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

The CO 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 [CO] (E-[CO]) by comparison to free-air CO enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well.

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

Methane (CH) 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 CH emission from paddy field. However, these EFs did not consider the effects of factors that influence CH emission (e.

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.

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.

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 ].

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.