aplication of the cropsyst model to mallee farming systems (australia)
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1
Application of the CropSyst model to Mallee farming
systemsCarlos G. Hernández Díaz-Ambrona Dpto. Producción Vegetal: Fitotecnia
Universidad Politécnica de Madrid
March 2001
Visiting Scientist Crop Production
ILFR The University of Melbourne
Joint Center for Crop Improvement
Mallee Research Station
2
Summary Overview
Mallee farming systems Problems Methodology Model performance Model application Future needs
3
Mallee farming systems
4
Mallee farming systemsEnvironment Geology
Murray-darling basin. Tertiary marine limestone capped by Pliocene sands
Topographycoastal plains with trend of sandridges, dunes
5
Mallee farming systems
Environment Soil solonized brown
Hill: sandy soil
Valley: sandy-clay soil
6
Mallee farming systemsEnvironment Natural vegetation
Relict: Mallee scrub (Eucalyptus dumosa)
7
Mallee farming systems
Tmax: 22.9 ºC [46.6ºC]Tmin: 9.6 ºC [-4.1ºC]T med: 16.5 ºCPrec: 340 mm y-1
Daily Sol. Rad.: 17.8 MJ m-2 d-1
Wind: 3.14 m s-1 ETo: 1500 mm y-1
Walpeup, BMSM 76064, 1939-2000
ClimateSemi-arid type Mediterranean
8
Mallee farming systems
Walpeup, BMSM 76064
9
Mallee farming systems Cropping land: 6 Mha (10 Mha) Wheat-fallow rotation Long fallow management
No tillTraditional till
10
Mallee farming systems Farm size: > 2 kha Paddock size 100-300 ha
11
Mallee farming systemsLand uses
Cereals 35 %
Pastures 30 %
Fallow 20 %
• Pulses 7 %• Oilseeds 1 %• Other 7 %
• 1.5 M Sheep• 0.9 M Meat cattle
12
Problems Problems
Low water useLow crop diversificationHigh risk of wind erosion
ConsequencesSoil salinitySoil erosionLow productivityLow farm income
ConstrainsSoilWeatherMarketComplexity
13
Problems Consequences
Soil erosion
Soil salinity
Low productivity
Low farm income
14
ObjectivesThere is an urgent environmental need to reduce the dependence on fallows and find alternative cropping systems that minimise deep drainage
Long term assessment of different crop management
15
Our framework «The key to success in farming
is to be able to identify and tactically adjust major control loops. The decision process is not as complex as it might seem. Once the decision about what crop to grow is made, choices of cultivar, planting date, land preparation, spacing, and fertilisation follow in sequence»
(Loomis and Connor, 1992 p 9)
16
Methodology Crop system processes Long term analyses Model applications
Which model?
17
MethodologyPrevious studies Drainage recharge modelling
O’Connell 1998: wheat crop and management tillage, stubble. Model fallow-wheat O’Leary-Connor (Vic)
Zhang et al, 1999: wheat, oat, mustard, field pea, lucerne and medic. Model WAVES (NSW & Vic)
Asseng et al., 2001: wheat crop and sowing dates, N fertiliser, residues and hypothetical cultivar. Model APSIM (WA)
Crop model in the Mallee Rimmington et al. 1987: wheat yield and long-term O’Leary & Connor 1995: wheat, water and nitrogen
18
Methodology Which studies do we want? Long term analysis Cropping system Water balance Farm or regional level
When using simulation models, it is important to understand how the model represents the physical, chemical, and biological processes involved in cropping system response to the environment and management
19
Methodology
CropSyst on-line Free Software www.bsyse.wsu.edu/CropSyst/ Water balance Farm or regional level Previous work: USA, Europe, Middle Est...
Cropping System Simulation model(Stöckle and Nelson, 2001)
20
Methodology Observed data (O’Connell, 1998)
Field experiment carried out MRS Walpeup from 1993-1997
Rotations FW Fallow-wheat FWP Fallow-wheat-pea WW Wheat-wheat MWP Mustard-wheat-pea
Field dataSoil water content evolution, phenology, LAI, crop coverage, biomass, yield ...
21
Model performanceSteps for model applications
1. Verification2. Calibration
sensibility analysis
3. Validationmodel acceptabilitymodel consistency
4. Applicationresults interpretation
22
How does CropSyst work?CropSyst model based on crop
approachDaily accumulation of crop
biomassMain process: Solar radiation and
temperature Water availability Nitrogen availability
23
What processes are simulated?
PhenologyBiomass partition (above, root, leaf)
Water balance (2 models)Nitrogen balance
Soil erosion USLESoil runoff (2 models)Soil and water salinitysoil freezing model (2 models)
Lineal CO2 responseManagement: sowing, fertilisation,
tillage, stubble, irrigation, clipping
24
What processes are NOT simulated?
Yield componentsPartitioning (yield comp.)Grain quality (N or protein content, oil)
All nutrients except NitrogenPest or diseasesWeedsOther abiotic stress (hail, soil
limitations as B, Al, Na, …)
Polycrop as individual cropsWind erosion
25
CropSyst input64 Crop parameters for each crop
or varieties
Soil parameters for each soil
Minimum texture by layer
Surface USLE, SCS Curve number
4 Nitrogen parameters
Daily Weather data (Tmax, Tmin, Prec, Radsol, HRmax, HRmin –DEWPT–, Wind)
Included ClimGen and works with Universal
Environmental Data file format
More CropSyst manual
26
CropSyst initial conditionsFor each soil layer:
Soil water contentNitrogen soil content (nitrate & ammonium)Salinity
and water table salinityExisting residuesCO2 concentration
27
CropSyst outputDaily (one day step or more)
Crop dataWater balanceNitrogen balanceSalinity balance
Harvest (crop data at harvest)Annual
AlsoSchedule (management)Summary (harvest report)
Output reports in format XLS, TXT, HTML, UED
28
CropSyst verificationDoes the model run well?
1. Last version 3.02.07 (16 Feb 2001)2. Run the examples3. Run our modified examples4. Display all outputs5. Some errors found in the
outputs but were not relevant (columns position, no use
routines)
6. Mass balances: water and N ok!
29
CropSyst calibrationCalibration can fit the model
close to 1:1But calibration parameters
must be explain the crop model physiology
Abolish unrealistic coefficient values for parameters calibration
Calibration starts with default parameters and it continues with well known parameters
30
CropSyst calibrationCrop parameters (64) for
Wheat, Mustard and Field pea
Parameters for a Sandy soilHydraulic properties (Permanent wilting point, field capacity, bulk density, and saturated hydraulic conductivity)
Also soil surface (Universal soil Loss Equation) and SCS Curve number
NitrogenWeather data from the MRSInitial condition = field experiment
31
CropSyst calibrationSummary of some key crop parameters
Variable Units Wheat Mustard Field peaThermal timeBase temperature ºC 0 0 0Emergence ºC days 130 150 150Begin flowering ºC days 750 950 1100Physiological maturity ºC days 1400 2000 1950Photo-periodDay length to inhibit flowering hours 16.5 ns nsDay length for insensitivity hours 8 ns nsCrop morphologyMaximum expected LAI m²/m² 5 5 5Specific leaf area m²/kg 20 22 24Stem/leaf partition coefficient 1-10 5 4 6Crop growthAbove ground biomass-transpiration efficiency kPa kg/m³ 5.8 6 3.25Radiation use efficiency RUE g/MJ 3 1.85 1.47Optimum mean daily temperature for growth ºC 20 15 10Extinction coefficient for solar radiation k 0-1 0.82 0.65 0.76Harvest indexUnstressed HI 0-1 0.4 0.2 0.25Nitrogen crop parametersMaximum N concentration during early growth kgN/kgDM 0.050 0.055 0.060Minimum N concentration at maturity kgN/kgDM 0.007 0.008 0.050Maximum N concentration at maturity kgN/kgDM 0.012 0.022 0.060Minimum N concentration of harvested material kgN/kgDM 0.030 0.030 0.030
32
CropSyst validationWater balance
for long fallow compared CropSyst vs. O’Leary-Connor wheat-fallow model
AndCropSyst vs. observed data
(O’Connell, 1998)
Crop performanceSimulated individual crops:
wheat, field pea, and mustard vs. observed data
Crops in rotation FW, WW, FWP, MWP
33
140
160
180
200
220
240
260
280
1993 1994 1995 1996 1997 1998 1999
So
il w
ate
r c
on
ten
t 0
-1m
(m
m)
CropSyst validationWater soil content (mm)
fallow phase
34
CropSyst validationWater soil content (mm)
fallow phase
140
160
180
200
220
240
260
280
35
CropSyst validationCrop performance
Biomass
y = 0.73x + 0.76
r2 = 0.79
0
2
4
6
8
0 2 4 6 8
Observed
Sim
ula
ted
Wheat
Mustard
Field pea
Yield
y = 0.84x + 0.10
r2 = 0.81
0
1
2
3
0 1 2 3
Observed
36
CropSyst validationCrop performance water use
Wheat
y = 0.61x + 94.91r2 = 0.50
y = 1.14x - 8.81r2 = 0.76
0
50
100
150
200
250
300
350
400
0 50 100 150 200 250 300 350 400
Observed (mm)
Sim
ula
ted
(m
m)
FW
MW
Field pea
y = 1.60x - 79.86r2 = 0.78
y = 1.64x - 74.66r2 = 0.81
0
50
100
150
200
250
300
350
400
0 50 100 150 200 250 300 350 400
Observed (mm)
Sim
ula
ted
(m
m)
FWP
MWP
Mustard
y = 1.02x + 8.07r2 = 0.57
0
50
100
150
200
250
300
350
400
0 50 100 150 200 250 300 350 400
Observed (mm)
Sim
ula
ted
(m
m)
37
CropSyst validationContinuos run
Water use
y = 1.34x - 54.03
r2 = 0.840
100
200
300
400
0 100 200 300 400
Observed (mm)
Sim
ula
ted
(m
m)
Yield
y = 0.83x - 0.07
r2 = 0.840
1
2
3
4
0 1 2 3 4
Observed (t ha-1)
Sim
ula
ted
(t
ha
-1)
Biomass
y = 0.71x - 0.003
r2 = 0.710
1
2
3
4
5
6
7
8
0 1 2 3 4 5 6 7 8
Observed (t ha-1)
Sim
ula
ted
(t
ha
-1)
sim. Cont.
sim. year by year
38
CropSyst validationMallee wheat performance(Sadras, 2001 -umpublished data-)
Monitored yield (t/ha)
0 1 2 3 4 5
Sim
ulated
yield (t/h
a)
0
1
2
3
4
5
intercept = 0.11 (s.e. = 0.177, P = 0.529)slope = 0.97 (s.e. = 0.082, P < 0.0001)
r2 = 0.72 (P < 0.0001)n = 55
Data: wheat crops managed by growers, three seasons and sites in South Australia, New South Wales and Victoria Mallee
39
Model application Analysis of some agronomic practices in the Victorian Mallee
In terms of: Water balance
Estimating drainage under different crop management
Also runoff Water use efficiency
Nitrogen uses Comparing rotations: Wheat continuous Fallow-wheat Fallow-wheat-pea Mustard-wheat-pea
Crop management effects Yield-profit efficiency
40
Model application Environmental conditions of the Victorian Mallee
61 year of weather data from Walpeup (1939-1999)Included several dry-wet seasons
Representative Mallee plain soil typeSandy soil
41
Experimental design 3 Tillage
CT Conventional till (4LF-3SF till)
MT Minimum tillage (2 till)ZT Zero till (0 till)
3 Stubble managementSR stubble retention (100 %)SG stubble grazing (65 %)SB stubble burning (10 %)
3 Fertilisation levelsF1 No N applied to any crop (minimum yield)F2 Current N fertiliser (Wheat & Mustard)F3 Simulation without N routine (potential yield)
4 Rotations and 3 cropsFW Fallow-wheat (50 %)FWP Fallow-wheat-pea (66 %)WW Wheat continuous (100%)MWP Mustard-wheat-pea (100%)
15 000 simulated years
42
Experimental design Soil profile 150 cm Soil drainage
Measured at 150 cmMaximum root depth 100 cm
Soil water balanceFinite diferenceUp-Down water flow
EvapotranspirationPriesley-Tailor
30 000 simulated years
43
Some results Water drainage Water runoff
Effect of stubble management in the water balance
Effect of fertilisation levelsYield potential on the Mallee (potential yield)
Annual variability
Effect of crop diversification Comments about not simulated effects
44
Model consistency
y = 15.82x
R2 = 0.63
0
1000
2000
3000
4000
5000
6000
0 50 100 150 200
Actual transpiration
Gra
in y
ield
(kg
ha
-1)
y = 13.09x - 1480.6
r2 = 0.43
0
1000
2000
3000
4000
5000
6000
0 100 200 300 400
Water use
Gra
in y
ield
(kg
ha
-1)
45
Model consistency
y = 0.37x - 551.14
r2 = 0.90
0
1000
2000
3000
4000
5000
6000
7000
0 2000 4000 6000 8000 10000 12000 14000
Biomass (kg ha-1)
Gra
in y
ield
(kg
ha
-1) Line 2:5
46
Sustainability approach Agronomy sustainability
Yield productivity Resources use efficiency Stability and trends
Environmental sustainability Minimize environmental impact
Reduce water drainageReduce water runoffReduce nitrogen loss
Maximize environmental gain Social sustainability
Gross margins and profit
47
Some results Average of Water drainageSTUBBLE ROTATION mm y-1 %
SB FW -2.4 76
FWP -8.6 12
MWP -9.8
WW -7.1 27
Total SB -7.4
SG FW 11.2 218
FWP -4.7 51
MWP -9.5
WW -2.3 76
Total SG -2.5
TILLAGE mm y-1 %
CT -6.1
MT -5.4 13
ZT -3.4 37
FERTIL mm y-1
F1 -3.3 54
F2 -4.3 40
F3 -7.2
48
Water drainageProbability of exceedence
0.0
0.2
0.4
0.6
0.8
1.0
-50 0 50 100 150 200
Drainage (mm)
FW
WW
FWP
MWP
ZT F2 SR
49
Water drainage
Accumulated deviation (mm)
-300
-250
-200
-150
-100
-50
0
50
100
0 10 20 30 40 50 60 70
-600
-400
-200
0
200
400
600
?
WW
+ Drainage
Drainage Rainfall
50
Water runoff Runoff events
Annual rainfall > 250 mm soil SCS curve number, slope < 1 % No differences among treatments
FW: Probability of exceedence
0.0
0.2
0.4
0.6
0.8
1.0
-50 0 50 100 150 200
Runoff (mm)
CTF2SG
MTF2SG
ZTF2SG
CTF2SB
MTF2SB
ZTF2SB
CTF2SR
MTF2SR
ZTF2SR
51
Crops and rotationsFW Fallow-wheat (50 %)FWP Fallow-wheat-pea (66 %)WW Wheat continuous (100%)MWP Mustard-wheat-pea (100%)
y = -1.0713x2 + 4223.3x - 4E+06r2 = 0.4775
0
1000
2000
3000
4000
5000
1938 1950 1962 1974 1986 1998
Year
Gra
in y
ield
(kg
ha-
1)
CT SB F1 WW wheat
y = -1.24x2 + 4886.3x - 5E+06r2 = 0.1607
0
1000
2000
3000
4000
5000
1938 1950 1962 1974 1986 1998
Year
Gra
in y
ield
(kg
ha-
1)
ZT SG F3 FW wheat
52
Crops and rotationsFallow-wheat (50 %)Fallow-wheat-pea (66 %)Wheat continuous (100%)Mustard-wheat-pea (100%)
Stability yield index
Stubble burnt + tillage +0N stubble grazed + no tillage+N CTF1SB CTF2SB CTF3SB MTF1SB MTF2SB MTF3SB ZTF1SB ZTF2SB ZTF3SB CTF1SG CTF2SG CTF3SG MTF1SG MTF2SG MTF3SG ZTF1SG ZTF2SG ZTF3SG
WheatFW 0.29 0.29 0.22 0.29 0.31 0.30 0.29 0.30 0.32 0.37 0.37 0.45 0.38 0.38 0.50 0.39 0.40 0.53FWP 0.25 0.25 0.26 0.29 0.28 0.28 0.28 0.29 0.28 0.32 0.35 0.40 0.38 0.38 0.43 0.38 0.39 0.47WW 0.21 0.19 0.02 0.18 0.09 0.03 0.20 0.22 0.03 0.27 0.25 0.12 0.33 0.26 0.18 0.35 0.39 0.32MWP 0.19 0.22 0.09 0.17 0.23 0.09 0.20 0.22 0.09 0.23 0.26 0.16 0.31 0.30 0.21 0.34 0.32 0.27
PeaFWP 0.23 0.19 0.17 0.22 0.21 0.14 0.21 0.22 0.19 0.29 0.26 0.31 0.34 0.31 0.36 0.37 0.36 0.43MWP 0.23 0.21 0.15 0.26 0.24 0.11 0.27 0.22 0.14 0.28 0.25 0.23 0.31 0.31 0.25 0.39 0.37 0.36
MustardMWP 0.24 0.30 0.23 0.27 0.28 0.20 0.30 0.29 0.23 0.40 0.40 0.29 0.44 0.40 0.26 0.42 0.43 0.30
53
Farmer decisionGross margins
-20
-10
0
10
20
30
40
50
60
F1
F2
F1
F2
F1
F2
F1
F2
F1
F2
F1
F2
F1
F2
F1
F2
F1
F2
F1
F2
F1
F2
F1
F2
F1
F2
F1
F2
F1
F2
F1
F2
F1
F2
F1
F2
CT MT ZT CT MT ZT CT MT ZT CT MT ZT CT MT ZT CT MT ZT
FW FWP WW FW FWP WW
SB SG
Annualized gross margins
STUBBLE ROTATION TILLAGE FERTIL
profit profit
54
Farmer decision
Lower (20%) Median Upper (80%)RotationFW 0N Yield kg ha-1 580 780 1046
Profit AUD ha-1 y-1 21 45 77+N Yield kg ha-1 619 803 961
Profit AUD ha-1 y-1 19 42 61WW 0N Yield kg ha-1 650 970 1391
Profit AUD ha-1 y-1 -19 19 69+N Yield kg ha-1 569 1026 1320
Profit AUD ha-1 y-1 -42 13 48FWP 0N Yield kg ha-1 566 862 1151
Profit AUD ha-1 y-1 -10 35 79+N Yield kg ha-1 555 867 1103
Profit AUD ha-1 y-1 -17 31 69MWP 0N Yield kg ha-1 487 773 1069
Profit AUD ha-1 y-1 -79 -32 17+N Yield kg ha-1 477 786 1046
Profit AUD ha-1 y-1 -97 -47 -2 Average of annualized yield
Seasonal variation in the anualized yield and profitability of rotations in the Victorian Mallee (Australia)
55
Farmer decision
Lower (20%) Median Upper (80%)RotationFW 0N Yield kg ha-1 580 780 1046
Profit AUD ha-1 y-1 21 45 77+N Yield kg ha-1 619 803 961
Profit AUD ha-1 y-1 19 42 61WW 0N Yield kg ha-1 650 970 1391
Profit AUD ha-1 y-1 -19 19 69+N Yield kg ha-1 569 1026 1320
Profit AUD ha-1 y-1 -42 13 48FWP 0N Yield kg ha-1 566 862 1151
Profit AUD ha-1 y-1 -10 35 79+N Yield kg ha-1 555 867 1103
Profit AUD ha-1 y-1 -17 31 69MWP 0N Yield kg ha-1 487 773 1069
Profit AUD ha-1 y-1 -79 -32 17+N Yield kg ha-1 477 786 1046
Profit AUD ha-1 y-1 -97 -47 -2 Average of annualized yield
Seasonal variation in the anualized yield and profitability of rotations in the Victorian Mallee (Australia)
100 100 100
92 92 79
-91 42 90
-197 28 62
-48 78 103
-78 69 89
-374 -71 22
-457 -103 -3
56
Farmer decision
0.0
0.2
0.4
0.6
0.8
1.0
0 1000 2000 3000 4000 5000 6000
Grain yield (kg/ha)
Pro
ba
bil
ity
of
ex
cee
de
nce CTF2SG
MTF2SG
ZTF2SG
ZTF3SG
CTF2SB
MTF2SB
ZTF2SB
ZTF3SB
FW
0.0
0.2
0.4
0.6
0.8
1.0
0 1000 2000 3000 4000 5000 6000
Grain yield (kg/ha)
Pro
ba
bil
ity
of
ex
cee
de
nce CTF2SG
MTF2SG
ZTF2SG
ZTF3SG
CTF2SB
MTF2SB
ZTF2SB
ZTF3SB
FWP
Wheat yields
57
Farmer decision
0.0
0.2
0.4
0.6
0.8
1.0
0 1000 2000 3000 4000 5000 6000
Grain yield (kg/ha)
Pro
ba
bil
ity
of
ex
cee
de
nce CTF2SG
MTF2SG
ZTF2SG
ZTF3SG
CTF2SB
MTF2SB
ZTF2SB
ZTF3SB
FWP
Wheat yields
0.0
0.2
0.4
0.6
0.8
1.0
0 1000 2000 3000 4000 5000 6000
Grain yield (kg/ha)
Pro
bab
ility
of
exce
eden
ce
Pea
Field peas
58
Some results Stubble management:
SR stubble retentionSG stubble grazingSB stubble burning
Maintenance of stubble increased the water retention
It had a positive effect on yield but also on water drainage
59
Some results Fertilisation levels
F1 No N applied to any crop (minimum yield)
F2 Current N fertiliser (Wheat & Mustard)There were little differences between F1 and F2
F3 without N simulation (potential yield)Showed that actual yield can be double with optimum N applicationIncreased stability in low intensity rotations but did not occur in high intensive land uses, water was the limiting factor
60
ConclusionsCropSyst showed a good
performance compared with observed data and similar other models
Long term application of CropSyst showed the effect of different management on drainage, runoff, crop yield and profitability
CropSyst appears ideal to address some of the Mallee issues
61
Conclusions
Also long term results obtained with CropSyst can explain some of the current farming systems of the Mallee, with advantages and limitations
Further improvements in the model should widen its aplication
62
Are model assumptions valid for this environment?
Mallee crops are crops?Continuous medium, were LAI
and k represents these crops
Low LAI and Low crop coverage do to think that crop are no continuos during long time of periods
Need other models for dryland areas?
63
Future work Spatial application of crop model for Mallee region
The drainage process is a biflow process in which some areas loss water and other gain water but which salt
Dune-slawe systems Paddock diversity and farming practices diversity among farmers
64
Future work (continued) The cereals (wheat and barley) are the main crops
Soils constrains and ‘low rainfall’ limit production
Need for new models to understand the processes that limited yield
Models versus long term experiment
Model for a paddock and model for spatial analyses
65
Acknowledgments
Thank you toMallee Research StationThe University of MelbourneThe Joint Centre for Crop Improvement
And special thanks toProf. David ConnorDr. Garry O’LearyMark O’Connell
Universidad Politécnica de Madrid for my fellowship
66
Publish
Environmental risk analysis of farming systems in a semi-arid environment: effect of rotations and management practices on deep drainage
Field Crops Research,
Volume 94, Issue 2-3, November 2005, Pages 257-271Diaz-Ambrona, C.G.H.; O\'Leary, G.J.; Sadras, V.O.; O\'Connell, M.G.; Connor, D.J.
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