MCCN Case Study 5 - Produce farm zone map
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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/10.26292/8679d473Case 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 5 - Produce farm zone mapDescriptionUse 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 SourcesUse 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).DependenciesThis notebook requires Python 3.10 or higherInstall relevant Python libraries with: pip install mccn-engine rocrate pykrige scikit-learnInstalling mccn-engine will install other dependencies
MCCN项目旨在提供工具，助力农业领域理解作物与环境的关系，具体而言是通过促进时空数据的数据立方体生成。本仓库包含Jupyter笔记本，用于演示MCCN数据立方体组件的功能。数据集包含案例研究的输入文件（data）、RO-Crate元数据（ro-crate-metadata.json）、案例研究结果（result）以及Jupyter笔记本（MCCN-CASE 5.ipynb） 研究活动标识符（RAiD） RAiD：https://doi.org/10.26292/8679d473 案例研究 本仓库包含以下案例研究的代码与样本数据。请注意，此处的分析仅用于演示软件功能，其结果不应被视为具有科学或统计意义。未针对样本偏差采取纠正措施，且样本数据的密度可能不足以支撑有效分析。所有案例研究均以生成RO-Crate数据包收尾，该数据包包含源数据、笔记本及生成的输出（包括数据立方体自身的netcdf导出文件） 案例研究5——生成农场分区图 描述 使用土壤样本数据与作物产量数据开发农场分区图。本研究演示：1）将异构数据源加载至数据立方体；2）使用pykrige与KMeans进行分析与可视化 数据来源 使用Llara-Campey数据（含产量值与土壤图）将农场区域划分为生产力相对相似的连续分区。变量应包含最小分区面积及待返回的最大分区类别数。 该笔记本可作为工具使用，用户可将自身数据以电子表格形式导入，表格需包含分析中需考虑的变量对应的点及关联值。数据需满足以下任一条件：区域具备全面的（栅格）覆盖；或每个变量拥有一组基于点的测量值（在此情况下将应用简单克里金法（kriging）或网格插值） 依赖项 该笔记本要求Python 3.10或更高版本 使用以下命令安装相关Python库：pip install mccn-engine rocrate pykrige scikit-learn 安装mccn-engine将自动安装其他依赖项
