substance flow data of Chinese Phosphorus cycle from 1949 to 2018
Description
The Substance Flow Data of Chinese P Cycle from 1949 to 2018 presents an annual data of 825 P flows transferring between natural and anthropogenic processes. The data were calculated based on the mass balance principle method —— substance flow analysis, with the unit of kt P/yr. The primary data are also included in the database. Measures of uncertainty of all are shown in the auxiliary dataset. With the data, the role of P in the resilient water–energy–food security nexus can be analyzed, the extended subsequent methods can also be applied.
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The SFA framework was constructed and optimized by referring to Liu et al., (2016). Followed the original divided 14 main processes, to track the economic and technological development, we added new subprocesses including postharvest handling, aquatic product handling, agricultural food products storage, distribution and retailing. And we added some production stages, they are: 1) LFP battery production to cope with the multiplying demand for electric vehicles; 2) maize starch processing, millet processing and sorghum brewing in food products processing; 3) feed-use maize processing in feed processing; 4) maize-based bioethanol production in non-food products processing; 5) meat processing so that there is a distinction between the slaughter of livestock and the processing of meat 6) and the corresponding balance terms to the above all. Secondly, we included in the updated phosphorus system calculation framework the amount of loss or waste of food during the food supply chain. Thirdly, to coordinate with the refinement of resource management policies and to facilitate researchers who focus on a specific point of the phosphorus system to obtain fine data, 191 main P flows are refined into 825 streams with the minor units of certain livestock or crop. To identify the role of exports in phosphorus loss, net exports were split into imports and exports. Finally, to enhance the reliability of the data, all the primary data, the source of the updated data, and the function are fully disclosed. A Monte Carlo simulation was used to qualify the influences of uncertainty and variability of the input data on the results in the propagation of the SFA processes. The formula and primary data collection can be found in the README doc. The format of the database is Microsoft Access (.accdb). The database contains three sheets. The fields in Sheet1 are START, END, FLOW NUMBER, DESCRIPTION and FUNCTION, representing the sequence number of the process in which a flow starts and ends and its calculation function. The fields contained in Sheet2 are PRIMARY DATA, UNIT, YEAR, DATA and distribution characteristics, representing information of all primary data in 1949~2018. Distribution characteristics include DIST, P1, P2 and P3, representing the distribution function and three distribution parameters. P1 and P2 are the minimum and maximum data values in the uniform distribution, respectively, and P3 is NA. In the triangle distribution, P1, P2 and P3 are the minimum, maximum and peak, respectively. Missing data or unchanged data in the time series are replaced by the previous value. The fields in Sheet3 are YEAR, FLOW NUMBER, MEAN, MEDIAN, standard deviation, coefficient of variation and percentile, providing the substance flow results after the uncertainty analysis. The unit of all data in Sheet3 is kt P.