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

(NCEP/CPC) (UF) (FSU/COAPS) (NCEP/CPC) (UM) (IRI) Muthuvel Chelliah Kingtse C. Mo James W. Jones James J. O’Brien R. Wayne Higgins Daniel Solis James W. Hansen Neil 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

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:

De-trended time series of cotton yields and ENSO phases (48-county average) Cotton yields (kg ha -1 ) Neutral La Niña El Niño

p < 0.00 r 0.00 – 0.25 t 0.25 – 0.50 v 0.50 – 0.75 x 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 ( ). 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 April and 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: MonthsGFI  = 0.01  = 0.05 Non significant April May June July August September October GFI = Average correlation over 48 counties FloweringFlowering MaturityMaturity toto 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 rainfall and low atmospheric humidity Under moderate to high rainfall and 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 and NOAA 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 normal increase air density producing subsidence Temperatures higher than normal decrease air density producing convection Temperatures lower than normal decrease absolute humidity, decreasing H 2 O available for condensation Land Ocean Highest cotton yieldLowest cotton yield July – August - September 200 hPa SST anomalies (°C) 200 hPa 850 hPa Sfc. Temperatures higher than normal increase absolute humidity, increasing H 2 O available for condensation Humid air convection

Climatology Highest yielding Lowest yielding Anomalies °C °C Depending on cotton varieties, temperatures higher than 32°C cause boll abortion decreasing boll retention Observed Mean Temperatures at Surface (July)

Observed anomalies of latent heat flux (July) Highest yieldingLowest yielding 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

R = ** 32 counties ** 15 counties * 1 county ns NOAA NCEP/NCAR Reanalysis data All-county average Cotton lint yields (kg.ha -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 the highest yielding years 500 hPa height anomalies during the lowest yielding years Observed Climatology of 500 hPa height

July R = PC of observed Z500 (NOAA NCEP/NCAR reanalysis) PC of observed cotton yields Canonical correlation

p < 0.00 r 0.00 – 0.25 t 0.25 – 0.50 v 0.50 – 0.75 x 0.75 – 1.00 no significant (based on 500 bootstrap samples, confidence level of 95%) Correlation ranges ENSO-based forecast NCEP Reanalysis-based prediction (Cross-validated)

Years Observed NCEP reanalysis-based prediction (cross-validated) ENSO-based hindcasted Cotton lint yields (kg.ha -1 ) R Obs = ** R Obs = ** R Obs = ** R ENSO = ns R ENSO = ns R ENSO = ns All-county average Best estimated county Bleckley - Georgia Worst estimated county Mitchell - Georgia ** Significant at the 0.01 probability * Significant at the 0.05 probability

July R = PC of CFS’s hindcasted circulation PC of observed cotton yields Canonical correlation

p < 0.00 r 0.00 – 0.25 t 0.25 – 0.50 v 0.50 – 0.75 x 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)

Years Observed CFS-based hindcasted (cross-validated) ENSO-based hindcasted Cotton lint yields (kg.ha -1 ) R CFS = ** R CFS = ** R CFS = * R ENSO = ns R ENSO = ns R ENSO = ns All-county average Best estimated county Terrell - Georgia Worst estimated county Madison - 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 where a 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