Rhizosphere interactions under diverse canola genotypes: a key component in nitrogen use efficiency
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Updated
Aug 5, 2022
Rhizosphere interactions under diverse canola genotypes: a key component in nitrogen use efficiency
Published original data and R code from “Above- and belowground plant pathogens along elevational gradients: patterns and potential mechanisms”
Ongoing research on several projects..
Brassica napus root and rhizosphere microbial nitrogen processing impacts on nitrogen use efficiency
SCMA work now in https://github.com/ANZSoilData/def-au-scma
A university project that aims to explore various data mining techniques like Data Exploration, Association Rule Mining, Supervised and Unsupervised Learning, applied to real-world datasets, focusing on soil fertility analysis and COVID-19 cases evolution over time.
Understanding the health of a farm based on soil organic carbon.
MASSAI multi-agent simulation of sustainable agricultural intensification
simple tool to find intersections
Comparing different data preprocessing methods to predict soil organic carbon content on soil spectra features
Reproducible Research Compendium for "Improving Models to Predict Holocellulose and Klason Lignin Contents for Peat Soil Organic Matter with Mid-Infrared Spectra" and "The need to reinterpret Hodgkins et al. (2018)".
Code for analysis of soils, crops, and nutrition along a distance-to-forest gradient in Ethiopia
Soil Data Access Scripts
Application for Soil health identification. Air health identification. Upcoming feature for agriculture, land Mining and other research purpose through using machine learning and artificial Intelligence and culminating into a vast application for computer vision also.
The scripts contained in this repository relate directly to the work conducted by the Tree Root Microbiome Project (TRMP) led by Dr Steve Wakelin.
Repository of Jupyter notebooks aimed at learning how to use Python to retrieve data from Google Earth Engine
SWAT+ model input data preparation helper
APIs for soil quality, types and other factors in Germany
🌱A machine learning web and mobile application for soil type classification for plant decision making
Large-scale digital mapping of soil organic carbon content by using machine learning algorithms
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