Clearing the fog on phosphate rock data – Uncertainties, fuzziness, and misunderstandings

Abstract Big Data , blockchains , and cloud computing have become ubiquitous in today's mass media and are universally known terms used in everyday speech. If we look behind these often misused buzzwords, we find at least one common element, namely data. Although we hardly use these terms in the “classic discipline” of mineral economics, we find various similarities. The case of phosphate data bears numerous challenges in multiple forms such as uncertainties, fuzziness, or misunderstandings. Often simulation models are used to support decision-making processes. For all these models, reliable and accurate sets of data are an essential premise. A significant number of data series relating to the phosphorus supply chain, including resource inventory or production, consumption, and trade data ranging from phosphate rock to intermediates like marketable concentrate to final phosphate fertilizers, is available. Data analysts and modelers must often choose from various sources, and they also depend on data access. Based on a transdisciplinary orientation, we aim to help colleagues in all fields by illustrating quantitative differences among the reported data, taking a somewhat engineering approach. We use common descriptive statistics to measure and causally explain discrepancies in global phosphate-rock production data issued by the US Geological Survey, the British Geological Survey, Austrian World Mining Data, the International Fertilizer Association, and CRU International over time, with a focus on the most recent years. Furthermore, we provide two snapshots of global-trade flows for phosphate-rock concentrate, in 2015 and 1985, and compare these to an approach using total-nutrient data. We find discrepancies of up to 30% in reported global production volume, whereby the major share could be assigned directly to China and Peru. Consequently, we call for a global, independent agency to collect and monitor phosphate data in order to reduce uncertainties or fuzziness and, thereby, ultimately support policy-making processes.