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

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Forecasting cotton yields over the southeastern US using NOAA NCEP/NCAR Reanalysis data and NOAA NCEP/CPC Coupled Forecast System. Guillermo A. Baigorria Postdoctoral Research Associate [email protected] (NCEP/CPC) (NCEP/CPC) (UF) (FSU/COAPS) (NCEP/CPC) (UM) (IRI) (IRI).

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slide1

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

Guillermo A. BaigorriaPostdoctoral Research Associate

[email protected]

slide2

(NCEP/CPC)

(NCEP/CPC)

(UF)

(FSU/COAPS)

(NCEP/CPC)

(UM)

(IRI)

(IRI)

Muthuvel Chelliah

Kingtse C. Mo

James W. Jones

James J. O’Brien

R. Wayne Higgins

Daniel Solis

James W. Hansen

Neil Wards

slide3

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

  • Climate Test Bed provided us with new research partners
slide4

Cotton in the Southeast US

  • 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
slide6

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

El Niño

Neutral

Cotton yields (kg ha-1)

La Niña

slide7

Cotton yield ENSO-based forecast

Correlation ranges

p< 0.00

r0.00 – 0.25

t0.25 – 0.50

v0.50 – 0.75

x0.75 – 1.00

no significant

slide8

What alternatives to ENSO do we have?

Global/Regional Circulation Models (GCM/RCM) better predict the interannual climate variability and circulation patterns rather than the absolute values of meteorological variables.

slide9

DATA

Climate data

  • NOAA NCEP/NCAR Reanalysis data (1970 – 2005)
  • NOAA NCEP/CPC Coupled Forecast System retrospective forecasts (1981-2005).

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)
slide10

Cotton lint yields in Alabama and Georgia

yields (kg ha-1)

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

slide11

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

G

r

o

w

t

h

F

l

o

w

e

r

i

n

g

M

a

t

u

r

i

t

y

t

o

GFI = Average correlation over 48 counties

slide13

Relationship between humidity and cotton yield

Under low to moderate rainfalland low atmospheric humidity

Under moderate to high rainfalland high atmospheric humidity

Hardlock of cotton

Boll rot

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

slide15

Wind field anomalies at 200 hPa and SST anomalies

Five years of highest yielding

Five years of lowest yielding

AMJ

JAS

Temperature anomaly

slide16

Surface temperatures

Meridional winds at 200 hPa

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.

slide17

July – August - September

Highest cotton yield

Lowest cotton yield

200 hPa

Temperatures lower than normalincrease air density producingsubsidence

Temperatures higher than normaldecrease air density producingconvection

850 hPa

Temperatures lower than normaldecrease absolute humidity,decreasing H2O available forcondensation

Temperatures higher than normalincrease absolute humidity,increasing H2O available forcondensation

Humid air

Humid air

convection

convection

Sfc.

Land Ocean

Land Ocean

850 hPa

200 hPa

850 hPa

200 hPa

SST anomalies (°C)

slide18

°C

Climatology 1981-2003

29

28

27

26

25

24

ObservedMean Temperaturesat Surface

(July)

Depending on cotton varieties, temperatures higher than 32°C cause boll abortion decreasing boll retention

°C

°C

Highest yielding

Lowest yielding

0

-0.2

-0.4

-0.6

-0.8

-1.0

0.8

0.6

0.4

0.2

0

-0.2

Anomalies °C

Anomalies °C

slide19

W m-2

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

Observed anomalies of latent heat flux (July)

Highest yielding

Lowest yielding

slide20

Methods

  • 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
slide21

Observed

NCEP reanalysis-based

prediction (cross-validated)

NOAA NCEP/NCAR Reanalysis data

All-county average

Cotton lint yields (kg.ha-1)

R = 0.6873 **

years

32 counties **

15 counties *

1 county ns

** Significant at the 0.01 probability

* Significant at the 0.05 probability

slide22

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
slide23

500 hPa height anomalies during thehighest yielding years

July

Observed Climatology of 500 hPa height

500 hPa height anomalies during the

lowest yielding years

slide24

PC of observed Z500 (NOAA NCEP/NCAR reanalysis)

PC of observed cotton yields

Canonical correlation

July

R = 0.9494

slide25

ENSO-based forecast

Correlation ranges

p< 0.00

r0.00 – 0.25

t0.25 – 0.50

v0.50 – 0.75

x0.75 – 1.00

no significant (based on 500 bootstrap samples, confidence level of 95%)

NCEP Reanalysis-basedprediction

(Cross-validated)

slide26

Observed

NCEP reanalysis-based

prediction (cross-validated)

ENSO-based hindcasted

All-county average

RObs= 0.8744**

RENSO= 0.2031ns

Best estimated county

Bleckley - Georgia

Cotton lint yields (kg.ha-1)

RObs= 0.8858**

RENSO= - 0.2248ns

Worst estimated county

Mitchell - Georgia

RObs= 0.6690**

RENSO= 0.0979ns

** Significant at the 0.01 probability

* Significant at the 0.05 probability

Years

slide27

PC of CFS’s hindcasted circulation

PC of observed cotton yields

Canonical correlation

July

R = 0.7876

slide28

ENSO-based forecast

Correlation ranges

p< 0.00

r0.00 – 0.25

t0.25 – 0.50

v0.50 – 0.75

x0.75 – 1.00

no significant (based on 500 bootstrap samples, confidence level of 95%)

CFS-based forecast

(Cross-validated)

slide29

Observed

CFS-based hindcasted

(cross-validated)

ENSO-based hindcasted

All-county average

RCFS= 0.7507**

RENSO= 0.2031ns

Best estimated county

Terrell - Georgia

Cotton lint yields (kg.ha-1)

RCFS= 0.7879 **

RENSO= - 0.2745ns

Worst estimated county

Madison - Alabama

RCFS= 0.4459 *

RENSO= 0.2436ns

** Significant at the 0.01 probability

* Significant at the 0.05 probability

Years

slide30

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.
slide31

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.
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