APSIM Use in Catchment Models and potential use in BYP ...reefcatchments.com.au/files/2013/12/Modelling... · APSIM Use in Catchment Models and potential use in BYP scenario analyses

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 *


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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) *


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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




Trash





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




Trash





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)




Trash





Sugar system:

Transpiration

Water uptake

N uptake

The crop (sugarcane):




Trash





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




Trash





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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APSIM Use in Catchment Models and potential use in BYP ...reefcatchments.com.au/files/2013/12/Modelling... · APSIM Use in Catchment Models and potential use in BYP scenario analyses