Jan Edwards – some thoughts on decision support systems and the
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Transcript Jan Edwards – some thoughts on decision support systems and the
Some thoughts on decision support systems
and the provision of advice to farmers
Jan Edwards
District Agronomist, Cowra
GRDC’s premise
The advice currently given to farmers by advisers is:
– inconsistent
– generic (rather than paddock specific)
– not the best available
– not rigorous enough
– more art than science
GRDC want to lift the standard of advice
Will a decision support tool help:
– diagnose why crops aren’t reaching their water limited yield
– assist GRDC make research investment decisions.
Why would advice differ?
Because advisers:
– use their training
– use past experience
– use trial data
– extrapolate from experience and data
– use rules of thumb
– use DSS
– use things learnt from training workshops
– ask other agronomists
– seek help from experts
– make educated guesses
This makes it individual
Is it the only reason outcomes differ?
Giving advice is an exchange
– Farmers are not the same
• age, debt level, family, education
• stage of life and family composition are important
– Farms are complex partnerships involving many people
Decision making is a very human thing
– Profit is not necessarily the main driving force
– It is also social (not just technical)
• A lot of adoption occurs when an idea or practice becomes part of ‘good
farm management’
Farmers construct their own knowledge
– Scientific advice
• is evaluated against other information, knowledge and beliefs
• does not automatically have credibility and legitimacy
• is used when consistent with their own understanding
• is often adapted to fit their own world view
Diagnostic agronomy
What is the limitation to consistent advice?
– Not enough information
– Too much information
– Confusing information
– Not enough time to integrate / summarise information
– Not enough strategic, whole farm thinking
– Complex situations
Diagnostic agronomy requires very effective monitoring
– Currently this is done on farm, mostly by farmers, and is very
subjective
There are only so many things it could be
– More likely it is a “combination of factors”
– Most of which you don’t need a model to figure out
What do you do after the problem is diagnosed?
Benchmarking performance
We have experience
– Collation of data over many seasons and paddocks
• Crop production groups
• CropCheck database
• TopCrop
When analysed:
– there was rarely a single identifiable reason for good
crop performance
– ‘rules’ for success were rarely to do with rates and dates
• so not easy to transfer
Didn’t deal with the ‘so what next?’ question
Can decision support systems help?
Computer based
– Simulation models
• GrassGro
• APSIM
– Predictive models
• WheatMan
• SowMan
– Websites
• CropMate
– Excel based
• Salvaging crops
calculator
• VarietyChooser
Paper based
– Rules of thumb
• Nitrogen budgeting
workshop
– References
• Wheat growth and
development book
• Sowing guides
• AgFacts
GRDC review has identified
about 60 DSS
Decision making and DSS
The promise of DSS
– they can organise and process the dazzling volume of crop
management information
Farming systems are complex
DSS tend to be deep but narrow
– Models are aimed at biophysical accuracy
• at the expense of realism in management?
– Also tend to focus on
• Tactical decisions (which crop, rates and dates)
– numerous, have lesser consequences and lower priorities
– less important than strategies?
– Rather than:
• Operational decisions (sowing, spraying, harvesting)
• Strategic (pasture to crop mix, land purchases)
Developing DSS
They take time and lots of money to develop
They need a champion, someone who believes in the concept
– The more they believe
• the more likely it is to be built
• the less likely it is that anyone else will understand its purpose or
be as passionate about using it
The more they mimic biological systems:
– the more data they need
– the more complex they are
– the harder they are to drive
The more complex the easier to create nonsense
They need updating or they get old very quickly
– they have to have current terminology and/or variety names or
people don’t relate to them
Using DSS
Tools like APSIM are fabulous
– they are the only way to handle very complex questions
– but without continued basic research they have limitations
• row spacing, lupins in NSW, frost, disease
‘Black box’ models put people off
– they instinctively want to change a value even if it is not relevant to
do so
Excel based spreadsheets are more flexible and adaptable
Simple systems
– to teach you a method or a principle which you then formulate into
your own rule of thumb
– You don’t have to use it every time you make a decision
DSS have a history of failed adoption
Farmers make good decisions without
DSS
Lack of demand for DSS
They are built by the people who will use
it
– Reflect the decision making style of
the user
– Don’t necessarily match users needs
– Users generally not involved in
development
The analysis is inferior to experienced
human judgement
– Often no link to what can be done
about the situation?
DSS focus on operational or tactical
decisions
They can take a lot of time to learn
– Unless they are used often you
forget how
Tedious data entry
Complicated set up process
Lack of software support post-release
Technical interpretation
Need large amounts of input data
– some of which the user can’t get
easily
Lack of computer use for management
Time constraints
Complex to use
Lack of local relevance
Poor marketing
• Hayman (2002)
• Robinson and Freebairn (2000)
Precision
Models give the illusion of precision
– The added precision may make no difference to the decision.
– There may be less to be gained from being precisely right with detailed simulation
• Especially when you factor in the risk of being precisely wrong
It may be better to be vaguely right than precisely wrong
to solve the whole problem roughly than part of the problem extremely well
• Malcolm (1994)
Decision makers are often confronted with situations that are:
– Obvious
• large response to N fertiliser; or very suboptimal sowing time
– Marginal
• gain in N fertiliser equal to the cost; or small differences if sown a week earlier or
later
Better decisions don’t necessarily follow from:
– Better information
– More information
What makes a DSS successful
Simple to use
Relevant
Able to be localised
Effective
Low cost
User-friendly
End-users involved in the development
Very clear purpose
Aimed at learning
– Modelling and simulation contribute to learning and knowledge
• but there is a weak link between modelling / simulation and
change
• Robinson and Freebairn (2000)
Other options?
DSS
– More time spent aggregating
data
– More literature reviews
– Publish summarised ‘guides
to …’
– Less concentration of trial
results, more concentration
on updating BMP
– More appropriate data
management
– Better organised website
– Rules of thumb
– Decision trees
– Paper-based tools
Consistent advice
– Accreditation for
agronomists, advisers and
consultants
– Membership of AACA or
similar
– Regular re-fresher training
– Training for farmers
– Less focus on individual trials
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