Original Article

Forecasting of Rice Production using the Meteorological Factor in Major States in India and its Role in Food Security

Year: 2021 | Month: March | Volume 14 | Issue 1

References (31)

1.Anonymous. 2011. www.indiastat.com, Last accessed December 11.2011.

View at Google Scholar

2.Anonymous. 2011. www.indiawaterportal.com, last accessed January 20.2011.

View at Google Scholar

3.Anonymous. 2017. Agriculture at a Glance. Available from http://www.agricoop.nic.in/Last accessed on 20 Oct. 2017.

View at Google Scholar

4.Anonymous. Various issue. Fertilizer statistics, Govt. of India.

View at Google Scholar

5.Biswas, R., Banerjee, S. and Bhattacharyya, B. 2018. Impact of temperature increase on performance of kharif rice at Kalyani, West Bengal using WOFOST model. J. Agrometeorol., 20(1): 28-30.

View at Google Scholar

6.Box, G.E.P. and Jenkins, G.M. 1976. Time Series Analysis: Forecasting and Control, Holden-Day, San Fransisco.

View at Google Scholar

7.Diebold, F.X. and Mariano, R.S. 1995. Comparing predictive accuracy. J. Bus. Econ. Stat., 13: 253-263.

View at Google Scholar

8.?urka Peter, Pastoreková Silvia. 2012.ARIMA vs. ARIMAX – which approach is better to analyze and forecast macroeconomic time series. Proceedings of 30th International Conference Mathematical Methods in Economics.

View at Google Scholar

9.Food and Agriculture Organisation of the United Nations 2002. Nutritional studies, F.A.O., Rome.

View at Google Scholar

10.Gideon E. Schwarz. 1978. Estimating the dimension of a model, Ann. Stat., 6(2): 461-464.

View at Google Scholar

11.Gouri K. Bhattacharyya. 1984. Tests of randomness against trend or serial correlations Handbook of Statistics, Elsevier, Volume 4, Pages 89-111, ISSN 0169-7161, ISBN 9780444868718, https://doi.org/10.1016/S0169- 7161(84)04007-4.

View at Google Scholar

12.Grubbs, F.E. 1950. Sample criteria for testing outlying observations. Ann. Math. Stat., 21: 27-58.

View at Google Scholar

13.Grubbs, F.E. 1969. Procedures for detecting outlying observations in samples. Technometrics, 11(1): 1-21.

View at Google Scholar

14.Grubbs, F.E. and Beck, G. 1972. Extension of sample sizes and percentage points for significance tests of outlying observations. Technometrics, 14: 847-854.

View at Google Scholar

15.Harvey, D., Leybourne, S. and Newbold, P. 1997. Testing the equality of prediction mean squared errors. Int. J. Forecast., 13(2): 281-291.

View at Google Scholar

16.Harvey, D., Leybourne, S. and Newbold, P. 1997. Testing the equality of prediction mean squared errors. Int. J. Forecast., 13(2): 281-291.

View at Google Scholar

17.Intriligator, M.D., Bodkin, R.G. and Hsio, C. 1996. Econometric Models, Techniques, and Applications”, Prentice Hall edition.

View at Google Scholar

18.Joshi, P.K., Gulati, A., Birthal, P.S. and Tewari, L. 2004. Agriculture diversification in South Asia: Patterns, determinants and policy implications. Econ. Polit. Wkly, 39(18): 2457-2467.

View at Google Scholar

19.Joshi, P.K., Tewari, L. and Birthal, P.S. 2006. Diversification and its impact on smallholders: Evidence from a study on vegetable production. Agric. Econ. Res. Rev., 19(2): 219-236.

View at Google Scholar

20.Kaul, S. 2006. Economic Analysis of Productivity of Rice Production- State-wise Analysis presented at the 14th Annual Conference of Agricultural Economics Research Association, held during Sept. 27-28, 2006 at G.B. Pant Univ. of Agri. & Tech.., Pant Nagar, Uttaranchal.

View at Google Scholar

21.Kebebe, Ergano, Mehta, V.P. and Dixit, P.N. 2000. Diversification of agriculture in Haryana: An empirical analysis. Agril. Situation in India, 57(8): 459-463.

View at Google Scholar

22.Khush, G. 2003. Productivity improvements in rice. Nutr. Rev., 61(6 Pt 2): S114-S116.

View at Google Scholar

23.Mishra, P., Fatih, C., Niranjan, H.K., Tiwari, S., Devi, M. and Dubey, A. 2020. Modelling and Forecasting of Milk Production in Chhattisgarh and India. Indian J. Anim. Res., 54(7): 912-917.

View at Google Scholar

24.Mishra, P., Fatih, C., Niranjan, H.K., Tiwari, S., Devi, M. and Dubey, A. 2020. Modelling and Forecasting of Milk Production in Chhattisgarh and India. Indian J. Anim. Res., 54(7): 912-917.

View at Google Scholar

25.Mishra, P., Sahu, P.K., Padmanaban, K., Vishwajith, K.P. and Dhekale, B.S. 2015. Study of Instability and Forecasting of Food Grain Production in India. Int. J. Agric. Sci., 7(3): 474-481.

View at Google Scholar

26.Mishra, P., Sahu, P.K., Dhekale, B.S. and Vishwajith, K.P. 2015. Modeling and forecasting of wheat in India and their yield Sustainability. Soc. Eco. Dev., 11(3): 637-647.

View at Google Scholar

27.Mishra, P., Matuka, A., Abotaleb, M.S.A., Weerasinghe, W.P.M.C.N, Karakaya, K. and Das, S.S. 2021. Modeling and Forecasting of Milk Production in the SAARC countries and China, Model. Earth Syst. Environ., pp. 1-9.

View at Google Scholar

28.Mishra, P. 2015. Study of instability and forecasting of food grain production in India. Int. J. Agric. Sci., ISSN, 0975- 3710.

View at Google Scholar

29.Raiger, H.L., Sahu, P.K., Behera, M.P. and Jajoriya, N.K. 2019. Variation and Character Association in Seed Yield and Related Traits in Rice Bean (Vigna umbellata). Int. J. Agric. Environ. Biotech., 12(1): 13-16.

View at Google Scholar

30.Ramesh, T., Rathika, S., Subramanian, E. and Ravi, V. 2020. Effect of Drip Fertigation on the Productivity of Hybrid Rice. Int. J. Agric. Environ. Biotech., 13(2): 219-225.

View at Google Scholar

31.Ravichandran, S., Muthuraman, P. and Rao, P.R. 2012. Time - series modelling and forecasting India’s rice production - arima vs stm Modelling approaches. Int. J. Agric. Stat. Sci., 8(1): 305-311.

View at Google Scholar

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