An application of principal component analysis for pre- harvest forecast model for rice crop based on biometrical characters
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Abstract
An application of principal component analysis for the development of suitable statistical models for preharvest forecast of rice yield based on biometrical characters has been dealt with in the present paper. The data obtained from the two experiments on rice have been utilised to develop the model. The forecast yields of based on
these models have been found to be 24.25, 22.60 and 21.10 q/ha against the actual yield of 28.00, 23.56 and 21.85 q/ha, respectively, in experiment –I. For experiment –II the forecast yields were found to be 24.62, 28.06 and 29.43 q/ha against the actual yield of 28.82, 29.31 and 26.59 q/ha, respectively. These forecast yields are subject to maximum of almost 10 percent standard error. In most of the cases, the forecast yields were found to be close to the actual yield except in some cases. The values of R2, i.e. 79.80 and 72.60 for experiment –I and II, respectively, indicate the validity of the models. Statistical tool like viz. principal component analysis (PCA) has been first time applied to develop pre-harvest forecast model based on experimental data.
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Biometrical characters, Pre-harvest forecast model, Principal component Analysis, Rice experiment
Agrawal, R., Jain, R.C., Jha, M.P. and Singh, D. (1980). Forecasting of rice yield using climatic variables. Ind. J. Agric. Sci., 50 (9), 680-684.
Agrawal, R.; Jain, R.C. and Jha, M.P. (1983). Joint effects of weather variables on rice yields. Mausam, 34 (2),189-194.
Anderson, T. W. (1974) An introduction to multivariate statistical analysis 2nd edition .Wiley eastern private limited New Delhi.
Annu, Sisodia, B.V.S. and Kumar, Sunil (2015). Pre-harvest forecast models for wheat yield based on biometrical characters. Economic Affairs, 60(1):89-93.
Drapper, N. R. and Smith, H. (1988). Applied regression analysis, second edition John Willy and sons, New York.
Jain, R. C., Agrawal, Ranjana and Singh, K.N.(1992b). A within year growth model for crop yield forecast. Biometrical Journal. 34(7), 789-799.
Jain, R. C., Sridharan, H. and Agrawal Ranjana (1984). Principal component technique for forecasting of sorghum yield. Indian Journal of Agril. Sci. 54 (6), 467-470.
Jain, R.C., Sridharan, H. and Agrawal, Ranjana, (1985),. Principal component technique for forecasting of sorghum yield. Ind. J. Agric. Sci., 54 (6), 467-470.
Mohd. Azfar, Sisodia, B. V. S., Rai, V. N. and Devi, Monika (2014). Pre-harvest of rapeseed and mustard yield based on weather variables- An application of discriminant function analysis. Int. J. Agri. and Statistical Science, 10(2): 497-502.
Mohd. Azfar, Sisodia, B. V. S. Rai, V. N. and Devi, Monika (2015). Pre-harvest of rapeseed & mustard yield based on weather variables- An application of principal component analysis of weather variables. Mausam Vol. 66 (4):761-766.
Pandey, K. K., Rai, V. N. and Sisodia, B. V. S. (2014). Weather variable based rice yield forecasting models for Faizabad district of eastern Uttar Pradesh. Int. J. Agri. and Statistical Science, 10(2): 381-385.
Singh, B. H. and Bapat, S. R. (1988)..Pre-harvest forecast models for prediction of sugarcane yield. Indian Journal of Agricultural Sciences, 58(6): 465-469.
Singh, D., Singh, H.P. and Singh, P (1986). Pre- harvest forecasting of rice yield. Ind. J. of Agric. Sci. 46, (10): 445-450.
Yadav, R. R., and Sisodia, B. V. S.(2015). Predictive models for Pigeon-pea yield using weather variables. Int. J. Agri. and Statistical Science, 11(2): 462-472
Yadav, R. R., Sisodia, B. V. S. and Kumar Sunil (2014). Application of principal component analysis in developing statistical models to forecast crop yield using weather variables. Mausam, 65 (3): 357-360.
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