Multiple model averaging methods for predicting regional rice yield

Abstract

AbstractUsing multiple crop models in an ensemble can generate more accurate crop growth predictions than only using an individual crop model. However, few studies have investigated the performances of individual crop models and multiple model averaging (MMA) methods for predicting regional rice (Oryza sativa L.) yield with limited inputs and observations. This study assessed the performances of three individual crop models, that is, AquaCrop, WOFOST, and Oryza version 3 (OryzaV3), and five MMA methods for predicting regional rice yield. Results showed that the AquaCrop model achieved better performances than the WOFOST and OryzaV3 models for predicting regional rice yield, especially for the early‐season rice yield, which implied that a crop model with a simple structure was powerful for making accurate regional rice yield predictions. The MMA methods achieved better performances than the AquaCrop model for predicting the late‐season rice yield; however, the AquaCrop model and three information criterion (IC) methods, that is, Akaike IC (AIC), Bayesian IC (BIC), and AICc (bias‐corrected), achieved more acceptable prediction accuracies of the early‐season rice yield than the other MMA methods. Overall, not all the MMA methods could necessarily produce more accurate regional rice yield prediction than individual crop models. This study suggested using the simple‐structure crop models and the AIC, AICc, and BIC methods for making accurate regional crop yield prediction when limited data are available for model parameters calibration.