Climatch

Land with a climate like Cândido Mota

🇧🇷Cândido Mota, São Paulo, Brazil

The towns with the closest growing climate to Cândido Mota are Santa Cruz do Rio Pardo, Brazil (99% match), Rancharia and Piraju. About 567,000 sq mi of land in 18 countries matches 80% or better, most of it in Mato Grosso do Sul, Brazil, São Paulo, Brazil and Minas Gerais, Brazil.The towns with the closest growing climate to Cândido Mota are Santa Cruz do Rio Pardo, Brazil (99% match), Rancharia and Piraju. About 1.5 million km² of land in 18 countries matches 80% or better, most of it in Mato Grosso do Sul, Brazil, São Paulo, Brazil and Minas Gerais, Brazil.

Your growing climate

Zone 11a · No frost · 1 chill hours · 46 in rainZone 11a · No frost · 1 chill hours · 1,166 mm rain

What grows in Cândido Mota →

Regions with the most land like yours

  1. 1

    🇧🇷Mato Grosso do Sul, Brazil

    87k sq mi226k km² matching 80% or better

    For example Nova Andradina (97), Bataguassu (96)

  2. 2

    🇧🇷São Paulo, Brazil

    79k sq mi204k km² matching 80% or better

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

  3. 3

    🇧🇷Minas Gerais, Brazil

    67k sq mi173k km² matching 80% or better

    For example Pouso Alegre (93), Borda da Mata (92)

  4. 4

    🇦🇷Chaco, Argentina

    15k sq mi39k km² matching 80% or better

    For example General José de San Martín (84), Resistencia (83)

  5. 5

    🇺🇸Florida, United States

    13k sq mi34k km² matching 80% or better

    For example Lake Wales (84), Haines City (84)

  6. 6

    🇦🇷Corrientes, Argentina

    13k sq mi34k km² matching 80% or better

    For example Ituzaingó (83), San Lorenzo (83)

  7. 7

    🇨🇳Yunnan, China

    10,000 sq mi26k km² matching 80% or better

    For example Mengla (85), Mengman (84)

  8. 8

    🇦🇷Misiones, Argentina

    9,900 sq mi26k km² matching 80% or better

    For example Puerto Iguazú (85), Jardín América (84)

  9. 9

    🇿🇦KwaZulu-Natal, South Africa

    9,400 sq mi24k km² matching 80% or better

    For example Empangeni (90), Mandeni (88)

  10. 10

    🇵🇾Concepción, Paraguay

    7,400 sq mi19k km² matching 80% or better

    For example Yby Yaú (89), Concepción (79)

  11. 11

    🇵🇾Caaguazú Department, Paraguay

    6,800 sq mi18k km² matching 80% or better

    For example Caaguazú (87), Doctor Juan Eulogio Estigarribia (86)

  12. 12

    🇲🇿Zambezia Province, Mozambique

    5,500 sq mi14k km² matching 80% or better

    For example Milange (84), Gurúè (82)

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

    🇧🇷Santa Cruz do Rio PardoSão Paulo, Brazil

    Zone 11a · No frost · 4 chill hours · 53 in rainZone 11a · No frost · 4 chill hours · 1,358 mm rain

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

    99%match
  2. 2

    🇧🇷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

    99%match
  3. 3

    🇧🇷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
  4. 4

    🇧🇷TupãSão Paulo, Brazil

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

    Shares 23 of your 23 great-fit crops

    99%match
  5. 5

    🇧🇷Terra RicaParaná, Brazil

    Zone 11a · No frost · 0 chill hours · 47 in rainZone 11a · No frost · 0 chill hours · 1,183 mm rain

    Shares 23 of your 23 great-fit crops

    99%match
  6. 6

    🇧🇷LinsSão Paulo, Brazil

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

    Shares 23 of your 23 great-fit crops

    99%match
  7. 7

    🇧🇷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
  8. 8

    🇧🇷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
  9. 9

    🇧🇷ColoradoParaná, Brazil

    Zone 11a · No frost · 0 chill hours · 49 in rainZone 11a · No frost · 0 chill hours · 1,253 mm rain

    Shares 23 of your 23 great-fit crops

    99%match
  10. 10

    🇧🇷LondrinaParaná, Brazil

    Zone 11a · No frost · 3 chill hours · 59 in rainZone 11a · No frost · 3 chill hours · 1,500 mm rain

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

    99%match
  11. 11

    🇧🇷LoandaParaná, Brazil

    Zone 11a · No frost · 0 chill hours · 49 in rainZone 11a · No frost · 0 chill hours · 1,232 mm rain

    Shares 23 of your 23 great-fit crops

    99%match
  12. 12

    🇧🇷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
  13. 13

    🇧🇷Mirante do ParanapanemaSão Paulo, Brazil

    Zone 11b · No frost · 0 chill hours · 45 in rainZone 11b · No frost · 0 chill hours · 1,153 mm rain

    Shares 22 of your 23 great-fit crops

    99%match
  14. 14

    🇧🇷GarçaSão Paulo, Brazil

    Zone 11a · No frost · 4 chill hours · 49 in rainZone 11a · No frost · 4 chill hours · 1,256 mm rain

    Shares 23 of your 23 great-fit crops

    99%match
  15. 15

    🇧🇷BauruSão Paulo, Brazil

    Zone 11b · No frost · 0 chill hours · 45 in rainZone 11b · No frost · 0 chill hours · 1,143 mm rain

    Shares 22 of your 23 great-fit crops

    99%match
  16. 16

    🇧🇷DracenaSão Paulo, Brazil

    Zone 11b · No frost · 0 chill hours · 47 in rainZone 11b · No frost · 0 chill hours · 1,201 mm rain

    Shares 22 of your 23 great-fit crops

    98%match
  17. 17

    🇧🇷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 23 great-fit crops

    98%match
  18. 18

    🇧🇷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
  19. 19

    🇧🇷ParanavaíParaná, Brazil

    Zone 11a · No frost · 4 chill hours · 48 in rainZone 11a · No frost · 4 chill hours · 1,219 mm rain

    Shares 22 of your 23 great-fit crops

    97%match
  20. 20

    🇧🇷SumaréSão Paulo, Brazil

    Zone 11a · No frost · 0 chill hours · 48 in rainZone 11a · No frost · 0 chill hours · 1,211 mm rain

    Shares 22 of your 23 great-fit crops

    97%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 Cândido Mota: 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.