Academic journal article Agricultural Economics Review

Supply Response in Rainfed Agriculture of Odisha, Eastern India: A Vector Error Correction Approach

Academic journal article Agricultural Economics Review

Supply Response in Rainfed Agriculture of Odisha, Eastern India: A Vector Error Correction Approach

Article excerpt

Abstract

This paper provides an empirical investigation of supply response in case of rice in rainfed agriculture of Odisha. A composite weather index, instead of rainfall has been incorporated in the model to monitor farmers ' supply response to weather. The vector error correction approach which avoids the unrealistic assumption of fixed supply on the basis of static expectation is applied. The empirical results reveal the fact that there is price inelasticity of supply whereas the supply elasticity with respect to weather is found to be very high. The policy implications suggest that there should be huge emphasis on irrigation as well as weather insurance. Assured irrigation minimizes the dependence on weather conditions and secures the crop from the vagaries of monsoon and thereby reduces the risk attached with farming.

Key Words: Supply response, Weather index, Error correction modeling

JEL Classification: C32, C51, Q11

(ProQuest: ... denotes formulae omitted.)

Introduction

The study of farmer's response and price elasticities has become a vital area of research since it is well established that the price mechanism plays a significant role in bringing both demand and supply for agricultural goods in equilibrium and correcting the imbalances (Lahiri and Roy, 1985; p.315). The role of other shifters in supply response analysis like weather, irrigation, area under HYVs and other inputs along with price cannot be ignored since the information about supply elasticity allows for the effective formulation of appropriate agricultural policies and helps predict short-run and long-run input changes on production (Moula, 2010; p.182). In India, the results of supply response studies are inconclusive as they vary from crop to crop, region to region as well as from one methodology to another. The studies in Indian agriculture like Krishna (1963), Narayanan and Parikh (1981), Lahiri and Roy (1985), Kumar and Rosegrant (1997), Gulati and Kelley (1999), Kanwar (2004) and so on conclude differently so far as the supply response to price is concerned. Krishna (1963) in that context was an outstanding one in the sense that it refuted the widely prevalent view that the peasants in underdeveloped countries do not respond or respond very little or negatively to price movements. Since then, many people have studied the nature of price responsiveness of Indian farmers in the case of different crops using different methodologies. Unfortunately all these studies suffer on several grounds. One such lacuna is that they all treated the farmers alike, ignoring the type of physical conditions that they operate in. Since most of the studies are based on aggregate data in the all India level, they overlook many peculiarities at regional levels. Some farmers operate in conditions where irrigation and other facilities are well facilitated. Thus, their reaction to price varies from the farmers who operate in rainfed agriculture. In fact, the farmers in rainfed agriculture respond more to weather conditions rather than price. Hence, analyzing the supply response on aggregate level ignores the regional specific characteristics. Second, most of the studies utilized ordinary least square (OLS) estimation technique which is not an appropriate one . Advances in time series techniques like Cointegration and Error Correction Mechanism (ECM) are more suitable in this regard. Error correction model performs well empirically and importantly it offers a scope of including the variables at levels alongside their differences and hence of modeling long-run as well as short-run relationships between integrated series4. The modeling of the short-run dynamics is consistent with any such long-run relationships (Hallam and Zanoli, 1993). Third, rainfall or rainfall index as a proxy for weather is incorporated commonly in a linear fashion along with prices and other shifters. But it is fairly established that both rainfall and temperature together affect the crop yields and also acreage devoted to cultivation5. …

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