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

The application of machine learning (ML) techniques in various fields of science has
increased rapidly, especially in the last 10 years. The increasing availability of soil data that …

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

This paper presents the second part of the map** of topsoil properties based on the Land
Use and Cover Area frame Survey (LUCAS). The first part described the physical properties …

80% of arable land in Africa has low soil fertility and suffers from physical soil problems.
Additionally, significant amounts of nutrients are lost every year due to unsustainable soil …

Abstract Machine-learning is the automated process of uncovering patterns in large datasets
using computer-based statistical models, where a fitted model may then be used for …

Precision agriculture represents the new age of conventional agriculture. This is made
possible by the advancement of various modern technologies such as the internet of things …

Although estimating the uncertainty of models used for modelling nitrate contamination of
groundwater is essential in groundwater management, it has been generally ignored. This …

This study aimed to map SOC lateral, and vertical variations down to 1 m depth in a semi-
arid region in Kurdistan Province, Iran. Six data mining techniques namely; artificial neural …

Estimation of soil organic carbon (SOC) is very useful for accurate monitoring of carbon
sequestration. However, there are still significant gaps in the knowledge of SOC reserves in …