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

U.A. Uwizeye*, Pierre J. Gerber, Evelyne A. Groen, Mark A. Dolman, Rogier P.O. Schulte, Imke J.M. de Boer

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 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.

Original languageEnglish
Pages (from-to)58-67
JournalEnvironmental Modelling & Software
Volume96
DOIs
Publication statusPublished - 2017

Keywords

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

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