Inter-comparison of soybean models for the simulation of evapotranspiration in a humid continental climate

Authors: FIGUEIREDO MOURA DA, EVANDRO - Federal University Of Sao Paulo; DA SILVA, MOURA - Federal University Of Sao Paulo; KOTHARI, KRITIKA - Indian Institute Of Technology; PATTEY, ELIZABETH - Agriculture And Agri-Food Canada; BATTISTI, RAFAEL - Universidade Federal De Alagoas; BOOTE, KENNETH - University Of Florida; ARCHONTOULIS, SOLTIRIOS - Iowa State University; CUADRA, SANTIAGO - Brazilian Agricultural Research Corporation (EMBRAPA); FAYE, BABACAR - University Of Sine-Saloum El Hadji Ibrahima Niasse; GRANT, BRIAN - Agriculture And Agri-Food Canada; HOOGENBOOM, GERRIT - University Of Florida; JING, QI - Agriculture And Agri-Food Canada; MARIN, FABIO - Federal University Of Sao Paulo; NENDEL, CLAAS - University Of Potsdam; QIAN, BUDONG - Agriculture And Agri-Food Canada; SMITH, WARD - Agriculture And Agri-Food Canada; SRIVASTAVA, AMIT - University Of Bonn; Thorp, Kelly; VIEIRA, NISON - Wageningen University And Research Center; SALMERON, MONSERRAT - University Of Kentucky

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/18/2025
Publication Date: 3/1/2025
Citation: Figueiredo Moura da Silva, Kothari, K., Pattey, E., Battisti, R., Boote, K.J., Archontoulis, S.V., Cuadra, S.V., Faye, B., Grant, B., Hoogenboom, G., Jing, Q., Marin, F.R., Nendel, C., Qian, B., Smith, W., Srivastava, A., Thorp, K.R., Vieira, N.A., Salmeron, M. 2025. Inter-comparison of soybean models for the simulation of evapotranspiration in a humid continental climate. Agricultural and Forest Meteorology. 365.110463. https://doi.org/10.1016/j.agrformet.2025.110463.
DOI: https://doi.org/10.1016/j.agrformet.2025.110463

Interpretive Summary: Addressing issues with water availability and shortage requires tools that can accurately calculate the use of water by agricultural crops. In this study, the accuracy of 15 computer models to estimate soybean water use was assessed for a site in eastern Canada. The study identified two models that were better able to simulate crop water use as compared to the others, but further studies are needed to better understand the differences between the measured data and model estimates. This research will benefit present-day and future farmers, water managers, agricultural researchers, and all food consumers.

Technical Abstract: Accurate simulation of evapotranspiration (ET) with crop models is essential for improving agricultural water management and yield forecasting. Six soybean [Glycine max (L.) Merr.] crop models with a total of 15 modeling approaches were inter-compared to simulate ET under a warm-summer humid continental climate in Ottawa, Canada. Models were evaluated using ET estimates from the eddy covariance technique from five soybean growing seasons. Models were first calibrated with phenology, in-season growth, and yield data. In a second step, models were calibrated with observed ET and soil water content (SWC) data. After the first calibration, simulated daily ET was higher on average than measured ET, particularly during the period of full canopy cover (normalized bias, nBias = 17.1 to 49.2% depending on the model). After the second calibration, simulated daily ET was closer to measured ET, but the bias persisted (nBias = 5.9 to 52.1% during full canopy). The ensemble median was useful to reduce uncertainty in simulation of daily ET compared to most models, but did not perform better than DNDC, the top ranking model (nRMSE = 0.7 mm d-1, nBias = 11.2%). The MONICA model performed best simulating cumulative ET (nRMSE = 39.9 mm d-1, B = 11.3%), whereas the CROPGRO models were the most accurate simulating SWC (RMSE= 0.04 to 0.05 m³ m-3, nBias = -0.6 to 11.4% depending on soil depth). This study was instrumental in better parametrizing soybean models for the simulation of ET, and to evidence a bias across models compared to estimated ET by eddy covariance in a humid environment. The results reveal the need to further investigate possible biases in ET estimates by eddy covariance over soybean canopies. Regarding model performance, the study indicated the need to review night-time dew contributions to ET in process-based models.