modelling crop sequences: a new approach. roger lawes
DESCRIPTION
A presentation at the WCCA 2011 event in Brisbane.TRANSCRIPT
Modelling crop sequences: a new approach
Roger Lawes and Michael Renton
Why not Wheat Wheat Wheat?
• Or what is the problem with a monoculture?
• Disease host is always present.
• Limited opportunity for Integrated Weed Management.
• Nutrients supplied entirely through fertiliser.
• No inputs from biologically fixed N.
CSIRO. Insert presentation title, do not remove CSIRO from start of footer
Trends in previous crop grown prior to wheat in Western Australia
1998 2000 2002 2004 2006
0.0
0.1
0.2
0.3
0.4
0.5
0.6
year
% p
revi
ou
s cr
op
wheatbarley
canolalupins
pastureOn-farm sequencessuggest a shift in attitude
Scientists have focused on thebiotic stresses
Farmers have focussed on their perception of economics
Who is right?
Disease incidence for take-all in NSW
Data courtesy John Kirkegaard
How much disease is tolerable?
Disease incidence
Fre
qu
en
cy
0.0 0.2 0.4 0.6 0.8 1.0
02
00
40
06
00
Low Disease High Disease
Relative cereal yields
Data courtesy John Kirkegaard
How much yield loss is economically acceptable?
Relative cereal yield
Fre
qu
en
cy
0.2 0.4 0.6 0.8 1.0
02
04
06
08
0
High yield loss Low yield loss
How can these break-crop effects be
quantified and analysed?
• MIDAS• Whole farm• But weeds, disease lumped together as ‘yield boost’ - one year only
• ROTAT• Uses expert opinion to define break crop response
• RIM (Ryegrass Integrated Management)• Models seedbank to get long-term weed effects• But disease and N lumped as one year ‘yield boost’, lots of detail
on herbicides etc
• LUSO (Land Use Sequence Optimiser)• Models seedbank and disease dynamics over multiple years• Separates weeds, disease, N effects
Single paddock
Flexible land use sequence
Modelling sequences with contemporary crop simulation models
• DSSAT
• CROPSYST
• APSIM
• APES
Integrating Disease, Weeds and N into a dynamic crop sequencing model (LUSO)
• A dynamic crop sequence model
• An optimisation model where:
• Crop species can be specified• N requirements can be specified• Disease population dynamics can be specified• Weed population dynamics can be specified• Costs and Prices can be varied across cropping options. • Duration of crop sequence altered• Discount rate included
• Objective function – maximise whole of sequence profit, given the dynamic processes.
Representing the effect of disease on crop yield.
1 2 3 4 5 6
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Year
Yie
ld t
/ha
Disease growth rate
LowMediumHigh
Deterministic model – constant disease processes through time
Disease growth as a stochastic process.
1 2 3 4 5 6
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Year
Yie
ld t
/ha
LUSO Modelling – application and use
• Framework allows • strategic analysis
(best land use sequence for a given set of assumptions)
• sensitivity analysis(magnitude of various drivers)
• tactical decision analysis(what is the optimal decision given the biotic stresses)
• Allows analysis of underlying drivers (weeds? disease?)
Underlying Drivers
Variation in economic return where weeds and disease effect crop yield over 6 years
$ return of sequence
Fre
quen
cy
0 500 1000 1500 2000 2500
020
4060
8010
012
014
0Sensitivity analysis on multiple drivers, ~1224 runs
Drilling down into the detail
Profit
Fre
quen
cy
1400 1600 1800 2000 2200 2400
010
2030
40
Rapid disease population growth and crop damaged by disease
Low disease growth
Evaluating the optimal sequence - economics and yield, developing rules of thumb.
0 1 2 3 4
0.5
1.0
1.5
2.0
2.5
Number of break crops
Bre
ak
cro
p v
alu
e /
Wh
ea
t cro
p v
alu
e
Unpacking the output
1.5 2 3 4
500
1500
Nitrogen cost ($/kg)
Pro
fit (
$)
0.02 0.03 0.05
500
1500
weed seed surival in wheatP
rofit
($)
1 2 3 4
500
1500
disease multiplier in wheat
Pro
fit (
$)
0.5 0.75 1
500
1500
disease effect on wheat
Pro
fit (
$)
And so…?
•Tool for guiding research •Tool for formulating general strategies
•Tool for formulating general tactical guidelines (if X then Y)
Future
• Improve disease model – calibrate from experimental data• Seasonal variability, uncertainty and risk
• Yield• Disease• Weeds? Like wheat yield?• Price?