Wheat Yield Prediction Using Remotely Sensed Agromet Trend-Based Models for Hoshiarpur District of Punjab, India

Estimation of crop production in advance of the harvest has been an intensively researched field in agriculture. The aim of present study was to predict wheat yield using different agrometeorological indices, spectral index (NDVI, Normalized Difference Vegetation Index) and Trend Estimated Yield (TEY) in Hoshiarpur district of Punjab for the years 2001-02 and 2002-03. On the basis of examination of Correlation Coefficients (R), Standard Error of Estimate (SEOE) and Relative Deviation (RD) values resulted from different agromet models, the best agromet subset were selected as Minimum Temperature (T_min), Maximum Temperature (T_max) and Accumulated Heliothermal Units (HTU) for Hoshiarpur district. In order to improve model accuracy the above mentioned agrometeorological indices together with NDVI and TEY were used as independent variables for yield prediction at reproductive stage (2nd week of March) of wheat. It was found that Agromet-Spectral-Trend-Yield model could explain 96% (SEOE = 87 kg ha^-1) of wheat yield variations for Hoshiarpur district.