Climatch

Land with a climate like Āyikudi

🇮🇳Āyikudi, Tamil Nadu, India

The towns with the closest growing climate to Āyikudi are Pānakkudi, India (97% match), Allinagaram and Aruppukkottai. About 61,000 sq mi of land in 8 countries matches 80% or better, most of it in Tamil Nadu, India, Zulia, Venezuela and Surat Thani, Thailand.The towns with the closest growing climate to Āyikudi are Pānakkudi, India (97% match), Allinagaram and Aruppukkottai. About 157,000 km² of land in 8 countries matches 80% or better, most of it in Tamil Nadu, India, Zulia, Venezuela and Surat Thani, Thailand.

Your growing climate

Zone 13a · No frost · 0 chill hours · 52 in rainZone 13a · No frost · 0 chill hours · 1,317 mm rain

What grows in Āyikudi →

Regions with the most land like yours

  1. 1

    🇮🇳Tamil Nadu, India

    21k sq mi55k km² matching 80% or better

    For example Pānakkudi (97), Chinnamanūr (97)

  2. 2

    🇻🇪Zulia, Venezuela

    5,900 sq mi15k km² matching 80% or better

    For example Pueblo Nuevo El Chivo (86), San Carlos del Zulia (84)

  3. 3

    🇹🇭Surat Thani, Thailand

    4,700 sq mi12k km² matching 80% or better

    For example Ko Samui (89), Surat Thani (84)

  4. 4

    🇹🇭Nakhon Si Thammarat, Thailand

    3,300 sq mi8,400 km² matching 80% or better

    For example Thung Song (85), Nakhon Si Thammarat (83)

  5. 5

    🇹🇭Songkhla, Thailand

    3,000 sq mi7,700 km² matching 80% or better

    For example Hat Yai (88), Ranot (86)

  6. 6

    🇨🇴Tolima Department, Colombia

    2,100 sq mi5,400 km² matching 80% or better

    For example Honda (87), Purificación (85)

  7. 7

    🇱🇰North Western Province, Sri Lanka

    2,100 sq mi5,400 km² matching 80% or better

    For example Chilaw (84), Kurunegala (83)

  8. 8

    🇮🇩Central Sulawesi, Indonesia

    1,500 sq mi3,900 km² matching 80% or better

    For example Palu (74), Luwuk (64)

  9. 9

    🇻🇪Mérida, Venezuela

    1,500 sq mi3,800 km² matching 80% or better

    For example Tucaní (85), El Vigía (83)

  10. 10

    🇮🇩Southeast Sulawesi, Indonesia

    900 sq mi2,300 km² matching 80% or better

    For example Kendari (75), Katobu (71)

  11. 11

    🇱🇰Southern Province, Sri Lanka

    900 sq mi2,300 km² matching 80% or better

    For example Hambantota (84), Ambalantota (83)

  12. 12

    🇻🇪Trujillo, Venezuela

    900 sq mi2,300 km² matching 80% or better

    For example Pampanito (83), El Dividive (83)

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

    🇮🇳PānakkudiTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 51 in rainZone 13a · No frost · 0 chill hours · 1,300 mm rain

    Shares 17 of your 18 great-fit crops

    97%match
  2. 2

    🇮🇳AllinagaramTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 35 in rainZone 13a · No frost · 0 chill hours · 889 mm rain

    Shares 15 of your 18 great-fit crops

    93%match
  3. 3

    🇮🇳AruppukkottaiTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 39 in rainZone 13a · No frost · 0 chill hours · 989 mm rain

    Shares 14 of your 18 great-fit crops

    90%match
  4. 4

    🇮🇳VadipattiTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 40 in rainZone 13a · No frost · 0 chill hours · 1,018 mm rain

    Shares 16 of your 18 great-fit crops

    90%match
  5. 5

    🇹🇭Ko SamuiSurat Thani, Thailand

    Zone 13b · No frost · 0 chill hours · 67 in rainZone 13b · No frost · 0 chill hours · 1,712 mm rain

    Shares 17 of your 18 great-fit crops · Also great there: tea

    89%match
  6. 6

    🇮🇳ParamagudiTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 41 in rainZone 13a · No frost · 0 chill hours · 1,031 mm rain

    Shares 15 of your 18 great-fit crops

    89%match
  7. 7

    🇹🇭Hat YaiSongkhla, Thailand

    Zone 13b · No frost · 0 chill hours · 74 in rainZone 13b · No frost · 0 chill hours · 1,879 mm rain

    Shares 16 of your 18 great-fit crops · Also great there: tea

    88%match
  8. 8

    🇮🇳MaduraiTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 42 in rainZone 13a · No frost · 0 chill hours · 1,062 mm rain

    Shares 16 of your 18 great-fit crops

    87%match
  9. 9

    🇻🇪HigueroteMiranda, Venezuela

    Zone 13a · No frost · 0 chill hours · 47 in rainZone 13a · No frost · 0 chill hours · 1,192 mm rain

    Shares 16 of your 18 great-fit crops

    87%match
  10. 10

    🇮🇳DevakottaiTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 44 in rainZone 13a · No frost · 0 chill hours · 1,124 mm rain

    Shares 15 of your 18 great-fit crops

    87%match
  11. 11

    🇮🇳KāraikkudiTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 44 in rainZone 13a · No frost · 0 chill hours · 1,116 mm rain

    Shares 15 of your 18 great-fit crops

    86%match
  12. 12

    🇹🇭YalaThailand

    Zone 13b · No frost · 0 chill hours · 77 in rainZone 13b · No frost · 0 chill hours · 1,966 mm rain

    Shares 16 of your 18 great-fit crops · Also great there: tea

    86%match
  13. 13

    🇹🇭PhatthalungThailand

    Zone 13b · No frost · 0 chill hours · 88 in rainZone 13b · No frost · 0 chill hours · 2,243 mm rain

    Shares 16 of your 18 great-fit crops

    85%match
  14. 14

    🇹🇭Thung SongNakhon Si Thammarat, Thailand

    Zone 13b · No frost · 0 chill hours · 78 in rainZone 13b · No frost · 0 chill hours · 1,975 mm rain

    Shares 16 of your 18 great-fit crops · Also great there: tea

    85%match
  15. 15

    🇻🇪San Carlos del ZuliaZulia, Venezuela

    Zone 13b · No frost · 0 chill hours · 57 in rainZone 13b · No frost · 0 chill hours · 1,449 mm rain

    Shares 16 of your 18 great-fit crops

    84%match
  16. 16

    🇮🇳ThanjavurTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 43 in rainZone 13a · No frost · 0 chill hours · 1,091 mm rain

    Shares 15 of your 18 great-fit crops

    84%match
  17. 17

    🇱🇰ChilawNorth Western Province, Sri Lanka

    Zone 13a · No frost · 0 chill hours · 66 in rainZone 13a · No frost · 0 chill hours · 1,685 mm rain

    Shares 16 of your 18 great-fit crops · Also great there: tea

    84%match
  18. 18

    🇮🇳PattukkottaiTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 51 in rainZone 13a · No frost · 0 chill hours · 1,297 mm rain

    Shares 17 of your 18 great-fit crops

    84%match
  19. 19

    🇨🇴La DoradaCaldas Department, Colombia

    Zone 13b · No frost · 0 chill hours · 81 in rainZone 13b · No frost · 0 chill hours · 2,068 mm rain

    Shares 15 of your 18 great-fit crops

    84%match
  20. 20

    🇮🇳VedaraniyamTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 66 in rainZone 13a · No frost · 0 chill hours · 1,669 mm rain

    Shares 16 of your 18 great-fit crops

    84%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 Āyikudi: 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.