cowfootR

Tools to estimate the carbon footprint of dairy farms.
Implements methods based on IDF (International Dairy Federation)
and IPCC guidelines for greenhouse gas accounting.

Overview

cowfootR provides a comprehensive toolkit for calculating carbon footprints of dairy farms following IPCC guidelines (IPCC 2019 Refinement) and International Dairy Federation guidance for the dairy sector (IDF Bulletin 520). The package includes:

• Individual emission calculations from enteric fermentation, manure, soil, energy, and inputs
• Batch processing capabilities for multiple farms
• Intensity metrics per liter of milk and per hectare
• System boundary flexibility (farm gate, cradle-to-farm gate, etc.)
• Excel integration for data input and report generation

Installation

install.packages("cowfootR")

Or install the development version:

devtools::install_github("juanmarcosmoreno-arch/cowfootR")

Quick Start

Below is a minimal, end-to-end example showing the core workflow of cowfootR for a single dairy farm.

library(cowfootR)

# 1. Define system boundaries
boundaries <- set_system_boundaries("farm_gate")

# 2. Calculate emissions by source
enteric <- calc_emissions_enteric(
  n_animals = 100,
  cattle_category = "dairy_cows",
  boundaries = boundaries
)

manure <- calc_emissions_manure(
  n_cows = 100,
  boundaries = boundaries
)

soil <- calc_emissions_soil(
  n_fertilizer_synthetic = 1500,
  n_excreta_pasture = 5000,
  area_ha = 120,
  boundaries = boundaries
)

energy <- calc_emissions_energy(
  diesel_l = 2000,
  electricity_kwh = 5000,
  boundaries = boundaries
)

inputs <- calc_emissions_inputs(
  conc_kg = 1000,
  fert_n_kg = 500,
  boundaries = boundaries
)

# 3. Aggregate total emissions
total_emissions <- calc_total_emissions(enteric, manure, soil, energy, inputs)
total_emissions
#> Carbon Footprint - Total Emissions
#> ==================================
#> Total CO2eq: 451512.6 kg
#> Number of sources: 5 
#> 
#> Breakdown by source:
#>   energy : 5740 kg CO2eq
#>   enteric : 312800 kg CO2eq
#>   inputs : 4000 kg CO2eq
#>   manure : 89880 kg CO2eq
#>   soil : 39092.62 kg CO2eq
#> 
#> Calculated on: 2026-01-08

# 4. Intensity metrics
milk_intensity <- calc_intensity_litre(
  total_emissions = total_emissions,
  milk_litres = 750000,
  fat = 4.0,
  protein = 3.3
)
milk_intensity
#> Carbon Footprint Intensity
#> ==========================
#> Intensity: 0.585 kg CO2eq/kg FPCM
#> 
#> Production data:
#>  Raw milk (L): 750,000 L
#>  Raw milk (kg): 772,500 kg
#>  FPCM (kg): 772,407 kg
#>  Fat content: 4 %
#>  Protein content: 3.3 %
#> 
#> Total emissions: 451,513 kg CO2eq
#> Calculated on: 2026-01-08

area_intensity <- calc_intensity_area(
  total_emissions = total_emissions,
  area_total_ha = 120
)
area_intensity
#> Carbon Footprint Area Intensity
#> ===============================
#> Intensity (total area): 3762.61 kg CO2eq/ha
#> Intensity (productive area): 3762.61 kg CO2eq/ha
#> 
#> Area summary:
#>  Total area: 120 ha
#>  Productive area: 120 ha
#>  Land use efficiency: 100%
#> 
#> Total emissions: 451,513 kg CO2eq
#> Calculated on: 2026-01-08

Batch processing (typical real-world use)

In practical applications, cowfootR is most often used to process data from multiple farms simultaneously. This is handled through the calc_batch() function, which applies the same methodological workflow across all farms in a structured dataset.

Below is a minimal example illustrating batch processing for multiple farms.

library(cowfootR)

# Example dataset with two farms
farms <- data.frame(
  FarmID = c("Farm_A", "Farm_B"),
  Year = c(2023, 2023),
  Milk_litres = c(500000, 750000),
  Cows_milking = c(90, 130),
  Area_total_ha = c(110, 160),
  Diesel_litres = c(4000, 6500),
  Electricity_kWh = c(18000, 26000),
  Concentrate_feed_kg = c(120000, 180000),
  stringsAsFactors = FALSE
)

# Define system boundaries
boundaries <- set_system_boundaries("farm_gate")

# Run batch carbon footprint calculation
batch_results <- calc_batch(
  data = farms,
  tier = 2,
  boundaries = boundaries,
  benchmark_region = "uruguay"
)
#> Batch: 2 rows; tier=2 ...

# Summary of batch processing
batch_results$summary
#> $n_farms_processed
#> [1] 2
#> 
#> $n_farms_successful
#> [1] 2
#> 
#> $n_farms_with_errors
#> [1] 0
#> 
#> $boundaries_used
#> $boundaries_used$scope
#> [1] "farm_gate"
#> 
#> $boundaries_used$include
#> [1] "enteric" "manure"  "soil"    "energy"  "inputs" 
#> 
#> 
#> $benchmark_region
#> [1] "uruguay"
#> 
#> $processing_date
#> [1] "2026-01-08"

# Farm-level results
batch_results$farm_results
#> [[1]]
#> [[1]]$success
#> [1] TRUE
#> 
#> [[1]]$farm_id
#> [1] "Farm_A"
#> 
#> [[1]]$year
#> [1] "2023"
#> 
#> [[1]]$emissions_enteric
#> [1] 230826.6
#> 
#> [[1]]$emissions_manure
#> [1] 183066.1
#> 
#> [[1]]$emissions_soil
#> [1] 0
#> 
#> [[1]]$emissions_energy
#> [1] 13794
#> 
#> [[1]]$emissions_inputs
#> [1] 84000
#> 
#> [[1]]$emissions_total
#> [1] 511686.8
#> 
#> [[1]]$intensity_milk_kg_co2eq_per_kg_fpcm
#> [1] 0.9936858
#> 
#> [[1]]$intensity_area_kg_co2eq_per_ha_total
#> [1] 4651.7
#> 
#> [[1]]$intensity_area_kg_co2eq_per_ha_productive
#> [1] 4651.7
#> 
#> [[1]]$fpcm_production_kg
#> [1] 514938.2
#> 
#> [[1]]$milk_production_kg
#> [1] 515000
#> 
#> [[1]]$milk_production_litres
#> [1] 5e+05
#> 
#> [[1]]$land_use_efficiency
#> [1] 1
#> 
#> [[1]]$total_animals
#> [1] 90
#> 
#> [[1]]$dairy_cows
#> [1] 90
#> 
#> [[1]]$benchmark_region
#> [1] "uruguay"
#> 
#> [[1]]$benchmark_performance
#> [1] "Excellent (below typical range)"
#> 
#> [[1]]$processing_date
#> [1] "2026-01-08"
#> 
#> [[1]]$boundaries_used
#> [1] "farm_gate"
#> 
#> [[1]]$tier_used
#> [1] "tier_2"
#> 
#> [[1]]$detailed_objects
#> NULL
#> 
#> 
#> [[2]]
#> [[2]]$success
#> [1] TRUE
#> 
#> [[2]]$farm_id
#> [1] "Farm_B"
#> 
#> [[2]]$year
#> [1] "2023"
#> 
#> [[2]]$emissions_enteric
#> [1] 333416.3
#> 
#> [[2]]$emissions_manure
#> [1] 264428.9
#> 
#> [[2]]$emissions_soil
#> [1] 0
#> 
#> [[2]]$emissions_energy
#> [1] 22142.25
#> 
#> [[2]]$emissions_inputs
#> [1] 126000
#> 
#> [[2]]$emissions_total
#> [1] 745987.4
#> 
#> [[2]]$intensity_milk_kg_co2eq_per_kg_fpcm
#> [1] 0.9657954
#> 
#> [[2]]$intensity_area_kg_co2eq_per_ha_total
#> [1] 4662.42
#> 
#> [[2]]$intensity_area_kg_co2eq_per_ha_productive
#> [1] 4662.42
#> 
#> [[2]]$fpcm_production_kg
#> [1] 772407.3
#> 
#> [[2]]$milk_production_kg
#> [1] 772500
#> 
#> [[2]]$milk_production_litres
#> [1] 750000
#> 
#> [[2]]$land_use_efficiency
#> [1] 1
#> 
#> [[2]]$total_animals
#> [1] 130
#> 
#> [[2]]$dairy_cows
#> [1] 130
#> 
#> [[2]]$benchmark_region
#> [1] "uruguay"
#> 
#> [[2]]$benchmark_performance
#> [1] "Excellent (below typical range)"
#> 
#> [[2]]$processing_date
#> [1] "2026-01-08"
#> 
#> [[2]]$boundaries_used
#> [1] "farm_gate"
#> 
#> [[2]]$tier_used
#> [1] "tier_2"
#> 
#> [[2]]$detailed_objects
#> NULL

# Export results to Excel
export_hdc_report(
  batch_results,
  file = "cowfootR_batch_report.xlsx"
)
#> Batch report saved to: cowfootR_batch_report.xlsx

Batch results can be directly exported to an Excel report using export_hdc_report(), facilitating integration with reporting workflows commonly used by consultants, researchers, and stakeholders.

Emission Sources Covered

• Enteric fermentation: CH₄ from ruminal fermentation
• Manure management: CH₄ and N₂O from manure systems
• Soil emissions: N₂O from fertilizer application and excreta
• Energy consumption: CO₂ from diesel, electricity, and other fuels
• External inputs: CO₂eq from feed, fertilizers, and materials

System Boundaries

boundaries_fg <- set_system_boundaries("farm_gate")
boundaries_cfg <- set_system_boundaries("cradle_to_farm_gate")

Intensity Metrics

The package calculates multiple intensity metrics:

• Per liter of milk: kg CO₂eq per liter
• Per kg FPCM: kg CO₂eq per kg Fat and Protein Corrected Milk
• Per hectare: kg CO₂eq per hectare (total and productive area)
• Land use efficiency: productive area / total area ratio

Data Requirements

Required Columns

• FarmID: Unique farm identifier
• Year: Year of data collection
  
• Milk_litres: Annual milk production (liters)
• Cows_milking: Number of milking cows
• Area_total_ha: Total farm area (hectares)

Optional Columns

• Animal data: Cows_dry, Heifers_total, Calves_total, Bulls_total
• Production: Fat_percent, Protein_percent, Milk_yield_kg_cow_year
• Feed: MS_intake_cows_milking_kg_day, Ym_percent, Concentrate_feed_kg
• Fertilizer: N_fertilizer_kg, N_fertilizer_organic_kg
• Energy: Diesel_litres, Electricity_kWh, Petrol_litres
• Land use: Area_productive_ha, Pasture_permanent_ha

Use cf_download_template() to get the complete column structure.

Error Handling

The package includes robust error handling for batch processing:

For batch processing, Excel templates, reporting, and error handling, please see the package vignettes and the documentation website.

Contributing

This package is under active development. Please report issues or suggest improvements on GitHub.

References

• IPCC 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html
• International Dairy Federation (IDF). 2022. The IDF global Carbon Footprint standard for the dairy sector https://shop.fil-idf.org/products/the-idf-global-carbon-footprint-standard-for-the-dairy-sector?_pos=1&_sid=8a3f414f8&_ss=r
• FAO. 2010. Greenhouse Gas Emissions from the Dairy Sector https://www.fao.org/4/k7930e/k7930e00.pdf

License

MIT License © 2025 Juan Moreno