diversification in australian broadacre farming: can simulation models handle the manager's...

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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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: Lindsay.Bell@csiro.au

Andrew MooreCSIRO Plant IndustryCanberra

Phone: +61 2 6246 5298Email: Andrew.Moore@csiro.au

Thank you