Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

Abstract

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO 2 concentration [CO 2 ]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO 2 ] and temperature.

Lessons from climate modeling on the design and use of ensembles for crop modeling

“…Many MME studies with crop models have remarked that the mean or median of the simulated values seems to give good agreement with observed yields (Asseng et al 2014;Bassu et al 2014;Li et al 2015;Palosuo et al 2011). Martre et al (2015) found that when multiple outputs are considered, the mean and median were both better predictors than even the best individual model, with the median being slightly better than the mean.…”

“…One approach with crop models has been to identify models that have similar equations for underlying processes, such as photosynthesis. In general, however, it has not been found that structural similarity leads to similarity in simulated values in crop MMEs (Palosuo et al 2011;Martre et al 2015;Li et al 2015). An alternative approach, proposed for climate models (Bishop and Abramowitz 2013), is to examine the covariance in model errors as the measure of model dependence.…”

“…Secondly, in impact assessment studies, the uncertainty in the climate projections must be taken into account. This has led to running crop models with multiple future climates (some recent examples are Asseng et al 2013;Li et al 2015).…”

“…These include multiple crop models with multiple climate models (Mearns et al 1999;Asseng et al 2013;Li et al 2015), multiple crop and climate models with multiple parameters (Tao et al 2009) and multiple parameter values with multiple values for inputs (Aggarwal 1995).…”

Uncertainty in simulating biomass yield and carbon–water fluxes from grasslands under climate change

“…This has urged for benchmarking actions at an international level, where estimation of processoriented epistemic uncertainties is done by running several models supposed to simulate the same reality (ensemble modelling) so as to generate an expanded envelope of uncertainty (Asseng et al, 2013;Bassu et al, 2014;Li et al, 2014). We address the same issues with grassland ecosystems in Europe and Israel, with focus on permanent, semi-natural or sown grasslands under management for at least 5 years, composed of multiple plant species.…”