Loading...

Indigenous Technical Knowledge based Rainfall Prediction: A Review

DOI: 10.18805/ag.R-2347    | Article Id: R-2347 | Page : 205-210
Citation :- Indigenous Technical Knowledge based Rainfall Prediction: A Review.Agricultural Reviews.2022.(43):205-210
A. Bheemappa, S.M. Shruthi, K.D. Maheshwari, Nagaratna Biradar shrumaya569@gmail.com
Address : Department of Agricultural Extension Education, College of Agriculture, University of Agricultural Sciences, Dharwad-580 005, Karnataka, India.
Submitted Date : 11-08-2021
Accepted Date : 3-11-2021

Abstract

Indigenous technical knowledge (ITK) is the actual knowledge of a given population that reflects the experiences based on tradition and includes more recent experiences with modern technologies. Traditionally, farmers have used traditional knowledge to understand weather and climate patterns in order to make decisions about crop and irrigation cycles. This knowledge has been gained through many decades of experience and has been passed on from previous generations. The present study was undertaken with the objective of collection and documenting the indigenous technical knowledge of farmers regarding rainfall prediction based on abiotic and biotic factors which is being practiced generation after generation. Here in this paper an effort has been made to collect the abiotic and biotic factors predicting rainfall, as a part of ICAR sponsored NASF ad-hoc research project entitled “Developing climate resilient adaptive strategies for empowerment of farmers” which has been implemented in University of Agricultural Sciences, Dharwad from 2019 to 2022. Various indigenous technical knowledge are collected by analyzing the journals and newsletters, deep interaction with the farmers of study area, contacting the local resource persons and documenting oral histories without scientific validation. The study found that traditional methods of rainfall forecasting can be utilized for the purpose of short-term and long-term seasonal rainfall predictions by local communities. All available abiotic and biotic indigenous rainfall forecasting techniques may serve as alternative to modern technologies.

Keywords

Abiotic factors Biotic factors Climate patterns Indigenous technical knowledge (ITK) Rainfall prediction

References

  1. Anju, R. and Bonny, B.P. (2019) Indigenous knowledge based abiotic indicators used in weather prediction by farmers of Wayanad, Kerala, India. Indian Journal of  Traditional Knowledge. 18(3): 565-572.
  2. Chhabra, V. and Haris, A.A. (2014), Nakshtra based rainfall analysis and its impact on rabi crops yield for Patna, Bihar. Sch. J. Agric. Vet. Sci. 1(4): 168-172.
  3. Chinlampianga, M. (2011), Traditional knowledge, weather prediction and bioindicators: A case study in Mizoram, north eastern India. Indian Journal of Traditional Knowledge. 10(11): 207-211.
  4. Das, H.P., Doblas, R.F.J., Garcia, A., Hansen, J., Mariani, L., Nain, A., Ramesh, K., Rathore, L.S. and Venkataraman, R. Weather and climate forecasts for agriculture. Guide to Agricultural Meteorological Practices (GAMP), (WMO, Geneva, Switzerland) 2010. 103. 
  5. Didal, V.K., Brijbhooshan, Todawat, A. and Choudhary, K., 2017. Weather forecasting in India: A review. Int. J. Curr. Microbiol. App. Sci. 6(11): 577-590. 
  6. Fand, B.B., Kamble, A.L. and Kumar, M. (2012) Will climate change pose serious threat to crop pest management: A critical review. International Journal of Scientific and Research Publications. 2(11): 1-14. Honey bee Network, https://www.honeybee.org/.
  7. Mishra, A., Singh, S.R.K., Raut, A.A. 2020, Traditional knowledge in agriculture, Division of Agricultural Extension, ICAR, New Delhi. 1-52.
  8. Netshiukhwi, G.Z., Stigter, K. and Walker, S. (2013), Use of traditional weather/climate knowledge by farmers in the south- western free state of South Africa: Agro Meteorological Learning by Scientists. 4: 383-410.
  9. Okwibale, P.M., Ansah, P., Boroto, R.J., Nkegbe, A., Deridder, B.S. et al., (2018). Compendium of community and indigenous strategies for climate change adaptation with focus on addressing water scarcity in agriculture. Report submitted to FAO: 1-113.
  10. Prakash, N., Roy, S.S. and Ngachan, S.V. (2012). http:/WWW. kiran. nic.in/pdf/publications/ITK.
  11. Praveen, N., Sreenivasa, R. and Sudha, R.V. (2018). Rationality and validity of ITKS on general agriculture by tribal farmers in Telangana region. Int. J. Curr. Microbiol. App. Sci. 7(11): 34-39.
  12. Rautela and Karki (2015). Weather forecasting: Traditional knowledge of the people of Uttarakhand Himalaya. Journal of Geography, Environment and Earth Science International. 3(3): 1-14.
  13. Ravi Shankar, K. Maraty, P., Murthy, V.R.K. and Ramakrishna, Y.S. (2008). Indigenous Rain Forecasting in Andhra Pradesh. Director, Central Research Institute for Dryland Agriculture, Santoshnagar, Saidabad P.O., Hyderabad.59: 1-75.
  14. Rengalakshmi Raj (2011). Linking traditional and scientific knowledge systems on climate prediction and utilization. M.S. Swaminathan Research Foundation Chennai, India. 1-11. 
  15. Santosh, T.H. and Chhetry, G.K.N. (2012). Agro-biodiversity management related ITKs in North-Eastern India. Journal of Biology, Agriculture and Healthcare. 2(6): 83-93.
  16. Shoko and Shoko (2017). Indigenous weather forecasting systems: A case study of the abiotic weather forecasting indicators for wards 12 and 13 in Mberengwa district Zimbabwe. Asian Social Science. 9(5): 285-297. 
  17. Sivaprakasam, S. and Kanakasabai, V. 2009, Traditional almanac predicted rainfall- A case study. Indian Journal of Technical Knowledge. 8(4): 621-625.
  18. Sumit, S. and Shivani, R., (2021). Indigenous Technical knowledge for sustainable agriculture in India. Agriculture and Food: E-Newsletter. 3(1): 30-36.
  19. Pandey, V., Mittal, R. and Sharma, P. (2017). Documentation and application of indigenous technical knowledge for sustainable agricultural development. Asian J. of Agricultural Extension, Economics and Sociology. 15(3): 1-9.
  20. Wang, G. (1988). Indigenous communication systems in research and development. J. Ext Sys: 75-86.

Global Footprints