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Title: Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models

Abstract

This study provides statistical emulators of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly summer weather variables for over a century at the grid cell level globally. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields, temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather, especially for extreme temperature and precipitation events, and now accounts for the effect of soil type. In- and out-of-sample validations show that the statistical emulators are able to replicate spatial patterns of yields crop levels and changes overtime projected by crop models reasonably well, although the accuracy of the emulators varies by model and by region. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools provide a computationally efficient method to account for uncertainty in climate change impact assessments.

Authors:
 [1]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Joint Program on the Science and Policy of Global Change
Publication Date:
Research Org.:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); USEPA
OSTI Identifier:
1380040
Alternate Identifier(s):
OSTI ID: 1424395
Grant/Contract Number:
FG02-94ER61937; XA-83600001-1
Resource Type:
Journal Article: Published Article
Journal Name:
Agricultural and Forest Meteorology
Additional Journal Information:
Journal Volume: 236; Journal ID: ISSN 0168-1923
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 60 APPLIED LIFE SCIENCES; crop yields; crop model; statistical model; climate change

Citation Formats

Blanc, Élodie. Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models. United States: N. p., 2017. Web. doi:10.1016/j.agrformet.2016.12.022.
Blanc, Élodie. Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models. United States. doi:10.1016/j.agrformet.2016.12.022.
Blanc, Élodie. Thu . "Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models". United States. doi:10.1016/j.agrformet.2016.12.022.
@article{osti_1380040,
title = {Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models},
author = {Blanc, Élodie},
abstractNote = {This study provides statistical emulators of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly summer weather variables for over a century at the grid cell level globally. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields, temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather, especially for extreme temperature and precipitation events, and now accounts for the effect of soil type. In- and out-of-sample validations show that the statistical emulators are able to replicate spatial patterns of yields crop levels and changes overtime projected by crop models reasonably well, although the accuracy of the emulators varies by model and by region. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools provide a computationally efficient method to account for uncertainty in climate change impact assessments.},
doi = {10.1016/j.agrformet.2016.12.022},
journal = {Agricultural and Forest Meteorology},
number = ,
volume = 236,
place = {United States},
year = {Thu Jan 26 00:00:00 EST 2017},
month = {Thu Jan 26 00:00:00 EST 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.agrformet.2016.12.022

Citation Metrics:
Cited by: 3works
Citation information provided by
Web of Science

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  • This study provides statistical emulators of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly summer weather variables for over a century at the grid cell level globally. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields, temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather,more » especially for extreme temperature and precipitation events, and now accounts for the effect of soil type. In- and out-of-sample validations show that the statistical emulators are able to replicate spatial patterns of yields crop levels and changes overtime projected by crop models reasonably well, although the accuracy of the emulators varies by model and by region. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools provide a computationally efficient method to account for uncertainty in climate change impact assessments.« less
  • Projected global environment changes will have major influences on crop yields. A very important aspects of crop yields, and societal adjustments to environmental changes, may well be altered year-year stability in yields. Simulations for three locations in the Midwest with mechanistic growth models for maize (Zea mays L.) and soybean [Glycine max (L.) Merr.] showed important species differences in projected yield stability. Maize yields under global environment changes were approximately equal to current levels and the year-to-year yield variability was unchanged. Soybean, however, had higher mean yields under changed global environments as compared to the current environment, and there wasmore » substantially greater year-to-year variability under possible future environments. This disparity among maize and soybean in mean yield changes and stability across years may have important consequences on crop management and stabilization of food and feed supplies. 27 refs., 2 figs., 1 tab.« less
  • We present protocols and input data for Phase 1 of the Global Gridded Crop Model Intercomparison, a project of the Agricultural Model Intercomparison and Improvement Project (AgMIP). The project consist of global simulations of yields, phenologies, and many land-surface fluxes using 12–15 modeling groups for many crops, climate forcing data sets, and scenarios over the historical period from 1948 to 2012. The primary outcomes of the project include (1) a detailed comparison of the major differences and similarities among global models commonly used for large-scale climate impact assessment, (2) an evaluation of model and ensemble hindcasting skill, (3) quantification ofmore » key uncertainties from climate input data, model choice, and other sources, and (4) a multi-model analysis of the agricultural impacts of large-scale climate extremes from the historical record.« less
  • We present protocols and input data for Phase 1 of the Global Gridded Crop Model Intercomparison, a project of the Agricultural Model Intercomparison and Improvement Project (AgMIP). The project consist of global simulations of yields, phenologies, and many land-surface fluxes using 12–15 modeling groups for many crops, climate forcing data sets, and scenarios over the historical period from 1948 to 2012. The primary outcomes of the project include (1) a detailed comparison of the major differences and similarities among global models commonly used for large-scale climate impact assessment, (2) an evaluation of model and ensemble hindcasting skill, (3) quantification ofmore » key uncertainties from climate input data, model choice, and other sources, and (4) a multi-model analysis of the agricultural impacts of large-scale climate extremes from the historical record.« less
  • Cited by 22