Climatch

Land with a climate like Igaraçu do Tietê

🇧🇷Igaraçu do Tietê, São Paulo, Brazil

The towns with the closest growing climate to Igaraçu do Tietê are Pirajuí, Brazil (99% match), Lins and Tupã. About 593,000 sq mi of land in 20 countries matches 80% or better, most of it in Minas Gerais, Brazil, Mato Grosso do Sul, Brazil and São Paulo, Brazil.The towns with the closest growing climate to Igaraçu do Tietê are Pirajuí, Brazil (99% match), Lins and Tupã. About 1.5 million km² of land in 20 countries matches 80% or better, most of it in Minas Gerais, Brazil, Mato Grosso do Sul, Brazil and São Paulo, Brazil.

Your growing climate

Zone 11a · No frost · 0 chill hours · 48 in rainZone 11a · No frost · 0 chill hours · 1,215 mm rain

What grows in Igaraçu do Tietê →

Regions with the most land like yours

  1. 1

    🇧🇷Minas Gerais, Brazil

    124k sq mi320k km² matching 80% or better

    For example Pouso Alegre (97), Jacutinga (96)

  2. 2

    🇧🇷Mato Grosso do Sul, Brazil

    93k sq mi240k km² matching 80% or better

    For example Aparecida do Taboado (96), Nova Andradina (95)

  3. 3

    🇧🇷São Paulo, Brazil

    84k sq mi217k km² matching 80% or better

    For example Pirajuí (99), Lins (99)

  4. 4

    🇨🇳Yunnan, China

    14k sq mi37k km² matching 80% or better

    For example Mengla (87), Mengman (87)

  5. 5

    🇿🇦KwaZulu-Natal, South Africa

    13k sq mi35k km² matching 80% or better

    For example Mandeni (88), Empangeni (88)

  6. 6

    🇲🇿Zambezia Province, Mozambique

    7,500 sq mi19k km² matching 80% or better

    For example Milange (84), Gurúè (81)

  7. 7

    🇻🇳Điện Biên Province, Vietnam

    7,000 sq mi18k km² matching 80% or better

    For example Mường Lay (80), Dien Bien Phu (78)

  8. 8

    🇧🇴Santa Cruz Department, Bolivia

    6,500 sq mi17k km² matching 80% or better

    For example El Torno (87), Warnes (78)

  9. 9

    🇲🇲Shan State, Myanmar

    6,400 sq mi17k km² matching 80% or better

    For example Kēng Tung (84), Möng La (83)

  10. 10

    🇲🇿Maputo Province, Mozambique

    5,400 sq mi14k km² matching 80% or better

    For example Manhiça (89), Boane (80)

  11. 11

    🇱🇦Luang Prabang Province, Laos

    5,100 sq mi13k km² matching 80% or better

    For example Luang Prabang (71)

  12. 12

    🇱🇦Oudômxai, Laos

    5,000 sq mi13k km² matching 80% or better

    For example Muang Xay (88)

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

    🇧🇷PirajuíSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 46 in rainZone 11a · No frost · 0 chill hours · 1,170 mm rain

    Shares 22 of your 22 great-fit crops

    99%match
  2. 2

    🇧🇷LinsSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 48 in rainZone 11a · No frost · 0 chill hours · 1,224 mm rain

    Shares 22 of your 22 great-fit crops · Also great there: banana

    99%match
  3. 3

    🇧🇷TupãSão Paulo, Brazil

    Zone 11a · No frost · 1 chill hours · 50 in rainZone 11a · No frost · 1 chill hours · 1,267 mm rain

    Shares 22 of your 22 great-fit crops · Also great there: banana

    99%match
  4. 4

    🇧🇷PiracicabaSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 48 in rainZone 11a · No frost · 0 chill hours · 1,230 mm rain

    Shares 22 of your 22 great-fit crops

    99%match
  5. 5

    🇧🇷LimeiraSão Paulo, Brazil

    Zone 11a · No frost · 2 chill hours · 49 in rainZone 11a · No frost · 2 chill hours · 1,239 mm rain

    Shares 21 of your 22 great-fit crops

    99%match
  6. 6

    🇧🇷Monte MorSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 46 in rainZone 11a · No frost · 0 chill hours · 1,170 mm rain

    Shares 22 of your 22 great-fit crops

    99%match
  7. 7

    🇧🇷CampinasSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 49 in rainZone 11a · No frost · 0 chill hours · 1,256 mm rain

    Shares 22 of your 22 great-fit crops · Also great there: banana

    99%match
  8. 8

    🇧🇷AraraquaraSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 51 in rainZone 11a · No frost · 0 chill hours · 1,301 mm rain

    Shares 22 of your 22 great-fit crops

    99%match
  9. 9

    🇧🇷Mogi GuaçuSão Paulo, Brazil

    Zone 11a · No frost · 4 chill hours · 52 in rainZone 11a · No frost · 4 chill hours · 1,329 mm rain

    Shares 22 of your 22 great-fit crops

    99%match
  10. 10

    🇧🇷IndaiatubaSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 48 in rainZone 11a · No frost · 0 chill hours · 1,212 mm rain

    Shares 22 of your 22 great-fit crops

    99%match
  11. 11

    🇧🇷IbitingaSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 47 in rainZone 11a · No frost · 0 chill hours · 1,198 mm rain

    Shares 21 of your 22 great-fit crops

    99%match
  12. 12

    🇧🇷MaríliaSão Paulo, Brazil

    Zone 11a · No frost · 4 chill hours · 50 in rainZone 11a · No frost · 4 chill hours · 1,273 mm rain

    Shares 22 of your 22 great-fit crops · Also great there: banana

    99%match
  13. 13

    🇧🇷CerquilhoSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 45 in rainZone 11a · No frost · 0 chill hours · 1,134 mm rain

    Shares 22 of your 22 great-fit crops · Also great there: wine grapes

    99%match
  14. 14

    🇧🇷LemeSão Paulo, Brazil

    Zone 11a · No frost · 1 chill hours · 51 in rainZone 11a · No frost · 1 chill hours · 1,293 mm rain

    Shares 21 of your 22 great-fit crops

    99%match
  15. 15

    🇧🇷AdamantinaSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 46 in rainZone 11a · No frost · 0 chill hours · 1,160 mm rain

    Shares 21 of your 22 great-fit crops

    99%match
  16. 16

    🇧🇷Novo HorizonteSão Paulo, Brazil

    Zone 11b · No frost · 0 chill hours · 48 in rainZone 11b · No frost · 0 chill hours · 1,209 mm rain

    Shares 20 of your 22 great-fit crops

    99%match
  17. 17

    🇧🇷AssisSão Paulo, Brazil

    Zone 11a · No frost · 2 chill hours · 47 in rainZone 11a · No frost · 2 chill hours · 1,192 mm rain

    Shares 21 of your 22 great-fit crops · Also great there: banana

    99%match
  18. 18

    🇧🇷GuaratinguetáSão Paulo, Brazil

    Zone 11b · No frost · 0 chill hours · 53 in rainZone 11b · No frost · 0 chill hours · 1,336 mm rain

    Shares 22 of your 22 great-fit crops

    99%match
  19. 19

    🇧🇷TaubatéSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 50 in rainZone 11a · No frost · 0 chill hours · 1,258 mm rain

    Shares 22 of your 22 great-fit crops

    99%match
  20. 20

    🇧🇷RanchariaSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 49 in rainZone 11a · No frost · 0 chill hours · 1,256 mm rain

    Shares 22 of your 22 great-fit crops · Also great there: banana

    99%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 Igaraçu do Tietê: 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.