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Diversification in Australian broadacre farming: can simulation models handle the manager’s objectives and constraints?
Andrew Moore & Lindsay Bell
Australian broadacre farming: the broad brush
Farms are large, and getting larger• Trend toward more cropping
Largely deregulated markets• Little direct government support• Exposed to price volatility
Multiple pressures on inputs• Long term cost-price squeeze• Labour shortages
Major “Millenium Drought” ABARES survey data
Biophysical simulation models of mixed farms
APSIM soil and crop models
GRAZPLAN pasture model • Common water uptake logic
GRAZPLAN ruminant model• Crop models extended for
defoliation
Event-based management• Full-featured management
scripting language
First applications in 2006
Barley Canola
Grass PhalarisClover Lucerne
Water Soil C+N Wheat
Paddock
Barley Canola
Grass PhalarisClover Lucerne
Water Soil C+N Wheat
Paddock
Barley Canola
Grass PhalarisClover Lucerne
Livestock
Cashbook
Water Soil C+N Wheat
Paddock
Barley Canola
Grass PhalarisClover Lucerne
Simulation
Manager
Weather
Water Soil C+N Wheat
Paddock
Key drivers and constraints on diversification
Risk mitigation• portfolio diversification reduces economic risk
Exploiting spatial variability• different land uses are optimal on different land classes
Production complementarities• legume N, crop disease breaks, forage supply
Management flexibility• divert resources between enterprises tactically
Maintenance of land & genetic resources• soil C levels, salinity management, herbicide resistance …
Resource allocation• Limited supplies of water, cash, machinery & labour
Management focus• “enterprises doubled, management squared”
1. Risk mitigation
Portfolio diversification reduces economic risk
Magnitude of this effect has not previously been quantified Simulation models are ideally suited to explore this question
Temora, New South Wales:Bell & Moore, this conference
2. Exploiting spatial variability
Different land uses are optimal on different land classes
Simulation models can capture key differences between soilsDifficult to assess typical levels of soil variability across a region•New mapping initiatives (e.g. GlobalSoilMap.net) may help
Australian Soil Resource Information System
3. Production complementarities
Simulation modelling the only way to extrapolate from experimentation
N supply through fixation by legumes• Captured by the models
Disease & weed management• Modelling crop & pasture diseases
is the next scientific challenge • Lawes’ talk at this conference
More diverse feed bases• Dual-purpose cereals• Stubbles: to graze or not to
graze?
Waikerie, South Australia:Descheemaeker & Moore, this conference
4. Management flexibility
Divert resources between enterprises tactically
Coolamon, New South Wales:Future Farming Industries CRC (unpublished)
(b) Murrumbidgee, NSW
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Soil C levels, salinity management, herbicide resistance …
The simulation models can do:• Water losses – deep drainage, runoff• Soil carbon changes (required precision is increasing)• Bare ground/erosion risk
5. Resource maintenance
Coolamon, New South Wales:Robertson et al. (2009)
5. Resource maintenance
Soil C levels, salinity management, herbicide resistance …
The simulation models can do:• Water losses – deep drainage, runoff• Soil carbon changes (required precision is increasing)• Bare ground/erosion risk
Soil acidity is a gap
Herbicide resistance management has generally been modelled using simpler approaches • Thornby et al. (2009) have linked weed population-genetic
models to APSIM using Vensim• Larger set of scientific questions around modelling
population genetics in agricultural systems
6. Resource allocation
Limited supplies of water, cash, machinery & labour
Typically done with linear programming “bio-economic” models• Use of simulation models to estimate (or constrain)
technical coefficients
6. Resource allocation
Limited supplies of water, cash, machinery & labour
Labour & machinery can be accounted for in the same way as cash flows
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Yiel
d -W
eedy
Stu
bble
sYield - Sprayed Stubbles
AAAAWCWB2nd Wheat
GRDC Water Use Efficiency Program
Allocation of resources between years and paddocks rather than enterprises:
• Soil water, via control of weeds in summer fallows
• Labour & machinery (e.g. sowing time allocation)
Hybrid modelling analyses needed• Use of simulation models to
estimate (or constrain) technical coefficients
7. Management focus
“Enterprises doubled, management squared”
Simulation analyses tend to assume a “perfect” manager
Area for future research (interface between “hard” & “soft” systems)
A final observation
These modelling analyses have treated mixed farming systems as stochastic but stationary processes• “Slow” variables held (or forced) constant
This assumption isn’t valid for some of the problems requiring analysis• Climate adaptation pathways• Carbon sequestration as a source of cash flow
How do we interpret modelling outputs in non-stationary contexts?
Lindsay BellCSIRO Ecosystem SciencesToowoomba
Phone: +61 7 4688 1221Email: [email protected]
Andrew MooreCSIRO Plant IndustryCanberra
Phone: +61 2 6246 5298Email: [email protected]
Thank you