Climatch

Land with a climate like Sundakkāmpālaiyam

🇮🇳Sundakkāmpālaiyam, Tamil Nadu, India

The towns with the closest growing climate to Sundakkāmpālaiyam are Mettupalayam, India (99% match), Dharmapuri and Mettur. About 152,000 sq mi of land in 10 countries matches 80% or better, most of it in Tamil Nadu, India, Maranhão, Brazil and Anzoátegui, Venezuela.The towns with the closest growing climate to Sundakkāmpālaiyam are Mettupalayam, India (99% match), Dharmapuri and Mettur. About 393,000 km² of land in 10 countries matches 80% or better, most of it in Tamil Nadu, India, Maranhão, Brazil and Anzoátegui, Venezuela.

Your growing climate

Zone 12b · No frost · 0 chill hours · 34 in rainZone 12b · No frost · 0 chill hours · 863 mm rain

What grows in Sundakkāmpālaiyam →

Regions with the most land like yours

  1. 1

    🇮🇳Tamil Nadu, India

    34k sq mi89k km² matching 80% or better

    For example Mettupalayam (99), Kāramadai (99)

  2. 2

    🇧🇷Maranhão, Brazil

    16k sq mi42k km² matching 80% or better

    For example Estreito (83), Santa Quitéria do Maranhão (83)

  3. 3

    🇻🇪Anzoátegui, Venezuela

    14k sq mi35k km² matching 80% or better

    For example Puerto Píritu (87), Lecherías (87)

  4. 4

    🇧🇷Piauí, Brazil

    11k sq mi28k km² matching 80% or better

    For example Batalha (83), Luzilândia (83)

  5. 5

    🇮🇳Andhra Pradesh, India

    9,300 sq mi24k km² matching 80% or better

    For example Chittoor (92), Pākāla (92)

  6. 6

    🇻🇪Zulia, Venezuela

    5,000 sq mi13k km² matching 80% or better

    For example La Concepción (82), Los Puertos de Altagracia (81)

  7. 7

    🇻🇪Falcón, Venezuela

    4,400 sq mi11k km² matching 80% or better

    For example Mene de Mauroa (87), Dabajuro (85)

  8. 8

    🇨🇴Magdalena Department, Colombia

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

    For example Fundación (83), Nueva Granada (82)

  9. 9

    🇨🇴La Guajira Department, Colombia

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

    For example Hatonuevo (88), San Juan del Cesar (88)

  10. 10

    🇨🇴Cesar Department, Colombia

    3,500 sq mi9,200 km² matching 80% or better

    For example Valledupar (88), Agustín Codazzi (86)

  11. 11

    🇮🇳Karnataka, India

    3,200 sq mi8,300 km² matching 80% or better

    For example Nanjangūd (84), Kollegāl (84)

  12. 12

    🇺🇬Western Region, Uganda

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

    For example Masindi (63), Kigumba (59)

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

    🇮🇳MettupalayamTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 37 in rainZone 12b · No frost · 0 chill hours · 940 mm rain

    Shares 16 of your 17 great-fit crops

    99%match
  2. 2

    🇮🇳DharmapuriTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 39 in rainZone 12b · No frost · 0 chill hours · 982 mm rain

    Shares 15 of your 17 great-fit crops

    98%match
  3. 3

    🇮🇳MetturTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 38 in rainZone 12b · No frost · 0 chill hours · 977 mm rain

    Shares 15 of your 17 great-fit crops

    97%match
  4. 4

    🇮🇳KrishnagiriTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 35 in rainZone 12b · No frost · 0 chill hours · 883 mm rain

    Shares 15 of your 17 great-fit crops

    96%match
  5. 5

    🇮🇳ChinnamanūrTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 44 in rainZone 13a · No frost · 0 chill hours · 1,106 mm rain

    Shares 16 of your 17 great-fit crops · Also great there: banana

    95%match
  6. 6

    🇮🇳ErodeTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 33 in rainZone 13a · No frost · 0 chill hours · 844 mm rain

    Shares 15 of your 17 great-fit crops

    95%match
  7. 7

    🇮🇳SalemTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 41 in rainZone 12b · No frost · 0 chill hours · 1,048 mm rain

    Shares 15 of your 17 great-fit crops

    94%match
  8. 8

    🇮🇳AmburTamil Nadu, India

    Zone 12a · No frost · 0 chill hours · 37 in rainZone 12a · No frost · 0 chill hours · 943 mm rain

    Shares 15 of your 17 great-fit crops

    94%match
  9. 9

    🇮🇳NāmakkalTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 35 in rainZone 13a · No frost · 0 chill hours · 887 mm rain

    Shares 15 of your 17 great-fit crops

    93%match
  10. 10

    🇮🇳AtturTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 42 in rainZone 12b · No frost · 0 chill hours · 1,062 mm rain

    Shares 15 of your 17 great-fit crops

    93%match
  11. 11

    🇮🇳DindigulTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 37 in rainZone 13a · No frost · 0 chill hours · 941 mm rain

    Shares 15 of your 17 great-fit crops

    93%match
  12. 12

    🇻🇪CaroraLara, Venezuela

    Zone 13a · No frost · 0 chill hours · 31 in rainZone 13a · No frost · 0 chill hours · 781 mm rain

    Shares 16 of your 17 great-fit crops

    92%match
  13. 13

    🇮🇳VelloreTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 42 in rainZone 12b · No frost · 0 chill hours · 1,070 mm rain

    Shares 15 of your 17 great-fit crops

    91%match
  14. 14

    🇮🇳UsilampattiTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 37 in rainZone 13a · No frost · 0 chill hours · 950 mm rain

    Shares 15 of your 17 great-fit crops

    90%match
  15. 15

    🇮🇳TiruvannamalaiTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 47 in rainZone 12b · No frost · 0 chill hours · 1,204 mm rain

    Shares 15 of your 17 great-fit crops

    90%match
  16. 16

    🇮🇳PīleruAndhra Pradesh, India

    Zone 12a · No frost · 0 chill hours · 30 in rainZone 12a · No frost · 0 chill hours · 768 mm rain

    Shares 15 of your 17 great-fit crops · Also great there: wheat

    90%match
  17. 17

    🇮🇳PalmanerAndhra Pradesh, India

    Zone 12a · No frost · 0 chill hours · 33 in rainZone 12a · No frost · 0 chill hours · 836 mm rain

    Shares 16 of your 17 great-fit crops · Also great there: wheat, canola (rapeseed)

    90%match
  18. 18

    🇨🇴San Juan del CesarLa Guajira Department, Colombia

    Zone 13a · No frost · 0 chill hours · 40 in rainZone 13a · No frost · 0 chill hours · 1,012 mm rain

    Shares 14 of your 17 great-fit crops

    88%match
  19. 19

    🇨🇴ValleduparCesar Department, Colombia

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

    Shares 14 of your 17 great-fit crops

    88%match
  20. 20

    🇻🇪Mene de MauroaFalcón, Venezuela

    Zone 13a · No frost · 0 chill hours · 35 in rainZone 13a · No frost · 0 chill hours · 891 mm rain

    Shares 15 of your 17 great-fit crops

    87%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 Sundakkāmpālaiyam: 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.