---
title: "Introduction_to_Dairy_LCA_cowfootR"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction_to_Dairy_LCA_cowfootR}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  fig.alt = "Figura generada por la viñeta; ver texto para detalles.",
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5,
  warning = FALSE,
  message = FALSE
)

# Load required packages
library(cowfootR)
library(ggplot2)
library(dplyr)
```

# Introduction to Dairy Life Cycle Assessment

## Overview

The dairy industry plays a crucial role in global food security, but it also contributes significantly to greenhouse gas emissions. Understanding and quantifying the environmental impact of dairy production is essential for sustainable development and climate change mitigation.

The **cowfootR** package provides a comprehensive toolkit for calculating dairy farm carbon footprints following internationally recognized standards, specifically the International Dairy Federation (IDF) 2022 guidelines and IPCC 2019 methodologies.

## Theoretical Background

### Life Cycle Assessment in Dairy Production

Life Cycle Assessment (LCA) is a systematic approach to evaluating the environmental impacts of a product or service throughout its entire life cycle. In dairy production, LCA helps quantify greenhouse gas emissions from various sources within the farm system.

### Key Emission Sources in Dairy Systems

Dairy farm emissions primarily originate from five main sources:

1. **Enteric Fermentation**: Methane (CH₄) produced during digestion in ruminants
2. **Manure Management**: CH₄ and nitrous oxide (N₂O) from manure storage and treatment
3. **Soil Emissions**: N₂O from nitrogen fertilizers and excreta deposition
4. **Energy Use**: Carbon dioxide (CO₂) from fossil fuel combustion and electricity
5. **Purchased Inputs**: Embodied emissions in feeds, fertilizers, and materials

### System Boundaries

System boundaries define which processes are included in the assessment:

- **Farm Gate**: Includes on-farm emissions only
- **Cradle-to-Farm Gate**: Includes upstream production of inputs
- **Partial**: Custom selection of emission sources

### Functional Units and Intensity Metrics

Results are expressed using functional units that allow meaningful comparisons:

- **kg CO₂eq per kg FPCM**: Fat and Protein Corrected Milk intensity
- **kg CO₂eq per hectare**: Land use intensity
- **Absolute emissions**: Total farm emissions in kg CO₂eq per year

## Getting Started with cowfootR

### Installation

```{r eval=FALSE}
# Install from CRAN (when available)
install.packages("cowfootR")

# Or install development version from GitHub
# devtools::install_github("yourusername/cowfootR")
```

### Loading the Package

```{r}
library(cowfootR)
```

### Basic Workflow

The typical cowfootR workflow involves four main steps:

1. **Define system boundaries**
2. **Calculate emissions by source**
3. **Aggregate total emissions**
4. **Calculate intensity metrics**

Let's walk through a simple example:

## Example: Basic Farm Assessment

### Step 1: Define System Boundaries

```{r}
# Define farm-gate boundaries (most common approach)
boundaries <- set_system_boundaries("farm_gate")
print(boundaries)
```

### Step 2: Basic Farm Data

For this example, we'll use data from a typical dairy farm:

```{r}
# Farm characteristics
farm_data <- list(
  # Herd composition
  dairy_cows = 100,
  heifers = 30,
  calves = 25,
  
  # Production
  milk_litres = 600000,  # Annual milk production
  milk_yield_per_cow = 6000,  # kg/cow/year
  
  # Farm area
  total_area_ha = 120,
  productive_area_ha = 110,
  
  # Inputs
  concentrate_kg = 180000,  # Annual concentrate use
  n_fertilizer_kg = 1500,   # Nitrogen fertilizer
  diesel_litres = 8000,     # Annual diesel consumption
  electricity_kwh = 35000   # Annual electricity use
)

print(farm_data)
```

### Step 3: Calculate Emissions by Source

Now we calculate emissions from each source using the individual calculation functions:

#### Enteric Fermentation

```{r}
# Calculate enteric methane emissions
enteric_emissions <- calc_emissions_enteric(
  n_animals = farm_data$dairy_cows,
  cattle_category = "dairy_cows",
  avg_milk_yield = farm_data$milk_yield_per_cow,
  tier = 2,  # Use Tier 2 for more accurate results
  boundaries = boundaries
)

print(enteric_emissions)
```

#### Manure Management

```{r}
# Calculate manure management emissions
manure_emissions <- calc_emissions_manure(
  n_cows = farm_data$dairy_cows,
  manure_system = "pasture",  # Typical for extensive systems
  tier = 2,
  include_indirect = TRUE,
  boundaries = boundaries
)

print(manure_emissions)
```

#### Soil Emissions

```{r}
# Calculate soil N2O emissions
soil_emissions <- calc_emissions_soil(
  n_fertilizer_synthetic = farm_data$n_fertilizer_kg,
  n_excreta_pasture = farm_data$dairy_cows * 100,  # Estimated N excretion
  area_ha = farm_data$total_area_ha,
  soil_type = "well_drained",
  climate = "temperate",
  include_indirect = TRUE,
  boundaries = boundaries
)

print(soil_emissions)
```

#### Energy Use

```{r}
# Calculate energy-related emissions
energy_emissions <- calc_emissions_energy(
  diesel_l = farm_data$diesel_litres,
  electricity_kwh = farm_data$electricity_kwh,
  country = "UY",  # Uruguay electricity grid
  boundaries = boundaries
)

print(energy_emissions)
```

#### Purchased Inputs

```{r}
# Calculate emissions from purchased inputs
input_emissions <- calc_emissions_inputs(
  conc_kg = farm_data$concentrate_kg,
  fert_n_kg = farm_data$n_fertilizer_kg,
  region = "global",  # Use global emission factors
  boundaries = boundaries
)

print(input_emissions)
```

### Step 4: Aggregate Total Emissions

```{r}
# Combine all emission sources
total_emissions <- calc_total_emissions(
  enteric_emissions,
  manure_emissions,
  soil_emissions,
  energy_emissions,
  input_emissions
)

total_emissions
```

### Step 5: Calculate Intensity Metrics

#### Milk Intensity

```{r}
# Calculate emissions per kg of milk (FPCM)
milk_intensity <- calc_intensity_litre(
  total_emissions = total_emissions,
  milk_litres = farm_data$milk_litres,
  fat = 3.8,      # Typical fat content
  protein = 3.2   # Typical protein content
)

print(milk_intensity)
```

#### Area Intensity

```{r}
# Calculate emissions per hectare
area_intensity <- calc_intensity_area(
  total_emissions = total_emissions,
  area_total_ha = farm_data$total_area_ha,
  area_productive_ha = farm_data$productive_area_ha,
  area_breakdown = list(
    pasture_permanent = 80,
    pasture_temporary = 20,
    crops_feed = 15,
    infrastructure = 5
  )
)

print(area_intensity)
```

## Visualizing Results

### Emission Source Breakdown

```{r}
# Create a data frame for plotting
emission_breakdown <- data.frame(
  Source = names(total_emissions$breakdown),
  Emissions = as.numeric(total_emissions$breakdown)
)

# Create pie chart
ggplot(emission_breakdown, aes(x = "", y = Emissions, fill = Source)) +
  geom_col(width = 1) +
  coord_polar("y", start = 0) +
  theme_void() +
  labs(title = "Farm Emissions by Source",
       subtitle = paste("Total:", round(total_emissions$total_co2eq), "kg CO₂eq/year")) +
  theme(plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5))
```

### Intensity Comparison

```{r}
# Create comparison chart
intensity_data <- data.frame(
  Metric = c("Milk Intensity\n(kg CO₂eq/kg FPCM)", 
             "Area Intensity\n(kg CO₂eq/ha)"),
  Value = c(milk_intensity$intensity_co2eq_per_kg_fpcm,
            area_intensity$intensity_per_productive_ha),
  Benchmark = c(1.2, 8000)  # Typical benchmark values
)

ggplot(intensity_data, aes(x = Metric)) +
  geom_col(aes(y = Value), fill = "steelblue", alpha = 0.7) +
  geom_point(aes(y = Benchmark), color = "red", size = 3) +
  geom_text(aes(y = Benchmark, label = "Benchmark"), 
            color = "red", vjust = -0.5) +
  labs(title = "Farm Intensity Metrics",
       y = "Value",
       x = "") +
  theme_minimal()
```

## Understanding the Results

### Interpreting Emission Factors

- **Enteric fermentation** typically represents 40-60% of total farm emissions
- **Purchased inputs** (especially protein feeds) can be 20-40% of emissions
- **Soil N₂O** usually contributes 5-15% of total emissions
- **Energy use** is generally the smallest component (2-8%)

### Benchmarking Performance

The calculated intensities can be compared against regional or global benchmarks:

- **Excellent performance**: < 1.0 kg CO₂eq/kg FPCM
- **Good performance**: 1.0-1.3 kg CO₂eq/kg FPCM  
- **Average performance**: 1.3-2.0 kg CO₂eq/kg FPCM
- **Poor performance**: > 2.0 kg CO₂eq/kg FPCM

## Data Quality Considerations

### Required vs Optional Data

**Essential data:**
- Herd size and composition
- Milk production
- Farm area
- Major input quantities

**Optional but recommended:**
- Detailed feed composition
- Animal weights and productivity
- Energy breakdown by use
- Soil and climate characteristics

### Common Issues

1. **Missing data**: The package provides reasonable defaults, but farm-specific data improves accuracy
2. **Unit consistency**: Ensure all inputs use the correct units (kg, litres, hectares)
3. **System boundaries**: Be consistent about what's included/excluded
4. **Temporal boundaries**: Use annual data for meaningful comparisons

## Next Steps

This introduction covered the basics of using cowfootR for single farm assessments. For more advanced topics, see:

- **Single Farm Analysis**: Detailed exploration of individual calculation functions
- **Batch Farm Assessment**: Processing multiple farms simultaneously
- **Methodology Comparison**: Understanding Tier 1 vs Tier 2 approaches
- **Regional Factors**: Using location-specific emission factors

## Key Takeaways

1. **cowfootR** follows internationally recognized LCA standards (IDF 2022, IPCC 2019)
2. The modular approach allows flexible assessment of different emission sources
3. Results should be interpreted in context of farm system and regional benchmarks
4. Data quality significantly affects accuracy - collect farm-specific data when possible
5. The package provides both absolute emissions and intensity metrics for comprehensive analysis

---

*For questions, bug reports, or contributions, visit the cowfootR GitHub repository or contact the development team.*
