Use of seasonal climate information to predict coconut production in Sri Lanka
T. S. G. Peiris; James W. Hansen; Lareef M. Zubair
- Use of seasonal climate information to predict coconut production in Sri Lanka
Peiris, T. S. G.
Hansen, James W.
Zubair, Lareef M.
- International Research Institute for Climate and Society
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- International Journal of Climatology
- Accurate forecasting of annual national coconut production (ANCP) is important for national agricultural planning and negotiating forward contracts. Climate and the long-term trends (attributed to 'technology') are major factors that determine ANCP. The effect of climate on ANCP of the following year was studied for the seven agro-ecological regions (AER's) in the principal coconut growing areas for the period 1950-2002. Climate was studied based on seasons aggregated by the monsoon calendar and by quarters that are consistent with the agricultural calendar. The use of quarterly seasons explained more of the variability of ANCP than the use of monsoon based seasons. January-March rainfall in all AER's and July-September rainfall in the wetter regions are positively correlated with the ANCP (p < 0.005). The technology effect was estimated using a log-linear trend model. The regression model integrates both climate and technology effects developed to predict ANCP with high fidelity (R2 = 0.94). The climate effect was estimated by regressing production data that had been de-trended to remove the technology effects with quarterly rainfall in the year prior to harvest. The most significant predictors were found to be the quarterly rainfall from the AER's in the coconut growing regions that are designated as wet and intermediate. Representative rainfall from each quarter was used in a regression model with corrections for the technology effect. The correlation between observed and predicted values of the ANCP was 0.83 (p < 0.001). The prediction of ANCP for 2003 and 2004 gave errors of only 6.5 and 7.0%. The estimated value of ANCP for 2005 is 2715 million nuts, which is 12% higher than the mean. The lead time of the prediction extends to 15 months but it may be extended with the use of seasonal climate forecasts to 24 months.
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