Simulation of soil temperature under maize: An inter-comparison among 33 maize models
Bruce Kimball
(1)
,
Kelly Thorp
(1)
,
Kenneth Boote
(2)
,
Claudio Stockle
(3)
,
Andrew Suyker
(4)
,
Steven Evett
(5)
,
David Brauer
(5)
,
Gwen Coyle
(5)
,
Karen Copeland
(5)
,
Gary Marek
(5)
,
Paul Colaizzi
(5)
,
Marco Acutis
(6)
,
Sotirios Archontoulis
(7)
,
Faye Babacar
(8)
,
Zoltán Barcza
(9, 10)
,
Bruno Basso
(11)
,
Patrick Bertuzzi
(12)
,
Massimiliano de Antoni Migliorati
(13)
,
Benjamin Dumont
(14)
,
Jean-Louis Durand
(15)
,
Nándor Fodor
(10, 16)
,
Thomas Gaiser
(17)
,
Sebastian Gayler
(18)
,
Robert Grant
(19)
,
Kaiyu Guan
(20)
,
Gerrit Hoogenboom
(2)
,
Qianjing Jiang
(21)
,
Soo-Hyung Kim
(22)
,
Isaya Kisekka
(23)
,
Jon Lizaso
(24)
,
Alessia Perego
(6)
,
Bin Peng
(20)
,
Eckart Priesack
(25)
,
Zhiming Qi
(21)
,
Vakhtang Shelia
(2)
,
Amit Kumar Srivastava
(17, 26)
,
Dennis Timlin
(27)
,
Heidi Webber
(26)
,
Tobias Weber
(28)
,
Karina Williams
(29, 30)
,
Michelle Viswanathan
(18)
,
Wang Zhou
(20)
1
USDA -
USDA Agricultural Research Service [Maricopa, AZ]
2 UF - University of Florida [Gainesville]
3 WSU - Washington State University
4 University of Nebraska–Lincoln
5 USDA-ARS : Agricultural Research Service
6 UNIMI - Università degli Studi di Milano = University of Milan
7 ISU - Iowa State University
8 USSEIN - Université du Sine Saloum El-Hadj Ibrahima NIASS
9 ELTE - Eötvös Loránd University
10 CZU - Czech University of Life Sciences Prague
11 Michigan State University [East Lansing]
12 AGROCLIM - Agroclim
13 DES - Department of Environment and Science [Queensland]
14 Unité de recherche TERRA [Gembloux]
15 INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
16 ATK MGI - Agricultural Institute [Budapest]
17 Universität Bonn = University of Bonn
18 University of Hohenheim
19 University of Alberta
20 UIUC - University of Illinois at Urbana-Champaign [Urbana]
21 McGill University = Université McGill [Montréal, Canada]
22 University of Washington [Seattle]
23 UC Davis - University of California [Davis]
24 UPM - Universidad Politécnica de Madrid
25 HMGU - Helmholtz Zentrum München = German Research Center for Environmental Health
26 ZALF - Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research
27 USDA Agricultural Research Service [Beltsville, Maryland]
28 Universität Kassel [Kassel]
29 MOHC - Met Office Hadley Centre
30 University of Exeter
2 UF - University of Florida [Gainesville]
3 WSU - Washington State University
4 University of Nebraska–Lincoln
5 USDA-ARS : Agricultural Research Service
6 UNIMI - Università degli Studi di Milano = University of Milan
7 ISU - Iowa State University
8 USSEIN - Université du Sine Saloum El-Hadj Ibrahima NIASS
9 ELTE - Eötvös Loránd University
10 CZU - Czech University of Life Sciences Prague
11 Michigan State University [East Lansing]
12 AGROCLIM - Agroclim
13 DES - Department of Environment and Science [Queensland]
14 Unité de recherche TERRA [Gembloux]
15 INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
16 ATK MGI - Agricultural Institute [Budapest]
17 Universität Bonn = University of Bonn
18 University of Hohenheim
19 University of Alberta
20 UIUC - University of Illinois at Urbana-Champaign [Urbana]
21 McGill University = Université McGill [Montréal, Canada]
22 University of Washington [Seattle]
23 UC Davis - University of California [Davis]
24 UPM - Universidad Politécnica de Madrid
25 HMGU - Helmholtz Zentrum München = German Research Center for Environmental Health
26 ZALF - Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research
27 USDA Agricultural Research Service [Beltsville, Maryland]
28 Universität Kassel [Kassel]
29 MOHC - Met Office Hadley Centre
30 University of Exeter
Bruce Kimball
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- ORCID : 0000-0003-3009-061X
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Kelly Thorp
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- ORCID : 0000-0001-9168-875X
Steven Evett
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- ORCID : 0000-0003-3418-5771
Karen Copeland
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- ORCID : 0000-0002-2826-0280
Paul Colaizzi
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- ORCID : 0000-0003-4251-8724
Faye Babacar
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Benjamin Dumont
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Jean-Louis Durand
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- ORCID : 0000-0003-2751-168X
Gerrit Hoogenboom
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- ORCID : 0000-0002-1555-0537
Soo-Hyung Kim
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- ORCID : 0000-0003-3879-4080
Isaya Kisekka
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- ORCID : 0000-0002-2460-7777
Vakhtang Shelia
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- ORCID : 0000-0002-9768-7958
Dennis Timlin
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- PersonId : 1389416
- ORCID : 0000-0003-4883-4664
Heidi Webber
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- PersonId : 801357
- ORCID : 0000-0001-8301-5424
Karina Williams
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- PersonId : 1161969
- ORCID : 0000-0002-1185-535X
Wang Zhou
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- ORCID : 0000-0002-8432-9828
Résumé
Accurate simulation of soil temperature can help improve the accuracy of crop growth models by improving the predictions of soil processes like seed germination, decomposition, nitrification, evaporation, and carbon sequestration. To assess how well such models can simulate soil temperature, herein we present results of an inter -comparison study of 33 maize ( Zea mays L.) growth models. Among the 33 models, four of the modeling groups contributed results using differing algorithms or "flavors" to simulate evapotranspiration within the same overall model family. The study used comprehensive datasets from two sites - Mead, Nebraska, USA and Bushland, Texas, USA wherein soil temperature was measured continually at several depths. The range of simulated soil temperatures was large (about 10-15 degrees 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 differing evapotranspiration method "flavors", 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 degrees C. The six models with the lowest RMSEs were APSIM, ecosys, JULES, Expert -N, SLFT, and MaizSim. Five of these best models used a numerical iterative approach to simulate soil temperature, which 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 improved routines for simulating soil thermal conductivity than the older routines now in use by the models.