ESTIMATION OF PINEAPPLE PRODUCTION IN MANIPUR - A STATISTICAL APPROACH
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Authors
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CHANGAMAYUM GIRIJA DEVI* and S. LOIDANG DEVI
Department of Statistics, Manipur University, Canchipur, Manipur - 795 001
Keywords:
ARIMA, Compound growth rate, Exponential smoothing model, Manipur, Pineapple.Abstract
The main objective of this paper was to compare the compound growth rate model, exponential
smoothing model and ARIMA model on pineapple production in Manipur. Time series secondary data
was utilized for this analysis. From the study, it was recorded that the value of R2 for the compound
growth rate model, exponential smoothing model and autoregressive integrated moving average
model of production, suggested that out of the total variation 83.2%, 75.4%, and 85.2%, respectively
was explained by the independent variable on the pineapple production of Manipur. In 2020 pineapple
production in Manipur was 166483 MT and in the years 2021, 2022, 2023, 2024 and 2025 it could be
160403 MT, 181650 MT, 175174 MT, 177148 MT, 176546 MT, respectively based on the fitted ARIMA
(1,1,2) model, in which the production fluctuates which might be due to the fluctuation in the past
years’ data of production.
References
1. Chanu, T.M., Baite, D.J., Singh, M.K and Rao,
2. D.U.M. 2014. Adoption of pineapple
3. cultivation practices by the farmers in
4. Manipur state. Indian Research Journal of
5. Extension Education. 14(1):17-20.
6. Chitra, N ., Shanmathi, R and Rajesh, R. 2015.
7. Application of ARIMA model using SPSS
8. software – A case study in supply chain
9. management. International Journal of
10. Science, Technology & Management. 4(1):
11. -215.
12. Government of Manipur. 2014. Mission for
13. integrated development of horticulture in
14. Manipur. Directorate of Horticulture and
15. Soil Conservation of Manipur.
16. Hossian, M.M and Abdulla, F. 2015. A time series
17. analysis for the pineapple production in
18. Bangladesh. Jahangirnagar University
19. Journal of Science. 38(2):49-59.
20. Panasa, V., Kumari. R.V., Ramakrishna, G and
21. Kaviraju, S. 2017. Maize price forecasting
22. using the autoregressive integrated
23. moving average (ARIMA) model.
24. International Journal of Current
25. Microbiology and Applied Sciences.
26. (8):2887-2895.
27. Sharma, A., Shastri, S and Mansoura, V. 2015.
28. Forecasting public healthcare services in
29. Jammu & Kashmir using time series data
30. mining. International Journal of Advanced
31. Research in Computer Science and
32. Software Engineering. 5(12): 570-575.
33. Shastri, S., Sharma, A., Mansoura, V., Sharma,
34. A., Bhadwal, A.S and Kumari, M. 2018. A
35. study on exponential smoothing method for
36. forecasting. International Journal of
37. Computer Sciences and Engineering.
38. (4):482-485.
39. Singh, N.A., Singh, R., Feroze, S.M., Singh, R.J
40. and Rani, P.M.N. 2016. Growth and
41. instability of pineapple production in
42. Manipur, India. Indian Journal of
43. Agricultural Research. 50(1): 88-91.
44. Vijay, N and Mishra, G.C. 2018. Time Series
45. forecasting using ARIMA and ANN Models
46. for production of pearl millet (BAJRA) crop
47. of Karnataka, India. International Journal
48. of Current Microbiology and Applied
49. Sciences.7(12): 880-889.
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