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

https://doi.org/10.1016/j.envsoft.2017.06.041Get rights and content

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.

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.

Section snippets

Software and data availability

The modelling done in this paper is performed in the R project for statistical computing. R is an open source statistics software and can be downloaded from https://www.r-project.org. Data from a global dataset were obtained from the Global Livestock Environmental Assessment Model (GLEAM) version 2, which was developed by the Animal Production and Health Division (AGA) of Food and Agriculture Organization of United Nations (FAO). The description of GLEAM can be found at //www.fao.org/gleam/en/

Description of the proposed method

The different steps of the proposed method are summarized in Fig. 1.

Uncertainties of N use indicators

The results of the uncertainty analysis for N use indicators in the Netherlands are shown in Fig. 2. The mean value of life-cycle-NUEN for FADNNL was similar to GLEAMNL (46%). For other indicators, however, there was a larger difference in mean values. The mean value of life-cycle-NNBN was lower for FADNNL (105 kg N ha−1) as compared to GLEAMNL (132 kg N ha−1). Regarding the NHIN, the mean value was higher for FADNNL (158%) as compared GLEAMNL (147%).

The distribution of NHIN was similarly

Conclusions

This study proposed a method to improve the local relevance of the environmental performance indicators computed from a global dataset, by identifying important input parameters through a GSA that shall be prioritized and established with high-quality data. We demonstrated that N use indicators computed from GLEAM dataset were relatively close to those estimated from farm survey data in the Netherlands than in Rwanda. However, by substituting the important input parameters for activity data

Acknowledgements

This work is supported by the Teagasc Walsh Fellowship Scheme (Ref: 2012230), the Livestock Environmental Assessment Performance (LEAP) Partnership (GCP/GLO/369/MUL) and the Livestock Information, Sector Analysis and Policy Branch (AGAL) of Food and Agriculture Organization of United Nations (FAO). We would like to thank Angel Bas for his statistical advice and GLEAM development team for their support in data handling.

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