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?