Climate-smart agriculture: Leveraging weekly rainfall trends for rainfed crops in West Bengal

TRISHA MANNA; ABIRA   BANERJEE; JOYDEEP  MUKHERJEE

doi:10.56093/ijas.v96i8.172590

Authors

TRISHA MANNA The Neotia University, Sarisha, 24 Parganas (South), West Bengal, India
ABIRA BANERJEE Polba Mahavidyalaya, Polba, Hooghly, West Bengal, India
JOYDEEP MUKHERJEE ICAR-Indian Agricultural Research Institute, New Delhi, 110 012, India

https://doi.org/10.56093/ijas.v96i8.172590

Keywords:

Conditional probability, Initial probability, Markov chain model, Rainfall probability, Wet and dry spell

Abstract

Rainfall variability has a significant impact on rainfed agriculture, crop planning and cropping patterns in diverse agro-climatic zones. Considering this important aspect of agriculture the present study, was carried out to analyse previously available meteorological data and find out different weather trends regarding rainfall and determine suitable time periods for agricultural activities in the study area. The study used incomplete gamma probability, initial and conditional probability and consecutive probability of weekly rainfall across Baharampur, Midnapore, Darjeeling, Sagar Island and Sriniketan regions of West Bengal. Conditional probability analysis was helpful for identifying the transition period from dry spell to wet spell and vice versa while consecutive probability analysis helped in identifying sustained rainfall or dry period. The study revealed that Baharampur had high probability of dry spell and limited sowing opportunities, while Midnapore recorded early and extended wet spell suitable for rainfed agriculture starting from 13^th standard meteorological week (SMW). Darjeeling experienced concentrated monsoon from 22^nd–40^th SMW and Sagar Island had wet spell from 23^rd–39^th SMW followed by long dry spell whereas Sriniketan depicted a widespread monsoon week starting from 16^th SMW to 43^rd SMW. Thus, the findings could help in planing crop cycle adjustment as well as contigency plans for a climate resilent agriculture.

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