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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
3
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?
11
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)
16
Jody Biggs, Marine Empson, Ken Rohde, Laura Esperandieu, Peter Thorburn, Steve Attard
Cane yield
17
Weekly runoff (mm/week)
Cyclon
e
Weekly NO3-‐N in runoff (kg/ha/week)
Cyclon
e
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 *
36
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
38
• 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
39
• 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
47
Modeling Carbon & Nitrogen in Soil
48
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
49
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
50
hcp://www.apsim.info
Agricultural Produc5on Systems SIMulator • Systems Model • Direct and indirect effects
• Complete balance • Carbon • Nitrogen • Water
• Daily 5me step • 1D
51
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 jody.biggs@csiro.au
Thank you
Weekly soil loss (kg/ha/week)
Cyclon
e
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