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Abstract

Although goat production plays a significant role in the livelihoods of smallholder farmers in Habru district, its potential to significantly improve rural livelihoods remains untapped. This study aimed to analyze factors that influence goat production participation and its constraints in Habru district, North Wollo Zone, Ethiopia. The study employed a multi-stage sampling technique to select 343 households (210 participants and 133 non-participants) from three rural kebeles of Habru district. Data was collected through a semi-structured questionnaire, key informant interviews, and focus group discussions. A double hurdle model was used to analyze the factors influencing both the decision to participate and the intensity of participation in goat production. The qualitative analysis revealed that income generation was the primary motive for goat keeping, cited by 59.25% of respondents, followed by meat consumption (16.7%) and savings/asset holding (10%). The study identified feed shortages, diseases, and drought as the most pressing challenges facing goat production, respectively. The econometric analysis, using a double hurdle model, revealed that male-headed households, larger family size, higher education levels, frequent extension contact, and smaller landholdings positively influenced the decision to participate in goat production. Furthermore, younger age, smaller landholdings, larger family size, higher education levels, frequent extension contact, and more farming experience were associated with larger herd sizes among participating farmers. The study recommends improving veterinary services, promoting disease prevention, providing farmer training, and sharing of experience. Furthermore, ensuring feed availability through fodder cultivation and alternative feed sources is crucial for sustainable goat production and improved rural livelihoods.

Keywords

double hurdle model Ethiopia goat production smallholder farmers participation

Introduction

Background of the Study

The largest livestock population in Africa is found in Ethiopia. According to CSA (2021) and Reis-Dennis (2020), the livestock subsector makes an enormous contribution to Ethiopia’s national economy and the improvement of smallholder farmers’ livelihood. Currently, the livestock sector accounts for 45% of the agricultural GDP, and approximately 84% of rural and urban poor households keep livestock in Ethiopia (FAO, 2017). There are about 70.3 million heads of cattle, 60 million poultry, 52.46 million goats, 42.9 million sheep, 10.79 million donkeys, 8.14 million camels, 2.15 million horses, and 6.98 million beehives in Ethiopia (CSA, 2021). Smallholder farmers’ welfare has been improved by diversifying in to livestock and the income obtained from the sale of livestock (Boka, 2020). The increasing demand for meat, milk, and other livestock products is likely to encourage small farmers to increase their herds and improve their efficiency. The raising of livestock has the potential to alleviate poverty for these families. In Ethiopia, there are roughly 14 million households that raise livestock, and two-thirds of them have herds smaller than three tropical livestock units (TLU) (FAO, 2017).

Ethiopia has obtained $66.59 million foreign exchange earnings from small ruminant meat and live shoat exports (Mijena & Getiso, 2022). Small ruminants play a decisive role in employment creation, generating income, improving nutrition, and food security for smallholder farmers. Small ruminants in Ethiopia are mostly raised in mixed crop-livestock production systems under a variety of agroecological conditions, which are determined by traditional husbandry practices and indigenous small ruminant breeds (Abera, 2021). The low cost of raising small livestock makes goat production an attractive option for low-income rural families seeking to improve their livelihoods and an accessible way for poor rural families to generate income (Namonje-Kapembwaa et al., 2022). Their merit lies in their rapid reproductive cycle, high market value, low operational costs, and thrive in harsh environments (Alilo, 2019). They rear for a variety of purposes, including income, saving/insurance, meat, and social and cultural functions (Sime et al., 2022).

In 2020/21, Ethiopia had 52.46 million estimated goats, of which Somalia (16,464,505), Afar (8,843,082), Oromia (8,425,727), and Amhara (7,045,305) accounted for the largest contribution to the country’s goat population, respectively (CSA, 2021). Goat production provides a number of socio-economic benefits that improve the livelihoods of communities. The major reasons for keeping goats are to generate regular cash income from the sale of goats (Byaruhanga et al., 2014). The increase in goat meat export demand has improved the recent goat meat market in Ethiopia. Approximately 12% of goat meat production is exported annually to Middle-East markets (Tsegaye et al., 2022).

Statement of the Problem

Climate change in arid and semi-arid environment, variable rainfall, and water scarcity are recurrent occurrences that affect food security and animal production (Assan, 2022). About 99.90% of goat in Ethiopia are indigenous breeds that survive in harsh environments (Mijena & Getiso, 2022), but they are characterized by low levels of production and productivity (Abera, 2021; Mijena & Getiso, 2022). Natural pastures supplemented with crop residues are the main source of feed (Gebrechristos & Dugma, 2013; Mekuria et al., 2018).

The main constraints of goat production in Ethiopia are diseases and parasites, feed shortage, labor scarcity, predators, land shortage, lack of improved goat breeds, and poor market access to sell goats and goat products (Gebrechristos & Dugma, 2013; Sime et al., 2022; Tadesse et al., 2022). A study by Mulie (2018) showed that disease was ranked as the first constraint, while feed shortage and predators were ranked second and third in all three agroecosystems. There were shortages of goat feed resources both in the dry and wet season of the year (Alilo, 2019). The area and its green coverage of both private and communal grazing lands are decreasing due to the increasing human population, resulting in deforestation for crop cultivation and the expansion of settlements (Alilo, 2019; Mekuria et al., 2018).

Goat production is practiced in vast geographical locations, mainly in low and middle-altitude areas, and supporting rural smallholder farmers’ livelihood in harsh environments, specifically in the study area. Despite its potential, it does not maximize the economic benefits of smallholder farmers (Gutema, 2022). Although there are rapidly growing small ruminant populations, especially goats, the sector is failing to specialize and intensify (Jemberu et al., 2022). The existing research emphasizes on goat breeding characteristics and management practices in different areas of Ethiopia. There are limited attention has been given to socio-economic of goat production in general and there is no research on goat production in the study in particular. Therefore, this study aimed to address the limitations of previous research on goat production and fills a critical gap in understanding smallholder goat production in arid and semi-arid areas of Ethiopia. It provides a more detailed analysis of the factors influencing participation and intensity, incorporating socioeconomic and demographic factors, contextual constraints, and the multi-faceted roles of goats in rural livelihoods.

Research Questions

This study aimed to provide empirical evidence to answer the following research questions.

What are the main constraints that hinder smallholder farmers’ goat production practices in the study area?

What factors influence the decision of smallholder farmers to participate in goat rearing in the study area?

What factors determine the intensity of goat production among participating farmers in the study area?

Research Objectives

The general objective of this study is to analyze the determinants of goat production and its constraints in Habru district. Specifically, the study aimed:

To identify the constraints of goat production practices in the study area.

To analyze the determinants of goat production participation decisions and the intensity of participation in the study area.

The study on smallholder farmers’ participation in goat production and its limitations offers valuable insight into the dynamics of goat production in arid and pastoral areas of Ethiopia. It provides context-specific information about the drivers and barriers of goat production in a rural Ethiopia. The findings of this study provides research-based evidence to policymakers on areas that require intervention to improve goat-based rural livelihoods. Ultimately, the research provides evidence-based recommendations to improve goat production, and improve rural livelihoods in the region.

Literature Review

Goat Production and Its Importance in Ethiopia

Goat production in Ethiopia operates under two main production systems: mixed crop-livestock systems and pastoral/agro-pastoral systems (Mijena & Getiso, 2022). In 2020/21, Ethiopia had 52.46 million estimated goats, of which Somalia (16,464,505), Afar (8,843,082), Oromia (8,425,727), and Amhara (7,045,305) accounted for the largest contribution to the country’s goat population, respectively (CSA, 2021). Goats raised in mixed systems exhibit higher reproductive performance due to better access to feed and water, unlike those in lowland pastoral systems (Getaneh et al., 2023).

Goat production is integral to Ethiopia’s smallholder livestock systems, which drive economic and social development (Kinati et al., 2023). Small ruminants, including goats, contribute to employment generation, household nutrition, and foreign exchange earnings. The sector’s importance is evidenced by its share of the national economy, contributing about 2% of the country’s annual GDP (Jemberu et al., 2022). Goats provide 16.8% of total meat and 16.7% of milk consumption in Ethiopia, with indigenous breeds being the predominant source (Adam & Mohammed, 2022). Goat production also supports traditional rituals, social obligations, and informal financial systems within rural communities (Derbib, 2021). Moreover, women play a vital role in goat production, using the income from livestock sales to support household nutrition and food security (Wodajo et al., 2020).

Constraints of Goat Production in Ethiopia

Goat production in Ethiopia is a vital component of smallholder livelihoods, contributing to food security, household income, and cultural practices. However, its development is constrained by feed shortages, diseases, water scarcity, predation, and market fluctuations. Despite the large population of goats, productivity remains low due to limited intensification, slow growth rates, and high kid mortality (Mijena & Getiso, 2022). Smallholder farmers’ practices, such as uncontrolled breeding, contribute to inbreeding, and reduced productivity (Tilahun, 2023). Multiple constraints hinder goat production in Ethiopia, limiting its potential to enhance livelihoods and economic development.

Feed Shortage: Feed scarcity is a major bottleneck in goat production, exacerbated by the reliance on natural pastures, crop residues, and indigenous browsing (Sime et al., 2022). Seasonal fluctuations in feed availability, coupled with the increasing demand for grazing land due to population growth, exacerbate the challenge (Mijena & Getiso, 2022). The limited availability of feed resources constrains herd size, production efficiency, and overall productivity.

Diseases and Veterinary Services: Diseases and lack of access to veterinary services are among the most frequently identified constraints to goat production in Ethiopia (Armson et al., 2020). Parasites and diseases significantly affect goat health, leading to production losses (Mahamed & Ali, 2023). Veterinary service gaps are more pronounced in rural and lowland areas, where preventive and curative measures are minimal.

Water Shortage and Drought Water scarcity is another critical challenge affecting goat production. Droughts intensify water shortages, making it difficult for farmers to provide adequate water for their livestock (Sime et al., 2022). Water availability influences production systems, with mixed crop-livestock systems often having better access to water than lowland pastoral areas (Getaneh et al., 2023).

Market Fluctuations and Infrastructure Market constraints, including price fluctuations and the lack of an organized marketing chain, affect the profitability of goat production (Armson et al., 2020). The absence of formalized marketing structures limits farmers’ ability to sell their goats at competitive prices. Poor infrastructure, such as inadequate transport and market facilities, further complicates farmers’ market access (Mahamed & Ali, 2023).

Theoretical Framework for Goat Production and Its Constraints in Ethiopia

The theoretical foundation for understanding farmers’ participation in goat production and its constraints in Ethiopia can be anchored in the principles of behavioral economics and the sustainable livelihoods framework. Behavioral economics integrates elements of psychology and sociology into traditional economic analysis, focusing on human decision-making in specific contexts. By using methods such as observation, investigation, and experimentation, behavioral economists aim to understand and predict economic behaviors in real-life situations (Luo, 2022). In the context of goat production, farmers’ participation is influenced by cognitive biases, social norms, and risk perceptions. Behavioral constraints, such as loss aversion, short-term thinking, and social pressure, shape farmers’ choices about adopting improved goat-rearing practices. Recognizing these behavioral dynamics is essential for designing policies and interventions that motivate farmers to engage in sustainable goat production.

The sustainable livelihoods framework (SLF) provides a complementary perspective on the constraints and enablers of goat production. It emphasizes the role of five key livelihood capitals (human capital, social capital, natural capital, physical capital, and financial capital) in shaping farmers’ capacity to engage in livestock production (Serrat, 2008). Livelihood resilience, which reflects the adaptive, absorptive, and transformative capacities of households, plays a critical role in sustaining livelihoods amid natural disasters and economic shocks (Sarker, 2019). In Ethiopia, constraints such as limited access to financial resources, inadequate veterinary services, and climate change-induced risks reduce farmers’ ability to maintain productive goat-rearing activities. The SLF highlights the role of enabling structures and processes, such as supportive government policies and access to agricultural extension services, in transforming livelihood strategies. By integrating the behavioral economics perspective with the SLF, a more holistic understanding of the constraints to goat production in Ethiopia emerges, allowing for more targeted and effective policy interventions.

The conceptual (Figure 1) framework outlines the relationships among key factors influencing goat production participation and herd size increase. It is structured in three main sets of characteristics: The framework centers on goat production participation and increase in goat herd size as the primary outcomes. Arrows indicate how demographic, institutional, and economic factors collectively influence these outcomes, emphasizing their interconnected roles in shaping participation and productivity in goat production systems.

Figure 1.

Conceptual framework of the study.

Research Methodology

This section discusses the sampling procedures, method of data collection, and technique of data analysis used in this study.

Study Area

North Wollo Zone is known for its significant reliance on livestock and Habru district’s arid and semi-arid climate supports goat production. As goats are known for their ability to thrive in harsh environments, making the area an ideal location to study goat farming practices. The study is conducted in Habru District, Amhara Regional State, Ethiopia (Figure 2). The district center, Mersa Town, is located 491 km northeast of Addis Ababa and 30 km south of Woldia. Habru district is located between 11°24′00″N–11°45′00″N latitude and 39°30′00″E–39°57′00″E longitude. It is bordered on the south by the Mille River, on the west by Guba-Lafto, on the north by the Alewuha River, and on the east by the Afar Region (HDAO, 2023). According to the Habru District Administration Ofﬁce (HDAO, 2023), the district comprises 35 rural and 3 suburban kebeles, covering a total area of 1,239.79 km² and supporting a projected population of 235,347 in 2016/17 (CSA, 2013).

Figure 2.

Map of the study area.

According to Habru District Office of Agriculture (HDOA, 2023), the agroecology zone of the study district are categorized into mid-highland and lowland. The major topography of the district is described as plain land, plateaus, and escarpment (Yimer et al., 2016). Crop-livestock mixed farming is the dominant source of livelihood for the majority of the population in the area. The district’s erratic rain-fed livelihood system makes the area susceptible to drought and food insecurity.

Data Collection Procedure

The primary data for the study was collected using a combination of semi-structured questionnaires, key informant interviews, and focus group discussions (FGDs) to ensure a comprehensive understanding of the factors affecting goat production participation. A semi-structured questionnaire was designed to collect quantitative and qualitative data on socio-economic characteristics, demographic factors, and the challenges faced by smallholder farmers in goat production. The questionnaire was developed based on a review of relevant literature and consultation with subject matter experts, ensuring that the questions were contextually relevant and aligned with the study’s objectives. Before full deployment, a pilot test was conducted with a small group of farmers to identify any ambiguities, inconsistencies, or redundant questions. Feedback from the pilot test was used to refine the questionnaire, enhancing its clarity, relevance, and comprehensiveness. Reliability was ensured through internal consistency checks, while content validity was established by seeking input from agricultural experts and stakeholders familiar with goat production in the study area.

Key informant interviews and FGDs were used to complement the data collected through questionnaires. Key informants, including local agricultural officers, extension agents, and experienced farmers, provided in-depth insights into the broader contextual factors influencing goat production. FGDs were conducted in each of the three sample kebeles, with eight participants per group, to capture diverse perspectives from farmers with practical experience in goat rearing.

Sampling Procedure and Sample Size

The research employed a multi-stage technique to select sample respondents. Firstly, Habru district was selected from the North Wollo Zone as it encompasses all agroecology in the district and it is goat producing district. Secondly, three rural kebeles were selected from the district. Following this, a list of household heads was obtained from the district agriculture office, and then households in each kebele of the district were categorized into goat production participants and non-participants. Finally, simple random sampling techniques were used to select a total of 343 household heads (210 participants and 133 non-participants) from the selected kebeles (Table 1).

Table 1.

Distribution of Sample Household Head.

Kebele	Total population HH	Sample HH
Kebele	Total population HH	Non-participant HHs	Participant HHs	Total sampled HHs
Menentila Kebele	1,050	44	70	114
Direroka keble	870	37	58	95
Kese Kebele	1,230	52	82	134
Total	3,150	133	210	343

Source. Compiled from HDOA, 2024.

The number of respondents was determined using the Cochran sample size determination formula (Cochran, 1977) to represent the target population in the Habru district. The Cochran formula is widely used when the study needs accurate variability and heterogeneity in the population. Accordingly, the required sample size was determined as follows:

n = \frac{Z^{2} pq}{e^{2}}

(1)

Where: n—is the sample size, z—Normal distribution, p—Estimated proportion that is present in the population, q = 1−p (proportion population not included in the sample) and e—the desired level of precision.

n_{0} = \frac{{1.96}^{2} 0.5 * 0.5}{{0.05}^{2}} = 384

For small populations of a known size, it uses Cochran’s equation together with a population correction to calculate the sample size.

n_{0} = \frac{n_{0}}{1 + (\frac{n_{0} - 1}{N})}

(2)

The appropriate sample size given the population size and the specified combination of precision, confidence, and variability is 343.

Methods of Data Analysis

The study used both descriptive and econometric methods to analyze the empirical data collected from sample respondents. Respondent characteristics were described using descriptive statistics such as mean, standard deviation, frequency, and percentage. In addition, the Likert-scale is used to identify the constraints of goat production in the study area. In addition, this study used a double hurdle model to estimate the determinants of smallholder farmers’ participation and intensity of participation in goat production.

The Double-Hurdle Model

The double-hurdle model is an econometric approach used to analyze two distinct but interrelated decisions: the decision to participate in an activity and the extent of participation (Cragg, 1971). Unlike the Tobit model, which assumes that the same factors influence both decisions, the double-hurdle model allows for the possibility that different factors affect each stage (Burke, 2009). This makes it a more flexible and realistic framework for analyzing smallholder farmers’ participation in goat production (Jones, 1989).

In datasets on goat production, there may be many zero observations for herd size. These zeros do not necessarily imply that farmers cannot manage large herds but rather that they have chosen not to rear goats at all. The Tobit model would treat these zero observations as corner solutions, but the double-hurdle model recognizes that zero herd sizes can arise from two separate decisions: the decision not to participate or the inability to rear goats due to other constraints (Burke, 2009). This dual process is better captured by the double-hurdle model.

The likelihood ratio test was used to determine whether the Tobit or double hurdle model best fits the data (Kelifa, 2023). It examines whether farmers make two-stage decisions simultaneously or separately. The Log-likelihood Ratio Test tests whether the two equations (participation and intensity) can be estimated separately. The test shows that there is no correlation between residuals of probit and truncated regression. The loglikelihood ratio test LR chi²(11) = 256.87 Prob > chi² = 0.0000 indicates the independent of error in the two hurdle equation and fitting of the double hurdle model in this context. The Shapiro-Wilk test result (p = .28425) tells normality of the data.

In the context of goat production, the first hurdle represents the decision of smallholder farmers to participate in goat production (whether or not to own goats). This was modeled using a Probit model, where the probability of participation is estimated as a function of socio-economic, demographic, and institutional factors (Jones, 1989). The outcome at this stage is binary (1 = participates, 0 = does not participate). The second hurdle examines the extent or intensity of participation, measured by the number of goats owned (herd size). This stage is modeled using a truncated regression, which accounts for the fact that herd size is only observed for those who have passed the first hurdle (those who decided to participate; Burke, 2009). By modeling the participation decision and intensity of participation separately, the double-hurdle model provides more precise policy insights. Policymakers can identify factors that influence both the decision to participate and the extent of participation, which is essential for designing targeted interventions to support smallholder goat farmers.

The model is specified as follows:

First Hurdle: The decision to participate can be modeled as (Cragg, 1971) specified as:

\begin{matrix} Y_{i}^{*} = {X_{i}}^{'} β + ε_{i} ε_{i} ~ N (0, δ^{2}) \\ Y_{i} = {\begin{matrix} 1 if Y_{i}^{*} > 0 \\ 0 if Y_{i}^{*} \leq 0 \end{matrix}} \end{matrix}

(3)

When $Y_{i}$ is the observed herd size, and $Y_{i}^{*}$ denotes the latent herd size. The latent dependent variable takes the value 1 if a smallholder farmer participated in goat production during the survey year, 0 otherwise, X is a vector of household characteristics.

In the second hurdle, the truncated regression model is used to analyze factors determining the intensity of participation in goat production. Truncated regression excludes part of the sample observations based on the value of the dependent variable (Wooldridge, 2010) and uses observations of respondents that have participated in goat production. This is specified as follows:

\begin{matrix} D_{i}^{*} = {X_{i}}^{'} γ + μ_{i} μ_{i} ~ N (0, δ^{2}) \\ D_{i} = {\begin{matrix} D_{i}^{*} if Y_{i}^{*} > 0 and Y_{i} = 1 \\ 0 Otherwise \end{matrix}} \end{matrix}

(4)

Where $D$ is the intensity of participation in goat production, which depends on a latent variable $D_{i}^{*}$ greater than zero, and conditional on the probability of participation $Y_{i}$ .

Variable Description and Hypothesis

Dependent Variable

Goat Production Participation: A binary variable indicating whether a household participates in goat production or not. It takes 1 If the household participates in goat production or 0 If the household does not participate in goat production

Intensity of Participation in Goat Production: This is a continuous variable that measures the extent of a household’s involvement in goat production. It is represented by the total number of goats owned by the household (goat herd size). This variable not only captures participation but also assesses the level or scale of a household’s engagement in goat production activities.

A summary of the independent variables used in the Double Hurdle model is presented in Table 2. The variables’ measurements and their expected effects, as informed by a review of relevant literature, are also provided.

Table 2.

Description and Hypothesis of Explanatory Variables.

Variable	Definition and measurement	Expected effect (Hypothesis)
Age of household head	Continuous, measured in years	+/− (Older individuals may have more experience but less physical capacity)
Sex of household head	Dummy, (Male = 1, Female = 0)	+ (Men may have more decision-making power and access to resources)
Family size	Continuous, measured in adult equivalents	+ (Larger families may have more labor for livestock care)
Education level of household head	Continuous, measured in years of formal schooling	+ (Education can improve knowledge of animal husbandry practices and market opportunities)
Farming experience	Continuous, measured in years of farming experience	+ (More experience can lead to better management practices and higher productivity)
Land holding	Continuous, measured in hectares of arable land holding	+/− (More land may enable diversification, but less land may necessitate livestock as a primary income source)
Extension contacts	Continuous, measured by the frequency of contact with extension agents	+ (Extension services can provide valuable information and training)
Distance to the nearest market	Continuous, measured in kilometers	− (Closer markets can facilitate sales and reduce transportation costs)
Distance to the veterinary center	Continuous, measured in kilometers	− (Closer veterinary centers can improve animal health and reduce mortality rates)
Off/Non-farm Activity	Dummy, (1 = yes, 0 = no)	+/− (Off-farm income can supplement household income, but may reduce time for livestock care)
Livestock in TLU	Continuous, livestock holding of a household in Tropical Livestock Units (TLU)	+/− (Larger herds may indicate wealth and diversification, but also higher management costs)

Source. Compiled from literature, 2024.

Result and Discussions

Purpose of Goat Keeping

Goat in the study area were kept for various purposes (Table 3). The Likert scale result below indicates that smallholder farmers predominantly keep goats to generate income for their livelihood. Smallholder farmers participate in goat keeping to generate income for their livelihood such as to buy food, cloth, transport, and medication. Beyond generating income for their livelihood, smallholder farmers keep goats for meat consumption, social or cultural roles, saving or asset holding, and manure, respectively. Similarly, Tesfaye and Tamir (2015) revealed that the source of income was ranked as the first purpose of goat production in Yabello District of Borana Zone. Moreover, Farmers in Korahey Zone raise goats for various purposes, including meat, milk, income, and as a form of savings (Tadesse et al., 2022).

Table 3.

Smallholder Farmers Purpose of Keeping Goats.

Purpose	Score	% of respondents’ first-order response	Rank
For income generation	2,300	59.25	1
Meat	650	16.72	2
Saving/assurance	412	10	3
Social and cultural function	340	8.74	4
Manure	185	4.75	5

Source. Own Survey, 2024.

Goat Production Constraints in the Study Area

The major goat production constraints in the district are presented in Table 4 below. Smallholder farmers face different constraints impeding the productivity of goat production. The fundamental constraints of goat production in the study area are feed shortages leading to flock death, and susceptibility to diseases. The problem is aggravated by erratic rainfall, which further deteriorates feeding availability. In addition, disease, water shortages, drought, predators, and inadequate government support are the main restraints of goat production. The finding is in line with the review conducted by Tsegaye et al. (2022), who revealed that goat productions is highly hindered by feed shortages, diseases, and climate change, market fluctuation, mortality, transport, and severe water shortages. In Ethiopia, shortages of feed, both in quantity and quality, diseases, and parasites are the major constraints of livestock production (Management Entity, 2021).

Table 4.

Constraints of Goat Production in the Study Area.

Constraints	Frequency					Total score	Mean	Rank
Constraints	SDA -1	DA-2	N-3	A-4	SA-5	Total score	Mean	Rank
Feed shortage	0	7	50	100	186	1,494	4.35	1
Disease	0	0	18	200	125	1,479	4.31	2
Drought	0	45	176	74	48	1,154	3.36	3
Water shortage	14	90	136	80	23	1,037	3.02	4
Lack of government support	10	147	122	64	0	926	2.70	5
Predator	56	134	153	0	0	783	2.28	6

Source. Own Survey, 2024.

Note. SDA = strongly disagree; DA = disagree; N = neutral; A = agree; SA = strongly agree.

Econometric Results

Descriptive Analysis

From the total of 343 respondents, 133 (38.78%) were non-participants, and 210 (61.22%) were participants in goat production. The Focus Group Discussion (FGD) revealed that farmers’ decisions not to participate in goat production are shaped by a range of socio-economic and environmental factors. A key constraint is the lack of access to critical resources, including startup capital, goat keepers, and adequate grazing land, which significantly discourages participation. Production risks, such as susceptibility to disease outbreaks and limited access to veterinary services, further dissuade farmers from engaging in goat rearing. Additionally, risk-averse farmers may not engage in goat production due to the perceived goat loss with disease outbreak. The household head sex composition of the sample household head indicates that out of the total respondents of 254 were male and 89 were female (Table 5).

Table 5.

Descriptive Statistics for Dummy Explanatory Variables.

		Participant HH (N = 210)		Non-participants (N = 133)		Total (N = 343)		$X^{2}$
Variables		Freq	%	Freq	%	Freq	%
Sex of HH	Female	50	23.81	39	29.32	89	25.95	1.2884
	Male	160	76.19	94	70.68	254	74.05
	Total	210	100	133	100	343	100
Off/non-farm income	No	142	67.62	74	55.64	216	62.97	5.012**
	Yes	68	32.38	59	44.36	127	37.03
	Total	210	100	133	100	343	100

Source. Own Survey, 2024.

Statistically significant at 5 percent.

The chi-square test revealed no significant variation in sex between participating and non-participant smallholder farmers. Out of the total sampled respondents, 63.53% reported off-farm income, while 36.47% did not. The chi-square test for off-farm income between the two groups was determined to be statistically significant at 5% level.

The mean age of the sampled household heads was 45 years, ranging from 21 to 71 years (Table 6). The result of the t-test for the differences in age distribution between the two groups was insignificant. The average family size of respondents in the study area was 2.25, ranging from 1.6 to 3.5 man-equivalents. The average family size of goat-producing farmers was 2.31 male-equivalent and that of non-participants was 2.16. The results indicate that participants in goat production households have a larger family size than non-participants. The mean difference between participants and non-participants family size was found to be statistically significant at 1% significant level. Education helps farmers acquire and understand the economic benefits of goat production.

Table 6.

Descriptive Statistics for Continuous Explanatory Variables.

Variables	Participant HH (N = 210)		Non-participant HH (N = 133)		Combined (N = 343)		t-Value
Variables	Mean	SD	Mean	SD	Mean	SD	t-Value
Age of HH head	45.171	11.452	45.571	11.289	45.326	11.374	−0.3169
Family size	2.312	0.249	2.161	0.219	2.253	0.249	5.688***
Education level	3.076	2.048	1.451	1.40	2.446	1.987	8.035***
Livestock holding	0.973	0.505	1.08	0.501	1.014	0.506	−1.9167*
Extension contacts	3.247	1.322	2.316	1.117	2.886	1.325	6.7456***
Farming experience	14.504	6.025	14.782	5.214	14.612	5.718	−0.4369
Distance to market	12.282	4.251	13.015	4.238	12.566	4.255	−1.5577
Distance to veterinary center	2.645	1.642	2.627	1.701	2.637	1.662	0.0959
TLU except goat	4.097	1.645	3.806	1.775	3.984	1.699	1.5464

Source. Own Survey, 2024.

***

, and * means statistically significant at 1%, and 10% respectively.

The mean average educational status of household heads was found to be 3 years for participants and 1.4 years for non-participants (Table 6). There is a significant variation between participants and non-participants at 1% significant level. Arable land holding negatively and significantly affects smallholder farmers’ goat production participation at 10% significance level. The survey indicated that the mean monthly extension contact for participants was 3.2, while 2.88 for non- participants. The sampled respondents have an average monthly extension contact of 3 times with a minimum and maximum of 1 and 6 times, respectively. The mean frequency of extension contact per month between the participant and non-participant was statistically significant at 1% significant level. The average number of years of farming experience was about 14 years, with a minimum and maximum of 4 and 28 years, respectively. The livestock holding except goat measured in terms of Tropical Livestock Unit (TLU) and the mean TLU was found to be 3.984. The mean distance of the market center from the respondent’s residence was 12.5 km, with a minimum of 7 km and maximum of 21 km distance from the nearest market center.

Econometrics Model Results

The likelihood ratio test for model comparison rejects the null hypothesis of equivalence between Tobit and the Double Hurdle econometric models. In addition, Heckman selection model result shows no selection bias as rho is insignificant in the model. Hence, this study employed a double hurdle model to analyze smallholder farmers’ participation and intensity of participation in goat production. The LR chi² (11) = 256.87 is significant at 1% level, which indicates that the overall good fit of the double hurdle model in the data. The result of double hurdle model presents the factors affecting smallholder farmers’ goat production (Table 7). The result of model estimation shows that out of the 11 explanatory variables, six were found to have significantly determined participation decision in the first hurdle and 7 variables were found significantly determined intensity of participation in the second hurdle.

Table 7.

Double Hurdle Model Result (Participation and Intensity of Participation in Goat Production).

Variable	First hurdle (Probit Model)			Second hurdle(truncated regression)
Variable	Coefficient (SE)	Z-value	dy/dx	Coefficient (SE)	Z-value
Sex	0.435** (0.2012)	2.16	0.549671	0235 (0.6438)	0.04
Age	0.0008 (0.0083)	0. 10	−0.03616	−0.056** (0.0246)	−2.29
Famsz	2.173*** (0.3847)	5.65	0.227532	2.781*** (1.0682)	2.60
Edulevl	0.446*** (0.062)	7.19	0.138131	0.923*** (0.1318)	7.00
landholding	−0.585*** (0.1767)	−3.31	−0.59162	−0.901* (0.5289)	−1.70
Extencontact	0.559*** (0.0801)	6.97	0.612198	1.061*** (0.2077)	5.11
experi	−0.025 (0.0157)	−1.61	−0.07546	0.014(0.0477)	0.29
DMkt	−0.056*** (0.0213)	−2.62	−0.38114	−0.28*** (0.0645)	−4.35
TLU except goat	−0.003 (0.0515)	−0.06	0.137501	0.215 (0.1604)	1.34
Distvet	−0.026 (0.0535)	−0.50	−0.09821	−0.012 (0.1657)	−0.08
Off/non-farm	−0.046(0.1793)	−0.26	−0.49621	−1.968*** (0.5667)	−3.47
Constant	−5.631 (1.0273)	−5.48		4.917 (3.0829)	1.60
Lnsigma	1.311 (0.0515)	25.42
Sigma	3.709 (0.1912)	-
		Number of obs.		343
LR chi²(11)	256.87***	Prob > chi²		0.0000

Source. Own Survey, 2024.

Note. SE = standard error.

***

, **, and *Implies that the estimated parameters are significantly different from zero at 1%, 5%, and 10% significance level, respectively.

Sex of household head (Sex): sex of household head positively and significantly affects participation in goat production at 5% significance level. The result indicates that male-headed households are more likely to engage in goat production, reflecting the cultural norms that position men as primary decision-makers in livestock management and farm activities. The marginal effect, which shows that being a male-headed household increases the likelihood of goat production participation by 54.96%, highlights the critical role of gender in shaping agricultural engagement (Table 7). Rural women manage the multifaceted household activities of goat-keeping. However, the foremost concern is that many of these activities are not effectively recognized (Narmatha et al., 2017). This implies that policies are essential to promote empowering omen’s capacity and decision-making power in goat production could increase overall participation rates and contribute to more inclusive and sustainable production systems.

Age of household head (Age): The age of the household head was found to significantly and negatively affect intensity of smallholder farmers’ participation in goat production at 5% significance level. Thus, the negative coefficient indicates that an increase in the household head’s age leads to a reduction in herd size, with each additional year of age resulting in a decrease of 0.056 units in herd size. This might be due to the fact that aged farmers are unable to manage a large number of goats due to difficulties in managing the day-to-day tasks of goat-keeping, as well as challenges related to labor, mobility, and other farm-related activities. Similarly, the study by Taruvinga et al. (2022) found that older farmers may struggle with the physical demands of managing larger herds, and maintain mobility. Consequently, as age increases, household heads may be less inclined to engage in labor-intensive activities or may prefer smaller, more manageable herds (Lucia, 2024).

Family size (famsz): As previously hypothesized, family size affected participation and intensity of participation of smallholder farmers’ goat production at 1% significance level. The result, indicating a 22.75% increase in the probability of household participation in goat production with each additional family member, highlights the critical role of labor availability in enhancing participation in goat production and increase herd size. Thus, the coefficient shows that each additional man equivalent would increase the herd size by 2.781 units. This is supported by Taruvinga et al. (2022), who revealed that households with more family members are likely to have surplus labor for goat management activities. Similarly, Hibso (2021) demonstrated a positive relationship between family size and the decision to engage in small ruminant livestock production. The positive association between family size and livestock production likely stems from the labor intensity of the practice, and larger families provide more manpower to efficiently manage and care for the animals (Belay et al., 2021).

Education level of Household Head (Edulevl): This was positively associated with farmers’ likelihood of participation and intensity of participation at 1% significance level. On average, each additional year of education increases the probability of a farmer’s participation in goat production by 13.81%. The result implies that for each additional increase in education level by 1 year, the smallholder proportion of farmers herd size increases by 0.923 units. This relationship may be attributed to the fact that education enhances farmers’ ability to comprehend the benefits of goat production, enabling them to adopt improved management practices that lead to greater economic returns. In line with the finding, Ayal and Mamo (2024) reported education enhance farmers ability to take informed decision and analyze relevant information and technology adoption.

Size of Arable landholding: landholding in hectares has a negative and significant effect the participation and extent of participation in goat production at 1% and 10%, respectively. A 1 ha increase in arable land reduces the probability of participation in goat production by 59.16% and decreases the goat herd size by .901 units. This negative effect may stem from a tendency among farmers to prioritize crop cultivation over livestock rearing, viewing farming as their primary livelihood. Furthermore, (Kechero et al., 2013) explored that the practice of subdividing household land among children as they form new families can result in progressively smaller landholdings, which may force farmers to diversify their livelihoods by incorporating goat production. This highlights the interplay between land availability, agricultural priorities, and the potential for smallholders to engage in alternative livelihood strategies such as goat farming.

Frequency of extension contact (Extencontact): It has a positive and significant effect on the participation and intensity of participation in goat production at 1%, respectively. The marginal effect shows that for each additional contact with the extension agents, the probability of participating in goat production increases by 61.23% and increases smallholder farmers herd size by 1.061 units. The positive relationship indicates that extension service is vital to enhance farmers’ participation and intensity of participation in goat production. The study finding is consistent with Hibso (2021) who found that access to contact extension has a positive effect on farmer’s small ruminant livestock production. The study conducted by Ayal and Mamo (2024) also illustrated that extension enhances smallholder farmer adoption of climate smart livestock production.

Distance to the nearest market: distance to the market negatively and significantly affect the participation and intensity of participation in goat production at 1% significance level. The finding showed that an increase in the distance of smallholder farmer’s residences to the nearest market decreased the probability of participation and herd size by 38.11% and .28 units, respectively. The accessibility of nearby markets increases smallholder farmers’ commercialization and thereby increases their participation and intensity of participation in goat production. Similarly, long-distance livestock markets are difficult for livestock commercialization (Belay et al., 2021).

Off/non-farm income participation: participation in off/non-farm activities negatively and significantly affects the extent of participation in goat production at 1%. It is expected that smallholder farmers participation in off/non-farm income-generating activities likely to leave goat production and focus on other livelihood activities. The findings of Key (2020) suggesting that when households receive off-farm income, they may prioritize other livelihood activities over livestock farming. In contrast, Hibso (2021) found that access to off-farm income positively and significantly influenced the probability of participating in small ruminant livestock production decisions.

Conclusion and Recommendations

The study on goat production in Habru District, Ethiopia, identified key determinants of smallholder farmers’ participation in goat production and its intensity. The findings revealed that participation in goat production is primarily driven by income generation, with over 59% of farmers citing it as their main motive. Major constraints include feed shortages, diseases, and water scarcity, which significantly hinder productivity. The econometric analysis revealed that male-headed households, larger family sizes, and higher levels of education among farmers significantly increased the likelihood of goat rearing. In contrast, households with larger landholdings tended to prioritize crop production over goat rearing. Likewise, herd size was positively associated with younger farmers, larger families, higher educational attainment, and greater farming experience. However, similar to the participation decision, land ownership exhibited a negative influence on herd size, suggesting a preference for crop production in households with larger landholding.

Based on the findings of this study, several policy recommendations can be made to enhance smallholder farmers’ participation in goat production and improve its intensity in the study area. First, targeted support should be provided to female-headed households and farmers with smaller family sizes, as these groups are less likely to participate in goat rearing. Extension services and training programs should focus on improving farmers’ education and technical skills, particularly for younger and less experienced farmers, as these factors positively influence herd size. Addressing key production constraints, such as feed shortages, water scarcity, and disease outbreaks, requires investment in sustainable feed production, water harvesting technologies, and access to affordable veterinary services. Finally, since income generation is a key driver of participation, supporting market access and facilitating better prices for goat products can further incentivize farmers to engage in goat production, thereby enhancing rural livelihoods and food security.

Limitations of This Study

This study does not provide comprehensive evidence on goat production in Ethiopia due to its limited scope and the specific issues addressed. The analysis is confined to the North Wollo Zone and focuses solely on smallholder farmers’ participation in goat production, the intensity of their participation, and the key production constraints. As a result, the findings may not be fully generalizable to other regions of Ethiopia or to other aspects of goat production beyond the identified context of the study.

Future Research Directions

Future research should adopt a broader geographic scope, incorporating multiple districts and agro-ecological zones to enhance the generalizability of findings and assess location-specific factors affecting goat production. Longitudinal studies are needed to examine the impact of climatic changes, such as droughts and water scarcity, on goat production and farmer livelihoods. Additionally, future studies should investigate the role of financial inclusion, including access to credit and insurance, in mitigating production risks and uncertainties for smallholder farmers.

Footnotes

Acknowledgements

We extend our sincere appreciation to the development agents in the study area for their invaluable support in facilitating the survey process. We also express our gratitude to the goat producers who generously provided their consent and thoughtful responses to the survey questions, thereby contributing to the success of this study.

ORCID iD

Ebrahim Endris

Statements and Declarations

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Determinants of Smallholder Farmers’ Participation in Goat Production and Its Constraints in North Wollo Zone,Amhara Region,Ethiopia

Abstract

Keywords

Introduction

Background of the Study

Statement of the Problem

Research Questions

Research Objectives

Literature Review

Goat Production and Its Importance in Ethiopia

Constraints of Goat Production in Ethiopia

Theoretical Framework for Goat Production and Its Constraints in Ethiopia

Research Methodology

Study Area

Data Collection Procedure

Sampling Procedure and Sample Size

Methods of Data Analysis

The Double-Hurdle Model

Variable Description and Hypothesis

Dependent Variable

Result and Discussions

Purpose of Goat Keeping

Goat Production Constraints in the Study Area

Econometric Results

Descriptive Analysis

Econometrics Model Results

Conclusion and Recommendations

Limitations of This Study

Future Research Directions

Footnotes

Acknowledgements

ORCID iD

Statements and Declarations

References