Evaluating the use of extended range forecasts in DSSAT for predicting rice yield: A case study of Madhya Pradesh, India

This study evaluates the potential of Extended Range Forecasts (ERFs) in improving rainfed rice yield simulations during three kharif seasons (2019–2021) using the DSSAT v4.8 model for Madhya Pradesh. Three weather datasets were evaluated: (1) observed weather, (2) observed + ERF + climatological normal and (3) observed + climatological normal. The ERF generated as weekly interval during the crop season with a total of 19 initial conditions (IC) were used for ERF dataset. The yields simulated using hybrid datasets (2 & 3) were related with those obtained with the observed weather data (1). Results indicated that integrating ERFs during the reproductive and ripening phases improves yield simulations, with the most notable improvements observed in 2021. However, benefits varied across seasons and growth phases. The findings highlight the potential of ERFs to enhance seasonal yield forecasts when applied strategically, particularly by bridging observed data and climatological normal during key crop phases.