PPM

Overview

PPM (Pesquisa da Pecuária Municipal - Municipal Livestock Survey) is Brazil’s comprehensive annual survey of livestock activities conducted by IBGE. This dataset provides:

  • Livestock inventories: Number of animals by species (cattle, pigs, poultry, sheep, horses, etc.)
  • Animal products: Production volumes and values of milk, eggs, honey, wool, and other animal-origin products
  • Dairy farming: Specialized data on milked cows, geographic distribution, productivity
  • Aquaculture: Fish farming, shrimp farming, and mollusk farming activities
  • Sheep specialization: Detailed shearing and wool production data
  • Multi-level geographic detail: Country, region, state, and municipality levels
  • Long historical series: Available from 1974 onwards
  • Economic value: Both production quantities and market values

PPM is the primary data source for understanding Brazil’s livestock sector, which is economically significant and globally important for beef, poultry, and dairy exports.

Data Source and Methodology

PPM data is compiled from: - Direct surveys of livestock producers - Agricultural censuses and administrative records - Municipal agriculture secretariats - Processed and validated by IBGE - Annual release with data for reference year

For more information, visit IBGE Livestock Statistics.

Available Datasets

1. ppm_livestock_inventory

Total livestock herds disaggregated by animal species.

  • Coverage: All livestock species across all Brazilian municipalities
  • Time period: 1974 onwards
  • Geographic levels: Country, Region, State, Municipality
  • Animal species: Cattle, pigs, chickens, sheep, horses, goats, buffalo, others
  • Variables: Number of animals by species, number of establishments
  • Use cases:
    • Identify regional livestock specialization
    • Track herd size trends
    • Analyze geographic concentration of livestock
    • Understand animal agriculture structure

2. ppm_sheep_farming

Specialized data on sheep production and wool/fleece harvest.

  • Coverage: Sheep farming across Brazil
  • Time period: 1974 onwards
  • Geographic levels: Country, Region, State, Municipality
  • Variables: Total sheep, sheared sheep, fleece weight, wool production
  • Use cases:
    • Analyze wool production and sheep farming specialization
    • Track shearing practices and yields
    • Regional wool industry assessment

3. ppm_animal_origin_production

Production of animal-based products (milk, eggs, honey, wool, etc.).

  • Coverage: All animal product production activities
  • Time period: 1974 onwards
  • Geographic levels: Country, Region, State, Municipality
  • Products included: Cow milk, goat milk, chicken eggs, quail eggs, honey, wool, hides, wax
  • Variables: Quantity produced and value of production
  • Use cases:
    • Track dairy and egg production
    • Analyze honey and other bee products
    • Economic analysis of animal product sectors

4. ppm_cow_farming

Detailed dairy cow farming data with milking and productivity metrics.

  • Coverage: Dairy cow operations
  • Time period: 1974 onwards
  • Geographic levels: Country, Region, State, Municipality
  • Variables: Milked cows, milk production volume, productivity (liters per cow)
  • Use cases:
    • Dairy sector analysis
    • Productivity assessment
    • Geographic specialization in dairy
    • Production trend analysis

5. ppm_aquaculture

Aquaculture activities including fish, shrimp, and mollusk farming.

  • Coverage: All aquaculture operations
  • Time period: 1974 onwards (though aquaculture data more recent)
  • Geographic levels: Country, Region, State, Municipality
  • Activities: Fish farming, shrimp farming, mollusk/oyster farming, other aquaculture
  • Variables: Quantity and value of aquaculture production by type
  • Use cases:
    • Aquaculture sector analysis
    • Regional aquaculture potential
    • Fish and seafood production trends
    • Emerging aquaculture development

Function Parameters

1. dataset

Selects which livestock/animal production dataset to download.

dataset = "ppm_livestock_inventory"     # Animal populations by species
dataset = "ppm_sheep_farming"           # Sheep and wool production
dataset = "ppm_animal_origin_production"  # Milk, eggs, honey, wool
dataset = "ppm_cow_farming"             # Dairy cow productivity
dataset = "ppm_aquaculture"             # Fish and aquaculture production

2. raw_data

Controls whether to download original or processed data.

  • TRUE: Returns raw IBGE format
  • FALSE: Returns treated data with English variable names and standardized units
raw_data = FALSE  # logical

3. geo_level

Specifies geographic aggregation level.

  • "country": National aggregate
  • "region": Brazilian geographic regions (5 regions)
  • "state": State-level data (27 units)
  • "municipality": All 5,570+ municipalities
geo_level = "state"  # character string

4. time_period

Specifies which year(s) to download.

time_period = 2020              # single year
time_period = c(2010, 2020)     # specific years
time_period = 2010:2020         # range of years

Note: All datasets available from 1974 onwards, though aquaculture more complete from 2000s.

5. language

Output language for variable names.

  • "pt": Portuguese
  • "eng": English
language = "eng"  # character string

Examples

Example 1: Livestock inventory by state

# download treated livestock inventory data at the state level for 2020
livestock <- load_ppm(
  dataset = "ppm_livestock_inventory",
  raw_data = FALSE,
  geo_level = "state",
  time_period = 2020,
  language = "eng"
)

Example 2: Dairy cow farming by state

# download treated dairy cow data at the state level for 2020
dairy <- load_ppm(
  dataset = "ppm_cow_farming",
  raw_data = FALSE,
  geo_level = "state",
  time_period = 2020,
  language = "eng"
)

Example 3: Animal origin production at the country level

# download treated animal origin production data at the country level for 2020
animal_products <- load_ppm(
  dataset = "ppm_animal_origin_production",
  raw_data = FALSE,
  geo_level = "country",
  time_period = 2020,
  language = "eng"
)

Example 4: Sheep farming by state

# download treated sheep farming data at the state level for 2020
sheep <- load_ppm(
  dataset = "ppm_sheep_farming",
  raw_data = FALSE,
  geo_level = "state",
  time_period = 2020,
  language = "eng"
)

Example 5: Aquaculture by state over time

# download treated aquaculture data at the state level for 2015 to 2020
aquaculture <- load_ppm(
  dataset = "ppm_aquaculture",
  raw_data = FALSE,
  geo_level = "state",
  time_period = 2015:2020,
  language = "eng"
)

Data Notes

Data Structure

Each record typically contains: - Geographic identifier (state or municipality) - Year - Animal species or product type - Quantity (number of animals or production volume) - Value (if applicable) - Number of establishments

Units of Measurement

  • Livestock counts: Number of animals
  • Milk: Liters
  • Eggs: Dozens or units (verify in data)
  • Honey: Kilograms
  • Wool: Kilograms
  • Aquaculture: Kilograms or tons

Raw vs. Treated Data

  • Raw data: IBGE original format, Portuguese names
  • Treated data: English variable names, standardized units

Important Limitations

  1. Survey-based data: Subject to sampling and reporting error
  2. Informal operations: May undercount small or informal livestock operations
  3. Data lag: Published with delay; recent years may not be available
  4. Aquaculture newer: Aquaculture data less complete for very early years
  5. Methodology changes: Survey methods may evolve; can affect comparability