Performance and Determinants of Household’s Participation ...

Global Journal of Emerging Trends in e-Business, Marketing and Consumer Psychology (GJETeMCP) An Online International Research Journal (ISSN: 2311-3170)

2015 Vol: 1 Issue 1

240 www.globalbizresearch.org

Performance and Determinants of Household’s Participation in

Dairy Marketing Cooperatives: The Case of Lemu-Arya and Bekoji

Dairy Marketing Cooperatives, Arsi Zone, Oromiya Region, Ethiopia

Eshetu Tefera,

Department of Agribusiness and Value Chain Management,

College of Agriculture and Environmental Sciences,

Arsi University, Ethiopia.

E-mail: [email protected]

Assefa Gebre Habte Wold,

Department of Agribusiness and Value Chain Management,

College of Agriculture and Environmental Sciences,

Arsi University, Ethiopia.

E-mail: [email protected]

___________________________________________________________________________

Abstract

The objectives of the study were to examine the financial performance of dairy marketing

cooperatives and to identify the major factors that affect households’ participation in these

cooperatives. Lemu-Araya and Bekoji dairy marketing cooperatives were purposively

selected and 40 members and 100 non-members respondents were used for primary data

collection. Ratios were analysed taking the three years’ financial data (2010, 2011 and

2012). The liquidity analysis showed that the cooperatives under investigation performed

above the desirable standard. The three years’ data of how the cooperatives financed showed

that creditors have supplied on average 21.5% of the cooperatives finance. The profitability

ratio of the cooperatives showed that it was weak. In this regard Lemu-Araya dairy marketing

cooperative earned a return on its asset below the interest rate of the financial institution

extend credit (4%). Descriptive statistics were used to compare the socio-economic, the

attitudes towards their cooperatives, services rendered by the cooperatives and other

institutional characteristics of the members and non-members of the cooperatives. Testing

differences between two samples were done using T-test and Chi-square test. To identify the

factors influencing farmers’ participation in dairy marketing cooperatives, Logit regression

model was used. The model results revealed that among thirteen explanatory variables

hypothesized to affect farmers' participation in dairy marketing cooperatives; eleven were

found to be statistically significant. Among these significant variables family size and distance

of the cooperative milk collection centre from the farmers’ house, were found to be

significantly and negatively related to the participation of farmers in dairy marketing

cooperatives. On the contrary, cooperatives price for milk and availability of other marketing

agents were not significant as opposed to the expected.

___________________________________________________________________________

Key words: dairy cooperatives, household’s participation, Agricultural development

mailto:[email protected]

mailto:[email protected]


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1. Introduction

1.1 Background and Justification of the Study

Agriculture is the basis of Ethiopia’s economy and is the most important economic sector in terms

of generation of foreign currency. The current Ethiopian agricultural policy, which advocates self-

sufficiency in food, has led the Ministry of Agriculture to spearhead the intensification of activities in

support of agricultural development. One concern is the overall improvement and development of the

livestock sector.

Livestock is a source of income, which can be used by rural population to purchase basic needs

and agricultural inputs. Livestock comes second to coffee in foreign exchange earnings in Ethiopia.

Its contribution can equally well be expressed at household level by its role in enhancing income, food

security and social status. Besides providing income- earning opportunities for the poor, dairy

development, especially at the smallholder sector level, can improve the nutritional status of Ethiopian

children by making available milk for consumption and increasing household income. The existing

high demand for dairy products in the country is expected to induce rapid growth in the dairy sector.

Factors contributing to this high demand include the rapid population growth which is estimated at 3

percent annually, increased urbanization and expected growth in income Tsehay, (1998).

Even though, the livestock sector in general and the dairy sector in particular have a huge

potential, it is constrained by shortage and fluctuation in quality and quantity of feed, poor and

eroding genetic resource base, poor management practices, diseases, poor market infrastructure, poor

service delivery and policy and institutional arrangements.

To ameliorate the development constraints and realize the benefits from the huge but untapped

livestock resource, efforts have been made in various aspects to develop the livestock sector. These

efforts include the provision of input and services such as animal health, breed improvement, feed

resources development, research, extension services and development, finance and marketing (Azage

et al., (2006).

In view of this, collective action is commonly supposed to assist small holders’ engagement in

markets, contributing to improvements in rural economies. Like in many other developing countries,

this perception is largely shared also amongst policy- makers in Ethiopia, who do not hesitate to

express their overwhelming confidence in cooperative organizations as a driving force for rural

development. The perception that collective action may contribute to boost the Ethiopian rural

economy includes the dairy sector.

Lemu Arya and Bekoji dairy cooperatives were established by dairy producer farmers of seven

different peasant associations. The cooperatives were established by members and registered in 1998

and 2000 respectively by the Oromiya Cooperative Promotion Bureau (OCPB). In the area, among

1313 households who have dairy cows only 170 households are members of the cooperatives.

Though, the two cooperatives are established as a means to increase efficiency of marketing of dairy


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products, households’ participation in the cooperatives is minimal. Moreover, knowledge about the

performance of the cooperatives as well as determinants of household participation in the cooperatives

is limited.

1.2 Research Questions

The study addresses the following research questions:

1. What does the financial performance of the cooperatives under investigation looks like?

2. What are the major factors that affect households’ participation in dairy marketing cooperatives?

1.3 Objectives of the Study

The main objective of the research is to investigate the performance of Lemu-Araya and Bekoji

dairy marketing cooperatives and identifying major determinants of households’ participation in these

cooperatives. The research focused on the following specific objectives:

1. To examine the financial performance of dairy marketing cooperatives in the study area.

2. To identify the major factors that affect households’ participation in dairy marketing cooperatives.

2. Methodology

2.1 Description of the Study Area

Lemu Arya and Bekoji dairy marketing cooperatives are found in Lemu Arya and Bekoji towns,

Lemu and Bilbilo district, Arsi Zone, at about 218 and 230 kms from Addis Ababa and 43 and 55 kms

away from Asella town. The altitude of the district ranges from 1500 to 4460 meters above sea level.

The mean monthly temperature ranges from 6oc to 20oc with an average of 13oc. The agro ecology of

the area is highly highland which accounts 85% of the total and mid highland which accounts 14%

from the total to low land which accounts only 1% (LBDAO, 2011). The rainfall of the area is

bimodal with short rainy season and the long (main) rainy season occurring in spring and summer,

respectively. The maximum rainfall occurs in August. The district receives mean annual rainfall of

1100 mm with the minimum and maximum being 800 and 1400 mm, respectively. The area is well

known by its crop-livestock mixed farming. Several cereal crops, predominantly barley, wheat,

linseed, teff, field pea, faba bean, rapeseed and lentil are produced. The livestock population in the

district includes cattle, goats, sheep, horses, donkeys, mules, poultry and bee colonies. Much of

livestock income is derived from the sale of milk and milk products, cattle, sheep and poultry. In the

district there are 10 registered primary dairy cooperatives which are established pursuant to the

Ethiopian Cooperative Proclamation Number 147/1998 and its amendment Proclamation number

402/2004 with a total members of 435 with only 12.87 % of women (LBDCPO, 2011).

2.2 Sampling Procedure

For this study 140 sample households were used (100 households from non-members and 40

households from members of the two cooperatives).

2.3 Method of Data Collection

Data were collected both from primary and secondary sources:


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3. Method of Data Analysis

3.1 Ratio Analysis

To meet the first objective of the study, different financial ratios were used. Financial ratios can

be designed to manage cooperative’s performance. Financial ratios enable to make comparison of

cooperative’s financial conditions over time or in relation to other cooperatives. Ratios standardize

various elements of financial data for differences in the size of a series of financial data when making

comparisons over time or among cooperatives.

3.1.1 Liquidity Ratio

A cooperative intends to remain viable business entity must have enough cash on hand to pay its

debts as they come due. Liquidity ratios are quick measure of cooperative’s ability to provide

sufficient cash to conduct business over the next few months. According to (Nevue (1985); Bringham

and Houston (1998) and (William et al. 2003) one of the most commonly used liquidity ratio is the

current ratio that is computed by dividing current asset by current liabilities.

Current ratio =Current Asset/Current Liability Eq (1)

3.1.2 Financial Leverage Management Ratio

Whenever, a cooperative finance a portion of asset with any type of financing such as debts, the

cooperative is said to be using financial leverage. According to (Bringham and Houston (1998) and

(William et al. 2003) financial leverage management ratio measures the degree to which a firm is

employing financial leverage. According to these authors, of the several types of financial leverage

ratios, debt ratio is commonly used. It measures the portion of a firm’s total asset that is financed with

creditors’ fund. It is computed by dividing total debt by total asset.

Debt ratio =Total Debt/Total Asset Eq (2)

3.1.3 Profitability Ratio

Profitability is the net effect of a number of policies and decisions. Profitability ratios measure

how effectively a firm’s management was generating profits on sales, total assets, most importantly

stockholders’ investment (Nevue, 1985; Bringham and Houston, 1998; (William et al., 2003). These

authors also suggested that the most commonly used profitability ratio is return on total asset, which is

computed by dividing net income by total asset.

Return on total asset =Net Income/ Total Asset Eq (3)

The core aim of the study was to identify factors affecting the participation of household’s in

dairy marketing cooperatives. The variable representing participation of household’s in dairy

cooperative is a dummy variable that takes a value of 1 for cooperative members or 0 for non

members. In this study to identify those factors which affects the participation of household’s in dairy

cooperatives an econometric model called logit model was used.


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3.1.4 Model Specification

Logistic model was used to identify the determinants of household’s participation in dairy

marketing cooperatives.

Where Pi = is a probability of being member of the cooperative ranges from 0 to 1

Zi = is a function of n explanatory variables (x) which is also expressed as:-

ß0 is an intercept

ß1, ß2 ------ ßn are slopes of the equation in the model

Li = is log of the odds ratio, which is not only linear in Xi but also linear in the parameters.

Xi = is vector of relevant household characteristics

If the disturbance term (Ui) is introduced, the logit model becomes

3.2 Definition of Variables and Hypothesis

a. Dependent Variable: Household’s Participation in Dairy Marketing Cooperatives

Is a dichotomous dependent variable in the model taking value of 1, if a household is member of

the dairy marketing cooperatives and, 0 for non-members of the cooperatives?

b. Independent variables

The major explanatory variables hypothesized to influence positively or negatively on the

households’ participation in dairy marketing cooperatives are listed below:

- Education Level (EDUCATION),

- Family Size (FAMILYSIZE),

- Participation in Off-farm activities (OFARM),

- Total Livestock Holding (TLSH),

- Credit (CREDIT),

- Number of Dairy Cow Holdings (DCOWH),

- Labor Availability (LABOR),

- Perception on Cooperative Organizations (PERC),

- Cooperative Price for Milk (COOPPM),

- Distance of the cooperative milk collection centre from the farmer house (DCMCFH),

- Availability of other Marketing Agents (OMKAG),

- Availability of Other Services (AOS),

- Access to Extension Services (EXSERV)

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4. Results and Discussion

4.1 Ratio Analysis

4.1.1 Liquidity analysis

The satisfactory rate of current ratio that is accepted by most lenders as condition for granting or

continuing commercial loan is greater or equal to two. With this yardstick when the reference years

(2010, 2011 and 2012) are observed, Both Bekoji and Lemu Araya dairy marketing cooperative

performed above the desirable standard with an average Liquidity ratio of 4.62 and 29.28 respectively,

hence lenders are highly interested to provide them loan since their current asset is rising higher than

their current liability. Compared to Bekoji dairy marketing cooperative the figure of Lemu-Araya is

much higher in its current ratio since the cooperative accumulated much fixed assets from donation.

Table 1: Financial ratios of the dairy marketing cooperatives

Cooperatives

CR

2010

CR

2011

CR

2012

DR

2010

DR

2011

DR

2012

ROTA

2010

ROTA

2011

ROTA

2012

Bekoji 2.34 2.50 9.03 0.32 0.19 0.12 0.22 0.08 0.19

Lemu Araya 38.87 20.66 28.30 0.21 0.25 0.19 0.03 0.02 0.06

Source: Own computation from the Audit Report

4.1.2 Financial Leverage Management Analysis

As indicated on Table 1 above, the average debt-asset ratio of Bekoji dairy marketing cooperative

was 21%, while that of Lemu-Araya was 22%. When we observe the three years data of how the

cooperatives were financed, creditors have supplied on average 21.5% of the cooperatives finance.

The smaller the proportion of debt-asset ratio (in most cases <50%) of the total asset financed by the

creditors, the smaller the risk that the firm unable to pay its debt (William et al., 2003). With these

lower debt-asset ratios, the two cooperatives can apply for loan to expand their business of doing

effective dairy marketing activities.

4.1.3 Profitability Analysis

The profitability ratios demonstrate how well the firm is making investment and financing

decisions. According to William et al. (2003) firms need to earn return on their asset that enables them

to pay the interest of the money they borrowed i.e. they need to have return on their asset which is

equal or better than the interest rate of the money they borrowed. One can observe from Table 1, the

profitability ratios of the cooperatives under investigation were too much low. When we look at the

earning of the cooperatives under investigation, the average profitability ratio for Bekoji Dairy

Cooperative was 16%, while that of Lemu-Araya was 4%. Even though there was improvement in

profitability ratio by Bekoji cooperative (16%), both cooperatives had less effective operation as the

profitability ratio show combined effects of liquidity, asset management and financial management.

Especially for Lemu-Araya cooperative, they couldn’t achieve the profitability ratio which is equal or

better than the interest rate (12%) with which they borrowed money from the financial institutions.

The plausible reasons for the difference in profitability among the cooperatives lies on how


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effectively the cooperative management is generating profit on sales, total assets, money they

borrowed and most importantly members’ investment (share capital).

4.2 Descriptive Analysis

Attempts were made to collect information on demographic characteristics of the sample

Households to provide information on some of the key variables in the study area.

4.2.1 Household Characteristics

Out of the sample farmers interviewed, 28.57% of them are members of the two dairy

cooperatives while the rest 71.43% are non members of the dairy cooperatives. The average age of the

sample farmers was about 46.17 years. The corresponding figure for the cooperative members and

non-members was about 46.6 and 46 years respectively (Table 2). There is no statistical significant

difference between cooperative members and non-members in age. The average family size of the

sample households was 6.74 persons, with maximum and minimum family size of 20 persons and 2

persons, respectively.

Table 2: Characteristics of the sample households

Characteristic Members

(N=40)

Non Members

(N=100)

Total Sample

(N=140)

Mean St.Dev Mean St.Dev Mean St.Dev

Age (Year) 46.60 10.98 46 12.12 46.17 11.77

Average Family Size

(number)

7.78 3.34 6.32 2.70 6.74 2.97

Children <15 (number) 3.34 1.51 3.58 1.89 3.44 1.67

15-64 years

(number)

3.41 1.48 3.39 1.74 3.40 1.59

>64 years

(number)

1.09 0.30 1.00 - 1.07 0.27

Active Labor

(Man equiv.)

3.36 1.26 3.22 1.49 3.30 1.36

Source: Computed from the field survey data.

Out of the total sample farmers studied 85.71% were male headed and 14.29% were female

headed (Table 3). The majority of the sample members of the cooperatives in the study area are male

(92.5%); compared to non-members, the participation of women as members of the cooperative is

minimal (Table 3). Most of the sample farmers (90.7%) are married while 3.6% and 5.7% are

divorced and widowed, respectively.

Table 3: Distribution of the sample farmers by sex of the household head

Sex Members

(N=40)

Non Members

(N=100)

Total Sample

(N=140)

n % n % n %

Male 37 92.50 83 83 120 85.71

Female 3 7.5 17 17 20 14.29


Among the sample dairy producer farmers, 14.3% were not received any education, while 13.6%

could only read and write. The rest attended from elementary to higher education level. More


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specifically, 40 %, 31.4% and 0.7% of the sample dairy producer farmers had attended elementary

school, high school and higher education respectively (Table 4).

Table 4: Educational status of the household head

Educational Status

Members

(N=40)

Non-Members

(N=100)

Total Sample

(N=140)

n % n % n %

Illiterate 3 7.5 17 17 20 14.30

Read and Write 5 12.5 14 14 19 13.6

Elementary School (1-6) 20 50 36 36 56 40

High School (7-12) 12 30 32 32 44 31.4

Higher Education (12+2) - - 1 1 1 0.7

Mean 2.03 1.86 2.88

T-Value 0.016***


Table 5 shows that 9.29% of the sample respondents had the farm size of 1.5 hectares and 38.57%

of the respondents had 1.60 to 2.50 hectares, while 7.86% of sample farmers had an average farm size

of greater than 5 hectares. About 72.50% of members of the cooperatives owned farm size greater

than 2.5 hectares and the proportion of non-members who owned farm size greater than 2.5 hectares

were about 44%. These figures imply that farmers with larger farm size were members of the dairy

marketing cooperatives.

Table 5: Farm Size by Farmers’ Groups

Farm Size (ha)

Members (N=40) Non-members (N=100) Total Sample (N=140)

n % n % n %

1.5 4 10 9 9 13 9.29

1.6-2.5 7 17.50 47 47 54 38.57

2.6-3.5 10 25 23 23 33 23.57

3.6-5 12 30 17 17 29 20.71

>5 7 17.50 4 4 11 7.86


4.2.2 Livestock Production

The livestock holding size varied between farmer categories: members and non-members. The

average livestock holding size was 15.765 TLU for members and 8.902 TLU for non-members and

the overall average for the sample farmers was 11.65 TLU (Table 6). The average number of livestock

was higher for members of the cooperatives when compared with non-members. The mean difference

test between members and non-members in terms of livestock holding was statistically significant.

This leads to the conclusion that members of the dairy cooperatives were in a better position with

respect livestock holding than non-members including dairy cows.


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Table 6: Average Livestock Holdings (TLU) by Farmer Groups

Livestock Type Members(N=40) Non-Members (N=100) Overall Mean

(N=140)

Cattle

Local breed

Cross breed

5.88

3.98

2.27

0.87

3.910

2.12

Sheep 1.54 1.56 1.51

Goat 0.03 0.08 0.054

Horses 1.77 1.64 1.651

Donkeys 1.74 1.61 1.572

Mules 0.025 0.022 0.021

Chicken 0.8 0.85 0.812

Total 15.765 8.902 11.65

4.2.3 Participation in off- farm Activities

Off-farm and non-farm activities are important activities through which rural households get

additional income. The income obtained from such activities helps farmers to purchase farm inputs

and outputs. Of the total sample members of the cooperatives 25% of them are involved on off-farm

activities while only 5% of non-members are involved in off-farm activities (Table 7). The mean

monthly off-farm income in 2012 was 295.22 birr with a minimum and maximum income of 61 and

2160 birr respectively. The categorical study also shows significance difference between members

and non members of the cooperative at less than 5% (x2 =5.492).

Table 7: Respondents’ participation in off -farm Activities

Response Category of the respondent

Members (n=40)

Non-members (n=100)

no % no %

Involve in off-farm

activities

Yes

N No

10

30

25

75

5

95

5

95

2 -value 5.492

**Significant at 5% significant level


4.2.4 Institutional Support

Agricultural Extension Services

The proportion of members (87.4%) who have got extension advisory service on the use and

benefit of the dairy marketing cooperatives especially from their own cooperatives is higher than non-

members (12.6%) who got the services only from Development agents. The 2 P analysis also

showed significant association between having extension service on the use and benefits of dairy

cooperatives and participation on cooperative enterprises (2 = 83.44) at less than 1% probability

level.


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Credit Services

Credit is important to resource poor farmers who cannot finance agricultural inputs as improved

dairy cows from their own savings. Results of sample households’ survey held with members and

non-members of the cooperative revealed that, no credit was given to the farmers for the purchase of

improved dairy cows and other related dairy inputs for the last three years. Unavailability of credit

directed to the purchase of dairy cows would, therefore, be one of the major bottlenecks for the

production of milk and low level of participation in the dairy marketing cooperatives.

Market Services

Most of the sampled dairy producer farmers have to walk a long distance from home to the

nearest cooperative milk collection centres to sale their milk. The average distance from home to the

milk collection centres for members of the cooperatives was found to be 3.5 km while that of non-

members was 7.78 km.

About 25.83% of the sample respondents had to travel more than 10 km to reach the nearest

cooperative milk collection centres and most of these farmers are found to be non-members of the

dairy cooperatives (Table 8). The independent sample t-test result indicates that the mean difference

between members and non-members of the dairy cooperatives in terms of distance of the cooperatives

milk collection centres from sample farmer's residence was significant at less than 1% probability

level. This leads to the conclusion that members of the cooperatives had better access to sale their

milk to the cooperatives than non-members.

Table 8: Distance from the cooperative milk collection centers by Farmers Group

Distance

(Km)

Members (N=40) Non-Members (N=100) Total Sample (N=140)

n % n % n %

<1km 5 12.5 3 3 8 5.71

1-5km 29 72.5 15 15 44 31.43

6-10km 6 15 47 47 53 37.86

11-15km - - 25 25 25 17.86

>15km - - 10 10 10 7.14

Total 40 100 100 100 140 100


During the survey time, it was also tried to assess, the availability of other marketing agents who

are collected milk other than the dairy cooperatives. The result showed that there are no private,

organized or licensed milk collectors/processors that collected milk from the farmers’ village; except

individual consumers and some hotels/cafeterias that collected milk from some producers in the

nearby areas of Bekoji town.


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4.2.5 Farmers’ Perceptions on Cooperative Organizations

Farmers’ perception on cooperative organizations can influences their decisions to be member of

the cooperatives. Respondents were asked to give their opinion about their perception with regarding

the current and future performance of the cooperatives. Based on that, most of the sample farmers (92

%) feel that the cooperative currently didn’t solve the major common problems of dairy producer

farmers (Table 9). These farmers were asked to rank their major common problems and all the 92%

raised the supply of major dairy services as AI, Feed, fodder seed, credit, veterinary and adequate

marketing services as their major common problems to be solved by the cooperatives.

Table 9: Distribution of the sample farmers by perception on the

current performance of the Cooperatives

Current

Performance

Members

(N=40)

Non-members

(N=100)

Total Sample

(N=140)

n % n % n %

Not Good 36 90 93 93 129 92

Good 4 10 7 7 11 8


4.3 Econometric Results

The purpose of this section is to identify the most important hypothesized independent variables

that influence the participation of households in dairy marketing cooperatives. Prior to running the

Logit model, the presence or absence of multicollinearity was checked. There are two measures that

are often suggested to test the existence of mulitcollinearity. These are: Variance Inflation Factor

(VIF) for association among the continuous explanatory variables and contingency coefficients for

dummy variables. A statistical package known as SPSS-version 16 was employed to compute these

values. The larger the value VIF, the more “troublesome” or collinear the variable Xi is. As a general

rule, if the VIF of a variable exceeds 10, there is multicollinearity. According to Gujarati, 2003, to

avoid serious problems of multicollinearity, it is quite essential to omit the variable with value 10 and

more from the logit analysis. Thus, the variable inflation factor (VIF) was employed to test the degree

of multicollinearity among the continuous variables.

Table 10: Variable inflation factor for the continuous explanatory variables

Variables Tolerance (R2i )

Variance Inflation

Factors (VIF)

Educational status 0.788 1.268

Family Size 0.742 1.347

Total Livestock Holding 0.698 1.432

Number of Dairy Cows Holding 0.715 1.398

Labor Availability 0.624 1.603

Distance of the Cooperatives

0.869 1.151

As shown above the values of the VIF for seven continuous variables were found to be small (i.e

VIF values less than 10) indicating that the data have no serious problem of multicollinearity. Hence,


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all the seven continuous explanatory variables were retained and entered into the binary logistics

analysis.

In a similar vein, contingency coefficients were computed from survey data to check the existence

of high degree of association problem among discrete independent variables. The decision rule for

contingency coefficients states that when its value approaches 1, there is a problem of association

between the discrete variables, i.e., the values of contingency coefficients ranges between 0 and 1,

with zero indicating no association between the variables and the values close to 1, indicating a high

degree of association.

Table 11: Contingency coefficients for Dummy Explanatory variables

OFARM CREDIT PERC COOPPM OMKAG EXSERV

AOS

OFARM 1 0.160 0.084 0.117 0.129 0.152 0.116

CREDIT 1 0.091 0.266 0.058 0.304 0.256

PERC 1 0.285 0.103 0.326 0.321

COOPPM 1 0.027 0.250 0.029

OMKAG 1 0.175 0.165

EXSERV 1 0.308

AOS

1

The results of the correlation coefficient reveal the absence of multicollinearity or high degree of

association problem among independent variables. All the screened variables, therefore, were decided

to be included in the model analyses. In this case, a dairy producer farmer who is member of the dairy

marketing cooperative is considered to be “participant”. The dependent variable is either members or

non-members of the dairy marketing cooperatives and logit model was employed to estimate the

effects of the hypothesized independent variables on the participation of dairy marketing cooperatives.

In doing so a total of thirteen independent variables were included in the model. These are

education level, family size, total livestock holding, number of dairy cow holding, economically

active household members, distance of the cooperative milk collection centres, participation in off-

farm activities , credit, perception on cooperative organizations, cooperative price for milk,

availability of other marketing agents, access to extension services and availability of other services.

These variables were selected in consultation of experts in the area, based on literatures, practical

situations, observation and experience of the researchers and the relevance of the variables.

Furthermore, they were selected by testing significant differences of the mean using t-test and 2 tests.

The various goodness of fit measure was checked and validate that the model fits the data. The

likelihood ratio test statistics exceeds the Chi-square critical value at less than 1% probability level.

This implies that the hypothesis, which says all coefficients except the intercept is zero, was rejected.


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The value of Pearson Chi-square test shows the overall goodness of fit of the model at less than 1%

probability level.

Another measure of goodness of fit of the model is based on a scheme that classifies the predicted

value of events as one if the estimated probability of an event is equal or greater than 0.5 and 0

otherwise.

Table 12: The Maximum Likelihood Estimates of the Binomial Logit Model.

HH Participation

(Dependent

Variable)

Estimated

Coefficient (B)

Odds Ratio

(S.E)

Wald

Statistics Sig. Level Exp (B)

EDUCATION 1.795 0.846 4.499 0.034** 6.017

FAMILYSIZE -0.509 0.304 2.804 0.094* 0.264

TLSH 0.148 0.070 4.400 0.036** 1.159

DCOWH 2.850 0.563 12.713 0.001*** 0.205

LABOR 0.335 0.156 4.596 0.032** 1.398

DCMCFH -0.435 0.150 8.358 0.004*** 0.647

OFARM 1.635 0.858 3.630 0.057* 5.129

CREDIT 2.036 1.112 3.351 0.067* 7.661

PERC 1.588 0.741 4.592 0.032** 4.896

COOPPM 0.038 0.227 0.029 0.866 1.039

OMKAG -0.356 0.676 0.277 0.599 0.700

EXSERV 1.792 0.843 4.466 0.032** 5.981

AOS 2.950 0.773 12.913 0.000*** 0.004

Constant -5.570 1.550 12.913 0.000 0.004

Notes: Exp (B) shows the predicted changes in odds for a unit increase in the predictor *Omnibus Tests of

model coefficients: Chi-square=159.824***, Sign 0.000;

-2log likelihood=79.321* Percentage of correct prediction=90.6; and *, **and ***Significant at 10%, 5%, and

1% Significant level

4.4. Interpretation of Empirical Results

As indicated in the previous sections, a number of independent explanatory factors (demographic,

social, economic, physical, psychological, technical and institutional) were postulated to influence the

participation of households in dairy marketing cooperatives. Out of thirteen explanatory variables

hypothesized to affect farmers' participation in dairy marketing cooperatives, eleven were found to be

statistically significant with expected signs. The results show that education level (EDUCATION),

total livestock holdings (TLSH), number of dairy cow holding (DCOWH), labor availability

(LABOR), participation in off-farm activities (OFARM), credit (CREDIT), perception on cooperative

organizations (PERC), availability of other services (AOS) and access to extension services

(EXSERV) were positively and significantly related to dairy producer farmers participation in dairy

marketing cooperatives. However, family size (FAMILYSIZE) and distance of the cooperative milk


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collection center from the farmers house (DCMCFH) had negative and significant influence on the

participation of the farmers in dairy marketing cooperatives.

On the contrary, cooperatives price for milk and availability of other marketing agents were not

significant as opposed to the expected. The effects of the model estimates were interpreted in relation

to the significant explanatory variables in the model as follows.

a. Education Level (EDUCATION): Formal education is statistically significant at less than 5%

probability level with expected sign. The model result confirms that educated farmers are more likely

to participate in dairy marketing cooperatives than those who are not educated. This result is

consistent with most participation studies (see Daniel, 2006). This result implies that education

enhances farmer’s awareness towards working in cooperatives. Educated farmers have more access to

information and they become aware to understand the use and benefits of cooperatives, and this

awareness enhances their participation in market oriented activities. The odds-ratio of 0.846 for

education implies that other things being kept constant, the probability of participating in dairy

marketing cooperatives increases by a factor of 6.017 as a farmer education level increase by one

grade.

b. Family Size (FAMILYSIZE): influenced negatively the probability of participating in dairy

marketing cooperatives (significant at 10%). As the family size increases by one adult equivalent

(AE), the probability of marketing of milk decreases by the factor of 0.264. This result shows that

households with larger family size consume more of what is produced in the house and small amount

is left to be marketed through the cooperatives.

c. Total Livestock Holding (TLSH): As of the hypothesis, this variable was found significant at less

than 5% probability level and affects the participation in dairy marketing cooperatives positively;

meaning as farmers own large livestock units, the probability to participate in dairy marketing

cooperatives increases. This is explained by the fact that herd size is a proxy for wealth status of

farmers. Those farmers with large herd size have better chance to earn more money to invest on

purchasing dairy inputs. This result is consistent with the findings of Mesfin, 2005. The odds ratio

0.070 for this variable indicates that the probability of participating in dairy marketing cooperatives

increases by a factor of 1.159 as livestock ownership increased by one tropical livestock unit.

d. Number of Dairy Cow Holdings (DCOWH): As of the hypothesis, this variable was found

significant at less than 1% probability level and affects the participation in dairy marketing

cooperatives positively; meaning as farmers own productive dairy cows, the probability to participate

in dairy marketing cooperatives increases. This is explained by the fact that having more number of

productive/cross breed dairy cows helps the farmers to supply adequate amount of milk to the market.

This result is consistent with the findings of Haji, 2005. The odds ratio 0.563 for this variable

indicates that the probability of participating in dairy marketing cooperatives increases by a factor of

0.205 as productive dairy cow ownership increased by one.


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e. Labor Availability (LABOR): As of the hypothesis, this variable was found significant at the

probability level of 5 %; indicated that households with high labor availability in man equivalent are

more likely to participate in dairy marketing cooperatives. Further observation of the result shows that

keeping all other things constant, the probability of participating in dairy marketing cooperatives

increases by a factor of 1.398 as labor availability increases by a single man equivalent unit.

f. Distance of the cooperative milk collection center from the farmers house (DCMCFH): As

expected, the relationship between market distance and participation in dairy marketing cooperatives

was negative and significant at 1% probability level. The implication is that the longer the distance

between farmers’ residence and the cooperatives milk collection centers, the lower will be the

probability of participation as members of the dairy cooperatives. Market accessibility through the

cooperative is very important for dairy farmers as it facilitates easy sale of milk they produce in

relatively large quantities and assists them to procure the necessary inputs at fair price.

Proximity to market also reduces marketing costs. The odds ratio of 0.150 for market distance indicate

that keeping the influence of all other factors constant, being member of the dairy cooperative will

decrease by a factor 0.647 as the distance increases by a single kilometer.

g. Participation in off-farm activities (OFARM): In line with our expectation, off farm income took

a positive sign with significant influence on participation in dairy marketing cooperatives at less than

10% level of probability. The result of the logit model signified that having extra income from off

farm activity provide financial freedom to farmers in turn positively influence farmers to invest on the

purchase of dairy inputs. According to this finding, involvement in off-farm activities increases the

probability of being members of the dairy cooperatives by a factor of 5.129. The finding on this

variable is in-line with Daniel (2006) on farmers’ participation in multi-purpose cooperatives.

h. Credit (CREDIT): Credit helps to improve the ability of farmers at critical times to purchase

dairy related inputs. The model result confirms that credit is statistically significant at 10% probability

level with the expected sign. The influence of credit on the participation of dairy marketing

cooperatives is very low when compared to most of the variables in the model. This is because as

discussed in section 4.2.7.2 the credit was not directed to the dairy development. However, the credit

used for other agricultural inputs improves their productivity and increase the farm income and wealth

status of the farmers. Those farmers with better wealth status participated in dairy marketing

cooperatives than the others. The odds-ratio of 1.112 indicates that, if other factors are kept constant,

the probability of participating in dairy marketing cooperatives increased by a factor of 7.661 for a

farmer who gets access to credit than those farmers who do not have access to credit. This result

indicates that those farmers who had access to credit were more likely to participate in dairy

marketing cooperatives than those who had no access to credit.

i. Perception on Cooperative Organizations (PERC): As of the hypothesis, this variable was found

significant at the probability level of 5 %; indicated that households with good perception about the


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current and future performances of the cooperatives are more likely to participate in dairy marketing

cooperatives. Further observation of the result shows that keeping all other things constant, the

probability of participating in dairy marketing cooperatives increases by a factor of 4.896 for those

farmers who perceived well about the current and future performances of the cooperatives.

j. Availability of other Services (AOS): As expected, this variable was positively and significantly

related to the participation of dairy producer farmers in dairy marketing cooperatives at less than 1%

probability level. This indicates that access to AI, fodder seed; concentrate feed and veterinary

services were the most important determinants of participating in dairy marketing cooperatives in the

area. The very strong relationship between AI, fodder seed, concentrate feed and veterinary services

and participation in dairy marketing cooperatives is that those farmers who had access to these

services through the cooperatives were more likely to be members of the cooperatives. The odds-ratio

of 0.773 indicates that, if other factors are kept constant, the probability of participation in dairy

marketing cooperatives increases by a factor of 0.004 for farmers who had access to AI, fodder seed,

concentrate feed and veterinary services than those farmers who did not have access to the services.

k. Access to Extension services (EXSERV): The logit model estimates indicated that this variable

was positively and significantly related to farmers' participation in dairy marketing cooperatives at 5%

probability level. Farmers who have regular access of extension advisory services either from the

cooperative or DAs were more likely to participate in dairy marketing cooperatives than those who

had no access to extension advice. This is because extension contact gives farmers access to

information. The odds ratio 0.843 is a witness for the probability that farmers who have access to

extension services would increase the probability of participating in dairy marketing cooperatives by

the factor of 5.981.

5. Summary, Conclusion and Recommendations

5.1 Summary

Dairy cooperatives operate in the agricultural sector of the national economy and they are

supposed to increase efficiency of the marketing system and promote agricultural development in the

rural area. They are also organized to render economic benefits such as economies of scale, market

power, risk pooling, coordination of demand and supply and guaranteed access to input and output

markets to the smallholders.

In this study, the financial performance of dairy cooperatives and identifying factors influencing

the participation of households in dairy marketing cooperatives were analyzed in Lemu-Bilbilo

districts of Arsi Zone. Primary data were collected from 140 smallholder dairy producer farmers from

both members and non-members of the dairy marketing cooperatives using personal interview

schedule. This was supplemented by information from focal group discussion with dairy producers,

board members of the cooperatives and key informants. Secondary data was collected from various

zonal and district offices to supplement the data obtained from the survey.


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The financial performance of the cooperatives is examined using the financial ratios. Current

ratio, debt ratio and return on total asset ratio indicators were used to examine the financial

performance of the cooperatives. Statistical software called "SPSS version 16” was employed to

analyze the collected data. Descriptive statistical tools such as percentage, frequency, tabulation, Chi-

square–test (for dummy /discrete variables) and t-test (for continuous variables) were also used to

analyze the collected data. Logit model was instrumented to estimate the effects of hypothesized

independent variables on dependent variables.

Ratios were analyzed taking the three years financial data (2010, 2011 and 2012). The liquidity

analysis showed that the cooperatives under investigation were performed above the desirable

standard. When we observe the three years data of how the cooperatives were financed, creditors have

supplied on average 21.5% of the cooperatives finance. With these lower debt-asset ratios, the two

cooperatives can apply for loan to expand their activities of doing effective dairy marketing activities.

The profitability ratio of the cooperatives under investigation showed that the profitability of the

cooperatives was weak. With this regard especially Lemu-Araya dairy marketing cooperatives earn

return on its asset below the interest rate the financial institution extend credit.

To identify the factors influencing farmers’ participation in dairy marketing cooperatives in the

study areas, Logit regression model was used. The model results revealed that among thirteen

explanatory variables hypothesized to affect farmers' participation in dairy marketing cooperatives;

eleven were found to be statistically significant. More specifically, these variables include: education

level (EDUCATION), total livestock holdings (TLSH), number of dairy cow holding (DCOWH),

labor availability (LABOR), participation in off-farm activities (OFARM), credit (CREDIT),

perception on cooperative organizations (PERC), availability of other services (AOS) and access to

extension services (EXSERV), family size (FAMILYSIZE) and distance of the cooperative milk

collection center from the farmers house (DCMCFH). And among these significant variables family

size and distance of the cooperative milk collection center from the farmers’ house, were found to be

significantly and negatively related to the participation of dairy producer farmers in dairy marketing

cooperatives. On the contrary, cooperatives price for milk (COOPPM) and availability of other

marketing agents (OMKAG) were not significant as opposed to the expected.

5.2 Conclusion and Recommendations

On the basis of this study, the following points are suggested for consideration in improving the

performances of the dairy cooperatives in the study area. These may be broadly viewed as improving

the financial condition of the cooperatives and identifying the possible factors that influence farmers’

participation in dairy marketing cooperatives.

1. The profitability ratio measures how effectively the cooperatives’ management is generating profits

on sales, total assets, money they borrowed and members’ investment (share capital). With regarding

to the profitability ratio both cooperatives in the study area perform below the desirable rate i.e. even


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the profitability ratio of Lemu-Araya dairy cooperative couldn’t reach bank interest rate with which

they borrowed money from financial institution. Increasing the qualified manpower in the field of

cooperative, upgrading the management capacity of the cooperatives’ management body (board of

directors and other employed workers) through education and trainings, improving the financial

capacity of the cooperatives through the sale of more shares and the active participation of the farmers

in the cooperative affairs are among the possible solutions.

2. Dairy producer farmers usage of the cooperative as marketing agent for their products increase if

the cooperative provide them with different dairy related services such as AI service, Fodder seed

supply, Concentrate feed supply, Veterinary services and other benefits. Hence, provision of different

dairy related services and benefits by the dairy marketing cooperatives will motivate the participation

of dairy producer farmers to actively involve as members of the dairy marketing cooperatives.

3. The empirical results of this study figures out that access to credit and number of productive dairy

cow holding are positively and significantly related to the participation of households in dairy

marketing cooperatives. One way of extending productive/crossbred dairy cows among farm

households is through distribution of crossbred heifers. As reported by the majority of sample

households, crossbred heifers or cows are expensive in the study area much beyond the financial

capacity of many farm households. On the other hand, the existing agricultural credit system focuses

on short-term credit, never targeted the dairy sector. The provision of medium and long-term credit

especially from formal sources directed to the promotion of dairy development would, therefore, is a

vital step to improve the sector.

4. The distance between farmers’ residence and the cooperatives milk collection centers has a

negative influence on the participation of households in dairy marketing cooperatives. The

establishment of additional fixed and satellite milk collection centers and improvement of marketing

infrastructure should receive due attention by the cooperatives and other concerned governmental and

non-governmental bodies to further enhance the participation of many dairy producer farmers as

members of the dairy marketing cooperatives.

5. The study revealed that extension contact significantly affects the participation of dairy producer

farmers to be members of the dairy marketing cooperatives. Hence, the extension service should be

further strengthened to change the current livestock production and marketing system of dairy

producer farmers through cooperative structures.

6. The study also revealed that negative perception of the dairy producer farmers on the performances

of the cooperatives can affect their participation. With this regard, the Board of directors of the

cooperatives together with the district cooperative officials should provide training and arrange visit

program to show the success history and good performances of selected dairy marketing cooperatives

in other areas of the country.


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