Price Fluctuation and Market Integration of Selected Cereal in North-Eastern Nigeria (2001-2010)


Prices contain information crucial to maximizing the returns to production and marketing investments. At planting time, a farmer’s planting decision depends on expected profits, which invariably hinge on the anticipated prices of the crop or mix of crops that would prevail in the market at the time of sale and on the farmer’s interpretation of those prices. A trader, in search of profitable arbitrage, reads and translates price signals in deciding on what crops to buy, where to buy, and when to sell. Apart from guiding production and marketing decisions, prices govern the optimal allocation of resources among competing uses. The accuracy, reliability, and promptness of market information are therefore critical in attaining pricing efficiency. Broadly, the study attempted to analyze the price fluctuation and market integration of selected cereal grains in North-eastern Nigeria. The specific objectives of the study were to: (i) estimate the extent of the various components of price; (ii) derive the probability distribution of cereal grain price in the long-run; (iii) determine the existence and level of inter-market price dependency; (iv) examine the speed of price adjustment to long-run equilibrium and (v) examine the Granger Causality among rural and urban cereal grain markets. The study was conducted in North-eastern Nigeria. Purposive sampling technique was used to select two states, of Adamawa and Taraba, from the six states that made up the North-east geopolitical zone. Only secondary data were used in the study. Secondary data on monthly bases for the prices of 100kg of three cereal grains, maize, rice and sorghum in both rural and urban markets in the study area were obtained from Adamawa and Taraba States Agricultural Development Program offices for a period of 10 years (2001-2010). Data were analyzed using descriptive statistics such as price decomposition technique, and inferential statistics such as Markov Chain, Vector Autoregressive and Error Correction Models. The results revealed that, the trend component showed an upward movement for all the three commodities. The seasonal variation had indexes ranged from 198.15 to 52.61, 142.83 to 61.88, and 141.44 to 66.25 for maize, rice and sorghum, respectively. The random and cyclical variations had negligible and insignificant indices with the former having 0.01 all through and the later ranging from 0.93 to 1.26. Probability distribution matrices of the three cereal grains were 0.18, 0.48 and 0.34 for maize, 0.27, 0.68 and 0.05 for rice and 0.48, 0.25 and 0.27 for sorghum. The Augmented Dickey-Fuller unit roots test indicated I(0), I(1) and I (1) for maize, rice and sorghum, respectively. Null hypothesis of β = 1 was rejected against β = 0. Trace

statistics for rural and urban markets were not significant ( Rural and urban prices of maize responded to shocks within and between each market. The speed with which the system adjusted to shocks and restored equilibrium between the short and the long-run were -0.170725 and -0.29517 for urban and 0.592237 and 0.38034 for rural prices of rice and sorghum, respectively. Granger Causality showed that a bi-directional flow of price signals existed between rural and urban prices of maize, while rural prices of rice and sorghum did not Granger-Cause urban prices of rice and sorghum. Also, urban prices of both rice and sorghum did not Granger-cause rural prices of both rice and sorghum. Findings of the study showed an imperfect market integration for North-eastern Nigeria cereal grain markets, this indicate that there may be substantial benefits in developing better infrastructure facilities to effectively link production centers to market centers and in improving market knowledge by providing more relevant, accurate, and timely public market information.



1.1 Background of the Study

The grain sub-sector plays an important role in the economic development of Nigeria. The output of the sub-sector (Ismaila, Gana, Twanya & Dogara 2010; Okunneye, 2003) constitutes a large proportion of staple food stuffs in Nigeria. Between 1985 and 1995, cereal grain accounted for almost 50 % of the total food supply in Nigeria when expressed in grain equivalent (Akpan & Udoh, 2009; Ukoha). On the other hand, Paulino and Sarma (1988) reported that about 70% of the total food crop area harvested in Nigeria was devoted to cereals and the remaining 30% to non-cereals.

The most important cereal grain crops grown and marketed in Nigeria are maize, rice, sorghum, millet and wheat (Akpan & Udoh, 2009; Global Information and Early Warning System on Food and Agriculture (GIEWS), 2008; Ismaila et al., 2010; Oguntunde, 1989; Wudiri, 1992). Of these, rice, maize, millet and sorghum are the major sources of energy staple food available and affordable in Nigeria, and are the commodities that are of considerable importance for food security, expenditure and income of households in Northern Nigeria (Ismaila et al., 2010; Maziya-Dixon et al., 2004).

In most parts of Asia and Africa, cereal products comprise 80% or more of the average diet, in central and western Europe, as much as 50% and in the United States, between 20 – 25% (Food and Agriculture Organization, 1996). Also, the increased demand for cereals, as a result of rapid urbanization, means that food crops must increasingly be produced to meet the needs of the rural and urban population (Balarabe, 2003). According to the Central Bank of Nigeria (CBN) (2000), Okunneye (2003) and Ukoha (2005), most Nigerians depended on cereal grains for their daily dietary needs and the price of these grains is one factor that determines the extent to which Nigerians can pay for these food commodities. Cereal grains availability and prices have become a major welfare determinant for the poorest segments of the Nigerian consumers who also are least food secured (Akande, 2001). Also, CBN (2000), Akande (2001) and Akpan and Udoh (2009) have affirmed that, the nominal or producer price of cereal grains have continuously fluctuated over the past years.

Spatial market integration of agricultural products has been widely used to indicate overall market performance (Faminow & Benson, 1990). In spatially integrated markets, competition among arbitragers will ensure that, a unique equilibrium is achieved where local prices in regional markets differ by no more than transportation and transaction costs. Information of spatial market integration, thus, provides indication of competitiveness, the effectiveness of arbitrage, and the efficiency of pricing (Sexton, Kling & Carman, 1991).

If price changes in one market are fully reflected in alternative market, these markets are said to be spatially integrated (Goodwin & Schroeder, 1991). Prices in spatially integrated markets are determined simultaneously in various locations, and information of any change in price in one market is transmitted to other markets (Gonzalez-Rivera & Helfand, 2001). Markets that are not integrated may convey inaccurate price signal that might distort producers’ marketing decisions and contribute to inefficient product movement (Goodwin & Schroeder 1991), and traders may exploit the market and benefit at the cost of producers and consumers. In more integrated markets, farmers specialize in production activities in which they are comparatively proficient, consumers pay lower prices for purchased goods, and society is better able to reap increasing returns from technological innovations and economies of scale (Vollrath, 2003). Market integration of agricultural products has retained importance in developing countries due to its potential application to policy making. Based on the information of the extent of market integration, government can formulate policies of providing infrastructure and information regulatory services to avoid market exploitation.

In theory, spatial price determination models suggest that, if two markets are linked by trade in a free market regime, excess demand or supply shocks in one market will have an equal impact on price in both markets. Given the wide range of ways prices may be related, the concept of price transmission can be thought of as being based on three notions, or components (Balcombe & Morisson, 2002; Prakash, 1998). These are:

Ø co-movement and completeness of adjustment which implies that changes in prices in one market are fully transmitted to the other at all points of time;

Ø dynamics and speed of adjustment which implies the process by, and rate at which, changes in prices in one market are filtered to the other market or levels; and,

Ø asymmetry of response which implies that upward and downward movements in the price in one market are symmetrically or asymmetrically to the other. Both the extent of completeness and the speed of the adjustment can be asymmetric.

Within this context, complete price transmission between two spatially separated markets is defined as a situation where changes in one price are completely and instantaneously transmitted to the other price, as postulated by the Law of One Price (LOP). In this case, spatially separated markets are integrated. In addition, this definition implies that if price changes are not passed-through instantaneously, but after some time, price transmission is incomplete in the short-run, but complete in the long run, as implied by spatial arbitrage condition. The distinction between short-run and long-run price transmission is important, and the speed by which prices adjust to their long-run relationship is essential in understanding the extent to which markets are integrated in the short-run. Changes in the price at one market may need some time to be transmitted to other markets for various reasons, such as policies, the number of stages in marketing and the corresponding contractual arrangements between economic agents, storage and inventory holding, delays caused in transportation or processing, or “price leveling” practices.

Fluctuation in prices seriously affects cereal productivity in Nigeria (Ismaila et al., 2010). For instance, the demise of poultry and poultry processing companies following the outbreak of avian influenza in Nigeria has adversely affected the demand for maize, a major component of poultry feeds across Nigeria. With last year’s stock of grains still in the market, serious concern has been raised about the impact of the abundant supplies on prices with the exception of sorghum which is commonly demanded by breweries and other drink manufacturing companies in Nigeria. Conversely, the low demand for maize has discouraged many farmers from maize production and consequently increased the price of maize in 2008.

The Nigerian government realizing the importance of the grain sub-sector had several times intervened in standardizing grain prices through agricultural price policy reformation. Some of the instruments used, as pointed out by Okoh and Akintola (2005) and Akpan and Udoh (2009), included input subsidies, strategic grain reserve scheme of 1976, ban on importation of rice and maize in 1985 and the liberalization of the economy in 1986 among other measures. Despite these lofty attempts, the producer prices of grains continued to fluctuate as presented in Table 1.

It is obvious from Table 1 that the major grain crops in Nigeria showed a broad dispersion in producer prices across the specified policy periods. For instance, between 1970 and 1974, the mean producer price of rice was N301.40/ton and 17.12 % coefficient of variability in prices. In 1975 to 1979 the mean price of rice increased by more than 100 % compared to the p tfre-Operation Feed the Nation (OFN) period. The fluctuations were the increasing function of time across the specified policy periods. Similar trends were obtained in the producer prices of maize, millet and sorghum. The highest coefficient of variability was obtained during the Structural Adjustment Programme (SAP) period for all the crops. It was 67.97 % for rice, 69.39 % for maize, 83.97 % for millet and 70.64 % for sorghum.

Table 1.1: Major Grain Prices under Different Agricultural Policy Regimes in Nigeria

Policy Regime





Mean price


Mean price


Mean price


Mean price