Increment-averaged kriging: a comparison with depth-harmonized mapping of soil exchangeable sodium percentage in a cropping region of eastern Australia

Lai, Y. R. ORCID: https://orcid.org/0000-0002-7135-7520 and Orton, T. G. and Pringle, M. J. and Menzies, N. W. and Dang, Y. P. (2020) Increment-averaged kriging: a comparison with depth-harmonized mapping of soil exchangeable sodium percentage in a cropping region of eastern Australia. Geoderma, 363:114151. ISSN 0016-7061


Abstract

Soil sodicity, generally measured through the exchangeable sodium percentage (ESP), is the most prevalent edaphic stress in Australia, particularly in the northern grains-growing region (NGR) of Australia. Farmers, scientists and policy-makers need accurate maps of sodicity for environmental modelling, and to make rational decisions for management. A common approach to mapping soil properties for multiple target depths is to first harmonize soil profile data to the target depth intervals, usually through equal-area spline functions, then undertake a two-dimensional spatial analysis (often regression kriging) for each interval; we refer to this as a ‘spline-then-krige’ (STK) approach. An alternative is to calibrate a single three-dimensional model that describes soil variation both horizontally and vertically, as is done in the increment-averaged kriging (IAK) approach. This study compares IAK and STK for mapping ESP in the NGR. A machine-learning algorithm was used to model trends based on environmental covariates in both approaches. Both IAK and STK captured the ESP variation reasonably well (concordance correlation coefficient values for IAK around 0.5 for all depths), with IAK giving slightly better predictive accuracies for all depths, most evidently in the topsoil. IAK allows the natural use of all available data, explicitly accounts for the sample support of the soil data and the horizontal and vertical auto-correlation between and within soil profiles, and enables the propagation of uncertainty from the original data through to the final predictions and prediction uncertainties. We conclude that IAK can provide a competitive alternative to STK approaches for mapping soil properties in three dimensions.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Sustainable Agricultural Systems (1 Aug 2018 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Sustainable Agricultural Systems (1 Aug 2018 -)
Date Deposited: 22 Mar 2021 07:11
Last Modified: 31 Mar 2021 05:33
Uncontrolled Keywords: digital soil mapping; disaggregation; geostatistics; kriging; REML: soil constraints
Fields of Research (2008): 05 Environmental Sciences > 0503 Soil Sciences > 050399 Soil Sciences not elsewhere classified
Fields of Research (2020): 41 ENVIRONMENTAL SCIENCES > 4106 Soil sciences > 410602 Pedology and pedometrics
41 ENVIRONMENTAL SCIENCES > 4106 Soil sciences > 410699 Soil sciences not elsewhere classified
Identification Number or DOI: https://doi.org/10.1016/j.geoderma.2019.114151
URI: http://eprints.usq.edu.au/id/eprint/41584

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