Assefa Gebre Habte Wold, Department of Agribusiness and Value Chain Management, Arsi University, Ethiopia.

Transcription

1 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. eshetugirma2011@gmail.com Assefa Gebre Habte Wold, Department of Agribusiness and Value Chain Management, College of Agriculture and Environmental Sciences, Arsi University, Ethiopia. assefagebre2003@yahoo.com 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 240

2 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 selfsufficiency 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 241

3 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 6 o c to 20 o c with an average of 13 o c. 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 % 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: 242

4 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 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) 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) 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. 243

5 3.1.4 Model Specification Logistic model was used to identify the determinants of household s participation in dairy marketing cooperatives. P i L i ln Z i β o β X 1 P 1 1 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:- Z ß 0 is an intercept i β o β 1 X 1 β 2 X 2... β β X... β n X 2 2 n ß 1, ß ß n are slopes of the equation in the model Li = is log of the odds ratio, which is not only linear in X i but also linear in the parameters. Xi = is vector of relevant household characteristics If the disturbance term (U i) is introduced, the logit model becomes Z i β o β X β X βnxn n X n U i 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) 244

6 4. Results and Discussion 4.1 Ratio Analysis 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 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 Lemu Araya Source: Own computation from the Audit Report 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 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 245

7 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 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 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) Average Family Size (number) Children <15 (number) years (number) >64 years (number) Active Labor (Man equiv.) 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 Female Source: Computed from the field survey data. 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 246

8 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). Educational Status Table 4: Educational status of the household head Members Non-Members (N=40) (N=100) Total Sample (N=140) n % n % n % Illiterate Read and Write Elementary School (1-6) High School (7-12) Higher Education (12+2) Mean T-Value 0.016*** Source: Computed from the field survey data. 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. Farm Size (ha) Table 5: Farm Size by Farmers Groups Members (N=40) Non-members (N=100) Total Sample (N=140) n % n % n % > Source: Computed from the field survey data Livestock Production The livestock holding size varied between farmer categories: members and non-members. The average livestock holding size was TLU for members and TLU for non-members and the overall average for the sample farmers was 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. 247

9 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 Sheep Goat Horses Donkeys Mules Chicken Total 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 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% (x 2 =5.492). Involve in off-farm activities Table 7: Respondents participation in off -farm Activities Response Category of the respondent Members (n=40) Non-members (n=100) Yes No no % no % value **Significant at 5% significant level Source: Computed from the field survey data 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 nonmembers (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 2 cooperatives and participation on cooperative enterprises ( = 83.44) at less than 1% probability level. 248

10 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 nonmembers 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. Distance (Km) Table 8: Distance from the cooperative milk collection centers by Farmers Group Members (N=40) Non-Members (N=100) Total Sample (N=140) n % n % n % <1km km km km >15km Total Source: Computed from the field survey data. 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. 249

11 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. Current Performance Table 9: Distribution of the sample farmers by perception on the current performance of the Cooperatives Members Non-members Total Sample (N=40) (N=100) (N=140) n % n % n % Not Good Good Source: Computed from the field survey data. 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 X i 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 (R 2 i ) Variance Inflation Factors (VIF) Educational status Family Size Total Livestock Holding Number of Dairy Cows Holding Labor Availability Distance of the Cooperatives 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, 250

12 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 CREDIT PERC COOPPM OMKAG EXSERV 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 offfarm 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. 251

13 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 (S.E) Ratio Wald Statistics Sig. Level Exp (B) EDUCATION ** FAMILYSIZE * TLSH ** DCOWH *** LABOR ** DCMCFH *** OFARM * CREDIT * PERC ** COOPPM OMKAG EXSERV ** AOS *** Constant Notes: Exp (B) shows the predicted changes in odds for a unit increase in the predictor *Omnibus Tests of model coefficients: Chi-square= ***, 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 252

14 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 for education implies that other things being kept constant, the probability of participating in dairy marketing cooperatives increases by a factor of 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 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, The odds ratio for this variable indicates that the probability of participating in dairy marketing cooperatives increases by a factor of 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, The odds ratio for this variable indicates that the probability of participating in dairy marketing cooperatives increases by a factor of as productive dairy cow ownership increased by one. 253

15 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 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 for market distance indicate that keeping the influence of all other factors constant, being member of the dairy cooperative will decrease by a factor 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 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 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 indicates that, if other factors are kept constant, the probability of participating in dairy marketing cooperatives increased by a factor of 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 254

16 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 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 indicates that, if other factors are kept constant, the probability of participation in dairy marketing cooperatives increases by a factor of 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 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 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. 255

17 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, Chisquare 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 256

18 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. 257

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