Articles | Volume 6, issue 2
SOIL, 6, 359–369, 2020
https://doi.org/10.5194/soil-6-359-2020
SOIL, 6, 359–369, 2020
https://doi.org/10.5194/soil-6-359-2020

Original research article 06 Aug 2020

Original research article | 06 Aug 2020

Disaggregating a regional-extent digital soil map using Bayesian area-to-point regression kriging for farm-scale soil carbon assessment

Sanjeewani Nimalka Somarathna Pallegedara Dewage et al.

Related authors

Additional soil organic carbon storage potential in global croplands
José Padarian, Budiman Minasny, Alex B. McBratney, and Pete Smith
SOIL Discuss., https://doi.org/10.5194/soil-2021-73,https://doi.org/10.5194/soil-2021-73, 2021
Manuscript not accepted for further review
Short summary
The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data
Wartini Ng, Budiman Minasny, Wanderson de Sousa Mendes, and José Alexandre Melo Demattê
SOIL, 6, 565–578, https://doi.org/10.5194/soil-6-565-2020,https://doi.org/10.5194/soil-6-565-2020, 2020
Short summary
Game theory interpretation of digital soil mapping convolutional neural networks
José Padarian, Alex B. McBratney, and Budiman Minasny
SOIL, 6, 389–397, https://doi.org/10.5194/soil-6-389-2020,https://doi.org/10.5194/soil-6-389-2020, 2020
Short summary
Comparing three approaches of spatial disaggregation of legacy soil maps based on the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm
Yosra Ellili-Bargaoui, Brendan Philip Malone, Didier Michot, Budiman Minasny, Sébastien Vincent, Christian Walter, and Blandine Lemercier
SOIL, 6, 371–388, https://doi.org/10.5194/soil-6-371-2020,https://doi.org/10.5194/soil-6-371-2020, 2020
Machine learning and soil sciences: a review aided by machine learning tools
José Padarian, Budiman Minasny, and Alex B. McBratney
SOIL, 6, 35–52, https://doi.org/10.5194/soil-6-35-2020,https://doi.org/10.5194/soil-6-35-2020, 2020
Short summary

Related subject area

Soils and the natural environment
Effects of environmental factors and soil properties on soil organic carbon stock in a natural dry tropical area of Cameroon
Désiré Tsozué, Nérine Mabelle Moudjie Noubissie, Estelle Lionelle Tamto Mamdem, Simon Djakba Basga, and Dieudonne Lucien Bitom Oyono
SOIL, 7, 677–691, https://doi.org/10.5194/soil-7-677-2021,https://doi.org/10.5194/soil-7-677-2021, 2021
Short summary
The role of ecosystem engineers in shaping the diversity and function of arid soil bacterial communities
Capucine Baubin, Arielle M. Farrell, Adam Št'ovíček, Lusine Ghazaryan, Itamar Giladi, and Osnat Gillor
SOIL, 7, 611–637, https://doi.org/10.5194/soil-7-611-2021,https://doi.org/10.5194/soil-7-611-2021, 2021
Short summary
SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty
Laura Poggio, Luis M. de Sousa, Niels H. Batjes, Gerard B. M. Heuvelink, Bas Kempen, Eloi Ribeiro, and David Rossiter
SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021,https://doi.org/10.5194/soil-7-217-2021, 2021
Short summary
Opportunities and limitations related to the application of plant-derived lipid molecular proxies in soil science
Boris Jansen and Guido L. B. Wiesenberg
SOIL, 3, 211–234, https://doi.org/10.5194/soil-3-211-2017,https://doi.org/10.5194/soil-3-211-2017, 2017
Short summary
Spatial variability in soil organic carbon in a tropical montane landscape: associations between soil organic carbon and land use, soil properties, vegetation, and topography vary across plot to landscape scales
Marleen de Blécourt, Marife D. Corre, Ekananda Paudel, Rhett D. Harrison, Rainer Brumme, and Edzo Veldkamp
SOIL, 3, 123–137, https://doi.org/10.5194/soil-3-123-2017,https://doi.org/10.5194/soil-3-123-2017, 2017
Short summary

Cited articles

Akpa, S., Odeh, I., Bishop, T., Hartemink, A., and Amapu, I.: Total soil organic carbon and carbon sequestration potential in Nigeria, Geoderma, 271, 202-215, https://doi.org/10.1016/j.geoderma.2016.02.021, 2016. 
Arrouays, D., McBratney, A. B., Minasny, B., Hempel, J. W., Heuvelink, G. B. M., MacMillan, R. A., Hartemink, A. E., Lagacherie, P., and McKenzie, N. J.: The GlobalSoilMap project specifications, Glob. Basis Glob. Spat. Soil Inf. Syst., Taylor & Francis Group, London, 9–12, 2014. 
Brus, D., Orton, T., Walvoort, D., Reijneveld, J., and Oenema, O.: Disaggregation of soil testing data on organic matter by the summary statistics approach to area-to-point kriging, Geoderma, 226–227, 151–159, https://doi.org/10.1016/j.geoderma.2014.02.011, 2014. 
Cheng, Q.: Modeling Local Scaling Properties for Multiscale Mapping, Vadose Zone J., 7, 525, https://doi.org/10.2136/vzj2007.0034, 2008. 
Cressie, N.: Statistics for spatial data, Wiley, New York, 1991. 
Download
Short summary
Most soil management activities are implemented at farm scale, yet digital soil maps are commonly available at regional/national scales. This study proposes Bayesian area-to-point kriging to downscale regional-/national-scale soil property maps to farm scale. A regional soil carbon map with a resolution of 100 m (block support) was disaggregated to 10 m (point support) information for a farm in northern NSW, Australia. Results are presented with the uncertainty of the downscaling process.