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03-parameter-standardization.md

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Parameter Standardization

Parameter standardization ensures that key data elements are consistently defined and used across the entire supply chain. This section describes the standardization of parameters used in the DIASCA project and explains which tables and fields utilize these parameters.

Enterprise Types

Defines the types of enterprises involved in the agro supply chain. This parameter is used in the type field of the Enterprises table.

Value Description
Farm An agricultural establishment primarily engaged in the cultivation of crops or raising livestock.
Processor An enterprise involved in the processing of raw agricultural products.
Importer An enterprise that imports agricultural products.
Exporter An enterprise that exports agricultural products.

Site Types

Defines the types of sites within the supply chain. This parameter is used in the type field of the Sites table.

Value Description
Farm A site where agricultural activities take place.
Processing facility A site where raw agricultural products are processed.
Warehouse A site used for the storage of agricultural products.

Transaction Types

Defines the types of transactions within the supply chain. This parameter is used in the type field of the Transactions table.

Value Description
Sale A transaction where products are sold.
Transfer A transaction where products are transferred between entities without a sale.
Return A transaction where products are returned.
Donation A transaction where products are donated.

Event Types

Defines the types of events that can occur within the supply chain. This parameter is used in the type field of the Events table.

Value Description
Planting An event where crops are planted.
Harvesting An event where crops are harvested.
Processing An event where products are processed.
Inspection An event where an inspection occurs.

Unit of Measure

Standardized units of measure for products. This parameter is used in the unit_of_measure field of the Products table.

Value Description
kg Kilograms
ton Metric Tons
l Liters
unit Individual units of product

Plot Parameters

Standardized parameters for plots within sites. These parameters are stored in the PlotParameters table.

Key Description Example
soil_type The type of soil in the plot Sandy, Clay, Loamy
crop_history The history of crops grown on the plot 2020: Wheat, 2021: Corn, 2022: Soybeans
type_of_shadow_trees Types of shadow trees present on the plot Mango, Acacia
other_crops Other crops grown on the plot Tomatoes, Peppers
avg_yields Average yields in kilograms 5000 kg/ha

Site Parameters

Stores custom key-value pairs for sites.

Key Description Example
irrigation_type Type of irrigation system used Drip, Sprinkler, Flood
ownership Ownership status of the site Owned, Leased
organic_certified Organic certification status Certified, In Conversion, Not Certified

People Parameters

Stores custom key-value pairs for people.

Key Description Example
training Type of training received Sustainable Agriculture, Pesticide Safety
years_of_experience Number of years of experience in the role 10 years, 5 years
certifications Certifications obtained ISO 9001, Fair Trade

Transaction Parameters

Stores custom key-value pairs for transactions.

Key Description Example
payment_method Method of payment used Credit, Cash, Bank Transfer
delivery_terms Terms of delivery FOB, CIF, Ex Works
quality_grade Quality grade of the product Grade A, Grade B

Creating Custom Keys for New Parameters

While the parameters listed above provide a standardized set of examples, users can create their own keys for new parameters to accommodate specific needs. Here are best practices for creating new key names:

Best Practices for Key Names

  1. Descriptive: Choose key names that clearly describe the parameter. This helps ensure that anyone using the data can easily understand what the parameter represents.
  2. Consistent: Use a consistent naming convention throughout your dataset. For example, if you use snake_case for one parameter, use it for all parameters.
  3. Concise: Keep key names short but informative. Avoid overly long names that can be cumbersome to use.
  4. Avoid Special Characters: Use only letters, numbers, and underscores. Avoid spaces and special characters to ensure compatibility with various systems and software.
  5. Contextual: Provide enough context within the key name to avoid ambiguity. For example, instead of using type, use soil_type or irrigation_type to make the context clear.

Examples of Custom Keys

Key Description Example
water_source Source of water for irrigation River, Well, Rainwater
labor_hours Total labor hours spent on the plot 100 hours
fertilizer_type Type of fertilizer used Organic, Inorganic
harvest_method Method used for harvesting Manual, Mechanical

By following these best practices, you can ensure that your custom keys are easily understandable, maintain consistency across datasets, and improve overall data quality.