APSIM Use in Catchment Models and potential use in BYP scenario analyses

Transcription

1 APSIM Use in Catchment Models and potential use in BYP scenario analyses Peter Thorburn & Jody Biggs CSIRO ECOSYSTEM SCIENCES

2 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?

3 Overview of Paddock- to- Reef evalua=on framework Prevalence (ABCD) at some time Effectiveness (ABCD) Paddock Modelling Paddock model into cat. model WQ outcomes Scenarios 3

4 The WQ challenge reduce N Surplus to 50 kg/ha?

5 N loss framework Thorburn & Wilkinson 2013 Management strategies (N recommendations) Biological N fixation N mineralised from organic sources Management tactics Placement (surface, bury) Split applications Carrier Timing Tillage Irrigation management N inputs N Surplus Input-crop N Crop size Climate Irrigation Crop husbandry Fallow mgt Partitioning to Runoff Deep drainage Atmosphere Soil type, climate N lost to water courses N lost to atmosphere

6 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)

7 Cause of N losses 1. N losses driven by fer5liser, esp. surplus 2. N surpluses kg/ha/yr in intensively managed crops N surplus and N rate Thorburn & Wilkinson (2013) Field scale Webster et al. (2012)

8 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)

9 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)

10 Can improved agricultural management meet water quality targets? Study / prac=ce Pollutant Fine sediments Total P Total N Dissolved inorganic N PSII herbicides Target: Thorburn and Wilkinson (2013) empirical modelling All BMP 15 nd nd All Agri Env Prac5ce* 19 nd nd Waters et al. (2013) paddock and catchment modelling All B- Class All A- Class *Defined as: N applica5ons to give a surplus of < 50 kg/ha

11 Why model? 11

12 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

13 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

14 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: Thorburn and Wilkinson (2013) empirical modelling All BMP 15 nd nd All Agri Env Prac5ce* 19 nd nd Waters et al. (2013) paddock and catchment modelling All B- Class All A- Class *Defined as: N applica5ons to give a surplus of < 50 kg/ha

15 The end (part 1)

16 _ 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 & 1 st ratoon) Jody Biggs, Marine Empson, Ken Rohde, Laura Esperandieu, Peter Thorburn, Steve Attard 16

17 Cane yield 17

18 Weekly runoff (mm/week) Cyclone

19 Weekly NO 3 - N in runoff (kg/ha/week) Cyclone

20 Simulated Season Totals Treatment Fer=liser N (kg/ha) Runoff (mm) NO 3 - N in Runoff (kg/ha) Soil loss (t/ha) Low N 1800mm 2009/ /11 Standard N 1500mm Low N 1800mm Standard N 1500mm

21 Simulated N loss pathways Treatment Fer=liser N (kg/ha) NO 3 - N in Runoff (kg/ha) NO 3 - N in Deep drainage (kg/ha) Denitrifica=on (kg/ha) Low N 1800mm 2009/ /11 Standard N 1500mm Low N 1800mm Standard N 1500mm

22 What s needed to model? InformaFon needs for paddock modelling N To run the model To check the model To use the model

23 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

24 Run the model: 2. History of the site A pre- history of the site Back to the end of the previous crop cycle. Example. Planted 14 th May Plant Crop: 15 months long Fallow length: 6 months long Fallow type: Bare OR Soybean Soybean variety Grain or catch crop 10 mth 5 mth 15 mth 11 mth 13 mth 12 mth

25 Run the model: 3. Treatment descrip=on Table or site map of the treatments Management of soil, fallow, N rate and 5llage. Replicates Treat ment Traffic Fallow N Fertiliser (kg/ha) plant / ratoons Tillages per crop cycle 1 controlled Soy (harvest) 0 / controlled Soy (cover) 0 / conventional Bare 144 / conventional Bare 192 /

26 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 (NH 4 ) 3 PO 4 ) 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)

27 Run the model: 4. Management details example =meline and diary Mona Park Management Diary tillage tillage 237 kgn/ha 247 kgn/ha 220 kgn/ha burn 70% burn 70% burn 70% sugar harvest Harvest Harvest Harvest 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 91mm 90mm 90mm 106mm 96mm 70mm 141mm 149mm 91mm 127mm 91mm 92mm 90mm 90mm 92mm 83mm 92mm 100mm 114mm 108mm 73mm 78mm 72mm 90mm 42mm 90mm 92mm 115mm 63mm 78mm 74mm 137mm 68mm 114mm

28 Check the model

29 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

30 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

31 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 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. NO 3 - N and NH 4 - N Three layers (0-10, 10-20, cm) Residue decomposi=on controlling both NO 3 - 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)

32 Thank you

33 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.

34 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 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, 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)

35 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 35

36 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

37 Simula=ng - Underlying processes Soil Water Soil water proper5es * Sat, DUL, LL, BD, internal drainage, CN Rainfall * Irriga5on * Poten5al evapora5on * Crop water uptake Crop residues Tillage * 37

38 Simula=ng - Underlying processes Crop residues Residues produced by crop Crop growth Ini5al residues * Residue decomposi5on Residue quality * Soil nitrate Climate and soil environ. Residue management (burnt, incorporated) * 38

39 Simula=ng - Underlying processes Crop growth Gene5c coefficients * RUE, thermal 5mes, etc. Plan5ng & harvest dates * Fallow management * Radia5on * Temperature * Soil water Soil N 39

40 Processes represented in a Crop/Soil/Environment simula=on (Daily) Sugar The crop system: (sugarcane): Establishment - Robertson 1998 plant or ratoon Climate Radiation, rain temperature Leaf Area Development Ritchie 1986; Inman- Bamber 1994 Ball- Coelho Root growth 1992 Glover and extension 1967 Inman- Bamber 1994 Tanner & Sinclair 1983 Sinclair 1986 Transpiration Monteith 1986 Beer s Cane law and Sugar Accumulation Muchow 1994, 1996, 1997 Robertson 1996 Hammer & Muchow 1994 Wilson 1995 Harvest Trash Thorburn etal 2001 Management Irrigation, fertiliser, Cv, timing. Fallow / ratoon plant Water uptake Monteith 1986 Godwin & Velk 1984 Muchow N & Robertson 1994 Catchpoole uptake & Kea=ng 1995 Van Keulen & Seligman

41 Sugar Soil water: system: Climate Radiation, rain temperature Transpiration Cane and Sugar Accumulation Management Irrigation, fertiliser, Cv, timing. Establishment - plant or ratoon Runoff Redistribution USDA Curve Number Probert etal 1998 Linleboy 1992 Jones and Kiniry 1986 Soil water Leaf Area Development Evap. Priestly & Taylor 1972 Ritchie 1972 Root growth and extension Water uptake N uptake Harvest Trash Fallow / ratoon plant Runoff & USDA Curve erosion Number Linleboy f(cover) 1992 a Drainage

42 Sugar system: Soil nitrogen (N): Climate Radiation, rain temperature Transpiration Cane and Sugar Accumulation Management Irrigation, fertiliser, Cv, timing. Establishment - plant or ratoon Runoff Redistribution Soil water Leaf Area Development Evap. Root growth and extension Water uptake Mineral N Harvest Trash Thorburn etal 2001 Residue / trash incorporation N uptake Meier etal 2006 Fallow / ratoon plant Runoff & erosion f(cover) a Denitrifica=on Thorburn etal 2010 Nitrogen Soil Organic in Probert etal 1998 Organic Matter Maner Denit. N (DIN & PN) in runoffæ Drainage Leaching 42 Impacts of management prac5ces on runoff and sediment losses from sugarcane produc5on: A simula5on study Marine Empson

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44 Processes represented in a Crop/Soil/Environment simula=on (Daily) Sugar The crop system: (sugarcane): Climate Radiation, rain temperature Transpiration Cane and Sugar Accumulation Management Irrigation, fertiliser, Cv, timing. Establishment - plant or ratoon Leaf Area Development Harvest Trash Fallow / ratoon plant Root growth and extension Water uptake N uptake

45 Sugar Soil water: system: Climate Radiation, rain temperature Transpiration Cane and Sugar Accumulation Management Irrigation, fertiliser, Cv, timing. Establishment - plant or ratoon Runoff Leaf Area Development Evap. Harvest Trash Fallow / ratoon plant Runoff & erosion f(cover) a Redistribution Soil water Root growth and extension Water uptake N uptake Drainage

46 Sugar system: Soil nitrogen (N): Climate Radiation, rain temperature Transpiration Cane and Sugar Accumulation Management Irrigation, fertiliser, Cv, timing. Establishment - plant or ratoon Runoff Redistribution Soil water Leaf Area Development Evap. Root growth and extension Water uptake Mineral N Harvest Trash Residue / trash incorporation N uptake Fallow / ratoon plant Runoff & erosion f(cover) a Denitrifica=on Nitrogen Soil Organic in Organic Matter Maner Denit. N (DIN & PN) in runoffæ Drainage Leaching 46 Impacts of management prac5ces on runoff and sediment losses from sugarcane produc5on: A simula5on study Marine Empson

47 Modeling Carbon & Nitrogen in Plant 47 Thorburn, Peter J., Elizabeth a. Meier, and Mervyn E. Probert Modelling nitrogen dynamics in sugarcane systems: Recent advances and applica5ons. Field Crops Research 92(2-3):

48 Modeling Carbon & Nitrogen in Soil Nitrous Oxide Nitrous Oxide 48 Thorburn, Peter J., Elizabeth a. Meier, and Mervyn E. Probert Modelling nitrogen dynamics in sugarcane systems: Recent advances and applica5ons. Field Crops Research 92(2-3):

49 Runoff 1- Dimensional Daily INPUT = OUTPUT R + I = ΔSW + Et + Es + RO + D hcp:// 49

50 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 hcp:// 50

51 Agricultural Produc5on Systems SIMulator Systems Model Direct and indirect effects Complete balance Carbon Nitrogen Water Daily 5me step 1D 51

52 Thank you ACKNOWLEDGMENT THIS PROJECT WAS SUPPORTED BY FUNDS FROM THE REEF RESCUE RESEARCH AND DEVELOPMENT PROGRAM CSIRO/ECOSYSTEM SCIENCES Jody Biggs T e jody.biggs@csiro.au

53 Weekly soil loss (kg/ha/week) Cyclone