Credit repayment is one of the dominant importance for viable financial institutions. This study was aimed to identify determinants of loan repayment capacity of smallholder farmers in Horro and Abay-Chomen Woredas. The study used primary data from a sample of formal credit borrower farmers in the two woredas through structured questionnaire. A total of 120 farm households were interviewed during data collection and secondary data were collected from different organizations. The logit model results indicated that a total of fourteen explanatory variables were included in the model of which six variables were found to be significant.; among these variables, family size and expenditure in social ceremonies negatively while, credit experience, livestock, extension contact and income from off-farm activities positively influenced the loan repayment performance of smallholder farmers in the study areas. Based on the result, the study recommended that the lending institution should give attention on loan supervision and management while the borrowers should give attention on generating alternative source of income to pay the loans which is vital as it provides information that would enable to undertake effective measures with the aim of improving loan repayment in the study area.

1. Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia
Determinants of Loan Repayment Performance of Smallholder
Farmers in Horro and Abay Choman woredas of Horoguduru-
Wollega Zone, Oromia Region, Ethiopia
*1Amsalu File, 2Oliyad Sori
1Wollega University, The Campus’s Finance Head, P.O. Box 38, Ethiopia
2Wollega University, Department of Agricultural Economics, P.O. Box 38, Ethiopia
Credit repayment is one of the dominant importance for viable financial institutions. This study
was aimed to identify determinants of loan repayment capacity of smallholder farmers in Horro
and Abay-Chomen Woredas. The study used primary data from a sample of formal credit borrower
farmers in the two woredas through structured questionnaire. A total of 120 farm households were
interviewed during data collection and secondary data were collected from different
organizations. The logit model results indicated that a total of fourteen explanatory variables were
included in the model of which six variables were found to be significant.; among these variables,
family size and expenditure in social ceremonies negatively while, credit experience, livestock,
extension contact and income from off-farm activities positively influenced the loan repayment
performance of smallholder farmers in the study areas. Based on the result, the study
recommended that the lending institution should give attention on loan supervision and
management while the borrowers should give attention on generating alternative source of
income to pay the loans which is vital as it provides information that would enable to undertake
effective measures with the aim of improving loan repayment in the study area.
Key words: Loan repayment performance, Smallholder farmers, logit model, Horro and Abbay Chomen Woredas
INTRODUCTION
The economic growth of developing countries depends to
a great extent on the growth of the agricultural sector.
Ethiopia is one example of a developing country,
characterized by a predominantly subsistence agrarian
economy. The nature of farming in Ethiopia is dominated
by traditional micro holdings of the subsistence type, with
less than two hectares of land being the average holding
(CSA, 2015).
The use of credit has been envisaged as one way of
promoting technology transfer, while the use of
recommended farm inputs is regarded as key to
agricultural development (Tomoya M. and Takashi, 2010).
(Medhin, 2015 and Million, 2014) have indicated that credit
is the largest source of farm capital in Ethiopia. Agricultural
credit has a key role for the development of different
sectors (Sileshi 2014, Tomoya and Takashi, 2010).
The provision of sustainable formal credit for agricultural
inputs is one of the most effective strategies for improving
productivity among the resource poor farmers. However,
lack of financial resource is one of the major problems
facing poor households. Formal financial institutions are
inefficient and inaccessible in providing credit facilities to
the poor. Delivering productive credit, low cost, efficient
credit services and recovering a high percentage of loans
granted are the ideal aims in rural finance (Wenner, 2015).
Over the last four decades the international donor
agencies and governments of less developing countries
have spent billions of dollars on projects, rapidly
expanding the volume of agricultural loan and the number
of rural institutions (Adams and Graham, 2011).
*Corresponding Author: Amsalu File, Wollega
University, The Campus’s Finance Head, P.O. Box 38,
Ethiopia. E-mail: amsemijena@gmail.com
Co-Author Email: oliyadsorizen@gmail.com
Research Article
Vol. 5(3), pp. 648-655, December, 2019. © www.premierpublishers.org, ISSN: 2167-0477
Journal of Agricultural Economics and Rural Development

2. Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia
File and Sori 649
The Loans taken from credit institutions vary from country
to country, region to region, sector to sector. But farmers
in the developing countries have been identified as the
most defaulting group of credit beneficiaries. While credit
remains the largest source of farm capital, prospective
borrowers are denied access to credit by financial
institutions as a result of high loan delinquency among
farmers. This phenomenon does not only reduce farmer
productivity but contributes also to dwindling household
income and food security. In order to improve agricultural
credit within financial institutions, it is very important to
examine the loan repayment capacity of farmers (Million,
2014).
Hunte (1996) argued that default problems destroy lending
capacity as the flow of repayment declines, transforming
lenders into welfare agencies and loan default is a disaster
because failing to implement appropriate lending
strategies and credible credit policies often result in
termination of credit institutions. Farmers incapable to
repay loans timely or they face a serious problem to repay
which is a problem for both agricultural credit institutions
and smallholder farmers (Million, 2014 and Amare, 2006).
According to Horro Guduru Wollega Rural Development
Office second Quarter Report (2015/2016), about 24.3
million birr loan which was given from 2010 to 2014, has
not been repaid in general and according to data obtained
from the institutions in Horro and Abay chomen districts in
(2016/2017), about 5.75 million birr loan, which was not
repaid in particular. Similarly, since farmers use loan for
non-productive purposes, they become unable to repay it
and even they borrow it for agricultural product which is
climate dependent, they fail to generate more profit.
Although there are such like problems that affect loan
repayment performance of small holders, there is no detail
study conducted which is related with detrminants of loan
repayment performances of smallholder farmers in the
study area. Therefore, this study was aimed at examining
the loan repayment performance of farm households in
Horro and Abay Choman woredas of Horro Guduru
Wollega administrative zone.
Research Methodology
a. Description of the Study Area
The study was conducted in the oromia region, Horro
Guduru Wollega zone specifically Horro and Abay
Chomen woredas. Shambu is the capital town of Horro
Guduru Wollega zone which is located at 315km away
from the capital city of Ethiopia Addis Ababa in Western
part of the country. Horro and Abay Chomen woredas are
among 9 Woreda’s of Horro Guduru Wollega zone.
According to CSA population projection, Horro and Abay
Chomen woredas have 97296 and 59371 total population,
respectively (CSA, 2015).
The woredas are bounded from the North by Jardaga Jarte
and Hababo Guduru woreda, in South by Jima Geneti and
Guduru woredas, from East by Hababo Guduru woreda,
from the West by Abe Dongoro woreda. Shambu and
Fincha town. Horro and Abay Chomen woredas are
comprised of the three main agro-ecological zones
namely, Woina Dega (moderate), Dega (cool) and Kola.
Woina Dega Zone lies almost at the middle of the Woredas
itself and having the average elevation between 1500-
2400 meters above the sea levels. There are different
crops produced in the study area’s agro-ecological zone
like maize, Teff, bean, wheat, sorghum, pea, barley (Zonal
Agricultural office report, 2015).
The main economic activity of the Woredas is agriculture,
which is based on land resource. However, due to rapid
population growth, per capita land holding is declining and
this result in a very intensive agriculture that degraded the
quality of the soil (Zonal agricultural office report, 2015).
The decline on the quality of the soil adversely affected the
land productivity. Rapid population growth also results in
high exploitation of the scarce water and forest resources.
The excessive deforestation and soil erosion caused by
very intensive agricultural system are some of the densely
populated part of the area has reached the stage where
the land resource can no longer support animal and human
lives (CSA, 2010).
b. Data Sources and Type
In order to under-take this study both primary and
secondary data were used. The primary data were
collected through personal interview and focused group
discussion through semi-structured questionnaires, which
was prepared for the study. The secondary data were
collected from available books, magazines, articles,
relevant research papers, annual reports and internet
sources.
c. Sample Size and Sampling procedure
In this study, two -stage random sampling procedure was
employed for the selection of the respondents. In the first
step of the sampling, In the first stage, forty-two kebeles in
the Woredas are listed and six kebeles (three from each
district) were selected using simple random sampling
technique.
In the second stage, from 2720 the total household in the
six kebeles were stratified in to two groups. These are 582
credit participants and 2138 non-participants of formal
source of financial institutions based on the household lists
which are obtained from the office of the kebeles and
formal financial institutions.
Finally, the list of farmers who have obtained loans from
formal credit sources were recorded from each kebeles
and a total of 120 farm households were selected
randomly using probability proportional to size sampling
technique.

3. Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia
J. Agric. Econ. Rural Devel. 650
The study used a simplified equation: 𝑛 = 𝑁
1+𝑁𝑒2 ,
where n is sample size, N is population size and e is level
of precision provided by Yamane (1967) to determine the
required sample size at 95% confident level.
Table 1: Sampled Households
No. Name of Kebele No. of borrowers of
formal financial
institutions in the
study area (in the
year 2017)
No. of
sampled
borrowers
1. Didibe Kistana 247 51
2. Doyo Bariso 121 25
3. Kombolcha Chanco 58 12
4. Homi 68 14
5. Dembal Gobaya 44 9
6. Digga Arbas 44 9
Total 582 120
Source: own calculation from total sample households.
d. Methods of Data Analysis
Descriptive statistics
Descriptive statistics such as mean, percentages,
frequencies, chi-square test, and standard deviations was
used to summarize data collected from a sample.
Econometric model
Specification of the logit model
This study is planned to analyze which and how much the
hypothesized regressors was related to the loan
repayment performance of rural households. The model
specifies the dependent variable is a dummy variable,
which take a value zero or one depending on whether or
not a borrower defaulted. However, the independent
variables were of both types, that is, continuous or
categorical.
Hosmer and Lemeshew (2013) pointed out that a logistic
distribution (logit) has got advantage over the others in the
analysis of dichotomous outcome variable in that it is
extremely flexible. Hence, the logistic model was selected
for this study. Therefore, the cumulative logistic probability
model is econometrically specified as follows:
𝑃𝑖 = 𝐹(𝑍𝑖) = 𝐹(𝛼 + ∑ 𝛽𝑖
𝑋𝑖) =
1
1+𝑒−𝑍𝑖 (1)
Pi is the probability of individual certain choice given Xi; e
denotes the base of natural logarithms, which is
approximately equal to 2.718; Xi is the ith explanatory
variables; and α and βi are parameters to be estimated.
Hosmer and Lemeshew (2013) pointed out that the logistic
model could be written in terms of the odds and log of
odds, which enables one to understand the interpretation
of the coefficients.
(1 − 𝑃𝑖) =
1
1+𝑒 𝑍𝑖
(2)
Therefore,
(
𝑃 𝑖
1−𝑃 𝑖
) = (
1+𝑒 𝑍𝑖
1+𝑒−𝑍𝑖
) = 𝑒 𝑍𝑖
(3)
(
𝑃 𝑖
1−𝑃 𝑖
) = (
1+𝑒 𝑍𝑖
1+𝑒−𝑍𝑖
) = 𝑒(𝛼+∑ 𝛽𝑖𝑥𝑖
) (4)
Taking the natural logarithm of equation (4)
𝑍𝑖 = 𝐿𝑛 (
𝑃 𝑖
1−𝑃 𝑖
) = 𝛼 + 𝛽1 𝑋1 + 𝛽2 𝑋2+. . . . . . . 𝛽 𝑚 𝑋 𝑚 (5)
If the disturbance term (ui) is taken into account, the logit
model becomes
=
++=
m
i
ii UXiiZ
1
 (6)
RESULTS AND DISCUSSION
Socio-Economic and Institutional Factors
(Continuous Variables)
Out of the total 120 sample interviewed farmer
household’s borrowers 99 (82.5%) were non-defaulters
and the remaining 21 (17.5%) were complete defaulters.
The descriptive Statistics in the table 2 shows that the
average age of households’ respondents was 41.82%
years with the maximum and minimum ages of
respondents observed were 65 and 24 years respectively.
In addition, the mean of non- defaulter was 41.36 years
while that of defaulters was 43.95 years with the mean
difference between the two groups was statistically
significant at 1 percent. This result showed that as mean
age increase default rate decreases.
As we observed in below table 2, the average family size
of the sample households was 7.42 with the maximum
family size 15 and minimum 3. The mean family size of
non-defaulter was 6.97 and with that of defaulters was 9.52
with statistically significant at 1% between means of the
two groups. Defaulters had on average slightly higher
family size than non-defaulters. This implies that the higher
the household size related with the higher the dependency
ratio for non-defaulters.

4. Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia
File and Sori 651
Table 2: Summary of continuous variables for defaulter and non-defaulter for all the respondents
Non-defaulters Defaulters Total Sample
Variable Characteristics (N=99) (N=21) T- Value (N=120)
Mean St.dev Mean St.dev Mean St.dev
AGE (year) 41.36 9.08 43.95 8.92 2.623*** 41.82 2.62
FSHH (family size in no) 6.97 2.78 9.52 2.93 2.495*** 7.42 2.96
EDUCTLVL (education in class) 6.82 3.57 5.57 3.14 2.263** 6.6 3.52
DFMHH (distance in km) 2.58 1.9 2.67 2.0 2.206** 2.59 1.92
SLIHH (land in hk) 2.28 1.34 2.02 1.35 2.651*** 2.23 1.36
TLUHHH (livestock in unit) 11.91 6.28 7.29 5.84 3.217*** 11.1 6.43
ExSocr (social ceremony in birr) 1432.32 652.28 1976.19 707.75 2.534*** 1527.5 691.12
AMBOH (money borrowed in birr) 5146.77 1605 5827.62 2166 1.69* 5265.92 411.62
PKGEPRC (exp.in agri. In year) 4.11 1.33 3.14 1.25 1.833* 3.94 1.36
Excon (extn. contact in no days) 1.67 0.705 1.53 0.86 2.453*** 1.56 0.73
Source: Own Survey, 2017
The descriptive statistics result revealed in table 2 above
show that the average education level of the entire sample
households was about 6.6 with maximum class of 12 and
minimum 0 classes. The average level of classes for
complete defaulters was 6 and for the non-defaulters was
7. The difference between the mean values of the two
groups was statistically significant at 5%. Possible
justification for this could be that more educated people
can properly use the loan for increase of agricultural
production. The better agricultural product will improve the
income of the household which contribute to better loan
repayment. The results also show that, non-defaulters are
more educated compared to defaulters which indicates the
importance of education in repaying loans on time.
The descriptive statistics in the table 2 indicated that the
average money borrowed were birr 5,265.92. The survey
results also revealed that on average Birr 5,146.77 was
borrowed by non-defaulters and defaulters borrowed Birr
5,827.62 with 10% level of significance. The mean
difference between the two groups was significant at 10%
level of significance.
Credit experience in extension package varied among the
sample borrowers from minimum value of two to a
maximum of 6 years’ experience. As observed from the
above table 2 the average Credit experience sample
house hold were 3.94, While non-defaulter participated on
average for higher number of years (4.11) as compared to
the defaulters who participated on average for 3.14 years.
The mean difference between the two groups was
statistically significant. That is, respondents who had
frequent in credit experience and contacts with
development agents settled their debt timely as compared
to those who had no or few contacts.
The descriptive statistics in table 2 above show that the
average mean of extension contact for the total sample
households was 1.56. In case of complete defaulters, it
was 1.53 and for non-defaulters it was 1.67. This result
shows as the mean of extension contact increase the loan
repayment performance increases. The mean difference
between the two groups was significant at 1% level of
significance. Possible justification for this is that as the
number of contact increase the farmers could get sufficient
technical supports that can help him/her to adopt modern
agricultural technologies that can improve productivity.
Hence, if productivity increases, the farmers can earn
better income from their agriculture, which can in turn
contribute to timely loan repayment.
Socio-economic and Institutional Characteristics of
(Discrete Variables)
The sample was composed of both male and female-
headed households. As depicted on table 3, among the
total sample household heads of 120, 89.17 percent were
male household heads and 10.83 percent were female
household heads. 90.91 percent of the non-defaulters and
9.09 percent of the non-defaulters were male and female-
headed households where as 80.95 percent of the
defaulters and 19.05 percent of the defaulters were male
and female-headed households respectively. The
differences in terms of sex among the two groups were not
significance.
Table 3: Sex of the Respondent
Non- default Defaulters Total
No. Percent No. Percent x2
-value No. percent
Sex 1.778
Male 90 90.91 17 80.95 107 89.17
Female 9 9.09 4 19.05 13 10.83
Source: Own Survey, 2017

5. Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia
J. Agric. Econ. Rural Devel. 652
Table 4: Source of Credit
Non- default Defaulters Total
No Percent No Percent x2
-value No Percent
Source of credit 0.123
OSCCO 56 56.57 11 52.38 67 55.83
Wasasa micro 43 43.43 10 47.62 53 44.17
Source: Own Survey, 2017
Table 5: The maximum likelihood estimates of the logit model
Variable Coefficient Std.Err. Z P>z Co.Marginal effect
Sex 0.199 0.054 -0.17 0.866 -0.009
Age -0.039 0.003 0.58 0.559 0.002
FSHH 0.333 0.010 -2.40 0.016** -0.026
EDUCTLVL -0.102 0.007 0.68 0.498 0.005
DFMHH 0.133 0.011 -0.55 0.582 -0.007
SLIHH -0.622 0.022 1.36 0.174 0.030
TLUHHH 0.191 0.005 1.73 0.084* 0.009
ExSocr -0.09 0.046 -1.95 0.054* -0.075
AMBOH 2.212 0.078 -1.37 0.171 -0.107
PKGEPRC 0.949 0.022 2.08 0.038** 0.046
Excon 1.023 0.028 1.75 0.080* 0.049
CRDTSRCE 0.265 0.039 -0.33 0.742 -0.012
Offr 0.000 0.000 3.97 0.00*** 0.000
PBROW 0.929 0.137 -0.29 0.769 -0.040
Logistic regresses
Number of obs = 120
LR χ2 (14) =55.97
Prob > χ2 = 000
Log likelihood = -27.66456
Pseudo R2 = 0.742
Source: Own Survey, 2017
Source of Credit
Farmers in the study area used credit from different
institutions (Oromiya credit and saving Share Company
and Wasasa micro finance). With regard to sources of
credit out of the total 55.83 percent borrowed from OSCCO
and the remaining 44.17 percent borrowed from Wasasa
micro finance. The performance of credit repayment
similar with respect to sources of credit. The proportion of
defaulter households (52.38 percent borrowed from
OSCCO as compared to Wasasa micro finance (47.62
percent). The difference between these percentage figures
was not significant (Table, 4).
Logit Model Results
To determine the explanatory variables which are good
indicators of the loan repayment performances of the
respondents, the logit regression model was estimated
using the Maximum Likelihood Estimation Method. The
results of the analysis are presented in the following Table.
The table 5, shows determinants loan repayment
performances of smallholder farmers and ***, ** and *
represent level of significance at1%, 5% and 10%
respectively
Out of the total fourteen variables which were
hypothesized to determine loan repayment performance of
small holder farmers six of them namely total of livestock
unit, expenditure on social festivals, number of extensions
contact, family size, credit experience in Extension
package and income from off-farm activities were found to
be statistically significant.
Out of the total significant factors of loan repayment in the
study area total livestock unit (TLUHH), expenditure on
social festivals (ExSocr) and number of extensions contact
(Excon), were significant explanatory variables at 10
percent level of significance, while family size (FHHS) and
credit experience in Extension package (PKGEXPRC)
where significant 5 percent. Moreover, the remaining
explanatory variable off-farm activities (Offr) were
significant factor at 1 percent in affecting loan repayment
performance of small holder farmers. The significant
explanatory variables are discussed below.
Family Size (FHHS): The result in table 5 above shows
that family size has a significant negative effect on the loan
repayment performance at 5 percent significant level.
From the above table we can observe that as the family
size increase by 1 person the loan repayment rate
decreases by 0.026 among the total sample households.

6. Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia
File and Sori 653
The result of logit model on the table 5 show that, as the
number of the family size increases by one person the
probability of being defaulter 0.026 percent. The possible
justification could be that, if family size increase food
requirement of the household could increase, so that most
of the agricultural product be used for consumption.
Hence, family size has negative effect on loan repayment
performance in the study area. The result is consistent with
the studies conducted by Sileshi, (2014), Daniel (2014),
inconsistent to Zelalem, G., Hassen,B,(.2012).
Total of Livestock unit (TLUHH): This is one of the
explanatory variables that positively affect the loan
repayment rate at 10 percent significant level. From the
logit result obtained in the table 5 above we can observe
that an increase in amount of livestock holding by one
Tropical Livestock Unit increases the loan repayment rate
by 0.009 units among the entire samples. An increase in
TLU increases the probability of being non-defaulter by
.009. The implication is that, Livestock is one of the
important household assets that can easily be changed to
cash. Whenever, the farmers face crop failure, the
immediate household asset they have to pay the loan is
the livestock. Hence, they are forced to sale it. In addition,
as a proxy to oxen ownership the result suggests that
farmers who have larger number of livestock have
sufficient number of oxen to plough their field timely and
as a result obtain high yield and income to repay loans.
The result is also supported by findings of Sileshi (2014),
Daniel (2014), Amare (2006) and Abebe (2011).
Expenditure on Social Festivals (ExSocr): This is a
continuous variable that shows frequency of social
celebration in the year 2016/2017. The ceremonies include
wedding, circumcision, funeral and engagement
celebrations. It is clear that such occasions cause over
expenditure of the limited incomes of the households on
practices that do not bring any income to the household.
The Logit result shows that celebration of social
ceremonies has negative impact on loan repayment rate
at 10 percent significance level. It revealed that an
increase in social celebration by one unit causes an
increase in default rate by 0.075 percent among the total
sample households. Furthermore, each additional social
festival increases the probability of being defaulter by
0.075 percent. The result of this study is consistent with
the result obtained by Belay (2002) and Shimelles (2009).
Credit Experience in Extension Package
(PKGEXPRC): Variables representing institutional service
have strongly influenced smallholder farmer’s loan
recovery. For instance, number of years of credit
experience in extension services (PKGEXPRC) is the
factor, which was positively related to the dependent
variable (significant at 5% level). Each additional year of
credit extension package experience increases the
probability of being non-defaulter by 4.6 percent. On
average, one-year additional participation in credit
experience extension package increases the rate of loan
repayment among the whole respondents. This implies
that credit experience of farmers in extension programs
have developed their credit utilization and management
skills that helped them to pay loans timely. In addition, as
a result of their participation in credit extension for a
number of years, these farmers are the beneficiary of the
use of improved agricultural technologies that would
increase their income generating capacity and these repay
loans timely. The result of this study is in line with the result
obtained by Assefa B.A. (2013) and Million (2014).
Number of Extension Contact (Excon): The number of
contact days that the household head has with extension
agents is another important institutional factor, which was
positively related to the dependent variable (significant at
10 percent level) for all the respondents. The result of logit
model on table 5 shows that each additional contact
increases the probability of being a non-defaulter by 4.9
percent. This implies that, farmers with more access to
technical assistance on agricultural activities were able to
repay their loan as promised, more than those who had
less or no assistance at all. The reason for this is that,
farmers who have frequent contact with development
agents are better to informed about markets, increase
productivity and production technologies. As a result, they
are motivated to repay their loans on time. Similar result
was also obtained by Chirwa E, (1997) and Belay (2002).
Income from Off-farm Activities (Offr): This variable
was positively affects the loan repayment rate at 1 percent
significance level in the study area. This might be due to
the fact that, off-farm activities were additional sources of
income for smallholders and the cash generated from
these activities could back up the farmers' income to settle
their debt. The logit result in the table 5 show that farmers'
participation in off-farm activity increases the probability of
being non-defaulter by 0.02 percent and on average
increases the rate of loan repayment by 0.002 percent for
all respondents. Possible reason is that borrowers who
had other alternative source of income were found to be
better payers relative to those who didn’t have other
sources of income. This result is contrary to results
obtained by Bekele (2001) and Belay (2005) but is in line
with that of Amare (2006) and Medhin (2015).
CONCLUSION
Ethiopia is one example of a developing country,
characterized by a predominantly subsistence agrarian
economy. The nature of farming in Ethiopia is dominated
by traditional micro holdings of the subsistence type, with
less than two hectares of land being the average holding.
The study was undertaken in Horro and Abay Choman
districts of Horoguduru Wollega Zone Ethiopia. The study
tried to identify determinants of loan repayment
performance in the study area. So, in order to under-take
this study both primary and secondary data were used.
The main data used for this study was collected from a

7. Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia
J. Agric. Econ. Rural Devel. 654
sample of formal credit borrower farmers through semi-
structured questionnaires, which was prepared for the
study. The secondary data were collected from available
books, magazines, articles, relevant research papers,
annual reports and internet sources. A multi-stage random
sampling procedure was employed for the selection of the
respondents. Data collected were analyzed by using
descriptive and econometric model.
From descriptive survey result sample households with
large family size were found to more defaulters than less
family size in the study area because most of the
dependent family members are in education that leads to
the dependency ratio to be high, which requires higher
utilization rate of loan or income for other purpose. In other
case the total livestock units are factors which were
positive significant influence on loan repayment
performance. That means livestock units’ increases the
recovery rate of repayment, therefore small holder farmers
must give attention on livestock production farming system
to overcome the challenges of repayment. Although, credit
experience and number of extension contact has a positive
impact on loan repayment performance of smallholder
farmers as compared to those who had less or no credit
experience and extension contact with development agent
and also with lending financial institutions.
The result of econometric model shows, out of the total
significant factors of loan repayment in the study area total
livestock unit, expenditure on social festivals and number
of extensions contact, were significant explanatory
variables, while family size and credit experience in
Extension package significantly affected loan repayment.
Moreover, the remaining explanatory variable off-farm
activities (Offr) were significant factor at 1 percent in
affecting loan repayment performance of small holder
farmers. The paper was faced the problem of well-
organized secondary data and by primary data sometimes
farmers are not willing to give detail information about
credit access and usage as they might use it for
nonproductive purposes. So, generally this research was
conducted to provide some knowledge bases for both
lenders and borrowers of credit and can help other
researchers as a reference for future credit loan
repayment performance related researches.
RECOMMENDATIONS
Concerned stakeholders, especially religion and
community leaders should teach the community under
their supervision about importance of family planning. It is
important that small holder farmers and the livestock
sector should give more attention for the following area:
Improved feeding system and management of livestock,
Genetic resource improvement, Control or preventions of
animal diseases and pesticides.
The econometric results also indicated that farmers who
engaged in off-farm activities earned more income and
were able to settle their debts in a more time manner, than
those who were not engaged in off farm activities. This
indicates that, rural development strategies and concerned
stakeholders should not only emphasize on increasing
agricultural production but simultaneous attention should
be given to alternative income generation activities that
promote off-farm activities in the rural areas
ACKNOWLEDGEMENTS
We give our great and special thanks to our God who
helped us to finish this research
REFERENCES
Abebe Mijena. 2011. Determinants of credit repayment
and fertilizer use by cooperative members in Ada
district, East Shoa Zone, Oromia Region. Msc. Thesis,
Haramaya University, Ethiopia
Adams, D. and D. Graham, 2011. A critique of traditional
agricultural credit project and polices. Journal of
Development Economics
Amare Birhanu 2006. Determinants Of Formal Source Of
Credit Loan Repayment Performance Of Smallholder
Farmers: The Case Of North Western Ethiopia, North
Gondar,MSc thesis, Haramaya University, Ethiopia.
Assefa B.A. 2013 ‘Factors influencing loan repayment of
rural women in Eastern Ethiopia: the case of Dire Dawa
Area’, A Thesis presented to the school of graduate
studies, AlemayaUniversity, Ethiopia
Bekele Hundie, (2001). Factors Influencing the Loan
Repayment Performance of Smallholders in Ethiopia.
M.Sc.Thesis,Alemaya University, Ethiopia
Belay Abebe, (2002) Factors Influencing Loan Repayment
of Rural Women in Eastern Ethiopia: The Case of Dire
Dawa Area. M.Sc.Thesis, Alemaya University, Ethiopia
Chirwa E. Wadonda,(1997) Econometric Analysis of the
Determinants of Agricultural Credit Repayment in
Malawi, African Review of Money, Finance and
Banking,No. 1-2,1997. Pp. 107-123.
CSA (Central Statistical Agency), 2015. Agriculture
sample survey volume VІІ report on crop and livestock
product utilization. Addis Ababa, Ethiopia
Daniel & Josephine. (2014). Factors Influencing Sacco
Members to Seek Services of Other Financial Service
Providers in Kenya. International Review of
Management and Business Research
Gujarati, D. N. (1995). Basic Econometrics, 3rd Edition.
McGraw-Hill, New York
Horo Guduru Wollega zone Rural Development and
information office second Quarter report, 2015/16).
Horo and Abay Chomen Woreda Agricultural Office Report
2015.
Hosmer D.W. and S. Lemeshew, 2013. Applied logistic
Regression

8. Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia
File and Sori 655
Hunte, C. K. (1996). Controlling loan default and improving
the lending technology in credit institution: AEMFI.
Saving and Development.
Medhin Mekonnen,(2015) Determinants of Loan
Repayment Performance of Rural Women Based
Saving and Credit Cooperatives’ Members: The Case
of Dire Dawa Administration
Million Sileshi, Rose Nyikal and Sabina Wangia 2014
Factors Affecting Loan Repayment Performance of
Smallholder Farmers in East Hararghe, Ethiopia. The
journal of Developing Country Studies, 11(2), 205-213.
Shimelles Tenaw and K.M. Zahidul I., 2009. Rural financial
services and effects of microfinance on agricultural
productivity and on poverty: Discussion Papers No: 37,
Helsinki
Takashi and Tomoya. 2010. The Impacts of Fertilizer
Credit on Crop Production and Incomein Ethiopia.
National Graduate Institute for Policy Studies, M.sc
Thesis Tokyo, Japan
Wenner, M. D. (1015). Group Credit: A Menace to Improve
Information Transfer and Loan Repayment
Performance. Journal of Development Studies.32 (2),
Pp 263- 281
Yamane Taro. 1967. Statistics: An Introductory Analysis,
2nd Ed. New York: Harper and Row
Zelalem, G., Hassen, B., (2013). Determinants of loan
repayment performance of small holders’ farmers.
International Journal of Economic Business and
Finance, 1(11), Pp 436-442
APPENDICES
Appendix 1: Conversion Factors
Appendix table 1 Conversion Factors used to Compute
Tropical Livestock unit (TLU)
Livestock type TLU (Tropical Livestock Unit)
Calf 0.2
Heifer 0.75
Cows/oxen 1
Horse/Mule 1.1
Donkey 0.7
Donkey (Young) 0.35
Sheep/Goat 0.13
Sheep/Goat (Young) 0.06
Livestock type TLU (Tropical Livestock Unit)
Appendix Table 2: Variance inflation factor for
continuous explanatory variable
Variable VIF 1/VIF
Age 2.40 0.416413
SLIHH 1.99 0.503422
TLUHHH 1.82 0.550637
ExSocr 1.71 0.583663
EDUCTLVL 1.70 0.589251
AMBOH 1.66 0.601778
FSHH 1.60 0.625184
PKGEPRC 1.50 0.664542
DFMHH 1.50 0.667884
Excon 1.15 0.871835
Mean of VIF 1.56
Source: own survey, 2017
Accepted 3 December 2019
Citation: File A, Sori O (2019). Determinants of Loan
Repayment Performance of Smallholder Farmers in Horro
and Abay Choman woredas of HoroguduruWollega Zone,
Oromia Region, Ethiopia. Journal of Agricultural
Economics and Rural Development, 5(3): 648-655.
Copyright: © 2019: File and Sori. This is an open-access
article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium,
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