AgMIP Soybean Phase 1: Multi-Model Sensitivity Analysis to CO2, Temperature, Water, and Nitrogen


Kritika Kothari, Montse Salmeron Cortasa, Rafael Battisti, Kenneth J. 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 C. Purcell, Budong Qian, Alex Ruane, Evandro da Silva, Ward Smith, Afshin Soltani, Amit Srivastava, Nilson Vieira. 2019. AgMIP Soybean Phase 1: Multi-Model Sensitivity Analysis to CO2, Temperature, Water, and Nitrogen. ASA, CSSA and SSSA International Annual Meetings, San Antonio, Texas, Nov 10-13, 2019.


Soybean is an important legume crop and its demand is expected to increase in the future. Future projections from global crop models show high uncertainty in soybean yield. Key factors in yield uncertainty include model responses to environmental factors (temperature, rainfall, CO2) and biological nitrogen fixation. The goal of the study was to test multi-model responses to changes in CO2, Temperature, Water, Nitrogen (CTWN), after two levels of calibration (phenology-only vs. full growth data). We quantify the error in estimation of observed variables such as yield, biomass, and grain N. and examine uncertainty in CTWN responses. A total of 10 soybean models were calibrated (against phenology) before generating CTWN responses. Model performance after blind calibration produced high RMSE in estimating grain yield, biomass, and grain nitrogen that was dependent on the model and location. The shape of CTWN response curve, yield response to Temperature and CO2, varied largely across models and locations. The error of the multi-model-ensemble was lower than the average error across models.