Climatch

Land with a climate like São João dos Patos

🇧🇷São João dos Patos, Maranhão, Brazil

The towns with the closest growing climate to São João dos Patos are Colinas, Brazil (99% match), São Domingos do Maranhão and Regeneração. About 393,000 sq mi of land in 22 countries matches 80% or better, most of it in Maranhão, Brazil, Piauí, Brazil and Karnataka, India.The towns with the closest growing climate to São João dos Patos are Colinas, Brazil (99% match), São Domingos do Maranhão and Regeneração. About 1.0 million km² of land in 22 countries matches 80% or better, most of it in Maranhão, Brazil, Piauí, Brazil and Karnataka, India.

Your growing climate

Zone 12b · No frost · 0 chill hours · 42 in rainZone 12b · No frost · 0 chill hours · 1,078 mm rain

What grows in São João dos Patos →

Regions with the most land like yours

  1. 1

    🇧🇷Maranhão, Brazil

    78k sq mi203k km² matching 80% or better

    For example Colinas (99), Fortuna (99)

  2. 2

    🇧🇷Piauí, Brazil

    34k sq mi88k km² matching 80% or better

    For example Água Branca (99), Regeneração (99)

  3. 3

    🇮🇳Karnataka, India

    21k sq mi54k km² matching 80% or better

    For example Kanakapura (87), Gauribidanur (86)

  4. 4

    🇻🇪Guárico, Venezuela

    18k sq mi46k km² matching 80% or better

    For example Valle de La Pascua (89), El Sombrero (89)

  5. 5

    🇧🇷Pará, Brazil

    16k sq mi41k km² matching 80% or better

    For example Rondon do Pará (86), São Geraldo do Araguaia (86)

  6. 6

    🇻🇪Anzoátegui, Venezuela

    8,300 sq mi21k km² matching 80% or better

    For example Pariaguán (88), San José de Guanipa (84)

  7. 7

    🇻🇪Portuguesa, Venezuela

    5,900 sq mi15k km² matching 80% or better

    For example Píritu (85), San Rafael de Onoto (84)

  8. 8

    🇮🇳Tamil Nadu, India

    5,600 sq mi14k km² matching 80% or better

    For example Rāmachettipālaiyam (87), Vadavalli (87)

  9. 9

    🇬🇳Kindia, Guinea

    5,300 sq mi14k km² matching 80% or better

    For example Kolenté (83), Télimélé (81)

  10. 10

    🇲🇲Mandalay Region, Myanmar

    4,700 sq mi12k km² matching 80% or better

    For example Yamethin (86), Pyawbwe (84)

  11. 11

    🇹🇿Mtwara, Tanzania

    4,400 sq mi11k km² matching 80% or better

    For example Masasi (88), Lukuledi (88)

  12. 12

    🇹🇭Lampang, Thailand

    4,300 sq mi11k km² matching 80% or better

    For example Lampang (87), Thoen (86)

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

    🇧🇷ColinasMaranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 44 in rainZone 12b · No frost · 0 chill hours · 1,122 mm rain

    Shares 15 of your 16 great-fit crops

    99%match
  2. 2

    🇧🇷São Domingos do MaranhãoMaranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 41 in rainZone 12b · No frost · 0 chill hours · 1,053 mm rain

    Shares 15 of your 16 great-fit crops

    99%match
  3. 3

    🇧🇷RegeneraçãoPiauí, Brazil

    Zone 12b · No frost · 0 chill hours · 46 in rainZone 12b · No frost · 0 chill hours · 1,163 mm rain

    Shares 15 of your 16 great-fit crops

    99%match
  4. 4

    🇧🇷Barra do CordaMaranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 41 in rainZone 12b · No frost · 0 chill hours · 1,048 mm rain

    Shares 15 of your 16 great-fit crops

    97%match
  5. 5

    🇧🇷Jenipapo dos VieirasMaranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 40 in rainZone 12b · No frost · 0 chill hours · 1,023 mm rain

    Shares 16 of your 16 great-fit crops

    96%match
  6. 6

    🇧🇷ParnaramaMaranhão, Brazil

    Zone 13a · No frost · 0 chill hours · 47 in rainZone 13a · No frost · 0 chill hours · 1,200 mm rain

    Shares 14 of your 16 great-fit crops

    96%match
  7. 7

    🇧🇷GrajaúMaranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 43 in rainZone 12b · No frost · 0 chill hours · 1,097 mm rain

    Shares 15 of your 16 great-fit crops

    96%match
  8. 8

    🇧🇷São João do SoterMaranhão, Brazil

    Zone 13a · No frost · 0 chill hours · 50 in rainZone 13a · No frost · 0 chill hours · 1,280 mm rain

    Shares 15 of your 16 great-fit crops

    95%match
  9. 9

    🇧🇷São Raimundo das MangabeirasMaranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 40 in rainZone 12b · No frost · 0 chill hours · 1,025 mm rain

    Shares 15 of your 16 great-fit crops

    95%match
  10. 10

    🇧🇷Formosa da Serra NegraMaranhão, Brazil

    Zone 12a · No frost · 0 chill hours · 45 in rainZone 12a · No frost · 0 chill hours · 1,132 mm rain

    Shares 16 of your 16 great-fit crops

    95%match
  11. 11

    🇧🇷TuntumMaranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 47 in rainZone 12b · No frost · 0 chill hours · 1,192 mm rain

    Shares 15 of your 16 great-fit crops

    95%match
  12. 12

    🇧🇷FlorianoPiauí, Brazil

    Zone 12b · No frost · 0 chill hours · 36 in rainZone 12b · No frost · 0 chill hours · 910 mm rain

    Shares 14 of your 16 great-fit crops

    95%match
  13. 13

    🇧🇷Valença do PiauíPiauí, Brazil

    Zone 12b · No frost · 0 chill hours · 35 in rainZone 12b · No frost · 0 chill hours · 878 mm rain

    Shares 16 of your 16 great-fit crops · Also great there: table and raisin grapes

    94%match
  14. 14

    🇧🇷ImperatrizMaranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 53 in rainZone 12b · No frost · 0 chill hours · 1,338 mm rain

    Shares 14 of your 16 great-fit crops

    93%match
  15. 15

    🇧🇷Amarante do MaranhãoMaranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 46 in rainZone 12b · No frost · 0 chill hours · 1,177 mm rain

    Shares 16 of your 16 great-fit crops

    93%match
  16. 16

    🇧🇷ArameMaranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 44 in rainZone 12b · No frost · 0 chill hours · 1,130 mm rain

    Shares 16 of your 16 great-fit crops

    93%match
  17. 17

    🇧🇷AltosPiauí, Brazil

    Zone 13a · No frost · 0 chill hours · 49 in rainZone 13a · No frost · 0 chill hours · 1,242 mm rain

    Shares 15 of your 16 great-fit crops

    92%match
  18. 18

    🇧🇷Demerval LobãoPiauí, Brazil

    Zone 13a · No frost · 0 chill hours · 48 in rainZone 13a · No frost · 0 chill hours · 1,216 mm rain

    Shares 14 of your 16 great-fit crops

    92%match
  19. 19

    🇧🇷RiachãoMaranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 45 in rainZone 12b · No frost · 0 chill hours · 1,145 mm rain

    Shares 16 of your 16 great-fit crops

    92%match
  20. 20

    🇧🇷CarolinaMaranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 56 in rainZone 12b · No frost · 0 chill hours · 1,425 mm rain

    Shares 15 of your 16 great-fit crops · Also great there: banana, cacao

    92%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 João dos Patos: 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.