- Open Access
Technical efficiency in milk production in underdeveloped production environment of India*
© Bardhan and Sharma; licensee Springer. 2013
- Received: 19 November 2012
- Accepted: 13 February 2013
- Published: 23 February 2013
The study was undertaken in Kumaon division of Uttarakhand state of India with the objective of estimating technical efficiency in milk production across different herd-size category households and factors influencing it. Total of 60 farm households having representation from different herd-size categories drawn from six randomly selected villages of plain and hilly regions of the division constituted the ultimate sampling units of the study. Stochastic frontier production function analysis was used to estimate the technical efficiency in milk production. Multivariate regression equations were fitted taking technical efficiency index as the regressand to identify the factors significantly influencing technical efficiency in milk production. The study revealed that variation in output across farms in the study area was due to difference in their technical efficiency levels. However, it was interesting to note that smallholder producers were more technically efficient in milk production than their larger counterparts, especially in the plains. Apart from herd size, intensity of market participation had significant and positive impact on technical efficiency in the plains. This provides definite indication that increasing the level of commercialization of dairy farms would have beneficial impact on their production efficiency.
- Stochastic frontier analysis
Despite of holding the number one position in global milk production, the milk productivity in India remains one of the lowest as compared to many leading countries of the world. At national level, milk yield of indigenous cow is about 3 to 3.5 litres, of buffalo 3.96 to 5.39 litres and of crossbred cow between 5.82 to 7.80 litres per day. As per FAO data, productivity of an average milch animal in India is even less than half of the world average. Productivity growth can be enhanced through two pathways – technological progress and technical efficiency improvement (Karanja et al. 2012). Technological progress requires substantial capital investment. In a developing country like India, it is important to know what policies and steps need to be taken for productivity enhancement before investing scarce capital to effect technological progress (Saha and Jain 2004). In this context, efficiency analysis assumes critical importance as technical efficiency improvement entails inefficient farmers adopting existing technologies and practices and thus saving scarce capital to get better results from them. Also, analysis of factors causing (in) efficiency offers crucial insights on key variables that might be worthy of consideration in policy making in order to ensure optimal capital and resource utilization.
Literature review has revealed that farmers in developing countries fail to exploit full potential of a technology and make allocative errors (Gelan et al. 2010; Otieno et al., 2012 and Rao and Rama 2012). Thus, increasing the efficiency in production assumes greater significance in attaining potential output at the farm level. Although several studies are available on analysis of technical efficiency in farm production in the Indian context (Narala and Zala 2010 and Mondal et al. 2012), studies on technical efficiency in milk production under mixed farming are rare (Saha and Jain 2004).
Efficiency analysis in milk production becomes all the more important in underdeveloped production environments of developing countries like India which are basically low-input and low-output environments characterized by subsistence holdings, resource poor locations with milch animals of low production potential and having poor infrastructural support system. In view of the above, the present study was carried out to examine the technical efficiency in milk production along with influence of various factors on this efficiency in Kumaon region of Uttarakhand state.
Sample and data
The state of Uttarakhand has two divisions, viz. Kumaon and Garhwal. The study was confined in Kumaon division on account of greater livestock density in this division (114 per sq. km. geographic area and 840 per 1000 rural population as compared to 78 per sq. km. geographic area and 795 per 1000 rural population in Garhwal division) (Bardhan et al. 2010). Two districts rich in livestock resources, viz. U.S. Nagar (located in the plains) and Almora (located in hills) from Kumaon region were chosen so as to have a comparative picture of milk production scenario in the plains and hills. A total of three villages falling outside milk-routes of the dairy cooperative network were selected randomly from each district. Ten milk selling households were selected from each such village having representation from different herd-size categories, viz. small, medium and large (identified on the basis of Standard Animal Units using cumulative square root frequency technique) on proportionate basis. The final sample size comprised of 60 milk producing households. Data for the present study were collected through personal interview method with the help of a well-structured, comprehensive and pretested interview schedule.
Estimation of technical efficiency in milk production
Where, Yk is the output of the kth farm, Xi ‘s are the inputs to the production process, vk is a random variable representing statistical noise and other stochastic shocks entering into the definition of the frontier. It is almost universal to specify this random term as independent normally distributed with zero mean and constant unknown variance σv 2, and independent of Xi, i.e. vk ~ N (0, σv 2). uk is a non-negative random variable representing technical inefficiency and is assumed to be distributed independently of vk and Xi . It can be measured by the difference between maximum output Y* (estimated through the stochastic frontier production function) and observed output, Yi. Thus, farm-specific inefficiency is the distance below the frontier (Yi – Y*). The above stochastic frontier production function can be estimated by maximum likelihood once a density function for uk is specified.
Since, the input-output relationship in this study has been explored at household level and not for individual species of animals, some of the important variables such as order of lactation of milch animals and stage of lactation of milch animals have been purposively eliminated due to difficulty incorporating these information at an aggregated milk production function. Hence, it was assumed that the eliminated variables were not significantly varying between farm households in the study area (Saha and Jain 2004).
To take care of variations in the type of fodder fed at different times and the mixture of fodder fed; the feed inputs were standardized to nutrition units in terms of a feed index developed from Digestible Crude Protein (DCP) and Total Digestible Nutrients (TDN) content in the feeds and fodder. The estimates of feed index were worked out for the feeds and fodder for individual farms using the formulae DCP + (TDN/7.5). The value of family labour was imputed upon the prevailing wage rate in the study area. This was measured by collecting information regarding the amount of time spent by different household members in various activities related to dairying, and converting them into equivalent mandays.
Factors influencing technical efficiency
Variables considered in the study
Education level of household age
0-Illiterate, 1-Read & write, 2-Primary, 3-Middle, 4-High school, 5-Intermediate, 6-Graduation & above
Age of household head
Number of years
Size of landholding of household
Number of milch animals owned by household
Measured as standard milch animal units
Whether a household member has non-farm income source
1 = Yes
0 = No
Prop. of output sold
Percentage of milk produced sold
Weighted average price received for each lire of milk normally sold
(In several instances, milk of different species and breeds like crossbred cow, indigenous cow and buffalo was sold by individual producer. Thus, the weighted average price of milk, taking quantity of each type of milk as weights, was considered in the study.)
Access to Information
Whether has easy access to information
1 = Yes
0 = No
Socio-economic profile of respondent households
Socio-economic profile of respondent households
I. Farmer Characteristics
Age-Household Head (No. of yrs.)
48.56 ± 10.44
50.82 ± 14.51
Education-Household Head # *
1.78 ± 1.77
2.79 ± 1.97
Farm Experience-Household Head (No. of yrs.)
39.72 ± 10.36
41.96 ± 13.70
Main occupation of HH Head (% of respondents surveyed in each category)***
Subsidiary Occupation of HH Head*
Agriculture + Animal Husbandry
Animal Husbandry + Others
II. Household Characteristics
Family Size (Adult Equivalents) ##
4.34 ± 1.40
3.80 ± 1.08
Household with at least one member having non-farm income (% of respondent Households)**
Households with at least one member who has migrated elsewhere for earning income (% of respondent HH’s)***
III. Farm Characteristics
Operational Land-holding (acres)**
2.01 ± 3.48
0.82 ± 0.77
Land used for Dairying (acres)
0.16 ± 0.26
0.00 ± 0.00
Dairy Animal Holding (Standard Animal Units)*
2.86 ± 2.45
1.79 ± 0.59
IV. Institutional Support Structure
Distance to nearest AHS centre (km)
5.47 ± 2.33
5.29 ± 0.66
Access to Credit (1 = Yes, 0 = No)
0.06 ± 0.24
0.01 ± 0.22
Has insured milch animals (1 = Yes, 0 = No)
0.00 ± 0.00
0.00 ± 0.00
Distance to market (km)**
5.39 ± 1.72
7.86 ± 7.82
Is arranging transportation a problem (1 = Yes, 0 = No)
0.06 ± 0.02
0.29 ± 0.46
Whether has easy access to information (1 = Yes, 0 = No)*
0.95 ± 0.17
0.50 ± 0.51
Milk production patterns
Milk production patterns (Per day)
Herd size categories
Herd-size wise contribution to total milk production and marketing
Contribution to production
Contribution to marketing
Frontier functional analysis for milk production
The level of technical efficiency of a particular farm is characterized by the relationship between observed production and some ideal or potential production. The measurement of farm-specific technical efficiency is based upon deviation of observed output from the best production or efficient production frontier. If a farm’s actual production point lies on the frontier, it is perfectly efficient. If it lies below the frontier, then it is technically inefficient, with the ratio of the actual to potential production defining the level of efficiency of the individual farm. Stochastic estimations incorporate a measure of random error. This involves the estimation of a stochastic production frontier, when the output of a farm is a function of a set of inputs, inefficiency and random error. Maximum likelihood estimation (MLE) technique was employed to estimate the parameters of the Cobb-Douglas production function using Frontier 4.1 version software package.
Maximum likelihood estimates of stochastic cobb-douglas frontier milk production functions of standard animal units of households
Green fodder index
Dry fodder index
γ = s2u / s2s
LR test of the one-sided error
In the plains, concentrate was found to be a significant factor negatively influencing milk production in MLE model, implying excessive feeding of concentrates to the dairy animals. The effect of dry fodder was on the other hand significant and positive implying that there is scope for profitably increasing the level of dry fodder fed to the animals. Miscellaneous expenditures also exerted significant and positive influence on milk production, implying suboptimal expenditures on miscellaneous items. Family labour was a significant variable and the effect was negative, implying that there is scope for curtailment in labour hours devoted to taking care of animals. Depreciation, veterinary expenditures and green fodder did not have significant influence on milk production. In case of the hills, no variable was observed to exert significant influence on milk production.
Estimation of technical efficiency
Mean technical efficiency estimates and increasing efficiency potential of different herd-size category households
Mean technical efficiency
Mean potential to increase efficiency
Mean technical efficiency
Mean potential to increase efficiency
Based on the technical efficiency of the most efficient farm in each herd-size category, the average potential to increase milk production was determined. The potential for technical efficiency improvement of milk production in terms of reducing milk production costs was higher for medium and large farms (14.62% and 6.51%, respectively) than that for small farms (5.34%) in the plains. Overall – for all categories of households - if the average farmer was to achieve the efficiency level of its most efficient counterpart, then he would realize a 9.18 per cent cost saving.
Mean potential to increase efficiency for small and medium category farmers in the hills were 8.62 per cent and 14.01 per cent. Mean potential to increase efficiency for overall category was 10.01 per cent. This implies, that if the average farm in the hills was to achieve the technical efficiency level of the most efficient farm, then the average farm would realize an 10.01 per cent cost saving.
Factors influencing farmers’ technical efficiency in milk production
Factors influencing technical efficiency of farms
Land holding size
Non-farm income (Y = 1; N = 0)
Prop. of output sold
Access to Info. (Y = 1; N = 0)
In the plains, the variable herd size significantly and negatively influenced technical efficiency indicating that farms with smaller herd size are more efficient in milk production. This finding is consistent with that obtained from mean technical efficiency analysis in the earlier section. Proportion of output sold had significant but positive influence on milk production efficiency implying that farmers with higher degree of intensity of market participation were more efficient in milk production. In the hills, only the variable age had significant influence on technical efficiency in milk production. The effect of age was negative implying that households with younger heads were more efficient in milk production. No other variables were found to impact level of technical efficiency, significantly.
The study was set out to measure and explain technical efficiency in milk production under mixed farming system in Kumaon division of Uttarakhand, which represents an underdeveloped production environment of India. Stochastic Frontier Analysis was used to estimate the technical efficiency scores. The study revealed that variation in output across farms in the study area was due to difference in their technical efficiency levels. Majority of farmers in the study area use available technology sub-optimally and produce less than potential output. The mean technical efficiency was found 91 per cent among the sample households in the plains and 89 per cent among the sample households in the hills. Further it was found that smaller herd size and higher level of commercialization contribute positively to efficiency in the plains and lower age of household heads contribute positively to efficiency in the hills. Thus, for technical efficiency improvement the policy makers should focus on households with larger herd size in the plains and households headed by older family members in the hills to enable them to utilize the potential of existing technologies more efficiently. Further, strong and effective linkage of farms to market would provide incentives towards increasing their efficiency in production and thus realize substantial cost savings, especially in the plains.
aThe following standards were used to standardize herd size of the farm households:
Milch buffalo 1.30
Milch Crossbred cow 1.40
Milch Indigenous cow 1.00
Dwaipayan Bardhan: Part of Ph.D. Thesis of first author.
The authors gratefully acknowledge Director Experiment Station, G.B. Pant University of Agriculture and Technology, Pantnagar for providing all the facilities for carrying out this research study. The support and help received from Dean, College of Veterinary and Animal Sciences, G.B. Pant University of Agriculture and Technology is also duly acknowledged.
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