Home About Browse Search
Svenska


Digital soil mapping of copper in Sweden: Using the prediction and uncertainty as decision support in crop micronutrient management

Adler, Karl and Piikki, Kristin and Söderström, Mats and Eriksson, Jan (2022). Digital soil mapping of copper in Sweden: Using the prediction and uncertainty as decision support in crop micronutrient management. Geoderma Regional. 30 , e00562
[Research article]

[img] PDF
4MB

Abstract

Digital soil mapping (DSM) of topsoil copper (Cu) concentrations and prediction intervals covering 90% of agricultural land in Sweden was performed, in order to identify areas at risk of Cu deficiency. A total of 12,527 soil samples were used to calibrate the DSM model, using airborne gamma radiation data, climate data, topographical data and soil texture class data. Among the samples included, 11,093 had no laboratory-analysed Cu concentrations, so their Cu concentrations were predicted using portable X-ray fluorescence (PXRF) measurements. Cross-validation of the PXRF model resulted in Nash-Sutcliffe model efficiency coefficient (E) of 0.66 and mean absolute error (MAE) of 3.3 mg kg−1. Cross-validation of the DSM model showed somewhat lower performance (E = 0.57, MAE = 4.1 mg kg−1). Based on the lower bound of the prediction interval (5th percentile), 48% of agricultural soils in Sweden are most likely not at risk of Cu deficiency (>7 mg kg−1). The Cu map was also validated against concentrations in soil samples from five fields (25–47 ha in size; four samples per ha). The field means were predicted with a MAE of 1.0 mg kg−1 and within-field variation was reproduced with a field-wise squared Pearson correlation coefficient (r2) of 0–0.36. The classification metric ‘recall’ showed that the map of soil Cu concentrations might not predict all possible areas at risk of being Cu deficient, as observational data indicates that about 22% of soils in the mapped area should have Cu concentrations below the risk limit. However, the metric ‘precision’ showed that when the soil map predicted a concentration at or below 7 mg kg−1, it was generally correct. Increasing the limit resulted in the recall and precision increasing rapidly. The remaining 52% of agricultural soils at risk of being below the Cu concentration limit can be targeted by laboratory analysis or monitoring.

Authors/Creators:Adler, Karl and Piikki, Kristin and Söderström, Mats and Eriksson, Jan
Title:Digital soil mapping of copper in Sweden: Using the prediction and uncertainty as decision support in crop micronutrient management
Series Name/Journal:Geoderma Regional
Year of publishing :2022
Volume:30
Article number:e00562
Number of Pages:12
ISSN:2352-0094
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Soil Science
Keywords:Digital soil mapping, Micronutrient, CopperMachine learning, PXRF, Cambisol, Regosol
URN:NBN:urn:nbn:se:slu:epsilon-p-118555
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-118555
Additional ID:
Type of IDID
DOI10.1016/j.geodrs.2022.e00562
ID Code:28554
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Soil and Environment
(S) > Dept. of Soil and Environment
Deposited By: SLUpub Connector
Deposited On:25 Aug 2022 10:34
Metadata Last Modified:25 Aug 2022 10:41

Repository Staff Only: item control page

Downloads

Downloads per year (since September 2012)

View more statistics

Downloads
Hits