Agronomy and Horticulture Department

 

Authors

Simon Bassu, Unite d’Agronomie, INRA-AgroParisTech
Nadine Brisson, Unite d’Agronomie, INRA-AgroParisTech
Jean-Louis Durand, Unite de Recherche Pluridisciplinaire sur la Prairie et les Plantes Fourrageres
Kenneth Boote, University of Florida
Jon Lizaso, University Politecnica of Madrid
James Jones, University of Florida
Cynthia Rosenzweig, Climate Impacts Group, NASA Goddard Institute for Space StudiesFollow
Alex Ruane, Climate Impacts Group, NASA Goddard Institute for Space Studies
Myriam Adam, UMR AGAP/PAM, CIRAD
Christian Baron, CIRAD, UMR TETIS
Bruno Basso, Michigan State University, University of Basilicata, Potenza,Italy
Christian Biernath, Institute für Bodenokologie
Hendrick Boogaard, Centre for Geo- Information
Sjaak Conijn, Wageningen University and Research Centre
Marc Corbeels, CIRAD-Annual Cropping Systems
Delphine Deryng, University of East Anglia
Giacomo de Sanctis, Unite AGROCLIM, INRA, Domaine st Paul Site Agroparc
Sebastian Gayler, University of Tubingen
Patricio Grassini, University of Nebraska-LincolnFollow
Jerry Hatfield, USDA-ARS
Steven Hoek, Centre for Geo- Information
Cesar Izaurralde, University of Maryland - College Park
Raymond Jongschaap, Wageningen University and Research Centre
Armen Kemanian, The Pennsylvania State University
Christian Kersebaum, Institute of Landscape Systems Analysis, ZALF, Leibniz-Centre for Agricultural Landscape Research
Soo-Hyung Kim, University of Washington
Naresh Kumar, Indian Agricultural Research Institute, Centre for Environment Science and Climate Resilient Agriculture, New Delhi
David Makowski, Unite d’Agronomie, INRA-AgroParisTech
Christoph Muller, Potsdam Institute for Climate Impact Research
Claas Nendel, Institute of Landscape Systems Analysis, ZALF, Leibniz-Centre for Agricultural Landscape Research
Eckart Priesack, Institute für Bodenokologie
Maria Virinia Pravia, The Pennsylvania State University
Federico Sau, University Politecnica of Madrid
Iurii Shcherbak, Michigan State University, University of Basilicata, Potenza,Italy
Fulu Tao, Chinese Academy of Sciences
Edmar Teixeira, The New Zealand Institute for Plant & Food Research Limited
Dennis Timlin, Crop Systems and Global Change Laboratory, USDA/ ARS
Katharina Waha, Indian Agricultural Research Institute, Centre for Environment Science and Climate Resilient Agriculture, New Delhi

Date of this Version

2014

Citation

Global Change Biology (2014), doi: 10.1111/gcb.12520

Comments

This article is a U.S. government work, and is not subject to copyright in the United States.

Abstract

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha 1 per °C. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

Share

COinS