# Supply Response of Wheat to Price and Non-Price Factors in Northern Pakistan (1980/81 to 2010/11).

Byline: Himayatullah Khana and Muhammad Niamatullah Khanb: Abstract

This paper estimates supply response of wheat crop to price and non-price factors in Khyber Pakhtunkhwa Province of Pakistan. The study uses Nerlovian adjustment model for supply response of wheat for the period 1980/81 to 2010/11. The study found that all the explanatory variables were stationary. Wheat price total cultivated area total cropped area and cropping intensity were statistically significant factors affecting wheat area positively. Total irrigated area has also positive but insignificant relationship with wheat area. Area cultivated to wheat is also directly but insignificantly related to its lagged value. A minor difference was found between short and long-run elasticities of regressors in case of wheat area. For wheat production response the study concluded that support price of wheat lagged price of wheat; total irrigated area and wheat production last year have positive but insignificant relationship with wheat production.

The support price policy if the government of Pakistan does not seem to be properly designed. The study recommends that proper agricultural policy needs to be prepared so that farmers can make timely decision regarding allocation of land to different crops keeping in view the comparative advantage of crop production. The government should invest further in education.

Keywords: Supply Response Wheat Nerlovian partial adjustment model PakistanIntroduction

Price is one of the most important factors affecting demand and supply of various goods and services. Consumers as well as producers keep in mind various determinants before they make any buying or selling decisions. Farmers being growers and seller not only keep in mind current prices of their produce their production decision is also affected by last year price as well as their expectation about price of their products after harvest. Similarly they also keep in mind other factors as well before they allocate land to various crops. They act as wise economic agents and keep in mind their comparative advantage in the production of various crops. They produce those crops in the production of which they have comparative advantage and purchase those crops which they can't grow with comparative advantage but they can purchase in the market.

The economy of Pakistan is predominantly based on agriculture. Agriculture accounts for about one-fifth of its gross domestic product (GDP). There are two main crop seasons in Pakistan. Rabi crops are grown in October and harvested in April- May. Kharif crops are grown in summer and harvested in September-November. Wheat is one of the most important food crops in Pakistan. This study therefore examines the supply response of wheat in Khyber Pakhtunkhwa province of Pakistan. In order to study farmer responsiveness (in terms of area and production) to various price and non-price factors affecting wheat crop in northern Pakistan we make use of the Nerlovian Adjustment Model one of the most widely used supply response model (Jati and Sfeir 1983). Various studies have shown that supply of crops can be studied using the Nerlovian adjustment model (Rahji et al. 2008 and Vinayakumar et al. 2007). The aim of this study is estimate supply response of wheat crop.

The study examines the effects of both the price and non-price factors on supply of wheat crops using time series date from 1980/81 to 2010/11.

Specification off Nerlovian Adjustment Model

Following the Nerlovian adjustment model we assume that actual adjustment of area planted to wheat in time t is equal to a proportion of the intended complete adjustment to the equilibrium production and its respective acreage At. Thus we specify this in the following equation.Equation

As the wheat growers make their crop plan on the basis of expected market prices in the country Therefore equation 2 and 3 were specified using the same format of adjustment with respect to market price of wheat crop too.Equation

Where Pt is the expected market price in respect of wheat in year t Pt-1 is the long run equilibrium market price of wheat in year t Pt is the actual market price in year t and is the adjustment coefficient for market price of wheat. We however specify the equilibrium area under wheat as a function of the expected market price and other non-price factors including cultivated area cropped area cropping intensity area irrigated by different sources all measured in time t. Thus using ordinary least squares method the following regression was run log-log form in which wheat area was regressed on wheat price (price response) and cultivated area cropped area cropping intensity area irrigated by different sources (as non- price response).Equation

Where At represents area (hectares) cultivated to wheat crop in year t Pt is market price of wheat (Rs. per 40 Kg) crop in year t CUt is total cultivated area in year t CRt is cropped area in year t CIt is cropping intensity in year t IRt is irrigated area in year t and At-1 is the lagged area i.e. area cultivated to wheat last year. ai's are regression coefficients and et is the stochastic error term in year t.

We also estimated the similar equation for wheat output in double log form as follows:Equation

Where Qt is wheat output in year t SPt is support price of wheat and IRt is irrigated area in time t Pt-1 and Qt-1 are wheat price and production respectively in last year. AYi's and i are regression coefficients and random error term respectively.

In addition we also used Augmented Dickey-Fuller test for unit root for determining the order of integration. Short-run and long-run elasticities were also computed.

Results and Discussion

Area Response

Unit Root Tests for Residuals of Wheat Acreage:

Data in table 1 shows the Augmented Dickey-Fuller Test for Unit Root was applied for the purpose of determining the order of integration. Following Augmented Dickey-Fuller Unit Root Test the null hypothesis of non-stationarity of all variables was rejected when the first and second order difference variables were used. This implied that the variables At Pt CUt CIt and At-1 were stationary of order 1 and 2.

Table 1: Unit Root Test for Residuals of Wheat Acreage

###ADF (Levels)###ADF in Differences

###Order of integration

###(Non-Stationarity)###(Stationarity)

Variables###through differencing

###Without###With###Without###With

###I( )

###Trend###Trend###Trend###Trend

###At###-1.2419###-1.8322###-4.9684###-5.4290###I(1)

###Pt###-1.0939###-2.4684###-5.1370###-5.1933###I(1)

###CUt###-1.5167###-1.9440###-5.5533###-6.9555###I(1)

###CRt###-3.4304###-3.5934###-7.5621###-7.3425###I(1)

###CIt###-1.5667###-1.6889###-5.4257###-6.8136###I(1)

###IRt###-1.5074###-1.1529###-4.2156###-4.1678###I(2)

###At-1###-0.95185###-1.5235###-4.9080###-5.2807###I(1)

Table 2 shows regressions results. All the explanatory variables have the correct expected algebraic signs. Wheat price total cultivated area total cropped area and cropping intensity constitute to be statistically significant factors affecting wheat area positively. Total irrigated area has also positive but insignificant relationship with

wheat area. Area cultivated to wheat is also directly but insignificantly related to its lagged value. The estimated coefficients are in natural logarithm form and thus show elasticities of wheat area with respect to various explanatory variables. The coefficient of determination shows that about 80 percent of total variation in area under wheat is explained by the explanatory by the variables included in this model. The F-test also shows that the overall estimated model is significant. Mushtaq (2000) William and Robert (2007) and Mythili (2008) also found similar results.

Table 2: Estimated Regression Function of Area under Wheat (1980/81-2010/11)

Explanatory Variables###Coefficient###Standard error###t-ration###P-value

###Constant###-0.001###0.006###-0.189###0.853

###Pt###0.03###0.009###3.33###0.013

###CUt###44.082###17.609###2.503###0.026

###CRt###42.909###17.495###2.453###0.029

###CIt###42.901###17.635###2.433###0.030

###AIt###0.068###0.154###0.439###0.668

###At-1###0.073###0.132###0.554###0.589

Short-run and Long-run Elasticities

Short-run and long-run elasticities of wheat area are given in Table 3. It is evident from the data in Table 3 that there was a very small difference between the short and long-run elasticities of various explanatory variables. The coefficient of adjustment was equal to 0.90. These results are in line with Mushtaq (2000) William and Robert (2007) and Mythili (2008).

Table 3: Adjustment Coefficient and Short and Long-run Elasticities of heat

###Acreage Response Function

Price Elasticities###Non-Price Elasticities

###Cultivated###Cropping

###Market Prices###Cropped area###Area irrigated

###area###intensity

Short###Long###Short###Long###Short###Long###Short###Long###Short###Long

Term###term###Term###term###Term###term###Term###term###Term###term

0.03###0.029###44.08###47.4###42.90###46.13###42.90 46.13###0.07###0.08

The high value of adjustment coefficient () as 0.93 implied a few obstacles is measuring the growers' expectation and adjustment of wheat acreage level (Fisher 1975). F ratio (Pless than 0.01) confirmed overall goodness of the Nerlovian Adjustment Model. Hence overall measured performance of the Nerlovian Adjustment Model in elaborating possible changes in wheat acreage seem to be quite interesting.

Wheat Production Response

Unit Root Tests for Residuals of Wheat Production:

Augmented Dickey-Fuller Test for Unit Root has been used for the purpose of determining the order of integration. It was found that Augmented Dickey-Fuller Unit Root Test rejected the null hypothesis of non-stationarity of the explanatory variables when 1st and 2nd order of integration. The results are given in Table 4.

Table 4: Unit Root Test for Wheat Production

###ADF (Levels)###ADF in Differences###Order of

###(Non-Stationarity)###(Stationarity)###integration

Variables###through

###Without###Without

###With Trend###With Trend###differencing

###Trend###Trend

###I( )

###Qt###-2.2937###-2.2616###-5.3885###-5.1379###I(2)

###SPt###-0.26385###-2.9900###-4.3285###-4.2206###I(2)

###Pt-1###-1.1904###-2.6385###-4.3935###-4.4214###I(1)

###IRt###-2.3522###-1.7856###-4.6173###-5.7420###I(1)

###Qt-1###-2.1929###-2.1460###-5.4441###-5.1922###I(2)

Estimated Supply Response Regression

The results given in Table 5 indicates that support price of wheat in the beginning of year t lagged price of wheat total irrigated area and wheat production last year have positive but insignificant relationship with wheat production. The estimated coefficients are in natural logarithm form and thus show elasticities of wheat production with respect to various explanatory variables. The reasons for such results may be that majority of farmers in the study area are not educated and they don't correctly incorporate the price policy announced by the government in to their expectations. They also don't grow wheat on commercial basis but for home consumption. The coefficient of determination shows that close to 50 per cent of variation in wheat production is explained by the model. These findings are in agreement with Umer et al. (2001) and Shah et al. (2002).

Table 5: Estimated Regression Function of Wheat Production (1980/81 to 2010/11)

Explanatory Variables###Coefficient###Standard error###t-ration###P-value

Constant###0.034###0.077###0.448###0.663

SPt###0.612###0.417###1.468###0.173

Pt-1###0.387###0.703###0.551###0.594

IRt###1.204###2.111###0.570###0.581

Qt-1###-0.550###0.290###-1.895###0.087

Conclusions and Recommendations

The study concluded that based on the Augmented Dickey-Fuller Unit Root Test the null hypothesis of non-stationarity of all variables was rejected for both wheat area and production. Thus all the explanatory variables were stationary. Wheat price total cultivated area total cropped area and cropping intensity were statistically significant factors affecting wheat area positively. Total irrigated area has also positive but insignificant relationship with wheat area. Area cultivated to wheat is also directly but insignificantly related to its lagged value. The study also concluded that there was a very minor difference difference between the short and long-run elasticities of various explanatory variables in case of wheat area. Regarding the wheat production response the study concluded that support price of wheat lagged price of wheat total irrigated area and wheat production last year have positive but insignificant relationship with wheat production. The support price policy if the government of Pakistan does not seem to be properly designed. Literacy in general in Pakistan and among farming community is low. The study recommends that proper agricultural policy needs to be prepared so that farmers can make timely decision regarding allocation of land to different crops keeping in view the comparative advantage of crop production. The government should invest further in education. Agricultural extension needs to be streamlined to the needs of the farmers. References

Fu T.T. S.M. Fletcher and J.E. Epperson. 1986. Acreage planting decision analysis of South Carolina tomatoes: Nerlovian versus just risk model. Southern J. Agric.Econ. 18(2): 65-71.

Jati K.S. and R.I. Sfeir R.I. 1983. Risk in supply response: an optimal control approach 1. Appl. Econ. 15(2): 255-265.

Mushtaq K. 2000. Supply response of major commodities in Pakistan Department of Agriculture Economics and Food Marketing University of Newcastle Upon Tyne.

Mythili G. 2008. Acreage and yield response of major crops in the pre- and post- reform periods in India a dynamic panel data approach. Indira Ghandhi Institute of Research Mumbai India. A report prepared for IGIDR-IRS/ USDA Project: Agricultural Markets and Policy.

Rahji M.A.Y. O.O. IIemobayo and S.B. Fakayode. 2008. Rise supply response in Nigeria: An application of Nerlovian Adjustment Model. Agric. J. 3(3): 229-234.

Shah N.A. S.M. Khair M. Afzal and M.A. Kasi. 2002. Determinanats of wheat productivity in irrigated Balochistan. Asian J. Plant Sci. 1(4): 373-375.

Umer F. T. Young N. Russell and M. Iqbal. 2001. The supply response of basmati rice growers in Punjab. Pakistan: price and non-price determinants. J. Int. Dev. 13(2): 227-237.

Vinayakumar B. N.N. Kollurmath L.B. Karnool S.K. Basavaraj and Vilaskulkarni. 2007. Supply Response of Rice and Maize in Karnataka Pre and Post WTO. J. Agric. Sci. 21(4): 535-537.

William L. and D. Robert. 2007. Supply Response under Risk: Implications for Counter-Cyclical Payments' Production Impact. Review Agric. Econ. 29(1): 64-86.

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Publication: | The Journal of Humanities and Social Sciences |
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Date: | Aug 31, 2013 |

Words: | 2363 |

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