Budiman Minasny

TL;DR: The generic framework, which the authors call the scorpanSSPFe (soil spatial prediction function with spatially autocorrelated errors) method, is particularly relevant for those places where soil resource information is limited.

TL;DR: In this paper, the authors surveyed the soil organic carbon (SOC) stock estimates and sequestration potentials from 20 regions in the world (New Zealand, Chile, South Africa, Australia, Tanzania, Indonesia, Kenya, Nigeria, India, China Taiwan, South Korea, China Mainland, United States of America, France, Canada, Belgium, England & Wales, Ireland, Scotland, and Russia).

TL;DR: In this article, a review highlights knowledge of the amount of carbon stored in soils globally, and the potential for carbon sequestration in soil, and discusses successful methods and models used to determine and estimate carbon pools and fluxes.

TL;DR: Increased demand and advanced techniques could lead to more refined mapping and management of soils, and conventional soil mapping delineates space mostly according to qualitative criteria and renders maps using a series of polygons, which limits resolution.

TL;DR: The cLHS method with a search algorithm based on heuristic rules combined with an annealing schedule is presented, illustrated with a simple 3-D example and an application in digital soil mapping of part of the Hunter Valley of New South Wales, Australia.