Complete_Parameter_Reference_Guide

Complete Parameter Reference Guide

This vignette provides a comprehensive reference for all parameters across cowfootR functions, including units, valid options, typical ranges, and data sources. Use this as a technical reference when setting up calculations or troubleshooting data issues.

Function Overview

cowfootR includes these main calculation functions: - calc_emissions_enteric() - Enteric fermentation methane - calc_emissions_manure() - Manure management CH4 and N2O
- calc_emissions_soil() - Soil N2O emissions - calc_emissions_energy() - Energy-related CO2 emissions - calc_emissions_inputs() - Purchased input emissions - calc_total_emissions() - Aggregation function - calc_intensity_litre() - Milk intensity calculations - calc_intensity_area() - Area intensity calculations - calc_batch() - Batch processing function

1. calc_emissions_enteric() Parameters

Required Parameters

Parameter Type Unit Description Valid Range Example
n_animals Numeric head Number of animals > 0 100

Animal Characteristics

Parameter Type Unit Description Valid Range Default Example
cattle_category Character - Type of cattle “dairy_cows”, “heifers”, “calves”, “bulls” “dairy_cows” “dairy_cows”
avg_body_weight Numeric kg Average live weight 100-800 550 (cows) 580
avg_milk_yield Numeric kg/year Annual milk yield per cow 1000-15000 6000 7200

Production System

Parameter Type Unit Description Valid Options Default Notes
production_system Character - System intensity “intensive”, “extensive”, “mixed” “mixed” Affects Tier 1 factors
dry_matter_intake Numeric kg/day Daily DM intake per animal 8-25 (cows) NULL Required for accurate Tier 2

Feed Parameters

Parameter Type Unit Description Valid Range Default Notes
feed_inputs Named list kg DM/year Annual feed consumption > 0 NULL Names: grain_dry, grain_wet, ration, byproducts, proteins
ym_percent Numeric % Methane conversion factor 4.0-8.0 6.5 Higher = more CH4 per unit energy

Methodology Control

Parameter Type Unit Description Valid Options Default Impact
tier Numeric - IPCC methodology tier 1, 2 1 Tier 2 more accurate with detailed data
emission_factor_ch4 Numeric kg CH4/head/year Custom CH4 factor 40-150 NULL Overrides tier calculations
gwp_ch4 Numeric kg CO2eq/kg CH4 Global Warming Potential 25-30 27.2 IPCC AR6 value

2. calc_emissions_manure() Parameters

Required Parameters

Parameter Type Unit Description Valid Range Default Example
n_cows Numeric head Total number of animals > 0 - 150

Manure System

Parameter Type Unit Description Valid Options Default CH4 Impact
manure_system Character - Management system type “pasture”, “solid_storage”, “liquid_storage”, “anaerobic_digester” “pasture” High variation
climate Character - Climate region “cold”, “temperate”, “warm” “temperate” Affects MCF

Tier 2 Specific

Parameter Type Unit Description Valid Range Default Required For
avg_body_weight Numeric kg Average live weight 400-700 600 Tier 2 VS calculation
diet_digestibility Numeric fraction Diet apparent digestibility 0.5-0.8 0.65 Tier 2 VS calculation
retention_days Numeric days Days manure in system 10-200 NULL System optimization
system_temperature Numeric °C Average system temperature 5-40 NULL MCF adjustment

Nitrogen Management

Parameter Type Unit Description Valid Range Default Impact
n_excreted Numeric kg N/cow/year N excretion per cow 80-150 100 N2O emissions
ef_n2o_direct Numeric kg N2O-N/kg N Direct N2O emission factor 0.005-0.03 0.02 IPCC 2019
include_indirect Logical - Include indirect N2O? TRUE/FALSE FALSE +20-30% N2O
protein_intake_kg Numeric kg/day Daily protein intake 1.5-4.0 NULL Refines N excretion

3. calc_emissions_soil() Parameters

Nitrogen Inputs

Parameter Type Unit Description Valid Range Typical Values Source
n_fertilizer_synthetic Numeric kg N/year Synthetic fertilizer N 0-5000 50-300 kg N/ha Purchase records
n_fertilizer_organic Numeric kg N/year Organic fertilizer N 0-3000 0-100 kg N/ha Application records
n_excreta_pasture Numeric kg N/year N deposited while grazing 0-20000 80-120 kg N/cow/year Calculated estimate
n_crop_residues Numeric kg N/year N in returned crop residues 0-2000 10-50 kg N/ha Crop management

Site Conditions

Parameter Type Unit Description Valid Options Default N2O Impact
soil_type Character - Soil drainage “well_drained”, “poorly_drained” “well_drained” 50% difference
climate Character - Climate classification “temperate”, “tropical” “temperate” 20% difference
area_ha Numeric hectares Total farm area > 0 NULL For per-hectare metrics

Emission Factors

Parameter Type Unit Description Valid Range Default Source
ef_direct Numeric kg N2O-N/kg N Direct emission factor 0.005-0.025 NULL IPCC 2019 by soil/climate
include_indirect Logical - Include volatilization/leaching TRUE/FALSE TRUE +30-50% total N2O
gwp_n2o Numeric kg CO2eq/kg N2O N2O warming potential 265-300 273 IPCC AR6

4. calc_emissions_energy() Parameters

Fuel Consumption

Parameter Type Unit Description Typical Range Default EF (kg CO2/unit)
diesel_l Numeric litres/year Diesel consumption 2000-15000 0 2.67
petrol_l Numeric litres/year Petrol/gasoline consumption 500-3000 0 2.31
lpg_kg Numeric kg/year LPG consumption 100-1000 0 3.0
natural_gas_m3 Numeric m³/year Natural gas consumption 0-5000 0 2.0
electricity_kwh Numeric kWh/year Electricity consumption 10000-100000 0 Variable by country

Location and Factors

Parameter Type Unit Description Valid Options Default Notes
country Character - Country for grid factors “UY”, “AR”, “BR”, “NZ”, “US”, “AU”, “DE”, etc. “UY” Major impact on electricity EF
ef_electricity Numeric kg CO2/kWh Custom electricity factor 0.05-1.0 NULL Overrides country default
include_upstream Logical - Include fuel production TRUE/FALSE FALSE +10-15% total

Grid Emission Factors (Built-in)

Country Code EF (kg CO2/kWh) Source
Uruguay UY 0.08 Clean grid (hydro)
Argentina AR 0.35 Mixed grid
Brazil BR 0.12 Hydro + renewables
New Zealand NZ 0.15 Renewable majority
United States US 0.45 Fossil majority
Australia AU 0.75 Coal dominant

5. calc_emissions_inputs() Parameters

Feed Inputs

Parameter Type Unit Description Typical Range Default EF Range (kg CO2eq/kg)
conc_kg Numeric kg/year Concentrate feed 50000-500000 0 0.5-1.2
feed_grain_dry_kg Numeric kg DM/year Dry grain feeds 20000-200000 0 0.3-0.6
feed_grain_wet_kg Numeric kg DM/year Wet grain/silage 10000-100000 0 0.25-0.45
feed_ration_kg Numeric kg DM/year Complete rations 30000-300000 0 0.4-0.8
feed_byproducts_kg Numeric kg DM/year Feed byproducts 5000-80000 0 0.1-0.25
feed_proteins_kg Numeric kg DM/year Protein supplements 5000-50000 0 1.2-2.8
feed_corn_kg Numeric kg DM/year Corn grain specific 10000-150000 0 0.35-0.65
feed_soy_kg Numeric kg DM/year Soybean meal 5000-40000 0 1.5-3.2
feed_wheat_kg Numeric kg DM/year Wheat grain 5000-100000 0 0.4-0.7

Other Inputs

Parameter Type Unit Description Typical Range Default EF (kg CO2eq/kg)
fert_n_kg Numeric kg N/year Nitrogen fertilizer 500-5000 0 5.5-8.5
plastic_kg Numeric kg/year Agricultural plastics 100-1000 0 1.8-3.8
transport_km Numeric km Average transport distance 50-500 NULL 1e-4 kg CO2/kg·km

Regional Factors

Parameter Type Unit Description Valid Options Default Impact
region Character - Regional emission factors “global”, “EU”, “US”, “Brazil”, “Argentina”, “Australia” “global” ±20% variation
fert_type Character - Fertilizer type “urea”, “ammonium_nitrate”, “mixed”, “organic” “mixed” ±15% variation
plastic_type Character - Plastic type “LDPE”, “HDPE”, “PP”, “mixed” “mixed” ±20% variation

Advanced Options

Parameter Type Unit Description Valid Range Default Purpose
include_uncertainty Logical - Run Monte Carlo analysis TRUE/FALSE FALSE Uncertainty quantification
ef_conc Numeric kg CO2eq/kg Override concentrate EF 0.3-1.5 NULL Custom factors
ef_fert Numeric kg CO2eq/kg N Override fertilizer EF 3.0-10.0 NULL Local studies
ef_plastic Numeric kg CO2eq/kg Override plastic EF 1.0-5.0 NULL Specific materials

6. calc_intensity_litre() Parameters

Required Parameters

Parameter Type Unit Description Valid Range Notes
total_emissions Numeric or cf_total kg CO2eq/year Total farm emissions > 0 From calc_total_emissions()
milk_litres Numeric litres/year Annual milk production > 0 Farm records

Milk Composition

Parameter Type Unit Description Valid Range Default Source
fat Numeric % Average fat content 2.5-6.0 4.0 Lab analysis or processor
protein Numeric % Average protein content 2.5-4.5 3.3 Lab analysis or processor
milk_density Numeric kg/L Milk density 1.025-1.035 1.03 Lab measurement

FPCM Calculation Formula

The Fat and Protein Corrected Milk (FPCM) formula used is:

FPCM (kg) = milk_kg × (0.1226 × fat% + 0.0776 × protein% + 0.2534)

This standardizes milk to 4.0% fat and 3.3% protein for fair comparison.

7. calc_intensity_area() Parameters

Required Parameters

Parameter Type Unit Description Valid Range Notes
total_emissions Numeric or cf_total kg CO2eq/year Total farm emissions > 0 From calc_total_emissions()
area_total_ha Numeric hectares Total farm area > 0 Property records

Area Breakdown

Parameter Type Unit Description Valid Range Default Notes
area_productive_ha Numeric hectares Productive/utilized area ≤ total area total area Agricultural use only
area_breakdown Named list hectares Detailed land use > 0 each NULL Must sum to total if validate=TRUE

Valid area_breakdown Names

Valid area_breakdown Names and Descriptions
Name Description Typical_Range
pasture_permanent Permanent grassland 40-80%
pasture_temporary Rotational/temporary pasture 5-20%
crops_feed Feed crop production 5-15%
crops_cash Cash crop production 0-10%
infrastructure Buildings, roads, facilities 2-5%
woodland Forest/trees 0-10%
wetlands Water bodies, wetlands 0-5%
other Other non-productive areas 0-5%

Validation

Parameter Type Unit Description Valid Options Default Purpose
validate_area_sum Logical - Check area breakdown sums TRUE/FALSE TRUE Data quality control

8. calc_batch() Parameters

Data Input

Parameter Type Unit Description Requirements Example
data data.frame - Farm data with template columns See template structure farm_data

Template Column Requirements

Template Structure (First 15 columns)
Column_Group Column_Name Data_Type Required
Identification FarmID character Yes
Identification Year character No
Production Milk_litres numeric Yes
Production Fat_percent numeric No
Production Protein_percent numeric No
Production Milk_density numeric No
Herd_Composition Cows_milking numeric Yes
Herd_Composition Cows_dry numeric No
Herd_Composition Heifers_total numeric No
Herd_Composition Calves_total numeric No
Herd_Composition Bulls_total numeric No
Animal_Weights Body_weight_cows_kg numeric No
Animal_Weights Body_weight_heifers_kg numeric No
Animal_Weights Body_weight_calves_kg numeric No
Animal_Weights Body_weight_bulls_kg numeric No

Processing Options

Parameter Type Unit Description Valid Options Default Impact
tier Numeric - IPCC methodology tier 1, 2 2 Accuracy vs data requirements
boundaries boundaries object - System boundaries From set_system_boundaries() “farm_gate” Scope of assessment
benchmark_region Character - Regional comparison “uruguay”, “argentina”, etc. NULL Performance context
save_detailed_objects Logical - Store detailed results TRUE/FALSE FALSE For debugging/analysis

9. Parameter Validation and Quality Control

Automatic Validations

Built-in Validation Rules
Parameter_Type Validation_Rules Error_Actions User_Guidance
Animal Numbers Must be positive integers Stop execution with error message Check data entry and farm records
Production Metrics Milk yield 1000-15000 kg/cow/year Warning with guidance on typical ranges Verify annual vs daily units
Area Data Area breakdown must sum to total (if validate=TRUE) Stop or warn based on validate_area_sum setting Review land use classification
Input Quantities All quantities ≥ 0 Stop with error message Check for data entry errors
Ratios Stocking rate 0.1-3.0 cows/ha Warning about unusual values Confirm farm characteristics

Data Quality Indicators

Data Quality Assessment Indicators
Indicator Formula Excellent_Range Good_Range Poor_Range Unit
Milk yield per cow Milk_litres / Cows_milking / 1000 7000-9000 6000-7000 <5000 or >10000 kg/cow/year
Stocking rate Cows_milking / Area_total_ha 1.2-1.8 0.8-1.2 <0.5 or >2.5 cows/ha
Feed conversion Milk_litres / Concentrate_feed_kg 3.0-5.0 2.0-3.0 <1.5 or >6.0 L milk/kg conc
Energy intensity Electricity_kWh / Milk_litres 0.04-0.06 0.06-0.08 >0.10 kWh/L milk

10. Common Parameter Issues and Solutions

Missing Data Handling

Handling Missing Parameters
Missing_Parameter Default_Used Accuracy_Impact Recommended_Action
Body weights Species-specific defaults Low Use literature values for breed/region
DM intake Calculated from body weight Medium Estimate from feeding standards
Feed breakdown Concentrate only High Collect detailed feed records
Area breakdown Total area only Medium Survey farm land use patterns
Ym factor 6.5% Medium Use regional studies or 6.0-6.8 range

Unit Conversion Guide

Unit Conversion Reference
Parameter Common_Units cowfootR_Unit Conversion_Factor Typical_Values
Milk production L, kg L/year kg = L × density 1.03 kg/L
Feed amounts kg fresh, kg DM, tons kg DM/year DM = fresh × (1 - moisture%) 35% DM corn silage
Fertilizer kg product, kg N kg N/year kg N = kg product × N% 46% N in urea
Body weight kg, lbs kg kg = lbs ÷ 2.205 580 kg dairy cow
Area ha, acres hectares ha = acres × 0.405 0.405 ha/acre

Regional Parameter Adjustments

Regional Emission Factor Variations
Region Soy_EF_Range Fertilizer_EF Key_Differences Use_When
EU 2.1-3.2 5.8-7.9 High soy transport costs European farms
US 1.2-2.2 5.3-7.6 Domestic grain production US/Canadian farms
Brazil 0.9-1.6 6.0-8.3 Local soy, high N fertilizer Brazilian operations
Argentina 0.8-1.5 5.8-8.1 Local grain/soy production Argentinian farms
Australia 1.8-3.0 5.4-7.7 High transport distances Australian/NZ farms
Global 1.5-2.8 5.5-7.8 Average of all regions Unknown/mixed sourcing

11. Parameter Sensitivity Rankings

High Impact Parameters (>15% result change)

High Impact Parameters (Priority for Accurate Data)
Parameter Function Impact_Direction Typical_Variation Result_Sensitivity Data_Priority
n_animals enteric Linear ±20% ±20% High
milk_litres intensity Inverse ±25% ±25% High
conc_kg inputs Linear ±30% ±25% High
ym_percent enteric Linear ±15% ±15% Medium
avg_body_weight enteric Linear ±10% ±8% Medium

Medium Impact Parameters (5-15% result change)

Medium Impact Parameters
Parameter Impact_Range Collection_Difficulty Recommendation
n_fertilizer_kg 5-12% Easy Get purchase records
diet_digestibility 8-15% Medium Estimate from feed quality
area_total_ha Area metrics only Easy Survey or property records
manure_system 10-25% manure Easy Observe system
region 5-20% inputs Easy Select best match

Low Impact Parameters (<5% result change)

Low Impact Parameters (Can Use Estimates)
Parameter Impact_Range Default_Approach Notes
plastic_kg <2% Estimate broadly Small contribution unless very large
lpg_kg <3% Estimate or ignore Often minimal in dairy
gwp values <5% Use package defaults IPCC AR6 values recommended
milk_density <2% Use 1.03 Varies little
transport_km <5% Estimate 100-200 km Affects feed emissions only

12. Troubleshooting Common Issues

Error Messages and Solutions

Common Error Messages and Solutions
Error_Type Common_Cause Solution Prevention
Invalid region Typo in region name Check spelling: ‘EU’, ‘US’, ‘Brazil’, ‘Argentina’, ‘Australia’ Use template dropdown lists
Negative values Data entry error or wrong units Verify all quantities ≥ 0 and units are correct Implement data validation in Excel
Area sum mismatch Land use breakdown doesn’t add up Review area_breakdown list or set validate_area_sum = FALSE Use GIS or survey data for areas
Missing required data Empty cells in required columns Fill required columns or use defaults Document data requirements clearly
Unrealistic results Wrong units or extreme outliers Check units, outliers, and parameter ranges Compare results with similar farms

Performance Optimization

For large batch processing:

Performance Optimization for Large Datasets
Aspect Recommendation Performance_Gain Implementation_Effort
Data Preparation Pre-validate data, use consistent formats, remove empty rows 50-70% Low
Processing Speed Process in chunks of 50-100 farms, use tier 1 for screening 30-50% Medium
Memory Management Set save_detailed_objects = FALSE for large batches 60-80% Low
Error Handling Implement robust error logging and recovery mechanisms Prevents crashes High
Result Storage Export results incrementally, use database for >1000 farms Scalable High

13. Advanced Parameter Combinations

Tier 2 Optimal Parameter Sets

Optimal Parameter Combinations by System Type
System_Type Key_Parameters Critical_Measurements Typical_Accuracy
Intensive Dairy High DM intake, concentrate feeds, precise body weights Feed composition, milk yield, system temperature ±10-15%
Extensive Grazing Pasture N excretion, extensive manure system, lower Ym Grazing management, soil conditions, climate data ±15-25%
Mixed System Balanced feed inputs, moderate intensities Feed efficiency ratios, land use breakdown ±12-20%
Organic System Organic fertilizers, lower input emissions, pasture focus Organic input quantities, certification requirements ±15-30%

Parameter Interaction Effects

Important Parameter Interactions
Parameter_Pair Interaction_Type Effect_Magnitude Management_Implication
Body weight + DM intake Multiplicative Medium Heavier cows need proportionally more feed
Ym% + Feed quality Exponential High Poor quality diets increase methane conversion
Climate + Soil type Additive Medium Tropical poorly-drained soils have highest N2O
Region + Feed sources Complex High Local feed sourcing reduces transport emissions
Manure system + Retention time Threshold Variable Short retention (<30 days) limits CH4 conversion

14. Data Collection Protocols

Minimum Data Requirements by Objective

Data Requirements by Assessment Objective
Assessment_Goal Essential_Data Time_Investment Accuracy_Target Tier_Recommendation
Screening Assessment Animal numbers, milk production, basic inputs 2-4 hours ±30% Tier 1
Management Planning Detailed feeds, precise areas, management practices 1-2 days ±15% Tier 2
Carbon Trading Verified production, third-party validated inputs 3-5 days ±10% Tier 2 + validation
Research Study Complete parameter set, uncertainty quantification 1-2 weeks ±5% Tier 2 + uncertainty

Data Collection Schedule

Recommended Data Collection Schedule
Data_Category Collection_Frequency Storage_Location Quality_Control
Production Records Monthly Farm office Cross-check with processor
Feed Purchases Each delivery Purchase invoices Verify units and quantities
Energy Consumption Monthly Utility bills Monitor seasonal patterns
Land Management Seasonal Management records Update land use changes
Animal Characteristics Annual Herd records Weigh representative sample

15. Quality Assurance Framework

Validation Hierarchy

Quality Assurance Validation Levels
Level Validation_Type Examples Error_Detection Implementation
Level 1: Range Checks Automatic Values within expected ranges, correct units 90% Built-in cowfootR
Level 2: Consistency Checks Automatic Milk yield vs feed intake, stocking rate vs area 70% Built-in cowfootR
Level 3: Benchmark Comparison Semi-automatic Results vs regional averages, peer farm comparison 50% User comparison
Level 4: Expert Review Manual Technical review by LCA specialist 95% External expert

Red Flag Indicators

Data Quality Red Flag Indicators
Indicator Warning_Threshold Likely_Cause Investigation_Priority
Milk intensity >2.5 kg CO2eq/kg FPCM Poor productivity or data errors High
Feed efficiency <1.0 L milk/kg concentrate Overestimated feed use or underestimated milk High
Energy use >0.15 kWh/L milk Energy-intensive processes or errors Medium
Emission ratios Enteric <30% of total Missing emission sources or calculation errors High
System consistency Intensive system + low inputs Inconsistent system classification Medium

Conclusion

This parameter reference guide provides comprehensive technical specifications for all cowfootR functions. Use it as a reference when:

For practical applications, start with the function-specific sections, then refer to validation and troubleshooting sections as needed. The parameter sensitivity rankings help prioritize data collection efforts for maximum accuracy improvement.

Remember that parameter accuracy requirements depend on the intended use of results. Screening assessments can tolerate higher uncertainty than management planning or carbon trading applications.

This reference guide covers cowfootR version 0.1.2 and follows IDF 2022 and IPCC 2019 methodological standards.