The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/96c54816-4e36-4285-89fd-a63e478f9acd

van Doorn, Maarten; Anatol Helfenstein; Ros, Gerard H.; Heuvelink, Gerard B.M.; van Rotterdam-Los, Debby et. al. (2024): Digital Soil Maps underlying the publication "high-resolution digital soil mapping of amorphous iron- and aluminium-(hydr)oxides to guide sustainable phosphorus and carbon management". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/96c54816-4e36-4285-89fd-a63e478f9acd.v1

Dataset

• Soils
• Land and Water Management
• Soil Sciences
• Environmental Science and Management
• Inorganic Chemistry
• Agriculture, Land and Farm Management
• Agricultural and Veterinary Sciences
• Environmental Sciences
• Environment
• Chemical Sciences

agriculture, aluminium, digital soil mapping, iron, netherlands, oxalate, phosphorus sorption capacity, soil functions, soil health

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Maarten van Doorn , Anatol Helfenstein , Gerard H. Ros , Gerard B.M. Heuvelink , Debby van Rotterdam-Los , Sven E. Verweij , Wim de Vries

This dataset contains digital soil maps (.tiff) of predicted soil contents of oxalate-extractable iron and aluminium at a 25 m spatial resolution across six depth layers (0-5 cm, 5-10 cm, 10-25 cm, 25-60 cm, 60-100 cm and 100-200 cm) for agricultural fields in the Netherlands. For each of these depth layers, there is a map of mean predictions, the 5th, 50th (median) and 95th quantile predictions, as well as the 90% prediction interval (PI90 = 95th - 5th quantile) and prediction interval ratio (PIR = PI90 / median). PI90 and PIR represent absolute and relative uncertainty predictions, respectively. The maps were created using Quantile Regression Forest models, which were calibrated using geo-referenced wet-chemical measurements (n = 12,110) and near-infrared (NIR) estimates (n = 102,393) of oxalate-extractable iron and aluminium and over 150 spatial covariates (spatially explicit environmental variables of soil forming factors). See publication for details, including the assessment of map quality using design-based statistical inference.

• 2024-02-29 first online, published, posted

4TU.ResearchData

tiff

High-resolution digital soil mapping of amorphous iron- and aluminium-(hydr)oxides to guide sustainable phosphorus and carbon management

Nutriënten Management Instituut; Wageningen University & Research;
ISRIC - World Soil Information