Modelling the impacts of weather and climate variability on crop productivity over a large area

A new process-based model development, optimization, and uncertainties analysis

Fulu Tao, Masayuki Yokosawa, Zhao Zhang

Research output: Contribution to journalArticle

118 Citations (Scopus)

Abstract

Process-based crop models are increasingly being used to investigate the impacts of weather and climate variability (change) on crop growth and production, especially at a large scale. Crop models that account for the key impact mechanisms of climate variability and are accurate over a large area must be developed. Here, we present a new process-based general Model to capture the Crop-Weather relationship over a Large Area (MCWLA). The MCWLA is optimized and tested for spring maize on the Northeast China Plain and summer maize on the North China Plain, respectively. We apply the Bayesian probability inversion and a Markov chain Monte Carlo (MCMC) technique to the MCWLA to analyze uncertainties in parameter estimation and model prediction and to optimize the model. Ensemble hindcasts (by perturbing model parameters) and deterministic hindcasts (using the optimal parameters set) were carried out and compared with the detrended long-term yields series both at the crop model grid (0.5° × 0.5°) and province scale. Agreement between observed and modelled yield was variable, with correlation coefficients ranging from 0.03 to 0.88 (p < 0.01) at the model grid scale and from 0.45 to 0.82 (p < 0.01) at the province scale. Ensemble hindcasts captured significantly the interannual variability in crop yield at all the four investigated provinces from 1985 to 2002. MCWLA includes the process-based representation of the coupled CO2 and H2O exchanges; its simulations on crop response to elevated CO2 concentration agree well with the controlled-environment experiments, suggesting its validity also in future climate. We demonstrate that the MCWLA, together with the Bayesian probability inversion and a MCMC technique, is an effective tool to investigate the impacts of climate variability on crop productivity over a large area, as well as the uncertainties.

Original languageEnglish
Pages (from-to)831-850
Number of pages20
JournalAgricultural and Forest Meteorology
Volume149
Issue number5
DOIs
Publication statusPublished - 2009 May 7
Externally publishedYes

Fingerprint

uncertainty analysis
weather
climate
crop models
productivity
crop
crops
modeling
Markov chain
parameter uncertainty
corn
China
maize
crop yield
uncertainty
development model
prediction
summer
methodology
climate change

Keywords

  • Agriculture
  • Climate change
  • CO fertilization effects
  • Transpiration
  • Water use efficiency
  • Yield prediction

ASJC Scopus subject areas

  • Forestry
  • Global and Planetary Change
  • Agronomy and Crop Science
  • Atmospheric Science

Cite this

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title = "Modelling the impacts of weather and climate variability on crop productivity over a large area: A new process-based model development, optimization, and uncertainties analysis",
abstract = "Process-based crop models are increasingly being used to investigate the impacts of weather and climate variability (change) on crop growth and production, especially at a large scale. Crop models that account for the key impact mechanisms of climate variability and are accurate over a large area must be developed. Here, we present a new process-based general Model to capture the Crop-Weather relationship over a Large Area (MCWLA). The MCWLA is optimized and tested for spring maize on the Northeast China Plain and summer maize on the North China Plain, respectively. We apply the Bayesian probability inversion and a Markov chain Monte Carlo (MCMC) technique to the MCWLA to analyze uncertainties in parameter estimation and model prediction and to optimize the model. Ensemble hindcasts (by perturbing model parameters) and deterministic hindcasts (using the optimal parameters set) were carried out and compared with the detrended long-term yields series both at the crop model grid (0.5° × 0.5°) and province scale. Agreement between observed and modelled yield was variable, with correlation coefficients ranging from 0.03 to 0.88 (p < 0.01) at the model grid scale and from 0.45 to 0.82 (p < 0.01) at the province scale. Ensemble hindcasts captured significantly the interannual variability in crop yield at all the four investigated provinces from 1985 to 2002. MCWLA includes the process-based representation of the coupled CO2 and H2O exchanges; its simulations on crop response to elevated CO2 concentration agree well with the controlled-environment experiments, suggesting its validity also in future climate. We demonstrate that the MCWLA, together with the Bayesian probability inversion and a MCMC technique, is an effective tool to investigate the impacts of climate variability on crop productivity over a large area, as well as the uncertainties.",
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AU - Zhang, Zhao

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AB - Process-based crop models are increasingly being used to investigate the impacts of weather and climate variability (change) on crop growth and production, especially at a large scale. Crop models that account for the key impact mechanisms of climate variability and are accurate over a large area must be developed. Here, we present a new process-based general Model to capture the Crop-Weather relationship over a Large Area (MCWLA). The MCWLA is optimized and tested for spring maize on the Northeast China Plain and summer maize on the North China Plain, respectively. We apply the Bayesian probability inversion and a Markov chain Monte Carlo (MCMC) technique to the MCWLA to analyze uncertainties in parameter estimation and model prediction and to optimize the model. Ensemble hindcasts (by perturbing model parameters) and deterministic hindcasts (using the optimal parameters set) were carried out and compared with the detrended long-term yields series both at the crop model grid (0.5° × 0.5°) and province scale. Agreement between observed and modelled yield was variable, with correlation coefficients ranging from 0.03 to 0.88 (p < 0.01) at the model grid scale and from 0.45 to 0.82 (p < 0.01) at the province scale. Ensemble hindcasts captured significantly the interannual variability in crop yield at all the four investigated provinces from 1985 to 2002. MCWLA includes the process-based representation of the coupled CO2 and H2O exchanges; its simulations on crop response to elevated CO2 concentration agree well with the controlled-environment experiments, suggesting its validity also in future climate. We demonstrate that the MCWLA, together with the Bayesian probability inversion and a MCMC technique, is an effective tool to investigate the impacts of climate variability on crop productivity over a large area, as well as the uncertainties.

KW - Agriculture

KW - Climate change

KW - CO fertilization effects

KW - Transpiration

KW - Water use efficiency

KW - Yield prediction

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