Linking Seasonal Forecast To A Crop Yield Model

SIMONE SIEVERT DA COSTADSA/CPTEC/INPE – CNPq

simone@cptec.inpe.brHOMERO BERGAMASCHI

UFRGS

First EUROBRISA Workshop 17-19 March 2008 –Paraty, RJ - Brazil

Talk Outline

AimStudy regionMotivationCrop model calibrated for South AmericaMethods (Bias Correction) Results Future Direction

AimProduce maize crop yield prediction based on climate information (seasonal forecasts).

Crop yield model

Climate Forecast

1000

1500

2000

2500

3000

3500

4000

1989 1991 1993 1995 1997 1999 2001 2003 2005

year

mai

ze g

rain

yie

ld (

kg h

a-1) National Statistics

Maize Grain YieldSource: IBGE

The Area of Study

Rio Grande Do Sul State (RS) “Long River of South”

27.2°- 29.8°S/51.2°- 56.0°W

About Maize in RS…After USA and China, Brazil is the main maize producer in the entire world, and RS is the second

greatest producer nationally (IBGE, 2006).

Sowing Date: Sep/OctHarvest: FebCrop cycle ~130 days

Santa Rosa Passo Fundo

Main producer region(all shaded area in the map)

Bergamaschi et al. 2008

Motivation: Crop and Climate RelationshipCorrelation btw obs.maize yield and rainfall

Data Source:crop yield (IBGE)rainfall (INMET, FEPAGRO)

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

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90

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91

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05

year

star

dis

ed a

no

mal

y

seasonal rainfallrainfall 30 days after tasselingyield

Standardised rainfall and yield anom.

• Rainfall in all cycle (~130-140 days) 0.56

• Rainfall in 0-30 days after tasselling 0.72

http://w3.ufsm.br/solos/boletim3.php

Bergamaschi et al. 2008

1000

1500

2000

2500

3000

3500

4000

1989 1991 1993 1995 1997 1999 2001 2003 2005

year

mai

ze g

rain

yie

ld (k

g ha

-1)

Can crop model to reproduce the interannual variability of maize yield?

Source: IBGE

SOIL WATER

TRANSPIRATION

BIOMASS

LEAF CANOPY

ROOT SYSTEM

Water Stress

Transpiration Efficiency

YIELDYIELD

DevelopmentStage

Yield is a time varying fraction of Biomass

Outputs

Yield GapParameter

Schematic diagram of GLAM (adapted from crop and climate group webpage-Reading)

Crop model: General Large Area Model Challinor et al., 2003 GLAM is a processed

based crop model, which simulates soil water budget, crop plant phenology, canopy growth, root growth, aerial dry mass and grain yield

Crop model: GLAM adapted to RS GLAM were initially tested for

groundnut yield across India (Challinor et al., 2003) , and it was adapted to simulate maize yield for RS (Bergamaschi et.al., 2008 in preparation.).

Calibration GLAM were based on observational data (soil and crop phenology). UFRGS, Eldorado do Sul Site, Brazil.

0

1

2

3

4

5

6

7

0 600 1200 1800 2400

degree-days

leaf

are

a in

dex

1993/94

1994/95

1995/96

1996/97

1997/98

1998/99

Muller et al., 2005

Input data to GLAM:

SOIL WATER

TRANSPIRATION

BIOMASS

LEAF CANOPY

ROOT SYSTEM

Water Stress

Transpiration Efficiency

YIELDYIELD

DevelopmentStage

Yield is a time varying fraction of Biomass

Outputs

Yield GapParameter

Daily data required:

Solar Radiation

Min. Temperature

Max. Temperature

Rainfall

Schematic diagram of GLAM (adapted from crop and climate group webpage-Reading)

Maize Grain yield Estimative using GLAM and observed weather data

Observed weather data from meteorological site (P, T, Rad.) FEPAGRO, INMET

GLAM

GLAM OBS (IBGE)

• Can GLAM be used to do crop prediction with daily seasonal forecast data?

Seasonal weather data:11 ensemble member ECMWF (single grid point)

0.00E+00

5.00E+03

1.00E+04

1.50E+04

2.00E+04

2.50E+04

0 50 100 150 200 250

days from first day of forecast

Acc

um

ula

ted

rai

nfa

l (m

m)

Seasonal weather data into crop model:

Forecast issued Sep. 1997

Daily Precipitation (a grid point) - 11 ensemble member from ECMWF model- first month of each forecasts initialized in Sep, Oct, Nov, Dec, Jan, Feb. (RS crop cycle)

Rad. & Temp.ObservationDaily mean climatologyfor wet and dry days

(1998 – 2005)

ECMWF Daily Climatology (1998-2005) for crop cycle

Mean Rainfall (mm/day)

sep oct nov dec jan fev month

obs Ensemble mean Indiv. Member

• Monthly Mean Rainfall R :

R(mm d-1) = I(mm wd-1) x f(wd d-1)

intensity frequency

d=daywd=wet day

(Ines and Hansen, 2006)

Rainfall decomposition:

5

6

7

8

9

10

0 1 2 3 4 5 6 7

inte

nsi

ty (

mm

/wet

day

)

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7

Intensity(mm/wd)

Frequency(wd/d)

sep oct nov dec jan fev

month

sep oct nov dec jan fev

month

Obs.Indiv. Member Emsemble Mean

Methods - Bias Correction of daily GCM: -Frequency (wd day-1) – wd = wet day

))~((~ 1 pFFp obsgcmgcm

F(pgcm=0)

F(pobs=0)

c)

P0 – used to truncate the GCM distribution=meanfrequency of rainfall abv p0

matches the obsv. Rainfall.

a)

b)

Daily rainfall (mm)

observation

GCM

Ines and Hansen (2006)Cum

ulat

ive

dist

ribut

ion

func

tion

F(pi)

b)

pgcm p’gcm

pp

pppFFp

i

iimcgobs

i ~0

~)((1

'

Methods - Bias Correction of daily GCM: -intensity (mm wd-1)

Ines and Hansen (2006)

observationGCM

Methods - Bias Correction of daily GCM: -Multiplicative Shift

mcg

obsmcgii p

ppp ,

'

sep oct nov dec jan fev

month

3.5

4.5

5.5

6.5

7.5

8.5

0 1 2 3 4 5 6 7

3.5

4.5

5.5

6.5

7.5

8.5

0 1 2 3 4 5 6 7

3.5

4.5

5.5

6.5

7.5

8.5

0 1 2 3 4 5 6 7sep oct nov dec jan fev

month

sep oct nov dec jan fev

month

Mean Rainfall(mm/day)

mult. shift

uncorrected

Bias correction

Obs.Indiv. MemberEnsemble Mean

sep oct nov dec jan fev

month

sep oct nov dec jan fev

month

sep oct nov dec jan fev

month

Intensity(mm/wd)

mult. shift

uncorrected

Bias correction

5

7

9

11

0 1 2 3 4 5 6 7

Obs.Indiv. MemberEnsemble Mean

5

7

9

11

0 1 2 3 4 5 6 7

sep oct nov dec jan fev

month

sep oct nov dec jan fev

month

sep oct nov dec jan fev

month

frequency(wd/d)

mult. shift

uncorrected

Bias correction

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7

0.5

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0.8

0.9

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0 1 2 3 4 5 6 7

Obs.11 GCM Mem. Mean GCM

0.5

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0.9

1

0 1 2 3 4 5 6 7

Hindcast of Yield– Main producer region

1000

2000

3000

4000

5000

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

year

yiel

d (

kg h

a-1)

GLAM(OBS) GLAM(Unc).

GLAM(MS) GLAM(IF)

2 std -GLAM(GG transf) 2std+GLAM(GG transf.)

DATA INPUT FORECAST:OBS = observed weather data UNC = uncorrected seasonal forecastMS = seasonal forecast corrected using multiplicative shiftIF = seasonal forecast, intensity & frequency correction method

Future Direction• Statistical downscaling – to take advantage of regional of

seasonal forecast skill (spatial calibration).

• Use of weather generator – to reproduce daily data (temporal disaggregation).

• Use of space-temporal downscaled daily prediction into crop model.

• Compared skill of different crop yield forecast approach (grid point and spatial downscaled data).

Thanks:

• Andrew Challinor (The University of Leeds-UK)

• Caio Coelho (CPTEC- Brazil)

• Homero Bergamaschi (UFRGS, Brazil)

• Tim Wheeler (The University of Reading-UK)

• Jim Hansen (IRI – USA)

• Walter Baethgen (IRI – USA)