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Mango on an average account approximately 75 per cent of total production quantity.  India is the largest mango producer, accounting for about half of the world-wide mango production. Forecasting of area, production and price fluctuations are the key to provide support in decision making and proper planning for sustainable growth of farmers and other people who are dependent on horticulture. The prices of mango are affected by cultivated area and yield of mango but in other ways pre or post-harvest management also affects it. The problems regarding the price fluctuations arise due to seasonality in arrival and its perishable nature. Therefore, the present study was carried out with time series intervention modelling in forecasting area, productivity and prices of mangoes. In the current investigation, simple exponential smoothing (SES) implemented to develop the forecasting models for area and productivity of mango. Under the SES, the error measurements at different values of alpha (a) for forecasting of area and productivity were observed that the value 0.8 and 0.9 of alpha (a) showed minimum Mean Absolute Percentage Error (MAPE) error i.e. 3.11 per cent, and 12.73 per cent, respectively. The study also developed time series ARIMA models for forecasting the prices of the mango (Keshar and Alphonso) for Valsad markets of Gujarat. It was showed that ARIMA (6, 1, 2) and ARIMA (1, 1, 2) were found good models for forecasting the prices of the Keshar and Alphonso, respectively in Valsad district of Gujarat


ARIMA Forecasting Mango price Simple exponential smoothing

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How to Cite
Garde, Y., Chavda, R. R., Thorat, V. S., Pisal, R. R., Shrivastava, A., & Varshney, N. (2023). Forecasting area, productivity and prices of mango in Valsad District of Gujarat: Time series analysis. Environment Conservation Journal, 24(2), 218–227.


  1. Anonymous (2021). Annual Report, Directorate of Horticulture, Agriculture, Farmers Welfare and Cooperation Department, Government of Gujarat (retrieve from:
  2. Areef, M., Rajeswari, S., Vani, N. & Naidu, G. M. (2020). Price behaviour and forecasting of Onion prices in Kurnoo market in Andhra Pradesh State. Economic Affairs, 65(1): 43-50. DOI:
  3. Box, G.E.P., Jenkins G.M., & Reinsel G.C. (1994). Time series analysis; forecasting and control. 3rd Edition, Prentice Hall, Englewood Cliff, New Jersey.
  4. Brockwell, P. J. & Davis R. A. (2002). An Introduction to Time Series and Forecasting, Springer-Verlag, New York, DOI: 10.1007/978-1-4757-2526-1 DOI:
  5. Deka, N. & Sarmah, A. K (2005). An analysis of growth trends in area, production and productivity of pineapple in Assam. Economic Affairs, 50(2): 110-115.
  6. Friedhelm B. (1973). Theil's forecast accuracy coefficient: a clarification. Journal of Marketing Research, Vol, (X), 444-446 DOI:
  7. Garde, Y. A. Chavda R. R., Thorat V. S. & Pisal R. R. (2022). Forecasting of area, productivity and prices of mango in Navsari district, Gujarat, Journal of Crop and Weed, 17(3): 17-28 DOI:
  8. Hamjah A.M. (2014). Forecasting major fruit crops productions in Bangladesh using Box-Jenkins ARIMA model. Journal of Economics and Sustainable Development, 5(7): 96-107
  9. Khan, M., Mustafa K., Shah M., Khan N., & Khan Z. (2008). Forecasting mango production Pakistan an economic model approach. Sarhad Journal of Agriculture, 24(2): 350-370.
  10. Kumar A. & Gupta R. K., (2020). Forecasting the production and area of Mango (Mangifera indica L.) in Himachal Pradesh by using different statistical models. International Journal of Bio-resource and Stress Management 2020, 11(1):014-019. DOI:
  11. Kumari, P., Mishra G. C. & Srivastava C. P. (2017). Forecasting models for predicting pod damage of pigeonpea in Varanasi region. Journal of Agrometeorology¸ 19(3), 265-269. DOI:
  12. MacKinnon, J. (1996). Numerical distribution functions for unit root and co-integration tests, Journal of Applied Econometrics, 11, 601-618 DOI:<601::AID-JAE417>3.0.CO;2-T
  13. Mazumdar, D. K. & Das, A. K. (1999). An analysis of production performance of pulses in Assam. Journal of Interacademica, 3(1):85-90.
  14. Moon, Mark A. (2013). Demand and supply integration: the key to world-class demand forecasting, pearson education, Inc. Publishing as FT Press Upper Saddle River, New Jersey 07458, pp 124-127
  15. Pardhi R., Singh R., & Paul R. (2018). Price forecasting of mango in Varanasi market of Uttar Pradesh. Current Agriculture Research Journal, 6(2): 218-224. DOI:
  16. Pradhan, P.C. (2012). Application of ARIMA model for forecasting agricultural productivity in India. Journal of Agriculture and Social Sciences, 8(2): 50-56.
  17. Qureshi M. N., Bilal M., Ayyub R.M, & Ayyub S. (2014). Modeling of mango production in Pakistan, Science International (Lahore), 26(3): 1227-1231.
  18. Rathod, S. & Mishra G.C. (2017). Weather based modeling for forecasting area and production of mango in Karnataka. International Journal of Agriculture, Environment and Biotechnology, 10(1): 149-162. DOI:
  19. Singh, S.P., Adarsha L.K., Nandi A.K., & Ome J. (2018). Production performance of fresh Mango in India: A growth and variability analysis. International Journal of Pure & Applied Bioscience, 6: 935-41. DOI:
  20. Sulaiman, Yusuf & Salau, Adekunle Sheu (2007). Forecasting mango and citrus production in Nigeria: A trend analysis, Munich Personal RePEc Archive (MPRA), online at
  21. Sundar Rajan & Palanivel., M. (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. DOI:
  22. Yusuf, S.A., & Salau, A.S., (2007). Forecasting mango and citrus production in Nigeria: trend analysis. Nigerian Agricultural Development Studies, 1(2), 1–19.