Information on the spatial distribution of soil pH is essential for assessing soil quality and soil
productivity. Digital soil mapping (DSM) is commonly used to predict soil characteristics over …

Digital soil mapping (DSM) increasingly makes use of machine learning algorithms to
identify relationships between soil properties and multiple covariates that can be detected …

Digital soil mapping combines soil plot data with environmental datasets to model variation
in soil properties across a landscape. The quality of a digital soil map depends on both the …

Soils are essential for supporting food production and providing ecosystem services but are
under pressure due to population growth, higher food demand, and land use competition …

A common practice in digital soil mapping (DSM) is to incorporate many environmental
covariates into a machine-learning algorithm to predict the spatial patterns of soil attributes …

Knowledge about soil properties and their variation in space and time is a key challenge for
understanding processes in the vadose zone. As we consider more complex models …

Spatial soil information is essential for informed decision-making in a wide range of fields.
Digital soil mapping (DSM) using machine learning algorithms has become a popular …

Although British Columbia (BC), Canada, has a rich history of producing conventional soil
maps (CSMs) between 1925 and 2000, the province still lacks a detailed soil map with a …

The uptake of machine learning (ML) algorithms in digital soil mapping (DSM) is
transforming the way soil scientists produce their maps. Within the past two decades, soil …

Digital soil mapping approaches predict soil properties based on the relationships between
soil observations and related environmental covariates using techniques such as machine …