In India, diverse climatic conditions pose risks to farmers’ livelihoods (Shukla et al., 2023; Singh et al., 2023), making livestock an essential support system, especially for small and landless farmers. It provides income, employment and food security (Kumari et al., 2020; Karunanayaka et al., 2022). Milk is an important source of income and nutrition to rural households (Dudi et al., 2022; Kumar et al., 2023). However, despite the availability of recommended cattle management practices, their adoption remains uneven among smallholder farmers (Quddus, 2012; Rathod et al., 2016), leading to persistent productivity gaps (Duong et al., 2023).
       
Earlier approaches emphasized awareness and information dissemination (Barakabitze et al., 2017), but adoption is now recognized as a complex process shaped by economic viability, risk perception and institutional access (Feder et al., 1985; Marra et al., 2003; Barham et al., 2015; Leeuwis and Aarts, 2020). Farmers evaluate technologies based on costs, labour and expected returns (Jack, 2013; Alexander et al., 2020). With increasing links between livestock and food security, improving adoption strategies is crucial (Duru and Therond, 2015; Abu Hatab  et al., 2020). Sustainable dairy development requires integrating behavioural insights, financial inclusion and institutional support (Kilelu et al., 2017; Gheorghe-Irimia  et al., 2023; Neethirajan, 2024).
       
Practices offering immediate economic benefits, such as feeding, are more readily adopted, whereas preventive healthcare and infrastructure investments remain limited (Quddus, 2012; Kebebe, 2019; Hennessy et al., 2019). This selective adoption creates vulnerabilities like disease risks and inefficiencies (Chaudhary et al., 2013; Yadav et al., 2014; Dhaka et al., 2017). Resource-poor farmers operate under uncertainty and prioritize short-term survival (Millar and Connell, 2010; Singh et al., 2024), while capital-intensive practices require reliable information and institutional support (Yadav et al., 2014; Padhy et al., 2024; Papakonstantinou et al., 2024;). This study examines adoption across breeding, feeding, housing and healthcare practices. It contributes to understanding constrained decision-making among farmers and provides insights for extension agencies and policymakers to address adoption gaps.

The study was conducted during 2024-25 under the institutional support of IIMT University, Meerut, Uttar Pradesh. A multistage sampling procedure was adopted. Bulandshahar and Meerut districts were selected purposively, followed by four blocks (Meerut, Daurala, Lakhaoti and Khurja) based on higher milk production. From each block, four villages were selected and a list of cattle-rearing farmers was prepared. Farmers with a minimum of four cattle were selected to ensure inclusion of commercially oriented and actively engaged dairy farmers, providing more reliable and experience-based data. This criterion helps improve the validity of findings related to management practices and adoption behavior, making a total sample of 320 respondents.
 
Development of measurement instrument
 
A structured and pre-tested interview schedule was developed based on recommended cattle management practices covering four domains: breeding, feeding, housing and health care. Data were collected through personal interviews. Adoption of practices was measured using a three-point continuum (always = 2, sometimes = 1, never = 0). Use of a three-point adoption scale ensures simplicity, clarity and ease of response for farmers. The interview schedule was validated by experts in Agricultural Extension and pre-tested in a non-sample area. Reliability was assessed through Split half method to ensure internal consistency of the tool. 
 

Total adoption score = (Always x 2) + (Sometimes x 1) + (Never x 0)

 
Statistical tools and techniques
 
Both descriptive and inferential statistical techniques were used. In descriptive, frequency and percentage, mean adoption scores and ranking of practices were calculated. In inferential, the chi-square test was used to test the association between selected independent variable and adoption levels.
 

 

 

   

Where,
χ²= Chi-square value.
O= Observed frequency.
E= Expected frequency.
Σ= Summation over all categories.

Profile of respondents
 
Data presented in Table 1 indicated that majority of the dairy farmers (44.69%) belonged to the old age category, followed by middle age farmers (36.56%), while only 18.75 per cent were young. These findings suggested that the dairy farmers were experienced and cattle managed by them, due to their practical and traditional knowledge developed over time. With regard to education, 35.31 per cent of respondents had secondary education, similar findings reported by followed by Khode  et al., 2009, 30.31 per cent were educated above secondary level. However, 13.44 per cent were illiterate. The relatively higher proportion of educated farmers indicates a favorable condition for adoption of scientific dairy practices. In terms of herd size, 43.44 per cent of respondents were maintained a medium herd size, while 38.75 per cent had large herd size. Only 17.81 per cent had small herds. This shows the commercialization of the dairy farming in the area, it also indicates that farmers increasingly maintaining moderate to large herd sizes to enhance income. Same pattern of herd size also reported by Godara et al., (2017). Landholding data shows that 42.81 per cent of respondents were small farmers, followed by marginal farmers (30.63%). Only 26.56 per cent farmers hold large land holdings. The pattern suggests that dairy farming acts as an important occupation for livelihood source and consistent income for small and marginal farmers. Family size revealed that nearly half of the respondents (48.75%) belonged to medium size families, followed by small families (28.44%). Larger families (22.81%) were comparatively fewer. Medium family size may provide adequate family labor for dairy operations without causing excessive economic burden.


 
Adoption of breeding practices
 
Table 2 reveals that the highest adopted practice was proper care during calving and post-calving heat (Mean score = 1.76; Rank I), followed by regular monitoring of heat symptoms (1.72; Rank II). These findings indicate that farmers are very conscious about practices that directly linked to animal productivity and survival. Similar with Rathore and Rathore (2014), reported that buffalo rearers prioritized practices which give immediate reproductive outcomes. The use of breeding bulls within the recommended age (1.37; Rank III) and timely insemination (1.24; Rank IV) were moderately adopted. Treatment of anoestrus and repeat breeding had satisfactory adoption (1.22; Rank V), suggesting awareness about the reproductive management. Panchbhai et al., (2017) reported moderate adoption of reproductive management practices and farmers respond actively when fertility problems affect milk production. However, very low adoption was observed in artificial insemination (0.43; Rank X) and pregnancy diagnosis (0.46; Rank IX). Due to lack of veterinary services, technical knowledge, or accessibility issues. Similar gap observed by Sarwar (2021). Providing exercise to pregnant cattle (0.62; Rank VIII) was also poorly adopted, possibly because farmers underestimate its importance. Contradictory findings were reported by Meena et al., (2012), higher adoption of institutional breeding support was observed in areas with strong extension contact. The overall mean score and adoption index was 1.10 and 54.49 per cent respectively which suggest moderate adoption of breeding practices. The results suggest that farmers prefer traditional and observable practices over technically specialized services. Rathore and Rathore (2014) also reported the same that mean score 1.05 and adoption was 52.18 percent among the buffalo rearers.



Adoption of feeding practices
 
Table 3 indicates that extra feeding to advanced pregnant animals recorded the highest adoption (1.74; Rank I), followed by grazing animals at suitable summer timings (1.65; Rank II). These practices are directly associated with visible animal health and milk yield, explaining their higher adoption. Chopping dry fodder (1.55; Rank III) and providing clean drinking water (1.47; Rank IV) also showed good adoption levels. Feeding balanced concentrate mixture ranked fifth (1.18), indicating moderate awareness about nutritional management. Very low adoption was recorded for preparation of hay and silage (0.21; Rank IX) and round the year green fodder supply (0.71; Rank VIII). Due to lack of proper planning, storage facilities and technical guidance. Similar behaviour of farmers has been documented globally like feeding and neglecting long-term fodder storage (FAO, 2011). Similarly, Meena et al., (2012) also indicated gap between scientific fodder management and reasonable awareness of basic feeding practices. Mahesh (2020) also reported some similar and some contrast findings due to location of study.


 
Adoption of housing practices
 
Table 4 shows that providing adequate floor space was get higher adoption in housing practices (1.86; Rank I), indicated that farmers give priority to the animal comfort. Proper orientation of cattle shed (1.65; Rank II) and loose housing system (1.51; Rank III) were also adopted widely. Moderate adoption was found in ventilation (1.33; Rank IV) and drainage (1.18; Rank V). Seasonal modification of housing (0.64; Rank VIII) was poorly adopted, due to lack of technical guidance and financial support. To modify the house or structural improvement required heavy capital investment, which may discourage smallholders. Conflicting results were found regarding pucca floors, pucca roof provisions and similar aspects like levelled floors, ventilation and practices to shield animals from extreme weather, as noted by Singh (2017); Meena (2017) and Kapadiya et al., (2022).


 
Adoption of health care practices
 
Table 5 reveals that most of the farmer adopted, daily observation for illness (1.72; Rank I) and cleaning and sanitation of sheds (1.65; Rank II). These practices are simple, low-cost and based on routine observation. Moderate adoption was seen in isolating sick animals (1.30), treatment of reproductive disorders (1.21) and parasite control (1.15). However, critical preventive measures such as regular deworming (0.40; Rank IX), timely vaccination (0.50; Rank VIII) and safe carcass disposal (0.28; Rank X) showed very low adoption. This indicates a reactive rather than preventive approach to animal health, where farmers focus more on visible illness than long-term disease prevention. Similar result in term of Proper veterinary treatment of sick animals and contradictory result for timely vaccination of animals, Isolating sick animals, Regular deworming of animals and cleaning and sanitation of shed were reported by the Kapadiya et al., (2022).


 
Relationship between selected variables and adoption of practices
 
The association between independent variables with adoption of recommended cattle management practices is presented in Table 6. Education had significant association with breeding (χ² = 15.49*), feeding (χ² = 16.21**) and health care practices (χ² = 19.47**), revealed that educated farmers were able to understand scientific management practices and also found reliable information from different sources. Education improves awareness, decision-making ability and openness to innovation. Similar findings were reported by Panchbhai et al., (2017), who observed that education influences adoption of improved dairy practices among farmers. Herd size also highly significant association with feeding (χ² = 22.14**) and housing (χ² = 19.43**) practices and significant with health care (χ² = 12.63*). Herd size influence for the reason that farmers had larger herd size operate their farm commercially. They also able to invest in housing and feeding. This pattern aligns with diffusion theory, where resource-rich farmers adopt innovations rapidly while lower perceived risk (Rogers, 2003). Landholding size had a highly significant on feeding (χ² = 30.42**) and housing (χ² = 27.56**) practices and a significant effect on health care (χ² = 10.15*), revealed that farmer with higher land holding were able to invest frequently in infrastructure and feeding. Comparable results that land ownership highly associated with scientific dairy management practices reported by Sarwar (2021). Family size showed a strong association with housing (χ² = 17.34**) and health care (χ² = 15.74**), likely due to more family member fill the requirement of labour for maintenance-intensive activities. Panchbhai et al., (2017) similarly found that labour availability increase adoption. Age was not significantly associated with Breeding, feeding and housing pattern. But it was significant with health care (χ² = 9.26*), suggested that age influence the decision about disease management more than production practices. This observation supports earlier work indicating that experiential knowledge often guides behaviour (Rogers, 2003). These findings confirm that adoption is shaped by education, resources and labour capacity, highlighting the need for targeted extension strategies for smallholders and less-educated farmers.

The findings of this study concluded that recommended cattle management practices moderately adopted with selective nature. Farmers were adopted productivity-oriented practices specially in housing and feeding. Regarding veterinary care and scientific breeding practices adopted poorly. These adoption pattern concluded with farmers were interested in investing short term economic gain over long term. Socio-economic factors influencing the adoption decision of farmers. Education, herd size and landholding found significantly associated with cattle management practices which leads to influence on capacity and willingness of farmers to implement improved practices. Educated and resource rich farmers practices adopted rapidly, emphasizing the information access and economic strength in dairy modernization.

The authors acknowledge IIMT University for institutional support and thank the dairy farmers of Bulandshahar and Meerut for their cooperation. No external funding was received.
 
Disclaimers
 
The views expressed are solely those of the authors and not of their institutions. The authors are responsible for the content.
 
Informed consent
 
Informed consent was obtained from all respondents; no animal experimentation was involved.

The authors declare no conflict of interest and no external influence on the study.