IPCC Methodology Tiers in cowfootR
In cowfootR: Dairy Farm Carbon Footprint Assessment

knitr::opts_chunk$set(
  fig.alt = "Figure generated by this vignette; see the surrounding text for details.",
  collapse = TRUE,
  comment = "#>",
  fig.width = 8,
  fig.height = 6,
  warning = FALSE,
  message = FALSE
)

library(cowfootR)
library(ggplot2)
library(dplyr)
library(knitr)
if (!requireNamespace("tidyr", quietly = TRUE)) {
  stop("This vignette requires 'tidyr'. Please install it with install.packages('tidyr').")
}

Understanding IPCC Methodology Tiers

The Intergovernmental Panel on Climate Change (IPCC) provides three tiers of methodological complexity for greenhouse gas inventory calculations. cowfootR implements Tier 1 and Tier 2 methodologies for dairy farm carbon footprint assessments. This vignette explains the differences, when to use each approach, and demonstrates their practical application.

Data Requirements Analysis

Tier 1 Data Needs

tier1_requirements <- data.frame(
  Category = c("Animal Data", "Production", "Management", "Optional"),
  Essential_Data = c(
    "Number by category (cows, heifers, calves)",
    "Annual milk production (litres)",
    "Manure system type, basic inputs",
    "Farm area, country location"
  ),
  Time_to_Collect = c("< 1 hour", "< 1 hour", "1-2 hours", "< 1 hour"),
  Data_Source = c("Farm records", "Milk processor", "Farmer interview", "Farm records")
)

kable(tier1_requirements, caption = "Tier 1 Data Requirements")

Tier 2 Additional Requirements

tier2_additional <- data.frame(
  Category = c("Animal Characteristics", "Nutrition", "Management Detail", "Environmental"),
  Additional_Data = c(
    "Body weights, milk yield per cow, breeding records",
    "Feed composition, DM intake, diet digestibility",
    "Precise input quantities, equipment usage",
    "Climate data, soil types, system temperatures"
  ),
  Time_to_Collect = c("2-4 hours", "4-8 hours", "2-4 hours", "1-2 hours"),
  Expertise_Level = c("Basic", "Intermediate", "Basic", "Basic")
)

kable(tier2_additional, caption = "Additional Tier 2 Data Requirements")

Input columns used by `calc_batch()` for Tier 2 calculations

When using calc_batch() with tier = 2, users may provide additional farm-level variables to enable more detailed IPCC Tier 2 calculations. All inputs represent one accounting year unless explicitly stated otherwise by the column name (e.g., *_kg_day).

The input data frame is flexible: only a small set of columns is strictly required, while the presence of additional columns enables more refined calculations. Missing optional columns do not cause errors; instead, cowfootR falls back to Tier 1–consistent default assumptions following IPCC and IDF guidance.

Core required columns (all tiers)

FarmID: Unique farm identifier
Milk_litres: Annual milk production (litres/year)
Cows_milking: Number of milking cows
Area_total_ha: Total managed farm area (hectares)

Tier 2–relevant optional columns by emission source

Enteric fermentation (Tier 2 refinement)

Providing one or more of the following columns enables Tier 2 enteric methane calculations based on animal performance and intake:

Milk_yield_kg_cow_year: Average annual milk yield per cow (kg/year)
Body_weight_cows_kg: Average body weight of milking cows (kg)
MS_intake_cows_milking_kg_day: Dry matter intake of milking cows (kg/day)
Ym_percent: Methane conversion factor (% of gross energy intake)

If none of these variables are provided, Tier 1 default emission factors are used.

Young stock (optional refinement)

When young stock is included, Tier 2 calculations may also use:

Body_weight_heifers_kg
Body_weight_calves_kg
Body_weight_bulls_kg
MS_intake_heifers_kg_day
MS_intake_calves_kg_day
MS_intake_bulls_kg_day

Manure management (Tier 2 refinement)

Tier 2 manure emissions may use additional information when available:

Manure_system: Manure management system (e.g., pasture, liquid storage)
Diet_digestibility: Diet digestibility (fraction)
Protein_intake_kg_day: Average protein intake per animal (kg/day)
Retention_days: Manure storage or retention time (days)
System_temperature: Average system temperature (°C)
Climate_zone: Climatic classification used for emission factors

Other emission sources

Soil, energy, and input-related emissions primarily rely on annual activity data (e.g., fertilizer use, fuel consumption, purchased inputs). For these sources, Tier 2 calculations are typically driven by regional emission factors rather than additional farm-specific columns.

Complete column specification

The full and authoritative list of supported input columns, including expected units and naming conventions, is provided by the Excel template generated with:

cf_download_template()

Theoretical Background

IPCC Tier System Overview

The IPCC tier system balances accuracy with data requirements and complexity:

Tier 1: Default emission factors with basic activity data
Tier 2: Country/region-specific factors with detailed activity data
Tier 3: Advanced modeling with comprehensive measurements (not implemented in cowfootR)

Key Differences in Dairy Applications

| Aspect | Tier 1 | Tier 2 | |--------|--------|--------| | Emission Factors | IPCC default values | Region/farm-specific values | | Data Requirements | Basic (animal numbers, production) | Detailed (weights, intake, composition) | | Expected precision | Lower (screening-level) | Higher (farm-specific when good data are available) | | Time Investment | Low (hours) | Medium (days) | | Suitable For | Screening, regional estimates | Farm management, policy |

## Methodological Differences by Emission Source

### Enteric Fermentation

#### Tier 1 Approach - Uses fixed emission factors by animal category and production system - Based on broad regional averages - No consideration of diet quality or animal performance

# Tier 1 enteric calculation example
enteric_tier1 <- calc_emissions_enteric(
  n_animals = 100,
  cattle_category = "dairy_cows",
  production_system = "mixed",
  tier = 1 # Uses default emission factors
)

print(enteric_tier1$emission_factors)

Tier 2 Approach

Calculates methane based on energy intake and conversion efficiency
Accounts for diet digestibility, animal productivity, and body weight
Uses farm-specific or regional Ym (methane conversion) factors

# Tier 2 enteric calculation with detailed parameters
enteric_tier2 <- calc_emissions_enteric(
  n_animals = 100,
  cattle_category = "dairy_cows",
  avg_milk_yield = 7200,
  avg_body_weight = 580,
  dry_matter_intake = 19.5,
  ym_percent = 6.2,
  tier = 2 # Uses energy-based calculation
)

print(enteric_tier2$emission_factors)

Manure Management

Tier 1 vs Tier 2 Comparison

# Tier 1: Simple emission factors
manure_tier1 <- calc_emissions_manure(
  n_cows = 100,
  manure_system = "liquid_storage",
  tier = 1
)

# Tier 2: VS and MCF-based calculation
manure_tier2 <- calc_emissions_manure(
  n_cows = 100,
  manure_system = "liquid_storage",
  tier = 2,
  avg_body_weight = 580,
  diet_digestibility = 0.68,
  climate = "temperate",
  retention_days = 90,
  system_temperature = 20
)

# Compare results
manure_comparison <- data.frame(
  Tier = c("Tier 1", "Tier 2"),
  CH4_kg = c(manure_tier1$ch4_kg, manure_tier2$ch4_kg),
  N2O_kg = c(manure_tier1$n2o_total_kg, manure_tier2$n2o_total_kg),
  CO2eq_kg = c(manure_tier1$co2eq_kg, manure_tier2$co2eq_kg),
  Method = c("Default factors", "VS + MCF calculation")
)

kable(manure_comparison, caption = "Manure Management: Tier 1 vs Tier 2")

Comprehensive Farm Comparison

Let's compare both tiers using a realistic farm example:

Farm Profile

# Define comprehensive farm data
farm_profile <- list(
  # Basic data (required for both tiers)
  dairy_cows = 120,
  heifers = 35,
  calves = 40,
  milk_production = 850000, # litres/year
  farm_area = 160, # hectares

  # Detailed data (enhances Tier 2)
  cow_body_weight = 580,
  heifer_body_weight = 380,
  calf_body_weight = 170,
  milk_yield_per_cow = 7080,
  cow_dm_intake = 19.2,
  heifer_dm_intake = 11.5,
  calf_dm_intake = 6.2,
  diet_digestibility = 0.67,
  ym_factor = 6.1,

  # Management data
  concentrate_feed = 195000, # kg/year
  n_fertilizer = 2200, # kg N/year
  diesel_use = 9500, # litres/year
  electricity = 52000 # kWh/year
)

print(farm_profile[1:8])

Tier 1 Assessment

# Complete Tier 1 assessment
boundaries <- set_system_boundaries("farm_gate")

# Tier 1 calculations
enteric_t1 <- calc_emissions_enteric(
  n_animals = farm_profile$dairy_cows,
  cattle_category = "dairy_cows",
  tier = 1,
  boundaries = boundaries
)

heifers_t1 <- calc_emissions_enteric(
  n_animals = farm_profile$heifers,
  cattle_category = "heifers",
  tier = 1,
  boundaries = boundaries
)

calves_t1 <- calc_emissions_enteric(
  n_animals = farm_profile$calves,
  cattle_category = "calves",
  tier = 1,
  boundaries = boundaries
)

manure_t1 <- calc_emissions_manure(
  n_cows = farm_profile$dairy_cows + farm_profile$heifers + farm_profile$calves,
  manure_system = "pasture",
  tier = 1,
  boundaries = boundaries
)

soil_t1 <- calc_emissions_soil(
  n_fertilizer_synthetic = farm_profile$n_fertilizer,
  n_excreta_pasture = (farm_profile$dairy_cows + farm_profile$heifers) * 100,
  area_ha = farm_profile$farm_area,
  boundaries = boundaries
)

energy_t1 <- calc_emissions_energy(
  diesel_l = farm_profile$diesel_use,
  electricity_kwh = farm_profile$electricity,
  country = "UY",
  boundaries = boundaries
)

inputs_t1 <- calc_emissions_inputs(
  conc_kg = farm_profile$concentrate_feed,
  fert_n_kg = farm_profile$n_fertilizer,
  boundaries = boundaries
)

# Aggregate Tier 1 results
enteric_combined_t1 <- list(
  source = "enteric",
  co2eq_kg = enteric_t1$co2eq_kg + heifers_t1$co2eq_kg + calves_t1$co2eq_kg
)

total_t1 <- calc_total_emissions(enteric_combined_t1, manure_t1, soil_t1, energy_t1, inputs_t1)

Tier 2 Assessment

# Complete Tier 2 assessment using detailed data
enteric_t2 <- calc_emissions_enteric(
  n_animals = farm_profile$dairy_cows,
  cattle_category = "dairy_cows",
  avg_milk_yield = farm_profile$milk_yield_per_cow,
  avg_body_weight = farm_profile$cow_body_weight,
  dry_matter_intake = farm_profile$cow_dm_intake,
  ym_percent = farm_profile$ym_factor,
  tier = 2,
  boundaries = boundaries
)

heifers_t2 <- calc_emissions_enteric(
  n_animals = farm_profile$heifers,
  cattle_category = "heifers",
  avg_body_weight = farm_profile$heifer_body_weight,
  dry_matter_intake = farm_profile$heifer_dm_intake,
  ym_percent = farm_profile$ym_factor,
  tier = 2,
  boundaries = boundaries
)

calves_t2 <- calc_emissions_enteric(
  n_animals = farm_profile$calves,
  cattle_category = "calves",
  avg_body_weight = farm_profile$calf_body_weight,
  dry_matter_intake = farm_profile$calf_dm_intake,
  tier = 2,
  boundaries = boundaries
)

manure_t2 <- calc_emissions_manure(
  n_cows = farm_profile$dairy_cows + farm_profile$heifers + farm_profile$calves,
  manure_system = "pasture",
  tier = 2,
  avg_body_weight = 500, # Weighted average
  diet_digestibility = farm_profile$diet_digestibility,
  climate = "temperate",
  boundaries = boundaries
)

# Soil and other sources remain the same
enteric_combined_t2 <- list(
  source = "enteric",
  co2eq_kg = enteric_t2$co2eq_kg + heifers_t2$co2eq_kg + calves_t2$co2eq_kg
)

total_t2 <- calc_total_emissions(enteric_combined_t2, manure_t2, soil_t1, energy_t1, inputs_t1)

Results Comparison

# Compare tier results
tier_comparison <- data.frame(
  Source = c("Enteric", "Manure", "Soil", "Energy", "Inputs", "TOTAL"),
  Tier1_kg = c(
    enteric_combined_t1$co2eq_kg,
    manure_t1$co2eq_kg,
    soil_t1$co2eq_kg,
    energy_t1$co2eq_kg,
    inputs_t1$total_co2eq_kg,
    total_t1$total_co2eq
  ),
  Tier2_kg = c(
    enteric_combined_t2$co2eq_kg,
    manure_t2$co2eq_kg,
    soil_t1$co2eq_kg,
    energy_t1$co2eq_kg,
    inputs_t1$total_co2eq_kg,
    total_t2$total_co2eq
  )
) %>%
  mutate(
    Difference_kg = Tier2_kg - Tier1_kg,
    Difference_pct = round((Tier2_kg - Tier1_kg) / Tier1_kg * 100, 1)
  )

kable(tier_comparison, caption = "Emission Source Comparison: Tier 1 vs Tier 2")

Visualization of Differences

# Prepare data for visualization
comparison_long <- tier_comparison %>%
  filter(Source != "TOTAL") %>%
  select(Source, Tier1_kg, Tier2_kg) %>%
  tidyr::pivot_longer(
    cols = c(Tier1_kg, Tier2_kg),
    names_to = "Tier", values_to = "Emissions"
  ) %>%
  mutate(Tier = gsub("_kg", "", Tier))

# Create comparison chart
ggplot(comparison_long, aes(x = Source, y = Emissions, fill = Tier)) +
  geom_col(position = "dodge", alpha = 0.8) +
  geom_text(aes(label = format(round(Emissions), big.mark = ",")),
    position = position_dodge(width = 0.9), vjust = -0.3, size = 3
  ) +
  labs(
    title = "Emission Estimates: Tier 1 vs Tier 2 Methodology",
    subtitle = "Same farm, different calculation approaches",
    x = "Emission Source",
    y = "Emissions (kg CO₂eq/year)"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 14, hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1)
  ) +
  scale_fill_brewer(type = "qual", palette = "Set1")

Impact on Intensity Metrics

# Calculate intensity metrics for both tiers
intensity_t1 <- calc_intensity_litre(
  total_emissions = total_t1,
  milk_litres = farm_profile$milk_production,
  fat = 3.7,
  protein = 3.2
)

intensity_t2 <- calc_intensity_litre(
  total_emissions = total_t2,
  milk_litres = farm_profile$milk_production,
  fat = 3.7,
  protein = 3.2
)

# Compare intensities
intensity_comparison <- data.frame(
  Metric = c(
    "Total Emissions (kg CO₂eq)", "Milk Intensity (kg CO₂eq/kg FPCM)",
    "FPCM Production (kg)", "Difference in Intensity (%)",
    "Management Classification"
  ),
  Tier1 = c(
    format(round(total_t1$total_co2eq), big.mark = ","),
    round(intensity_t1$intensity_co2eq_per_kg_fpcm, 3),
    format(round(intensity_t1$fpcm_production_kg), big.mark = ","),
    "-",
    ifelse(intensity_t1$intensity_co2eq_per_kg_fpcm < 1.2, "Good", "Needs Improvement")
  ),
  Tier2 = c(
    format(round(total_t2$total_co2eq), big.mark = ","),
    round(intensity_t2$intensity_co2eq_per_kg_fpcm, 3),
    format(round(intensity_t2$fpcm_production_kg), big.mark = ","),
    round((intensity_t2$intensity_co2eq_per_kg_fpcm - intensity_t1$intensity_co2eq_per_kg_fpcm) /
      intensity_t1$intensity_co2eq_per_kg_fpcm * 100, 1),
    ifelse(intensity_t2$intensity_co2eq_per_kg_fpcm < 1.2, "Good", "Needs Improvement")
  )
)

kable(intensity_comparison, caption = "Intensity Metrics: Tier 1 vs Tier 2")

Accuracy and Uncertainty

Uncertainty Ranges by Tier

uncertainty_analysis <- data.frame(
  Source = c("Enteric", "Manure", "Soil", "Energy", "Inputs"),
  Tier1_Uncertainty = c("Higher", "Higher", "Higher", "Lower", "Medium"),
  Tier2_Uncertainty = c("Medium", "Medium", "Medium", "Lower", "Medium"),
  Key_Improvement = c(
    "Diet-specific Ym factors",
    "VS calculation from intake",
    "Site-specific soil factors",
    "No significant change",
    "Regional emission factors"
  )
)

kable(uncertainty_analysis, caption = "Uncertainty Comparison by Emission Source")

Factors Affecting Accuracy

# Create accuracy comparison visualization
accuracy_data <- data.frame(
  Factor = c(
    "Enteric - Default EF", "Enteric - Energy Method", "Manure - Default",
    "Manure - VS/MCF", "Soil - Standard", "Energy - Standard", "Inputs - Default"
  ),
  Tier = c("Tier 1", "Tier 2", "Tier 1", "Tier 2", "Both", "Both", "Both"),
  Uncertainty_Low = c(70, 85, 60, 75, 50, 85, 75),
  Uncertainty_High = c(130, 115, 140, 125, 150, 115, 125),
  Method_Complexity = c(1, 3, 1, 3, 2, 2, 2)
)

accuracy_data$Uncertainty_Mid <- (accuracy_data$Uncertainty_Low + accuracy_data$Uncertainty_High) / 2

ggplot(accuracy_data, aes(
  x = reorder(Factor, Method_Complexity),
  y = Uncertainty_Mid, color = Tier
)) +
  geom_pointrange(aes(ymin = Uncertainty_Low, ymax = Uncertainty_High),
    size = 0.8, alpha = 0.8
  ) +
  geom_hline(yintercept = 100, linetype = "dashed", color = "gray50") +
  coord_flip() +
  labs(
    title = "Accuracy Ranges by Methodology and Tier",
    subtitle = "100 = Perfect accuracy, wider ranges = higher uncertainty",
    x = "Calculation Method",
    y = "Accuracy Range (% of true value)",
    color = "IPCC Tier"
  ) +
  theme_minimal() +
  theme(plot.title = element_text(size = 14, hjust = 0.5)) +
  scale_color_brewer(type = "qual", palette = "Set1")

Decision Framework: When to Use Each Tier

Tier Selection Criteria

decision_framework <- data.frame(
  Criterion = c(
    "Purpose", "Data Availability", "Time Available", "Expertise Level",
    "Accuracy Needs", "Budget", "Follow-up Actions"
  ),
  Use_Tier1 = c(
    "Regional estimates, screening",
    "Basic farm records only",
    "< 1 day",
    "Basic agricultural knowledge",
    "Screening-level precision",
    "Minimal cost",
    "Awareness, general comparison"
  ),
  Use_Tier2 = c(
    "Farm management, mitigation",
    "Detailed records + measurements",
    "2-5 days",
    "Nutrition/LCA knowledge helpful",
    "Better precision when detailed inputs are available",
    "Moderate investment",
    "Specific interventions, monitoring"
  )
)

kable(decision_framework, caption = "Tier Selection Decision Framework")

Cost-Benefit Analysis

# Cost-benefit comparison
cost_benefit <- data.frame(
  Aspect = c(
    "Data Collection Cost", "Technical Expertise", "Processing Time",
    "Result Accuracy", "Management Value", "Policy Applicability"
  ),
  Tier1_Score = c(1, 1, 1, 2, 2, 3), # 1=low, 3=high
  Tier2_Score = c(3, 2, 2, 3, 3, 2),
  Weight = c(0.2, 0.15, 0.15, 0.25, 0.15, 0.1) # Importance weights
)

cost_benefit$Tier1_Weighted <- cost_benefit$Tier1_Score * cost_benefit$Weight
cost_benefit$Tier2_Weighted <- cost_benefit$Tier2_Score * cost_benefit$Weight

tier1_total <- sum(cost_benefit$Tier1_Weighted)
tier2_total <- sum(cost_benefit$Tier2_Weighted)

cat("Weighted Decision Scores:\n")
cat("Tier 1:", round(tier1_total, 2), "\n")
cat("Tier 2:", round(tier2_total, 2), "\n")
cat(
  "\nRecommendation: Use", ifelse(tier2_total > tier1_total, "Tier 2", "Tier 1"),
  "for most farm-level assessments\n"
)

Sensitivity Analysis

Parameter Sensitivity in Tier 2

# Test sensitivity of key Tier 2 parameters
sensitivity_tests <- list(
  baseline = list(ym = 6.1, body_weight = 580, dm_intake = 19.2),
  high_ym = list(ym = 6.8, body_weight = 580, dm_intake = 19.2),
  low_ym = list(ym = 5.4, body_weight = 580, dm_intake = 19.2),
  heavy_cows = list(ym = 6.1, body_weight = 650, dm_intake = 19.2),
  light_cows = list(ym = 6.1, body_weight = 510, dm_intake = 19.2),
  high_intake = list(ym = 6.1, body_weight = 580, dm_intake = 21.5),
  low_intake = list(ym = 6.1, body_weight = 580, dm_intake = 16.9)
)

sensitivity_results <- lapply(names(sensitivity_tests), function(scenario) {
  params <- sensitivity_tests[[scenario]]

  enteric_test <- calc_emissions_enteric(
    n_animals = farm_profile$dairy_cows,
    cattle_category = "dairy_cows",
    avg_milk_yield = farm_profile$milk_yield_per_cow,
    avg_body_weight = params$body_weight,
    dry_matter_intake = params$dm_intake,
    ym_percent = params$ym,
    tier = 2
  )

  data.frame(
    Scenario = scenario,
    CH4_kg = enteric_test$ch4_kg,
    CO2eq_kg = enteric_test$co2eq_kg
  )
})

sensitivity_df <- do.call(rbind, sensitivity_results) %>%
  mutate(
    Change_from_baseline = round((CO2eq_kg - CO2eq_kg[Scenario == "baseline"]) /
      CO2eq_kg[Scenario == "baseline"] * 100, 1)
  )

kable(sensitivity_df, caption = "Tier 2 Parameter Sensitivity Analysis")

Impact on Farm Rankings

# Create hypothetical farm comparison
set.seed(456)
farm_comparison <- data.frame(
  Farm = paste0("Farm_", LETTERS[1:6]),
  Tier1_Intensity = c(1.15, 1.42, 0.98, 1.65, 1.28, 1.33),
  Tier2_Intensity = c(1.08, 1.51, 1.12, 1.48, 1.35, 1.29)
) %>%
  mutate(
    Tier1_Rank = rank(Tier1_Intensity),
    Tier2_Rank = rank(Tier2_Intensity),
    Rank_Change = Tier2_Rank - Tier1_Rank
  )

# Visualize ranking changes
ranking_plot_data <- farm_comparison %>%
  select(Farm, Tier1_Rank, Tier2_Rank) %>%
  tidyr::pivot_longer(
    cols = c(Tier1_Rank, Tier2_Rank),
    names_to = "Tier", values_to = "Rank"
  ) %>%
  mutate(Tier = gsub("_Rank", "", Tier))

ggplot(ranking_plot_data, aes(x = Tier, y = Rank, group = Farm, color = Farm)) +
  geom_line(size = 1.2, alpha = 0.7) +
  geom_point(size = 3) +
  geom_text(aes(label = Farm), vjust = -0.8, size = 3) +
  scale_y_reverse(breaks = 1:6, labels = paste0("#", 1:6)) +
  labs(
    title = "Farm Ranking Changes: Tier 1 vs Tier 2",
    subtitle = "Lines show how farm rankings change between methodologies",
    x = "Methodology Tier",
    y = "Performance Rank (1 = best)"
  ) +
  theme_minimal() +
  theme(
    legend.position = "none",
    plot.title = element_text(size = 14, hjust = 0.5)
  )

kable(farm_comparison[, c("Farm", "Tier1_Intensity", "Tier2_Intensity", "Rank_Change")],
  caption = "Impact of Methodology on Farm Rankings"
)

Practical Recommendations

Implementation Strategy

Based on the analysis, here are practical recommendations:

For Research and Policy

Use Tier 2 for developing mitigation strategies
Use Tier 1 for initial screening and regional inventories
Combine approaches for cost-effective large-scale assessments

For Farm Advisors

Start with Tier 1 to identify high-impact farms
Upgrade to Tier 2 for detailed improvement plans
Focus data collection on parameters with highest uncertainty

For Farmers

Tier 1 benefits: Quick assessment, low cost, baseline establishment
Tier 2 benefits: Precise targeting, monitoring progress, optimization

Quality Assurance

# Quality control recommendations
quality_control <- data.frame(
  Tier = c("Tier 1", "Tier 1", "Tier 2", "Tier 2", "Both"),
  Check_Type = c(
    "Data Range", "Internal Consistency", "Parameter Validation",
    "Results Plausibility", "Cross-Validation"
  ),
  Description = c(
    "Verify animal numbers and production within expected ranges",
    "Check milk per cow, stocking rates against system type",
    "Validate body weights, intakes against literature values",
    "Compare results with similar farms and published studies",
    "Run both tiers where possible, investigate large differences"
  ),
  Critical_Level = c("Medium", "High", "High", "Medium", "High")
)

kable(quality_control, caption = "Quality Assurance Recommendations by Tier")

Conclusions

Key Findings

Tier differences: Tier 2 can differ from Tier 1 because it uses more farm-specific parameters (e.g., intake, body weight, digestibility, Ym). The magnitude and direction of the change depend on the farm system and the quality of the Tier 2 inputs.
Source-specific impacts: Enteric fermentation and manure management are often the most sensitive to tier choice, because Tier 2 relies on more detailed animal and diet information.
Data investment: Tier 2 typically requires more detailed data collection and more careful parameter checking, but it can provide results that are more representative of the specific farm when those inputs are reliable.

Selection Guidelines

Choose Tier 1 when: - Conducting regional assessments with limited resources - Screening large numbers of farms quickly - Data availability is severely limited - Results are for general awareness or policy screening

Choose Tier 2 when: - Developing farm-specific mitigation strategies - Monitoring progress over time - Detailed data is available or can be collected - Results will guide significant investments

Use both when: - Resources allow for comprehensive analysis - Validation of results is critical - Training purposes or methodology development

The choice between Tier 1 and Tier 2 should align with the intended use of results, available resources, and required accuracy. cowfootR's flexible implementation of both tiers enables users to select the most appropriate methodology for their specific needs.

This analysis demonstrates cowfootR's implementation of IPCC 2019 guidelines. For detailed single-farm analysis, see the "Single Farm Analysis" vignette. For processing multiple farms, consult the "Batch Farm Assessment" vignette.

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cowfootR documentation built on Jan. 13, 2026, 5:07 p.m.

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cowfootR Dairy Farm Carbon Footprint Assessment

IPCC Methodology Tiers in cowfootR In cowfootR: Dairy Farm Carbon Footprint Assessment