This document summarizes recent trends in whole-farm modeling approaches. It finds that most studies use representative farms and static optimization to focus on profit, with few accounting for seasonal variability, price fluctuations or spatial heterogeneity. Newer approaches model smallholder farms in developing countries to assess food security and incorporate more dynamic simulation of biophysical processes under resource constraints. Overall, there is a need for whole-farm models to more clearly describe their intended users and validate their assumptions and parameters against real farm data.

1. Whole-farm models – some recent trends
Michael Robertson
CSIRO Sustainable Agriculture Flagship and Ecosystem Sciences
David Pannell & Morteza Chalak
University of Western Australia

2. 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?

3. 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

4. Constrained resources
• 68% of studies
• Primary economic emphasis
• Constraints on labour, machinery of
“This small amount
fertiliser is all you need
or expenditure plant”
for each plant”
• Not in dynamic biophysical
models

5. Dynamics – within year, between years
• Within year – 28% (livestock emphasis)
• Between years – 8% (cropping emphasis)
• Both – 43%
• Neither – 8%

6. 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

7. 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

8. 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

9. 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

10. 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

11. Emergent approaches (1)
• Static optimisation in
industrialised
agriculture
• Technically focussed
• Resource constrained
• Multiple activities
• Seasonal variability not
accounted for
• E.g. MIDAS

12. 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

13. Emergent approaches (3)
• Biophysical simulation
• Farm inputs are supplied Rainfall Runoff
Soil
exogenously. f1
Water
evaporation
Soil water Drainage
• Greater specification of Weed
transpiration
f2
Transpiration
management options & f3
seasonal variability. Root Shoot
• Little application to spatially biomass biomass
heterogeneous situations or
Biomass
Soil C f6 f5 f4
f8
developing country situations Surface
Biomass
f7 Feed
Consumed
Fodder
Conserved
Grain
Harvested
• Resource constraints not GHG f9
Meat, wool
imposed, though may be production
accounted for in the costs of
f10
production. Money
Input costs Gross income
f11
Gross Margin
• E.g. APSIM-FARMWI$E

14. “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

15. “New” approaches: Regional-scale adoption studies
Maximum potential Actual adoption
adoption
Proportion farmers
Impact of climate, growing break crops?
commodity prices, costs?
Proportion of farm
Impact of yields being under break crops?
attained, break crop
Yields being attained?
effect?
Can the difference between
surveyed and modelled
area of break crops on farm
be explained ?

16. 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

17. 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

18. Thank you
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