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")

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")

cf_download_template()

# 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)
#> $emission_factor_ch4
#> [1] 115
#> 
#> $ym_percent
#> [1] 6.5
#> 
#> $gwp_ch4
#> [1] 27.2
#> 
#> $method_used
#> [1] "Tier 1"

# 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])
#> $dairy_cows
#> [1] 120
#> 
#> $heifers
#> [1] 35
#> 
#> $calves
#> [1] 40
#> 
#> $milk_production
#> [1] 850000
#> 
#> $farm_area
#> [1] 160
#> 
#> $cow_body_weight
#> [1] 580
#> 
#> $heifer_body_weight
#> [1] 380
#> 
#> $calf_body_weight
#> [1] 170

# 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)

# 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)

# 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")

# 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")

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")

# 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 <- 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 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")
#> Weighted Decision Scores:
cat("Tier 1:", round(tier1_total, 2), "\n")
#> Tier 1: 1.6
cat("Tier 2:", round(tier2_total, 2), "\n")
#> Tier 2: 2.6
cat(
  "\nRecommendation: Use", ifelse(tier2_total > tier1_total, "Tier 2", "Tier 1"),
  "for most farm-level assessments\n"
)
#> 
#> Recommendation: Use Tier 2 for most farm-level assessments

# 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")

# 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"
)

# 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")