decision support systems in practice - some observations. david freebairn
DESCRIPTION
Presentation from the WCCA 2011 conference in Brisbane, Australia.TRANSCRIPT
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Decision support systems in practice- some observations
David FreebairnRPS Brisbane, Australia
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Outline
• Simplicity and transparency• Who says the world has to be complex?• Acknowledge stakeholders as experts• Modesty and limitations
• Ideas, comments, suggestions
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Experience -background
And many good ideas from my colleagues in DPI, DERM and CSIRO
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• 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
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Complexity and Generality
Relativeaerial
applicability
Detail
RulesOf thumb
Simplesimulation
Complex simulationDSS
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The struggle between usefulness (goodness) and complexity
http://www.dau.mil/pubscats/PubsCats/atl/2005_11_12/war_nd05.pdf
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Soil management, water conservation, erosion
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Rainfall simulation - a research and extension tool
Bare soilBare soilStubble coverStubble cover
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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.
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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.
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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.
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Modesty and limitations
• Acknowledge external “experts” have small roles to play
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• 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?
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• 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?
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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)
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Estimating soil moisture- the simple “push” probe
“2 feet of moisture”
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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
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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
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Seeing, feeling, trialling
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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?
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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?
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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
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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
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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
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Enlightened DSS design• Question focused, client focused
• Easy to use and ready access
• Multiple access points
• Transparency
• Information, not advice
• Efficient
• Recognise life cycle
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How do we ensure we move 1, 2, 4?
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Thankyou
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