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Plant Science Today (PST)

Research Articles

Vol. 12 No. 4 (2025)

Ginger price dynamics in the Eastern Himalayan Region: A case study of Meghalaya

DOI
https://doi.org/10.14719/pst.9291
Submitted
4 May 2025
Published
07-09-2025 — Updated on 01-10-2025
Versions

Abstract

Ginger contributes substantially to the agricultural sector in Meghalaya, yet comprehensive forecasting analyses that integrate both traditional and volatility-sensitive time series approaches to study its price movements are still scarce. In this study, we explore common time series yet very powerful forecast models, namely the autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and ARIMA with exogenous inputs (ARIMAX), along with autoregressive conditional heteroscedasticity (ARCH) and generalized autoregressive conditional heteroscedasticity (GARCH) models, to forecast the monthly price of ginger in Meghalaya. During the estimation process, attention was given to the intrinsic forecasting strengths and limitations of these models, as well as to essential time series diagnostics, including stationarity, parsimony and overfitting. The discussion in this study is based on the forecast results obtained from a real-time monthly price dataset spanning 10 years. While fitting the models to the dataset, special care was taken to select the most parsimonious model. To evaluate forecast accuracy and compare the performance of the different models applied to the time series of monthly ginger prices, we used seven forecast performance measures: ME (mean error), RMSE (root mean square error), MAE (mean absolute error), MAPE (mean absolute percentage error), MASE (mean absolute scaled error), AIC (Akaike information criterion) and BIC (Bayesian information criterion). The GARCH (1,1) model outperformed the others, yielding the lowest MAE (1,212.28), MASE (0.2817) and a high persistence in volatility (β₁ = 0.99772). The average price during the study period was ₹4400.47. The forecasts indicated a decline in prices from ₹11910 in March 2024 to ₹10938 in July 2024.

References

  1. 1. Das A, Layek J, Babu S, Kumar M, Yadav G, Patel D, et al. Influence of land configuration and organic sources of nutrient supply on productivity and quality of ginger (Zingiber officinale Rosc.) grown in Eastern Himalayas, India. Environ Sustain. 2020;3:59–67. https://doi.org/10.1007/s42398-020-00098-x
  2. 2. Giri NC, Mohanty RC. Traditional versus underneath farming of turmeric and ginger in Odisha, India. Int J Agric Stat Sci. 2022;18(1):189–95.
  3. 3. Food and Agricultural Organization. FAOSTAT [Internet]. Rome: FAO; 2020. Available from: https://www.fao.org/faostat/
  4. 4. Kumar S, Singh SP, Sharma RR. Constraints perceived by the farmers in adoption of improved ginger production technology: A study of low hills of Himachal Pradesh. Int J Bioresour Stress Manag. 2018;9(6):740–44. https://doi.org/10.23910/IJBSM/2018.9.6.1911
  5. 5. Shahrajabian MH, Sun W, Cheng Q. Clinical aspects and health benefits of ginger (Zingiber officinale) in both traditional Chinese medicine and modern industry. Acta Agric Scand B Soil Plant Sci. 2019;69(6):546–56. https://doi.org/10.1080/09064710.2019.1606930
  6. 6. Wang Y, Liu P, Zhu K, Liu L, Zhang Y, Xu G. A garlic-price-prediction approach based on combined LSTM and GARCH-family model. Appl Sci. 2022;12(22):11366. https://doi.org/10.3390/app122211366
  7. 7. Wang B, Liu P, Zhang C, Wang J, Peng L. Prediction of garlic price based on ARIMA model. In: Liu J, Yang Y, Ma J, Chen J, editors. Cloud computing and security: Proceedings of the 4th International Conference, ICCCS 2018; 2018 Jun 8–10; Haikou, China. Part I. Cham: Springer; 2018. p. 731–39. http://dx.doi.org/10.1007/978-3-030-00006-6_66
  8. 8. Gogoi M. Production performance of ginger: A study among Indian states. Int J Manag. 2021;11(12):845–50. https://doi.org/10.34218/IJM.11.12.2020.078
  9. 9. District Crop Forecast Committee. District crop forecast report for the month of January 2021. Tura: The Committee; 2021.
  10. 10. Mathew M, Vani N, Aparna B. Economics of production of ginger in Wayanad district of Kerala, India. Econ Aff. 2018;63(3):627–32. https://doi.org/10.30954/0424-2513.3.2018.6
  11. 11. Zhang XX, Liu L, Su CW, Tao R, Lobonţ OR, Moldovan NC. Bubbles in agricultural commodity markets of China. Complexity. 2019;2019(1):2896479. https://doi.org/10.1155/2019/2896479
  12. 12. Hasan MK, Haque MM, Rabbani MG. Production and price relationship of ginger (Zingiber officinale) in Bangladesh: A distributed lag model. Agriculturists. 2017;15(1):1–9. https://doi.org/10.3329/agric.v15i1.33423
  13. 13. Geta E, Kifle A. Production, processing and marketing of ginger in Southern Ethiopia. J Hortic For. 2011;3(7):207–13.
  14. 14. Jhade S, Singh A. Integration and causality of major garlic markets in Uttar Pradesh state. J Res ANGRAU. 2021;49(2):100–106.
  15. 15. Kithu CJ. Marketing and export prospects of ginger. In: Singh HP, Tamil Selvan M, editors. Indian ginger – Production and utilization. Kerala: Directorate of Arecanut and Spice Development; 2003.
  16. 16. Moktan MW, Dhiman Mukherjee DM. Trade and marketing strategy for spice crops in Darjeeling district. Environ Ecol. 2008;26(3A):1302–305.
  17. 17. Saini AS, Sood AK, Thakur DR. Evaluation of ginger marketing system in Himachal Pradesh. In: Singh LP, Garg S, editors. Agricultural marketing. Jaipur: Arihant; 1992. p. 260–82.
  18. 18. George L. The ginger economy of Kerala [PhD dissertation]. Kerala: Cochin University of Science and Technology; 1996.
  19. 19. Datta SK, Gurdev S, Millindo C. Management of marketing and export of ginger with special reference to Eastern Himalayan region. In: Singh HP, Tamil Selvan M, editors. Indian ginger – Production and utilization. Kerala: Directorate of Arecanut and Spice Development; 2003.
  20. 20. Aleem M, Khan MI, Shakshaz FA, Akbari N, Anwar D. Botany, phytochemistry and antimicrobial activity of ginger (Zingiber officinale): A review. Int J Herb Med. 2020;8(6):36–49. https://doi.org/10.22271/flora.2020.v8.i6a.705
  21. 21. Rymbai H, Jha AK, Talang HD, Deshmukh NA, Verma VK, Baiswar P, et al. Organic ginger cultivation in North Eastern region. Umiam (Meghalaya): ICAR Research Complex for NEH Region; 2018. https://doi.org/10.13140/RG.2.2.11901.64482
  22. 22. Gosh D. Shifting cultivation in northeast India: A historical perspective. In: Bhattacharyya RHHW, Das RT, editors. Agriculture in northeast India. New Delhi: B.R. Publishing Corporation; 1984. p. 45–59.
  23. 23. Zonuntluanga R. Problems and prospects of agricultural marketing in Mizoram [PhD dissertation]. Aizawl: Mizoram University; 2002.
  24. 24. Rahman H, Ramaiyan K, Kishore K, Denzongpa R. Traditional practices of ginger cultivation in Northeast India. Indian J Tradit Knowl. 2009;8(1):23–28.
  25. 25. Kumar A, Avasthe RK, Lepcha B, Mohanty AK, Shukla G. Impact of frontline demonstrations on yield enhancement of ginger (var. Majauley) in tribal reserve biosphere of Sikkim Himalaya. J Agric Sci. 2012;3(2):121–23. https://doi.org/10.1080/09766898.2012.11884693
  26. 26. Jha AK, Deka BC. Present status and prospects of ginger and turmeric in NE States. Meghalaya: Kiran Publications, ICAR Research Complex for NEH Region; 2012. p. 1–5.
  27. 27. Mawlong M. Ginger cultivation in Umroi, Ri Bhoi District, Meghalaya. IOSR J Humanit Soc Sci. 2017;22(7):36–45. https://doi.org/10.9790/0837-2207033645
  28. 28. Sharath AM, Dhanajaya KB. Organic farming in India – A study of ginger cultivation. Excellence Int J Edu Res. 2015;2(6):50–61. https://www.researchgate.net/publication/333207594_Organic_Farming_in_India_-_a_study_of_Ginger_Cultivation
  29. 29. Panme PH, Thangjom LS. Prospects and challenges in ginger cultivation: Livelihood of the rural farmers in Dima Hasao district, Assam. Int J Agric Sci. 2021;17(2):724–28. https://doi.org/10.15740/HAS/IJAS/17.2/724-728
  30. 30. Sharma V, Longkumer J, Mary NO, Das S. Constraints in ginger cultivation by farmers of West Garo Hills district of Meghalaya. Int J Econ Plants. 2023;10(3):252–55. https://doi.org/10.23910/2/2023.526d
  31. 31. Enders W. Applied econometric time series. 4th ed. New York: University of Alabama; 2015.
  32. 32. Hyndman RJ, Talagala TS, Athanasopoulos G. Meta-learning how to forecast time series. Monash Econom Bus Stat Work Pap. 2018;6(18):16–44. https://business.monash.edu/econometrics-and-business-statistics/research/publications
  33. 33. Taylor JW. Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles. Int J Forecast. 2010;26(4):627–46. https://doi.org/10.1016/j.ijforecast.2010.02.009
  34. 34. Wei H, Lai KK, Yu L, Wang S. A novel nonlinear neural network ensemble model for financial time series forecasting. In: Proceedings of the 6th International Conference in Computational Science; 2006 May 28; UK. Berlin: Springer; 2006. p. 790–3. https://doi.org/10.1007/11758501_106
  35. 35. Claeskens G, Hjort NL. Model selection and model averaging. Cambridge (England): Cambridge University Press; 2008. https://doi.org/10.1007/S11336-011-9219-3
  36. 36. Chatfield C, Xing H. The analysis of time series: An introduction with R. New York: Chapman and Hall; 2019. https://doi.org/10.1201/9781351259446
  37. 37. Box GEP, Jenkins GM. Time series analysis: Forecasting and control. San Francisco: Holden Day; 1976.
  38. 38. Hipel KW, McLeod AI. Time series modelling of water resources and environmental systems. Amsterdam: Elsevier; 1994. https://doi.org/10.1002/iroh.19950800107
  39. 39. Dickey DA, Fuller WA. Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc. 1979;74(366a):427–31. https://doi.org/10.2307/2286348
  40. 40. Lombardo R, Flaherty J. Modelling private new housing starts in Australia. Paper presented at: Pacific-Rim Real Estate Society Conference; 2002; Sydney, Australia.
  41. 41. Webel K, Ollech D. An overall seasonality test based on recursive feature elimination in conditional random forests. In: Proceedings of the 5th International Conference on Time Series and Forecasting; 2018; Granda. p. 20–31.
  42. 42. Armstrong JS, Collopy F. Error measures for generalizing about forecasting methods: Empirical comparisons. Int J Forecast. 1992;8(1):69–80. https://doi.org/10.1016/0169-2070(92)90008-W
  43. 43. Armstrong JS. Evaluating forecasting methods. In: Armstrong JS, editor. Principles of forecasting: A handbook for researchers and practitioners. Boston: Kluwer Academic Publishers; 2001. p. 443–72. https://doi.org/10.1007/978-0-306-47630-3_20
  44. 44. Gooijer JGD, Hyndman RJ. 25 years of time series forecasting. Int J Forecast. 2006;22(3):443–73. https://doi.org/10.1016/j.ijforecast.2006.01.001
  45. 45. Hyndman RJ, Koehler AB. Another look at measures of forecast accuracy. Int J Forecast. 2006;22(4):679–88. https://doi.org/10.1016/j.ijforecast.2006.03.001
  46. 46. Yeasin M, Sharma P, Paul RK, Meena DC, Anwer ME. Understanding price volatility and seasonality in agricultural commodities in India. Agric Econ Res Rev. 2023;36(2):177–88. https://doi.org/10.5958/0974-0279.2023.00031.9
  47. 47. Ospina R, Gondim JA, Leiva V, Castro C. An overview of forecast analysis with ARIMA models during the COVID-19 pandemic: Methodology and case study in Brazil. Math. 2023;11(14):3069. https://doi.org/10.3390/math11143069
  48. 48. Pandit P, Sagar A, Ghose B, Paul M, Kisi O, Vishwakarma DK, et al. Hybrid modelling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks. Sci Rep. 2024;14(1):26639. https://doi.org/10.1038/s41598-024-74503-4
  49. 49. Li F, Meng X, Zhu K, Yan J, Liu L, Liu P. Research on ginger price prediction model based on deep learning. Agriculture. 2025;15(6):596. https://doi.org/10.3390/agriculture15060596
  50. 50. Ministry of Earth Sciences. India Meteorological Department. Rainfall statistics of India – 2023 [Internet]. Available from: https://mausam.imd.gov.in
  51. 51. Kumari P, Goswami VNH, Pundir RS. Recurrent neural network architecture for forecasting banana prices in Gujarat, India. PLoS One. 2023;18(6):e0275702. https://doi.org/10.1371/journal.pone.0275702
  52. 52. Uma M, Rajarathinam P, Venkatachalam S, Baskaran T. Growth and forecast performance of turmeric in India: An empirical analysis. Madras Agric J. 2022;109(1–3):1–6. https://doi.org/10.29321/MAJ.10.000658
  53. 53. Nath B, Bhattacharya D. Modeling onion price volatility with AR–GARCH framework. Environ Ecol. 2025;43(1):79–88. https://doi.org/10.60151/envec/twwk2894
  54. 54. Shankar SV, Chandel A, Gupta RK, Sharma S, Chand H, Kumar R, Gowsar SN. Exploring the dynamics of arrivals and prices volatility in onion (Allium cepa) using advanced time series techniques. Front Sustain Food Syst. 2023;7:1208898. https://doi.org/10.3389/fsufs.2023.1208898
  55. 55. Setiawan B, Ben Abdallah M, Fekete-Farkas M, Nathan RJ, Zeman Z. GARCH (1, 1) models and analysis of stock market turmoil during COVID-19 outbreak in an emerging and developed economy. J Risk Financ Manag. 2021;14(12):576. https://doi.org/10.3390/jrfm14120576
  56. 56. Mani JR, Hudu MI, Ali A. Price variation of tomatoes and ginger in Giwa Market, Kaduna State, Nigeria. J Agric Ext. 2018;22(1):91–4. https://doi.org/10.4314/jae.v22i1.9
  57. 57. Das TK, Singh M, Borah S. Value chain and constraints analysis of ginger in West Garo Hills district of Meghalaya. J Krishi Vigyan. 2024;12(2):435–40. https://doi.org/10.5958/2349-4433.2024.00076.4

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