Climatch

Land with a climate like Avalēpalli

🇮🇳Avalēpalli, Tamil Nadu, India

The towns with the closest growing climate to Avalēpalli are Chik Ballāpur, India (99% match), Nanjangūd and Mysuru. About 177,000 sq mi of land in 15 countries matches 80% or better, most of it in Karnataka, India, Maranhão, Brazil and Tamil Nadu, India.The towns with the closest growing climate to Avalēpalli are Chik Ballāpur, India (99% match), Nanjangūd and Mysuru. About 459,000 km² of land in 15 countries matches 80% or better, most of it in Karnataka, India, Maranhão, Brazil and Tamil Nadu, India.

Your growing climate

Zone 12a · No frost · 0 chill hours · 34 in rainZone 12a · No frost · 0 chill hours · 853 mm rain

What grows in Avalēpalli →

Regions with the most land like yours

  1. 1

    🇮🇳Karnataka, India

    33k sq mi84k km² matching 80% or better

    For example Chik Ballāpur (99), Mandya (99)

  2. 2

    🇧🇷Maranhão, Brazil

    31k sq mi81k km² matching 80% or better

    For example São João dos Patos (86), Buriti Bravo (85)

  3. 3

    🇮🇳Tamil Nadu, India

    9,900 sq mi26k km² matching 80% or better

    For example Alangāyam (92), Rakkiyapālaiyam (88)

  4. 4

    🇮🇳Andhra Pradesh, India

    8,400 sq mi22k km² matching 80% or better

    For example Punganūru (98), Palmaner (97)

  5. 5

    🇲🇲Mandalay Region, Myanmar

    7,000 sq mi18k km² matching 80% or better

    For example Yamethin (83), Pyawbwe (80)

  6. 6

    🇧🇷Piauí, Brazil

    5,300 sq mi14k km² matching 80% or better

    For example Água Branca (82), Valença do Piauí (82)

  7. 7

    🇹🇭Chiang Mai, Thailand

    5,100 sq mi13k km² matching 80% or better

    For example San Pa Tong (86), Hang Dong (86)

  8. 8

    🇹🇭Lampang, Thailand

    4,800 sq mi13k km² matching 80% or better

    For example Lampang (85), Thoen (84)

  9. 9

    🇲🇲Magway, Myanmar

    3,600 sq mi9,500 km² matching 80% or better

    For example Taungdwingyi (79), Myaydo (72)

  10. 10

    🇹🇭Tak, Thailand

    3,400 sq mi8,900 km² matching 80% or better

    For example Ban Tak (77), Mae Ramat (72)

  11. 11

    🇲🇲Kayah State, Myanmar

    3,400 sq mi8,800 km² matching 80% or better

    For example Loikaw (82)

  12. 12

    🇹🇿Lindi, Tanzania

    2,600 sq mi6,900 km² matching 80% or better

    For example Nachingwea (86), Ruangwa (84)

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

    🇮🇳Chik BallāpurKarnataka, India

    Zone 11b · No frost · 0 chill hours · 31 in rainZone 11b · No frost · 0 chill hours · 784 mm rain

    Shares 20 of your 20 great-fit crops

    99%match
  2. 2

    🇮🇳NanjangūdKarnataka, India

    Zone 12a · No frost · 0 chill hours · 31 in rainZone 12a · No frost · 0 chill hours · 794 mm rain

    Shares 17 of your 20 great-fit crops

    98%match
  3. 3

    🇮🇳MysuruKarnataka, India

    Zone 12b · No frost · 0 chill hours · 31 in rainZone 12b · No frost · 0 chill hours · 781 mm rain

    Shares 17 of your 20 great-fit crops

    97%match
  4. 4

    🇮🇳ChamrajnagarKarnataka, India

    Zone 12a · No frost · 0 chill hours · 28 in rainZone 12a · No frost · 0 chill hours · 717 mm rain

    Shares 16 of your 20 great-fit crops

    97%match
  5. 5

    🇮🇳MadanapalleAndhra Pradesh, India

    Zone 12a · No frost · 0 chill hours · 27 in rainZone 12a · No frost · 0 chill hours · 692 mm rain

    Shares 17 of your 20 great-fit crops

    96%match
  6. 6

    🇮🇳KoratagereKarnataka, India

    Zone 12a · No frost · 0 chill hours · 29 in rainZone 12a · No frost · 0 chill hours · 740 mm rain

    Shares 17 of your 20 great-fit crops

    96%match
  7. 7

    🇮🇳TumkūrKarnataka, India

    Zone 12a · No frost · 0 chill hours · 31 in rainZone 12a · No frost · 0 chill hours · 786 mm rain

    Shares 20 of your 20 great-fit crops

    95%match
  8. 8

    🇮🇳TiptūrKarnataka, India

    Zone 12a · No frost · 0 chill hours · 24 in rainZone 12a · No frost · 0 chill hours · 615 mm rain

    Shares 17 of your 20 great-fit crops

    94%match
  9. 9

    🇮🇳HassanKarnataka, India

    Zone 12a · No frost · 0 chill hours · 35 in rainZone 12a · No frost · 0 chill hours · 891 mm rain

    Shares 20 of your 20 great-fit crops · Also great there: macadamia, potato

    93%match
  10. 10

    🇮🇳AlangāyamTamil Nadu, India

    Zone 12a · No frost · 0 chill hours · 40 in rainZone 12a · No frost · 0 chill hours · 1,011 mm rain

    Shares 18 of your 20 great-fit crops

    92%match
  11. 11

    🇮🇳TirumalaAndhra Pradesh, India

    Zone 12a · No frost · 0 chill hours · 42 in rainZone 12a · No frost · 0 chill hours · 1,074 mm rain

    Shares 19 of your 20 great-fit crops · Also great there: macadamia, potato

    92%match
  12. 12

    🇮🇳PiriyāpatnaKarnataka, India

    Zone 12a · No frost · 0 chill hours · 46 in rainZone 12a · No frost · 0 chill hours · 1,160 mm rain

    Shares 19 of your 20 great-fit crops · Also great there: macadamia

    92%match
  13. 13

    🇮🇳HindupurAndhra Pradesh, India

    Zone 12a · No frost · 0 chill hours · 27 in rainZone 12a · No frost · 0 chill hours · 687 mm rain

    Shares 15 of your 20 great-fit crops

    91%match
  14. 14

    🇮🇳ShivamoggaKarnataka, India

    Zone 12a · No frost · 0 chill hours · 41 in rainZone 12a · No frost · 0 chill hours · 1,050 mm rain

    Shares 17 of your 20 great-fit crops

    87%match
  15. 15

    🇮🇳SīraKarnataka, India

    Zone 12a · No frost · 0 chill hours · 22 in rainZone 12a · No frost · 0 chill hours · 569 mm rain

    Shares 14 of your 20 great-fit crops

    87%match
  16. 16

    🇮🇳HubballiKarnataka, India

    Zone 12a · No frost · 0 chill hours · 35 in rainZone 12a · No frost · 0 chill hours · 895 mm rain

    Shares 18 of your 20 great-fit crops

    87%match
  17. 17

    🇧🇷São João dos Patos↻Seasons flipped. Maranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 42 in rainZone 12b · No frost · 0 chill hours · 1,078 mm rain

    Shares 16 of your 20 great-fit crops

    86%match
  18. 18

    🇳🇮OcotalNueva Segovia Department, Nicaragua

    Zone 12a · No frost · 0 chill hours · 42 in rainZone 12a · No frost · 0 chill hours · 1,078 mm rain

    Shares 19 of your 20 great-fit crops · Also great there: macadamia

    86%match
  19. 19

    🇬🇹San Luis JilotepequeJalapa, Guatemala

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

    Shares 18 of your 20 great-fit crops · Also great there: macadamia

    86%match
  20. 20

    🇧🇷Buriti Bravo↻Seasons flipped. Maranhão, Brazil

    Zone 12b · No frost · 0 chill hours · 42 in rainZone 12b · No frost · 0 chill hours · 1,069 mm rain

    Shares 15 of your 20 great-fit crops

    85%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 Avalēpalli: 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.