Nepal, a landlocked South Asian nation, is characterized by its diverse topography, spanning approximately 147,181 square kilometers, with the Himalayan ranges in the north and fertile plains in the south. The country, home to 29.16 million people (CBS, 2021), remains predominantly agricultural, where livestock significantly contributes to household income, nutrition, draft power and manure (Khanal et al., 2022). The agricultural sector accounts for 23.95% of Nepal’s GDP, with livestock contributing 6.23% and the dairy sector playing a pivotal role. Notably, cattle’s raw milk contributes 4.63% to agricultural GDP, while buffalo’s raw milk accounts for 7.85% (MoALD, 2022). Dairy agriculture’s origins trace back to the domestication of cattle, goats and sheep (Zeuner, 1963; Reed, 1984). Technological advancements and globalization have reshaped dairy production, with countries like Brazil, China, Germany, India and the USA leading global output as shown in Fig 1 (FAOSTAT, 2021). In Nepal, agriculture underpins livelihoods for 3.8 million households, of which 95% engage in dairy farming; however, only 14% sell in markets, with most producing for self-consumption (CASA, 2020). Similar patterns appear in India’s major milk-producing states-Punjab, Bihar and Uttar Pradesh-where smallholders dominate, marketing largely occurs through informal channels and socio-economic constraints limit technology adoption (Kumar and Parappurathu, 2014; Kaur and Toor, 2024).


       
Annual milk production in Nepal stands at 2.48 million metric tons, with buffalo milk (57%) surpassing cow milk (43%) in contribution. Between 2011/12 and 2020/21, production increased by 52.82%, reflecting substantial sectoral growth as shown in Fig 2 and 3 (MoALD, 2021). Regional disparities in livestock populations and milk output emphasize the need for targeted value chain improvements, as seen in the dominance of buffalo milk and provincial variations in productivity. Similar findings on regional disparities and the economic role of dairy are reported in drought-prone areas of Maharashtra, India, where crossbred cow and buffalo systems also reveal differences in productivity and costs (Sapkal et al., 2025). Despite growing demand, Nepal’s dairy sector faces challenges such as inefficiencies in production, processing and distribution, particularly in urban areas like Kathmandu valley. Fragmentation among small-scale producers, limited technical expertise, inadequate rural infrastructure and insufficient advanced processing facilities hinder sectoral growth (Mahato et al., 2019; Shrestha et al., 2017). Quality and safety concerns, including adulteration and contamination, persist due to inadequate knowledge and testing technologies (Dhakal et al., 2019). Similar gaps in adoption of key practices such as mastitis screening and cattle insurance have been documented among Indian dairy farmers even after formal training interventions (Thakur et al., 2022). In the context of Kathmandu Valley, these challenges are more pronounced due to high consumer demand, dependence on smallholder farmers with low bargaining power, weak cold chain facilities, seasonal fluctuations in supply and dominance of informal milk marketing channels. These systemic problems reduce efficiency and profitability along the value chain, ultimately affecting both producers and consumers.




       
Value chain analysis, as outlined by Roduner (2007) and Zamora (2016), identifies inefficiencies and opportunities within production, transportation and consumer delivery systems. However, existing studies on Nepal’s dairy sector have largely focused on production trends and consumption patterns, with limited attention to how value is distributed among actors, where inefficiencies emerge and what constraints farmers and intermediaries face within an urbanized market system like Kathmandu Valley. This lack of comprehensive value chain analysis constitutes a significant research gap. This research evaluates the milk value chain in Kathmandu valley, focusing on farmers’ socioeconomic profiles, production costs, marketing efficiency and sectoral constraints. The findings aim to inform evidence-based interventions, enhancing income generation, rural development and consumer access to safe and nutritious milk. By addressing the identified gap, this study contributes empirical evidence on the structure, dynamics and constraints of the milk value chain in Kathmandu Valley, thereby guiding policy and investment strategies for sustainable growth.

Selection of site
 
Kathmandu Valley, comprising Kathmandu, Bhaktapur and Lalitpur districts, was purposively chosen due to its role as Nepal’s economic, cultural and administrative hub. Its dense population, strong urban-rural connections and significant dairy industry make it an ideal setting to study the dairy value chain’s adaptation to urbanized markets.
 
Population and sampling
 
The study utilized purposive sampling to select the Kathmandu Valley, which has a daily milk demand of 75,000 litres (NDDB, 2022) and ranks fifth in cattle population nationwide (MoALD, 2021). Municipalities-Shankharapur, Suryabinayak and Godawari were targeted, focusing on wards with dense farmer populations and high milk collection rates (DDC, 2023). A total of 150 households were randomly selected, with 50 households from each ward with a milk collection center.
 
Data collection
 
Primary data were collected between April and September 2023, at Kathmandu Valley, Nepal using pretested questionnaires and structured interviews. Field observations, key informant interviews (e.g., with collection center managers and retailers) and focus group discussions with producers, collectors and consumers further enriched data. Secondary data were sourced from reports, journals and statistical records of organizations such as NDDB, NDA, DLS, MoALD and online materials.
 
Analytical concepts
 
The cost analysis encompassed fixed and variable costs. Fixed costs included depreciation of fixed assets, while variable costs covered feed, fodder, labor, veterinary expenses and miscellaneous expenditures. Feed and fodder costs accounted for green and dry fodder, concentrates and mineral mixtures, valued at market prices. Labor costs included hired labor, valued based on task-specific wages and family labor, estimated using prevailing rates. Veterinary expenses encompassed pharmaceuticals, vaccination fees, artificial insemination charges and veterinary fees. Miscellaneous costs included electricity, water services, repairs, insurance and related expenses. Depreciation was calculated using the straight-line method (Chaudhary, 2011), considering zero salvage value and useful lives of 12.5 years for animals and 30 years for farm buildings (Mohapatra et al., 2021), with an 11% annual interest rate.

 
Cost concepts
 

i. 

 

ii.

iii. Gross returns (GR): Calculated by multiplying milk yield with fat content and unit fat content price. In case of cooperative.
GR = (Milk produced / household / day * Fat content * Unit fat content price of milk) + value of dung (Rs)
Or, GR = (Milk produced / household / day * Price of milk / liter) + Value of dung (Rs)
iv. Net returns (NR) = Gross returns (GR) - Net cost (NC)
v. Flush and lean season: Flush Season (August-February): High milk production due to favorable conditions, abundant feed and increased yields. Lean season (March-July): Low milk production due to dry weather and limited pasture, leading to reduced yields and higher feeding costs (NDDB, 2020; CASA, 2020).
vi. Marketing cost: Total marketing expenses incurred by producers and intermediaries (Satashia and Pundir, 2021).

vii. Marketing efficiency (ME): Calculated by using Acharya method.

Where,
FP = Price received by the farmer.
MC = Net marketing cost.
MM = Net marketing margins.
 

vii. 

Garrett’s ranking technique
 
This was employed to assess production and marketing constraints. Respondents ranked challenges by significance, with scores derived from Garrett’s Table. Average scores for each factor were calculated and ranked in descending order to identify the most influential factors (Garrett and Woodworth, 1969).

Where,
R_ij = Rank given for ith variable by jth respondents.
N_j = Number of variable ranked by jth respondents.

Socio-demographic characters of farmers
 
Table 1 shows that a majority of households (60.67%) were male-headed, with 39.33% female-headed, which is higher than the national average of 25% reported for livestock-based households (NDDB, 2018) and substantially higher than 6% in Bangladesh (Kabir et al., 2018). Most households (79.33%) were nuclear families with an average size of 5.44, above the national average (4.37) (CBS, 2021), suggesting relatively larger household labor availability. The literacy rate (79.33%) exceeded the national rate (76.2%). Income sources included agriculture (68%), pensions, while 10% were fully dependent on livestock. Land ownership varied, with 7.33% holding 1-3 ropani and 52% owning 3-6 ropani. Average milking animals per household was 2.01, with Lalitpur highest at 2.32. These findings indicate that Kathmandu Valley farmers are relatively better educated and resource-endowed, which could facilitate adoption of improved practices. However, the higher proportion of female-headed households highlights increasing feminization of agriculture, likely due to male outmigration, aligning with broader South Asian trends (CASA, 2020). This feminization has policy implications, particularly in ensuring women’s access to extension, finance and markets.


 
Involvement of women in production and marketing of milk
 
Table 2 shows over 60% of women engaged in production tasks such as forage cutting, shed cleaning and feeding, while men were more active in marketing and purchasing inputs. Similar trends were observed by CASA (2020), where women dominated on-farm labor but had limited decision-making roles in marketing. There was relatively equal involvement in milking. These results reinforce that women’s contribution in dairy farming is substantial but undervalued. While they provide most production labor, their limited role in marketing perpetuates gender gaps in income control. Addressing these gaps could empower women and enhance household earnings.



Economic analysis of milk production
 
Table 3 presents costs and revenues while Table 4 explains about cost and production of milk in Kathmandu valley. Bhaktapur had total costs of NRs.337,418.75 with net revenue N Rs.96,280.57; Kathmandu costs N Rs.378,580.66 with net revenue NRs.69,653.53; Lalitpur costs N Rs.378,043.32 with net revenue N Rs.60,073.56. Feed emerged as the largest cost, followed by labor, corroborating (Satashia and Pundir 2021). Average yields were 12.72 liters (Bhaktapur), 12.96 (Kathmandu) and 14.45 (Lalitpur), mean 13.38 liters-slightly higher than Bhandari (2015), who reported 10-12 liters. Production costs averaged NRs.74.25/liter, higher than national range NRs.45-67 (NDDB, 2021). This confirms earlier observations by Timsina (2010) that household-level costs often exceed official estimates. Overall, while productivity in Kathmandu Valley is above average, elevated costs reduce competitiveness, especially for smallholders. Without interventions in feed efficiency, input cost reduction and cooperative marketing, farmers’ margins will remain constrained.




 
Marketing channels, cost, margins, efficiency and producer share
 
The study area observed various marketing channels for transferring the milk produced on farms to consumers. The milk supply pattern varied across three different villages in the Kathmandu Valley. The main marketing channels identified in the study area were.


       
Table 5, 6 and 7 provide a comparative analysis of milk marketing across Bhaktapur, Kathmandu and Lalitpur districts, highlighting variations in cost structure, profit distribution and efficiency across different marketing channels. In Bhaktapur, two channels were observed. Channel-I, where farmers sell directly to consumers, yields the highest marketing efficiency (145.66) and producer’s share (99.32%). In contrast, Channel-II, involving retailers, shows lower efficiency (4.911) and producer’s share (83.33%). In Kathmandu, four channels were analyzed. Channel-I remains the most efficient, while Channels III and IV (with collection and chilling centers) further reduce efficiency and producer’s share, with Channel-III showing the lowest efficiency (1.9). Similarly, in Lalitpur, Channel-I emerges as the most favorable for farmers, while Channels III and IV offer the lowest efficiency and producer share. These findings underscore that direct-to-consumer sales are the most beneficial for farmers, but such channels are limited in scale and reach. Reliance on intermediaries significantly reduces producer margins, echoing previous studies (Shrestha et al., 2017). This points to structural inefficiencies in the milk value chain, highlighting the need for stronger cooperative structures and transparent pricing systems.






 
Value chain actors and their function
 
This section outlines the roles of key actors, including input suppliers, producers, collection centers, chilling centers, processors, wholesalers, retailers and consumers, along with supporting institutions such as DLS, DoC, NDDB and DDC as shown in Fig 4. The multiplicity of actors reflects both opportunities and inefficiencies. While institutions like DDC and NDDB provide critical support, weak coordination among actor’s results in fragmented markets and inefficiencies, a finding consistent with Mahato et al. (2019). Strengthening linkages among value chain actors could enhance both productivity and farmer incomes.


 
Problems faced by farmer in Production and marketing of milk
 
Table 8 highlights challenges such as the high cost of feed and fodder, limited availability of green grass, rising veterinary service costs, disease outbreaks and lack of standardized milk pricing. Other issues include delayed payments, poor genetic potential of animals and irregular milk supply. These problems are consistent with Dhakal et al. (2019) and NDDB (2021), suggesting that systemic challenges persist in Nepal’s dairy sector. The implications are that without policy support for feed resources, veterinary access and transparent milk pricing mechanisms, profitability and sustainability of smallholder dairy farming will remain at risk. Limitations of this study include its focus on Kathmandu Valley, meaning results may not fully generalize to rural or remote districts with differing agro-ecological contexts.

Milk production, even for non-commercial producers, is a profitable venture, yielding a net revenue of NRs.75,335.89/year/cow. However, small-scale farmers face high feed, forage and veterinary costs, which impact the milk cost. Profits increase when there are fewer intermediaries between the producer and consumer. The pricing structure based on milk’s fat content leads to reduced farmer earnings. Collection and chilling centers break even, while processors profit from extracting fat to produce value-added items. Direct sales to consumers ensure fresh, high-quality milk, while other channels lower quality, especially fat content. Channel I is the most efficient for marketing raw milk, with farmers often selling partially to collection or chilling centers and partially directly to consumers for higher revenue. Aging cows reduce milk quality and revenue, posing a significant challenge for farmers in Nepal.
 
Suggestions
 
Most milk producers rear only one or two milch animals, reflecting the non-commercial nature of the sector. Enhancing herd size through targeted interventions, promoting year-round fodder cultivation and strengthening farmer-led cooperatives can improve efficiency. Government support should prioritize veterinary services, artificial insemination and accessible credit. Ensuring hygiene through quality control at collection and processing centers, location-specific milk pricing and training on feed, nutrition and hygiene are essential. Policies on unproductive cattle must also respect religious and cultural sensitivities.

The authors wish to thank anonymous referees and the editor who helped to substantially improve the paper.
 
Disclaimers
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.
 
Informed consent
 
All animal procedures for experiments were approved by the Committee of Experimental Animal care and handling techniques were approved by the University of Animal Care Committee.

The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.