Forecasting cotton yields over the southeastern USusing NOAA NCEP/NCAR Reanalysis data and NOAA NCEP/CPC Coupled Forecast System

Guillermo A. BaigorriaPostdoctoral Research Associate

gbaigorr@ifas.ufl.edu

(NCEP/CPC)

(NCEP/CPC)

(UF)

(FSU/COAPS)

(NCEP/CPC) (UM) (IRI) (IRI)

Muthuvel ChelliahKingtse C. Mo

James W. JonesJames J. O’Brien

R. Wayne HigginsDaniel Solis

James W. HansenNeil Wards

1. We developed a system potentially useful to forecast cotton yields in the SE-USA

2. Climate Test Bed provided us with new research partners

Lin

t yi

eld

(kg

ha-

1)

0

50

100

150

200

250

300

350

400

1970 1975 1980 1985 1990 1995 2000 20050

50

100

150

200

250

300

350

400

0

50

100

150

200

250

300

350

400

1970 1975 1980 1985 1990 1995 2000 2005

0

50

100

150

200

250

300

350

400

Yield

Production

Lin

t pro

ductio

n (M

g)

Year

Alabama

Georgia

• US$ 826 million (1997) in Alabama and Georgia (USDA/NASS)

• In the last 30 years cotton increased by 800 thousand hectares in the US.

• Much of this increase occurred in the Southeast where yields have also increased during this period

• US cotton exports have more than doubled in the last 5 years

• Sensible to plagues and diseases (i.e. Hardlock of cotton [Fusarium verticillioides]) reduced yields by about 50% in 2002 in the Panhandle of Florida

Cotton in the Southeast US

Source: http://agclimate.org

De-trended time series of cotton yields and ENSO phases(48-county average)

300

400

500

600

700

800

1970

1973

1976

1979

1982

1985

1988

1991

1994

1997

2000

2003

Cot

ton

yiel

ds (

kg h

a-1)

Neutral

La Niña

El Niño

p < 0.00r 0.00 – 0.25t 0.25 – 0.50v 0.50 – 0.75x 0.75 – 1.00 no significant

Correlation ranges

Cotton yield ENSO-based forecast

Global/Regional Circulation Models (GCM/RCM) better

predict the interannual climate variability and circulation

patterns rather than the absolute values of meteorological

variables.

What alternatives to ENSO do we have?

DATA

- NOAA NCEP/NCAR Reanalysis data (1970 – 2005)- NOAA NCEP/CPC Coupled Forecast System retrospective forecasts

(1981-2005).

Climate data

Agricultural data

- National Agricultural Statistics Service (NASS) 211 counties in

Alabama (67), Florida (16) and Georgia (128) producing cotton.

Only 48 counties were selected because of significant cotton

production areas (35-year average ranging from 1,500 to 22,000 ha)

Cotton lint yields in Alabama and Georgia

Most of the cotton in the southeastern US is planted between March and Apriland harvested between September and October

yields (kg ha-1)

Goodness-of-Fit index (GFI) between observed rainfall at weather station and observed cotton yield anomalies

Percentage of counties significant at:

Months GFI = 0.01 = 0.05 Non significant

April -0.101 0 0 100

May -0.117 0 0 100

June -0.042 0 0 100

July 0.485 69 15 16

August 0.132 2 11 87

September 0.098 2 6 92

October 0.042 0 0 100

GFI = Average correlation over 48 counties

Flowering

Maturity

to

G r o w t h

Relationship between humidity and cotton yield

In the SE-USA this vulnerable-window period occurs during July and early August

Boll rot Hardlock of cotton

Under low to moderate rainfalland low atmospheric humidity

Under moderate to high rainfalland high atmospheric humidity

Five years of highest yielding Five years of lowest yielding

AMJ

JAS

Wind field anomalies at 200 hPa and SST anomalies

Temperature anomaly

Correlation maps between de-trended cotton yields andNOAA NCEP/NCAR reanalysis data of:

Reference: Baigorria, G.A., Hansen, J.W., Ward, N., Jones, J.W. and O’Brien, J.J. Assessing predictability of cotton yields in the Southeastern USA based on regional atmospheric circulation and surface temperatures. Journal of Applied Meteorology and Climatology. In press.

Surface temperatures

Meridional winds at 200 hPa

850 hPa

200 hPa Temperatures lower than normalincrease air density producing

subsidence

Temperatures higher than normaldecrease air density producing

convection

Temperatures lower than normaldecrease absolute humidity,

decreasing H2O available forcondensation

Land Ocean Land Ocean

Highest cotton yield Lowest cotton yield

July – August - September

200 hPa

SST anomalies (°C)

200 hPa850 hPa 850 hPa

Sfc.

Temperatures higher than normalincrease absolute humidity,

increasing H2O available forcondensation

Humid airHumid air convectionconvection

Climatology 1981-2003

Highest yielding Lowest yielding

Anomalies °C Anomalies °C

29

28

27

26

25

24

0.8

0.6

0.4

0.2

0

-0.2

0

-0.2

-0.4

-0.6

-0.8

-1.0

°C

°C °C

Depending on cotton varieties,

temperatures higher than 32°C

cause boll abortion

decreasing boll retention

ObservedMean

Temperaturesat Surface

(July)

Observed anomalies of latent heat flux (July)

Highest yielding Lowest yielding

-12 -10 -8 -6 -4 -2 0 2 4 6 8 W m-2

• Use as predictors the spatial structure of the NOAA NCEP/NCAR

reanalysis 200 hPa meridional winds and surface temperatures

from July to September captured by principal components

• Use as predictors the spatial structure of the NOAA NCEP/CPC CFS

200 hPa meridional winds and surface temperatures from July to

September captured by principal components

• Applied canonical correlation analysis and leave-one-out

cross-validation to predict the interannual variability of cotton

yields in the 48 counties

Methods

100

200

300

400

500

600

700

800

90019

70

1973

1976

1979

1982

1985

1988

1991

1994

1997

2000

R = 0.6873 **

32 counties **15 counties * 1 county ns

NOAA NCEP/NCAR Reanalysis data

All-county averageC

ott

on

lin

t yi

eld

s (k

g.h

a-1)

years

Observed

NCEP reanalysis-based prediction (cross-validated)

** Significant at the 0.01 probability* Significant at the 0.05 probability

How this can support a farmer?

- External symptoms of hardlock of cotton appear just previous to the

harvest when there is nothing to do

- Farmers usually do not apply fungicides because they don’t see the

effects and they try to reduce costs

- To wait for observed July data from NOAA NCEP/NCAR reanalysis will

help farmers in early August to know if they will have harvest losses in

November. It doesn’t help a lot, does it?

- But what if at least we can forecast July conditions later June – early

July (CFS 0-1 month in advance)? Farmers will have the information to

help them in the decision whether applying fungicides just when the

attack is beginning

July

500 hPa height anomalies during thehighest yielding years

500 hPa height anomalies during the lowest yielding years

Observed Climatology of 500 hPa height

July R = 0.9494

PC of observed Z500 (NOAA NCEP/NCAR reanalysis)

PC of observed cotton yields

Canonical correlation

p < 0.00r 0.00 – 0.25t 0.25 – 0.50v 0.50 – 0.75x 0.75 – 1.00 no significant (based on 500 bootstrap samples, confidence level of 95%)

Correlation ranges

ENSO-based forecast

NCEP Reanalysis-basedprediction

(Cross-validated)

200

300

400

500

600

700

800

900

1000

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

200

300

400

500

600

700

800

900

100019

81

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

200

300

400

500

600

700

800

900

1000

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Years

Observed

NCEP reanalysis-based prediction (cross-validated)ENSO-based hindcasted

Co

tto

n l

int

yiel

ds

(kg

.ha-1

)

RObs = 0.8744**

RObs= 0.8858**

RObs = 0.6690**

RENSO = 0.2031ns

RENSO= - 0.2248ns

RENSO = 0.0979ns

All-county average

Best estimated countyBleckley - Georgia

Worst estimated countyMitchell - Georgia

** Significant at the 0.01 probability* Significant at the 0.05 probability

July R = 0.7876

PC of CFS’s hindcasted circulation

PC of observed cotton yields

Canonical correlation

p < 0.00r 0.00 – 0.25t 0.25 – 0.50v 0.50 – 0.75x 0.75 – 1.00 no significant (based on 500 bootstrap samples, confidence level of 95%)

Correlation ranges

ENSO-based forecast

CFS-based forecast(Cross-validated)

100

200

300

400

500

600

700

800

900

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

100

200

300

400

500

600

700

800

90019

81

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

100

200

300

400

500

600

700

800

900

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Years

Observed

CFS-based hindcasted(cross-validated)ENSO-based hindcasted

Co

tto

n l

int

yiel

ds

(kg

.ha-1

)

RCFS = 0.7507**

RCFS= 0.7879 **

RCFS = 0.4459 *

RENSO = 0.2031ns

RENSO= - 0.2745ns

RENSO = 0.2436ns

All-county average

Best estimated countyTerrell - Georgia

Worst estimated countyMadison - Alabama

** Significant at the 0.01 probability* Significant at the 0.05 probability

Conclusions

• Based on the previous almost total lack of predictability skills in the region during summertime, the method increased the probabilities to forecast cotton yields beyond the chances in up to 67%.

• Specific atmospheric circulation patterns that favor higher humidity, temperatures and rainfall during summertime caused a tendency to lower cotton yields, which is consistent with boll abortion and higher than normal incidence of diseases during flowering and harvest.

• In the case of predicting cotton yield, the dual effect of water during anthesis and boll maturity creates important challenges wherea multi-disciplinarily approach is the only way to tackle the issue.

Conclusions

• It is necessary to go further in to investigate the physical relationship between the circulation patterns and the regional conditions where cotton are growing during summertime in the SE-USA.

• It is necessary to analyze CFS’s forecasts made with more than one month in advance in order to assess the predictability levels with more time in advance.

Thank you