IBGE - Brazilian Institute of Geography and Statistics¶

Overview¶

Field	Value
Institution	Federal Government
Website	ibge.gov.br
API	SIDRA
agrobr access	Via SIDRA API (JSON)

Data Origin¶

Source¶

API: https://sidra.ibge.gov.br/
Format: JSON
Access: Public, no authentication

Available Surveys¶

PAM - Municipal Agricultural Production¶

SIDRA table: 5457 (new series 2018+)
Coverage: All municipalities
Frequency: Annual

LSPA - Systematic Survey of Agricultural Production¶

SIDRA table: 6588
Coverage: National/state
Frequency: Monthly

PPM - Municipal Livestock Survey¶

SIDRA tables: 3939 (herds), 74 (animal-origin production)
Coverage: All municipalities
Frequency: Annual
Series: 1974-present (51 years)

Slaughter - Quarterly Animal Slaughter Survey¶

SIDRA tables: 1092 (cattle), 1093 (hogs), 1094 (chickens)
Coverage: Brazil + states (27 states)
Frequency: Quarterly
Series: 1997-present
Species: cattle, hog, chicken
Variables: animals slaughtered (head), carcass weight (kg)

Agricultural Census 1995/2006/2017¶

SIDRA tables 2017: 6907 (livestock numbers), 6881 (land use), 6957 (temporary crops), 6956 (permanent crops), 6855 (soil preparation), 6848 (fertilization), 6849 (liming), 6851 (pesticides), 8561 (agricultural practices), 6857 (irrigation)
SIDRA tables 2006: 791 (soil preparation), 1249 (fertilization), 1245 (liming), 1459 (pesticides), 837 (agricultural practices), 855 (irrigation)
SIDRA tables 1995: 323 (livestock numbers), 316/311 (land use), 497/492/503 (temporary crops), 509/504/510 (permanent crops)
Coverage: Brazil + state + municipality
Frequency: Decennial
Periods: 1995, 2006 and 2017 (by theme)
Themes: efetivo_rebanho, uso_terra, lavoura_temporaria, lavoura_permanente, preparo_solo, adubacao, calagem, agrotoxicos, praticas_agricolas, irrigacao
Format: Long format (variable/value per row)

Agricultural Census — Historical Series (1920-2006)¶

SIDRA tables: 263 (establishments/area), 264 (land use), 265 (personnel/tractors), 280 (producer status), 281 (animal numbers), 282 (animal production), 283 (crop production), 1730 (permanent crops), 1731 (temporary crops)
Coverage: Brazil + region + state (municipal NOT available in SIDRA)
Frequency: Decennial censuses (1920-2006, by table)
Periods: up to 10 censuses per theme (1920, 1940, 1950, 1960, 1970, 1975, 1980, 1985, 1995, 2006)
Themes: 9 themes with a long historical series
Quirks: Poultry in thousand head (tab 281), mixed units by category (tabs 282/283/1730/1731), classifications without Total (tabs 281/282/283/1730/1731)

Agricultural Census 1985 — Municipal Data (OCR PDFs)¶

Source: State PDFs from the IBGE Library
Format: CSVs extracted via hybrid OCR (PyMuPDF coords + OCR correction)
Coverage: 22 states, down to municipality (mesoregion, microregion, municipality)
Frequency: One-off (1985 Census)
Themes: 53 themes (property, land use, personnel, mechanization, livestock, crops, production)
Excluded states: MA, PI, CE, RN (PDFs without an OCR layer)
Access: Data bundled in the package (agrobr/data/censo_1985/)
Quality: confianca field (alta/media/baixa), 77.9% state↔national cross-validation
Catalog URL: https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=768

Agricultural Census 1995/96 — Legacy Themes (FTP)¶

Source: IBGE FTP (ftp.ibge.gov.br)
Format: Legacy XLS (xlrd)
Coverage: Brazil (mesoregions, microregions, municipalities)
Frequency: One-off (1995/96 Census)
Themes: tecnologia, pessoal_ocupado, maquinas, producao_animal, valor_producao, financeiro
Access: Public, no authentication

PEVS — Silviculture¶

SIDRA tables: 291 (production, classification c194) + 5930 (planted area, classification c734)
Coverage: All municipalities
Frequency: Annual
Series: 1986-present
Products: carvao, lenha, madeira_tora, madeira_celulose, acacia_negra, eucalipto_folha, resina (14 total)
Area species: eucalipto, pinus, outras
Variables: quantidade_produzida (var 142), valor_producao (var 143), area (var 6549)
Units: Tonnes or cubic meters (by product)

PEVS — Plant Extraction¶

SIDRA table: 289 (classification c193)
Coverage: All municipalities
Frequency: Annual
Series: 1986-present
Products: 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 (21 total)
Variables: quantidade_produzida (var 144), valor_producao (var 145)
Units: Tonnes (most) or cubic meters (lenha, madeira_tora)

Quarterly Milk — Quarterly Milk Survey¶

SIDRA table: 1086
Coverage: Brazil + states (27 states)
Frequency: Quarterly
Series: 1997-present
Variables: milk acquired (var 282, thousand liters), milk industrialized (var 283, thousand liters), average price (var 2522, R$/liter)
Output: Wide format (3 variables as columns)

Agricultural GDP — Quarterly National Accounts¶

SIDRA tables: 1846 (current prices, var 585) + 6612 (real prices, 1995 base, var 9318)
Coverage: Brazil (national level)
Frequency: Quarterly
Series: 1996-present
Sectors: agropecuaria (90687), industria (90691), servicos (90696), pib_total (90707)
Classification: c11255
Unit: Millions of Reais

Variables¶

Code	Name	Unit
214	Planted area	hectares
215	Harvested area	hectares
216	Quantity produced	tonnes
112	Average yield	kg/ha

Usage - PAM¶

Basic¶

import asyncio
from agrobr import ibge

async def main():
    # Soybean data by state
    df = await ibge.pam('soja', ano=2023)

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

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

    # Municipality level
    df = await ibge.pam('arroz', ano=2023, nivel='municipio', uf='RS')

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

asyncio.run(main())

Territorial Levels¶

Level	Description
`brasil`	National total
`uf`	By Federative Unit
`municipio`	By municipality

Usage - LSPA¶

Basic¶

# Estimates for the year
df = await ibge.lspa('soja', ano=2024)

# Specific month
df = await ibge.lspa('milho_1', ano=2024, mes=6)

# Filter by state
df = await ibge.lspa('soja', ano=2024, uf='PR')

# With metadata
df, meta = await ibge.lspa('soja', ano=2024, return_meta=True)

Schema - PAM¶

Column	Type	Description
`ano`	int	Reference year
`localidade`	str	Locality name
`produto`	str	Product name
`area_plantada`	float	Hectares
`area_colhida`	float	Hectares
`producao`	float	Tonnes
`rendimento`	float	kg/ha
`valor_producao`	float	Thousand reais
`fonte`	str	"ibge_pam"

Schema - LSPA¶

Column	Type	Description
`ano`	int	Reference year
`mes`	int	Reference month
`variavel`	str	Variable name
`valor`	float	Variable value
`produto`	str	Product name
`fonte`	str	"ibge_lspa"

PAM Products¶

produtos = await ibge.produtos_pam()
# ['soja', 'milho', 'arroz', 'feijao', 'trigo', 'cafe', ...]

LSPA Products¶

produtos = await ibge.produtos_lspa()
# ['soja', 'milho_1', 'milho_2', 'arroz', 'feijao_1', 'feijao_2', ...]

Note: In LSPA, milho_1 and milho_2 refer to the first and second crop years.

Available States¶

ufs = await ibge.ufs()
# ['AC', 'AL', 'AM', 'AP', 'BA', 'CE', 'DF', ...]

Usage - PPM¶

Basic¶

import asyncio
from agrobr import ibge

async def main():
    # Cattle herd by state
    df = await ibge.ppm('bovino', ano=2023)

    # Milk production
    df = await ibge.ppm('leite', ano=2023)

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

    # Filter by state
    df = await ibge.ppm('bovino', ano=2023, uf='MT')

    # Municipality level
    df = await ibge.ppm('bovino', ano=2023, nivel='municipio', uf='MS')

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

asyncio.run(main())

Schema - PPM¶

Column	Type	Description
`ano`	int	Reference year
`localidade`	str	Locality name
`localidade_cod`	int	IBGE code of the locality
`especie`	str	Species/product name
`valor`	float	Value (head, thousand liters, etc.)
`unidade`	str	Unit of measure
`fonte`	str	"ibge_ppm"

PPM Species/Products¶

Herds (table 3939)¶

Code	Species	Unit
`bovino`	Cattle	head
`bubalino`	Buffalo	head
`equino`	Equine	head
`suino_total`	Hog (total)	head
`suino_matrizes`	Breeding sows	head
`caprino`	Goat	head
`ovino`	Sheep	head
`galinaceos_total`	Poultry (total)	head
`galinhas_poedeiras`	Laying hens	head
`codornas`	Quails	head

Animal-origin production (table 74)¶

Code	Product	Unit
`leite`	Milk	thousand liters
`ovos_galinha`	Hen eggs	thousand dozens
`ovos_codorna`	Quail eggs	thousand dozens
`mel`	Bee honey	kg
`casulos`	Silkworm cocoons	kg
`la`	Wool	kg

especies = await ibge.especies_ppm()
# ['bovino', 'bubalino', 'caprino', 'casulos', 'codornas', ...]

Usage - Quarterly Slaughter¶

Basic¶

import asyncio
from agrobr import ibge

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

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

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

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

asyncio.run(main())

Schema - Quarterly Slaughter¶

Column	Type	Description
`trimestre`	str	Quarter in YYYYQQ format
`localidade`	str	State
`localidade_cod`	int	IBGE code of the locality
`especie`	str	bovino, suino or frango
`animais_abatidos`	float	Quantity slaughtered (head)
`peso_carcacas`	float	Total carcass weight (kg)
`fonte`	str	"ibge_abate"

Slaughter Species¶

Code	Species	SIDRA Table
`bovino`	Cattle	1092
`suino`	Hog	1093
`frango`	Chicken	1094

especies = await ibge.especies_abate()
# ['bovino', 'suino', 'frango']

Usage - Agricultural Census¶

Basic¶

import asyncio
from agrobr import ibge

async def main():
    # Livestock numbers by state
    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')

    # Permanent crops — Brazil
    df = await ibge.censo_agro('lavoura_permanente')

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

asyncio.run(main())

Schema - Agricultural Census¶

Column	Type	Description
`ano`	int	Reference year (1995, 2006 or 2017)
`localidade`	str	Locality name
`localidade_cod`	int	IBGE code of the locality
`tema`	str	Census theme
`categoria`	str	Category within the theme
`variavel`	str	Variable name
`valor`	float	Variable value
`unidade`	str	Unit of measure
`fonte`	str	"ibge_censo_agro"

Agricultural Census Themes¶

Code	Theme	SIDRA Table 1995	SIDRA Table 2006	SIDRA Table 2017
`efetivo_rebanho`	Livestock numbers	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

temas = await ibge.temas_censo_agro()
# ['efetivo_rebanho', 'uso_terra', 'lavoura_temporaria', 'lavoura_permanente',
#  'preparo_solo', 'adubacao', 'calagem', 'agrotoxicos', 'praticas_agricolas', 'irrigacao']

Cache¶

Survey	TTL	Maximum stale
PAM	7 days	90 days
LSPA	24 hours	30 days
PPM	7 days	90 days
Slaughter	7 days	90 days
Agricultural Census	30 days	90 days
Legacy Agricultural Census	90 days	90 days
Silviculture (PEVS)	7 days	90 days
Plant Extraction (PEVS)	7 days	90 days
Quarterly Milk	7 days	90 days
Agricultural GDP	7 days	90 days

Update¶

Survey	Frequency
PAM	Annual (August-September)
LSPA	Monthly
PPM	Annual (September)
Slaughter	Quarterly (T+2 months)
Agricultural Census	Decennial (latest: 2017)
Silviculture (PEVS)	Annual (August-September)
Plant Extraction (PEVS)	Annual (August-September)
Quarterly Milk	Quarterly (T+2 months)
Agricultural GDP	Quarterly (T+2 months)

Usage - Silviculture (PEVS)¶

Basic¶

import asyncio
from agrobr import ibge

async def main():
    # Roundwood production by state
    df = await ibge.silvicultura('madeira_tora', ano=2023)

    # Planted area of eucalyptus
    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', return_meta=True)

asyncio.run(main())

Schema - Silviculture¶

Column	Type	Description
`ano`	int	Reference year
`localidade`	str	Locality name
`localidade_cod`	int	IBGE code of the locality
`produto`	str	Product name
`valor`	float	Value (Tonnes, cubic meters or Hectares)
`unidade`	str	Unit of measure
`fonte`	str	"ibge_silvicultura"

Usage - Plant Extraction (PEVS)¶

Basic¶

import asyncio
from agrobr import ibge

async def main():
    # 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')

    # Production value
    df = await ibge.extracao_vegetal('acai', variavel='valor_producao')

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

asyncio.run(main())

Schema - Plant Extraction¶

Column	Type	Description
`ano`	int	Reference year
`localidade`	str	Locality name
`localidade_cod`	int	IBGE code of the locality
`produto`	str	Product name
`valor`	float	Value (Tonnes or cubic meters)
`unidade`	str	Unit of measure
`fonte`	str	"ibge_extracao_vegetal"

Usage - Quarterly Milk¶

Basic¶

import asyncio
from agrobr import ibge

async def main():
    # 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(return_meta=True)

asyncio.run(main())

Schema - Quarterly Milk¶

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

Usage - Agricultural GDP¶

Basic¶

import asyncio
from agrobr import ibge

async def main():
    # 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
    df = await ibge.pib_agro(trimestre='202501', setor='pib_total')

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

asyncio.run(main())

Schema - Agricultural GDP¶

Column	Type	Description
`trimestre`	str	Quarter YYYYQQ
`valor`	float	Value (Millions of Reais)
`unidade`	str	Unit of measure
`setor`	str	Economic sector
`fonte`	str	"ibge_pib"

Notes¶

PEVS Silviculture: 14 products, annual data since 1986. Planted area (tab 5930) with 3 species. Cache 7 days
PEVS Plant Extraction: 21 products, annual data since 1986. Mixed units (Tonnes vs cubic meters). Cache 7 days
Quarterly Milk: table 1086, 3 variables pivoted into wide columns. Series since 1997. Cache 7 days
Agricultural GDP: tabs 1846/6612, 4 sectors, Brazil level. Series since 1996. No contract (macro view). Cache 7 days