Journal of Agricultural Economics and Sustainable Development Photon 106 (2017) 219-232 https://sites.google.com/site/photonfoundationorganization/home/journal-of-agricultural-economics-and-sustainable-development Original Research Article. ISJN: 7755-2467: Impact Index: 4.18

Journal of Agricultural Economics and Sustainable Development

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Determinants of Access to Formal Credit for Agricultural Purposes in Wukari -Taraba State, Nigeria G.O. Onogwu.*, Audu I.A., Igbodor F.O Department of Agricultural Economics and Extension Services, Federal University Wukari, Taraba State, Nigeria Article history: Received: 12 May, 2017 Accepted: 15 May, 2017 Available online: 09 September, 2017 Keywords: Access to Agricultural Credit, Small Scale Farmers, Formal Financial Institutions, Farmers’ and Institutional Characteristics, Effects Abbreviations: LGA: Local Government Area, GDP: Gross Domestic Product, FAO: Food and Agricultural Organization, WB: World Bank, CBN: Central Bank of Nigeria Corresponding Author: G.O. Onogwu.* Lecturer I Email: gonogwu ( at ) yahoo ( dot ) com ( dot ) sg Audu I.A. Assistant Lecturer Igbodor F.O. Assistant Lecturer

Abstract Farmers do not have access to formal credit and the factors affecting access has not been identified in study

area located between latitude 60 301 and 90361 north and longitude 90101 and 110501 east of the Greenwich meridian (see figure1). The main aim of study was to determine the major factors affecting smallholders’ access to formal credit for agricultural purposes. A total of 150 respondents were randomly selected using multistage and simple random sampling techniques. Results revealed that Gender, farming experience, ownership of bank account, access to extension agent and interest rates were significant at 5%, while farm size and number of years in school were significant at 10% levels. It is recommended that lending institutions should offer agricultural loans at one digit interest rate to enable the farmers make gains for themselves and save expenditures on costs of capital that would normally be passed on to consumers in form of price, presence of which engenders a food secured nation and region. Citation: G.O. Onogwu.*, Audu I. A., Igbodor F.O., 2017. Determinants of Access to Formal Credit for Agricultural Purposes in Wukari -Taraba State, Nigeria. Journal of Agricultural Economics and Sustainable Development. Photon 106, 219-232 All Rights Reserved with Photon. Photon Ignitor: ISJN77552467D868012092017

1. Introduction 1.1 Trends of Economic Relevance of Agriculture in Nigeria In Nigeria, the economic relevance of the agricultural sector has since declined, with the share of agriculture in GDP falling to 32.2% in the 1975–1979 periods (Adewuyi, 2002) and averaging 35% between 1981 and 2006. The fall of agriculture in export share has been even more precipitous. From 1960–1970, the export crop subsector contributed 58.4% annually on averages to the total foreign exchange revenue. This declined to 5.2% over the period 1971–85 and then further to 3% from 1995–1999 (Adewuyi, 2002). Similarly, the growth of output in the agricultural sector declined from 3.8% in the 1987–1990 period to 2.2% between 1992–1995 (Adewuyi, 2002). In spite of the oil, agriculture remains the base of the Ph ton

Nigerian economy, providing the main source of livelihood for most Nigerians. The sector faces many challenges, notably an outdated land tenure system that constrains access to land (1.8 ha/farming household), a very low level of irrigation development (less than 1 percent of cropped land under irrigation), limited adoption of research findings and technologies, high cost of farm inputs, poor access to credit, inefficient fertilizer procurement and distribution, inadequate storage facilities and poor access to markets have all combined to keep agricultural productivity low (average of 1.2 metric tons of cereals/ha) with high postharvest losses and waste (http://www.fao.org/nigeria/fao-in-nigeria/nigeriaat-a-glance/en/).

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Even though agriculture still remains the largest sector of the Nigerian economy and employs twothirds of the entire labour force, the production hurdles have significantly stifled the performance of the sector. Over the past 20 years, value-added per capita in agriculture has risen by less than 1 percent annually. It is estimated that Nigeria has lost USD 10 billion in annual export opportunity from groundnut, palm oil, cocoa and cotton alone due to continuous decline in the production of those commodities. Food (crop) production increases have not kept pace with population growth, resulting in rising food imports and declining levels of national food self-sufficiency (FMARD, 2008). The main factors undermining production include reliance on rain fed agriculture, smallholder land holding, and low productivity due to poor planting material, low fertilizer application, a weak agricultural extension system and poor access to agricultural credit amongst others. As a result of this slow growth in output, Nigeria moved from a food sufficient country in the 1960s to a major importer of food in the 1980s. The estimated current 3.7% food production growth rate cannot keep pace with the 6.5% food demand and fueled by a high rate of population increase, moderately rapid income growth, and relatively high elasticities of expenditure for food (Egwuda 2001,Oviasogie 2005, Mellor, 1988). Agriculture is a major contributor to Nigeria’s GDP and smallscale farmers play a dominant role in this contribution (Rahji and Fakayode 2009), but their productivity and growth are hindered by limited access to credit facilities (Odoemenem and Obinne, 2010).Modernizing agriculture requires large infusion of credit to finance the use of purchased inputs such as fertilizers, improved seeds, insecticides, additional labour and so on. In this regard, the provision of agricultural credit can be a powerful economic force for development if used to inject appropriate capital for the purchase of agricultural inputs that are not otherwise available to farmers from their own financial, physical and labour resources (Ololade&Olagunju, 2013). 1.2 Credit Need for Agricultural Purposes Institutional supply of agricultural credit has remained inadequate to date; and this continues to impede the transfer of technology and investment into agriculture (Olagunju and Ajiboye, 2010). Osuntogun, 1975, holds the view that ‘unless production credit is made available on suitable terms, the majority of the small farmers will be seriously handicapped in adopting profitable technology. Also he outlined three reasons why credit is demanded by small scale farmers. The farmers require credit for production purposes; Credit is required for the payment of wages, procurement of inputs, like fertilizers, herbicides and improved seeds; Credit is needed for marketing Ph ton

of produce like transportation, storage, processing and other marketing related functions. According to Ijere, 1986 credit is a catalyst which drives the machinery of production to optimum performance. McNamara, 1975, is of the opinion that access to credit is crucial to small scale farmers operations, no matter how realistic and essential the land reform is design or adopted. A well-motivated farmer without credit cannot buy improved seeds, fertilizers and chemicals. Hence the small scale farmer generally spend less than 20 percent of what is required on such items because they do not have access to credit disbursement. Mbata, 1991 opined that credit is pertinent to increase efficiency required by the small scale farmers. Harsch, 1994 also, noted that farmers in Africa have demonstrated that when given the opportunity to earn higher incomes they can be dynamic producers. Kitbur, 1990 submitted that modernization of agriculture demands increased use of modern inputs which consequently increase the demand for credit. Galbraith, 1952, contents that at a certain stage of agricultural development, agricultural credit clearly does not become a strong force for further improvement when a man with energy and initiative who lacks only resource for more and efficient production is enable by use of credit to eliminate the one block on his path to improvement and this is consistent with the views of Taylor et al., 1986. 1.3 Problem Statement The smallholders seem to have reached the limit in their production potential because in an attempt to increase production, smallholders will either have to undertake area expansion or intensification of current practices which require external funding (Dzade el al, 2012).Despite efforts to overcome the widespread lack of financial services especially among smallholders in rural areas of the country, the majority still have limited access to bank services to support private initiatives. Financing of agricultural inputs and labour wages requires liquid cash which often is not readily available to the smallholder farmers. Therefore, it is essential to find ways of expanding formal credit to smallholders to improve agricultural productivity Credit is a very important resource that allows farmers to expand their operations or adopt new technologies. The World Bank, 1996 opined that credit is necessary for small-scale farmers to increase their agricultural productivity and farm income; however their access to institutional credit is curtailed. Credit has done a thousand times more to enrich mankind than the gold mines in the world. However, merely recognition of credit as a condition for agricultural growth is not sufficient to guarantee increased agricultural productivity and farm income.

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Agricultural credit plays an important role in the development of agriculture and supporting employment opportunities in the rural areas, Cygnus Business Consulting Report, 2004. De Janvry and Sadoulet,1995;Dzadzeet al, 2012 observed that agricultural household models suggest that farm credit is not only necessitated by the limitations of self-finance, but also by uncertainty pertaining to the level of output and the time lag between inputs and outputs. Access to formal financial services by smallholder farmers in rural areas is lacking despite the general growth in financial service delivery worldwide; World Bank, 2007.The likely small number of rural households in Nigeria having bank accounts is an indication of an even weaker access to formal financial services. Similarly, only 27% of marginal and small farmers have access to formal sources of credit in Bangladesh (Khalily et al, 2002). 1.4 Objectives of the study The main focus ofthis study is to examine the factors influencing access to formal financial services in the study area.Specifically, the study will sought to:Determine the major factors that affect smallholders’ access to formal credit, Assess loan application requirements and criteria for credit allocation employed by lending institutions in the area, and Determine major reasons for farm credit application rejection by formal lending institutions in the study area. 1.5 Hypotheses of the study The null hypotheses of this study include: Characteristic factors of the farmers, the farm and the formal financial institutions do not significantly influence access to agricultural credits. 1.6 Justification of the Study The data obtained provides a useful basis for forming recommendations for improving access to credit by majority of the small scale farmers in the area. And in the enhancement of the effectiveness and efficiency of the credit delivery and recovery mechanisms of various credit institutions and micro- credit programmes. Increase the income of small scale farmers in the community; Increase the volume of production of staple foodstuff in the country; ensure sustenance of food security of the country; lead to increased adoption of new technologies due to availability of credit facilities to improve productivity. The effects of socioeconomic characteristics of smallholder farmers in accessing credit from formal credit facilities is the major focus of this study. This information is vital for policy makers in taking appropriate actions toward facilitating the establishment of Ph ton

comprehensive and sustainable plan for the development of the rural farmers and agriculture in general, given their characteristics. The study results will also benefit the development partners and civil society organizations involved in the provision of credit facilities to small farmers and rural micro- enterprise sector in modifying the lending policies and conditions to better serve the specific credit needs of their beneficiaries. Moreover, it is also hoped that the study will highlight on the possible link between credit use and increase in income and more importantly on its contribution to poverty alleviation. About 70% of Nigerian population lives below the poverty line of less than one dollar per day, majority of whom live in the rural sector of the economy (Nosiru, 2010). But these poor did not have practical access to institutional credit; the reason is that they are not credit worthy. Therefore, they could not borrow from banks or other financial institutions. The informal financial markets provide loans but at exorbitant or high interest rates. Credit to the smallholder farmers will go a long way to boost agricultural production and alleviate poverty in the country. 2. Literature Review 2.1 The Formal and Informal Financial Systems The formal and informal financial systems co-exist and operate side by side with one another (Kessler et al, 1985). The reality of operations of the two forms of market, however, is more complex and the dividing line is not so clear-cut (Chandavarkar, 1987). Zeller, 1994 observes that each segment of the financial market provides credit services that differ from each other with respect to target group, loan duration, and amount of loan, its use, interest rates and transaction costs. Formal institutions are more inclined to provide its services to the public sector, upper-income households, large-scale enterprises and non-agricultural activities, while the informal financial institutions tend to match their products (Mohamed, 2003).Formal institutions are more inclined to provide its services to the public sector, upper-income households, large-scale enterprises and non-agricultural activities, while the informal financial institutions tend to match their products and services to the characteristics and demand of the predominantly private, low-income, small-scale and rural population of most developing countries (Germidis, Kessler and Meghir, 1991). The coexistence of informal finance serving the latter market can be seen as “healthy and dynamic, permitting more people to participate in financial markets” (Von Pischke, 1991). Literature has viewed the informal sector as the consequence of policy distortions and emphasized the negative consequences of financial dualism for al locative efficiency, equity and 221

economic development (Roe, 1990). However, the recent development have seen the informal financial institutions to have a comparative advantage in some market segments due to its ability to enhance efficiency in resource allocation by mobilizing household savings and financing small business activities that are beyond the reach of the formal system (Ghate, 1988; Adams, 1992). According to Srivasta and Basu, 2004, as in Essien et al, 2013, a recent World Bank survey on rural access to finance indicates that 70% of the rural poor do not have a bank account and 87 % have no access to credit from a formal source. Informal sector lenders remain a strong presence in many less developed economies, delivering finance to the poor on frequently extortionary terms, and access to other financial services such as savings accounts, life, health and crop insurance also remains limited to the rural poor(Essien et al, 2013). The important role of credit in agricultural enterprises development and sustainability prompted the federal government of Nigeria to establish credit schemes such as the Agricultural Credit Guarantee Scheme (ACGS) and the Agricultural credit support Scheme (ACSS) to ensure farmers’ access to agricultural credit, yet the situation has not improved substantially (Badiru, 2010). Based on 2006 core welfare indication questionnaire survey, it is estimated that only 18% of farm households made up of mainly small scale farmers, have access to financial services (Akramov, 2009). Central Bank of Nigeria (2005) noted that the formal financial system provides services to about 35% of the economically active population while the remaining 65% are excluded from access to financial services. According to the apex financial body, these 65% are often served by the informal sector through NGO-MFIs, friends, relations and credit unions. The failure of formal financial sector in most developing economies as Nigeria to serve the poor, has forced majority of rural farmers to rely on informal finance sources ( Musinguzi and Smith, 2000; Fraslin, 2003;Udoh, 2005) as in (Essien et al, 2013) . Despite evidence of credit

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constraints among micro and small businesses in the country, limited attempts has been made to mitigate the financing constraints. 2.2 Financial Markets in Low Income Countries (LICs) Financial Markets in Low Income Countries are characterized by fragmentations and imperfect market conditions. Ghate, 1992categorizes the market into two forms: formal and informal financial markets. He defines formal financial markets as those financial market activities that are controlled by government, which are largely urbanoriented in terms of distribution of bank branches and the concentration of deposits and lending activities. Informal financial markets are defined as activities of various financial intermediaries ranging from farmers, money-lenders, friends, relatives, shopkeepers, merchants, traders, and Rotating Savings and Credit Associations (ROSCAs). 3. Research Methodology 3.1 The Study Area The study will be conducted in Wukari LGA of Taraba State. The state covers a land area of 60291.82 square kilometres and has a population of 2, 300,736 persons, 1,199,849 males, 1,100,887 females. It is located between latitude 60 301 and 90361 north and longitude 90101 and 110501 east of the Greenwich meridian (Taraba State Investors Guide, Undated). Agriculture is the bedrock of the state economy. Taraba State lies largely within the middle of Nigeria and consist of undulating landscape dotted with a few mountainous features. They include the scenic and prominent Mambila Plateau. The State lies largely within the tropical zone and has a vegetation of low forest in the southern part and grassland in the northern part. The Mambila Plateau with an altitude of 1800 meters (1600 ft) above sea level has a temperate climate all year round.

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Figure 1: Map of Taraba State showing Local Government Areas

Source:https://www.researchgate.net/figure/271830626_fig1_Figure-1-Map-of-Taraba-State-showingLocal-Government-Areas-Including-Gassol-Local 3.2 Sampling Procedure: Multistage and simple random sampling techniques were adopted in sampling respondents. In the first stage, three LGAs were purposefully selected as the only LGAs in the State’s agricultural zone II. In the second stage five farmers’ cooperative societies in each of the selected LGAs were randomly selected, giving a total of fifteen (15) farmers’

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cooperative societies and in stage three ten farmers each from the fifteen selected farmers’ cooperative societies were randomly selected, giving a total of 150 farmers for the study. And all the credit officers of the identified institutional financial organizations were used to confirm some of the claims of the farmers studied.

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AEA = Access to Extension Agent (1 if up to 3 visits per year, 0 otherwise) MTHLS = Farmer's Monthly Savings Habit (1 if up to 35,000, 0 otherwise) SECI = Secondary Income AMTDD = (Size of credit demanded during the 2013 farming season (N) (0 if up to N 50,000.00, 1 if otherwise) COLLAT = Collateral Security of the Loan requested INTRATE = Interest Rate GRPRD = Grace Period NONSGUA = Non-Salary Guarantor β= Logistic Coefficients for independent variables µ= Error term. Where Y= credit access, Xi= determinants of formal loan, βoβi= coefficients, µ= error term.

3.3 Data Analysis: Objectives (iii) and (iv) were analyzed using descriptive statistics such as, tabulation, frequency distribution and percentages, while objectives (i) and (ii) were achieved using the binary logit analytical tool which according to literature best fits the analysis of determinant factors of smallholder farmers’ access to formal credit (Yehuala, 2008). The binary logistic regression analyses were carried out to ascertain the effects of these characteristics on access to formal credit on the farmers of the area. The generic form of the logistic model for farmers’ access to formal credit as adopted from Mohamed, 2003 is; Logit P(Y) = β0 +∑β1X1 +µ

4. Results and Discussion Where, Y= (1 respondent has access to formal credit, 0 if otherwise) GNDR = (1 if male, 0 if otherwise) EXP = Farming Experience in years (0 if less than 5 years, 1 if otherwise) BKA = (0 if respondent does not have a bank account, 1 if otherwise) DIST = Distant from Farmer’s Home to the Bank (Km; (0 if less than 5km, 1 if otherwise) FARMS = Farm Size (Km) 1 if more than 4, 0 otherwise FARMW = (0 if less than N 50,000.00, 1 if otherwise) FOB = Membership of Farm Based Organization (1 if respondent is a member of a farmer based organization (FBO); 0 if otherwise) YRSS = Literacy level (1 if respondent has formal education, 0 otherwise) SGUA = Access to salaried guarantor (1 if respondent has access, 0 if otherwise) RPDL = Default on loan repayment (1 if respondent defaulted before, 0 if otherwise) WSECE = Worth of secondary Enterprise (1 if up to 25,000, 0 otherwise)

During the study, descriptive and inferential statistics were used to assess the determinants of access to formal credits in Nigeria, with special reference to Taraba State Agricultural zone II. It was discovered that the average age, experience, distance from Bank, farm size, and number of years in school of the surveyed farmers were, 41 years, 19 years, 12.05km, 2.98ha and 13.25 years, respectively. While 62% of the surveyed farmers had Bank accounts, 38% did not. Also, 68% of the farmers were males, while 32% were female. And while 55% of them belonged to a farm based organization, 45% did not. 4.1 Distribution of the Farmers According to Age The distribution of the age of the respondent farmers shows that it ranges from twenty-one to seventy years. However, it can be observed from table 1 that majority of the farmers (44.67%) falls between the ages of 31 - 40, followed by the age group of between 41-50 years (34.67%) of the surveyed farmers. The distribution depicts that the farmers of Taraba State Agricultural Zone II comprises 79.34% of active productive group between 31-50 years.

Table 1: Distribution of the Farmers According to Age

Age Group 021-030 031-040 041-050 051-060 061-070 Total

Frequency 16 67 52 11 4 150

Percent(%) 10.67 44.67 34.67 7.3 2.67 100

Source: Survey of Taraba State Agricultural Zone II, 2015. 4.2 Distribution of the Farmers According to Age

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Percent distribution of farmers according to their farming experiences reveals that 38% of the farmers have been farming for the past 11-20 years, 224

while 30.67% have farming experiences of between experience in farming. This is informed from the 1-10 years. And less of the farmers are within the fact that up to 68.67% of the farmers are within the age limit of 41-50 years. This suggests that the farming experience range of 1-20 years as farming population take to farming very early in buttressed by table 2 below. life, hence ability to acquire so many years of Table 2: Distribution of Farmers According to Years of Farming Experience

Years Group 001-10 011-20 021-30 031-40 041-50 Total

Frequency 46 57 30 14 3 150

Percent (%) 30.67 38 20 9.33 2 100

Source: Survey of Taraba State Agricultural Zone II, 2015. farmers have a minimum of two hectares which depicts that most of the farmers are indeed small scale, requiring formal loan or credit to improve their scale and productivity. More so, the regression analysis shows farm size as a significant and determining factor of productivity.

4.3 Distribution of Farmers According to Farm Size The frequency distribution of the farmers according to farm sizes x-rayed the area of farm holdings in hectares. It can be observed in table 3 that frequency of 82, corresponding to 54.67% of the

Table 4.3: Distribution of Farmers According to Farm Size

Number of Hectares Frequency 1 1 2 82 3 0 4 44 5 23 Total 150

Percent (%) 0.67 54.67 0 29.33 15.33 100

Source: Survey of Taraba State Agricultural Zone II, 2015. 4.4 Distribution of Farmers According to Distance from Formal Credit Institution (Km) Table 4 reveals that up to 56.66% of the farmers cover a minimum distance of between 1-10 kilometres before getting to a formal financial institution where they request for formal credit to embark on farming activities. Since up to 39.34%

of the farmers have to trek up to 11- 30 kilometres before getting to a formal financial institution, one suffices to say that formal finance institutions that can offer credit to these farmers lack spread within Taraba State Agricultural Zone II.

Table 4: Distribution of Farmers According to Distance from Formal Credit Institution (Km)

Distance(Km) 001-010 011-020 021-030 031-040 041-050 Total

Frequency 85 43 16 0 6 150

Percent (%) 56.66 28.67 10.67 0 4 100

Source: Survey of Taraba State Agricultural Zone II, 2015. 4.5 Distribution of Farmers According to Number of Years in School Table 5 reveals that most of the farmers (50%) have spent between 13-18 years in school, while

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34.67% spent between 7-12 years in school. This implies that graduates make up half of the farming population within the agricultural zone, while first

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school leaving certificate holders make up about 13.33%. Table 5: Distribution of Farmers According to Number of Years in School

Years in School 00-06 007-012 013-018 019-25 Total

Frequency 20 52 75 3 150

Percentage 13.33 34.67 50 2 100

Source: Survey of Taraba State Agricultural Zone II, 2015. Also, the details of the analyses of the factors determining productivity and access to formal credit by farmers in Taraba State agricultural zone II outputs are discussed below under logistic classification, binary logistic regression output, model summary, and test of model coefficients sub headings as shown.

4.6 Logistic Classification of Access to Formal Agricultural Credit in in the Study Area The classification table for access to formal credit shows that our observations of farmers coded as (value = 0), and observations (value =1), yielded a total correct classification of 86.1%. This means that the model distinguished successfully between farmers who had access to formal credit and those who did not given the logistic predicted values and the cut values. (See classification table 6).

Table 6: Classification Table for Farmers’ Access to Formal Credit in the Study Area Classification Table Observed Predicted Access to Formal Loan or Credit Percentage Correct 0 1 84 11 88.4 Access to Formal Loan or0 Credit Step 1 1 10 46 82.1 Overall Percentage 86.1 a. The cut value is .500 freedom which for categorical variables, the Wald 4.7 Factors Determining Access to Formal statistics has degree of freedom equal to one less Agricultural Credit by Farmers in Taraba State the number of categories; (in our scenario 2-1=1) Agricultural Zone II. i.e. for dependent and independent variables. The Table 7 presents the logistic regression output of variables that were found to have significant at 5% the factors determining access to formal credit level of significance include, gender, experience, among farmers of Taraba State agricultural zone II. Ownership of bank account, access to extension In the table, the first column represents the agent and interest rate; while those variables found coefficients (B) of the model equation. The second to be significance at 10% levels of significance column gives the standard error (S.E.) of each of include, farm size and number of years in school. the variables. The Wald is the test of significance The explanations of the relevance of the significant (it tests whether the coefficient equals 0, and or the variables in lieu of access to formal credit cum unique contribution of each predictor in the context productivity are hereunder presented. of other predictors- i.e. holding constant other predictors). The next column (df) is the degree of Table 7: Factors Determining Access to Formal Credit in the Study Area Variable Description B S.E. Wald Df Age .006 .044 .018 1 2.343 .746 9.856 1 Gender -.150 .063 5.694 1 Experience 3.634 1.154 9.922 1 BankAccount Step 1a Distance -.072 .053 1.857 1 .513 .312 2.704 1 FarmSize Farmworth .251 .948 .070 1 FBO .534 .619 .745 1 Ph ton

Sig. .894 .002* .017* .002* .173 .100* .792 .388

Exp(B) 1.006 10.415 .860 37.876 .930 1.670 1.285 1.707 226

YearsinSch SalariedGuara LoanRepaid Enterpriseworth AccessEA MonthlySavings SecIncome AmtDD Collateral IntRate GracePrd NonSGuarantor Constant

-.287 .208 .102 .000 4.408 .625 -.069 .000 -.590 -.137 .592 -.365 -2.186

.104 .977 .659 .000 1.395 .766 .882 .000 1.137 .065 .811 .917 1.910

7.555 .045 .024 1.594 9.983 .665 .006 .054 .269 4.495 .533 .158 1.309

1 1 1 1 1 1 1 1 1 1 1 1 1

.006* .831 .877 .207 .002* .415 .938 .816 .604 .034* .465 .691 .253

.751 1.231 1.107 1.000 82.103 1.868 .933 1.000 .554 .872 1.808 .694 .112

*= significant at 5% level of significance Gender: The coefficient (B) is 2.34 and its standard error is 0.75. The Wald statistics 9.86, which means that gender, is highly significant at 5% level of significance. The Exp (B) or odds multiplier gives the average impact of any of the predictor variables on reporting. Here, the positive logistic coefficients indicates that gender as a variable, on the average increases the odds of reporting i.e. increases the chances of access to formal credit by 10.42. This means that since 68% of the sample population were males, being a male increase the access to formal credit in the study area at the survey period (See Table 7 above). This finding is in tandem with that in the study of Mduduzi Biyase and Bianca Fisher, 2017. In their study, “determinants of Access to Formal Credit by the Poor Households in South Africa, gender, educational level and age among others affect the propensity to borrow by poor households in South Africa. Experience: The logistic coefficient is -0.15, while the standard error is 0.06, which yielded a Wald statistics of 5.69, indicating that experience as a variable is highly significant at 5% level of significance. The negative value of the logistic coefficient indicates that the lower the experience the farmers have, the more would their access to formal loan or credit be lowered by the odd of reporting 0.86, i.e., access will be reduced. Bank Account: The coefficient is 3.63, while the standard error is 1.15. A Wald statistic of 9.92 is significant at 5% level. The positive value of the logistic coefficient means that, farmers possession of a Bank account number increases the chances of access to formal loan or credit by 9.92 (Table 7). Farm Size: The logistic (B) coefficient is 0.51 and the standard error is 0.31. The Wald statistic is 2.70, which is Ph ton

significant at 10% level of significance. The positive logistic coefficient value indicates that the variable increases the odds of reporting. So, it was inferred that, as farm size increases, the chances of access to formal loan or credit would increase by 2.70 among the farmers within the Taraba State agricultural zone II area. This finding shares the same view with the result of Olugbenga Omotayo Alabi, Alimi Folorunsho Lawal, and Henry Onyebuchi, Chiogor. 2016, that farm size, has significant positive influence on access to formal agricultural credit among the respondents in Gwagwalada Area Council, Abuja, Nigeria. Number of Years in School: The logistic (B) coefficient is -0.29 and the standard error is 0.10. The Wald statistic is 7.56, which is significant at 6% level of significance. The negative logistic coefficient value indicates that the variable decreases the odds of reporting. So, it was inferred that, as number of years in school decreases, access to formal loan or credit would decrease by 7.56 among the farmers within the zone. This result agrees with Mduduzi Biyase and Bianca Fisher, 2017; K. I. Etonihu, S. A. Rahmanand S. Usman, 2013.In the study of the later, “determinants of access to agricultural credit among crop farmers in a farming community of Nasarawa state, Nigeria”, education was found to be one of the main determinants of farmers’ access to formal credit. Access to Extension Agent(s): The coefficient is 4.41, while the standard error is 1.4. The Wald Statistics is 9.98 indicating that this variable is significant at 2% level of significant. The positive value of the logistic coefficient indicates that the variable has direct relationships with access to formal agricultural loan or credit. This means that, the more access to agricultural extension agents, the more the farmers would have access to formal loan. The value of Wald Statistics or odds of reporting of suggests that access to 227

formal loan or credit increases by 9.98 given evidence of farmer’s access to extension agent. Put differently, farmers who have no access to extension agents were not considered for acquisition of loan. The appropriate policy option that would be offered is to deploy appropriate number of extension agents to the farming populace to educate and improve their productivity by reducing cost through the adoption of cost saving options and technologies in the value chain during production, processing and packaging of all agricultural products. This result is in line with the findings of Lighton, Dube, Tatenda Mariga and May Mrema, 2015, on the determinants of access to formal credit by smallholder Tobacco farmers in Makoni District, Zimbabwe, in which access or contact with extension agents was a determining factor of ac access to formal credit by farmers.

Interest Rate: The coefficient is -0.14 and its standard error are 0.07. The Wald statistics as shown in the table above is 4.5. Therefore, interest rate is highly significant at 3% level of significance. The Exp (B) or odds multiplier gives the average impact of any of the predictor variables on reporting. Here, the negative logistic coefficient indicates that the prevailing interest rate on the average decreases the odds of reporting i.e., decreases the chances of access to formal loan or credit of the survey respondents by 4.5. This means that as interest rate decreases, access to credit decreases by the value of odds multiplier (Table 7). 4.8 Model Summary The average R  68% , indicating that 68% of the variations in access to formal credit data were explained by the determining variables 2

Table 8: Model Summary Step -2 Log likelihood Cox & Snell R Nagelkerke R Square Square a 1 95.094 .498 .680 a. Estimation terminated at iteration number 7 because parameter estimates changed by less than .001. 4.9 Test of Hypothesis: Testing the overall significance of the Binary Logistic Model Coefficients H o : b1  b2 ...bn  0, (That Null Hypothesis: Farmers’, socio-economic characteristics do not significantly influence access to formal agricultural loan or credit). Against the alternative hypothesis: H1 : b1  b2 ...bn  0 (That Farmers’ socio-economic Table 9: Omnibus Tests of Model Chi-square Step 104.049 Step 1 Block 104.049 Model 104.049

Coefficients Df Sig. 20 .000 20 .000 20 .000

Decision Rules: Since the model p=.0001, the model is significant, which means that not all b ' s are zero. Therefore, the null hypothesis is rejected; since gender, experience, ownership of bank account, farm size, number of years in school, access to extension agent and interest rate are significant, at 5% level of significance. The inference drawn are that these variables mentioned have significant influence and are the determining factors of access to formal loan or credit by the surveyed farmers of Taraba State agricultural Zone II.

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characteristics have significant influence on access to formal agricultural loan or credit).From the model chi-square, we see that the model is adequate (p=.0001). This was concluded from the following output. That the model (p = .0001) means the model is significant beyond (p = .0001) (Table 9).

Summary During the study, descriptive and binary logistic statistics were used to assess the determining factors of access to formal credit in Taraba State Agricultural Zone II. The specific objectives of the study were to identify the factors that influence access to formal credit by the farmers within the survey area. Data collected were primary. The data set used was elicited from the farmers during the 2015 farming year. In order to determine the effects of farmers’ socio-economic characteristics on access to formal credit, the dependent variable 228

(access to credit) was coded 1 if farmers took loan whose amount is equal to or greater than a threshold (average) of all credit obtained by the farmers and zero, otherwise. The independent variables included those specified in the model generic form. The outputs were discussed under logistic classification, regression output, model summary, and test of model coefficients and policy decision given the results. A total of 150 farmers were surveyed. The selection was through a multistage random sampling as fully specified in the sampling procedure of the study methodology. The results obtained indicate that the socioeconomic characteristic of the farmers influenced their access to formal credit. The variables that explain why some farmers have access to formal credit, while others do not are x-rayed and highlighted in table, 4.7 above. Conclusion The major reason for assessing the determining factors of access to formal credit is to properly inform the farming populace of the survey area of the factors deterring them from obtaining formal credit for agricultural purposes. As such, these impacting factors should be guarded against in order to ensure access to formal credit, thereby improving and sustaining farm scale and productivity. This will ensure food security for the state and nation at large. These could be achieved through access to more credit, education by extension agents, increase in farm size, increase in savings by farmers, improved farming experiences through exposures from outreach programmes and spat plots; as well as ability to pay bank interest rates, among others. These activities can only be achieved with a given threshold of access to farm loan, which the formal or informal sources can provide, but calls for prudence on the side of the farmers. This is however not lacking because the farmers were able to repay borrowed funds and provide collateral security as depicted by the insignificance statuses of repayment and collateral variables. Acknowledgement This article forms part of a granted study titled, “Factors Influencing Access to Formal Credit in Nigeria: A Case Study of Smallholder Farmers in Taraba State Agricultural Zone II”, by the Nigerian Tertiary Education Fund (TET fund), via Federal University Wukari. The authors are grateful to TET fund and Federal University Wukari Institutional Based Research Unit for her useful criticisms in an earlier draft that informed this study.

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Recommendations (1) Given the negative effect of interest rate on farmers’ access to formal credit, it is recommended that lending institutions should offer agricultural loans at one digit interest rate to enable the farmers make gains for themselves and save expenditures on costs of capital that would normally be passed on to consumers in form of price. Those costs that are saved would on the long run reduce the unit price of agricultural commodities and the final consumption expenditure of an average person in the nation would pave way to sustain consumption. (2) Considering the positive effects of having bank account and farm size on access to credit, it is recommended that those who qualify by having bank accounts should be given enough loans to improve scale and farm size since certificate as collateral security is a insignificant factor in this regards. (3) Considering the effects of farm size on credit access, Federal and State governments should not only acquire land, but lease them to needing farmers to enable them gain access and improve productivity. (4) Given the negative and positive effects of number of years in school and access to extension agents on access to credit, respectively. It is recommended that in order to make formal credit available to many more farmers, those farmers that have poor educational background should be considered for loan on the basis of years of experience in farming. (5) Federal and state governments should put in place policies aimed at providing free educative seminars to farmers that did not spend more than 6 years in school to teach them possible ways and methods of acquiring credit and should ensure mass attendance to seminars by offering incentive to farmer-participants. (6) Considering that access to extension agents is significantly related to access to credit, governments revitalize extension services policy to make extension activities vigorous and rural farmer oriented. References Adams D.W., 1992. “Taking a Fresh look at Informal Finance”, in Adams, D. W and D. A. Fitchett (eds) Informal Finance in Low- Income Countries, Boulder, Co: Westview Press. Adewuyi, S.A., 2002. “Resource Use Productivity In Food Crop Production In Kwara State, Nigeria.” Ph.d

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G.O. Onogwu

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G. O. Onogwu hails from Nru, Nsukka LGA of Enugu State, Nigeria. He attended Central School Nru-Nsukka and Nsukka High School from where he acquired his primary and secondary education. His first school leaving certificate (FSLC) and West African School Certificate (WASC) were obtained in 1978 and 1980, respectively. G. O. Onogwu obtained his first, second and third degrees from University of Nigeria Nsukka in 1994, 2006 and 2011, respectively of the Department of Agricultural Economics. G.O Onogwu worked with the Spiritan Farms Co. Ltd, Orkija Anambra State from 1997 to 2001 as the operations Manager before starting his M.Sc. programme in 2002. G. O. Onogwu worked with the Richmond Open University from 2008/2009 session to 2011/2012 session when he joined the Federal University Wukari, Taraba State, Nigeria as a Lecturer I in October 2012. He served as the Head of the Department of Agricultural Economics and Extension from 2011/2012 session to 2016/2017 session. He is still with the Federal University Wukari to date hoping to bang his senior Lecturing position by October, 2017. His major academic laurels include the African Economic Research Consortium (AERC) and 231

Tertiary Education Trust Funds (TetFund) research grants of 2010 and 2013, respectively.

Agricultural Cooperatives Research method and Statistics, etc. he is married with five (5) children.

Audu, I.A

Ignatius AnguumAudu was born on the 31st July, 1965 in Wukari of Taraba State, Nigeria. He attend Takum Local Government Primary School Bete from 1974 t0 1979 and proceeds to Government Secondary School Bali from 1980 to 1984. Graduates from the Federal University of Technology in 1997 and did his compulsory National Youth Service (NYSC) at Ifewara High School, Ifewara in 1998. Ignatius joined Taraba State Teaching Service Board, Jalingo in July, 1998 and was posted to Marmara Government Girls Secondary School, Wukari from July, 1998 to December, 2005 where he transferred his service to the Taraba State Ministry of Agriculture and Natural Resources. He holds a Master of Science (M.Sc) degree from the University of Nigeria, Nsukka in December, 2008. Until he withdrawn his services in 2012, he was an Assistant Chief Planning Officer (GL.13). After withdrawing his from Taraba State Civil Service, he joined the Services of Federal University, Wukari in October, 2012 as an Assistant Lecturer. Igbodor, F.O.

Igbodor, Francis Onwe joined the Department of Agricultural Economics and Extension Federal University Wukari as an Assistant Lecturer in 2013. He obtained a Bachelor of Agriculture degree in Agricultural Economics and Extension in 2006 and also a Master of Science in Agricultural Economics development and policy in 2013 both from the University of Calabar, Calabar, Cross River State, Nigeria. He taught and is still teaching courses like principles of Agricultural Economics, Resource and Environmental Economics Ph ton

For publications/ Enquiries/ Submissions: Email: [email protected]

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