MCCN Case Study 5 - Produce farm zone map

Full description

The MCCN project is to deliver tools to assist the agricultural sector to understand crop-environment relationships, specifically by facilitating generation of data cubes for spatiotemporal data. This repository contains Jupyter notebooks to demonstrate the functionality of the MCCN data cube components.

The dataset contains input files for the case study (data), RO-Crate metadata (ro-crate-metadata.json), results from the case study (result), and Jupyter Notebook (MCCN-CASE 5.ipynb)

Research Activity Identifier (RAiD)

RAiD: https://doi.org/10.26292/8679d473

Case Studies

This repository contains code and sample data for the following case studies. Note that the analyses here are to demonstrate the software and result should not be considered scientifically or statistically meaningful. No effort has been made to address bias in samples, and sample data may not be available at sufficient density to warrant analysis. All case studies end with generation of an RO-Crate data package including the source data, the notebook and generated outputs, including netcdf exports of the datacubes themselves.

Case Study 5 - Produce farm zone map

Description

Use soil sample data and crop yield data to develop a zone map for a farm. This study demonstrates: 1) Loading heterogeneous data sources into a cube, and 2) Analysis and visualisation using pykrige and KMeans.

Data Sources

Use Llara-Campey data including yield values and soil maps to develop classification of farm area into contiguous zones of relatively self-similar productivity. Variables should include the minimum zone area and the maximum number of zone classes to return.

This notebook can be delivered as a tool into which the user can load their own data in the form of spreadsheets containing points and associated values for the variables to take into account in the analysis. The requirement is either for comprehensive (raster) coverage for the area or of a set of point-based measurements for each variable (in which case a simple kriging or mesh interpolation will be applied).

Dependencies

• This notebook requires Python 3.10 or higher
• Install relevant Python libraries with: pip install mccn-engine rocrate pykrige scikit-learn
• Installing mccn-engine will install other dependencies