whole-farm models - some recent trends. mike robertson
DESCRIPTION
A presentation at the WCCA 2011 event in Brisbane.TRANSCRIPT
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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