Abstract. There is some evidence that rice cultivars respond differently to elevated CO 2 concentrations ([CO 2 ]), but [CO 2 ] Â cultivar interaction has never been tested under open-field conditions across different sites. Here, we report on trials conducted at free-air CO 2 enrichment (FACE) facilities at two sites in Japan, Shizukuishi (2007 and2008) 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 [CO 2 ] Â 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 [CO 2 ] 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 [CO 2 ] Â cultivar interaction for percentage of ripened spikelets. These results suggest that a large sink is a prerequisite for higher productivity under elevated [CO 2 ], but that improving carbon allocation by increasing grain setting may also be a practical way of increasing the yield response to elevated [CO 2 ].

The CO2 fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO2] (E-[CO2]) by comparison to free-air CO2 enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well. However, yield prediction in response to E-[CO2] varied significantly among the rice models. The variation was not random: models that overestimated at one experiment simulated greater yield enhancements at the others. The variation was not associated with model structure or magnitude of photosynthetic response to E-[CO2] but was significantly associated with the predictions of leaf area. This suggests that modelled secondary effects of E-[CO2] on morphological development, primarily leaf area, are the sources of model uncertainty. Rice morphological development is conservative to carbon acquisition. Uncertainty will be reduced by incorporating this conservative nature of the morphological response to E-[CO2] into the models. Nitrogen levels, particularly under limited situations, make the prediction more uncertain. Improving models to account for [CO2] × N interactions is necessary to better evaluate management practices under climate change.

Enhancing crop yield response to elevated CO 2 concentrations (E-[CO 2 ]) is an important adaptation measure to climate change. A high-yielding indica rice cultivar “Takanari” has recently been identified as a potential candidate for high productivity in E-[CO 2 ] resulting from its large sink and source capacities. To fully utilize these traits, nitrogen should play a major role, but it is unknown how N levels influence the yield response of Takanari to E-[CO 2 ]. We therefore compared grain yield and quality of Takanari with those of Koshihikari, a standard japonica cultivar, in response to Free-Air CO 2 enrichment (FACE, +200 μmol mol −1 ) under three N levels (0, 8, and 12 g m −2 ) over three seasons. The biomass of both cultivars increased under E-[CO 2 ] at all N levels; however, the harvest index decreased under E-[CO 2 ] in the N-limited treatment for Koshihikari but not for Takanari. The decreased harvest index of Koshihikari resulted from limited enhancement of spikelet number under N-limitation. In contrast, spikelet number increased in E-[CO 2 ] in Takanari even without N application, resulting in significant yield enhancement, averaging 18% over 3 years, whereas Koshihikari exhibited virtually no increase in yield in E-[CO 2 ] under the N-limited condition. Grain appearance quality of Koshihikari was severely reduced by E-[CO 2 ], most notably in N-limited and hot conditions, by a substantial increase in chalky grain, but chalky grain % did not increase in E-[CO 2 ] even without N fertilizer. These results indicated that Takanari could retain its high yield advantage over Koshihikari with limited increase in chalkiness even under limited N conditions and that it could be a useful genetic resource for improving N use efficiency under E-[CO 2 ].

Genomic prediction (GP) is expected to become a powerful technology for accelerating the genetic improvement of complex crop traits. Several GP models have been proposed to enhance their applications in plant breeding, including environmental effects and genotypeby-environment interactions (G×E). In this study, we proposed a two-step model for plant biomass prediction wherein environmental information and growth-related traits were considered. First, the growth-related traits were predicted by GP. Second, the biomass was predicted from the GP-predicted values and environmental data using machine learning or crop growth modeling. We applied the model to a 2-year-old field trial dataset of recombinant inbred lines of japonica rice and evaluated the prediction accuracy with training and testing data by cross-validation performed over two years. Therefore, the proposed model achieved an equivalent or a higher correlation between the observed and predicted values (0.53 and 0.65 for each year, respectively) than the model in which biomass was directly predicted by GP (0.40 and 0.65 for each year, respectively). This result indicated that including growthrelated traits enhanced accuracy of biomass prediction. Our findings are expected to contribute to the spread of the use of GP in crop breeding by enabling more precise prediction of environmental effects on crop traits.

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.

The aims of this study are to 1) examine the relationships between various indices of rice quality and agro-climatological elements, and to 2) contribute to the evaluation of projecting climate change impact on rice quality in Japan. We used a large collection of rice quality dataset from the Crop Survey database provided ad hoc by the Ministry of Agriculture, Forestry and Fisheries of Japan. Firstly, we conducted statistical analysis to determine the interrelationships among the various indices of rice quality such as the percentages of the first-grade rice, undamaged rice and chalky grain, then also analyzed their relationships with surface meteorological elements and their composite indices. The results showed that the indices of rice qualities were well correlated with a heat stress index HD_m26, that is a cumulative deviation of daily averaged surface air temperature from a base of 26 °C during the 20days after heading. Especially, the percentage of chalky grain was better explained in relation with the HD_m26 than that of simple 20 days-averaged air temperature during the corresponding period. In addition, the Quantile Regression analysis was applied to regress the percentage of chalky grain based on the HD_m26. Although the result showed the significant linear, quadratic and cubic curves regression in each percentile values of the chalky grain with the zone of HD_m26 values, varietal differences were perceived in these statistical relationships.

Rice is the most important cereal crop in Japan, and therefore the impact of projected climate change on its production and quality has been assessed using rice growth models accounting for the effects of rising temperature and atmospheric CO 2 concentration [ CO 2 ] on important growth processes. Recent experimental studies, however, have shown some negative effects of interactions between [ CO 2 ] and temperature on yield and quality of rice which were not accounted for by previous impact assessments. This study examined the importance of [ CO 2 ] temperature interactions in the nationwide impacts of climate change on grain yield and quality of rice in Japan by 2100. We introduced new functions accounting for the effects of interactions on yield. Then we adopted the acceleration by elevated [ CO 2 ] in the estimation of the occurrence of chalky grains, an indicator of appearance quality of rice. We applied the modified model to Japan at a spatial resolution of 1 km using 10 climate scenarios 5 Global Circulation Models 2 representative concentration pathways [ RCPs ] from 1981 to 2100. The effects of the newly introduced negative effects of [ CO 2 ] temperature were evaluated by comparing simulations with and without the interaction in each scenario. Nationwide production was estimated to decrease by up to 28 and the percentage of white chalky grains to increase up to 16 relative to the previous assessment results, especially in RCP8.5, in which larger increases were projected in both temperature and [ CO 2 ] . The result suggests that the positive effect of elevated [ CO 2 ] , which had been expected to offset the negative effect of increased temperature on rice productivity, may be limited in the future, and rice quality degradation may be more severe than predicted previously.

Food security faces increasing threats from anthropogenic climate change (Wheeler & von Braun, 2013), with current assessment methods predicting between 8 and 80 million additional people at risk of hunger by 2050 (Mbow et al., 2019). While there is general consensus that climate change will undermine food security by causing production losses (Tomoko Hasegawa et al., 2021;Zhu et al., 2021), there is little consensus on the magnitude and even direction of yield response to different climate change factors, as well as the extent to which yield losses can be mitigated by adaptive management. Model projections of yield changes also vary widely from study to study and from region to region, and there is therefore an urgent need to provide reliable large-scale but regionally