# Presentation: 722541

## Harnessing automatic data collection to enhance genetic improvement programs 

- Abstr.
- 422

## Questions

- What should we be thinking about to prepare for the future?
- How can we best exploit technology that is/will be available?
- How can we minimize the impact of negative trends?

## History of innovation

- 1950s	Computerization
- 1960s	Laboratories for component testing
- 1970s	Farm to mainframe connection for input and reports
- 1980s	Electronic data transfer from farms and laboratories; on-farm data entry
- 1990s	Robotic/voluntary milking systems
- 2000s	Handheld devices for data collection and access; RFID

## Automaticdata collection

- Continued adoption of technology
- Includes equipment or procedures that aid data collection

## Electronic milk meters

- Currently supply 7% of data
- Can provide	−Total yield
- 			− Milking speed
- 			− Milk conductivity
- May provide	− Progesterone levels
- 			− Milk temperature
- 			− Component concentration
- RFID may improve reliability of cow ID associated with meter data

## Voluntary milking systems

- Also known as robotic parlors
- Most common in Europe
- Depend heavily on automatic data collection
- Require adaptation by DHI to be included
- May provide data not available elsewhere

## Other data collection devices

- Electronic scales
- Handheld computers to record health
- Activity monitors
- Weather stations

## New traits

- Diagnostic test results
- Heat tolerance
- Lameness

## Limitations

- Electronic ID read errors
- Equipment failure
- Staff capability
- Cost

## Why farms should invest in automatic data collection

- Better management
- More accurate data
- More characteristics
- Greater quality control
- Food quality assurance and traceability
- Help genetic improvement?

## Trends

- More traits recorded
- Larger herds
- Improved equipment for electronic recording
- Increased worldwide competition among AI organizations
- Demand for increased data accuracy and comprehensiveness, especially for traits with low heritability

## Needs of geneticimprovement program

- Continued participation
- Maintained or improved data quality
- Adaptation to change

## Benefits to genetic improvement from automatic data collection

- Improved accuracy
- Reduced cost
- More traits

## Tradeoffs in adding traits

- Low heritability
- Recording errors
- Difficulty in estimating economic value
- Dissipation of selection differential

## Why more traits?

- Goal of a profitable cow
- Selection index
- Evaluations weighted by economic contribution
- More precise measurement of profitability
- More accurate profit tracking
- More accurate selection

## How to connect genetic improvement to on-farm data

- Provide value
- Genetic evaluations
- Data backup
- Data quality control
- Compensate for data as a dairy product (like milk)
- Promote connection ease and security

## On-farm software

- Must be maintained
- Support
- Extremely labor intensive
- Expensive if many platforms
- Central control of updates attractive
- Dedicated uniform hardware?

## Systems for farms to provide data

- Current system
- AI organizations pay for progeny-test daughters
- Bundled with DHI program
- System managed by AI organizations
- AI organizations connect to on-farm computers
- Data quality monitored by AI organizations
- Farm as data vendor
- Farm markets data to AI organizations
- Compensation based on quality

## Who is in charge?

- AI organizations
- Establish data connection with progeny-test herds
- DRPC
- Offer test plans that provide desired data
- Farm
- Market data based on quality
- Cooperation
- Establish mechanism for equitable resolution of competing interests

## Measures of data quality

- Consistency
- Milk weights *vs.* milk shipped
- Calving, progeny birth, breeding, dry dates
- ID accuracy from parentage verification
- Electronic ID
- Protocols to detect misreads
- Portion of duplicate or missing cows
- Within-herd heritability

## Management vs.genetic improvement

- Large-herd management based on cow groups
- Selection based on evaluation of individuals
- Genetic improvement needs data from individuals

## Where genetic improvement needs to go beyond herd management needs

- Accurate ID
- Access to all data
- Allow efficient research and development of new trait evaluations
- Sufficient incentive for herds to participate

## Herd of the future

- Every milking recorded and components determined
- All calves genotyped
- Parentage verification
- Genomic-based evaluation
- Data delivered to evaluation center daily

## Hurdles for SNP

- Benefits justified by cost
- Convenient DNA collection and accurate labels
- Timely and adequately accurate genomic prediction
- For parentage verification/discovery, genotypes from same SNPs required for potential parents

## Evaluations on demand

- Estimates of SNP effects updated several times each year
- Genomic prediction calculated as soon as genotype available

## Best practice

- Collection of accurate data for all relevant traits
- Seamless transfer to evaluation center
- Evaluations calculated with test-day model and including genomic data
- Results available as needed

## What to expect from automatic data collection

## Incentives

- Quality data have value
- Computer capacity on farm minimizes need for central computing
- Economic incentive required for dairies to contribute data to national evaluations
- Appropriate to have incentive based on data quality

## Benefits to herds

- Improved management information
- Incentives from AI organizations for providing data
- Improved pedigree accuracy from parentage validation

## Impact on national evaluations 

- More traits
- Body condition score based on electronic scales
- Mobility
- Higher quality
- Electronic recording and monitoring
- Lower cost
- Less labor required

## Lower cost

- Technician cost on test-day eliminated
- On-farm component determination

## Conclusions

- Automated data collection
- Growing
- Can improve data quality
- Genetic improvement programs
- More traits
- Better inputs
- Tighter connection to sources