Over time, the stimulative effect of elevated CO2 on the photosynthesis of rice crops is likely to be reduced with increasing duration of CO2 exposure, but the resultant effects on crop productivity remain unclear. To investigate seasonal changes in the effect of elevated CO2 on the growth of rice (Oryza sativa L.) crops, a free air CO2 enrichment (FACE) experiment was conducted at Shizukuishi, Iwate, Japan in 1998–2000. The target CO2 concentration of the FACE plots was 200 µmol mol−1 above that of ambient. Three levels of nitrogen (N) were supplied: low (LN, 4 g N m−2), medium [MN, 8 (1998) and 9 (1999, 2000) g N m−2] and high N (HN, 12 and 15 g N m−2). For MN and HN but not for LN, elevated CO2 increased tiller number at panicle initiation (PI) but this positive response decreased with crop development. As a result, the response of green leaf area index (GLAI) to elevated CO2 greatly varied with development, showing positive responses during vegetative stages and negative responses after PI. Elevated CO2 decreased leaf N concentration over the season, except during early stage of development. For MN crops, total biomass increased with elevated CO2, but the response declined linearly with development, with average increases of 32, 28, 21, 15 and 12% at tillering, PI, anthesis, mid‐ripening and grain maturity, respectively. This decline is likely to be due to decreases in the positive effects of elevated CO2 on canopy photosynthesis because of reductions in both GLAI and leaf N. Up to PI, LN‐crops tended to have a lower response to elevated CO2 than MN‐ and HN‐crops, though by final harvest the total biomass response was similar for all N levels. For MN‐ and HN‐crops, the positive response of grain yield (ca. 15%) to elevated CO2 was slightly greater than the response of final total biomass while for LN‐crops it was less. We conclude that most of the seasonal changes in crop response to elevated CO2 are directly or indirectly associated with N uptake.

Summary• The effects of elevated CO 2 are reported here on the uptake of nitrogen (N) and its relationships with growth and grain yield in rice ( Oryza sativa ).• Using free-air CO 2 enrichment (FACE), rice crops were grown at ambient or elevated ( c . 300 µ mol mol -1 above ambient) CO 2 and supplied with low, medium or high levels of N.• For the medium and high N treatments, FACE increased N uptake at panicle initiation but not at maturity. For total dry matter, as well as spikelet number and grain yield, positive interactions between CO 2 and N uptake were observed. Furthermore, spikelet number was closely associated with N uptake at panicle initiation.• These results indicate that, to maximize rice grain yield under elevated CO 2 , it is important to supply sufficient N over the whole season, in order to maintain the enhancement in dry matter production. In addition, N availability must be coordinated with the developmental stage of the crop, specifically to ensure that sufficient N is available at panicle initiation in order to maximize spikelet number and grain yield.

Summary• A free air CO 2 enrichment (FACE) system in which rice was grown under elevated CO 2 conditions by releasing high pressure, pure CO 2 from emission tubes surrounding the crop is described here. Unlike other (FACE) systems, blowers were not used to mix the emitted CO 2 with the surrounding air.• Four 12-m diameter emission structures ('rings') were constructed. Monitoring and control of CO 2 emission was carried out by a series of CO 2 and wind sensors, data loggers, controllers and valves. The target CO 2 concentration ( [CO 2 ] ) was 200 µ mol mol -1 above ambient; enrichment was carried out continuously.• Temporal [CO 2 ] control was adequate, with c . 60 and 90% of the air samples at ring center having a [CO 2 ] within 10 and 20% of the target, respectively. Spatial [CO 2 ] distribution was also adequate, with 60% of the ring area having a [CO 2 ] that was within 15% of that at the center.• At comparable wind speeds, the pure CO 2 injection FACE system described here had a similar performance to that of FACE designs that use blowers to mix the injected CO 2 with the air.

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N O emissions. Yield-scaled N O emissions (N O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N O emissions at field scale is discussed.

No abstract