Forecasting rainfed rice yield with biomass of early phenophases, peak intercepted PAR and ground based remotely sensed vegetation indices

Rice is the main staple food of country but crop productivity in some years declines due to erratic monsoon and non-uniformity spatial temporal distribution rainfall. Hence, assessing rice advance using meteorological plant physiological attributes will be helpful for planners take decision on contingency measures. In this investigation, dry biomass early phenophases (active tillering, panicle initiation, boot leaf stages, flowering),peak intercepted photosynthetically active radiation (IPAR), peak spectral reflectance based vegetation indices 3 varieties under nitrogen levels (50, 100 130 kg ha-1) were made correlated with grain yield.Based interrelationship it was found that flowering period better yield R2 value 0.75.Inter-relationship between IPAR(%), remotely sensed simple ratio index (IR/R) normalized difference (NDVI)with also established. Multiple regression modelwas developed by interrelating as dependant variable stage, IPAR(%) IR/R NDVI independent variables which may used an effective tool prediction yield, at least 30-40 days advance. The estimated through algorithm MODIS satellite derived compared actual yield.