Agricultural Production Systems Simulator (APSIM)

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Agricultural Production Systems Simulator (APSIM)

• Simulates
• yield of crops, pastures, trees, weeds ...
• key soil processes (water, N, P, carbon, pH)
• surface residue dynamics erosion
• range of management options
• crop rotations fallowing mixtures
• short or long term effects
• one or two dimensions
• high software engineering standards
• BUT, not yet pests nor diseases

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APSIM - developmental goals

• Production and profit
• sought to retain yield prediction in relation to
  management options and environment (c/f -
  CERES, CROPGRO models)
• Fate of the soil resource
• sought valid long-term simulation of key soil
  processes (c/f - CENTURY, EPIC)
• Impacts off-farm
• predict loss of soil, water, nutrients off-site
  (c/f - EPIC)

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APSIM - some statistics

• Development team
• 7 programmers / model support staff
• 12 scientist / modellers
• User base
• 180 licensed users
• 9 countries, 4 continents
• Product Suite
• ca. 450,000 lines of code
• 4 languages
• 38 modules
• 12 interfaces or major tools

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Developing our knowledge capability - APSIM
modules

• Crop/pasture/tree
• wheat sorghum
• sugarcane chickpea
• mungbean soybean
• barley groundnut
• maize sunflower
• hemp lucerne
• fababean canola
• lupin mucuna
• cowpea Pinus radiata Eucalyptus sp.
• cotton - CSIRO PI
• pearl millet - ICRISAT
• pigeonpea - ICRISAT

• Soil
• SoilWat
• SWIM
• SoilN
• SoilP
• SoilpH
• Solute
• Residue
• Manure - ICRISAT

Management Sowing Tillage Irrigate Fertilize Inter
crop/mixture competition
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Multiple user interfaces e.g. APSFront interface
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APSIM has been used to simulate

• Some examples

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Pigeonpea qualitative photoperiod response
physiological processes
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plant organs
Tiller leaf area in millet
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crop growth development
Growth development of pigeonpea
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yield of experimental crops
Chickpea
Mungbean
Cowpea
3000
5000
yields
1200
4500
11 line
2500
regression
4000
y 1.0631x - 70.964
900
3500
2000
2
R
0.7924
3000
Predicted
Predicted
1500
600
2500
Predicted
2000
1000
11 line
1500
300
Grain (g/m2)
y 0.87x 221.44
1000
500
Biomass (g/m2)
R
2
0.77
500
0
0
0
0
300
600
900
1200
0.0
500.0
1000.0
1500.0
2000.0
2500.0
3000.0
0
1000
2000
3000
4000
5000
Observed
Observed
Observed
Prediction n regression line R2 slope in
tercept wheat grain 43 1.07 -13.0 0.79 maize
grain 111 0.98 (? 0.04) -5.5 (?
240) 0.85 chickpea grain 60 0.90 (? 0.07) 163
(? 172) 0.76 mungbean grain 47 1.07 (?
0.10) -27.2 (? 128) 0.72 cowpea grain 15 0.93
(? 0.08) -31.6 (? 34.6) 0.91 stylo
biomass 63 0.84 (? 0.06) -131.7 (? 171) 0.78
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yield of commercial crops

• APSIM tested against data from commercial farms
• Crops include cotton, sorghum, mungbean, wheat,
  chickpea

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yield of smallholder crops
Maize response to N in Malawi Maize response to N
manure in Kenya
Maize response to N at Makoholi
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N response in smallholder crops
Testing simulation of maize response to N at
Makoholi over 7 seasons 1991-1997
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seasonal perspectives
How representative were the seasons 91-98 at
Makoholi?
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yield of crops in rotation
Lines predicted Symbols observed
Wheat-Sorghum Long Fallow rotation
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soil water of crops in rotation
Wheat
Sorgham
Wheat-Sorghum Long Fallow rotation
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ET of crops in rotation
93 Wheat, 94-97 Lucerne measured in lysimeter
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legume rotation effects
Maize response (TBM) to fertiliser N following
pigeonpea, India
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consequence of crop rotations
GM
drainage
wheat-wheat-mungbean-sorghum-chickpea rotation
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soil organic matter changes
Farming systems on a vertisol at Dalby, Qld.
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crop-weed competition
Maize volunteer stylo
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response to manure application
High low quality manure applied to maize
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response to N, P fertilizer manure
Maize response to P rates in Kenya
Response to N, P and manure, India
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on-farm constraints

• Response to 36 kg N/ha

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agroforestry systems

• Enabling landholder assessment of the
  productivity and risk of commercial agroforestry
  investment on grain farms in Australias medium
  to low rainfall regions

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change in wheat production under climate change
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but can you use such technical information with
farmers?
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YESbut the information needs to be made
relevant to farmers realities
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Source Peter Carberry CSIRO, Australia
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