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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2020)
[Effective from January 1, 2020]
For more details click here

ICV 2019: 96.39
Index Copernicus ICI Journals Master List 2019 - IJCMAS--ICV 2019: 96.39
For more details click here

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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2018.7(7): 2001-2006
DOI: https://doi.org/10.20546/ijcmas.2018.707.236


Weather Forecast Models of Potato Yield Using Principal Componant Analysis for Sultanpur District of Eastern Uttar Pradesh
Snehdeep, B.V.S. Sisodia, V.N. Rai and Sunil Kumar
Department of Agricultural Statistics Narendra Dev University of Agriculture and Technology, Kumarganj, Faizabad, U.P., India
*Corresponding author
Abstract:

The present investigation entitled “Forecast Models of Potato Yield Using Principal Component Analysis for Sultanpur District of Eastern Uttar Pradesh.” Time series data on yield of potato and weekly data from 40th SMW of the previous year to 6th SMW of the following year on five weather variables viz., Minimum Temperature, Maximum Temperature, Relative humidity 08.30hrs, Relative humidity 17.30hrs, and Wind-Velocity covering the period from 1990-91 to 2011-12 have been utilized for development of pre-harvest forecast model. Statistical methodologies using multiple regression, principal component analysis for developing pre-harvest forecast model have been described. In both models (one based on regression and one from principal component) have been developed. The Model-Ist is based on step wise regression, and IInd based on principal component analysis. Models have been developed on the basis of adjR2, RMSE and %SE, the best model obtained by the application of step-wise regression analysis of weekly weather data are Model-Ist for Sultanpur have further reduced the percentage standard error of the forecast yield to some extent. These models can be used to get the reliable forecast of potato yield two and half months before the harvest.


Keywords: pre-harvest forecast, Statistical model, Weather variables, Principal componant
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How to cite this article:

Snehdeep, B.V.S. Sisodia, V.N. Rai and Sunil Kumar. 2018. Weather Forecast Models of Potato Yield Using Principal Componant Analysis for Sultanpur District of Eastern Uttar Pradesh.Int.J.Curr.Microbiol.App.Sci. 7(7): 2001-2006. doi: https://doi.org/10.20546/ijcmas.2018.707.236