Using daily data from seasonal forecasts in dynamic crop models for yield prediction: A case study for rice in Nepal’s Terai

Abstract

Skillful seasonal climate predictions paired with a dynamic crop model can assist agricultural management and help farmers minimize risk. For crop yield predictions, the skill in generating realistic distributions of weather for the crop growing season matters more than the skill of forecasting the mean seasonal climate itself. In this regard, the ensemble of daily fields of the Seasonal Prediction Systems (SPSs) output could be a potential alternative to other methods that are available to generate daily weather from the monthly or seasonal mean forecasts. However, the SPSs are not expected to forecast individual weather events at a given grid point (deterministic forecast), but if the statistics of the predicted weather are correct, an ensemble of yield predictions using individual realizations of the ensemble seasonal forecast may produce a more skillful yield forecast. So far, the potential of this new approach has not been tested. The goal of this study was to determine the potential applicability of using daily data from SPSs to predict rice yield through a case study of Nepal’s Terai. The study used 28 years (1983–2010) daily hindcasts of the coupled forecast system model version 2 (CFSv2) SPS into a Cropping System Model (CSM)-CERES-Rice. The hindcasts of the CFSv2, initialized at different lead times, were used in various ways to simulate rice yield, which were then compared to the reference yield and to the simulated yield using climatology alone to examine the predictive skill at different lead times. The results from this study indicate that unlike the typical ensemble averaging approach commonly used in seasonal climate forecasting, averaging the simulated yield using individual member does not guarantee better yield prediction. Further analyses should be made, including alternative downscaling methods as well as a similar analysis for an area where quality meteorological and agricultural data are available and where the seasonal forecasts exhibit better skill.