Estimation of annual compound growth rates of guava (Psidium guajava L.) fruit in Haryana using Non linear model
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Abstract
Computation of growth rates plays an important role in agricultural and economic research to study growth pattern of a various commodities. Many of the research workers used the parametric approach for computation of annual growth rate but not use the concept of non-linear model. In this paper, an attempt has been made to study growth rates of guava for three districts (Hisar, and Kurukshetra) and Haryana state as a whole using different non-linear models. The time series data on annual area and production of guava (Psidium guajava L.) in different districts of Haryana from 1990-91 to 2015-16 were collected to fit non linear models. Growth rates were computed through best fitted non-linear models. It was found that Logistic model could be best fit for computation of growth rates of area for guava fruit in Hisar and Kurukshetra district and Haryana state as a whole whereas Gompertz model was best fit for Yamunanagar district based on high R2 and least MSE and RMSE values. It was also observed that monomolecular model was best fit for production of guava fruits in Hisar and Yamunanagar district whereas Logistic model was best fit for production of guava fruit in Kurukshetra and Haryana state as a whole because of high R2 and least MSE and RMSE values. R and excel software have been used for fitting the non linear model and computation of growth rates for area and production of guava fruit for the year 1990-91 to 2015-16. None has been used the non linear model growth model for computation of annual growth rate of guava fruit for area and production of Haryana state. But in this work non linear growth model has been used for computation of growth rate instead of parametric approaches.
Article Details
Article Details
Annual growth rate, Coefficient of determination, Non linear model, Relative mean square error
Anonymous (2015). Department of Horticulture, Government of Haryana. Retrieved from http://hortharyana.gov.in/en/statisticaldata.pdf
Draper, N. R. and H. Smith, (1998). Applied Regression Analysis, 3rd Edn., New York, USA: John Wiley & Sons.
Prajneshu, and Chandran, K.P. (2005). Computation of Compound Growth Rates in Agriculture: Revisited. Agricultural Economics Research Review, 18:317-324. https://DOI:10.5958/0974-0279.2019.00001.6
Rajarathinam, A., Parmar, R.S. & Vaishnav, P.R. (2010). Estimating Models for Area, Production and Productivity Trends of Tobacco (Nicotiana tabacum) Crop for Anand Region of Gujarat State. Indian Journal of Applied Sciences, 10:2419-2425. https://DOI;10.15373/2249555X
Singh, P.S.; Adarsha, L.K., Nandi; A.K. andJopir, O. (2018). Production Performance of Fresh Mango in India: A Growth and Variability Analysis. International Journal of Pure Applied Biosciences, 6(2):935- 941. https:// DOI: 10.18782/2320-7051
Singh, Rajender; Kumar, Manoj; Dahiya, Mamta and Baloda, Satpal (2019). Development of growth model for Ber powdery mildew in relation to weather parameters. Indian Phytopathology. pp1-7 72(1). https://doi.org/10.1038/164690b0
Kumar, Manoj; Battan, K.R. and Sheoran, O.P. (2019). Pre-harvest forecast model for rice yield using principal component regression based on biometrical character with R-software. Int. J. Agricult. Stat. Sci.,15, (1): 323-326 https:// doi:10.1029/20 08WR007163.
Mukherjee, Deep Naryana; Vasudev, N; Sushasini,K.; and Kumari, R. Vijaya (2016). Application of nonlinear growth models for estimation for annual compound growth rates of major pulse crops in the Telangana State.. The J. Res. PJTSAU 44(1&2)27-33 https:// doi: 10.3389/fphys.2015.00119
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