Determinants of Productivity and Sustainability in Small‐Scale Dairy Industries: Evidence From the Salale Zone of Oromia, Ethiopia

ABSTRACT

The small‐scale dairy industry plays a crucial role in improving rural livelihoods, nutrition and income generation in Ethiopia. However, its productivity and sustainability are constrained by socio‐economic, institutional and climatic factors. This study identifies the key determinants influencing productivity and sustainability in the small‐scale dairy sector of the Salale Zone, Oromia, Ethiopia. Primary data were collected from 250 smallholder dairy farmers using structured questionnaires. Ordered Logit regression model with marginal effects estimation was employed to assess factors affecting the likelihood of achieving higher dairy productivity under varying household, institutional and environmental conditions. The results indicate that age of household head (β = 0.012), education level (β = 0.040), land ownership (β = 0.490), access to social networks (β = 0.188), infrastructure (β = 0.150) and veterinary services (β = 0.160) significantly enhanced productivity. Conversely, larger household size (β = –0.038), low economic status (β = –0.275), market inefficiencies (β = –0.230), high feed prices (β = –0.290), milk adulteration (β = –0.270), price fluctuations (β = –0.340), seasonal feed shortages (β = –0.175) and climatic variability (β = –0.250) reduced performance. Marginal effects analysis showed that market inefficiencies, adulteration and price fluctuations decreased dairy success by 35%, 29% and 22%, respectively, while improved feed access, infrastructure, finance and training increased it by 31%, 27%, 24% and 18%. The findings highlight the combined influence of socio‐economic, institutional and climatic factors on dairy productivity and provide empirical insights for enhancing efficiency, profitability and sustainability in Ethiopia's small‐scale dairy industry.

Small‐scale dairy productivity in Ethiopia is significantly shaped by socio‐economic, institutional and climatic factors. Education, land ownership, infrastructure and veterinary access enhance performance, while market inefficiencies, feed costs and climate variability constrain success, underscoring the need for integrated interventions to improve sustainability and profitability

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1. Introduction

Small‐scale dairy farming is a cornerstone of Ethiopia's agricultural sector, particularly in the Salale Zone, where it supports rural livelihoods and contributes substantially to household nutrition and local economic growth. The rising population, urbanization and dietary shifts have led to increasing demand for dairy products in Ethiopia, creating opportunities for sectoral expansion and modernization (Gizaw 2021; FAO 2022). However, traditional smallholder dairy systems in the Salale Zone are under pressure from modernization and market demands. These systems, while historically resilient, are often characterized by low productivity, limited technology adoption and weak value chain integration (Zeleke and Lema 2023). In contrast, modernization efforts emphasize improved breeding, efficient feed management and enhanced milk processing technologies (Hedlund 2021). This tension between tradition and modernization presents both challenges and opportunities for advancing the dairy value chain in the region (Ayele et al. 2024; Kassie et al. 2022).

Milk quality and adulteration represent major challenges within the Salale Zone's dairy sector. Practices such as water dilution and chemical adulteration undermine product safety, consumer confidence and market pricing (D. Tadesse et al. 2020; H. Mekonnen and Haji 2021). Economic fluctuations, including volatile milk prices and rising production costs, further constrain profitability. Climatic variability—manifested through erratic rainfall and temperature extremes—reduces forage availability and animal productivity (A. Tadesse 2023; Mulugeta et al. 2022). Moreover, the genetic potential of local breeds remains limited, restricting both milk yield and quality (Tesfaye 2024; Tiruneh et al. 2021). The economic viability of small‐scale dairying is also shaped by market inefficiencies, weak infrastructure and constrained market access (Kebede 2023). Meanwhile, feed shortages, aggravated by land use change and climatic stress, continue to impede production (Beyene 2022; M. M. Mekonnen and Hoekstra 2022). Although policy interventions exist, they are often fragmented or poorly aligned with the needs of smallholder farmers (Meles 2023; Seyoum and Wolde 2023).

Despite its importance, comprehensive research on the determinants of small‐scale dairy productivity and sustainability in the Salale Zone remains limited. Prior studies have largely focused on isolated aspects such as milk quality, feed management or market issues, but have not integrated socio‐economic, institutional and climatic dimensions into a unified analytical framework. Furthermore, limited attention has been given to how traditional practices are evolving under modernization pressures and how such transitions affect the efficiency and equity of the dairy value chain (B. Lemma and Adane 2024).

This research is particularly timely and relevant because the Salale Zone part of Ethiopia's ‘dairy belt’ possesses favourable agro‐ecological conditions and a long‐standing dairy tradition, yet its production trends have recently declined due to feed scarcity, inadequate breeding and poor market access. Accordingly, this study aims to analyse the determinants influencing the productivity and sustainability of small‐scale dairy farming in the Salale Zone of Oromia, Ethiopia, by identifying and evaluating the socio‐demographic, economic, institutional and climatic factors shaping dairy performance and proposing evidence‐based strategies to enhance resilience, profitability and long‐term sustainability. The findings are expected to provide practical insights for policymakers, development practitioners and researchers in strengthening the smallholder dairy value chain, improving milk quality and feed systems, and promoting sustainable modernization in Ethiopia's dairy sector.

2. Materials and Methods

2.1. Description of the Study Area

The study was conducted in the Salale Zone (officially the North Shewa Zone) of the Oromia Regional State, Ethiopia. The zone is situated in the central highlands north of Addis Ababa and is characterized by a temperate highland climate, with elevations ranging from 1500 to over 3000 m above sea level, annual rainfall between 800 and 1200 mm, and mean annual temperatures from 10°C to 20°C. The climate is marked by moderate temperatures, variable rainfall and occasional extreme weather events that directly affect dairy production (M. Tadesse et al. 2022).

These agro‐climatic conditions are generally favourable for livestock production, particularly for dairy farming. The Selale Dairy Cooperative Union (SDCU), based in Fiche, plays a central role in coordinating more than 30 primary cooperatives, categorized by daily milk supply capacity as high (>400 L/day), medium (200–400 L/day) and low (<200 L/day) producers. Empirical data indicate that approximately 86.4% of the milk produced by cooperative members is marketed, with the majority sold through the union. Dairy farming thus serves as a major livelihood source for local households, with a gradual transition toward crossbred cows producing 5–10 L of milk per day, compared to 1.5–2 L from indigenous breeds.

Despite this potential, the sector faces persistent constraints, including inadequate infrastructure (such as milk collection and cooling facilities), limited access to quality feed, veterinary care and artificial insemination, and weak institutional and policy support. These challenges make the Salale Zone an ideal case for analysing small‐scale dairy productivity and sustainability, given its strategic market access, active cooperative system and well‐documented production challenges and opportunities.

2.2. Research Design and Sampling Strategy

A cross‐sectional survey design was employed to assess the determinants influencing the productivity and sustainability of small‐scale dairy farming. This design was selected because it enables the collection of data at a single point in time, providing a comprehensive snapshot of prevailing conditions and relationships among socio‐demographic, economic, climatic and institutional variables (Creswell 2014).

A stratified random sampling approach was used to ensure adequate representation across the zone's heterogeneous dairy systems. The population was divided into strata based on agro‐ecological zones, farm sizes and management practices to capture variation across production environments (Mugenda and Mugenda 2003).

2.3. Sampling Procedure and Sample Size Determination

A multi‐stage sampling procedure was adopted. In the first stage, districts with high engagement in small‐scale dairy farming and active milk‐producer cooperatives were purposively selected. In the second stage, kebeles (the smallest administrative units) within these districts were randomly chosen to represent variations in production systems, market access and infrastructure. In the final stage, households engaged in dairy farming were randomly selected from lists obtained from district livestock and agriculture offices, which served as the sampling frame.

The sample size was determined using Cochran's (1977) formula, assuming a 95% confidence level and a 5% margin of error, yielding a final sample of 250 smallholder dairy farmers. This ensured both statistical reliability and adequate representation of the study population.

2.4. Data Collection Methods

Data were collected from both primary and secondary sources. Primary data were obtained through a structured questionnaire administered to smallholder dairy farmers to gather quantitative information on household demographics, production inputs, feeding systems, productivity levels, market participation and sustainability practices.

Key informant interviews were conducted with cooperative leaders, extension agents and local policymakers to capture insights on institutional support, market dynamics and policy implementation gaps.

Focus group discussions (FGDs) were held to explore farmers’ perceptions regarding modernization, milk quality and value‐chain constraints. Secondary data were compiled from district livestock reports, cooperative records, agricultural offices and published literature, complementing and validating the primary data.

2.5. Definition, Measurement and Expected Relationship of Variables

2.5.1. Dependent Variable

The dependent variable in the model is dairy productivity status, which is categorized into discrete and mutually exclusive groups based on the level of dairy production performance observed among sampled households. The categories represent different productivity levels (e.g., low, medium and high productivity). Each household is assigned to one category according to predefined productivity indicators such as milk yield per cow and overall dairy output. Since these outcomes are nominal and not inherently ordered, the multinomial logit model is considered the most appropriate analytical framework.

2.5.2. Independent Variables

The explanatory variables included in the model represent key socio‐economic, institutional and farm resource characteristics that are hypothesized to influence dairy productivity. These variables were selected based on theoretical considerations and empirical evidence from previous studies on agricultural productivity and technology adoption.

The independent variables and their measurements are summarized as follows:

• Age of household head (years): Continuous variable representing the age of the household head. Older farmers may possess greater farming experience, which could positively influence dairy productivity, although ageing may also reduce labour capacity.

• Education level (years of schooling): Continuous variable measuring the number of years of formal education completed by the household head. Higher education levels are expected to enhance managerial ability and adoption of improved dairy management practices, thereby positively affecting productivity.

• Household size (number of persons): Continuous variable representing the total number of household members. Larger households may provide more labour for dairy management, potentially increasing productivity.

• Landholding size (hectares): Continuous variable indicating the total agricultural land owned or operated by the household. Larger landholdings can facilitate greater fodder production and improved dairy management.

• Livestock holding (tropical livestock units—TLU): Continuous variable measuring the total livestock owned by the household expressed in TLU. Higher livestock assets may reflect better resource availability and investment capacity for dairy production.

• Extension contact (number of contacts per year): Continuous variable representing the frequency of interaction between farmers and agricultural extension agents. Regular extension contact is expected to improve farmers’ access to technical information and best management practices, thereby enhancing dairy productivity.

• Access to credit (dummy variable): Binary variable coded as 1 if the household had access to credit services during the study period and 0 otherwise. Access to credit is expected to positively influence dairy productivity by enabling investment in improved inputs, feed and veterinary services.

• Market access (distance to market in kilometres): Continuous variable representing the distance from the household to the nearest dairy market. Greater distance may negatively affect productivity due to higher transaction and transportation costs.

2.5.3. Expected Association Between Variables

The selected explanatory variables are hypothesized to influence dairy productivity through different mechanisms. Socio‐economic characteristics such as education, age and household size affect managerial capacity and labour availability. Resource endowments, including land and livestock holdings, determine the physical capacity for dairy production. Institutional factors such as extension services and credit access facilitate knowledge transfer, technology adoption and financial investment. Finally, market access influences the economic incentives for dairy production and commercialization.

2.6. Data Analysis

Data analysis involved both descriptive and inferential statistical methods. Descriptive statistics including means, percentages and standard deviations were used to summarize key characteristics of the respondents and production systems.

To identify the determinants of dairy productivity and sustainability, an ordered logit regression model was employed. This econometric approach was selected because the dependent variable dairy farm performance was categorized into three ordered levels (Low, Moderate and High) based on a composite index derived from key performance indicators, including milk yield, feed efficiency and farm profitability. Given the inherent ranking among these categories, the ordered logit model provides a more appropriate framework for estimating the influence of explanatory variables on the likelihood of farms achieving higher levels of productivity and sustainability.

The explanatory variables included a range of socio‐economic, institutional and environmental factors that are theoretically and empirically associated with dairy farm performance. Socio‐economic variables comprised household characteristics such as age of the household head, educational attainment, household size and landholding size. Institutional factors included cooperative membership, access to credit services, access to veterinary services and extension support. In addition, selected environmental and climatic variables, such as rainfall variability and feed availability, were incorporated to capture the broader agro‐ecological conditions affecting dairy production.

The ordered logit model is based on an underlying latent (unobserved) variable that represents the true level of dairy farm productivity and sustainability. The econometric specification can be expressed as follows:

$$\mathrm{Y}{\mathrm{i}}^{\ast }=\beta {\mathrm{X}}_{\mathrm{i}}+{\epsilon }_{\mathrm{i}}$$

where Y _i ^∗​ denotes the latent level of dairy farm performance for household i, X _i​ represents a vector of explanatory variables, β is the vector of parameters to be estimated and ε_i​ is the stochastic error term.

The observed ordinal outcome variable Y_i ​, representing dairy productivity levels, is determined by threshold parameters as follows:

$$\mathrm{Yi}\begin{array}{c}1.\mathrm{if}{{\mathrm{Y}}_{\mathrm{i}}}^{\ast }\le {\mu }_1\left(\mathrm{Low}\mathrm{productivity}\right)\hfill \\ 2.\mathrm{if}{\mu }_1<\mathrm{Yi}\ast \le {\mu }_2\left(\mathrm{Moderate}\mathrm{productivity}\right)\hfill \\ 3.\mathrm{if}{{\mathrm{Y}}_{\mathrm{i}}}^{\ast }>{\mu }_2\left(\mathrm{High}\mathrm{productivity}\right)\hfill \end{array}$$

where μ ₁​ and μ ₂​ represent the threshold parameters separating the ordered outcome categories. The ordered logit model estimates the cumulative probability that an observation falls within a given category or below, conditional on the explanatory variables.

The estimated coefficients indicate the direction and magnitude of the influence of explanatory variables on the latent productivity level. For ease of interpretation, odds ratios (ORs) were computed by exponentiating the estimated coefficients. These ORs provide an intuitive interpretation of how changes in explanatory variables affect the likelihood of dairy farms moving toward higher productivity and sustainability categories.

To complement the quantitative analysis, qualitative data obtained from key informant interviews and FGDs were analysed using a thematic analysis approach. This qualitative analysis provided contextual insights into the socio‐economic, institutional and environmental factors influencing dairy farming practices in the study area. The thematic analysis followed the methodological framework proposed by Virginia Braun and Victoria Clarke in their widely cited work on thematic analysis. The process involved systematic coding of interview transcripts, identification of recurring patterns and organization of codes into broader themes reflecting farmers’ experiences, perceived constraints and opportunities within the dairy sector.

The integration of quantitative econometric analysis and qualitative thematic insights enabled a more comprehensive understanding of the determinants of dairy productivity and sustainability. This mixed‐methods approach allowed the study to capture not only statistically significant relationships but also the underlying contextual factors influencing dairy production systems in the Salale Zone.

2.7. Replication and Validity

Replication was ensured by using standardized data collection instruments and procedures across all sampling sites. Internal validity was enhanced through triangulation of quantitative and qualitative data sources, while external validity was achieved by ensuring representative sampling across diverse agro‐ecological conditions. Reliability tests (Cronbach's α ≥ 0.7) were conducted to confirm internal consistency of key questionnaire constructs.

3. Results and Discussion

3.1. Socio‐Demographic Characteristics of Smallholder Dairy Farmers

Table 1 summarizes the results of socio‐demographic factors affecting small‐scale dairy industry in Salale Zone of Oromia, Ethiopia

TABLE 1.

Descriptive statistics of socio‐demographic factors affecting small‐scale dairy industry in Salale Zone of Oromia, Ethiopia.

Variable	Mean	Standard deviation	Minimum	Maximum	N
Age of farm household head (years)	47.2	11	22	72	250
Gender of household head (Male: 1, Female: 0)	0.7	0.46	0	1	250
Household size (members)	6.5	2.3	2	14	250
Education level of household head (years)	8.2	3.8	0	16	250
Farmers' experience in dairy farming (years)	13.1	8.7	1	40	250
Economic status of the household (USD)	1350	600	250	3000	250
Access to social networks and support systems (Yes: 1, No: 0)	0.72	0.45	0	1	250
Land ownership and access (Owned: 1, Rented: 0)	0.6	0.49	0	1	250
Cultural attitudes and practices (Positive Attitude: 1, Negative Attitude: 0)	0.78	0.42	0	1	250
Migration patterns for seasonal conflict (Affected: 1, Not Affected: 0)	0.28	0.45	0	1	250

3.1.1. Age of Household Head

The average age of household heads was 47.2 years (SD = 11.0), reflecting a mature demographic likely to possess substantial experience in dairy farming (Table 1). While experience can enhance farm management, older age may also limit adoption of new technologies or innovative practices, potentially affecting farm efficiency (R. Smith and Jones 2021). Most smallholder dairy farmers in the Salale Zone fall within the productive age range of 30–55 years, consistent with findings by Kassie et al. (2017), who reported that farmers in this age bracket are more likely to adopt improved agricultural technologies due to their physical capacity and openness to innovation. Similarly, D. Lemma et al. (2020) found that younger and middle‐aged farmers are more responsive to training and advisory services, which is critical for enhancing dairy productivity.

3.1.2. Gender of Household Head

Approximately 70% of household heads are male (SD = 0.46) (Table 1). This gender imbalance may influence various aspects of dairy farming, including access to productive resources, decision‐making and farm management practices, which can, in turn, affect productivity and sustainability (Doe and Green 2022). In Salale, although women are heavily involved in daily dairy operations such as feeding and milking, men typically control income, cooperative membership and resource allocation.

3.1.3. Household Size and Education Level

The mean household size was 6.5 members (SD = 2.3), indicating a relatively large family unit (Table 1). Larger households may provide additional labour for dairy operations, but they also face higher economic pressures and resource demands, which could constrain farm management and productivity (Brown et al. 2023). Berhanu and Melesse (2018) similarly noted that family labour compensates for limited access to hired labour, particularly in rural Ethiopian settings.

The average education level of household heads was 8.2 years of formal schooling (SD = 3.8). Education is a key determinant of adoption of modern farming practices, improved feeding strategies and participation in cooperatives (Table 1). Higher education levels are generally associated with enhanced farm management and increased productivity (R. White and Lee 2023). In the Salale Zone, farmers with at least primary education exhibit higher adoption rates of crossbred cows and modern feeding systems, consistent with findings by M. Tadesse and Belay (2021), who highlighted that education improves comprehension and implementation of livestock management innovations.

3.1.4. Farmers’ Experience in Dairy Farming

Farmers in the Salale Zone reported an average of 13.1 years of dairy farming experience (SD = 8.7), indicating substantial practical knowledge (Table 1). Such experience is critical for effective farm management, disease control, feeding strategies and breeding decisions. However, variability in experience levels may lead to differences in farm performance across households (Johnson and Anderson 2024). Experienced farmers are generally better equipped to manage operational challenges, mitigate risks and respond to production constraints, which aligns with the conclusions of Mohammed et al. (2020), who found that experience enhances problem‐solving abilities and risk management in livestock enterprises.

3.1.5. Economic Status of Households

Dairy farming contributes significantly to household incomes in Ethiopian highlands (Table 1). For example, in Holleta, dairying accounts for approximately 34% of total household income (Zewde and Mustefa 2022). Although this figure pertains to Holleta, it provides a reference point for the potential economic significance of dairy farming in similar agro‐ecological zones, including Salale. Assuming a household owns two crossbred cows, each producing 10 L of milk per day, and sells milk at ETB 51 per litre, the estimated daily income would be:

$$\mathrm{Daily}\mathrm{income}=2\mathrm{cows}\times 10\mathrm{L}\times \mathrm{ETB}51=\mathrm{ETB}1020$$

This moderate‐income level influences households’ capacity to invest in farm improvements, as limited financial resources may constrain productivity, expansion and overall sustainability (Taylor and Martinez 2022).

3.1.6. Access to Social Networks and Support Systems

Approximately 72% of households (SD = 0.45) reported access to social networks and support systems (Table 1). Access to such networks facilitates knowledge exchange, resource sharing and peer support, which can enhance farming practices and help mitigate production challenges (Miller and Brown 2021). Farmers with strong social connections are generally more likely to adopt innovative practices and participate in cooperative activities.

3.1.7. Land Ownership and Access

About 60% of households (SD = 0.49) reported owning land (Table 1). Land ownership provides long‐term stability and security, enabling households to make investments in infrastructure, pasture improvement and dairy management. In contrast, renting land often leads to uncertainty and limited investment, which can negatively affect productivity (H. White 2023).

3.1.8. Cultural Attitudes and Practices

Approximately 78% of households (SD = 0.42) expressed a positive attitude toward dairy farming (Table 1). Favourable cultural attitudes can facilitate the adoption of best practices, enhance motivation and support industry growth, whereas negative perceptions may hinder innovation and sector development (Clark and Roberts 2023).

3.1.9. Migration Patterns Due to Seasonal Conflicts

Around 28% of households (SD = 0.45) reported migration patterns resulting from seasonal conflicts (Table 1). Such mobility can disrupt daily dairy operations, reduce labour availability and affect income stability. Understanding these patterns is essential for designing targeted interventions and resilience strategies for smallholder dairy systems (P. Smith 2022).

3.2. Climatic Factors Affecting Dairy Production

3.2.1. Average Temperature

The mean annual temperature in the Salale Zone was 22.5°C (SD = 2.7), ranging from 18.0°C to 27.0°C (Table 2). Moderate temperatures are generally conducive to dairy farming; however, significant fluctuations can stress animals and reduce milk yield and quality. Maintaining stable, moderate temperatures is crucial for optimal dairy cattle performance.

TABLE 2.

Descriptive statistics of climatic factors influencing small‐scale dairy farming in the Salale Zone, Oromia, Ethiopia.

Climatic factor	Mean	Standard deviation	Minimum	Maximum	Sample size
Average Temperature (°C)	22.5	2.7	18	27	250
Annual rainfall (mm)	875.3	120.4	600	1200	250
Number of rainy days (days/year)	90	15.6	60	120	250
Dry season length (months)	4.2	1	2	6	250
Humidity (relative humidity %)	65.7	8.9	50	85	250
Occurrence of extreme weather events (number/year)	3.4	1.2	1	6	250

3.2.2. Annual Rainfall

The average annual rainfall was 875.3 mm (SD = 120.4), with a range of 600–1200 mm, sufficient to support pasture growth and water availability (Table 2). However, excessive rainfall may lead to waterlogging and increased disease incidence, emphasizing the need for effective water and pasture management.

3.2.3. Number of Rainy Days

On average, there were 90 rainy days per year (SD = 15.6), ranging from 60 to 120 days (Table 2). While frequent rainfall benefits pasture growth, it can also create muddy conditions and heightened disease risk, necessitating adapted farm management practices to maintain animal health.

3.2.4. Dry Season Length

The mean dry season length was 4.2 months (SD = 1.0), with a range of 2–6 months. Extended dry periods can reduce pasture availability and water resources, increasing costs for supplementary feed (Table 2). Strategies such as improved feed storage and alternative feed sources are critical during prolonged dry periods.

3.2.5. Humidity

Relative humidity averaged 65.7% (SD = 8.9), ranging from 50% to 85% (Table 2). High humidity can contribute to heat stress, respiratory problems and disease susceptibility in dairy cattle. Adequate ventilation and cooling systems in housing are recommended to mitigate these effects.

3.2.6. Occurrence of Extreme Weather Events

Farmers reported an average of 3.4 extreme weather events per year (SD = 1.2), ranging from 1 to 6 events (Table 2). Extreme events, such as droughts, storms or floods, can disrupt feed availability, damage infrastructure and reduce milk production. Implementing risk management measures and building resilient infrastructure is essential to minimize their impact on dairy production.

3.3. Determinants of Small‐Scale Dairy Industry Productivity: Ordered Logit Regression Model Analysis

Table 3 presents the results of an ordered logit regression model analysis, examining the determinants of small‐scale dairy productivity in the Salale Zone of Oromia, Ethiopia. The dependent variable was a categorical measure of dairy farming success, classified as low success, moderate success and high success. The analysis identifies key socio‐economic, institutional and resource‐related factors influencing productivity.

TABLE 3.

Ordered logit regression model results for determinant factors affecting small‐scale dairy industry Salale Zone of Oromia, Ethiopia.

Variable	Coefficient	Standard error	z‐Value	p‐Value
Age of farm household head	0.012	0.005	2.4	0.016
Household size	−0.038	0.017	−2.24	0.025
Education level of household head	0.04	0.01	4.00	0.001
Economic status of the household	−0.275	0.089	−3.09	0.002
Access to social networks and support systems	0.188	0.07	2.69	0.007
Land ownership and access	0.49	0.11	4.45	0.001
Access to veterinary services	0.16	0.075	2.13	0.033
Dairy product market problems	−0.23	0.085	−2.71	0.007
Increase price of concentrate feed	−0.29	0.098	−2.96	0.003
Milk adulteration by collectors and suppliers	−0.27	0.092	−2.93	0.003
Fluctuation of milk prices	−0.34	0.10	−3.40	0.001
Seasonal security problem	−0.175	0.08	−2.19	0.028
Infrastructure (e.g., road)	0.15	0.06	2.50	0.012
Climatic factors	−0.25	0.09	−2.78	0.050

3.3.1. Age of Farm Household Head

The coefficient for age was β = 0.012 (p = 0.016), indicating that older household heads are positively associated with higher dairy productivity (Table 3). This relationship likely reflects the accumulated experience, practical knowledge and problem‐solving capacity of mature farmers. However, older farmers may face limitations in adopting modern technologies, which could moderate the positive effects of experience (Abdulai and Huffman 2014).

3.3.2. Household Size

Household size exhibited a negative association (β = −0.038, p = 0.025) with dairy productivity (Table 3). Larger households may experience resource dilution, increased economic pressures and more complex farm management challenges, which can reduce overall efficiency and success (Ameye et al. 2023).

3.3.3. Education Level of Household Head

The education level of household heads had a positive and significant effect (β = 0.040, p = 0.001) (Table 3). Educated farmers are generally more capable of accessing, interpreting and applying information, adopting modern feeding, breeding and management practices, and participating effectively in cooperatives and extension programs (Amare and Mekonnen 2023).

3.3.4. Economic Status of the Household

Economic status showed a negative and significant coefficient (β = −0.275, p = 0.002), indicating that households with lower financial capacity are less likely to achieve higher dairy productivity (Table 3). Economic constraints limit the ability to invest in quality inputs, crossbred animals, feed supplementation and veterinary care, reducing both productivity and profitability. This finding is consistent with previous studies highlighting financial limitations as a key barrier to technology adoption among smallholder dairy farmers in Ethiopia (Korir et al. 2023).

3.3.5. Access to Social Networks and Support Systems

Access to social networks and support systems was positively associated with dairy productivity (β = 0.188, p = 0.007) (Table 3). Such networks facilitate knowledge exchange, resource sharing, peer support and access to extension services, thereby enhancing farm management and productivity outcomes (Davis et al. 2010).

3.3.6. Land Ownership and Access

Land ownership and access demonstrated a strong positive effect on dairy productivity (β = 0.490, p = 0.001) (Table 3). Ownership provides security and stability, enabling long‐term investment in infrastructure, feed production and herd management, which are essential for achieving higher productivity levels (Aklilu and Catley 2010).

3.3.7. Access to Veterinary Services

The positive coefficient (β = 0.160, p = 0.033) highlights the crucial role of veterinary services in maintaining animal health and enhancing productivity (Table 3). Regular veterinary support, including vaccinations, disease management and reproductive health services, contributes significantly to better dairy farming outcomes (Kumar et al. 2012).

3.3.8. Dairy Product Market Problems

Market‐related challenges, including limited access and inadequate pricing mechanisms, were negatively associated with dairy productivity (β = −0.230, p = 0.007) (Table 3). Such constraints hinder farmers’ ability to effectively participate in markets, thereby reducing profitability and sustainability. These results are consistent with Ethiopian studies demonstrating that lack of market information, poor infrastructure and price fluctuations are major barriers for smallholder dairy producers (Kena et al. 2022).

3.3.9. Increase in Price of Concentrate Feed

Rising feed costs negatively impacted dairy productivity (β = −0.290, p = 0.003) (Table 3). Increased prices of concentrate feed directly affect production costs and profitability, limiting farmers’ capacity to maintain herd nutrition and productivity (Niu et al. 2016).

3.3.10. Milk Adulteration by Collectors and Suppliers

Milk adulteration significantly undermined dairy productivity (β = −0.270, p = 0.003) (Table 3). Practices such as dilution with water or other substances reduce milk quality, erode consumer trust and depress market prices, adversely affecting farmer income and sustainability (Ghosh et al. 2013).

3.3.11. Fluctuation of Milk Prices

Price volatility showed a strong negative effect (β = −0.340, p = 0.001) (Table 3). Unstable milk prices challenge farmers’ planning and investment decisions, reduce profitability and hinder sustainable production (Lal 2015).

3.3.12. Seasonal Security Problems

Seasonal security issues were negatively associated with productivity (β = −0.175, p = 0.028) (Table 3). Conflicts or unrest during certain periods disrupt farm operations, labour availability and market access, reducing dairy performance (Eckert 2011).

3.3.13. Infrastructure (Roads)

Infrastructure improvements, particularly road networks, positively influenced dairy productivity (β = 0.150, p = 0.012) (Table 3). Better roads enhance market access, reduce transaction costs and facilitate input delivery, which collectively improve farm efficiency and profitability (Fafchamps and Minten 2012).

3.3.14. Climatic Factors

Climatic variability exerted a significant negative effect on sustainability outcomes (β = −0.25, p = 0.005) (Table 3), indicating that increasing environmental stress reduces the relative utility of sustainable dairy production systems. Variations in temperature, rainfall patterns and the frequency of extreme weather events adversely affect feed availability, water resources, livestock health and milk productivity, ultimately constraining household profitability and system resilience. These findings underscore the vulnerability of smallholder dairy systems to climate‐induced shocks and are consistent with recent empirical evidence from Ethiopia, which highlights the critical role of climate variability in shaping livestock productivity and sustainability trajectories (Gutu and Asfaw 2023).

3.4. Marginal Effects of Determinant Factors Affecting Small‐Scale Dairy Industry in Salale Zone, Oromia

Table 4 presents the marginal effects of determinant factors on the probability of achieving different levels of success in the small‐scale dairy industry in Salale Zone. Marginal effects quantify the change in the probability of being in a specific outcome category (e.g., ‘High Success’) resulting from a one‐unit change in each predictor variable, holding other factors constant. This analysis provides insights into the relative influence of socio‐economic, institutional and infrastructural factors on dairy productivity.

TABLE 4.

The marginal effects estimation of various determinants factors affecting small‐scale dairy industry in Salale Zone of Oromia, Ethiopia.

Determinant factor	Marginal effect	Standard error	p‐Value	Significance level (0.05)
Market problems	−0.35	0.05	0.001	Highly significant
Feed access	0.31	0.05	0.001	Highly significant
Milk adulteration	−0.29	0.06	0.001	Highly significant
Fluctuation of milk prices	−0.22	0.07	0.002	Highly significant
Financial resources	0.24	0.06	0.001	Highly significant
Seasonal security	0.2	0.06	0.007	Highly significant
Infrastructure (e.g., roads)	0.27	0.05	0.001	Highly significant
Training and extension services	0.18	0.07	0.014	Highly significant
Breed quality	0.15	0.06	0.022	Significant
Access to veterinary services	0.13	0.07	0.06	Significant
Experience	0.17	0.06	0.018	Significant
Education level	0.16	0.06	0.027	Significant
Climatic factors	0.19	0.06	0.014	Significant

Note: Marginal effects indicate the change in the dependent variable associated with a one‐unit change in the predictor variable, holding all other variables constant. Significance levels are based on a p‐value threshold of 0.05.

3.4.1. Market Problems

Market problems exhibited a marginal effect of −0.35 (SE = 0.05, p = 0.001), indicating a strong and highly significant negative impact on dairy success (Table 4). Poor market infrastructure, limited access and inefficient pricing mechanisms restrict farmers’ ability to sell milk effectively, reducing profitability and sustainability.

3.4.2. Feed Access

Access to feed showed a marginal effect of 0.31 (SE = 0.05, p = 0.001), reflecting a highly significant positive impact (Table 4). Adequate and reliable feed availability is crucial for animal health, milk yield and overall farm productivity.

3.4.3. Milk Adulteration

Milk adulteration had a marginal effect of −0.29 (SE = 0.06, p = 0.001), demonstrating a significant negative influence on productivity (Table 4). Adulteration reduces milk quality and market value, undermining consumer confidence and lowering farm income.

3.4.4. Fluctuation of Milk Prices

Price volatility showed a marginal effect of −0.22 (SE = 0.07, p = 0.002), indicating a significant negative impact on dairy farming success (Table 4). Unstable milk prices create income uncertainty and impede investment decisions, which can hinder farm sustainability. This finding is consistent with Hegde et al. (2020), who note that price fluctuations reduce farmers’ incentives to adopt improved practices.

3.4.5. Financial Resources

Access to financial resources exhibited a marginal effect of 0.24 (SE = 0.06, p = 0.001), reflecting a highly significant positive impact (Table 4). Availability of credit or capital allows farmers to invest in quality inputs, modern technologies and infrastructure, thereby enhancing productivity.

3.4.6. Seasonal Security

Security showed a marginal effect of 0.20 (SE = 0.06, p = 0.007), indicating a significant positive influence (Table 4). A secure environment reduces risks such as theft, vandalism and seasonal conflicts, allowing for uninterrupted farm operations.

3.4.7. Infrastructure (Roads)

Infrastructure improvements, particularly road networks, exhibited a marginal effect of 0.27 (SE = 0.05, p = 0.001), showing a highly significant positive effect (Table 4). Efficient infrastructure facilitates timely transportation of dairy products to markets, reduces transaction costs and enhances operational efficiency. M. Tadesse et al. (2022) emphasize that investments in roads and market access infrastructure are critical for supporting smallholder dairy development.

3.4.8. Training and Extension Services

Training and extension services exhibited a marginal effect of 0.18 (SE = 0.07, p = 0.014), indicating a highly significant positive impact (Table 4). Access to effective training improves farmers’ skills, knowledge and decision‐making, leading to enhanced management practices and higher productivity.

3.4.9. Breed Quality

Breed quality had a marginal effect of 0.15 (SE = 0.06, p = 0.022), showing a significant positive influence (Table 4). High‐quality breeds contribute to greater milk yield and improved herd performance.

3.4.10. Access to Veterinary Services

Access to veterinary services showed a marginal effect of 0.13 (SE = 0.07, p = 0.060), reflecting a positive but moderately significant impact (Table 4). Veterinary services are vital for disease prevention, reproductive health and overall animal well‐being, but their immediate effect on productivity may be less pronounced compared to other direct determinants such as feed access or market conditions. This underscores that veterinary support complements, rather than substitutes, other productivity‐enhancing measures.

3.4.11. Experience of the Households

Farmer experience exhibited a marginal effect of 0.17 (SE = 0.06, p = 0.018), indicating a significant positive impact (Table 4). Experienced farmers typically demonstrate better problem‐solving skills, efficient resource management and improved decision‐making, which enhance productivity. Muriuki et al. (2019) support that experience strengthens farm management and operational efficiency, contributing to higher dairy outputs.

3.4.12. Education Level of the Households

Education level had a marginal effect of 0.16 (SE = 0.06, p = 0.027), reflecting a significant positive impact (Table 4). Farmers with higher education are generally more capable of adopting modern technologies, understanding best management practices and efficiently utilizing resources, which enhances productivity. Desta and Melaku (2017) emphasize that education is a critical enabler for technology adoption and performance improvement in dairy farming.

3.4.13. Climatic Factors

Climatic factors exhibited a positive and statistically significant marginal effect on sustainability outcomes (ME = 0.19, SE = 0.06, p = 0.014) (Table 4), indicating that improvements in climatic conditions enhance the probability of achieving sustainable dairy production. Favourable temperature regimes and rainfall patterns support feed quality, water availability and livestock health, thereby promoting productivity, milk yield stability and overall system resilience. These findings corroborate earlier empirical evidence emphasizing the central role of climatic conditions in shaping agricultural performance and underscore the necessity of adaptive management strategies to mitigate climate‐related risks.

Notably, the observed divergence between the negative order logit coefficient and the positive marginal effect reflects the nonlinear structure of the multinomial logit model and suggests that climatic stress may trigger adaptive behavioural responses among farming households. Such responses include the adoption of improved herd management practices, increased engagement with institutional support systems, livelihood diversification and investment in climate‐resilient technologies. Consequently, although climatic challenges reduce relative utility, they may simultaneously stimulate proactive adaptation, thereby enhancing the probability of sustainable production outcomes.

This result highlights the critical importance of climate adaptation policies, including the promotion of climate‐smart agriculture, early warning systems, drought‐tolerant forage development and strengthened agricultural extension services, to bolster household resilience and ensure long‐term sustainability under escalating climatic uncertainty.

4. Research Implications, Limitations and Future Directions

The findings of this study have important implications for policy makers, development practitioners and stakeholders in the dairy sector, as they highlight the critical role of socio‐demographic, market, economic and climatic factors in shaping the productivity and sustainability of small‐scale dairy enterprises in the Salale Zone. Interventions that enhance access to education, financial services, market infrastructure and climate adaptation strategies are essential for improving dairy performance in the region. However, the study is not without limitations; it relies primarily on cross‐sectional data, which may not fully capture temporal dynamics and causal relationships, and its geographic focus on a single zone may limit the generalizability of results to other contexts. Future research should consider employing longitudinal designs and broader geographic coverage to validate findings and explore the long‐term impacts of interventions, while also integrating qualitative insights to better understand farmer decision‐making and resilience mechanisms under changing socio‐economic and climatic conditions.

5. Conclusion

This study underscores the substantial influence of socio‐demographic, climatic and market‐related factors on the performance of small‐scale dairy farming in the Salale Zone. Socio‐demographic attributes such as educational attainment, farming experience and household economic status were found to positively contribute to dairy productivity. Favourable climatic conditions, particularly moderate temperatures and reliable rainfall, further enhance the viability of dairy operations in the region. Nevertheless, persistent challenges including limited market access, price volatility, feed shortages and inadequate infrastructure continue to undermine the sustainability and profitability of the sector. Addressing these constraints calls for a holistic and coordinated approach that includes targeted training programs, financial and technical support, climate‐resilient strategies and the development of efficient and equitable market systems to empower small‐scale dairy producers.

Author Contributions

Dr. Girma Megersa contributed significantly to all aspects of this study, from its inception to completion. He was responsible for the conceptualization of the research, design of the survey, and data collection. Dr. Megersa meticulously analyzed the data, interpreted the findings, and wrote the manuscript. His dedication and expertise were critical in conducting the study on ‘Assessing Determinant Factors Affecting Small‐Scale Dairy Industry Productivity and Sustainability in Salale Zone of Oromia, Ethiopia’. His hard work and commitment to advancing knowledge in sustainable agriculture were vital to the success of this research.

Funding

The author has nothing to report.

Ethics Statement

Ethical approval for the study titled ‘Determinants of Productivity and Sustainability in Small‐Scale Dairy Industries: Evidence from the Salale Zone of Oromia, Ethiopia’ was obtained from the Ethics Committee chaired by Dr. Solomon Balami at Salale University. Approval was granted under Reference/Permit Number 127/2025, dated 21 January 2025. All research procedures were conducted in accordance with the ethical standards of the approving committee, institutional guidelines, applicable national regulations, and the 2013 revision of the Declaration of Helsinki for studies involving human participants.

Conflicts of Interest

The author declares no conflicts of interest.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.