A presentation made at the WCCA 2011 event in Brisbane, Australia.

1. David Parsons Building a model based on
Chuck Nicholson APSIM that simulates
Bob Blake
smallholder crop-livestock
Quirine Ketterings
Luis Ramirez systems
Danny Fox
Luis Tedeschi
Jerry Cherney
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2. Yucatan Mexico
Milpa – shifting cultivation
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3. Hair sheep in Yucatan
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4. The role for modelling mixed
systems in developing countries
• There is a general lack of knowledge of what actually goes on
in these complex smallholder mixed systems.
• “Modelling realistically offers the only way of identifying and
quantifying the subtle but highly significant interactions that
occur between the various components of smallholders’
systems” (Thornton & Herrero, 2001).
• Modelling is a method for integrating information in a rational
way.
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5. Challenges for crop-livestock models
• Often sufficient in one discipline (soils, crops,
livestock, economics) but not the others
• May not be dynamic between components
• Modelling can be very time consuming
• Construction of a model for a specific
application is costly, therefore a generic
modelling framework is preferable.
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6. Objectives
• Develop a crop-livestock model to assess the
biophysical and economic consequences of
management decisions/farming practices evident in
Yucatan mixed systems.
• Dynamically link all components of the model
• Be descriptive of the system, predictive in relation to
outcomes (given specific farmer decisions), but not
prescriptive (suggesting what farmers should be
doing).
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7. Movement of nutrients through
sheep and fodder
Forest
Home garden Cut & carry
Within village Graze
Tree purchase & Sheep pens
Forage purchase Cultivated forage Field Crops
Graze residues
Graze
Tree harvest Cut & carry residues
Cut & carry Grain
Cultivated forage
Cut & carry
Graze
Movement of fodder
Outside village
Movement of sheep By-product purchase
Grain purchase
Supplement purchase
Buy and sell sheep
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8. Components of the integrated
model
• APSIM (Agricultural production simulator)
– Simulate crop production (soils, crops, weather)
• Vensim
– Icon-based modeling software
– ‘System dynamics’ software
• SRNS (Small Ruminant Nutrition System)
– Based on CNCPS-S (Cornell Net Carbohydrate and
Protein System for Sheep)
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9. The Integrated model
APSIM Vensim™
Climate Flock dynamics
Soil organic matter, Livestock feeding
nutrient ,and water
Venlink Nutrient allocations
dynamics
interface Management
Surface organic matter
variables
Labor
Plant growth
Economics
Crop
Grass
Corral
SRNS data
Intake
Weight gain
Manure quantity
Manure quality
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10. Venlink interface variables
Crop Grass Corral
APSIM Grain harvested Grass leaf and stem Manure in pile
Grain protein available Manure N in pile
to
Stover harvested Grass leaf and stem Refusals in pile
Vensim protein
Stover protein Refusal N in pile
Vensim Manure to milpa Manure to grass Manure to corral
to Manure C:N Manure C:N Manure C:N
Refusals to milpa Refusals to grass Refusals to corral
APSIM
Refusals C:N Refusals C:N Refusals C:N
Milpa cultivation Urea to grass Empty manure
cycle pile signal
Urea to milpa
Fraction of stover
harvested
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11. Example - manure
APSIM Vensim™
Crop Manure Manure allocation
allocation, C:N calculations
Effects of applied and use info
manure Manure C:N
calculations
Grass Define manure use
Effects of applied Quantity and
manure C:N of stored
manure
Corral Manure quantity
Manure quality
Breakdown of stored
manure
SRNS data
Manure quantity
Manure quality
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12. Example Vensim screen
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13. Some model limitations
• Not all modules in APSIM are P responsive.
• SRNS also does not track P (yet).
• Wider range of tropical crop modules needed in
APSIM, particularly forage crops.
• Simulation time
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14. Possible model improvements
• More choices of soils and crops.
• Lack of knowledge of the underlying processes of manure
decomposition:
– Manure decomposition in soil is ok
– Manure on surface and in piles not as well understood
– Technologies that improve manure management
• Secondary feed quality data is needed to generate SRNS
runs, i.e. data that is not generated by the APSIM model
section.
• A dynamic SRNS would offer numerous benefits.
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15. Why not code it all in APSIM?
• APSIM has great flexibility to write ‘manager’
code
• Visual nature of Vensim is a great help
• Partial model testing
• Accessing text/excel data using Vensim
• Sensitivity analysis, optimization
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16. Types of scenario analyses
• What are the biophysical and household
outcomes from differing:
– Types of farms (livestock vs. crop vs. livestock & crop)
– Manure management and use practices
– Livestock feeding practices
– etc.
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17. Example Implications of model outputs
1. Logical for smallholders to make use of the natural resources
available.
Focus on using common land
2. Cut and carry systems can be more labor efficient than
common land grazing systems (where continuous supervision
is needed).
3. Investment in increased integration through the use of crop
by-products may not be a favorable option while common
land is available.
4. Investment in infrastructure to grow improved forages may
lead to decreased returns to labor and net income.
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18. Review of this modeling approach
• Crop-livestock systems, particularly those in developing
countries, are variable and complex, making it difficult for a
particular modeling package to be applicable to every
situation.
• Our model builds on a foundation of well a established soil-
crop-climate model (APSIM) and sheep model (SRNS)
• Links with Vensim allow flexibility to develop model structure
to simulate individual systems and address particular research
questions
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