P.Steduto.pdf

8/11/2019 P.Steduto.pdf

1/23

AquaCrop

A new model for crop prediction

under water deficit conditionsPasquale STEDUTO1, Theodore C. HSIAO2, Elias FERERES3,

Dirk RAES4, Lee HENG5, G. Izzi1 and J. Hoogeven1

Drought Management: Scientific and technological innovationsZaragoza, SPAIN, 12-14 June, 2008

1 Land and Water Division, FAO, United Nations, Rome, Italy2 Dept. of Land, Air and Water Resources, Univ. of California, Davis, USA3 IAS-CSIC and Univ. of Cordoba, Spain4 Dept. of Land Management and Economics, K. Univ. Leuven, Belgium5 Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture,

IAEA, Vienna, Austria


2/23

Background

Revision of the 1979 FAO I & D Paper no.33,

Yield Response to Water

Consultative process with experts

Separation between herbaceous-crops andtrees: AquaCrop & Guidelines

AquaCrop as one model with crop-specific

parameters


3/23

Evolution from Paper 33

(b)(a)

=

x

axy

x

ax

ET

ETET

Y

YYk

CanopyCanopy

TranspirationTranspiration

(c)

(a) (b)

BIOMASSBIOMASS

CropCropEvapotEvapotranspirationranspiration

YIELDYIELD(c)

EHI

Paper 33Paper 33

{AquaCropAquaCrop

long-term sums

daily time-steps

Ky

WP


4/23

Atmosphere

Soil

Crop

AquaCrop Conceptual Framework

Manag

ement


5/23

CLIMATE

Rain

AquaCrop Conceptual Framework | Atmosphere

RS, T, RH, u

ETo

T (oC) CO2


6/23

T (oC) Es TaETo

Phenology Canopy CoverLeaf expansion gs

Senescence

Biomass

WP

YieldHI

Rooting depth

AquaCrop Conceptual Framework | Crop

CO2


7/23

Canopy Cover (CC)

CGC

CCx

start of canopy senescence

time to harvest

canopy

cover

timetime to full canopy

CDC

CCo

CC follows the exponential

growth during the first halfof the full development (Eq. 1)

and an exponential decay

during the second half of thefull development (Eq. 2)

tCGCoeCCCC

= (1) tCGCoxx

e)CCCC(CCCC = (2)


8/23

WPWater Productivity

(g m-2 mm-1)

=

CT

BiomassWP

(g m-2)

)2000(COO

C

*

2

ETT

BiomassWP

=

ETc (mm x 1000)0.0 0.3 0.6 0.9

Biomass

(kgm

-2)

0

1

2

3Sorghum

Sunflower

Chickpea

Wheat

CT (ETc/ETo)0 40 80 120 160

Biomas

s

(kgm-2)

0

1

2

3SorghumSunflowerChickpeaWheat

)ET/T( OC


9/23


10/23

Soil water (& salt) balance

Infiltration

UptakeRedistribution

Runoff

Rain

KsEs Ta

AquaCrop Conceptual Framework | Soil

deeppercolationcapillaryrise

Leaf expansiongs

Senescence

WP

HI

Texture 1

Texture 2Texture

Ksat

FCPWP

Irrig.

Runoff


11/23

Stresses

Ks for leaf expansion, maize

Ksfor stomata, maize

Ks for senescence, maize


12/23


13/23

Field Management

Water Management

Fertility level (non-limiting; moderate; poor)

Field-surface practices (mulching; soil bunds)

Rainfed

User defined schedule (timing and depth) Model-generated schedule (fixed interval; fixed depth;

% of RAW) Irrigation method (drip; sprinkler;

surface basin; border; furrow)

Irrigation

AquaCrop Conceptual Framework | Management


14/23

AquaCrop Flow-Chart


15/23

AquaCrop Tests

Wheat

TexasTexas

KansasKansas

SardiniaSardinia

QuzhouQuzhou

LuanchengLuancheng

FengquiFengqui


16/23

China Quzhou Wheat 98-99

0

5

10

15

20

25

30

35

40

0 50 100 150 200 250DAP

Bio

mass(t/ha)


-20

-10

0

10

20

30

40

0 50 100 150 200

DAP

MinandMaxTemper

ature(oC)


25

50

75

100

0 50 100 150 200 250

DAP

CanopyCover(%

)

YieldYield

(ton ha(ton ha--11

))

MeasuredMeasured SimulatedSimulated

6.726.72 6.506.50 3%3%

China


17/23

0

5

10

15

20

25

30

35

40

0 50 100 150 200 250

DAP

Bio

mass(t/ha)



-20

-10

0

10

20

30

40

1 31 61 91 121 151 181 211

DAP

MinandMaxTemperature(oC)

0

25

50

75

100

0 50 100 150 200 250

DAP

CanopyCover(%

)


YieldYield

(ton ha(ton ha--11

))

MeasuredMeasured SimulatedSimulated

6.226.22 7.097.09 14%14%


18/23

Maize

TexasTexas

CaliforniaCalifornia

FloridaFlorida

SpainSpainQuzhouQuzhou


19/23

I55 treatment biom ass

0

500

1000

1500

2000

2500

3000

0 20 40 60 80 100 120 140

DAP

cumD

M

(g/m2)

DM

Biomass

Treatments:

Irrigated (I)Rainfed (NI)

Irrig. day 55 onward (I55)

I

NII55

Biomass

24.3

16.821.2

Yield

11.4

5.210.3

NI treatm ent biom as s

0

500

1000

1500

2000

2500

3000

0 20 40 60 80 100 120 140

DAP

cumD

M

(g/m2)

DM

Biomass

I55 (IRR on Day55) tr eatm ent Ground Cove r

0

20

40

60

80

100

0 20 40 60 80 100 120 140

DAP

GC(%)

Calc GC

CC

22.7

16.822.4

10.8

6.210.6

measured

simulated

California

NI (No IRR) tr eatm ent Ground Cover

0

1020

30

40

50

60

70

80

90

100

0 20 40 60 80 100 120 140

DAP

GC(%)

Calc GC

CC

I treatm ent biom ass

0

500

1000

1500

2000

2500

3000

0 20 40 60 80 100 120 140

DAP

cumD

M

(g/m2)

DM

Biomass

I (Full IRR) treatm ent Ground Cover

0

20

40

60

80

100

0 20 40 60 80 100 120 140

DAP

GC(%)

Calc GC

CC


20/23

Cotton

SpainSpainTexasTexas

GreeceGreece

TurkeyTurkey

SyriaSyria


21/23

0

20

40

60

80

100

0 20 40 60 80 100 120 140 160

DAS

GC

(%)

Measur

Model

0

2

4

6

8

10

12

14

0 2 4 6 8 10 12 14

Obser ved (t ha-1)

Simulated(tha-1)

Yield

Biomass

1:1

Cordoba (SPAIN)Cordoba (SPAIN)


22/23

Conclusions

AquaCrop is explicit and mostly intuitive, and maintains anoptimum balance between simplicity, accuracy and robustness

AquaCrop differs from other models for being water-driven,and for its relatively small number of parameters

AquaCrop is aimed at practical end-users, as those in farmers

and irrigation associations, extension services, governmentalagencies and NGOs, for devising water management and savingstrategies

AquaCrop is also particularly suited for perspective studies(e.g., under different climate change scenarios)

AquaCrop is also aimed at planners and economists who needestimates of production for given amounts of water


23/23

Thank You

www.fao.org/nr/water

Documents

P.Steduto.pdf