2237-1 joint ictp-iaea conference on coping with climate...

2237-1

Joint ICTP-IAEA Conference on Coping with Climate Change and Variability in Agriculture through Minimizing Soil Evaporation

Wastage and Enhancing More Crops per Drop

Theodore C. HSIAO

9 - 13 May 2011

Dept. of Land, Air and Water Resources University of California

Davis USA

Lee HENGJoint FAO/IAEA Div.of Nuclear Techniques in Food and Agric.

IAEA Vienna Austria

AquaCrop, the FAO simulation model for crop water productivity, irrigation management and hydrologic assessment

AquaCrop, the FAO simulation model for crop water productivity, irrigation

management and hydrologic assessment

Theodore C. HSIAO1 and Lee HENG2

To downloan AquaCrop:

1 Dept. of Land, Air and Water Resources, Univ. of California, Davis, USA

2Joint FAO/IAEA Div. of Nuclear Techniques in Food and Agric., IAEA, Vienna, Austria

ICTP-IAEA Conference, Trieste, May, 2011

WWW.fao.org/nr/water/aquacrop.html

Water use is highly unequal among nations

• Rising world population• Improved living conditions • Increasing recreational use

• Shifting to diets higher in animal products

Globally, water is a finite resourceDemand and competition for water is increasing

Example in the Mediterranean countries

• Rising world population• Improved living conditions • Increasing recreational use• Shifting to diets higher in animal products

Where there is rain, there is food production

NDVI—indicator of green biomass, May 2005

To ameliorate the problem of water scarcity for agriculture, need to quantify water availability and use for food production, and seek improvements in water use efficiency through management changes

AquaCrop is designed by FAO with those purposes in mind for the potential use to:• Improve irrigation management by reducing water use while

maintaining crop productivity, i.e., improve water use efficiency• Quantify crop ET and production from field to landscape scale

in the hydrological analysis of water resources• Develop water production functions for economical analysis to

optimize water use and farm income• Predict crop water use and productivity for climate change

scenarios

Esoil TrETo

Phenology Canopy CoverLeaf expansion gs

SenescenceBiomass

WP

YieldHI

Rooting depth

AquaCrop Conceptual Framework |Crop and Climate

CO2

T (oC)

Climate, weather

Soil water (& salt) balanceInfiltration

UptakeRedistribution

Runoff

Rain

Ks

Es Ta

AquaCrop Conceptual Framework | Soil and Water

deeppercolation

capillaryrise

Leaf expansiongs

Senescence

WPHI

Texture 1

Texture 2

Texture …

Ksat

FC

PWP

Irrig.

Runoff

Stresses

Ks

(1) leaf expansion

WP(4)

HI(5)

(2) gs

(3) senescence

Ks for leaf expansion, maize

Ks for stomata, maize

Ks for senescence, maize

Key steps of AquaCrop• Uses growing degree day as driver (can switch to calendar time)• Calculates canopy cover (CC) first, daily, and not use LAI• Transpiration is calculated from CC, daily ETo and a crop coef. for full CC,

but with empirical adjustment for daily integral and for interrow advection• Soil evaporation is calculated from CC, daily ETo based on Stage 1 & 2

drying, and ET is obtained as the sum• Water productivity normalized for atmosphere evaporative demand & CO2,

a constant, is used to convert transpiration to biomass production, daily• Yield is calculated from biomass and harvest index (HI)• HI is assumed to increase from anthesis onward, linearly after a lag phase• Water stress is dependent on fractional depletion of available soil water, via

several water stress functions, each with its own sensitivity threshold• The stress functions are: leaf growth, stomata, early canopy senescence,

and changes in HI • Impact of transpiration rate on plant water status are indirectly accounted

for by adjusting the sensitivity thresholds according to the daily ETo

Canopy Cover (CC)

CGC

CCx

start of canopy senescence

time to harvest

cano

py c

over

timetime to full canopy

CDC

CCo• Canopy cover (CC) follows theexponential growth during thefirst half of its development(Eq. 1), and an exponentialdecay during the second half of its development (Eq. 2)

tCGCoeCCCC (1)

Canopy Cover (CC)

CGC

CCx

start of canopy senescence

time to harvest

cano

py c

over

timetime to full canopy

CDC

CCo

tCGCoeCCCC (1) tCGC

oxx e)CCCC(CCCC (2)

• AquaCrop calculates soil evaporation (Esoil) and crop transpiration (Tr) separately

• Canopy cover (CC) is calculated first as it affects the separation between Esoiland Tr

• Initial canopy cover (CCo) is calculated from plant density and CC per seedling

(B)Cotton, 1997

C(%

)

0

20

40

60

80

100 (D)Cottonnormal vs. high density

(A)Cotton, 1981

C(%

)

0

20

40

60

80

100

(C)Cotton, 1999

SimulatedMeasured

SimulatedMeasured

SimulatedMeasured

Days after planting0 20 40 60 80 100

(F)Cotton, 1983Dwarf cultivar

(E)Cotton, 1982Dwarf cultivar

Days after planting0 20 40 60 80 100

C(%

)

0

20

40

60

80

100

SimulatedMeasured

SimulatedMeasured

Measured, normalMeasured, highSimulated, normalSimulated, high

Hsiao et al unpublished

CC simulation using the same CGC and initial CC per seedling

WPWater Productivity

(g m-2 mm-1)CT

BiomassWP(g m-2)

)2000(COO

C

*

2ETT

BiomassWP

ETc (mm x 1000)0.0 0.3 0.6 0.9

Bio

mas

s (k

g m

-2)

0

1

2

3

SorghumSunflowerChickpeaWheat

CT (ETc/ETo)

0 40 80 120 160

Bio

mas

s (k

g m

-2)

0

1

2

3

SorghumSunflowerChickpeaWheat

)ET/T( OC

General features

• 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, governmental agencies and NGOs, for devising water management and saving strategies

• AquaCrop is suitable for perspective studies(e.g., different irrigation strategies, climate change scenarios)

• AquaCrop is also aimed at planners and economists who need estimates of production for given amounts of water

How does AquaCrop perform?

Example of application to maize

• Initial canopy cover per seedling (cm2)• Canopy growth coefficient (% of existing canopy)• Maximum canopy cover (plant density dependent)• Canopy decline coefficient (% per day)

• Average deepening rate (optimal soil conditions, cm/day)

• Crop coefficient for transpiration (full canopy)• Decline in green canopy activity with age (% per day)

• Normalized WP (optimal conditions, year 2000, g m2) • Reference HI (%)

Plant parameters, basic, wide applicability

Canopy

Root system

Transpiration

Production

For maize6.516--14

2.4

1.030.3

33.048

We consider these as conservative (constant) parameters

Ks function for leaf expansion (canopy growth)• p upper for leaf expansion• p lower • Curve shape

Ks function for stomata (canopy transpiration)• p upper • Curve shape

Ks function for acceleration of canopy senescence• p upper • Curve shape

Stress effects on HI• Positive effects of stress inhibition of leaf growth • Negative effects of stress inhibition of stomata

Plant stress parameters, basic, wide applicability

For maize0.180.76

+3

0.76+6

0.78+3

Stress effects on WP* 0

Also considered as conservative (constant) parameters

• Daily weather (ETo, rainfall, and temperature)

• Soil water characteristics of depth layers

• Initial soil water content of depth layers

• Rooting depth as affected by restrictive soil layers

• Irrigation schedule (time and amount)

• % of soil surface wetted by irrigation

• Plant density

• Crop phenology (cultivar specific), including flowering, senescence start, and physiological maturity time

Parameters specific to each location & season

1974 ExperimentTreatments:

Irrigated (I)Rainfed (NI)Irrig. day 55 onward (I55)

• Early senescence of NI fairly well simulated

• Biomass time course and difference fairly well simulated

INII55

Biomass24.316.821.2

Yield11.45.2

10.3

22.716.822.4

10.86.2

10.6White—measuredBlue—simulated

1974 Irrigated ( I )

0102030405060708090

100

0 20 40 60 80 100 120 140DAP

Can

opy

cove

r (%

)

MeasuredSimulated

1974 Rainfed (NI)

0102030405060708090

100

0 20 40 60 80 100 120 140DAP

Can

opy

cove

r (%

)

Measured

Simulated

1974 Rainfed (NI)

0

5

10

15

20

25

30

0 20 40 60 80 100 120DAP

Bio

mas

s (t

on/h

a)

Measured

Simulated

1974 Irrigated day 55 onward (I55)

0102030405060708090

100

0 20 40 60 80 100 120 140DAP

Can

opy

cove

r (%

)

Measured

Simulated

1974 Irrigated 55 day onward (I55)

0

5

10

15

20

25

30

0 20 40 60 80 100 120D A P

Bio

mas

s (t

on/h

a)

Measured

Simulated


0

5

10

15

20

25

30

0 20 40 60 80 100 120DAP

Bio

mas

s (t

on/h

a)

Measured

Simulated

• Effects of early stress and late irrigation fairly well simulated

ITreatment

I 55Treatment

NITreatment


Irrigated (I)Late dry (LD, dry day 56 on)

ILD

Biomass26.922.7

Yield12.010.1

26.923.9

12.410.7

White—measuredBlue—simulated


0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100 120 140DAP

Can

opy

cove

r (fr

actio

n)

Measured

Simulated

1990 Late Dry (LD)

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100 120 140DAP

Can

opy

cove

r (fr

actio

n)

Mearured

Simulated

• Canopy decline of LD under-simulated slightly

• Reduced late biomass accumulation of LD well simulated


0

5

10

15

20

25

30

0 20 40 60 80 100 120 140DAP

Bio

mas

s (t

on/h

a)

Simulated

Measured

1990 Late Dry (LD)

0

5

10

15

20

25

30

0 20 40 60 80 100 120 140DAP

Bio

mas

s (t

on/h

a)

Measured

Simulated


Irrigated (I)Limited irrig. (LI)

ILI

Biomass27.622.2

Yield12.710.2

29.825.4

14.112.1

White—measuredBlue—simulated


0

20

40

60

80

100

0 20 40 60 80 100 120 140DAP

Can

opy

cove

r (%

)

MeasuredSimulated

1994 Limited Irrigation (LI)

0

20

40

60

80

100

0 20 40 60 80 100 120 140DAP

Can

opy

cove

r (%

)

SimulatedMeasured

• Effects of irrigation in delaying senescence well simulated

• Recovery in biomass growth upon irrigation simulated


0

5

10

15

20

25

30

35

0 20 40 60 80 100 120 140DAP

Bio

mas

s (to

n/ha

)

Simulated

Measured

1994 Limited Irrigation (LI)

0

5

10

15

20

25

30

35

0 20 40 60 80 100 120 140DAP

Bio

mas

s (to

n/ha

)

Simulated

Measured

1994

irrigated

Limitedirrigation

Range of 6 different years in Davis simulated (same basic parameters)

• Three different cultivars• Planting dates: May 14 to June 16• Time to maturity: 118 to 138 days• Substantial range of evaporative conditions• Variety of watering regimes:

• Rainfed• No irrigation first half of season• No irrigation last half of season• Limited irrigation in mid season• Fully irrigated

ETo, 15-day running mean

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

0 20 40 60 80 100 120 140DAP

ETo

(mm

/day

)

19741990

Testing for other locations:

• Bushland Texas—high wind and very high ETosoil water holding capacity = 15%

• Gainsville Florida—sandy soil and humid, low ETosoil water holding capacity = 5 to 10%

• Zaragoza Spain—sandy loam and 3 irrigation regimessoil water holding capacity = 17 to 20%

Heng L. et al, 2009. Agron J.

All using same conservative parameters as Davis

Coefficients of efficiency for the simulations was 0.9 or higher for the large majorities of the test cases

So far AquaCrop has been parameterized for only a number of crops:

• Maize• Cotton• Wheat (mild winter)• Paddy rice

• Soybean• Sunflower• Sugar beet• Quinoa• Potato

Rel

iabi

lity

Highest

Lowest

• Sorghum• Tomato• Sugar cane• Barley

Working on:

• Wheat (cold winter)• Alfalfa

Next to do:

Available now:

Full irrig.

65% irrig.

24% irrig.

• Need to make canopy growth coefficient (CGC) more sensitive to water stress• Need to make transpiration less sensitive • Need to make the HI vs. DAP function a curve instead of straight line

PotatoDavis 80

What about potato water use, simulated?

• Water balance is simulated OK in this case• Hard to find data where ET, CC or LAI, and

biomas are sampled over the season, along with detailed weather data

• Daily weather (rainfall, radiation, temperature, humidity, and wind)• Soil water characteristics of depth layers• Initial soil water content of depth layers• Irrigation schedule (time and amount)• Soil water content at different times• Plant density• Canopy cover (CC from LAI) progression with time• Rooting depth at different times• Transpiration (or ET) as function of time• Biomass at different times and final harvest• Yield

Data desired for parameterization and testing

Most experiments not provide all desired dataHave to test various aspects separately

www.fao.org/nr/water

Now demonstration of model use

2237-1 joint ictp-iaea conference on coping with climate...

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