake is not cost-effective. However, this trait could be amenable to genomic selection based on estimated single nucleotide polymorphism effects from experimental, nucleus, or contract herds. 49

52 APPENDIX D CALVING ABILITY Calving ability is an ongoing problem in North American Holsteins and, to a lesser extent, Brown Swiss. Despite the availability of genetic evaluations for direct (service sire) calving ease for more than two decades, genetic progress has been negligible. The reason is threefold. First, pedigree breeders who are interested in winning dairy cattle shows have intentionally favored sires that produce enormous calves that can compete in the show ring. Second, calf size has increased modestly as a correlated response to selection for other traits, including milk yield, udder depth, and overall conformation score. Third, little direct selection for reduced calving difficulty has occurred, because farmers have instead used calving ease information as a corrective mating tool. In other words, they have used calving ease information to identify bulls that sire small calves and can safely be used as mates for yearling heifers. Conversely, bulls that sire large calves have been used as mates for lactating cows. As such, bulls of the latter type have continued to provide significant genetic contributions to future generations. Only recently, after the introduction of genetic evaluations for maternal calving ease, direct stillbirth rate, and maternal stillbirth rate, has information regarding calving ability been incorporated into our national selection indices. The addition of genetic evaluations for stillbirth rate has been quite valuable, as research has confirmed that significant genetic variation exists in the direct and maternal components of stillbirth rate, even after accounting for differences in calving difficulty. Previously it was thought that selection for both calving ease and stillbirth rate would be redundant. The next frontier with regard to calving ability, at least in the US, is consideration of gestation length. Research in this area began within the last five years, largely because bulls from one or two very popular sire families tended to transmit significantly (i.e., 3 to 5 day) shorter gestation length when used as mating sires; This decrease in direct gestation length was accompanied by surprisingly low (i.e. favorable) genetic evaluations for calving ease and stillbirth rate among these sires. As noted by Cromie et al. (2008), who described genetic evaluations for direct and maternal gestation length in Ireland, the direct heritability is much greater (0.36 to 0.44) than the maternal heritability (0.04 to 0.08), as it is well known that the fetus triggers parturition. Lopez de Maturana et al. (2009) used recursive models to describe the biological relationships between direct and maternal aspects of calving ease, stillbirth rate, and gestation length in Holstein cattle. This study noted that the optimum gestation length, with respect to minimizing calving difficulty and stillbirth rate, may be slightly (i.e., 1 to 2 days) less than the current average in US Holstein cattle. Furthermore, this study noted that the genetic correlation between calving difficulty and stillbirth rate differed in both magnitude and sign between groups of animals that had been divided according to gestation length. This finding precludes the idea of simply including gestation length as a correlated trait in a multiple-trait best linear unbiased prediction model and suggests that a more creative strategy is needed for incorporating gestation length data into genetic improvement programs. Lastly, the influence of herd management on calving traits should be considered. Swalve et al. (2006) indicated that estimates of direct and maternal heritability for stillbirth rate were significantly higher among unsupervised births than among supervised births, in which death of the calf occurred despite assistance by the farmer. Furthermore, estimated genetic correlations between the direct and maternal components of stillbirth rate differed considerably between supervised and unsupervised births. 50

53 APPENDIX D Clearly one cannot expect farmers to refrain from providing calving assistance when it is needed, but this study suggests that programs for recording calving ease and stillbirth data should capture information regarding the extent of supervision provided at each birth event. Overall, selection for calving performance using an index of direct and maternal calving ease, direct and maternal stillbirth rate, and direct gestation length seems to be the best strategy for reducing the incidence of problems at calving time and minimizing calvingrelated health and fertility disorders in the subsequent lactation. REPRODUCTIVE PERFORMANCE The decline in reproductive performance of Holstein cattle and, to a lesser extent, cattle of other dairy breeds, has been well documented. In response, nearly every leading dairy country has implemented genetic evaluations for one or more measures of female fertility in the past decade. This reaction contradicts our way of thinking with regard to genetic selection for low heritability traits, that is, the opinion that traits with low heritability (e.g., less than 10 to 15%) should be improved by management rather than selection. We now realize that significant genetic variation exists in traits such as female fertility, despite the simultaneous existence of tremendous variation due to management practices and environmental conditions. It is clear that these new selection tools, perhaps coupled with advanced reproductive management programs (i.e., hormonal synchronization), have begun to stabilize or reverse the trend of declining fertility in high-producing dairy cows. Improvements in selection for female fertility traits will most likely come in three areas: improved data collection and statistical modelling for existing traits, correlated responses to selection for other traits that influence fertility indirectly, and automated recording of fertilityrelated attributes on the farm. Modelling of reproductive traits in genetic evaluation programs can be challenging for a variety of reasons, including the binary (all or none) nature of traits such as conception rate, and the censoring of traits such as services per conception or days open for cows that never become pregnant. More importantly, fertility data are often of poor quality, perhaps not so much as a consequence of inaccurate reporting for the traits of interest, but rather as a consequence of missing information for key management activities that can influence these traits (e.g., unreported inseminations by natural service bulls, unreported do not breed designations for cows that have been marked for culling, and extensive and differential usage of hormonal synchronization programs). Goodling et al. (2005) described the effects of hormonal synchronization programs on genetic evaluations for days open and other measures of female fertility. These programs are extremely popular in North America, but they introduce a large dose of non-genetic variation into many measures of reproductive performance. Many different programs exist, because individual farmers and veterinarians are keen to customize their synchronization protocols based on the latest research or anecdotal evidence. As shown in this study, information regarding the extent of hormonal synchronization in a given herd can be mined from simple variables such as the distribution of inseminations over days of the week (e.g., high proportion of inseminations on Thursdays), and this information can be useful for grouping herds or developing adjustment factors. 51

54 APPENDIX D A particularly concerning source of inaccuracy in genetic evaluations for female fertility traits is the bias introduced by differential reproductive management of individual animals or groups of animals within the herd. Such differences can occur in the application of hormonal synchronization programs. For example, these programs may be used only for animals that fail to demonstrate a visual heat within a certain number of days postpartum. Likewise, differences in length of the voluntary waiting period after calving, whether due to age, milk yield, body condition score, or other factors, can cause havoc in genetic evaluations for interval traits such as days open, particularly in herds where high-producing cows are intentionally subject to a longer voluntary waiting period for economic, rather than genetic, reasons. Furthermore, information reported by farmers regarding length of the voluntary waiting period is often inaccurate. Chang et al. (2007) developed a model in which length of the voluntary waiting period was treated as an unknown parameter and was estimated from the data of individual herds or sub-groups of animals within a herd. Lastly, reproductive status can have indirect effects on genetic evaluations for other traits. For example, Bohmanova et al. (2008) described a method for including a pregnancy effect in a test-day model for production traits, noting that models that ignore this effect tend to overestimate the breeding values of non-pregnant cows. Strong relationships exist between female fertility and other trait groups, most notably calving ability and metabolic health. It is well known that cows that experience a difficult calving, as well as cows that give birth to stillborn calves, tend to exhibit extended days to first heat, poorer conception rates, and greater days open in the subsequent lactation. Similarly, it is well known that cows that lack adequate body condition tend to exhibit higher incidence of anoestrus, poorer conception rates, and greater incidence of embryonic loss. These traits are discussed in detail in other sections of this manuscript. Butler and Butler (2008) noted that when a cow enters negative energy balance and begins to mobilize body energy reserves to meet lactation demands, which receive higher priority than other physiological processes, including reproduction, anoestrus can extend for several months and conception rates can be unacceptably low. In several countries (e.g., Biffani et al., 2008), an aggregate index comprised of traits such as days to first insemination, conception rate at first service, days open, calving interval, and indirect traits such as angularity, is used to select for improved female fertility. Automated systems for on-farm monitoring of reproductive performance will be aggressively pursued for reasons of herd management, but useful information for genetic selection may be captured as well. Postpartum measures of luteal activity based on measurement of milk progesterone are highly heritable, as compared with other measures of female fertility. For example, Petersson et al. (2007) reported heritability estimates for days to first luteal activity of 0.30, 0.25, 0.20, and 0.14 for thrice weekly, weekly, biweekly, and monthly measurement of milk progesterone level, respectively. The authors argued that monthly progesterone testing, based on samples collected in routine milk recording programs, could provide reliabilities of 0.80 for genetic evaluations of days to first luteal activity based on 50 milk-recorded daughters per sire. Of greater interest are automated systems for measurement of milk progesterone, and as noted by Miglior et al. (2008), automated on-farm measurement of milk progesterone may be possible using the Lattec Herd Navigator System. This will allow detection of individuals or sire families with atypical progesterone profiles that may not be conducive to reproductive efficiency (Petersson et al., 2006). Furthermore, daily measures of traits such as body temperature or physical activity, which provide insufficient data for making management or genetic selection decisions on their own, might be very useful when combined with other indicator variables. 52

55 APPENDIX D Lastly, little effort has been made to exploit genetic variation in maternal traits related to embryo transfer success, such as the number of flushed oocytes, transferable embryos, degenerated embryos, or unfertilized oocytes (Koenig et al., 2006). However, such traits may become more interesting in the era of genomic selection, as breeding companies attempt to capture synergies between advanced reproductive and genetic technologies. Overall, it is clear that, through more diligent data recording and more creative statistical modelling, there is significant room for additional improvement of selection programs for existing measures of female fertility. Furthermore, the realization that calving problems, inadequate body condition scores, and early postpartum health disorders can tremendously impair reproductive performance will help focus attention on these traits and will lead to favorable correlated responses in female fertility. Lastly, online measurement of milk progesterone level, if it proves to be accurate and cost-effective, will introduce many new and exciting possibilities for genetic and genomic selection of fertility, as well as enhanced reproductive management. UDDER HEALTH Improvement of udder health, namely resistance to mastitis and teat injuries, is of tremendous economic importance due to the relatively high prevalence of mastitis, the corresponding costs of veterinary treatment, lost milk yield, discarded milk, and involuntary culling, and the risk of antibiotic residues. Great improvements have been achieved in udder conformation through selection for traits such as udder depth, fore udder attachment, rear udder height and width, udder cleft, and teat placement. In fact, improvements in these traits have been so dramatic that the rear teats of some cows are now too close for efficient milking, and selection pressure on these traits has been relaxed. Unfortunately, genetic correlations between udder conformation and clinical mastitis are modest, so the aforementioned improvements in udder traits have not been accompanied by a similar decrease in the incidence of mastitis. Genetic evaluations for somatic cell count have been available in most countries for 10 to 15 years, and fortunately the correlation between this indicator trait and clinical mastitis is much greater (about 0.6). Furthermore, somatic cell counts reflect both clinical and subclinical mastitis. The main disadvantage, however, is that monthly or bi-monthly measurement of somatic cell count in conventional milk recording programs is insufficient to capture day-to-day variation in the udder health of individual cows (which often suffer from mastitis between monthly or bimonthly milk tests). Future improvements in the measurement and evaluation of udder health will come in three areas: use of mastitis treatment data from on-farm herd management software, identification of specific mastitis pathogens for genetic or genomic selection, and automated recording of somatic cell count or related variables. Recording of clinical mastitis data and use of this information in genetic improvement programs has been the norm in the Nordic countries for more than two decades. On one hand, the presence of a national veterinary recording system (coupled with stringent rules on the use of antibiotics) is conducive to the collection of accurate and reliable mastitis data for genetic selection. On the other hand, small average herd size in these countries greatly complicates the use of this information in genetic evaluations, as many contemporary groups have few or no reported cases of mastitis, and random variation due to small sample size precludes an accurate assessment of the environmental conditions within a particular herd at a given time. 53

56 APPENDIX D At the other extreme lies the capture of mastitis treatment data from on-farm herd management software on large commercial dairy farms in North America. Zwald et al. (2004a, 2004b) described procedures for gathering farmer-reported mastitis data from the DairyComp 305, PC Dart, and DHI-Plus software programs into a central database for the purpose of genetic evaluation. Heterogeneity in the acronyms used to record mastitis events on individual farms (e.g., MAST, MASTLF, RRMAST, etc.) was accommodated via inspection of the data and pooling of event codes that seemed to represent mastitis. Furthermore, nonspecific event codes (e.g., ILLNESS, HOSPITAL, SICK, etc.) could often be recognized as mastitis by scanning the accompanying remarks and identifying the specific antibiotics that were used. In these studies, heritability estimates for mastitis matched or exceeded estimates from the Nordic countries, despite the apparent lack of control in the data recording system. Furthermore, heterogeneity between herds in the procedures for diagnosing specific health disorders or their severity tended to average out across herds, because individual sires were used across multiple herds, and because genetic evaluations were based on comparison of daughters of these sires within large contemporary groups (often containing 200 or more animals per herd-year-season). More recently, Koenen and Steeneveld (2006) reported on Dutch efforts to capture clinical mastitis data from on-farm management software and noted that heritability estimates in the range of 0.03 to 0.05 were achieved in a preliminary study involving 19,000 cows on 200 farms. With advanced statistical models, such as multiple ordered categorical threshold models or test-day threshold models, heritability estimates for clinical mastitis as high as 0.12 to 0.15 have been reported using field data from commercial farms (Zwald et al., 2006; Hinrichs et al., 2008). Several recent studies have considered the use of pathogen-specific mastitis data in genetic selection programs. For example, Holmberg et al. (2008) considered selection for resistance to S. aureus, coagulase-negative staphylococci, Str. uberis, Str. dysgalactiae, E. coli, and other streptococci, and in a German study, Schafberg et al. (2006) reported heritability estimates of 0.06 to 0.09 for pathogen-specific mastitis, particularly S. aureus and coagulasenegative staphylococci. In a similar study, Sorensen et al. (2008) reported heritability estimates for pathogen-specific mastitis ranging from for S. aureus to for S. uberis. These estimates tended to be lower than the estimated heritability of unspecified (i.e., grouped) cases of clinical mastitis, most likely due to a greater incidence rate of the latter. Estimated genetic correlations between susceptibility to specific mastitis pathogens ranged from 0.45 to 0.76, indicating the possibility of effective selection for nonspecific resistance or immune response, and estimates tended to be greater among closely related pairs of pathogens (e.g., S. dysgalactiae and S. uberis) than distantly related pairs of pathogens (e.g., S. aureus and E. coli). Miglior et al. (2008) noted that the Afilab TM system from S.A.E. Afikim offers online analysis of somatic cell count, as well as blood in the milk, whereas the DeLaval online cell counter offers accurate, fully automated on-farm measurement of somatic cell count in a composite sample from each cow at every milking. Similarly, the Lattec Herd Navigator System offers on-farm analysis of lactate dehydrogenase, which is an enzyme associated with early detection of mastitis. This information, coupled with daily milk weights from the milking parlor, mastitis treatment events from on-farm herd management software, and data from other real-time sensors (e.g., activity or body temperature) could be used to produce a formidable set of diagnostic tools for herd management, as well as interesting new information for genetic selection. In this manner, indicator traits such as electrical conductivity (e.g., Dal Zotta et al., 2006), which lack the sensitivity and specificity to serve as useful stand-alone selection tools, 54

57 APPENDIX D can be combined with other variables to form a useful portfolio of udder health traits for use in management and selection applications. Kramer et al. (2008) suggested the use of statistical process control methods for detection of mastitis and other disorders, such as lameness. A multivariate fuzzy logic model comprised of weekly somatic cell count data and clinical mastitis treatments was used to identify new cases of mastitis, whereas univariate control charts were used to identify new cases of lameness. Error rates (false positives) tended to be high in this study, but as additional variables become available through automated recording systems it is likely that acceptable sensitivity and specificity levels can be achieved. Overall, it is clear that farmer-reported data from on-farm herd management software will play a large role in selection programs for improved udder health in the future, particularly as breeding companies continue to concentrate their product development (e.g., progeny testing or genomic selection) efforts in larger herds. On the other hand, pathogen-specific mastitis data will probably be more useful for applications involving genomic selection, because identification of genotypes or haplotypes associated with resistance to a specific pathogen may be more likely than identification of genotypes or haplotypes associated with general immunity. Furthermore, many well-managed dairy farms in North America have extremely low incidence rates for contagious mastitis pathogens, and knowledge regarding contagious versus environmental pathogens could be useful in sire selection programs involving such herds. The ability to capture real-time measurements of somatic cell count and related variables in an automated manner will open many new management and selection possibilities, and it is likely that the coupling of advanced statistical models with daily observations for these traits will lead to significantly greater estimates of heritability parameters. A note of caution, however, is that the availability of daily, online measurement of somatic cell count on the farm could have a significant adverse impact on participation in traditional milk recording programs. MOBILITY Lameness and associated mobility problems cause considerable economic losses on North American dairy farms, and this trait group is of particular interest on farms where cows spend the vast majority of their time on concrete. Selection for mobility using conformation traits, most notably foot angle and rear legs side view, was the approach of choice for more than two decades. However, selection for steeper foot angle was complicated by low heritability, which was in turn due to the influence of hoof trimming and an inability of classifiers to score this trait accurately in unfavorable environmental conditions (i.e., mud or manure). In addition, selection for rear legs side view was complicated by the fact that this trait has an intermediate optimum and is therefore more challenging to improve through directional selection. More recently, rear legs rear view was added to the classification portfolio, and early indications are that this trait will significantly enhance our ability to improve mobility through genetic selection. Locomotion scoring, which is a commonly used management tool for assessing the degree of lameness in commercial herds, has recently been added to the type classification program in many countries. While this trait is strongly correlated with lameness and longevity, it is also very highly correlated with rear legs rear view and feet and legs score (a subjective major category score assigned in type classification programs), and as such it may provide little additional information for selection programs. As a note of caution, measurement of locomotion scores for management purposes is subject to severe bias if one chooses to evaluate only a subset of animals in a given herd. The best place to measure this trait is in the parlor return lane, and because lame cows typically lag behind the rest of the group they will tend to congregate at the end of the milking string. 55

58 APPENDIX D Therefore, scoring of the entire herd is generally recommended. Recently, there has been a flurry of research regarding the possibility of using data from claw trimmers for genetic selection purposes, particularly in Germany and the Nordic countries. Konig et al. (2008) described the current status of laminitis recording by claw trimmers in Germany and evaluated the usefulness of this information in selection programs. In this study, the estimated heritability of laminitis was approximately 0.12, and the incidence of laminitis was correlated with higher milk yield and somatic cell score. In addition, Swalve et al. (2008) noted that substantial genetic variation exists among German Holsteins in a broad array of claw health parameters, including laminitis, dermatitis digitalis, dermatitis interdigitalis, white line disease, sole ulcer, rotation, tylom (interdigital growth), and thick hocks. Heritability estimates for these claw disorders ranged from 0.05 to More importantly, correlations with estimated breeding values for the aforementioned conformation traits, such as foot angle and rear leg set, were marginal. This indicates that genetic selection for linear type traits will not lead to significant improvements in claw health and, conversely, that data collected during claw trimming can provide new and useful information for genetic selection programs. Boelling et al. (2008) described the Swedish program for recording of claw disorders by claw trimmers and suggested the development of a selection index comprised of claw disorders, claw trimming, locomotion scores, and rear legs rear view scores. Naeslund (2008a, 2008b) described results of a genetic analysis of digital dermatitis, heel horn erosion, sole hemorrhage, and sole ulcer in Swedish Red and Swedish Holstein cattle using data provided by claw trimmers. Incidence rates per lactation ranged from a low of 3 to 5% for sole ulcer to a high of 25 to 27% for sole hemorrhage, whereas corresponding heritability estimates ranged from 0.03 to 0.09 for these traits. The authors also suggested the possibility of combining digital dermatitis and heel horn erosion into a single trait, and likewise for sole hemorrhage and sole ulcer, and although incidence rates for the combined traits tended to be higher, heritability estimates increased only slightly. Lastly, Uggla et al. (2008) noted that estimated genetic correlations between common claw diseases, including digital dermatitis, heel horn erosion, sole hemorrhage, and sole ulcer, and linear type traits, including rear legs side view, rear legs rear view, hock quality, bone structure, and foot angle, tended to be quite low. Another interesting possibility is automated detection of lameness based on the distribution of weight observed using walk-over scales. Rajkondawar et al. (2006) proposed a system for automated detection of lameness using vertical ground reaction force measurements of individual limbs. Vertical forces were measured over time during the weight-bearing and nonweight-bearing phases of the gait, and these were used to calculate various movement variables for each limb, including peak ground reaction force, average ground reaction force, stance time, impulse (integral of ground reaction force with respect to time), and area under the Fourier-transformed curve of ground reaction force signature. The accuracy of gait-score models for prediction of clinical lameness in this study ranged from 0.63 to 0.73, which was slightly lower the accuracy of lesion-score models for prediction of clinical lameness (0.75 to 0.84). However, gait scores can be measured by automated systems, whereas lesion scores must be measured manually. 56

59 APPENDIX D Overall, it appears that additional improvements in the recording and evaluation of mobility through linear type traits, including locomotion scores, will be negligible. Data provided by claw trimmers contain sufficient genetic variation for inclusion in selection programs. Furthermore, these data are disorder-specific, and such detailed information may be valuable if heterogeneity exists in the economic value of various disorders, or if susceptibility to individual disorders can be associated with specific genotypes or haplotypes. The primary challenge in using data from claw trimmers will be achieving consistency in the diagnosis and recording of specific disorders and their severity, which will likely require the use of hand-held computers and standardized software. A promising alternative is automated and objective, albeit nonspecific, measurement of lameness using weight distribution data from walk-over scales, as one could use this approach to develop a massive database of daily assessments of mobility for individual animals. EARLY POSTPARTUM HEALTH Early postpartum health disorders are common problem among high-producing dairy cows and a costly source of frustration for their owners. Several of the aforementioned selection tools for improving calving ability, mobility, and udder health can provide favorable correlated responses in early postpartum health, particularly when used in conjunction with genetic evaluations for length of productive life, but in the future a direct, timely, and proactive approach to improving resistance to infectious diseases and metabolic disorders will be needed. As noted by Bertoni and Trevisi (2008), herd management information, including vaccination and treatment records, can be combined with physiological measures, such as cytokines or acute phase proteins, to provide an overall assessment of animal health. As such, future programs for improving postpartum health will be based on a combination of farmer-recorded disease treatment records and a portfolio of physiological indicators. Selection for length of productive life clearly favors families that can balance high milk production with early postpartum health and fertility. However, because genetic evaluations for productive life are based on culling data, they fail to provide accurate information in a timely manner. Furthermore, culling rates (i.e., herd turnover rates) are subject to large external sources of variation, such as milk prices, replacement heifer prices, herd expansion plans, and milk quota restrictions, and farmer-reported reasons for culling are inherently unreliable. On the other hand, information regarding the timing of culling can be useful as a diagnostic tool for evaluating herd management problems, and it may be possible to uncover genetic variation between families in culling patterns within the lactation (at least for popular sires with thousands of offspring). A common approach is to compute the percentage of cows that were culled during each of a dozen or more predetermined time intervals (e.g., 3-week or 4-week periods, beginning at parturition), as a proportion of all cows that were culled from the herd in a given year. Problems with the management of transition cows near calving time are indicated by a high culling rate in early lactation (e.g., < 60 days in milk), whereas problems with mastitis and lameness are indicated by a high culling rate in mid lactation (e.g., 60 to 250 days in milk), and reproductive problems are indicated by a high culling rate in late lactation (e.g., > 250 days in milk). Routine recording of body condition score, a popular tool for herd management, can provide useful data for genetic evaluation of the cow s ability to maintain adequate tissue reserves while producing large volumes of milk. However, the genetic correlation between body condition score and dairy form (or dairy character), which is already measured as part of the linear type classification program in many countries, is extremely high, and the marginal benefit of measuring condition scores for the purpose of genetic selection may be negligible. 57

60 APPENDIX D On the other hand, body condition scores are extremely valuable for herd management decisions, and one could develop monitoring tools that, for example, could identify cows that have less than optimal condition scores for a given age, stage of lactation, and level of milk yield (Bastin et al., 2008). Automated methods for assessing body condition score have been considered in many studies, including methods that utilize commercially available digital cameras or thermal cameras (Klopcic et al., 2008). When coupled with advanced image analysis techniques, it may soon be possible to achieve automated, objective monitoring of body condition scores in commercial herds. Efforts to select for overall robustness of dairy cattle are under way in several countries. For example, Dutch researchers have considered above average breeding values for body condition score, body depth, chest width, and rump width as possible indicators of the cow s propensity to maintain adequate udder health, fertility, and mobility (Van Pelt and de Jong, 2008); Wall et al; (2008) noted the importance of coping ability of the dairy cow, with respect to herd management practices in the United Kingdom, and suggested that future farming systems should ensure a match between genotypes and environmental conditions. Many studies have attempted to predict genetic differences in susceptibility to early postpartum health disorders from variables measured in routine national milk recording programs. Roelofs and de Jong (2004) noted that indicators of negative energy balance derived from milk recording data, such as minima, maxima, ratios, and differences of fat percentage and protein percentage, have poor sensitivity and specificity for detecting cows that are at risk for health problems. La Croix and Nordlund (2008) described the manner in which the transition cow index, which is based on comparing the predicted and actual milk yield of each cow at the first test day, can be used to identify herds that appear to have management problems at calving time and shortly thereafter. Recently, de Jong et al. (2007) described efforts to detect subclinical ketosis using Fourier transformed infrared spectrometry measurements of monthly milk samples to predict serum concentrations of acetone, -hydroxybutyrate. The authors concluded that predictions of acetone and -hydroxybutyrate can be combined with fat percentage and protein percentage data to identify cows that are at risk for subclinical ketosis. Such a tool will have limited diagnostic value for individual cows in herds that have only monthly or bimonthly testing of milk samples, but it could provide useful information regarding the overall management level of the herd. As noted earlier, Zwald et al. (2004a, 2004b) described procedures for pooling farmerreported health events from herd management software programs into a central database for the purpose of genetic evaluation. Information regarding treatments for ketosis, displaced abomasums, mastitis, lameness, and metritis or retained placenta was used to estimate genetic parameters and predict sire breeding values for liability of their daughters to the aforementioned disorders. Despite inconsistencies between farms in the diagnosis and recording of health problems, heritability estimates matched or exceeded other estimates reported in the scientific literature, including those from national health reporting systems in the Nordic countries. Later, Johansson et al. (2008) described the analysis of early reproductive diseases (retained placenta and other disorders prior to 40 days postpartum), late reproductive diseases (disorders between 41 and 305 days postpartum), metabolic diseases (ketosis, milk fever, etc.), feet and leg problems, and mastitis in Denmark, Finland, and Sweden. Heritability estimates for these traits were lower than estimates reported by Zwald et al. (2004a, 2004b), perhaps due to smaller average herd size, but estimated genetic 58

61 APPENDIX D correlations between traits were positive, indicating that selection for overall health may be possible. Lastly, Moemke et al. (2008) reported the results of a genome-wide association study for left displaced abomasums in Holstein cattle and suggested that genomic selection for resistance to displaced abomasums and other health disorders may be possible. Selection for resistance to endemic infectious diseases may be more difficult, although Bishop (2008) suggested that mastitis, bovine leukemia, gastrointestinal parasites, tuberculosis, and paratuberculosis may be good candidates for genetic or genomic improvement in dairy cattle. Earlier, Raadsma and Fullard (2006) reviewed the possibilities for improving gastrointestinal nematodes, mastitis, trypanosomiasis, dermatophilosis, footrot, fasciolosis, tick, and tickborne diseases in cattle. Physiological indicators of animal health may be useful for genetic selection purposes, and in the future such indicators could be used to quantify the animal welfare conditions under which animal agriculture products originate. French et al. (2006) reported that plasma levels of nonesterified fatty acids were similar in Holstein and Jersey cows up to 5 days prepartum, but levels were higher in Holstein cows thereafter, as was the magnitude of depression in dry matter intake. Furthermore, plasma level of non-esterified fatty acids was significantly correlated with the prepartum decline in dry matter intake in Holstein cattle, but not in Jersey cattle. In addition, subacute ruminal acidosis, which is an extended period of low rumen ph (in the range of 5.5 to 5.6), can have serious implications on animal health, welfare, and feeding behavior. Wireless rumen sensors could be an interesting tool for monitoring rumen ph, as well as internal body temperature. Byrem et al. (2008) described the possibilities associated with detailed testing of milk samples collected routinely in milk recording programs, including those based on an enzyme-linked immunosorbent assay or polymerase chain reaction. Thus far, tests have been developed for paratuberculosis (Johne s disease), bovine leukosis, bovine viral diarrhea, and milk progesterone level. High accuracy can be achieved in the aforementioned traits and, although monthly progesterone testing is of little value with respect to the management of individual cows, test results for a group of cows that are known to be at a specific stage of the hormonal synchronization program can be used to evaluate employees compliance with the herd s reproductive management protocol. Arazi (2008) described potential applications of the S.A.E. Afikim online milk analyzer and behavior meter. The author speculated that familiarity with individual cows, which was possible historically in small herds, will be replaced in large herds by real-time identification of the management and veterinary needs of individual cows using an ensemble of data that were collected in an automated manner. Daily analysis of milk components may indicate nutritional changes that can lead to metabolic or reproductive problems, and continuous monitoring of the ratio of fat percentage to protein percentage of individual cows (along with other indicator traits) may help identify cows that are at risk for negative energy balance and subacute ruminal acidosis. Likewise, continuous monitoring of the activity of individual cows, including number of steps per hour, number of lying bouts, and total lying time, is possible using a three-dimensional sensor. Determination of the routine behavior pattern of a given animal allows subsequent monitoring of deviations that may indicate health and fertility events or other stressful situations (e.g., estrous, impending parturition, heat stress). 59

62 APPENDIX D In summary, the development of tools for monitoring and evaluating the health and welfare of dairy cattle during the early postpartum period will be of critical importance with respect to economic and animal welfare concerns. Farmer-recorded information regarding the treatment of infectious diseases and metabolic disorders can presently be used for routine genetic evaluation of dairy sires. Furthermore, the development of systems for continuous, automated monitoring of useful physiological indicators on commercial farms is imminent, and strategies for using this information effectively in selection programs are needed. IMPACT OF GENOMICS Any discussion of the future of genetic improvement programs would be woefully inadequate if it did not consider the impact of recent developments in the area of genomics. As a result of the bovine genome sequencing initiative, hundreds of thousands of potentially useful single nucleotide polymorphism (SNP) markers have become available. Unlike the labor intensive genotyping of microsatellite markers in the 1990s, the genotyping of SNPs is fully amenable to automation. As such, breeding companies and pedigree breeders can obtain gains of 20 to 40% in the reliability of genetic predictions of young bulls, heifers, and calves (as compared with the reliability of their parent averages) for dozens of economically important traits. Genomic selection, also known as whole genome selection, has received much attention because of the possibility that it will replace progeny testing, which has been the cornerstone of dairy cattle breeding for nearly a half century. Of equal importance, and of greater relevance to the topic at hand, is the suite of opportunities that will arise for genomic improvement of novel traits related to animal health and product quality traits that were not improved successfully via conventional selection. Nontraditional sources of data can be of great value in the genomics era, because the presence of costly systems for the routine recording of performance and pedigree data on all animals in the population (or at least a very large proportion of them) is no longer a prerequisite. For example, one could improve calf health and survival by measuring immunoglobulin concentration in serum samples from young calves and subsequently associating variation in immunoglobulin phenotypes with SNP genotypes in a genome-wide association study or a whole genome selection program. All of this work could be carried out on a few large calf ranches, on which thousands of young calves from commercial farms are reared under highly consistent management conditions. Likewise, the animal health databases maintained by state and federal veterinarians, which contain information regarding disease status for many nonrandom samples from commercial dairy cows and their herdmates but lack corresponding pedigree data, could be used to construct case-control studies of the association between SNP genotypes and susceptibility or resistance to various infectious diseases. Non-additive genetic effects have largely been ignored in dairy cattle breeding, partly due to our great success in exploiting additive genetic variation, and partly due to the lack of large full-sib families in which to estimate non-additive (particularly dominance) effects. This will change with the advent of widespread SNP genotyping of cows on commercial dairy farms, because non-additive effects can be estimated at the genomic level. This information can be used in selection, mating, and management programs. A key factor will be the availability of SNP genotypes and corresponding phenotypes for females, as most of the genomic selection work to date has been based on associations between SNP genotypes and estimated breeding values of progeny tested bulls (which do not reflect nonadditive genetic variation). 60

63 APPENDIX D Computerized mating programs, whether delivered by handheld devices carried by inseminators or integrated into herd management software programs, will be able to consider not only inbreeding, inherited defects, and type faults, but genomic complementarity as well. Mating algorithms based on high-density (50,000) SNP genotypes for males and low-density (300 to 1000) SNP genotypes for commercial females will be developed, with the objective of maximizing genomic heterozygosity or complementarity, considering both additive and nonadditive genetic effects. Lastly, interactions between SNP genotypes and environmental or management conditions can be considered. Long et al. (2008) investigated the possibility of genotype by environment interactions in a genomic selection study involving chick mortality in a commercial line of broilers. A panel of SNPs associated with mortality in a low hygiene environment performed well (in terms of predictive ability) when applied to an independent data set from another low hygiene environment, but performance was much poorer when this panel of SNPs was applied to a data set from a high hygiene environment, and vice-versa. This suggests that low-density SNP assays should be developed with a particular target environment in mind. SUMMARY AND RECOMMENDATIONS As herds become larger, the challenge of managing individual cows in the absence of familiarity with specific animals will become critical (Pinsky et al., 2008). Effective management of cows that require individualized attention will be possible with radio-frequency identification (RFID) coupled with various management sensors that provide real-time analysis of milk, activity, behavior, and other key parameters. For example, pedometers have been used successfully on some farms, and while implementing timed artificial insemination based solely on changes in activity is not recommended, activity data can be merged with other information to make such decisions. Behavior meters that record lying and standing time can provide additional data, as can daily milk meters, and changes in health status or feeding behavior of specific animals are quickly reflected in abnormal test-day yields and milk composition parameters. Accurate and up-do-date feeding recommendations can be developed, and early diagnosis of health problems, such as subacute ruminal acidosis or subclinical mastitis, will be possible based on such information. The first step is to obtain accurate, objective, and complete data effortlessly though automated data collection systems and sensors, coupled with reliable RFID. The second step is to develop new statistical methods to assess the multifactorial causes of various health problems and management needs, using modern data mining tools. Katz and Pinsky (2008) discuss statistical aspects associated with evaluation, maintenance, surveillance, and control of an automated, on-farm, multi-sensor data collection system. Although automated data collection offers many potential advantages, there are some disadvantages as well, as noted earlier. Cantin (2008) describes some of the challenges in detail. First, even after decades of commercialization of automated systems for animal identification and recording of milk weights, many data quality issues remain. These include RFID reading errors, reuse of RFID tags, and failure of metering equipment, and such problems lead to extensive editing or complete discarding of data for certain herds, time periods, or traits. However, such errors can be identified with relative ease using statistical process control methodology. Second, the cost of creating an electronic pipeline to exchange data between hundreds or thousands of commercial farms and one or more central data processing centers and, more importantly, the cost of maintaining this pipeline, managing the interfaces, and keeping up with manufacturers changes in hardware and software could be substantial. 61

64 APPENDIX D Third, the expectation that farmers will automatically participate in such a system for the greater good of the industry, even if their own herd management needs can be met by a simpler and cheaper on-farm system, is unreasonable. Industry users of these data must be prepared to reimburse farmers for the added costs and labor associated with such systems, and even if such funds are provided it will be extremely difficult to force farmers to record variables that do not provide a direct, on-farm economic benefit. The logical solution is to pursue simultaneous development of genetic and management applications that can utilize the data from automated recording systems, such that the on-farm decision-making value of new management tools outweighs the additional costs or headaches associated with participating in genetic improvement activities. It is important to recognize that future data collection and genetic evaluation systems must break the mold, in the sense that recording of a given trait by the vast majority of herds in the national dairy population should no longer be a prerequisite. Subsets of herds will provide data for key traits of economic importance, for subsequent use in genetic or genomic evaluations that can be applied to the population as a whole. Swalve (2008) discussed the importance of contract herds, with respect to the collection of suitable data from progeny test daughters and their contemporaries. In particular, the author noted that data regarding fertility, mobility, and udder health should be emphasized, and that both clinical and sub-clinical disease should be recorded. The author also noted that, when evaluating health traits, contemporary groups should be defined in terms of the time of exposure, rather than the time of calving. Contract or cooperator herds have been used for decades for the purpose of progeny testing, but historically most breeding companies had 2000 to 4000 progeny test herds apiece, and each herd contributed a relatively small amount of data. Two North American breeding companies have recently trimmed their lists of cooperator herds by more than 90%, such that virtually all data used for product development are recorded in 200 to 300 large, dedicated commercial herds. Essentially, this is a realization of the data farm concept of _antin (2008), in which breeding companies or other users of on-farm pedigree, performance, and management information fund the data collection process. Changes in data collection systems cannot take place in a vacuum, and changes in our methods for data analysis will be needed as well. Traditional statistical methods used in the field of animal science typically require knowledge of all traits and all explanatory variables for every animal. Such data could arise from designed experiments, such as those used in nutrition or reproduction studies, or from extensive editing and discarding of records with incomplete or unusable information for specific traits or variables. In genetic evaluation programs, the latter strategy is usually employed, and thousands or millions of records with unknown or errant data regarding sire identification, birth date, calving date, and other traits or explanatory variables are discarded. However, non-traditional methods, such as those used in the fields of data mining and machine learning, can readily accommodate missing values for specific traits or explanatory variables, and these methods can be extremely useful for extracting information from large, messy data sets derived from various sources. For example, Caraviello et al. (2006) used an alternating decision tree algorithm to identify key explanatory variables and to build a model for reproductive performance in lactating Holstein cows in a data set comprised of roughly 350 management and environmental variables, many of which were missing or errant for individual animals or herds. In the future, data mining and machine learning approaches will be of greater importance, because we will find ourselves attempting to extract useful genetic and management information from pooled warehouses of data in which great differences exist in the attributes measured on specific farms or specific animals within a farm. 62

65 APPENDIX D For example, some animals will have high-density (50,000) SNP genotypes, some animals will have low-density SNP genotypes (from multiple public or proprietary chips, each containing 300 to 1000 or more different SNPs), some animals will have ultra-high-density (300,000 to 1,000,000) SNP genotypes, some animals will have full or partial DNA sequence data, and many animals will have no genotypic data at all. As another example, most herds will have milk-recording data, some herds will have data regarding infectious diseases or metabolic disorders, and some herds will have data regarding feed intake, hormonal status, blood parameters, and other traits of interest. Discarding all of herds with incomplete data and working with the least common denominator, as we have done historically, will not be an option. Methods to assess the quality of data from each herd will be needed as well, such as the suggestion of Miglior et al. (2008) and Dechow et al. (2007, 2008) to use within-herd heritability estimates as a parameter for routine screening of the quality of pedigree and performance data prior to inclusion in genetic evaluations (or for weighting of records). It may be possible to mask many of the underlying differences in data availability and data structure to the end users, particularly in genetic selection programs. One approach is the development of composite traits or indices corresponding to key biological functions. In this manner, a farmer will see estimated breeding values and corresponding reliabilities for the overall categories of production efficiency, calving ability, reproductive performance, udder health, mobility, and postpartum health. However, information regarding the underlying phenotypes and genotypes that were used to derive each composite or index will remain transparent to the end user; For example, a sire s breeding value for udder health may be derived from a composite of somatic cell count information from 1000 daughters, udder conformation measurements from 300 daughters, clinical mastitis treatment data from 400 daughters, electrical conductivity data from 500 daughters, and genomic predictions for lactate dehydrogenase and pathogen-specific mastitis that were derived from high-density SNP genotypes and phenotypes of 3000 cows on 15 contracted data farms. New traits or data sources can be added as needed, and this new information will be reflected in the reliability for a given composite index, but the change will be transparent to the end users. Such information can also be valuable for the development of new herd management tools, as noted earlier. Handheld devices have become increasingly popular (Dukas, 2008), especially among larger farms, and these confer numerous advantages with respect to data recording and animal management, particularly when coupled with RFID readers. Errors are minimized due to real-time error checking and elimination of the need to enter identification numbers and the need to copy records from paper clipboards. Subsequently, management applications that incorporate audio playback features are especially helpful, as farm staff can scan RFID tags to identify cows that require specific actions such as veterinary treatment, pen change, artificial insemination, hormonal synchronization, or vaccination. Feedback of genetic information into herd management reports and software programs has been lacking in the past, but this will be important in the future. For example, knowledge of genetic differences in gestation length can be used to inform producers which animals will calve earlier or later than expected, thereby enhancing management efficiency. The aforementioned data mining tools can be valuable for enhancing internal efficiencies of breeding companies and other service providers as well. For example, Shneider et al. (2008) documented the gains that could be achieved in mean daily traveling time and, more importantly in the standard deviation of daily work time, for artificial insemination technicians by using a planning agent algorithm to predict the amount of time needed at each farm and the optimum route between farms. 63

66 APPENDIX D In conclusion, the future is very bright with respect to the contributions that RFID and automated data collection systems will offer to genetic improvement programs and herd management applications. Flexibility will be critical, because the capabilities of individual farms for providing various types of data and, in turn, for using the resulting genetic and management information effectively, will vary greatly. Likewise, skilled scientists will be needed to translate all of this data into useful information. REFERENCES Amodeo, P., and A. Tondo Official milk recording with automatic milking systems: The Italian situation. Proceedings of the 34 th Biennial Session of ICAR, pp Arazi, A Automated daily analysis of milk components and automated cow behaviour meter: Developing new applications in the dairy farm. Proceedings of the 36 th ICAR Session, Niagra Falls, NY. Arthur, P. F., and R. M. Herd Genetic relationships between residual feed intake and other economically important traits in beef cattle. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Bastin, C., L. Laloux, A. Gillion, C. Bertozzi, and N. Gengler Alternative modeling of body condition score from Walloon Holstein cows to develop management tools. Proceedings of the 36 th ICAR Session, Niagara Falls, NY. Bertoni, G., and E. Trevisi Diet-health relationship in the transition period: Consequences on energy balance and efficiency. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p. 85. Biffani, S., M. Marusi, F. Canavesi, and F. Biscarini An aggregate index for dairy cattle fertility using direct and correlated traits. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communicatino Bijma, P., and W.M. Muir Genetic analysis and improvement of traits affected by behavourial or other interactions among individuals. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Bishop, S.C Breeding for improved disease resistance in ruminants. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p Bobe, G., J.A. Minick Bormann, G. L. Lindberg, A.E. Freeman, and D. C. Beitz Estimates of genetic variation of milk fatty acids in US Holstein cows. Journal of Dairy Science 91: Boelling, D., M. Vesterager Laursen, and T. Mark Claw trimming records and locomotion scores can improve selection for feet and legs. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p.211. Bohmanova, J., F.Miglior, J. Jamrozik, L. R. Schaeffer, and S. Loker Accounting for the effect of pregnancy in the Canadian test day model. Interbull Bulletin No. 38: Butler, S. T., and W. R. Butler The interface between bioenergetic status and the reproductive axis in lactating dairy cows. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p. 74. Byrem, T., M. Adams, and B. Voisnet Supplemental testing on milk recording samples. Proceedings of the 36 th ICAR Session, Niagara Falls, NY. Cantin, R On-farm technologies. Challenges and opportunities for DHI and genetic improvement programs. ICAR Technical Series 13: Caraviello, D. Z., K. A. Weigel, M. Craven, D. Gianola, N. B. Cook, K. Nordlund, P. M. Fricke, and M. L. Wiltbank Analysis of the reproductive performance of lactating 64

67 APPENDIX D Holstein cows on large dairy farms using machine learning algorithms. Journal of Dairy Science 89: Cassandro, M., A. Comin, M. Ojala, R. Dal Zotto, M. De Marchi, L. Gallo, P. Carnier, and G. Bittante Genetic parameters of milk coagulation properties and their relationships with milk yield and quality traits in Italian Holstein cows. Journal of Science 91: Cecchinato, A., M. De Marchi, R. Dal Zotto, L. Gallo, G. Bittante, and P. Carnier Genetic correlations between measures of milk coagulation properties and their predictions by mid-infrared spectrometry. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p Chang, Y. M., O. Gonzalez-Recio, K. A. Weigel, adn P. M. Fricke Genetic analysis of 21-day pregnancy rate in US Holsteins using an ordinal censored threshold model with unknown voluntary waiting period. Journal of Science 90: Cole, J. B Data Collection Ratings and best prediction of lactation yields. Proceedings of the 36 th ICAR Session, Niagara Falls, NY. Coleman, S. W Challenges to assessing forage intake by grazing ruminants. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Cromie, A., F. Kearney, R. Evans, D.Berry, and D. Wihelmus Use of inseminating data in cattle breeding: Some experiences from Ireland. Proceedings of the 36 th ICAR Session, Niagara Falls, NY. Dal Zotto, R., M. Povinelli, L. Gallo, P. Carnier, M. Cassandro, and G. Bittante Estimated breeding values for milk electrical conductivity and their relation with somatic cell score in Italian Brown cattle. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Dechow, C. D., and H. D. Norman Within-herd heritability estimated with daughterparent regression for yield and somatic cell score. Journal of Dairy Science 90: Dechow, C.D., H. D. Norman, N. R. Zwald, C. M. Cowan, and O. M. Meland Relationship between individual herd-heritability estimates and sire misidentification rate. Journal of Dairy Science 91: de Jong, G., A. P. W. de Roos, H. J. C. M. van den Bijgaart, and J. Horlyk Screening for subclinical ketosis in dairy cattle by Fourier transform infrared spectrometry. Journal of Dairy Science 90: Dukas, P. A Using PocketDairy with FRID for herd management. Proceedings of the 36 th ICAR Session, Niagara Falls, NY. French, P.D Dry matter intake and blood parameters of nonlactating Holstein and Jersey cows in late gestation. Journal of Dairy Science 89: Goodling, R. C., Jr., G. E. Shook, K. A. Weigel, and N. R. Zwald The effect of synchronization on genetic parameters of reproductive traits in dairy cattle. Journal of Dairy Science 88: Guinard-Flament, J., Y. Gallard, and H. Larroque Estimation of dairy cows ability to tolerate once-dairy milking. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p. 3. Hinrichs, D., J. Bennewitz, E. Stamer, and G. Thaller The use of multiple ordered categorical threshold model for the estimation of genetic parameters for the liability to mastitis in dairy cattle. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p

68 APPENDIX D Holmberg, M., W. F. Fikse, L. Andersson-Eklund, K. Artursson, and A. Lunden Genetic analysis of pathogen-specific mastitis. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p. 74. Ingvartsen, K. L Use of early diagnostics and precision management tools to limit subclinical diseases in intensive dairy farming systems. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p. 2. Johansson, K., J. Poso, U. Sander Nielsen, J. A. Eriksson, and G. Pedersen Aamand Joint genetic evaluation of other disease traits in Denmark, Finland, and Sweden. Interbull Bulletin No. 38: Katz, G., and N. Pinsky A new approach to perform analysis of milk components incorporating statistical methods adapted in real time sensor. ICAR Technical Series 13: Klopcic, M., I. Halachmi, P. Polak, A. White, R. Boyce, and D. Roberts Applying parameter of body-shape in the automation of condition scoring. Proceedings of the 36 th ICAR Session, Niagara Falls, NY. Koenen, E. P. C., and W. Steeneveld Genetic parameters of clinical mastitis for Dutch Holstein cattle based on farm management software data. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Konig, S., F. Bosselmann, and H. Simianer Synergistic models for the joint estimation of variance components for ovulation rate, embryonic survival and pregnancy from embryo transfer results in dairy cattle. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Konig, S., M. Tietze, D. Landmann, and H. Simianer Application of a multivariate random regression sire model to estimate genetic parameters among milk yield, somatic cell count, and laminitis. Interbull Bulletin No. 38: Kramer, E., D. Cavero, and J. Krieter Mastitis and lameness detection using different statistical methods. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p La Croix, R., and K. Nordlund Two years experience in use of the Transition Cow Index as a tool to improve dairy herd management. Proceedings of the 36 th ICAR Session, Niagara Falls, NY. Long, N., D. Gianola, G.J. M. Rosa, K. A. Weigel, and S. Avendan Markerassisted assessment of genotype by environment interaction: A case study of single nucleotide polymorphism-mortality association in broilers in two hygiene environments. Journal of Animal Science 86: , Lopez de Maturana, E., X. L. Wu, D. Gianola, K. A. Weigel, and G. J. M. Rosa Exploring biological relationships between calving traits in primiparous cattle with a Bayesian recursive model. Genetics 181: Martini, M., F. Cecchi, C. Scolozzi, F. Salari, F. Chiatti, S. Chessa, and A. Caroli Morphometric characteristics of milk fat globules in Italian Friesian dairy cows. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Miglior, F., G. R. Wiggans, M. A. Faust, and B. J. Van Doormal Impact of new onfarm technologies in dairy cattle breeding. ICAR Technical Series 13: Misztal, I., J. Bohmanova, M. Freitas, S. Tsuruta, H. D. Norman, and T. J. Lawlor Issues in genetic evaluation of dairy cattle for heat tolerance. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication

69 APPENDIX D Moemke, S., W. Brade, O. Distl, F. Reinhardt, and R. Reents Genomic markers for left-sided displaced abomasums in German Holstein dairy cows. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p Muir, W. M., and P. Bijma Incorporation of competitive effects in breeding programs for improved performance and animal well-being. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Mulder, H. A., W. G. Hill, A. Vereijken, and R. F. Veerkamp Estimation of genetic variation in residual variance in female and male broiler chickens. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p Naeslund, S., J. H. Jakobsen, J. A. Eriksson, and E. Strandberg. 2008a. Genetic parameters for dairy cattle claw health traits recorded by claw trimmers. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p Naeslund, S., J. H. Jakobsen, J. A. Eriksson, and E. Strandberg. 2008b. Genetic correlations between combined claw health traits measured at claw trimmings of Swedish Holsteins and Swedish Red dairy cows. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p Palucci, V., L. R. Schaeffer, F. Miglior, and V. Osborne Non-additive genetic effects for fertility traits in Canadian Holsteins. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Pegolo, N. T., H. N. De Oliveira, L. G. Albuquerque, and R. B. Lobo Environmental sensitivity in cattle studied by linear adaptive reaction norms. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Petersson, K. J., B. Berglund, E. Strandberg, H. Gustafsson, A. P. F. Flint, J. A. Woolliams, and M. D. Royal Genetic analysis of postpartum measures of luteal activity in dairy cows. Journal of Dairy Science 90: Petersson, K. J., H. Gustafsson, E. Strandberg, and B. Berglund Atypical progesterone profiles and fertility in Swedish dairy cows. Journal of Dairy Science 89: Pinsky, N., U. Golan, A. Arazi, and G. Katz Management of individual cows in large herds A challenge to modern dairy farming. Proceedings of the 36 th ICAR Session, Niagara Falls, NY. Raadsma, H. W., and K. J. Fullard QTL mapping and gene markers for resistance to infectious diseases in sheep and cattle. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Rajkondawar, P. G., M. Liu, R. M. Dyer, N. K. Neerchal, U. Tasch, A. M. Lefcourt, B. Erez, and M. A. Varner Comparison of models to identify lame cows based on gait and lesion scores, and limb movement variables. Journal of Dairy Science 89: Roelofs, R. M. G., and G. de Jong Detection of consequences of NEB based on milk production records of Dutch Black-and-white Holsteins. Proceedings of the 34 th Biennial Session of ICAR, pp Rossoni, A., O. Bonetti, C. Nicoletti, A. Samore, and A. Bagnato Genetic evaluation for casein contents in Italian Brown Swiss: Preliminary results. Interbull bulletin No. 38: Sanchez, J. P., R. Rekaya, I. Aguilar, and I. Misztal Genetic variation in the threshold of sensitivity to heat stress on milk production in dairy cattle. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p Schafberg, R., F. Rosner, and H. H. Swalve Examinations of intramammary infections in dairy cows based on pathogen-specific data. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication

70 APPENDIX D Schopen, G. C. B., J. M. L. Heck, H. Bovenhuis, M. H. P. W. Visker, H. J. F. Van Valenberg, and J. A. M. Van Arendonk Genetic parameters for milk protein composition of dairy cows. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p 115. Shneider, B., M. Even Chaime, D. Gilad, and I. Halachmi Planning agents work in cows artificial insemination services. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p Sorensen, L. P., P. Madsen, T. Mark, and M. S. Lund Genetic parameters for pathogen-specific mastitis in Danish Holstein cattle. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p Soyeurt, H., D. Bruwier, P. Dardenne, J. M. Romnee, and N. Gengler Potential estimation of mineral contents in cow milk using mid-infrared spectrometry. Proceedings of the 36 th ICAR Session, Niagara Falls, NY. Soyeurt, H., P. Dardenne, P. Mayeres, C. Croquet, S. Vanderick, C. Bertozzi, and N. Gengler Within and across breed differences in fatty acid profiles of milk and milk fat in dairy cows. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Soyeurt, H., F. Dehareng, C. Bertozzi, and N. Gengler Genetic parameters of butter hardness estimated by test-day model. Interbull Bulletin No. 37: Stoop, W. M., J. A. M. van Arendonk, J. M. L. Heck, H. J. F. Van Valenberg, and H. Bovenhuis Genetic parameters for major milk fatty acids and milk production traits of Dutch Holstein Friesians. Journal of Dairy Science 91: Strabel, T., and J. Jamrozik Alternative measures of lactation persistency from random regression models with Legendre polynomials. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Strandberg, E Analysis of genotype by environment interaction using random regression models. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Suzuki, M. Y. Masuda, S. Ohashi, and T. Kawahara Genetic differences for lifetime production and rate of maturity in milk of Japanese Holsteins. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Swalve, H. H Recording of functional traits in contract herds for progeny testing of bulls in dairy cattle breeding programs. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p Swalve, H. H., H. Alkhoder, and R. Pijl Estimates of breeding values for sires based on diagnoses recorded at hoof trimming: Relationships with EBV for conformation traits. Interbull Bulletin No. 38: Swalve, H. H., R. Schafberg, and F. Rosner Genetic parameters for categories of stillbirth in dairy cattle. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication Uggla, E., J. H. Jakobsen, C. Bergsten, J. A. Eriksson, and E. Strandberg Genetic correlations between claw health and feet and leg conformation traits in Swedish dairy cows. Interbull Bulletin No. 38: Van Pelt, M. L., and G. de Jong Robustness: Breeding for optimum traits. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p Wade, K. M Automated on-farm recording systems and the challenge of utilizing them for breeding programs in dairy cattle. Proceedings of the 8 th World Congress on Genetics Applied to Livestock Production, Communication

71 APPENDIX D Wall, E., M. P. Coffey, and M. J. Haskell Genetic and environmental effects on fitness traits in dairy cattle. Proceedings of the 59 th Annual Meeting of the European Association of Animal Production, p Zwald, N. R., K. A. Weigel, Y. M. Chang, R. D. Welper, and J. S. Clay Genetic analysis of clinical mastitis data from on-farm management software using threshold models. Journal of Dairy Science 89: Zwald, N. R., K. A Weigel, Y. M. Chang, R. D. Welper and J. S. Clay Genetic evaluation of dairy sires for milking duration using electronically-recorded milking times of their daughters. Journal of Dairy Science 88: Zwald, N. R., K. A. Weigel, Y. M. Chang, R. D. Welper, and J. S. Clay Genetic selection for health traits using producer-recorded data. I. Incidence rates, heritability estimates, and sire breeding values. Journal of Dairy Science 87: Zwald, N. R., K. A. Weigel, Y. M. Chang, R. D. Welper, adn J. S. Clay Genetic selection for health traits using producer-recorded data. II. Genetic correlations, disease probabilities, and relationships with existing traits. Journal of Dairy Science 87:

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73 WRITTEN SUBMISSIONS APPENDIX E The Review Committee received volunteered submissions from the following parties. These submissions were received in confidence. Organisations Abacus Bio Limited CRV Limited Dairy Automation Limited Dairy Companies Association Of New Zealand De Laval Limited Federated Farmers Of New Zealand Fonterra Milk Supply Landcorp Farming Limited Liberty Genetics Limited Livestock Improvement Corporation Limited Livestock Improvement Corporation Shareholder Council National Animal Identification And Tracing Project New Zealand Ayrshire Association (Incorporated) New Zealand Jersey Cattle Breeders Association (Incorporated) New Zealand Holstein-Friesian Association (Incorporated) New Zealand Milking Shorthorn Association Incorporated Sheep Improvement Limited Tru-Test Corporation Limited Individuals Don Blumhardt Cathy Brown Colin Holmes John Lay 71

74 APPENDIX F INTERVIEWS The Review Committee interviewed representatives of the following parties as part of the conduct of the Review. Organisations Abacus Bio Limited CRV Limited Dairy Companies Association Of New Zealand De Laval Limited Landcorp Farming Limited Liberty Genetics Limited Livestock Improvement Corporation Limited Livestock Improvement Corporation Shareholder Council National Animal Identification And Tracing Project Sheep Improvement Limited 72

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