Climatch

Land with a climate like Palāsa

🇮🇳Palāsa, Andhra Pradesh, India

The towns with the closest growing climate to Palāsa are Srikakulam, India (99% match), Hinjilicut and Vizianagaram. About 168,000 sq mi of land in 14 countries matches 80% or better, most of it in Andhra Pradesh, India, Odisha, India and East Nusa Tenggara, Indonesia.The towns with the closest growing climate to Palāsa are Srikakulam, India (99% match), Hinjilicut and Vizianagaram. About 436,000 km² of land in 14 countries matches 80% or better, most of it in Andhra Pradesh, India, Odisha, India and East Nusa Tenggara, Indonesia.

Your growing climate

Zone 12a · No frost · 0 chill hours · 56 in rainZone 12a · No frost · 0 chill hours · 1,429 mm rain

What grows in Palāsa →

Regions with the most land like yours

  1. 1

    🇮🇳Andhra Pradesh, India

    22k sq mi56k km² matching 80% or better

    For example Srikakulam (99), Nellimarla (99)

  2. 2

    🇮🇳Odisha, India

    20k sq mi51k km² matching 80% or better

    For example Chhatrapur (99), Purushottampur (99)

  3. 3

    🇮🇩East Nusa Tenggara, Indonesia

    15k sq mi40k km² matching 80% or better

    For example Waingapu (85), Maumere (83)

  4. 4

    🇮🇳West Bengal, India

    10,000 sq mi26k km² matching 80% or better

    For example Kakdwip (90), Haldia (89)

  5. 5

    🇹🇿Lindi, Tanzania

    6,500 sq mi17k km² matching 80% or better

    For example Kilwa Masoko (89), Lindi (89)

  6. 6

    🇹🇿Morogoro, Tanzania

    5,000 sq mi13k km² matching 80% or better

    For example Ifakara (85), Kidodi (76)

  7. 7

    🇹🇿Pwani, Tanzania

    3,800 sq mi10,000 km² matching 80% or better

    For example Ikwiriri (84), Kibiti (79)

  8. 8

    🇲🇿Cabo Delgado Province, Mozambique

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

    For example Mocímboa da Praia (86), Mocímboa (85)

  9. 9

    🇹🇭Sukhothai, Thailand

    2,900 sq mi7,400 km² matching 80% or better

    For example Sukhothai (84), Sawankhalok (84)

  10. 10

    🇮🇩West Nusa Tenggara, Indonesia

    2,700 sq mi6,900 km² matching 80% or better

    For example Bima (83), Taliwang (80)

  11. 11

    🇧🇩Khulna Division, Bangladesh

    2,200 sq mi5,700 km² matching 80% or better

    For example Sātkhira (82), Kesabpur (79)

  12. 12

    🇹🇭Kamphaeng Phet, Thailand

    2,000 sq mi5,200 km² matching 80% or better

    For example Khanu Woralaksaburi (80), Kamphaeng Phet (80)

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

    🇮🇳SrikakulamAndhra Pradesh, India

    Zone 12a · No frost · 0 chill hours · 46 in rainZone 12a · No frost · 0 chill hours · 1,179 mm rain

    Shares 15 of your 18 great-fit crops

    99%match
  2. 2

    🇮🇳HinjilicutOdisha, India

    Zone 11b · No frost · 0 chill hours · 62 in rainZone 11b · No frost · 0 chill hours · 1,584 mm rain

    Shares 17 of your 18 great-fit crops

    99%match
  3. 3

    🇮🇳VizianagaramAndhra Pradesh, India

    Zone 12a · No frost · 0 chill hours · 50 in rainZone 12a · No frost · 0 chill hours · 1,271 mm rain

    Shares 16 of your 18 great-fit crops

    99%match
  4. 4

    🇮🇳RasapūdipalemAndhra Pradesh, India

    Zone 12b · No frost · 0 chill hours · 46 in rainZone 12b · No frost · 0 chill hours · 1,171 mm rain

    Shares 16 of your 18 great-fit crops

    99%match
  5. 5

    🇮🇳BālugānOdisha, India

    Zone 12a · No frost · 0 chill hours · 63 in rainZone 12a · No frost · 0 chill hours · 1,593 mm rain

    Shares 17 of your 18 great-fit crops

    99%match
  6. 6

    🇮🇳Parādīp GarhOdisha, India

    Zone 11b · No frost · 0 chill hours · 62 in rainZone 11b · No frost · 0 chill hours · 1,586 mm rain

    Shares 17 of your 18 great-fit crops

    98%match
  7. 7

    🇮🇳PuriOdisha, India

    Zone 12a · No frost · 0 chill hours · 63 in rainZone 12a · No frost · 0 chill hours · 1,591 mm rain

    Shares 16 of your 18 great-fit crops

    97%match
  8. 8

    🇮🇳TuniAndhra Pradesh, India

    Zone 12b · No frost · 0 chill hours · 48 in rainZone 12b · No frost · 0 chill hours · 1,217 mm rain

    Shares 15 of your 18 great-fit crops

    97%match
  9. 9

    🇮🇳BobbiliAndhra Pradesh, India

    Zone 12a · No frost · 0 chill hours · 56 in rainZone 12a · No frost · 0 chill hours · 1,417 mm rain

    Shares 17 of your 18 great-fit crops

    97%match
  10. 10

    🇮🇳ChodavaramAndhra Pradesh, India

    Zone 12a · No frost · 0 chill hours · 58 in rainZone 12a · No frost · 0 chill hours · 1,468 mm rain

    Shares 16 of your 18 great-fit crops

    96%match
  11. 11

    🇮🇳JataniOdisha, India

    Zone 11b · No frost · 0 chill hours · 68 in rainZone 11b · No frost · 0 chill hours · 1,721 mm rain

    Shares 16 of your 18 great-fit crops

    95%match
  12. 12

    🇮🇳BhubaneswarOdisha, India

    Zone 11b · No frost · 0 chill hours · 69 in rainZone 11b · No frost · 0 chill hours · 1,752 mm rain

    Shares 16 of your 18 great-fit crops

    94%match
  13. 13

    🇮🇳KākinādaAndhra Pradesh, India

    Zone 12b · No frost · 0 chill hours · 46 in rainZone 12b · No frost · 0 chill hours · 1,157 mm rain

    Shares 15 of your 18 great-fit crops

    94%match
  14. 14

    🇮🇳BhanjanagarOdisha, India

    Zone 11b · No frost · 0 chill hours · 61 in rainZone 11b · No frost · 0 chill hours · 1,543 mm rain

    Shares 16 of your 18 great-fit crops · Also great there: canola (rapeseed)

    92%match
  15. 15

    🇹🇿Kilwa Masoko↻Seasons flipped. Lindi, Tanzania

    Zone 13a · No frost · 0 chill hours · 33 in rainZone 13a · No frost · 0 chill hours · 842 mm rain

    Shares 15 of your 18 great-fit crops

    89%match
  16. 16

    🇮🇳BālugaonOdisha, India

    Zone 11b · No frost · 0 chill hours · 61 in rainZone 11b · No frost · 0 chill hours · 1,548 mm rain

    Shares 16 of your 18 great-fit crops

    89%match
  17. 17

    🇹🇿Mtwara↻Seasons flipped. Tanzania

    Zone 12b · No frost · 0 chill hours · 38 in rainZone 12b · No frost · 0 chill hours · 970 mm rain

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

    89%match
  18. 18

    🇮🇳HaldiaWest Bengal, India

    Zone 11b · No frost · 0 chill hours · 68 in rainZone 11b · No frost · 0 chill hours · 1,735 mm rain

    Shares 17 of your 18 great-fit crops · Also great there: canola (rapeseed)

    89%match
  19. 19

    🇹🇿Lindi↻Seasons flipped. Tanzania

    Zone 12b · No frost · 0 chill hours · 34 in rainZone 12b · No frost · 0 chill hours · 876 mm rain

    Shares 16 of your 18 great-fit crops · Also great there: table and raisin grapes

    89%match
  20. 20

    🇮🇳ContaiWest Bengal, India

    Zone 11b · No frost · 0 chill hours · 67 in rainZone 11b · No frost · 0 chill hours · 1,694 mm rain

    Shares 17 of your 18 great-fit crops · Also great there: canola (rapeseed)

    88%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 Palāsa: 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.