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Statistical Analysis of Large Simulated Yield Datasets for Studying Climate EffectsMany studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects.
Document ID
20150008975
Acquisition Source
Goddard Space Flight Center
Document Type
Book Chapter
Authors
Makowski, David
(Institut National de la Recherche Agronomique Thiverval-Grignon, France)
Asseng, Senthold
(Florida Univ. Gainesville, FL, United States)
Ewert, Frank
(Bonn Univ. Germany)
Bassu, Simona
(Institut National de la Recherche Agronomique Thiverval-Grignon, France)
Durand, Jean-Louis
(Haute-Alsace Univ. Mulhouse, France)
Martre, Pierre
(Universite Blaise Pascal Aubiere, France)
Adam, Myriam
(Centre de Cooperation Internationale en Recherche Agronomique pour le Developpement Montpellier, France)
Aggarwal, Pramod K.
(International Water Management Institute New Delhi, INDIA)
Angulo, Carlos
(Bonn Univ. Germany)
Baron, Chritian
(Centre de Cooperation Internationale en Recherche Agronomique pour le Developpement Montpellier, France)
Basso, Bruno
(Michigan State Univ. East Lansing, MI, United States)
Bertuzzi, Patrick
(Institut National de la Recherche Agronomique Avignon, France)
Biemath, Christian
(Helmholtz Zentrum Munchen Neuherberg, Germany)
Boogaard, Hendrik
(Wageningen Univ. Wageningen, Netherlands)
Boote, Kenneth J.
(Florida Univ. Gainesville, FL, United States)
Brisson, Nadine
(Institut National de la Recherche Agronomique Thiverval-Grignon, France)
Cammarano, Davide
(Florida Univ. Gainesville, FL, United States)
Challinor, Andrew J.
(Leeds Univ. United Kingdom)
Conijn, Sjakk J. G.
(Wageningen Univ. Wageningen, Netherlands)
Corbeels, Marc
(Academia Sinica Beijing, China)
Deryng, Delphine
(East Anglia Univ. Norwich, United Kingdom)
De Sanctis, Giacomo
(Institut National de la Recherche Agronomique Avignon, France)
Doltra, Jordi
(Cantabrian Agricultural Research and Training Centre Muriedas, Spain)
Gayler, Sebastian
(Tuebingen Univ. Germany)
Goldberg, Richard A.
(Columbia Univ. New York, NY, United States)
Grassini, Patricio
(Nebraska Univ. Lincoln, NE, United States)
Hatfield, Jerry L.
(Iowa State Univ. Ames, IA, United States)
Heng, Lee
(International Atomic Energy Agency Vienna, Austria)
Hoek, Steven
(Wageningen Univ. Wageningen, Netherlands)
Hooker, Josh
(Reading Univ. United Kingdom)
Hunt, Tony L. A.
(Guelph Univ. Ontario, Canada)
Ingwersen, Joachim
(Hohenheim Univ. Stuttgart, Germany)
Izaurralde, Cesar
(Maryland Univ. College Park, MD, United States)
Jongschaap, Raymond E. E.
(Wageningen Univ. Wageningen, Netherlands)
Rosenzweig, Cynthia
(NASA Goddard Inst. for Space Studies New York, NY, United States)
Date Acquired
May 28, 2015
Publication Date
March 25, 2015
Publication Information
Publisher: Imperial College Press
Subject Category
Statistics And Probability
Meteorology And Climatology
Report/Patent Number
GSFC-E-DAA-TN22468
Funding Number(s)
CONTRACT_GRANT: NNX10AU63A
WBS: WBS 281945.02.03.03.96
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
climate
climate change
farm crops
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