Vol 4 - Cont J. Agric Econs.pdf 2010

8/8/2019 Vol 4 - Cont J. Agric Econs.pdf 2010

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Continental J. Agricultural Economics 4: 1 - 8, 2010 ISSN: 2141 4130

Wilolud Journals, 2010 http://www.wiloludjournal.com

ANALYSIS OF RETURNS TO SOCIAL CAPITAL AMONG TIMBER MARKETERS IN ONDO STATE.

Awoyemi, T. T1

and Ogunyinka, A. I.2

1Department of Agricultural Economics, University of Ibadan, Ibadan, Oyo State, Nigeria.

2Department of Agricultural Extension and Management, Federal College of Agriculture, Akure, Ondo State.

Nigeria.

ABSTRACT

This study examines the returns to social capital among timber marketers in Ondo State.

Purposive sampling was used in the data collection as four sawmills was identified and one

hundred and twenty respondents were randomly selected from the sawmills. Questionnaire was

used to obtain information from the marketers. Results show that over 75% of the members

attend meetings regularly with an index of between 20 and 50 percent of the highest time

allocated to meeting attendance. The decision-making index of the respondents shows thatmembers with the highest decision making index have high social capital than those with low

or intermediate index and are most committed to the course of the association. Result shows

that marketers with high income from the business tends to be more involved in local

association activities as a result of social capital accumulated. Social capital dimension showsthat index of participation and cash contribution was significant at 10 percent showing that as

respondents participate in local association activities more social capital was accumulated.

KEY WORDS: Social capital, gross margin, marketing, local association

INTRODUCTION

Social capital has become a topic of interest in a large number of policy areas. Definitions vary but it is often

understood to be a social resource which is created through formal and informal relationships between people within

a community. It describes the social environment that people live in, and is the collective resources to which

individuals, families, neighbourhoods and communities have access. The World Bank (1999) defines social capital

as the institutions, relationship and norms that shape the quality and quantity of a society interaction. Increasing

evidence show that social cohesion is critical for societies to prosper economically and for development to be

sustainable.

Social capital has been found to have great impact on the income and welfare of the poor, by improving the outcome

of activities that affects them. Rural people coming together to achieve a common goal through social capital, will

improve the efficiency of rural development programs by increasing agricultural productivity, facilitation, the

management of common resources making rural trading more profitable and improve access of people or household

to water, sanitation, credit and education in rural and urban areas (Grootaert and Bastelaer, 2001). This is why

social capital refers to connections among individuals and the social networks of reciprocity that arises from

them.

Social capital is one among several factors of production, along with human capital, financial capital, physical andnatural resources (Crudeli, 2005; Grootaert and Narayan, 1999; Serageldin 1996). Thus, there is a growing

recognition (Grootaert, 2005, Okunmadewa et al, 2004) that difference in economic outcomes, whether at the level

of the individual or household or at the level of the state, cannot be explained fully by difference in the traditional

inputs such as labour, land and physical capital. The role of social capital plays in affecting the well being of

household and the level of development of communities and nations are been documented (Serageldin 1996 and

Grootaert, 1999), these scholars argued that social capital is an input in a households or a nations productions and

has major implications for development policy and project design. This suggests that acquisition of human capital

and establishment of physical infrastructure needs to be complemented by institutional development in order to reap

the full benefits of the investments (Grootaert, 1999, Svendsen, 2000; Knack 1999). Social capital describes

activities familiar in everyday life in rural and pre-industrial societies around the world, cooperation between

individuals within their household and outside it to meet their everyday needs (Halpern, 2001). Yet social capitalhas not been easily accounted for in the money terms (Woolcock, 2001), its significance has tended to be overlooked


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Awoyemi, T. T and Ogunyinka, A. I: Continental J. Agricultural Economics 4: 1 - 8, 2010

(Lorenz, 1988). However, it ought to be of major importance in developing countries like Nigeria where so mucheconomic activity is not yet fully monetized and extended family ties are primary (Okunmadewa et al, 2004).

Certainly, the case for massive investment in social capital has be made, investing in social capital, although, there

are number of time-tested approaches in investing in social capital that are available such as building schools,

training teachers, developing appropriate curricula and so forth. Equivalent which have proven fruitful but

documentation in investing in social capital have not yet emerged (Grootaert and Bestelaer, 2001).

Consequently, this study is designed to assess returns to social capital among timber marketers in Ondo State,

specifically; the study developed a social capital index and the index was used to categories social capital formation

available to marketers in the study area, evaluate the effect of social capital index on gross margin, asses the degree

of linkage between social capital and income of timber marketers.

METHODOLOGY

Area of Study

This study was conducted in Ondo State of Nigeria (2009). Ondo State is situated in the south western geo-political

region of Nigeria, which comprises of 18 Local Government Areas. The state has a land area of 14,973 square

kilometer and projected population of 5,691,843 (NPC, 1991). It is bounded in the North by Ekiti and Kogi State, in

the East by Edo and Delta States, in the West by Osun and Ogun State and in the south by the Atlantic Ocean. Ondo

State falls within the tropical forest with total rainfall of about 1,250mm-1,500mm annually and it has a bio-modal

distribution between April-August and August-November. The maximum temperature ranges between 12oC-23

oC,

while humidity is relatively high.

Agriculture is the main occupationof the people of Ondo State. Majority of the people in the area are producers

who produce and market some agricultural produce like maize, rice, yam, plantain, tomato e. t. c. including livestock

production. The people are predominantly farmers. The farming population is scattered all over the villages in the

Local Government Areas.

Sources of DataThe data for this study were obtained mainly from primary sources. Information was collected on the socio-

economic/demographic characteristics of food marketers, costs and returns of each timber marketer, social capital

indices such as level of trust, Heterogeneity index, Density of membership, meeting attendance and active

participation index.

Sampling Procedure

The study covers Akure South Local Government Area of Ondo State. Since timber marketing is a lucrative

business in Ondo State, four sawmills were chosen from the Local Government, they are at Ogbese, Oba-ile, Ilara-

makin and Awule. From each of the sawmill, thirty marketers was randomly selected to make a sample size of 120

respondents.

Analytical Techniques

The analytical framework for this study includes descriptive, gross margin and regression analyses. The descriptiveanalysis encompasses frequency distribution, mean, median and mode as well as coefficient of variation. In addition,

different social capital dimension indices are constructed. The regression analysis attempts to model the Social

Capital Index through identifying and listing of all social capital dimensions attaching scores and weight

respectively. Social capital index (SCI), Human Capital (HC) and Socio-economic variables (SEV) of the marketers

were measured against their Gross margin/Total sales.

The Gross margin analysis is used to determine the profitability of the business. It is the difference between the

Total Revenue and the Total variable cost.

Gross margin = Total Revenue Total variable Cost


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The regression analysis is further elaborated upon in the subsequent paragraphs.The implicit form of the model is given by

Q= f (SCI, HC, SEV)

Where Q= Gross Margin/Total sale as dependent variable

SCI= Social capital index

HU= Human Capital

SEV= Socio-economic variables

Variables Definition

(A) The social capital variables that were used in the regression analysis include:

The indices used are density of membership, heterogeneity index, meeting attendance index, cash contribution,

labour contribution and decision making index. The measurement of these six social capital indices is as explained

below. This follows the approach used by Grootaert, et al (2002). The measurement of each is as described below.

1. Density of membership: this is captured by the summation of the total number of associations to which each

household belongs. In other words, the membership of associations by individuals in the household is summed up.

2. Heterogeneity index: this is an aggregation of the responses of each household to the questions on the diversity of

members of the most important institutions to the households.

3. Meeting attendance index: this is obtained by summing up the attendance of household members at meetings and

relating it to the number of scheduled meetings by the associations they belong to.

4. Cash contribution: This was obtained by the summation of the total cash contributed to the various associations

which the household belong.

5. Labour contribution: this is the number of days that household members belonging to institutions claimed to haveworked for their institutions.

6. Decision making index: this was calculated by summation of the subjective responses of households on their

rating in the participation in the decision making of the three most important institutions to them.

Aggregate social capital index: this is obtained by the multiplication of density of membership, heterogeneity index

and decision making index (Grootaert, 1999). The resultant index is renormalized to maximum value of 100.

(B) The human capital variable was measured by the average years of formal education of the head of the

household.

(C) The household characteristics used are:

(i) Marital status of household head (1 if married, 0 if otherwise)(ii) Household size (actual number of people in the household)

(iii) Gender of household head (D=l if male, 0 if otherwise)

(iv) Age of household head in years

RESULTS AND DISCUSSION

Selected Household Characteristics and Dimensions of Social Capital.

Selected Household Characteristics of Respondents:Table 1 presents the selected socio-economic characteristics of

the sampled respondents. Most of the marketers selected are male (79.2%) while 20.8% of the marketers are female;

the competing demand for production and reproduction may be responsible for low involvement (Adekoya, 2007).

Age-wise, most of the respondents are in their economic active age. Most of the marketers are in the middle age

falling between 36-55years (60%). This implies that risk element could be potent in the enterprise, in that is assumed

that the older the marketers the more risk averse he becomes. Table 1 also shows that the marketers have household


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size of between 5-10 members this implies that the respondents have a relatively large family. This may be as aresult of the need to have more helping hands with the business.

The level of educational attainment shows that majority of the respondents had access to formal education, 88.4%

had one form of formal education or the other, the recorded level of education might influence marketers level of

exposure and be more involved in social activities.

Table 1 also shows that majority of the marketers had between 1-10 years of experience in the business (59.9%),

while 25.8% have been into timber marketing for over 10years. The result implies that more experience people are

involved in the business and this enables them to relate more with each other and build a strong trust among

themselves.

Table 1: Selected Household Characteristics of Respondents

Variable Frequency Percentage (%)

Gender

Male

Female

95

25

79.2

20.8

Age:

26-36yrs

36-45yrs

46-55yrs

Above 55yrs

9

45

27

39

7.5

37.5

22.5

32.5

Household size

Less than 5

5-10

Above 10

25

83

10

20.8

70.7

8.5

Level of Education

No formal educationPrimary

Secondary

Tertiary

Others specify

1435

45

20

6

11.629.1

37.4

16.6

5.0

Years of Experience

1-10yrs

11-20yrs

21-30yrs

72

31

17

59.9

25.8

14.2

Total 120 100.0

Source: Field Survey, 2009

Social Capital index

Table 2 shows the social capital index, the result shows that majority of the marketers belong to at least oneassociation in the study area. While 16.6% belong to 2-3 association in the area. This shows that the marketersbelong to least one local association where they interact. On the cash contribution of the marketers, the results

shows that about 79.8% contribute less than 4 percents of total cash contribution, while 20% contribute more than 4

percent of the highest cash contribution within the study area. The labour contribution index shows that 70% of the

marketers gave less than 20 percent of the highest time allocated to any local association in the study area, while

30% gave more than 20 percent of the highest time allocated to any association within the study area. The

Heterogeneity index involve using socio-economic factors such as religion, age, level of education, gender to

construct heterogeneity index, this depicts the internal homogeneity of the group. The result shows that about 21.6%

of the marketers have an heterogeneity index of less than 20 in their local associations and about 28.2% have an

heterogeneity index of between 20 and 50 while 50.% of the marketers were on heterogeneity index that is greater

than 50. A high degree of heterogeneity in an association usually has negative implication, because it makes it more

difficult for members to trust each other, since it implies lesser degree of homogeneity. In term of meeting


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attendance, it seem that meetings are most frequent in the study area occurring on the average, every ten days, thetable shows that the higher the meeting attendance index by members, the more the participation in the associations

activities. The result shows that over 75% of the members attend meetings more regularly with an index of between

20 and 50 percent of the highest time allocated to meeting attendance. The decision-making index of the

respondents shows that members with the highest decision making index have high social capital than those with

low or intermediate index. This may be so because those with high decision making index are likely to be most

committed to the course of the association and those with very low value of decision making index, they seem not to

be committed to the activities of the associations and hence lower social capital. The result shows that 82.5% of the

marketers have above 20 percent decision making index.

Table 2: Social Capital Indices

Frequency Percentage (%)

Density of Association

Less than 2

2-3

Above 3

92

20

8

76.5

16.6

6.6

Cash Contribution

Less than 4

4-10

11-20

Above 20

96

15

6

3

79.8

12.5

5.0

2.5

Labour Contribution

Less than 20

20-50

Above 50

74

27

9

70.0

12.5

7.8

Heterogeneity Index

Less than 20

20-50Above 50

26

3460

21.6

28.250.0

Meeting Attendance

Less than 20

20-50

Above 50

27

90

3

22.7

75.0

2.5

Decision Making

Less than 20

20-50

Above 50

9

84

27

7.8

70.0

12.5

Total 120 100.0

Source: Field Survey, 2009.

Gross Margin AnalysisGross margin is used to determine the profitability of the business. It is the difference between the Total Revenue

and the Total variable cost.

Gross margin = Total Revenue Total variable Cost

The total revenue is the amount of money collected by the timber merchants on the sale of timber. The total variable

cost is the cost incurred in the running of the business which include labour, wages, offices and administrative

expensive, fueling and vehicle maintenance, electricity dues e.t.c.


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Result of Cost and Returns AnalysisTotal variable Cost N65026800

Total Revenue N287292400

Gross Margin N222265600

Gross Margin per marketer = Total Gross margin

Number of marketer

= 222265600

120

= N1852213.33

From the result, the total variable cost was N65026800 while the total revenue was N287292400, when the gross

margin per marketer was N1852213.33. The size and positive value obtained from the gross margin confirmed that

timber marketing was able to cover the operating expense therefore profitable in the study area.

Regression Analysis

Table 3 and 4 show the effect of socio-economic variables, human capital variable and social capital index variables

on respondents gross margin. The education variables in Table 3 was disintegrated into primary, secondary and

tertiary variables, while the aggregate social capital index was disintegrated into its components indices, which are

Heterogeneity index, decision making index, cash contribution index, labour contribution index, meeting attendance,

index of participation in Table 4.

(a) Socio-Economic variables

From Table 3, two of the five variables in the index were significant and these are years of experience (at 5percent)

and years of education (at 5percent). The interpretation of the result shows that years of experience in the business

enhance participation in social association because of the benefit derived from the association which in turns

increase the profit realized from the business. Also the result suggests that being educated and accumulating social

capital would improve the performance on the business. This is so since the higher the level of education of the

marketers the more their human capital and thus increased income.

The insignificant variables are sex, age, and family size at 5percent this is because sex does not affect participation

in local association in the study area. Age of the respondents have little effect on the social capital formation.

(b) Human Capital variable

The human capital variable considered is the years of education and result from table 3 confirm that it is an

important variable, thus impact accumulating social capital in the area. This shows that the more educated the

respondents are the more social capital they can accumulate.

(c) Social Capital Index variables

In Table 3, the social capital index does not have a significant effect on the marketers gross margin, however

variables such as index of participation, cash contribution, were significant at 10 percent level of significance. Theimplication of these findings is that the proportion of participation and cash contribution of the respondents increase

in association, so will more social capital be accumulated. Also labour contribution and Heterogeneity index is

significant at 5percent.


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Table 3 Regression Analysis Result IVariables Coefficient

Parameter

Standard Error t-ratio IP(/T/>t)

Constant -254.364 222.067 -1.145 .2540

Sex 40.061 33.132 1.239 .1785

Age 6.694 8.234 .754 .4607

Family size -5.546 7.923 -.557 .450

Years of Experience 5.498 2.023 1.304 .033**

Years of Education 56.410 27.678 1.864 .543**

Social Capital index 0.0057 2.18 -1.479 .1234

Membership in

Association

0.0825 0.432 0.581 0.543

Index of Participation 1.869 5.858 0.319 0.750*

Heterogeneity index 0.427 0.736 0.580 0.563Meeting Attendance -1.214 0.710 -1.609 0.034

Cash Contribution 0.231 1.347 0.145 0.0765*

Labour Contribution 0.979 1.157 0.787 0.392

Source: Field Survey Data 2009,* Significant at 5%, ** Significant 10%

Table 4 Regression Analysis Result II

Variables Coefficient

Parameter

Standard Error t-ratio IP(/T/>t)

Constant -203.032 211.324 -.823 0.321

Sex 87.761 34.453 1.002 0.245

Age 6.354 9.342 0.761 0.423

Tertiary Education 89.91 45.34 2.120 0.035

Primary Education 26.76 42.341 0.542 0.4304Secondary Education 41.353 45.042 0.931 0.346

Social Capital index 0.0014 0.054 2.18 0.123

Membership in

Association

0.0741 0.321 0.831 0.435

Index of Participation 1.869 5.858 0.419 0.650

Heterogeneity index 0.474 0.643 0.580 0.563

Meeting Attendance -1.134 0.510 -1.739 0.052

Cash Contribution 0.361 1.256 0.134 0.0589

Labour Contribution 0.798 1.231 0.864 0.278

Source: Field Survey Data 2009.

CONCLUSION

Social capital has been found to have great impact on the income and welfare of the poor, by improving the outcomeof activities that affects them. Rural people coming together to achieve a common goal through social capital

accumulation. As empirical findings from this study show that marketers with high income from the business tends

to be more involved in local association as a result of the social capital accumulated. Social capital dimension shows

that index of participation and cash contribution was significant at 10 percent showing that as respondents

participate in local association more social capital was accumulated. Also labour contribution and Heterogeneity

index was significant at 5percent showing that marketers are more directly involved in the activities of the local

association which will influence social capital accumulation.

The income realized shows that timber marketing is a profitable venture in the area, this influence participation in

local association as shown in the cash contribution of the marketers, income generated from ones business activities

enable the people to participate in local association and this in turn influences social capital accumulation.


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REFERENCESAdekoya, A. E. (2007): Determinant of Productivity level of Commercial Grasscutter farmers in Oyo State. In

African Journal of Livestock Extension, Vol. 5, No 1. July 2007, Pp71-75.

Crudeli, L. (2005): Social Capital and economic Opportunities. The Journal of Socio-economic

www.elsevier.com/locate/econibase.

Grootaert, C. (1999): Social capital, household welfare and poverty in Indonesia. Local Level Institutions Study,

Working Paper No. 6, Social Development Department, World Bank, Washington D.C.

Grootaert, C. (2005): Social Capital, Household welfare and Poverty in Indonesia, Local level institutions study,

Social Development Department, The World Bank.

Grootaert, C. and D. Narayan (1999): Local Institutions, Poverty and Household Welfare in Bolivia Mimeo,

Social Development Department. Washington D. C. World Bank.

Grootaert C., Oh, G. T. and Swamy, A. (2002): Social Capital, Household Welfare and Poverty in Burkina Faso.

Journal of African Economies, 11(1): 4-38.

Grootaert, C., and T. Van Bastelaer (2001): Understanding and measuring social capital: A synthesis of findings

and recommendations from the social capital initiative. Washington, D. C.: World Bank.

Halpern, D. S. (2001): Moral values, social trust and inequality: can values explain crime?British Journal of

Criminology, 2001.

Knack, S. (1999): "Social Capital, Growth and Poverty: A Survey and Extensions" Social Capital Initiative Working

Paper, Social Development Department. Washington, D.C. World Bank.

Lorenz, E. H. (1988): Neither friends nor strangers: Informal Networks of Subcontracting in French Industry In:

Gambetta D (ed): Trust: Making and Breaking Cooperatives Relations. New York Basil Blackwell.

NPC (2006): National Population Commission Census Figure 2006.

Okunmadewa, F. Y., Yusuf. S. A. and Omonona, B. T. (2004): Social capital and Poverty Reduction in Nigeria.

Report Submitted to Africa economic Research Consortium (AERC).

Serageldin, I. (1996): Sustainability as Opportunity and the Problem of Social Capital Brown Journal of World

Affairs 3(2): 187-203.

Svendsen, S. (2000): Social capital, the economy and education in historical perspective in Baron S., Field J. and

Schuller T., Social capital : critical perspectives, Oxford University Press, 2000.

Woolcock, M. (2001): The place of social capital in understanding social and economic outcomes, Canadian

Journal of Policy Research 2 (1): 11-17.

World Bank (1999) World Development Report1999.

Received for Publication: 14/07/2010, Accepted for Publication: 18/08/2010

Corresponding Author

Ogunyinka, A. I

Department of Agricultural Extension and Management, Federal College of Agriculture, Akure, Ondo State.

Nigeria.


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Continental J. Agricultural Economics 4: 9 - 18, 2010 ISSN: 2141 4130

Wilolud Journals, 2010 http://www.wiloludjournal.com

EFFICIENCY OF VEGETABLE PRODUCTION UNDER IRRIGATION SYSTEM IN ILORIN METROPOLIS:A CASE STUDY OF FLUTED PUMPKIN (Telferia occidentalis).

Nwauwa, Linus Onyeka Ezealaji and Omonona, Bola T . *

Department of Agricultural Economics, University of Ibadan, Ibadan, Nigeria.

ABSTRACT

The study was carried out in Ilorin metropolis of Kwara State, Nigeria. It investigated the costs

and return analysis of the respondents and the stochastic frontiers production analysis was

applied to estimate the technical, allocative and economic efficiency among fluted pumpkin

farming households in the metropolis. The result of the gross margin analysis showed that the

average gross margin per farmer was 21,252. The results of economic efficiency also

revealed an average of 0.904 while the mean technical and allocative efficiency were 0.978

and 0.925 respectively. Stochastic frontier production model showed that fertilizer and labour

were found to be significant factors that contributed for the technical efficiency of the farmers

while plot size and labour also were significant factors for allocative efficiency. The results

therefore concluded that only years of experience and size of plot were the significant factors

in the inefficiency sources model. On the basis of the findings, the study recommends that the

government should provide conducive environment for the establishment of modern irrigation

facilities for dry season farming, encourage more citizenry, especially the youths to practice

dry season vegetable farming in a bid to alleviate poverty status and unemployment in the state

and the country at large.

KEYWORDS: Fluted pumpkin, farming, technical, allocative and economic efficiency.

INTRODUCTION

Telfairia occidentalis otherwise called fluted pumpkin is one of the commonest, popular cut herbs grown mainly in

southeastern Nigeria and belongs to the cucurbitaceace family. The crop, which originated from West Africa, is aperennial climber grown for its leaves and seeds, which are very nutritious (Schippers, 2000). Fluted pumpkins can

be cultivated on the flat land or on mounds. In home gardens, they are frequently grown along a fence or next to a

tree, thus allowing the fruit to hang from a branch. They are also raised along stakes of various types including

bamboo [Akoroda, 1990]. Telfairia does best at the lower altitudes and medium to high rainfall and will do well on

sandier soil provided fertilizer is applied but has a more robust growth in rich well drained soil. When planting for

leaves, the usual spacing is 50 x 50cm for a mono-crop or occasionally even closer. Some farmers plant in the

middle of a 1.20m- wide bed at 40cm interval, and others plant on a mound next to a stake.

There is a clear need for location- specific plant density trials. When seed supply is not a limiting factor, farmers like

to plant two (or three) seeds/hole just in case seeds fail to germinate [Odiaka, 1997]. Nitrogen is essential for

adequate vegetation and should ideally be given in the form of manure, applied before planting. The use of well-

decomposed manure is essential for fruit production and in this case it is recommended that about 1 kg manure/

plant be applied. For maximum leaf yields, it is advisable to top dress with a nitrogen fertilizer immediately aftereach harvest. The maturity period for vegetative growth is between one to six months while for fruits, it is 6-8

months. Harvesting of shoots up to 50cm long can begin 1 month after germination followed by 3-4 week intervals

when new shoots are formed. Fresh shoot yields is usually about 500-1000kg/harvesting/ha, but could be more if the

crop receives adequate manure or when fertilizers are applied after each picking [Akinsami, 1975; Schippers, 2000].

The major crops grown under irrigation are vegetables, wheat and rice with initial bias for vegetables [Olugbemi,

1989]. Vegetables, which are rich sources of vitamins, minerals, carbohydrates, protein and dietary fibres are

important to the human diet. A balanced diet should contain 250-325g of vegetables and the average human

requirement for vegetable is 285g/person/day for a balanced diet [Nwachukwu, and Onyenweaku, 2007]. Over

dependence on rain-fed agriculture has led to seasonal vegetable shortage, fluctuation in vegetable prices, nutritional

inadequacy, which dry season vegetable production would have solved [Ayoade, 1988]. Outside Nigeria, where


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Nwauwa, Linus Onyeka Ezealaji and Omonona, Bola T: Continental J. Agricultural Economics 4: 9 - 18, 2010

fluted pumpkin is frequently eaten by up to 35 million people, and apart from West Cameroon, it is far less wellknown and, if so, then mainly for its immature edible seeds rather than for its shoots and leaves.

Indigenous vegetable production in Nigeria is rapidly decreasing due to water scarcity problem associated with the

cropping season. In the past, indigenous vegetables were largely grown under rain fed condition. However, pure rain

fed cultivation especially in the dry zones of Nigeria can seldom be practiced at present due to erratic nature of

rainfall. The present rainfall pattern in Nigeria creates prolonged dry season period during cropping season which

affects crop development and compel the need for crop irrigation. Irrigation method practiced currently for

vegetables is manual that consumes high labour cost as well as large amount of water (Narvaratne, 2009). Hence

farmers hesitate to grow vegetable under irrigation conditions, even though the economic value of these vegetables

is high compared to other crops. If it becomes a long term practice, it would cause disappearance of indigenous

vegetables indirectly which has high nutritional and medicinal value. As such, development of an appropriate

irrigation method which has high water use efficiency and low labour requirement has become an urgent need to

develop indigenous vegetables.

Vegetables have tremendous potentials to address poverty alleviation and nutritional security because they are

affordable and easily available, easy to grow, require minimum production inputs, rich in vitamins and minerals, and

are loaded with phytochemicals and anti-oxidants properties (Eusebio, 2009). Food security remains a challenge for

Africa and other developing countries. More than half of the population studied in Africa between 1995 and 2000

experienced food insecurity. Stunting as well as high levels of vitamins A and iron deficiencies, due to inadequate

dietary intake, is one of the major causes (Averbeke,2009). The use of Western vegetable has declined in Africa in

the last 20 years. The consumption of indigenous food plant has gone up. Many of the indigenous plants are

harvested from the wild. With increased demand, it becomes imperative to cultivate selected crops most suitable for

addressing nutrient deficiencies. Some of these crops have tremendous potentials to address food insecurity. Of

these, fluted pumpkin seems most appropriate for the African region mostly affected by food insecurity. Recent

work by Okokoh (2005), reveals that fluted pumpkin either as juice or pulse has high level medicinal value in

treatment of sexual impotence, maintenance of prostate gland, urinary and digestive disorders and acts as immuno-

stimulant and vermifuge. And according to Lithan (2005) sexual ability and general healthcare are directly related tonutrition.

Efficiently combining inputs to yield output is the primary task of farm management. When two firms in an industry

use the same inputs and employ the same technology, yet produce different quantities of output, the implication is

that at least one firm is producing inefficiently. The technical efficiency indicates the producers ability to achieve

maximum output from a given quantities of input and existing technology. Most recent studies have failed to

critically examine the importance of producing fluted pumpkin during dry season under irrigation system against the

popular rain fed system with a view of ascertaining their economic efficiency. If fluted pumpkin is to play a vital

role in ensuring future food availability for food security and nutrition in the country, this sector has to develop and

expand in an economically viable and environmentally sustainable manner.

The efficient allocation of resources at the farm level has implication for investment and employment at the national

level. It is also the indicator by which success of production units are evaluated. When measured correctly, it makesit easier to separate its effects from the effects of production units thereby enabling the enactment of sound policies

by which farm level performance could be improved (Ayanwale and Abiola, 2008). Among many other factors,

increasing efficiency of resource use and productivity at the farm level is one of the pre-requisites for sustainable

agriculture (FAO, 1997). Measuring technical efficiency at the farm level, identifying important factors associated

with the efficient production system would serve as a panacea to assessing potential for developing sustainable

vegetable production.

Economic efficiency is therefore derived from a cross product of the technical efficiency and allocative efficiency

(i.e. technical efficiency x allocative efficiency). The technical efficiency of an individual firm is defined as the ratio

of the observed output to the corresponding frontier output, given the available technology while allocative

efficiency reflects the ability of the producers to use inputs in optimal proportions given their respective prices

(Ajibefun and Daramola, 1999). There are four major approaches to measure and estimated efficiency (Dey et al,


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2000). These are the non-parametric programming approach , the parametric programming approach (Aigner andChu, 1968; Ali and Chaudhry, 1990], the deterministic statistical approach [Schmidt, 1976;] and the stochastic

frontier production function approach [Aigner et al, 1976; Aigner etal, 1977; Meeusen and Van Den Broeck, 1977].

Among these, the stochastic frontier production function and non-parametric programming, known as data

envelopment analysis (DEA), are the most popular approaches. The stochastic frontier approach is preferred for

assessing efficiency in agriculture because of the inherent stochasticity involved. [Fare et al, 1985; Kirkley et al,

1995; Coelli et al, 1998]. Economic efficiency however depends on market forces, which in turn are influenced by

the sectoral and marketing policies of the country. Empirical literature has shown that efficiency could be measured

from a production function or a profit function approaches. The profit function approach is much more helpful when

individual or sole enterprises are considered [Nwachukwu and Onyenweaku (2007)].

Apart from several studies by Nwachukwu and Onyenweaku (2007); Ayanwale and Abiola (2008) and Odiaka et al

(2008) conducted in fluted pumpkin production in the country, a stochastic production frontier has not been widely

applied to determine the production efficiency of the fluted pumpkin producers under irrigation system.

The objectives of this research are to: (1) find the socio-economic characteristics of the fluted pumpkin farmers, (2)

to estimate the technical, allocative and economic efficiency among the fluted pumpkin farmers using irrigation

system and (3) identifying the specific factors affecting fluted pumpkin enterprise in the state. Research hypotheses

will address the following:

H01 : Inefficiency sources model do not have effects in the use of resources.

H02 : Inefficiency sources model have effects in the use of resources.

METHODOLOGY

Area of the Study: The study was carried out in Ilorin, the Kwara state capital. The state serves as a bridge state

between the Northern and South-Western Nigeria. It shares its boundaries with Ondo, Oyo, Osun, Niger and Kogi

states in Nigeria and an international border with the Republic of Benin. The state has a population of about

2.37million people (NPC, 2006). The state has two distinct seasons annually: the dry and wet seasons. It has sizeable

expanse of arable land, rich fertile soils which is good for the cultivation of a wide variety of food crops like yam,cassava, maize, cowpea, fruits and vegetables. Fluted pumpkin, amaranthus and cochorus are significant vegetable

crops commonly grown in the area throughout the year. Dry season vegetable production is done along the coastal

areas of Asa River and other smaller streams that run across the metropolis. Cultivation and consumption of fluted

pumpkin (Telferia occidentalis) is alien to the state. T. occidentalis originated from the oriental states of Nigeria

from where it was introduced to some different parts of Nigeria. Hence majority of the correspondents used in this

study were from the Eastern part of Nigeria resident in the state involved in the production of fluted pumpkin.

Cultural diffusion and free trade across the country paved way for the production and consumption of fluted

pumpkin by majority of the citizenry. Local vegetables such as Amaranthus spp. and celosia argente etc are

gradually giving way to fluted pumpkin as a major vegetable food among the people of the state. The vegetable has

no local name hence it is still widely referred to as ugu in the state, the original name it is called in the East.

Fluted pumpkin is mainly produced in Ilorin metropolis for pumpkin consuming population and sometimes

marketers go as far as Ibadan and Lagos to buy in order to augment local production. There is no evidence ofcommercial fluted pumpkin production in the other parts of the state.

Sample Selection

The target population of this study is the households that produce fluted pumpkin under irrigation system. A two-

stage sampling procedure was used to select a representative sample for the study. The first stage was the random

selection of 10 areas along the coastline in the zone and the second stage involved the random selection of 10

household- respondents from each of the coastal areas engaged in dry season fluted pumpkin production, making a

total of 100 respondents. The data for the study were extracted from the respondents through questionnaire method

followed with personal interview by the researcher where necessary. Additional information for the study was

sourced from secondary sources such as journals and periodicals, Food and Agricultural Organisation circulars, etc.


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Theoretical Underpinning/Conceptual FrameworkFollowing Farells (1957) article on efficiency measurement which led to the development of several approaches to

efficiency and productivity analysis, among these is the Data Envelopment Analysis (AEA). As noted by Coelli et

al, (1998), the stochastic frontier is considered more appropriate than DEA in agricultural applications especially in

developing countries where the data is likely to be influenced by measurement errors and effects of weather

conditions, disease etc. This equally applies to the applications of frontier techniques to agriculture, including fluted

pumpkin production. However, the modeling and estimation of frontier production function has been a subject of

considerable interest in econometrics and applied economic analysis during the last two decades. Review of frontier

production is given by Forsund, et al (1980), Bauer (1990) and Battese and Coelli (1992). The stochastic frontier

production proposed by Battese and Coelli (1992) assumed that a random sample of farms is observed over t-period

such that the production of n farms over time is a given function of input variables and random variables which

involve the traditional random error and non-negative random variable which are associated with technical

inefficiencies of production. One of the earliest empirical studies in stochastic frontier production function was an

analysis of the source of technical inefficiency in the Indonesian Wheat Industry by Pit and Lee, (1983). The study

estimated a stochastic frontier production function by the method of maximum likelihood and the prediction of

technical inefficiencies were then regressed upon size of firm, age and ownership structure of each firm. These

variables were found to have significant effect on the degree of technical inefficiency of the firms.

Battese and Coelli, (1992) also investigated factors which influenced the technical inefficiency of Indian Farmers

using a stochastic frontier production function which incorporated a model for the technical inefficiency effects,

results found out that some farmers were able to achieve maximum efficiency while others were technically

inefficient. Onu et al, (2000) similarly investigated the determinants of cotton production and economic efficiency

using a stochastic frontier production function, which incorporated a model of inefficiency effects. The results

indicated that labour and material input were the major factors associated with changes in the output of cotton.

Farmers specific variables which comprise status of farmers, education, experience, and access to credit facilities

were found to be significant factors that accounted for the observed variation in inefficiency among the cotton

producers.

The frontier production model analysis for cross sectional data can be defined by considering a stochastic production

function with a multiplicative disturbance term of the form:

Y = f(Xa ) . (1)

Where,

Y = the quantity of the original output

Xa = a vector of input quantities

= a vector of parameters and = error term Where is a stochastic disturbance term consisting of two independent elements and v

where, = + v ...(2)

The symmetric component v accounts for random variation in output due to factors outside the farmers controlsuch as weather and disease. It is assumed to be independently and normally distributed with zero mean and constant

variance as N (0,2

v). A one sided component < 0 reflects technical inefficiency relative to the stochastic frontier,

(f(xa,) ). Thus, = 0 for a farm output which lies on the frontier and < 0 for one whose output is below the

frontier as [N (0,2u)], that is , the distribution of is half normal.

The frontier of the farm is given by combining (1) and (2).

Y = f f(xa,) (u+v)

..(3)

Measures of efficiency for each farm can be calculated as:

TE = exp. [E{ /}] .(4)


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And in equation (4) is defined as :

= f (zb, ) ..(5)

Where zb = a vector farmer specific factor.

= a vector of parameters.

The parameters for the stochastic production frontier model in equation (3) and those for the technical inefficiency

model in equation (5) were estimated simultaneously using the maximum-likelihood estimation (MLE) programme ,

FRONTIER 4.1 (Coelli, 1994), which gives the variance parameter of the likelihood function in terms of2

= 2

u +

2

v ,

= 2u /

2

In terms of its value and significance, is an important parameter in determining the existence of a stochastic

frontier: rejection of the null hypothesis. Ho1 : = 0 implies the existence of a stochastic production frontier.

Similarly, = 1 implies that all the deviation from the frontier are due mainly to technical inefficiency (Coelli, et al.,

1998).

Data Analysis

The tools employed for the analysis of this study were descriptive and stochastic frontier production function. The

descriptive statistical tool comprised frequency counts, percentages and means, which were used to analyse the

socio-economic characteristics of the fluted pumpkin producers in the state. The stochastic frontier production

function was used to estimate the efficiencies of the producers.

Analytical procedures

Descriptive statistics was used to describe the costs and return of the fluted pumpkin farming households in the

study area.

The Empirical Stochastic Frontier Production Model

Following the standard assumption that farmers maximize expected profits (Zellner et al, 1966), a single equation

Cobb-Douglas stochastic production frontier was applied to the analysis of fluted pumpkin farmers in the state

specified as follows:

Qi = f(x1, i) exp (vi - ui) (implicit) (5)

lnQi = 0 + 1lnx1 + 2lnx2+,,+ nlnxn + vi-ui (explicit) (6)

For technical efficiency specification:

Where Qi = output of the i-th farm in kilogrmme (kg)

Plot(x1) = size of plot/farm (acre)

Fert.(x2) = quantity of fertilizer used (kg)

Seed(x3) = quantity of seed for planting material

Labour(x4) = total labour used (family and hired labour) in man days

OtherMat(x5)= other materials used (quantity/month)

ln = natural logarithm.

0 = constant

1 = coefficient to be estimated

For allocative Efficiency Specification:

Qi = revenue from sales (output price x out of the i-th farm in (kg)

cplot(x1) = cost of plot (acre)

cFert.(x2) = cost of fertilizer used (kg)

cSeed(x3) = quantity of seed as planting material (kg)

clabour(x4) = monetary value of total labour used (family and hired labour)


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cotherMat.(x5) = cost of other materials used (quantity/month)

RESULTS AND DISCUSION

Costs and Return Analysis of fluted pumpkin farming households.

Table1 explains the objective of determining the cost and return of fluted pumpkin farming households in Ilorin

metropolis. The result showed that variable cost of production was the major cost involved in the production of

fluted pumpkin by the households. They are mainly peasant farmers who rent all the equipment used for the

production which would have constituted the fixed cost such as water pump, land and tilling implements. Labour

constituted about 38.31% of the total variable cost which indicates the low level of mechanization of the farms. The

average total revenue of the farmers was 97,709 for the period under review. The revenue was entirely from the

sales of fluted pumpkin leaves. The farmers do not undertake pod production since according to them it is not as

profitable the former.

Table 1: costs and return Analysis of an average fluted pumpkin farming household in Ilorin.

Item value ( /season) value (/season)

A: Revenue (output x price)

Leaf

Pod

97,709

-

B: Variable cost

Seed

Fertilizer

Rent of farm plot

Labour

Others (levies, cost of sales)

Total

24,085

14,000

8,500

30,009

1,710

78,304

C: Total Average Gross Margin (A-B) 21,252

Source: Field survey, 2009.

Relative Efficiency Indices

The estimation of economic efficiency (Table 2) shows the relative efficiency indices by age category for fluted

pumpkin farming households. The farmers operated at a high level of both average technical and allocative

efficiency of 0.90% and above for all the age categories. Though, analysis revealed that farmers operated at a high

economic efficiency level, but age group 40-49 operated at 0.87% which is far below average compared to the other

groups. The results support the assertion of Kalirajan and Shand (1989), Shapiro and Muller (1977) that given a

technology to transform physical inputs into output, some farmers are able to achieve maximum efficiency up to

100% while others are technically inefficient.

Table2: Relative Efficiency indices by age category for fluted pumpkin farmers in Ilorin: Estimation of

Economic Efficiency.

Agecategory A No. offarmers B Sum ofTech.Eff. C Sum of Allo.Eff. D Av.Tech.Eff E

(C/B)

Av. Allo. Eff.(%)

F (D/B)

Av. Ecco .Eff.(%) E x F

60

Total

9

23

29

39

100

8.66

22.43

28.42

38.61

8.46

20.7

27.55

35.49

0.962

0.975

0.98

0.99

0.94

0.90

0.95

0.91

0.904

0.878

0.931

0.901


Stochastic Frontier Models

The results of the stochastic frontier model estimated further showed that there are significant differences in the

technical, allocative and economic efficiency of the farmers in the study area. Quantity of fertilizer used and number


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of labour (both family and hired) were found to be significant factors that were associated with technical efficiency,while cost of plot and labour were also found to be significant under allocative efficiency (Table3). The inefficiency

sources model showed that years of experience and farm size contributed significantly to the explanation of

efficiency tables of the farmers.

Table3:Result of Maximum likelihood estimate of the Cobb-Douglas frontier production functions for technical and

allocative efficiency of the fluted pumpkin farmers.

Variable per parameter estimates Coefficient Std. error t-value

A: Technical Efficiency

Constant (0)Ln plot (1)

Ln Fert. (2)

Ln Seed (3)

Ln Labour(4)Ln Other materials (5)

Sigma-squared (s2= u

2+v

2)

Gamma ( = u2/v

2

Log (likelihood) (0)

Mean technical Efficiency

0.369**-0.522

0.117***

-0.142

0.310*0.241

0.517

0.535

0.150

0.978

0.9630.228

0.684

0.261

0.7430.421

0.180

0.117

0.383-0.229

0.171

-0.543

0.4170.445

0.287

0.459

B: Allocative Efficiency

Constant (0)

Ln cplot (1)

Ln cFert. (2)

Ln cSeed(3)

Ln clabour(4)

Ln cotherMat.(5)

Sigma-squared (s2

= u2

+v2

)Gamma ( = u

2/v

2

Log (likelihood) (0)Mean Allocative Efficiency

0.250**

0.500***

0.12

-0.166

0.313*

0.213

0.1030.900

0.8960.925

0.559

0.200

-NAN

0.309

0.367

0.432

0.1400.232

0.447

0.250

-NAN

-0.537

0.851

0.256

0.3120.243

C: Estimate of the Inefficiency sources model for the farmers.

Constant (0)

Age (1)

Household size (2)

Level of education(3)

Experience(yrs) 5

Farm size (5)

Sigma-squared (s2= u

2+v

2)

Gamma ( = u2/v

2

Log (likelihood) (0)Mean technical Efficiency

-0.678

-0.176

-0.182

-0.783

0.965*

0.151**

0.517

0.535

0.1500.978

0.153

0.796

0.156

0.717

0.971

0.924

0.180

0.117

-0.444

-0.223

-0.117

-0.109

0.994

0.163

0.287

0.459


* Significant at 1%, ** Significant at 5%, *** Significant at 10%. Other materials (e.g. miscellaneous expenses such

as levies, cost of sales, etc).

Hypotheses

Tables 3 and 4 showed that the null hypothesis which specified that inefficiency sources model do not have effects

in the use of resources is accepted. Moreso, = 1, = =2, =, = 50. This implies that the entire delta ()

estimates are not zero. It further revealed that the delta variables estimated contributed significantly to the

inefficiency of the fluted pumpkin farmers in the study area. Also, that the 2-calculated is less than the

2-tabulated

(table 4) indicating the relevance of the variables in fluted pumpkin production.


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Table4: The generalized likelihood ratio test for the parameter of the inefficiency sources model.Log(likelihood)

2Statistics

2V.095 Decision

0.150 3.08 24.62 Accept Ho


CONCLUSION

This study focused on the analysis of economic efficiency of fluted pumpkin farming households in Ilorin, Kwara

State. The findings showed that all the farmers were operating at a high technical, allocative and economic

efficiency level of 90% or more, though not at exactly 100% level. The result agreed with the findings of Ayanwaleand Abiola (2008) who found that an average fluted pumpkin farmer in Edo State of Nigeria utilized resources

below optimum level. The research therefore concluded that it is more advisable for fluted pumpkin farmers in the

study area to adopt this technology with a view to make more profit and to be more economically efficient in their

investment decision.

The results further, concluded that year of experience was found to be statistically significant at 1 per cent. The

results of the hypotheses which showed that the beta () are different from zero also revealed the production

variables: plot, fertilizer, labour, seed and other materials are relevant to the technical and allocative efficiency.

More so, delta () values representing the farmers specific variables (years of experience, age, household size, and

level of education of farmers) are also relevant in the production system.

The inefficiency sources model showed that only years of experience and size of plot (farm size) are significant

factors. Thus it can therefore be concluded that farming experience and size of plot influenced level of inefficiency

among the producers. On the basis of the findings, the study therefore recommends that the government should

provide a conducive environment for the establishment of modern irrigation facilities for dry season farming,

encourage more citizenry, especially the youths to practice dry season vegetable farming in a bid to alleviate poverty

status and unemployment in the state and the country at large.

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Received for Publication: 14/07/2010,

Accepted for Publication: 18/08/2010

Corresponding Author

Nwauwa, Linus Onyeka Ezealaji

Department of Agricultural Economics, University of Ibadan, Ibadan, Nigeria.

Email: [email protected]


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