{"type": "FeatureCollection", "features": [{"id": "10.1016/j.still.2020.104672", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:17:10Z", "type": "Journal Article", "created": "2020-05-15", "title": "Can pedotransfer functions based on environmental variables improve soil total nutrient mapping at a regional scale?", "description": "Abstract   Numerous pedotransfer functions (PTFs) have been developed to predict the soil properties of interest from other soil properties and, less commonly, from environmental variables. However, only a few PTFs have been developed to predict soil nutrients using environmental variables and to extrapolate them to characterize spatial soil variations at a regional scale. In this study, we attempted to develop PTFs for the total nitrogen (TN), total phosphorus (TP) and total potassium (TK) concentrations in three typical pedo-climatic areas of China (Fujian Province, Jiangsu Province and Qilian Mountains) with diverse climate, terrain and soil types. A series of linear PTFs were developed to quantify the effect of terrain and climate on the predictive relations between the soil nutrients and other measured soil properties and environmental variables. In addition, digital soil mapping (DSM) based on the random forest (RF) technique was performed to test the hypothesis that the best-fit PTFs could be extrapolated, based on soil maps and environmental variables, to describe regional soil variations in the soil nutrients. The root mean square errors (RMSEs) of the best-fit PTFs for TN, TP and TK ranged from 0.21 to 0.79 g kg\u22121, 0.20 to 0.58 g kg\u22121, and 3.68 to 5.00 g kg\u22121, respectively. 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Each parameter is distributed as a single-band GeoTIFF (compressed into ZIP archives), with additional preview images for quick inspection. \u00a0  Reference laboratory methods for comparability include: \u00a0- Nitrogen \u2013 ISO 11261:1995 (Modified Kjeldahl) \u00a0- Phosphorus \u2013 ISO 11263:1994 (Spectrometric) \u00a0- Potassium \u2013 USDA\u2013NRCS (NH\u2084OAc extraction + AAS) \u00a0- Organic Carbon \u2013 ISO 10694:1995 (Dry combustion) \u00a0- pH \u2013 ISO 10390:2005 (Glass electrode) \u00a0  The Murcia release forms part of a broader open dataset series curated by Siora, covering multiple countries and regions. 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Reports, articles, papers, scientific and non - scientific works of any form, including tables, maps, or any other kind of output, in printed or electronic form, based in whole or in part on the data supplied, must contain an acknowledgement of the form: \"Data reused from the BonaRes Data Centre www.bonares.de. This data were created as part of the Other's research activities.\" Although every care has been taken in preparing and testing the data, the Other and the BonaRes Data Centre cannot guarantee that the data are correct; neither does the Other and the BonaRes Data Centre accept any liability whatsoever for any error, missing data or omission in the data, or for any loss or damage arising from its use. 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