AN EARLY WARNING SYSTEM FOR AGRICULTURAL DROUGHT IN AN ARID REGION USING LIMITED DATA

In this paper, an early warning system for agricultural drought in an arid region (Jodhpur district, Rajasthan, India) is developed by estim ating grain yield of pearl millet, the major food crop in the region. Two multiple linear regression models, namely the intermediate warning (IW) model that estimates the yield about a month before the crop is harvested, and the final warning (FW) model that estimates the yield a t about harvest time, are developed for the purpose. Explanatory varia bles used in the models are delay in crop sowing, monthly rainfall, an d number of rainy days in a month. These variables are derived from th e daily rainfall data which are easily available in acid regions. Usin g the 1963-1987 data for model development, up to 74% and 81% yield va riations could be explained by the IW and FW models, respectively. Whe n these models were tested on the 1988-1991 data, mean absolute per ce nt error in the estimated yields was found to be 18.5% and 11.2% for t he IW and the FW model, respectively. Models are evaluated and some me asures for their improvement discussed. (C) 1998 Academic Press.