SJA_42_4_1337-1353

Introduction

The economy of Nepal is mainly based on agriculture, with 61% of the labor force engaged in this sector (World Bank, 2020). The livestock sector contributes 24.01%, while the dairy sector accounts for 11.2% to Agricultural Gross Domestic Product (AGDP) (MoALD, 2023). Cattle and buffalo are the primary dairy animals in Nepal. The country has a high potential for milk production due to favorable climatic conditions, particularly in Ilam and Morang districts of Koshi Province, as well as in Chitwan and Kavre districts of Bagmati Province (MoALD, 2023). The intake of milk and dairy-based products plays a vital role in household nutrition, providing essential proteins, vitamins, and minerals (Miller et al., 2020). Among the 5.20 million total cattle, 1.06 million are milking cattle, representing 61% of dairy animals (MoALD, 2025). There are 6,486 commercial livestock farms in the country, comprising 1,233 cattle farms and 914 buffalo farms, with improved breeds’ accounts for 75% and an average milk yield of 10 liters (GoN, 2022). The country has 765 Small and Medium-sized Enterprises (SMEs), 36 district milk producer cooperatives, more than 1800 primary dairy cooperatives, and 55 large milk chilling centers (NDDB, 2021). Milk output rose from 1.68 million tons in 2013 to 2.61 million tons in 2023 (MoALD, 2024). These trends reflect a gradual shift from subsistence-based dairy farming to market-oriented production.

To achieve better dairy outcomes, national policies have prioritized the commercialization of dairy farms (ADS, 2015; NPC, 2022). The Government of Nepal (GoN) also supports initiatives such as improved breeds, fodder missions, Artificial Insemination (AI), veterinary services, and insurance schemes. The sixteenth periodic plan (2024/25–2028/29) further emphasizes the need for a market-based approach (NPC, 2024). Collectively, these policies aim to enhance dairy farming from a traditional farm-based system to a more commercial production system.

However, the commercialization of dairy cattle farms is significantly low. Farmers still engage in subsistence production, resulting in limited livestock marketing (Chapagaee et al., 2024; Dhakal and Mueser, 2023; Yogi et al., 2025). In total, 95% households rear dairy animals, while only 15% produce marketable dairy products (CASA, 2020). This situation is impeded by weak institutional mechanisms (Shrestha and Singh, 2023), poor market infrastructure (Ghimire, 2021), limited dairy extension services (Dhungana et al., 2024; Neopane et al., 2022), high cost of production (Upadhyay et al., 2021), insufficient credit facilities (Devkota et al., 2022), and limited availability of modern technology (Yogi et al., 2025).

Although the issue has been recognized, previous studies have not sufficiently addressed the problem of commercialization, particularly in Nepal. Most prior studies have focused on the commercialization of crops in Africa (Gebiso et al., 2023; Paul et al., 2022; Saha et al., 2022) and some aspects of agriculture in Nepal (Joshi and Piya, 2021). Extensive studies have examined the factors influencing dairy market participation in Ethiopia (Bahta et al., 2025; Degefa et al., 2022; Jebessa et al., 2024). The existing studies in Nepal have primarily concentrated on topics such as production (Bhandari, 2022; Panta, 2022; Poudel et al., 2023), efficiency (Dhakal, 2022a), insurance (Ghimire et al., 2023), agri-enterprises (Bhatta and Mishra, 2022), and reproductive performance (Bhattarai et al., 2022). However, these studies do not sufficiently address the challenges related to milk market participation and its components. Currently, no research specifically examines the socio-economic, institutional, and farm-level factors that determine the commercialization of dairy cattle farms in Nepal. These knowledge gap limits policy-makers’ potential to formulate tailored policies that promote market-oriented dairy cattle farming.

The rapid changes in agri-food chains have created opportunities for dairy production in both national and global markets (Tadele et al., 2025). The production of marketable commodities plays a key role in income generation (Belay et al., 2021; Saha et al., 2022), enhances food security (Ahmed et al., 2024; Manda et al., 2021), and contributes to poverty reduction (Mgomezulu et al., 2024; Singh et al., 2023). In this context, the study of determinants of farm commercialization is highly relevant (Gayathri et al., 2023; Kim et al., 2016; Mamo et al., 2022; Paudel et al., 2021; Von Braun et al., 1994).

To address this knowledge gap, the study intends to identify the determinants of dairy cattle farm commercialization by focusing on two key research questions: (a) What factors affect households’ decisions to participate in the dairy market? (b) What are the determinants of the intensity of milk sales among households that do participate in the market? This study applies a double-hurdle model to analyze market participation decisions and intensity of milk sales, thereby providing a better understanding of the commercialization of milk. The findings are expected to enhance the commercialization of dairy farms and provide relevant policy insights to promote market-oriented dairy development in Nepal.

Materials and Methods

Description of the study sites

For this study, Morang and Ilam from the Koshi province, along with Chitwan and Kavre from the Bagmati province were selected. These districts were chosen for their dominance of cattle populations and presence of dairy farms with diverse agro-ecological conditions. The Ilam and Kavre districts have milking cattle populations of 30,901 and 21,442, respectively. Meanwhile, the Morang and Chitwan districts report milking cattle populations of 85,098 and 23,064, respectively (MoALD, 2025). All four districts are recognized for high milk production potential.

Types and sources of data

The study utilized an explanatory sequential mixed-methods design (Creswell, 2003; Subedi, 2023), comprising two stages of data collection: a quantitative stage using a household survey, followed by a qualitative stage. A closed-ended, structured questionnaire was used to collect data. The questionnaire was pre-tested with non-sampled households and revised based on field feedback. The data were collected from households on dairy cattle farms in selected municipalities from February to October 2024. To obtain qualitative information, the study employed eight Key Informant Interviews (KIIs) and three Focus Group Discussions (FGDs). Official reports, literature reviews, and relevant empirical evidence were also used to enhance the interpretation of the research findings.

Sampling procedure and sample size

The dairy cattle rearing households were the study population. Sample sites were chosen through a multistage random sampling procedure (Bhandari, 2022). Three criteria were set, including the number of high AI users (NLBO, 2022), the presence of a large number of dairy cattle farms (MoICS, 2023), and high milk production volume (MoALD, 2023). The stages of sample selection were: (i) provinces, (ii) districts, (iii) municipalities, and (iv) dairy-cattle rearing households. Five municipalities per district were selected. The sampling frame was identified from selected municipalities covering four districts (Table 1). The inclusion criteria for the household survey were that households must be rearing dairy cattle and have at least one milking cattle.

The final sampling units were selected through simple random sampling (Noor et al., 2022). The sample size was determined using the Probability Proportional to Size (PPS) sampling method, with the Krejcie and Morgan (1970) formula. This formula was used for populations of 5,000 or more, with a 95% confidence interval and 5% margin of error (Wangu et al., 2021). The total population of dairy cattle households across the selected districts was framed at N=5,194, which justifies the use of this method. The sample size was calculated using the following formula:

Where Z is the z-value 1.96 at 95% confidence, p=probability of happening=0.5, 1-p=1-0.5=0.5

e=margin of error=5%=0.05. Sample size (n) = ((1.96) ^2×0.5× (1-0.5))/ (0.05) ^2 =384, plus 23 (6% buffer), n=407. The sample size was calculated proportionately, including Ilam (86), Morang (114), Chitwan (116), and Kavre (91) (Table 1).

Table 1: Study population and sample size

Districts	Municipalities	Population (N)	Sample size (n)	Districts	Municipalities	Population (N)	Sample size(n)
Ilam (86)	Suryodaya	252	20	Kavre (91)	Dhulikhel	349	27
	Ilam	231	18		Panchkhal	221	17
	Rong	187	15		Banepa	267	21
	Sandakpur	143	11		Mandandeupur	151	12
	Deumai	285	22		Panauti	174	14
Sub-total		1098	86			1162	91
Morang (114)	Biratnagar	496	39	Chitwan (116)	Bharatpur	534	42
	Sundarharaicha	204	16		Khairahani	236	18
	Pathari	191	15		Ratnanagar	385	30
	Katahari	267	21		Madi (new)	164	13
	Dhanpalthan	292	23		Rapti(new)	165	13
Sub-total		1450	114			1484	116

Source: Field Survey, 2024

Data analysis

The data were examined using Microsoft Excel and STATA 15, with both descriptive and econometric approaches. The means, frequencies, and percentages were calculated. The T-test and Chi-square test were used to determine the statistical variations between groups (Gautam and Khadka, 2022). In this study, milk sales in the market were used as a proxy for the commercialization of dairy cattle farms (Bahta et al., 2021). The proxy variable captured the market participation, specifying whether farmers sell milk in the market or not, as well as the intensity of milk sales in the market. The double-hurdle model was chosen for its ability to capture two distinct hurdles (Cragg, 1971). In the first stage (hurdle 1), farmers decide to participate in the dairy market. In the second stage (hurdle 2), dairy farmers determine how much milk to sell (Jannat et al., 2026). The model employs a two-stage process that combines the probit model with truncated regression (Asfaw et al., 2023; Workie and Tasew, 2023). The second-stage dependent variable included some observations with zero values, for which Ordinary Least Squares (OLS) estimation seems inappropriate due to the lack of variance (Gujarati, 2004).

Conceptual framework of the study

Commercialization refers to the transition from subsistence farming to market-oriented production (GC and Hall, 2020; Von Braun et al., 1994). The commercial farming aims to generate income, whereas subsistence farming produces food for own consumption only (Joshi and Piya, 2021). The market participation theory explains this concept of how farm households decide whether and to what extent to engage in market activities (Cragg, 1971; Goetz, 1992). This decision is influenced by market access (Turkelboom et al., 1996), policy and institutional support (Pingali and Rosegrant, 1995), and personal characteristics (Schultz, 1964). According to Goletti et al. (2003), the main drivers of commercialization include technology adoption, credit and insurance schemes, and the role of farmers’ organizations. The behavior of farmers in commercial production is shaped by a variety of inter-connected socio-economic, institutional and farm characteristics rather than by a single determinant (Fountas et al., 2006). This process involves not only the production of surplus goods but also household decision-making.

The theoretical foundations outlined here form the basis for the conceptual framework shown in Figure 1, which guides variable selection and knowledge of their relationships. This study hypothesized two models, with the dependent variables being market participation (yes/no) and milk sales intensity. The independent variables were socio-economic factors (age, gender, family size, education, off-farm work, and experience in dairy farming), institutional factors (access to extension services, membership in dairy cooperatives, credit, subsidy, training, and distance to milk sales centers), and farm characteristics (milk yield, the number of technologies used, and the cost of milk production).

Farmers typically have two approaches to milk production: subsistence or market-oriented. The market-oriented approach often leads to greater engagement in dairy markets, which can significantly increase milk supply and improve farmers’ livelihoods.

Specification of the double-hurdle model

In the first stage, the influencing factors of farmers’ decisions to participate in the dairy market were regressed using a binary probit model (Khanal et al., 2024). The model is specified below in equation (2):

Di* = αZi +μi; Di = 1, if Di*>0 and Di = 0, if Di*< 0 (2)

Where, Di* is a binary dependent’s latent variable, takes the value of one for those who sell milk in the market and zero otherwise. Zi, α, and μi represent vectors of explanatory variables, parameter estimates, and error terms, respectively, for those market participants.

In the second stage, a truncated regression model was used, which excludes the portion of the sampled observations on the dependent variables (Wooldridge, 2002). Only households that choose market participation (value=1) are included in the regression, which limits the dependent variable to positive values and excludes zero from the estimation. The lower left censored truncation is used, equal to 0 values of dependent variables, which was described in equation (3):

γi= βiXi + μλi+ εi

γi*= βiXi + νi,

γi= {γi* if γi* >0 and Di=1; 0 otherwise}

Where γi* and γi are the latent and observed intensity of participation, respectively, Xi, β, and εi represent the vectors of explanatory variables, parameter estimates, and error terms respectively, for market participants. It is assumed that the error terms have an independent, normal distribution, reflecting the fact that each decision made by the individual producer is independent, namely: ui ~ N (0, 1) and vi ~ N (0, σ2).

The study tested two hypotheses as given below:

Hypothesis 1: Households’ decision to participate in the dairy market depends on the age of HH, gender of HH, Education of HH, family size, extension services, cooperative membership, off-farm work, and milk yield
Hypothesis 2: The intensity of milk sales depends upon the experience in dairy farming, access to extension services, credit use, subsidy, training, technology, distance to milk sales center, and cost of milk production

Table 2: Description of the variables used in the study

Variables	Type	Description
Market participation Intensity of milk sales	Dummy Continuous	1 if the household decides to sell milk in the market or 0 otherwise A continuous variable with the daily volume of milk sales in liters
Age	Continuous	Age of household in number of years
Gender	Dummy	1 if the household head is male or 0 otherwise
Education	Continuous	Schooling in years of household head
Family Size	Continuous	Number of members in the family
Extension	Dummy	1 if the household has access to extension services or 0 otherwise
Membership	Dummy	1 if the household has a membership in the dairy cooperative or 0 otherwise
Off-farm work	Dummy	1 if households have off-farm work or 0 otherwise
Milk yield	Continuous	Milk per cattle per day in liters
Experience	Continuous	Dairy farming experience in number of years
Credit use	Dummy	1 if the household used credit in previous years, or 0 otherwise
Subsidy	Dummy	1 if the household received subsidy, 0 otherwise
Training	Dummy	1 if the household received training or 0 otherwise
Technology	Continuous	Number of technologies used in the cattle farms
Distance	Continuous	Distance to milk selling center measured in Kilometers
Cost	Continuous	Cost of milk production in Nepalese Rupees (NPR)

Results and Discussion

Descriptive statistics

Table 3 presents the differences in continuous variables between milk market participants and non-participants. Out of total 407 dairy farms, 83% farmers sell milk, while 17% farmers do not sell milk in the market. The t-test results showed that the means for household head education, family size, milk yield, number of technologies, distance to the sales

Table 3: Differences between market participants and non-participants

Variables		Participants (n=336)	Non-participants (n=71)	Total (n-=407)	t-value
Continuous		Mean			t-value
Age of HH		49	51	49	1.4558
Education of HH		9.2	4.49	8.38	-8.15***
Family size		5	2.87	4.7	-10.5914***
Milk yield		11	9.41	10.78	−5.69***
Experience		8.71	8.13	8.61	-1.1361
Technology		6.24	2.05	5.51	-8.8772***
Distance		1.46	2.51	1.64	5.5100***
Cost		44	54.94	44.9	6.1086***
Categorical		Frequency given in percentage (%)			χ2 value
Gender	Male	79	73	78	1.2053
Gender	Female	21	27	22	1.2053
Extension	Yes	56	31	52	14.9851 ***
Extension	No	44	69	48	14.9851 ***
Membership	Yes No	69 31	34 66	63 37	31.2008***
Off-farm	Yes No	55 45	94 6	66 34	30.2535***
Credit use	Yes No	39 61	59 5	36 64	33.2867***
Subsidy	Yes No	44 56	39 7	37 63)	42.3190***
Training	Yes No	60 40	43 57	57 43	6.46***

Notes: *** represents statistical significance at 1% levels

center, and milk production costs were statistically significant at 1% level between the two groups (Table 3). The mean years of educational attainment among participants’ household heads was 9.2, almost double that of non-participants (4.49). The average family size was larger among participants, at 5 members, compared to 2.9 among non-participants (Table 3).

Market participants adopted more improved farm technologies (6.24), achieved higher milk yields (11 liters), and owned more dairy cattle (10.85) than non-participants. The average distance to the milk sales center was shorter for participants (1.46 km) compared to non-participants (2.51 km). The cost of milk production was lower for participants (NPR 44) compared to non-participants (NPR 54). No substantial differences were observed between the two groups in the age of the household head or years of dairy farming experience.

The chi-square test revealed that access to extension service, membership, off-farm work, credit use, and subsidy are significant at 1% level (Table 3). Most of the respondents (56%) had access to an extension service, and 69% of households had a membership in a dairy cooperative. Only 3% of non-participant farmers received government subsidy, however, 93% of them were engaged in off-farm activities.

Econometric analysis

The double-hurdle model with a probit regression was used to test the first hypothesis, while a truncated regression was used for the second hypothesis. Before running the regression analysis, the Variance Inflation Factor (VIF) was tested, which had a value of 1.23. It indicates the low multicollinearity among the predictor variables. Heteroskedasticity was examined through the Breusch-Pagan and White tests in the second stage of the model.

Factors influencing households’ decisions to participate in the dairy market

A binary probit model was used to examine the factors influencing a household’s decision to participate in

Table 4: Factors influencing the market participation decision of dairy cattle farms

Variables	Coefficient.	Std. Err.	Marginal effect (dy/dx)	P>\|z\|
Age	0.0122075	0.013575	0.0010173	0.369
Gender	0.2279547	0.3218805	0.0189959	0.479
Education	0.0884483	0 .0276903	0.0073706	0.001***
Family Size	0.4078813	0.0895037	0.0339896	0.000***
Extension	0.9563612	0.3428712	0.0796956	0.005***
Membership	1.308253	0.3289223	0.1090194	0.000***
Off-farm work	-1.241427	0.3639559	-0.1034507	0.001***
Milk Yield	0.0318071	0.0595192	0.0026506	0.593
_Cons	3.166669	1.174581		0.007***
LR chi2(8)=255.63, Prob > chi2=0.0000, Log likelihood= -60.570452 Pseudo R2= 0.6785

Notes: *** represent statistical significance of factors at 1% levels, respectively

the dairy market. The dependent variable is binary: 1 if the household participates in the dairy market and 0 otherwise. Out of eight hypothesized explanatory variables, family size, the education (number of schooling years) of the household head, access to extension services, membership in a dairy cooperative, and off-farm work were found to be significant (Table 4).

The gender and age of the household head were found to be insignificant. Marginal effects were calculated to interpret the influence of explanatory variables. The likelihood ratio test was found to be 255.63, with a log-likelihood of -60.570, indicating good model fit. A p-value of less than 0.0001 confirmed the statistical significance. The pseudo R-squared 0.6785 indicates that the model accounts for 67.85% of the variation in market participation decisions. The following factors were found to be significant:

Education

The probit regression results show that the years of schooling of the household head are positively and significantly associated with participation in the dairy market (Table 4). Each additional year of schooling increased the likelihood of market participation by 0.74%. Educated farmers often have better access to information and negotiation skills, which can facilitate their market engagement. Similar findings in Nepal showed that the educational attainment of the household head positively affected farm production (Dhakal, 2022a). Studies from Ethiopia (Ordofa et al., 2021) and Kenya (Wangu et al., 2021) also found that the higher participation of educated farmers in marketing activities. A positive relationship was found between education and market awareness (Cheng et al., 2025; Paul et al., 2022). On the other hand, Haile et al. (2022) reported that each additional year of education reduced market participation by 2.74%, suggesting that more educated farmers may prefer non-farm jobs. In conclusion, these results suggest that educating farmers, particularly in financial literacy, is essential.

Family size

According to the probit model results, family size has a significant, positive effect on the household’s decision to participate in the dairy market (Table 4). Each extra family member raises the probability of market participation by 3.4%. The availability of farm labor is one of the advantages of a large family size. In a similar line, previous research from Bangladesh (Nath et al., 2025) and Ethiopia (Jebessa et al., 2024) found that the likelihood of market participation increased by 3.3% and 12.4%, respectively, for each extra family member. However, the increasing trend of youth out-migration in Nepal has reduced the family size, resulting in a shortage of family labor (Chhetri et al., 2020). In this context, labor-saving technologies and mechanization can enhance labor efficiency, particularly in smaller family sizes.

Access to extension services

The households receiving extension services are 7.97% more likely to participate in the dairy market (Table 4). Extension services, such as veterinary care, market information, and technical advice help farmers engage in marketing activities. Regular contact with local extension agents and their services enables farmers to acquire technical knowledge and market information. Similar findings have been documented by previous research in Nepal (Jaishi et al., 2024), Ethiopia (Degefa et al., 2022), Pakistan (Abbasi and Nawab, 2021), Nigeria (Lai-Solarin et al., 2024), and Kenya (Ogola et al., 2023). They reported that regular extension services to farmers led to higher milk yields. Despite the government’s provision of one technician per ward in Nepal, village-level technicians are insufficient to reach farmers (KII with farmer, Kavre). The agricultural extension system is constrained by poor institutional structure and limited technical human resources (Timsina et al., 2023). Thus, they advised promoting extension programs based on commodities and communities. In contrast, a study in Bangladesh (Eyasmin and Ghosh, 2024) found that extension visits led to low levels of market activity. This was due to informal payments to extension agents, which made an additional cost burden to farmers. These insights highlighted the need for policy interventions that promote regular, farmer-focused, and accessible dairy-focused extension services to enhance milk marketing.

Cooperative membership

The analysis shows that households with cooperative membership are 10.90% more likely to participate in the dairy market (Table 4). Local cooperatives assist farmers to access in farm inputs and encourage them to sell their commodities. This finding concurs with earlier studies that highlighted the role of cooperatives in facilitating access to inputs, sharing information, and establishing market linkages (Panta et al., 2023; Van Koppen et al., 2022). A previous study in Nepal found that cooperative members earned agricultural incomes four times higher than non-members (Dhakal et al., 2021). Studies in Nepal found positive impacts of cooperatives on trust building (Mishra and Bhatta, 2021), gender balance (Kharel et al., 2020), and formation of social capital (Dhakal, 2022b). The Constitution of Nepal 2015 also recognizes cooperatives as a third pillar of development. Thus, significant contribution of dairy cooperatives includes establish a strong market network, provide non-financial services, and connect farmers with livestock insurance (FGDs, August 2024). Similar impacts have been observed in Lesotho (Rantlo et al., 2020) and Ethiopia (Jebessa et al., 2023), where cooperatives reduced transaction costs, enhanced bargaining power, and supported economies of scale. Given these benefits, dairy cooperatives should prioritize membership expansion in underserved areas to strengthen markets.

Off-farm work

The results reveal that households engaged in off-farm work (salaried jobs, remittance income, daily wage work, business) are 10.35% have a less likely to engage in the dairy market (Table 4). The negative coefficient indicates that income from off-farm sources may decrease their involvement in farm activities. Many Nepalese households diversify into non-farm activities to reduce risk, thereby weakens their participation in the agricultural market (World Bank, 2020). Participants engaged in off-farm activities for better income sources, hassle-free work, and greater interest in out-migration jobs (FGDs, August 2024). Haile et al. (2022) found that off-farm income reduced the capacity of households to produce marketable farm commodities by diverting family labor. Unlike this result, a study in the Asia region, including Nepal, found that off-farm employment significantly increased the likelihood of livestock raising (Bai et al., 2024). Non-farm activities have had a positive influence on farmers’ income in Kenya (Onyango et al., 2023) and Ghana (Twumasi et al., 2022), and increased access to farm inputs in China (Zheng et al., 2023). These mixed findings indicate that income from off-farm work may be used for farm investment or redirected to marketing activities.

Determinants of the intensity of milk sales in the dairy market

The intensity of dairy farm commercialization was measured using daily milk sales volume (Table 5). Out of the eight explanatory variables, five variables, such as credit use, subsidy, technology, distance, and cost of milk production were found to be statistically significant. The model showed high explanatory power, with a log-likelihood of -221.98955 and a Wald chi-square statistic of 246.27, indicating a good overall fit. A detailed discussion of significant variables is described below:

Credit use

The analysis shows that the quantity of credit used in dairy farms during the previous year influenced milk sales, increasing them by 13.52% (Table 5). The credit enables investment in essential farm inputs. The interview with farmers in study areas revealed that the credit is beneficial for the purchase of improved breeds, shed construction, and machinery. Farmers usually access credit through cooperatives, microfinance institutions, and commercial banks. In a similar line, the study found the statistically significant positive relationship between credit use and agricultural growth in Nepal (Lamsal et al., 2025). The regulations in Nepal also mandate that banks allocate at least 15% of their entire credit to the agriculture sector (Pandey, 2022). These findings are related to studies from Kenya that found that access to credit enhanced production and sales of milk (Kurgat et al., 2023) and supported technology adoption (Okello et al., 2021). The results differ from those of Bahta et al. (2025) in Ethiopia, who reported that the use of credit reduced milk supply by 4.2 liters per household. This is because households diverted the funds for investment in non-farm goods. Therefore, credit programs offered by financial institutions need to be adjusted to fulfill the specific needs of farmers and closely monitored to ensure funds are utilized for productive purposes.

Subsidy

The analysis reveals a positive correlation between subsidies and milk sales volume, with each subsidy associated with 30.79% increase in milk sales (Table 5). These findings are consistent with the studies from Nepal (Kharel et al., 2025; Sapkota and Khatiwada, 2024; Thapa et al., 2023) and Sri Lanka (Nadheer and Ummah, 2020). They found a significant relationship between farm subsidies and marketable surplus. A study in Nepal found a strong positive correlation (r=0.72) between production and input subsidy (Paudel, 2024). The subsidy contributed to a 15% reduction in costs (Bhatta et al., 2025). Farmers participating in FGDs reported that they were benefited from various forms of support, including grants, loan subsidies, insurance, farm inputs, and top-up payments based on milk sales volume. They received provincial subsidies for equipment such as milking and silage machines; however, the lack of a local custom hiring center has left these machines unused (KIIs with farmers, September 2024). Local governments in Nepal offer support for animal insurance, farming tools, and machinery (Gautam et al., 2021). These subsidies help farmers overcome production constraints. The findings indicate that ongoing support for output-based and targeted subsidy programs is essential.

Technology

The analysis reveals that increased use of dairy technologies had a positive impact on milk sales (Table 5). Each additional technology leads to 5.91% increase in daily milk sales intensity. The most commonly adopted technologies in Nepal include improved cattle breeds, AI, commercial fodder, hay, silage, improved sheds, clean milking practices, regular vaccinations, tools and machinery, and digital media (FGDs, producer groups, September 2024). This result aligns with previous research studies in Nepal. The authors reported that overall farm productivity is directly affected by the use of technology (Kharel and Dahal, 2023; Poudel et al., 2025). However, as mandated by the Good Husbandry Practices (GHP) implementation directive 2023, the rate of technology adoption is inadequate (DoLS, 2023). The technology developed by NARC has not reached smallholders adequately due to weak extension services. Similar to this study, Korir et al. (2023) identified 19 farm technologies used by Ethiopian dairy producers. The analysis found a significant association between improved practices and milk production. Okello et al. (2021) reported that the intensity of technology positively affected the number of dairy cattle. The use of improved technologies in farms helps to improve farm efficiency, promote good animal health, and achieve higher yields (Zegeye et al., 2022). These findings suggest that increased adoption of diverse and appropriate dairy technologies could enhance marketable surplus and serve as a key strategy to improve outcomes in the dairy sector.

Table 5: Determinants of intensity of milk sales in the dairy market

Variables	Coefficient.	Std. Err.	P>\|z\|
Experience	0.0033357	0.006783	0.623
Extension	0.0620119	0.0604862	0.305
Credit use	0.1351691	0 .0554767	0.015**
Subsidy	0.3078877	0.0659531	0.000***
Training	0.0957961	0.07053	0.174
Technology	0.0590868	0.0077368	0.004***
Distance	-0.0519447	0.0179255	0.004***
Cost	-0.054057	0.0018927	0.004***
_cons \|	3.503154	0.1281778	0.000***
/sigma	0.4684831	0.0180721	0.000***
No. of observations =336, Log likelihood = -221.98955 , Prob > chi2= 0.0000, Wald chi2(8) =246.27

Notes: *** represent statistical significance of factors at 5% and 1% levels, respectively

Market distance

The analysis indicates that proximity to the milk sales center has a significant negative impact on milk sales. Specifically, for every additional kilometer of distance from the sales center, milk sales decrease by 5.19% (Table 5). Due to the high perishable nature of milk, longer transport times increase the risk of spoilage and discourage regular sales. Participants from remote, hilly areas reported that reaching the milk chilling center typically requires them to walk for at least 30 minutes to an hour (KIIs, September 2024). In contrast, farmers located closer to sales centers benefit from reduced transportation time. This evidence is supported by previous studies by Ejeta and Bai (2025) and Kassahun et al. (2020), who reported the negative relationship between distance and sales. Jebessa et al. (2024) found that milk sales decrease by 2.4% for every additional kilometer to the sales center. Therefore, better road connections and shorter distances to milk sales centers can significantly enhance farmers’ ability to sell milk, ultimately increasing their income.

Cost of milk production

The analysis shows that higher costs negatively affect milk sales. A one-rupee increase in the cost of milk results in a 5.41% decline in milk sales (Table 5). It aligns with the results of Thapa et al. (2025), which reported an NPR 74.25 per liter, higher than the national average. The high production costs are largely driven by high feed expenses, which account for 48% of costs in Nepal (Panta, 2022) and 64% in India (Gadhvi et al., 2024). A key concern raised by farmers was the high production cost, driven by greater reliance on purchased pellet feed and manual labor (KII with farmers, Chitwan, August 2024). To address these issues, initiatives have been taken to reduce costs, such as forage missions and the distribution of small farm tools and machinery (KII with the agriculture officer, October 2024). In contrast, Uganda reports lower feed costs (11.4%), primarily due to widespread adoption of local fodder and pasture (Waiswa and Gunlu, 2022). Large-scale commercial farms in India (George et al., 2021) and Turkey (Sarica et al., 2022) benefit from lower production costs compared to smaller farms due to their modern farming practices. Therefore, farmers need access to improved feed options and cost-effective technologies (Khanal et al., 2022), such as hay and silage, better feeding practices, and machinery.

Conclusions and Recommendations

Dairy farming in Nepal remains a prominent sector for addressing poverty and food insecurity. Farmers are also gradually shifting from subsistence to market-oriented production in order to commercialize their commodities. However, the rate of commercialization in the sector remains low. In response to this emerging need, the study aimed to identify the determinants of dairy cattle farm commercialization, focusing on market participation choices of households in the dairy market and milk sales intensity. Double-hurdle model results showed that market engagement is markedly influenced by the education level of the household head, family size, access to extension services, cooperative membership, and off-farm work. The intensity of milk sales is positively influenced by credit use, subsidies, and technology adoption, whereas distance to the milk sales center and production costs negatively affects milk sales. The following recommendations have been made in considering the findings mentioned above:

Short term

There is a need for policy interventions that promote regular, dairy-focused, and accessible extension services by increasing human resources at the ward level. These services should emphasize good husbandry practices in milk production and marketing, as mandated by the GHP directive. In addition, the expansion of dairy cooperative membership in low-access areas can connect farmers to markets and improve their negotiation skills.

Long term

It is essential to promote the adoption of suitable, labor-saving, cost-reducing farm technologies. This includes practices such as hay and silage conservation, improved feeding methods, and the use of tools and machinery to replace manual labor. The public policy should focus on strengthen livestock credit schemes that offer soft loans and providing targeted subsidies based on output to support market-driven milk production.

Acknowledgments

The author would like to thank all dairy farmers and research participants. Special thanks are extended to Dr. Himal Luitel and Gopal Khadka for their substantial support and valuable suggestions in finalizing this article.

Novelty Statement

This study uniquely applies a double-hurdle model to analyze both the decision to participate in the dairy market and the intensity of milk sales among dairy cattle farmers in Nepal. It offers policy-relevant insights by identifying household-specific socio-economic, institutional, and farm-related factors that influence commercialization, addressing a critical gap in dairy cattle farms.

Authors’ Contribution

Jyoti Dhungana: Conceptualization, data curation, formal analysis, investigation, methodology, resources, writing original draft, Devendra Gauchan, Krishna Prasad Timsina, Hari Krishna Panta, Lok Nath Paudel: Conceptualization, supervision, validation, review and editing

Generative AI and AI-assisted technology statement

The authors declare that no generative AI and AI assisted technology was used in the creation of this manuscript.

Conflict of interest

The authors have declared no conflict of interest.

Funding

This research did not receive any specific funding.

References

Abbasi, S. and K. Nawab. 2021. Determinants of the dairy technology adoption by the rural milk producers in district Muzaffarabad, Azad Jammu and Kashmir. Sar. J. Agric., 37(3):921-929. https://doi.org/10.17582/journal.sja/2021/37.3.921.929

ADS. 2015. Agriculture Development Strategy (ADS) 2015–2035. Government of Nepal, Ministry of Agricultural and Livestock Development, Singha Durbar, Kathmandu, Nepal

Ahmed, U.I., N. Nadeem, C.I. Ali, A.U. Rehman, S. Nasir, M.A. Tahir and U. Kamal. 2024. Subsistence farmer’s production diversity and market access: impact on rural women and children’s dietary diversity. Agric. Sci. J., (1): 21-36. https://doi.org/10.56520/asj.24.356

Asfaw, D.M., A.A. Belete, A.G. Nigatu and G.M. Habtie. 2023. Status and determinants of saving behavior and intensity in pastoral and agro-pastoral communities of a far regional state, Ethiopia. PLoS One., 18(2): e0281629.https://doi.org/10.1371/journal.pone.0281629

Bahta, M., M. Tesfay and L. Zemedu. 2025. Factors affecting milk market participation in Bahir Dar Milk Shed area, Ethiopia. Ethiop. J. Agric. Sci., 35(1): 68–83

Bahta, S., A. Omore, D. Baker, I. Okike, B. Gebremedhin and F. Wanyoike. 2021. An analysis of technical efficiency in the presence of developments toward commercialization: Evidence from Tanzania’s milk producers Europ. J. Develop. Res., 33: 502–525. https://doi.org/10.1057/s41287-020-00279-8

Bai, Y., X. Zeng, C. Fu and L. Zhang. 2024. Off-farm employment, agriculture production activities, and household dietary diversity in environmentally and economically vulnerable areas of Asia. J. Integrat. Agric., 23(2): 359–373. https://doi.org/10.1016/j.jia.2023.11.016

Belay, G.H., K.A. Mengstu, K.H. Mehammedberhan, G. Hosseininia, A.I. Özgüven, A.H. Viira and H. Azadi. 2021. Determinants of smallholder commercialization of livestock: A case study from Tigray, Ethiopia. Cogent Food Agric., 7(1): 1921950. https://doi.org/10.1080/23311932.2021.1921950.

Bhandari, P. 2022. Land and livestock ownership and household food security in Nepal. pp. 259–277. In J. Timilsina et al. (eds.), Agriculture, Natural Resources and Food Security: Lessons from Nepal. Springer Publishers. https://link.springer.com/book/10.1007/978-3-031-09555-9

Bhatta, R., S. Karki, D. Devkota and A. Acharya. 2025. Effect of agricultural subsidies on vegetable production in Kathmandu, Nepal. Russ. J. Agric. Socio-Econ. Sci., 165(9): 76–87

Bhatta, S. and B.P. Mishra. 2022. Success factors for micro and small agribusiness enterprises in Chitwan district, Nepal. Nepalese J. Agric. Sci., 23: 32-38

Bhattarai, N., J. Poudel, M.R. Kolakshyapati, M.P. Sharma, N.A. Gorkhali, A. Sigdel, S. Upadhayayaand and S. Sapkota. 2022. Evaluation of reproductive performance and litter traits of Khari, Jamunapari and Sirohi crossbred goats in Surkhet district of Karnali province, Nepal. J. Agric. Forest. Uni., 5: 71-80

CASA. 2020. Dairy sector strategy, Nepal. Commercial Agriculture for Smallholders and Agribusiness. https://www.casaprogramme. com/wp-content/uploads/CASA

Chapagaee, P., A. Kunwar, D.R. Bist and L. Khatri. 2024. Socioeconomic impact of livestock farming: A case study of Baitadi and Darchula districts, Nepal. Int. J. Appl. Sci. Biotechnol., 12(3): 166–179. https://doi.org/10.3126/ijasbt.v12i3.70155

Cheng, H., J.N. Ng’ombe, Y. Choi, T.H. Kalinda and S. Zheng. 2025. Understanding the drivers of smallholder dairy cooperative participation in developing countries: Evidence from rural Zambia. Agric. Sys., 224: 104261. https://doi.org/10.1016/j.agsy.2025.104261

Chhetri, K.R., P.K.C. and S.C. Dhakal. 2020. Remittance and its impact on Nepalese economy. Acta Sci. Agric., 4(3): 1–5. https://doi.org/10.31080/asag.2020.04.0818

Cragg, J.G. 1971. Some statistical models for limited dependent variables with application to the demand for durable goods. Economet., 39(5): 829–844. https://doi.org/10.2307/1909582

Creswell, J.W. 2003. Research design: Qualitative, quantitative, and mixed methods approaches (2nd ed.). Sage Publication

Degefa, K., G. Abebe and G. Biru. 2022. Determinants of market participation decision and intensity of market participation in western Ethiopia: Evidence from smallholder tef producers. I. J. Agric. Sci. Food Technol., 8(2): 125–133. https://doi.org/10.17352/2455-815X.000153

Devkota, N., D.K. Shreebastab, J. Korpysa, K. Bhattarai and U.R. Paudel. 2022. Determinants of successful entrepreneurship in a developing nation: Empirical evaluation using an ordered logit model. J. Int. Stud., 15(1): 181–196. https://doi.org/10.14254/2071-8330.2022/15-1/12.

Dhakal, D. and P. Mueser. 2023. Agricultural cooperatives and the failure to achieve commercialization of agriculture in Nepal: A case study of the Chitwan district. Res. Globalizat., 7: 100165. https://doi.org/10.1016/j.resglo.2023.100165

Dhakal, D., D. O’Brien and P. Mueser. 2021. Government policy and performance of agricultural cooperatives: A case study in Chitwan district, Nepal. Sustain., 13(21): 12282. https://doi.org/10.3390/su132112282

Dhakal, S.C. 2022a. Determinants of technical efficiency among dairy farms in Chitwan, Nepal. J. Agric. Forest. Uni., 5: 149. https://doi.org/10.3126/jafu.v5i1.48459

Dhakal, S.C. 2022b. Factors affecting adoption of improved shed as climate change adaptation strategy by dairy farmers in Terai Region of Nepal. Nepalese J. Agric. Sci., 19: 1. https://www.cabidigitallibrary.org/doi/pdf/10.5555/20220262726

Dhungana, J., D. Gauchan, K.P. Timsina and H.K. Panta. 2024. Insight into policy provisions and their gaps for dairy sector development in Nepal. J. Agric. Food Res., 16: 101134. https://doi.org/10.1016/j.jafr.2024.101134

DoLS. 2023. Good Husbandry implementation directive 2023. Ministry of Agriculture and Livestock Development, Department of Livestock Services, Harihar Bhawan, Pulchok, Lalitpur, Nepal

Ejeta, T.T. and X. Bai. 2025. The effect of sustainable agricultural practices on crop productivity in Ethiopia: Insights from a meta-analysis. Front. Front. Sustain. Food Sys., 8:1499412. https://doi.org/10.3389/fsufs.2024.1499412

Eyasmin, F. and B.C. Ghosh. 2024. Analyzing the determinants of beef cattle commercialization and its market inefficiency: A case study of Pabna district, Bangladesh. PLoS ONE., 19(3): e0300034. https://doi.org/10.1371/journal.pone.0300034

Fountas, S., D. Wulfsohn, B.S. Blackmore, H.L. Jacobsen and S.M. Pedersen. 2006. A model of decision-making and information flows for information-intensive agriculture. Agric. Syst., 87: 192–210.

Gadhvi, D.N., L.M. Sorathiya and A.L. Rathva. 2024. Comparative profitability analysis of specialized cattle and buffalo farms. Agric. Sci. Digest., 44(4): 774–778. https://doi.org/10.18805/ag.D-5354

Gautam, G. and U. Khadka. 2022. Reproductive and productive performance of crossbred and Terai cattle in Bardiya District of Nepal. J. Nepal Agric. Res. Counc., 8: 64–71. https://doi.org/10.3126/jnarc.v8i.44862

Gautam, S., N. Neupane, B. Dhital, H. Neupane and S.P. Bhatta. 2021. Status of cattle and buffalo farming in Banepa Kavrepalanchowk district Nepal. J. Livestock Sci., 12: 125–131. https://doi.org/10.33259/JLivestSci.2021.125-131

Gayathri, S.L., M. Bhakat and T.K. Mohanty. 2023. An outlook on commercial dairy farming in India: : A review. Ind. J. Anim. Prod. Manage., 37(1): 45–56

GC, R.K. and R.P. Hall. 2020. The commercialization of smallholder farming—a case study from the rural western middle hills of Nepal. Agric., 10(5): 143. https://doi.org/10.3390/agriculture10050143

Gebiso, T., M. Ketema, A. Shumetie and G. Leggesse. 2023. Drivers of crop commercialization in central and southern Oromia, Ethiopia. Discover Food., 3: 15. https://doi.org/10.1007/s44187-023-00055-7

George, S., P.C. Saseendran, K.S. Anil, V.L. Gleeja, E.D. Benjamin, S. Pramod and M.K. Aslam. 2021. Cost and returns of milk production under different types of dairy farms in Kerala. J. Anim. Res., 11(6). https://doi.org/10.30954/2277-940X.06.2021.23

Ghimire, B., S.C. Dhakal, S. Khanal, S. Sharma and M. Poudel. 2023. Insurance scheme and its adoption determinants among livestock farms in Bardiya, Nepal. Lumb. Agric. J., 57

Ghimire. R.P. 2021. Fodder and Pasture Research in Nepal: Status, Challenges and Way Forward. Proceedings of 12th National Workshop on Livestock and Fisheries Research in Nepal

Goetz, S.J. 1992. A selectivity model of household food marketing behavior in sub-Saharan Africa. Americ. J. Agric. Econ., 74(2): 444–452. https://doi.org/10.2307/1242498.

Goletti, F., T. Purcell and D. Smith. 2003. Concepts of commercialization and agricultural development. Agrifood Consult. Int. Discuss. Paper Ser., 8: 3–18.

GoN. 2022. Commercial Livestock Integrated Survey 2021. Government of Nepal, Central Bureau of Statistics and Ministry of Agriculture and Livestock Development, Singha Durbar Kathmandu, Nepal.

Gujarati, D.N. 2004. Basic Econometrics (4th ed.). McGraw-Hill

Haile, K., E. Gebre and A. Workye. 2022. Determinants of market participation among smallholder farmers in Southwest Ethiopia: Double-hurdle model approach. Agric. Food Secur., 11(1): 18. https://doi.org/10.1186/s40066-022-00358-5

Jaishi, M., T. Singh, N. Teufel and P. Varijakshapanicker. 2024. Village livestock promoters (VLPs): A new approach for integrated delivery of livestock inputs and services in Nepal. https://cgspace.cgiar.org/server/api/core/ bitstreams/3e23feda-f58f-4567 -9f5e-ef48dce90306/content

Jannat, A., M. Salman, J.U. Ahmed, A.G. Geffersa, and M.M. Islam. 2026. Livestock production and dairy sector performance in Bangladesh: Does dairy productivity promote smallholder commercialization? Food Ener. Secur., 15(1). https://doi.org/10.1002/fes3.70171.

Jebessa, G.M., B. Fikadu and T. Chalchisa. 2023. Impacts of crossbreed dairy cow adoption on women dietary diversity in Southwestern Ethiopia. J. Agric. Food Res., 12: 100544. https://doi.org/10.1016/j.jafr.2023.100544

Jebessa, G.M., G. Beriso, E. Yacoob and T. Chalchisa. 2024. Dairy product market participation and its impacts on household food security of smallholder farmers in Jimma zone Ethiopia: A PSM approach. Cogent Food Agric., 10(1): 2300875. https://doi.org/10.1080/23311932.2023.2300875

Joshi, N.P. and L. Piya. 2021. Determinants of small-scale commercial vegetable farming among vegetable growers in Nepal. Sage Open., 11(2). https://doi.org/10.1177/21582440211010168

Kassahun, Y., M. Ketema and Z. Shumeta. 2020. Determinants of participation decision and levels of participation in small ruminants market. Sustain. Agric. Res., 9(1): 56–66. https://doi.org/10.5539/sar.v9n1p56

Khanal, P., R. Dhakal, T. Khanal, D. Pandey, N.R. Devkota and M.O Nielsen. 2022. Sustainable Livestock Production in Nepal: A Focus on Animal Nutrition Strategies. Agric., 12(5): 679. https:// doi.org/10.3390/agriculture12050679

Khanal, S., U. Tiwari and S. Khanal. 2024. Willingness to pay for cattle insurance in Nepal: A double-hurdle model perspective. Glob. J. Agric. Alli. Sci., 5(1): 7–14. https://doi.org/10.35251/gjaas.2024.002

Kharel, K.R., P.L. Adhikari, D.R. Chalise, and B.P. Gyawali. 2020. Evolution of cooperatives through development plans and resource mobilization in Nepal. J. Advan. Res. Manag. Soc. Sci., 9(11): 39–63.

Kharel, S. and S. Dahal. 2023. Study of livestock production system and management practices adopted by small ruminants’ farmers in Bagmati Province, Nepal. Nepal. J. Agric. Sci., 25: 259–268

Kharel, S., R.B. Pasa and P.M. Pradhan. 2025. Assessing the agricultural service delivery functions of local governments in Nepal. Geograph. J. Nep., 18:113–135. https://doi.org/10.3126/gjn.v18i1.77065

Kim, C.S., J. Abafita and J. Atkinson. 2016. Smallholder commercialization in Ethiopia: Market orientation and participation. J. Administ. Bus. Stud., 2(1):19–28. https://doi.org/10.20474/jabs-2.2.3

Korir, L., L. Manning, H.L Moore, J.F Lindahl, G. Gemechu, A. Mihret and N.S. Nyokabi. 2023. Adoption of dairy technologies in smallholder dairy farms in Ethiopia. Front. Sustain. Food Syst., 7: 1070349. https://doi.org/10.3389/fsufs.2023.1070349

Krejcie, R.V. and D.W. Morgan. 1970. Determining sample size for research activities. Educat. Psychol. Measure., 30(3): 607–610.

Kurgat, K.R., J.K. Lagat and E.W. Gathungu. 2023. Factors influencing choice of milk market outlets among smallholder dairy farmers in Kuresoi North Sub-county, Kenya. Afric. J. Agric. Res., 19(2): 189–195. https://doi.org/10.5897/AJAR2020.15069

Lai-Solarin, W.I., J. Bamidele, O.J. Joel, T. Tata, U.P.E. Abubakar, A.F. Joel and S.O. Sennuga. 2024. Challenges and benefits of extension service delivery for dairy cooperatives in Kaduna State, Nigeria. J. Agric. Exten. Rur. Econ., 1(1): 1–14. https://doi.org/10.31248/JAERE2024.00

Lamsal, M.R., N. . Kanel, M.P. Dahal and S. Soneja. 2025. An analysis of commercial banks’ lending and agricultural growth in Nepal. Econ. Rev. Nepal., 8(1): 53–68. https://doi.org/10.3126/ern.v8i1.80744

Mamo, F.H. and A.K. Beguije. 2022. The determinants of commercialization to smallholder farmers in West Hararghe Zone, Oromia Region, J. World Econ. Res., 11(2): 64–76. https://doi.org/10.11648/j.jwer.20221102.11

Manda, J., C. Azzarri, S. Feleke, B. Kotu, L. Claessens and M. Bekunda. 2021. Welfare impacts of smallholder farmers’ participation in multiple output markets: Empirical evidence from Tanzania. PLoS One., 16(5): e0250848. https://doi.org/10.1371/journal.pone.0250848

Mgomezulu, W.R., M.M. Chitete, B.B. Maonga, L. Kachingwe, H.H. Phiri, M. Mambosasa and L. Folias. 2024. Does shifting from subsistence to commercial farming improve household nutrition and poverty? Evidence from Malawi, Tanzania and Nigeria. Res. Glob., 8: 100201. https://doi.org/10.1016/j.resglo.2024.100201

Miller, L.C., S. Neupane, N. Joshi and M. Lohani. 2020. MILK Symposium review: Milk consumption is associated with better height and weight in rural Nepali children over 60 months of age and better head circumference in children 24 to 60 months of age. J. dairy Sci., 103(11): 9700-9714. https://doi.org/10.3168/jds.2020-18289

Mishra, B.P. and S. Bhatta. 2021. Factors affecting women farmers’ participation in agricultural cooperatives in Lamjung and Nawalpur districts, Nepal. Nepalese J. Agric. Sci., 19: 4.

MoALD. 2023. Statistical Information on Nepalese Agriculture 2021/2022. Ministry of Agriculture and Livestock Development (MoALD), Singha Durbar, Kathmandu, Nepal

MoALD. 2024. Statistical Information on Nepalese Agriculture 2022/2023. Ministry of Agriculture and Livestock Development (MoALD), Singha Durbar, Kathmandu, Nepal

MoALD. 2025. Statistical Information on Nepalese Agriculture 2023/2024. Ministry of Agriculture and Livestock Development (MoALD), Singha Durbar, Kathmandu, Nepal

MoICS. 2023. Micro, Small and Cottage Industry Report 2022/23. Ministry of Industry, Commerce and Supplies (MoICS), Government of Nepal, Singha Durbar, Kathmandu, Nepal

Nadheer, M.A. and M.A.C. Salfiya Ummah. 2020. Factors contributing to the business growth of small and medium dairy enterprises in Sri Lanka. J. Bus. Econ., 2(2): 1-12

Nath, T.D., A. Biswas and S.F. Ahmed. 2025. Livestock farming adoption in rural Bangladesh: Determinants and impacts on household income and women’s dietary diversity. Food Ener. Secur., 14(2): e70074. https://doi.org/10.1002/fes3.70074

NDDB. 2021. Study on Cost of Milk Production in Nepal. National Dairy Development Board. Right to Access Nepal (RAN), Hariharbhawan, Pulchowk, Lalitpur, Nepal

Neopane, S.P., B.S. Shrestha and D. Gauchan. 2022. Livestock contribution to food and nutrition security in Nepal, in: Agriculture, Natural Resources and Food Security: Lessons from Nepal, Springer International Publishing, Cham, pp. 241–258

NLBO. 2022. Annual Technical Report. National Livestock Breeding Office, Department of Livestock Services, Pokhara, Kaski, Nepal. http://nlbo.gov.np/downloadfile/NLBO_ POKHARA__Annual_Book_NLBO__2076_1611801190.pdf

Noor, S., O. Tajik and J. Golzar. 2022. Simple random sampling. Int. J. Educat. Lang. Stud., 1(2):78–82.

NPC. 2022. National Dairy Development Policy 2021. National Planning Commission (NPC). Singha Durbar, Kathmandu, Nepal.

NPC. 2024. Sixteen Five-Year periodic plan (2024/25–2028/29). National Planning Commission (NPC). Singha Durbar, Kathmandu, Nepal

Ogola, P.A., F. Ngesa and D.L. Makanji. 2023. Influence of access to extension services on milk productivity among smallholder dairy farmers in Njoro Sub-County, Nakuru County, Kenya. Heliyon., 9(9). https://doi.org/10.1016/j.heliyon.2023.e20210

Okello, D., G. Owuor, C. Larochelle, E. Gathungu and P. Mshenga. 2021. Determinants of utilization of agricultural technologies among smallholder dairy farmers in Kenya. J. Agric. Food Res., 6: 100213. https://doi.org/10.1016/j.jafr.2021.100213

Onyango, V.A., G. Owuor, E.J. Rao and D.J. Otieno. 2023. Impact of cooperatives on smallholder dairy farmers’ income in Kenya. Cogent Food Agric., 9(2): 2291225. https://doi.org/10.1080/23311932.2023.2291225

Ordofa, G., L. Zemedu and B. Tegegne. 2021. Structure, conduct and performance of dairy market in Ada’a Berga District, Ethiopia. Cogent Food Agric., 7(1): 1918878. https://doi.org/10.1080/23311932.2021.1918878

Pandey, A. 2022. Credit and financial access in Nepalese agriculture: Prospects and challenges. J. Agric. Environ., pp. 56–70.

Panta, H.K. 2022. Financial profitability of production and marketing of buffalo milk in Lalitpur District, Nepal. Econ. J. Develop. Issue., 34(1–2): 1–14. https://doi.org/10.3126/ejdi.v34i1-2.6365

Panta, R., V.K. Paswan, P. Kanetkar, D.S. Bunkar, H. Rose and S. Bakshi. 2023. Exploring trade prospects of Chhurpi and the present status of Chhurpi producers and exporters of Nepal. J. Ethnic Food., 10(1): 1. https://doi.org/10.1186/s42779-022-00165-0

Paudel, S., S.H. Ghimire and Y.R. Pandey. 2021. Cattle research in Nepal: Current status, challenges and way forward. In: Proc. 12th National Workshop on Livestock and Fisheries Research in Nepal, Nepal Agric. Res. Council., pp. 14–21

Paudel, U.K. 2024. Relationship between Agriculture Subsidy and Agricultural Production in Nepal. Curricul. Develop. J., 32(46): 234–243. https://doi.org/10.3126/cdj.v32i46.77037

Paul, M., T. Ojong, M. Hauser and K. Mausch. 2022. Does agricultural commercialization increase asset and livestock accumulation on smallholder farms in Ethiopia. J. Develop. Stud., 58(3): 524–544. https://doi.org/10.1080/00220388.2021.1983170

Pingali, L.P. and M.W. Rosegrant. 1995. Agricultural commercialization and diversification: Process and policies. Food Polic., 20(3): 171–185. https://doi.org/10.1016/0306-9192(95)00012-4

Poudel, S., D. Chhetri and S.R. Barsila. 2023. Productive and reproductive performance of farmers managed dairy cattle in western Chitwan, Nepal. J. Instit. Agric. Anim. Sci., 37: 160-172. https://doi.org/10.3126/jiaas.v37i1.57003

Poudel, S., R. Karki, and S. Sharma. 2025. Government Intervention in Agriculture Market Management. Nepalese J. Agric. Sci., 29: 174–188.

Rantlo, A.M., M. Tsoako and B. Muroyiwa. 2020. Institutional factors influencing dairy farmers’ participation in formal and informal milk markets in Maseru Urban, Lesotho, South Africa. J. Agricultural Exten., 24(2): 48–59. https://doi.org/10.4314/jae.v24i2.6

Saha, A., R. Sabates-Wheeler and J. Thompson. 2022. Insights into smallholder capacity for agricultural commercialization: Evidence from four African contexts. Europ. J. Develop. Res., 34(4): 1757–1802. https://doi.org/10.1057/s41287-021-00414-z

Sapkota, K.N. and B. Khatiwada. 2024. Economic impact of Chhurpi production on farmers’ livelihood. Europ. J. Soc. Sci. Stud., 10(5). https://doi.org/10.46827/ejsss.v10i5.1875

Sarica, D.V., A. Demircan, O. Naziroglu, H. Aydin and H. Koknaroglu. 2022. The cost and profitability analysis of different dairy farm sizes. Trop. Anim. Heal. Prod., 54(5):320. https://doi.org/10.1007/s11250-022-03321-5

Schultz, T.W. 1964. Transforming traditional agriculture. Yale University Press, New Haven, CT.

Shrestha, R. and S. Singh. 2023. Presence of E. coli in raw and pasteurized milk in Bagmati Province, Nepal. Nepalese J. Agric. Sci., 25: 225-236

Singh, N.K., N.H. Sunitha, G. Tripathi, D.R.K. Saikanth and A. Sharma. 2023. A.E. Jose and M.V.K.J. Mary. Impact of digital technologies in agricultural extension. Asian J. Agric. Exten. Econ. Sociol., 41(9): 963–970. https://doi.org/10.9734/AJAEES/2023/v41i92127

Subedi, M. 2023. Use of Mixed Methods in Social Sciences Research. Nepalese J. Develop. Rur. Stud., 20(1): 96-105. https://doi. org/10.3126/njdrs.v20i01.64166

Tadele, E., D. Worku, D. Yigzaw, T. Muluneh and A. Melese. 2025. Precision of dairy farming: Navigating challenges and seizing opportunities for sustainable dairy production in Africa. Front. Anim. Sci., 6: 1541838. https://doi.org/10.3389/fanim.2025.1541838

Thapa, G., N.S. Teli and A. Mangalasseri. 2025. Value Chain Analysis of Milk in Kathmandu Valley-Nepal. Asian J. Dairy Food Res., 1-9. https://doi.org/10.18805/ajdfr.DR-2309

Thapa, S., H.K. Panta, S. Poudel, A. GC, K. Regmi, and M. Basnet. 2023. Factors affecting farmers’ access to agricultural subsidy in Makwanpur and Dhading districts of Nepal. SAARC J. Agric., 21(2): 263-276. https://doi.org/10.3329/sja.v21i2.68550

Timsina, K.P., S. Subedi, S. Pandey, D. Devkota, A. Aryal and J. Lamichhane. 2023. Reforming agricultural extension system in Nepal: What can we adopt from selected agriculture led countries? J. Nepal Agric. Res. Council., 9(1): 1-21. https://doi.org/10.3126/jnarc.v9i1.61513

Turkelboom, F., K.K. Van, S. Ongprasert, P. Sutigoolabud and J. Pelletier. 1996. The changing landscape of the northern Thai hills: adaptive strategies to increasing land pressure. In: Montane Mainland Southeast Asia in Transition. Chiang Mai University, Thailand. pp. 25–45

Twumasi, M.A., I.O. Asante, G. Essilfie, G. Ntim-Amo, S.K. Modzi and Y. Jiang. 2022. Household income improvement among Ghanaian livestock farmers: Does climate change adaptation strategies matter? Environ. Develop., 44: 100737. https://doi.org/10.1016/j.envdev.2022.100737

Upadhyay, N., B. Khanal, Y. Acharya and K.P. Timsina. 2021. Nepalese legal standard of milk and common milk products and its implications. J. Agric. Natur.Resour., 4(2): 284–294. https://doi.org/10.3126/janr.v4i2.33924

Van Koppen, B., M. Raut, A. Rajouria, M. Khadka, P. Pradhan, R.K. GC, L. Colavito, C. O’Hara and S.L. Rautanen. 2022. Gender equality and social inclusion in community-led multiple use water services in Nepal. IWMI Working Paper, 203

Von Braun, J., H. Bousi and E. Kennedy. 1994. Conceptual framework. In J. von Braun and E. Kennedy (eds.), Agric. Commercializ. Econ. Develop. Nutrit., pp. 9–33. John Hopkins University Press, Baltimore, MD

Waiswa, D. and A. Gunlu. 2022. Economic analysis of dairy production in Uganda: A case study on the performance of dairy cattle enterprises in Southwestern Uganda. Asian J. Agric., 6(2): 61–67. https://doi.org/10.13057/asianjagric/g06020

Wangu, J., E. Mangnus and A.C. van Westen. 2021. Recognizing determinants to smallholders’ market orientation and marketing arrangements: Building on a case of dairy farming in rural Kenya. Land., 10(6): 572. https://doi.org/10.3390/land10060572

Wooldridge, J.M. 2002. Econometric analysis of cross section and panel data MIT press. Cambridge, ma., 108(2): 245-254.

Workie, D.M. and W. Tasew. 2023. Adoption and intensity use of malt barley technology package by smallholder farmers in Ethiopia: A double hurdle model approach. Heliyon., 9(8). https://doi.org/10.1016/j.heliyon.2023.e18477

World Bank. 2023. Agriculture Contribution to GDP, World Bank Database, 2023. https://data.worldbank.org

Yogi, L.N., T. Thalal and S. Bhandari. 2025. The role of agriculture in Nepal’s economic development: Challenges, opportunities, and pathways for modernization. Heliyon., 11(2). https://doi.org/10.1016/j.heliyon.2025.e41860

Zegeye, M.B., A.H. Fikire and G.B. Meshesha. 2022. Determinants of multiple agricultural technology adoption: evidence from rural Amhara region, Ethiopia. Cogent Econ. Finan., 10(1): 1-23. https://doi.org/10.1080/23322039.2022.2058189

Zheng, H., P. Vatsa, W. Ma and D.B. Rahut. 2023. Does agricultural cooperative membership influence the off-farm work decisions of farm couples? Ann. Public Cooperat Econ., 94(3): 831–855. https://doi.org/10.1111/apce.12417