Commodity Outlook and Price Forecasting

Suresh Pal, Raka Saxena and Abhimanyu Jhajhria

ICAR-National Institute of Agricultural Economics and Policy Research

New Delhi-12

ICAR-National Institute of Agricultural Economics and Policy Research

Agricultural Markets

• The main problem with agricultural markets is information asymmetry, putting farmers at disadvantage

• Large variations in product quality and prices but seldom captured in the data

• Small product lots of farmers and rural connectivity of agricultural markets

– storage capacity

• Market information system and analysis

– Commodity outlook and price forecasting

ICAR-National Institute of Agricultural Economics and Policy Research

The Approach

• Agricultural production projections and commodity outlook

– Supply and demand conditions, international scenario

• Market behavior and price forecasting

– Historical trends in the production and prices

– Present market scenario like market arrivals, trade scenario

• Policy interventions to manage the shocks

ICAR-National Institute of Agricultural Economics and Policy Research

Commodity Outlook Model

Commodity outlook models are

– comprehensive multi-regional and multi-dimensional, simultaneous equation models

– to effectively depict the food production scenario and performance of commodity markets

– with short, medium and long-term projections on key parameters.

ICAR-National Institute of Agricultural Economics and Policy Research

Key Components of the Outlook Model

Producer Core System ◦ Area equation ◦ Yield equation ◦ Production equation ◦ Supply equation

Consumer Core System ◦ Household food demand equation ◦ Home-away demand equation ◦ Other uses demand equation ◦ Total demand equation

Trade Core System ◦ Export-import balance

Price linkage equation

ICAR-National Institute of Agricultural Economics and Policy Research

Commodity Outlook

Rice 2011/12 2016/17 2020/21 2025/26 Obs., 2016-17

Area (M ha) 44.17 44.20 44.34 44.53 43.84

Yield (t/ha) 2.26 2.37 2.47 2.61 2.43

Production (Mt) 100.01 104.82 109.60 116.07 106.53

Consumption (Mt) 96.55 100.81 105.72 112.03

End stock (Mt) 18.63 18.42 18.01 17.99

Net trade (Mt) 4.27 4.04 4.03 4.04

Maize 2011/12 2016/17 2020/21 2025/26 Obs., 2016-17

Area (M ha) 9.04 9.26 9.19 9.14 8.38

Yield (t/ha) 2.62 2.73 2.86 2.98 2.49

Prod.(Mt) 23.72 25.26 26.31 27.20 24.21

Food & Other (Mt) 10.22 11.04 11.65 12.10

Feed (Mt) 10.43 11.51 11.85 12.24

Consumption (Mt) 20.64 22.55 23.50 24.34

End stock (Mt) 678 630 635 634

Net trade (Mt) 3.16 2.72 2.81 2.86

ICAR-National Institute of Agricultural Economics and Policy Research

Commodity Price Forecasts, 2013-17

Objectives

To provide short term price forecasts to farmers for selected agricultural commodities for effective decision

making

To conduct regional case studies on price movements, marketing infrastructure and farmers’ decision making

13 states with 40 commodities

ICAR-National Institute of Agricultural Economics and Policy Research

Markets and data Selection of markets Collection of historical data on price, arrivals, trade variables, rainfall and other important variables

Forecast schedule and technique Forecasting time and Choice of technique: ARIMA, SARIMA, ARCH/GARCH, ANN , VAR etc.

Forecasts validation

Validation for hold-out dataset

Incorporating farmers’, traders’ and other stakeholders’ perception on market trends

Other Considerations

Accounting for changes in climatic variables like rainfall, supply changes

Forecasts Dissemination

Dissemination of price forecasts through personal interactions, print media, electronic media, social networking, farmers’ meetings and fairs, APMC and other relevant platforms

ICAR-National Institute of Agricultural Economics and Policy Research

Models Used for Price Forecasting

Univariate linear time series models: Exponential Smoothing, ARIMA, ARIMAX, Seasonal Decomposition etc.

Univariate non-linear time series models: GARCH, EGARCH, TGARCH etc.

Machine learning techniques: ANN, SVR, Lasso, Random Forest, Deep Learning etc.

Hybrid Models: Wavelet-ANN, GA-ANN, Wavelet-SVR, EMD-ANN etc.

Multivariate models: VAR, VECM, MGARCH and other variants.

ICAR-National Institute of Agricultural Economics and Policy Research

Forecast Inaccuracy under High Price Volatility

2 4 5

65

5 2

89

18 14

5 8 7

33

3 5

15

35

14

6 9

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Ginger Onion Potato Tomato

2014 2015 2016

ICAR-National Institute of Agricultural Economics and Policy Research

Institutionalization of the Capacity

• Building database, analytical capacity and institutional responsibility

– Reliability of database: consistency, uniformity, availability

• Development of research capacity for commodity outlook

– International experience and hands on training

• Implementation of price forecasting and dissemination

– For select commodities and states

– Development of dissemination mechanism and feedback

• Institutions

– Analytical capacity building in academic institutions

– Strengthening state agencies with manpower to create the capacity

ICAR-National Institute of Agricultural Economics and Policy Research

Price Forecasts for Onion

Weeks Delhi Lasalgaon Bengaluru

Actual Forecast Actual Forecast Actual Forecast

March-week1 747.14 715.23 696.14 529.57 671.43 734.46

March-week2 654.29 730.72 768.29 550.91 650.00 750.53

March-week3 640.00 736.85 825.00 569.56 700.00 769.38

March-week4 640.00 756.81 847.14 588.02 742.86 788.80

April-week1 640.00 778.76 867.29 606.98 750.00 809.26

April-week2 707.86 642.72 900.43 914.15 600.00 771.12

April-week3 750.71 663.51 827.43 944.38 785.71 785.59

April-week4 815.00 683.56 820.71 973.86 742.86 802.04

May-Week1 815.00 705.17 870.57 1021.73 657.14 823.91

May-Week2 822.14 708.85 910.14 1070.78 750.00 845.57

May-Week3 815.86 718.21 1047.57 1118.55 878.57 867.79

May-Week4 833.57 730.21 1140.43 1164.66 1071.43 892.00

June –Week1 861.00 741.70 1165.00 1200.03 1121.43 917.25

June-Week2 975.71 752.50 1277.43 1232.24 1114.29 944.08

June –Week3 1008.00 767.36 1214.29 1263.22 1121.43 972.38

June –Week4 1008.00 783.43 1236.17 1289.39 1100.00 1001.57

MAPE (%) 15 15 12

ICAR-National Institute of Agricultural Economics and Policy Research

Institutional Partners

Data Base Providers Data

Directorate of Marketing and

Inspection(DMI)

Arrival and price of whole sale and retail

market

Directorate of Economics and Statistics

(DES)

Area, yield, production data, advance

estimates, cost data

India Meteorological Department (IMD) Weather data

Department of Commerce and

Industry/Directorate General of Commercial

Intelligence and Statistics

Monthly trade statistics

National Agricultural Cooperative Marketing

Federation of India Ltd(NAFED)

Procurement and stock

Food Corporation of India (FCI) and

Department of Food and Public Distribution

Procurement, stock and distribution

Directorate of Sugar & Vegetable Oils Edible oil and sugar

Department of Consumer Affairs Market price and distribution through PDS

National Informatics Centre(NIC) Development of decision support system

NCDEX,MCX Future market price on agricultural

commodities

ICAR-National Institute of Agricultural Economics and Policy Research

Thank You !