Crop yield predictions using seasonal climate forecasts

Crop yield predictions using seasonal climate forecasts

SIMONE SIEVERT DA COSTADSA/CPTEC/INPE

simone.sievert@cptec.inpe.br

Second EUROBRISA Workshop July 2009 – Dartmoor, Devon- UK

AimInvestigate the potential of using seasonal climate forecasts for producing maize yield predictions

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 Study Area

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 in Brazil (IBGE, 2006).

Sowing Date: Sep/OctHarvest: Feb/MarchCrop cycle ~130 days

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)

Crop model: General Large Area Model Challinor et al., 2003

Calibration GLAM was 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

GLAM model• Challinor et al. 2003

Morse et al. (2005) Tellus, 57A(3), 464-475

Maize yield predictions

Meteorological stations:Daily data

ECMWF System 3:Anderson et al. (2007)ECMWF Tech. Memo,

503, pp 56

Rain, T, S (climat)

Sim. crop Fcst. crop

Hindcast period: 1989-20050 to 5 month lead predictions; 11 ensemble members

B.corr. RainT, S (climat.)

Seasonal weather data into crop model: Monthly Mean Rainfall ECMWF (bias corrected) forecasts, 11 ensemble members initialized in Sept. (for Sept, Oct, Nov, Dec, Jan, Feb)

Radiation & TemperatureDaily mean observed climatology for wet and dry days (1989 – 2005).

Correlation Between ECMWF monthly Forecasts and Obs. Rainfall Anomalies (1981-2005), Issue Sep.

sept. nov.

dec. jan.

oct.

feb.

Time disaggregation: Monthly mean to daily rainfall using a weather generator

• Stochastic weather generator (first order Markov chain) based on gamma rainfall PDF (Moron, 2005)

• Input data: daily rainfall observations and monthly mean rainfall predictions

• Output data: daily rainfall sequences

Daily Rainfall Histogram for a county - Sept-Feb (1989-2005)

WG (ECMWF)for 2 members

Observation

Daily Rainfall Sequence for All Januaries (1989-2005)

Obs.

WG (ECMWF-Mb. 5)

1989 1994 1999 2004

1989 1994 1999 2004

Daily Rainfall Sequence for All Octobers (1989-2005)

Obs.

WG (ECMWF- Mb. 5)

1989 1994 1999 2004

1989 1994 1999 2004

Grid Point 1

Grid Point 2

fcstobs

Grain yield RS state produced six months in advance

Grain yield prediction for indiv. County

produced six months in advance

3

5

7

Grid Point 1

fcstobs

3

5

7

Grid Point 2

Grain yield predictionfor indiv. County

produced six months in advance

fcstobs

Summary• Stochastic weather generator: powerful tool for

making use of monthly mean rainfall forecasts from coupled seasonal forecast models for producing crop yield predictions

• Preliminary results show promising usefulness of monthly mean rainfall forecasts produced by ECMWF coupled model for producing maize yield predictions for RS six months in advance

Future Directions• Use monthly mean rainfall forecasts from other coupled

models (e.g. CPTEC, UK Met Office and Meteo-France) into weather generator for use in maize yield crop model

• Compared skill of different crop yield forecasts produced using different coupled model monthly mean rainfall forecasts

• Further investigate potential of using seasonal climate forecasts for producing maize yield predictions for other locations (e.g. Uruguay)

Thanks:

• Caio Coelho (CPTEC- Brazil)

• Homero Bergamaschi (UFRGS, Brazil)

• Andrew Challinor (The University of Leeds-UK)