Climatch

Land with a climate like Xapuri

🇧🇷Xapuri, Acre, Brazil

The towns with the closest growing climate to Xapuri are Plácido de Castro, Brazil (99% match), Porto Acre and Boca do Acre. About 1.8 million sq mi of land in 21 countries matches 80% or better, most of it in Pará, Brazil, Amazonas, Brazil and Mato Grosso, Brazil.The towns with the closest growing climate to Xapuri are Plácido de Castro, Brazil (99% match), Porto Acre and Boca do Acre. About 4.7 million km² of land in 21 countries matches 80% or better, most of it in Pará, Brazil, Amazonas, Brazil and Mato Grosso, Brazil.

Your growing climate

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

What grows in Xapuri →

Regions with the most land like yours

  1. 1

    🇧🇷Pará, Brazil

    97k sq mi251k km² matching 80% or better

    For example Canaã dos Carajás (92), São Félix do Xingu (92)

  2. 2

    🇧🇷Amazonas, Brazil

    88k sq mi227k km² matching 80% or better

    For example Boca do Acre (99), Pauini (97)

  3. 3

    🇧🇷Mato Grosso, Brazil

    85k sq mi219k km² matching 80% or better

    For example Vila Bela da Santíssima Trindade (93), Pontes e Lacerda (93)

  4. 4

    🇨🇩Kwilu, Democratic Republic of the Congo

    28k sq mi72k km² matching 80% or better

    For example Mangai (92), Dibaya-Lubwe (92)

  5. 5

    🇨🇩Mai-Ndombe, Democratic Republic of the Congo

    27k sq mi69k km² matching 80% or better

    For example Dungu (92), Mushie (88)

  6. 6

    🇧🇴Beni Department, Bolivia

    22k sq mi56k km² matching 80% or better

    For example Trinidad (98), Guayaramerín (97)

  7. 7

    🇨🇩Mongala, Democratic Republic of the Congo

    18k sq mi46k km² matching 80% or better

    For example Binga (87), Lisala (84)

  8. 8

    🇨🇲Centre, Cameroon

    18k sq mi46k km² matching 80% or better

    For example Akonolinga (90), Eséka (88)

  9. 9

    🇧🇴Santa Cruz Department, Bolivia

    16k sq mi41k km² matching 80% or better

    For example Ascención de Guarayos (89), San Ignacio de Velasco (84)

  10. 10

    🇨🇲East, Cameroon

    14k sq mi36k km² matching 80% or better

    For example Abong Mbang (90), Yokadouma (84)

  11. 11

    🇨🇬Cuvette, Republic of the Congo

    13k sq mi34k km² matching 80% or better

    For example Oyo (86), Mossaka (84)

  12. 12

    🇵🇪Ucayali, Peru

    13k sq mi33k km² matching 80% or better

    For example Atalaya (95), San Fernando (91)

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

    🇧🇷Plácido de CastroAcre, Brazil

    Zone 12b · No frost · 0 chill hours · 69 in rainZone 12b · No frost · 0 chill hours · 1,746 mm rain

    Shares 19 of your 19 great-fit crops

    99%match
  2. 2

    🇧🇷Porto AcreAcre, Brazil

    Zone 12b · No frost · 0 chill hours · 72 in rainZone 12b · No frost · 0 chill hours · 1,825 mm rain

    Shares 19 of your 19 great-fit crops

    99%match
  3. 3

    🇧🇷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

    99%match
  4. 4

    🇧🇷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
  5. 5

    🇧🇷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
  6. 6

    🇧🇷Nova MamoréRondônia, Brazil

    Zone 12b · No frost · 0 chill hours · 72 in rainZone 12b · No frost · 0 chill hours · 1,840 mm rain

    Shares 16 of your 19 great-fit crops

    99%match
  7. 7

    🇧🇷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
  8. 8

    🇧🇴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

    98%match
  9. 9

    🇧🇷Guajará MirimRondônia, Brazil

    Zone 13a · No frost · 0 chill hours · 70 in rainZone 13a · No frost · 0 chill hours · 1,789 mm rain

    Shares 15 of your 19 great-fit crops

    97%match
  10. 10

    🇧🇷São Miguel do GuaporéRondônia, Brazil

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

    Shares 19 of your 19 great-fit crops

    97%match
  11. 11

    🇵🇪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

    97%match
  12. 12

    🇧🇷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

    97%match
  13. 13

    🇧🇷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
  14. 14

    🇧🇷Senador GuiomardAcre, Brazil

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

    Shares 19 of your 19 great-fit crops

    97%match
  15. 15

    🇧🇷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

    97%match
  16. 16

    🇧🇷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

    97%match
  17. 17

    🇧🇷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

    97%match
  18. 18

    🇧🇷ApuíAmazonas, Brazil

    Zone 13a · No frost · 0 chill hours · 76 in rainZone 13a · No frost · 0 chill hours · 1,943 mm rain

    Shares 15 of your 19 great-fit crops

    97%match
  19. 19

    🇧🇷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

    96%match
  20. 20

    🇧🇷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

    96%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 Xapuri: 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.