Climatch

Land with a climate like Marília

🇧🇷Marília, São Paulo, Brazil

The towns with the closest growing climate to Marília are Piraju, Brazil (99% match), Lençóis Paulista and Jaú. About 482,000 sq mi of land in 17 countries matches 80% or better, most of it in Minas Gerais, Brazil, São Paulo, Brazil and Mato Grosso do Sul, Brazil.The towns with the closest growing climate to Marília are Piraju, Brazil (99% match), Lençóis Paulista and Jaú. About 1.2 million km² of land in 17 countries matches 80% or better, most of it in Minas Gerais, Brazil, São Paulo, Brazil and Mato Grosso do Sul, Brazil.

Your growing climate

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

What grows in Marília →

Regions with the most land like yours

  1. 1

    🇧🇷Minas Gerais, Brazil

    99k sq mi256k km² matching 80% or better

    For example Pouso Alegre (98), Borda da Mata (98)

  2. 2

    🇧🇷São Paulo, Brazil

    84k sq mi219k km² matching 80% or better

    For example Piraju (99), Santa Cruz do Rio Pardo (99)

  3. 3

    🇧🇷Mato Grosso do Sul, Brazil

    67k sq mi173k km² matching 80% or better

    For example Nova Andradina (92), Naviraí (90)

  4. 4

    🇨🇳Yunnan, China

    19k sq mi49k km² matching 80% or better

    For example Mengman (87), Pu'er (85)

  5. 5

    🇿🇦KwaZulu-Natal, South Africa

    8,600 sq mi22k km² matching 80% or better

    For example Mandeni (88), Empangeni (87)

  6. 6

    🇻🇳Điện Biên Province, Vietnam

    7,000 sq mi18k km² matching 80% or better

    For example Mường Lay (72), Dien Bien Phu (66)

  7. 7

    🇲🇲Shan State, Myanmar

    4,700 sq mi12k km² matching 80% or better

    For example Kēng Tung (80), Möng La (78)

  8. 8

    🇦🇷Chaco, Argentina

    4,500 sq mi12k km² matching 80% or better

    For example Resistencia (79), General José de San Martín (78)

  9. 9

    🇱🇦Oudômxai, Laos

    4,200 sq mi11k km² matching 80% or better

    For example Muang Xay (80)

  10. 10

    🇲🇿Zambezia Province, Mozambique

    4,000 sq mi10k km² matching 80% or better

    For example Milange (86), Gurúè (79)

  11. 11

    🇱🇦Xiangkhoang, Laos

    3,900 sq mi10k km² matching 80% or better

    For example Muang Phônsavan (80)

  12. 12

    🇵🇪Junin, Peru

    3,800 sq mi9,900 km² matching 80% or better

    For example San Martín de Pangoa (76), Satipo (76)

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

    🇧🇷PirajuSão Paulo, Brazil

    Zone 11a · No frost · 4 chill hours · 49 in rainZone 11a · No frost · 4 chill hours · 1,245 mm rain

    Shares 22 of your 23 great-fit crops

    99%match
  2. 2

    🇧🇷Lençóis PaulistaSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 47 in rainZone 11a · No frost · 0 chill hours · 1,185 mm rain

    Shares 23 of your 23 great-fit crops

    99%match
  3. 3

    🇧🇷JaúSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 49 in rainZone 11a · No frost · 0 chill hours · 1,243 mm rain

    Shares 22 of your 23 great-fit crops

    99%match
  4. 4

    🇧🇷AvaréSão Paulo, Brazil

    Zone 11a · No frost · 8 chill hours · 49 in rainZone 11a · No frost · 8 chill hours · 1,233 mm rain

    Shares 22 of your 23 great-fit crops · Also great there: wine grapes

    99%match
  5. 5

    🇧🇷TaquaritubaSão Paulo, Brazil

    Zone 11a · No frost · 7 chill hours · 47 in rainZone 11a · No frost · 7 chill hours · 1,202 mm rain

    Shares 22 of your 23 great-fit crops · Also great there: wine grapes

    99%match
  6. 6

    🇧🇷OurinhosSão Paulo, Brazil

    Zone 11a · No frost · 4 chill hours · 50 in rainZone 11a · No frost · 4 chill hours · 1,275 mm rain

    Shares 23 of your 23 great-fit crops

    99%match
  7. 7

    🇧🇷AngatubaSão Paulo, Brazil

    Zone 11a · No frost · 5 chill hours · 47 in rainZone 11a · No frost · 5 chill hours · 1,187 mm rain

    Shares 22 of your 23 great-fit crops · Also great there: wine grapes

    99%match
  8. 8

    🇧🇷CampinasSão Paulo, Brazil

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

    Shares 23 of your 23 great-fit crops

    99%match
  9. 9

    🇧🇷BotucatuSão Paulo, Brazil

    Zone 11a · No frost · 13 chill hours · 51 in rainZone 11a · No frost · 13 chill hours · 1,306 mm rain

    Shares 21 of your 23 great-fit crops · Also great there: tea

    99%match
  10. 10

    🇧🇷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 23 great-fit crops

    99%match
  11. 11

    🇧🇷TatuíSão Paulo, Brazil

    Zone 11a · No frost · 4 chill hours · 46 in rainZone 11a · No frost · 4 chill hours · 1,158 mm rain

    Shares 22 of your 23 great-fit crops · Also great there: wine grapes

    99%match
  12. 12

    🇧🇷SaltoSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 47 in rainZone 11a · No frost · 0 chill hours · 1,205 mm rain

    Shares 22 of your 23 great-fit crops

    99%match
  13. 13

    🇧🇷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 23 great-fit crops

    99%match
  14. 14

    🇧🇷Cornélio ProcópioParaná, Brazil

    Zone 11a · No frost · 4 chill hours · 49 in rainZone 11a · No frost · 4 chill hours · 1,245 mm rain

    Shares 22 of your 23 great-fit crops

    99%match
  15. 15

    🇧🇷SocorroSão Paulo, Brazil

    Zone 11a · No frost · 17 chill hours · 52 in rainZone 11a · No frost · 17 chill hours · 1,333 mm rain

    Shares 21 of your 23 great-fit crops · Also great there: tea

    99%match
  16. 16

    🇧🇷Pouso AlegreMinas Gerais, Brazil

    Zone 10b · No frost · 31 chill hours · 59 in rainZone 10b · No frost · 31 chill hours · 1,502 mm rain

    Shares 21 of your 23 great-fit crops · Also great there: tea

    98%match
  17. 17

    🇧🇷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 23 great-fit crops

    98%match
  18. 18

    🇧🇷São João da Boa VistaSão Paulo, Brazil

    Zone 11a · No frost · 5 chill hours · 56 in rainZone 11a · No frost · 5 chill hours · 1,420 mm rain

    Shares 23 of your 23 great-fit crops · Also great there: tea

    98%match
  19. 19

    🇧🇷Siqueira CamposParaná, Brazil

    Zone 11a · No frost · 10 chill hours · 47 in rainZone 11a · No frost · 10 chill hours · 1,194 mm rain

    Shares 22 of your 23 great-fit crops · Also great there: wine grapes

    98%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 23 of your 23 great-fit crops

    98%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 Marília: 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.