In the digital soil mapping (DSM) framework, machine learning models quantify the
relationship between soil observations and environmental covariates. Generally, the most …

Abstract Knowledge about distribution of soil properties over the landscape is required for a
variety of land management applications and resources, modeling, and monitoring …

Desertification is a serious and perilous threat to natural ecosystems. Understanding the
spatio-temporal characteristics of this process through the evaluation and monitoring of …

This study investigates the ability of different digital soil mapping (DSM) approaches to
predict some of physical and chemical topsoil properties in the Shahrekord plain of …

In this study, using Analytic Hierarchy Process (AHP) and Geographic Information System
(GIS), the sensitivity to the occurrence of different types of droughts including meteorological …

Since in most mapping models geometric mean of different criteria are used to determine the
desertification intensity, one of the most important issues in desertification studies is …

In southwestern Tunisia, the intense exploitation of the natural resources (soil, water,
hydrocarbons, etc.) bears unrecoverable consequences on the local environment. In fact …

This study tested and evaluated a suite of nine individual base learners and seven model
averaging techniques for predicting the spatial distribution of soil properties in central Iran …

Climate change effects on crop production are of high concern in arid regions that are
suffering from increased drought. We parameterized the APSIM-Wheat (The Agricultural …

Vulnerabilities of different regions to drought are under the influence of different factors such
as the land use, accessibility to the surface and subsurface water resources, etc. In this …