TRENDS AND PATTERNS OF DRY SPELLS IN KARNATAKA
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Authors
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ARPITA PANDEY* and SRINIVASA SASDHAR PONNALURU
Department of Economics, Vijayanagara Srikrishnadevaraya University, Bellary - 583 105
Keywords:
Drought, El Niño, Spatial Analysis, SPI, South-West Monsoon.Abstract
Drought affects agricultural growth by its duration and frequency. Karnataka an agrarian-based
economy, experienced frequent intense droughts in the last two decades. This study analyzed the
intensity and duration of dry spells that occurred in Karnataka between 1990 and 2020 in the spatial
context by using SPI. The Standardized Precipitation Index (SPI) helped in identifying four significant
drought incidents in the last three decades. Mann-Kendall Test was used to identify the trend in the
SPI values. The results showed that the droughts in last decade were more intensive compared to
previous two decades. The duration of recent dry spells got overlapped between two different drought
periods. The northern districts of State get more frequent dry spells compared to the southern districts.
References
1. APDES. 2020. Agricultural statistics at a glance:
2. -20. Directorate of Economics and
3. Statistics, Andhra Pradesh.
4. Areef, M and Radha. 2020. Application of
5. GARCH and ANN models for potato price
6. forecasting: A case study of Bangalore
7. market, Karnataka state. Indian Journal of
8. Agricultural Marketing. 34(3): 44- 52.
9. Andhra Prabha. 2021.Chilli exports to London.
10. News written by Prabha News Bureau.
11. Guntur, 8th May 2021. 5. Retrieved from the
12. website (https://epaper.prabhanews.com/c/
13. on 8th May, 2021.
14. Girish, K. J and Kanchan, S. 2013. Agricultural
15. price forecasting using neural network
16. model: an innovative information delivery
17. system. Agricultural Economics Research
18. Review. 26(2): 229-239.
19. Hyndman, R J. 2017. Forecast: Forecasting
20. functions for time series and linear models.
21. R package version 8.1.
22. Jha, G. K., Thulasiraman, P and Thulasiram, R.
23. K. 2009. PSO based neural network for time
24. series forecasting. Proceedings of the
25. International Joint Conference on Neural
26. Networks. Atlanta, USA. pp. 1422-1427.
27. Lakshmi, S. B. R., Devi, B. L and Prasad, S. V.
28. Arrival pattern and price trends of
29. chillies (Dry) in Guntur market of Andhra
30. Pradesh. Indian Journal of Agricultural
31. Marketing. 28(1): 63-69.
32. Rathod, S., Singh, K. N., Patil, S. G.,
33. Ravindrakumar, H. N., Mrinmoy. R and
34. Vikram, S. M. 2018. Modeling and
35. forecasting of oilseed production of India
36. through artificial intelligence techniques.
37. Indian Journal of Agricultural Sciences.
38. (1): 22-27.
39. Srikala. M., Devi, B. I and Ananda, T. 2016. Price
40. behaviour of chillies in Guntur market of
41. Andhra Pradesh, India. Indian Journal of
42. Agricultural Research. 50(5): 471-474.
43. Vijay, N and Mishra, G.C. 2018. Time series
44. forecasting using ARIMA and ANN models
45. for production of pearl millet (Bajra) crop of
46. Karnataka, India. International Journal
47. Current Microbiology and Applied Sciences.
48. (12): 880-889.
49. Zhang, G., Patuwo, B. E and Hu, M. Y. 1998.
50. Forecasting with artificial neural networks:
51. The state of the art. International Journal
52. of Forecasting. 14: 35-62.
53. Dickey, D. A and Fuller, W. A. 1979. Distribution
54. of the estimators for autoregressive time
55. series with a unit root. Journal of American
56. Statistical Association. 74(6): 427–431.
57. Engle, R and Yoo, B. S. 1987. Forecasting and
58. testing in cointegrated system. Journal of
59. Econometrics. 35(1): 143-159.
60. Padhan, P. C. 2012. Application of ARIMA model
61. for forecasting agricultural productivity in
62. India. Journal of Agriculture and Social
63. Sciences. 8(1):50-56.
64. Hamjah, M. A. 2014. Forecasting major fruit crops
65. productions in Bangladesh using Box-
66. Jenkins ARIMA model. Journal of Economics
67. and Sustainable Development. 5(7): 96-107.
68. Rahman, F. N. M., Baten, M. A., Roy, A and
69. Hossain, M. M. 2013. Forecasting of lentil
70. pulse production: An econometric analysis.
71. Australian Journal of Basic and Applied
72. Sciences.7(2): 819-824.
73. Mahapatra, S. K and Dash, A. 2019. Forecasting
74. area and production of mung bean in
75. Odisha using ARIMA model. International
76. Journal of Chemical Studies. 7(3): 3899-
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