Invited paper presented at the 6th African

Conference of Agricultural Economists, September 23-26, 2019, ,

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EFFECTS OF BANK OF AGRICULTURE’S (BOA) CREDIT ON YAM FARMERS’ PRODUCTIVITY IN FEDERAL CAPITAL TERRITORY, ABUJA, NIGERIA

C.O ADEBAYO, E.S YISA and SAMUEL, D.C Department of Agricultural Economics and Farm Management, Federal University of Technology, Minna [email protected] & [email protected]

Abstract

The study determined the effect of Bank of Agriculture (BOA) credit on productivity of yam farmers in Federal Capital Territory (FCT), Abuja, Nigeria. Multi – stage sampling technique was used to select 92 Bank of Agriculture beneficiaries and 92 non-beneficiaries to give a total of 184 respondents on which structured questionnaire complemented with interview schedule was employed to collect primary data. The result of the analysis obtained shows that majority of the beneficiaries (68.47%) and non-beneficiaries (78.26%) were with a mean age of 42 years for the beneficiaries and 41 years for non-beneficiaries. Mean farming experience was 17.8 years for the beneficiaries and 19.8 years for non-beneficiaries. The mean credit accessed by the beneficiaries was ₦135,652.2. Total factor productivity revealed minimum productivity value of the beneficiaries and non-beneficiaries to be 0.5 and 0.06, respectively. The result of the multiple regression analysis revealed that farmers level of education, membership of cooperative society, access to BOA credit and farm size were significantly explained the effect of BOA credit on yam farmers’ productivity. Major constraint to access to BOA credit was inadequate credit availability. It was therefore recommended that the amount of credit disbursed by the bank need to be increased.

Key words: Bank of Agriculture, productivity, credit

JEL: Q2, Q14, Q18

INTRODUCTION

Nigeria is a leading yam producing country (FAO, 2015). Yam as a tuber crop is a staple food for most people in Nigeria (regardless of their status in both rural and urban communities) for food security, poverty alleviation, and unemployment reduction among others. This is because of its capacity to yield under marginal soil condition and its tolerance of drought (IITA, 2004). The farm hectares of yam production have been increasing with corresponding increases in the usage of inputs. Unfortunately, the increase in output seems not to have been commensurable with those

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input usage (Jonathan and Anthony, 2012). Amaefula et al., (2018) identified lack of fund as one

of the major limitation to yam production enterprise development in Nigeria. On the other hand,

Abdullahi (2015) identified inadequate access to credit facilities as a major constraint yam farmers

face in .

Agricultural credit is the process whereby the control over the use of money, goods and service is

obtained in exchange for a promise to repay at a future date (Onyebinama, 2000). Credit is needed

by farmers for farming activities, family consumption expenses, enhancing productivity and

promotes standards of living by breaking the vicious cycle of poverty especially among small scale

farmers. According to Nwaru (2003), it is difficult to accumulate the amount of financial capital

required to purchase new production technologies, considering the very low level of income of

farmers in Nigeria hence, external sources of funds to farm household are required in order to

increase their incomes and agricultural productivity. Low farm output resulting to low income

generation after sales of produce cannot help the farmers to expand their operations, enjoy a

prosperous living or acquire new production technology. However, a notable increase in

production efficiency is a function of the quality of resources used or made available as well as

their efficient use. The quantity and increased resource mobilization help to account for high

productivity. Ekpebu (2006) posited that the continuous problems facing agricultural sector is that

of insufficient funding, low technology base, inadequate extension services, high cost of farm

inputs, and inadequate infrastructural facilities. There is an increase in the number of farmers

demanding for agricultural loan (credit) to enhance production. Agricultural loan helps to improve

the well-being of the farmers as it removes the financial constraints facing the farmers and increases their chances of embracing new technologies. Therefore, the problem facing yam farmers in the country is their inability to access loan facilities from various sources available. However,

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problems emanating from the informal financial institution (local money lenders) sources are that

credit supply is very expensive to access, scarce and unreliable with a high interest rate. Farmers

sometimes lose their belongings and valuables assets like houses due to inability to off-set the loan as agreed upon within a specified period of time. On the other hand, small holder yam farmers find it difficult to access loans from formal financial institutions as a result of loan rationing mechanisms adopted by these institutions which posed a major constraint, excluding these farmers from having access to loans.

One of the numerous efforts of the government to enhance productivity through the provision of agricultural finance is the establishment of the Bank of Agriculture (BOA) Limited. The bank was incorporated in 1972 as Nigerian Agricultural Bank (NAB) It has metamorphosed over the years and finally adopted BOA as its name in October 2010 to reflect its transformation programme

(BOA, 2017). The mandate of the BOA aimed at increasing agricultural production and productivity among farmers through the provision of agricultural credit as a significant ingredient for improving productivity in Nigeria.

Many researches on yam production in Nigeria has been on profitability, efficiency, constraints and resource use (Amaefuna, 2018; Abdullahi, 2015 and Jonathan and Anthony, 2012). The aim of this research is to determine the effect of BOA credit on yam farmers’ productivity in Federal

Capital Territory, Abuja. The specific objectives are to: (i.) describe the socio-economic characteristics of yam crop farmers in the study area; (ii) determine the total factor productivity of yam farmers; (iii). determine the factors influencing yam crop farmers’ access to BOA credit; vi. identify the constraints to access BOA credit in the study area.

Hypothesis of the Study

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H0: Bank of Agriculture’s credit did not have significant effect on the yam crop farmers’

productivity.

Methodology

Study Area

The study was carried out in the Federal Capital Territory (FCT), Abuja, Nigeria. Federal Capital

Territory falls within Latitudes 70° 201 North and Longitudes 60° 451 and 70° 391 East of the

Equator. It has a land area of 8,000 square kilometers and bounded on the North by ,

the West by Niger State, the East and South-east by Nasarawa State and the South-west by Kogi

State. There are six (6) Area Councils in the Federal Capital Territory; these include Gwagwalada,

Kuje, Kwali, Bwari, Abaji and the Abuja municipal area council (FCDA, 2015). FCT has an estimated population of 3,324,000 persons in 2015 as estimated by the United Nations Fund for

Population Activities (UNFPA, 2015).

FCT experiences three weather conditions annually, which includes a warm, humid rainy and a blistering dry season. There is a brief interlude of harmattan occasioned by the North-east trade wind, with the main feature of dust haze and dryness. The rainy season begins from April and ends in October, while day-time temperature reach 28oC (82.4oF) to 30oC (86.0oF) and night-time

temperatures hovers around 22oC (71.6oF) to 23oC (73.4oF). During the dry season, day-time

temperatures can soar as high as 40oC (104.0oF) and night-time temperatures can dip to 12oC

(53.6oF). The high altitudes and undulating terrain of the FCT has a moderating influence on the

weather of the territory. Rainfall in the FCT reflects the territory’s location on the wind ward side

of Jos Plateau and the zone of rising air masses with the city receiving frequent rainfall during the

rainy season from March to November every year. The annual rainfall is between 1100mm to

1600mm (FCDA, 2015).

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Economic activities in FCT, Abuja includes agriculture such as farming, livestock production and

fish production. However, occupation such as weaving, craftsmanship, trading, public and civil

servants are present in the study area. Major crops grown are yam, maize, millet, sorghum, rice,

cassava, groundnut and cowpea, while mineral deposits are marble, tin, stones, talc, iron ore, gold

and lead (FCDA, 2015).

Sampling Procedures and Sample Size

The respondents for this study were mainly yam farmers residing in Federal Capital Territory,

Abuja who are categorized into BOA credit beneficiaries (i.e the treatment) and Non-BOA credit beneficiaries (i.e the control). A multi-stage sampling technique was used to select the total respondents. First stage involved random selection of three (3) Area Councils (Abaji, Gwagwalada and Kwali) out of the six (6) Area councils in the study area. The second stage involved random selection of four villages from each of the Area Council selected (two villages each for the treatment and control). In the third stage, yam crop farmers were proportionately (20%) selected from the sample frame of Registered yam crop farmers from Federal Capital Territory Agricultural

Development Programme (FCTADP) and BOA credit users list (which constitutes the beneficiaries and non-beneficiaries of the Bank of Agriculture credit scheme) to make up of 92

Bank of Agriculture beneficiaries and 92 non-beneficiaries giving a total of 184 yam crop farmers used as respondents for the study.

Method of Data Collection

Data for this study were obtained from primary source with the use of structured questionnaire

complemented with interview schedule. In the collection of data, household questionnaire was

administered to elicit information such as socio-economic characteristics of yam crop farmers,

Page 5 of 18 factors influencing accessibility of BOA credit, information on inputs and output of yam and constraints to access BOA credit in the study area.

Method of Data Analysis

The data collected were analyzed using both descriptive statistics (such as means, percentages and frequency distributions) and inferential statistics (multiple regression model). Total Factor

Productivity (TFP) was used to determine the productivity of the yam farmers.

Model Specification

Total factor productivity

According to Rogers (1998), Total Factor Productivity (TFP) can be measured as the inverse of unit variable cost. It is the ratio of the output to the Total Variable Cost (TVC) which was used to determine the productivity of the yam farmers. This is mathematically expressed as:

TFP = (1) 𝑌𝑌 𝑇𝑇𝑇𝑇𝑇𝑇 Where;

Y = Value of output (₦) and TVC = Total Variable Cost (₦)

Alternatively,

TFP = (2) 𝑌𝑌 ∑𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 Where;

Y = Value of output in naira

Pi = unit price of ith variable input and Xi = quantity of ith variable input.

Multiple regression model

Effect of the BOA credit on the yam farmers’ productivity was achieved using multiple

regression model. First step in the application of regression analysis was the specification of

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the dependent and explanatory variables, as well as the functional forms of the model. The

general multiple regression model in its implicit form is expressed as:

Y = f (X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12) (3)

The explicit forms of the model are expressed as follows:

Linear form

Y = α + β1X1 + β2X2 + β3X3 + β4X4 +β5X5 + β6X6 +...... + β12X12 + e (4)

Double-log form: lnY = lnα + β1lnX1 + β2lnX2 + β3 lnX3 + β4 lnX4 + β5 lnX5 + . . . + β12X12 + e (5)

Semi-log form:

Y = lnα + β1lnX1 + β2lnX2 + β3 lnX3 + β4 lnX4 + β5 lnX5 + . . . + β12X12 + e (6)

Exponential form:

lnY = α + β1X1 + β2X2 + β3X3 + β4X4 +β5X5 + β6X6 + . . . + β12X12 + e (7)

Where;

Y = Productivity index (obtained from Total Factor Productivity)

X1= Age of farmers (years)

X2 = Educational level (years)

X3 = Farm experience (years)

X4 = Household size

X5 = Extension contact (number of visit)

X6 = Cooperative membership (years)

X7 = Access to BOA credit (access = 1, otherwise = 0)

X8 = Farm size (Ha)

X9 = Labour usage (mandays)

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X10 = Yam seeds

X11 = Fertilizer application (kg)

X12 = Agro-chemicals (₦)

e = error term

β0 = Intercept to be estimated and

β1 – β8 = Coefficients to be estimated.

Results and discussion

Socio-Economic Characteristics of the Respondents

The age of farmers determines the quality and quantity of work he can do on his/her farm. Table

1 reveals that majority of the beneficiaries (68.47%) and non-beneficiaries (78.26%) were within

the age range of 30 – 49 years with a mean age of 42 years for the beneficiaries and 41 years for

non-beneficiaries. This implies that respondents were in their mid-age and most productive stage

of life; hence have the capacity to carry out yam production. This finding is in agreement with

Okunade et al., (2005) who stated that most of the yam farmers in their study area were active and mostly in their mid-ages (between 36 – 56 years of age).

Table 1: Socio-economic Characteristics of the Respondents Beneficiaries (n=92) Non-beneficiaries((n=92) Variables Freq. % Freq. % Age (years) < 30 1 1.09 1 1.09 30 – 39 15 16.30 17 18.48 40 – 49 48 52.17 55 59.78 50 – 59 23 25.00 18 19.57 > 59 5 5.43 1 1.09 Mean 42 41 Gender Male 90 97.83 89 96.74 Female 2 2.17 3 3.26 Marital status

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Single 5 5.43 6 6.52 Married 72 78.26 77 83.70 Divorced 6 6.52 2 2.17 Widowed 3 3.26 2 2.17 Separated 6 6.52 5 5.43 Education level (years) Primary 14 15.22 57 61.96 Secondary 23 25.00 17 18.48 Tertiary 55 59.78 8 8.70 Non-formal 0 0 10 10.86 Mean 12 6.5 Experience (years) < 6 2 2.17 5 5.43 6 – 10 15 16.30 10 10.87 11 – 15 27 29.35 18 19.57 16 – 20 16 17.39 17 18.48 > 20 32 34.78 42 45.65 Mean 17.8 19.8 Household size (No.) < 6 21 22.83 44 47.82 6 – 10 36 39.13 37 40.22 11 – 15 31 33.70 9 9.78 > 15 4 4.35 2 2.17 Mean 9 7 Farm size (Hectares) < 1 20 21.74 56 60.87 1 – 2 12 13.04 16 17.39 3 – 4 35 38.04 15 16.30 > 4 25 27.17 5 5.43 Mean 3.9 2.4 Credit (₦) < 100,000 29 31.53 0 0 100,000 – 200,000 61 66.30 0 0 > 200,000 2 2.17 0 0 Mean 135,652.2 Source: Field Survey, 2017 Table 4.1 revealed that majority (97.83%) of the beneficiaries and 96.74% of the non-beneficiaries were males implying that males are predominant yam farmers which could be due to its tedious nature. This finding is in agreement Nlerun (2012) and Abdullahi (2015), they found out that yam production was male dominated in Rivers and Niger states of Nigeria respectively. As shown in

Table 1, majority (74.7%) of the beneficiaries and (83.70%) of the non-beneficiaries were married.

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This implies that married individuals are more into yam production in the study area which could help them to take care of their responsibility by providing the food needs of the family. Married household heads are likely to have larger household sizes when compared to single household heads (Amao and Omonona, 2011).

Further analysis revealed that all (100%) of the beneficiaries and (90%) of the non-beneficiaries had one form of formal education or the other; which could be the reason they were able to access

BOA credit for yam production. This is in corroboration with the findings of Ayamgu et al., (2006) who found out that education significantly influences the decision to participate in formal credit schemes. Farmers gain experience while carrying out farming operation which is an indication of farming expertise. Table 1 revealed that majority (63.14%) of the beneficiaries and (48.89%) of the non-beneficiaries had farming experience within the range of 6 – 20 years with an average farming experience of 17.8 years for the beneficiaries and 19.8 years for non-beneficiaries in the study area. This implies that the respondents have been into yam production over a long period of time and could easily influence their decision to access BOA credit for an increased production.

Household size refers to the total number of people living together under the same roof. As revealed in Table 1, majority (72.83%) of the beneficiaries and (50.00%) of the non-beneficiaries had household size within the range of 6 – 15 people with an average of 8 people for the beneficiaries and 7 people for non-beneficiaries. This implies that there is fairly large number of people eating from the same cooking pots and living together in the study area. This could be a good source of family labour, hence the capacity to engage in yam production. The result is in line with the findings of Oladeebo et al., (2013) who found out that yam farmers in , Nigeria had a large household size which was an important source of labour.

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Farm size is the total area of land that is put into agricultural production and an important fixed

factor of production. As revealed in Table 1, more than half (51.08%) of the beneficiaries and

33.69% of the non-beneficiaries had farm size within the range of 1 – 4 hectares, while about

21.74% of the beneficiaries and 60.87% of the non-beneficiaries had farm size less than 1 hectare

with mean farm size of 2.9 and 1.4, respectively. This implies that the beneficiaries in the study

area were small scale farmers and have more farmland compare to the non-beneficiaries which

could be due to their ability to access BOA credit for yam production. Distribution of the

respondents based on the amount of credit accessed by the respondents is presented as presented

in Table 1 revealed that majority (66.30%) of the beneficiaries were able to access BOA credit

between ₦100,000 - ₦200,000, 31.53% of the beneficiaries accessed BOA credits of less than

₦100,000, while few (2.17%) of the beneficiaries accessed BOA credit greater than ₦200,000

with mean BOA credit accessed to be ₦135,652.20.

Total Factor Productivity of the Respondents

The result of the total factor productivity of the beneficiaries and non-beneficiaries is presented in

Table 2 and 3. It shows that the minimum productivity of yam farmers who are the beneficiaries of BOA credit was 0.5, while the maximum productivity was 48.16 with an average total factor productivity of 5.65. This implies that yam farmers in the study area are producing beyond the optimum level as indicated by the mean productivity. However, given favourable conditions and appropriate combination of available resources, an individual yam farmer under BOA credit can produce at a capacity they can sufficiently repay credit granted to them by Bank of Agriculture.

Table 2: Distribution of Beneficiaries Total Factor Productivity Level Productivity Class Frequency Percentage < 11 82 89.13 11 – 20 6 6.53

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21 – 30 2 2.17 > 30 2 2.17 Total 92 100.00 Mean 5.65 Minimum value 0.5 Maximum value 48.16 Source: Field Survey, 2017 Furthermore, Table 3 shows that the minimum productivity of the non-beneficiaries of BOA credit

was 0.06, while the maximum productivity was 9.71 with an average productivity of 1.61. This

implies that the non-beneficiaries in the study area are also producing beyond the optimum level

as indicated by the mean productivity of 1.61, but not as compared to the beneficiaries.

Table 3: Distribution of non-beneficiaries Total Factor Productivity Level Productivity Class Frequency Percentage < 11 92 100.00 Total 92 100.00 Mean 1.61 Minimum value 0.06 Maximum value 9.71 Source: Field Survey, 2017

Effect of BOA Credit on the Beneficiaries’ Yam Productivity

The result of the four functional forms of the multiple regressions is presented in Table 5. From

the regression analysis, output of the semi-log gave the best fit based on the number and signs of

significant parameters, the value of the coefficient of determinations (R2) and F-statistics.

However, double – log was chosen as the best fit due to significance of access to BOA credit in

conformity with a priori expectation as it was the variable of interest. The double – log have adjusted R-Square (R2) value of 0.5008 implying that about 50% of the variation in yam productivity was due to the independent variables included in the model, while unaccounted 50%

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could be due to some externalities beyond the control of the researcher. The F-statistic value was

8.61 and significant at 1% level of probability indicating the model goodness of fit.

Table 4: Regression estimates of the effect of BOA Credit on Yam Productivity Variables Linear Semi – log Double – log Exponential Constant 0.2476 -0.0919 -1.7827 -4.9345 (-0.05) (-0.12) (-1.02) (-0.41) Age -0.0871 -0.0158 -0.0876 0.2130 (-0.61) (-0.78) (-0.31) (0.11) Education 0.1734 0.0155 0.7465 3.4473 (0.98) (0.62) (2.89) ** (1.93)* Experience 0.0557 0.0029 0.0217 1.5570 (0.40) (0.15) (0.08) (0.81) Household size 0.5247 0.1184 0.2404 0.5366 (1.60) (2.55)*** (0.87) (0.28) Extension 1.5474 0.3516 1.4742 13.9137 (0.63) (1.01) (1.41) (1.93)* Cooperative 0.2024 -1.1318 13.1319 -35.3637 (0.06) (-2.34)** (2.42)** (-0.94) Credit -3.35e-06 1.12e-06 0.2006 0.4065 (-0.51) (1.21) (5.47)*** (1.60) Farm size 0.9476 0.5367 0.8670 2.3364 (0.78) (3.14)*** (2.78)*** (1.08) Labour 0.0029 0.0024 0.1695 0.8258 (0.16) (0.92) (0.78) (0.55) Yam seeds -0.0003 -0.0001 -0.3206 -1.3896 (-1.90)* (-4.85)*** (-4.15)*** (-2.60)*** Fertilizer -0.0004 -0.0002 0.0105 0.1807 (-0.51) (-2.43)** (0.13) (0.31) Agrochemicals 0.0683 0.0670 -0.1333 -1.7886 (0.26) (1.77)* (-0.52) (-1.01) R2 0.2186 0.5700 0.5666 0.2465 R2 – adjusted 0.0999 0.5047 0.5008 0.1320 F – ratio 1.84*** 8.73*** 8.61*** 2.15*** Source: Field Survey, 2017 Note: *** implies significant at 1%, ** implies significant at 5% and * implies significant at 10%, while figures in parenthesis are the t – values.

From the twelve explanatory variables specified in the model, four variables namely educational

level, membership of cooperatives, credit and farm size had positive coefficients and statistically

significant, implying that an increase in any of the independent variables will lead to a Page 13 of 18

corresponding increase in yam productivity, while variable such as yam seeds had negative

coefficients and statistically significant, implying that an increase in yam seeds will lead to a

corresponding decrease in productivity.

Level of education had a positive coefficient and statistically significant (5%) implying that

educational level has a direct relationship with yam farmers’ productivity. This means that high

literacy rate ceteris paribus would enable yam farmers to adopt new technologies that will lead to more productivity. The coefficient of membership of cooperative (13.1319) was positive and statistically significant at 5% level of probability. This implies that 1 percent increase in cooperative membership of the beneficiaries will increase yam productivity by 13 percent. This is because participation in cooperative helps in share of vital information relating to yam production, training and capacity building that could boast effective and efficient performance of an individual.

This finding is in line with that of Effiong (2005).

The coefficient of access to BOA credit (0.2006) was positive and statistically significant at the

1% level of probability implying that 1 percent increase in access to credit by the beneficiaries will lead to about 0.2 percent increase in yam productivity. This is in conformity with the a priori expectation as access to BOA credit by the beneficiaries is expected to enhance yam productivity in the study area. Therefore, as expected, BOA credit has significant and positive effect on yam productivity of the beneficiaries. The higher the amount of credit access, all other things being equal, the higher the productivity leading to an increase output and revenue generation. This finding is in corroboration with the work of Samboko (2011) who reported positive and highly significant influence of credit in yam production in his study area.

The coefficient of farm size (0.8670) was positive and statistically significant at 1% level of probability implying that 1 percent increase in the farm size of the beneficiaries will lead to about Page 14 of 18

0.9 percent increase in yam productivity. This is because access to more farmland enhances

individual farmers’ productivity as it increases area of yam cultivation for an increase output and

income.

Hypothesis testing

The null hypothesis that Bank of Agriculture (BOA) credit did not have significant effect on the

yam crop farmers’ productivity was tested using the t – value of the chosen best fit functional form

of the multiple regression analysis as presented in Table 4. From t – value of the double – log

functional form which was chosen as the lead equation, access to BOA credit (5.47) was positive

and statistically significant at 1% level of probability, hence we hereby reject the null hypothesis

and accept the alternative

Constraints to Access to BOA Credit

The constraints identified by the beneficiaries of BOA in the study area were presented in Table

5. Based on the 5-point Likert scale used to categorize the severity of the constraints, cut-off mean score of 3.0 was used for decision making. As revealed in the Table, inadequate credit availability

( = 4.16), problem of collateral ( = 4.01), high interest rate ( = 3.99), non-availability of capital

(𝑋𝑋� = 3.41) and lack of technical know𝑋𝑋� -how ( = 3.34) were the𝑋𝑋� constraints considered to be severe by𝑋𝑋� the beneficiaries of BOA credit in the study𝑋𝑋� area. This result is somehow in agreement with that of Balami et al. (2011) who reported that factors militating against the realization of potentials of food crops productivity in sub-Saharan Africa include: poor access to credit, high cost of farm inputs, low adoption of research findings, inefficient fertilizer procurement and distribution, and inadequate access to markets.

Table 5: Constraints Faced by the Beneficiaries (n = 92) Constraints VS(5) S(4) UN(3) NS(2) NVS(1) Sum Mean RMK

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Inadequate credit availability 35 45 5 6 1 383 4.16 S Problem of collateral 39 31 12 4 6 369 4.01 S High interest rate 28 43 16 2 3 367 3.99 S Non availability of capital 13 46 7 8 18 314 3.41 S Lack of technical know how 14 3 75 0 0 307 3.34 S Weather condition fluctuation 1 20 27 27 17 237 2.57 NS Unstable market for products 6 4 21 46 15 216 2.35 NS Lack of irrigation facilities 2 7 29 33 21 212 2.30 NS High cost of input 7 17 9 30 29 211 2.29 NS Labour unavailability 2 6 6 51 27 181 1.97 NS Poor storage facilities 10 2 9 23 48 179 1.95 NS Inadequate processing facilities 5 5 9 31 42 176 1.91 NS Source: Field Survey, 2017 Note: VS = Very Severe, S = Severe, UN = Undecided, NS = Not Severe, NVS = Not Very Severe Cut- off mean is 3.0.

Conclusion and recommendations

Access to Bank of Agriculture (BOA) credit has a positive impact on yam farmers’ productivity in the study area. The major constraints to access to BOA credit was inadequate credit facility.

Based on the findings the following recommendations were made: (i). The amount of credit disbursed by BOA to yam farmers need to be increased substantially to increase productivity. (ii)

Credit should be extended to other group of yam farmers who did not have access to BOA credit

(especially the non-beneficiaries identified) in order to improve the overall productivity of yam in the study area and Nigeria in general. (iii). Beneficiaries of BOA credit should improve on their productivity through judicious utilization of available resources in order to reach the optimum level of production. (iv). It is also recommended that financial institutions especially Bank of

Agriculture (BOA) should come up with flexible policy on credit that will enhance access to credit by resources poor farmers who did not have suitable collaterals with single digit interest rate.

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