Climatch

Land with a climate like Matões

🇧🇷Matões, Maranhão, Brazil

The towns with the closest growing climate to Matões are Regeneração, Brazil (99% match), São João do Soter and Altos. About 236,000 sq mi of land in 14 countries matches 80% or better, most of it in Maranhão, Brazil, Piauí, Brazil and Guárico, Venezuela.The towns with the closest growing climate to Matões are Regeneração, Brazil (99% match), São João do Soter and Altos. About 612,000 km² of land in 14 countries matches 80% or better, most of it in Maranhão, Brazil, Piauí, Brazil and Guárico, Venezuela.

Your growing climate

Zone 12b · No frost · 0 chill hours · 51 in rainZone 12b · No frost · 0 chill hours · 1,299 mm rain

What grows in Matões →

Regions with the most land like yours

  1. 1

    🇧🇷Maranhão, Brazil

    82k sq mi212k km² matching 80% or better

    For example São João do Soter (99), Buriti Bravo (99)

  2. 2

    🇧🇷Piauí, Brazil

    36k sq mi94k km² matching 80% or better

    For example Regeneração (99), Altos (99)

  3. 3

    🇻🇪Guárico, Venezuela

    17k sq mi43k km² matching 80% or better

    For example El Sombrero (89), Valle de La Pascua (86)

  4. 4

    🇮🇳Tamil Nadu, India

    9,100 sq mi23k km² matching 80% or better

    For example Pennāgaram (89), Dharmapuri (89)

  5. 5

    🇮🇳Andhra Pradesh, India

    9,000 sq mi23k km² matching 80% or better

    For example Kuppam (85), Pīleru (84)

  6. 6

    🇧🇷Pará, Brazil

    8,000 sq mi21k km² matching 80% or better

    For example Rondon do Pará (86), Bom Jesus do Tocantins (84)

  7. 7

    🇻🇪Anzoátegui, Venezuela

    7,700 sq mi20k km² matching 80% or better

    For example Pariaguán (86), Aragua de Barcelona (84)

  8. 8

    🇻🇪Portuguesa, Venezuela

    6,200 sq mi16k km² matching 80% or better

    For example San Rafael de Onoto (84), Píritu (83)

  9. 9

    🇬🇳Kindia, Guinea

    4,700 sq mi12k km² matching 80% or better

    For example Kolenté (76), Télimélé (76)

  10. 10

    🇮🇳Karnataka, India

    2,900 sq mi7,500 km² matching 80% or better

    For example Kanakapura (84), Gauribidanur (84)

  11. 11

    🇹🇿Morogoro, Tanzania

    2,700 sq mi6,900 km² matching 80% or better

    For example Ifakara (80), Kidodi (79)

  12. 12

    🇹🇿Mtwara, Tanzania

    2,400 sq mi6,100 km² matching 80% or better

    For example Lukuledi (87), Nanganga (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

    🇧🇷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 15 great-fit crops

    99%match
  2. 2

    🇧🇷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 15 great-fit crops

    99%match
  3. 3

    🇧🇷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 15 great-fit crops

    99%match
  4. 4

    🇧🇷Buriti BravoMaranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 42 in rainZone 12b · No frost · 0 chill hours · 1,069 mm rain

    Shares 15 of your 15 great-fit crops

    99%match
  5. 5

    🇧🇷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 15 great-fit crops

    99%match
  6. 6

    🇧🇷CaxiasMaranhão, Brazil

    Zone 13a · No frost · 0 chill hours · 56 in rainZone 13a · No frost · 0 chill hours · 1,414 mm rain

    Shares 14 of your 15 great-fit crops · Also great there: date palm

    99%match
  7. 7

    🇧🇷Presidente DutraMaranhão, Brazil

    Zone 13a · No frost · 0 chill hours · 45 in rainZone 13a · No frost · 0 chill hours · 1,150 mm rain

    Shares 15 of your 15 great-fit crops

    99%match
  8. 8

    🇧🇷MiradorMaranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 47 in rainZone 12b · No frost · 0 chill hours · 1,203 mm rain

    Shares 15 of your 15 great-fit crops

    99%match
  9. 9

    🇧🇷UniãoPiauí, Brazil

    Zone 13a · No frost · 0 chill hours · 55 in rainZone 13a · No frost · 0 chill hours · 1,405 mm rain

    Shares 14 of your 15 great-fit crops · Also great there: date palm

    97%match
  10. 10

    🇧🇷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 15 great-fit crops

    97%match
  11. 11

    🇧🇷São João dos PatosMaranhão, Brazil

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

    Shares 15 of your 15 great-fit crops · Also great there: avocado

    97%match
  12. 12

    🇧🇷PedreirasMaranhão, Brazil

    Zone 13a · No frost · 0 chill hours · 59 in rainZone 13a · No frost · 0 chill hours · 1,492 mm rain

    Shares 14 of your 15 great-fit crops · Also great there: date palm

    97%match
  13. 13

    🇧🇷Pastos BonsMaranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 45 in rainZone 12b · No frost · 0 chill hours · 1,146 mm rain

    Shares 15 of your 15 great-fit crops

    96%match
  14. 14

    🇧🇷EsperantinópolisMaranhão, Brazil

    Zone 13a · No frost · 0 chill hours · 63 in rainZone 13a · No frost · 0 chill hours · 1,603 mm rain

    Shares 14 of your 15 great-fit crops

    96%match
  15. 15

    🇧🇷CodóMaranhão, Brazil

    Zone 13a · No frost · 0 chill hours · 55 in rainZone 13a · No frost · 0 chill hours · 1,408 mm rain

    Shares 14 of your 15 great-fit crops · Also great there: date palm

    96%match
  16. 16

    🇧🇷PiripiriPiauí, Brazil

    Zone 13a · No frost · 0 chill hours · 53 in rainZone 13a · No frost · 0 chill hours · 1,341 mm rain

    Shares 15 of your 15 great-fit crops

    96%match
  17. 17

    🇧🇷Lago da PedraMaranhão, Brazil

    Zone 13a · No frost · 0 chill hours · 59 in rainZone 13a · No frost · 0 chill hours · 1,506 mm rain

    Shares 13 of your 15 great-fit crops

    96%match
  18. 18

    🇧🇷Valença do PiauíPiauí, Brazil

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

    Shares 15 of your 15 great-fit crops · Also great there: table and raisin grapes, avocado

    96%match
  19. 19

    🇧🇷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 15 great-fit crops

    95%match
  20. 20

    🇧🇷São Mateus do MaranhãoMaranhão, Brazil

    Zone 13a · No frost · 0 chill hours · 61 in rainZone 13a · No frost · 0 chill hours · 1,555 mm rain

    Shares 13 of your 15 great-fit crops · Also great there: date palm

    94%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 Matões: 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.