TY - JOUR
T1 - Selective improvement of global datasets for the computation of locally relevant environmental indicators
T2 - A method based on global sensitivity analysis
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
AN - SCOPUS:85021717220
SN - 1364-8152
VL - 96
SP - 58
EP - 67
JO - Environmental Modelling & Software
JF - Environmental Modelling & Software
ER -