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Ravita Ravita Urmil Verma

Abstract

Parameter estimation in statistical modelling plays a crucial role in the real world phenomena. Several alternative analyses may be required for the purpose. In this paper, standard linear regression and multivariate statistical analyses were carried out to achieve the district-level rapeseed-mustard yield estimation in Haryana State (India). The study revealed that the zonal weather models incorporating crop condition term as dummy regressor(s) had the desired predictive accuracy. The model based mustard yield(s) indicated good agreement with State Department of Agriculture (DOA) yield estimates by showing 5-10 percent deviations in most of the mustard growing districts however for two-three districts, it gave 12-13 percent deviations possibly due to the smaller set of data available for those districts. The crop yield estimates on the basis of developed models may be obtained 4-5 weeks in advance of the harvest time.

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Keywords

Clustering, Dummy variables, Linear time trend, Multiple linear regression, Principal component scores

References
Adrian, D. (2012). A model-based approach to forecasting corn and soybean yields. USDA, National Agricultural Statistics Service, Research & Development Division.
Ahmad, T. and Kathuria, O.P. (2010). Estimation of crop yield at block level. Adv. Appl. Res. 2(2), 164-172.
Andarzian, B., Bakhshandeh, A.M., Bannayan, M., Emam, Y., Fathi, G. and Alami, S. (2008). Wheat Pot : a simple model for spring wheat Yp using monthly weather data. Biosyst. Eng. 99, 487–495.
Azfar, M., Sisodia, B. V. S., Rai, V. N. and Devi, M. (2015). Pre-harvest forecast models for rapeseed & mustard yield using principal component analysis of weather variables, Mausam 66(4), 761-766.
Dadhwal, V. K., Sehgal, V. K., Singh, R. P. and Rajak, D. R. (2003). Wheat yield modeling using satellite remote sensing with weather data; recent Indian experience. Mausam 54, 253-262.
Draper, N. and Smith, H. (1981). Applied Regression Analysis. 2nd edition. New York, Wiley.
Kaiser, H.F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement 20, 141–151.
Kandiannan, K., Chandaragiri, K. K., Sankaran, N., Balasubramanian, T. N. and Kailasam, C. (2002). Crop-weather model for turmeric yield forecasting for Coimbatore District, Tamil Nadu, India, Agricultural and Forest Meteorology 112, 133-137.
Shabnam, Bansal, S. K. and Dabas, D. S. (2013). Use of time-series data of temperature and yield to assess the impact of climate change on crop yield using mustard in Haryana, International Research Journal of Social Sciences 2(4), 31-33.
Shaw P.J.A. (2003). Multivariate statistics for the Environmental Sciences, Hodder-arnold ISBN 0-3408-0763-6.
Field, A. (2000). Discovering Statistics using SPSS for Windows, London – Thousand Oaks – New Delhi, Sage publications.
Goyal, M. and Verma, U. (2015). Development of weather-spectral models for pre-harvest wheat yield prediction on agro-climatic zone basis in Haryana, International J. of Agricultural and Statistical Sciences 11(1), 73-79.
Verma, U., Dabas, D. S. and Singh, J. P. (2011). Regression, multivariate techniques, time series and mixed modelling in context of pre-harvest wheat, mustard, cotton and sugarcane yield prediction in Haryana using the SAS system, Research Bulletin, Department of Soil Science, CCS Haryana Agricultural University, Hisar, Haryana, pp.1-154,
Citation Format
How to Cite
Ravita, R., & Verma, U. (2017). Use of crop condition based dummy regressor and weather input for parameter estimation of mustard yield forecast models. Journal of Applied and Natural Science, 9(3), 1703-1709. https://doi.org/10.31018/jans.v9i3.1425
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Research Articles