Improving Rice Models for More Reliable Prediction of Responses of Rice Yield to CO2 and Temperature Elevation
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Date
2016
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Abstract
Increased CO2 concentration and air temperature are two very important variables associated with global warming and climate change. Assessing the putative impacts of these factors on rice production is crucial for global food security due to rice being the staple food for more than half of the world population. Rice crop models are useful for predicting rice productivity under climate change. However, model predictions have uncertainties arisen due to the inaccurate inputs and the varying capabilities of models to capture yield performance. A series of modeling activities were implemented by the AgMIP Rice Team (consisting of 16 rice models currently) to improve the model capability for reducing the uncertainties of model prediction.