Price Forecasting Model for Perishable Commodities: A Case of Tomatoes in Punjab, Pakistan.

Muhammad Aamir, Shahzad (2017): Price Forecasting Model for Perishable Commodities: A Case of Tomatoes in Punjab, Pakistan.

Abstract

This study focused on developing forecasting model for perishable commodities and tomato is taken as a case to study. The model is developed on in-depth analysis of market dynamics and structure. An estimable theoretically founded model is the major output of this study which is based on true structure of the market. Complete model is comprised of inverted demand equation, Plantation and yield equations and the role of price expectations. The study reveals the fact that the farmers’ production decisions are affected by the expected profitability which is based on the expected output prices. However, due to the involvement of certain intermediaries the farmers couldn’t get the proper prices of its output whereas the domestic production meets 31.5% to its total demand only and the deficit is imported from other provinces of the country and from India. Low per acre yield and inefficient management practices, non-availability of hybrid seed, weather conditions and less profit margins and declining area of production causes the production to fall short of its potential maximum. Moreover, the increased reliance on imports and the increased demand due to increase in population causes the domestic prices to becomes more volatile. The majority of the small farmers sell their product through commission agents and wholesaler that cause imperfections in the market. Tomatoes value chain have certain problems like there exists a disparity between the small and large farmers in cost of production, yield and profitability. The model may forecast the prices on monthly or weekly basis depending upon the data availability.

• Item Type: MPRA Paper
• Language: English
• Keywords: Forecasting, Tomato, Market, Price
• Subjects: E - Macroeconomics and Monetary Economics > E1 - General Aggregative Models > E17 - Forecasting and Simulation: Models and Applications; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E31 - Price Level ; Inflation ; Deflation; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q1 - Agriculture > Q11 - Aggregate Supply and Demand Analysis ; Prices

References

Aidoo, Robert, Rita A. Danfoku, and James Osei Mensah. "Determinants of postharvest losses in tomato production in the Offinso North district of Ghana." Journal of Development and Agricultural Economics 6.8 (2014): 338-344.

Anwar, Dr, et al. (2015) "Determinants of Potato Prices and its Forecasting: A Case Study of Punjab,Pakistan."

Arshad, F. M., & Hameed, A. A. A. (2009). The long run relationship between petroleum and cereals prices. Global Economy & Finance Journal, 2(2), 91-100.

Asante, B. O., et al. "Producer characteristics and determinants of technical efficiency of tomato based production systems in Ghana." Journal of Development and Agricultural Economics 5.3 (2013): 92- 103.

Askari, H., & Cummings, J. T. (1977). Agricultural supply response. A survey of the econometric evidence. Praeger Publishers.

Ayinde, O. E., Bessler, D. A., & Oni, F. E. (2014, July). Analysis Of Supply Response And Price Risk On Rice Production In Nigeria. In Selected Paper prepared for presentation at the Agricultural & Applied Economics Association’s 2014 AAEA Annual meeting, Minneapolis MN, July (pp. 27-29).

Ayyub, R. M., M. Bilal, and M. Ahmed. "Meat prices hikes and its forecasting in Pakistan." J. Anim. Pl. Sci 21.2 (2011): 256-259.

Baumeister, C., & Kilian, L. (2014). Do oil price increases cause higher food prices?. Economic Policy, 29(80), 691-747.

Binuomote, S. O., & Odeniyi, K. A. (2013). Effect of crude oil price on agricultural productivity in Nigeria (1981-2010). International Journal of Applied Agriculture and Apiculture Research, 9(1-2), 131-139.

Box, G. E. P. and G. M. Jenkins (1970). Time series analysis: Forecasting and control. , Holdon-dav, San Francisco

Camillus, A. W., Stephen, O. M., Alexander, A., & Lydia, A. K. (2014). Economics of tomato marketing in Ashanti Region, Ghana. Russian Journal of Agricultural and Socio-Economic Sciences, 26(2).

Chern, W. S., & Just, R. E. (1978). Econometric Analysis of Supply Response and Demand for Processing Tomatoes in California (No. 37). California Agricultural Experiment Station.

Cioffi, A., Santeramo, F. G., & Vitale, C. (2009). The Price Stabilisation Effects of the EU import regime of fruit and vegetables: the case of tomatoes.

Dillon, B., & Barrett, C. B. (2013, September). Global crude to local food: an empirical study of global oil price pass-through to maize prices in East Africa. In International Conference on Food Price Volatility,“Causes and Challenges”, New York City, February (pp. 25-26).

Eckstein, Zvi. "A rational expectations model of agricultural supply." The Journal of Political Economy (1984): 1-19.

Eltoum, Yasir Ahmed Abdalla. Evaluation of the Factors Affecting the Production and Marketing of Tomato Crop In Khartoum State, Sudan. Diss. UOFK, 2015.

Engle, Robert F. "Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation." Econometrica: Journal of the Econometric Society (1982): 987-1007.

Griliches, Z. (1961). Hedonic price indexes for automobiles: An econometric of quality change. In The Price Statistics of the Federal Goverment (pp. 173-196). NBER.

Hernández, Ricardo, Thomas Reardon, and Julio Berdegué. "Supermarkets, wholesalers, and tomato growers in Guatemala." Agricultural Economics 36.3 (2007): 281-290.

Jordan, K. H., & VanSickle, J. J. (1995). Integration and behavior in the US winter market for fresh tomatoes. Journal of Agricultural and Applied Economics, 27(01), 127-137.

Limsombunchao, V. (2004). House price prediction: hedonic price model vs. artificial neural network.

Lopez, R. A., Adelaja, A. O., & Andrews, M. S. (1988). The effects of suburbanization on agriculture. American journal of agricultural economics, 346-358.

Lucier, Gary, et al. "Factors affecting tomato consumption in the United States." Vegetables and Specialties/VGS 282 (2000): 26-32.

Murthy, D. S., Sudha, M., Hegde, M. R., & Dakshinamoorthy, V. (2009). T*echnical efficiency and its determinants in tomato production in Karnataka, India: data envelopment analysis (DEA) approach. Agricultural Economics Research Review, 22(2), 215-224.

Naz, Falak, et al. "Studies on growth, yield and nutritional composition of different tomato cultivars in Battal valley of district Mansehra, Khyber Pakhtunkhwa, Pakistan." Sarhad Journal of Agriculture 27.4 (2011): 1-3.

Nwaru, Jude Chukwudi, Ubon Asuquo Essien, and Robert Enyeribe Onuoha. "Determinants of informal credit demand and supply among food crop farmers in Akwa Ibom State, Nigeria." Journal of Rural and Community Development 6.1 (2011): 129-139.

Paltasingh, K. R., & Goyari, P. (2013). Supply response in rainfed agriculture of Odisha, Eastern India: A vector error correction approach. Agricultural Economics Review, 14(2), 89.

Paltasingh, K. R., & Goyari, P. (2013). Supply response in rainfed agriculture of Odisha, Eastern India: A vector error correction approach. Agricultural Economics Review, 14(2), 89.

Roeger, E., & Leibtag, E. (2011, July). Produce sourcing and transportation cost effects on wholesale fresh fruit and vegetable prices.

Schultz, T. W., & Brownlee, O. H. (1942). Two trials to determine expectation models applicable to agriculture. The Quarterly Journal of Economics, 56(3), 487-496.

Sharif, M., W. Akhter, N.Akmal and Ayesha Tahir (2010). “Tomato Value Chain Analysis in Punjab Province of Pakistan”.

Shonkwiler, J. S., & Emerson, R. D. (1982). Imports and the supply of winter tomatoes: An application of rational expectations. American Journal of Agricultural Economics, 64(4), 634-641.

Trostle, R. (2008). Global agricultural supply and demand: factors contributing to the recent increase in food commodity prices. Washington, DC: US Department of Agriculture, Economic Research Service.

Yeh, Chi-Yuan, Chi-Wei Huang, and Shie-Jue Lee. "A multiple-kernel support vector regression approach for stock market price forecasting." Expert Systems with Applications 38.3 (2011): 2177-2186.