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VeRCYe Pipeline Inputs Documentation

Overview

The VeRCYe pipeline enables large-scale yield studies by organizing simulation data within a structured directory and processing it with snakemake. This document details the required input files, directory structure, and configurable parameters.

1. Yield Study Setup

To run a yieldstudy, you will need two things:

  1. Base Directory: A directory that includes all your regions of interest, APSIM configurations and Reference Data.
  2. Configuration: A configuration file for VeRCYe, specifying different parameters for the study.

We provide an example setup under Example Setup.

In this document we detail the steps for the input setup. However, we recommend using the setup_helper.ipynb as outlined in Running VeRCYe.

Simulation Head Directory

Your yield study is structured within a single directory, referred to as the simulation head directory. This directory must follow a predefined structure to ensure compatibility with the pipeline.

Directory Structure

head_dir/
|   snakemake_config.yaml
|---Year1/
|   |   groundtruth-primary-Year1.csv (optional)
|   |---TimePoint-1/
|   |   |---region1/
|   |   |   |   region1.geojson
|   |   |   |   region1_template.apsimx
|   |---TimePoint-N/
|       |---regionN/
|           |   regionN.geojson
|           |   regionN_template.apsimx
|---Year2/
    ...

Each year contains timepoints, and each timepoint contains regions with their respective geojson and apsimx template files. The names in this structure are just descriptive placeholders and should be adjusted as described below.

2. Region GeoJSON Files

Purpose

Each Region of Interest (ROI) is represented as a GeoJSON file within its respective timepoint directory.

Converting Shapefiles to GeoJSON

We expect your data to initially be a shapefile (.shp). To extract individual regions into GeoJSONs, we provide a conversion script. If your shapefile has

  • A: Mixed Administrative Levels: Use apsim/prepare_shapefile.py to standardize the shapefile before conversion. Then proceed with B.
  • B: Single Administrative Level: Use apsim/convert_shapefile_to_geojson.py if the shapefile has a uniform administrative level.

[!Warning] Ensure your shapefiles contains only geometries at the same administrative level if skipping prepare_shapefile.py!

File Naming Convention

Each GeoJSON file must follow the format:

regionname.geojson

For example, if studying California, the file structure would be:

2024/T-0/california/california.geojson

If you do not use our conversion from shapefile to GeoJSON, you will need to manually ensure that each GeoJSON contains a centroid column that has the same format as extracted in convert_shapefile_to_geojson.py.

3. Years and Timepoints

Defining Years

Years should be named numerically (e.g., 2024, 2025) to represent different simulation periods.

Defining Timepoints

Each timepoint represents a simulation scenario, e.g., using all available meteorological data vs. limiting it to 30 days before the latest observation.

Each timepoint must define:

  • APSIM Simulation Start & End Dates
  • Meteorological Data Start & End Dates
  • LAI (Leaf Area Index) Data Start & End Dates

Years and Timepoints are referenced in snakemake_config.yaml using their respective names. Therefore the folder names must match these.

4. APSIMX Templates

Purpose

Each region and timepoint requires an APSIMX template (regionname_template.apsimx). This file defines crop and soil and other parameters for APSIM. The dates (Models.Clock) in the APSIM file must align with the simulation dates set in snakemake_config.yaml! This is often the only change that needs to be applied for using the same APSIM files for different years.

If you are not using the setup_helper.ipynb you will likely want to copy the same file to a number of directories (regions):

cd path/to/apsim_simulation_20240607/2021/T-0  # Path where regions exist (e.g., from above shapefile conversion)
source_file="/path/to/my/template.apsimx"; for dir in *; do cp "$source_file" "${dir}/${dir}_template.apsimx"; done

Adjustments for e.g soil properties and simulation constraints must be manually configured with domain knowledge.

5. Validation Data (Optional)

If ground-truth yield data is available, it should be included as groundtruth_{AggregationLevel}-{Year}.csv in the corresponding year directory. Hereby, aggregation level, specifies how the simulation level regions should be aggregated and must be aliged with the eval_params.aggregation_levels in your snakemake_config.yaml. The year must match the corresponding year directory.

Reference CSV Specification

Column Name Description
region Name matching GeoJSON folder (for primary aggregation level) or matching attribute table column values for custom aggregation level
reported_mean_yield_kg_ha Mean yield (kg/ha), if available
reported_production_kg Total production (kg) (optional, used to derive mean yield)

If reported_production_kg is provided, the mean yield is computed as:

mean_yield_kg_ha = reported_production_kg / cropland_area_ha

with cropland_area_ha being the computed cropland_area_ha based on the provided cropland map!.

Validation data is optional and can also be provided for a subset of years where it is available. In this case a total reference value is ommited to avoid misinterpretation.

6. Snakemake Configuration

This file defines the study parameters and links the simulation head directory with the pipeline. You will have to adapt this fill for each yield study. An example configuration can be found here example configuration file. We reccomend, to use this as a template for adjustment. The following section describes the meaning of the paramters, but does not represent the syntac for how to organize the config. For this please refer to the example.

Key Parameters

General Settings

  • platform: Choose between 'local' or 'umd' for dependency management.
  • sim_study_head_dir: Path to the simulation head directory.
  • regions: List of included regions. Must match folder names of regions.
  • years: List of included years (int). Must match year folder names.
  • timepoints: List of included timepoints. Must match timepoint folder names.
  • study_id: Id/Name of that identifies the study. Freely choosable up to a length of 25 characters.
  • keep_apsim_db_files: Delete actual APSIM DB files after processing and reporting to free space.(True/False).
  • create_per_region_html_report: Create HTML reports of predicted yield curves per region. Requires a lot of space. (True/False).
  • title: Free choice of a title, used in the report.
  • description: Custom description (freetext) of the study.
  • lai_shp_name: Name of the shapefile used for LAI generation. Not used for processing, simply for reference.
  • regions_shp_name: Name of the shapefile from which the GeoJSONs where created. Not used for processing, simply for reference.

APSIM Parameters (apsim_params)

  • met_source: 'NASA_POWER' or 'ERA5'. If using ERA5 ensure you call earthengine authenticate from your terminal before starting the pipeline.
  • met_agg_method" 'centroid' or 'mean'. Aggregation method for the pixels within a region. NASA_Power only supports centroid.
  • precipitation_source: Choose between 'NASA_POWER' or 'CHIRPS'. If you are using CHIRPS, you will have to manually download the precipitation data before starting the pipeline (apsim/download_chirps_data.py).
  • precipitation_agg_method: Aggregation method for precipitation data (mean or centroid). 'NASA_POWER' only supporting centroid currently.
  • fallback_precipitation: Set True to use the original precipitation data (NASAPower or ERA5) if CHIRPS is unavailable. CHIRPS only provides coverage from -50 to 50 degrees.
  • chirps_dir: Directory containing global CHIRPS precipitation data (tiffs).
  • nasapower_cache_dir: Directory (typically outside of your yieldstudy, globally) containing already fetched dates of nasapower met data per region.
  • era5_cache_dir: Directory (typically outside of your yieldstudy, globally) containing already fetched dates of nasapower met data per region.
  • ee_project: Project ID in google earth engine. Required if using ERA5 meteorological data.
  • time_bounds: Defines timepoint parameters:
  • sim_start_date, sim_end_date: Start/End date of the simulation in APSIM.
  • met_start_date, met_end_date: Start/End date from when to include metereological data into APSIM.

LAI Parameters (lai_params)

  • lai_dir: Directory for LAI data.
  • lai_region: Region name with naming convention ({region}_{date}_{resolution}m_LAI.tif).
  • lai_resolution: Spatial resolutin, used for matching as above. In meters/pixel.
  • crop_name: Specify crop (wheat or maize). Is related to the cropname defined in APSIM.
  • use_crop_adjusted_lai: Adjust LAI data for the crop specified (True/False).
  • lai_analysis_mode: Set to 'raster'.
  • time_bounds: LAI start and end dates for each year.
  • lai_aggregation: Method how to choose a representative LAI value from a region of pixels. 'mean' or 'median'.
  • smoothed: Whether to smoth the original remotely sensed LAI curve using Sav-Gol Method. (True/False).
  • file_ext: LAI files extension. If produced with GEE pipeline and regions are not merged, use tif. Else use vrt. (tif/vrt).
  • min_cropland_pixel_threshold: Minimum number of cropland pixels each simulation ROI must contain. If region has less it is skipped. Cropmask pixels are counted at the resolution of the LAI data.
  • cloudcov_threshold: Maximum cloud coverage percentage of a region for LAI of a date to be considered valid. E.g if set to 0.9, 90% of the pixels in the LAI data of a specific date must not be clouds/snow, otherwise this date will be ignored in the LAI curve.
  • crop_mask: Dict[year, cropmaskpath]. Path to crop mask files for each year. The cropmask must be binary, with 1 indicating the crop and 0 otherwise.
  • do_cropmask_reprojection: The cropmask must match the resolution and CRS of the LAI data. If set to True it will be automatically reprojected to match the LAI data. (True/False).

Matching Parameters (matching_params)

  • target_crs: CRS string for coordinate reference system used for area calculation. Either a proj string (e.g '"+proj=aea +lat_1=29 +lat_2=36 +lat_0=32.5 +lon_0=-5 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"') or authority string (e.g 'epsg:1234'). Should be choosen with care to minimize distortions. If using a proj string ensure to encolose it with additional quotes as in the example.

APSIM Execution (apsim_execution)

  • use_docker: Set True to run APSIM in Docker.
  • docker.image: Docker image (apsiminitiative/apsimng).
  • docker.platform: Device platform (e.g., 'linux/amd64').
  • local.executable_fpath: Path to the local APSIM executable. Follow the setup instruction to install it and replace this path if not using docker.
  • local.n_jobs: Number of threads (-1 uses APSIM default).

Evaluation Parameters

  • aggregation_levels: Dictionary where the keys are names for levels at which results should be aggregated for evaluation and values are columns in the shapefile allowing to aggregate simulation level results. E.g a the shapefile might have a column Admin2 and the simulations are run at Admin3. So all predictions that share the same Admin2 region would be aggregated and metrics would be computed for these.

Scripts Configuration

  • Paths to scripts used within the Snakemake pipeline. See the example for details