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 CO₂ concentration [CO₂]. 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 [CO₂] and temperature.

There is some evidence that rice cultivars respond differently to elevated CO2 concentrations ([CO2]), but [CO2] × cultivar interaction has never been tested under open-field conditions across different sites. Here, we report on trials conducted at free-air CO2 enrichment (FACE) facilities at two sites in Japan, Shizukuishi (2007 and 2008) and Tsukuba (2010). The average growing-season air temperature was more than 5°C warmer at Tsukuba than at Shizukuishi. For four cultivars tested at both sites, the [CO2] × cultivar interaction was significant for brown rice yield, but there was no significant interaction with site-year. Higher-yielding cultivars with a large sink size showed a greater [CO2] response. The Tsukuba FACE experiment, which included eight cultivars, revealed a wider range of yield enhancement (3–36%) than the multi-site experiment. All of the tested yield components contributed to this enhancement, but there was a highly significant [CO2] × cultivar interaction for percentage of ripened spikelets. These results suggest that a large sink is a prerequisite for higher productivity under elevated [CO2], but that improving carbon allocation by increasing grain setting may also be a practical way of increasing the yield response to elevated [CO2].

• Background and Aims The morphogenesis and architecture of a rice plant, Oryza sativa, are critical factors in the yield equation, but they are not well studied because of the lack of appropriate tools for 3D measurement. The architecture of rice plants is characterized by a large number of tillers and leaves. The aims of this study were to specify rice plant architecture and to find appropriate functions to represent the 3D growth across all growth stages. • Methods A japonica type rice, ‘Namaga’, was grown in pots under outdoor conditions. A 3D digitizer was used to measure the rice plant structure at intervals from the young seedling stage to maturity. The L-system formalism was applied to create ‘3D virtual rice’ plants, incorporating models of phenological development and leaf emergence period as a function of temperature and photoperiod, which were used to determine the timing of tiller emergence. • Key Results The relationships between the nodal positions and leaf lengths, leaf angles and tiller angles were analysed and used to determine growth functions for the models. The ‘3D virtual rice’ reproduces the structural development of isolated plants and provides a good estimation of the tillering process, and of the accumulation of leaves. • Conclusions The results indicated that the ‘3D virtual rice’ has a possibility to demonstrate the differences in the structure and development between cultivars and under different environmental conditions. Future work, necessary to reflect both cultivar and environmental effects on the model performance, and to link with physiological models, is proposed in the discussion.

Reliable estimates of the impacts of climate change on crop production are critical for assessing the sustainability of food systems. Global, regional, and site-specific crop simulation studies have been conducted for nearly four decades, representing valuable sources of information for climate change impact assessments. However, the wealth of data produced by these studies has not been made publicly available. Here, we develop a global dataset by consolidating previously published meta-analyses and data collected through a new literature search covering recent crop simulations. The new global dataset builds on 8703 simulations from 202 studies published between 1984 and 2020. It contains projected yields of four major crops (maize, rice, soybean, and wheat) in 91 countries under major emission scenarios for the 21st century, with and without adaptation measures, along with geographical coordinates, current temperature and precipitation levels, projected temperature and precipitation changes. This dataset provides a solid basis for a quantitative assessment of the impacts of climate change on crop production and will facilitate the rapidly developing data-driven machine learning applications.