Simulation of soil temperature under maize: an inter-comparison among 33 maize models

Authors: Thorp, Kelly; BOOTE, KENNETH - University Of Florida; STOCKLE, CLAUDIO - Washington State University; SUYKER, ANDREW - University Of Nebraska; Evett, Steven - Steve; Brauer, David; Coyle, Gwen; Copeland, Karen; Marek, Gary; Colaizzi, Paul; ACUTIS, MARCO - University Of Milan; ARCHONTOULIS, SOTIRIOS - Iowa State University; BABACAR, FAYE - University Of Sine-Saloum El Hadji Ibrahima Niasse; BARCZA, ZOLTAN - Eotvos Lorand University; BASSO, BRUNO - Michigan State University; KIMBALL, BRUCE - Retired ARS Employee; DE ANTONI MIGLIORATI, MASSIMILIANO - Queensland Department Of Environmental Science; ZHOU, WANG - University Of Illinois; Timlin, Dennis

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/6/2024
Publication Date: 4/23/2024
Citation: Thorp, K.R., Boote, K.J., Stockle, C., Suyker, A.E., Evett, S.R., Brauer, D.K., Coyle, G.G., Copeland, K.S., Marek, G.W., Colaizzi, P.D., Acutis, M., Archontoulis, S., Babacar, F., Barcza, Z., Basso, B., Kimball, B.A., De Antoni Migliorati, M., Zhou, W., Timlin, D.J. 2024. Simulation of soil temperature under maize: an inter-comparison among 33 maize models. Agricultural and Forest Meteorology. 351. Article 110003. https://doi.org/10.1016/j.agrformet.2024.110003.
DOI: https://doi.org/10.1016/j.agrformet.2024.110003

Interpretive Summary: Accurate simulation of soil temperature can help improve the accuracy of crop growth models by improving the predicted time from planting to germination, as well as improving the predictions of other soil processes like nitrification, carbon sequestration, and soil respiration. To determine how well maize growth models can simulate soil temperature, we conducted an inter-comparison of 33 maize models that was conducted under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). The study used comprehensive datasets from two sites - Mead, Nebraska, USA and Bushland, Texas, USA wherein soil temperature was measured. The average simulation errors ranged about 1.5 to 5.1°C (3 to 9°F). The six best models were identified, five of which used a “numeric” method to simulate soil temperature. Model improvement can come by wider adoption of the numeric method as well as adoption of newer routines to calculate soil thermal conductivity. This research will help present-day and future farmers and agricultural researchers, and of course all food consumers.

Technical Abstract: Accurate simulation of soil temperature can help improve the accuracy of crop growth models by improving the predicted time from planting to germination, as well as improving the predictions of other soil processes like nitrification, carbon sequestration, and soil respiration. To determine how well maize (Zea mays L.) growth models can simulate soil temperature, herein we present results of an inter-comparison study of 33 maize models that was conducted under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). The study used comprehensive datasets from two sites - Mead, Nebraska, USA and Bushland, Texas, USA wherein soil temperature was measured continually at several depths. There were 20 treatment-years with varying irrigation levels over multiple seasons at both sites. The range of simulated soil temperatures was large (about 10-15°C) from the coolest to warmest models across whole growing seasons from bare soil to full canopy and at both shallow and deeper depths. Within model families, there were no significant differences among their simulations of soil temperature due to their “flavor” methods for simulating evapotranspiration , so root-mean-square-errors (RMSE) were averaged within families, which reduced the number of soil temperature model families to 13. The model family RMSEs averaged over all 20 treatment-years and 2 depths ranged from about 1.5 to 5.1°C. The six models with the lowest RMSEs were APSIM, ecosys, JULES, Expert-N, SLFT, and MaizSim. Five of them used a “numeric” approach to simulate soil temperature, which essentially entailed using an energy balance on each soil layer. whereby the change in heat storage during a time step equals the difference between the heat flow into and that out of the layer. Further improvements in the best models for simulating soil temperature might be possible with the incorporation of more recently developed routines for simulating soil thermal conductivity.