Soybean multi‐model sensitivity analysis for prediction of seed nitrogen, biological N fixation, and N cycling
Montse Salmeron Cortasa, Kritika Kothari, 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. Soybean multi‐model sensitivity analysis for prediction of seed nitrogen, biological N fixation, and N cycling. ICROPM 2020, Montpellier, France, Feb 3-5, 2020
There is limited work on model evaluation for prediction of seed protein and N2-fixation in soybean production. The objectives of the study were to calibrate soybean models under baseline conditions with data from 5 locations worldwide and to quantify model uncertainty under variable atmospheric [CO2], Temperature, Water, and N fertilizer (CTWN sensitivity analysis). Model error due to a systematic over-prediction of total aboveground and seed N content (kg N ha-1), and seed protein (%) was reduced in most models after full calibration. Optimization of critical seed N concentration to minimize error in seed protein (%) may reduce bias but not improve predictability across environments -> need of more multi-environment data with same genotypes. N2-fixation showed the expected response: increased due to greater photosynthesis under high CO2, and decreased under increasing N fertilizer applications. However, the rate of response was widely variable across models. Differences in how models predict N demand for seed growth may introduce high uncertainty in model responses to CO2, Temperature, and N fertilizer.