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.
| Language | English |
|---|---|
| Pages | 58-67 |
| Journal | Environmental Modelling & Software |
| Volume | 96 |
| DOIs | |
| Publication status | Published - 2017 |
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Keywords
- Decision-making
- Environmental modelling
- Global datasets
- Global sensitivity analysis
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Selective improvement of global datasets for the computation of locally relevant environmental indicators : A method based on global sensitivity analysis. / Uwizeye, U.A.; Gerber, Pierre J.; Groen, Evelyne A.; Dolman, Mark A.; Schulte, Rogier P.O.; de Boer, Imke J.M.
In: Environmental Modelling & Software, Vol. 96, 2017, p. 58-67.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Selective improvement of global datasets for the computation of locally relevant environmental indicators
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
U2 - 10.1016/j.envsoft.2017.06.041
DO - 10.1016/j.envsoft.2017.06.041
M3 - Article
VL - 96
SP - 58
EP - 67
JO - Environmental Modelling & Software
JF - Environmental Modelling & Software
SN - 1364-8152
ER -