Climatch

Land with a climate like Senador Guiomard

🇧🇷Senador Guiomard, Acre, Brazil

The towns with the closest growing climate to Senador Guiomard are Marechal Thaumaturgo, Brazil (99% match), Cruzeiro do Sul and Tarauacá. About 1.9 million sq mi of land in 20 countries matches 80% or better, most of it in Amazonas, Brazil, Rondônia, Brazil and Pará, Brazil.The towns with the closest growing climate to Senador Guiomard are Marechal Thaumaturgo, Brazil (99% match), Cruzeiro do Sul and Tarauacá. About 5.0 million km² of land in 20 countries matches 80% or better, most of it in Amazonas, Brazil, Rondônia, Brazil and Pará, Brazil.

Your growing climate

Zone 12b · No frost · 0 chill hours · 65 in rainZone 12b · No frost · 0 chill hours · 1,650 mm rain

What grows in Senador Guiomard →

Regions with the most land like yours

  1. 1

    🇧🇷Amazonas, Brazil

    179k sq mi463k km² matching 80% or better

    For example Guajará (99), Pauini (97)

  2. 2

    🇧🇷Rondônia, Brazil

    60k sq mi156k km² matching 80% or better

    For example Porto Velho (96), Candeias do Jamari (93)

  3. 3

    🇧🇷Pará, Brazil

    51k sq mi131k km² matching 80% or better

    For example Trairão (91), Rurópolis (91)

  4. 4

    🇨🇩Mai-Ndombe, Democratic Republic of the Congo

    26k sq mi67k km² matching 80% or better

    For example Kutu (90), Inongo (88)

  5. 5

    🇵🇪Loreto, Peru

    25k sq mi66k km² matching 80% or better

    For example Contamana (93), Requena (90)

  6. 6

    🇨🇩Tshopo, Democratic Republic of the Congo

    22k sq mi58k km² matching 80% or better

    For example Kisangani (89), Yangambi (89)

  7. 7

    🇧🇴Beni Department, Bolivia

    22k sq mi56k km² matching 80% or better

    For example Trinidad (93), San Borja (90)

  8. 8

    🇨🇩Mongala, Democratic Republic of the Congo

    18k sq mi46k km² matching 80% or better

    For example Binga (90), Bumba (87)

  9. 9

    🇨🇬Cuvette, Republic of the Congo

    16k sq mi43k km² matching 80% or better

    For example Mossaka (86), Oyo (85)

  10. 10

    🇵🇪Ucayali, Peru

    14k sq mi35k km² matching 80% or better

    For example San Fernando (97), Pucallpa (95)

  11. 11

    🇵🇪San Martín Department, Peru

    12k sq mi32k km² matching 80% or better

    For example La Banda (93), Moyobamba (90)

  12. 12

    🇨🇬Likouala, Republic of the Congo

    12k sq mi31k km² matching 80% or better

    For example Impfondo (91), Bétou (84)

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

    🇧🇷Marechal ThaumaturgoAcre, Brazil

    Zone 12b · No frost · 0 chill hours · 71 in rainZone 12b · No frost · 0 chill hours · 1,803 mm rain

    Shares 19 of your 19 great-fit crops

    99%match
  2. 2

    🇧🇷Cruzeiro do SulAcre, Brazil

    Zone 12b · No frost · 0 chill hours · 82 in rainZone 12b · No frost · 0 chill hours · 2,088 mm rain

    Shares 19 of your 19 great-fit crops

    99%match
  3. 3

    🇧🇷TarauacáAcre, Brazil

    Zone 12b · No frost · 0 chill hours · 94 in rainZone 12b · No frost · 0 chill hours · 2,387 mm rain

    Shares 19 of your 19 great-fit crops

    99%match
  4. 4

    🇧🇷FeijóAcre, Brazil

    Zone 12b · No frost · 0 chill hours · 87 in rainZone 12b · No frost · 0 chill hours · 2,220 mm rain

    Shares 19 of your 19 great-fit crops

    98%match
  5. 5

    🇵🇪Puerto MaldonadoMadre de Dios, Peru

    Zone 12b · No frost · 0 chill hours · 88 in rainZone 12b · No frost · 0 chill hours · 2,242 mm rain

    Shares 19 of your 19 great-fit crops

    98%match
  6. 6

    🇧🇷PauiniAmazonas, Brazil

    Zone 13a · No frost · 0 chill hours · 88 in rainZone 13a · No frost · 0 chill hours · 2,243 mm rain

    Shares 19 of your 19 great-fit crops

    97%match
  7. 7

    🇧🇷EirunepéAmazonas, Brazil

    Zone 13a · No frost · 0 chill hours · 98 in rainZone 13a · No frost · 0 chill hours · 2,500 mm rain

    Shares 19 of your 19 great-fit crops

    97%match
  8. 8

    🇧🇷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
  9. 9

    🇧🇷IpixunaAmazonas, Brazil

    Zone 13a · No frost · 0 chill hours · 98 in rainZone 13a · No frost · 0 chill hours · 2,499 mm rain

    Shares 19 of your 19 great-fit crops

    96%match
  10. 10

    🇧🇷Sena MadureiraAcre, Brazil

    Zone 12a · No frost · 0 chill hours · 82 in rainZone 12a · No frost · 0 chill hours · 2,078 mm rain

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

    96%match
  11. 11

    🇧🇷Porto VelhoRondônia, Brazil

    Zone 12b · No frost · 0 chill hours · 96 in rainZone 12b · No frost · 0 chill hours · 2,449 mm rain

    Shares 18 of your 19 great-fit crops

    96%match
  12. 12

    🇧🇷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

    96%match
  13. 13

    🇧🇷Boca do AcreAmazonas, Brazil

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

    Shares 19 of your 19 great-fit crops

    96%match
  14. 14

    🇵🇪PucallpaUcayali, Peru

    Zone 13a · No frost · 0 chill hours · 72 in rainZone 13a · No frost · 0 chill hours · 1,817 mm rain

    Shares 18 of your 19 great-fit crops

    95%match
  15. 15

    🇧🇷HumaitáAmazonas, Brazil

    Zone 13a · No frost · 0 chill hours · 94 in rainZone 13a · No frost · 0 chill hours · 2,385 mm rain

    Shares 18 of your 19 great-fit crops

    94%match
  16. 16

    🇧🇷CanutamaAmazonas, Brazil

    Zone 13a · No frost · 0 chill hours · 97 in rainZone 13a · No frost · 0 chill hours · 2,470 mm rain

    Shares 17 of your 19 great-fit crops

    94%match
  17. 17

    🇨🇩LodjaSankuru, Democratic Republic of the Congo

    Zone 12b · No frost · 0 chill hours · 55 in rainZone 12b · No frost · 0 chill hours · 1,388 mm rain

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

    93%match
  18. 18

    🇵🇪AtalayaUcayali, Peru

    Zone 12b · No frost · 0 chill hours · 62 in rainZone 12b · No frost · 0 chill hours · 1,565 mm rain

    Shares 19 of your 19 great-fit crops

    93%match
  19. 19

    🇵🇪La BandaSan Martín Department, Peru

    Zone 13a · No frost · 0 chill hours · 58 in rainZone 13a · No frost · 0 chill hours · 1,485 mm rain

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

    93%match
  20. 20

    🇧🇴TrinidadBeni Department, Bolivia

    Zone 12a · No frost · 0 chill hours · 71 in rainZone 12a · No frost · 0 chill hours · 1,801 mm rain

    Shares 18 of your 19 great-fit crops

    93%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 Senador Guiomard: 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.