Managing Global Resources for a Secure Future

2017 Annual Meeting | Oct. 22-25 | Tampa, FL

66-2 Prediction of Evapotranspiration and Yields of Maize: An Inter-Comparison Among 31 Maize Models.

See more from this Division: ASA Section: Climatology and Modeling
See more from this Session: AgMIP: Recent Findings of the Agmip Projects

Monday, October 23, 2017: 10:40 AM
Tampa Convention Center, Room 6

Bruce A. Kimball1, Kenneth J. Boote2, Jerry L. Hatfield3, Lajpat R. Ahuja4, Claudio O. Stockle5, Sotirios V Archontoulis6, Christian Baron7, Bruno Basso8, Patrick Bertuzzi9, Ming Chen10, Julie Constantin11, Delphine Deryng12, Benjamin Dumont13, Jean-Louis Durand14, Frank Ewert15, Thomas Gaiser16, Sebastian Gayler17, Tim Griffis18, Munir Hoffman19, Qianjing Jiang20, Soo-Hyung Kim21, Jon I Lizaso22, Sophie Moulin23, Claas Nendel24, Philip Parker25, Taru I Palosuo26, Eckart Priesack27, Zhiming Qi28, Amit Srivastava29, Tommaso Stella25, Fulu Tao30, Kelly Thorp31, Dennis J. Timlin32, Tracy Twine33, Heidi Webber34, Magali Willaume35 and Karina Williams36, (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)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)Department of Soil, Water, and Climate, University of Minnesota, Falcon Heights, MN
(11)AGIR, Université de Toulouse, INRA, INPT, INP- EI PURPAN, CastanetTolosan, France
(12)Computation Institute, University of Chicago, Chicago, IL
(13)Environmental Sciences and Technologies, ULg - Gembloux Agro-Bio Tech, Gembloux, Belgium
(14)Unité de Recherche Pluridisciplinaire Prairie et Plantes Fourragères, INRA, Lusignan, France
(15)Leibniz Centre for Agricultural Landscape Research, Müncheberg, GERMANY
(16)Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
(17)Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany
(18)Department of Soil, Water, and Climate, University of Minnesota, St. Paul, MN
(19)Crop Production Systems in the Tropics, Georg-August-Universität, Göttingen, Germany
(20)Department of Bioresource Engineering, Macdonald Campus, McGill University, Sanite-Anne-de-Bellevue, QC, Canada
(21)School of Environmental and Forest Sciences, University of Washington, Seattle, WA
(22)Dep. Producción Vegetal, Univ. Politécnica of Madrid, Madrid, Spain
(23)Département Environnement & Agronomie, INRA, centre de recherche PACA, Centre de recherche Provence-Alpes-Côte d’Azur, Avignon, France
(24)ZALF - Leibniz Centre for Agricultural Landscape Research, Muencheberg, Germany
(25)Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
(26)Climate Impacts Group, Natural Resources Institute Finland (Luke), Helsinki, Finland
(27)Helmholtz Zentrum München, Neuherberg, Germany
(28)21, 111 Lakeshore Road, McGill University - MacDonald Campus, Ste-Anne-de-Bellevue, QC, CANADA
(29)Institute of Crop Science and Resource Conservation, University of Bonn,, Bonn, Germany
(30)Climate Impacts Group, Natural Resources Institute Finland, Helsinki, Finland
(31)21881 N Cardon Ln, USDA-ARS, Maricopa, AZ
(32)10300 Baltimore Ave., USDA-ARS, Beltsville, MD
(33)Department of Soil, Water, & Climate, Univeristy of Minnesota, St. Paul, MN
(34)Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, Germany
(35)UMR INRA-ENSAT 1248 AGIR - AGroécologie, Innovations & teRritoires, INRA - Centre de recherche de Toulouse, Castanet-Tolosan Cedex, France
(36)Climate Adaptation Scientist Meteorological Office, Devon, United Kingdom
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 31 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 in later stages when the modelers were provided additional leaf area, growth, and the actual ET observations 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: Recent Findings of the Agmip Projects