IBGE API¶

The IBGE module provides access to data from the IBGE Automatic Retrieval System (SIDRA).

Functions¶

`pam`¶

Retrieves Municipal Agricultural Production (PAM) data.

async def pam(
    produto: str,
    ano: int | str | list[int] | None = None,
    uf: str | None = None,
    nivel: Literal['brasil', 'uf', 'municipio'] = 'uf',
    variaveis: list[str] | None = None,
    as_polars: bool = False,
    return_meta: bool = False,
) -> pd.DataFrame | pl.DataFrame  # (df, MetaInfo) when return_meta=True

Parameters:

Parameter	Type	Description
`produto`	`str`	Product code (e.g. 'soja', 'milho')
`ano`	`int \\| str \\| list[int] \\| None`	Year(s). Default: latest available
`uf`	`str \\| None`	Filter by state (e.g. 'MT')
`nivel`	`Literal['brasil', 'uf', 'municipio']`	Level: 'brasil', 'uf', 'municipio'
`variaveis`	`list[str] \\| None`	Specific variables
`as_polars`	`bool`	Return as polars.DataFrame
`return_meta`	`bool`	Returns a `(df, MetaInfo)` tuple with provenance

Available variables:

Code	Variable
`area_plantada`	Planted area (hectares)
`area_colhida`	Harvested area (hectares)
`producao`	Quantity produced (tonnes)
`rendimento`	Average yield (kg/ha)

Example:

from agrobr import ibge

# PAM by state
df = await ibge.pam('soja', ano=2023, nivel='uf')

# Multiple years
df = await ibge.pam('soja', ano=[2020, 2021, 2022, 2023])

# By municipality (filter state to reduce volume)
df = await ibge.pam('soja', ano=2023, nivel='municipio', uf='MT')

# Specific variables
df = await ibge.pam('soja', ano=2023, variaveis=['producao', 'area_plantada'])

`lspa`¶

Retrieves Systematic Survey of Agricultural Production (LSPA) data.

async def lspa(
    produto: str,
    ano: int | str | None = None,
    mes: int | str | None = None,
    uf: str | None = None,
    as_polars: bool = False,
    return_meta: bool = False,
) -> pd.DataFrame | pl.DataFrame  # (df, MetaInfo) when return_meta=True

Parameters:

Parameter	Type	Description
`produto`	`str`	Product code
`ano`	`int \\| str \\| None`	Year. Default: current
`mes`	`int \\| str \\| None`	Month (1-12). Default: latest
`uf`	`str \\| None`	Filter by state
`as_polars`	`bool`	Return as polars.DataFrame
`return_meta`	`bool`	Returns a `(df, MetaInfo)` tuple with provenance

LSPA products:

Code	Product
`soja`	Soybean
`milho_1`	Corn 1st crop
`milho_2`	Corn 2nd crop
`arroz`	Rice
`feijao_1`	Beans 1st crop
`feijao_2`	Beans 2nd crop
`feijao_3`	Beans 3rd crop
`trigo`	Wheat
`algodao`	Herbaceous cotton
`amendoim_1`	Peanut 1st crop
`amendoim_2`	Peanut 2nd crop
`batata_1`	Potato 1st crop
`batata_2`	Potato 2nd crop

Generic aliases:

Generic names automatically expand into sub-crops and return a concatenated DataFrame:

Alias	Expands to
`milho`	`milho_1` + `milho_2`
`feijao`	`feijao_1` + `feijao_2` + `feijao_3`
`amendoim`	`amendoim_1` + `amendoim_2`
`batata`	`batata_1` + `batata_2`

Example:

from agrobr import ibge

# Monthly LSPA
df = await ibge.lspa('soja', ano=2024, mes=6)

# Corn 2nd crop
df = await ibge.lspa('milho_2', ano=2024)

# Generic alias — returns milho_1 + milho_2 concatenated
df = await ibge.lspa('milho', ano=2024)

# By state
df = await ibge.lspa('soja', ano=2024, uf='MT')

`produtos_pam`¶

Lists products available in PAM.

async def produtos_pam() -> list[str]

`produtos_lspa`¶

Lists products available in LSPA.

async def produtos_lspa() -> list[str]

`ppm`¶

Retrieves Municipal Livestock Survey (PPM) data.

async def ppm(
    especie: str,
    ano: int | str | list[int] | None = None,
    uf: str | None = None,
    nivel: Literal['brasil', 'uf', 'municipio'] = 'uf',
    as_polars: bool = False,
    return_meta: bool = False,
) -> pd.DataFrame | pl.DataFrame  # (df, MetaInfo) when return_meta=True

Parameters:

Parameter	Type	Description
`especie`	`str`	Species or product (e.g. 'bovino', 'leite')
`ano`	`int \\| str \\| list[int] \\| None`	Year(s). Default: latest available
`uf`	`str \\| None`	Filter by state (e.g. 'MT')
`nivel`	`Literal['brasil', 'uf', 'municipio']`	Level: 'brasil', 'uf', 'municipio'
`as_polars`	`bool`	Return as polars.DataFrame
`return_meta`	`bool`	Returns a `(df, MetaInfo)` tuple with provenance

Available species (herds):

Code	Species
`bovino`	Cattle
`bubalino`	Buffalo
`equino`	Horse
`suino_total`	Swine (total)
`suino_matrizes`	Sows
`caprino`	Goat
`ovino`	Sheep
`galinaceos_total`	Chickens (total)
`galinhas_poedeiras`	Laying hens
`codornas`	Quail

Animal-origin products:

Code	Product	Unit
`leite`	Milk	thousand liters
`ovos_galinha`	Chicken eggs	thousand dozen
`ovos_codorna`	Quail eggs	thousand dozen
`mel`	Honey	kg
`casulos`	Silkworm cocoons	kg
`la`	Wool	kg

Example:

from agrobr import ibge

# Cattle herd by state
df = await ibge.ppm('bovino', ano=2023, nivel='uf')

# Milk production by municipality in MG
df = await ibge.ppm('leite', ano=2023, nivel='municipio', uf='MG')

# Historical series
df = await ibge.ppm('bovino', ano=[2019, 2020, 2021, 2022, 2023])

# With metadata
df, meta = await ibge.ppm('bovino', ano=2023, return_meta=True)

`especies_ppm`¶

Lists species and products available in PPM.

async def especies_ppm() -> list[str]

`abate`¶

Retrieves Quarterly Animal Slaughter Survey data.

async def abate(
    especie: str,
    trimestre: str | list[str] | None = None,
    uf: str | None = None,
    as_polars: bool = False,
    return_meta: bool = False,
) -> pd.DataFrame | pl.DataFrame  # (df, MetaInfo) when return_meta=True

Parameters:

Parameter	Type	Description
`especie`	`str`	Species: 'bovino', 'suino', 'frango'
`trimestre`	`str \\| list[str] \\| None`	Quarter YYYYQQ (e.g. '202303'). Default: latest available
`uf`	`str \\| None`	Filter by state (e.g. 'PR')
`as_polars`	`bool`	Return as polars.DataFrame
`return_meta`	`bool`	Returns a `(df, MetaInfo)` tuple with provenance

Available species:

Code	Species	SIDRA table
`bovino`	Cattle	1092
`suino`	Swine	1093
`frango`	Chicken	1094

Returned variables:

Variable	Description	Unit
`animais_abatidos`	Number of slaughtered animals	head
`peso_carcacas`	Total carcass weight	kg

Example:

from agrobr import ibge

# Cattle slaughter by state
df = await ibge.abate('bovino', trimestre='202303')

# Chicken slaughter in Paraná
df = await ibge.abate('frango', trimestre='202303', uf='PR')

# Swine slaughter — Brazil
df = await ibge.abate('suino', trimestre='202304')

# With metadata
df, meta = await ibge.abate('bovino', trimestre='202303', return_meta=True)

`especies_abate`¶

Lists species available in the Quarterly Slaughter survey.

async def especies_abate() -> list[str]

`censo_agro`¶

Retrieves Agricultural Census data (1995, 2006 and 2017).

async def censo_agro(
    tema: str,
    ano: int | str | None = None,
    uf: str | None = None,
    nivel: Literal['brasil', 'uf', 'municipio'] = 'uf',
    as_polars: bool = False,
    return_meta: bool = False,
) -> pd.DataFrame | pl.DataFrame  # (df, MetaInfo) when return_meta=True

Parameters:

Parameter	Type	Description
`tema`	`str`	Census theme (see table below)
`ano`	`int \\| str \\| None`	Census year (1995, 2006 or 2017). Default: all available years
`uf`	`str \\| None`	Filter by state (e.g. 'MT')
`nivel`	`Literal['brasil', 'uf', 'municipio']`	Level: 'brasil', 'uf', 'municipio'
`as_polars`	`bool`	Return as polars.DataFrame
`return_meta`	`bool`	Returns a `(df, MetaInfo)` tuple with provenance

Available themes:

Code	Theme	Table 1995	Table 2006	Table 2017
`efetivo_rebanho`	Herd inventory	323	—	6907
`uso_terra`	Land use	316/311	—	6881
`lavoura_temporaria`	Temporary crops	497/492/503	—	6957
`lavoura_permanente`	Permanent crops	509/504/510	—	6956
`preparo_solo`	Soil preparation	—	791	6855
`adubacao`	Fertilization	—	1249	6848
`calagem`	Liming	—	1245	6849
`agrotoxicos`	Pesticide use	—	1459	6851
`praticas_agricolas`	Agricultural practices	—	837	8561
`irrigacao`	Irrigation	—	855	6857

Variables returned per theme (original themes):

Theme	Variable	Unit
`efetivo_rebanho`	`estabelecimentos`	units
`efetivo_rebanho`	`cabecas`	head
`uso_terra`	`estabelecimentos`	units
`uso_terra`	`area`	hectares
`lavoura_temporaria`	`estabelecimentos`	units
`lavoura_temporaria`	`producao`	varies
`lavoura_temporaria`	`area_colhida`	hectares
`lavoura_permanente`	`estabelecimentos`	units
`lavoura_permanente`	`producao`	varies
`lavoura_permanente`	`area_colhida`	hectares

Categories of the newer themes (examples):

Theme	Categories (examples)
`preparo_solo`	Conventional tillage, Minimum tillage, No-till on straw
`adubacao`	Chemical, Organic, Green manure
`calagem`	Applied, Not applied
`agrotoxicos`	Used, Did not use
`praticas_agricolas`	Contour planting, Crop rotation, Fallow
`irrigacao`	Drip, Center pivot, Flooding, Sprinkler

Example:

from agrobr import ibge

# Herd inventory by state (2017)
df = await ibge.censo_agro('efetivo_rebanho')

# Land use in Mato Grosso
df = await ibge.censo_agro('uso_terra', uf='MT')

# Temporary crops by municipality
df = await ibge.censo_agro('lavoura_temporaria', nivel='municipio', uf='PR')

# Soil preparation — both years (2006 + 2017)
df = await ibge.censo_agro('preparo_solo')

# Irrigation 2017 only
df = await ibge.censo_agro('irrigacao', ano=2017)

# Fertilization in 2006, filtered by state
df = await ibge.censo_agro('adubacao', ano=2006, uf='SP')

# With metadata
df, meta = await ibge.censo_agro('efetivo_rebanho', return_meta=True)

`temas_censo_agro`¶

Lists themes available in the Agricultural Census.

async def temas_censo_agro() -> list[str]

`censo_agro_legado`¶

Retrieves Agricultural Census 1995/96 data — 6 legacy themes via FTP (XLS).

Note: nivel='uf' returns data by mesoregion (not by individual state) — a quirk of the legacy format; nivel='municipio' and nivel='brasil' work as expected.

async def censo_agro_legado(
    tema: str,
    uf: str | None = None,
    nivel: Literal['brasil', 'uf', 'municipio'] = 'uf',
    as_polars: bool = False,
    return_meta: bool = False,
) -> pd.DataFrame | pl.DataFrame  # (df, MetaInfo) when return_meta=True

Parameters:

Parameter	Type	Description
`tema`	`str`	Legacy theme (see table below)
`uf`	`str \\| None`	Filter by state (e.g. 'SP')
`nivel`	`Literal['brasil', 'uf', 'municipio']`	Level: 'brasil', 'uf', 'municipio'
`as_polars`	`bool`	Return as polars.DataFrame
`return_meta`	`bool`	Returns a `(df, MetaInfo)` tuple with provenance

Available themes:

Code	Theme
`tecnologia`	Technology (technical assistance, irrigation, fertilizers, etc.)
`pessoal_ocupado`	Persons employed (total, family, permanent, temporary)
`maquinas`	Machinery and equipment (tractors by HP range)
`producao_animal`	Animal production (milk, wool, eggs)
`valor_producao`	Value of production (crop, animal, subtypes)
`financeiro`	Financial data (investments, financing, expenses, revenue)

Example:

from agrobr import ibge

# Technology by mesoregion
df = await ibge.censo_agro_legado('tecnologia')

# Persons employed in São Paulo
df = await ibge.censo_agro_legado('pessoal_ocupado', uf='SP')

# Machinery — municipality level
df = await ibge.censo_agro_legado('maquinas', nivel='municipio')

# With metadata
df, meta = await ibge.censo_agro_legado('tecnologia', return_meta=True)

`temas_censo_agro_legado`¶

Lists themes available in the Legacy Agricultural Census (FTP).

async def temas_censo_agro_legado() -> list[str]

`censo_agro_historico`¶

Retrieves the Agricultural Census historical series (1920-2006, state level maximum).

async def censo_agro_historico(
    tema: str,
    ano: int | list[int] | None = None,
    uf: str | None = None,
    nivel: Literal['brasil', 'regiao', 'uf'] = 'uf',
    as_polars: bool = False,
    return_meta: bool = False,
) -> pd.DataFrame | pl.DataFrame  # (df, MetaInfo) when return_meta=True

Parameters:

Parameter	Type	Description
`tema`	`str`	Historical series theme (see table below)
`ano`	`int \\| list[int] \\| None`	Census year(s). Default: all available
`uf`	`str \\| None`	Filter by state (e.g. 'SP'). Only applied at nivel='uf'
`nivel`	`Literal['brasil', 'regiao', 'uf']`	Level: 'brasil', 'regiao', 'uf' (municipal NOT available)
`as_polars`	`bool`	Return as polars.DataFrame
`return_meta`	`bool`	Returns a `(df, MetaInfo)` tuple with provenance

Available themes:

Code	Theme	SIDRA table	Periods
`estabelecimentos_area`	Establishments and area by area group	263	1920-2006 (10 censuses)
`uso_terra`	Area by land use	264	1970-2006 (6 censuses)
`pessoal_tratores`	Persons employed and tractors	265	1970-2006 (6 censuses)
`condicao_produtor`	Establishments by producer status	280	1920-2006 (10 censuses)
`efetivo_animais`	Animal inventory by species	281	1970-2006 (6 censuses)
`producao_animal`	Animal production by type	282	1920-2006 (10 censuses)
`producao_vegetal`	Crop production and harvested area	283	1920-2006 (10 censuses)
`lavoura_permanente`	Quantity produced — permanent crops	1730	1940-2006 (9 censuses)
`lavoura_temporaria`	Quantity produced — temporary crops	1731	1940-2006 (9 censuses)

Example:

from agrobr import ibge

# Establishments and area, Brazil, 1985
df = await ibge.censo_agro_historico('estabelecimentos_area', ano=1985, nivel='brasil')

# Animal inventory, all states, all censuses
df = await ibge.censo_agro_historico('efetivo_animais')

# Persons and tractors in São Paulo, 1980 and 1985
df = await ibge.censo_agro_historico('pessoal_tratores', ano=[1980, 1985], uf='SP')

# Crop production, region level
df = await ibge.censo_agro_historico('producao_vegetal', nivel='regiao')

# With metadata
df, meta = await ibge.censo_agro_historico('uso_terra', ano=1985, return_meta=True)

`temas_censo_agro_historico`¶

Lists themes available in the Agricultural Census historical series.

async def temas_censo_agro_historico() -> list[str]

CLI¶

# All establishments/area data by state
agrobr ibge censo-historico estabelecimentos_area

# Specific year, CSV format
agrobr ibge censo-historico uso_terra --ano 1985 --formato csv

# Multiple years, Brazil level
agrobr ibge censo-historico efetivo_animais --ano 1970,1985,2006 --nivel brasil

# Filter by state
agrobr ibge censo-historico pessoal_tratores --ano 1985 --uf SP

# List available themes
agrobr ibge temas-historico

`censo_agro_municipal_1985`¶

Retrieves Agricultural Census 1985 data at municipal level (extracted via OCR from IBGE PDFs).

async def censo_agro_municipal_1985(
    tema: str,
    *,
    uf: str | None = None,
    nivel: str | None = None,
    as_polars: bool = False,
    return_meta: bool = False,
) -> pd.DataFrame | tuple[pd.DataFrame, MetaInfo]

Parameters:

Parameter	Type	Description
`tema`	`str`	Theme (53 available — use `temas_censo_agro_municipal_1985()`)
`uf`	`str \\| None`	Filter by state (22 states available)
`nivel`	`str \\| None`	Filter: total, mesorregiao, microrregiao, municipio
`as_polars`	`bool`	Return as polars.DataFrame
`return_meta`	`bool`	Return MetaInfo

Example:

from agrobr import ibge

# Land ownership in São Paulo
df = await ibge.censo_agro_municipal_1985('propriedade_terras', uf='SP')

# Municipalities only
df = await ibge.censo_agro_municipal_1985('bovinos', nivel='municipio')

# With metadata
df, meta = await ibge.censo_agro_municipal_1985('propriedade_terras', return_meta=True)

`temas_censo_agro_municipal_1985`¶

Lists themes available in the Municipal Agricultural Census 1985.

async def temas_censo_agro_municipal_1985() -> list[str]

CLI¶

# Land ownership data in SP
agrobr ibge censo-municipal-1985 propriedade_terras --uf SP

# CSV format
agrobr ibge censo-municipal-1985 bovinos --formato csv

# Filter by level
agrobr ibge censo-municipal-1985 uso_terra_lavoura --nivel municipio --uf MG

# List available themes
agrobr ibge temas-municipal-1985

`ufs`¶

Lists available states.

async def ufs() -> list[str]

`silvicultura`¶

Retrieves Plant Extraction and Silviculture Production (PEVS) data — silviculture.

async def silvicultura(
    produto: str,
    ano: int | str | list[int] | None = None,
    uf: str | None = None,
    nivel: Literal['brasil', 'uf', 'municipio'] = 'uf',
    variavel: str = 'quantidade_produzida',
    as_polars: bool = False,
    return_meta: bool = False,
) -> pd.DataFrame | pl.DataFrame

Parameters:

Parameter	Type	Description
`produto`	`str`	Product code (e.g. 'madeira_tora', 'carvao') or area species (e.g. 'eucalipto')
`ano`	`int \\| str \\| list[int] \\| None`	Year(s). Default: latest available
`uf`	`str \\| None`	Filter by state (e.g. 'MG')
`nivel`	`Literal['brasil', 'uf', 'municipio']`	Level: 'brasil', 'uf', 'municipio'
`variavel`	`str`	'quantidade_produzida', 'valor_producao' (table 291) or 'area' (table 5930)
`as_polars`	`bool`	Return as polars.DataFrame
`return_meta`	`bool`	Return MetaInfo

Products (table 291, classification c194):

carvao, carvao_eucalipto, carvao_pinus, carvao_outras, lenha, lenha_eucalipto, lenha_pinus, lenha_outras, madeira_tora, madeira_celulose, madeira_outras_finalidades, acacia_negra, eucalipto_folha, resina

Area species (table 5930, classification c734) — variavel='area':

eucalipto, pinus, outras

Example:

from agrobr import ibge

# Log wood production by state
df = await ibge.silvicultura('madeira_tora', ano=2023)

# Eucalyptus planted area
df = await ibge.silvicultura('eucalipto', variavel='area')

# Charcoal in MG
df = await ibge.silvicultura('carvao', ano=2023, uf='MG')

# With metadata
df, meta = await ibge.silvicultura('madeira_tora', ano=2023, return_meta=True)

`produtos_silvicultura`¶

Lists products available in silviculture (table 291).

async def produtos_silvicultura() -> list[str]

`especies_silvicultura_area`¶

Lists species available for planted area (table 5930).

async def especies_silvicultura_area() -> list[str]

`extracao_vegetal`¶

Retrieves Plant Extraction and Silviculture Production (PEVS) data — plant extraction.

async def extracao_vegetal(
    produto: str,
    ano: int | str | list[int] | None = None,
    uf: str | None = None,
    nivel: Literal['brasil', 'uf', 'municipio'] = 'uf',
    variavel: str = 'quantidade_produzida',
    as_polars: bool = False,
    return_meta: bool = False,
) -> pd.DataFrame | pl.DataFrame

Parameters:

Parameter	Type	Description
`produto`	`str`	Product code (e.g. 'acai', 'castanha_para')
`ano`	`int \\| str \\| list[int] \\| None`	Year(s). Default: latest available
`uf`	`str \\| None`	Filter by state
`nivel`	`Literal['brasil', 'uf', 'municipio']`	Level: 'brasil', 'uf', 'municipio'
`variavel`	`str`	'quantidade_produzida' or 'valor_producao'
`as_polars`	`bool`	Return as polars.DataFrame
`return_meta`	`bool`	Return MetaInfo

Products (table 289, classification c193):

acai, castanha_caju, castanha_para, erva_mate, mangaba, palmito, pequi_fruto, pinhao, umbu, hevea_coagulado, hevea_liquido, carnauba_cera, carnauba_po, piacava, carvao, lenha, madeira_tora, babacu, copaiba, cumaru, pequi_amendoa

Example:

from agrobr import ibge

# Açaí production by state
df = await ibge.extracao_vegetal('acai', ano=2023)

# Brazil nut in Amazonas
df = await ibge.extracao_vegetal('castanha_para', ano=2023, uf='AM')

# Value of production
df = await ibge.extracao_vegetal('acai', ano=2023, variavel='valor_producao')

# With metadata
df, meta = await ibge.extracao_vegetal('acai', ano=2023, return_meta=True)

`produtos_extracao_vegetal`¶

Lists products available in plant extraction (table 289).

async def produtos_extracao_vegetal() -> list[str]

`leite_trimestral`¶

Retrieves Quarterly Milk Survey data — acquisition, processing and average price.

async def leite_trimestral(
    trimestre: str | list[str] | None = None,
    uf: str | None = None,
    as_polars: bool = False,
    return_meta: bool = False,
) -> pd.DataFrame | pl.DataFrame

Parameters:

Parameter	Type	Description
`trimestre`	`str \\| list[str] \\| None`	Quarter YYYYQQ (e.g. '202303'). Default: latest
`uf`	`str \\| None`	Filter by state
`as_polars`	`bool`	Return as polars.DataFrame
`return_meta`	`bool`	Return MetaInfo

Returned columns (wide pivot):

Column	Type	Description
`trimestre`	str	Quarter YYYYQQ
`localidade`	str	State
`localidade_cod`	int	IBGE code
`leite_adquirido`	float	Raw milk acquired (thousand liters)
`leite_industrializado`	float	Raw milk processed (thousand liters)
`preco_medio`	float	Average price paid to producer (BRL/liter)
`fonte`	str	"ibge_leite_trimestral"

Example:

from agrobr import ibge

# Quarterly milk by state
df = await ibge.leite_trimestral(trimestre='202303')

# Filter by state
df = await ibge.leite_trimestral(trimestre='202303', uf='MG')

# Multiple quarters
df = await ibge.leite_trimestral(trimestre=['202301', '202302', '202303'])

# With metadata
df, meta = await ibge.leite_trimestral(trimestre='202303', return_meta=True)

`pib_agro`¶

Retrieves the quarterly agricultural GDP (Quarterly National Accounts).

async def pib_agro(
    trimestre: str | list[str] | None = None,
    precos: str = 'corrente',
    setor: str = 'agropecuaria',
    as_polars: bool = False,
    return_meta: bool = False,
) -> pd.DataFrame | pl.DataFrame

Parameters:

Parameter	Type	Description
`trimestre`	`str \\| list[str] \\| None`	Quarter YYYYQQ. Default: latest
`precos`	`str`	'corrente' (table 1846) or 'real_1995' (table 6612)
`setor`	`str`	'agropecuaria', 'industria', 'servicos' or 'pib_total'
`as_polars`	`bool`	Return as polars.DataFrame
`return_meta`	`bool`	Return MetaInfo

Example:

from agrobr import ibge

# Agricultural GDP at current prices
df = await ibge.pib_agro(trimestre='202501')

# GDP at real prices (1995 base)
df = await ibge.pib_agro(trimestre='202501', precos='real_1995')

# Total GDP (all sectors)
df = await ibge.pib_agro(trimestre='202501', setor='pib_total')

# With metadata
df, meta = await ibge.pib_agro(return_meta=True)

PAM vs LSPA vs PPM vs Slaughter vs PEVS vs Milk vs GDP vs Agri Census vs Historical Series vs Municipal 1985¶

Aspect	PAM	LSPA	PPM	Slaughter	PEVS	Milk	GDP	Agri Census	Legacy Census	Historical Series	Municipal 1985
Frequency	Annual	Monthly	Annual	Quarterly	Annual	Quarterly	Quarterly	Decennial	One-off (1995/96)	Decennial	One-off (1985)
Granularity	To municipality	To state	To municipality	Brazil + state	To municipality	State	Brazil	To municipality	To municipality	Brazil/Region/State	To municipality
Type	Consolidated	Estimates	Consolidated	Consolidated	Consolidated	Consolidated	Estimates	Census	Census (FTP)	Census	Census (OCR)
Availability	Y+1 year	Y+1 month	Y+1 year	Q+2 months	Y+1 year	Q+2 months	Q+2 months	Post-census	Static	Static	Static
Scope	Crops	Crops	Livestock	Slaughter	Silviculture + Plant extraction	Milk (acquisition, processing)	Sector GDP	Agri structure	6 legacy themes	9 themes (1920-2006)	53 themes (1985)

SIDRA Tables Used¶

Table	Description
5457	PAM - New series (2018+)
6588	LSPA - Monthly estimates
1612	PAM - Temporary crops (historical)
3939	PPM - Herd inventory
74	PPM - Animal-origin production
1092	Slaughter - Cattle
1093	Slaughter - Swine
1094	Slaughter - Chickens
323	Agri Census 1995 - Herd inventory
316 / 311	Agri Census 1995 - Land use
497 / 492 / 503	Agri Census 1995 - Temporary crops
509 / 504 / 510	Agri Census 1995 - Permanent crops
6907	Agri Census 2017 - Herd inventory
6881	Agri Census 2017 - Land use
6957	Agri Census 2017 - Temporary crops
6956	Agri Census 2017 - Permanent crops
791 / 6855	Agri Census 2006/2017 - Soil preparation
1249 / 6848	Agri Census 2006/2017 - Fertilization
1245 / 6849	Agri Census 2006/2017 - Liming
1459 / 6851	Agri Census 2006/2017 - Pesticides
837 / 8561	Agri Census 2006/2017 - Agricultural practices
855 / 6857	Agri Census 2006/2017 - Irrigation
263	Historical Series - Establishments and area
264	Historical Series - Land use
265	Historical Series - Persons and tractors
280	Historical Series - Producer status
281	Historical Series - Animal inventory
282	Historical Series - Animal production
283	Historical Series - Crop production
1730	Historical Series - Permanent crops
1731	Historical Series - Temporary crops
289	PEVS - Plant extraction (c193)
291	PEVS - Silviculture production (c194)
5930	PEVS - Silviculture area (c734)
1086	Milk - Quarterly Milk Survey
1846	GDP - National Accounts at current prices
6612	GDP - National Accounts at real prices (1995)

Synchronous Version¶

from agrobr.sync import ibge

df = ibge.pam('soja', ano=2023)
df = ibge.lspa('milho_1', ano=2024, mes=6)
df = ibge.ppm('bovino', ano=2023)
df = ibge.abate('bovino', trimestre='202303')
df = ibge.censo_agro('efetivo_rebanho')
df = ibge.censo_agro('preparo_solo', ano=2017)
df = ibge.censo_agro_legado('tecnologia')
df = ibge.censo_agro_legado('pessoal_ocupado', uf='SP')
df = ibge.censo_agro_historico('estabelecimentos_area', ano=1985)
df = ibge.censo_agro_municipal_1985('propriedade_terras', uf='SP')
df = ibge.silvicultura('madeira_tora', ano=2023)
df = ibge.extracao_vegetal('acai', ano=2023)
df = ibge.leite_trimestral(trimestre='202303')
df = ibge.pib_agro(trimestre='202501')

Notes¶

Municipality-level queries generate large data volumes
Filtering by state is recommended when using municipality level
LSPA is updated monthly by IBGE
PAM is consolidated annually after harvest
PPM is consolidated annually (September), series since 1974
Quarterly Slaughter available since 1997, updated each quarter (Q+2 months)
Agricultural Census: 10 themes, data from 1995, 2006 and/or 2017 depending on availability. 2017 reference: Oct/2016 to Sep/2017. 30-day cache
Legacy Agricultural Census: 6 FTP themes (tecnologia, pessoal_ocupado, maquinas, producao_animal, valor_producao, financeiro). Fixed year 1995. 90-day cache
Historical Series: 9 themes, 1920-2006, up to state (municipal NOT available). Mixed units per category (Poultry=Thousand head, etc). 30-day cache
Municipal Census 1985: 53 themes, municipal data for 22 states, extracted via OCR from IBGE state PDFs. Static (bundled) data. The confianca field indicates OCR quality ```

IBGE API¶

Functions¶

pam¶

lspa¶

produtos_pam¶

produtos_lspa¶

ppm¶

especies_ppm¶

abate¶

especies_abate¶

censo_agro¶

temas_censo_agro¶

censo_agro_legado¶

temas_censo_agro_legado¶

censo_agro_historico¶

temas_censo_agro_historico¶

CLI¶

censo_agro_municipal_1985¶

temas_censo_agro_municipal_1985¶

CLI¶

ufs¶

silvicultura¶

produtos_silvicultura¶

especies_silvicultura_area¶

extracao_vegetal¶

produtos_extracao_vegetal¶

leite_trimestral¶

pib_agro¶

PAM vs LSPA vs PPM vs Slaughter vs PEVS vs Milk vs GDP vs Agri Census vs Historical Series vs Municipal 1985¶

SIDRA Tables Used¶

Synchronous Version¶

Notes¶

`pam`¶

`lspa`¶

`produtos_pam`¶

`produtos_lspa`¶

`ppm`¶

`especies_ppm`¶

`abate`¶

`especies_abate`¶

`censo_agro`¶

`temas_censo_agro`¶

`censo_agro_legado`¶

`temas_censo_agro_legado`¶

`censo_agro_historico`¶

`temas_censo_agro_historico`¶

`censo_agro_municipal_1985`¶

`temas_censo_agro_municipal_1985`¶

`ufs`¶

`silvicultura`¶

`produtos_silvicultura`¶

`especies_silvicultura_area`¶

`extracao_vegetal`¶

`produtos_extracao_vegetal`¶

`leite_trimestral`¶

`pib_agro`¶