Application of Regression Models for Area, Production and Productivity Growth Trends of Cotton Crop in India

M. Sundar Rajan; M. Palanivel

doi:doi:10.11648/j.ijsd.20180401.11

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Application of Regression Models for Area, Production and Productivity Growth Trends of Cotton Crop in India

M. Sundar Rajan, M. Palanivel

Published in International Journal of Statistical Distributions and Applications (Volume 4, Issue 1)

Received: 11 September 2017 Accepted: 11 November 2017 Published: 19 January 2018

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Abstract

Computing the growth of any entity over a time period is important for understanding the past behaviour and for future planning. ‘Compound growth rate’ is one of the frequently used methods for calculating the growth rate models. Among the statistical study was carried out on different growth models viz., linear, quadratic, cubic, exponential, compound, logarithmic, inverse, power, growth and S-curve models to project the area, production and productivity cotton crop in India for 1951 to 2013. The study revealed that through all models exhibited significant; the cubic model is the best fitted, for its highest adjusted R² on increasing future projection trends with respect to area, production and productivity of cotton in India.

Published in	International Journal of Statistical Distributions and Applications (Volume 4, Issue 1)
DOI	10.11648/j.ijsd.20180401.11
Page(s)	1-5
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Regression Growth Models, Area, Production, Productivity, Cotton, Adjusted R², Growth Models

References

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[6]	D. P. Sing et al., (2014). Developing statistical models to study the growth rates of paddy crops in different districts of Chhattisgarh, American International Journal of Research in Formal, Applied & Natural Science. 5 (1), 102-104.
[7]	Finger, R., (2007). Evidence of slowing yield growth the example of swiss cereal yield. Agri-Food and Agri-Environmental Economics Group, ETH Zurich, Switzerland.
[8]	Hussaini. Mairiga, (2014). Trend Analysis of Productivity of Some Selected Cereal Crops in Nigeria: 1983-2008. Tahir, Research on Humanities and Social Sciences. Vol. 4, No. 8, 2014.
[9]	Karim, M. R., M. A. Awal and M. Akter, (2010). Forecasting of wheat production in Bangladesh. Bangladesh J. Agric. Res., 35: 17-28.
[10]	Kvalseth, T. O. (1985). Cautionary note about （R²）, Amer. Statistician, 39, 279-85.
[11]	Martin and Yeh, H. 1965. Yield predictions for 1965 wheat, oats and barely in Manitoba. Canadian Journal of Agricultural Economic. 13: 306-309.
[12]	Mohan Naidu. G., (2015). Selection of appropriate growth model for projection of sugarcane area, production and productivity of Andhra Pradesh. International Journal Agricultural Statistical Science. Vol. 11, No. 1, pp. 215-218.
[13]	Rajarathinam, A., Parmar, R. S. and Vaishnav P. R. 2010. Estimating Models for Area, Production and Productivity Trends of Tobacco (Nicotiana tabacum) Crop for Anand Region of Gujarat State, India. J. App. Sci., 10: 2419-25.
[14]	Rimi, R. H., S. H. Rahman, S. Karmakar and S. G. Hussain, (2009). Trend analysis of climate change and investigation on its probable impacts on rice production at Sathkhira, Bangladesh. Pak. J. Meteorol., 6: 37-50.
[15]	Srinivasa Rao, V and Srinivasulu, R. (2006). Growth comparisons of turmeric up to 2020 AD. The Andhra Agricultural Journal. 53 (1&2): 108-109.

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APA Style

M. Sundar Rajan, M. Palanivel. (2018). Application of Regression Models for Area, Production and Productivity Growth Trends of Cotton Crop in India. International Journal of Statistical Distributions and Applications, 4(1), 1-5. https://doi.org/10.11648/j.ijsd.20180401.11

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ACS Style

M. Sundar Rajan; M. Palanivel. Application of Regression Models for Area, Production and Productivity Growth Trends of Cotton Crop in India. Int. J. Stat. Distrib. Appl. 2018, 4(1), 1-5. doi: 10.11648/j.ijsd.20180401.11

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AMA Style

M. Sundar Rajan, M. Palanivel. Application of Regression Models for Area, Production and Productivity Growth Trends of Cotton Crop in India. Int J Stat Distrib Appl. 2018;4(1):1-5. doi: 10.11648/j.ijsd.20180401.11

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@article{10.11648/j.ijsd.20180401.11,
  author = {M. Sundar Rajan and M. Palanivel},
  title = {Application of Regression Models for Area, Production and Productivity Growth Trends of Cotton Crop in India},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {4},
  number = {1},
  pages = {1-5},
  doi = {10.11648/j.ijsd.20180401.11},
  url = {https://doi.org/10.11648/j.ijsd.20180401.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20180401.11},
  abstract = {Computing the growth of any entity over a time period is important for understanding the past behaviour and for future planning. ‘Compound growth rate’ is one of the frequently used methods for calculating the growth rate models. Among the statistical study was carried out on different growth models viz., linear, quadratic, cubic, exponential, compound, logarithmic, inverse, power, growth and S-curve models to project the area, production and productivity cotton crop in India for 1951 to 2013. The study revealed that through all models exhibited significant; the cubic model is the best fitted, for its highest adjusted R2 on increasing future projection trends with respect to area, production and productivity of cotton in India.},
 year = {2018}
}

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TY  - JOUR
T1  - Application of Regression Models for Area, Production and Productivity Growth Trends of Cotton Crop in India
AU  - M. Sundar Rajan
AU  - M. Palanivel
Y1  - 2018/01/19
PY  - 2018
N1  - https://doi.org/10.11648/j.ijsd.20180401.11
DO  - 10.11648/j.ijsd.20180401.11
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 1
EP  - 5
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20180401.11
AB  - Computing the growth of any entity over a time period is important for understanding the past behaviour and for future planning. ‘Compound growth rate’ is one of the frequently used methods for calculating the growth rate models. Among the statistical study was carried out on different growth models viz., linear, quadratic, cubic, exponential, compound, logarithmic, inverse, power, growth and S-curve models to project the area, production and productivity cotton crop in India for 1951 to 2013. The study revealed that through all models exhibited significant; the cubic model is the best fitted, for its highest adjusted R2 on increasing future projection trends with respect to area, production and productivity of cotton in India.
VL  - 4
IS  - 1
ER  -

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Author Information

M. Sundar Rajan

Department of Statistics, Govt. Arts College (Affiliated Bharathiar University), Coimbatore, India
M. Palanivel

Department of Statistics, Govt. Arts College (Affiliated Bharathiar University), Coimbatore, India

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APA Style

M. Sundar Rajan, M. Palanivel. (2018). Application of Regression Models for Area, Production and Productivity Growth Trends of Cotton Crop in India. International Journal of Statistical Distributions and Applications, 4(1), 1-5. https://doi.org/10.11648/j.ijsd.20180401.11

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ACS Style

M. Sundar Rajan; M. Palanivel. Application of Regression Models for Area, Production and Productivity Growth Trends of Cotton Crop in India. Int. J. Stat. Distrib. Appl. 2018, 4(1), 1-5. doi: 10.11648/j.ijsd.20180401.11

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AMA Style

M. Sundar Rajan, M. Palanivel. Application of Regression Models for Area, Production and Productivity Growth Trends of Cotton Crop in India. Int J Stat Distrib Appl. 2018;4(1):1-5. doi: 10.11648/j.ijsd.20180401.11

Copy | Download

@article{10.11648/j.ijsd.20180401.11,
  author = {M. Sundar Rajan and M. Palanivel},
  title = {Application of Regression Models for Area, Production and Productivity Growth Trends of Cotton Crop in India},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {4},
  number = {1},
  pages = {1-5},
  doi = {10.11648/j.ijsd.20180401.11},
  url = {https://doi.org/10.11648/j.ijsd.20180401.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20180401.11},
  abstract = {Computing the growth of any entity over a time period is important for understanding the past behaviour and for future planning. ‘Compound growth rate’ is one of the frequently used methods for calculating the growth rate models. Among the statistical study was carried out on different growth models viz., linear, quadratic, cubic, exponential, compound, logarithmic, inverse, power, growth and S-curve models to project the area, production and productivity cotton crop in India for 1951 to 2013. The study revealed that through all models exhibited significant; the cubic model is the best fitted, for its highest adjusted R2 on increasing future projection trends with respect to area, production and productivity of cotton in India.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Application of Regression Models for Area, Production and Productivity Growth Trends of Cotton Crop in India
AU  - M. Sundar Rajan
AU  - M. Palanivel
Y1  - 2018/01/19
PY  - 2018
N1  - https://doi.org/10.11648/j.ijsd.20180401.11
DO  - 10.11648/j.ijsd.20180401.11
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 1
EP  - 5
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20180401.11
AB  - Computing the growth of any entity over a time period is important for understanding the past behaviour and for future planning. ‘Compound growth rate’ is one of the frequently used methods for calculating the growth rate models. Among the statistical study was carried out on different growth models viz., linear, quadratic, cubic, exponential, compound, logarithmic, inverse, power, growth and S-curve models to project the area, production and productivity cotton crop in India for 1951 to 2013. The study revealed that through all models exhibited significant; the cubic model is the best fitted, for its highest adjusted R2 on increasing future projection trends with respect to area, production and productivity of cotton in India.
VL  - 4
IS  - 1
ER  -

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