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

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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

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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

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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.

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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.

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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.

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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

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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:

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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

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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.

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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.

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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.

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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.

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References

Brander, L., Gomez-Baggethun, E., Margin, L. & Verma, M. ( 2010) The Economics of Valuing

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