More Related Content More from Joanna Hicks (20) The expected value of seasonal streamflow forecasts to a grain-cotton irrigator in the Condomine-Balone catchment. Brendan Power1. Department of Employment, Economic Development and Innovation
The expected value of seasonal stream-
flow forecasts to a grain-cotton irrigator
in the Condamine-Balonne catchment.
Brendan Power, Daniel Rodriguez,
Jeff Perkins and Claire Hawksworth
2. Overview
• Seasonal stream flow forecasts
• Whole-farm economic modelling of
irrigated grain/cotton farms with
APSIM
• Case study results
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 2
3. Seasonal streamflow forecasts
www.bom.gov.au/water/ssf
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 3
4. Forecast skill
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 4
5. What is APSIM?
A farming systems model created …
• to model the farming system performance over
time
• with equal emphasis on crop and soil
dimensions of agricultural systems
• with a capability to deal comprehensively with
management
– e.g. planting times, N, irrigation
• wide scope of application, from genetics
(breeding) through farming systems to policy.
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 5
6. APSIM
Manager
Soilwat SoilPH
Paddock
SoilN SoilWat
SWIM
SurfaceOM
SoilP
Chickpea
Maize Erosion
Wheat
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 6
7. Irrigated APSIM farms
Crops Crops
Crops Residue Residue
Crops
Residue Weeds Weeds
paddock2 paddock3 Residue
Soil Soil
Weeds
paddock1 paddock4 Weeds
Soil Soil
Waters Pump cap. Met Water
storages Farm sources
Economics Manager
Soil
Soilwat
Soil paddock8 paddock5
Weeds
SoilN
Weeds
Residue
Residue Soil Soil
paddock7 paddock6
Crops
Crops Weeds Weeds
Residue Residue
Crops Crops
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 7
8. Case studies
Planting window: Sep 15 – Oct 15
Stored water > 4ML/ha
Existing farm area planted to maize or
sorghum less than 50%
Planting window: Oct 16 – Jan 15
Rain over 4 days > 25mm
ESW > 150mm
maize or sorghum area < 50% Oct 15 – Nov15
Stored water > 4ML/ha
cotton area < 50%
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 8
9. Validation
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 9
10. Validation
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 10
11. Dalby case study farm
Dalby
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 11
12. Dalby case study farm
• Total cropping area - 615ha
• Furrow irrigation
• 3 Storages - 2400 ML at 70ML/day
• Sources of water
– On-farm runoff
– Off-farm overland flow ~tr(0,200,400) ML/yr
– Bores (172ML/yr at 12ML/day)
– River flow
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 12
13. IQQM modelled Condamine river flow
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 13
14. Proxy streamflow forecasts
• Streamflow forecasts not currently
available in QLD (available from July
2012)
• The NINO3 index with a 2 month lag
has the best skill at predicting summer
(DJF) rainfall at Dalby (Schepen et
al., 2011) .
• NINO3 index used as a proxy forecast
for Condamine River flows.
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 14
15. Oct NINO3 index
Summer (DJF) rainfall Summer (DJF) river flows
Probability
Probability
Positive forecast
Negative forecast
Positive forecast No forecast
Negative forecast
No forecast
River flow (ML)
Rainfall (mm)
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 15
16. Change in management
• Vary cotton area based on the streamflow prediction
• Assumed monoculture cotton for simplicity
• Cotton area depends on the amount of stored water at
sowing
• Optimised the cotton sowing rule based on NINO3
values w.r.t. required stored water (ML/ha) at sowing.
• Results:
– Positive NINO3 predictions - 0 ML/ha (ie plant entire
farm to cotton)
– Negative NINO3 – no change in management (ie 4
ML/ha)
– 20 out of 51 years predicted high river flows.
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 16
17. Results
High flow seasons All seasons
AU$31,000/year
Current management Current management
Adaptive management Adaptive management
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 17
18. What next?
• Realistic crop rotations
• Implication for risk
• Different farms and rivers
• Out-of-sample tests
• Environmental trade-offs
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 18
19. Summary:
• Seasonal stream flow forecasts
• Whole-farm economic modelling of
irrigated grain/cotton farms with
APSIM
• Case study results
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 19
20. Thanks.
References
• Australian Bureau of Meteorology (2010) Information sheet 9 Streamflow
forecasting: Days to seasons, Australian Government, retrieved from
www.bom.gov.au/water/about/publications/document/InfoSheet_9.pdf
• Hammer GL, Nicholls N, Mitchell C (2000) Applications of Seasonal Climate
Forecasting in Agricultural and Natural Ecosystems: The Australian
Experience. Kluwer Academic,
• Power B, Rodriguez D, deVoil P, Harris G, Payero J (2011) A multi-field bio-
economic model of irrigated grain-cotton farming systems. Field Crop Res.
• Schepen A, Wang QJ, Robertson D, (2011) Evidence for using climate
indices to forecast Australian seasonal rainfall, J. Climate.
• The State of Queensland (DERM) (2010). Integrated Quantity and Quality
Model (IQQM) output data for the Condamine Balonne ROP
www.derm.qld.gov.au
© The State of Queensland, Department of Employment, Economic Development and Innovation, 2011 20