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Whole-farm models - some recent trends. Mike Robertson
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Whole-farm models - some recent trends. Mike Robertson

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A presentation at the WCCA 2011 event in Brisbane.

A presentation at the WCCA 2011 event in Brisbane.

Published in: Education, Technology
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  • 1. Whole-farm models – some recent trendsMichael RobertsonCSIRO Sustainable Agriculture Flagship and Ecosystem SciencesDavid Pannell & Morteza ChalakUniversity 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 Contact Us Phone: 1300 363 400 or +61 3 9545 2176 Email: enquiries@csiro.au Web: www.csiro.au

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