APSIM Use in Catchment Models and potential use in BYP...
Transcript of APSIM Use in Catchment Models and potential use in BYP...
APSIM Use in Catchment Models and potential use in BYP scenario analyses
CSIRO ECOSYSTEM SCIENCES
Peter Thorburn & Jody Biggs
Outline
• Paddock modelling in the Paddock-‐to-‐Reef evalua5on
• The WQ challenge _ how low does N need to go?
• Why model, what can it offer? • General ideas • Example
• What’s needed to model?
Overview of Paddock-‐to-‐Reef evalua=on framework
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Effectiveness (ABCD)
Paddock Modelling
Prevalence (ABCD) at some time
Paddock model into cat. model
WQ outcomes Scenarios
The WQ challenge – reduce N Surplus to 50 kg/ha?
N loss framework Thorburn & Wilkinson 2013
N inputs
Crop size•Climate•Irrigation•Crop husbandry•Fallow mgt
N SurplusInput-crop N
Partitioning to•Runoff•Deep drainage•Atmosphere
N lost to water courses
Management ‘tactics’•Placement (surface, bury)•Split applications•Carrier•Timing•Tillage•Irrigation management
Soil type, climate
Management ‘strategies’
(N recommendations)
N mineralised from organic
sourcesBiological N fixation
N lost to atmosphere
Cause of N losses 1. N losses driven by fer5liser, esp. surplus
• Reef behaves same as everywhere else
Basin scale Thorburn et al (2013)
Field scale Webster et al. (2012)
Cause of N losses 1. N losses driven by fer5liser, esp. surplus 2. N surpluses 100-‐250 kg/ha/yr in intensively
managed crops
N surplus and N rate Thorburn & Wilkinson (2013)
Field scale Webster et al. (2012)
Re-‐framing nutrient management • Apply nutrients for the crops actually grown in each field (as
opposed to wide scale poten5als) • What is the minimum N Surplus needed to maintain crop
yields ? • 50 kg N ha-‐1 ? Sugarcane N response and surplus
(Thorburn et al. 2003, 2013)
Is it really possible to grow sugarcane with a N surplus of ~ 50 kg/ha/crop?
Results from five sites from Bundaberg to Mulgrave
^N surplus per crop ^N rate per crop
Thorburn et al. (2010, 2011)
Can improved agricultural management meet water quality targets?
Study / prac=ce
Pollutant Fine sediments
Total P Total N Dissolved inorganic N
PSII herbicides
Target:
20 50 50 50 50
Thorburn and Wilkinson (2013) – empirical modelling All BMP 15 nd 14 12 nd
All Agri Env Prac5ce*
19 nd 24 59 nd
Waters et al. (2013) – paddock and catchment modelling All B-‐Class
13 22 17 27 62
All A-‐Class
25 33 24 34 91
*Defined as: N applica5ons to give a surplus of < 50 kg/ha
Why model?
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Why model? • Fill in ‘missing’ data • Forced check of results • some things just don’t make sense • e.g., runoff > rainfall
• Gain insights into acributes not measured • _ Have more complete picture of the experiment than provided by data alone
• ‘Trail run’ of management ideas prior to inves5ng in field experiments
• Test hypotheses / Generate hypotheses
• Extrapolate results beyond those in the experiment
Why model? • ‘Trail run’ of management ideas prior to inves5ng in field experiments • N Replacement – Field results consistent with before-‐experiment predic5ons – Specific ‘Replacement’ rules for different soils & climates
• Test hypotheses / Generate hypotheses • What limits crop yields? • How sensi5ve are yields to limited roo5ng depth? • How does soil water holding capacity and carbon affect – Yields – Interac5ons with N management
• How responsive are yields to 5ming of N inputs? • Controlled release / nitrifica5on inhibi5on fer5liser
Extrapola=on: Can improved agricultural management meet water quality targets?
Study / prac=ce
Pollutant Fine sediments
Total P Total N Dissolved inorganic N
PSII herbicides
Target:
20 50 50 50 50
Thorburn and Wilkinson (2013) – empirical modelling All BMP 15 nd 14 12 nd
All Agri Env Prac5ce*
19 nd 24 59 nd
Waters et al. (2013) – paddock and catchment modelling All B-‐Class
13 22 17 27 62
All A-‐Class
25 33 24 34 91
*Defined as: N applica5ons to give a surplus of < 50 kg/ha
The end (part 1)
_ Have more complete picture of the experiment than provided by data alone
Example from simula=ng Victoria Plains (Mackay) experiment
• Low N / 1800 mm row spacing • Std N / 1500 mm row spacing • Two seasons (Plant & 1st ratoon)
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Jody Biggs, Marine Empson, Ken Rohde, Laura Esperandieu, Peter Thorburn, Steve Attard
Cane yield
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Weekly runoff (mm/week)
Cyclon
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Weekly NO3-‐N in runoff (kg/ha/week)
Cyclon
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Simulated Season Totals
2009/10 2010/11 Treatment Low N
1800mm Standard N 1500mm
Low N 1800mm
Standard N 1500mm
Fer=liser N (kg/ha)
38 133 136 200
Runoff (mm)
956 1083 1793 2035
NO3-‐N in Runoff (kg/ha)
10 27 7 9
Soil loss (t/ha)
5 5 2 3
Simulated N loss pathways 2009/10 2010/11
Treatment Low N 1800mm
Standard N 1500mm
Low N 1800mm
Standard N 1500mm
Fer=liser N (kg/ha)
38 133 136 200
NO3-‐N in Runoff (kg/ha)
10 27 7 9
NO3-‐N in Deep drainage (kg/ha)
6 1 21 35
Denitrifica=on (kg/ha)
93 81 136 167
What’s needed to model? InformaFon needs for paddock modelling N
• To run the model
• To check the model
• To use the model
Run the model: 1. Ini=al condi=ons
Soil profile characteris5cs – Date – By depth – With units
• Bulk density, Org C & N, pH, EC. • Water holding capacity. – i.e. lower limit & drained upper limit
• Water table depth & salinity • Roo5ng depth constraints • Slope
Other • Crop residue (!!) – Type – Amount – C/N
• Measured/esBmate of curve number
Run the model: 2. History of the site
A pre-‐history of the site • Back to the end of the previous crop cycle.
15 mth 10 mth 12 mth 11 mth 13 mth 5 mth
• Example. • Planted 14th May • Plant Crop: 15 months long • Fallow length: 6 months long • Fallow type: Bare OR Soybean • Soybean variety • Grain or ‘catch’ crop
Run the model: 3. Treatment descrip=on
Table or site map of the treatments • Management of soil, fallow, N rate and 5llage. • Replicates
Treatment
Traffic Fallow N Fertiliser (kg/ha) plant / ratoons
Tillages per crop cycle
1 controlled Soy (harvest) 0 / 85 1
2 controlled Soy (cover) 0 / 40 2
3 conventional Bare 144 / 180 11
4 conventional Bare 192 / 240 20
Run the model: 4. Management details • Daily climate (rain, temp radia5on, etc) • Nutrients • Date of applica5on • Type of nutrient and product (e.g. millmud, urea or (NH4)3PO4) • Amount of nutrient (e.g. kg N / ha)
• Irriga5on • Date • Type of irriga5on (furrow, OHLP, pivot) • Amount / day (mm)
• Tillage • Date • Type of 5llage (disc, centrebust) • Amount -‐ Effect on surface residues and on soil disturbance
• Harvest • Date • Type (pre-‐burnt, post-‐burnt or green)
Mona Park Management Diary
91mm
100mm
90mm
114mm
127mm
Harvest
63mm
92mm
90mm
92mm
114mm
tillage
237 kgN/ha
91mm
91mm
burn 70%
sugar harvest
108mm
106mm
115mm
90mm
73mm
68mm
42mm
92mm
149mm
tillage
220 kgN/ha
burn 70%
Harvest
141mm
137mm
90mm
83mm
70mm
74mm
72mm
92mm
96mm
247 kgN/ha
burn 70%
Harvest
78mm
78mm
90mm
90mm
Apr-04 Jun-04 Aug-04 Oct-04 Jan-05 Mar-05 May-05 Aug-05 Oct-05 Dec-05 Mar-06 May-06 Jul-06 Sep-06 Dec-06 Feb-07 Apr-07 Jul-07 Sep-07 Nov-07 Feb-08
Run the model: 4. Management details example =meline and diary
Check the model
Check the model: 1. Crop measurements
• Crop Yield (cane, legume) • Amount of crop (N) removed. • Cane yield • Grain yield • Legume harvested
• Amount of crop (N) returned. • Surface residues
• Residue prior to harvest • photos
Check the model: 2. Ongoing measurements
• Soil nitrogen • At harvest, start & end of fallow
• Soil water • At plan5ng and harvest • Wecest & driest condi5ons
• Runoff &/or drainage • Amount of water • Amount of N, P, etc
• Other • Nutrients/pes5cides in irriga5on
Lessons from Victoria Plains (The Gie of Hindsight)… How we could have reduced key uncertain=es • What were the soybean residues (site history)? • Impacts on N immobilisa5on / mineralisa5on rates • Sugges5on: Obtain informa5on: – Soybean above ground biomass weight (and N) – Depth of incorpora5on – Propor5on incorporated
• Lots of SMN simulated prior to the 26-‐Jan-‐2010 runoff event that drove N lost. • Simula5ng very large amounts of NO3-‐N in top 30cm • Sugges5on: Conduct within season 0-‐30 cm SMN sampling. – NO3-‐N and NH4-‐N – Three layers (0-‐10, 10-‐20, 20-‐30 cm)
• Residue decomposi=on controlling both NO3-‐N in runoff and soil loss • Impacts on N immobilisa5on / mineralisa5on and ground cover. • Sugges5on: Conduct within season es5mates of residue. – Amount of trash (same 5me as soil sampling)
Thank you
Summary
• Simulated very large amounts of N mineralised following the soybean crop.
• Surface residue is important.
• Full effect of controlled traffic on runoff possibly not realised in 2 years. • 6% reduc5on in Curve Number compared 15% reduc5on used to simulate Bronwyn Master’s long running trial.
• Nitrate lost via runoff and deep drainage similar. • N denitrified > sum of NO3 lost via runoff and deep drainage.
The Gie of Hindsight… How we could have reduced key uncertain=es. • What were the soybean residues (site history)? • Impacts on N immobilisa5on / mineralisa5on rates • Sugges5on: Obtain informa5on: – Soybean above ground biomass weight (and N) – Depth of incorpora5on – Propor5on incorporated
• Lots of SMN simulated prior to the 26-‐Jan-‐2010 runoff event that drove N lost. • Simula5ng very large amounts of NO3-‐N in top 30cm • Sugges5on: Conduct within season 0-‐30 cm SMN sampling. – NO3-‐N and NH4-‐N – Three layers (0-‐10, 10-‐20, 20-‐30 cm)
• Residue decomposi=on controlling both NO3N in runoff and soil loss • Impacts on N immobilisa5on / mineralisa5on and ground cover. • Sugges5on: Conduct within season es5mates of residue. – Amount of trash (same 5me as soil sampling)
Simula=ng -‐ Soil Loss
• Slope * • Runoff • Rainfall * • Irriga5on * • Infiltra5on – Soil type – Soil water deficit
• Crop growth, weather, ground cover • Residue cover – Crop growth, management – Decomposi5on
• Soil nitrogen and soil water • Tillage * • Soil Water
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Simula=ng -‐ Dissolved N in runoff
• Runoff • Soil nitrate • Soil N – Ini5al soil mineral N * – Ini5al soil organic N * – N fer5liser * – Leaching (soil water)
• Soil Organic Macer (mineralisa5on/immobilisa5on/denitrifica5on) – Total soil carbon and carbon frac5ons * – Soil N and Soil Water – Residues – Soil temperature – Crop growth and N uptake – Soil N
• Soil Water • Enrichment type factor *
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Simula=ng -‐ Underlying processes
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• Soil Water • Soil water proper5es *
• Sat, DUL, LL, BD, internal drainage, CN • Rainfall * • Irriga5on * • Poten5al evapora5on * • Crop water uptake • Crop residues • Tillage *
Simula=ng -‐ Underlying processes
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• Crop residues • Residues produced by crop
• Crop growth • Ini5al residues * • Residue decomposi5on
• Residue quality * • Soil nitrate • Climate and soil environ.
• Residue management (burnt, incorporated) *
Simula=ng -‐ Underlying processes
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• Crop growth • Gene5c coefficients *
• RUE, thermal 5mes, etc. • Plan5ng & harvest dates * • Fallow management * • Radia5on * • Temperature * • Soil water • Soil N
Processes represented in a Crop/Soil/Environment simula=on (Daily)
Establishment -plant or ratoon
Leaf AreaDevelopment
HarvestFallow / ratoonplant
Trash
Root growth and extension
Cane and SugarAccumulation
ClimateRadiation, raintemperature
ManagementIrrigation, fertiliser, Cv, timing.
Sugar system:
Transpiration
Water uptake
N uptake
The crop (sugarcane):
Monteith 1986
Ritchie 1986; Inman-‐Bamber 1994 Robertson 1998
Ball-‐Coelho 1992 Glover 1967
Beer’s law Muchow 1994, 1996, 1997 Robertson 1996 Hammer & Muchow 1994 Wilson 1995
Inman-‐Bamber 1994 Tanner & Sinclair 1983 Sinclair 1986 Monteith 1986
Godwin & Velk 1984 Muchow & Robertson 1994 Catchpoole & Kea=ng 1995 Van Keulen & Seligman
Thorburn etal 2001
Establishment -plant or ratoon
Leaf AreaDevelopment
HarvestFallow / ratoonplant
Trash
Root growth and extension
Cane and SugarAccumulation
ClimateRadiation, raintemperature
ManagementIrrigation, fertiliser, Cv, timing.
Sugar system:
Evap.
Transpiration
Soil water
Drainage
Redistribution
Runoff
Water uptake
N uptake
Soil water:
Runoff & erosion
f(cover) a
Soil water
Probert etal 1998 Linleboy 1992 Jones and Kiniry 1986
USDA Curve Number USDA Curve Number Linleboy 1992
Priestly & Taylor 1972 Ritchie 1972
Impacts of management prac5ces on runoff and sediment losses from sugarcane produc5on: A simula5on study | Marine Empson 42
Establishment -plant or ratoon
Leaf AreaDevelopment
HarvestFallow / ratoonplant
Trash
Root growth and extension
Cane and SugarAccumulation
ClimateRadiation, raintemperature
ManagementIrrigation, fertiliser, Cv, timing.
Sugar system:
Evap.
Transpiration
Soil water
Drainage
Redistribution
Runoff
Soil Organic MatterMineral N
Leaching
Residue / trashincorporation
Denit.
Water uptake
N uptake Nitrogen in
Organic Maner
Soil nitrogen (N):
Denitrifica=on
Runoff & erosion
f(cover) a
N (DIN & PN) in runoffÆ
Soil water
Thorburn etal 2010
Meier etal 2006
Thorburn etal 2001
Probert etal 1998
Processes represented in a Crop/Soil/Environment simula=on (Daily)
Establishment -plant or ratoon
Leaf AreaDevelopment
HarvestFallow / ratoonplant
Trash
Root growth and extension
Cane and SugarAccumulation
ClimateRadiation, raintemperature
ManagementIrrigation, fertiliser, Cv, timing.
Sugar system:
Transpiration
Water uptake
N uptake
The crop (sugarcane):
Establishment -plant or ratoon
Leaf AreaDevelopment
HarvestFallow / ratoonplant
Trash
Root growth and extension
Cane and SugarAccumulation
ClimateRadiation, raintemperature
ManagementIrrigation, fertiliser, Cv, timing.
Sugar system:
Evap.
Transpiration
Soil water
Drainage
Redistribution
Runoff
Water uptake
N uptake
Soil water:
Runoff & erosion
f(cover) a
Soil water
Impacts of management prac5ces on runoff and sediment losses from sugarcane produc5on: A simula5on study | Marine Empson 46
Establishment -plant or ratoon
Leaf AreaDevelopment
HarvestFallow / ratoonplant
Trash
Root growth and extension
Cane and SugarAccumulation
ClimateRadiation, raintemperature
ManagementIrrigation, fertiliser, Cv, timing.
Sugar system:
Evap.
Transpiration
Soil water
Drainage
Redistribution
Runoff
Soil Organic MatterMineral N
Leaching
Residue / trashincorporation
Denit.
Water uptake
N uptake Nitrogen in
Organic Maner
Soil nitrogen (N):
Denitrifica=on
Runoff & erosion
f(cover) a
N (DIN & PN) in runoffÆ
Soil water
Thorburn, Peter J., Elizabeth a. Meier, and Mervyn E. Probert. 2005. “Modelling nitrogen dynamics in sugarcane systems: Recent advances and applica5ons.” Field Crops Research 92(2-‐3): 337–351.
Modeling Carbon & Nitrogen in Plant
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Modeling Carbon & Nitrogen in Soil
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Thorburn, Peter J., Elizabeth a. Meier, and Mervyn E. Probert. 2005. “Modelling nitrogen dynamics in sugarcane systems: Recent advances and applica5ons.” Field Crops Research 92(2-‐3): 337–351.
Nitrous Oxide
Nitrous Oxide
Runoff
• 1-‐Dimensional • Daily • INPUT = OUTPUT • R + I = ΔSW + Et + Es + RO + D
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hcp://www.apsim.info
Runoff • USDA Curve Number (CN) technique.
• Ini5al CN • Average condi5ons preceding rainfall.
• Bare soil • Soil texture
• Runoff • Ini5al curve number • Soil moisture content • Volume of rain/irrig.
• Management effects • Crop/ground cover • Soil disturbance
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hcp://www.apsim.info
Agricultural Produc5on Systems SIMulator • Systems Model • Direct and indirect effects
• Complete balance • Carbon • Nitrogen • Water
• Daily 5me step • 1D
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ACKNOWLEDGMENT THIS PROJECT WAS SUPPORTED BY FUNDS FROM THE REEF RESCUE RESEARCH AND DEVELOPMENT PROGRAM CSIRO/ECOSYSTEM SCIENCES Jody Biggs T 07 3833 5704 e [email protected]
Thank you
Weekly soil loss (kg/ha/week)
Cyclon
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