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Linear Modelling of The State-Wise Yield of Principal Crops in India.

Modelling techniques are applied in agriculture field. Yield of rice is modelled using the method of least squares in Time Series Analysis and linear equations are fitted for the state-wise average yield of crops in kg per hectare in India and also for the average yield of various principal crops in Tamil Nadu.

Author’s

P Gayathri and K R Subramanian

KEYWORDS

Crop Modelling, Time Series Analysis, Average Yield of Crops in India.

PUBLISHED DATE

6 March 2019

PUBLISHER

The Author(s) 2019. This article is published with open access at www.chitkara.edu.in/publications

ABSTRACT

Modelling techniques are applied in agriculture field. Yield of rice is modelled using the method of least squares in Time Series Analysis and linear equations are fitted for the state-wise average yield of crops in kg per hectare in India and also for the average yield of various principal crops in Tamil Nadu.

Page(s)

69-76

URL

https://mjis.chitkara.edu.in/index.php/mjis/article/view/174/113

ISSN

Print : 2278-9561, Online : 2278-957X

DOI

https://doi.org/1015415/mjis.2019.72009

REFERENCES

Efstathios, P (2018).Sieve bootstrap for functional time series, The annals of Statistics, 46, 3510–3538.https://doi.org/10.1214/17-AOS1667.
Gayathri, P. and Subramanian, K. R. (2016). Statistical analysis of observation on Pisum Sativum production, Jamal Academic Research Journal,239–244.
Hongyuan, Cao., Weidong, Liu., Zhou Zhou., Bernoulli (2018). Simultaneous non parametric regression analysis of sparse longitudinal data, 24, 3013–3038. https://doi.org/10.3150/17-BEJ952.
John, R. (1984). Bandwidth Choice for Nonparametric Regression, The Annals of Statistics, 12, 1215-1230 https://www.jstor.org/stable/2240998.https://doi.org/10.1214/aos/1176346788
Meena, K., Subramanian, K. R., Gayathri, P. (2014). Haridhra – turmeric (curcuma longa) production – a multivariant analytical and data mining based observation, Agro Biodiversity Informatics, National Academy of Agricultural Research Management (NAARM), 6, 123–140.
Siegfried, H. Piotr, K. and Gilles, N. (2018). The annals of Statistics, Testing for periodicity in functional time series, 46, 2960–2984. https://doi.org/10.1214/17-AOS1645.
Somu, J. (2015). Prediction on rice production in india through multivariate regression analysis,Journal of Business and Management Sciences, 3, 26–31. https://doi.org/10.12691/jbms-3-1-4.
Yudo, P. et. al. (2018). Rice productivity prediction model design based on linear regression of spectral value using NDVI and LSWI combination on landsat-8 imagery, IOP conference series: earth and environmental science, 165. https://doi.org/10.1088/1755-1315/165/1/012002