Research on World Agricultural Economy

Comparative Analysis of Price Forecasting Models for Garlic (Allium sativum L.) in Kota District of Rajasthan, India

Surjeet Singh Dhaka

Department of Applied Agriculture, Central University of Punjab, Bathinda, Punjab, 151401, India

Urmila

Department of Applied Agriculture, Central University of Punjab, Bathinda, Punjab, 151401, India

Dharavath Poolsingh

Department of Applied Agriculture, Central University of Punjab, Bathinda, Punjab, 151401, India

DOI: https://doi.org/10.36956/rwae.v4i4.915

Received: 30 July 2023; Received in revised form: 10 September 2023; Accepted: 25 September 2023; Published: 16 October 2023

---

Abstract

Garlic is a well-known spice in India, and Rajasthan is the country's second-largest producer of garlic after Madhya Pradesh. Accurate price predictions are crucial for agricultural commodities, as they significantly impact the accessibility of food for consumers and the livelihoods of farmers, governments, and agribusiness industries. Governments also use these forecasts to support the agricultural sector and ensure food security. A study was conducted in Rajasthan's Kota district to analyze the wholesale price of garlic using data from July 2021 to July 2023 from the Kota fruit and vegetable market. The study used simple moving average (SMA), simple exponential smoothing (SES), and autoregressive integrated moving average (ARIMA) models to forecast garlic prices. The models were validated through mean absolute deviation (MAD), mean squared error (MSE), mean absolute percentage error (MAPE), root mean squared error (RMSE), correlation coefficient (r), and coefficient of variation (CV). The research was conducted utilizing Microsoft Excel and R Studio version 4.2.2 for Windows, and the results showed that the ARIMA (1,0,0) with a non-zero mean model had a strong correlation coefficient (r = 0.91**) and accurately predicted the variation in garlic prices. Based on the analysis, it is recommended to use this model for forecasting and making informed decisions.

Keywords: Agricultural commodities, ARIMA model, Garlic, Informed decisions, Market intelligence, Price forecasting models

---

References

[1] Baliga, M.S., Shivashankara, A.R., Palatty, P.L., et al., 2013. Protective effect of garlic (Allium sativum L.) against atherosclerosis. Bioactive food as dietary interventions for cardiovascular disease. Academic Press: Cambridge. pp. 591-607. DOI: https://doi.org/10.1016/B978-0-12-396485-4.00031-1

[2] Amagase, H., Petesch, B.L., Matsuura, H., et al., 2001. Intake of garlic and its bioactive components. The Journal of Nutrition. 131(3), 955S-962S. DOI: https://doi.org/10.1093/jn/131.3.955s

[3] Banerjee, S.K., Maulik, S.K., 2002. Effect of garlic on cardiovascular disorders: A review. Nutrition Journal. 1(1), 1-14. DOI: https://doi.org/10.1186/1475-2891-1-4

[4] Spices Industry and Export in India [Internet]. IBEF; 2023. Available from: https://www.ibef.org/exports/spice-industry-indias#:~:text=India%20is%20the%20world's%20largest,stood%20at%2010.87%20million%20tonnes

[5] Keelery, S., 2023. Area under Spices Cultivation India FY 2010-2022 [Internet] [cited 2023 May 16]. Available from: https://www.statista.com/statistics/1377943/india-spices-cultivation-area/#:~:text=In%20financial%20year%202022%2C%20the,among%20a%20variety%20of%20others

[6] Keelery, S., 2023. Production Volume of Spices India FY 2009-2022 [Internet] [cited 2023 May 16]. Available from: https://www.statista.com/statistics/622453/spice-production-india/

[7] Krishnakumar, P.K., 2023. Garlic Emerges as the Leader in India’s Spice Export Basket [Internet] [cited 2023 Jun 22]. Available from: https://www.moneycontrol.com/news/business/economy/garlic-emerges-as-the-leader-in-indias-spice-export-basket-10535171.html

[8] Area, Production and Productivity of Garlic in India (1970-1971, 1974-1975 to 2022-2023-1st Advance Estimates) [Internet]. Indiastat; 2023 [cited 2023 Jun 20]. Available from: https://www.indiastat.com/table/garlic/area-production-productivity-garlic-india-1970-197/337535

[9] Selected State-wise Area, Production and Productivity of Garlic in India [Internet]. Indiastat; 2023 [cited 2023 Jun 20]. Available from: https://www.indiastat.com/table/garlic/selected-state-wise-area-production-productivity-g/1424608

[10] Indiastat [Internet] [cited 2023 Jun 20]. Available from: https://www.indiastat.com/rajasthan-state/data/agriculture/garlic

[11] Feng, Y., 2021. Garlic price forecast based on the combined model of time-frequency decomposition and neural network. Academic Journal of Computing & Information Science. 4(6), 86-96. DOI: https://dx.doi.org/10.25236/AJCIS.2021.040615

[12] Wang, B., Liu, P., Zhang, C., et al., 2018. Prediction of garlic price based on ARIMA model. Cloud Computing and Security: 4th International Conference, ICCCS 2018; 2018 Jun 8-10; Haikou, China. Berlin: Springer International Publishing. p. 731-739. DOI: https://doi.org/10.1007/978-3-030-00006-6_66

[13] Lianlian, L., Chao, Z., Junmei, W., et al. (editors), 2021. The effect of COVID-19 on garlic prices. 2021 IEEE International Conference on Smart Internet of Things (SmartIoT); 2021 Aug 13-15; Jeju, Korea. New York: IEEE. p. 322-326. DOI: https://doi.org/10.1109/SmartIoT52359.2021.00058

[14] Al-Mamun, M.A., Sayem, S.M., Rahman, K.M.M., et al., 2020. A statistical analysis on forecasting prices of some important food commodities in Bangladesh: A statistical analysis on forecasting prices. Sultan Qaboos University Journal for Science [SQUJS]. 25(2), 130-137.

[15] Wu, G., Liu, P., Chen, W., et al. (editors), 2018. Analysis of price fluctuation characteristics and influencing factors of garlic based on HP filter method. Cloud Computing and Security: 4th International Conference, ICCCS 2018; 2018 Jun 8-10; Haikou, China. Berlin: Springer International Publishing. p. 334-342. DOI: https://doi.org/10.1007/978-3-030-00006-6_30

[16] Mathur, S., Dhaka, S.S., 2017. Impact assessment of chickpea (chana) price forecast advice on economic status of the farmers. Indian Journal of Economics and Development. 13(2a), 304-308. DOI: http://dx.doi.org/10.5958/2322-0430.2017.00085.3

[17] Bannor, R.K., Dhaka, S., 2015. Integration of cluster beans markets in Rajasthan state of India. Economic Affairs. 60(3), 529-538. DOI: http://dx.doi.org/10.5958/0976-4666.2015.00074.1

[18] Bannor, R.K., Sharma, M., Dhaka, S., 2016. Integration of cumin markets in Rajasthan. Journal of Energy and Natural Resource Management. 3(2).

[19] Dhaka S.S., Urmila, 2023. Youth participation in agriculture and allied sectors: Empirical evidence from Rajasthan. Indian Journal of Economics and Development. 19(1), 58-68. DOI: https://doi.org/10.35716/IJED/21324

[20] Allen, P.G., 1994. Economic forecasting in agriculture. International Journal of Forecasting. 10(1), 81-135. DOI: https://doi.org/10.1016/0169-2070(94)90052-3

[21] District Kota, Government of Rajasthan, 2023. About District [Internet]. Available from: https://kota.rajasthan.gov.in/sm/jankalyan-category-and-entry-type/12744/44/4/1

[22] District Profile [Internet]. Krishi Vigyan Kendra, Borkhera, Kota. Available from: https://kota.kvk2.in/district-profile.php

[23] Losada, L., 2022. Time Series Analysis with Auto. Arima in R [Internet] [cited 2023 Jun 23]. Available from: https://towardsdatascience.com/time-series-analysis-with-auto-arima-in-r-2b220b20e8ab

[24] Hyndman, R.J., Khandakar, Y., 2008. Automatic time series forecasting: The forecast package for R. Journal of Statistical Software. 27, 1-22. DOI: https://doi.org/10.18637/jss.v027.i03

[25] Hyndman, R.J., Athanasopoulos, G., 2018. Forecasting: Principles and Practice, 2nd Edition [Internet] [cited 2023 Jun 23]. Available from: https://otexts.com/fpp2/

[26] The R Project for Statistical Computing [Internet]. Available from: https://www.R-project.org/

[27] Box, G.E.P., Jenkins, G.M., Reinsel, G.C., et al., 2015. Time series analysis: Forecasting and control (5th ed). John Wiley & Sons: Hoboken, New Jersey.

[28] Kathayat, B., Dixit, A.K., 2021. Paddy price forecasting in India using ARIMA model. Journal of Crop and Weed. 17(1), 48-55. DOI: https://doi.org/10.22271/09746315.2021.v17.i1.1405

[29] Verma, V.K., Kumar, P., Singh, H., 2016. Use of ARIMA modeling in forecasting coriander prices for Rajasthan. International Journal of Seed Spices. 6(2), 40-45.

[30] Yang, S., Chen, H.C., Chen, W.C., et al., 2020. Forecasting outbound student mobility: A machine learning approach. PloS One. 15(9), e0238129. DOI: https://doi.org/10.1371/journal.pone.0238129

[31] Sen, S., Das, M.N., Chatterjee, R. (editors), 2016. A weighted kNN approach to estimate missing values. 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN); 2016 Feb 11-12; Noida, India. New York: IEEE. p. 210-214. DOI: https://doi.org/10.1109/SPIN.2016.7566690

[32] Lee, H., 2017. Comparison of time series forecasting models in garlic's wholesale price. Journal of Rural Development/Nongchon-Gyeongje. 40(2), 55-73. DOI: http://dx.doi.org/10.22004/ag.econ.330724

[33] Rosli, N.S., Ibrahim, R., Ismail, I., et al., 2022. Modeling of high voltage induction motor cooling system using linear regression mathematical models. Plos One. 17(11), e0276142. DOI: https://doi.org/10.1371/journal.pone.0276142

[34] Ostertagova, E., Ostertag, O., 2012. Forecasting using simple exponential smoothing method. Acta Electrotechnica et Informatica. 12(3), 62. DOI: http://dx.doi.org/10.2478/v10198-012-0034-2

[35] Brown, R.G., Meyer, R.F., 1961. The fundamental theorem of exponential smoothing. Operations Research. 9(5), 673-685. DOI: https://doi.org/10.1287/opre.9.5.673

[36] Kim, S., Kim, H., 2016. A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting. 32(3), 669-679. DOI: https://doi.org/10.1016/j.ijforecast.2015.12.003