Biases in simulation of the rice phenology models when applied in warmer climates

Abstract:

The current model inter-comparison studies highlight the difference in projections between crop models when they are applied to warmer climates, but these studies do not provide results on how the accuracy of the models would change in these projections because the adequate observations under largely diverse growing season temperature (GST) are often unavailable. Here, we investigate the potential changes in the accuracy of rice phenology models when these models were applied to a significantly warmer climate. We collected phenology data from 775 trials with 19 cultivars in 5 Asian countries (China, India, Philippines, Bangladesh and Thailand). Each cultivar encompasses the phenology observations under diverse GST regimes. For a given rice cultivar in different trials, the GST difference reaches 2.2 to 8.2°C, which allows us to calibrate the models under lower GST and validate under higher GST (i.e., warmer climates). Four common phenology models representing major algorithms on simulations of rice phenology, and three model calibration experiments were conducted. The results suggest that the bilinear and beta models resulted in gradually increasing phenology bias (Figure) and double yield bias per percent increase in phenology bias, whereas the growing-degree-day (GDD) and exponential models maintained a comparatively constant bias when applied in warmer climates (Figure). Moreover, the bias of phenology estimated by the bilinear and beta models did not reduce with increase in GST when all data were used to calibrate models. These suggest that variations in phenology bias are primarily attributed to intrinsic properties of the respective phenology model rather than on the calibration dataset. Therefore we conclude that using the GDD and exponential models has more chances of predicting rice phenology correctly and thus, production under warmer climates, and result in effective agricultural strategic adaptation to and mitigation of climate change.