Diagnosis frameworks for multiple and complex soil constraints

The purpose of this project is to diagnose and prioritise multiple and interacting soil constraints at the sub-field level using farmer generated and publicly available data. The project is developing underpinning data-centric methods as a software code framework that future decision support tools will use to diagnose soil constraints.

The goal of this research is to reduce the cost barrier (the need for detailed soil sampling at depth) to farmers diagnosing complex and multiple soil constraints in their fields.

It is achieving this by developing a hybrid modelling and diagnostic approach that brings together biophysical models, artificial intelligence (AI) and statistical approaches to analysing farmer and publicly available data to identify and diagnose soil constraints at a subfield level.