An Effective Multiple Linear Regression-Based Forecasting Model for Demand-Based Constructive Farming

1. Introduction

The farmers in developing countries like India are being faced by the age-old problems in the field of agriculture like there is no reliable and easy access to accurate weather forecasting, there is no easy access to government market portals, there is no common forum to consult the agricultural experts for discussion, there is no system to inform the farmers about new tools, technologies, and new governmental schemes, there is no synchronization between the demand and supply of food crops and list goes on. Demand-supply problem is one of the major problems being faced by the farmer community, where there is no synchronization in the production and demand for food crops. Due to this either farmer is failing to get good market prices when there is more supply than demand or consumer suffers high prices due to less production. To effectively tackle this problem, there is a need for some forum that forecasts the societal demand for different food crops and assists the farmers in growing the crops accordingly.

Demand forecasting plays an important role in the success of any business. As the demand forecasting helps in efficient planning for production, supply, maintenance, and other activity, the performance of a business depends on the accuracy of forecasting. Demand forecasting also plays a strategic role in the field of agriculture, where a farmer can plan for the crop production according to the need of society. So, this work attempts to develop a model to forecast the demand for the food crops and assists the farmers in selecting a crop based on this demand.

The demand for any product can be defined as, the aspiration for a product/commodity reinforced by the ability and willingness to buy it(Triplett, 1976). Demand is a function of Price of the Commodity P_c, Relative Price R_p, Income I, Taste and Preference T_P, Future Expectation F_e and other factors O (“Demand, Supply, and Market Equilibrium,” 2011; Whelan & Forrester, 1996). The relation of Demand with these parameters can be expressed as:

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  Price of the Commodity (P_c): Demand is contrariwise of the price i.e. the demand for a commodity will decrease with the increase in the price of a commodity and vice versa;

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  Relative Price of Commodity (R_p): For certain products, the price are directly related to associated product price, those products are called relative products. The relative price of the product is directly proportional to the associated product;

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  The income (I): The demand for a product will directly depend on the income of the people, an increase in income results in an increase in demand for the products. Consequently, the demand will decree with the decreasing income;

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  Taste and Preferences (T_P): Taste and preferences of a consumers influence the demand for a commodity. With the change in taste and preferences of a consumer will affects the level of demand for various goods. Taste and Preferences may change due to the change in lifestyle, habits, etc.;

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  Future Expectation (F_e): Expectation in the future price variations of goods will also affect their demand predominantly for durable goods. The expectation of an increase in price will decrease the demand and vice versa;

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  Other Factors (O): Other factors such as population, weather and climate condition, taxation, advertisement etc. will also affect the demand of a commodity.

The Demand ‘D’ of the product could be represented as a function of the above parameters as D= f (P_c, R_p, I, T_P, F_e, O).