Invited review: a position on The Global Livestock Environmental Assessment Model (GLEAM)
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The livestock sector is one of the fastest growing subsectors of the agricultural economy and, while it makes a major contribution to global food supply and economic development, it also consumes significant amounts of natural resources and alters the environment. In order to improve our understanding of the global environmental impact of livestock supply chains, FAO has developed GLEAM, the Global Livestock Environmental Assessment Model. The purpose of this paper is to provide a review of GLEAM. Specifically, it explains the model architecture, methods and functionality, i.e. the types of analysis that the model can perform. The model focuses primarily on the quantification of GHG emissions arising from the production of the 11 main livestock commodities. The model inputs and outputs are managed and produced as raster datasets, with spatial resolution of 0.05 decimal degrees. GLEAM v 1.0 consists of five distinct modules: (a) the Herd Module; (b) the Manure Module; (c) the Feed Module; (d) the System Module; (e) the Allocation Module. In terms of the modelling approach, GLEAM has several advantages. For example spatial information on livestock distributions and crops yields enables rations to be derived that reflect the local availability of feed resources in developing countries. GLEAM also contains a herd model that enables livestock statistics to be disaggregated and variation in livestock performance and management to be captured. Priorities for future development of GLEAM include: improving data quality and the methods used to perform emissions calculations; extending the scope of the model to include selected additional environmental impacts and to enable predictive modelling; and improving the utility of GLEAM output.
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Copyright © The Animal Consortium 2017 This is an Accepted Manuscript of an article published by Cambridge University Press in a revised form with their editorial input. The final published version is available online: https://doi.org/10.1017/S1751731117001847