Estimation of Farm Supply Response and Acreage Allocation: A Case Study of Indian Agriculture

Parikh, K.S. & Narayana, N.S.S. (1980). Estimation of Farm Supply Response and Acreage Allocation: A Case Study of Indian Agriculture. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-80-007

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Abstract

Some of the most important decisions in agricultural production, such as what crops to grow and on how much land, have to be taken in an uncertain environment of future rain, yield and prices. This paper aims at modeling the land allocation decisions of the Indian farmers as an important first step in developing a model for Indian Agricultural Policy. The approach adopted is consistent with the basic premise that farmers behave rationally and that rational farmers react in a way that maximizes their utility in the contexts of opportunities, uncertainties and risks as perceived by them.

After a brief review of the available approaches towards estimating the farm supply response, a summary of a few important studies in this connection was provided which are essentially based on the traditional Nerlovian model.

Nerlovian model, based on adaptive expectations and adjustment schemes, is quite general and is applicable for the study of acreage response even for developing economies like India. However, there seems to be a serious misspecification involved in this model as far as the formulation of the price expectation function is concerned. Nerlovian specification does not separate the actually realized prices in the past into "stationary" (or expected) and random components, and attaches the same weights to the two components for predicting expected prices.

This paper deviates from the traditional Nerlovian model on two counts mainly:

1. Acreage response to different crops was estimated using expected revenue instead of expected price as a proxy for expected profits.

2. First, an appropriate revenue (or price as the case may be) expectation function was formulated for each crop by clearly identifying the "stationary" and random components involved in the past values of the variable, and attaching suitable weights to these components for prediction purposes. An Auto Regressive Integrated Moving Average (ARIMA) type model was postulated towards this purpose and Box-Jenkins methodology was made use of in estimating these functions.

Almost all the crops grown in India were considered in our study. Based on sowing and harvesting periods and also some important data, an overall substitution pattern among the crops at all-India level was drawn up. This pattern permits classification of the crops into ten groups where the crops in different groups are usually grown in different soils and/or different seasons. The essential data for estimating the acreage response consists of area, production, yield, irrigation, prices and rainfall.

The revenue expectation functions for different crops estimated as mentioned earlier, were later lugged in the Nerlovian model and the acreage response equations were estimated.

Later, an area-allocation scheme was formulated so that the individually estimated areas of different crops would add up to the exogenously specified total gross cropped area in the country.

Finally, the estimated equations were all subjected to a validation exercise to judge the performance of the model; particularly its ability to predict the turning points.

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