Continental J. Agricultural Economics - Volume 4, 2010

<!-- /* Font Definitions */ @font-face {font-family:SimSun; panose-1:2 1 6 0 3 1 1 1 1 1; mso-font-alt:宋体; mso-font-charset:134; mso-generic-font-family:auto; mso-font-pitch:variable; mso-font-signature:3 680460288 22 0 262145 0;} @font-face {font-family:Vrinda; panose-1:2 11 5 2 4 2 4 2 2 3; mso-font-charset:0; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:65539 0 0 0 1 0;} @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-1610611985 1107304683 0 0 415 0;} @font-face {font-family:Cambria; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-1610611985 1073741899 0 0 415 0;} @font-face {font-family:Calibri; panose-1:2 15 5 2 2 2 4 3 2 4; mso-font-charset:0; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:-520092929 1073786111 9 0 415 0;} @font-face {font-family:Tahoma; panose-1:2 11 6 4 3 5 4 4 2 4; mso-font-charset:0; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:-520081665 -1073717157 41 0 66047 0;} @font-face {font-family:"\@SimSun"; panose-1:2 1 6 0 3 1 1 1 1 1; mso-font-charset:134; mso-generic-font-family:auto; mso-font-pitch:variable; mso-font-signature:3 680460288 22 0 262145 0;} /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin-top:0cm; margin-right:0cm; margin-bottom:10.0pt; margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-fareast-font-family:Calibri; mso-bidi-font-family:"Times New Roman"; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} h1 {mso-style-priority:9; mso-style-unhide:no; mso-style-qformat:yes; mso-style-link:"Heading 1 Char"; mso-style-next:Normal; margin-top:12.0pt; margin-right:0cm; margin-bottom:3.0pt; margin-left:0cm; mso-pagination:widow-orphan; page-break-after:avoid; mso-outline-level:1; font-size:16.0pt; font-family:"Cambria","serif"; mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"; mso-font-kerning:16.0pt; mso-ansi-language:EN-US; mso-fareast-language:ZH-CN; font-weight:bold;} p.MsoCommentText, li.MsoCommentText, div.MsoCommentText {mso-style-noshow:yes; mso-style-priority:99; mso-style-link:"Comment Text Char"; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif"; mso-fareast-font-family:SimSun; mso-ansi-language:EN-US; mso-fareast-language:ZH-CN;} p.MsoHeader, li.MsoHeader, div.MsoHeader {mso-style-noshow:yes; mso-style-priority:99; mso-style-link:"Header Char"; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; tab-stops:center 234.0pt right 468.0pt; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-fareast-font-family:Calibri; mso-bidi-font-family:"Times New Roman"; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} p.MsoFooter, li.MsoFooter, div.MsoFooter {mso-style-priority:99; mso-style-link:"Footer Char"; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; tab-stops:center 234.0pt right 468.0pt; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-fareast-font-family:Calibri; mso-bidi-font-family:"Times New Roman"; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} span.MsoCommentReference {mso-style-noshow:yes; mso-style-priority:99; mso-ansi-font-size:8.0pt; mso-bidi-font-size:8.0pt;} a:link, span.MsoHyperlink {mso-style-priority:99; color:blue; text-decoration:underline; text-underline:single;} a:visited, span.MsoHyperlinkFollowed {mso-style-noshow:yes; mso-style-priority:99; color:purple; mso-themecolor:followedhyperlink; text-decoration:underline; text-underline:single;} p {mso-style-noshow:yes; mso-style-priority:99; mso-margin-top-alt:auto; margin-right:0cm; mso-margin-bottom-alt:auto; margin-left:0cm; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman","serif"; mso-fareast-font-family:"Times New Roman"; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} p.MsoAcetate, li.MsoAcetate, div.MsoAcetate {mso-style-noshow:yes; mso-style-priority:99; mso-style-link:"Balloon Text Char"; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:8.0pt; font-family:"Tahoma","sans-serif"; mso-fareast-font-family:Calibri; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} p.MsoNoSpacing, li.MsoNoSpacing, div.MsoNoSpacing {mso-style-priority:1; mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman","serif"; mso-fareast-font-family:SimSun; mso-ansi-language:EN-US; mso-fareast-language:ZH-CN;} span.Heading1Char {mso-style-name:"Heading 1 Char"; mso-style-priority:9; mso-style-unhide:no; mso-style-locked:yes; mso-style-link:"Heading 1"; mso-ansi-font-size:16.0pt; mso-bidi-font-size:16.0pt; font-family:"Cambria","serif"; mso-ascii-font-family:Cambria; mso-fareast-font-family:"Times New Roman"; mso-hansi-font-family:Cambria; mso-bidi-font-family:"Times New Roman"; mso-font-kerning:16.0pt; mso-ansi-language:EN-US; mso-fareast-language:ZH-CN; font-weight:bold;} span.HeaderChar {mso-style-name:"Header Char"; mso-style-noshow:yes; mso-style-priority:99; mso-style-unhide:no; mso-style-locked:yes; mso-style-link:Header; mso-ansi-font-size:11.0pt; mso-bidi-font-size:11.0pt; font-family:"Times New Roman","serif"; mso-bidi-font-family:"Times New Roman"; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} span.FooterChar {mso-style-name:"Footer Char"; mso-style-priority:99; mso-style-unhide:no; mso-style-locked:yes; mso-style-link:Footer; mso-ansi-font-size:11.0pt; mso-bidi-font-size:11.0pt; font-family:"Times New Roman","serif"; mso-bidi-font-family:"Times New Roman"; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} span.CommentTextChar {mso-style-name:"Comment Text Char"; mso-style-noshow:yes; mso-style-priority:99; mso-style-unhide:no; mso-style-locked:yes; mso-style-link:"Comment Text"; font-family:"Times New Roman","serif"; mso-ascii-font-family:"Times New Roman"; mso-fareast-font-family:SimSun; mso-hansi-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"; mso-ansi-language:EN-US; mso-fareast-language:ZH-CN;} span.BalloonTextChar {mso-style-name:"Balloon Text Char"; mso-style-noshow:yes; mso-style-priority:99; mso-style-unhide:no; mso-style-locked:yes; mso-style-link:"Balloon Text"; mso-ansi-font-size:8.0pt; mso-bidi-font-size:8.0pt; font-family:"Tahoma","sans-serif"; mso-ascii-font-family:Tahoma; mso-hansi-font-family:Tahoma; mso-bidi-font-family:Tahoma; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} span.cgselectable {mso-style-name:cgselectable; mso-style-unhide:no;} .MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:10.0pt; mso-ansi-font-size:10.0pt; mso-bidi-font-size:10.0pt; mso-ascii-font-family:Calibri; mso-fareast-font-family:Calibri; mso-hansi-font-family:Calibri; mso-bidi-font-family:Arial;} @page Section1 {size:612.0pt 792.0pt; margin:72.0pt 72.0pt 72.0pt 72.0pt; mso-header-margin:36.0pt; mso-footer-margin:36.0pt; mso-paper-source:0;} div.Section1 {page:Section1;} -->

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 that members 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 shows that 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 and natural 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 household’s or a nation’s 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 capital has not been easily accounted for in the money terms (Woolcock, 2001), its significance has tended to be overlooked

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 much economic 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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">METHODOLOGY

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Area of Study

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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-23oC, while humidity is relatively high.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.undefined

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Sources of Data

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">The 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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Sampling Procedure

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Analytical Techniques

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">The analytical framework for this study includes descriptive, gross margin and regression analyses. The descriptive analysis 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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Gross margin = Total Revenue – Total variable Cost

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Awoyemi, T. T and Ogunyinka, A. I: Continental J. Agricultural Economics 4: 1 - 8, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">The regression analysis is further elaborated upon in the subsequent paragraphs.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">The implicit form of the model is given by

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Q= f (SCI, HC, SEV)

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Where Q= Gross Margin/Total sale as dependent variable

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">SCI= Social capital index

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">HU= Human Capital

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">SEV= Socio-economic variables

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Variables Definition

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">(A) The social capital variables that were used in the regression analysis include:

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">4. Cash contribution: This was obtained by the summation of the total cash contributed to the various associations which the household belong.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">5. Labour contribution: this is the number of days that household members belonging to institutions claimed to have worked for their institutions.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">(B) The human capital variable was measured by the average years of formal education of the head of the household.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">(C) The household characteristics used are:

<p style="margin-bottom: 0.0001pt; text-align: justify; text-indent: 36pt; line-height: normal;">(i) Marital status of household head (1 if married, 0 if otherwise)

<p style="margin-bottom: 0.0001pt; text-align: justify; text-indent: 36pt; line-height: normal;">(ii) Household size (actual number of people in the household)

<p style="margin-bottom: 0.0001pt; text-align: justify; text-indent: 36pt; line-height: normal;">(iii) Gender of household head (D=l if male, 0 if otherwise)

<p style="margin-bottom: 0.0001pt; text-align: justify; text-indent: 36pt; line-height: normal;">(iv) Age of household head in years

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">RESULTS AND DISCUSSION

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Selected Household Characteristics and Dimensions of Social Capital.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Awoyemi, T. T and Ogunyinka, A. I: Continental J. Agricultural Economics 4: 1 - 8, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">size of between 5-10 members this implies that the respondents have a relatively large family. This may be as a result of the need to have more helping hands with the business.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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 marketer’s level of exposure and be more involved in social activities.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin: 0cm 0cm 0.0001pt 36pt; text-align: justify; text-indent: 36pt; line-height: normal;">Table 1: Selected Household Characteristics of Respondents <p style="margin: 0cm 0cm 0.0001pt 36pt; text-align: justify; text-indent: 36pt; line-height: normal;">Source: Field Survey, 2009

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Social Capital index

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table 2 shows the social capital index, the result shows that majority of the marketers belong to at least one association in the study area. While 16.6% belong to 2-3 association in the area. This shows that the marketers belong 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

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Awoyemi, T. T and Ogunyinka, A. I: Continental J. Agricultural Economics 4: 1 - 8, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">attendance, it seem that meetings are most frequent in the study area occurring on the average, every ten days, the table shows that the higher the meeting attendance index by members, the more the participation in the association’s 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.

<p style="margin: 0cm 0cm 0.0001pt 36pt; text-align: justify; text-indent: 36pt; line-height: normal;">Table 2: Social Capital Indices <p style="margin: 0cm 0cm 0.0001pt 36pt; text-align: justify; text-indent: 36pt; line-height: normal;">Source: Field Survey, 2009.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Gross Margin Analysis

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Gross margin is used to determine the profitability of the business. It is the difference between the Total Revenue and the Total variable cost.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Gross margin = Total Revenue – Total variable Cost

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Awoyemi, T. T and Ogunyinka, A. I: Continental J. Agricultural Economics 4: 1 - 8, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; text-indent: 36pt; line-height: normal;">Result of Cost and Returns Analysis

<p style="margin-bottom: 0.0001pt; text-align: justify; text-indent: 36pt; line-height: normal;">Total variable Cost N65026800

<p style="margin-bottom: 0.0001pt; text-align: justify; text-indent: 36pt; line-height: normal;">Total Revenue N287292400

<p style="margin-bottom: 0.0001pt; text-align: justify; text-indent: 36pt; line-height: normal;">Gross Margin N222265600

<p style="margin-bottom: 0.0001pt; text-align: justify; text-indent: 36pt; line-height: normal;">Gross Margin per marketer = Total Gross margin

<p style="margin: 0cm 0cm 0.0001pt 144pt; text-align: justify; text-indent: 36pt; line-height: normal;">Number of marketer

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">= 222265600

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">120

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">= N1852213.33

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Regression Analysis

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table 3 and 4 show the effect of socio-economic variables, human capital variable and social capital index variables on respondent’s 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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">(a) Socio-Economic variables

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">(b) Human Capital variable

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">(c) Social Capital Index variables

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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. The implication 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.

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Awoyemi, T. T and Ogunyinka, A. I: Continental J. Agricultural Economics 4: 1 - 8, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; text-indent: 36pt; line-height: normal;">Table 3 Regression Analysis Result I <p style="margin-bottom: 0.0001pt; text-align: justify; text-indent: 36pt; line-height: normal;">Source: Field Survey Data 2009,* Significant at 5%, ** Significant 10%undefined

<p style="margin-bottom: 0.0001pt; text-align: justify; text-indent: 36pt; line-height: normal;">Table 4 Regression Analysis Result II <p style="margin-bottom: 0.0001pt; text-align: justify; text-indent: 36pt; line-height: normal;">Source: Field Survey Data 2009.

<p style="margin-bottom: 0.0001pt; line-height: normal;">CONCLUSION

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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 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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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 one’s business activities enable the people to participate in local association and this in turn influences social capital accumulation.

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Awoyemi, T. T and Ogunyinka, A. I: Continental J. Agricultural Economics 4: 1 - 8, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">REFERENCES

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Adekoya, 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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Crudeli, L. (2005): Social Capital and economic Opportunities. The Journal of Socio-economic www.elsevier.com/locate/econibase.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Grootaert, C. (2005): Social Capital, Household welfare and Poverty in Indonesia, Local level institutions study, Social Development Department, The World Bank.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Grootaert, C. and D. Narayan (1999): “Local Institutions, Poverty and Household Welfare in Bolivia” Mimeo, Social Development Department. Washington D. C. World Bank.

<p style="margin-bottom: 0.0001pt; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Halpern, D. S. (2001): ''Moral values, social trust and inequality: can values explain crime? ''British Journal of Criminology, 2001.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">NPC (2006): National Population Commission Census Figure 2006.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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).

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Serageldin, I. (1996): “Sustainability as Opportunity and the Problem of Social Capital” Brown Journal of World Affairs 3(2): 187-203.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Svendsen, S. (2000): 'Social capital, the economy and education in historical perspective in Baron S., Field J. and 

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Schuller T., Social capital : critical perspectives, Oxford University Press, 2000.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Woolcock, M. (2001): “The place of social capital in understanding social and economic outcomes”, Canadian Journal of Policy Research 2 (1): 11-17.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">World Bank (1999) World Development Report 1999.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Received for Publication: 14/07/2010, Accepted for Publication: 18/08/2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Corresponding Author

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Ogunyinka, A. I

<p style="margin-bottom: 0.0001pt; line-height: normal;">Department of Agricultural Extension and Management, Federal College of Agriculture, Akure, Ondo State. Nigeria.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Continental J. Agricultural Economics 4: 9 - 18, 2010 ISSN: 2141 – 4130

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">© Wilolud Journals, 2010 http://www.wiloludjournal.com

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">EFFICIENCY OF VEGETABLE PRODUCTION UNDER IRRIGATION SYSTEM IN ILORIN METROPOLIS: A CASE STUDY OF FLUTED PUMPKIN (Telferia occidentalis).

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Nwauwa, Linus Onyeka Ezealaji and Omonona, Bola T. *

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Department of Agricultural Economics, University of Ibadan, Ibadan, Nigeria.

<p style="margin: 0cm 43.2pt 0.0001pt; text-align: justify; line-height: normal;">ABSTRACT

<p style="margin: 0cm 43.2pt 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin: 0cm 43.2pt 0.0001pt; text-align: justify; line-height: normal;">KEYWORDS: Fluted pumpkin, farming, technical, allocative and economic efficiency.

<p style="text-align: justify;">INTRODUCTION

<p style="text-align: justify;">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 a perennial 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.

<p style="text-align: justify;">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 after each 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].

<p style="text-align: justify;">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

<p style="text-align: center;">Nwauwa, Linus Onyeka Ezealaji and Omonona, Bola T: Continental J. Agricultural Economics 4: 9 - 18, 2010

<p style="text-align: justify;">fluted pumpkin is frequently eaten by up to 35 million people, and apart from West Cameroon, it is far less well known and, if so, then mainly for its immature edible seeds rather than for its shoots and leaves.

<p style="text-align: justify;">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.

<p style="text-align: justify;">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 to nutrition.

<p style="text-align: justify;">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 producer’s 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.

<p style="text-align: justify;">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 makes it 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.

<p style="text-align: justify;">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, 

<p style="text-align: center;">Nwauwa, Linus Onyeka Ezealaji and Omonona, Bola T: Continental J. Agricultural Economics 4: 9 - 18, 2010

<p style="text-align: justify;">2000). These are the non-parametric programming approach, the parametric programming approach (Aigner and Chu, 1968; Ali and Chaudhry, 1990], the deterministic statistical approach [Schmidt, 1976;] and the stochastic frontier production function approach [Aigner et al, 1976; Aigner et al, 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)].

<p style="text-align: justify;">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.

<p style="text-align: justify;">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:

<p style="text-align: justify;">H01: Inefficiency sources model do not have effects in the use of resources.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">H02 : Inefficiency sources model have effects in the use of resources. undefinedundefined

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">METHODOLOGY

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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 of commercial fluted pumpkin production in the other parts of the state.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Sample Selection

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="text-align: center;">Nwauwa, Linus Onyeka Ezealaji and Omonona, Bola T: Continental J. Agricultural Economics 4: 9 - 18, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Theoretical Underpinning/Conceptual Framework

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Following Farell’s (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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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:

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Y = f(Xaβ) ℮ε …………………………. (1)

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Where,

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Y = the quantity of the original output

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Xa= a vector of input quantities

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">β = a vector of parameters and

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">εundefined= error term undefinedWhere ‘ε’ is a stochastic disturbance term consisting of two independent elements ‘μ’ and ‘v’ undefined

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">where, ε = μ + v …………………………………...(2)

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">The symmetric component ‘v’ accounts for random variation in output due to factors outside the farmers control such as weather and disease. It is assumed to be independently and normally distributed with zero mean and constant variance as N ̴ (0,σ2v). A one sided component μ < 0 reflects technical inefficiency relative to the stochastic frontier, (f(xa,β) ℮ε). Thus, μ = 0 for a farm output which lies on the frontier and μ < 0 for one whose output is below the frontier as [N ̴ (0,σ2u)], that is, the distribution of ‘μ’ is half normal.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">The frontier of the farm is given by combining (1) and (2).

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Y = f f(xa,β) ℮(u+v)…………………………………..(3)

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Measures of efficiency for each farm can be calculated as:

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">TE = exp. [E{ μ/ε}] …………………………………….(4)

<p style="text-align: center;">Nwauwa, Linus Onyeka Ezealaji and Omonona, Bola T: Continental J. Agricultural Economics 4: 9 - 18, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">And ‘μ’ in equation (4) is defined as :

<p style="margin: 0cm 0cm 0.0001pt 36pt; text-align: justify; text-indent: 36pt; line-height: normal;">μ = f (zb, σ) ……………………………………………..(5)

<p style="margin: 0cm 0cm 0.0001pt 36pt; text-align: justify; text-indent: 36pt; line-height: normal;">Where zb = a vector farmer specific factor.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">σ = a vector of parameters.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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 of σ2 = σ2u + σ2v ,

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">γ = σ2u / σ2

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">In terms of its value and significance, γ is an important parameter in determining the existence of a stochastic frontier: rejection of the null hypothesis. Ho1 : γ = 0 implies the existence of a stochastic production frontier. Similarly, γ = 1 implies that all the deviation from the frontier are due mainly to technical inefficiency (Coelli, et al., 1998).

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Data Analysis

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Analytical procedures

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Descriptive statistics was used to describe the costs and return of the fluted pumpkin farming households in the study area.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">The Empirical Stochastic Frontier Production Model

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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:

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Qi= f(x1, βi) exp (vi- ui) (implicit) …………………………………(5)

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">lnQi = β0 + β1lnx1 + β2lnx2+,…,+ βnlnxn + vi-ui (explicit) ……………………(6)

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">For technical efficiency specification:

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Where Qi = output of the i-th farm in kilogrmme (kg)

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Plot(x1) = size of plot/farm (acre)

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Fert.(x2) = quantity of fertilizer used (kg)

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Seed(x3) = quantity of seed for planting material <p style="border: medium none ; padding: 0cm; margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Labour(x4) = total labour used (family and hired labour) in man days <p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">OtherMat(x5)= other materials used (quantity/month)

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">ln = natural logarithm.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">β0= constant

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">β1= coefficient to be estimated

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">For allocative Efficiency Specification:

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Qi = revenue from sales (output price x out of the i-th farm in (kg)

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">cplot(x1) = cost of plot (acre)

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">cFert.(x2) = cost of fertilizer used (kg)

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">cSeed(x3) = quantity of seed as planting material (kg)

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">clabour(x4) = monetary value of total labour used (family and hired labour)

<p style="text-align: center;">Nwauwa, Linus Onyeka Ezealaji and Omonona, Bola T: Continental J. Agricultural Economics 4: 9 - 18, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">cotherMat.(x5) = cost of other materials used (quantity/month)

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">RESULTS AND DISCUSION

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Costs and Return Analysis of fluted pumpkin farming households.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table 1: costs and return Analysis of an average fluted pumpkin farming household in Ilorin. <p style="margin-bottom: 0.0001pt; text-align: justify; text-indent: 36pt; line-height: normal;">Source: Field survey, 2009.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Relative Efficiency Indices

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table2: Relative Efficiency indices by age category for fluted pumpkin farmers in Ilorin: Estimation of Economic Efficiency. <p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Source: Field survey, 2009.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Stochastic Frontier Models

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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

<p style="text-align: center;">Nwauwa, Linus Onyeka Ezealaji and Omonona, Bola T: Continental J. Agricultural Economics 4: 9 - 18, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table3:Result of Maximum likelihood estimate of the Cobb-Douglas frontier production functions for technical and allocative efficiency of the fluted pumpkin farmers. <p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Source: Field survey, 2009.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">* Significant at 1%, ** Significant at 5%, *** Significant at 10%. Other materials (e.g. miscellaneous expenses such as levies, cost of sales, etc).

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Hypotheses

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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, δ = 1, = δ =2, =…, δ = 5≠0. This implies that the entire delta (δ) 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 χ2-calculated is less than the χ2-tabulated (table 4) indicating the relevance of the variables in fluted pumpkin production.

<p style="text-align: center;">Nwauwa, Linus Onyeka Ezealaji and Omonona, Bola T: Continental J. Agricultural Economics 4: 9 - 18, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table4: The generalized likelihood ratio test for the parameter of the inefficiency sources model. <p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Source: Field survey, 2009.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">CONCLUSION

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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 Ayanwale and 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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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 (β) 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 (δ) 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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">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.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">REFERENCE:

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Aigner, D.J., Amemiya, T. and Porier, D.J (1976) On the Estimation of Production Frontiers: Maximum Likelihood Estimation of the Parameters of a Discontinuous Density Function. International Economic Review 17: 377-396 ''

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Aigner, D.J. and Chu, S.F (1968) On Estimating the Industry Production Function. American Economic Review 58:826-839 ''

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Aigner, D, Lovell, C.A.K and Schmidt, P (1977) Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics 6:21-37''

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Ajibefun, I. A. and Daramola, A (1999): Inefficiency of production of farmers under the National Directorate of in Ondo State, Nigeria. Applied Economic ''Letters, Routledge, 6. 111-114.''

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Akinsami, O (1975) Certificate Agricultural Science. Longman Group Limited. First Edition.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Akoroda, M.O (1990) Ethno Botany of Telfairia occidentalis (cucurbitaceae) among Igbos of Nigeria. Economic Botany 44:1 ''

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Ali, M. and Chaudhry, M.A (1990) Inter-regional Farm Efficiency in Pakistan’s Punjab: A Frontier Production Function Study. ''Journal of Agricultural Economics 41:62- 74. 

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Averbeke, W.V. (2009): ‘Water Use of Indigenous Crops for Improved Livelihood’. International Conference on the Nutritional Value of Indigenous Crops Held at Technical University of Tshwane.

<p style="text-align: center;">Nwauwa, Linus Onyeka Ezealaji and Omonona, Bola T: Continental J. Agricultural Economics 4: 9 - 18, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Ayanwale A.B.and Abiola M.O. (2008): Efficiency of fluted pumpkin production under Tropical conditions. International Journal of vegetable science Vol.13, (3) 35-49.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Ayoade, J.O (1988) Tropical Hydrology and water Resources. Macmillian Publishers Ltd. London.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Battese, G.E. and Coelli, T.J. (1992): Frontier Production, Technical Efficiency and Panel Data With Application to paddy Rice Farmers in India. ''Journal of Productivity Analysis. 3: 153-169.''

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Battese, G.E. and Coelli, T.J. (1995): A Model for Technical Inefficiency Effects in a stochastic Frontier production function for Panel data. Journal of Econometrics 13, 5-25

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Bauer, P.W.(1990): Recent Development in Econometric Estimation of Frontiers. Journal of Econometrics 46:39 - 56.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Coelli, T.J. (1994): A Guide to Frontier Version 4.1 a Computer Programme for Stochastic Frontier Production and Cost function Estimation. Department of Econometrics, University of New England, Amidale, Aust.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Coelli, T.J., Rao, D.S.P. and Battese, C.E.(1998): An introduction to Efficiency and Productivity analysis. Kluwer Academic Publishers, Boston U.S.A.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Dey, M.M., Paraguas, F.J. and Bimbao, G.B (2000) Technical Efficiency of Tilapia Growout pond Operations in the Phillipines. Aquaculture Economics and Management 4:33 - 47 ''

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Eusebio, J.E. (2009): ‘Promoting Utilisation of Indigenous Vegetables for Improved Nutrition in the Philippines’. First International Conference on Indigenous Vegetables and Legumes, RWC Auditorium. 12-15 Dec.

Fare, R.S., Grosskopf and Lovell, C.A.K (1985) The Measurement of Efficiency of Production. Kluwer-Nijhoff Publishing, Boston.

Farell, M.J. (1957): The Measurement of productive Efficiency. J. Royal Statistics Society Series A (general) 21: 253-281).

Food and Agricultural Organisation (FAO) (1997): Survey and analysis of aquaculture development research priorities and capabilities in Asia. FAO Fisheries Circular, No. 930, Rome.

Food and Agricultural Organisation (FAO) (1990): Second World Climate Conference. Geneva; and UNEP. 1992. The state of the environment.

Forsund F, Knox-Lovell, C.A. and Schmidt, P (1998): A Survey of Frontier Production Function and their Relationship to Efficiency Measurement: ''American Journal of Agric. Economics, 77: 675-685.''

Kilirajan, K.P., and Shand, R.T. (1989): A generalized measure of technical efficiency. Applied Econ. 21: 25-34.

Kirkley, J.E., Squires, D. and Strand, I.E (1995) Assessing Technical Efficiency in Commercial Fisheries:The Mid-Atlantic Sea Scallop Fishery. ''American Journal of Agricultural Economics 20: 31-34. ''

Lithan J. (2005): ‘Healing Power of Fruits and Vegetables, MCall, December PP.34.

Meeusen, W. and Van Den Broeck, J (1977) Efficiency Estimation from Cob-Douglas Production Function With Composed Error. International Economic Review 18: 435-444 

National Population Commission (NPC) 2006: Kwara State Population, NPC, Abuja.

<p style="text-align: center;">Nwauwa, Linus Onyeka Ezealaji and Omonona, Bola T: Continental J. Agricultural Economics 4: 9 - 18, 2010

Navaratne, C.M. and Kodithuwakku,W.K.K.S.P.I. (2009): ‘Improvement of Indigenous Vegetable Production in Sri Lanka Through Low Cost- Cost Micro-Irrigation Approach’. International Conference on Indigenous vegetables. University of Ruhuma, Mapalana.

Nwachukwu, I. N. and Onyenweaku, C.E (2007): Economic Efficiency Of Fadama Telfairia Production In Imo State Nigeria: A Translog Profit Function Approach. Published in: Journal of Agricultural Research and Policies, Nigeria 2 4 (2007): pp. 87-93.

Odiaka, N.I. (1997) Aspect of Seeds Quality in Fluted Pumpkin. M.Phil thesis Submitted to the Faculty of Agriculture and Forestry, University of Ibadan. Nigeria

Odiaka N. I, Akoroda M.O and Odiaka E.C (2008): Diversity and production methods of fluted pumpkin(Telfairia occidentalis Hook F.); Experience with vegetable farmers in Makurdi, Nigeria''. African Journal of Biotechnology Vol. 7 (8), pp. 944-954. ''Available online at http://www.academicjournals.org/AJBISSN 1684– 5315 © 2008 Academic Journals

Okokoh, L. (2005): Foods that Heal and Foods that Kill. Capstone Natural health Center (Nig.) Ltd., Lagos.

Olugbemi, l. B (1989) National Wheat Requirement Versus Fadama potentials. A Paper Presented at the National Workshop on Fadama and Irrigation Development, Zaranda Hotel, Bauchi, October 23-26.

Onu, J.I., Amaza,P.S. and Okumadewa, F. (2000): Determinants of Cotton Production and Economic Efficiency in Nig. African Journal of Business and Economics1(2)34-40

Pitt, M.M and Lee, L.F. (1983): Measurement and Sources of Technical Inefficiency in the Indonesian Weaving Industry. Journal of development Economics 9:43-64.

Sanders, D.C. (2009): ‘Vegetable Crop Irrigation’. A Hand Book for Extension Horticultural Specialist. North Carolina Cooperative Extension Services, North Carolina State University.

Schippers, R.R (2000) African Indigenous Vegetables. An Overview of the Cultivated Species. Chathan UK: Natural Resources Institute/ACP-EU Technical Centre for Agricultural and Rural Cooperation.

Schmidt, P. (1976): On the Estimation of Parametric Frontier Production Function. ''Review of Economics and Statistics 58: 238-239. ''

Zellner, A., Kmenta J. and Dorez, J. (1966): Specification and Estimation of Cobb- Douglas Production Function Model. Econometrica, 34: 785-795.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Received for Publication: 14/07/2010,

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Accepted for Publication: 18/08/2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Corresponding Author

<p style="margin-bottom: 0.0001pt; line-height: normal;">Nwauwa, Linus Onyeka Ezealaji

<p style="margin-bottom: 0.0001pt; line-height: normal;">Department of Agricultural Economics, University of Ibadan, Ibadan, Nigeria.

<p style="text-align: justify; line-height: 150%;">Email: [mailto:linusezealaji@yahoo.com linusezealaji@yahoo.com]

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Continental J. Agricultural Economics 4: 19 - 25, 2010 ISSN: 2141 – 4130

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">© Wilolud Journals, 2010 http://www.wiloludjournal.com

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">FARMERS PERCEPTION OF IMPACT OF CLIMATE CHANGES ON FOOD CROP PRODUCTION IN OGBOMOSO AGRICULTURAL ZONE OF OYO STATE, NIGERIA.

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Ayanwuyi, E1 Kuponiyi, F. A1. Ogunlade, I2 and Oyetoro J. O1.

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">1Department of Agricultural Extension and Rural Development, Ladoke Akintola University of Technology Ogbomoso, Oyo State, Nigeria. 2Department of Agricultural Extension and Rural Development, University of Ilorin, Ilorin Nigeria.

<p style="margin: 0cm 17pt 0.0001pt; line-height: normal;">ABSTRACT

<p style="margin: 0cm 17pt 0.0001pt; text-align: justify; line-height: normal;">The study assessed farmer’s perception of impact of climate change on food crop production in Ogbomoso Agricultural zone of Oyo State, Nigeria. It highlights the socio-economic characteristics of the farmers, farmer’s perception on climate change, impact of climate change on crop production and adaptation strategies adopted to mitigate the effect of climate change. Data were collected by using structured interview schedule administered on 360 farmers randomly selected from the three agricultural extension blocks in the study area. Description and analysis of data were carried out using frequency counts, percentages means and tables, while multiple regression was used to test the hypothesis. About 72.0% of the respondents were male, and 95.8% were between 31 and above 51 years old. While 29.4% had no formal education,70.6% have various levels of formal education. About 90% of the farmers had many years of farming experience ranging from6years to 21years and above. Only 31.1% and 24.7% of the respondents indicated delayed rainfall and higher temperature respectively as their perception of climate change. About 12% indicated unusual heavy rainfall, 9.4% indicated undefined season, while 4.4% and 4.2% respectively indicated flood with serious consequences and later fruiting of tree crops respectively as their perception of climate change. About 80.3% of the respondents mentioned low yield of crops as the impact of climate change on crop production, stunted growth (37.2%), ease spread of pest and diseases attack on crops (31.1%). Even though only 68.3% indicated increased water conservation as adaptation strategies, 74.7% mentioned planting of different crops while 54.4% change row orientation with respect to slope, as the adaptation strategies to mitigate impact of climate changes. A significant relationship at 0.05 significant level with coefficient of (R2 = 0.612) was found between perceived climate change and adaptation strategies. Therefore Arable food crop farmers are more knowledgeable of climate change and even its impacts on their livelihood that should be considered in policy formulation on adaptation of agricultural production systems to climate change.

<p style="margin: 0cm 17pt 0.0001pt; text-align: justify; line-height: normal;">Keywords: Farmers Perception, impact, climate change, Food Crop Production, Agricultural Zone, Oyo State.

<p style="margin-bottom: 0.0001pt; line-height: normal;">INTRODUCTION

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Agriculture places heavy burden on the environment in the process of providing humanity with food and fiber, while climate is the primary determinant of agricultural productivity (Apata et al 2 009). The fundamental role of agriculture in human welfare, concern has been expressed by federal agencies and others regarding the potential effects of climate change on agricultural productivity. Because the effectiveness of rainfall for crop and fish production is a function of the temperature values which affect evaporation and transpiration (Rudolf and Harmann 2009) Smith and Skinner (2002) asserted that climate plays a dominant role in agriculture having a direct impact on the productivity of physical production factors, for example the soil’s moisture and fertility. Adverse climate effects can influence farming outputs at any stage from cultivation through the final harvest. Even if there is sufficient rain, its irregularity can affect yields adversely if rains fail to arrive during the crucial growing stage of the crops (Mowa and Lambi, 2006, Rudolf and Hermann 2009). Interest in this issue has motivated a substantial body of research on climate change and agriculture (Lobell et al, 2008) climate change is expected to influence crop and livestock production, hydrologic balance input supplies and other components of agricultural systems. However, the nature of these biophysical effects and the human responses to them are complex and uncertain. It is evidence that climate change will have a strong impact on Nigeria particularly in the areas of agriculture, land use, energy consumption, biodiversity health and water resources (Apata et al, 2009). Nigeria like all the countries of sub-Saharan Africa is highly vulnerable to the impacts of climate change (NEST, 2004, IPCC 2007 and Apata et al, 2009). Though climate change is a threat to agriculture and non-agricultural socio-economic development, agricultural production activities

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Ayanwuyi, E et al: Continental J. Agricultural Economics 4: 19 - 25, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">are generally more vulnerable to climate change than other sectors. (Kurukulasuriya, et al, 2006). Ole et al, (2009) asserted that analysis of 9000 farmers in 11 African countries predicted falling in farm revenues with current climate scenarios. Also Butt et al, (2005) predicted future economic losses and increased the risk of hunger due to climate change. It seems clear the combination of high climatic variability poor infrastructure, economic poverty, drought, excess rainfall, poor livestock health, reduced crop yields, low productivity and a range of other problems associated with climate variability will constitute important challenges for Africa countries in particular (Adger et al, 2007).

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Food crop farmers in Oyo State Nigeria provide the bulk of arable crops that are consumed locally. The local farmers are experiencing climate change even though they have not considered it deeper implications (Apata et al, 2009). This is evidenced in the late arrival of rain, the drying-up of stream and Small Rivers that usually flows year round. Also the gradual disappearances of flood-recession cropping in riverine areas of Ondo State are among the effects of climate disturbances in some communities of south-western Nigeria. (BNRCC, 2008 and Apata et al,  2009). InSri-lanka when precipitation increases it results in a positive and significant impact on farmers revenues, whereas temperature has a strong negative impact. Also in Cameroon net revenue falls as precipitation decreases or as temperature increases while in South Africa climate change has significant effects on net revenue per hectares of sugarcane with higher sensitivity to future increase in temperature than precipitation Ole et al, 2009).

<p style="margin-bottom: 0.0001pt; line-height: normal;">STRATEGIES TO MITIGATE IMPACTS OF CLIMATE CHANGES

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">To approach the issue of climate change appropriately, one must take into account local communities understanding of climate change, since they perceive climate as having a strong spiritual, emotional, and physical dimension. It is therefore assumed that these communities have an inborn, adaptive knowledge from which to draw and survive in high-stress ecological and socio-economic conditions. Thus, the human responses are critical to understanding and estimating the effects of climate change on production and food supply for ease of adaptation. Accounting for these adaptations and adjustments is necessary in order to estimate climate change mitigations and responses (Apata et al, 2009 SPORE, 2008 BNRCC 2008).

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">There is identification of production systems which are most resilient to climate variability that is production systems with the ability to adjust or recover from negative impacts and take advantage of positive impacts of the current climate variability. One of the factors that contribute to increasing resiliency of agricultural systems is the identification of appropriate mixes of production activities. For example, establishing crop/livestock mixed systems, using a mix of crop species, cultivar types and sowing dates, combining less productive drought - resistant cultivars and high yield but water sensitive crops. In other words, modifying the production systems by introducing four strategies:

<p style="margin: 0cm 0cm 0.0001pt 36pt; text-align: justify; text-indent: -36pt; line-height: normal;">(a) Increased diversification including activities that are less sensitive to drought and/or temperature stresses.

<p style="margin: 0cm 0cm 0.0001pt 36pt; text-align: justify; text-indent: -36pt; line-height: normal;">(b) Compatibility: activities that take full advantage of beneficial climate conditions.

<p style="margin: 0cm 0cm 0.0001pt 36pt; text-align: justify; text-indent: -36pt; line-height: normal;">(c) Escaping sensitive growth stages: This is by establishing crop practices that avoid the concentration of sensitive growth stages in the same period of the year (e.g different season lengths, sowing dates etc

<p style="margin: 0cm 0cm 0.0001pt 36pt; text-align: justify; text-indent: -36pt; line-height: normal;">(d) Elimination: another pathway for increasing resiliency is by eliminating climate related factor which is most limiting to crop productivity (e.g. introducing irrigation in water-limited summer crops)(IPCC, 2007). Nevertheless rural communities in Nigeria have always managed their resources and livelihoods in the face of challenging environmental and socio-economic conditions (Mortimore and Adams 2001, and Ole et al 2009). They have to a large extent been able to develop their livelihood strategies in a way which enables them to constantly cope with and adapt to an erratic climate change, severe pest attack, changing agricultural policies at local, national, global levels and other natural factors (BNRCC,2008 Apata et al 2009,IPCC,2007, ODI,2007and Molua 2008). There is need to gain as much information as possible, and learn the positions of rural farmers and their needs, about what they know about climate change, in order to offer adaptation practices that meet these needs. (Royal Society 2005 and Apata et al, 2009 Lobell et al, 2008, Hassan and Nhemachem 2008).This study therefore intends to assess farmers perception of impact of climate change on food crop production, it also describe socio-economic characteristics of the respondents, examine farmers perception on climate change, identify impact of climate change on crop production and to ascertain adaptation strategies adopted to mitigate effect of climate change.

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Ayanwuyi, E et al: Continental J. Agricultural Economics 4: 19 - 25, 2010

<p style="margin: 0cm 0cm 0.0001pt 36pt; text-align: justify; text-indent: -36pt; line-height: normal;">METHODOLOGY

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">The study was conducted in Ogbomoso Agricultural zone of Oyo State. This zone consist of five agricultural extension blocks out of which three namely, Ikoyi, Ajaawa and Iresa were purposively selected for the study due to rural based of those extension blocks. Multistage sampling technique was used to select, Three hundred and sixty (360) farmers for the study. Four extension cells, out of eight cells in each selected extension block were randomly selected and two communities were selected randomly from each cell making a total of 24 communities selected for the study. Fifteen (15) respondents were systematically selected from each community making One hundred and twenty (120) respondents chosen from each extension block. Finally, a total of three hundred and sixty (360) respondents constituted the sample size for the study.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Structured interview schedule was administered on respondents through Personal contact, with assistance of employed trained e numerators in their various communities. The adaptation strategies adopted by the respondents to mitigate effect of climate change on food crop production was grouped into five categories. These strategies are (i) soil water management (ii) Farming operations (iii) Protection measure (iv) household livelihood and (v) education and finance. Data collected were subjected to descriptive statistics, such as frequency counts, tables and percentages. However for testing research hypothesis multiple regressions was used.

<p style="margin-bottom: 0.0001pt; line-height: normal;">RESULTS AND DISCUSSION

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Socio-Economic Characteristics of the Respondents

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table 1 shows that 22.2% of the respondents were above 51 years of age, 21.7% fell between the age range of 41-45 while 21.1% and 17.5% were between the age ranges of 46-50,and 36-40 years respectively, also 13.3% fell between the age range of 31-35 and 4.2% were less than 30 years of age. Data further shows that about 72.0% of the respondents were male, 70.5% were literate and 90.3% of the respondents had involved in farming for more than 5years. This implies that majority of the respondents had being in farming for many years.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Farmers’ perception on climate change

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Data in Table 2 revealed the responses of sampled farmers perception on climate change in their area as 31.1% indicated delayed rainfall, 24.7% indicated higher temperature 11.7% indicated unusual heavy rainfall, 9.4% indicated fast water evaporation and undefined season while 5.0%, 4.4% and 4.2% indicated more longer days than knight, flood with serious consequence and late fruiting of tree crops respectively as the determinant of climate change in their environment. This result conform with Lobell,(2008) Apata et al, (2009) who reported that 89.0%, 72.0% and 65.0% of the respondents respectively indicated higher temperature, water evaporation from the ground is fast and delayed rainfall as the determinants of climate change.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Impact of Climate Change on Crop Production

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table 3 reveals that low yield on crop is the dominant impact (80.3%) stunted growth of crop (37.2%) ease spread of pest and diseases attack on crops. (31.1%) Drying of seedling after germination (27.2%) and ineffectiveness of agriculture chemicals due to delayed of rainfall 26.9%. These agree with findings of Molua (2008) who reported that performance of agriculture sector depends largely on the return of good rains and the timely and adequate provision of agricultural inputs.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Perceived Adaptation strategies to mitigate impact of climate change on crop production.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table 4 presents adaptation strategies actually adopted by the respondents. These strategies are: increase water conservation (68.3%), shading and shelter/ mulching (59.4%) soil conservation (55.0%) move to different site (38.3%) while 34.2%, 20.6% and 19.2% of the respondents implement water conservation techniques practice, increase irrigation, and increase or reduction in land size cultivated respectively. Also respondents adopted planting of different crops (74.7%) Treat seed with fungicides before sowing (60.0%) planting different varieties of crops (58.9%), mixed cropping (96.7%) change use of chemical (68.9%). Furthermore 54.4% and 52.5% of the respondents adopted change of row orientation with respect to slope and Application of soil amendments e.g. farmyard manure respectively as the strategies to mitigate effect of climate change. Table 4 further revealed that 96.1% adopted ration food, 86.7% reduce expenditure and 59.2% avoid selling remaining food stocks. However, 86.1% and 11.9% revealed that adequate access to extension facilities and credit facilities are the strategies adopted to mitigate effects of climate change on crop production in the study area.

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Ayanwuyi, E et al: Continental J. Agricultural Economics 4: 19 - 25, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">These results are in line with Molua (2008) Rudolf and Hermann (2009) and Apata et al, (2009) who reported that main strategies for reducing climate risk is to diversify production and livelihood systems like soil and water management measures, and plant protection measures that varied to maintain adequate crop yields.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Results of regression analysis in Table 5 shows that increased or reduced land size cultivated (X1), shading and shelter /mulching (X2) mixed cropping (X3) change row orientation with respect to slope (X4) Access to extension facilities (X5) Access to credit facilities (X6) education level (X7) years of farming experience (X8) and zero tillage (X9) had positive significant relationship with the dependent variable and predicted 60% of the variations in the farmers perceptions of impact of climate change. This explains that the more the perceived impact of climate changes the more the adoption of adaptation strategies to mitigate climate change impact on food crop production.

<p style="margin-bottom: 0.0001pt; line-height: normal;">CONCLUSION AND RECOMMENDATION

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">It is established from this study that farmers were aware of climate change and its impacts on food crop production. Furthermore, they are able to develop their livelihood and adaptation strategies in a way that enables them to constantly cope with an erratic impact of climate change on food crop production.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Increase/reduce farm size, mulching, mixed cropping, row orientation with respect to slope, access to extension facilities credit facilities, zero tillage, educational level and years of farming experience were found to be significantly related to the perceived climate change in the study area. Hence, there should be off-farm employment that could stabilize income in the face of low crop production as a result of impact of climate change on crop production, also small scale irrigation project are of more sustainable nature that show a promising effect on climate change, income and risk reduction and there should be formulation of policy that considered arable food crop farmers experience in climate change with reliable and effective measure of adaptation that need to be implemented that must be easily accessible to the end users.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table1 Socio Economic Characteristics of Respondents <p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Source: Field Survey 2009

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Ayanwuyi, E et al: Continental J. Agricultural Economics 4: 19 - 25, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table 2:

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Farmers perception of Climate Change <p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Source: Field Survey 2009

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table 3: Impact of Climate Change on Crop Production <p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Source: Field Survey 2009

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">*multipleresponses

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table 4: Adaptation strategies to Climate Change <p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Source: Field Survey 2009

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">*Multipleresponses

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table 5: Regression analysis on perception of Climate Change and adaptation strategies. <p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Source: Data Analysis 2009

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">R2 - 0.612

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">AdJ R - 0.734

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">F - Value - 4.5

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">* Significant at 0.05 level

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">REFERENCES

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Apata T. G. Samuel, K. D. and Adeola, A. O. (2009) Analysis of Climate change perception and Adaptation among Arable Food Crop Farmers in south Western Nigeria paper presented at the conference of International Association of Agricultural Economics pp. 2-9.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Butt, T. A. McCari, B. A. Angerer, J Dyke, P. T. and Stuth, J. W (2005). The economic and food security implications of climate change in Mali ''Journal Climatic change 6(8) 355-378. ''

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Building Nigeria’s Response to Climate change (BNRCC) (2008): The Recent Global and Local Action on Climate change paper presented at Annual Workshop of Nigerian Environmental Study Team (NEST) held at Hotel Millennium, Abuja, Nigeria 8-9th October 2008 p. 2-4.

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Ayanwuyi, E et al: Continental J. Agricultural Economics 4: 19 - 25, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Hassan, R and Nhemachena, C (2008) Determinants of African Farmers’ Strategies for adaptation to climate change African Journal of Resource Economics 2 (1) pp 83-104.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Intergovernmental panel on climate change (IPCC) (2007): Climate change impacts, Adaptation and vulnerability. ''In Third Assessment Report of the Intergovernmental Panel on Climate Change (eds) Parry, M. L. Canziani, O. F. Palutikof, J. P, Vanderlinden, P. J, and Hasson, C. E. Cambridge University Press, Cambridge, United Kingdom pp. 80-96. ''

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Kurukulasuriya, P. Mendelsohn, R. Hassan, R, Benhin, J. Deressa, T. Dip. M. Fosu, K. Y. Jain, S. Mano, R. Molua E. Ouda, S. Sene, I, Seo S. N. and Dinar, A. (2006): Will African agriculture survive climate change? World Bank Economic Review 20 (3) 67-88.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Lobell, D. B. Burke, M. B, Tebaldi C Manstrandrea, M. D, Fakon, W. P. & Naylor R. L. (2008) Prioritizing Climate change adaptation needs for food security in 2030 International Journal of Science 31 (9) pp. 60-71.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Molua, E. L. and Lambi, C. M. (2007) Economic Impact of Climate change on agriculture in Cameroon. Policy Research paper No 4364 World Bank, Washington, D. C. pp. 51-55.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Mortimore, M. J. and Adams W.M. (2001) Farmer adaptation change and crisis in the Sahel Global Environmental change Human and Policy Dimensions. ''Journal of Environmental Management 4(3) 604-616. ''

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Molua, E. L. (2008) Turning up the heat on African Agriculture: The impact of climate change on Cameroon’s agriculture, ''African Journal of Agriculture and Resource Economics 2 (1) pp 45-64. ''

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Nigerian Environmental Study Team (NEST) (2004) Regional Climate modeling and climate scenarios Development in support of vulnerability and adaptation studies: Outcome of Regional Climate modeling Efforts over Nigeria, NEST, Ibadan Nigeria. Pp12-20

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Overseas Development Institute (ODI) (2007) Climate Change Agricultural Policy and Poverty Reduction how much do we know? Overseas Development Institute 2007 pp. 14-21.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Ole, M. Cheikh, M. Anette, R. and Awa, D. (2009) Farmers Perceptions of Climate Change and Agricultural Strategies in Rural Sahel. ''Journal of Environmental Management 4(3) 804-816. ''

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Royal Society (2005) Impact of Climate Change on Crops Worse Than previously thought http://royalsociety.org/news.asp''? Accessed September, 2010.''

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Rudolf, W. Hermann, W. (2009) Climate risk and farming Systems in Rural Cameroon. Institute of Development and Agricultural Economics. University of Hannover, Germany pp. 21-24.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Smith, B. and Skinner, M. (2002) Adaptation options in Agriculture to climate change: A typology, mitigation and Adaptation Strategies for Global Change. African Journal of Agriculture and Resource Economics 3(5) pp. 78-82.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Spore (2008) Climate Change Spore Special Issue - August, 2009. pp. 9-13.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Received for Publication: 18/10/2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Accepted for Publication: 24/11/2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Corresponding Author

<p style="margin-bottom: 0.0001pt; line-height: normal;">Ayanwuyi, E

<p style="margin-bottom: 0.0001pt; line-height: normal;">Department of Agricultural Extension and Rural Development, Ladoke Akintola University of Technology Ogbomoso, Oyo State, Nigeria.

<p style="margin-bottom: 0.0001pt; line-height: normal;">Email:ayanshola2005@yahoo.com

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Continental J. Agricultural Economics 4: 26 - 31, 2010 ISSN: 2141 – 4130

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">© Wilolud Journals, 2010 http://www.wiloludjournal.com

<p style="margin: 0cm 4.15pt 0.0001pt 0cm; text-align: center; line-height: normal;">MARKETING ECONOMICS OF MEAT POULTRY IN KHARTOUM STATE, SUDAN

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Abda Abdalla Emam

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Department of Agricultural Economics, College of Agricultural Studies, Sudan University of Science and Technology, Sudan, Khartoum North, P.O Box 71

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Email address: safarefga@hotmail.com

<p style="margin: 0cm 17pt 0.0001pt; text-align: justify; line-height: normal;">ABSTRACT

<p style="margin: 0cm 17pt 0.0001pt; text-align: justify; line-height: normal;">The study aims to measure the marketing efficiency of meat poultry in Khartoum State in the year 2010. The study depended mainly on primary data which were collected through questionnaire. About 10 and 20 of wholesaler and retailer were selected, respectively. Secondary data were also collected from sources related to topic of the study. The data were analyzed using descriptive statistics tool. Also, quantitative analysis techniques were used to calculate net marketing margins and marketing efficiency for wholesalers and retailers. The study described marketing channels of meat poultry beside constraints that facing marketer of meat poultry in Khartoum State. The study revealed that; the majority of respondents were aged ranged from 21 to 40 years and about 100% of respondents be male. About 10%, 40% and 50% were represented primary, secondary and university education level for wholesalers, respectively. The retailers had education level as follows: secondary (20%) and university (80%). The wholesalers shared about 53.71% of the total marketing costs while retailers shared only about 46.28% of them. The results reflected to the fact that rent, transportation and taxes costs represented higher percentages in the total marketing costs for each trader. Retailers got higher marketing efficiency than wholesalers. Retailers got higher net marketing margins (1.26 SG/Keg) than wholesalers (1.099 SG/Keg). Meat poultry channels in Khartoum State markets passes from producer to consumer through: wholesaler, wholesaler and processor or wholesaler and retailer. Also, the study showed that about 50% and 35% of wholesalers and retailers, respectively, were facing obstacles in transportation. 90% of wholesalers and 75% of retailers were facing constraints in poor extension services. Increasing marketing efficiency at wholesaler’s meat poultry in Khartoum State market through reducing marketing costs, Provision of extension and credit services and encourage investment in this efficiency activity represented the main recommendations that drown from the results.

<p style="margin: 0cm 17pt 0.0001pt; text-align: justify; line-height: normal;">KEYWORDS: Marketing Efficiency, Marketing Channel Wholesaler, Retailer,

<p style="margin: 0cm 4.15pt 0.0001pt 0cm; text-align: justify; line-height: normal;">INTRODUCTION

<p style="margin: 0cm 4.15pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Poultry are domesticated birds which provide human with nutritional and economical products under his management. They include many and among them are chickens (Hassan, 1998). The poultry production sector creates about 30000 positions (Ministry of Animal Resources and Fisheries, 2006). Poultry is classified as white meat and its consumption about 26% of total meat consumption in Khartoum State (State Ministry of Agriculture, 2004). The population of Khartoum State has grown by tenfold since 1956 and continues to grow by about 4% annually, faster than national average of 2.8% (FAO, 2002). In spite of the increasing investment in this industry, there are an obvious gap between the production of meat poultry and its needs (Ministry of agriculture, Animal wealth and irrigation, 2005). Although there increase in poultry production, but still the prices are increasing, and it is not well known whether this is due to increase in demand or to high cost of inputs. The main objective of the study is to evaluate marketing economics of meat poultry in Khartoum State in 2010. Specifics objectives are to highlight on: socioeconomics characteristics of traders, net marketing margins, marketing efficiency, Marketing channels and marketing constraints that facing traders.

<p style="margin: 0cm 4.2pt 0.0001pt 0cm; text-align: justify; line-height: normal;">METHODOLOGY

<p style="margin: 0cm 4.2pt 0.0001pt 0cm; text-align: justify; line-height: normal;">The study depended mainly on primary data while secondary data were also collected. The primary data were gathered through a questionnaire given to 10 and 20 of wholesalers and retailers of meat poultry in Khartoum State, respectively. The secondary data were collected from different sources related to the topic of the study.

<p style="margin: 0cm 4.2pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Data Analysis

<p style="margin: 0cm 4.2pt 0.0001pt 0cm; text-align: justify; line-height: normal;">The descriptive statistics, marketing margin and the marketing efficiency analyses were used in analyzing the

<p style="margin: 0cm 4.2pt 0.0001pt 0cm; text-align: center; line-height: normal;">Abda Abdalla Emam: Continental J. Agricultural Economics 4: 26 - 31, 2010

<p style="margin: 0cm 4.2pt 0.0001pt 0cm; text-align: justify; line-height: normal;">data for the study.

<p style="margin: 0cm 4.25pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Descriptive Statistics

<p style="margin: 0cm 4.25pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Descriptive statistic was used to analyze the data gathered on the socio-economic characteristics of meat poultry marketers in the study area.

<p style="margin: 0cm 4.15pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Marketing Margin Analysis

<p style="margin: 0cm 4.15pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Marketing margin analysis was used to estimate the margin in terms of revenue and profit that accrue to the meat poultry marketers. According to Kohls (1980), marketing margin can be estimated as

<p style="margin: 0cm 4.1pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Marketing Margin = Selling Price – Cost Price

<p style="margin: 0cm 4.1pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Net Marketing Margin = Marketing Margin – Marketing Cost

<p style="margin: 0cm 4.2pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Net Marketing Margin for Wholesaler = Wholesale Marketing Margin – Wholesale Marketing Cost

<p style="margin: 0cm 4.2pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Net Marketing Margin for Retailer = Retailer Marketing Margin – Retailer Marketing cost

<p style="margin: 0cm 4.1pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Gross Marketing Margin = Wholesaler Selling Price – Retailers Cost Price

<p style="margin: 0cm 4.25pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Marketing Efficiency: For the Marketing efficiency analysis, marketing efficiency is measured as

<p style="margin: 0cm 4.25pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Marketing Efficiency = (Gross Marketing Margin÷ Marketing Cost) × 100

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">RESULTS AND DISCUSSION

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Socioeconomics Characteristics:

<p style="margin: 0cm 4.15pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Age: Table 1 showed that the majority of respondents were aged group between 21 and 40 years for wholesalers and retailers of meat poultry. The implication of the results showed that most of the traders were within the economically active age. These finding agreed with previous study (Adinya and et al, 2008). The study found that people in age group of 21-60 years are more economically active and independent than those in the age group less than 21 years and above 60 years. Upton (1987) recorded that age influence managerial decision making.

<p style="margin-bottom: 0.0001pt; text-align: justify; text-indent: 36pt; line-height: normal;">Table 1: Socio-economic Characteristics of Wholesalers and Retailers of Meat Poultry <p style="text-align: justify; line-height: normal;">Source: Data collected and calculated, 2010.

<p style="margin: 0cm 4.2pt 0.0001pt 0cm; text-align: center; line-height: normal;">Abda Abdalla Emam: Continental J. Agricultural Economics 4: 26 - 31, 2010

<p style="margin: 0cm 4.15pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Gender: About 100% of respondents are male (Table 1).

<p style="margin: 0cm 4.15pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Education: Table 1 illustrated that about 10%, 40% and 50% represented primary, secondary and university education level for wholesalers, respectively. The retailers had education level as follows: secondary (20%) and university (80%). Upton (1987) reported that education has an important influence in managerial ability and decision making. This means that the meat poultry marketing is practically done by experienced traders.

<p style="margin: 0cm 4.15pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Analysis of Marketing Cost at Wholesalers and Retailers: Table 2 showed that total marketing costs was 3.539 SG/Keg (1.901 SG/Keg for wholesalers + 1.638 SG/Keg for retailers). The wholesalers shared about 53.71% of the total marketing costs while retailers shared only about 46.28% of it. The higher shared of wholesaler in the total marketing costs was reflected mainly to the fact that wholesalers conducted many marketing functions than retailers. Emam (2002) reported that wholesalers run many marketing functions than retailers. The table illustrated that the total marketing costs items were distributed as the descending order for different traders. At wholesalers: rent cost (25.25%), others (22.57%), transportation (20.52%), taxes (16.83%), storage (9.31%), handling (7.89%), packing (1.58%) and sorting and grading (1.35%). They were at retailers as: rent cost (34.80%), others (24.85%), transportation (10.38%), taxes (9.77%), handling (9.16%), storage (8.18%) and packing (2.87%). The results indicated that rent, transportation and taxes costs represented higher percentages in the total marketing costs of each trader.

<p style="text-align: justify; line-height: normal;">Table 2: Marketing Costs at Wholesaler and Retailer of Meat Poultry <p style="line-height: normal;">Source: Data collected and calculated, 2010.

<p style="margin: 0cm 4.15pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Gross Marketing Margins: Table 3 showed the Gross Marketing Margins for wholesalers and retailers of meat poultry in Khartoum State. Wholesalers (3.00 SG/Keg) generally got higher Gross Marketing Margins than retailers (2.90SG/Keg). This may be due to higher marketing costs at wholesaler (1.901 SG/Keg) than retailers (1.638SG/Keg).

<p style="margin: 0cm 4.2pt 0.0001pt 0cm; text-align: center; line-height: normal;">Abda Abdalla Emam: Continental J. Agricultural Economics 4: 26 - 31, 2010

<p style="text-align: justify; text-indent: 36pt; line-height: normal;">Table 3: Net Marketing Margins at Wholesaler and Retailer Level of Meat Poultry <p style="text-align: justify; line-height: normal;">Source: Data collected and calculated, 2010.

<p style="margin: 0cm 4.15pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Net Marketing Margins: Table 4 illustrated that Net Marketing Margins at meat poultry wholesalers and retailers. Retailers got higher Net Marketing Margins (1.262 SG/Keg) than wholesalers (1.099 SG/Keg). The lower Net Marketing Margins of wholesalers was reflected to the higher marketing cost which came as a results of higher transportation (0.390 SG/Keg), storage (0.177 SG/Keg), taxes (0.320 SG/Keg) and sorting (0.025 SG/Keg).

<p style="text-align: justify; line-height: normal;">Table 4: Marketing Efficiency of Traders (S. G/Keg) <p style="text-align: justify; line-height: normal;">Source: Data collected and calculated, 2010.

<p style="margin: 0cm 4.15pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Marketing Efficiency: A market that is efficient does not only bring sellers and buyers together, it enables entrepreneurs to take advantage of opportunities, to innovate and improve in response to demand and price changes (Fakayode et al, 2010). Table 4 indicated that retailers got higher marketing efficiency than wholesalers. The result indicated that the meat poultry product is efficient in the study area.

<p style="margin: 0cm 4.15pt 0.0001pt 0cm; text-align: justify; line-height: normal;">Marketing Channel: Figure 1 showed meat poultry channels in Khartoum State Markets. It passes from producer to consumer through; wholesaler, wholesaler and processor or wholesaler and retailer. Many traders in marketing channels lead to increase marketing costs and hence constituted welfare cross to the final consumers (Ugwumba and Okoh, 2010).

<p style="margin: 0cm 4.2pt 0.0001pt 0cm; text-align: center; line-height: normal;">Abda Abdalla Emam: Continental J. Agricultural Economics 4: 26 - 31, 2010

<p style="text-align: justify; line-height: normal;">Figure (1): Meat poultry channels in Khartoum State, 2010.

<p style="text-align: justify; line-height: normal;">Source: Field Survey, 2010.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Marketing Constraints: Table 5 depicted the percentage of traders according to marketing constraints of Meat Poultry in Khartoum State. The table showed many constraints that facing traders of such commodity. It illustrated that about 50% and 35% of wholesalers and retailers, respectively, face obstacles on transportation. 90% of wholesalers and 75% of retailers were face constraints on poor extension services. This result was supported by analysis of marketing costs this study especially on the transportation cost. Transportation cost got high percentage of marketing costs on each trader (Table 2). Also, the results was assured with previous study (Emuron and etal, 2010). The study recorded that one of major constraints in the marketing of local chicken in Kampala city markets is costly transport (22.4%).

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table 5: Percentage of Traders According to Marketing Constraints of Meat Poultry in Khartoum State <p style="text-align: justify; line-height: normal;">Source: Data collected and calculated, 2010.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">RECOMMENDATIONS

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Increasing marketing efficiency at wholesaler’s meat poultry in Khartoum market through reducing marketing costs (rent, transportation and taxes cost items) must be ensured. Provision of extension and credit services should be done. Encourage investment in this efficiency activity.

<p style="margin: 0cm 4.2pt 0.0001pt 0cm; text-align: center; line-height: normal;">Abda Abdalla Emam: Continental J. Agricultural Economics 4: 26 - 31, 2010

<p style="text-align: justify; line-height: normal;">REFERENCES

<p style="text-align: justify; line-height: normal;">Adinya, I. B., Ayuk, E. A., Akpet, S. O. and Agiopu, B. F. (2008). Determining Costs- Returns Profitability in Honey Marketing in Cross River State, Nigeria. Continental Journal of Agricultural Economics, Vol. 2, pp44-51.

<p style="text-align: justify; line-height: normal;">Emam, A. A. (2002). Agricultural Marketing. Printed by Ministry of Education, Khartoum, Sudan. (Arabic version).

<p style="text-align: justify; line-height: normal;">Emuron, N., Magala, H., Ryazze, F. B., kugonza, D. R. and Kyarisiima, C. C. (2010). Factors influencing the Trade of Local Chickens in Kampala City Markets. Livestock Research for Rural Development. Vol. 22, No. 4.

<p style="text-align: justify; line-height: normal;">Fakayode, S. B., Omotesho, O. A., Babatunde, R. O. and Momoh, A. A. (2010). The Sweet Orange Market in Nigeria, How Viable? Research Journal of Agricultural and Biological Sciences, 6(4): 395-400, 2010.

<p style="text-align: justify; line-height: normal;">FAO (2002). Diary Sub-Sector Development Project: socio-economic Marketing Study. Rome, Italy.

<p style="text-align: justify; line-height: normal;">Hassan, S. A. (1998). Critical Study of Theses on Poultry Science in Sudan University from 1973 to 1997, M.Sc. Thesis. Faculty of Agriculture. University of Khartoum.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Kohls, R. L. (1980). Marketing of Agricultural Products. Macmillian Publishers.New York.

<p style="text-align: justify; line-height: normal;">Khartoum State Ministry of Agriculture (2004). Food Consumption and Nutritional Situation in Khartoum State, Sudan.

<p style="text-align: justify; line-height: normal;">Ministry of agriculture, Animal Wealth and Irrigation (2005). Khartoum State, poultry survey in Khartoum State.

<p style="text-align: justify; line-height: normal;">Ministry of Animal Resources and Fisheries (2006). Future Vision for Poultry Sector. Technical Committee, Khartoum, Sudan.

<p style="text-align: justify; line-height: normal;">Ugwumba, C. O. A. and Okoh, R. N. (2010). Price Spread and the Determinants of Catfish Marketing Income in Anambra State, Nigeria. Journal of Agriculture and Social Sciences. 2010/6-4-73-78.www. fspublisher.org.

<p style="text-align: justify; line-height: normal;">Upton (1987). "African farm management" Cambridge University Press.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Continental J. Agricultural Economics 4: 32 - 38, 2010 ISSN: 2141 – 4130

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">© Wilolud Journals, 2010 http://www.wiloludjournal.com

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">SOCIO-ECONOMIC FACTORS INFLUENCING AWARENESS AND USE OF ORGANIC FARMING PRACTICES BY HORTICULTURAL FARMERS IN OYO STATE NIGERIA.

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Adebisi-Adelani, O.1, Ibe, R. B.1, Olajide-Taiwo, L. O.1, Amao, I. O.1, Olajide-Taiwo, F. B.1 Usman, J.M2

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">1 National Horticultural Research Institute (NIHORT), P.M.B. 5432, Jericho, Idi-ishin, Ibadan, Nigeria.

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">2Federal College of Forestry, P.M.B. 5087, Ibadan, Nigeria

<p style="margin: 0cm 17pt 0.0001pt; line-height: normal;">ABSTRACT

<p style="margin: 0cm 17pt 0.0001pt; text-align: justify; line-height: normal;">The health hazards posed by inorganic fertilizers, pesticides and herbicides cannot be overemphasized. Despite this, there is still the need to know empirically the level of awareness and use of organic farming practices and socio-economic factors influencing it among horticultural farmers. Data for the study were collected through qualitative and quantitative methods involving Focus Group Discussion (FGD) as well as the use of structured interview schedule. A total number of 62 respondents were sampled and they were selected through multi-stage and purposive sampling techniques from the four zones of Oyo State Agricultural Development Programme (OYSADEP). Data were analyzed using descriptive and inferential statistics such as frequency counts, percentages, means and Chi-square analysis. Half of the respondents’ falls between the ages brackets of 30-40 years (50%). 46.8% of the respondents have a household size of 4-6, while 72.6% are males and 87.1% were married. Awareness score of 85.5% of the respondents about organic farming was high while 61.3% were into organic farming though unconsciously. Most (96.8%) of the respondents identified time consuming as the highest constraints in organic farming practices. Chi-square analysis revealed that, land acquisition, educational qualification and source of information have significant influence on horticultural farmers' awareness and use of organic farming practices at P=0.05. It was therefore recommended that, female farmers should be encouraged and empowered in organic farming practices. Capacity building through training of respondents on newly generated technologies in organic farming practices should be embarked upon to improve the health and livelihood of farmers.

<p style="margin: 0cm 17pt 0.0001pt; text-align: justify; line-height: normal;">KEYWORDS: Organic farming practices, awareness and use, horticultural farmers

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">INTRODUCTION

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Horticulture is an aspect of agriculture and is the mainstay of some African countries livelihoods (Mendelsohn et al 2000; Devereux and Maxwell, 2001). Horticulture deals with the production, distribution, and utilization, of fruits, vegetables, spices, and ornamental plants. Among the rungs of the ladder of man's basic needs is food self-sufficiency which is a state in which the daily intake of calories, proteins, vitamins and minerals form the pre-requisites for normal, mental and physical development is guaranteed (Olufolaji, 2009). About 65% of Nigerians are involved in horticultural activities, horticulture accounted for 25-60% of annual Gross Domestic Product between 1962 – 1985 attracting 1 - 14% of the government's capital expenditure during successive development plans for that period. Horticultural crop production creates jobs; and because of its intensive nature, it provides twice the amount of employment per hectare compared to cereal crop production (Alli et al, 2002). Also, horticultural crops can play a vital role in solving the global micro-nutrient crisis for over two billion people, the majority of whom are women and children (UN/SCN, 2004).

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Organic farming is one of several approaches to sustainable agriculture, and many of the techniques used such as inter-cropping, mulching, and integration of crops and livestock - are practiced under various agricultural systems. What makes organic agriculture unique is that, under various laws and certification programmes, almost all synthetic inputs are prohibited and "soil building" crop rotations are mandatory (FAO, 1999). Organic farming means using methods in tune with nature, enhancing the local eco-system, without adding synthetic substances such as chemical fertilizers and pesticides. (FAO 2001) reported that ‘organic farming can reduce hunger and environmental damage.’

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Thus, organic agriculture can go a long way to mitigate against the effect of global climate change it is the best form of agriculture that can overcome the harmful impacts of the Green Revolution on soil, air, water, landscapes and humans globally (Christian et al, 2005). Significant development in certified organic farming has been attained with nearly 31 million hectares of land currently being managed organically in 120 countries of the world. The world,

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Adebisi-Adelani, O et al.,: Continental J. Agricultural Economics 4: 32 - 38, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">market for organic foods and beverages was US$ 40 billion in 2006 which is 2% of total food retail in the developed countries (Willer and Yussefi, 2007). Unfortunately, in this big organic market the share of the developing countries like Nigeria is really very little or virtually nothing. Though Kilcher (2002), Mc Neely and Scherr (2002) and Yussefi and Willer (2003) strongly recommended that organic agriculture is not just a resolution for more wealthy countries but also effective in poorer countries; for purposeful socio-economic and ecological sustainability. Thus, this study was an attempt to explore the socio-economic factors influencing awareness and use of organic farming practices among horticultural farmers in Oyo State Nigeria. The specific objectives are to:

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">(i) Identify the socio-economic characteristics of horticultural farmers.

<p style="margin: 0cm 0cm 0.0001pt 13.5pt; text-align: justify; text-indent: 22.5pt; line-height: normal;">(ii) Determine awareness and use of organic farming practices by horticultural farmers

<p style="margin: 0cm 0cm 0.0001pt 13.5pt; text-align: justify; text-indent: 22.5pt; line-height: normal;">(iii) Identify constraints faced by horticultural farmers in the use of organic farming practices.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Hypothesis of the study:

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">There is no significant relationship between socio-economic characteristics and awareness and use of organic farming practices among horticultural farmers.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">METHODOLOGY

<p style="margin: 0cm 7.2pt 0.0001pt 0cm; text-align: justify; line-height: normal;">The study area was Oyo State, boarded by Benin Republic in the west, in the North and East by Kwara and Osun States respectively and Ogun state in the south. Oyo State covers a land area of 27,000 sq kilometers and is made up of 33 Local government areas. The State is divided into four agricultural zones by ADPs namely: Ibadan / Ibarapa, Oyo, Ogbomosho and Saki zones. Based on the prevailing climatic and soil characteristic, three vegetation zones are identifiable in Oyo State. These are forest, savanna, and derived savanna. The forest zone with high relative humidity favours the cultivation of tree crops such as Cocoa, Kola, Citrus, and Oil Palm as well as arable crops like maize, cassava, yam and rice. Areas within Ibadan zone and up to Fiditi town fall within the forest zone. The derived savanna has a mixture of forest and savanna vegetation. Oyo, Ogbomosho, Ilora, Fasola, Eruwa and Lagelu fall within this zone. The savanna zone favours mainly arable crops such as sorghum, maize, cowpea and yam with some parcel of land, which can support tree crops. The wide expanse of land covered by Oyo / Ogbomosho zone in the south to Saki zone is savannah (MANR, Oyo State 2001).

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">For the purpose of this study Oyo and Ogbomosho zone were purposively sampled because they have being identified to have a lot of horticultural produce. In fact a particular town Fiditi in the zone is being referred to as “home of fruits”. Sixty –two respondents were sampled in all. Those considered as horticultural farmers in this study are those that grows all kinds of fruits and vegetables. Data was collected through the use of Focus Group Discussion (Qualitative) and structured interview guide (Quantitative). It was analyzed using both descriptive (frequencies, percentages) and inferential statistics.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">RESULTS AND DISCUSSION

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Socio-economic characteristics of respondents

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Majority (50.0%) of the farmers falls within the age group of 30-50 years, while 37.1% are within the age group of 51-70.(Table 1). Showing that majority of the farmers are within their economic and productive ages, since age of the farmer is important in determining productivity and adoption of innovation (Chikwendu et al, 1994; Kebede, 2001; Nwaru, 2004). This result implies that there is great prospect for increased and sustainable organic horticultural production among younger farmers in the study areas. More than one-third (46.8%) of the horticultural farmers had household size of 4-6 persons with 40.3% having 3-4 children. This means availability of cheap labour in the household for increased horticultural production using organic farming practices. Majority (72.6%) of the farmers was male, (74.2%) were Christians, and 43.6% of farmers inherited the land which is a common way of acquiring land in the zone. Also the table revealed that over half (54.8%) of the farmers made use of hired laborers on their farm. Most of these labourers are from Cross-River, Benue and Nassarawa States in the country. More than half (58%) the population are illiterate and with primary school education, 51.6% of the population had 10-30 years of farming experience. This gives an indication of the practical skill and knowledge which the respondents must have acquired for horticultural production in the area. However, (29.0%) of the farmers had 10-30 years of organic farming experience by default in horticulture and other cropping activities. Though the percentage is small it still means that it will not be difficult to work with this set of farmers on organic farming practices. The FGD revealed that there is the need to enlighten the farmer better on the differences between organic farming and traditional farming.

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Adebisi-Adelani, O et al.,: Continental J. Agricultural Economics 4: 32 - 38, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table 1: Percentage distribution of horticultural farmers based on Socio-economic characteristics. <p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Years of farming experience in organic.

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Adebisi-Adelani, O et al.,: Continental J. Agricultural Economics 4: 32 - 38, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Production constraints encountered by horticultural farmers on the use of organic farming practices.

<p style="margin: 0cm 0cm 0.0001pt; text-align: justify;">The result in Table 2 indicates some of the production constraints encountered by horticultural farmers in the use of organic farming practices. Most important ones were: time consuming (96.8%), transportation, inadequate credit facilities and inadequate storage facility (91.9%), climate change (87.1%), capital intensive (79.0%), lack of extension agents (75.8%), inadequate technical know-how (74.2%),inadequate information (72.6%)and so on. The above result conforms to the findings of (FAO 1999) which stated that “organic farmers face huge uncertainties. Lack of information is a major obstacle to organic conversion, according to 73% of North American organic farmers. Extension personnel rarely receive adequate training in organic methods and studies have shown that they sometimes discourage farmers from converting. Furthermore, institutional support in developing countries is scarce - professional institutions capable of assisting farmers throughout production, post-production and marketing processes are non-existent in many developing countries”. During the FGD the farmers expressed their feelings that the use of poultry manure, farm yard manure consumes a lot of time and that it is not always easy to transport it to the farm sites compared to inorganic fertilizers.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Awareness and usage of organic farming practices by horticultural farmers

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">The highest organic farming practices farmers were aware of was crop rotation (95.2%) while the lowest was the use of biological agents for pest control (32.3%).Most (79.0%) of the respondents were using crop rotation and mixed cropping. Only 32.3% of the farmers use minimum tillage (Table 3). The FGD reveals that farmers use of organic farming practices is based on their Indigenous Technical Knowledge (I.T.K) and not as a result of conscious application of organic management practices as internationally recognized.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Result of Chi-square analysis.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Chi-square analysis (Table 4) revealed that, land acquisition, educational qualification and source of information have significant relationship with horticultural farmers' awareness and use of organic farming practices at P=0.05. This is in agreement with a priori expectation and it is consistent with Onyenweaku and Nwaru (2005) for food crop farmers in Imo state Nigeria. Education and training help to unlock the natural talents and inherent enterprising qualities of the farmer, enhances his ability to understand and evaluate new production techniques leading to increased productivity and income (Nwaru, 2007).

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table 2: Percentage distribution table showing constraints being faced by horticultural farmers in the use of organic farming practices. <p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Source: Field survey, 2009.

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Adebisi-Adelani, O et al.,: Continental J. Agricultural Economics 4: 32 - 38, 2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table3: Frequency Distribution Table of Awareness and usage of organic farming practices by horticultural farmers <p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Source: Field survey, 2009.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Table 4: Chi-square analysis showing relationship between socio-economic characteristics and awareness and usage of organic farming practices <p style="margin-bottom: 0.0001pt; line-height: normal;">Source: Field survey, 2009., NS- Not Significant, S-Significant

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Adebisi-Adelani, O et al.,: Continental J. Agricultural Economics 4: 32 - 38, 2010

<p style="margin-bottom: 0.0001pt; line-height: normal;">CONCLUSION AND RECOMMENDATION.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Results from the study show that majority of the horticultural farmers in Oyo State were in their economic and productive ages and high level of farming experience. Hence there is hope for increased and sustainable organic horticultural production in the state. They were aware of all the organic farming practices though they are not using them regularly as it ought to probably due to some constraints such as time consuming, transportation, inadequate credit facilities, and inadequate storage facility, climate change, capital intensive, lack of extension agents,

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Technical know-how.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Sex and Educational qualification are the socio-economic status that affects the awareness and use of organic farming practices. Based on the findings of the study it was recommended that the government should encourage older and experienced farmers to be involved in organic horticultural production by empowering the horticultural farmers. This could be done through training and capacity building, provision of loan, education and market information on organic farming. Also Non-governmental and governmental organizations which specialize on organic agriculture should go a long way to encourage and educate farmers who are into horticulture on the health importance of consistence use of organic farming practices in the study area. Provision of market information on where and how to sell their organic produce should be made available to them.

<p style="margin-bottom: 0.0001pt; line-height: normal;">REFERENCES.

<p style="margin-bottom: 0.0001pt; line-height: normal;">Ali, M., Farooq, U. and Shih, Y.Y. (2002) Vegetable research and development in the ASEAN region: a guideline for setting priorities. ln: C.G. Kuo (ed). Perspectives of ASEAN cooperation in vegetable research and development

<p style="margin-bottom: 0.0001pt; line-height: normal;">Ashanhua, Taiwan: Asian Vegetable Research and Development Center. p. 20-64.

<p style="margin-bottom: 0.0001pt; line-height: normal;">Chikwendu, D.O., C.C Chinaka and A.M Omotayo (1994). Adoption of Minisett technique of seed yam production by farmers in the eastern forest zone of Nigeria. Discovery and Innovation .7(4): 367-375

<p style="margin-bottom: 0.0001pt; line-height: normal;">Christian, R.V., L. Kilcher and H. Schmidt, (2005) Are standards and regulations of organic farming moving away from small farmers’ knowledge? J.Sustainable Agric., 26 (1):5- 26.

<p style="margin-bottom: 0.0001pt; line-height: normal;">Devereux, S. and Maxwell, S. (2001) Food Security in Sub-Saharan Africa. ITDG Publishing, Pietermaritzburg, 361 pp.

<p style="margin-bottom: 0.0001pt; line-height: normal;">FAO, (1999) Agriculture, Food and Nutrition for Africa. Food and Nutrition Division of Food and Agriculture Organization of the United Nations, Rome, 412 pp.

<p style="margin-bottom: 0.0001pt; line-height: normal;">FAO, (2001) Production Year Book, Food and Agriculture Organization. Vol. 1.55, Global Environmental Outlook Report, 76 pp; http://www.unep.org/geo/yearbook/

<p style="margin-bottom: 0.0001pt; line-height: normal;">Kebede,T.A.(2001).Farm household technical efficiency: A stochastic frontier analysis of a study of rice producers in Mardi-Watershed in the Western Development of Nepal.An unpublished Masters Thesis in Department of Economics and Social Stidies,Agricultural University of Norway.56 pages.

<p style="margin-bottom: 0.0001pt; line-height: normal;">Kilcher, L. (2002) Production and trade constraints of organic products from developing countries. In: Proceedings of the 14th IFOAM Organic World Congress, August 2002, pp: 23.

<p style="margin-bottom: 0.0001pt; line-height: normal;">Mc Neely, J.A. and S.J. Scherr, (2002) Eco Agriculture-Strategies to Feed the World and Save.

<p style="margin-bottom: 0.0001pt; line-height: normal;">Mendelsohn, R., W. Morrison, M.E. Schlesinger and Andronova, N.G. (2000) Country-specific market impacts from climate change. Climatic Change, 45,553- 569.

<p style="margin-bottom: 0.0001pt; line-height: normal;">M.A.N.R. (2001) Ministry of Agriculture and Natural Resource, Oyo State, 2001

<p style="margin-bottom: 0.0001pt; text-align: center; line-height: normal;">Adebisi-Adelani, O et al.,: Continental J. Agricultural Economics 4: 32 - 38, 2010

<p style="margin-bottom: 0.0001pt; line-height: normal;">Nwaru,J.C.(2004).Rural credit markets and arable crop production in Imo state of Nigeria. An unpublished Ph.D. Dissertation .Michael Okpara University of Agriculture, Umudike, Abia state, Nigeria.123 pages.

<p style="margin-bottom: 0.0001pt; line-height: normal;">Nwaru,J.C.(2007). Gender and relative technical efficiency in smallholder arable crop production in Abia state of Nigeria. International Journal of Agriculture and Rural Development, 10 (12):25-34.

<p style="margin-bottom: 0.0001pt; line-height: normal;">Olufolaji, A. O. (2009) Country Report in Nigeria; National Horticultural Research Institute. A report submitted to Food and Agriculture in Nigeria.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Onyenweaku,C.E and Nwaru,J.C.(2005). Application of stochastic frontier production function to the measurement of technical efficiency in food crop production in Imo state of Nigeria. The Nigerian Agricultural Journal,36: 1-12.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">United Nations System Standing Committee on Nutrition. (UN/SCN, (2004) 5th Report on the world nutrition situation. Nutrition for improved development outcomes. Geneva: World Health Organization. United Nations System Standing Committee onNutrition.Wild Biodiversity. Island Press, Washington, USA.

<p style="margin-bottom: 0.0001pt; line-height: normal;">Willer, H. and M. Yussefi, (2007.) The World of Organic Agriculture-Statistics and Emerging Trends 2007.

<p style="margin-bottom: 0.0001pt; line-height: normal;">International Federation of Organic Agriculture Movements IFOAM, Bonn, Germany and Research Institute of Organic Agriculture FiBL, Ackerstrasse, Switzerland.

<p style="margin-bottom: 0.0001pt; line-height: normal;">Yussefi, M. and H. Willer, (2003). The World of Organic Agriculture Statistics of 2003 and Future Prospects. Retrieved from www.ifoam.org,on April 10, 2007.

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Received for Publication: 12/11/2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Accepted for Publication: 29/12/2010

<p style="margin-bottom: 0.0001pt; text-align: justify; line-height: normal;">Corresponding Author

<p style="margin-bottom: 0.0001pt; line-height: normal;">Adebisi-Adelani, O

<p style="margin-bottom: 0.0001pt; line-height: normal;">National Horticultural Research Institute (NIHORT), P.M.B. 5432, Jericho, Idi-ishin, Ibadan, Nigeria.

<p style="margin-bottom: 0.0001pt; line-height: normal;">Email: adelanidotol@yahoo.com