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Maryam Mohammadi
Contribution of Dairy Farming to Households’
Income and Food Security - Case Study of Balkh
Province
Volume | 003 Bochum/Kabul | 2016 www.afghaneconomicsociety.org
1
Contribution of Dairy Farming to Households’ Income and Food Security
Case Study of Balkh Province
Maryam Mohammadi
Keywords
Household, Milk Collection Center, Productivity Method, Milk Production, Production Function, Production Costs, Household’s Total Income, Poverty Reduction, Food Security
Exchange rate:
AFN (Afghani Rupee) 68.61 = USD 1
(Average exchange rate: January-February 2016)
Abstract
This study investigates the determinants of the households’ (HH) milk production output, costs,
and estimates their dairy income in Dehdadi district of Balkh province in Afghanistan. The primary
data for the study was collected from 200 female milk producers using a random sampling
procedure. In addition, qualitative information from semi-structured interviews with four milk
collectors and two milk-processing plants’ executive managers complements the quantitative
records from the households’ survey.
The productivity method under the header of the revealed preferences was identified as an
appropriate method to assess the impact of dairy income on HHs’ total income and the CES
production function was used to analyze the milk production function.
The results of estimation showed that there is a significant and positive relationship between dairy
income and HH’s total income. The results also revealed that dairy production has a positive
impact on the female milk producers’ self-sufficiency as well as it influences the food security of
both dairy farming and non-dairy farming households. The study further revealed that there is a
link between poverty and dairy farming in the area. Therefore, this agricultural sub-sector can be
considered as a potential area for improving the livelihood of small-scale farmers especially the
poor in the rural areas and reducing their poverty to some extent.
Description of Data
To identify the determinants of households' milk production output and costs and understand the
characteristics of small-scale dairy farming in Afghanistan, both primary and secondary data were
used. There exist few accurate and detailed studies on dairy farming in Afghanistan, particularly
the study area of Balkh province. Therefore, primary data at the household level were collected
through households’ survey using structured questionnaire. Furthermore, semi-structured
2
interviews were conducted with four milk collectors and the executive managers of two active
dairy processing plants in Balkh province to enhance the quantitative findings from the survey. A
random sample of 200 milk producer households was selected as the target interviewees.
The data collected for this study included yearly amount of milk produced, yearly amount of milk
sold, yearly amount of feed, total number of cattle, number of milking cows, average time spent
on various dairying activities, number of family and paid laborers involved in dairy farming, and
households’ income from various economic activities.
The standardized questionnaire for the household survey was developed after a field visit and
preliminary discussion with milk collectors and dairy farmers. The first hypothesis regarding
contributive factors to income, production, and cost functions was made. The pilot survey with 10
households was conducted to examine the accuracy of the questions before the main survey and
then the questionnaire was modified to fit the real situation.
The questionnaire accounted for the changes in the households’ milk production and total income
through questions covering issues such as the level of milk production, production costs, and farm
and non-farm income. It comprised five sections:
1. General information about the interview
2. Households’ composition and their social and economic characteristics
3. Households’ land ownership and their agricultural output
4. Milk producers’ experience and time spent on dairying activities
5. Households’ total income/revenue from different sources and impact of training and extension
services on the level of milk production, and the right of women to use earned cash income from
the sale of milk produced.
Research Questions/Theoretical contextualization
The dairy sector plays an important role in the agricultural economy of the most developed and
developing countries. Over the last 24 years, global milk production has increased by 32 percent
while the per capita milk production has declined by nine percent. The data indicates that world
population growth has not kept pace with the world milk production (Knips, 2005, p. 2).
Afghanistan’s dairy sector is a significant part of the agriculture sector and it is of particular
importance in the economy and nutrition of many people ranging from milk producers to
processors and consumers. According to last livestock census in the year 2002-2003,
Afghanistan has 3.7 million cattle with 60 percent milking cows from the total population at the
time of the survey (FAO, 2008). The milk produced is not sufficient for domestic consumption and
3
there is a gap between demand and supply of dairy products, which is filled by the imported dairy
products from neighboring countries mainly Iran and Pakistan.
To identify the characteristics of small-scale dairy farming and assess the impact of milk
production on the sample households’ income and livelihood, this research intended to answer
the following questions:
• What are the determinants of households' milk production output and costs?
• Does dairy income have an impact on food security and total households’ income?
• Does dairy production have a positive impact on poverty reduction and empowerment of
women?
To estimate the contribution of dairy farming income to overall household income a theory based
approach subjected to a preference-based method and empirical evidence was proposed. “The
preference-based methods rely on models of human behavior. It rests on the assumption that
values arise from the subjective preferences of individuals (Brander, et al., 2010, p. 11)”. In
general, for the valuation of private goods such as the one targeted in this study, the actual
preference approach is used, with the condition that perfect information including statistics on
price, quantity, and quality of the products is available and accessible. With respect to developing
countries, accurate data on people’s actual behavior are not available, which means data needs
to be generated through interviews. The productivity method, which was proposed as a technique to value the impact of dairy
production on the household income, has its roots in public goods valuation and discussed in the
literature as a revealed preference method (Mishra, 2006). The productivity method has also its
basis in the estimation of the production function, which is not related to the public goods. Since
there are no reliable data available on the dairy production, the idea of estimating the production
function approach from the revealed preference methods was employed. In the absence of market
data, the results of interviews were used to apply the productivity method. Therefore, the
employed approach still can be called the application of productivity method under the header of
actual preferences because individuals were asked about their actual choices rather than their
valuation, but market data are replaced by data from interviews.
The household production function was employed to estimate the contribution of dairy income to
total family income, production output, and production costs. A production function is a functional
relationship between physical quantities of output and inputs; it shows that to what extent output
changes with variation in input factors (Lomax, 1946, p. 146). The employed theoretical model in
the following is an adaptation of the approach that has been used by Löwenstein, et al., 2015 for
impact evaluation of a water project and agricultural output in Sri –Lanka, which was adjusted to
dairy production in Afghanistan.
4
The income function for the targeted households was formulated as an empirical model for the
further estimation. The households in the targeted areas earned their total cash income (Ytot) from
different sources including agricultural income (through sales of agricultural products other than
dairy products and livestock, Yag), dairy income (Ydr), other laborer paid income (wage and salary
paid income, small businesses,Yol), and transferred income (remittances and financial aids, Ytr).
The household total income (Ytot) is expressed as follow1:
1) 𝑌𝑌𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡 = 𝑌𝑌𝑖𝑖𝑑𝑑𝑑𝑑 + 𝑌𝑌𝑖𝑖𝑎𝑎𝑎𝑎 + 𝑌𝑌𝑖𝑖𝑡𝑡𝑜𝑜 + 𝑌𝑌𝑡𝑡𝑖𝑖𝑡𝑡𝑑𝑑 With i= 1,…, n (number of milk producer households)
It should be considered that farming and other laborer paid income are not directly affected by
the dairy income since the dairying activities are carried out by female members of the households
those who are involved in farming and other productive activities. It may perhaps have an indirect
effect on the households’ leisure and working time allocation as milk produced generates a
considerable income and it can encourage the active members of the household to allocate more
time for leisure than work. The household dairying production function contains the following
explanatory variables:
2) 𝑋𝑋𝑖𝑖𝑑𝑑𝑑𝑑 = 𝑓𝑓[𝐴𝐴, 𝐾𝐾𝑖𝑖+, 𝐿𝐿𝑖𝑖+, 𝐼𝐼𝑛𝑛𝑖𝑖+]
In general, Dairy output (Xdr) is produced combining laborer (L), physical capital, the number of
milking cows (K), and intermediate inputs such as fodder (In) at given technology. Dairy farming
is a small-scale and labor-intensive activity performed in a very traditional way with basic tools
and skills. Households’ female members make up the labor force (L) for dairy production that do
the milking, feeding, and cleaning. In the case of increasing the number of laborers, the impact
on the level of production is questionable since the marginal productivity of laborer tends to
decrease and even approaches to zero with the increasing number of laborers at the given capital.
Milking cows are the fundamental production factor and increment in the quality and quantity of
this factor will positively affect the volume of milk produced. Feeding, which is intermediate
production input, has a significant influence on the level of output. It is often composed of fresh
fodder, crop residual from the household’s own farm, and purchased forage.
In addition, different trainings on cattle management and agricultural extension services are
provided to dairy farmers in the target area, which also may have an influence on the total output.
One can argue that training may have an effect on laborer (L) through providing knowledge on
how to improve the dairy farming activities and use of extension services may affect the
intermediate inputs of production (ln). The combined use of both training and services may
potentially affect the level of technology (A).
1 The total income equation was adopted from the Sri Lank study (Löwenstein et al. 2015) but in this study, dairy income was distinguished from agricultural income.
5
To measure the generated revenue, the yearly production at the household level was calculated
and multiplied by the constant price (P) per liter paid by milk collection centers. The households’
gain (income) can be measured by subtracting the production’s fixed cost (Cf) and variable cost
(Cv):
3) 𝑌𝑌𝑖𝑖𝑑𝑑𝑑𝑑 = (𝑓𝑓[𝐴𝐴, 𝐾𝐾𝑖𝑖+ , 𝐿𝐿𝑖𝑖+, 𝐼𝐼𝑛𝑛𝑖𝑖+])𝑃𝑃 − 𝐶𝐶𝑖𝑖𝑑𝑑𝑑𝑑, where 𝐶𝐶𝑖𝑖𝑑𝑑𝑑𝑑 = 𝐶𝐶𝑖𝑖𝑓𝑓 + 𝐶𝐶𝑖𝑖𝑣𝑣(𝑋𝑋𝑖𝑖)
To estimate the production costs, first, the cost determinative factors for milk production were
identified. The variable production costs were composed of laborer cost (the salary of paid laborer,
Lc ), feeding cost (Inc), and Artificial (AI c) and natural (NI c ) insemination:
4) 𝐶𝐶𝑖𝑖𝑣𝑣 = 𝑓𝑓[ 𝐿𝐿𝑐𝑐𝑖𝑖+ , 𝐼𝐼𝑛𝑛𝑐𝑐𝑖𝑖
+ , 𝐴𝐴𝐼𝐼𝑐𝑐𝑖𝑖+ ,𝑁𝑁𝐼𝐼𝑐𝑐𝑖𝑖
+ ]
The production costs were estimated for the duration of one year, in the same manner as
production and income. The positive sign above the variable denotes the expected impact of the
each explanatory variable on the production costs.
Field research design/ Methods of data gathering
The survey was conducted in Dehdadi district of Balkh Province, located in the North Afghanistan.
Ten milking collection centers (MCCs) exist in the area that supply milk to one of the two active
dairy processing plants, Balkh Livestock and Dairy Union (BLDU, Balkh Dairy). Based on the field
visit and preliminary discussion with the Balkh Dairy, four MCCs out of 10 with the population of
2000 milk producer households were selected as a target area for the survey. These four MCCs
were registered in Balkh Dairy and were mainly located in the area of Dehdadi district, 15 km east
of Mazar-e-Sharif city. The main criterion for the selection of these centers was security condition
and closeness to the city. After receiving the list of 384 milk producer households from these
MCCs, a random sample of 200 out 384 milk producer households was selected. In order to show
equal representation of the sampled MCCs, 50 households were selected from the lists provided
by the MCCs in each location. Due to cultural issues, the households were registered under the
name of one male member of the families. Therefore, 50 names were chosen randomly in order
to call their female milk producer for the interview (see Table 1). The data show slightly different
percentages in interviewed milk producers, with a maximum of 44 percent and a minimum of 39
percent, although the numbers of interviewed people were the same in all MCCs. The difference
in the total population of milk producers at each MCC explains the difference in the percentages.
6
Table 1 Sample Composition
Milk producers registered and surveyed
MCC 1 MCC 2 MCC 3 MCC 4 Total
No. of registered Milk producers providing milk at time of the survey
129 113 113 125 380
No. of interviewed milk producers (random sampling)
50 50 50 50 200
% of registered milk producers interviewed
39 44 44 40 53
At each location, the milk collector from the MCC was met prior to the survey to explain the
purpose of the research and receive the list of beneficiaries (interviewees). Groups of five to 10
milk producers were gathered in each location in order to explain the aim of the survey and
afterward the questionnaires were completed. In some places, due to the problem of distance,
households were visited individually. The female milk producers were considered as interviewees.
In most cases, they were cooperative, and only in a few cases, the interviewees did refuse to
cooperate and answer the questions.
Identifying the Functional Form
Determining the correct functional form for a given relationship, input and output, is almost not
possible2. The challenge is to select the best form for the given task (Griffin, et al., 1987, p. 220).
In the work of Griffin et al (1987), twenty functional forms with their properties and algebraic forms
are presented. In addition, a group of criteria has been proposed in order to assist in the selection
of true functional form, which depends on the following conditions:
1. Maintenance of hypothesis regarding objectives in the presence of theoretical and empirical
basis
2. Availability of data and computing procedure
3. Data characteristics and conformance
4. Specific features of the application
Estimation of households’ income, production output, and production costs were the main
concerns of this study. Therefore, the functional form of each function was identified to run the
2 The way that we approach the problem of identifying the correct functional form is a way, which can be found in the work of Löwenstein et al. (2015) for the impact evaluation of the water projects in Sri Lanka
7
regression. For estimating the household’s milk production, the linear approach cannot be used,
as it is impossible that a farmer without milking cows could produce milk, i.e. milking cows are the
prior independent variable and no milk is produced without that, but under linear approach, the
level of production can be positive even without this variable. In addition, the first derivative of the
linear production function indicates that the physical marginal product is a constant value, which
is not in line with reality, as the addition of one more cows does not result in a constant increase
in the level of produced milk. Therefore, if production function is not linear, other options should
be considered. In the case of this study, we propose a multiplicative combination of production
factors in which each production factor is not entering one by one but to the power of a number.
This type of functions modifies the impact of each individual factor. We chose the Constant
Elasticity of Substitution (CES) function as a generalization of the Cobb-Douglas function, which
allows for any (non-negative constant) elasticity of substitution (Henningsen & Henningsen, 2011,
p. 1). The formal setting of CES production function with two inputs is as follow:
5) y = F �αX1ρ + (1 − α)X2
ρ�1ρ Where y is the output, X1, and X2 are the inputs quantities, α and
1 − α are distribution parameters that sum up to one and determines the factors’ respective
shares, and ρ is substitution parameter that is used to derive the elasticity of substitution σ = 11+ρ
(Miller, 2008, p. 8). If ρ → 0 σ approaches 1, CES turns to the Cobb-Douglas form. For ρ →∞,
σ approaches 0 and CES turn to Leontief and in the case that ρ →−1 , σ approaches infinity and
CES turns to linear production function (Henningsen & Henningsen, 2011, p. 1). To use the
multiplicative form in order to apply the CES production function, the dependent (output) and
independent (input factors) variables were translated to natural log to obtain a linear approach
with a multiplicative combination of production factors. Afterward, the linear regression taking
natural log was run to linearize the multiplicative level approach to the additive log approach.
However, the linear functional form was considered appropriate for total households’ revenue and
production costs function as it meets the criteria of this form of relation.
Estimating the Impact of Milk Production on Income with Respect to Incurred Cost
From the research questions, the dependent variables of the study were identified as the measure
of dairy income contribution to the households’ total income, yearly milk production, and yearly
production costs. First, the yearly amount of milk production and incurred costs were estimated.
The multiple regression techniques were employed to describe the relation between the
research’s dependent and independent variables. Multiple regression analysis produces Beta
coefficient, which indicates the relative contribution of each independent variable and shows the
significance of the contribution under the P-value. It was necessary to conduct the estimated
regression for milk production, which was simplified as follows:
8
6) LnYPMi = Ln β0 ̂ + B1�LnNo. mcowsi + β2�Lnfeedingi + β3 �Lnwhwi + β4 �Lnstai + ϵi
where YPM is the yearly milk produced by the household (i); nomcows is the number of milking
cows, feeding accounts for different feed items consumed for milk production, whw is the working
hours per week spent on dairying activities, and sta shows the estimated size of stalls where the
cows are kept. These variables are determinants in producing milk. βi is the estimated impact of
one unit of explanatory variables on the household’s yearly milk production and ϵi is the error
term. A similar regression is run to estimate the influence of production factors on milk production
costs:
7) YPCi = β0� + B1�feedingi + β2�vetci + β3�lci + β4�aici + β5�nici + ϵi
Where YPC is the yearly production costs, feeding indicates the yearly amount of different feed
items, vetc is the number of times veterinary care has been used including vaccination, aic, and
nic are the number of artificial and natural inseminations. βi is the estimated impact of one unit of
explanatory variables on the household yearly production costs, and ϵi is the error term
9
Results
Demographic and Income Composition On average, the 200 sample households have 6.53 members. From these, 3.35 are in working
age between 16 and 65. The rest are children, less than 16, or family elders, above 65; these two
groups are considered economically inactive and compose 51 percent of households’ members.
Female members consist of 51 percent of the households and the remaining 49 percent are male
members. The sample households earn average yearly cash income of 176,857AFN from various
sources of economic activities. The per capita income is equivalent to 1.27$US, 77AFN, per day.
The majority of the sample households (93%) earn income from various sources of economic
activities; of these, 91 percent have cash income from sale of milk plus other sources of income.
Two percent live from the sale of the agricultural products, laborer paid income (wage or salary
paid laborer and small businesses), and the remaining seven percent only rely on one source of
income for living. Estimating Total Household Income The result of regression (Table 2) shows that variables describing household labor force
(HH_Work_Age), households’ members with primary education (HH_PE), total size of land owned
by household (Land_S), sales of crops (Sell_Crops) including wheat and vegetables, and yearly
amount of milk sold (ASM) are significantly different from zero, indicating that these factors
influence the households’ total income. Although the income from the source of milk sold is
statistically significant, the size of the coefficient is very small 0.000122. It indicates that with an
additional litre of the sold milk, the total income would increase by 0.0122 percent.
Estimating the Household Milk Production and Revenue
Dairy farming is the major sub-farming activity in the target area. Each household owns on
average 2.76 cows, of which 1.18 are milking cows and the rest are dry cows or calves. The
average quantity of milk produced varies from 7.18 liters per day in winter to 12.17 liters per day
in summer, while the average quantity of milk sold ranges from 5.66 litres per day in winter to 10
litres per day in summer. The estimated regression demonstrated that the number of milking
cows, number of cross-breeds, yearly amount of consumed feed including oil cake, stale bread,
wheat straw, and wheat bran, receiving training and services together, and the control variable
for one of the milk collection centers influence the level of yearly milk production significantly.
None of laborer explanatory variables including family laborer, paid laborer, or the number of
working hours per week is significant contrary to the expectation. In addition, the size of the stall
is also insignificant due to underutilization of the stalls’ capacity. Interestingly, the number of
cross-breed cows is significant. It can be reasoned from the fact that households who have a
larger number of cross-breeds have a larger share of cross-breeds among their milking cows,
which influences the level of production.
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Table 2 Estimation of the Sample Households’ Total Income (Ytot)
Variables Ytot (Model 1)
(at household level)
Yearly cash income
Ytot (Model 2)
(at household level)
Yearly cash income
Ytot (Model 3)
(at household level)
Yearly cash income
HH_Work_Age 0.051375 0.071824 0.064234 (0.0920) (0.0070) (0.0130) HH_PE 0.062736 0.057621 0.067696 (0.0085) (0.0168) (0.0249) HH_SE 0.033629 (0.3710) HH_NE -0.003971 -0.018364 (0.8836) (0.4682) Land_S 0.017249 0.025347 0.014825 (0.0364) (0.0006) (0.0251) Sell_Crops 0.258879 0.262711 (0.0051) (0.0051) No_Cows -0.033383 (0.2882) No_MCows -0.014270 (0.8761) ASM 0.000122 0.000116 0.000113 (0.0000) (0.0000) (0.0000) YTinc_AFN 2.85E-06 3.19E-06
(0.5725) (0.5335)
Constant 11.21188 11.25494 11.12875 (0.0000) (0.0000) (0.0000)
Observation 199 199 199
Adjusted R-squared 0.255999 0.225347 0.261926
Prob>F 0.00000 0.00000 0.00000
Significant coefficients are in bold; p-values are in parentheses. The relevant data for 199 households out
of 200 households were available.
In addition, the result showed that increasing income from non-dairying activities did not withdraw
laborers from dairy to other economic activities.3 In order to estimate the predicted revenue, the
predicted amount of yearly milk produced was multiplied by the constant price of 20AFN per liter.
3 This approach is consistent with the work of Löwenstein et al. (2015) for assessing the impact of non-farming income on the farming activities.
11
The result showed that yearly-predicted revenue varies from 15,040AFN to 146,680AFN for
household milk producers.
Estimating the Household Cost Function The model indicated that annual milk production cost is a function of the amount of feed items,
paid laborer, veterinary services, NI and AI, the number of dairy animals, and stall size. A
combination of variables based on the quantities of mentioned factors were used for statistical
purposes. The finding showed that the feed items: the yearly amount of consumed oil cake, stale
bread, wheat straw, and wheat bran were highly significant under t-test. It means with increasing
amount of any feed item by one unit (kg), the level of total cost increased by the price of that feed
item per unit. In addition, the Paid laborers and use of veterinary services were also significant
that indicates with an additional number of paid laborer and use of vaccination, the costs
increased by the wage per laborer and cost of the vaccination. The total number of cows and
milking cows failed to explain the households’ production costs. The feeding system of sample
households may explain this insignificance. The empirical data and observation from the field
showed that households feed their animals mostly based on the feed resources they own rather
than the amount of feed needed for the increased number of cows. Therefore, increasing one
more cow may not always translate into a higher quantity of feeding and higher production costs.
The stall size is also insignificant since the households already own it and do not pay an extra
cost for it.
The Households Dairy income
In general, the viability of small-scale milk production mainly depends on the level of dairy
production’s revenue and income. Dairy income was calculated by subtracting the predicted
yearly production costs from the predicted yearly revenue. The income and cost estimation of the
households’ milk production is to determine whether dairy income can cover the costs. In order
to check for the viability of the small-scale dairy farming in the sample area, the operating ratio
(total operating costs over gross income from milk production) was calculated that indicates the
extent to which the income from milk production covers the expenses of feeding, health care, and
other variable costs involved in the production. The result showed that the operating ratio varies
from 0.07 to 4.7, which clearly indicates that milk production for some of the households is not
cost-effective since one unit of gross income is generated by more than one unit of variable cost.
This calls into question the profitability of milk production for sample small-scale dairy farmers. In
order to investigate whether the households with lower productivity are poor or rich, the
productivity of milking cows (income/milking cow) was compared to the per capita income, which
is the indicator of wealth at the household level.
12
Figure 1 Income per Milking Cow and Per Capita Income at Household Level
The findings showed that there is a systematic difference between poor and rich households in
terms of milking cows’ productivity, as the income per milking cow is higher for the poor
households than rich households. It can be argued that households with higher operating ratio
are those who do not depend on dairy farming and have other sources of income and may keep
the cows only as a source of capital reserve that they can sell at the time of need.
Dairy Production, Poverty Reduction, Food Security, and Women’s Empowerment The negative correlation between households’ per capita income and share of dairy income in
total households’ income (-0.257792) indicates that small-scale dairy farming is poverty-oriented
and can be considered as a poverty reduction strategy in rural areas. With respect to the impact
of dairy production on the food security, the increasing quantity of the locally produced milk
produced with a lower price than UHT milk means that poor consumers are now able to purchase
inexpensive milk to complement their diet, which is a contribution to the food security of non-dairy
farming households. In addition, the difference between the amount of milk produced and sold
indicates that dairy production influences on the farmers own food security as well. Regarding the
impact of dairy production on the women’s empowerment, the findings of the field research
showed that women who are highly involved in the cattle management and earn dairy income,
have control over use of cash earned from milk production, and even six percent of them can
influence the use of income earned by other family members.
13
Discussion & Conclusion
Milk production by small- scale farmers in the study area in particular and in Afghanistan in
general can play an important role in poverty alleviation and generate income especially for poor
households in rural areas. The presence of dairy income in income profile of 91 percent of sample
households indicated the importance of dairy production in the study area. The increasing quantity
of milk supplied to dairy plants over the last decade also demonstrate the growth of dairy
production and the interest of dairy farmers in earning regular dairy cash income. However, the
productivity of milking of cows was higher for poor households, which means that milk production
was more cost effective for them than rich households. On the other hand, the negative correlation
between per capita income and share of dairy incomes supported the above argument that dairy
farming is an activity of poor households. These findings can be considered in rural development
interventions that aim to change the pattern of dairy production, especially for poor dairy farming
households. In addition, dairy production can influence food security of both dairy farming and
non-dairy farming households.
Furthermore, the social effects of the production should not be overlooked. Almost all female milk
producers spoke the satisfaction of having regular cash income and the right to use it in the way
they desire. However, it should also be considered that dairy production is not viable for all
households, and some of them have to cover the production costs from other sources of income.
Finally, it should be stressed that the link between poverty and dairy farming reflects the potential
of this agricultural sub-sector for improving the livelihood of the poor in rural areas and reducing
their poverty to some extent. Therefore, governmental organizations and rural development
programs can bring significant changes in the rural households’ income, especially income of
poor households, by enhancing the productivity of dairy farming through the provision of improved
feeding and extension services and adopting new technologies and methods. The development
of cost effective and profitable dairy farming in Afghanistan is a long-term process and needs
governmental and institutional support to enhance the financial and technical capacity of dairy
farmers to take the improved technologies and methods and to invest in the accumulation of more
productive dairy animals, consumption of improved feeds, and the use of improved healthcare. It
is obvious that the more productive the dairy cattle, the higher the level of revenue and income.
The economic viability of milk production strongly depends on the number of productive cattle.
Several methods are proposed to increase the level of milk production. Increasing the number of
milking cows is one method and boosting the productivity of milking cows through improved
feeding and health care is another method. However, improving the local cows’ genetic is also an
important factor, which should be highly considered.
14
References
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