Selective improvement of global datasets for the computation of locally relevant environmental indicators: A method based on global sensitivity analysis. (October 2017) - Uwizeye, Aimable; Gerber, Pierre J.; Groen, Evelyne A.; Dolman, Mark A.; Schulte, Rogier P.O.; de Boer, Imke J.M.
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. Highlights: We propose a method to improve the local relevance of environmental indicators. We use a global sensitivity analysis to select important input parameters. Important parameters contribute more than 82% to the variance of indicators. Establishing a few parameters with good data improve the reliability of indicators.