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Presentation from the WCCA 2011 conference in Brisbane, Australia.
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Decision support systems in practice- some observations
David FreebairnRPS Brisbane, Australia
Outline
• Simplicity and transparency• Who says the world has to be complex?• Acknowledge stakeholders as experts• Modesty and limitations
• Ideas, comments, suggestions
Experience -background
And many good ideas from my colleagues in DPI, DERM and CSIRO
• Many decisions are simpler than we think
• Many analytic tools are complex, inaccessible or opaque
• Computers are good at simple tasks (e.g. arithmetic)
• Humans are good at complex tasks (e.g. decision making)
General observations
Complexity and Generality
Relativeaerial
applicability
Detail
RulesOf thumb
Simplesimulation
Complex simulationDSS
The struggle between usefulness (goodness) and complexity
http://www.dau.mil/pubscats/PubsCats/atl/2005_11_12/war_nd05.pdf
Soil management, water conservation, erosion
Rainfall simulation - a research and extension tool
Bare soilBare soilStubble coverStubble cover
Simplicity and transparency
• The simplest things generally work best, and the simpler the better.
• The easier a decision support tool is to use and support.
• More complex >> less transparent.• Active demonstrations are most effective
learning tools.
Who says farming is complex?
• Increased complexity is a common pathway for scientists.
• What challenge farm decision making though is uncertainty.
• There is a view that many models should be used in an “instructive” mode.
Acknowledge stakeholders as experts
• Remember who has the greatest vested interest in problem solving.
• The farmer is clearly the best expert, and expert farmers often use a range of other experts to support them.
• Being useful to decision makers requires getting into their shoes.
Modesty and limitations
• Acknowledge external “experts” have small roles to play
• System status-history (weather, previous crops)
-monitoring (soil water, weeds, disease)
• Weather futures - based on history
- forecasts
• Market futures
• Fit in the system
• Personal preferences
Decision point
Tactical decision making- where is the niche for improved information?
• System status-history (weather, previous crops)
-monitoring (soil water, weeds, disease)
• Weather futures - based on history
- forecasts
• Market futures
• Fit in the system
• Personal preferences
Decision point
Tactical decision making- how do farmers view this?
Importance of various elements in decision making – e.g. planting
Climate forecast
adjustment
Gut feeling
Weeds
Price
Soil N
Seed availability
Starting soil water
20%
15%
30%8%
8%
8%
8%
8%
Diseaserisk
Note:Use this figure to
focus discussion on what are the issues and their relative
importance
(no correct answers)
Estimating soil moisture- the simple “push” probe
“2 feet of moisture”
Simple vs. less simple
0
50
100
150
200
250
300
0 50 100 150 200 250 300
Observed (mm)
Acland
Capella
Greenmount
Wallumbilla
Warra
RMSD = 38 mm 1:1 Line
y = 0.76x + 47
R2 = 0.56
0
50
100
150
200
250
300
0 50 100 150 200 250 300
Observed (mm)
Pre
dict
ed (
mm
)
Acland
Capella
Greenmount
Wallumbilla
Warra
RMSD = 28 mm
1:1 Line
y = 0.82x + 29.6
R2 = 0.72
Fallow efficiency
-20% fallow rainfall
HOWWET?
-daily model
Soil cover (%)
0
10
20
30
40
50
0 20 40 60 80 100
Bare fallow
Stubble incorporated
Stubble mulch Zero-till Pasture
Average annual soil loss (t/ha)
Influence of stubble cover on soil erosion
Greenmount (Qld) 1978-88
Seeing, feeling, trialling
Some issues Queensland farmers consider
• What are the chances of a planting rain?• What are current moisture, nitrogen
conditions?• What are implications for yields? • Input needs?
Component questions for simple models
• What are current conditions (e.g. moisture heat sum)?
• What are the chances of a future event (e.g. planting rain, frost, wet harvest)?
• What is skill in a forecast?
• What are the implications of above, and what management options are there to adjust?
Recent Histor
y
Now(the decision
point)
Futureoutcome
RainfallTemperature
Previous crop Soil type
Management
Range of Options
andoutcomes
Current conditions
Soil waterNutritionDiseaseWeeds
-supported by new
observation
Linking conditions NOW and Future probabilities
Expected drivers• Rainfall
• Temperature
Based on • History
• Persistence• forecasts
Time line
Rainfall mm Temperature > OC Temperature < oC Heat sum oC days
What are the chances of getting …
50 3 30 200
In days, between 10
Occurs in % of years between 54 1912-2010
Maximum
in eachyear
Previous analysis
Rainfall Max. temp. stress days Min. temp days Heat sum oC days
How is the season progressing?
Between
Previous analysis
Season to date rainfall from dd/mm/yyyy to dd/mm/yyyy9th , 5th and 1st decile
Enlightened DSS design• Question focused, client focused
• Easy to use and ready access
• Multiple access points
• Transparency
• Information, not advice
• Efficient
• Recognise life cycle
How do we ensure we move 1, 2, 4?
Thankyou
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