EFFECT OF RURAL INFRASTRUCTURE ON PROFITABILITY AND PRODUCTIVITY OF CASSAVA-BASED FARMS IN ODOGBOLU LOCAL GOVERNMENT AREA , OGUN STATE , NIGERIA

Infrastructural development in Nigeria has been historically linked to the development of agriculture, exploitation of natural resources and public policies. This study examined the effect of rural infrastructures on profitability and productivity of cassava-based farms in Odogbolu local government area of Ogun state, Nigeria. The study was based on a cross-sectional survey of 120 cassava farmers selected with a multistage random sampling technique from 10 villages. Descriptive statistics were used to generate the composite rural infrastructure index which revealed that 5 out of the 10 sampled villages were under-developed. Economic efficiency in the developed and under-developed areas shows that farmers in the developed areas are better off compared to their counterparts in the under-developed areas. Farm size, years of farming experience and infrastructural development index (INF) were statistically significant with negative influence on productivity of cassava-based farmers. The significance and indirect relationship of the years of farming experience and infrastructural development index at p<0.05 and farm size (p<0.01) regarding the total factor productivity (TFP) implied that these variables decrease TFP. Similarly, the negative sign of the coefficient of INF of -0.742 at p<0.05 shows that the under-development of infrastructural facilities observed in the study area is capable of jeopardizing efforts at improving the productivity of cassava-based farmers. Therefore, farmer in the developed areas can generally produce more output at lower cost if there is an improvement in infrastructural facilities in the study area.


Introduction
Recent literature indicates the significance of rural infrastructure in improving agricultural productivity in developing economies (Anderson and Shimokawa, 2006).Effective infrastructure has been described as a key foundation for strong economic growth (International Labour Organization, 2010; National Audit Office 2013) that plays a critical role in ensuring efficient operations in the cassava value chain (Oni, 2013).Infrastructural facilities are basic services without which the needed environment as well as primary, secondary and tertiary productive activities will not be able to function.Infrastructural facilities can be physical (such as roads, water, rural electrification, storage and processing facilities), social infrastructure (health and educational facilities, community centres, fire and security services) and institutional infrastructure (credit and financial institutions, agricultural research facilities) (Rahji, 2007).Availability of adequate infrastructure facilities is an important pre-requisite for sustainable economic and social development.
Increasing agricultural productivity depends on good infrastructural facilities and is an instrument to improve the economy (Calderon and Serve, 2008;Egbetokun, 2009;Patra and Acharya, 2011).Adequate infrastructures can reduce the cost of production which affects productivity (Oyewole and Oloko, 2006).Infrastructures are key stimulants to agricultural development and growth (FAO, 1996).But most developing countries including Nigeria still suffer from poor rural infrastructural facilities (Olayiwola and Adeleye, 2005;Umoren et al., 2009).Even though Nigeria government initiated several projects to improve the quality and quantity of infrastructure in the rural areas through programmes such as the construction of small dams and boreholes for rural water supply and the clearing of feeder roads for the evacuation of agricultural produce, the supply of electricity to rural areas from large irrigation dams, the establishment of eleven River Basin Development Authorities (RBDAs), Directorate for Food, Roads and Rural Infrastructure (DFRRI), the Poverty Relief and Infrastructure Investment Fund and the Comprehensive Agricultural Support Programme, the impact of such programmes on the lives of many rural people in the country is still considered to be limited (Ale et al., 2011).The neglect of rural infrastructure (such as roads) impedes the profitability of agricultural production, marketing of agricultural commodities and prevents farmers from selling their produce at reasonable price due to spoilage (IFAD, 2011;Akpan, 2012).Limited accessibility to infrastructures such as road and credit cuts small-scale farmers off from sources of inputs, equipment and new technology and keeps yields low.Inadequate infrastructures also affect the level of productivity through ineffective time allocation (Ondiege et al., 2013), poor health and quality of life (Kessides, 1993;Alaba, 2001), poverty reduction, economic growth and employment for the rural poor (United Nations, 2011), ineffective marketing and price transmission, thereby inhibiting full utilization of potentials of farm households.Many poor farm households tend to live in isolated villages which are virtually inaccessible during the rainy seasons (Alaba, 2001).This study will be very important even though the effect of rural infrastructure on productivity has been examined in Nigeria recently, the specific effect on cassava-based farms was however not sharply focused (Fakayode et al., 2008;Ashagidigbi et al., 2011;Odoemenem and Otanwa, 2011;Olagunju et al., 2012;Adepoju and Salman, 2013).This study will bridge this gap through empirical analysis of the effect of infrastructural deficiency on productivity of cassava-based farms as well as correct the bias in methodology that uses distance (Fakayode et al., 2008) in the computation of infrastructural index.The use of cost incurred in accessing the various infrastructures is more appropriate in the context of a rural community where the study is carried.The study is also important as the provision of infrastructure is a necessary pre-requisite for policy formulation that will aid the much needed increase in the productivity of cassava-based farm which is one of the strategic plans in the Nigeria's transformational agenda.The present paper will contribute to relevant literature by testing the significance of infrastructure among a set of other variables in the determination of the overall goodness of a model.Specifically, the ordinary least square regression was used and the result was compared to that of step-wise regression model.The distinction in the two models is that the step-wise regression allows all specified variables to enter into the model in order of importance.This systematically ensures that only the important variables are included in the model.
The broad objective of this study was to examine the effect of rural infrastructures on productivity of cassava-based farming system in the study area.The specific objectives were to determine the extent of rural infrastructure development in the study area and to estimate the effect of the infrastructural facilities on profitability and productivity of cassava-based farms.

Material and Methods
The study was conducted in Odogbolu local government area of Ogun state, Nigeria.It is located at latitude 65°0'N and longitude 34°6'E in the north-western part of the state.The area occupies a land mass of 541 km² and a population of 12,123 (NPC, 2006).It consists of tropical rain forest and a small stretch of derived savannah.The people are predominantly farmers growing food crops in virtually all the parts of the local government.The local government comprises 15 wards.The study was a cross-sectional survey using structured interview guides for focus group discussion and questionnaires for individual farming households.The study sample was comprised of two sets viz. a focus group of at least ten people per community and individual farming household.A total of 120 cassava farmers were selected in a multistage random sampling technique.In the first stage, 10 wards were randomly selected from the 15 wards in the local government.In the next stage, a community per ward was randomly selected.In final stage, 12 farm households were randomly selected for interview to culminate into the 120 total samples used for the study.Data on socioeconomic characteristics of cassava farmers and production activities were collected.Information on cost of transportation to the various infrastructures was obtained from at least one focus group chosen from each community.The infrastructures used in this study were potable water source, schools, health centre, market and agro-service centre.The data were subjected to both descriptive and inferential analysis.
The infrastructural index used for this study is based on the sampled communities' level data in line with Fakayode et al. (2008).The composite degree of infrastructure development used was adopted following Adeoye et al. (2011), Ashagidigbi et al. (2011), Balogun et al. (2012), Bulus and Adefila (2014) and Babatunde et al. (2014).It was obtained in a process listed in the equations below.Individual transportation cost (IDCi) of the respondents in each of the 10 villages was obtained by summing up the individual cost of access (TCi) to five basic infrastructure elements in this study.An average total cost (ATC) of getting to each of these infrastructure elements across these communities was computed and used to divide the average costs (ACi) of getting to a particular infrastructure facility in each of the 10 communities.The outcome of this step was summed up to obtain the infrastructural index (INF).The infrastructural index (INF) indicates the degree of underdevelopment, thus, the higher the value of infrastructural index, the more under-developed the village is considered (Ahmed and Hossain, 1990).

∑
(5) Where: = individual transportation cost of getting to each infrastructure by the respondents in each community (N); = average cost of transportation in each community to a particular infrastructure (N); = total cost of transportation to a particular infrastructure across communities (N); = average cost of transportation to a particular infrastructure across communities (N); = weight of average transportation cost attached to infrastructure in each community; = total number of communities; = number of respondents in each community.Total factor productivity (TFP) is a method of calculating agricultural productivity by comparing an index of agricultural inputs to an index of outputs (Jean-Paul, 2009).This can be computed following Key and McBride (2005) as the ratio of the output to the total variable cost (TVC): where: Y = quantity of cassava, TVC = total variable cost, = unit price of i th variable input, and = quantity of i th variable input.This methodology ignores the role of total fixed cost (TFC) as it does not affect either the profit maximization or the resource-use efficiency conditions (Fakayode et al., 2008).Therefore, equation 6 can be rewritten as: (7) The effect of various factors on TFP was hypothesized using the Cobb-Douglas functional form of multiple regression model as follows: TFP f X , X , … … … … X (8) where: = age of the farmer (years), X = household size, X farming experience (years), X = farm size (ha) and X infrastructural index.Furthermore, a step-wise regression model was used to determine the importance of entry of all the variables.
The economic efficiency (EE) is computed as in equation 9: (9) where TVP is the total value of product and TC is total cost.

Results and Discussion
The results in Table 1 show that the majority of the respondents are male (86.7%), married (81.7%) and 31.7% of them have no formal education.The higher proportion of male implied that the males are more concerned about rural infrastructures than their female counterparts in the study area.Also, the wide margin between the married and unmarried may not be unrelated to the culture, religion and norms of the people.It also shows the importance attached to marriage institution by farmers in rural area in Nigeria for the benefit of having children to help in farming activities.We can also infer from the result that the literacy level is high in the study area indicating the possibility of maintaining infrastructures if provided.The largest proportion (35.8%) has no other occupation than cassavabased farming indicating the extent to which the respondents depend on farming for income.This depicts the necessity to make rural life better through adequate provision of infrastructures such as good road and storage facility capable of transforming lives.Furthermore, Table 1 shows that the farmers are mainly aged between 46 and 60 years (55.8%) with the mean age of 55.3 years, which indicates that they are still economically active and productive.An average farming household in the study area is made of about 7 individuals.This is an indication that they may be ready source of family labour on the farm in agreement with other findings such as those of Bulus and Adefila (2014).The majority (69.2%) of the respondents cultivated between 0.4 and 0.8 hectares, which indicates that the respondents are peasant farmers.This shows that farmers in the study area were producing at subsistence level probably as result of the condition of infrastructural facilities that may not support large-scale and commercial production.
The average cost to all infrastructures in the study area is presented in Table 2.The estimations were in line with Wanmali (1985) and Bhatia and Rai (2008) that measured access to various infrastructures as the physical distance in kilometers or transport cost between the households and the centers where these services are provided.The result confirms that cost of transportation to the drinking water sources (N31.92) is the lowest, school (N44.64),health centre (N48.22),agroservice centre while the cost to market is the highest (N50.11).This amount spent to sources of water is, however, lower than the one reported by Adeoye et al. (2011) in Oyo state where Fadama farmers spent an average of N50.04.However, cassava-based farmers spent a higher amount in getting to markets as against N44.44 (Adeoye et al., 2011) spent by Fadama farmers in getting to market in Oyo state.The residents of Ilado are paying the highest cost of accessing health centres (N60.00),market (N60.00)and agro-service centres (N60.00).This shows how far the infrastructures are from them.On the contrary, the Ososa people are paying the lowest amount to get to water sources (N26.52),school (N34.35), and market (N41.74).Incidentally, the cost of accessing all infrastructures in Ososa was also observed as the cheapest (N39.22) in the study area.The result of the composite infrastructural index in Table 3 shows that Ososa is the most developed ward with infrastructure index of 0.77; this is followed by Idowa with an index of 0.91.However, Ibefun and Okun-Owa are the least developed, having an index of 1.15 each, which is above the average index value of 1.00.The cost and return profile of the developed and under-developed areas show that the labour cost is significantly higher in the under-developed area when compared to the developed areas.This may not be unconnected with the fact that the farm labour may prefer to stay in more developed areas where infrastructural supplies are better.This singular effect also accounts for the increase in the total variable cost (TVC) of production in the under-developed area as against the developed areas.Interestingly, this increase in TVC did not reflect in the profit as there was no difference in the profit of both the farmers in the under-developed and developed areas.
Table 4 depicts cost and return structure of cassava-based farms per hectare in developed and under-developed villages.Farmers in the under-developed areas may be enjoying the opportunity of selling at an increased price as against those in the developed areas that are often faced with flooded markets that tend to lower prices as they are price takers.The result of economic efficiency indicates that the farmers in the developed areas are better off as they will recoup 39k on every N2 (i.e.N18.50k on N1) invested as against their counterparts in the under-developed areas who will only get 24k on every N2 (i.e.N12.00k on N1) invested in cassavabased farms.These values are significantly different at 5% stressing the debilitating effect of poor infrastructure on economy of the farmers and they are in agreement with past findings which showed that good infrastructure facilities improved farm profitability and productivity (Manalili and Gonzales, 2009).Table 5 shows the effect of infrastructure on productivity of cassava farmers.The model shows that all the variables were significantly related to TFP, F (5, 115) = 5.268, p <0.001.The adjusted coefficient of determination ( ) of 0.72 indicates that 72 percent of the variations in the productivity of the farmers in the study area could be explained by the considered explanatory variables (socioeconomic and infrastructure).Farm size, years of farming experience and infrastructural development index were found to be statistically significant revealing a negative influence on productivity of cassava-based farmers in the study area.The significance and indirect relationship of the years of farming experience, infrastructural index at p<0.05 and farm size (p<0.01)regarding the TFP implied that these variables decrease TFP contrary to past findings such as those of Adepoju and Salman (2013) and Babatunde et al. (2014), who found a positive and significant relationship between farm size and farm productivity.Similarly, the negative sign of the coefficient of infrastructural development index which is less than 1 (-0.742) at p<0.05 shows that the underdevelopment of infrastructural facilities observed in the study area is capable of jeopardizing efforts at improving the productivity of cassava-based farmers in the study area.Therefore, farmer in the developed areas can generally produce more output at lower cost if there is an improvement in infrastructural facilities.In addition, the results of our step-wise multiple regression (Table 6) show the importance of the infrastructural index in the model predicting TFP.At step 1 of the analysis, farm size was entered into the regression equation and it was significantly related to TFP, F (1, 119) = 10.711 at p<0.001 with adjusted of 0.38.This indicates that approximately 38% of the variance in the TFP could be accounted for by farm size.At step 2, farm size and experience were entered into the equation and the equation became better as it was significantly related to TFP, F (2, 118) = 10.608 at p < 0.001 with a higher adjusted of 0.72 showing that 72% of the variance in the TFP was accounted for by farm size and experience.A further inclusion of our variable of interest (infrastructural index) into the model in step 3 shows that farm size, year of farming experience were significantly related to TFP with F (3, 118) = 8.491 at p<0.001 and infrastructural index at p<0.005 while the explanatory power of the model also increased from 72% to 86% from the value of 0.72 and 0.86 respectively.Other socioeconomic variables such as age and household size were not entered into the equation at step 3 of the analysis.This confirms the ordinary least square multiple regression results (Table 5) which showed that inclusion of infrastructural development on TFP is important and could not be neglected or omitted in the productivity of the cassavabased farms in the study area.

Table 1 .
Socioeconomic characteristics of cassava-based farmers.

Table 2 .
Cost of transportation to infrastructures in naira (N).

Table 3 .
The result of the composite infrastructural index (INF).

Table 4 .
Cost and return structure (N per hectare) of cassava-based farms in developed and under-developed villages.

Table 5 .
The effect of infrastructure and other socioeconomic factors on productivity of cassava-based farming system.

Table 6 .
Step-wise regression analysis to determine the effects of rural infrastructure on total factor productivity (TFP).