Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

Crop growth models are increasingly used as part of research into areas such as climate change and
bioenergy, so it is particularly important to understand the effects of environmental inputs on model
results. Rather than investigating the effects of separate input parameters, we assess results obtained
from a crop growth model using a selection of entire meteorological and soil input datasets, since these
define modelled conditions. Yields are found to vary significantly only where the combination of inputs
makes the crop vulnerable to drought, rather than being especially sensitive to any single input. Results
highlight the significance of soil water parameters, which are likely to become increasingly critical in
areas affected by climate change. Differences between datasets demonstrate the need to consider the
dataset-dependence of parameterised model terms, both for model validation and predictions based on
alternative datasets.
Original languageEnglish
Pages (from-to)37-43
Number of pages16
JournalEnvironmental Modelling and Software
Volume29
Issue number1
Early online date16 Nov 2011
DOIs
Publication statusPublished - Mar 2012

Keywords

  • Crop growth model
  • Input data
  • Sensitivity analysis
  • Soil water
  • Drought
  • Parameterisation
  • Climate-change
  • Growth-model
  • Miscanthus
  • Uncertainty
  • Variability
  • Efficiency
  • Parameter
  • Europe
  • Maize
  • Yield

Cite this

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title = "Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought",
abstract = "Crop growth models are increasingly used as part of research into areas such as climate change andbioenergy, so it is particularly important to understand the effects of environmental inputs on modelresults. Rather than investigating the effects of separate input parameters, we assess results obtainedfrom a crop growth model using a selection of entire meteorological and soil input datasets, since thesedefine modelled conditions. Yields are found to vary significantly only where the combination of inputsmakes the crop vulnerable to drought, rather than being especially sensitive to any single input. Resultshighlight the significance of soil water parameters, which are likely to become increasingly critical inareas affected by climate change. Differences between datasets demonstrate the need to consider thedataset-dependence of parameterised model terms, both for model validation and predictions based onalternative datasets.",
keywords = "Crop growth model, Input data, Sensitivity analysis, Soil water, Drought, Parameterisation, Climate-change, Growth-model, Miscanthus, Uncertainty, Variability, Efficiency, Parameter, Europe, Maize, Yield",
author = "Mark Pogson and Astley Hastings and Pete Smith",
year = "2012",
month = "3",
doi = "10.1016/j.envsoft.2011.10.008",
language = "English",
volume = "29",
pages = "37--43",
journal = "Environmental Modelling and Software",
issn = "1364-8152",
publisher = "ELSEVIER APPL SCI PUBL LTD",
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T1 - Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought

AU - Pogson, Mark

AU - Hastings, Astley

AU - Smith, Pete

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N2 - Crop growth models are increasingly used as part of research into areas such as climate change andbioenergy, so it is particularly important to understand the effects of environmental inputs on modelresults. Rather than investigating the effects of separate input parameters, we assess results obtainedfrom a crop growth model using a selection of entire meteorological and soil input datasets, since thesedefine modelled conditions. Yields are found to vary significantly only where the combination of inputsmakes the crop vulnerable to drought, rather than being especially sensitive to any single input. Resultshighlight the significance of soil water parameters, which are likely to become increasingly critical inareas affected by climate change. Differences between datasets demonstrate the need to consider thedataset-dependence of parameterised model terms, both for model validation and predictions based onalternative datasets.

AB - Crop growth models are increasingly used as part of research into areas such as climate change andbioenergy, so it is particularly important to understand the effects of environmental inputs on modelresults. Rather than investigating the effects of separate input parameters, we assess results obtainedfrom a crop growth model using a selection of entire meteorological and soil input datasets, since thesedefine modelled conditions. Yields are found to vary significantly only where the combination of inputsmakes the crop vulnerable to drought, rather than being especially sensitive to any single input. Resultshighlight the significance of soil water parameters, which are likely to become increasingly critical inareas affected by climate change. Differences between datasets demonstrate the need to consider thedataset-dependence of parameterised model terms, both for model validation and predictions based onalternative datasets.

KW - Crop growth model

KW - Input data

KW - Sensitivity analysis

KW - Soil water

KW - Drought

KW - Parameterisation

KW - Climate-change

KW - Growth-model

KW - Miscanthus

KW - Uncertainty

KW - Variability

KW - Efficiency

KW - Parameter

KW - Europe

KW - Maize

KW - Yield

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