Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments.

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.

Estimation of total CH4 emission from Japanese rice paddies using a new estimation method based on the DNDC-Rice simulation model.

Methane (CH4) is a greenhouse gas, and paddy fields are one of its main anthropogenic sources. In Japan, country-specific emission factors (EFs) have been applied since 2003 to estimate national-scale CH4 emission from paddy field. However, these EFs did not consider the effects of factors that influence CH4 emission (e.g., amount of organic C inputs, field drainage rate, climate) and can therefore produce estimates with high uncertainty. To improve the reliability of national-scale estimates, we revised the EFs based on simulations by the DeNitrification-DeComposition-Rice (DNDC-Rice) model in a previous study. Here, we estimated total CH4 emission from paddy fields in Japan from 1990 to 2010 using these revised EFs and databases on independent variables that influence emission (organic C application rate, paddy area, proportions of paddy area for each drainage rate class and water management regime). CH4 emission ranged from 323 to 455ktCyr-1 (1.1 to 2.2 times the range of 206 to 285ktCyr-1 calculated using previous EFs). Although our method may have overestimated CH4 emissions, most of the abovementioned differences were presumably caused by underestimation by the previous method due to a lack of emission data from slow-drainage fields, lower organic C inputs than recent levels, neglect of regional climatic differences, and underestimation of the area of continuously flooded paddies. Our estimate (406ktC in 2000) was higher than that by the IPCC Tier 1 method (305ktC in 2000), presumably because regional variations in CH4 emission rates are not accounted for by the Tier 1 method.

Prediction of future methane emission from irrigated rice paddies in central Thailand under different water management practices.

There is concern about positive feedbacks between climate change and methane (CH4) emission from rice paddies. However, appropriate water management may mitigate the problem. We tested this hypothesis at six field sites in central Thailand, where the irrigated area is rapidly increasing. We used DNDC-Rice, a process-based biogeochemistry model adjusted based on rice growth data at each site to simulate CH4 emission from a rice-rice double cropping system from 2001 to 2060. Future climate change scenarios consisting of four representative concentration pathways (RCPs) and seven global climate models were generated by statistical downscaling. We then simulated CH4 emission in three water management practices: continuous flooding (CF), single aeration (SA), and multiple aeration (MA). The adjusted model reproduced the observed rice yield and CH4 emission well at each site. The simulated CH4 emissions in CF from 2051 to 2060 were 5.3 to 7.8%, 9.6 to 16.0%, 7.3 to 18.0%, and 13.6 to 19.0% higher than those from 2001 to 2010 in RCPs 2.6, 4.5, 6.0, and 8.5, respectively, at the six sites. Regionally, SA and MA mitigated CH4 emission by 21.9 to 22.9% and 53.5 to 55.2%, respectively, relative to CF among the four RCPs. These mitigation potentials by SA and MA were comparable to those from 2001 to 2010. Our results indicate that climate change in the next several decades will not attenuate the quantitative effect of water management practices on mitigating CH4 emission from irrigated rice paddies in central Thailand.

Development of a method for estimating total CH4 emission from rice paddies in Japan using the DNDC-Rice model.

Methane (CH4) is a greenhouse gas, and paddy fields are one of its main anthropogenic emission sources. To mitigate this emission based on effective management measures, CH4 emission from paddy fields must be quantified at a national scale. In Japan, country-specific emission factors have been applied since 2003 to estimate national CH4 emission from paddy fields. However, this method cannot account for the effects of weather conditions and temporal variability of nitrogen fertilizer and organic matter application rates; thus, the estimated emission is highly uncertain. To improve the accuracy of national-scale estimates, we calculated country-specific emission factors using the DeNitrification-DeComposition-Rice (DNDC-Rice) model. First, we calculated CH4 emission from 1981 to 2010 using 986 datasets that included soil properties, meteorological data, and field management data. Using the simulated site-specific emission, we calculated annual mean emission for each of Japan's seven administrative regions, two water management regimes (continuous flooding and conventional mid-season drainage), and three soil drainage rates (slow, moderate, and fast). The mean emission was positively correlated with organic carbon input to the field, and we developed linear regressions for the relationships among the regions, water management regimes, and drainage rates. The regression results were within the range of published observation values for site-specific relationships between CH4 emission and organic carbon input rates. This suggests that the regressions provide a simplified method for estimating CH4 emission from Japanese paddy fields, though some modifications can further improve the estimation accuracy.

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

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.

An empirical model of soil chemical properties that regulate methane production in Japanese rice paddy soils.

To understand which soil chemical properties are the best predictors of CH4 production in rice paddy soils, a model was developed with empirical data from nine types of rice soils collected around Japan and anaerobically incubated at 30 degrees C for 16 wk in laboratory conditions. After 1, 2, 4, 8, and 16 wk of incubation, CO2, CH4, and Fe(II) were measured to understand soil organic matter decomposition and iron (Fe) reduction. Available N (N ava) was also measured at the end of incubation. The results showed that decomposable C and reducible Fe are two key parameters that regulate soil CH(4) production (P CH4). There was a significant relationship between decomposable C and available N (N ava) (r2 = 0.975**). Except for a sandy soil sample, a significant relationship between total Fe (Fe total) and reducible Fe was found. From this experiment, a simple model of soil CH4 production was developed: P CH4 = 1.593N(ava) - 2.460Fe total/1000 (each unit was mg kg(-1) soil). After simulated CH4 production by two soil chemical properties as above, there was a significant consistency between model simulation and actual measurement (r2 = 0.831**).