Trend and Forecasting of Area, Production and Productivity of Mango Crop in Karnataka, India

A. Varalakshmi *

Department of Agricultural Statistics, Applied Mathematics and Computer Application, GKVK, UAS, Bengaluru-560065, Karnataka, India.

G. H. Harish Nayak

ICAR-Indian Agricultural Statistics Research Institute, ICAR-IARI, New Delhi-110012, India.

M. G. Manjunath

Department of Agronomy, College of Agriculture, Dharwad-580005, Karnataka, India.

H. T. Vinay

Department of Agricultural Statistics, Uttar Banga Krishi Viswavidyalaya, Pundibari, Coochbehar-736165, India.

*Author to whom correspondence should be addressed.


Abstract

The current study was carried out to analyze the trend and forecast in area, production and productivity of mango crop in Karnataka. It was determined by using the secondary data of area, production and productivity of mango for the period of 18 years (2000-01 to 2017-18) was collected from Directorate of Economics and Statistics, Karnataka. To estimate the trend and its forecast for the next 5 years, up to 2022-23, linear, quadratic, exponential, logistic and Gompertz models were fitted and the best-fitted model was selected based on lowest MAPE. Result revealed that exponential model was best-fitted for area and production of mango, and the logistic model was found to be the best-fitted model for the mango productivity. The result also explored that the area, production and productivity of mango crop have an upward trend in Karnataka state in above study period. Based on this trend to forecast area, production and productivity of mango crop for the period from 2018-19 to 2022-23.

Keywords: Linear and nonlinear models, Mean Average Percentage Error (MAPE), Shapiro-Wilks test; run test, mango, area, production, productivity


How to Cite

Varalakshmi , A., G. H. Harish Nayak, M. G. Manjunath, and H. T. Vinay. 2023. “Trend and Forecasting of Area, Production and Productivity of Mango Crop in Karnataka, India”. Journal of Experimental Agriculture International 45 (5):16-23. https://doi.org/10.9734/jeai/2023/v45i52116.

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