447-2 Prediction of Evapotranspiration and Yields of Maize: An Inter-Comparison Among 29 Maize Models.

See more from this Division: ASA Section: Climatology and Modeling
See more from this Session: AgMIP: Advances in Crop & Soil Model Intercomparison and Improvement Oral

Wednesday, November 9, 2016: 2:25 PM
Phoenix Convention Center North, Room 228 A

Bruce A. Kimball1, Kenneth J. Boote2, Jerry L. Hatfield3, Lajpat R. Ahuja4, Claudio O. Stockle5, Sotiris V Archontoulis6, Christian Baron7, Bruno Basso8, Patrick Bertuzzi9, Julie Constantin10, Delphine Deryng11, Benjamin Dumont12, Frank Ewert13, Thomas Gaiser14, Tim Griffis15, Munir Hoffman16, Qianjing Jiang17, Soo-Hyung Kim18, Jon I Lizaso19, Sophie Moulin20, Philip Parker21, Taru I Palosuo22, Zhiming Qi17, Amit Srivastava23, Fulu Tao24, Kelly Thorp25, Dennis Timlin26, Heidi Webber27, Magali Willaume28, Karina Williams29, Ming Chen15, Jean-Louis Durand30, Sebastian Gayler31, Eckart Priesack32 and Tracy Twine33, (1)USDA-ARS, Maricopa, AZ
(2)Agronomy Dept., 3105 McCarty Hall, University of Florida, Gainesville, FL
(3)USDA-ARS National Laboratory for Agriculture and the Environment, Ames, IA
(4)Agricultural Systems Research Unit, USDA-ARS, Fort Collins, CO
(5)Washington State University, Pullman, WA
(6)Department of Agronomy, Iowa State University, Ames, IA
(7)CIRAD, Montpellier, France
(8)Michigan State University, Michigan State University, East Lansing, MI
(9)Département Environnement & Agronomie, Centre de recherche Provence-Alpes-Côte d’Azur, Avignon, France
(10)UMR INRA-ENSAT 1248 AGIR - AGroécologie, Innovations & teRritoires, INRA - Centre de recherche de Toulouse, Castanet-Tolosan, France
(11)Computation Institute, University of Chicago, Chicago, IL
(12)Environmental Sciences and Technologies, ULg - Gembloux Agro-Bio Tech, Gembloux, Belgium
(13)Leibniz Centre for Agricultural Landscape Research, Müncheberg, GERMANY
(14)Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
(15)Department of Soil, Water, and Climate, University of Minnesota, Falcon Heights, MN
(16)Crop Production Systems in the Tropics, Georg-August-Universität, Göttingen, Germany
(17)Department of Bioresource Engineering, Macdonald Campus, McGill University, Sanite-Anne-de-Bellevue, QC, Canada
(18)School of Environmental and Forest Sciences, University of Washington, Seattle, WA
(19)Dep. Producción Vegetal, Univ. Politécnica of Madrid, Madrid, Spain
(20)Département Environnement & Agronomie, INRA, centre de recherche PACA, Centre de recherche Provence-Alpes-Côte d’Azur, Avignon, France
(21)Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
(22)Climate Impacts Group, Natural Resources Institute Finland (Luke), Helsinki, Finland
(23)Institute of Crop Science and Resource Conservation, University of Bonn,, Bonn, Germany
(24)Chinese Academy of Sciences, Institute of Geographical Sciences and Natural Resources Research, Beijing, China
(25)21881 N Cardon Ln, USDA-ARS, Maricopa, AZ
(26)Crop System & Global Change, USDA-ARS, Beltsville, MD
(27)Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, Germany
(28)UMR INRA-ENSAT 1248 AGIR - AGroécologie, Innovations & teRritoires, INRA - Centre de recherche de Toulouse, Castanet-Tolosan Cedex, France
(29)Climate Adaptation Scientist Meteorological Office, Devon, United Kingdom
(30)INRA, Lusignan, France
(31)Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany
(32)Helmholtz Zentrum München, Neuherberg, Germany
(33)Department of Soil, Water, & Climate, Univeristy of Minnesota, St. Paul, MN
Abstract:
An important aspect that determines the ability of crop growth models to predict growth and yield is their ability to predict the rate of water consumption or evapotranspiration (ET) of the crop, especially for rain-fed crops. If, for example, the predicted ET rate is too high, the simulated crop may exhaust its soil water supply before the next rain event, thereby causing growth and yield predictions that are too low. In a prior inter-comparison among maize growth models, ET predictions varied widely, but no observations of actual ET were available for comparison. Therefore, another study has been initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). This time observations of ET using the eddy covariance technique from an 8-year-long experiment conducted at Ames, IA are being used as the standard. Simulation results from 29 models have been completed. In the first “blind” phase for which only weather, soils, and management information were furnished to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. A detailed statistical analysis of the daily ET data from 2011, a “typical” rainfall year, showed that, as expected, the median of all the models was more accurate across several criteria (correlation, root mean square error, average difference, regression slope) than any particular model. However, some individual models were better than the median for a particular criteria. Predictions improved somewhat in later stages when the modelers were provided additional leaf area and growth information that allowed them to “calibrate” some of the parameters in their models to account for varietal characteristics, etc.

See more from this Division: ASA Section: Climatology and Modeling
See more from this Session: AgMIP: Advances in Crop & Soil Model Intercomparison and Improvement Oral