Prediction of Evapotranspiration and Yields of Maize: An Inter-comparison among 31 Maize Models - Archive ouverte HAL
Communication Dans Un Congrès Année : 2017

Prediction of Evapotranspiration and Yields of Maize: An Inter-comparison among 31 Maize Models

1 USDA-ARS : Agricultural Research Service
2 Agronomy Department
3 National laboratory for agriculture and the environment
4 Agricultural Systems Research Unit, USDA, Agricultural Research Service
5 Biological Systems Engineering
6 Department of Agronomy
7 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
8 Department of Geological Sciences and W. K. Kellogg Biological Station
9 AGROCLIM - Agroclim
10 Department of Soil, Water, and Climate
11 AGIR - AGroécologie, Innovations, teRritoires
12 Computation Institute
13 ULg-GxABT, Gembloux Agro-Bio Tech Faculty, Dpt. Agronomy, Bio-Engineering and Chemistry, Crop Science Unit
14 P3F - Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères
15 ZALF - Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research
16 INRES - Institute of Crop Science and Resource Conservation [Bonn]
17 Institute of Soil Science and Land Evaluation, Biogeophysics
18 Crop Production Systems in the Tropics
19 Department of Bioresource Engineering, Macdonald Campus
20 Center for Urban Horticulture [Seattle]
21 Dept. Producción Agraria-CEIGRAM
22 EMMAH - Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes
23 LUKE - Natural Resources Institute Finland
24 Institute of Biochemical Plant Pathology
25 IGSNRR - Institute of Geographic Sciences and Natural Resources Research
26 Crop Systems and Global Change Research Unit
27 Climate Adaptation Scientist Meteorological Office
K. Boote
  • Fonction : Auteur
Patrick Bertuzzi
  • Fonction : Auteur
M. Chen
D. Derying
  • Fonction : Auteur
Magali Willaume

Résumé

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. Bruce
Fichier principal
Vignette du fichier
2017_Kimball_ASA 2017_Abstract_1.pdf (307.48 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01604530 , version 1 (05-06-2020)

Licence

Identifiants

  • HAL Id : hal-01604530 , version 1
  • PRODINRA : 407685

Citer

Bruce Kimball, K. Boote, J. Hatfield, L. Ahuja, C. Stockle, et al.. Prediction of Evapotranspiration and Yields of Maize: An Inter-comparison among 31 Maize Models. Meeting of working Group Medicago sativa, Oct 2017, Tampa, United States. pp.1. ⟨hal-01604530⟩
719 Consultations
73 Téléchargements

Partager

More