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Determinants of Smallholder Farmers’ Participation in Improved Sheep Production: The Case of Doyogena District, Kembata Tembaro Zone, Southern Ethiopia

Determinants of Smallholder Farmers’ Participation in Improved Sheep Production: The Case of... Hindawi Advances in Agriculture Volume 2021, Article ID 5514315, 17 pages https://doi.org/10.1155/2021/5514315 Research Article Determinants of Smallholder Farmers’ Participation in Improved Sheep Production: The Case of Doyogena District, Kembata Tembaro Zone, Southern Ethiopia 1 2 2 3 Teketel Mathewos, Daniel Temesgen, Dereje Hamza, and Haben Fesseha College of Agriculture, Wolaita Sodo University, P.O. Box 138, Wolaita Sodo, Ethiopia College of Agriculture, Jimma University, Jimma, Ethiopia School of Veterinary Medicine, Wolaita Sodo University, P.O. Box 138, Wolaita Sodo, Ethiopia Correspondence should be addressed to Haben Fesseha; haben.senbetu@wsu.edu.et Received 9 January 2021; Accepted 30 August 2021; Published 14 September 2021 Academic Editor: Yunchao Tang Copyright © 2021 Teketel Mathewos et al. *is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Improved sheep production practices are becoming relevant, but smallholder farmers’ involvement in improved sheep production was below expectations and detailed studies were restricted on the determinants of the participation of smallholder farmers in improved sheep production. *is research was conducted to examine the determinants of the involvement of smallholder farmers in enhanced sheep production in the study area. Methods. Multi-stages sampling techniques were used for this study. Firstly, improved sheep production had a big effect on the incomes of households of participants and nonparticipants. Finally, three kebeles were chosen by basic random sampling, and the third was picked for systematic sampling by 144 survey respondents. Quantitative and qualitative data were gathered from primary and secondary sources. Data collection techniques were undertaken by surveys, focus group discussions, and key informants. Quantitative data were used to evaluate descriptive statistics, such as average, frequency, standard deviation, scope, and inferential t-test and chi-square statistics, and a logit model. Qualitative data obtained from focus group discussion and key informants were analyzed by narrative and used for survey data triangulation. Results. Out of the 144 samples, 51 were participants and 93 were nonparticipants. Participation determinants found in this research field were household labor, age, communication frequency with developers, membership in cooperatives, land ownership, participation in credit, and off-farm income. Improved sheep production had a major effect on the incomes of households of participants and nonparticipants. Multiple determinants typically affect the role of smallholder farmers in raising sheep production. Conclusion. Future initiatives under a successful policy should aim at accelerating agricultural and rural growth by efficiently leveraging enhanced sheep capacity in particular in the study region and Ethiopia in general. land, the position of large ruminants is decreased [3]. Sheep 1. Introduction need small investments, need shorter production cycles, have higher rates of growth and environmental adaptability Ethiopia, which accounts for 50% of the total agricultural share of the gross domestic product (GDP), 85% of exports, 80% of in contrast to large ruminants, and thus are exceptional for the total jobs as well as domestic raw material for the small- smallholder farms [4]. Sheep are mainly used for cash scale industry of this region, has been thought to have the production where precipitation is unpredictable, and where largest livestock population in Africa [1]. It is used to manage poor people are interested in owning and sustaining their food, crop input, soil fertility, industry raw materials, cash sheep for immediate generation of income [5]. Sheep usage income, saving, fuel, social functions, and employment [2]. as a source of income for farm inputs reduces risks related to *e pressure of populations decreases farm size and at crop production in mixed farming and creates employment, the same time, because of its lower competition for arable saving, and money [6]. 2 Advances in Agriculture A few studies have been performed in the field of re- According to Lakew et al. [5], sheep in Ethiopia play a multifunctional role in the cash, meat, skin, manure, and search on sheep production, but not explicitly, to explain the determinants of the involvement of smallholder farmers in long hairy fleece with regard to smallholder farmers. In- creased production of ovine animals is thus necessary to improved sheep production in the region. For example, the fulfill the demands of the rising human population. On the study conducted by Getachew et al. [19] and Gizaw et al. [20] other hand, improving the productivity of sheep and export was not focused on identifying the determinants of farmers’ earnings will improve the income of the household [7]. *e participation in improved sheep production. While agri- study done by Gowane et al. [8] confirmed that sheep are cultural development workers and researchers are making relatively resilient to higher temperatures than cattle and big and ongoing efforts, involvement in improved pro- duction of ovine animals has not reached the desired level. rising temperatures will lead to a growing increase of sheep due to climate change. *e research will also help to gain accurate knowledge that can be useful to promote and enhance the production and *e production of sheep provides protection in times of crop failure, which is projected to be “near-cash” equity [9]. productivity of improved sheep, as well as to recognize and interfere with negative participatory determinants of the Similarly, Ahmed [10] insists that sheep enterprise in Ethiopia is the source of cash income and provides social smallholder farmers. *us, this study was designed to an- security in the bad crop years. Moreover, in Ethiopia, sheep alyze the determinants of smallholder farmers’ participation provide almost 15% of fresh skins and hide production and in improved sheep production in Doyogena district, Kem- 72% of semiprocessed skins and hide export trade [11]. *e bata Tembaro Zone, Southern Ethiopia. annual mutton production in the country is estimated at 78,000 Metric Ton (MT) [6]. Sheep are important in terms of 2. Methodology food security and poverty, particularly for the poor and the women who are often vulnerable in society and who con- 2.1. Study Area. Doyogena is one of the districts in the tribute to the generation of cash income [12]. Kembata Tembaro zone, Southern Nations, Nationalities, According to Central Statistics Authority (CSA) [13], a and People’s Region which is a high land area. *e area is survey in Ethiopia indicated that the potential of sheep is about 258 km from Addis Ababa in the southern direction very high; 30.70 million sheep are estimated to be found in and 171 km southwest of Hawassa. *e district comprises the country, out of which about 5,087,007(17%) are found in mostly high land agro-ecological zone and its altitude ranges SNNPR, and from 5,087,007 around 109, 732 (2.2%) are from 1900 to 2800 m.a.s.l. Annual rainfall is 1200–1800 mm found in the Kembata Tembaro zone, and from 109,732 ° and the mean temperature varies from 10 to 18 C [21]. *e about 32920 (30%) are found in the Doyogena district. *is total population of the district is 116,048, comprising 56,863 zone is noted for mixed farming activity that has a high males and 59,185 females. *e district has a total of 32,920 cultivation and livestock potential. *e biodiversity of the sheep, out of which 10,534 were improved. *e majority of region is mainly high ground. Highlands in Ethiopia are the population is dependent on mixed agriculture, with considered to be potential for sheep production [14]. income of 60% of the households (HH) from crop pro- Improved livestock production became significant, duction and 40% of households from livestock production. but the involvement of small-scale farmers in the pro- Among livestock production, 19% is from sheep and 21% duction of sheep was not as anticipated and the study did from other species (Figure 1) [22]. not show why participation was reduced [15]. Ethiopian national sheep production program aims to increase sheep per capita rather than maintain an incredibly large 2.2. Study Design. A cross-sectional design was used in this number of unproductive ovine animals that lead to land study. Quantitative data were collected and appropriate loss, food shortages, and consumption of large resources analytic techniques were employed to meet the objectives of [16]. Despite the well-adapted and large sheep population, the study. Effective measurement methods to fulfill the aims current productivity and involvement in improved sheep of this study were employed and quantitative data were production for smallholders is poor and for different obtained. Using an interview schedule, quantitative data reasons, the country cannot achieve the anticipated were gathered to assess the significant data, and general- benefit from ovine production [17]. izations were drawn from the result. Several small-scale farmers have been focusing on de- veloping sheep production programs in southern Ethiopia. However, smallholder farmers are still in doubt and for 2.3. Sampling Method and Sample Size Determination. various factors decided to control increased livestock pro- For this study, the multi-stage sampling technique was used. duction [1]. Studies in Mareko district, Gurage zone show Firstly, the Doyogena district was selected purposively, due that farmers’ decision to participate in improved sheep to the existence of improved sheep production experiences production was determined by the combined effects of and its accessibility. Secondly, three kebeles, namely, Serara several factors such as lack of access to improved breeds, age, Bokata, Ancha Sedicho, and Hawora Arara were selected by socioeconomic (membership of cooperative, land size, farm simple random sampling technique due to their similar income, labor) and institutional factors like access to ani- production potential. *irdly, 144 sample households were mals’ health services, credit utilization distance from kebele selected by systematic random sampling methods from all center, and extension contact [18]. three kebeles. Advances in Agriculture 3 Map of study Area 0 2.25 4.5 9 13.5 18 Kilometers Ethio map kambate_tembaro South_map Doyogana Figure 1: Map of the study area. *e sample size determination was computed by using (1) Individual Interview. Totally, 144 sample respondents were Yamane [23] sampling formula at 95% confidence interval, selected and considered for an interview. *ree enumerators who have a college diploma and experience in agricultural with the level of precision of 8% activities were recruited and trained to implement both n � , qualitative and quantitative data collection using an interview 1 + N(e) schedule. Before data collection, the interview schedule was (1) translated into the local language (Kembatigna) and pretested n � � 144, on nine farmers who were not included in the final sample 1 + 1900(.08) households. Hence, appropriate modifications and correc- tions were made to the questionnaire, and data were collected where n is the sample size, N is the population size (total under the continuous supervision of the researcher. household heads size), and e is the level of precision. In general, the sample size, total number of sheep (2) Key Informants’ Interview. For this study, in addition to producers’ household heads from the kebeles, and the individual interviews, data from key informants (KI) were proportion of sample size are summarized in Table 1. also collected from development agents (coordinators), managers of respective kebeles, district’s livestock and fishery, 2.4. Methods of Data Collection. Both quantitative and agriculture, and natural resources, cooperative development, trade, and the industry as well as administration offices ex- qualitative types of data were gathered through different data collection methods from primary and secondary sources. pertise focal persons (total of eleven key informants). *ese participants were selected purposively to obtain relevant data. *e data from both primary and secondary sources were collected and used to generate valuable information. Pri- mary data sources were 144 sample respondents, key in- (3) Focus Group Discussion. Focus group discussions were formants, and focus group discussions. Secondary data held with three groups, one in each kebele (including twelve sources were Areka research center (branch at Doyogena), members in each group). *e composition of groups was farmers such as development group leaders, model, and district’s livestock and fishery, cooperative development, agriculture and natural resources, finance and economy and nonmodel farmers as well as respective kebeles’ leaders who were selected purposively for seeking appropriate infor- trade and industry offices, and relevant published and un- published reports. mation. It was also aimed to increase the reliability and 4 Advances in Agriculture trustworthiness of the information. *e group members methods employed in analysis using logistic regression were familiarized with the discussion points and encouraged follow the same general principles used in linear regression [25]. to forward their opinion without any reservation. *e idea of dominance was tried to control as much as possible to avoid *e probability model, which expresses the dichotomous sampling bias. Both key informants and focus group dis- dependent variable (Y ) as a linear function of the explan- cussions were mainly used to generate qualitative data that atory variables (X ), is called the linear probability model supported the findings of the survey based on predetermined (LPM). Due to econometric shortcomings like nonnormality checklists. of the disturbances (Ui), heteroscedastic variances of the disturbances, nonfulfillment of 0 <E (Yi/Xi) < 1, and lower value of R2, linear probability model (LPM), as a measure of 2.5. Data Management and Data Analysis. After compiling goodness of fit, failed to test the statistical significance of and screening, the interview data of 144 respondents were estimated coefficients. In the case of logit and probit, the analyzed. Descriptive and econometric analyses were used to estimated probabilities lay between logical limit 0 and 1, and analyze the data collected from respondents. Both de- they are the most frequently used models when the de- scriptive and econometric methods were employed to an- pendent variable happens to be dichotomous, as well as the alyze the relationship between dependent and explanatory choice between these two models revolves around practical variables by using the Statistical Package for Social Sciences concerns such as the availability and flexibility of computer (SPSS, version 20). Inferential statistics such as Chi-square program, personal preference, experience, and other facil- (X ) and t-tests were used. Also, qualitative analysis was used ities. In fact, it represents a close approximation to the to compare the socioeconomic, demographic, and situation cumulative normal distribution [24]. of respondents as well as triangulating survey data. Crowder [26] pointed out that a logistic distribution has got advantages over others in the analyses of the dichoto- 2.5.1. Econometric Analysis. *e purpose of this study was to mous outcome variables. *ere are two primary reasons for analyze the determinants of participation of smallholder choosing the logistic distribution. *ese are: (a) from a farmers in improved sheep production. *e dependent mathematical point of view, it is an extremely flexible and variable, in this case, is a dichotomous variable, which takes a easily used function and (b) it tends itself to a logically value of 1 if the household participates, and otherwise 0. meaningful interpretation also states that the logit model is Demographic and socioeconomic characteristics, as well as simpler in estimation than the probit model. After reviewing institutional factors that were assumed to be correlated with the strengths, drawbacks, and assumptions of different the participation of improved sheep production, were en- models, the binary logistic regression model was employed tered along with these classifications. to address the core objective of the study, i.e., analyzing Models, which include a “yes’’ or “no’’ type-dependent determinants of participation of smallholder farmers in variable, are called dichotomous or dummy variable re- improved sheep production gression models. Such models approximate the mathe- matical relationship between explanatory variables and the P(x) � E Y � , 􏼠 􏼡 dependent variable that is always assigned qualitative re- (2) sponse variables [24]. *e four most commonly used ap- 1 1 proaches to estimate dummy-dependent variable regression P(x) � E􏼒y � 􏼓 � . x 1 + e − (B0 + BiXi) models are (a) the linear probability model (LPM), (b) the logit, (c) the probit, and (d) the Tobit model. *ey are For ease of exposition, we write equation (2) as applicable to a wide variety of fields [24]. *e major point that distinguishes these functions from P(x) � , (3) − zi the linear regression model is that the outcome variable in 1 + e these functions is binary or dichotomous. Besides, the dif- where P(x) � is a probability of being participant and ranges ference between logistic and linear regression is reflected from 0 to 1; Z � is a function of n-explanatory variables (x) both in the choice of a parametric model and in the as- which is also expressed as sumptions. Once this difference is accounted for, the Z � B + B X + B X + · · · + B X , i 0 1 1 2 2 n n PART � β + β AG + β SEX + β EDU + β LOBOUR + β LAND SZ + β FI (4) 0 1 2 3 4 5 6 + β OFI + β CU + β MSC + β DFKC + β FDC + β AHS + β DFNMC, 7 8 9 10 11 12 13 where, X � Age of the household head, X � Sex of house- HH, X � Farm income, X Off-farm income, X � Credit 1 2 6 7 � 8 hold head, X Education level of the household head, utilization, X � Membership of cooperatives, X � Distance 3 � 9 10 X � Household labor size in ME, X Land size owned by from kebele center, X � Frequency of contact with 4 5 � 11 Advances in Agriculture 5 Table 1: Sample size determination from selected kebeles checked and the association among discrete variables was also verified by checking covariance. *e existence of Population (HH Sample size multicollinearity seriously affects the parameter estimates. In heads) Kebeles short, the coefficients of the interaction of the variables Male Female Total Male Female Total indicate whether one of the two associated variables should Serera-Bokata 342 242 584 32 13 45 be eliminated from the model analysis [27]. Ancha-Sedicho 396 288 684 36 16 52 Accordingly, Variance Inflation Factors (VIF) technique Hawora-Arara 353 279 632 32 15 47 was employed to distinguish the problem of multi- Total 1091 809 1900 100 44 144 collinearity for continuous explanatory variables [24]. Each Source: computed from own survey data, 2018. selected continuous variable was regressed on the other continuous explanatory variables and an evaluation was development agents, X � Access to animals’ health serves, 12 made on the coefficient of determination (R ). If an ap- X � Distance from near market center, B � intercept, B , proximately linear relationship exists among the explanatory 13 0 1 B . . . B � Slopes of the equation in the model. 2 n variables, then this results in a “large” value for R in at least *e probability that a given household participant is one of the test regressions. A popular measure of multi- expressed by equation (3) while the probability of not collinearity is VIF defined as participating is VIF􏼐X 􏼑 � . j (9) 1 2 1 − R 1 − P(x) � . (5) j zi 1 + e A rise in the value of R , which is an increase in the *erefore, we can write degree of collinearity, does indeed lead to an increase in the zi variances and standard errors. A VIF value greater than or P(x) 1 + e zi (6) � � e . equal to 10 is used as a signal for the strong collinearity. In − zi 1 − P(x) 1 + e the same way, it is necessary to test whether there is or not Now P(x)/(1 P(x)) is simply the odds ratio in favor of the interaction between discrete variables that can lead to the participation. It is the ratio of the probability that a problem of association among each other using coefficients household participated to the probability that did not of contingency (CC). If the value of CC is greater than or participate. Finally, taking the natural log of equation (6) we equal to 0.75 it is used as a signal for the existence of strong obtain association among the discrete variables [24]. 􏽳������ ln[P(x)] � � Z , i i (10) CC � , 1 − P(x) (7) n + X Z � B + B X + B X + · · · + B X . i 0 1 1 2 2 n n where CC is the coefficient of contingence, x is the chi- square test, and n is the total sample size. If the disturbance term, (U ) is introduced, the logit model becomes 2.6. Definition of Variables Z � B + B X + B X + · · · + B X + U , (8) i 0 1 1 2 2 n n i 2.6.1. Dependent Variable of the Model. *e dependent where L � log of the odds ratio, which is not only linear in X i i variable for this study was smallholder farmers’ participation but also linear in the parameters, X � Vector of relevant in improved sheep production. Participation “(Participating explanatory variables. in Improved Sheep Production, “PISP”)” which was the Changing an independent variable, in this case, was dependent variable for the binary logit analysis as a di- expected to alter the probability that a given individual chotomous variable and represented by 1 for participant and becomes a participant, and this helped to predict the 0 for nonparticipant household heads. probability of participating. 2.6.2. Independent Variables of the Model. After the logical 2.5.2. Estimation Procedure. Given that the model selected procedure was clearly delineated, the potential explanatory for analysis was the binary logit model, the dependent variables were identified that determined the participation of variable was assigned by a value of 1 or 0, representing smallholder farmers in improved sheep production. *e participant or nonparticipant, respectively. Estimating the independent variables of the study are variables that are values of B and B ’s, a set of data were fitted into equation expected to influence farmers’ participation in improved 0 i (8). Since the method of Ordinary Least Square (OLS) does sheep production and can be of many types. Here, an ex- not make any assumption about the probabilistic nature of planation of the thirteen potential hypothesized explanatory the disturbance term (U ), the parameters of the model are variables was presented. estimated using the maximum likelihood (ML) method [24]. Consequently, a review of literature, past research Before employing the logit model, the existence of findings, and expert opinions were used to identify the multicollinearity among the continuous variables was potential determinants of the participation of farmers in 6 Advances in Agriculture large area of land can be a means of accumulating wealth and improved sheep production in the study area. *us, taking participation as the dependent variable, the following ex- a source of animal feed. Households who have better landholding have a better capacity to participate in improved planatory variables were identified and their influence on the participation of smallholder farmers examined. sheep production. According to Mueller et al. [32], a large size of land implies more possibility of having a large flock (1) Age of the Rural Farm Household Head (AG). It is a size and availability of feeds. In this study, it was hypoth- continuous variable, defined as the farm household head’s esized that the size of landholding by the household has a age and measured as the number of years from the dates of positive and significant influence on the participation of birth to the day of the survey interview. When farmers’ age improved sheep production. increases, their maturity also increases and they will be eager to apply new technology. According to Assan [28], those (6) Farm Income (FI). It is a continuous variable measured in the amount of money the household earns annually from the household heads of a matured age due to a good farm ex- perience have a much better association with more pro- sale of agricultural products (both crop and livestock) in ET B. *e increase in the productivity of agriculture enables to get ductivity. Hence, in this study, it was hypothesized that when the household head’s age increases, it affects participation in huge money and enhancing improved breed and other pro- improved sheep production positively and significantly. duction input purchasing power. According to Rasch et al. [33], rural households with better farm income have a better pos- (2) Sex of the Household Head (SEX). this is a dummy sibility of participating in improved sheep production. In this variable that assumes a value of “1” if the head of the study, it was hypothesized that better farm income influences household is male, and “0” otherwise. Sex is a biological smallholder farmers’ participation in improved sheep production difference between being male or female respondents. With positively and significantly. this background, male-headed households have a better (7) Off-Farm Income (OFI). It is a continuous variable probability of mobility, participate in different meetings, and have more exposure to information about better production measured by the amount of money the household earns annually from the sale of the family business, remittance, a participation. According to Urgessa [29], women-headed households are less likely to control economic resources and day’s labor in others̓ farm, or nonfarm activities, and any the nature of their economic activity. *en, it was hy- other income sources in ET Br. When households get ad- pothesized that male-headed households have more chance equate off-farm income, they can have the capacity to run to participate in improved sheep production and positively improved sheep production. According to Babatunde et al. and significantly influence it. [34], off-farm income is a determining explanatory variable that can positively affect the probability of participation in (3) Education Level of Household Head (EDU). It is a con- improved sheep production. In this study, it was hypothe- tinuous variable and is measured by years of schooling. sized that getting off-farm income influences smallholder farmers’ participation positively and significantly. When the education levels of farmers increase, they have a better ability to identify the problem of their farm income as (8) Credit Participation (CP). It is a dummy variable that well as calculate its costs and benefits. According to Mathebula [30], a high level of education was expected to takes the value of 1 if the household utilized credit, and 0 facilitate more exposure to the external environment and otherwise. Credit is an important instrument to solve the accumulation of knowledge on farming practices. *erefore, liquidity problem that farm households are facing. House- in this study, it was hypothesized that advanced school levels holds who participated in credit could purchase agricultural affect participation in improved sheep production positively inputs including livestock. According to Kebebe [35], credit and significantly. participation can ensure that households purchase improved breed and other production inputs. In this study, the credit (4) Household (HH) Labor in Me. It is a continuous variable part was hypothesized that credit participation influences smallholder farmers’ participation in improved sheep pro- and is measured by several members under the control of one HH head in man equivalent (ME) ratio. Sheep man- duction positively and significantly. agement involves time-consuming labor and availability of labor can ease the management of sheep in a household. (9) Membership of Cooperatives (MSC). *is was coded as a According to Haile et al. [31], in farming households, for dummy variable, which took the value of 1 if the farmer was improved sheep rearing and routine management practices, a member of cooperatives, and 0 otherwise. Cooperative the availability of productive labor is mandatory. Hence, in societies are one of the important institutions in rural and this study, availability of labor was hypothesized, which agricultural development. Cooperatives serve as an important affects the participation of smallholder farmers in improved source of rural credit and producers who are a member of cooperatives are likely to get inputs and production infor- sheep production positively and significantly. mation and thus could participate and supply sheep to the market than nonmembers. According to Hennessy et al. [36], (5) Size of Land in Hectares (LAND SZ). Land is a continuous variable measured in the number of hectares by the membership in farmers’ cooperatives significantly raised the household. Land is one of the key productive resources for probability of technology adoption. *erefore, in this study, smallholder farmers to generate their livelihood. Owning a being a member of a cooperative was hypothesized that Advances in Agriculture 7 membership of cooperatives has a positive and significant households’ heads were considered in this study. As relationship with participation in improved sheep production. shown in Table 2, out of the total sample respondents, 100 (69.4%) and 44 (30.6%) were male- and female-headed, (10) Distance from the Kebele Center (DFKC). It is a con- respectively. *e overall mean age of the sampled tinuous variable measured by several kilometers from the household head was 53 years with a standard deviation of center of the kebele to their home. Distance from the kebele 5.67; this implies that the majority of them were in the center is the number of kilometers farmers walk to reach the working age group, and the age difference among farmers kebele center. Farmers living closer to kebele centers are was 5.67 years. likely to get updated information and adopt improved sheep *e other demographic characteristic was education breeds than those who are living far. According to Deresse level. *e average education grade level was grade 4 with a et al. [37], when farmers come from far, the probability of standard deviation of 1.29, which indicates that the major improving agricultural technology adoption decreases. It group of farmers had similar lower grades. *e average land was hypothesized that distance affects participation in im- holding was 1.47 hectares with a standard deviation of 0.39. proved sheep production negatively and significantly. *is is less than the national average, which is 1.37 hectares, but it can vary from place to place [1]. (11) Frequency of Extension Contact (FDC). It is a continuous As described in Table 3, the major crop in the study variable measured by the number frequency of contacts per area was wheat, which covers 42.5% of the total cultivable year that the respondent makes with development agents. *e land. Potato (14%) is the second most produced crop in frequency of extension contact is one type of sharing knowl- cultivable lands of the highland areas. *e livestock edge and experience with development agents. According to production was also another farm practice that used the Elias et al. [38], farmers who make contact with development mixed approach and the most dominant livestock types agents frequently have better access to information on tech- were cattle and sheep. nology and have a better possibility to translate their intentions As described in Table 4, the study area is known for land into action. In this study, it was hypothesized that maximum shortage, as most of (67.7%) the land is cultivable land, frequency of extension contact with development agents has a 19.75% is forest land, and only 6.14% is grazing land that can positive and significant influence on smallholder farmers’ be used for livestock production. participation in improved sheep production. (12) Access to Animals’ Health Service (AHSC). It is a dummy 3.2. Descriptive Statistics Analysis for Discrete Variables variable; it takes the value 1 if the respondent gets access, and “0” otherwise. Access to health services is a very critical 3.2.1. Household Heads’ Sex. *e result in Table 5 shows that 144 respondents were included in this study: 100 (69.4%) variable that can affect the motivation of farmers to par- ticipate in improved sheep production. When sheep health and 44 (30.6%) from male-headed and female-headed households, respectively. Out of 51 participants, 42 (82.4%) care access is improved, productivity will increase as well as farmers will be encouraged to participate in improved sheep were from male-headed households and 9 (17.6%) were from female-headed HHs. But in the nonparticipant group, out of production. According to Robinson et al. [39], unless a 93 nonparticipants, 58 (62.4%) were from male-headed HHs farmer having access to health services, he/she cannot decide to participate in improved sheep production. So, in this and 35 (37.6%) were from female-headed HHs. *e par- ticipation of females in improved sheep production is still study, it was hypothesized that improvement in access to animals’ health services affects the decision to participate in very least. *e study result showed that the biological differences improved sheep production positively and significantly. between males and females influenced participation sig- ∗∗∗ nificantly at a 1% significance level (X � 6.201 ; p ≤ 0.01). (13) Distance to the Nearest Market Center (DTNMC). It is a continuous variable that can be measured by the number of Based on evidence obtained from focus group discussions, male dominance on resources was very high. Due to this, kilometers it takes from their home to reach the nearest the market. *e closer they are to the nearest market, the more contacts with development agents to share new information and knowledge enabled males better than females. It is in likely they have updated market information and are enabled to participate in improved and intensive farming activities. line with the study of Musgrave [42], and production of Meanwhile, for the farmers who live far away from market sheep needs high production resources and power due to places, the likelihood of adopting the technology will de- this most of the time males have been suitable to undertake crease [40,41]. So, in this study, it was hypothesized that heavy management activities than females. distance from the nearest market to their home is expected to influence participation in improved sheep production 3.2.2. Access to Animal Health Services. Accessibility of negatively and significantly. animal health services is one of the crucial factors for the production and productivity of sheep. *e result of this 3. Results and Discussion study, as shown in Table 5, indicated that out of 51 par- ticipants, 36 (70.6%) participants in improved sheep pro- 3.1. Demographic and Socioeconomic Characteristics of Sheep duction had access to animal health services. Out of 51 Producer Sample Households. In total, 144 sample 8 Advances in Agriculture Table 2: Demographic and socioeconomic characteristics of sample sheep producers. Variable Frequency Percentage Male 100 69.4 Sex Female 44 30.6 Mean Standard deviation Age in year (AGE cont) 53 5.6695 Educational status (EDU) 4 1.2975 Land size 0.935 0.356 Source: computed from own survey data, 2018. Table 3: *e Livestock and crop types in the study area. Types of crops grown in the study districts Land coverage (Ha) Land coverage in percent Types of livestock Number of livestock Wheat 5200 42.5 Total sheep 32920 Barley 1221 10 Improved sheep 10534 Teff 1450 12 Goat 4501 Pulse crops Pack animals 10213 Faba bean 1036 8 Bee in hive 3101 Haricot bean 450 4 Field pea 295 2 Potato 1750 14 Enset 714 6 Oil crops 20 0.5 Others 112.60 1 Total 12,248.60 Source: computed from own survey data, 2018. Table 4: *e land use of farmers in the study area. Types of land use Coverage in hectare (Ha) Percentage (%) Cultivated land 12,248.6 67.7 Grazing land 1110 6.14 Forest land 3573 19.75 Degraded land 435 2.4 Swampy land 358.33 1.98 Potentially cultivable land 202.4 1.13 Others 164.01 0.9 Total 18,091.34 Source: computed from own survey data, 2018. Table 5: Descriptive statistics’ results of discrete explanatory variables. Participants Nonparticipants Variables P value Chi-square Freq. % Freq. % ∗∗∗ Male 42 82.4 58 62.4 0.009 6.201 Sex Female 9 17.6 35 37.6 Total 51 100 93 100 ∗∗∗ Yes 36 70.6 24 25.8 0.0001 27.176 Access to animals health services (AHS) No 15 29.4 69 74.2 Total 51 100 93 100 ∗∗∗ Yes 38 74.5 25 26.9 0.0001 30.361 Membership of cooperatives (MSC) No 13 25.5 68 73.1 Total 51 100 95 100 ∗∗∗ Yes 40 78.4 30 32.3 0.0001 28.110 Credit utilization (CU) No 11 21.6 63 67.7 Total 51 100 93 100 ∗∗∗ ∗∗ (Source: computed from own survey data, 2018); Freq. � frequency; % � percentage; p � probability; , (1% and 5%) significance, respectively. Advances in Agriculture 9 participants, 15 (29.4%) did not have access to animal health and there was a significant relationship that exists at a 1% services. significance level (X � 28.110, p ≤ 0.01). According to focus In the current study, out of total 93 nonparticipants, 24 group discussions and key informants’ responses, the credit (25.8%) of them got access to animal health services and 69 utilization difference between participants and nonpartici- (74.2%) did not get access to animal health services. *ere is pants was due to the presence of different governmental and an association between access to animal health services and nongovernmental organizations that facilitated the saving participation in improved sheep production. Farmers during and credit associations, mainly among participants. focus group discussions and at key informants’ level con- However, the majority of respondents criticized the firmed that there was a shortage of health posts and animal OMO microfinance service for its high interest rate, inability health officers. It was observed that only one animal health to procure the loan despite their request, and lack of other expert was assigned for three kebeles during the survey. For lending institutions as an alternative. *is result is in line this reason, farmers complained about inappropriate and with Silong [45] who states that participation in credit can inadequate animal health services. Also, focus group par- influence the adoption of new agricultural technology ticipants explained that the experts focused on larger ru- positively and significantly. minants than small ruminants like sheep. Unfortunately, those participating in improved sheep 3.3. Descriptive Statistics Analysis Results for Continuous production were having animal health services from different Variables sources such as nongovernmental organizations, research centers (Areka research center branch), and community-based 3.3.1. Age of Household Head. Table 6 has shown that the breed selection cooperatives. *e accessibility of health services average ages of participants and nonparticipants were 44.53 influenced smallholder farmers’ participation significantly. *is and 61.27 years, respectively. *is has indicated that the result is similar to Getachew et al. [19], and the health ac- younger age group was the one who participated more than the cessibility influenced the participation in improved sheep older ones. Youngsters were better capable of managing assets production significantly at a 1% significance level (χ �27.176, and are more productive than older-aged households. *e age p ≤ 0.01). Moreover, it is in line with Pulina et al. [43] who have variances were 6.775 and 4.564 between participants and specified that animal health services encourage farmers to nonparticipants, respectively. *is indicates that the house- participate in improved sheep production significantly. holds who participated in improved sheep production had younger people than the nonparticipants. *e age variation among nonparticipants was very low compared to participants. 3.2.3. Membership of Cooperatives. As indicated in Table 5, In another way, elders were at a similar age level. *e age the existence and operation of institutions such as cooperatives influenced participation significantly at a 1% significance level for marketing, saving, and credit can enhance the livelihood of (t � 17.642, p ≤ 0.01). *is result is in line with the study of smallholder farmers and be alternative sources of information, Bhattarai et al. [46] and stated that the level of innovativeness in knowledge, and credit to members. Out of 51 participants, 38 agricultural technology adoption is lower among older farmers. (74.5%) have membership of cooperatives and 13 (25.5%) are not members of any cooperatives. Out of 93 participants, nonparticipants were 68 (73.1%) and they were not members of 3.3.2. Educational Status of Household Heads. As indicated any cooperative organization, whereas only 25 (26.9%) were in Table 6, the educational level of respondents’ mean grades members of cooperatives. *is study result has shown that of participants and nonparticipants were 5 and 2, respec- membership of cooperatives plays a significant role at 1% tively. Household heads with a high level of education significance level (X � 30.361, p ≤ 0.01), which influenced the participated in improved sheep production, more than those participation of smallholder farmers in improved sheep pro- who had less educational level. Education has a relationship duction, and similar to the study of Yin et al. [44] membership with participation at 5% significance level (t � 17.404, of cooperatives is one way of transferring knowledge and p < 0.05). *e variations of education level were 1.568 and getting credit for production, which influences the participa- 1.027 among participants and nonparticipants, respectively. tion of farmers positively and significantly. *is means variation among nonparticipants was very less (nonparticipants achieved similarly lower grades). Hence, the data analysis showed that a high level of 3.2.4. Credit Participation. In situations where the financial education had a significant association with the trends of capacity of an individual can limit the expansion of pro- participating in improved sheep production. *is finding is duction activities, participating in credit from any source in line with Tegegne [47] who found that educated house- influenced new technology practices. As described in holds tend to have higher productivity, use of information, Table 5, in the study area, out of 51 participants on improved and are able to adopt new production techniques than the sheep producers, 40 (78.4%) participated in credit from any less educated households. of the organizations. But, out of 51 participants, 11(21.6%) did not utilize credit from any organizations. Out of 95 nonparticipants, 30 (32.3%) did utilize credit but did not participate in improved sheep production. *is 3.3.3. Distance from Kebele Center. Distance from the kebele result has shown that credit participation had a strong as- center to their home plays a vital role in rural communities sociation with participation in improved sheep production in case of knowledge exchange at the kebele (farmers training 10 Advances in Agriculture Table 6: Descriptive statistics’ results of continuous explanatory variables. Participants Nonparticipants Variable P value T value Mean Stand. Dev Mean Stand. Dev. ∗∗∗ Age in year (AGE cont) 44.53 6.775 61.27 4.564 0.002 17.642 ∗∗ Educational status (EDU) 5.000 1.568 2.000 1.027 0.013 17.407 ∗∗ Distance from Kebele center (DFKC) 1.180 0.478 2.91 0.351 0.014 24.927 ∗∗∗ Land size (LAND cont) 1.120 0.448 0.750 0.264 0.001 6.179 ∗∗∗ Farm income (FI) 9627.45 2999.739 6105.38 2079.839 0.004 8.272 ∗∗∗ Off-farm income (NFI) 2196.08 626.725 1066.77 466.679 0.001 12.261 ∗∗∗ Household labor in ME (HHLME) 4.980 1.295 2.36 0.602 0.0001 16.641 Distance from near market center to their home (DFNMC) 4.450 1.487 6.000 1.707 0.328 5.444NS ∗∗∗ Frequency of development agents contact (FDC) 37.290 2.773 14.77 6.478 0.0001 23.639 ∗∗∗ ∗∗ Source: computed from own survey data, 2018; , , NS shows significance level at 1%, 5%, not significant, respectively. center). *e result of the study, as indicated in Table 6, shows could be used for the purchase of farm inputs. Improved sheep production often requires intensive input which has that the average distance between participants and non- participants were 1.18 and 2.91 kilometers, with a standard great implications on the cost of production. Due to this, deviation of 0.478 and 0.351, respectively. *is indicated that improved sheep production needs to have the required most of the participants lived around the kebele’s center amount of income from their agricultural activities to run compared to nonparticipants, and the variation of distances the improved sheep production activities. According to among them has shown that those farmers who lived far Table 6, the average annual farm income of the participating away were ignored. and nonparticipating sample households was 9627.45 and Generally, the farmers who live far from the kebele’s 6105.38 birr as well as a standard deviation of 2999.739 and center faced the problem of participation in improved sheep 2079.839, respectively. In the current research, the farm income variation between production. In this study, distance from the kebele center influenced the participation significantly at a 5% significance participants and nonparticipants group indicated that non- participants groups has less farm income as compared to the level; t � 24.927; p < 0.05). Brown et al. [9] reported a similar relationship between distance from the kebele and partici- participant groups. In this study, agricultural income influ- pation in improved livestock technology in the Dejen enced participation positively at a 1% significance level district. (t � 8.272, p ≤ 0.01). *erefore, a household with relatively higher farm income was expected to better adopt an improved sheep production package and it is in line with the study of 3.3.4. Size of Landholding. Responses of focus group dis- Olson, [49]. cussions implied that most of the smallholder farmers in the study area use their land only for all farming activities, which 3.3.6. Households’ Off-Farm Income in a Production Year. include production of food crops and cash crops, house construction, tethering livestock during the rainy season, Households’ income sources in rural areas are as diverse as and tree planting. *e sampled households did not get extra households’ activities even within the agricultural sector. land even for renting. Table 6 has shown that the annual off-farm incomes among As described in Table 6, the mean land holding of participants and nonparticipants were 2196.08 and 1066.77 participants and nonparticipants in the study area was ETB, respectively, and standard deviations among each 1.12 ha and 0.75 ha, respectively, and a standard deviation of other were 626.725 and 466.679, respectively. Based on 0.448 and 0.264, respectively. *is has shown that there was a Focus Group Discussion (FGD), most of the farmers who get significant difference among participants and nonpartici- remittances from abroad and from different sources in the pants, and nonparticipants had similarly very low land- country were more likely to participate in improved sheep holdings. Landholding affected participation at 1% production. *e households who had better off-farm income had a higher probability of participating in improved sheep significance level (t � 6.179; p ≤ 0.01). *is trend is similar to the South Nations, Nationalities, and People’s Region where production and affected participation significantly at a 1% 81.8% of the households own less than one hectare and only significance level (t � 12.261; p ≤ 0.01). It is in consonance 3.8% of the farming households own greater than 2 ha with the study of Asante et al. [50] that off-farm income (Teffera et al.) [48], which could be due to variations in the enables farmers to purchase new agricultural technology. population density. 3.3.7. Household Labor. *e overall mean of family labor 3.3.5. Farm Income of the Household. Households’ farm size in man equivalent for sheep producers was 4.98 and 2.36 for participants and nonparticipants, respectively, and the income is one of the important factors determining the adoption of improved technologies. *e amount of variation of labor size for participant and nonparticipant labor was 1.295 and 0.602, respectively. Table 6 has indicated household income obtained from the sale of crops and livestock after the household consumption requirement there were very few variations of labor size among Advances in Agriculture 11 population and tests of the association between the de- nonparticipants compared to participants (nonparticipant households had similarly less labor among them). *e result pendent and explanatory variables using the chi-square and t-tests. However, identification of these factors alone is not of test statistics has shown that the availability of labor influenced participation significantly at 1% significance level enough to stimulate policy actions unless the relative in- (t � 16.553, p ≤ 0.01). fluence of each factor is known for priority-based inter- According to focus group discussions and key infor- vention. In this section, an econometric model (binary logit) mants, a household with a large working labor force was in a was used to see the relative influence of different demo- position to manage the labor-intensive agricultural activities, graphic, socioeconomic, and institutional variables on the including livestock production such as rearing and watering participation of farm households in improved sheep activities that are accomplished by boys and girls. Tethering, production. providing feeds, cleaning the shed are activities of women Determinants that had a significant relationship with the dependent variable were included in the Logit model. Gen- and children while taking to medication are the responsi- bilities of adult men and women. Selling and purchasing of erally, twelve out of thirteen variables that had a significant relationship with the dependent variables during descriptive sheep are the responsibilities of the owner, who in most instances the head of the household. *e findings are in statistics analysis were included in the binary logit model. agreement with those of Cafer and Rikoon [51], who re- Before running the binary logit model, all the hypothesized ported that the described availability of enough labor in the explanatory variables were checked for the existence of a family is expected to be significantly and positively related to multicollinearity problem. Contingency coefficients were the adoption of improved agricultural technology. computed for discrete variables and described in Table 3. Similarly, the VIF values diagnosed to check the multi- collinearity of continuous variables are displayed in Table 4. In 3.3.8. Distance from near Market Center to Ieir Home both cases, variables have no strong collinearity problem. Based (DFNMC). Distance from near the market center plays a on the above test, both the hypothesized continuous and vital role in rural communities in case of market information discrete variables were included in the model. exchange. *e result of the study indicates in Table 6 that the average distances between participants and nonparticipants were 4.45 and 6 kilometers, and standard deviations were 3.4.1. Determinants of Participation in Improved Sheep 1.487 and 1.707, respectively. *e result indicates that Production. Estimates of the parameters of the variables participants living far from the markets’ center faced the expected to determine the participation of improved sheep problem of not having updated market information and production are displayed in Table 7. From the total of participated less in improved sheep production compared to thirteen potential explanatory variables, twelve were in- the one who lives near the market. But, it is not significantly corporated into the econometric model out of which the associated with participation (t � 5.444; p > 0.05). *e focus following seven variables influenced the participation of group discussion responses state that even though the smallholder farmers in improved sheep production signif- market problem was common, this area has not had a serious icantly, namely, labor size (HHLME), age (AGE), frequency problem of market access. of contact with development agents (FDC), membership of cooperatives (MSC), land size (LANDSZ), credit utilization (CU), and off-farm income (OFI). *ey are discussed in the 3.3.9. Frequency of Contact with Development Agents. following paragraphs. Extension contact is supposed to have a direct influence on the behavior of farmers to intensify and improve their (1) Labor Availability. Participation in improved sheep production through resolving problems and improving ef- production requires adequate labor supply to carry out the ficiency to make use of opportunities. When there is contact production processes. It was hypothesized that the avail- with extension agents (DA), there is a greater possibility of ability of labor positively influences participation in im- farmers being influenced to adopt agricultural innovations proved sheep technology. *e finding of this study was and improve their productivity. similar to the hypothesis that described that the size of In Table 6, the average contact with development agents household labor (HHLME) influenced the participation of for participants and nonparticipants was 37.29 and 14.77 smallholder farmers in improved sheep production signif- with a standard deviation of 2.773 and 6.478, respectively. icantly and positively at 1% (p < 0.01). When labor increases *is implied that participants made a lot of contacts with by units, participation increases by an odds ratio of (12.061) development agents with the very minimum differences or by a 6.1% probability level. *us, households with large among each other compared to nonparticipants. It affected family sizes tend to improve their participation in the significance at 1% level (t � 23.639, p ≤ 0.01); this finding is production of improved sheep. It is similar to the findings of in line with the study of Vince et al. [52], which has indicated Lima et al. [53], who reported that labor affects new tech- that the livestock production systems require knowledge nology adoption, production, and productivity significantly change according to contact with extension workers. and positively. 3.4. Results of the Econometric Model/Logit. *e previous (2) Age. *e result of the study shows that the age of the section mainly had dealt with descriptions of the sample household head influenced participation in improved sheep 12 Advances in Agriculture of this study, the result of this study has shown that land size production negatively at 5% (p < 0.05). *is is different from the hypothesis of this study. When age increased by a year, influenced participation decisions in improved sheep pro- duction significantly and positively at a 1% significance level participation in improved sheep production decreased by odds of 3.466 or by a probability level of 47%. *is is a fact (p ≤ 0.01). When land increases by one hectare, the prob- indicated by focus group discussions and key informants in ability of participation increased by odds of 29.283 or by a the study area, as older people fear risk because sheep 28% probability level. production involves high risks like heavy management tasks, *e data gathered qualitatively from focus group dis- fear of serious respiratory diseases, and feed shortage. *ese cussions and key informants assured that participants had were some possible reasons for the negative relationship more land compared to nonparticipants in the study area between the age of the household head and participation in and thus, nonparticipants keep their sheep more frequently improved sheep production. *is result of the current study under stall feeding or cut and carry system, and also use more of other types of feed such as supplements and ex- is in line with the study of Bhattarai et al. [46] who reported that the level of innovativeness was found to be lower among pensive industrial by-products. It is in line with Mishra et al. [57] who reported that land is a very crucial input for older farmers. Also, this finding is in consonance with a study con- livestock production and that it can influence the production ducted by Danso-Abbeam et al. [54] on the adoption of of improved livestock production significantly and improved livestock technology which has reported that positively. younger farmers were more likely to adopt and the effect of age on the probability of adoption was elastic. Moreover, (6) Credit Participation. *is is a very important deter- Gunte [55] found that smallholders’ adoption of small ru- minant for households’ decision to take more risks and minants in the South-Eastern highlands of Ethiopia reported enhance their financial capacity to purchase inputs that that age had a negative effect on the adoption of new complements the package of sheep technologies, im- proved breed purchasing, veterinary purpose, and other technology. management activities. In this study, credit participation was similar to the hypothesis and was influenced signif- (3) Frequency of Extension Contact. Development agents visit farmers and would enable the farmers to develop a icantly and positively at a 5% significant level (p < 0.05). positive attitude towards participation in improved sheep *e probability of participation in improved sheep pro- production. *e finding was similar to the hypothesis of duction increased by odds of 10.026 or by a 3% probability this study which implied that contact with development level as compared to nonparticipants of credit. Partici- agents personally as well as engaging them in field days and pation in credit affects an improvement of participation in training influenced positively and significantly at 1% livestock technology production positively and signifi- cantly, and this is in consonance with the finding of Silong (p ≤ 0.01). *e odds ratio (1.019) indicates that the par- ticipation in improved sheep production increases by a [45]. factor of 1.019 or by a 2% probability level as the result of one unit increase of the extension contact for the house- (7) Off-Farm Income. Households’ income position and resource ownership were found to be important determi- holds. *is finding is consistent with the findings of Vince et al. [52] which has indicated that the livestock production nants in the participation of improved sheep production. systems require knowledge change through contacting Similar to the hypothesis, the result of this study indicated extension workers. that households who had better off-farm income from different sources participated well compared to those who (4) Membership of Cooperatives. Cooperatives are one of the did not get access to off-farm income. It influenced the important organizations in rural and agricultural development participation of smallholder farmers in improved sheep production positively and significantly at 1% p ≤ 0.001). which serve as an important source of information, knowledge transfer, and rural credit. In this study, similar to the hy- When off-farm income increased by one thousand ETB, the probability of participation increased by odds of 1.002 or by pothesis, participation in cooperatives had a significant and positive influence on the participation of smallholder farmers a 0.2% probability level. *is means that a farmer who had in improved sheep production at 1% (p ≤ 0.01), and the better off-farm income from different sources was more probability of cooperative members participation in improved likely to adopt improved sheep production. *is is in line sheep production increased by odds of 21.802 or by 80% with a study conducted by Mwangi and Kariuki [58], who probability level as compared to nonmembers of the cooper- reported that petty trades, daily labor on others’ farms, and ative. It is in line with Fufa [56], who reported that organizing nonfarm activities as well as small businesses enable farmers farmers in a cooperative society would facilitate access to credit, to get additional income to have production inputs and can extension information, and market. *is implies being a influence positively and significantly new agricultural technology adoption. member of rural cooperatives can enhance the adoption of new agricultural technology. (5) Land Owned by Households. Results showed that re- 3.5. Impact of Participation in Improved Sheep Production on spondents’ less participation in improved sheep production Smallholder Farmers’ Income. Sheep is one of the most af- was due to scarcity of rangelands. Similar to the hypothesis fordable animals in the world and can be accommodated in Advances in Agriculture 13 Table 7: *e results of the binary logit model. Variable (B) S.E Wald statistics Sig. Level Exp (B) ∗∗∗ Household labor in ME 2.49 0.679 13.51 0.001 12.061 ∗∗ Age of the rural farm household head (AG) − 1.243 0.589 4.456 0.035 3.466 Sex of the household head (SEX) − 0.242 1.092 0.049 0.824 1.274 ∗∗ Frequency of extension contact (FDC) 0.019 6 11.552 0.001 1.019 Education level of household head (EDU) 0.395 0.322 1.501 0.220 1.484 ∗∗∗ Membership of cooperatives (MSC) 3.082 1.147 7.215 0.007 21.802 ∗∗∗ Size of land in hectares (LAND SZ) 3.377 1.171 8.324 0.004 29.283 ∗∗ Credit participation (CP) 2.305 1.119 4.24 0.039 10.026 Farm income (FI) 0.000 0.000 0.705 0.401 1.000 Distance from market (DTNMC) 0.330 1.173 0.079 0.779 1.391 ∗∗∗ Off-farm income (OFI) 0.002 0.001 8.596 0.003 1.002 Access to animal health service (AHS) 5.821 0.606 5.537 0.217 333.309 ∗∗ Number of Obs. �144; p≤ 0.001; Nagelkerke R Square � 78.947. Notes: Exp (B) shows the predicted changes in odds for a unit increase in the predictor; and ∗∗∗ significant at 5%, and 1% levels (source: computed from own survey data, 2018). Table 8: Farm income of smallholder farmers in 2017. Participants Nonparticipants Variable t value Mean Stand. Deviation Mean Stand. Deviation ∗∗∗ Farm income 9627.45 2999.74 6105.38 2079.84 8.272 ∗∗∗ Income from sheep production 3447.06 1584.34 1380.65 230.43 12.38 ∗∗∗ Source: computed from own survey data, 2018; � 1% significance level. Participation of small holder farmers in improved sheep production No Yes Income from sheep production Figure 2: *e farm income of participant and nonparticipant smallholder farmers (source: computed from own survey data, 2018). any kind of weather condition. *ey are also called poor and key informants’ data confirmed that the income source man’s cows, and rearing sheep contributes a lot to the of smallholder farmers is mainly farming (both livestock and economy by generating household income, providing local crop), but the sheep production due to its potential in the employment, and export [59]. area made a great difference in smallholder household According to the survey results, the average farm income income. of participants and nonparticipants were 9627.45 and Sheep production is increasing constantly in the study area, due to the constant decrease of arable land as well as the 6105.38 ETB with a standard deviation of 2999.74 and 2079.84, respectively. Generally, farm income differences trend of improving the genotype of sheep (improving breed) between participants and nonparticipants of improved sheep at the community level, which initiates smallholder farmers producers were significant. Both focus group discussions (there is community-based improved breed selection Cumulative Frequency 8000 14 Advances in Agriculture Participation of small holder farmers in improved sheep production No Yes Farm income Figure 3: *e income of participant and nonparticipant smallholder farmers from sheep production. (source: computed from own survey data, 2018). practice by different stakeholders). As described in Table 8, improved sheep production earn more income due to pe- the average income from sheep production was 3447.06 and culiar characteristics of the Doyogena sheep breed (short- term reproduction rate, early weaning of weight, and better 1380.65 ETB with a standard deviation of 1584.34 and 230.43 for participants and nonparticipants, respectively. It has physical appearance), and it mostly favors the higher pro- ductivity of participants in improved sheep production. It is shown that participants’ average income was 3447.06 ETB, but in the case of nonparticipants it was only 1380.65 ETB. in line with the study of Legese et al. [60] who reported that Hence, there is a difference of 2066.41 ETB between par- improving the sheep breed can enhance productivity as well ticipants and nonparticipants. as the livelihood of smallholder farmers. Generally, participants of focus group discussions and Also, participants of focus group discussions clarified key informants suggested that the impact of participation in that improved sheep production showed a significant impact improved sheep production was significant and made a on the livelihood of smallholder farmers. Most households difference among smallholder farmers’ living styles. in the study area had short-term income as these smallholder As described in Figure 2, average income gained from farmers were challenged to cover food costs, education fees, clothes, and agricultural inputs. However, the living style of sheep production in the case of participants’ income ob- tained from sheep production ranges from 1,800 to 6,000 those who participated in improved sheep production has been changed, and it enables them to cover such costs easily ETB, whereas nonparticipants’ income ranged from 800 to 1,800 ETB. *is implies that participants gained better in- compared to nonparticipants. come from their sheep production compared to nonpar- ticipants in improved sheep production. Based on the 4. Conclusion response to focus group discussions and key informant data, this income difference is due to better market and financial *e expected output of the research was identifying the most support for participants from governmental and nongov- likely determining factors, income contribution, and chal- ernmental organizations as well as technical support from lenges related to improved sheep production regardless of Areka research center branch at Doyogena. Again, they smallholder farmers. In this study, 100 males and 44 females suggested that sheep production needs both technical and were included’ out of a total of 144 samples, 51 (35.4%) sheep financial support, so such reasons contributed to the income producers participated in improved sheep production. All difference between participants and nonparticipants. variables have a significant relationship with participation As shown in Figure 3, the farm income (which was except distance from the near market center. Determinants obtained both from livestock and crop) ranged from 4500 to that significantly limited participation of smallholder 17,000 and 500 to 10,000 ETB for participants and non- farmers in improved sheep production in the study area participants, respectively. Most of the participants obtained were labor, age, frequency of contact with development much income from sheep production due to the short-term agents, land size, off-farm income, membership of coop- reproduction rate, early weaning of weight, better price of eratives, and participation in credit. According to survey the improved breed, and weighing sell trend for improved results and focus group discussion responses, the households sheep in the study area. *is result was confirmed by focus which had large size labor undertook these activities than group discussions; the farmers in the study area have almost those that had a small size of labor and participated well in the same land size but the ones who participated in improved sheep production simultaneously. Count 16000 Advances in Agriculture 15 [4] H. Desta, Gendered Priority Livestock Species and Roles in Based on focus groups and key informants’ responses, Small Ruminant Production, CRP Livestock and IFAD, Addis aged farmers feared risk and management activities of Ababa, Ethiopia, 2017. improved sheep production, because of the shortage of [5] A. Lakew, A. Melesse, and S. Banerjee, “Traditional sheep communal land for free grazing (the only means of feeding production systems and breeding practice in Wolayita Zone sheep was carry and cut system), and housing was also of Southern Ethiopia,” African Journal of Agricultural Re- another challenge for elders. Another determinant that search, vol. 12, no. 20, pp. 1689–1701, 2017. influenced participation was land size owned by households [6] G. Gebrehiwot, T. Negesse, and A. Abebe, “Effect of feeding which affected significantly and positively; farmers who leucaena leucocephala leaves and pods on feed intake, di- owned large tracts of land for free grazing, producing im- gestibility, body weight change and carcass characteristic of proved fodders, housing, and other management practices central-highland sheep fed basal diet wheat bran and natural participated in improved sheep production better than the pasture hay in tigray, Ethiopia,” International Journal of ones that had small-size landholdings. Agriculture Environment & Biotechnology, vol. 10, no. 3, pp. 367–376, 2017. *e impact of participation in improved sheep pro- [7] S. Gizaw, A. Abebe, A. Bisrat, T. Zewdie, and A. Tegegne, duction on the economy of smallholder farmers’ income was “Defining smallholders’ sheep breeding objectives using identified. Income of smallholder households that partici- farmers trait preferences versus bio-economic modelling,” pated in improved sheep production was enhanced, and it Livestock Science, vol. 214, pp. 120–128, 2018. fetched them 2066.41 additional ETB compared to non- [8] G. R. Gowane, Y. P. Gadekar, V. Prakash, V. Kadam, participants. *is amount of money regardless of the type of A. Chopra, and L. L. L. Prince, “Climate change impact on farmers was very high and made a difference in their living sheep production: growth, milk, wool, and meat,” Sheep style. *us, the future effort through an effective policy Production Adapting to Climate Change, Springer, Berlin, should be intended to accelerate agricultural and rural de- Germany, 2017. velopment through effective utilization of improved sheep [9] M. E. 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Determinants of Smallholder Farmers’ Participation in Improved Sheep Production: The Case of Doyogena District, Kembata Tembaro Zone, Southern Ethiopia

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Hindawi Advances in Agriculture Volume 2021, Article ID 5514315, 17 pages https://doi.org/10.1155/2021/5514315 Research Article Determinants of Smallholder Farmers’ Participation in Improved Sheep Production: The Case of Doyogena District, Kembata Tembaro Zone, Southern Ethiopia 1 2 2 3 Teketel Mathewos, Daniel Temesgen, Dereje Hamza, and Haben Fesseha College of Agriculture, Wolaita Sodo University, P.O. Box 138, Wolaita Sodo, Ethiopia College of Agriculture, Jimma University, Jimma, Ethiopia School of Veterinary Medicine, Wolaita Sodo University, P.O. Box 138, Wolaita Sodo, Ethiopia Correspondence should be addressed to Haben Fesseha; haben.senbetu@wsu.edu.et Received 9 January 2021; Accepted 30 August 2021; Published 14 September 2021 Academic Editor: Yunchao Tang Copyright © 2021 Teketel Mathewos et al. *is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Improved sheep production practices are becoming relevant, but smallholder farmers’ involvement in improved sheep production was below expectations and detailed studies were restricted on the determinants of the participation of smallholder farmers in improved sheep production. *is research was conducted to examine the determinants of the involvement of smallholder farmers in enhanced sheep production in the study area. Methods. Multi-stages sampling techniques were used for this study. Firstly, improved sheep production had a big effect on the incomes of households of participants and nonparticipants. Finally, three kebeles were chosen by basic random sampling, and the third was picked for systematic sampling by 144 survey respondents. Quantitative and qualitative data were gathered from primary and secondary sources. Data collection techniques were undertaken by surveys, focus group discussions, and key informants. Quantitative data were used to evaluate descriptive statistics, such as average, frequency, standard deviation, scope, and inferential t-test and chi-square statistics, and a logit model. Qualitative data obtained from focus group discussion and key informants were analyzed by narrative and used for survey data triangulation. Results. Out of the 144 samples, 51 were participants and 93 were nonparticipants. Participation determinants found in this research field were household labor, age, communication frequency with developers, membership in cooperatives, land ownership, participation in credit, and off-farm income. Improved sheep production had a major effect on the incomes of households of participants and nonparticipants. Multiple determinants typically affect the role of smallholder farmers in raising sheep production. Conclusion. Future initiatives under a successful policy should aim at accelerating agricultural and rural growth by efficiently leveraging enhanced sheep capacity in particular in the study region and Ethiopia in general. land, the position of large ruminants is decreased [3]. Sheep 1. Introduction need small investments, need shorter production cycles, have higher rates of growth and environmental adaptability Ethiopia, which accounts for 50% of the total agricultural share of the gross domestic product (GDP), 85% of exports, 80% of in contrast to large ruminants, and thus are exceptional for the total jobs as well as domestic raw material for the small- smallholder farms [4]. Sheep are mainly used for cash scale industry of this region, has been thought to have the production where precipitation is unpredictable, and where largest livestock population in Africa [1]. It is used to manage poor people are interested in owning and sustaining their food, crop input, soil fertility, industry raw materials, cash sheep for immediate generation of income [5]. Sheep usage income, saving, fuel, social functions, and employment [2]. as a source of income for farm inputs reduces risks related to *e pressure of populations decreases farm size and at crop production in mixed farming and creates employment, the same time, because of its lower competition for arable saving, and money [6]. 2 Advances in Agriculture A few studies have been performed in the field of re- According to Lakew et al. [5], sheep in Ethiopia play a multifunctional role in the cash, meat, skin, manure, and search on sheep production, but not explicitly, to explain the determinants of the involvement of smallholder farmers in long hairy fleece with regard to smallholder farmers. In- creased production of ovine animals is thus necessary to improved sheep production in the region. For example, the fulfill the demands of the rising human population. On the study conducted by Getachew et al. [19] and Gizaw et al. [20] other hand, improving the productivity of sheep and export was not focused on identifying the determinants of farmers’ earnings will improve the income of the household [7]. *e participation in improved sheep production. While agri- study done by Gowane et al. [8] confirmed that sheep are cultural development workers and researchers are making relatively resilient to higher temperatures than cattle and big and ongoing efforts, involvement in improved pro- duction of ovine animals has not reached the desired level. rising temperatures will lead to a growing increase of sheep due to climate change. *e research will also help to gain accurate knowledge that can be useful to promote and enhance the production and *e production of sheep provides protection in times of crop failure, which is projected to be “near-cash” equity [9]. productivity of improved sheep, as well as to recognize and interfere with negative participatory determinants of the Similarly, Ahmed [10] insists that sheep enterprise in Ethiopia is the source of cash income and provides social smallholder farmers. *us, this study was designed to an- security in the bad crop years. Moreover, in Ethiopia, sheep alyze the determinants of smallholder farmers’ participation provide almost 15% of fresh skins and hide production and in improved sheep production in Doyogena district, Kem- 72% of semiprocessed skins and hide export trade [11]. *e bata Tembaro Zone, Southern Ethiopia. annual mutton production in the country is estimated at 78,000 Metric Ton (MT) [6]. Sheep are important in terms of 2. Methodology food security and poverty, particularly for the poor and the women who are often vulnerable in society and who con- 2.1. Study Area. Doyogena is one of the districts in the tribute to the generation of cash income [12]. Kembata Tembaro zone, Southern Nations, Nationalities, According to Central Statistics Authority (CSA) [13], a and People’s Region which is a high land area. *e area is survey in Ethiopia indicated that the potential of sheep is about 258 km from Addis Ababa in the southern direction very high; 30.70 million sheep are estimated to be found in and 171 km southwest of Hawassa. *e district comprises the country, out of which about 5,087,007(17%) are found in mostly high land agro-ecological zone and its altitude ranges SNNPR, and from 5,087,007 around 109, 732 (2.2%) are from 1900 to 2800 m.a.s.l. Annual rainfall is 1200–1800 mm found in the Kembata Tembaro zone, and from 109,732 ° and the mean temperature varies from 10 to 18 C [21]. *e about 32920 (30%) are found in the Doyogena district. *is total population of the district is 116,048, comprising 56,863 zone is noted for mixed farming activity that has a high males and 59,185 females. *e district has a total of 32,920 cultivation and livestock potential. *e biodiversity of the sheep, out of which 10,534 were improved. *e majority of region is mainly high ground. Highlands in Ethiopia are the population is dependent on mixed agriculture, with considered to be potential for sheep production [14]. income of 60% of the households (HH) from crop pro- Improved livestock production became significant, duction and 40% of households from livestock production. but the involvement of small-scale farmers in the pro- Among livestock production, 19% is from sheep and 21% duction of sheep was not as anticipated and the study did from other species (Figure 1) [22]. not show why participation was reduced [15]. Ethiopian national sheep production program aims to increase sheep per capita rather than maintain an incredibly large 2.2. Study Design. A cross-sectional design was used in this number of unproductive ovine animals that lead to land study. Quantitative data were collected and appropriate loss, food shortages, and consumption of large resources analytic techniques were employed to meet the objectives of [16]. Despite the well-adapted and large sheep population, the study. Effective measurement methods to fulfill the aims current productivity and involvement in improved sheep of this study were employed and quantitative data were production for smallholders is poor and for different obtained. Using an interview schedule, quantitative data reasons, the country cannot achieve the anticipated were gathered to assess the significant data, and general- benefit from ovine production [17]. izations were drawn from the result. Several small-scale farmers have been focusing on de- veloping sheep production programs in southern Ethiopia. However, smallholder farmers are still in doubt and for 2.3. Sampling Method and Sample Size Determination. various factors decided to control increased livestock pro- For this study, the multi-stage sampling technique was used. duction [1]. Studies in Mareko district, Gurage zone show Firstly, the Doyogena district was selected purposively, due that farmers’ decision to participate in improved sheep to the existence of improved sheep production experiences production was determined by the combined effects of and its accessibility. Secondly, three kebeles, namely, Serara several factors such as lack of access to improved breeds, age, Bokata, Ancha Sedicho, and Hawora Arara were selected by socioeconomic (membership of cooperative, land size, farm simple random sampling technique due to their similar income, labor) and institutional factors like access to ani- production potential. *irdly, 144 sample households were mals’ health services, credit utilization distance from kebele selected by systematic random sampling methods from all center, and extension contact [18]. three kebeles. Advances in Agriculture 3 Map of study Area 0 2.25 4.5 9 13.5 18 Kilometers Ethio map kambate_tembaro South_map Doyogana Figure 1: Map of the study area. *e sample size determination was computed by using (1) Individual Interview. Totally, 144 sample respondents were Yamane [23] sampling formula at 95% confidence interval, selected and considered for an interview. *ree enumerators who have a college diploma and experience in agricultural with the level of precision of 8% activities were recruited and trained to implement both n � , qualitative and quantitative data collection using an interview 1 + N(e) schedule. Before data collection, the interview schedule was (1) translated into the local language (Kembatigna) and pretested n � � 144, on nine farmers who were not included in the final sample 1 + 1900(.08) households. Hence, appropriate modifications and correc- tions were made to the questionnaire, and data were collected where n is the sample size, N is the population size (total under the continuous supervision of the researcher. household heads size), and e is the level of precision. In general, the sample size, total number of sheep (2) Key Informants’ Interview. For this study, in addition to producers’ household heads from the kebeles, and the individual interviews, data from key informants (KI) were proportion of sample size are summarized in Table 1. also collected from development agents (coordinators), managers of respective kebeles, district’s livestock and fishery, 2.4. Methods of Data Collection. Both quantitative and agriculture, and natural resources, cooperative development, trade, and the industry as well as administration offices ex- qualitative types of data were gathered through different data collection methods from primary and secondary sources. pertise focal persons (total of eleven key informants). *ese participants were selected purposively to obtain relevant data. *e data from both primary and secondary sources were collected and used to generate valuable information. Pri- mary data sources were 144 sample respondents, key in- (3) Focus Group Discussion. Focus group discussions were formants, and focus group discussions. Secondary data held with three groups, one in each kebele (including twelve sources were Areka research center (branch at Doyogena), members in each group). *e composition of groups was farmers such as development group leaders, model, and district’s livestock and fishery, cooperative development, agriculture and natural resources, finance and economy and nonmodel farmers as well as respective kebeles’ leaders who were selected purposively for seeking appropriate infor- trade and industry offices, and relevant published and un- published reports. mation. It was also aimed to increase the reliability and 4 Advances in Agriculture trustworthiness of the information. *e group members methods employed in analysis using logistic regression were familiarized with the discussion points and encouraged follow the same general principles used in linear regression [25]. to forward their opinion without any reservation. *e idea of dominance was tried to control as much as possible to avoid *e probability model, which expresses the dichotomous sampling bias. Both key informants and focus group dis- dependent variable (Y ) as a linear function of the explan- cussions were mainly used to generate qualitative data that atory variables (X ), is called the linear probability model supported the findings of the survey based on predetermined (LPM). Due to econometric shortcomings like nonnormality checklists. of the disturbances (Ui), heteroscedastic variances of the disturbances, nonfulfillment of 0 <E (Yi/Xi) < 1, and lower value of R2, linear probability model (LPM), as a measure of 2.5. Data Management and Data Analysis. After compiling goodness of fit, failed to test the statistical significance of and screening, the interview data of 144 respondents were estimated coefficients. In the case of logit and probit, the analyzed. Descriptive and econometric analyses were used to estimated probabilities lay between logical limit 0 and 1, and analyze the data collected from respondents. Both de- they are the most frequently used models when the de- scriptive and econometric methods were employed to an- pendent variable happens to be dichotomous, as well as the alyze the relationship between dependent and explanatory choice between these two models revolves around practical variables by using the Statistical Package for Social Sciences concerns such as the availability and flexibility of computer (SPSS, version 20). Inferential statistics such as Chi-square program, personal preference, experience, and other facil- (X ) and t-tests were used. Also, qualitative analysis was used ities. In fact, it represents a close approximation to the to compare the socioeconomic, demographic, and situation cumulative normal distribution [24]. of respondents as well as triangulating survey data. Crowder [26] pointed out that a logistic distribution has got advantages over others in the analyses of the dichoto- 2.5.1. Econometric Analysis. *e purpose of this study was to mous outcome variables. *ere are two primary reasons for analyze the determinants of participation of smallholder choosing the logistic distribution. *ese are: (a) from a farmers in improved sheep production. *e dependent mathematical point of view, it is an extremely flexible and variable, in this case, is a dichotomous variable, which takes a easily used function and (b) it tends itself to a logically value of 1 if the household participates, and otherwise 0. meaningful interpretation also states that the logit model is Demographic and socioeconomic characteristics, as well as simpler in estimation than the probit model. After reviewing institutional factors that were assumed to be correlated with the strengths, drawbacks, and assumptions of different the participation of improved sheep production, were en- models, the binary logistic regression model was employed tered along with these classifications. to address the core objective of the study, i.e., analyzing Models, which include a “yes’’ or “no’’ type-dependent determinants of participation of smallholder farmers in variable, are called dichotomous or dummy variable re- improved sheep production gression models. Such models approximate the mathe- matical relationship between explanatory variables and the P(x) � E Y � , 􏼠 􏼡 dependent variable that is always assigned qualitative re- (2) sponse variables [24]. *e four most commonly used ap- 1 1 proaches to estimate dummy-dependent variable regression P(x) � E􏼒y � 􏼓 � . x 1 + e − (B0 + BiXi) models are (a) the linear probability model (LPM), (b) the logit, (c) the probit, and (d) the Tobit model. *ey are For ease of exposition, we write equation (2) as applicable to a wide variety of fields [24]. *e major point that distinguishes these functions from P(x) � , (3) − zi the linear regression model is that the outcome variable in 1 + e these functions is binary or dichotomous. Besides, the dif- where P(x) � is a probability of being participant and ranges ference between logistic and linear regression is reflected from 0 to 1; Z � is a function of n-explanatory variables (x) both in the choice of a parametric model and in the as- which is also expressed as sumptions. Once this difference is accounted for, the Z � B + B X + B X + · · · + B X , i 0 1 1 2 2 n n PART � β + β AG + β SEX + β EDU + β LOBOUR + β LAND SZ + β FI (4) 0 1 2 3 4 5 6 + β OFI + β CU + β MSC + β DFKC + β FDC + β AHS + β DFNMC, 7 8 9 10 11 12 13 where, X � Age of the household head, X � Sex of house- HH, X � Farm income, X Off-farm income, X � Credit 1 2 6 7 � 8 hold head, X Education level of the household head, utilization, X � Membership of cooperatives, X � Distance 3 � 9 10 X � Household labor size in ME, X Land size owned by from kebele center, X � Frequency of contact with 4 5 � 11 Advances in Agriculture 5 Table 1: Sample size determination from selected kebeles checked and the association among discrete variables was also verified by checking covariance. *e existence of Population (HH Sample size multicollinearity seriously affects the parameter estimates. In heads) Kebeles short, the coefficients of the interaction of the variables Male Female Total Male Female Total indicate whether one of the two associated variables should Serera-Bokata 342 242 584 32 13 45 be eliminated from the model analysis [27]. Ancha-Sedicho 396 288 684 36 16 52 Accordingly, Variance Inflation Factors (VIF) technique Hawora-Arara 353 279 632 32 15 47 was employed to distinguish the problem of multi- Total 1091 809 1900 100 44 144 collinearity for continuous explanatory variables [24]. Each Source: computed from own survey data, 2018. selected continuous variable was regressed on the other continuous explanatory variables and an evaluation was development agents, X � Access to animals’ health serves, 12 made on the coefficient of determination (R ). If an ap- X � Distance from near market center, B � intercept, B , proximately linear relationship exists among the explanatory 13 0 1 B . . . B � Slopes of the equation in the model. 2 n variables, then this results in a “large” value for R in at least *e probability that a given household participant is one of the test regressions. A popular measure of multi- expressed by equation (3) while the probability of not collinearity is VIF defined as participating is VIF􏼐X 􏼑 � . j (9) 1 2 1 − R 1 − P(x) � . (5) j zi 1 + e A rise in the value of R , which is an increase in the *erefore, we can write degree of collinearity, does indeed lead to an increase in the zi variances and standard errors. A VIF value greater than or P(x) 1 + e zi (6) � � e . equal to 10 is used as a signal for the strong collinearity. In − zi 1 − P(x) 1 + e the same way, it is necessary to test whether there is or not Now P(x)/(1 P(x)) is simply the odds ratio in favor of the interaction between discrete variables that can lead to the participation. It is the ratio of the probability that a problem of association among each other using coefficients household participated to the probability that did not of contingency (CC). If the value of CC is greater than or participate. Finally, taking the natural log of equation (6) we equal to 0.75 it is used as a signal for the existence of strong obtain association among the discrete variables [24]. 􏽳������ ln[P(x)] � � Z , i i (10) CC � , 1 − P(x) (7) n + X Z � B + B X + B X + · · · + B X . i 0 1 1 2 2 n n where CC is the coefficient of contingence, x is the chi- square test, and n is the total sample size. If the disturbance term, (U ) is introduced, the logit model becomes 2.6. Definition of Variables Z � B + B X + B X + · · · + B X + U , (8) i 0 1 1 2 2 n n i 2.6.1. Dependent Variable of the Model. *e dependent where L � log of the odds ratio, which is not only linear in X i i variable for this study was smallholder farmers’ participation but also linear in the parameters, X � Vector of relevant in improved sheep production. Participation “(Participating explanatory variables. in Improved Sheep Production, “PISP”)” which was the Changing an independent variable, in this case, was dependent variable for the binary logit analysis as a di- expected to alter the probability that a given individual chotomous variable and represented by 1 for participant and becomes a participant, and this helped to predict the 0 for nonparticipant household heads. probability of participating. 2.6.2. Independent Variables of the Model. After the logical 2.5.2. Estimation Procedure. Given that the model selected procedure was clearly delineated, the potential explanatory for analysis was the binary logit model, the dependent variables were identified that determined the participation of variable was assigned by a value of 1 or 0, representing smallholder farmers in improved sheep production. *e participant or nonparticipant, respectively. Estimating the independent variables of the study are variables that are values of B and B ’s, a set of data were fitted into equation expected to influence farmers’ participation in improved 0 i (8). Since the method of Ordinary Least Square (OLS) does sheep production and can be of many types. Here, an ex- not make any assumption about the probabilistic nature of planation of the thirteen potential hypothesized explanatory the disturbance term (U ), the parameters of the model are variables was presented. estimated using the maximum likelihood (ML) method [24]. Consequently, a review of literature, past research Before employing the logit model, the existence of findings, and expert opinions were used to identify the multicollinearity among the continuous variables was potential determinants of the participation of farmers in 6 Advances in Agriculture large area of land can be a means of accumulating wealth and improved sheep production in the study area. *us, taking participation as the dependent variable, the following ex- a source of animal feed. Households who have better landholding have a better capacity to participate in improved planatory variables were identified and their influence on the participation of smallholder farmers examined. sheep production. According to Mueller et al. [32], a large size of land implies more possibility of having a large flock (1) Age of the Rural Farm Household Head (AG). It is a size and availability of feeds. In this study, it was hypoth- continuous variable, defined as the farm household head’s esized that the size of landholding by the household has a age and measured as the number of years from the dates of positive and significant influence on the participation of birth to the day of the survey interview. When farmers’ age improved sheep production. increases, their maturity also increases and they will be eager to apply new technology. According to Assan [28], those (6) Farm Income (FI). It is a continuous variable measured in the amount of money the household earns annually from the household heads of a matured age due to a good farm ex- perience have a much better association with more pro- sale of agricultural products (both crop and livestock) in ET B. *e increase in the productivity of agriculture enables to get ductivity. Hence, in this study, it was hypothesized that when the household head’s age increases, it affects participation in huge money and enhancing improved breed and other pro- improved sheep production positively and significantly. duction input purchasing power. According to Rasch et al. [33], rural households with better farm income have a better pos- (2) Sex of the Household Head (SEX). this is a dummy sibility of participating in improved sheep production. In this variable that assumes a value of “1” if the head of the study, it was hypothesized that better farm income influences household is male, and “0” otherwise. Sex is a biological smallholder farmers’ participation in improved sheep production difference between being male or female respondents. With positively and significantly. this background, male-headed households have a better (7) Off-Farm Income (OFI). It is a continuous variable probability of mobility, participate in different meetings, and have more exposure to information about better production measured by the amount of money the household earns annually from the sale of the family business, remittance, a participation. According to Urgessa [29], women-headed households are less likely to control economic resources and day’s labor in others̓ farm, or nonfarm activities, and any the nature of their economic activity. *en, it was hy- other income sources in ET Br. When households get ad- pothesized that male-headed households have more chance equate off-farm income, they can have the capacity to run to participate in improved sheep production and positively improved sheep production. According to Babatunde et al. and significantly influence it. [34], off-farm income is a determining explanatory variable that can positively affect the probability of participation in (3) Education Level of Household Head (EDU). It is a con- improved sheep production. In this study, it was hypothe- tinuous variable and is measured by years of schooling. sized that getting off-farm income influences smallholder farmers’ participation positively and significantly. When the education levels of farmers increase, they have a better ability to identify the problem of their farm income as (8) Credit Participation (CP). It is a dummy variable that well as calculate its costs and benefits. According to Mathebula [30], a high level of education was expected to takes the value of 1 if the household utilized credit, and 0 facilitate more exposure to the external environment and otherwise. Credit is an important instrument to solve the accumulation of knowledge on farming practices. *erefore, liquidity problem that farm households are facing. House- in this study, it was hypothesized that advanced school levels holds who participated in credit could purchase agricultural affect participation in improved sheep production positively inputs including livestock. According to Kebebe [35], credit and significantly. participation can ensure that households purchase improved breed and other production inputs. In this study, the credit (4) Household (HH) Labor in Me. It is a continuous variable part was hypothesized that credit participation influences smallholder farmers’ participation in improved sheep pro- and is measured by several members under the control of one HH head in man equivalent (ME) ratio. Sheep man- duction positively and significantly. agement involves time-consuming labor and availability of labor can ease the management of sheep in a household. (9) Membership of Cooperatives (MSC). *is was coded as a According to Haile et al. [31], in farming households, for dummy variable, which took the value of 1 if the farmer was improved sheep rearing and routine management practices, a member of cooperatives, and 0 otherwise. Cooperative the availability of productive labor is mandatory. Hence, in societies are one of the important institutions in rural and this study, availability of labor was hypothesized, which agricultural development. Cooperatives serve as an important affects the participation of smallholder farmers in improved source of rural credit and producers who are a member of cooperatives are likely to get inputs and production infor- sheep production positively and significantly. mation and thus could participate and supply sheep to the market than nonmembers. According to Hennessy et al. [36], (5) Size of Land in Hectares (LAND SZ). Land is a continuous variable measured in the number of hectares by the membership in farmers’ cooperatives significantly raised the household. Land is one of the key productive resources for probability of technology adoption. *erefore, in this study, smallholder farmers to generate their livelihood. Owning a being a member of a cooperative was hypothesized that Advances in Agriculture 7 membership of cooperatives has a positive and significant households’ heads were considered in this study. As relationship with participation in improved sheep production. shown in Table 2, out of the total sample respondents, 100 (69.4%) and 44 (30.6%) were male- and female-headed, (10) Distance from the Kebele Center (DFKC). It is a con- respectively. *e overall mean age of the sampled tinuous variable measured by several kilometers from the household head was 53 years with a standard deviation of center of the kebele to their home. Distance from the kebele 5.67; this implies that the majority of them were in the center is the number of kilometers farmers walk to reach the working age group, and the age difference among farmers kebele center. Farmers living closer to kebele centers are was 5.67 years. likely to get updated information and adopt improved sheep *e other demographic characteristic was education breeds than those who are living far. According to Deresse level. *e average education grade level was grade 4 with a et al. [37], when farmers come from far, the probability of standard deviation of 1.29, which indicates that the major improving agricultural technology adoption decreases. It group of farmers had similar lower grades. *e average land was hypothesized that distance affects participation in im- holding was 1.47 hectares with a standard deviation of 0.39. proved sheep production negatively and significantly. *is is less than the national average, which is 1.37 hectares, but it can vary from place to place [1]. (11) Frequency of Extension Contact (FDC). It is a continuous As described in Table 3, the major crop in the study variable measured by the number frequency of contacts per area was wheat, which covers 42.5% of the total cultivable year that the respondent makes with development agents. *e land. Potato (14%) is the second most produced crop in frequency of extension contact is one type of sharing knowl- cultivable lands of the highland areas. *e livestock edge and experience with development agents. According to production was also another farm practice that used the Elias et al. [38], farmers who make contact with development mixed approach and the most dominant livestock types agents frequently have better access to information on tech- were cattle and sheep. nology and have a better possibility to translate their intentions As described in Table 4, the study area is known for land into action. In this study, it was hypothesized that maximum shortage, as most of (67.7%) the land is cultivable land, frequency of extension contact with development agents has a 19.75% is forest land, and only 6.14% is grazing land that can positive and significant influence on smallholder farmers’ be used for livestock production. participation in improved sheep production. (12) Access to Animals’ Health Service (AHSC). It is a dummy 3.2. Descriptive Statistics Analysis for Discrete Variables variable; it takes the value 1 if the respondent gets access, and “0” otherwise. Access to health services is a very critical 3.2.1. Household Heads’ Sex. *e result in Table 5 shows that 144 respondents were included in this study: 100 (69.4%) variable that can affect the motivation of farmers to par- ticipate in improved sheep production. When sheep health and 44 (30.6%) from male-headed and female-headed households, respectively. Out of 51 participants, 42 (82.4%) care access is improved, productivity will increase as well as farmers will be encouraged to participate in improved sheep were from male-headed households and 9 (17.6%) were from female-headed HHs. But in the nonparticipant group, out of production. According to Robinson et al. [39], unless a 93 nonparticipants, 58 (62.4%) were from male-headed HHs farmer having access to health services, he/she cannot decide to participate in improved sheep production. So, in this and 35 (37.6%) were from female-headed HHs. *e par- ticipation of females in improved sheep production is still study, it was hypothesized that improvement in access to animals’ health services affects the decision to participate in very least. *e study result showed that the biological differences improved sheep production positively and significantly. between males and females influenced participation sig- ∗∗∗ nificantly at a 1% significance level (X � 6.201 ; p ≤ 0.01). (13) Distance to the Nearest Market Center (DTNMC). It is a continuous variable that can be measured by the number of Based on evidence obtained from focus group discussions, male dominance on resources was very high. Due to this, kilometers it takes from their home to reach the nearest the market. *e closer they are to the nearest market, the more contacts with development agents to share new information and knowledge enabled males better than females. It is in likely they have updated market information and are enabled to participate in improved and intensive farming activities. line with the study of Musgrave [42], and production of Meanwhile, for the farmers who live far away from market sheep needs high production resources and power due to places, the likelihood of adopting the technology will de- this most of the time males have been suitable to undertake crease [40,41]. So, in this study, it was hypothesized that heavy management activities than females. distance from the nearest market to their home is expected to influence participation in improved sheep production 3.2.2. Access to Animal Health Services. Accessibility of negatively and significantly. animal health services is one of the crucial factors for the production and productivity of sheep. *e result of this 3. Results and Discussion study, as shown in Table 5, indicated that out of 51 par- ticipants, 36 (70.6%) participants in improved sheep pro- 3.1. Demographic and Socioeconomic Characteristics of Sheep duction had access to animal health services. Out of 51 Producer Sample Households. In total, 144 sample 8 Advances in Agriculture Table 2: Demographic and socioeconomic characteristics of sample sheep producers. Variable Frequency Percentage Male 100 69.4 Sex Female 44 30.6 Mean Standard deviation Age in year (AGE cont) 53 5.6695 Educational status (EDU) 4 1.2975 Land size 0.935 0.356 Source: computed from own survey data, 2018. Table 3: *e Livestock and crop types in the study area. Types of crops grown in the study districts Land coverage (Ha) Land coverage in percent Types of livestock Number of livestock Wheat 5200 42.5 Total sheep 32920 Barley 1221 10 Improved sheep 10534 Teff 1450 12 Goat 4501 Pulse crops Pack animals 10213 Faba bean 1036 8 Bee in hive 3101 Haricot bean 450 4 Field pea 295 2 Potato 1750 14 Enset 714 6 Oil crops 20 0.5 Others 112.60 1 Total 12,248.60 Source: computed from own survey data, 2018. Table 4: *e land use of farmers in the study area. Types of land use Coverage in hectare (Ha) Percentage (%) Cultivated land 12,248.6 67.7 Grazing land 1110 6.14 Forest land 3573 19.75 Degraded land 435 2.4 Swampy land 358.33 1.98 Potentially cultivable land 202.4 1.13 Others 164.01 0.9 Total 18,091.34 Source: computed from own survey data, 2018. Table 5: Descriptive statistics’ results of discrete explanatory variables. Participants Nonparticipants Variables P value Chi-square Freq. % Freq. % ∗∗∗ Male 42 82.4 58 62.4 0.009 6.201 Sex Female 9 17.6 35 37.6 Total 51 100 93 100 ∗∗∗ Yes 36 70.6 24 25.8 0.0001 27.176 Access to animals health services (AHS) No 15 29.4 69 74.2 Total 51 100 93 100 ∗∗∗ Yes 38 74.5 25 26.9 0.0001 30.361 Membership of cooperatives (MSC) No 13 25.5 68 73.1 Total 51 100 95 100 ∗∗∗ Yes 40 78.4 30 32.3 0.0001 28.110 Credit utilization (CU) No 11 21.6 63 67.7 Total 51 100 93 100 ∗∗∗ ∗∗ (Source: computed from own survey data, 2018); Freq. � frequency; % � percentage; p � probability; , (1% and 5%) significance, respectively. Advances in Agriculture 9 participants, 15 (29.4%) did not have access to animal health and there was a significant relationship that exists at a 1% services. significance level (X � 28.110, p ≤ 0.01). According to focus In the current study, out of total 93 nonparticipants, 24 group discussions and key informants’ responses, the credit (25.8%) of them got access to animal health services and 69 utilization difference between participants and nonpartici- (74.2%) did not get access to animal health services. *ere is pants was due to the presence of different governmental and an association between access to animal health services and nongovernmental organizations that facilitated the saving participation in improved sheep production. Farmers during and credit associations, mainly among participants. focus group discussions and at key informants’ level con- However, the majority of respondents criticized the firmed that there was a shortage of health posts and animal OMO microfinance service for its high interest rate, inability health officers. It was observed that only one animal health to procure the loan despite their request, and lack of other expert was assigned for three kebeles during the survey. For lending institutions as an alternative. *is result is in line this reason, farmers complained about inappropriate and with Silong [45] who states that participation in credit can inadequate animal health services. Also, focus group par- influence the adoption of new agricultural technology ticipants explained that the experts focused on larger ru- positively and significantly. minants than small ruminants like sheep. Unfortunately, those participating in improved sheep 3.3. Descriptive Statistics Analysis Results for Continuous production were having animal health services from different Variables sources such as nongovernmental organizations, research centers (Areka research center branch), and community-based 3.3.1. Age of Household Head. Table 6 has shown that the breed selection cooperatives. *e accessibility of health services average ages of participants and nonparticipants were 44.53 influenced smallholder farmers’ participation significantly. *is and 61.27 years, respectively. *is has indicated that the result is similar to Getachew et al. [19], and the health ac- younger age group was the one who participated more than the cessibility influenced the participation in improved sheep older ones. Youngsters were better capable of managing assets production significantly at a 1% significance level (χ �27.176, and are more productive than older-aged households. *e age p ≤ 0.01). Moreover, it is in line with Pulina et al. [43] who have variances were 6.775 and 4.564 between participants and specified that animal health services encourage farmers to nonparticipants, respectively. *is indicates that the house- participate in improved sheep production significantly. holds who participated in improved sheep production had younger people than the nonparticipants. *e age variation among nonparticipants was very low compared to participants. 3.2.3. Membership of Cooperatives. As indicated in Table 5, In another way, elders were at a similar age level. *e age the existence and operation of institutions such as cooperatives influenced participation significantly at a 1% significance level for marketing, saving, and credit can enhance the livelihood of (t � 17.642, p ≤ 0.01). *is result is in line with the study of smallholder farmers and be alternative sources of information, Bhattarai et al. [46] and stated that the level of innovativeness in knowledge, and credit to members. Out of 51 participants, 38 agricultural technology adoption is lower among older farmers. (74.5%) have membership of cooperatives and 13 (25.5%) are not members of any cooperatives. Out of 93 participants, nonparticipants were 68 (73.1%) and they were not members of 3.3.2. Educational Status of Household Heads. As indicated any cooperative organization, whereas only 25 (26.9%) were in Table 6, the educational level of respondents’ mean grades members of cooperatives. *is study result has shown that of participants and nonparticipants were 5 and 2, respec- membership of cooperatives plays a significant role at 1% tively. Household heads with a high level of education significance level (X � 30.361, p ≤ 0.01), which influenced the participated in improved sheep production, more than those participation of smallholder farmers in improved sheep pro- who had less educational level. Education has a relationship duction, and similar to the study of Yin et al. [44] membership with participation at 5% significance level (t � 17.404, of cooperatives is one way of transferring knowledge and p < 0.05). *e variations of education level were 1.568 and getting credit for production, which influences the participa- 1.027 among participants and nonparticipants, respectively. tion of farmers positively and significantly. *is means variation among nonparticipants was very less (nonparticipants achieved similarly lower grades). Hence, the data analysis showed that a high level of 3.2.4. Credit Participation. In situations where the financial education had a significant association with the trends of capacity of an individual can limit the expansion of pro- participating in improved sheep production. *is finding is duction activities, participating in credit from any source in line with Tegegne [47] who found that educated house- influenced new technology practices. As described in holds tend to have higher productivity, use of information, Table 5, in the study area, out of 51 participants on improved and are able to adopt new production techniques than the sheep producers, 40 (78.4%) participated in credit from any less educated households. of the organizations. But, out of 51 participants, 11(21.6%) did not utilize credit from any organizations. Out of 95 nonparticipants, 30 (32.3%) did utilize credit but did not participate in improved sheep production. *is 3.3.3. Distance from Kebele Center. Distance from the kebele result has shown that credit participation had a strong as- center to their home plays a vital role in rural communities sociation with participation in improved sheep production in case of knowledge exchange at the kebele (farmers training 10 Advances in Agriculture Table 6: Descriptive statistics’ results of continuous explanatory variables. Participants Nonparticipants Variable P value T value Mean Stand. Dev Mean Stand. Dev. ∗∗∗ Age in year (AGE cont) 44.53 6.775 61.27 4.564 0.002 17.642 ∗∗ Educational status (EDU) 5.000 1.568 2.000 1.027 0.013 17.407 ∗∗ Distance from Kebele center (DFKC) 1.180 0.478 2.91 0.351 0.014 24.927 ∗∗∗ Land size (LAND cont) 1.120 0.448 0.750 0.264 0.001 6.179 ∗∗∗ Farm income (FI) 9627.45 2999.739 6105.38 2079.839 0.004 8.272 ∗∗∗ Off-farm income (NFI) 2196.08 626.725 1066.77 466.679 0.001 12.261 ∗∗∗ Household labor in ME (HHLME) 4.980 1.295 2.36 0.602 0.0001 16.641 Distance from near market center to their home (DFNMC) 4.450 1.487 6.000 1.707 0.328 5.444NS ∗∗∗ Frequency of development agents contact (FDC) 37.290 2.773 14.77 6.478 0.0001 23.639 ∗∗∗ ∗∗ Source: computed from own survey data, 2018; , , NS shows significance level at 1%, 5%, not significant, respectively. center). *e result of the study, as indicated in Table 6, shows could be used for the purchase of farm inputs. Improved sheep production often requires intensive input which has that the average distance between participants and non- participants were 1.18 and 2.91 kilometers, with a standard great implications on the cost of production. Due to this, deviation of 0.478 and 0.351, respectively. *is indicated that improved sheep production needs to have the required most of the participants lived around the kebele’s center amount of income from their agricultural activities to run compared to nonparticipants, and the variation of distances the improved sheep production activities. According to among them has shown that those farmers who lived far Table 6, the average annual farm income of the participating away were ignored. and nonparticipating sample households was 9627.45 and Generally, the farmers who live far from the kebele’s 6105.38 birr as well as a standard deviation of 2999.739 and center faced the problem of participation in improved sheep 2079.839, respectively. In the current research, the farm income variation between production. In this study, distance from the kebele center influenced the participation significantly at a 5% significance participants and nonparticipants group indicated that non- participants groups has less farm income as compared to the level; t � 24.927; p < 0.05). Brown et al. [9] reported a similar relationship between distance from the kebele and partici- participant groups. In this study, agricultural income influ- pation in improved livestock technology in the Dejen enced participation positively at a 1% significance level district. (t � 8.272, p ≤ 0.01). *erefore, a household with relatively higher farm income was expected to better adopt an improved sheep production package and it is in line with the study of 3.3.4. Size of Landholding. Responses of focus group dis- Olson, [49]. cussions implied that most of the smallholder farmers in the study area use their land only for all farming activities, which 3.3.6. Households’ Off-Farm Income in a Production Year. include production of food crops and cash crops, house construction, tethering livestock during the rainy season, Households’ income sources in rural areas are as diverse as and tree planting. *e sampled households did not get extra households’ activities even within the agricultural sector. land even for renting. Table 6 has shown that the annual off-farm incomes among As described in Table 6, the mean land holding of participants and nonparticipants were 2196.08 and 1066.77 participants and nonparticipants in the study area was ETB, respectively, and standard deviations among each 1.12 ha and 0.75 ha, respectively, and a standard deviation of other were 626.725 and 466.679, respectively. Based on 0.448 and 0.264, respectively. *is has shown that there was a Focus Group Discussion (FGD), most of the farmers who get significant difference among participants and nonpartici- remittances from abroad and from different sources in the pants, and nonparticipants had similarly very low land- country were more likely to participate in improved sheep holdings. Landholding affected participation at 1% production. *e households who had better off-farm income had a higher probability of participating in improved sheep significance level (t � 6.179; p ≤ 0.01). *is trend is similar to the South Nations, Nationalities, and People’s Region where production and affected participation significantly at a 1% 81.8% of the households own less than one hectare and only significance level (t � 12.261; p ≤ 0.01). It is in consonance 3.8% of the farming households own greater than 2 ha with the study of Asante et al. [50] that off-farm income (Teffera et al.) [48], which could be due to variations in the enables farmers to purchase new agricultural technology. population density. 3.3.7. Household Labor. *e overall mean of family labor 3.3.5. Farm Income of the Household. Households’ farm size in man equivalent for sheep producers was 4.98 and 2.36 for participants and nonparticipants, respectively, and the income is one of the important factors determining the adoption of improved technologies. *e amount of variation of labor size for participant and nonparticipant labor was 1.295 and 0.602, respectively. Table 6 has indicated household income obtained from the sale of crops and livestock after the household consumption requirement there were very few variations of labor size among Advances in Agriculture 11 population and tests of the association between the de- nonparticipants compared to participants (nonparticipant households had similarly less labor among them). *e result pendent and explanatory variables using the chi-square and t-tests. However, identification of these factors alone is not of test statistics has shown that the availability of labor influenced participation significantly at 1% significance level enough to stimulate policy actions unless the relative in- (t � 16.553, p ≤ 0.01). fluence of each factor is known for priority-based inter- According to focus group discussions and key infor- vention. In this section, an econometric model (binary logit) mants, a household with a large working labor force was in a was used to see the relative influence of different demo- position to manage the labor-intensive agricultural activities, graphic, socioeconomic, and institutional variables on the including livestock production such as rearing and watering participation of farm households in improved sheep activities that are accomplished by boys and girls. Tethering, production. providing feeds, cleaning the shed are activities of women Determinants that had a significant relationship with the dependent variable were included in the Logit model. Gen- and children while taking to medication are the responsi- bilities of adult men and women. Selling and purchasing of erally, twelve out of thirteen variables that had a significant relationship with the dependent variables during descriptive sheep are the responsibilities of the owner, who in most instances the head of the household. *e findings are in statistics analysis were included in the binary logit model. agreement with those of Cafer and Rikoon [51], who re- Before running the binary logit model, all the hypothesized ported that the described availability of enough labor in the explanatory variables were checked for the existence of a family is expected to be significantly and positively related to multicollinearity problem. Contingency coefficients were the adoption of improved agricultural technology. computed for discrete variables and described in Table 3. Similarly, the VIF values diagnosed to check the multi- collinearity of continuous variables are displayed in Table 4. In 3.3.8. Distance from near Market Center to Ieir Home both cases, variables have no strong collinearity problem. Based (DFNMC). Distance from near the market center plays a on the above test, both the hypothesized continuous and vital role in rural communities in case of market information discrete variables were included in the model. exchange. *e result of the study indicates in Table 6 that the average distances between participants and nonparticipants were 4.45 and 6 kilometers, and standard deviations were 3.4.1. Determinants of Participation in Improved Sheep 1.487 and 1.707, respectively. *e result indicates that Production. Estimates of the parameters of the variables participants living far from the markets’ center faced the expected to determine the participation of improved sheep problem of not having updated market information and production are displayed in Table 7. From the total of participated less in improved sheep production compared to thirteen potential explanatory variables, twelve were in- the one who lives near the market. But, it is not significantly corporated into the econometric model out of which the associated with participation (t � 5.444; p > 0.05). *e focus following seven variables influenced the participation of group discussion responses state that even though the smallholder farmers in improved sheep production signif- market problem was common, this area has not had a serious icantly, namely, labor size (HHLME), age (AGE), frequency problem of market access. of contact with development agents (FDC), membership of cooperatives (MSC), land size (LANDSZ), credit utilization (CU), and off-farm income (OFI). *ey are discussed in the 3.3.9. Frequency of Contact with Development Agents. following paragraphs. Extension contact is supposed to have a direct influence on the behavior of farmers to intensify and improve their (1) Labor Availability. Participation in improved sheep production through resolving problems and improving ef- production requires adequate labor supply to carry out the ficiency to make use of opportunities. When there is contact production processes. It was hypothesized that the avail- with extension agents (DA), there is a greater possibility of ability of labor positively influences participation in im- farmers being influenced to adopt agricultural innovations proved sheep technology. *e finding of this study was and improve their productivity. similar to the hypothesis that described that the size of In Table 6, the average contact with development agents household labor (HHLME) influenced the participation of for participants and nonparticipants was 37.29 and 14.77 smallholder farmers in improved sheep production signif- with a standard deviation of 2.773 and 6.478, respectively. icantly and positively at 1% (p < 0.01). When labor increases *is implied that participants made a lot of contacts with by units, participation increases by an odds ratio of (12.061) development agents with the very minimum differences or by a 6.1% probability level. *us, households with large among each other compared to nonparticipants. It affected family sizes tend to improve their participation in the significance at 1% level (t � 23.639, p ≤ 0.01); this finding is production of improved sheep. It is similar to the findings of in line with the study of Vince et al. [52], which has indicated Lima et al. [53], who reported that labor affects new tech- that the livestock production systems require knowledge nology adoption, production, and productivity significantly change according to contact with extension workers. and positively. 3.4. Results of the Econometric Model/Logit. *e previous (2) Age. *e result of the study shows that the age of the section mainly had dealt with descriptions of the sample household head influenced participation in improved sheep 12 Advances in Agriculture of this study, the result of this study has shown that land size production negatively at 5% (p < 0.05). *is is different from the hypothesis of this study. When age increased by a year, influenced participation decisions in improved sheep pro- duction significantly and positively at a 1% significance level participation in improved sheep production decreased by odds of 3.466 or by a probability level of 47%. *is is a fact (p ≤ 0.01). When land increases by one hectare, the prob- indicated by focus group discussions and key informants in ability of participation increased by odds of 29.283 or by a the study area, as older people fear risk because sheep 28% probability level. production involves high risks like heavy management tasks, *e data gathered qualitatively from focus group dis- fear of serious respiratory diseases, and feed shortage. *ese cussions and key informants assured that participants had were some possible reasons for the negative relationship more land compared to nonparticipants in the study area between the age of the household head and participation in and thus, nonparticipants keep their sheep more frequently improved sheep production. *is result of the current study under stall feeding or cut and carry system, and also use more of other types of feed such as supplements and ex- is in line with the study of Bhattarai et al. [46] who reported that the level of innovativeness was found to be lower among pensive industrial by-products. It is in line with Mishra et al. [57] who reported that land is a very crucial input for older farmers. Also, this finding is in consonance with a study con- livestock production and that it can influence the production ducted by Danso-Abbeam et al. [54] on the adoption of of improved livestock production significantly and improved livestock technology which has reported that positively. younger farmers were more likely to adopt and the effect of age on the probability of adoption was elastic. Moreover, (6) Credit Participation. *is is a very important deter- Gunte [55] found that smallholders’ adoption of small ru- minant for households’ decision to take more risks and minants in the South-Eastern highlands of Ethiopia reported enhance their financial capacity to purchase inputs that that age had a negative effect on the adoption of new complements the package of sheep technologies, im- proved breed purchasing, veterinary purpose, and other technology. management activities. In this study, credit participation was similar to the hypothesis and was influenced signif- (3) Frequency of Extension Contact. Development agents visit farmers and would enable the farmers to develop a icantly and positively at a 5% significant level (p < 0.05). positive attitude towards participation in improved sheep *e probability of participation in improved sheep pro- production. *e finding was similar to the hypothesis of duction increased by odds of 10.026 or by a 3% probability this study which implied that contact with development level as compared to nonparticipants of credit. Partici- agents personally as well as engaging them in field days and pation in credit affects an improvement of participation in training influenced positively and significantly at 1% livestock technology production positively and signifi- cantly, and this is in consonance with the finding of Silong (p ≤ 0.01). *e odds ratio (1.019) indicates that the par- ticipation in improved sheep production increases by a [45]. factor of 1.019 or by a 2% probability level as the result of one unit increase of the extension contact for the house- (7) Off-Farm Income. Households’ income position and resource ownership were found to be important determi- holds. *is finding is consistent with the findings of Vince et al. [52] which has indicated that the livestock production nants in the participation of improved sheep production. systems require knowledge change through contacting Similar to the hypothesis, the result of this study indicated extension workers. that households who had better off-farm income from different sources participated well compared to those who (4) Membership of Cooperatives. Cooperatives are one of the did not get access to off-farm income. It influenced the important organizations in rural and agricultural development participation of smallholder farmers in improved sheep production positively and significantly at 1% p ≤ 0.001). which serve as an important source of information, knowledge transfer, and rural credit. In this study, similar to the hy- When off-farm income increased by one thousand ETB, the probability of participation increased by odds of 1.002 or by pothesis, participation in cooperatives had a significant and positive influence on the participation of smallholder farmers a 0.2% probability level. *is means that a farmer who had in improved sheep production at 1% (p ≤ 0.01), and the better off-farm income from different sources was more probability of cooperative members participation in improved likely to adopt improved sheep production. *is is in line sheep production increased by odds of 21.802 or by 80% with a study conducted by Mwangi and Kariuki [58], who probability level as compared to nonmembers of the cooper- reported that petty trades, daily labor on others’ farms, and ative. It is in line with Fufa [56], who reported that organizing nonfarm activities as well as small businesses enable farmers farmers in a cooperative society would facilitate access to credit, to get additional income to have production inputs and can extension information, and market. *is implies being a influence positively and significantly new agricultural technology adoption. member of rural cooperatives can enhance the adoption of new agricultural technology. (5) Land Owned by Households. Results showed that re- 3.5. Impact of Participation in Improved Sheep Production on spondents’ less participation in improved sheep production Smallholder Farmers’ Income. Sheep is one of the most af- was due to scarcity of rangelands. Similar to the hypothesis fordable animals in the world and can be accommodated in Advances in Agriculture 13 Table 7: *e results of the binary logit model. Variable (B) S.E Wald statistics Sig. Level Exp (B) ∗∗∗ Household labor in ME 2.49 0.679 13.51 0.001 12.061 ∗∗ Age of the rural farm household head (AG) − 1.243 0.589 4.456 0.035 3.466 Sex of the household head (SEX) − 0.242 1.092 0.049 0.824 1.274 ∗∗ Frequency of extension contact (FDC) 0.019 6 11.552 0.001 1.019 Education level of household head (EDU) 0.395 0.322 1.501 0.220 1.484 ∗∗∗ Membership of cooperatives (MSC) 3.082 1.147 7.215 0.007 21.802 ∗∗∗ Size of land in hectares (LAND SZ) 3.377 1.171 8.324 0.004 29.283 ∗∗ Credit participation (CP) 2.305 1.119 4.24 0.039 10.026 Farm income (FI) 0.000 0.000 0.705 0.401 1.000 Distance from market (DTNMC) 0.330 1.173 0.079 0.779 1.391 ∗∗∗ Off-farm income (OFI) 0.002 0.001 8.596 0.003 1.002 Access to animal health service (AHS) 5.821 0.606 5.537 0.217 333.309 ∗∗ Number of Obs. �144; p≤ 0.001; Nagelkerke R Square � 78.947. Notes: Exp (B) shows the predicted changes in odds for a unit increase in the predictor; and ∗∗∗ significant at 5%, and 1% levels (source: computed from own survey data, 2018). Table 8: Farm income of smallholder farmers in 2017. Participants Nonparticipants Variable t value Mean Stand. Deviation Mean Stand. Deviation ∗∗∗ Farm income 9627.45 2999.74 6105.38 2079.84 8.272 ∗∗∗ Income from sheep production 3447.06 1584.34 1380.65 230.43 12.38 ∗∗∗ Source: computed from own survey data, 2018; � 1% significance level. Participation of small holder farmers in improved sheep production No Yes Income from sheep production Figure 2: *e farm income of participant and nonparticipant smallholder farmers (source: computed from own survey data, 2018). any kind of weather condition. *ey are also called poor and key informants’ data confirmed that the income source man’s cows, and rearing sheep contributes a lot to the of smallholder farmers is mainly farming (both livestock and economy by generating household income, providing local crop), but the sheep production due to its potential in the employment, and export [59]. area made a great difference in smallholder household According to the survey results, the average farm income income. of participants and nonparticipants were 9627.45 and Sheep production is increasing constantly in the study area, due to the constant decrease of arable land as well as the 6105.38 ETB with a standard deviation of 2999.74 and 2079.84, respectively. Generally, farm income differences trend of improving the genotype of sheep (improving breed) between participants and nonparticipants of improved sheep at the community level, which initiates smallholder farmers producers were significant. Both focus group discussions (there is community-based improved breed selection Cumulative Frequency 8000 14 Advances in Agriculture Participation of small holder farmers in improved sheep production No Yes Farm income Figure 3: *e income of participant and nonparticipant smallholder farmers from sheep production. (source: computed from own survey data, 2018). practice by different stakeholders). As described in Table 8, improved sheep production earn more income due to pe- the average income from sheep production was 3447.06 and culiar characteristics of the Doyogena sheep breed (short- term reproduction rate, early weaning of weight, and better 1380.65 ETB with a standard deviation of 1584.34 and 230.43 for participants and nonparticipants, respectively. It has physical appearance), and it mostly favors the higher pro- ductivity of participants in improved sheep production. It is shown that participants’ average income was 3447.06 ETB, but in the case of nonparticipants it was only 1380.65 ETB. in line with the study of Legese et al. [60] who reported that Hence, there is a difference of 2066.41 ETB between par- improving the sheep breed can enhance productivity as well ticipants and nonparticipants. as the livelihood of smallholder farmers. Generally, participants of focus group discussions and Also, participants of focus group discussions clarified key informants suggested that the impact of participation in that improved sheep production showed a significant impact improved sheep production was significant and made a on the livelihood of smallholder farmers. Most households difference among smallholder farmers’ living styles. in the study area had short-term income as these smallholder As described in Figure 2, average income gained from farmers were challenged to cover food costs, education fees, clothes, and agricultural inputs. However, the living style of sheep production in the case of participants’ income ob- tained from sheep production ranges from 1,800 to 6,000 those who participated in improved sheep production has been changed, and it enables them to cover such costs easily ETB, whereas nonparticipants’ income ranged from 800 to 1,800 ETB. *is implies that participants gained better in- compared to nonparticipants. come from their sheep production compared to nonpar- ticipants in improved sheep production. Based on the 4. Conclusion response to focus group discussions and key informant data, this income difference is due to better market and financial *e expected output of the research was identifying the most support for participants from governmental and nongov- likely determining factors, income contribution, and chal- ernmental organizations as well as technical support from lenges related to improved sheep production regardless of Areka research center branch at Doyogena. Again, they smallholder farmers. In this study, 100 males and 44 females suggested that sheep production needs both technical and were included’ out of a total of 144 samples, 51 (35.4%) sheep financial support, so such reasons contributed to the income producers participated in improved sheep production. All difference between participants and nonparticipants. variables have a significant relationship with participation As shown in Figure 3, the farm income (which was except distance from the near market center. Determinants obtained both from livestock and crop) ranged from 4500 to that significantly limited participation of smallholder 17,000 and 500 to 10,000 ETB for participants and non- farmers in improved sheep production in the study area participants, respectively. Most of the participants obtained were labor, age, frequency of contact with development much income from sheep production due to the short-term agents, land size, off-farm income, membership of coop- reproduction rate, early weaning of weight, better price of eratives, and participation in credit. According to survey the improved breed, and weighing sell trend for improved results and focus group discussion responses, the households sheep in the study area. *is result was confirmed by focus which had large size labor undertook these activities than group discussions; the farmers in the study area have almost those that had a small size of labor and participated well in the same land size but the ones who participated in improved sheep production simultaneously. Count 16000 Advances in Agriculture 15 [4] H. Desta, Gendered Priority Livestock Species and Roles in Based on focus groups and key informants’ responses, Small Ruminant Production, CRP Livestock and IFAD, Addis aged farmers feared risk and management activities of Ababa, Ethiopia, 2017. improved sheep production, because of the shortage of [5] A. Lakew, A. Melesse, and S. Banerjee, “Traditional sheep communal land for free grazing (the only means of feeding production systems and breeding practice in Wolayita Zone sheep was carry and cut system), and housing was also of Southern Ethiopia,” African Journal of Agricultural Re- another challenge for elders. Another determinant that search, vol. 12, no. 20, pp. 1689–1701, 2017. influenced participation was land size owned by households [6] G. Gebrehiwot, T. Negesse, and A. 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Advances in AgricultureHindawi Publishing Corporation

Published: Sep 14, 2021

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