Whole-farm models – some recent trends

Michael Robertson CSIRO Sustainable Agriculture Flagship and Ecosystem Sciences

David Pannell & Morteza Chalak University of Western Australia

The issue

• Extrapolating from field to farm scale

• Guidelines on types of approach

• Comprehensiveness vs. complexity

• Optimisation vs. non-optimisation approaches

• Accounting for variability (seasonal, spatial, economic)

• Interactions between activities• Ex-ante research evaluation vs.

engagement with farmers and advisors.

• Emergence of a focus on smallholder in developing world

• One tool or many tools?

Review of the literature

• Papers using WFMs 2006 -2011• 53 studies utilising 42 models• 21% studies on smallholders in LDCs• Classified according to criteria:

• Constrained resources• Dynamics – within year, between years• Seasonal and price variation• Mixed farming or monoculture• Spatial heterogeneity• Real vs. “representative” farms• Objective – profit, risk, natural resources etc

Constrained resources

• 68% of studies• Primary economic emphasis• Constraints on labour, machinery

or expenditure• Not in dynamic biophysical

models

““This small amount of This small amount of fertiliser is all you need fertiliser is all you need for each plantfor each plant””

““This small amount of This small amount of fertiliser is all you need fertiliser is all you need for each plantfor each plant””

Dynamics – within year, between years

• Within year – 28% (livestock emphasis)• Between years – 8% (cropping emphasis)• Both – 43%• Neither – 8%

Seasonal and price variation

• Price only – 13%• Seasonal only – 17%• Both – 21%• Neither – 49%

• No studies used a distribution or sequence of prices.

• Many models used a sequence of years to calculate a long-term mean without analysing the shape of the distribution

Mixed vs. monoculture

• Mixed crop-livestock systems – 49% of studies

• A feature of smallholder systems in LDCs

• 74% of studies on mixed systems treated activities as discrete

Spatial heterogeneity

• Half of studies specified spatial heterogeneity in land-use units within the farm

• Land use units varied in production potential and costs of production

Real vs. “representative” farms

• 75% of studies used representative farms (often based on surveys)

• Surprisingly, few models varied key characteristics of the representative farm in sensitivity analyses

Objective – profit, risk, natural resources, social outcomes

• Household food security in LDCs – 21%

• Industrialised countries - Profit – 79%• 21% additional objective e.g. GHGs,

energy use, soil carbon, nutrient losses

• Social (max. labour use) – 1 study• Risk reduction – 1 study

Emergent approaches (1)

• Static optimisation in industrialised agriculture

• Technically focussed• Resource constrained• Multiple activities• Seasonal variability not

accounted for

• E.g. MIDAS

Emergent approaches (2)

• Household models in the developing world

• Household food security• Spatial heterogeneity• Resource endowments of

farmers (surveys)• Optimisation & non-

optimisation• Short & long-term effects

• E.g. IMPACT, NUANCES, IAT

Emergent approaches (3)

• Biophysical simulation• Farm inputs are supplied

exogenously. • Greater specification of

management options & seasonal variability.

• Little application to spatially heterogeneous situations or developing country situations

• Resource constraints not imposed, though may be accounted for in the costs of production.

• E.g. APSIM-FARMWI$E

Legend

f1 Rainfall capture efficiency

f2 Soil water utilisation efficiency

f3 Shoot Biomass Transpiration efficiency

f4 Grain harvest index

f5 Fodder conservation efficiency

f6 Feed utilisation efficiency

f7 Rate of excreta return

f8 Surface biomass decomposition efficiency

f9 Feed conversion efficiency

f10 Price

f11 Margin

Rainfall

Transpiration

Shootbiomass

GrainHarvested

FeedConsumed

Surface Biomass

Meat, wool production

Gross income

f2

Runoff

Drainage

Soilevaporation

Soil water

Fodder Conserved

Rootbiomass

Soil C

Weedtranspiration

GHG

Gross Margin

Wat

erBi

omas

sM

oney

f1

f3

f10

f9

f8

f7

f6 f5 f4

f11

Input costs

Rainfall

Transpiration

Shootbiomass

GrainHarvested

FeedConsumed

Surface Biomass

Meat, wool production

Gross income

f2

Runoff

Drainage

Soilevaporation

Soil water

Fodder Conserved

Rootbiomass

Soil C

Weedtranspiration

GHG

Gross Margin

Wat

erBi

omas

sM

oney

f1

f3

f10

f9

f8

f7

f6 f5 f4

f11

Input costs

“New” approaches: Dynamic simulation under resource constraints

•Two approaches:• Resource constrained models used to define

farm configuration for dynamic simulation• Resource use an output variable, against

which scenarios evaluated

“New” approaches: Regional-scale adoption studies

Actual adoption

Proportion farmers growing break crops?

Proportion of farm under break crops?

Yields being attained?

Maximum potential adoption

Impact of climate, commodity prices, costs?

Impact of yields being attained, break crop

effect?

Can the difference between surveyed and modelled

area of break crops on farm be explained ?

Overall observations

• Deficiencies:• Clear description of audience for the work• Justification for biophysical parameters• Assumptions about resource endowments of farmers• Explicit statement of what inputs are exogenous or endogenous

to the model• Sensitivity analysis around prices, seasonal conditions and

farm configuration.• “Validation” - combination of subjective (“sensibility testing”) and

objective methods (comparisons with farm surveys, etc).• The focus on most studies is still policy guidance and

research prioritisation, • Few studies attempting to engage with farm managers

Evidence of impact?

• Lessons from engagement with MIDAS in Western Australia (Pannell 1997)

• brought together researchers (of various disciplines) and extension agents who otherwise would interact little

• allows scientists and extension agents to assess the economic significance of particular biological or physical information

• influenced the thinking of researchers and extension agents about the whole-farm system

• highlighted a large number of data deficiencies and allowed prioritization of research to overcome them

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