MCCN Case Study 6 - Environmental Correlates for Productivity

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 (source_data), RO-Crate metadata (ro-crate-metadata.json), results from the case study (results), and Jupyter Notebook (MCCN-CASE 6.ipynb)Research Activity Identifier (RAiD)RAiD: https://doi.org/10.26292/8679d473Case StudiesThis 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 6 - Environmental Correlates for ProductivityDescriptionAnalyse relationship between different environmental drivers and plant yield. This study demonstrates: 1) Loading heterogeneous data sources into a cube, and 2) Analysis and visualisation of drivers. This study combines a suite of spatial variables at different scales across multiple sites to analyse the factors correlated with a variable of interest.Data SourcesThe dataset includes the Gilbert site in Queensland which has multiple standard sized plots for three years. We are using data from 2022. The source files are part pf the larger collection - Chapman, Scott and Smith, Daniel (2023). INVITA Core site UAV dataset. The University of Queensland. Data Collection. https://doi.org/10.48610/951f13cBoundary file - This is a shapefile defining the boundaries of all field plots at the Gilbert site. Each polygon represents a single plot and is associated with a unique Plot ID (e.g., 03_03_1). These plot IDs are essential for joining and aligning data across the orthomosaics and plot-level measurements.https://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/shp.zip.Orthomosaics - The site was imaged by UAV flights multiple times throughout the 2022 growing season, spanning from June to October. Each flight produced an orthorectified mosaic image using RGB and Multispectral (MS) sensors.https://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/2022-09-18.tifhttps://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/UQ_GilbertN_danNVT_2022-07-28_10-00-00_Altum_bgren_20m_transparent_reflectance_packed.tifhttps://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/UQ_GilbertN_danNVT_2022-08-08_10-00-00_Altum_bgren_20m_transparent_reflectance_packed.tifPlot level measurements - Multispectral Traits: Calculated from MS sensor imagery and include indices NDVI, NDRE, SAVI and Biomass Cuts: Field-measured biomass sampled during different growth stages (used as a proxy for yield).https://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/filtered_biomass_updated.csvhttps://object-store.rc.nectar.org.au/v1/AUTH_2b454f47f2654ab58698afd4b4d5eba7/mccn-test-data/case-study-5-files/filtered_multispec_aggregated.csv