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The FarmData2 Data Model

This document describes the details of the FarmData2 data model. Note that FarmData2 is built on top of farmOS and largely makes use of the standard farmOS data model.

The sections below describe each part of the sample database provided with FarmData2 for development and testing. Several pieces of information are provided for each part of the sample database:

  • The scripts that create each part of the sample data are indicated and provide a good reference for how to create new records in the database.
  • An API request is given for each type of data as well. Accessing these APIs using a tool like Hoppscotch or Postman is a good way to understand the structure of the data returned by API calls in FarmData2.

The Sample Database

A sample database is provided with FarmData2 for development and testing purposes.

Some useful properties of the sample data:

  • It is real data from the Dickinson College farm.
  • The data runs from Jan 1, 2019 - July 15, 2020. So it includes:
    • One completed growing season (Jan 1 - Dec 31, 2019).
    • One in-progress growing season (Jan 1 - July 15, 2020).

More details are included in the table below:

Asset/Log Type 2017 2019 2020 Total Notes
Direct Seeding 3 182 101 286 2017 seedings were harvested in 2019.
Tray Seeding - 378 313 691
Total Seedings 3 560 414 977
Planting 3 347 248 595 A planting may have multiple seedings. A few plantings have no seedings.
Transplanting - 171 117 288 Plantings are transplanted and thus may include multiple tray seedings. A few transplantings do not have a tray seeding.
Harvest - 1592 487 2079

The sub-sections below give more complete details of each part of the sample database.

People

The FarmData2 sample database contains the following People (i.e. users):

User Name Password Notes
manager1 farmdata2 Full edit permissions
manager2 farmdata2
worker1 farmdata2 Limited edit permissions
worker2 farmdata2
worker3 farmdata2
worker4 farmdata2
worker5 farmdata2
guest farmdata2 No edit permissions
restws1 farmdata2 For REST API access only.

The users in the FarmData2 sample database are created by the addPeople.bash script.

Vocabularies

A vocabulary consists of a list of terms and the relationships between them. There are a number of vocabularies that play important roles in FarmData2.

Farm Areas

The Farm Areas vocabulary defines each of the areas (fields, greenhouses, beds) on the farm. This vocabulary allows areas to have sub-areas. For example, an area that is a greenhouse can have beds that are sub-types.

The terms for the Farm Areas vocabulary can be accessed using the API request:

GET http://localhost/taxonomy_term.json?bundle=farm_areas

The areas in the FarmData2 sample database are created by the addAreas.py script using the data in the sampleData/areas.csv file.

Farm Crop Families and Farm Crops/Varieties

The Farm Crop Families vocabulary defines the crop category names from the USDA Organic INTEGRITY Database.

The terms for the Farm Crop Families vocabulary can be accessed with the request:

GET http://localhost/taxonomy_term.json?bundle=farm_crop_families

The Farm Crops/Varieties Vocabulary defines all of the crops that appear in the FarmData2 database. Each crop is assigned to one of the crop categories defined in the Farm Crop Families vocabulary. Crops can also be parent or child-crops. For example LETTUCE is a parent crop to LETTUCE-ROMAINE and LETTUCE-GREEN, and conversely they are child crops to LETTUCE. In addition, each crop has a default unit from the Farm Quantity Units vocabulary (see below) and also a list of conversion factors for converting from the default units to any other unit that may be used for the crop.

The terms for the Farm Crops vocabulary can be accessed with the request:

GET http://localhost/taxonomy_term.json?bundle=farm_crops

The crop families and varieties in the FarmData2 sample database are created by the addCrops.py script using the data in the sampleData/crops.csv file.

Farm Log Categories

The Farm Log Categories vocabulary is used to categorize the log entries by what they pertain to (e.g. Equipment, Animals, Plantings, etc.). Of particular interest are the categories of Direct Seeding and Tray Seeding that are added to the standard farmOS vocabulary. These are sub-terms of the Planting term and are used to categorize the different types of seeding.

The terms for the Farm Log Categories vocabulary can be accessed with the request:

GET http://localhost/taxonomy_term.json?bundle=farm_log_categories

The Direct Seeding and Tray Seeding categories in the FarmData2 sample database are created by the addSeedings.py script.

Farm Quantity Units

The Farm Quantity Units vocabulary defines the units that can be assigned to logs (e.g. Row Feet, Counts, Hours, Bunches, Pounds, etc.). The units are divided into categories (e.g. Count, Weight, Length/depth, etc).

The terms for the Farm Quantity Units vocabulary can be accessed with the request:

GET http://localhost/taxonomy_term.json?bundle=farm_quantity_units

The quantity units in the FarmData2 sample database are created by the addUnits.py script using the data in the sampleData/units.csv file.

Seeding Logs and Planting Assets

A seeding is a crop that has been planted from seed either directly in the ground (a Direct Seeding) or in a seeding tray (a Tray Seeding). There is one Seeding Log for every seeding and each is categorized as either a Direct Seeding or a Tray Seeding.

Every Seeding Log is associated with a Planting Asset, which represents the crop that resulted from the planting. Planting Assets indicate the crops that are available for future operations (e.g. observation, transplanting, harvesting, etc.).

The Planting Asset must be created before the Seeding Log because the Seeding Log must reference the Planting Asset that it creates.

The Seeding Logs and associated Planting Assets in the FarmData2 sample database are created by the addDirectSeedings.py and addTraySeedings.py scripts using the data in the sampleData/directSeedings.csv and sampleData/traySeedings.csv files.

Seeding Logs

All Seeding Logs can be accessed with the request:

GET http://localhost/log.json?type=farm_seeding

Notes:

  • The log_category attribute can be used to distinguish between logs for Direct Seedings and logs for Tray Seedings.
  • The data attribute will contain an object that provides the crop_tid of the crop that was seeded (e.g. { crop_tid: 115 }). This can be used to get the crop name without retrieving the Planting Asset.

Planting Assets

All Planting Assets can be accessed with the request:

GET http://localhost/farm_asset.json?type=planting

The Planting Assets in the FarmData2 sample database are created primarily by the addDirectSeeding.py and addTraySeeding.py scripts when new seedings are created, as described above. However, a few are created by addTransplanting.py as well when a transplanting record was found without a matching seeding record.

Notes:

  • The Planting Asset does not itself have a location. The location of a Planting Asset is assigned to the location given in the Seeding Log that references it. Similarly, if the Planting Asset was created by a tray seeding, then its location can also be changed by a transplanting operation.

Transplanting Logs

Each Transplanting Log corresponds to the transplanting of a Planting Asset created by one or more Tray Seedings from a greenhouse to a field or bed. When a Transplanting Log is created it includes a Movement attribute that indicates the new location of the planting. The farmOS system uses the Movement attribute to automatically create a Movement Log indicating the new location of the Planting Asset. Future requests for the Planting Asset will then show it in the updated location.

All Transplanting Logs can be accessed with the request:

GET http://localhost/log.json?type=farm_transplanting

The Transplanting Logs in the FarmData2 sample database are created by the addTransplantings.py script using the data in the sampleData/transplantings.csv file.

Notes:

  • The data attribute will contain an object that provides the crop_tid of the crop that was transplanted (e.g. { crop_tid: 115 }). This can be used to get the crop name without retrieving the Planting Asset.

Harvest Logs

Each Harvest Log represents one harvesting event and is linked to the Planting Asset from which the harvest occurred.

All Harvest Logs can be accessed with the request:

GET http://localhost/log.json?type=farm_harvest

The Harvest Logs in the FarmData2 sample database are created by the addHarvests.py script using the data in the sampleData/harvests.csv file.

Notes:

  • The data attribute will contain an object that provides the crop_tid of the crop that was transplanted (e.g. { crop_tid: 115 }). This can be used to get the crop name without retrieving the Planting Asset.

Building the Provided Databases

The following sections detail how to build the empty and sample databases that are provided for development with the FarmData2 repo.

Building the Empty Database

When the Drupal or farmOS images are updated, it is sometimes necessary to rebuild the empty database to allow the full sample database to be built on top of it. The following steps outline how to build the empty database.

  1. Change to the docker directory in FarmData2.
  2. rm -rf db
  3. rm settings.php
  4. cp settings-default.php settingsp.php
  5. ./fd2-up.bash
  6. Visit http:\\localhost in a browser.
  7. Follow the install instructions with the following information:
    1. Verify Requirements
      1. Database name: farm
      2. Database username: farm
      3. Database password: farm
      4. Advanced Options:
        1. Database host: fd2_mariadb
    2. Configure Site
      1. Site name: Sample Farm
      2. Site e-mail address: [email protected]
      3. Username: admin
      4. E-mail address: [email protected]
      5. Password: farmdata2
      6. Default Country: United States
      7. Default time zone: America\New York
      8. Check for updates automatically: unchecked
      9. Receive e-mail notifications: unchecked
    3. Configure farmOS
      1. System of measurement: US/Imperial
    4. Finished
      1. Click Visit your new site
  8. Click Manage
    1. Click Modules
      1. Turn on modules for:
        • FarmData2 BarnKit
        • FarmData2 Example
        • FarmData2 FieldKit
        • FarmData2 School
      2. Click Save configuration
    2. Click Appearance
      1. Click Logo image settings
        1. Uncheck Use the default logo
        2. Path to custom logo: farmdata2logo.png
      2. Click Save configuration
        1. Logo should change to FarmData2 logo.
  9. Click Log out
  10. ./fd2-down.bash
  11. rm db.empty.tar.bz2
  12. sudo tar cjvf db.empty.tar.bz2 db

Building the Sample Database

The sample database is built on top of the empty database. The csv files in the docker/sampleData directory provide the data for the sample database. Each file contains a detailed description of its purpose, contents, and format in the comments at the top of the file. The buildSampleDB.bash script and its sub-scripts use the contents of the csv files in the sampleData directory to create the sample database on top of the provided empty database. The following steps will rebuild the sample database. Note: Building the sample database typically takes 10-20 minutes.

  1. Ensure that python3 is installed in /usr/bin
  2. python3 -m pip install requests
  3. Change to the docker directory in FarmData2.
  4. rm db.sample.tar.bz2
  5. Change to the sampleDB directory in FarmData2/docker.
  6. ./buildSampleDB.bash