building the evidence and rigour – combining science and experience bill ryan
TRANSCRIPT
Outcome we want
Development of technologies and innovations that, when implemented, makes the system more productive
Science and agribusiness contribute to this process – but they are different
Science
Understanding of the foundations and principles that underpin production
Development of new technologies and management systems for primary industry
Typically involves a reductionist approach and is not time bound
Science
Needs to be peer reviewed
Need to maintain our standards and capability
We need to do the best science that we can for a range of reasons
Strong Scientific rigour is an important part of the process
Agribusiness
Technical aspects
Financial management
Labour and staff matters
Logistics
Other drivers for the business
Involves all aspects of the business
Agribusiness
Technical matter are only part of the complexity
Can play a role in innovation – the “D” in all parts
It is time bound and decisions have to be made
Has its own rigour – if you don’t make money you don’t stay in business
Deals with all the complexity of the business
Address three aspects that impact the pathway of technology development and adoption
Information and data
Innovation in implementing technology
Other drivers for the business
Explore how the two components interact
Underpins the foundation of all aspects of the business
Often viewed differently by science and agribusiness
Science aims to get the best possible data
May not always be required
Information and data
Analyse a given site in any year
Only right 68% of the time
Analyse with additional years and relevant sites increases the accuracy by 40% (MET analysis)
Statisticians view MET analysis as superior
Growers want individual site data – they do their own analysis
National Variety testing
Neither position is right or wrong – just different
Early weaning example
Experiment at Flora Valley
By year two had fed about 250 small calves
Challenged by management for a diet to early wean 100,000 cows
Making decisions on incomplete data
Result was an additional 20,000 calves
Agribusiness often makes sound commercial decisions on limited data
Data and information often viewed differently by science and agribusiness
If working together need to understand and appreciate the differences
Conclusions
Some innovation in research phase
Often much more in the implementation or “D” phase
Implementing early weaning on a whole station basis – implementing some of my own researchRequired significant investmentConvince management and staff that it was
feasible and would increase productivity
Innovation in implementing technologies
First year 2 staff members Cost of $3.27 / kg of gain
By Year three 0.5 staff members Cost of $0.56 / kg of gain
Branding percentage increased 10%
Unexpected benefits Simplified management system on the station Animals educated to feeding systems
Innovation during implementation
No progress unless all involved are committed to the change
Much more innovation during the implementation phase than the research phase Example in soil amelioration work today
More progress during this phase – often drives new research
Fostering innovation Real challenge for RDCs
Key lessons
Key driver – making a profit
Once this is consistently achieved other drivers can be many and varied
Impacted by personal situations
ExamplesSchoolingFinishing harvest by Christmas
Other drivers for the business
At 6 km/hr cover 7.7 ha/hr with 29 tonne throughput – high level of efficiency
At 8 km/hr cover 10.3 ha/hr with 40 tonne throughput – greatly reduced efficiency8% of all grain entering header is thrown
out the backLoss of 270 kg /ha or $80/ha
Interaction of harvesting speed and crop density
While not logical decisions are legitimate