Masters thesis defense on milk market chain

Masters thesis defense
The market chain analysis of milk production : The case
debere Berhan town, Amahara region, Ethiopia
By: Dereje Admassu Reta
Advisor: Tsega A. (Phd)
July , 2020
Debre Berhan, Ethiopia
112/7/2020

OUTLINE
1. Introduction
1.1. Background of the study
1.2. Statement of the problem
1.3. Objectives of the study
1.4. Research questions
1.5. Hypothesis
2. Conceptual framework
3. Methodology
3.1. Description of study area
3.2. Sample methods and size
3.3. Research Design ,Data source
and collection methods
3.4. Data analysis methods
3.5. Pilot survey summery
3.6. Limitation
4. Major results and discussion(10)
4.1. Results of descriptive analysis of milk
producer
4.2. Results of descriptive analysis of
actors
4.3. Results from the econometrics model
4.4. Prospects and major constraints
5. Conclusion, policy implications &Suggestion
5.2. Conclusion
5.2. Recommendation
5.3. Suggestion
References
12/7/2020 2

1. INTRODUCTION
 Globally milk production 2017-2018
was produced 843 million ton &
increased by 2.2% on the nations
India, turkey, European union,
Pakistan, the USA. However, to
some extent declines in china,
Ukraine and others (FAO, 2019).
1.1. Background of the study
 In Africa, milk output was estimated
350 million tones & increase by 1.1
% on the nation Kenya, South
Africa, Algeria and Morocco.
However; partially offset by decreases
elsewhere, especially Mali and Niger
(FAO, 2019).
12/7/2020 3

Cont.
 In Ethiopia & Amhara region the main
formal marketing chain actors in cow
milk are
o processors, traders (collectors,
wholesalers, retailers and cafe/hotel
owners), and consumers (Ketema,2016;
Abu,2016; Ali, 2017).
o Enters formal channel in Amhara
region accepted figure is less than
10% others informal (traditional)
(Feleke G. , 2003; Land O’Lakes, 2010).
 In Ethiopia milk production was
indicated
o 3.1 billion liters (RuLIS dataset ;FAO
;CSA, 2014).
o The priority on GTP II period(2015-2020)
increase annual growth rate by 15.5% ,
from 5,304 million litters to 9,418
million litters (GTP, 2016).
o However; this production is very low with
comparable to African and World
12/7/2020 4

Cont.
• Sex (gander),
• Age,
• Education level,
• Family size,
• Number of children of the household
• Land holding size,
• Experience milk production ,
The fresh row cow milk participation and supply to the market to affected by:
• Access of market information,
• Access of credit,
• Extension serves,
• Number of cross breed milking cows,
• Price per liter offered at the market,
• Income of households and
• Members of cooperative.
Sintayehu Yigrem, 2008; Negassa, 2009; Anjani Kumar, 2010; Berhanu, 2012; Meryem, 2013;
Berhanu Gebremedhin, 2013; Tadele Mamo, 2014; Burke W, 2015; Mekonnen, 2015; Benyam
Tadesse, 2016; Gemechu, 2016; Ali, 2017 and Tsega Lemma, 2017
12/7/2020 5

1.2. Statement of the problem
The motivation of this study is to full fill the gap such as;
 Others academics was not covered this potential study area;
 Ethiopian and in particular in this study area smallholder dairy producers are
facing by numbers of problems. So that;
o To analyzed factors affecting on participation decision & volume of milk
supplied to the market ;
oTo full fill the linkage of ;
 producers(farmers) with actors or the relation between supply with
demand;
 producers with supportive institutions in particular credit institution;
 To show the price margin between producers and consumer seem very
large( the profit of actors from 5-8 birr per liters before value add)
 Lastly; to contribute for City administration, Policy makers, Review Literature
and Other actors.12/7/2020 6

1.3. Goal/Objectives of the research
 General objective:
 The general objective of the study is the Market Chain Analysis of
milk production on Debere Berhan Town.
Specific objectives:
 To identify the key milk marketing channels, and margins it in Debere
Berhan Town.
 To analyze factors affecting participation decision and level of participation
of smallholder milk producer households in milk market supply in Debere
Berhan Town.
 To assess the prospects and major constraints of milk production and
marketing in Debere Berhan Town.
12/7/2020
7

1.4. Research Questions
Who are the key milk market chain, the milk marketing
channels actors their functions and what does marketing
margins along the chain looks like?
 What are the factors that affect farmers in production milk
deciding participation and farm level milk product supply to
the market?
What are the prospects and major constraints of milk
production and marketing?
12/7/2020 8

1.5. Hypothesis
 has positive correlation with
 Age of the household
 Education level of the household
 Family size of the household
 Access of credit
 Extension serves
 Access of market information
 Number of cross breed milking cows
 Price per liter
 While it is negatively
associated with
 Number of children of the
household≤6
 Land holding size(Hr.)
 Experience milk production
 Membership to milk producers’
cooperative
The milk market participation decision & volume of milk supplied
to the market smallholder milk producer households statues
12/7/2020 9

2. Conceptual Framework
Figure: 2 Conceptual framework model of the factors of farmer’s market deciding participation and farm level
milk product supply to the market.
Source: Own Reorganizing Based On (Mekonnen,2015), (Ali,2017)12/7/2020 10

3.1. Description of study area
Figure 3: Map of the study area
This study was applied
in Debere Berhan town
specifically from nine
administration kebeles in
01,06,07,08 and 09
kebeles
3. Methodology
12/7/2020 11

3.2. Sample methods and size
• The sample methods and size for this study was employed such as;
 1st , from nine administration kebeles were selected 01,06,07,08
and 09 kebeles based on number of farmers.
 2nd from selected fives administration kebeles population were
selected the sample farmers using simple random sampling
procedure.
 Additionally, most of participant on milk traders such as
wholesalers /processors, milk retailing kiosks/middlemen
was engaged in study area.
12/7/2020 12

Cont.
No. Item Population
Plan of Sample
size
Performance of
sample size
NO- % NO- %
1 Total of 01,06,07,08 & 09 Kebeles
farmers
2008 307 15.3% 303 98.7%
2 Milk market intermediary (2.1.,2.2.) 35 25 71.4% 22 88%
2.1. Wholesalers/Processors 12 12 100% 12 100%
2.2. Retailing /Middleman 23 13 56.5% 10 76.92%
Total (1+2) 2043 332 16.3% 324 97.6%
Table: 3 Distribution of sample dairy farmers included in the survey by kebeles
Source: North Showa Zone Trade & Industry and fish and livestock Department Annual Report ,2019.
The sample size (yamane taro, 1967) n=
𝑵
𝟏+𝑵(𝒆) 𝟐 Where: n = sample size, N =
population size, A 95% confidence level & e = level of error margin (±5).
12/7/2020 13
Note: The remaining not responds different aspect. Such as ; COVID -19 & other case.

3.3. Research Design ,Data Source & Collection Methods
 Research Design
o A cross-sectional study was
employed with data type approach
 Qualitative ( for s-c-p & observation) &
 Quantitative(for statistical conclusions )
 Data source
o Primary; &
o Secondary
 Data collection methods
o Household survey
questionnaires such as
from farmers, traders
o Observation;&
o Analysis of relevant
document such as reports
of ministries, journals,
books, CSA and internet
browsing, national
policies, zonal and town
reports… etc.
12/7/2020 14

3.4.1. Description statistics
3.4.1.1. Analysis of structure ,conduct and
performance
A. Market concentration ratio
Si =
Vi
Vi
Where:
 𝑆𝑖= Market share of buyer i
 𝑉𝑖 = Amount of product handled by buyer i
 𝑉𝑖 = Total amount of product handled
C =
i=1
r
Si i = 1.2.3.4 … r
Where:
C = concentration ratio
Si = the percentage market shares of the ith firm and
r = is the number of largest firms for which the ratio is going
to be calculated.
o perfect competition an oligopoly:
Between 0% to 40% to
o Medium concentration an oligopoly:
between 40% to 70%.
o High concentration from an oligopoly
to monopoly : between 70% to 100%.
o extremely concentrated oligopoly:
100% (London economics in
association with global energy
decisions, 26 February 2007). Hence,
the market concentration ratio, which
refers to the number and relative size
of buyers in the market (Meryem,
2013).
3.4. Data analysis methods
12/7/2020 15

C. Marketing margin
• Total Gross Marketing Margin (TGMM):
TGMM =
End buyer price − First seller price
End buyer price
• Gross Marketing Margin percentage (GMMP):
GMMP =
End buyer price − Gross Marketing margin
End buyer price
X 100
• Net marketing margin percentage (NMMP):
NMMP =
Gross margin − Marketing cos𝑡
End buyer price
X 100
 In this study area were analyzed market
margin (Farris, Bendle, Pfeiffer, & Reibstein,
2010) and (Hailegiorgis, 2015).
12/7/2020 16
B. Market conduct
In this study area were
analyzed the behavior &
adopting technology
farmers & traders/firm
(Bain, 1968; Abbott &
Makeham ,1981).

3.4.2. Econometric model
 Probit mode function ;meaningful
interpretation of a dummy dependent
variable was estimated (Aldrich, 1984).
And this model was adopted the
researchers (Sintayehu Yigrem, 2008);
(Anjani Kumar, 2010); (Berhanu, 2012) ;
(Berhanu Gebremedhin, 2013) & (Tadele
Mamo, 2014)
To analyze factors that affect
farmers in production milk
participation decision
 Let y denotes participation deciding and we
write
𝑌𝑖
∗
= 𝛽0 + 𝛽𝑖 𝐷𝑖 + 𝑈𝑖
𝑌𝑖 =
1 𝑖𝑓 𝑌𝑖
∗
> 0
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Where,
o 𝑌𝑖 Is not observable variable, 𝑌𝑖 =1 when, 𝑌𝑖
>0 (Participated), 𝑌𝑖 =0 Otherwise (Not
participated)
o 𝐷𝑖 is explanatory variables listed under, i.e.
=D1, D2, D3, D4 … 𝑒𝑡𝑐
o β𝑖=a vector of parameters to be estimated,
i.e.= β1, β2, β3, β4 …etc.
o U = disturbance term
12/7/2020 17

Cont.
• Tobit model function
;meaningful interpretation of
continues dependent variable
was estimated (Tobin, 1958). And
this model adopted the
researcher (Gemechu, 2016)
• To analyze factors affecting
volume of milk supplied to
market
 Let y denote volume of milk supply and we write
𝑌𝑖
∗
= 𝛽𝑖 𝐷𝑖 + 𝑈𝑖
𝑌𝑖 =
𝑌𝑖
∗
= 𝛽𝑖 𝐷𝑖 + 𝑈𝑖 𝑖𝑓 𝑌𝑖
∗
> 0
0 𝑖𝑓 𝑌𝑖
∗
≤ 0
𝑈𝑖~𝐼𝑁 0, 𝜎2
Where,
 Yi = Volume of milk supplied to market ;
 Di = a vector of explanatory variables, i. e =
D1, D2, D3, D4 … 𝑒𝑡𝑐
 𝛽𝑖 vector of parameters to be estimated, i. e 𝛽1, 𝛽2 , 𝛽3 … 𝑒𝑡𝑐
and
 𝑌𝑖
∗
> 0 𝑣𝑜𝑙𝑚𝑒 𝑜𝑓 𝑚𝑎𝑟𝑘𝑒𝑡 𝑠𝑢𝑝𝑝𝑙𝑖𝑒𝑑 𝑡𝑜 𝑚𝑎𝑟𝑘𝑒𝑡
 𝑌𝑖
∗
≤ 0 𝑛𝑜𝑡 𝑣𝑜𝑙𝑚𝑒 𝑜𝑓 𝑚𝑎𝑟𝑘𝑒𝑡 𝑠𝑢𝑝𝑝𝑙𝑖𝑒𝑑 𝑡𝑜 𝑚𝑎𝑟𝑘𝑒𝑡
 U = disturbance term
12/7/2020 18

3.5. Pilot survey summery
 Reliability and validity :
 Explanatory variables correlations (covariance’s) on dependent
variables were found that ;
o Number of items in the scale: 14
o Reliability coefficient: 0.7926 or 0.7926 ≈ 0.8903
mean that the explanatory variables interpreter or estimated
the dependent variables vary high (Cronbach, 1951)
12/7/2020 19

3.6. Limitations
 The study has limited.
o This study were not focused in practical on value add fresh row cow
milk (butter, cheese etc.) Supply and marketing.
o The methodology of this study was limited to the sample selected only
five kebeles (01,06,07,08,09)in Debere Berhan town due to budgetary
and time limitations.
12/7/2020 20

4.1.1. The influences of demographic characteristic
Mean Std.Err Mean Std.Err Mean Std.Err
total Participate Non-Participate
Participate per HH 75.58 2.47
Females participant per HH 60.01 2.8 58.01 3.25 67.57 5.78
Age of participant per HH 11.62 0.31 11.37 35.8 12.34 0.58
Land/Hec of participant per HH 1.93 0.41 1.53 0.05 3.13 1.67
Experience of participant per HH(-ve **) 7.59 0.24 7.11 0.24 9 0.58
0
10
20
30
40
50
60
70
80
%,Year&Hec.
On Fresh Row Milk production participation , Female, age, Land ,& Experience on Per HH Debere Berhan Town
Obs. 303
Source: own computation from survey data (2020)
Figure-4: Socio-demographic characteristics of framing households milk production (in average, years and %)
12/7/2020 21
4. Major results and discussion
4.1.Results of descriptive analysis of milk producer

Cont.
Freq. % Freq. % Freq. %
Illiterates (+ve ** &***) 115 37.95 65 28.14 51 68.92
1-4 grade 58 19.14 44 19.05 14 18.92
5-8 grade 64 21.12 63 27.27 1 1.35
9-12 grade 32 10.56 28 12.12 5 6.76
Certificate –diploma grade 29 9.57 26 11.26 3 4.05
Degree and above grade 5 1.65 5 2.16
0
20
40
60
80
100
120
140
Freq.,%
Total,particpate,Non- participate
Education level for Milk market participant per- hh of urban/per urban in Debre Berhan
Obs.303
Figure 5: education level for milk market participant farmer of urban/per urban Debre Berhan
12/7/2020 22

Cont.
One (-ve***) 204 67.33 151 65.37 54 72.97
Two 85 28.05 68 29.44 18 24.32
Three 13 4.29 11 4.76 2 2.7
Four and above 1 0.33 1 0.43
0
50
100
150
200
250
FReq.&%
number of children per hh
Number of childern per HH of farmer in Debere Berhan Town Obs.303
Figure 6: Number of children per HH in dairy farming in Debere Berhan town.
12/7/2020 23

Cont.
1-3 Per HH(+ve ***) 143 47.19 82 35.5 62 83.78
4-5 per HH 73 24.09 69 29.87 5 6.76
6 -8 per HH 61 20.13 56 24.2 5 6.76
9 and above per HH 26 8.58 24 10.39 2 2.7
0
20
40
60
80
100
120
140
160
Freq.&%
Number of HH
Number of HH in Dairy farming in Urban/per-Urban of Debere Berhan
Obs. 303
Figure 7: Number of HH in dairy farming in urban/per- urban Debre Berhan
Source: own computation from survey data (2020)12/7/2020 24

4.1.2. Influence economic factors on milk production
Figure 8: Number Cross Breeding per of HH in dairy farming in urban/per- urban Debre Berhan
12/7/2020 25
1.3, 42%
1.67, 54%
0.11, 4%
Cross Breeding per HH Debre Berhan town
Obs. 303
total Mean(***)
Participate Mean
Non-Participate Mean

Cont.
Price per liter offered at the market:
 Mean value was found 18.73 Birr ± 0.12 .
 However, statistically non-significant for participate decision and
farm level of milk product supply to the market (Annex -3).
12/7/2020 26

Cont.
Table 6: Major means of income sources for farming households
Variables Total Participate Non-Participate
Rank
Mean Std.Err Mean Std.Err Mean Std.Err
Incomesources
crop 13,283.61 316.68 1,3057 379.86 14,180.28 529.21 2nd
livestock 79.6 79.6 0 0 335.21 335.21 4th
dairy 33,657.69 1,913.37 44,415.33 20,95 0 0 1st
Non-farming 1,351.17 302.20 973.33 318 2521.13 750.42 3rd
total 47,903.18 1,879.05 57,476.67 2,108.39 18,639.44 1,281.24
12/7/2020 27

4.1.3. Influences of institutional factors on milk production
Access of credit Accesses of market information
Membership milk producers of
cooperative(**)
total Mean 44.55 37.95 43.9
total Std.Err 2.86 2.79 2.85
Participate Mean 48.92 46.75 43.72
Participate Std.Err 3.3 3.3 3.3
Non-Participate Mean 29.73 9.46 43.24
Non-Participate Std.Err 5.35 5.8 5.8
44.55
37.95
43.9
2.86
2.79
2.85
48.92
46.75
43.72
3.3
3.3
3.3
29.73
9.46
43.24
5.35
5.8
5.8
Meanvalue
Institutional supports
Access Of Credit , information Membership In Milk Producers To Coo. Per HH Of Farmers In Debere
Berhan Obs. =303
Figure 9: access of credit, information and member of cooperative dairy farming per HH in Debre Berhan town

Cont.
Extension service for dairy farmers per HH in urban/per-urban debere Berhan Obs. 303
Title Item Total Participate Non-
Participate
Extension serves Never sport 21 6.93 7 3.03 14 18.92
Weekly 35 11.55 28 12.12 7 9.46
Two times a month 83 27.39 62 26.84 21 28.38
Three times a month 62 20.46 49 21.21 13 17.57
Monthly 76 25.08 62 26.84 16 21.62
Others( when we want
supports)
26 8.58 23 9.96 3 4.05
Table 7: the extension service for dairy farmers per households Debere Berhan
town
12/7/2020 29

4.1. Results of descriptive analysis of actors
Institute service for fresh row milk actors in Debre Berhan
Variables Item wholesalers/process
or
retailers/middlem
en
Total Obs. 12 Total Obs. 10
Freq. % Freq. %
Access of credit
Banks 1 9.09
Ngo’s 1 9.09 2 20
Micro finance institution - - - -
Relatives and friends 4 36.36 5 50
Traders 2 18.18 -
Others (by Owen startup
capital
3 27.27 3 30
Means of access of
information
Via radio - -
Via written pamphlets - -
Retailers - -
Consumer/cafes/hotels - - 1 10
Whole sellers 12 100 9 90
Table 8: access of credit and information for fresh row milk for actors
12/7/2020 30

4.1.1. Market participants, their roles and linkages in of milk
value chain and channels
1. Channel 1: producers  consumers (95.37 tons / 96,900 liters)
2. Channel 2: producers  wholesalers/processors  consumers
(139.32tons/ 141559 litres)
3. Channel 3: producers  wholesalers/processors  retailers
/middlemen  consumers (278.69tons /283.160.53 litres)
4. Channel 4: producers  retailers- consumers (139.32 tons / 141559
liters)
12/7/2020 31

4.1.2. Market chain map of row milk urban/per-urban of Debre Berhan
Figure 11: Market chain map of fresh row milk urban/per-urban of Debre Berhan

Cont.
Figure 12: Fresh row milk marketing channels 2020 (percentage and tons)
12/7/2020 33

4.1.3. Structure conduct and performance (s-c-p) of milk market
4.1.3.1. Market concentration ratio /Structure : which refers to the number and
relative size of buyers in the market (Kuru, 2013).
o From 22 valid buyer the volume of row cow milk was found 28% of market
share & other traders from 1% up to 15%.So this structure is relatively
perfect competition to an oligopoly on milk marketing(see annex 6).
4.1.3.2. Market conduct: The behavior & adopting technology farmers (Bain,
1968; Abbott & Makeham ,1981) were found that;
o Almost noun of own milk producer’s cooperative
o Lake of bargain power with buyer based on these 11% and above sell for
informal market system
o Fresh row cow milk dissolved with water,
o Do not sell at moment of after milking cow and
o low numbers cross breed milking cow
12/7/2020 34

4.1.3.3. Milk market performance (marketing costs and margin)
Milk Marketing channels
Marketing actors of
milk Descriptions/particulars
Channels
1 2 3 4
Producers Production cost 5.90 5.78 6.00 6.80
Selling price 19.05 18.81 18.72 18.68
MM 13.15 13.03 12.72 11.88
GMMP(%) 30.97 30.34 31.50 35.70
NMMP(%) 69.03 69.27 67.95 63.60
wholesalers/processor Buying price cost 18.25 17.64
Market cost 1.43 1.80
Selling price 24.25 24.29
MM 6.00 6.65
GMMP(%) 24.74 27.38
NMMP(%) 18.85 19.97
Retailers/middlemen Buying price cost 19.05
Market cost 0.44
Selling price 33.20
MM 14.15
GMMP(%) 57.38
NMMP(%) 41.30
Producers portion( GMMP)(%) 30.97 30.64 31.5 35.7
Rank of channels by producers’ share 3 4 2 1
Table 9: Performance of milk marketing in different channels of the study area
12/7/2020 35

4.3.1. Factor affecting of milk market participation decision
Variables with symbols Coefficient Marginal effect ( 𝒅𝒚 𝒊
𝒅𝑫 𝒊
)/ Z P>|z|
Constant -1.701(±1.340 ) -
Age of the household(𝐃 𝟐) 0.071(±0.035) 0.005(±0.003 ) 1.89 0.059
Education level of the household(𝑫 𝟑) 0.368(± 𝟎. 𝟏𝟒𝟎) 0.028(±𝟎. 𝟎𝟏𝟏 ) 2.52 0.012**
Family size of the household(𝑫 𝟒) 0.742(±𝟎. 𝟐𝟎𝟏 ) 0.056(±𝟎. 𝟎𝟏𝟔 ) 3.43 0.001***
Number of children of the household≤ 𝟔(𝑫 𝟓) -1.007(±𝟎. 𝟑𝟖𝟎 ) -0.076(±𝟎. 𝟎𝟐𝟕 ) -2.80 0.005***
Land holding size(Hr.)(𝐃 𝟔) -0.268(± 0.198) -0.020(±0.014 ) -1.44 0.150
Experience milk production(𝑫 𝟕) -0.115(± 𝟎. 𝟎𝟒𝟒 ) -0.009(±𝟎. 𝟎𝟎𝟒 ) -2.31 0.021**
Access of credit (𝐃 𝟖) 0.095(±0.294 ) 0.007(±0.022 ) 0.33 0.744
Extension serves (𝐃 𝟗) 0.052(±0.078 ) 0.004(±0.006 ) 0.68 0.494
Access of market information(𝐃 𝟏𝟎) 0.488(±0.363 ) 0.037(±0.026 ) 1.42 0.155
Membership to milk producers’ cooperative(𝑫 𝟏𝟏) -0.650(±𝟎. 𝟐𝟖𝟎 ) -0.049(±𝟎. 𝟎𝟐𝟑 ) -2.11 0.035**
Number of cross breed milking cows(𝑫 𝟏𝟐) 2.365(±𝟎. 𝟑𝟖𝟓 ) 0.178(±𝟎. 𝟎𝟏𝟕) 10.73 0.000***
Price per liter offered at the market(𝐃 𝟏𝟑) 0.041(±0.055 ) 0.003(±0.004) 0.73 0.467
Number of observations = 303
number of covariate patterns = 302
Goodness-of-fit test: Pearson chi2(289) = 493.01 Prob > chi2 = 0.0000***
LR chi2(12) = 256.45 Prob > chi2 = 0.0000
Log likelihood = -40.214677
pseudo R2 = 0.7612
Observed probability =75.57%
Predicted probability =96.70%
The dependent variable is a dummy variable that takes on the value 1 if the farmer had probability participation decision on fresh row cow
milk, 0 otherwise .When the marginal effect(dy/dx) robust *** & ** indicate statistical significance (P>|t|) at 1% and 5% respectively.
Table 10- The probit regression the determinates factors affecting of milk market participation decision
12/7/2020 36
4.3. Results from the econometrics model

4.3.2. Factors affecting the volume of milk supplied to the market
Variables with Symbol Coefficient Marginal effect ( 𝒅𝒚 𝒊
𝒅𝑫𝒊
) Z P>|z|
Constant -2.391(±2.966) - -0.81 0.421
sigma 4.560(±0.380) -
Age of the household(D2) 0.054(±0.063) 0.054 (±0.063 ) 0.85 0.395
Education level of the household(𝑫 𝟑) 0.604(±0.212) 0.604(±𝟎. 𝟐𝟏𝟐) 2.86 0.004***
Family size of the household(D4) 0.324(±0.342) 0.324 (±0.342) 0.95 0.343
Number of children of the household≤ 𝟔(𝑫 𝟓) -1.981(±0.551) -1.981(±𝟎. 𝟓𝟓𝟏 ) -3.59 0.000***
Land holding size(Hr.)(D6) -0.036(±0.028) -0.036(±0.028 ) -1.31 0.189
Experience milk production(D7) -0.087(±0.078) -0.087(±0.078) -1.12 0.265
Access of credit (D8) 0.659(±0.551) 0.659 (±0.551) 1.20 0.232
Extension serves (D9) 0.089(±0.186) 0.089(±0.186) 0.48 0.632
Access of market information(D10) 0.636(±0.660) 0.636 (±0.660) 0.96 0.336
Membership to milk producers’ cooperative(D11) -0.231(±0.591) -0.231 (±0.591 ) -0.39 0.695
Number of cross breed milking cows(𝑫 𝟏𝟐) 8.339(±0.487) 8.339(±𝟎. 𝟒𝟖𝟕) 17.12 0.000***
Price per liter offered at the market(D13) 0.051(±0.137) 0.051(±0.137) 0.38 0.708
Number of obs. = 303
o 72 left-censored observations at supply to market ≤ 𝟎
o 231 uncensored observations, 0 right-censored observations.
LR chi2(12) = 453.65 Prob > chi2 = 0.0000
Wald test of liner hypotheses fit model: F (14, 291) = 190.16, Prob > F = 0.0000***
Log likelihood = -714.84792
Pseudo R2 = 0.2409
The dependent variable was a continues variable that takes on the value two; if the probability level of milk supply to market the marginal
effect(dy/dx) robust *** indicate statistical significance (P>|t|) at 1%.
Table 11: The tobit model regression the determinates of the milk supply to market
12/7/2020
37

4.4.1. Major constraints of dairy production and marketing/ actors
 were found that;
1) High price cross breed milk cow for milk producer’s was indicated 1st rank and 100(33%)
2) shortage of feed for cow milk producer’s was found the 2nd rank and 79(26.1%).
3) Lack of credit to buy cow and others input for milk producer’s & actors were indicated
producer’s 3rd , wholesalers 2nd and retailers 3rd and 32(10.6%), 3(27.27%) and 1(10%)
respectively.
4) Low breed performance for cow milk producer’s was induced that; the 2nd rank and 35(11.6%).
(Simon, 2016).
5) Season with Related to religion for cow milk producer’s, wholesalers and retailers were found
5th ,1st and 2nd ranked and 24(7.9%), 7(58.33%) & retailers 3(30%) respectively (ASL, 2050) &
(CSA, 2017).
6) Quality fresh row milk problem for wholesalers and retelrirs were found t 1st and 2nd ranked and
7(58.33%) and 3(30%) respectively (See Annex 10).12/7/2020 38
4.4. Prospects and major constraints of cow milk market value chain

4.4.2. Major prospects of dairy production and marketing
 The future prospective fresh row cow milk producer’s & actors in this
study area were found that;
Fresh row cow milk producers
o Better market access, available veterinary service and livestock professional extension
support for producers were found totally 108(35.64%).
o Market access 2nd rank and 102(33.66%) ,
o Availability of veterinary service 4th rank and 29(9.57%)
o Availability of livestock professionals for extension support 5th rank and 22(7.26%)
o Relatively well - developed infrastructures 3rd rank and 42(13.86%)
• Fresh row cow milk market actors
o Huge market potential , availability of suitable agro-ecology and well-developed
infrastructures such as asphalted & rural road access to major towns and communication
facilities and telephone access the 1st rank and for wholesalers/processors 8(66.66%)
and retailers/middlemen 6(60%) respectively .
12/7/2020 39

5.1. Conclusion
The results showed that the Average of fresh row milk yield 6.985 liters per day per
households was produced, out of which 5.69 liters were sold to the market in Debere
Berhan town.
Structure conduct and performance (s-c-p) of milk market
o The structure was found relatively perfect competition to an oligopoly in case of the
market share less than 28%
o Market conduct were found Almost noun of own milk producer’s cooperative, Lake of
bargain power with buyer based on these 11% and above sell for informal market
system low numbers cross breed milking cow
o The market margin using producers portion ( GMMP in %) to computed in rank of
channels four (4), three (3), one (1) and last two (2) was found 35.7%,31.5%,30.97% and
30.34% respectively.
The probit model for this research was reveled that, educational level of the household,
family size of the household, number of cross breed milking cows, to affect positively and
number of children under six years old, experience in milk production, membership to
milk producers’ cooperative to affect negatively on the factors affecting participation decision
milk market. 12/7/2020 40
5. Conclusion , policy implications &Suggestion

Cont.
Tobit model for this research was reveled that, educational level of the
household and number of cross breed milking cows to affect positively
and also number of children under six years old to affect negatively level
of participation to supply of milk to the market.
The constraints in this study were found that, high price of dairy cow
,shortage of feed ,lack of credit to buy cow and others ,low breed
performance ,related to season and religion ,quality fresh row milk
problem.
The major future prospect were found that, available veterinary service;
livestock professional extension support; relatively well - developed
infrastructure; huge market potential , availability of suitable agro-
ecology.
12/7/2020 41

5.2. Policy implications
Improve cross- breeding by adjust the linkage of access information, veterinary
service & credit and promote investment on dairy production for the purpose of
skill developments or technology transfer for improve
Through short and practical orated training
o Increase the knowledge and skills up the small household farmers
o For capacity family planning per household’s farmers in particular females
o To improve the family division of labors forces increase the capacity of all age
particularly 15 up to 64 age per HH
 The government and other dairy sector development partners in
particular this study area and nationally to give attention . Such
as;
12/7/2020 42

Cont.
To improve the capacity of business or additional investment with related
dairy production per HH for minimize discourages
To give land near to the living house of memberships of milk producer’s
cooperative for milk collecting centers
To decrease shortage feed : Feeds specific type of animals like milking cow
when hay and silage making for critical periods, link to credit services for
improved forage.
12/7/2020
43

Cont.
• To remove cash lack borrowing money from family, relatives/friends and traders,
NGO’s and properly link for three years long credit/loan between farmers and
micro finance institution, private and governmental banks considering a pregnant
cross breeding milking cow insurance system.
• To remove the constraint season with related to religion the farmers sell milk
other religion person and also done to processes partially on vast fasting time of
Orthodox Christianity.
• To improve aptitudes about feed milking cow not only increasing milk
production and quality of milk
12/7/2020 44

5.3. Suggestion for further research
This study was covered only the market chain of fresh row cow
milk production in case of budget and time constraint at 2020.
Therefore, my suggestion for further research, if you can be
doing value add of the marketing chain of dairy production.
12/7/2020 45

References
• Aldrich, J. H. (1984). Linear Probability, Logit, And Probit Models. . Newbury Park, CA: Sage.
• Ali, T. (2017). Value Chain Analysis Of Milk: The Case Of Dessie Zuria District, South Wollo Zone, Northern Ethiopia. Africa.
Vol.34, 2017. www. iste. Org , Pp. 1-122. Doi: ISSN 2422-8451
• Berhanu, K. (2012). Market Access And Value Chain Analysis Of Dairy Industry In Ethiopia The Case Of Wolaita Zone. In
Partial Fulfillment Of The Requirement For The Degree Of Philosophy Of Doctor In Agriculture (Agricultural Economics),
Haramaya University.
• Cronbach, L. J. (1951). Coefficient Alpha And The Internal Structure Of Tests. Psychometrika.
• Farris, P. W., Bendle, N. T., Pfeifer, P. E., & Reibstein, D. J. (2010). The Definitive Guide To Measuring Marketing Performance.
Upper Saddle River, New Jersey: Pearson Education, Inc. ISBN 0-13-705829-2.
• Gemechu, B. T. (2016). Determinants Of Market Participation And Financial Profitability Of Smallholder Dairy Farming: The
Case Of Bako Tibe, West Showa, Ethiopia. Trends In Agricultural Economics, 9, 29-44. Doi:10.3923/Tae.2016.29.44
• Kuru, M. (2013). Analysis Of Cow Milk Market Chain: The Case Of Sululta District, Oromia Special Zone Surrounding Finfinnee,
Ethiopia.
• RuLIS Dataset ;FAO ;CSA. (2014). Agricultural Sample Survey.
• Tobin, J. (1958). Estimation Of Relationship For Limited Dependent Variable,” Econometrical. (Vol. 26).
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