Abstract

The economy and livelihood of the State of West Bengal in India are mainly dependent on agriculture. Rice is one of the major crops of this state, and it contributes a significant proportion to the rice production of India. The present study deals with the prediction of Kharif rice over Gangetic ... Show moreThe economy and livelihood of the State of West Bengal in India are mainly dependent on agriculture. Rice is one of the major crops of this state, and it contributes a significant proportion to the rice production of India. The present study deals with the prediction of Kharif rice over Gangetic West Bengal using forecast products from IITM-IMD extended range prediction (ERP) system. It is a multi-model ensemble prediction system that comprises 16 different ensemble members obtained from the Climate Forecast System (CFSv2) and the stand-alone atmospheric component of CFSv2 (i.e., GFSv2). The performance of ERP rainfall forecast has been assessed over a relatively larger domain covering Indian landmass before its utilization in crop yield prediction. Satisfactory skills, e.g., higher correlation and lower normalized root mean squared error (nRMSE), have been found in ERP rainfall forecast during the first 3 weeks for most of the initial conditions (ICs). Next, bias-corrected ERP weekly forecast data of incoming solar radiation, rainfall, and maximum and minimum temperatures have been incorporated into a process-based crop model (CERES-rice) available in the Decision Support System for Agro-technology Transfer (DSSAT). ERP-driven crop model has performed better to reproduce inter-annual variability of observed rice yield compared to yield simulated using crop model driven by climatology alone. Also, the ERP-driven model has been able to capture the below- and above-normal yield categories relatively better than the climatology-driven model. Hence, the incorporation of ERP in crop models may provide value-added prediction, which will be helpful for the stakeholders and decision-makers. Show less