MATHEMATICAL MODELING ON RAINFALL AND GROUNDNUT PRODUCTIVITY IN BHAVNAGAR DISTRICT OF GUJARAT

G.B. CHAUDHARI¹, D.K. PARMAR², R.S. PARMAR³, H.K. PATEL⁴*, B.K. DABHI^5
1College of Agricultural Information Technology, Anand Agricultural University, Anand, 388 110, Gujarat, India
²College of Agricultural Information Technology, Anand Agricultural University, Anand, 388 110, Gujarat, India
³College of Agricultural Information Technology, Anand Agricultural University, Anand, 388 110, Gujarat, India
⁴Main Forage Research Station, Anand Agricultural University, Anand, 388 110, Gujarat, India
⁵Dairy Science College, Anand Agricultural University, Anand, 388 110, Gujarat, India
* Corresponding Author : hirenubi@gmail.com

Received : 28-05-2018     Accepted : 06-06-2018     Published : 15-06-2018
Volume : 10     Issue : 11       Pages : 6198 - 6199
Int J Agr Sci 10.11 (2018):6198-6199

Keywords : Groundnut, Rainfall distribution
Conflict of Interest : None declared
Acknowledgements/Funding : Authors are thankful to College of Agricultural Information Technology, Anand Agricultural University, Anand, 388 110, Gujarat, India
Author Contribution : All author equally contributed

The district-wise average yield data of groundnut and daily rainfall data were used over a period of 47 years i.e., from 1970-2017. Aggregate rainfall approach and different models viz., Linear, Quadratic, Cubic, Quartic, Exponential, Wilmink, Parabolic Exponential, Wood’s Gamma, Inverse Quadratic, Mixed Log, Heat Capacity, Monomolecular were tried to fit the mathematical models on rainfall and groundnut productivity in Bhavnagar district of Gujarat. The appropriate mathematical model was selected on the basis of adjusted R2, significant regression co-efficient, co-efficient of determination (R2) and RMSE. Among the different linear and non-linear models fitted, the maximum adjusted R2 of 40 percent was observed in case of Quadratic model with the comparatively lower values of root mean square (324.0) in comparison to that of other fitted models. In general, it could be observed that, none of the fitted models could be recommended as perharvest forecast models, because of low to moderate predictability. This suggests that inclusion of certain other weather parameters like temperature, relative humidity, daily sunshine and wind speed in the models, may improve the predictability of the groundnut productivity.

1. Khatri T.J. and Patel R.M. (1982) Guj. Stat. Review., 9 (2), 49-58.
2. Suryanarayana G., Rajashekara B.G., Jagannatha M.K. and Venkataramu M.M. (1982) Journal of Agri. Sci., 16(2), 128-133.
3. Sahu D.D., Golakiya B.A. and Patoliya B.M. (2004) Journal of Agrometeorlogy, 6(2), 249-253.
4. Patel J.S. and Vaishnav M.R. (1990) Journal of Ind. Soc. Ag. Stat. 4(3): 335.
5. Khatri T.J. and Patel R.M. (1990) Journal of Ind. Soc. Ag. Stat., 42(3), 334.
6. Singh Jagmohan and Singh B.H. (1994) Journal of Ind. Soc. Ag. Stat. 46(1), 99.
7. Parmar B.A., Sahu D.D., Dixit S.K. and Patoliya B.M. (2004) J. of Agrometeorol., 6(1), 1-8.
8. Anonymous (2018) Published by Directorate of Economics and Statistics, Department of Agriculture and Cooperation, Min. of Agriculture, GOI, New Delhi.
9. Ramachandran V. S. and Rajegowda M.B. (1987) Annals of Arid Zone, 26 (4), 273-79.
10. Khichar M.L. Ram Singh, Bishnoi O.P. (1996) Haryana Agric. J. Res., 26,35-38.