INTRODUCTION Sweet orange (Citrus sinensis) and pineapple (Ananas comosus) are tropical fruits in greatest demand, grown both for the fresh and processed market (FAO, 2009). Sweet orange is the most widespread species of citrus in the world (Vacaante, 2010). Food and Agriculture Organization estimated that in the year 2007 worldwide orange cultivation covered a total area of 3,905,780 hectares variously distributed in different continents (FAOSTAT, 2008). Citrus in Nigeria is rated to be among the ten most important fruit tree crops (Adelaja and Olaniyan, 2000; Oyedele, 2000). According to United Nations Conference on Trade and Development UNCTAD (Accessed February,

2014), Nigeria ranked first in small citrus production. Citrus fruits are rich in Vitamin C, Folic acid, as well as a good source of fiber, and they are fat free, sodium free and cholesterol free. Oyo State is one of the major producers of sweet orange in Nigeria. This is due to the comparative advantage the State has in the production of the fruit. Nigeria was ranked seventh on the list of world pineapple producers as well as the leading pineapple producer in Africa with a production of 1,400,000 MT (FAOSTAT, 2011). Pineapple is a wonderful tropical fruit having exceptional juiciness, vibrant flavor and immense health benefits (Joy, 2010). Also, pineapple wastes from processing the fruit are now further processed into sugar,

Journal of Applied Agricultural Research 2014, 6(2): 55-64 ISSN 2006-7496 © Agricultural Research Council of Nigeria, 2014

MARKET INTEGRATION FOR SWEET ORANGE AND PINEAPPLE IN OYO STATE, NIGERIA: 2003-2012

*Adeoye, I. B.1, Fashogbon, A. E.2, Layade, A. A.1, Adegbite, O. O.1 and F. O. Fajimi2

1National Horticultural Research Institute, P.M.B. 5432, Ibadan, Oyo State, Nigeria 2Department of Agricultural Economics, University of Ibadan, Ibadan, Nigeria

*iyabs1@gmail.com _______________________________________________________________________

ABSTRACT Sweet orange (Citrus sinensis) and pineapple (Ananas comosus) are perishable commodities thus the need for their markets to be integrated in order to bridge supply and demand. The study investigated integration in urban and rural market of sweet orange and pineapple in Oyo State. Monthly retail prices (₦/kg) from 2003 to 2012 were obtained from Oyo State Agricultural Development Programme. Data were analysed using Johansen co-integration test, Granger causality test and index of market connection. Prices in rural and urban markets were not stationary at level form but became stationary at first difference for both sweet orange and pineapple. The result also revealed that the markets of the two commodities were cointegrated at 1% level indicating existence of long run price relationship for the two commodities. Granger causality test showed that the rural market has no causal relationship with urban market of sweet orange and pineapple. The Index of market connection (IMC) indicates that both markets exhibit low short-run market integration indicating low transmission of prices between rural and urban markets of sweet orange and pineapple. The study recommends development of market infrastructure and efficient market information as well as formation of market cooperatives for the market to be well integrated. Keywords: Sweet orange, pineapple, co-integration, Granger causality, Oyo State ________________________________________________________________________

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wines, vinegar, animal feed, alcohol, citric acid, calcium citrate (Office of the gene technology regulator, 2003). Trade prospects for pineapple in Nigeria are very promising and can increase national income through the expansion of local industries and higher incomes for farmers involved in its production (Fawole, 2008). The agricultural marketing system plays an important role in determining the prices received by the farmers and those paid by the consumer (Acharya, 2005). Development of efficient agricultural markets has a large impact on the economic opportunities of rural households (Minot and Hill, 2007). There is the need for efficient marketing of horticultural crops because they are perishable and are traded in a very free market where price is determined by supply and demand (Dixie, 2005). Market integration can be defined as a measure of the extent to which demand and supply in one location are transmitted to the other (Negassa et al., 2003). Market integration as an important aspect of market research provides the basic data for understanding how specific markets work (Okoh, 2005). It ensures that a regional balance occurs among food-deficit and food-surplus regions and regions producing non-food cash crops. Market locations across space often lack integration due to inadequate provision of public goods (such as infrastructure), inefficient flow of information, imperfect competition, and incomplete or missing institutions for risk management, like credit and insurance, all of which qualify as sources of market failures (Rashid et al., 2010). The absence of market integration or complete pass-through of price changes from one market to another has important implications for economic welfare (Barett, 2001). The production and trade of agricultural commodities, particularly highly perishable horticultural products, requires that producers have a

working knowledge of local, regional and export markets and the ability to readily access those markets and the essential market information (USAID, 2005). Previous studies on market analysis and market integration in Nigeria include those of Dittoh (1994), Eze et al. (2005), Nkang et al. (2007), Oladapo et al. (2007), Adesope et al. (2009), Adeoye et al. (2011) and Amao et al. (2011). The reviewed work dwelt on pineapple, banana and plantain, cocoa, oil palm and leafy vegetables. This study is different from the reviewed works because it examined the extent of market integration in sweet orange and pineapple using secondary data from Oyo State Agricultural Development Programme. The aim of this study, therefore, was to investigate integration in rural and urban market of sweet orange and pineapple in Oyo State in order to determine the efficiency and price relationship in the markets.

METHODOLOGY Study area and data collection The study was carried out in Oyo, State, (coordinate ‘8°00′N 4°00′E’) Nigeria. Oyo State occupies an area of about 28,454 Square Kilometers and has a population of 5,591,589 (National Population Commission; NPC 2006). The mean annual rainfall ranges from 1000 to 1500 mm. The agricultural sector of the state is dominated by food crops as well as cash crops. Orange and pineapple were selected due to the fact that the State is one of the major producers of these crops. The commodities are also among the most popular horticultural crops in the state. Secondary data on monthly retail prices of selected horticultural crops from 2003 to 2012 were obtained from the Oyo State Agricultural Development Programme (OYSADEP). The data were subjected to graphical analysis to establish and describe trend, and time series-inferential analysis to

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determine flow of market price on the selected commodities in both short and long runs. Analytical methods This analysis started with an investigation of the stationarity properties of each time-series. This study applied a standard augmented Dickey-Fuller (ADF) test of the following form to each price variable.

…1 Where: Δ = First difference operator, ∂ = 0, implies the existence of a unit root in Pit or that the price series is non-stationary, i = commodity price series, i.e. citrus and pineapple in ₦/kg, t = time indicator (monthly), eit is the error term.

The process is considered stationary if /∂ / < 1, thus testing for stationarity is equivalent with testing for unit roots (∂<1). The second stage of the analysis is the Co-integration analysis which involved establishing statistically sound long-run relationships between time series data. The Engle and Granger (1987) test and the Johansen test are the two most common approaches to testing for cointegration of data that are not stationary. However, the Engel-Granger approach requires normalization of all time-series, that is, all variables that will enter into the cointegration equation be I(1), but the Johansen approach relaxes this assumptions with variables of different order of integrations permitted in the analysis (Wee and Tan, 2013). Hence, the Johansen method was adopted in this study.

…2 where:

Xt is an (N x 1) vector containing the series of interest (citrus and pineapple spatial prices series), Γ and Π = matrices of parameters, K = number of lags, and should be adequately large enough both to capture the short-run dynamics of the underlying Vector Auto-Regressive (VAR) and to produce normally distributed white noise residuals, εt = vector of white noise errors. Usually, Trace and Maximum Eigen value test statistics are used for the null hypothesis of no cointegration. Trace statistic evaluates the null hypothesis that there are at most r-cointegrating vector of X; and it is represented as:

…3 where: λ denotes p-r smallest correlation of vector of εt; and T is the number of observations. On the other hand, the maximum Engel value statistics tests for the null hypothesis that there are exactly r cointegrating vectors in X and derived as:

…4 Note, if none of the k null hypotheses are rejected under both tests, it indicates there is no co-integration (Ssekuma, 2011). The third step is the Granger causality test which was carried out to determine the direction of causality. When Granger causality run one way (uni-directional), the market which Granger-causes the other is tagged the exogenous market. It could also be bi-directional which indicates that both series influence each other (e.g. X causes Y, and Y also causes X). More so, there could be no causality, that is, independence (Mayaka, 2013). This could result from market segmentation which occurs when prices in regional markets are independent of lagged prices in central market. The Granger model used in this study can be represented by:

itt

P

itit l+∆Ρ+Ρ∂=∆Ρ

−=− ∑

1111 β

tkt

k

ittt εµ +ΠΧ+ΓΧ+=∆Χ −

−−∑

11

∑+=

−−=k

ritrace Tr

1

)ˆ1ln()( λλ

)ˆ1ln()1,( 1max +−−=+= rTrr λλ

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Market Integration for Sweet Orange and Pineapple

... 5 where; m and n are the numbers of lags determined by a suitable information criterion. Index of market connection The index of market connection was used to measure price relationship between integrated markets.

… 6 where: Rt= urban price (₦) Pt = rural price (₦) Rt-1 = lagged price for urban market (₦) Rt - Rt-1 = difference between urban price and its lag (in Naira) Et = error term βo = constant term β1= coefficient of rural lagged price β2= coefficient of Rt - Rt-1 β3 = coefficient of urban lagged price

where 0≤ IMC ≤ ∞ IMC < 1 implies high short run market integration IMC > 1 implies low short run market integration IMC = ∞ implies no market integration IMC = 1 high or short run market integration.

RESULTS AND DISCUSSION There are fluctuations in the prices of oranges in rural and urban markets of Oyo State. Prices are lower in the fourth quarter of the year, and which coincides with the peak season of oranges and there is

abundant supply in the market. Prices are higher in June, July and August, months which are off-season. This is the period of rainfall and supply is drastically reduced with attendant high prices in the markets. Prices are lower in the rural markets compared to the urban counterparts. The maximum price of oranges during the period of survey was (₦153.69/kg recorded in the month of May, 2011 at the rural market. Maximum price of orange in the urban market was ₦170.83/kg recorded in the month of March, 2009. The minimum prices recorded were ₦32.54/kg and ₦20.00/kg in the rural and urban market, respectively. This was obtained in the month of February, 2005 and July, 2003 for both rural and urban markets. It can also be seen from Figure 1 that price of oranges doubled between 2005 and 2012. The maximum price of pineapple in rural market was ₦123.04/kg and was obtained in April, 2004 while the minimum price of pineapple in rural market was ₦29.00/kg and was obtained in April 2005, June, July, August and September in 2007, July in 2008, November and December in 2009 and January in 2010. For urban market, maximum price (₦146.80/kg) was attained in February, 2004 and minimum price (₦39.90/kg) was obtained in June, 2005 (Figure 2). This indicated that prices were lower in the rural markets compared to the urban counterparts. According to Oladapo et al. (2007), the reason for the non-corresponding peaks of rural and urban markets could have been that fewer pineapple fruits were being supplied from the rural to urban markets. Thus urban markets had low supply of pineapple fruits as against the high demand of pineapple fruits in the urban markets. However, the fluctuations in the prices of pineapple in rural and urban markets of Oyo State over the years could be as a result of climate contingencies that affect the volume of supply and quality, production

∑ ∑= =

−− +∆Ρ+∆Ρ=∆Ρm

1it

n

1j)1t(jj)1t(jiit aa l

( ) tttttt RRRPP εββββ ++−++= −−− 1312110

3

1

ββ

=IMC

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Adeoye et al., 2014

contingencies due to diseases or pests. This leads to differences in supply between markets, shortages, abundant supplies or temporary delays which influence the final price. For the earlier years (2003 to 2004), the prices of pineapple were higher, which declined from 2005 to 2009 and later increased again from 2010 to 2012. This may

be due to the forces of supply in the market which definitely influence the price. Unit root test The result of the unit root test is depicted in Table 1. With the application of the Augmented Dickey Fuller test, the prices

Figure 1: Urban and rural retail market price of sweet orange in Oyo State (2003-2012)

Figure 2: Urban and rural retail market price of pineapple in Oyo State (2003-2012)

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Market Integration for Sweet Orange and Pineapple

were not stationary at level form. This is indicated by their respective critical value which was larger than the ADF statistics. Therefore, the hypothesis of the presence of unit root test could not be rejected at the level form. The price series became stationary at first difference indicating that the ADF statistics were higher in absolute terms than the critical values. The null

hypothesis of presence of unit root was, therefore, rejected at first difference. This conforms to earlier findings of Okoh and Egbon (2003), Chirwa (2000) and Yusuf et al. (2006). They also reported that commodity prices are stationary at the order of first difference. Critical value at 1% for level form and first difference is -3.490772 for sweet orange rural market. Critical value at

Table 1: Unit Root test for sweet orange in Oyo State

Variable ADF (Level form)

Remark ADF (1st

Difference) Remark

Rural sweet orange -0.612 Non stationary -6.589** Stationary Urban sweet orange -2.979 Non stationary -9.114** Stationary Rural pineapple -2.250 Non stationary -7.036** Stationary Urban pineapple -2.524 Non stationary -6.538** Stationary

Field Survey, 2013 **Significant at 1% level.

Table 2: Co-integration test for sweet orange and pineapple

Market pairs Hypothesized No of CE (s)

Eigenvalue Trace statistic 5% Critical value

1% Critical value

Urban and Rural Citrus markets

None** 0.117293 20.04126 15.41 20.04 At most 1* 0.045465 5.444114 3.76 6.65

Urban and Rural pineapple markets

None** 0.134913 20.95406 15.41 20.04 At most 1* 0.038136 4.432540 3.76 6.65

Source: Field Survey, 2013 *Significant at 5%, **Significant at 1%

Table 3: Granger causality test for citrus and pineapple

Null Hypothesis Obs. F-statistic Probability URPCT does not Granger Cause RUPCT 118 4.52536 0.01286* RUPCT does not Granger Cause URPCT 1.41766 0.24656 URPPI does not Granger Cause RUPPI 118 0.35532 0.70173 RUPPI does not Granger Cause URPPI 0.94430 0.39200

Source: Field Survey, 2013 *Significant at 5% level

Table 4: Indices of Market Concentration (IMC)

Market pairs Crops R2 Adjusted R2

F-statistics DW IMC classification

Rural and urban

Citrus 0.489 0.485 38.090* 2.093 3.44 Low S/R market

integration Rural and urban

Pineapple 0.449 0.434 31.134 * 2.365 9.04 Low S/R market

integration Source: Field Survey, 2013 *Significant at 1%, S/R-short run

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1% for level form and first difference are -3.486551 and -3.487550, respectively, for sweet orange urban market. Critical value at 1% for level form and first difference are -3.487550 and -3.489117, respectively, for pineapple rural market. Critical value at 1% for level form and first difference are -3.488585 and -3.489117 respectively for pineapple urban market. Co-integration Test for sweet orange and pineapple Co-integration test was carried out to determine the existence of long-run relationship between the price variables, using Johansen Maximum Likelihood test to determine the number of co-integrating relations. The trace statistic showed that the null hypothesis of no co-integration was rejected at 1% α-level in both sweet orange and pineapple market pairs indicating long-run market integration (Table 2). This also indicates that the rural and urban market prices of sweet orange and pineapple are tied together in the long-run. Granger causality test for citrus and pineapple prices Granger causality Wald tests was performed to test the causality of prices (Granger, 1969). Out of 4 market pairs, only one rejected their respective null hypothesis of no granger causality. The null hypothesis that urban price of sweet orange does not cause rural price (test 1) was rejected at 5%. Thus, urban price of sweet orange causes rural price. This indicates uni-directional causality between sweet orange urban and rural market, which implies that sweet orange urban market is exogenous market. This implies that price formation of sweet orange in the State is largely done on the demand side and that farmers and assemblers who largely form the rural market contribute nothing or little to signaling of sweet orange price in the State. This asymmetric information flow will leave

the farmers worse off. This may lead to losses, especially in the rural areas where there is no arbitrage, and excess product in the rural areas will not be conveyed to the urban area. This may arise from inadequate flow of information from rural areas to urban areas as a result of poor market power which could come from non-formation into farmers groups. For pineapple urban and rural markets, no causality exists. It also implies that there is no information flow between rural and urban pineapple markets and that the pair markets are segmented from each other. This could be attributed to the fact that most of the production sites are not motorable. This also hinders the movement of the commodity from the rural areas to urban areas and vice-versa. The result in Table 3, shows an inter-market information flow between sweet orange and pineapple markets within the rural areas of the State. The index of market connection between rural and urban The result of the indices of market connection (IMC) is presented in Table 4. The IMC for sweet orange and pineapple markets were 3.44 and 9.04, respectively. The IMCs for this market pair were greater than one, thus indicating low short run market integration. The result implies that price changes in the rural market do not cause immediate change in the prices in the urban sweet orange and pineapple markets.

CONCLUSION AND RECOMMENDATIONS

The study has investigated the level of market integration between rural and urban market of sweet orange and pineapple in Oyo State, Nigeria using monthly retail price data for the period from 2003 to 2012 sourced from Oyo ADP. Based on the result of the analysis, prices in urban and rural

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Market Integration for Sweet Orange and Pineapple

market of sweet orange and pineapple are tied together in the long-run but exhibit low short-run market integration. Also, there is asymmetric information flow on sweet orange between urban and rural markets in the State, with the urban market only dictating the price in the short run. The pineapple markets are segmented in the short-run with no significant direction of information flow. Based on the result of the analysis, the following are recommended: There should be appropriate dissemination of market information to different stakeholders in the rural and urban markets. Improvement in rural infrastructure such as good road network: this will facilitate movement of commodities from rural centre to urban centers. This has been the bane in the conveyance of products from rural areas to urban centers. Farmers should organize themselves into commodity groups to enable them amass market power in order to translate all cost into price of their commodities and not be ripped off in the market.

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