%0 Generic
%A van Doorn, Maarten
%A Helfenstein, Anatol
%A Ros, Gerard H.
%A Heuvelink, Gerard B.M.
%A van Rotterdam-Los, Debby
%A Verweij, Sven E.
%A de Vries, Wim
%D 2024
%T 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"
%U 
%R 10.4121/96c54816-4e36-4285-89fd-a63e478f9acd.v1
%K oxalate
%K digital soil mapping
%K iron
%K aluminium
%K agriculture
%K netherlands
%K soil health
%K soil functions
%K phosphorus sorption capacity
%X <p>This dataset contains digital soil maps (.tiff) of predicted soil contents of oxalate-extractable iron and aluminium at a&nbsp;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&nbsp;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.</p><p><br></p>
%I 4TU.ResearchData