A set of hands-on coding exercises to solve common tasks and simple problems in agricultural sciences.

The soilspec package: data and functions for the book 'Soil Spectral Inference with R'

The soil mate app provides a simple and convenient way to collect soil data at sample locations in the field. The Soil Mate app is targeted across multiple industries, including agriculture, environmental science, geology, and mining. The current version of the app collects soil texture data.

An R implementation of the DSMART algorithm

Soil Sample and Soil Profile Datasets: an Open Compilation

Expandable And Scalable Infrastructure for Finite Element Methods, EASIFEM, is [Modern Fortran](https://fortran-lang.org) framework for solving partial differential equations (PDEs) using finite element methods. EASIFEM "eases" the efforts to develop scientific programs in Fortran.

Conformal Prediction for Digital Soil Mapping

R scripts for predicting soil organic carbon using soil spectral library from visible, near-infrared and shortwave-infrared (VNIR) and middle-infrared (MIR) using LASSO and PLS regression methods and the target-oriented cross-validation strategy.

This repository contains files for automated soil sampling selection using the K-Means algorithm in R. The repository is intended for researchers and practitioners interested in automated soil sampling selection using the K-Means algorithm.

The scripts contained in this repository relate directly to the work conducted by the Tree Root Microbiome Project (TRMP) led by Dr Steve Wakelin.

Soil Heating in Fire (SheFire) Model: Annotated .Rmd scripts and an R package to build and use a SheFire model for how different soil depths heat and cool during fires

Course material for LSU AGRO 4092: R for Spatial Analysis & Visualization

Welcome to the Geographic AI for Soil Assessment gaia interface, your ultimate companion in Predicting and visualizing soil microbial biodiversity.

R script for analyzing soil texture based on Geophilus measurements. Generates comprehensive reports with detailed visualizations and statistical summaries of sand, silt, and clay percentages.

Comparing different data preprocessing methods to predict soil organic carbon content on soil spectra features

A package to support sediment source fingerprinting studies: characterising your dataset, selecting tracers (three-step method), modelling source contribution (BMM) and assessing the quality of modelling predictions using virtual mixtures (support BMM and MixSIAR).

A proof of concept of how a holistic data-driven approach could be used to make better future planning decisions for land zoning.

Personal blog

This repository collects material (code, presentation, images, test data) prepared for the webinar series of the Excalibur H2020 Training

Ongoing research on several projects..