grassland_production.utilisation_rate
This module contains the UtilisationRate class, which is focused on calculating and managing the utilisation rates of grassland for different farm types across various scenarios. This class is a crucial part of the grassland_production package, integrating components like grassland yield, fertilisation, and dry matter production for comprehensive analysis.
- Classes:
UtilisationRate: Manages and computes utilisation rates of grassland.
Module Contents
Classes
A class to manage and compute the utilisation rates of grassland for different farm types and scenarios. |
- class grassland_production.utilisation_rate.UtilisationRate(ef_country, calibration_year, target_year, scenario_data, scenario_animals_df, baseline_animals_df, yield_class=None, fertilisation_class=None, dry_matter_class=None)[source]
A class to manage and compute the utilisation rates of grassland for different farm types and scenarios.
The UtilisationRate class calculates the utilisation rate, defined as the ratio of dry matter required by livestock to the dry matter produced on grasslands. It integrates data from various sources, including grass yield, fertilisation, and livestock needs, to compute the utilisation rates for dairy, beef, and sheep farms under various scenarios. This information is vital for sustainable grassland and livestock management.
- Parameters:
ef_country (str) – The country for which the analysis is performed.
calibration_year (int) – The calibration year.
target_year (int) – The target year for future scenario projections.
scenario_inputs_df (DataFrame) – DataFrame containing scenario input variables data.
scenario_animals_df (DataFrame) – DataFrame containing scenario animal data.
baseline_animals_df (DataFrame) – DataFrame containing baseline animal data.
yield_class (Yield, optional) – An instance of the Yield class. If not provided, a new instance is created with default parameters.
fertilisation_class (Fertilisation, optional) – An instance of the Fertilisation class. If not provided, a new instance is created with default parameters.
dry_matter_class (DryMatter, optional) – An instance of the DryMatter class. If not provided, a new instance is created with default parameters.
- sc_class
Fetches scenario data.
- Type:
- scenario_list
List of scenarios.
- Type:
list
- data_manager_class
Manages and processes grassland and livestock data.
- Type:
- calibration_year
Year of data calibration.
- Type:
int
- target_year
Target year for data analysis.
- Type:
int
- default_calibration_year
Default year used for calibration in case of data discrepancies.
- Type:
int
- fertiliser_class
Class for managing fertilisation data.
- Type:
- cattle_grass_feed_class
Class for calculating grass feed for cattle.
- Type:
cattle_lca.GrassFeed
- sheep_grass_feed_class
Class for calculating grass feed for sheep.
- Type:
sheep_lca.GrassFeed
- concentrate_feed_class
Class for calculating energy requirements for cattle.
- Type:
cattle_lca.Energy
- sheep_concentrate_feed_class
Class for calculating energy requirements for sheep.
- Type:
sheep_lca.Energy
- get_farm_type_dry_matter_produced()[source]
Calculates dry matter produced by each farm type (uses NFS areas).
- get_farm_type_dry_matter_required()[source]
Calculates dry matter required by each farm type (uses NFS areas).
- get_farm_based_utilisation_rate()[source]
Computes utilisation rate based on farm type and scenario.
- get_dynamic_utilisation_rate()[source]
Calculates dynamic utilisation rate considering scenario-specific parameters.
- get_farm_type_dry_matter_produced()[source]
Calculates and returns the dry matter produced by different farm types across various scenarios.
This method utilizes the grass yield per hectare, obtained from the Yield class, combined with area data from the National Farm Survey (NFS) to compute the total dry matter produced on dairy, beef, and sheep farms. The calculations are performed for both the calibration year and the target year.
The method iterates over each scenario and farm type, summing the product of yield per hectare and the corresponding area for each type of grassland.
- Returns:
- A dictionary where each key is a scenario, and its value is a DataFrame. Each DataFrame
has years as the index and farm types (dairy, beef, sheep) as columns. Each cell contains the total dry matter produced for that farm type in the respective year.
- Return type:
dict
- get_farm_type_dry_matter_required()[source]
Calculates and returns the dry matter required by the National Farm Survey (NFS) average livestock population on each of the three farm types (dairy, beef, sheep).
This method determines the amount of dry matter required by dairy, beef, and sheep farms based on the average livestock populations from NFS data. It involves converting the NFS data into dry matter requirements from grass. The calculation is performed for both the calibration year and the target year across various scenarios.
The method iterates through each scenario, farm type, and animal cohort, using the grass feed class to calculate the dry matter required from grass for each animal type. This provides essential insights into the feed requirements of different farm types.
- Returns:
- A dictionary where each key is a scenario, and its value is a DataFrame. Each DataFrame has years
as the index and farm types (dairy, beef, sheep) as columns. Each cell contains the total dry matter required for that farm type in the respective year.
- Return type:
dict
- get_farm_based_utilisation_rate()[source]
Calculates and returns the utilisation rate of grassland for different farm types across various scenarios.
This method computes the grassland utilisation rate, defined as the ratio of dry matter demand to dry matter availability, for dairy, beef, and sheep farms. The calculations consider both the calibration year and the target year, and they incorporate scenario-specific adjustments for dairy and beef Grass Use Efficiency (GUE) increases.
This method incorporates NFS data related to dry matter demand and dry matter availability.
The utilisation rate is calculated separately for each scenario and farm type. For years other than the target year, it is the ratio of the dry matter demand in the calibration year to the dry matter availability in the calibration year. For the target year, the utilisation rate includes additional adjustments based on the scenario-specific GUE increases for dairy and beef farms.
- Returns:
- A dictionary where each key is a scenario, and its value is a DataFrame. The DataFrame has years
as the index and farm types (dairy, beef, sheep) as columns. Each cell contains the utilisation rate for that farm type in the respective year and scenario.
- Return type:
dict
- get_dynamic_utilisation_rate()[source]
Calculates and returns a dynamic utilisation rate for each livestock system and scenario.
This method computes the utilisation rate, which is the ratio of dry matter required to dry matter produced, for dairy, beef, and sheep farms. For years prior to the target year, the utilisation rate is calculated by aggregating the dry matter produced for each livestock system and grassland type, then dividing the dry matter requirement by this aggregated amount. For the target year, the utilisation rate is determined by adding the utilisation rate increase specified in the scenario parameters to the utilisation rate of the calibration year.
The method considers various scenarios and adjusts the utilisation rates based on scenario-specific parameters for dairy and beef Grass Use Efficiency (GUE) increases.
- Returns:
- A dictionary where each key is a scenario, and its value is a DataFrame. The DataFrame has years
as the index and farm types (dairy, beef, sheep) as columns. Each cell contains the dynamic utilisation rate for that farm type in the respective year and scenario.
- Return type:
dict