Climatch

Land with a climate like São Miguel do Guaporé

🇧🇷São Miguel do Guaporé, Rondônia, Brazil

The towns with the closest growing climate to São Miguel do Guaporé are Jaru, Brazil (99% match), São Francisco do Guaporé and Monte Negro. About 1.4 million sq mi of land in 18 countries matches 80% or better, most of it in Mato Grosso, Brazil, Pará, Brazil and Rondônia, Brazil.The towns with the closest growing climate to São Miguel do Guaporé are Jaru, Brazil (99% match), São Francisco do Guaporé and Monte Negro. About 3.7 million km² of land in 18 countries matches 80% or better, most of it in Mato Grosso, Brazil, Pará, Brazil and Rondônia, Brazil.

Your growing climate

Zone 12b · No frost · 0 chill hours · 69 in rainZone 12b · No frost · 0 chill hours · 1,741 mm rain

What grows in São Miguel do Guaporé →

Regions with the most land like yours

  1. 1

    🇧🇷Mato Grosso, Brazil

    138k sq mi358k km² matching 80% or better

    For example Aripuanã (98), Colniza (97)

  2. 2

    🇧🇷Pará, Brazil

    82k sq mi212k km² matching 80% or better

    For example Ourilândia do Norte (98), Tucumã (97)

  3. 3

    🇧🇷Rondônia, Brazil

    81k sq mi210k km² matching 80% or better

    For example Jaru (99), São Francisco do Guaporé (99)

  4. 4

    🇨🇩Kwilu, Democratic Republic of the Congo

    29k sq mi74k km² matching 80% or better

    For example Bulungu (89), Masi-Manimba (88)

  5. 5

    🇨🇲East, Cameroon

    20k sq mi51k km² matching 80% or better

    For example Abong Mbang (92), Bertoua (87)

  6. 6

    🇨🇲Centre, Cameroon

    18k sq mi48k km² matching 80% or better

    For example Bafia (89), Nanga Eboko (89)

  7. 7

    🇧🇴Beni Department, Bolivia

    17k sq mi43k km² matching 80% or better

    For example Guayaramerín (90), Trinidad (89)

  8. 8

    🇨🇩Sankuru, Democratic Republic of the Congo

    15k sq mi39k km² matching 80% or better

    For example Lusambo (87), Lukula (87)

  9. 9

    🇨🇩Maniema, Democratic Republic of the Congo

    14k sq mi36k km² matching 80% or better

    For example Kasongo (84), Kampene (82)

  10. 10

    🇨🇮Montagnes, Ivory Coast

    11k sq mi29k km² matching 80% or better

    For example Logoualé (90), Gohouo (90)

  11. 11

    🇧🇴Santa Cruz Department, Bolivia

    11k sq mi28k km² matching 80% or better

    For example San Ignacio de Velasco (82), Ascención de Guarayos (81)

  12. 12

    🇨🇬Plateaux, Republic of the Congo

    10k sq mi26k km² matching 80% or better

    For example Djambala (83), Gamboma (79)

Regions are states and provinces, counting land within 75 km of a town. At most three per country, so the list covers the world.

Closest towns

  1. 1

    🇧🇷JaruRondônia, Brazil

    Zone 12b · No frost · 0 chill hours · 82 in rainZone 12b · No frost · 0 chill hours · 2,086 mm rain

    Shares 18 of your 19 great-fit crops

    99%match
  2. 2

    🇧🇷São Francisco do GuaporéRondônia, Brazil

    Zone 12b · No frost · 0 chill hours · 64 in rainZone 12b · No frost · 0 chill hours · 1,620 mm rain

    Shares 18 of your 19 great-fit crops

    99%match
  3. 3

    🇧🇷Monte NegroRondônia, Brazil

    Zone 12b · No frost · 0 chill hours · 83 in rainZone 12b · No frost · 0 chill hours · 2,096 mm rain

    Shares 18 of your 19 great-fit crops

    99%match
  4. 4

    🇧🇷CacoalRondônia, Brazil

    Zone 12a · No frost · 0 chill hours · 81 in rainZone 12a · No frost · 0 chill hours · 2,062 mm rain

    Shares 18 of your 19 great-fit crops

    99%match
  5. 5

    🇧🇷Espigão dOesteRondônia, Brazil

    Zone 12a · No frost · 0 chill hours · 80 in rainZone 12a · No frost · 0 chill hours · 2,036 mm rain

    Shares 18 of your 19 great-fit crops · Also great there: wheat

    99%match
  6. 6

    🇧🇷Ouro Preto do OesteRondônia, Brazil

    Zone 12a · No frost · 0 chill hours · 80 in rainZone 12a · No frost · 0 chill hours · 2,024 mm rain

    Shares 19 of your 19 great-fit crops · Also great there: wheat

    99%match
  7. 7

    🇧🇷Rolim de MouraRondônia, Brazil

    Zone 12a · No frost · 0 chill hours · 69 in rainZone 12a · No frost · 0 chill hours · 1,751 mm rain

    Shares 19 of your 19 great-fit crops

    99%match
  8. 8

    🇧🇷AriquemesRondônia, Brazil

    Zone 12b · No frost · 0 chill hours · 88 in rainZone 12b · No frost · 0 chill hours · 2,223 mm rain

    Shares 18 of your 19 great-fit crops

    99%match
  9. 9

    🇧🇷CerejeirasRondônia, Brazil

    Zone 12a · No frost · 0 chill hours · 73 in rainZone 12a · No frost · 0 chill hours · 1,851 mm rain

    Shares 18 of your 19 great-fit crops · Also great there: wheat

    99%match
  10. 10

    🇧🇷Ji ParanáRondônia, Brazil

    Zone 12a · No frost · 0 chill hours · 85 in rainZone 12a · No frost · 0 chill hours · 2,168 mm rain

    Shares 18 of your 19 great-fit crops

    99%match
  11. 11

    🇧🇷Rolim de Moura do GuaporéRondônia, Brazil

    Zone 12b · No frost · 0 chill hours · 59 in rainZone 12b · No frost · 0 chill hours · 1,493 mm rain

    Shares 19 of your 19 great-fit crops · Also great there: fig

    99%match
  12. 12

    🇧🇷BuritisRondônia, Brazil

    Zone 12b · No frost · 0 chill hours · 86 in rainZone 12b · No frost · 0 chill hours · 2,173 mm rain

    Shares 18 of your 19 great-fit crops

    99%match
  13. 13

    🇧🇷Machadinho d'OesteRondônia, Brazil

    Zone 12b · No frost · 0 chill hours · 92 in rainZone 12b · No frost · 0 chill hours · 2,337 mm rain

    Shares 17 of your 19 great-fit crops

    99%match
  14. 14

    🇧🇷AripuanãMato Grosso, Brazil

    Zone 12b · No frost · 0 chill hours · 80 in rainZone 12b · No frost · 0 chill hours · 2,027 mm rain

    Shares 16 of your 19 great-fit crops

    98%match
  15. 15

    🇧🇷TucumãPará, Brazil

    Zone 12b · No frost · 0 chill hours · 70 in rainZone 12b · No frost · 0 chill hours · 1,770 mm rain

    Shares 17 of your 19 great-fit crops

    97%match
  16. 16

    🇧🇷Água Azul do NortePará, Brazil

    Zone 12b · No frost · 0 chill hours · 68 in rainZone 12b · No frost · 0 chill hours · 1,730 mm rain

    Shares 17 of your 19 great-fit crops

    97%match
  17. 17

    🇧🇷XapuriAcre, Brazil

    Zone 12b · No frost · 0 chill hours · 71 in rainZone 12b · No frost · 0 chill hours · 1,813 mm rain

    Shares 19 of your 19 great-fit crops

    97%match
  18. 18

    🇧🇷São Félix do XinguPará, Brazil

    Zone 12b · No frost · 0 chill hours · 73 in rainZone 12b · No frost · 0 chill hours · 1,847 mm rain

    Shares 17 of your 19 great-fit crops

    97%match
  19. 19

    🇧🇷VilhenaRondônia, Brazil

    Zone 12a · No frost · 0 chill hours · 81 in rainZone 12a · No frost · 0 chill hours · 2,070 mm rain

    Shares 19 of your 19 great-fit crops · Also great there: wheat, canola (rapeseed)

    97%match
  20. 20

    🇧🇷BrasiléiaAcre, Brazil

    Zone 12b · No frost · 0 chill hours · 70 in rainZone 12b · No frost · 0 chill hours · 1,770 mm rain

    Shares 19 of your 19 great-fit crops

    97%match

Any town over 15,000 people, skipping those within 75 km of you. ↻ = seasons flipped across the equator.

How matching works

We compare all twelve months of average highs and lows, rain, humidity and sunshine, the same way as Climatch's places-like search, but weighted for growing: winter lows count most, since frost, chill and hardiness decide which perennials survive, then summer highs and rain. Humidity counts least.

Scores show how unusual a match this close is: 80 or better is closer than all but about 2% of random pairs of places. Across the equator seasons are lined up (↻), so a southern farm's January is compared with a northern July. Climate is only part of it: check soil, water and day length (it matters for photoperiod crops) before buying land.

Looking for land?

We're building detailed reports on land like São Miguel do Guaporé: frost risk year by year, water, and how each match's climate is shifting by 2050.

We'll only use your email for the report and related updates.

Climate data: TerraClimate 1991–2020 monthly averages (Abatzoglou, J.T. et al., 2018, Scientific Data 5:170191). Humidity is derived from TerraClimate vapor pressure; “sunshine” is solar radiation.

Places: GeoNames (CC BY 4.0). Map: © OpenStreetMap contributors, tiles by OpenFreeMap.

Grower tools: crop ranges from FAO ECOCROP and university extension guides (cited per crop). Daily weather, frost dates and chill are estimated from the monthly averages; chill portions use the Dynamic Model (Fishman, Erez & Couvillon, 1987), hardiness zones follow the USDA definition.