Supply Response of Cereal Crop Farmers to Price and Non-Price Factors in Rajasthan State of India

Journal of Agricultural Economics and Rural Development Vol. 3(2), pp. 230-235, August, 2017. www.premierpublishers.org. ISSN: XXXX-XXXX AJAERD Review Article Supply Response of Cereal Crop Farmers to Price and Non-Price Factors in Rajasthan State of India Sadiq MS 1,2*, Singh IP 2, Karunakaran N 3 1 Department of Agricultural Economics and Extension Technology, FUT, Minna, Nigeria 2 Department of Agricultural Economics, SKRAU, Bikaner, India 3 Department of Economics, EKNM Government College Elerithattu, Kerela, India The present study investigated cereal crop farmers acreage response to price and non-price factors in Rajasthan State of India using time series data spanning from 1981 to 2014. The cereal crops considered for the study were jowar, maize, bajra, wheat and barley. Furthermore, the Nerlovian model was used for data synthesis. From the results, it was observed that farmers in the state were not price responsive except for maize. The growers of these crops considered the lagged area and lagged price of competing crops to be the major factors for area allocation decision. The lagged price and lagged yield emerged as an important factor in deciding area allocation to maize and barley crops Therefore, findings showed that farmers decision on cereal crops acreage allocation were governed by both price and non-price factors. Hence price incentive alone was not sufficient in bringing desirable change in cropping pattern as well as the production of these crops. Therefore, the creation of other infrastructural facilities like irrigation is important to increase acreage and production with stability in the studied area. Keywords: Acreage; response; Nerlovian model; cereal crops; Rajasthan state; India INTRODUCTION A disquieting feature of Indian agricultural economy is the uncertain trend in the movement of prices of agricultural products. In a developing country like India where the emphasis is being laid on planned development of the economy, the role of price mechanism cannot be undermined in effecting desired changes in production. Supply response function is based on generally accepted notion that current experience relating to the past decisions influences decisions. Farmers are rational in allocating the land to a particular crop, hence, it is important to identify and examine important price and nonprice factors which affect this rational decision. Nerlove (1958) as cited by Maddala and Lahiri (2009) and Subba et al. (2016) developed two distributed lag models, and estimated the supply elasticity of corn, cotton and wheat in U.S.A. In the distributed lag model based on price expectations, he assumed past price experience influences formation of expected price, which in turn, influenced acreage allocation decision. Since then, the Nerlovian lagged adjustment model has been used by researchers for analysis, for example, Subba et al. (2016); Abujam et al. (2015), because Nerlove introduced the element of dynamism by incorporating the concept of distributed lag in the analysis of crop acreage response. Another advantage for the choice of this particular model is that it facilitates separate estimation of the long-run and short-run elasticities. Following this suite, this research aimed at investigating empirically factors beyond price that influence farmers acreage response in Rajasthan State of India. * Corresponding author: Sadiq MS, Department of Agricultural Economics and Extension Technology, FUT, Minna, Nigeria. Department of Agricultural Economics, SKRAU, Bikaner, India. Email: sadiqsanusi30@gmail.com; Tel:+2347037690123

Sadiq et al. 231 RESEARCH METHODOLOGY The study made use of time series data spanning from 1981-2014, covering the area, production, productivity, farm harvest prices and wholesale prices of selected crops. The criteria used for the identification of major cereal crops was that the crop should have an average area of at least 2-3 lakh hectares during the last five years, while district which accounts for 50 percent share in the gross area under a particular crop in Rajasthan was chosen. Data sources were the statistical abstract of Rajasthan State, Directorate of Economics and Statistics, AGMARKNET etc. Nerlovian model was used for data analysis. Table 1: Selected districts for each crop Crops Jowar Maize Bajra Wheat Barley Districts Nagaur and Tonk Chittore and Udaipur Nagaur, Churu and Sri-Ganganagar Jaipur, Alwar and Sri-Ganganagar Jaipur, Alwar and Sri-Ganganagar Empirical model specification The basic model which has come to be called as Nerlovian price expectation model is as follows: At = α + βip*t + U. (1) (Pt* - P*t-1) = β(pt-1 P*t-1) 0<β<1 (2) Where; At = Actual acreage under the crop in year t P*t = expected price of the crop in year t P*t-1 = expected price of the crop in year t-1 Pt-1 = actual price of the crop in year t-1 Ut = Stochastic term β= the coefficient of price expectation, and; α and β are parameters to be estimated. The hypothesis described through equation (2) is price expectation hypothesis. The expression on the left-hand side of this equation is the revision in price expectation from year to year. On the right-hand side, the expression is the error made by the farmers in predicting the price during t-1. The coefficient of price expectation (β) indicates that only a fraction of last year s error in price prediction is translated into revision in expected price during the current year. Nerlovian model depicting farmer s behavior in its simplest form is given below: At* = β0 + β1pt-1 + β2yt-1 + β3rf_irr + β4yrt + β5prt + β6cyt-1 + β7cpt-1 + DD + Ut (3) At-At-1 =B (At*-At-1) (Nerlovian adjustment equation) (4) As expected variables are not observable, for estimation purpose, a reduced form containing only observable variables may be written after substituting the value of At* from equation (4) into equation (3), as follows: At = β0 + β1pt-1 + β2yt-1 + β3rf_irr + β4yrt + β5prt + β6cyt-1 + β7cpt-1+ β8at-1 + DD + Ut.. (5) The step-up/ backward regression technique was used, as this technique allows the variable explaining maximum variability in the dependent variable to enter first in order of their explanatory power. The first equation is a behavioral equation, stating that desired acreage (At*) depend upon following independent variables. Where, At = current year under study crop; Pt-1 = one year lagged price of study crop; Yt-1= one lagged yield of study crop; RF_IRR = seasonal rainfall and/ or irrigated area under study crop; YRt = yield risk of study crop measured by standard deviation of three preceding years; PRt = price risk of studied crop measured by standard deviation of three preceding years; CYt-1= lagged yield of competing crop; CPt-1 = lagged price of competing crop; At-1 = lagged area of study crop; DD = District Dummy; β0 = intercept; β1-n = parameter coefficients; and, Ut = stochastic term. The extent of adjustment to changes in the price and/or non-price factors is measured in terms of coefficient of adjustment. The adjustment takes place in accordance with the actual planted area in the preceding year. If the coefficient of adjustment is one, farmers fully adjust area under the crop in the current year itself and there were no lags in adjustment. But if the coefficient of adjustment is less than one, the adjustment goes on and gives rise to the lags, which are distributed over time. The number of years required for 95 percent of the effect of the price to materialize is given as follows: (1-r) n = 0.05 Where; r = coefficient of adjustment (1-coefficient of lagged area); and, n = number of year. The proportionate change in area under the crop (At) with respect to a proportionate change in any of the factors which cause variation in At is called elasticity of At with respect to that variables. In the present study, both short run (SRE) and long run (LRE) elasticities of the area under the crop with respect to price were estimated to examine

J. Agric. Econ. Rural Devel. 232 and compare the effect of price on the responsiveness of area in the short-run as well as in the long-run. The price elasticities for linear regression model are given below: Short-run elasticity (SRE) = Price coefficient X Long-run elasticity (LRE) = RESULTS AND DISCUSSION SRE of adjustment Mean of price Mean of area This part empirically investigated the supply response of farmers with the motive to tentatively study the relevance of policy in the process of decision making by the farmers. Two folds exist in which a farmer s supply response can manifest itself: either to make adjustments in crop cultivated acreage, or vary the pattern of input use and try to adjust the output of crop to market conditions. Farmers allocate land to different crops depending on expected revenue from different crops: assume input costs are the same or move uniformly overtime for different crops, expected revenue would depend on expected prices and expected yields. If yield levels remain constant overtime due to inadequate significant technological changes, output response equals the acreage response. The acreage response functions using Nerlovian adjustment lag model were fitted through step-wise regression technique, in order to allow the variable explaining maximum variability in explained variable to enter first in order of their explanatory power. The regression results with respect to the cereal crops studied are presented below: Jowar (Sorghum) crop The R 2 of acreage under Jowar was 0.67, implying that 67 percent of the variation in acreage under Jowar was explained jointly by the explanatory variables included in the model (Table 2). In the allocation of acreage for Jowar crop, the significant influencing factors were lagged area under Jowar and lagged yield of competing crop. Current area under Jowar was positively influenced by lagged area of the crop which is statistically significant at 1 percent probability level. The estimated adjustment coefficient (0.44) was moderate, indicating moderate adjustment of the area under jowar crop by the farmers. The coefficient of lagged yield of competing crop (maize) was negative and statistically significant at 5 percent probability level, meaning acreage under Jowar cultivation decreased over the years mainly due to better yield performance of competing crops. As the productivity of competing crop (Maize) increased, acreage under Jowar cultivation decreased significantly. However, the dummy variable for districts indicated that there were some un-measureable attributes in Nagaur and Tonk districts which steered area allocation to Jowar crop. Table 2: Acreage response function of Jowar crop Intercept 0.65 (0.11)*** Lagged price of jowar 0.23 (0.16) NS Lagged area of jowar 0.56 (0.21)*** Lagged yield of maize -0.11 (0.05)** Lagged price of maize 0.13 (0.11) NS Nagaur -0.33 (0.182)* Tonk 0.08 (0.045)* R 2 0.67 Note: ***, **,* denotes significance at 1, 5 and 10% level of probability Maize crop Four variables significantly influenced the current acreage under maize cultivation viz. lagged price of maize, lagged area of maize, rainfall and dummy district-chittore were statistically significant at 1 percent probability level (Table 3). The acreage allocation of maize crop was significantly affected by lagged price of maize, and the relationship turned out to be negative, due to subsistence farming system associated with maize producing areas, thus, a certain amount of production irrespective of the prevailing price in the market. Also, the negative relationship is attributed to non-availability of substitute crops to cultivate, as substitute crops require extra capital for cultivation, well known that capital is the major constraint affecting small and marginal farmers, they tend to glue themselves to the cultivation of maize crop. The adjustment coefficient was 0.81 which was very high, thus, indicating very rapid adjustments of the area under maize crop by the farmers. Other significant variables observed are lagged area and rainfall that exert positive significant influence on acreage allocation under the crop. The coefficient of determination of acreage under the crop was satisfactory as exogenous variables included in the model explained 65 percent variation in current acreage under maize cultivation. District dummy variable: Chittore explaining the existence of un-measureable characteristics influencing area allocation under the crop. The negative sign of the district dummy implied less area allocation to maize as compared to another district. Table 3: Acreage response function of maize crop Intercept 0.98 (0.732) NS Lagged price of maize -0.51(0.13)*** Lagged area of maize 0.19(0.05)*** Lagged yield of maize 0.44 (0.41) NS Price risk of maize 0.11(0.08) NS Rainfall 0.09 (0.031)*** Chittore -0.521 (0.112)*** Udaipur 0.545 (0.521) NS R 2 0.65

Sadiq et al. 233 Bajra (Millet) crop All variables included in the model to determine acreage response of Bajra were negative (Table 4) due to decline over the years in the area under Bajra cultivation. Only intervening variables were significant factors influencing area allocation to Bajra crop, and the possible reason was, there were no substitute crops to cultivate if the price of Bajra decline and the alternative crops requires extra capital which these paucity poor resource farmers are capacity-wise bereft. Furthermore, the adjustment coefficient was high (0.87), implying rapid adjustments of the area under Bajra crop in the current year. As Jodhpur district was assumed as the base line, all the district dummy variables were significant, expressing unmeasurable district characteristics influencing acreage under Bajra. However, the coefficient of multiple determination was good as it explained 79 percent variation in the current area under allocation of Bajra crop. Table 4: Acreage response function of Bajra crop Intercept 1.23 (0.99) NS Lagged price of bajra -0.71 (0.591) NS Lagged area of bajra -0.13 (0.11) NS Lagged yield of maize -0.65 (0.631) NS Lagged price of maize -0.04 (0.032) NS Nagaur -0.10 (0.06)* Churu -0.24 (0.113)** Sri-Ganganagar 0.19 (0.101)* R 2 0.79 Wheat crop The R 2 of the acreage response function was 0.83; meaning that 83 percent variation in acreage under wheat crop cultivation was explained by the exogenous variables included in the model. The lagged area under wheat, yield risk and availability of irrigation facilities with the farmers were the identified significant parameters influencing area under wheat crop. The lagged acreage of wheat and availability of irrigation facilities exert a positive influence on current acreage under wheat production, implying that with assured irrigation facilities to farmers, sustainable wheat production could be achieved. The acreage adjustment coefficient was low (0.39), meaning that the adjustment rate of area under wheat crop was very low. Yield risk had a negative impact on the current acreage devoted to wheat cultivation, meaning farmers react to yield risk involved. Districts dummy explained the nonimportance of Jaipur, Alwar and Sri-Ganganagar districts, respectively, in wheat production, as their estimated coefficients were not significant (Table 5). Table 5: Acreage response function of Wheat crop Intercept 0.432 (0.212)** Lagged price of wheat 0.311 (0.235 NS Lagged area of wheat 0.612 (0.321)* Yield risk of wheat -0.821 (0.482)* Irrigated area under wheat 0.10 (0.052)* Lagged yield of rice 0.321 (0.298) NS Lagged price of rice 0.09 (0.07) NS Jaipur 0.271 (0.191) NS Alwar 0.111 (0.103) NS Sri-Ganganagar 0.218 9 (0.189) NS R 2 0.83 Barley crop The acreage response function of barley explained 84 percent variation in the area under Barley cultivation. A perusal of Table 6 revealed that farmers considered lagged acreage, lagged yield and availability of irrigation water as significant parameters in area allocation decision to Barley crop. The area under barley was declining, as depicted by the negative and significant coefficient of lagged yield of barley, and maybe due to the fact that competing crops are more remunerative in term of price and yield as reflected by the non-significant coefficient of lagged price of barley (non-remunerative price). The estimated adjustment coefficient (0.32) was very low, indicating a very low rate of adjustment of the area under barley by the farmers. Assuming Sikar district as the base for dummy variable, all the district dummy variables were significant in expressing un-measureable district characteristics. Table 6: Acreage response function of Barley crop Intercept -0.09 (0.32)** Lagged price of barley 0.073 (0.0617) NS Lagged area of barley 0.677 (0.393)* Lagged yield of barley -0.83 (0.491)* Irrigated area under barley 0.24 (0.125)* Lagged yield of rice 0.17(0.154) NS Lagged price of rice 0.19 (0.134) NS Jaipur 0.23 (0.129)* Alwar 0.09 (0.051)* Sri-Ganganagar 0.32 (0.171)* R 2 0.84 Short-run and Long-run elasticities Owned lagged price variables were found to be positive for three crops viz., jowar, wheat and barley, while negative

J. Agric. Econ. Rural Devel. 234 Table 7: Short-run and Long-run price elasticity Crops Short-run elasticity Long-run elasticity Year(s) required for price effect to materialize Jowar 0.23 0.52 3.61 Maize -0.51-0.63 14.13 Bajra -0.71-0.82 - Wheat 0.311 0.79 3.18 Barley 0.073 0.23 2.65 Source: Authors computation, 2017 for the remaining cereal crops under consideration viz., maize and bajra (Table 7). Out of the aforementioned crops, only maize crop coefficient of lagged price of itself was significant (negative signed). The short run elasticity revealed acreage responsiveness of a crop to price changes in preceding crop period, and the elasticity for these crops ranged from -0.21 to 0.094: negative price response was observed in maize and bajra (noncommercial crops). However, it should be noted that negative supply response is not an uncommon feature on supply response as seen in earlier studies: Sud and Kahlon (1969) observed negative price coefficients in nearly six gram cultivating districts in Punjab; Cumming (1975) also observed negative price coefficient in nearly half of the 100 wheat cultivating districts in India; Jhala (1979) also observed negative price response in six out of fourteen cases he studied on groundnut crop. In studies of Rao and Krishna (1965); Krishna and Rao (1967) and Bhowmick and Goswami (1998), this kind of conflicting estimates were reported. The long run elasticity reflects the acreage responsiveness of a crop to price change given sufficient time for adjustment. None of the crop under consideration showed very high long-run elasticity as such the impact of price policy on these crops would be mild/light in the long-run. The number of years required for price effect to materialize depends on the technological and institutional constraints faced by the farmers for a particular crop. The higher the constraints, the more is the time required for adjustment. It was observed that Jowar and maize crops, respectively, took medium time for adjustment, while barley and wheat crops, respectively; take very small time for adjustment. The smaller the time for adjustment, the more effective is the price policy instruments in bringing desired change in the supply of a crop. In the case of bajra, the number of years required for the price to materialize was indeterminate. CONCLUSION AND RECOMMENDATION The study empirically investigated farmers supply response with the aim of studying the relevance of policy in the process of decision-making by farmers, and other relevant stakeholders involved in the business of agriculture directly and indirectly. Empirical investigation showed that the acreage response of cereal crop in Rajasthan state was governed by price and non-price factors; hence, price incentive alone was not sufficient in bringing desirable change in cropping pattern as well production of the cereal crop. Non-price factors like yield risk, the yield of own and competing crops, rainfall and irrigation facilities, investigated were relevant explanatory variables. Therefore, policy for better implementation of price support system, development of consistently performing varieties and further enhancement of irrigation facilities will go a long way in ensuring agricultural stability in the state. REFERENCES Abujam AD, Chahal SS (2015). Acreage response of sugarcane to price and non-price factors in Punjab. Indian Journal of Economics and Development.11 (3):623-630 Bhowmick BC, Goswami J (1998). Supply response of some important crops in Assam-An inter-district analysis. Agricultural Situation in India. 55(6):349-356 Cummings JT (1975). The supply responsiveness of Indian farmers in the post-independence period: Major cereal and cash crops. Indian Journal of Agricultural Economics, 30(1):25 Jhala ML (1979). Farmers' response to economic incentives: An analysis of interregional groundnut supply response in India. Indian Journal of Agricultural Economics, 34(1):55 Krishna J, Rao MS (1967). Dynamics of acreage allocation for wheat in uttar pradesh-a study in supply response. Indian Journal of Agricultural Economics, 22(1):37-43 Maddala GS, Lahiri K (2009). Introduction to Econometrics-Fourth edition. John Wiley and Sons Nerlove M (1958). The dynamics of supply: Estimation of farmers response to price. John Hopkins, Baltimore, U.S.A Rao MS, Krishna J. (1965). Price expectation and acreage response for wheat in Uttar Pradesh. Indian Journal of Agricultural Economics, 20(1):22-27 Subba RS, Raghu RP, Neelakanta STV, Bhavani DI (2016). Agricultural Economics-Second Edition. Oxford and IBH Publishing Company PVT. LTD. Sud L, Kahlon AS (1969). Estimation of acreage response to price of selected crops in Punjab state. Indian Journal of Agricultural Economics, 24(3):46

Sadiq et al. 235 Accepted 24 July 2017 Citation: Sadiq MS, Singh IP, Karunakaran N (2017). Supply Response of Cereal Crop Farmers to Price and Non-Price Factors in Rajasthan State of India. Journal of Agricultural Economics and Rural Development, 3(2): 230-235. Copyright: 2017 Sadiq et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.