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Research Article

volume 37 issue 3 (september 2017) : 191-196,   Doi: 10.18805/asd.v37i03.8987

Fitting of appropriate model to study growth rate and instability of mango production in India

A
Abhiram Dash
D
D.S. Dhakre
D
Debasis Bhattacharya
1Institute of Agriculture, Visva-Bharati University, Bolpur - 731 235 West Bengal, India
Cite article:- Dash Abhiram, Dhakre D.S., Bhattacharya Debasis (2017). Fitting of appropriate model to study growth rate and instability of mango production in India. Agricultural Science Digest. 37(3): 191-196. doi: 10.18805/asd.v37i03.8987.

ABSTRACT

The growth rate and instability of mango production is studied for the period 1992-93 to 2013-14. For studying the growth rate, linear and compound models are fitted with and without spline. Spline model is fitted to take care of abrupt jumps in the data and evaluate the change in the trend of time series data.The entire period of study is divided into two sub-periods, 1992-93 to 2002-03 as sub period-I and 2003-04 to 2013-14 as sub-period-II as there is maximum percentage change in area, production and yield of mango in the year 2002-03. The best fitted model is selected by considering the model evaluation criteria, such as, Adj. R2 and Root Mean Square Error (RMSE). The compound model without spline, compound model with spline and linear model with spline was found to be best fit forarea, production and yield respectively. The growth rate in area, production and yield is found by using the best fit model.  The growth rate for area is found to be positive and significant but for yield it is found to be significantly negative for each sub period and for the entire period of study. For this, though the production shows positive growth rate but it is insignificant for sub-period-Iand also for the whole period. The instability in mango production is studied with the help of coefficient of variation.As coefficient of variation is affected by long term trend, it is computed after eliminating trend from the data.From study of instability, it is found that area under mango in India shows lesser instability than production and yield. Though there is increase in production of  mango but the increase is mainly due to the positive growth in area under mango. The yield of mango is declining at a significant rate. Decomposition analysis carried out by hazel’s decomposition technique reveals that change in mean area made a great positive contribution towards the change in mean production, whereas, change in mean yield and change in area – yield interaction both made negative contribution towards change in mean productionfrom sub-period I to II.

REFERENCES

  1. Chand, R. and Raju, S.S. (2009). Instability in Indian agriculture during different phases of technology and policy.Indian Journal. of Agricultural. Economics, 64(2) : 283-285.
  2. Hazel, P.B.R., (1984). Sources of increased instability in Indian Cereal product.American Journal.of Agricultural. Economics, 32(20): 302-311.
  3. https://www.indiastat.in
  4. Kagerer, K., (2013). A short introduction to splines in least square regression analysis.University of Regensburg Working Papers in Business, Economics and Management Information Systems. 
  5. Sarvida, R.L. and Baguio, C.B., (2010). On the efficiency of spline regression for time series data. 11th National Convention on Statistics (NCS) EDSA Shangri-La Hotel 
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    Research Article

    volume 37 issue 3 (september 2017) : 191-196,   Doi: 10.18805/asd.v37i03.8987

    Fitting of appropriate model to study growth rate and instability of mango production in India

    A
    Abhiram Dash
    D
    D.S. Dhakre
    D
    Debasis Bhattacharya
    1Institute of Agriculture, Visva-Bharati University, Bolpur - 731 235 West Bengal, India
    Cite article:- Dash Abhiram, Dhakre D.S., Bhattacharya Debasis (2017). Fitting of appropriate model to study growth rate and instability of mango production in India. Agricultural Science Digest. 37(3): 191-196. doi: 10.18805/asd.v37i03.8987.

    ABSTRACT

    The growth rate and instability of mango production is studied for the period 1992-93 to 2013-14. For studying the growth rate, linear and compound models are fitted with and without spline. Spline model is fitted to take care of abrupt jumps in the data and evaluate the change in the trend of time series data.The entire period of study is divided into two sub-periods, 1992-93 to 2002-03 as sub period-I and 2003-04 to 2013-14 as sub-period-II as there is maximum percentage change in area, production and yield of mango in the year 2002-03. The best fitted model is selected by considering the model evaluation criteria, such as, Adj. R2 and Root Mean Square Error (RMSE). The compound model without spline, compound model with spline and linear model with spline was found to be best fit forarea, production and yield respectively. The growth rate in area, production and yield is found by using the best fit model.  The growth rate for area is found to be positive and significant but for yield it is found to be significantly negative for each sub period and for the entire period of study. For this, though the production shows positive growth rate but it is insignificant for sub-period-Iand also for the whole period. The instability in mango production is studied with the help of coefficient of variation.As coefficient of variation is affected by long term trend, it is computed after eliminating trend from the data.From study of instability, it is found that area under mango in India shows lesser instability than production and yield. Though there is increase in production of  mango but the increase is mainly due to the positive growth in area under mango. The yield of mango is declining at a significant rate. Decomposition analysis carried out by hazel’s decomposition technique reveals that change in mean area made a great positive contribution towards the change in mean production, whereas, change in mean yield and change in area – yield interaction both made negative contribution towards change in mean productionfrom sub-period I to II.

    REFERENCES

    1. Chand, R. and Raju, S.S. (2009). Instability in Indian agriculture during different phases of technology and policy.Indian Journal. of Agricultural. Economics, 64(2) : 283-285.
    2. Hazel, P.B.R., (1984). Sources of increased instability in Indian Cereal product.American Journal.of Agricultural. Economics, 32(20): 302-311.
    3. https://www.indiastat.in
    4. Kagerer, K., (2013). A short introduction to splines in least square regression analysis.University of Regensburg Working Papers in Business, Economics and Management Information Systems. 
    5. Sarvida, R.L. and Baguio, C.B., (2010). On the efficiency of spline regression for time series data. 11th National Convention on Statistics (NCS) EDSA Shangri-La Hotel 
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