First Soybean Multi-model Sensitivity Analysis to CO 2 , Temperature, Water, and Nitrogen

Abstract : Crop model responses to changes in climatic and management factors could be highly variable due to variation in model coding and prior calibration with limited observed data, thus introducing high uncertainty in food projections. Coordinated multi-model comparison studies play a crucial role in evaluating model uncertainty and improving existing models. The present study is the first model intercomparison effort on a legume, soybean (Glycine max L. (Merr.)), with special emphasis on the crop N processes. Previous multi-model studies in cereal grain crops identified the value of model ensembles to reduce uncertainties in yield projections (Bassu et al., 2014; Martre et al., 2015). Interestingly, model responses to changes in temperature and atmospheric carbon dioxide concentration [CO 2 ] were not affected by the level of observed data available for calibration (Bassu et al., 2014). We propose to test: 1) whether soybean multi-model ensembles are better estimators of yield, crop growth and grain nitrogen concentration (grain N%) than individual models; and 2) whether the level of observed data available during calibration influences model responses to variation in climatic and N fertilization factors ([CO 2 ], Temperature, Water, and Nitrogen; CTWN sensitivity analysis).