Selective improvement of global datasets for the computation of locally relevant environmental indicators: A method based on global sensitivity analysis

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4 Citations (Scopus)

Abstract

Several global datasets are available for environmental modelling, but information provided is hardly used for decision-making at a country-level. Here we propose a method, which relies on global sensitivity analysis, to improve local relevance of environmental indicators from global datasets. This method is tested on nitrogen use framework for two contrasted case studies: mixed dairy supply chains in Rwanda and the Netherlands. To achieve this, we evaluate how indicators computed from a global dataset diverge from same indicators computed from survey data. Second, we identify important input parameters that explain the variance of indicators. Subsequently, we fix non-important ones to their average values and substitute important ones with field data. Finally, we evaluate the effect of this substitution. This method improved relevance of nitrogen use indicators; therefore, it can be applied to any environmental modelling using global datasets to improve their relevance by prioritizing important parameters for additional data collection.

LanguageEnglish
Pages58-67
JournalEnvironmental Modelling & Software
Volume96
DOIs
Publication statusPublished - 2017

Fingerprint

environmental indicator
Sensitivity analysis
sensitivity analysis
Nitrogen
environmental modeling
Dairies
Supply chains
Substitution reactions
Decision making
nitrogen
substitution
decision making
indicator
method
parameter

Keywords

  • Decision-making
  • Environmental modelling
  • Global datasets
  • Global sensitivity analysis

Cite this

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title = "Selective improvement of global datasets for the computation of locally relevant environmental indicators: A method based on global sensitivity analysis",
abstract = "Several global datasets are available for environmental modelling, but information provided is hardly used for decision-making at a country-level. Here we propose a method, which relies on global sensitivity analysis, to improve local relevance of environmental indicators from global datasets. This method is tested on nitrogen use framework for two contrasted case studies: mixed dairy supply chains in Rwanda and the Netherlands. To achieve this, we evaluate how indicators computed from a global dataset diverge from same indicators computed from survey data. Second, we identify important input parameters that explain the variance of indicators. Subsequently, we fix non-important ones to their average values and substitute important ones with field data. Finally, we evaluate the effect of this substitution. This method improved relevance of nitrogen use indicators; therefore, it can be applied to any environmental modelling using global datasets to improve their relevance by prioritizing important parameters for additional data collection.",
keywords = "Decision-making, Environmental modelling, Global datasets, Global sensitivity analysis",
author = "U.A. Uwizeye and Gerber, {Pierre J.} and Groen, {Evelyne A.} and Dolman, {Mark A.} and Schulte, {Rogier P.O.} and {de Boer}, {Imke J.M.}",
year = "2017",
doi = "10.1016/j.envsoft.2017.06.041",
language = "English",
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pages = "58--67",
journal = "Environmental Modelling & Software",
issn = "1364-8152",
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T2 - Environmental Modelling & Software

AU - Uwizeye, U.A.

AU - Gerber, Pierre J.

AU - Groen, Evelyne A.

AU - Dolman, Mark A.

AU - Schulte, Rogier P.O.

AU - de Boer, Imke J.M.

PY - 2017

Y1 - 2017

N2 - Several global datasets are available for environmental modelling, but information provided is hardly used for decision-making at a country-level. Here we propose a method, which relies on global sensitivity analysis, to improve local relevance of environmental indicators from global datasets. This method is tested on nitrogen use framework for two contrasted case studies: mixed dairy supply chains in Rwanda and the Netherlands. To achieve this, we evaluate how indicators computed from a global dataset diverge from same indicators computed from survey data. Second, we identify important input parameters that explain the variance of indicators. Subsequently, we fix non-important ones to their average values and substitute important ones with field data. Finally, we evaluate the effect of this substitution. This method improved relevance of nitrogen use indicators; therefore, it can be applied to any environmental modelling using global datasets to improve their relevance by prioritizing important parameters for additional data collection.

AB - Several global datasets are available for environmental modelling, but information provided is hardly used for decision-making at a country-level. Here we propose a method, which relies on global sensitivity analysis, to improve local relevance of environmental indicators from global datasets. This method is tested on nitrogen use framework for two contrasted case studies: mixed dairy supply chains in Rwanda and the Netherlands. To achieve this, we evaluate how indicators computed from a global dataset diverge from same indicators computed from survey data. Second, we identify important input parameters that explain the variance of indicators. Subsequently, we fix non-important ones to their average values and substitute important ones with field data. Finally, we evaluate the effect of this substitution. This method improved relevance of nitrogen use indicators; therefore, it can be applied to any environmental modelling using global datasets to improve their relevance by prioritizing important parameters for additional data collection.

KW - Decision-making

KW - Environmental modelling

KW - Global datasets

KW - Global sensitivity analysis

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