MAPA PSR API¶

The MAPA PSR module provides data on Brazilian rural insurance policies and claims with federal premium subsidy, published by SISSER/MAPA. Namespace: agrobr.alt.mapa_psr.

Functions¶

`sinistros`¶

Rural insurance claims — indemnities paid by crop/municipality.

async def sinistros(
    cultura: str | None = None,
    uf: str | None = None,
    ano: int | None = None,
    ano_inicio: int | None = None,
    ano_fim: int | None = None,
    municipio: str | None = None,
    evento: str | None = None,
    as_polars: bool = False,
    return_meta: bool = False,
) -> pd.DataFrame | tuple[pd.DataFrame, MetaInfo]

Parameters:

Parameter	Type	Description
`cultura`	`str \\| None`	Crop filter (partial, accent-insensitive match, e.g. "cafe" matches "CAFE ARABICA")
`uf`	`str \\| None`	State filter (abbreviation, e.g. "MT")
`ano`	`int \\| None`	Single-year filter (e.g. 2023)
`ano_inicio`	`int \\| None`	Start year of the range (inclusive)
`ano_fim`	`int \\| None`	End year of the range (inclusive)
`municipio`	`str \\| None`	Municipality filter (partial match)
`evento`	`str \\| None`	Filter by predominant event (e.g. "seca")
`as_polars`	`bool`	If True, returns a polars.DataFrame
`return_meta`	`bool`	If True, returns a (DataFrame, MetaInfo) tuple

Returns:

DataFrame with columns: nr_apolice, ano_apolice, uf, municipio, cd_ibge, cultura, classificacao, evento, area_total, valor_indenizacao, valor_premio, valor_subvencao, valor_limite_garantia, produtividade_estimada, produtividade_segurada, nivel_cobertura, seguradora

Example:

from agrobr.alt import mapa_psr

# All claims
df = await mapa_psr.sinistros()

# Soybean claims in MT
df = await mapa_psr.sinistros(cultura="SOJA", uf="MT")

# Drought claims in 2023
df = await mapa_psr.sinistros(evento="seca", ano=2023)

# Year range
df = await mapa_psr.sinistros(ano_inicio=2020, ano_fim=2024)

`apolices`¶

All rural insurance policies with federal subsidy.

async def apolices(
    cultura: str | None = None,
    uf: str | None = None,
    ano: int | None = None,
    ano_inicio: int | None = None,
    ano_fim: int | None = None,
    municipio: str | None = None,
    as_polars: bool = False,
    return_meta: bool = False,
) -> pd.DataFrame | tuple[pd.DataFrame, MetaInfo]

Parameters:

Parameter	Type	Description
`cultura`	`str \\| None`	Crop filter (partial, accent-insensitive match, e.g. "cafe" matches "CAFE ARABICA")
`uf`	`str \\| None`	State filter (abbreviation, e.g. "MT")
`ano`	`int \\| None`	Single-year filter (e.g. 2023)
`ano_inicio`	`int \\| None`	Start year of the range (inclusive)
`ano_fim`	`int \\| None`	End year of the range (inclusive)
`municipio`	`str \\| None`	Municipality filter (partial match)
`as_polars`	`bool`	If True, returns a polars.DataFrame
`return_meta`	`bool`	If True, returns a (DataFrame, MetaInfo) tuple

Returns:

DataFrame with columns: nr_apolice, ano_apolice, uf, municipio, cd_ibge, cultura, classificacao, area_total, valor_premio, valor_subvencao, valor_limite_garantia, valor_indenizacao, evento, produtividade_estimada, produtividade_segurada, nivel_cobertura, taxa, seguradora

Example:

from agrobr.alt import mapa_psr

# All policies
df = await mapa_psr.apolices()

# Corn policies in PR
df = await mapa_psr.apolices(cultura="MILHO", uf="PR")

# 2023 policies
df = await mapa_psr.apolices(ano=2023)

Synchronous Version¶

from agrobr.sync import alt

df = alt.mapa_psr.sinistros(cultura="SOJA", uf="MT")
df = alt.mapa_psr.apolices(ano=2023)

Notes¶

Source: SISSER/MAPA — livre license (CC-BY)
Data: bulk CSV (3 files: 2006-2015, 2016-2024, 2025)
PII removed automatically (NM_SEGURADO, NR_DOCUMENTO_SEGURADO)
Geolocation removed (LATITUDE, LONGITUDE, degrees/min/sec)
CSVs can be large (~500k rows in the 2006-2015 period)
Read timeout: 180 seconds