First Soybean Multi-model Sensitivity Analysis to CO2, Temperature, Water, and Nitrogen (CTWN)
Kritika Kothari, Montserrat Salmeron, Rafael Battisti, Kenneth Boote, Sotirios Archontoulis, Adriana Confalone, Julie Constantin, Santiago Cuadra, Philippe Debaeke, Babacar Faye, Brian Grant, Gerrit Hoogenboom, Qi Jing, Michael van der Laan, Fernando Macena, Fabio Marin, Alireza Nehbandani, Claas Nendel, Larry Purcell, Budong Qian, Alex Ruane, Evandro da Silva, Ward Smith, Afshin Soltani, Amit Srivastava, Nilson Vieira Jr. Poster: First Soybean Multi-model Sensitivity Analysis to CO2, Temperature, Water, and Nitrogen (CTWN). ICROPM 2020, Montpellier, France, Feb 3-5, 2020.
Coordinated multi-model comparisons play a crucial role in evaluating model uncertainties and identifying avenues for model improvement. Soybean is a major legume, with a production of 353 million tons worldwide (FAO, 2017). Previous results from global gridded models indicate high uncertainty in future yield projections. A multi-model comparison study in soybean is needed to identify limitations and strengths in current models that address future food projections under climate change. A study was undertaken to test whether or not the amount of data used for model calibration reduces error in simulating grain yield and biomass and to evaluate uncertainties and differences in multi-model responses to variation in atmospheric CO2, Temperature, Water, and Nitrogen (CTWN) factors. It was found that increasing number of models in the ensemble reduced error in grain yield predictions, consistent with previous studies. Large variations in the rate of responses to increasing CO2 and temperature across models may be embedded in model structures and will not be influenced by calibration. The preliminary results suggest that uncertainty/variability in yield projections under climate change may be more influenced by different responses of individual models, than by the amount of data used for calibration.