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.

67

68

69